ALBERT

Description: Crowdsourcing is a new mode of value creation in which organizations leverage numerous Internet users to accomplish tasks. However, because these workers have different backgrounds and intentions, crowdsourcing suffers from quality concerns. In the literature, tracing the behavior of workers is preferred over other methodologies such as consensus methods and gold standard approaches. This paper proposes two novel models based on workers’ behavior for task classification. These models newly benefit from time-series features and characteristics. The first model uses multiple time-series features with a machine learning classifier. The second model converts time series into images using the recurrent characteristic and applies a convolutional neural network classifier. The proposed models surpass the current state of-the-art baselines in terms of performance. In terms of accuracy, our feature-based model achieved 83.8%, whereas our convolutional neural network model achieved 76.6%.

Description: In this paper, a robust observer-based control strategy for n-DOF uncertain robot manipulators with fixed-time stability was developed. The novel fixed-time nonsingular sliding mode surface enables control errors to converge to the equilibrium point quickly within fixed time without singularity. The development of the novel fixed-time disturbance observer based on a uniform robust exact differentiator also allows uncertain terms and exterior disturbances to be proactively addressed. The designed observer can accurately approximate uncertain terms within a fixed time and contribute to significant chattering reduction in the traditional sliding mode control. A robust observer-based control strategy was formulated, according to a combination of the fixed-time nonsingular terminal sliding mode control method and the designed observer, to yield global fixed time stability for n-DOF uncertain robot manipulators. The proposed controller proved definitively that it was able to obtain global stabilization in fixed time. The approximation capability of the proposed observer, the convergence of the proposed sliding surface, and the effectiveness of the proposed control strategy in fixed time were fully confirmed by simulation performance on an industrial robot manipulator.

Description: This paper is focused on the analysis of unfairness of random media access in Local Operating Networks (LON), which is one of the commercial platforms of the Industrial Internet of Things (IIoT). The unfairness in accessing the LON channel is introduced by a collision avoidance mechanism in the predictive p-persistent CSMA protocol adopted at the media access control layer. The study on the bandwidth share in predictive p-persistent CSMA calls for the analysis of multiple memoryless backoff. In this paper, it is shown that the channel access in LON systems is unfair in the short term for medium traffic load conditions, and in the long term for heavy loaded networks. Furthermore, it is explained that the average bandwidth allocated to a particular node is determined implicitly by the load scenario, while an actual node bandwidth fluctuates in time according to stochastic dynamics of the predictive p-persistent CSMA. Next, it is formally proven that the average bandwidth available to a node is a linear function of its backoff state and does not depend on backoff states of the other stations. Finally, it is demonstrated that possibly unfair bandwidth share in LON networks determined implicitly by load scenario is stable because, with lowering a fraction of actual network bandwidth accessible by a given station, the probability to decrease it in the future also drops.

Description: For the sound field reconstruction of large conical surfaces, current statistical optimal near-field acoustic holography (SONAH) methods have relatively poor applicability and low accuracy. To overcome this problem, conical SONAH based on cylindrical SONAH is proposed in this paper. Firstly, elementary cylindrical waves are transformed into those suitable for the radiated sound field of the conical surface through cylinder-cone coordinates transformation, which forms the matrix of characteristic elementary waves in the conical spatial domain. Secondly, the sound pressure is expressed as the superposition of those characteristic elementary waves, and the superposition coefficients are solved according to the principle of superposition of wave field. Finally, the reconstructed conical pressure is expressed as a linear superposition of the holographic conical pressure. Furthermore, to overcome ill-posed problems, a regularization method combining truncated singular value decomposition (TSVD) and Tikhonov regularization is proposed. Large singular values before the truncation point of TSVD are not processed and remaining small singular values representing high-frequency noise are modified by Tikhonov regularization. Numerical and experimental case studies are carried out to validate the effectiveness of the proposed conical SONAH and the combined regularization method, which can provide reliable evidence for noise monitoring and control of mechanical systems.

Description: Voids in polymer matrix composites are one of the most common manufacturing defects, which may influence the mechanical properties and structural behavior of the final parts made of composites by various manufacturing methods. Therefore, numerous non-destructive testing (NDT) techniques were developed and applied for quality control and in-service testing of such structures. In this paper, the authors analyzed various alternatives to the reference technique, X-ray computed tomography (XCT) NDT, which is used for industrial testing of composite disks having defects in the form of the lower density areas. Different candidates, namely: vibration-based testing, infrared thermography, vibro-thermography, as well as ultrasonic testing were analyzed in terms of their sensitivity and technical feasibility. The quality of the results, the complexity of the testing procedure, time and labor consumption, and the cost of the equipment were analyzed and compared with the reference technique. Based on the conducted research the authors finally proposed a hybrid approach to quality control, using a combination of two NDT techniques–infrared thermography (for initial scanning and detection of near-surface defects) and ultrasonic testing (for a more detailed analysis of products that pass the first testing procedure). It allowed for replacing the costly XCT diagnostics with a much cheaper, but almost equally effective, alternative.

Description: Mycotoxins are toxic secondary metabolites produced by fungi on agricultural produce. Mycotoxins can be cytotoxic, genotoxic, mutagenic, and teratogenic, and they are persistent threats to human and animal health. Consumption of mycotoxin-contaminated maize can cause cancer and even sudden death. Health hazards can also occur from consuming products from animals fed with mycotoxin-contaminated feed or forage. The main mode of spread of mycotoxigenic fungi is through air-borne spores originating from soil or plant debris, although some fungi can also spread through infected seed-to-seedling transmission, ultimately followed by contamination of the harvestable product. This perspective assesses opportunities to prevent mycotoxigenic fungal infection in maize seeds produced for sowing as an important starting point of crop contamination. A case study of Nigeria showed infection in all tested farmer-produced, seed company, and foundation seed samples. A schematic overview of the formal and informal seed systems is presented to analyze their contribution to fungal infection and mycotoxin contamination in the maize value chain, as well as to set criteria for successful control. We recommend an integrated approach to control mycotoxigenic fungal infection, including resistant varieties and other control methods during seed production, grain production, and grain storage, with an important role in maintaining seed health.

Description: In many embedded systems, we face the problem of correlating signals characterising device operation (e.g., performance parameters, anomalies) with events describing internal device activities. This leads to the investigation of two types of data: time series, representing signal periodic samples in a background of noise, and sporadic event logs. The correlation process must take into account clock inconsistencies between the data acquisition and monitored devices, which provide time series signals and event logs, respectively. The idea of the presented solution is to classify event logs based on the introduced similarity metric and deriving their distribution in time. The identified event log sequences are matched with time intervals corresponding to specified sample patterns (objects) in the registered signal time series. The matching (correlation) process involves iterative time offset adjustment. The paper presents original algorithms to investigate correlation problems using the object-oriented data models corresponding to two monitoring sources. The effectiveness of this approach has been verified in power consumption analysis using real data collected from the developed Holter device. It is quite universal and can be easily adapted to other device optimisation problems.

Description: The traction power supply system of an Electrical Multiple Unit (EMU) often suffers from overvoltage impact. As an important protection device for on-board electrical equipment, the working environment of a roof arrester is worse than that of a power system. In recent years, the explosion failure of the roof arresters of an EMU has occurred from time to time, which seriously endangers the safe operation of high-speed railways. In this paper, the electrical performance test and material micro test of roof arrester in three states of normal, defect, and exploded, are carried out in order to study the internal causes of roof arrester explosion and clarify its deterioration mechanism. Using the DC reference voltage test and leakage current test, the electrical performance differences of normal, defective, and exploded arresters are obtained. By studying the disassembly of an arrester, the appearance characteristics of arrester varistor in three states are obtained. The micro morphology and chemical elements of the varistor are analyzed by Scanning Electron Microscope and Energy Dispersive Spectrometer. The deterioration mechanism of the arrester varistor is then revealed, and preventive measures for the explosion failure of the roof arrester are put forward. The obtained results show that, during the long-term operation of the roof arrester of an EMU, the varistor may be damp, and therefore the aluminum electrode layer and side insulation layer of the varistor may deteriorate. After the deterioration of the aluminum electrode layer, the content of the O element increases, and multiple film structures are formed on the surface. After the deterioration of the side insulating layer, the content of the O element increases, and the surface becomes uneven. Improving the sealing performance requirements of the roof arrester and optimizing the maintenance process can reduce its explosion failure.

Description: Poa annua is a cosmopolitan, cool-season grass species regarded as one of the most significant weeds of turfgrass. It is mainly controlled by herbicides; however, repeated use of herbicides in golf turf has resulted in the evolution of multiple-herbicide resistant P. annua. Four field experiments were performed in autumn and spring in golf turf to identify effective herbicide options to control multiple herbicide-resistant P. annua. In herbicide resistance screening, the trial site population (SA1) was found to be susceptible to amicarbazone and terbuthylazine, but resistant to simazine and metribuzin at the field rate of each herbicide. Consistent with the results of the pot study, the PSII-inhibiting herbicides amicarbazone and terbuthylazine provided the best control (80–100%) of P. annua in both autumn and spring trials with minimal damage to the turf. In contrast, the other two PSII-inhibiting herbicides, metribuzin and simazine, were relatively ineffective in controlling P. annua in the field. Indaziflam also performed well in both autumn trials and reduced P. annua occurrence by 〉75%. Pyroxasulfone and s-metolachlor only provided moderate weed control in both the autumn and spring trials, reducing P. annua occurrence by 50%. Among the nine different herbicides, amicarbazone and terbuthylazine were found to be most effective for spring and autumn application in turf. As resistance to some PSII-inhibiting herbicides has already evolved in this field population, the use of amicarbazone and terbuthylazine needs to be integrated with other herbicide modes of action and non-chemical tactics to delay the onset of resistance to them.

Description: Cooperative driving is an essential component of intelligent transport systems (ITSs). It promises greater safety, reduced accidents, efficient traffic flow, and fuel consumption reduction. Vehicle platooning is a representative service model for ITS. The principal sub-systems of platooning systems for connected and automated vehicles (CAVs) are cooperative adaptive cruise control (CACC) systems and platoon management systems. Based on vehicle state information received through vehicle-to-vehicle (V2V) communication, the CACC system allows platoon vehicles to maintain a narrower safety distance. In addition, the platoon management system using V2V communications allows vehicles to perform platoon maneuvers reliably and accurately. In this paper, we propose a CACC system with a variable time headway and a decentralized platoon join-in-middle maneuver protocol with a trajectory planning system considering the V2V communication delay for CAVs. The platoon join-in-middle maneuver is a challenging research subject as the research must consider the requirement of a more precise management protocol and lateral control for platoon safety and string stability. These CACC systems and protocols are implemented on a simulator for a connected and automated vehicle system, PreScan, and we validated our approach using a realistic control system and V2V communication system provided by PreScan.

