ALBERT

Description: In the current era of a strongly competitive business environment, it is more difficult for companies to attract customers. Consumer neuroscience has growing potential here, as it reveals internal consumer preferences by using innovative methods and tools, which can effectively examine consumer behavior and attract new customers. In particular, smell has a great ability to subconsciously influence customers and, thus, support profitability. This paper examines the importance of consumer neuroscience and its modern technologies used for exploring human perceptions to influence customers and benefit from the aromatization of business spaces. We focused our analysis on various service sectors. Despite the potential of the examined issue, there are a limited number of studies in the field of service providers that use neuroscience tools to examine the effect of aromas on human emotions. Most studies took place in laboratory conditions, and the used methodological procedures varied widely. Our analysis showed that, in spite of the positive impact of aromatization in the majority of aromatized spaces, service companies still do not use the potential of consumer neuroscience and aroma marketing to a sufficient degree. Innovative methods and tools, in particular, are still very underused.

Description: The Kuroshio is the strongest warm current in the western North Pacific, which plays a crucial role in climate and human activities. In terms of this, the accurate acquisition of ocean surface current velocity and direction in the Kuroshio region is of great research value. Gaofen-3 synthetic aperture radar (SAR) provides data support for the study of ocean surface current measurements in the Kuroshio region, but no relevant experimental result has been published yet. In this paper, four available stripmap mode SARs’ data acquired by Gaofen-3 in the Kuroshio region are used for measuring the ocean surface current field. In general, the Doppler centroid anomaly (DCA) estimation is a common method to infer ocean surface currents from single-antenna stripmap data, but only the radial velocity component can be retrieved. In order to measure current vectors, a novel method combining the sub-aperture processing and the least squares (LS) technology is suggested and demonstrated by applying to the Gaofen-3 SAR data processing. The experiment’s results agree well with model-derived ocean current data, indicating that the Gaofen-3 SAR has the capability to accurately retrieve the ocean surface current field in the Kuroshio region and motivate further research by providing more data.

Description: Increasing the bearing capacity of reinforced concrete structures, reducing material consumption, and ensuring quality are critical in modern construction. The article presents an experimental study of the ultimate compressive strains of short fiber basalt reinforced concrete columns and provides recommendations for increasing the bearing capacity using steel reinforcement bars with greater strength. The columns were tested in an upright position using a hydraulic press. Strains were measured with dial indicators and a strain gauge station. It was shown that the addition of 10% coarse basalt fiber increased the ultimate compressibility of concrete on ordinary crushed stone by 19.8%, and expanded clay concrete by 26.1%, which led to the strain hardening of concrete under compression by 9.0% and 12%, respectively. Ultimate compressive strains in fiber-reinforced concrete short columns with combined reinforcement increased 1.42 times in columns on a lightweight aggregate and 1.19 times on heavy aggregate. An increase in the ultimate compressibility of concrete makes it possible to use steel reinforcement with greater strength in compressed elements as the concrete crushing during compression occurs primarily due to the reaching of critical values by tensile stresses in the transverse direction. This makes it possible to manufacture structures with a higher load-bearing capacity and less material consumption. A practical example of the application of the proposed approach is given.

Description: Power Factor Correction (PFC) single-phase AC/DC converters are used in several power electronics applications as full wave control rectifiers improving power quality and providing high standards of efficiency. Many papers dealing with the description or use of such topologies have been published in recent years; however, a review that describes and organizes their specific details has not been reported in the technical literature. Therefore, this paper presents an extensive review of PFC single-phase AC/DC converters operating with the Boost converter topology for low and medium voltage as well as and power appliances. A categorization of bridge, semi-bridgeless, and bridgeless, in accordance with the construction characteristics, was carried out in order to unify the technical terminology. Benefits and disadvantages are described and analyzed in detail. Furthermore, a comparison performance in terms of PFC, Total Harmonic Distortion (THD), power capacity, electromagnetic compatibility (EMC), number of elements, and efficiency is included.

Description: Evaluation and monitoring of wireless sensor networks (WSN) and the parameters defining their operations and design, such as energy consumption, latency, and stability, is a complex task due to interaction with real devices. For greater control of these variables, the use of simulators arises as an alternative. Cooja is a WSN simulator/emulator which handles the devices’ controllers and multiple communication protocol implementations, such as RPL (RPL is one of the most used protocol in IoT). However, Cooja does not consider either the implementation of an energy model (it has infinite energy consumption) nor the visual behavior of the topology construction, although these aspects are crucial for effective network analysis and decision taking. This paper presents the design and the implementation of ViTool-BC, a software built on top of Cooja, which allows the creation of different energy estimation models and also to visualize in real time the behavior of WSN topology construction. In addition, ViTool-BC offers a heat map of energy consumption traces. Therefore, this tool helps researchers to monitor in real time the topology construction, node disconnection, and battery depletion, aspects to be considered in the analysis of the available routing protocols in Cooja.

Description: Cervical laminoplasty is a valuable procedure for myelopathy but it is associated with complications such as increased kyphosis. The effect of ligament damage during cervical laminoplasty on biomechanics is not well understood. We developed the C2–C7 cervical spine finite element model and simulated C3–C6 double-door laminoplasty. Three models were created (a) intact, (b) laminoplasty-pre (model assuming that the ligamentum flavum (LF) between C3–C6 was preserved during surgery), and (c) laminoplasty-res (model assuming that the LF between C3–C6 was resected during surgery). The models were subjected to physiological loading, and the range of motion (ROM), intervertebral nucleus stress, and facet contact forces were analyzed under flexion/extension, lateral