Description: The battery storage system (BSS) is one of the key components in many modern power applications, such as in renewable energy systems and electric vehicles. However, charge imbalance among batteries is very common in BSSs, which may impair the power efficiency, reliability, and safety. Hence, various battery equalization methods have been proposed in the literature. Among these techniques, switched-capacitor (SC)-based battery equalizers (BEs) have attracted much attention due to their low cost, small size, and controllability. In this paper, seven types of SC-based BEs are studied, including conventional, double-tiered, modularized, chain structure types I and II, series-parallel, and single SC-based BEs. Mathematical models that describe the charge–discharge behaviors are first derived. Next, a statistical analysis based on MATLAB simulation is carried out to compare the performance of these seven BEs. Finally, a summary of the circuit design complexity, balancing speed, and practical implementation options for these seven topologies is provided.

Description: 3D visual recognition is a prerequisite for most autonomous robotic systems operating in the real world. It empowers robots to perform a variety of tasks, such as tracking, understanding the environment, and human–robot interaction. Autonomous robots equipped with 3D recognition capability can better perform their social roles through supportive task assistance in professional jobs and effective domestic services. For active assistance, social robots must recognize their surroundings, including objects and places to perform the task more efficiently. This article first highlights the value-centric role of social robots in society by presenting recently developed robots and describes their main features. Instigated by the recognition capability of social robots, we present the analysis of data representation methods based on sensor modalities for 3D object and place recognition using deep learning models. In this direction, we delineate the research gaps that need to be addressed, summarize 3D recognition datasets, and present performance comparisons. Finally, a discussion of future research directions concludes the article. This survey is intended to show how recent developments in 3D visual recognition based on sensor modalities using deep-learning-based approaches can lay the groundwork to inspire further research and serves as a guide to those who are interested in vision-based robotics applications.

Description: This paper presents an adaptive protection scheme (APS) for solving the coordination problem that deals with coordination directional overcurrent relays (DOCRs) and distance relays second zone time, in relation to coordination with DOCRs. The coordination problem becomes more complex with the impact of renewable energy sources (RES) when added to the distribution grid. This leads to a change in the grid topology, caused by the on/off states of the distribution generators (DG). The frequency of topological changes in distribution grids poses a challenge to the power system’s protection components. The change in the state of DGs leads to malfunction in reliability and miscoordination between protection relays, since that causes a direct effect to the short circuit currents. This paper used the school-based optimization (SBO) algorithm, which simulates the educational process, in order to deal with coordination problems. That algorithm is modified (MSBO) by modified both learning and teaching processes. The IEEE 8-bus test system and IEEE 14-bus distribution network are used to validate the proposed coordination system’s effectiveness when dealing with the coordination process between distance and DOCRs, at both the near- and far-end in the typical topological grid and with DGs in working order.

Description: With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

Description: This paper proposes an AC/DC single-stage structure by integrating a boost topology and an active clamp flyback (ACF) circuit with power-factor-correction (PFC) function. The PFC function can be achieved by controlling a boost PFC topology operated in the discontinuous conduction mode. With the coordination of active clamping components, a resonant technique is obtained and zero-voltage-switching (ZVS) can be achieved. The proposed converter is combined with the advantages of: (1) compared with two-stage circuit, a single stage circuit decreases the component of the main circuit and reduces the complexity of the control circuit; (2) a boost topology with PFC function operated in discontinuous conduction mode can be accomplished without adding any current detecting technique or detecting input signal; (3) by using the inductor from the PFC stage, ZVS function can be achieved without any additional inductor; (4) the increment of switching frequency facilitates the optimization of power density; (5) the conducting loss at the secondary side can be reduced by adding the synchronous rectification; (6) in this proposed scheme, the dual transformers with series-parallel connection are utilized, the current at the secondary side can be shared for lowering the conduction loss of the synchronous transistors. Finally, a prototype converter with AC 110 V input and DC 19 V/6.32 A (120 W) output under 300 kHz switching frequency is implemented. The efficiency of the proposed converter reaches 88.20% and 0.984 power factor in full load condition.

Description: The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster–Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy’s average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.

Description: Calmodulin (CaM), as an important factor in the calcium signaling pathway, is widely involved in plant growth and development regulation and responses to external stimuli. In this study, the full-length sequence of the ScCaM gene (GenBank: GQ246454) was isolated from the leaves of a Saccharum spp. hybrid. Prokaryotic expression showed that ScCaM could be solubly expressed and purified in Escherichia coli BL21. Subcellular localization confirmed that ScCaM was localized in the plasma membrane and nucleus of cells. A quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis revealed that ScCaM can be induced by various stresses, including sodium chloride (NaCl), chromium trichloride (CrCl3), salicylic acid (SA), and methyl jasmonate (MeJA). Ectopic expression in Arabidopsis thaliana demonstrated that ScCaM can affect the growth and development of transgenic plants. Moreover, the qRT-PCR analysis indicated that the overexpression of the allogenic ScCaM gene inhibits the expression of AtSTM, leading to the phenomenon of multiple-tillering in transgenic A. thaliana. The present study provided valuable information and facilitates further investigation into the function of ScCaM in the future.

Description: Strong evidence from studies on primates and rodents shows that changes in pupil diameter may reflect neural activity in the locus coeruleus (LC). Pupillometry is the only available non-invasive technique that could be used as a reliable and easily accessible real-time biomarker of changes in the in vivo activity of the LC. However, the application of pupillometry to preclinical research in rodents is not yet fully standardized. A lack of consensus on the technical specifications of some of the components used for image recording or positioning of the animal and cameras have been recorded in recent scientific literature. In this study, a novel pupillometry system to indirectly assess, in real-time, the function of the LC in anesthetized rodents is presented. The system comprises a deep learning SOLOv2 instance-based fast segmentation framework and a platform designed to place the experimental subject, the video cameras for data acquisition, and the light source. The performance of the proposed setup was assessed and compared to other baseline methods using a validation and an external test set. In the latter, the calculated intersection over the union was 0.93 and the mean absolute percentage error was 1.89% for the selected method. The Bland–Altman analysis depicted an excellent agreement. The results confirmed a high accuracy that makes the system suitable for real-time pupil size tracking, regardless of the pupil’s size, light intensity, or any features typical of the recording process in sedated mice. The framework could be used in any neurophysiological study with sedated or fixed-head animals.

Description: The reniform nematode, Rotylenchulus reniformis (Linford and Oliveira), remains a common, widespread nematode pest of cotton across the southern United States. Trials were conducted during 2017 at three non-irrigated locations: one location in Hamilton, MS, and two locations in Tchula, MS, in field settings with a history of cotton production and documented economically-damaging reniform nematode populations. Trials were designed to evaluate the response of two cotton cultivars to in-furrow nematicides consisting of aldicarb, 1,3-dichloropropene, and a non-treated control applied for nematode suppression. No significant interactions between cotton cultivar and nematicide were observed. However, treatment with 1,3-dichloropropene produced greater plant biomass, and plant height compared to aldicarb-treated cotton and the nontreated. Nematode densities were suppressed with the use of 1,3-dichloropropene compared to aldicarb and the non-treated control. The use of 1,3-dichloropropene resulted in positive early-season plant growth responses; however, these responses did not translate into greater yield.

Description: The Poisson–Boltzmann equation (PBE) arises in various disciplines including biophysics, electrochemistry, and colloid chemistry, leading to the need for efficient and accurate simulations of PBE. However, most of the finite difference/element methods developed so far are rather complicated to implement. In this study, we develop a ResNet-based artificial neural network (ANN) to predict solutions of PBE. Our networks are robust with respect to the locations of charges and shapes of solvent–solute interfaces. To generate train and test sets, we have solved PBE using immersed finite element method (IFEM) proposed in (Kwon, I.; Kwak, D. Y. Discontinuous bubble immersed finite element method for Poisson–Boltzmann equation. Communications in Computational Physics 2019, 25, pp. 928–946). Once the proposed ANNs are trained, one can predict solutions of PBE in almost real time by a simple substitution of information of charges/interfaces into the networks. Thus, our algorithms can be used effectively in various biomolecular simulations including ion-channeling simulations and calculations of diffusion-controlled enzyme reaction rate. The performance of the ANN is reported in the result section. The comparison between IFEM-generated solutions and network-generated solutions shows that root mean squared error are below 5·10−7. Additionally, blow-ups of electrostatic potentials near the singular charge region and abrupt decreases near the interfaces are represented in a reasonable way.

Description: This study is a specific contribution to investigating normalities in prices to a well-established cointegrated vector autoregressive model (VAR). While the role of prices in computational economics has been investigated, the real prices vis-à-vis nominal prices in the decision process has been neglected. The paper investigates the transition from nominal to real time-series of prices without losing information in the data set when deflating or de-seasonalizing. The likelihood approach is based on careful specifications of the (co)integration characteristics of tourism prices. The results confirm that the transmission of tourism prices in the Eurozone positively impacts Slovenian tourism prices when the spatial consolidated cointegrated VAR model is used. The theoretical-conceptual and empirical contribution is twofold: first, the study develops and empirically applies bona fide divisor of normality consolidation for time-series in levels instead of routinely utilised inflation integers, and second, the study introduces perfection of prices on a long-run time-series treatment.

Description: Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

Description: Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling.

Description: High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.

Description: This research was undertaken to perform and evaluate the temperature measurement in the ground utilized as an energy source with the goal to determine whether significant temperature variations occur in the subsurface during the heating season. The research infrastructure situated on our University campus was used to assess any variations. The observations were made at the so called “Small Research Polygon” that consists of 8 monitoring boreholes (Borehole Heat Exchangers) situated around a borehole used as an energy source. During the heating season, a series of monthly measurements are made in the monitoring boreholes using a distributed temperature system (DTS). Raman back-scattered light is analysed using Optical Frequency Time Domain Reflectometry (OTDR). Our results indicate that no noticeable changes in temperature occur during the heating season. We have observed an influence of long-term variations of the atmospheric conditions up to the depth of a conventional BHE (≈100 m). The resulting uncertainty in related design input parameters (ground thermal conductivity) was evaluated by using a heat production simulation. Production data during one heating season at our research facilities were evaluated against the design of the system. It is possible to construct smaller geothermal installations with appropriate BHE design that will have a minimal impact on the temperature of the surrounding rock mass and the system performance.

Description: Defining the most profitable remote sensing platforms is a difficult decision-making process, as it requires agronomic and economic considerations. In this paper, the price and profitability of three levels of remote sensing platforms were evaluated to define a decision-making process. Prices of satellite, plane and UAV-acquired vegetation indices were collected in Italy during 2020 and compared to the economic benefits resulting from variable rate nitrogen application, according to a bibliographic meta-analysis performed on grains. The quality comparison of these three technologies was performed considering the error propagation in the NDVI formula. The errors of the single bands were used to assess the optical properties of the sensors. Results showed that medium-resolution satellite data with good optical properties could be profitably used for variable rate nitrogen applications starting from 2.5 hectares, in case of medium resolution with good optical properties. High-resolution satellites with lower optical quality were profitable starting from 13.2 hectares, while very high-resolution satellites with good optical properties could be profitably used starting from 76.8 hectares. Plane-acquired images, which have good optical properties, were profitable starting from 66.4 hectares. Additionally, a reference model for satellite image price is proposed.

Description: The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

Description: The Y Balance Test (YBT) is a dynamic balance assessment typically used in sports medicine. This work proposes a deep learning approach to automatically score this YBT by estimating the normalized reach distance (NRD) using a wearable sensor to register inertial signals during the movement. This paper evaluates several signal processing techniques to extract relevant information to feed the deep neural network. This evaluation was performed using a state-of-the-art human activity recognition system based on recurrent neural networks (RNNs). This deep neural network includes long short-term memory (LSTM) layers to learn features from time series by modeling temporal patterns and an additional fully connected layer to estimate the NRD (normalized by the leg length). All analyses were carried out using a dataset with YBT assessments from 407 subjects, including young and middle-aged volunteers and athletes from different sports. This dataset allowed developing a global and robust solution for scoring the YBT in a wide range of applications. The experimentation setup considered a 10-fold subject-wise cross-validation using training, validation, and testing subsets. The mean absolute percentage error (MAPE) obtained was 7.88 ± 0.20%. Moreover, this work proposes specific regression systems to estimate the NRD for each direction separately, obtaining an average MAPE of 7.33 ± 0.26%. This deep learning approach was compared to a previous work using dynamic time warping and k-NN algorithms, obtaining a relative MAPE reduction of 10%.

Description: Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were trained using the proximal policy optimization algorithm. A validation by comparison with a model-based controller was performed. The performance of the trained controllers was verified with respect to the mean energy consumption and the root mean squared distance error. In our evaluation scenarios, the learning-based controllers reduced energy consumption in comparison to the model-based controller by 17.9% on average.

Description: Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumulation and are not fast enough. Therefore, we propose a deep learning algorithm that takes into account the continuity information of adjacent image frames, including image sequence processing and an end-to-end trainable multi-input single-output network to jointly process the segmentation of lanes and road markers. In order to emphasize the location of the target with high probability in the adjacent frames and to refine the segmentation result of the current frame, we explicitly consider the time consistency between frames, expand the segmentation region of the previous frame, and use the optical flow of the adjacent frames to reverse the past prediction, then use it as an additional input of the network in training and reasoning, thereby improving the network’s attention to the target area of the past frame. We segmented lanes and road markers on the Baidu Apolloscape lanemark segmentation dataset and CULane dataset, and present benchmarks for different networks. The experimental results show that this method accelerates the segmentation speed of video lanes and road markers by 2.5 times, increases accuracy by 1.4%, and reduces temporal consistency by only 2.2% at most.

Description: In this paper, a dual-polarized four-port 2 × 2 series fed antenna array operating at 28 GHz with beam-switching capability is proposed. The antenna array uses a simple passive beamforming network to switch the main beam. The presented antenna design is suitable for 5G user equipment and high data rates applications by which it has a compact size with low cost and complexity. The size of the antenna is 37.2 × 37.2 mm2 including the ground plane, and it produces 10 different switched beams by using only two simple 3 dB/90∘ couplers which create the required amplitudes and phase excitations for the antenna elements. A one-port simple feeding mechanism including Peregrine PE42525 SPDT switch modules and a power divider is used to generate and measure the 10 switched beams. The antenna design is implemented on a two-layer 0.203 mm thick low-loss (tanδ = 0.0027) Rogers 4003C substrate, and it has a measured 10 dB impedance bandwidth of 4 GHz (14.3%, from 26 GHz to 30 GHz) for all ports. Measured peak isolation between any dual-polarized ports of the antenna is better than 30 dB. The antenna has an average measured realized gain of 8.9 dBi and around 10 dB side lobe level (SLL) for all beams. The antenna has 3-dB coverage of 80∘ to 90∘ in 2D space and it has a maximum of ±26∘ beam-steering angle. The antenna is designed and simulated using Ansys HFSS and fabricated using regular PCB processing.

Description: High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.

Description: Sowing time, as an element, is important to improving the adaptation of cultivars to environmental conditions and to achieving high seed yields. The field experiment was conducted from 2018–2019 at the Experimental Station of Vytautas Magnus University Agriculture Academy. The experimental design included treatments with different sowing dates: eight sowing dates in 2018 and 10 sowing dates in 2019. The first sowing of spring rapeseed was carried out when the soil reached its physical maturity, i.e., it did not stick to agricultural implements and it crumbled well. The other sowing dates were every seven subsequent days. From 2018–2019, the rapeseed emerged as best in early May (3 and 4 May), and later sowing reduced the emergence of rapeseed. In 2018, most pods were formed on one plant when the rapeseed was sown (on 1 June), compared to other sowings, on average 2.8 times more. In 2019, most pods were formed by the latest-sown rapeseed (7 June), from 1.4 to 2.7 times more compared to previously sown crops. In 2018, the sowing time of spring rapeseed did not have a significant effect on the number of seeds in one pod. In 2019, it was found that the rapeseed formed most of the seeds in the pod at a similar time as in 2018: the sowings of 19 April and 7 June. The average number of seeds in the pod was significantly reduced by early sowing (5 April). In 2019, the highest 1000-seed weight was found at the earliest-sown crop (5 April), which was on average 18.0% higher compared to the later sowings. The 1000-seed weight of the last-sown rapeseed (7 June) was the lowest. In 2018, the yields of early-sown (20 April) spring rapeseed were the highest. Later sowing significantly reduced the yields by 20.7 to 48.2%. In 2019, the highest seed yield was obtained after sowing spring rapeseed in late April (26 April); it was significant, on average, 1.9 times higher than the yields of spring rapeseed sown from 3 May to 7 June. Meteorological conditions had a stronger effect on the field emergence and yield components of spring rapeseed than the sowing date.

Description: The Internet of Robotic Things (IoRT) has emerged as a promising computing paradigm integrating the cloud/fog/edge computing continuum in the Internet of Things (IoT) to optimize the operations of intelligent robotic agents in factories. A single robot agent at the edge of the network can comprise hundreds of sensors and actuators; thus, the tasks performed by multiple agents can be computationally expensive, which are often possible by offloading the computing tasks to the distant computing resources in the cloud or fog computing layers. In this context, it is of paramount importance to assimilate the performance impact of different system components and parameters in an IoRT infrastructure to provide IoRT system designers with tools to assess the performance of their manufacturing projects at different stages of development. Therefore, we propose in this article a performance evaluation methodology based on the D/M/c/K/FCFS queuing network pattern and present a queuing-network-based performance model for the performance assessment of compatible IoRT systems associated with the edge, fog, and cloud computing paradigms. To find the factors that expose the highest impact on the system performance in practical scenarios, a sensitivity analysis using the Design of Experiments (DoE) was performed on the proposed performance model. On the outputs obtained by the DoE, comprehensive performance analyses were conducted to assimilate the impact of different routing strategies and the variation in the capacity of the system components. The analysis results indicated that the proposed model enables the evaluation of how different configurations of the components of the IoRT architecture impact the system performance through different performance metrics of interest including the (i) mean response time, (ii) utilization of components, (iii) number of messages, and (iv) drop rate. This study can help improve the operation and management of IoRT infrastructures associated with the cloud/fog/edge computing continuum in practice.

Description: Magnetic micro- and nanoparticles (MPs)-based composite materials are widely used in various applications in electronics, biotechnology, and medicine. This group of silicone composites have advantageous magnetic and mechanical properties as well as sufficient flexibility and biocompatibility. These composites can be applied in medicine for biological sensing, drug delivery, tissue engineering, and as remote-controlled microrobots operating in vivo. In this work, the properties of polydimethylsiloxane (PDMS)-based composites with different percentages (30 wt.%, 50 wt.%, 70 wt.%) of NdFeB microparticles as a filler were characterized. The novelty of the work was to determine the influence of the percentage of MP content and physiological conditioning on the properties of the PDMS-MP composites after in vitro incubation. An important essence of the work was a comprehensive study of the properties of materials important from the point of view of medical applications. Materials were tested before and after conditioning in 0.9 wt.% NaCl solution at a temperature of 37 °C. Several studies were carried out, including thermal, physicochemical, and rheological tests. The results show that with an increase of the incubation time, most of the measured thermal and physicochemical parameters decreased. The presence of the magnetic filler, especially at a concentration of 70 wt.%, has a positive effect on thermal stability and physicochemical and rheological properties. The performed tests provided important results, which can lead to further research for a broader application of magnetic composites in the biomedical field.

Description: Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

Description: Climate change, increasing environmental pollution, continuous loss of biodiversity, and a growing human population with increasing food demand, threaten the functioning of agro-ecosystems and their contribution to people and society. Agroforestry systems promise a number of benefits to enhance nature’s contributions to people. There are a wide range of agroforestry systems implemented representing different levels of establishment across the globe. This range and the long time periods for the establishment of these systems make empirical assessments of impacts on ecosystem functions difficult. In this study we investigate how simulation models can help to assess and predict the role of agroforestry in nature’s contributions. The review of existing models to simulate agroforestry systems reveals that most models predict mainly biomass production and yield. Regulating ecosystem services are mostly considered as a means for the assessment of yield only. Generic agroecosystem models with agroforestry extensions provide a broader scope, but the interaction between trees and crops is often addressed in a simplistic way. The application of existing models for agroforestry systems is particularly hindered by issues related to code structure, licences or availability. Therefore, we call for a community effort to connect existing agroforestry models with ecosystem effect models towards an open-source, multi-effect agroforestry modelling framework.

Description: The study aimed to evaluate the changes in the quantitative composition of a soil bacterial community near a municipal waste landfill, and attempted to use a bacteriological coefficient to assess the degree of soil degradation. The research was carried out near a landfill site located in southern Poland. Soil samples were collected from plots on which spring wheat, field bean and potato were cultivated. Microbiological analyses included the determination of the total number of bacteria in active and dormant (sporulating) stages. The highest ratio of sporulating bacteria in relation to vegetative bacteria was found in the reclaimed sector of the landfill site. The proposed bacteriological indicator of soil quality (i.e., the ratio of the number of sporulating bacteria to the number of vegetative forms) seems to be a good index for the assessment of soil quality near the landfill site.

Description: This paper studies the power of online search intensity metrics, measured by Google, for examining and forecasting exchange rates. We use panel data consisting of quarterly time series from 2004 to 2018 and ten international countries with the highest currency trading volume. Newly, we include various Google search intensity metrics to our panel data. We find that online search improves the overall econometric models and fits. First, four out of ten search variables are robustly significant at one percent and enhance the macroeconomic exchange rate models. Second, country regressions corroborate the panel results, yet the predictive power of search intensity with regard to exchange rates vary by country. Third, we find higher prediction performance for our exchange rate models with search intensity, particularly in regard to the direction of the exchange rate. Overall, our approach reveals a value-added of search intensity in exchange rate models.

Description: At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. In this paper, we describe the development of a tire inspection system for the tire industry. We provide methods for processing tire sidewall data obtained from a camera and a laser sensor. The captured data comprise visual and geometric data characterizing the tire surface, providing a real representation of the captured tire sidewall. We use an unfolding process, that is, a polar transform, to further process the camera-obtained data. The principles and automation of the designed polar transform, based on polynomial regression (i.e., supervised learning), are presented. Based on the data from the laser sensor, the detection of abnormalities is performed using an unsupervised clustering method, followed by the classification of defects using the VGG-16 neural network. The inspection system aims to detect trained and untrained abnormalities, namely defects, as opposed to using only supervised learning methods.

Description: In this paper, we introduce the MSCI China A-shares index (MCASI) and analyze MCASI’s properties. From the perspective of index investment, we found that MCASI’s investor sentiments, both overnight sentiment and BW sentiment, provide significant predictability for future MCASI returns, supported by the in-sample and out-of-sample results. From the perspective of sector investment, we show that the sector portfolio of “information transfer, software and information technology services” performs the best among the 10 sector portfolios. In addition, seven approaches of the optimal portfolio in ten sectors are examined, and the results suggest that the classic Markowitz portfolio approach is recommended. Our empirical analysis is helpful for domestic and foreign investors seeking to form investment strategies for MSCI China A-shares.

Description: Hot pepper (Capsicum annum L.) is a major spice crop and is used worldwide for its nutritional value. In the field, its plant is susceptible to various fungal diseases, including fusarium wilt, caused by soil-borne fungus Fusarium oxysporum f. sp. capsici, which can survive in the soil for several years. The infected plant can be recognized by the yellowing of older leaves and downward curling of apical shoots, followed by plant wilting and ultimately the death of the plant. The resistance mechanism in plants is controlled by a single dominant gene, and conventional plant breeding techniques are used to develop a wilt-resistant germplasm. Non-conventional techniques such as gene pyramiding and expression enhancement of antifungal genes could be used to shorten the time to develop resistance against fusarium wilt in hot peppers. In this review, we discuss different aspects of the disease and the molecular basis of resistance in chili/hot pepper plants. Furthermore, this review covers the scope of conventional and non-conventional breeding strategies and different management approaches used to tackle the disease.

Description: Feature Pyramid Network (FPN) is used as the neck of current popular object detection networks. Research has shown that the structure of FPN has some defects. In addition to the loss of information caused by the reduction of the channel number, the features scale of different levels are also different, and the corresponding information at different abstract levels are also different, resulting in a semantic gap between each level. We call the semantic gap level imbalance. Correlation convolution is a way to alleviate the imbalance between adjacent layers; however, how to alleviate imbalance between all levels is another problem. In this article, we propose a new simple but effective network structure called Scale-Equalizing Feature Pyramid Network (SEFPN), which generates multiple features of different scales by iteratively fusing the features of each level. SEFPN improves the overall performance of the network by balancing the semantic representation of each layer of features. The experimental results on the MS-COCO2017 dataset show that the integration of SEFPN as a standalone module into the one-stage network can further improve the performance of the detector, by ∼1AP, and improve the detection performance of Faster R-CNN, a typical two-stage network, especially for large object detection APL∼2AP.

Description: In the differential diagnosis of nonspecific white matter lesions (NSWMLs) detected on magnetic resonance imaging (MRI), multiple sclerosis (MS) should be taken into consideration. Optical coherence tomography (OCT) is a promising tool applied in the differential diagnostic process of MS. We tested whether OCT may be useful in distinguishing between MS and NSWMLs patients. In patients with MS (n = 41) and NSWMLs (n = 19), the following OCT parameters were measured: thickness of the peripapillary Retinal Nerve Fibre Layer (pRNFL) in superior, inferior, nasal, and temporal segments; thickness of the ganglion cell-inner plexiform layer (GCIPL); thickness of macular RNFL (mRNFL); and macular volume (MV). In MS patients, GCIPL was significantly lower than in NSWMLs patients (p = 0.024). Additionally, in MS patients, mRNFL was significantly lower than in NSWMLs patients (p = 0.030). The average segmental pRNFL and MV did not differ between MS and NSWMLs patients (p 〉 0.05). GCIPL and macular RNFL thinning significantly influenced the risk of MS (18.6% [95% CI 2.7%, 25.3%]; 27.4% [95% CI 4.5%, 62.3%]), and reduced GCIPL thickness appeared to be the best predictor of MS. We conclude that OCT may be helpful in the differential diagnosis of MS and NSWMLs patients in real-world settings.

Description: Many studies have found that gold nanoparticles with branched surfaces (nanoflowers) are markers for immunosensors that provide higher sensitivity gains than the commonly used spherical gold nanoparticles. Although the analytical characteristics of nanoparticle-using systems vary significantly depending on their size and shape, the question of choosing the best gold nanoflowers remains open. This work presents a comparative study of a panel of 33 preparations of gold nanoflowers formed by varying several parameters: the size of spherical nanoparticles-nuclei, the concentrations of nuclei, and tetrachloroauric acid during growth. The sizes of the resulting particles, their sorption capacity under antibody immobilization, mobility along membranes for lateral flow assays, and the effects of these parameters on the limits of detection of lateral flow immunoassay are characterized. The optimality of preparations obtained by growing a 0.2% v/v solution of nuclei with a diameter of 10 or 20 nm with tetrachloroauric acid at a concentration of 0.12 mM was shown. With their use, lateral flow immune tests were developed to determine markers of acute myocardial infarction—fatty acids binding protein and troponins I and T. The use of gold nanoflowers obtained under the proposed protocols led to significant gains in the limits of detection—3 to 10 times under visual detection and over 100 times under instrumental detection—compared to spherical gold nanoparticles. The significant increase under instrumental detection is due to the label’s low nonspecific binding.

Description: In the past few decades, biosensors have been gradually developed for the rapid detection and monitoring of human diseases. Recently, functional nucleic-acid (FNA) biosensors have attracted the attention of scholars due to a series of advantages such as high stability and strong specificity, as well as the significant progress they have made in terms of biomedical applications. However, there are few reports that systematically and comprehensively summarize its working principles, classification and application. In this review, we primarily introduce functional modes of biosensors that combine functional nucleic acids with different signal output modes. In addition, the mechanisms of action of several media of the FNA biosensor are introduced. Finally, the practical application and existing problems of FNA sensors are discussed, and the future development directions and application prospects of functional nucleic acid sensors are prospected.

Description: This paper investigates the hybrid source localization problem using differential received signal strength (DRSS) and angle of arrival (AOA) measurements. The main advantage of hybrid measurements is to improve the localization accuracy with respect to a single sensor modality. For sufficiently short wavelengths, AOA sensors can be constructed with size, weight, power and cost (SWAP-C) requirements in mind, making the proposed hybrid DRSS-AOA sensing feasible at a low cost. Firstly the maximum likelihood estimation solution is derived, which is computationally expensive and likely to become unstable for large noise levels. Then a novel closed-form pseudolinear estimation method is developed by incorporating the AOA measurements into a linearized form of DRSS equations. This method eliminates the nuisance parameter associated with linearized DRSS equations, hence improving the estimation performance. The estimation bias arising from the injection of measurement noise into the pseudolinear data matrix is examined. The method of instrumental variables is employed to reduce this bias. As the performance of the resulting weighted instrumental variable (WIV) estimator depends on the correlation between the IV matrix and data matrix, a selected-hybrid-measurement WIV (SHM-WIV) estimator is proposed to maintain a strong correlation. The superior bias and mean-squared error performance of the new SHM-WIV estimator is illustrated with simulation examples.

Description: Lithium-ion capacitors (LICs) have gained significant attention due to the combination on the advantages of electric double-layer capacitors (EDLCs) and lithium-ion batteries (LIBs). Herein, the LIC pouch cell was fabricated by an activated carbon (AC) cathode and a Li4Ti5O12 (LTO) anode. Two organic electrolytes (1 mol L−1 LiBF4/acetonitrile (AN) and 1 mol L−1 LiPF6/ ethylene carbonate (EC) + ethyl methyl carbonate (EMC) + dimethyl carbonate (DMC)) were chosen and the gas swelling behavior was studied. Compared with the ester-based LIC, the AN-based LIC displays higher energy density of 13.31 Wh kg−1 at 11.4 W kg−1 and even provides a value of 9.1 Wh kg−1 at 1075 W kg−1. Because of the lower DC Resistance of 0.761 mΩ, the maximum power density of the AN-based LIC reaches 12.5 kW kg−1. The AN-based LIC delivers good stability with an energy retention of 88.3% after 900 cycles. It is discovered that the swelling behavior of AN-based LICs is more serious and the major component is H2. The difference of swelling behavior among the LICs, lithium nickel cobalt manganese oxide (NCM)/LTO LIB and AC/AC EDLC is proposed to be caused by the AC electrode and the interfacial reaction of LTO.

Description: While there is increasing interest in crypto assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we considered a unique dataset of 144 exchanges, active from the first quarter of 2018 to the first quarter of 2021. We analyzed the determinants surrounding the decision to close an exchange using credit scoring and machine learning techniques. Cybersecurity grades, having a public developer team, the age of the exchange, and the number of available traded cryptocurrencies are the main significant covariates across different model specifications. Both in-sample and out-of-sample analyzes confirm these findings. These results are robust in regard to the inclusion of additional variables, considering the country of registration of these exchanges and whether they are centralized or decentralized.

Description: Although a positive relationship between tourism and quality of life is the premise of using tourism to support biodiversity conservation, tourism scholars rarely assess the relationship between tourism and community livelihoods with rigorous empirical methods, even less so in African contexts. Focusing on communities in the Greater Virunga Landscape in Rwanda and Uganda, we conducted a household survey to acquire empirical data to test novel hypotheses about tourism’s influence on capital assets, household resiliency, and subjective wellbeing. Using inferential statistical analyses (e.g., analysis of variance, chi-square difference test, and independent sample t-tests), we compared the responses from 346 residents who have direct access to tourism livelihoods with responses collected from 224 residents not engaged in tourism. Contrary to expectations, our findings suggest that tourism may not lead to dramatic differences in access to capital assets. However, we did discover moderate influences on household resiliency and subjective wellbeing. These intangible and subjective wellbeing outcomes of tourism-based livelihood programs are challenging to assess empirically. Yet, they may be among some of the most important from a human development standpoint. As a first effort to integrate three theoretical frameworks that have, to date, seen limited application in tourism research, this study has opened the door to further work at the intersections of capital assets, family resilience, and wellbeing theories. In conclusion, we argue that incentivizing the protection of local environments through tourism must be extended to other forms of capital, while also considering more nuanced manifestations of intangible wellbeing outcomes. As such, this paper makes a significant empirical contribution to the ongoing theoretical and practical debates about the tourism-conservation relationship.

Description: The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

Description: This paper presents a microwave sensor based on a two-ports network for liquid characterizations. The proposed sensor is constructed as a miniaturized microwave resonator based on Moore fractal geometry of the 4th iteration. The T-resonator is combined with the proposed structure to increase the sensor quality factor. The proposed sensor occupies an area of 50 × 50 × 1.6 mm3 printed on an FR4 substrate. Analytically, a theoretical study is conducted to explain the proposed sensor operation. The proposed sensor was fabricated and experimentally tested for validation. Later, two pans were printed on the sensor to hold the Sample Under Test (SUT) of crude oil. The frequency resonance of the proposed structure before loading SUT was found to be 0.8 GHz. After printing the pans, a 150 MHz frequency shift was accrued to the first resonance. The sensing part was accomplished by monitoring the S-parameters in terms of S12 regarding the water concentration change in the crude oil samples. Therefore, 10 different samples with different water percentages were introduced to the proposed sensor to be tested for detecting the water content. Finally, the measurements of the proposed process were found to agree very well with their relative simulated results.

Description: Strigolactones (SLs) are a prime example of allelochemicals, promoting parasitic plant germination and certain hyphal branching factors associated to the growth of symbiotic arbuscular mycorrhizal fungi (AMF). However, the study of SLs is complex, and various issues have yet to be studied in depth. This review intends to provide an overview of the works that have been conducted on the identification, isolation, and evaluation of the allelopathic activity of natural canonical and non-canonical SLs on parasitic weeds and AMF growth. These topics were related with their application in agriculture through trap crops, suicidal germination or intercropping strategies. The high applicability of SLs in agriculture, for example, as preventing herbicides for parasitic weed control, has increased the interest for these compounds and the number of research articles published. This review updates and discusses the last findings in this field, with special emphasis in the results published since 2015, using tables and graphs to summarize and discuss that information. The promising results and conclusions obtained from the bioassays herein presented provide a good reason to encourage and support further research works on these natural products, which must also consider the disadvantages or current limitations that SLs present.

Description: Alfalfa and red clover are important perennial legumes for the production of high-quality fodder. The improvement of the forage quality of legumes is one of the strategic goals of breeding programs. Variation in quality traits (protein content (CP), neutral detergent fiber content (NDF), and acid detergent fiber content (ADF)) and relative feed value (RFV) among seven cultivars and 39 elite breeding populations of alfalfa and red clover was evaluated in the study. Significant differences were determined among the investigated cultivars/populations. Alfalfa populations L-8, 10, 12, 15, and 20 were characterized by a high CP content (up to 23.47%) and/or low NDF and ADF contents. The highest CP content in red clover was recorded in population CD-18 (21.89%), while the lowest NDF and ADF contents were determined in populations CD-19 and CD-4, respectively. High RFV was determined in alfalfa populations L-10, 12, 20, 15, 16, 8, 11, and 17 (prime fodder), and in red clover populations CD-4, 8, 16, 14, and 19 (premium-quality fodder). The identified superior alfalfa and red clover populations will be used to improve the nutritional value of forage crops in our breeding program, which will lead to the release of novel cultivars with improved forage quality.

Description: Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.

Description: Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process. For a collaborative SLAM system that generally uses both intra-robot and inter-robot loop closures to optimize the pose graph, it is a tough job to reject those false positive loop closures without a reliable a priori knowledge of the relative pose transformation between robots. Aiming at this solving problem, this paper proposes a two-stage false positive loop-closure rejection method based on three types of consistency checks. Firstly, a multi-robot pose-graph optimization model is given which transforms the multi-robot pose optimization problem into a maximum likelihood estimation model. Then, the principle of the false positive loop-closure rejection method based on test is proposed, in which clustering is used to reject those intra-robot false loop-closures in the first step, and a largest mutually consistent loop-based test is constructed to reject inter-robot false loop closures in the second step. Finally, an open dataset and synthetic data are used to evaluate the performance of the algorithms. The experimental results demonstrate that our method improves the accuracy and robustness of the back-end pose-graph optimization with a strong ability to reject false positive loop closures, and it is not sensitive to the initial pose at the same time. In the Computer Science and Artificial Intelligence Lab (CSAIL) dataset, the absolute position error is reduced by 55.37% compared to the dynamic scaling covariance method, and the absolute rotation error is reduced by 77.27%; in the city10,000 synthetic dataset, the absolute position error is reduced by 89.37% compared to the pairwise consistency maximization (PCM) and the absolute rotation error is reduced by 97.9%.

Description: Panax ginseng is a well-known medicinal plant that achieves strong resistance against plant pathogens while growing in the wild. Due to the high market demand for ginseng as a health food source, ginseng cultivation is prevalent in South Korea. However, continuous monocropping creates problems like irregular growth or vulnerability to crop diseases. Quorum sensing (QS) deals with the intracellular communication of bacteria and plays a role in dynamic changes in the soil microbiome. Here, we investigated how acyl-homoserine lactone (AHL) signaling molecules in QS (C8, C10, and C12) improve plant growth and induce shifts in the soil microbiome. To assess the effects, we recorded root and shoot growth of ginseng seedlings and checked the changes in the soil microbiome during different time points (0, 2, 4, and 8) after 8 weeks of growth. We observed that soils treated with N-decanoyl-L-homoserine lactone (C10) showed the most pronounced effects. Very striking was that C10 had the lowest alpha diversity. Using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2), we observed a high number of QS-related functional genes, with the highest count occurring in the untreated planted soil (W). Together with the known direct and beneficial effects of AHLs on plant development, AHLs treated mono-cropped soil showed trends in the microbiome community.

Description: This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

Description: Fault tolerance in IoT systems is challenging to overcome due to its complexity, dynamicity, and heterogeneity. IoT systems are typically designed and constructed in layers. Every layer has its requirements and fault tolerance strategies. However, errors in one layer can propagate and cause effects on others. Thus, it is impractical to consider a centralized fault tolerance approach for an entire system. Consequently, it is vital to consider multiple layers in order to enable collaboration and information exchange when addressing fault tolerance. The purpose of this study is to propose a multi-layer fault tolerance approach, granting interconnection among IoT system layers, allowing information exchange and collaboration in order to attain the property of dependability. Therefore, we define an event-driven framework called FaTEMa (Fault Tolerance Event Manager) that creates a dedicated fault-related communication channel in order to propagate events across the levels of the system. The implemented framework assist with error detection and continued service. Additionally, it offers extension points to support heterogeneous communication protocols and evolve new capabilities. Our empirical results show that introducing FaTEMa provided improvements to the error detection and error resolution time, consequently improving system availability. In addition, the use of Fatema provided a reliability improvement and a reduction in the number of failures produced.

Description: This paper proposes a new single-image dehazing method, which is an important preprocessing step in vision applications to overcome the limitations of the conventional dark channel prior. The dark channel prior has a tendency to underestimate transmissions of bright regions or objects that can generate color distortions during the process of dehazing. In order to suppress the distortions in a large sky area or a bright white object, the sky probabilities and the white-object probabilities calculated in the non-sky area are proposed. The sky area is detected by combining the advantages of a region-based and a boundary-based sky segmentation in order to consider various sky shapes in road scenes. The performance of the proposed methods is evaluated using synthetic and real-world datasets. When compared to conventional methods in the reviewed literature, the proposed method produces significant improvements concerning visual and numerical criteria.

Description: As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM process. In situ monitoring is a vital technique to detect the defects in advance, which is expected to reduce the defects. This work proposed a method that combined acoustic signals with a deep learning algorithm to monitor the spatter behaviors. The acoustic signals were recorded by a microphone and the spatter information was collected by a coaxial high-speed camera simultaneously. The signals were divided into two types according to the number and intensity of spatter during the SLM process with different combinations of processing parameters. Deep learning models, one-dimensional Convolutional Neural Network (1D-CNN), two-dimensional Convolutional Neural Network (2D-CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were trained to establish the relationships between the acoustic signals and characteristics of spatter. After K-fold verification, the highest classification confidence of models is 85.08%. This work demonstrates that it is feasible to use acoustic signals in monitoring the spatter defect during the SLM process. It is possible to use cheap and simple microphones instead of expensive and complicated high-speed cameras for monitoring spatter behaviors.

Description: The activities performed by nurses in their daily activities involve frequent forward bending and awkward back postures. These movements contribute to the prevalence and development of low back pain (LBP). In previous studies, it has been shown that modifying their posture by education and training in proper lifting techniques decreases the prevalence of LBP. However, this education and training needs to be implemented daily. Hence, implementing the use of a wearable device to monitor the back posture with haptic feedback would be of importance to prevent LBP. This paper proposes a wearable device to monitor the back posture of the user and provide feedback when the participant is performing a possible hurtful movement. In this study, a group of participants was asked to wear the device while performing three of the most common activities performed by nurses. The study was divided into three sessions: In the first session, the participants performed the activities without feedback (baseline). During the second session, the participants received feedback from the wearable device (training) while performing the three tasks. Finally, for the third session, the participants performed the three tasks again, but the haptic feedback was turned off (validation). We found an improvement in the posture of more than 40% for the pitch (lateral bending) and roll (forward/backward bending) axes and 7% for the yaw (twisting) axis when comparing to the results from session 1 and session 2. The comparison between session 1 and session 3 showed an overall improvement of more than 50% for the pitch (lateral bending) and roll (forward/backward bending) axes and more than 20% for the yaw axis. These results hinted at the impact of the haptic feedback on the participants to correct their posture.

Description: Ischemic stroke is one of the leading causes of death among the aged population in the world. Experimental stroke models with rodents play a fundamental role in the investigation of the mechanism and impairment of cerebral ischemia. For its celerity and veracity, the 2,3,5-triphenyltetrazolium chloride (TTC) staining of rat brains has been extensively adopted to visualize the infarction, which is subsequently photographed for further processing. Two important tasks are to segment the brain regions and to compute the midline that separates the brain. This paper investigates automatic brain extraction and hemisphere segmentation algorithms in camera-based TTC-stained rat images. For rat brain extraction, a saliency region detection scheme on a superpixel image is exploited to extract the brain regions from the raw complicated image. Subsequently, the initial brain slices are refined using a parametric deformable model associated with color image transformation. For rat hemisphere segmentation, open curve evolution guided by the gradient vector flow in a medial subimage is developed to compute the midline. A wide variety of TTC-stained rat brain images captured by a smartphone were produced and utilized to evaluate the proposed segmentation frameworks. Experimental results on the segmentation of rat brains and cerebral hemispheres indicated that the developed schemes achieved high accuracy with average Dice scores of 92.33% and 97.15%, respectively. The established segmentation algorithms are believed to be potential and beneficial to facilitate experimental stroke study with TTC-stained rat brain images.

Description: This study presents tributyl acetylcitrate (TBAC) as a novel ecofriendly high flash point and high boiling point solvent for electrolytes in lithium-ion batteries. The flash point (TFP=217∘C) and the boiling point (TBP=331∘C) of TBAC are approximately 200 K greater than that of conventional linear carbonate components, such as ethyl methyl carbonate (EMC) or diethyl carbonate (DEC). The melting point (TMP=−80∘C) is more than 100 K lower than that of ethylene carbonate (EC). Furthermore, TBAC is known as an ecofriendly solvent from other industrial sectors. A life cycle test of a graphite/NCM cell with 1 M lithium hexafluorophosphate (LiPF6) in TBAC:EC:EMC:DEC (60:15:5:20 wt) achieved a coulombic efficiency of above 99% and the remaining capacity resulted in 90 percent after 100 cycles (C/4) of testing. As a result, TBAC is considered a viable option for improving the thermal stability of lithium-ion batteries.

Description: Human–robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implement method. In this paper, we proposed to add human–machine differentiation to the speed and separation monitoring in human–robot collaborative applications. The formula for the protective separation distance was extended with a variable for the kind of object that approaches the robot. Different sensors for differentiation of human and non-human objects are presented. Thermal cameras are used to take measurements in a proof of concept. Through differentiation of human and non-human objects, it is possible to decrease the protective separation distance between the robot and the object and therefore increase the overall efficiency of the collaborative application.

Description: The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power levels of the microwave sources. A moisture controller can be designed to achieve this goal; however, a process model is required to design model-based controllers. Since complex physics governs the microwave drying process, system identification tools are employed in this paper to exploit the process input and output information and find a simplified yet accurate model of the process. The moisture content of the foam that is the process output is measured using a designed electrical capacitance tomography (ECT) sensor. The ECT sensor estimates the 2D permittivity distribution of moving foams, which correlates with the foam moisture. Experiments are conducted to collect the ECT measurements while giving different inputs to the microwave sources. A state-space model is estimated using one of the collected datasets and is validated using the other datasets. The comparison between the model response and the actual measurements shows that the model is accurate enough to design a controller for the microwave drying process.

Description: Loran-C is the most essential backup and supplementary system for the global navigation satellite system (GNSS). Continuous wave interference (CWI) is one of the main interferences in the Loran-C system, which will cause errors in the measurement of the time of arrival, thereby affecting positioning performance. The traditional adaptive notch filter method needs to know the frequency of CWI when removing it, and the number is limited. This paper presents a method based on sparseness to suppress the CWI in the Loran-C signal. According to the different morphological characteristics of the Loran-C signal and the CWI, we construct dictionaries suitable for the two components, respectively. We use the tunable Q-factor wavelet transform and the discrete cosine transform to make the two components obtain a good sparse representation in their respective dictionaries. Then, the two components are separated using the morphological component analysis theory. We illustrate this method using both synthetic data and actual data. A huge advantage of the proposed method is that there is no need to know the frequencies of the CWI for it can better cope with frequency changes of the CWI in the actual environments. Compared with the adaptive notch filter method, the results of the proposed method show that our approach is more effective and convenient.

Description: Defoliation (DF) on peach (Prunus persica L.) and Japanese apricot (Prunus mume Sieb. et Zucc.) trees caused by a hailstorm in 2017, year 1, was investigated for its effects on growth and fruit yield in South Korea over four years, comparing with recovery effects of the DF trees treated with repeated immediate pruning (IP) right after the storm. Treatments included 0–10, 10–40, 40–70, and 70–100% of DF trees, with 0–10, 10–40, 40–70, and 70–100% of DF + IP trees. The hailstorms increased the damages to shoots for peaches and to shoots and scaffold for Japanese apricot trees in year 1, with fruit yield reduced more than 80% observed on 10–100 DF of both fruit species. The IP treatment increased the number of new shoots in years 2–4 but reduced shoot length and diameter of peach and Japanese apricot trees. Tree canopy in years 2–4 was reduced on 40–100 DF of peach trees and on 70–100 of DF and DF + IP of Japanese apricot trees. The 40–100 DF Japanese apricot trees resulted in a fruit yield index of less than 90% for years 2–4, which was observed on 40–100 DF+IP trees only in year 2 due to balanced tree vegetative and reproductive growth.

Description: Legume crops have played a significant role in the historical dietary regime of Afghan peoples. Recently, production of common beans has increased on Afghan farms relative to other leguminous crops. However, compared with other pulse crops, common beans are more prone to water stress. To select drought resistant common beans, several varieties were assessed in the field during a sequence of restricted water supplies for two years and the local drought regime was analyzed for a 12-yr period. The first experiment in 2018 compared five bean varieties under four irrigation regimes. White and black beans with long maturation periods and climber habits, and motley beans, characterized by moderate maturity and semi-climber structures, were susceptible to drought and did not mature well under restricted irrigation and ambient climate conditions. The other two varieties, red and pied beans, adapted to restricted water supplies and the long dry summers; these two varieties were assessed again in 2019. Statistical analyses and inferences based on the 2019 study suggest that red beans are more adaptable to water deficit treatments compared to pied beans. Therefore, red beans are considered a better option given the frequent mid- to late-summer droughts that occur in this region, together with the generally harsh mountain climate and short growing season of the central Afghanistan highlands. As a second varietal choice, pied beans are reasonably drought tolerant based on our findings.

Description: As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks’ layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision.

Description: The aim of this work was to answer the following question: can influential points modify the recommendation of genotypes, based on regression methods, in the presence of genotype × environment (G × E)? Therefore, we compared the parameters of the adaptability and stability of three methodologies based on regression in the presence of influential points. Specifically, were evaluated methods based on simple, non-parametric and quantile (τ = 0.50) regressions. The dataset used in this work corresponds to 18 variety trials of cotton cultivars that were conducted in the 2013–2014 and 2014–2015 crop seasons. The evaluated variable was the cotton fiber yield (kg/ha). Once we noticed that the effect of G × E interaction is significant, the statistical procedures adopted for the adaptability and stability analysis of the genotypes. To verify the presence of a possible influential point, we used the leverage values, studentized residuals (SR), DFBETAS and Cook’s distance. As a result, the influential points can modify the recommendation of genotypes, based on regression methods, in the presence of G × E interaction. The non-parametric and quantile (τ = 0.50) regressions, which are based on median estimators, are less sensitive to the presence of influential points avoiding misleading recommendations of genotypes in terms of adaptability.

Description: Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in this work, we propose a cooperative spectrum detection system based on a residual neural network architecture combined with feature extractor and random forest classifier. The objective of this paper is to propose a cooperative spectrum sensing approach that can achieve high accuracy in higher levels of noise power density with less unlicensed users cooperating in the system. Therefore, we propose to extract features of the sensing information of each unlicensed user, then we use a random forest to classify if there is a presence of a licensed user in each band analyzed by the unlicensed user. Then, information from several unlicensed users are shared to a fusion center, where the decision about the presence or absence of a licensed user is accomplished by a model trained by a residual neural network. In our work, we achieved a high level of accuracy even when the noise power density is high, which means that our proposed approach is able to recognize the presence of a licensed user in 98% of the cases when the evaluated channel suffers a high level of noise power density (−134 dBm/Hz). This result was achieved with the cooperation of 10 unlicensed users.

Description: High-throughput computational screening (HTCS) is an effective tool to accelerate the discovery of active materials for Li-ion batteries. For the evaluation of organic cathode materials, the effectiveness of HTCS depends on the accuracy of the employed chemical descriptors and their computing cost. This work was focused on evaluating the performance of computational chemistry methods, including semi-empirical quantum mechanics (SEQM), density-functional tight-binding (DFTB), and density functional theory (DFT), for the prediction of the redox potentials of quinone-based cathode materials for Li-ion batteries. In addition, we evaluated the accuracy of three energy-related descriptors: (1) the redox reaction energy, (2) the lowest unoccupied molecular orbital (LUMO) energy of reactant molecules, and (3) the highest occupied molecular orbital (HOMO) energy of lithiated product molecules. Among them, the LUMO energy of the reactant compounds, regardless of the level of theory used for its calculation, showed the best performance as a descriptor for the prediction of experimental redox potentials. This finding contrasts with our earlier results on the calculation of quinone redox potentials in aqueous media for redox flow batteries, for which the redox reaction energy was the best descriptor. Furthermore, the combination of geometry optimization using low-level methods (e.g., SEQM or DFTB) followed by energy calculation with DFT yielded accuracy as good as the full optimization of geometry using the DFT calculations. Thus, the proposed calculation scheme is useful for both the optimum use of computational resources and the systematic generation of robust calculation data on quinone-based cathode compounds for the training of data-driven material discovery models.

Description: In this paper, we provide external image features and use the internal attention mechanism to solve the VQA problem given a dataset of textual questions and related images. Most previous models for VQA use a pair of images and questions as input. In addition, the model adopts a question-oriented attention mechanism to extract the features of the entire image and then perform feature fusion. However, the shortcoming of these models is that they cannot effectively eliminate the irrelevant features of the image. In addition, the problem-oriented attention mechanism lacks in the mining of image features, which will bring in redundant image features. In this paper, we propose a VQA model based on adversarial learning and bidirectional attention. We exploit external image features that are not related to the question to form an adversarial mechanism to boost the accuracy of the model. Target detection is performed on the image—that is, the image-oriented attention mechanism. The bidirectional attention mechanism is conducive to promoting model attention and eliminating interference. Experimental results are evaluated on benchmark datasets, and our model performs better than other models based on attention methods. In addition, the qualitative results show the attention maps on the images and leads to predicting correct answers.

Description: In recent years, tryptophan metabolism via the kynurenine pathway has become one of the most active research areas thanks to its involvement in a variety of physiological processes, especially in conditions associated with immune dysfunction, central nervous system disorders, autoimmunity, infection, diabetes, and cancer. The kynurenine pathway generates several metabolites with immunosuppressive functions or neuroprotective, antioxidant, or toxic properties. An increasing body of work on this topic uncovers a need for reliable analytical methods to help identify and quantify tryptophan metabolites at physiological concentrations in biological samples of different origins. Recent methodological advances in the fabrication and application of electrochemical sensors promise a rise in the future generation of novel analytical systems. This work summarizes current knowledge and provides important suggestions with respect to direct electrochemical determinations of kynurenine pathway metabolites (kynurenines) in complex biological matrices. Measurement challenges, limitations, and future opportunities of electroanalytical methods to advance study of the implementation of kynurenines in disease conditions are discussed.

Description: Wireless technologies are increasingly relevant in different activities and lines of the economy, as well as in the daily life of people and companies. The advent of fifth generation networks (5G) implies a promising synergy with the Internet of Things (IoT), allowing for more automations in production processes and an increase in the efficiency of information transmission, managing to improve the efficiency in decision-making through tools such as big data and artificial intelligence. This article presents a description of the 5G implementation process in Colombia, as well as a revision of opportunities when combining with IoT in featured sectors of the departmental development plans, such as agriculture, tourism, health, the environment, and industry. Results shows that the startup of 5G in Colombia has been a slow process, but there are comparisons with similar procedures in other developed countries. Additionally, we present examples of 5G and IoT applications which can be promoted in Colombia, aimed at improving the quality of life of their habitants and promoting economic development.

Description: Strategies for waste valorisation from domestic and agro-industrial activities must be pursued, and its use as a soil amendment is an interesting possibility. In this four-year study, the effect of applying municipal solid waste (MSW), farmyard manure (FYM), bottom wood ash supplemented with nitrogen (Ash + N), the inorganic fertilization common in the region (50 kg ha-1 N, P2O5 and K2O) (Control) and this inorganic fertilization supplemented with 70 kg N ha-1 (High N) was assessed in a rainfed olive grove planted in a shallow soil with low organic matter and managed with conventional tillage. The High N treatment significantly increased olive yield in comparison to the other treatments (165% more than MSW), and soil available N proved to be the main driver for tree productivity. MSW and FYM increased soil organic matter, as well as the levels of phosphorus and cation exchange capacity, leaving good indications for future production cycles, although during the four years of the study these treatments provided little N to the trees. The High N treatment significantly reduced soil organic matter (63% less than MSW). The result was attributed in part to the soil management system that did not allow the development of herbaceous vegetation, but also to an effect known as “added N interaction”, in which the excess of inorganic N in the soil might have contributed to accelerate the mineralization of native soil organic matter, an aspect that compromises the sustainability of this fertilization strategy. Although MSW and wood ash are sometimes associated with risks of environmental contamination with heavy metals, in this study the levels of heavy metals in soils and in plant tissues were not of concern.

Description: This research concerns a design and construction of a bone mineral density (BMD) and bone mineral content (BMC) measurement system based on dual energy x-ray absorptiometry (DEXA). An indirect x-ray detector is designed by optical coupling CMOS sensor with image on the intensifying screen. A dedicated microcontroller x-ray apparatus is used as an x-ray source to capture two energy level x-ray of middle phalanges bone of middle finger. The captured image is processed based on modified Beer-Lambert law to compute bone mineral density. Bone mineral content is also computed by determining the area of the phalanges bone using active contour. The designed bone mineral density (BMD) and bone mineral content (BMC) measurement system is low-cost and hence can be distributed at district hospital for screening purposes of Osteoporosis of the elderly. Compared with BMD measured from commercial model, BMD measurement of our system acquires linear relation with R2 equals 0.969. The mean square error between the normalized BMD value and that of the commercial model is 0.0000981.

Description: Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period.

Description: The signal-to-noise ratios (SNR) of ultrasonic imaging and non-destructive evaluation (NDE) applications can be greatly improved by driving each piezoelectric transducer (single or in array) with tuned HV capacitive-discharge drivers. These can deliver spikes with kW pulsed power at PRF ≈ 5000 spikes/s, achieving levels higher even than in CW high-power ultrasound: up to 5 kWpp. These conclusions are reached here by applying a new strategy proposed for the accurate modeling of own-design re-configurable HV capacitive drivers. To obtain such rigorous spike modeling, the real effects of very high levels of pulsed intensities (3–10 A) and voltages (300–700 V) were computed. Unexpected phenomena were found: intense brief pulses of driving power and probe emitted force, as well as nonlinearities in semiconductors, though their catalog data include only linear ranges. Fortunately, our piezoelectric and circuital devices working in such an intense regime have not shown serious heating problems, since the finally consumed “average” power is rather small. Intensity, power, and voltage, driving wideband transducers from our capacitive drivers, are researched here in order to drastically improve (∆ 〉〉 40 dB) their ultrasonic “net dynamic range available” (NDRA), achieving emitted forces 〉 240 Newtonspp and receiving ultrasonic signals of up to 76–205 Vpp. These measurements of ultrasonic pulsed voltages, received in NDE and Imaging, are approximately 10,000 larger than those usual today. Thus, NDRA ranges were optimized for three laboratory capacitive drivers (with six commercial transducers), which were successfully applied in the aircraft industry for imaging landing flaps in Boeing wings, despite suffering acoustic losses 〉 120 dB.

Description: This paper introduces a cognitive psychological experiment that was conducted to analyze how traditional film editing methods and the application of cognitive event segmentation theory perform in virtual reality (VR). Thirty volunteers were recruited and asked to watch a series of short VR videos designed in three dimensions: time, action (characters), and space. Electroencephalograms (EEG) were recorded simultaneously during their participation. Subjective results show that any of the editing methods used would lead to an increased load and reduced immersion. Furthermore, the cognition of event segmentation theory also plays an instructive role in VR editing, with differences mainly focusing on frontal, parietal, and central regions. On this basis, visual evoked potential (VEP) analysis was performed, and the standardized low-resolution brain electromagnetic tomography algorithm (sLORETA) traceability method was used to analyze the data. The results of the VEP analysis suggest that shearing usually elicits a late event-related potential component, while the sources of VEP are mainly the frontal and parietal lobes. The insights derived from this work can be used as guidance for VR content creation, allowing VR image editing to reveal greater richness and unique beauty.

Description: This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.

Description: Recently, the field of musical co-creativity has gained some momentum. In this context, our goal is twofold: to develop an intelligent listening and predictive module of chord sequences, and to propose an adapted evaluation of the associated Music Information Retrieval (MIR) tasks that are the real-time extraction of musical chord labels from a live audio stream and the prediction of a possible continuation of the extracted symbolic sequence. Indeed, this application case invites us to raise questions about the evaluation processes and methodology that are currently applied to chord-based MIR models. In this paper, we focus on musical chords since these mid-level features are frequently used to describe harmonic progressions in Western music. In the case of chords, there exists some strong inherent hierarchical and functional relationships. However, most of the research in the field of MIR focuses mainly on the performance of chord-based statistical models, without considering music-based evaluation or learning. Indeed, usual evaluations are based on a binary qualification of the classification outputs (right chord predicted versus wrong chord predicted). Therefore, we present a specifically-tailored chord analyser to measure the performances of chord-based models in terms of functional qualification of the classification outputs (by taking into account the harmonic function of the chords). Then, in order to introduce musical knowledge into the learning process for the automatic chord extraction task, we propose a specific musical distance for comparing predicted and labeled chords. Finally, we conduct investigations into the impact of including high-level metadata in chord sequence prediction learning (such as information on key or downbeat position). We show that a model can obtain better performances in terms of accuracy or perplexity, but output biased results. At the same time, a model with a lower accuracy score can output errors with more musical meaning. Therefore, performing a goal-oriented evaluation allows a better understanding of the results and a more adapted design of MIR models.

Description: Renewable sources on islands seem to be the most attractive option to decarbonize and lower the price of electricity; currently, most islands do so by replacing their diesel generators with wind or solar sources, along with energy storage. The Galapagos Islands are no exception. This study presents a techno-economic analysis of hybrid renewable systems in the Galapagos Islands, considering the repowering of its renewable sources and reduction in the penetration of diesel generators. This study uses EnergyPlan software, where the best option is chosen based on technical, economic, and environmental indicators. Finally, several sensitivity analyses are done. The results show that by increasing the capacity of current wind and photovoltaic systems, the total annual cost reduces by 20% and 10.31%, respectively; this is a specific result of this study. Moreover, there is a reduction in CO2 emissions produced by diesel generators, up to 38.96%.

Description: Background: The new demands of the current market including for space should be satisfied by designing modern material flow systems. Designing warehouses using effective material handling equipment significantly supports cost reduction and efficient space utilization. Sequencing of items is an important process that leads to enhanced logistics operations. Current approaches are not capable of fully fulfilling dynamic changes. Methods: In this paper, a puzzle-based sequencing system with a high density and highly efficient floor space utilization was successfully developed. Accordingly, two solving methods were investigated: game tree and pathfinding algorithms. A-star was chosen based on pathfinding algorithms in order to find the shortest solution of the puzzle in which the sequencing time was decreased. The pre-sorting strategy was proposed to overcome the unsolvable configuration issue that cannot be solved by the aforementioned methods. Moreover, the shape of the puzzle was considered. Results: Based on numerical calculations, we found that a square shape was better than a rectangle in terms of solution steps, and we confirmed the direct relationship between the aspect ratio and rectilinear distance, which directly affects the pre-sorting steps. Conclusions: Our results prove that the puzzle-based sequencing system should be highly preferred for effective floor space utilization compared to the current systems.

Description: Software-defined networking (SDN) separates the control plane and the data plane, which provides network applications with global network topology and the flexibility to customize packet forwarding rules. SDN has a wide range of innovative applications in 5G, Internet of Things, and information center networks. However, the match-action programming model represented by OpenFlow / Protocol Oblivious Forwarding (POF) in SDN can only process limited types of data such as packets and metadata, making it hard to fulfill future network applications. In this paper, data type and data location are added in the matching fields and actions to make the match-action table (MAT) compatible with multiple types of data, hence improving the data plane’s programmability. Data type helps the MAT to perceive multiple types of data, allowing them to be processed by a single MAT. Data location allows MAT to be decoupled from data meaning, quickly locating specific data in the switch. Based on Intel’s Data Plane Development Kit (DPDK), we design and implement a pipeline that is compatible with multiple types of data processing. Protocol and data type oblivious match-action tables and atomic instructions are included in the pipeline. Experiments show that representing data with data type and data location makes the pipeline compatible with multiple types of data without sacrificing forwarding performance, fulfilling the needs of network applications to handle a variety of types of data while avoiding repeating hardware design.

Description: This paper proposes a hybrid beamforming design algorithm for a multi-user physical layer security modulation technique. The hybrid beamforming scheme is used in the base station to generate multi-beams according to the direction angle of the target users. The base station first uses a secure analog beamforming scheme to generate analog beams in multiple desired directions, then uses minimum mean square error (MMSE) to design the digital beamforming matrix to eliminate inter-beam interference. Due to randomly selecting a subset of antennas to transmit signals at the symbol rate, the base station transmits the defined constellation to the target users and projects the randomized constellation in the other angles. In addition, the superposition of signals is affected by a randomly selected antennas subset, resulting in higher sidelobe energy. However, due to the integer optimization target, the optimization problem of antenna subsets is non-trivial. Therefore, this paper proposes a cross-entropy iteration method to choose the optimal antenna combination to reduce the sidelobe energy. The simulation shows that the proposed method in this paper has about 10 dB lower sidelobe energy than the random selection method. Besides, the eavesdropper’s symbol error rate of QPSK is always 0.75, while the multi-target users meet the quality of service requirements.

Description: This study presents the optimization of the lateral device geometry and thickness of the channel and barrier layers of AlGaN/GaN high electron mobility transistors (HEMTs) for the enhancement of breakdown voltage (VBR) characteristics using a TCAD simulation. The effect of device geometry on the device performance was explored by varying the device design parameters, such as the field plate length (LFP), gate-to-drain length (LGD), gate-to-source length (LGS), gate length (LG), thickness of the Si3N4 passivation layer (Tox), thickness of the GaN channel (Tch), and AlGaN barrier (Tbarrier). The VBR was estimated from the off-state drain current versus the drain voltage (IDS–VDS) curve, and it exhibited a strong dependence on the length and thickness of the parameters. The optimum values of VBR for all the device’s geometrical parameters were evaluated, based on which, an optimized device geometry of the field-plated AlGaN/GaN HEMT structure was proposed. The optimized AlGaN/GaN HEMT structure exhibited VBR = 970 V at IGS = 0.14 A/mm, which was considerably higher than the results obtained in previous studies. The results obtained in this study could provide vital information for the selection of the device geometry for the implementation of HEMT structures.

Description: The last decade has seen exponential growth in the field of deep learning with deep learning on microcontrollers a new frontier for this research area. This paper presents a case study about machine learning on microcontrollers, with a focus on human activity recognition using accelerometer data. We build machine learning classifiers suitable for execution on modern microcontrollers and evaluate their performance. Specifically, we compare Random Forests (RF), a classical machine learning technique, with Convolutional Neural Networks (CNN), in terms of classification accuracy and inference speed. The results show that RF classifiers achieve similar levels of classification accuracy while being several times faster than a small custom CNN model designed for the task. The RF and the custom CNN are also several orders of magnitude faster than state-of-the-art deep learning models. On the one hand, these findings confirm the feasibility of using deep learning on modern microcontrollers. On the other hand, they cast doubt on whether deep learning is the best approach for this application, especially if high inference speed and, thus, low energy consumption is the key objective.

Description: The utilization of groundwater is becoming increasingly popular for heating and cooling buildings, as well as to regulate the temperature needs of industrial processes. Groundwater has excellent energy potential from various factors, of which environmental acceptability stands out, as groundwater is considered a source of renewable energy. Due to the water table depth below the surface, atmospheric conditions have a negligible effect on the temperature of groundwater, resulting only in minor annual temperature variations, thus also making groundwater a source of reliable renewable energy. This paper presents some aspects of the groundwater heat pump (GWHP) system’s design and addresses a particular problem on the influence of recharge temperature field as well as local utility lines on the pumping well water temperature. An example is given of a system designed for a production hall in the northern part of Croatia. Geological and hydrogeological conditions at the site are highly favourable regarding the groundwater temperature and aquifer parameters. For the needs of this research, precise electronic sensors with data loggers were installed inside the wells. Probe type GSR 120 NT manufactured by Eltratec, Slovenia, is capable of monitoring level, temperature, and electrical conductivity, including telemetric data transfer to the remote server. Mapping the obtained data revealed significant temperature breakthroughs from the recharge wells, as well as local temperature field deviation near the sanitary and precipitation drainage collectors. Utility installation seepage influence was differentiated by the increase in groundwater electrical conductivity measured at the pumping wells. Results show that not only distance between the wells, as the main parameter that affects the system, but also industrial utility lines can have an influence on thermal field breakthrough.

Description: Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time–space evolution require information about the core characteristics of volcanic particles, such as their granulometry. Typically, such information is gained by the spot direct observation of the ash collected at the ground or by using expensive instrumentation. In this paper, a vision-based methodology aimed at the estimation of ash granulometry is presented. A dedicated image processing paradigm was developed and implemented in LabVIEW™. The methodology was validated experimentally using digital reference images resembling different operating conditions. The outcome of the assessment procedure was very encouraging, showing an accuracy of the image processing algorithm of 1.76%.

Description: The growing availability of mobile devices has lead to an arising development of smart cities services that share a huge amount of (personal) information and data. Without accurate and verified management, they could become severe back-doors for security and privacy. In this paper, we propose a smart city infrastructure able to integrate a distributed privacy-preserving identity management solution based on attribute-based credentials (p-ABC), a user-centric Consent Manager, and a GDPR-based Access Control mechanism so as to guarantee the enforcement of the GDPR’s provisions. Thus, the infrastructure supports the definition of specific purpose, collection of data, regulation of access to personal data, and users’ consents, while ensuring selective and minimal disclosure of personal information as well as user’s unlinkability across service and identity providers. The proposal has been implemented, integrated, and evaluated in a fully-fledged environment consisting of MiMurcia, the Smart City project for the city of Murcia, CaPe, an industrial consent management system, and GENERAL_D, an academic GDPR-based access control system, showing the feasibility.

Description: The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster.

Description: Heavy metal pollution of soil restricts the sustainable use of land and poses risks to human health throughout the world. Changes in the physicochemical properties of soil may increase the mobility of heavy metals in the soil ecosystem and lead to groundwater pollution. In this study, the effects of different salt solutions (NaCl, CaCl2, NaNO3, MgCl2, Na2SO4, and mixed salts) on the release of Cd from soil were investigated by batch desorption tests and the Freundlich isothermal sorption model. Increased concentrations of the salts, except for NaNO3, significantly promoted Cd release (R2 〉 0.9, p 〈 0.01). Under the salt stress, Cd release from the test soils was promoted more by CaCl2 and MgCl2 than by the other salts, and the average desorption rates of eight soil samples at 3.5% salt concentration were 11.15% and 10.80%, respectively, which were much higher than those of NaCl (4.05%), Na2SO4 (0.41%), and NaNO3 (0.33%). Ca2+ and Mg2+ showed better ion exchange capacity than Na+ to promote Cd release; for anions, Cl− formed hydrophilic Cd chloride complexes with Cd in soil. In addition, principal component analysis results revealed that Cd release was mainly influenced by soil texture, cation exchange capacity, and iron–manganese oxide content of the soil. The Cd release level for different soil samples was most closely related to the proportion of fine particles in the soil. The higher the clay content was, the higher the Cd desorption rate.

Description: Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches.

Description: Sustainability is one of the main components of precision farming that will lead to food security and production resources for current and future generations. The selection of suitable hybrids and fertilizers is among the methods that can directly influence sustainable agriculture and economic efficiency at the farm level, providing accurate site-specific nutrient management strategies for yield maximization. This experiment included two fertilizer sources in ten maize hybrids in four replications for three consecutive years (2018–2020). The experiment was carried out at the Látókép Crop Production Experimental Site of the University of Debrecen, Hungary. The results of the ANOVA showed that genotype, year, and fertilizer levels had various effects on grain yield, oil, protein, and starch content. FAO340 had maximum grain yield on different fertilizers (NPK and N), and FAO350 had maximum protein content. To gain the best performance and maximum yield of maize on protein and oil, FAO350 is recommended for protein and FAO340 for oil content. The parameters of grain yield, oil content, protein content, and starch content affected by NPK fertilizer provide the stability of grain yield parameters. FAO360, FAO420, and FAO320 hybrids had their maximum desirable N fertilizer doses and NPK fertilizer stability in this research. These results indicate that FAO360, FAO420, and FAO330 hybrids had their maximum potential yield in different fertilizer and environmental conditions. Based on this multi-year study, the complete NPK fertilizer with 150 kg/ha nitrogen, 115 kg/ha potassium, 135 kg/ha phosphorus is recommended to be used on maize hybrids.

Description: Biochemical stability of soil humus is an important factor affecting soil quality. Fungi are among the most efficient decomposers of humic matter due to presence of oxidative enzymes, including phenoloxidase laccase. Production of laccase by zygomycetes, a group of cellulolytic fungi widespread in soil, is poorly studied. The potential role of laccase from zygomycetes in humus turnover is unknown. Here, we show for the first time that laccase of zygomycetous fungus Mortierella elasson can effectively depolymerize humic acids in vitro. The fungus produced laccase extracellularly in a liquid culture medium. Unlike in case of laccases in ligninolytic basidiomycetes, attempts to increase enzyme activity using inductors, changes in the source of nitrogen and carbon failed to lead to any increase in laccase production. Laccase was purified using ion exchange chromatography and gel filtration. The molecular weight of the laccase was 51.75 kDa. The laccase catalyzed the oxidation of ABTS and K4[Fe(CN)6], phenolic compounds, but not tyrosine. The laccase activity was inhibited by NaN3 and NaF. The pH optimum of the laccase activity was 3.0 for ABTS and 5.0 for 2,6-dimethoxy phenol. The enzyme had moderate thermal stability and was rapidly inactivated at 70 °C. Purified laccase depolymerized humic acids from retisol, compost and peat more effectively than culture liquid containing laccase. The results of the study extend our knowledge of the role of laccases from different producers in the transformation of natural organic matter.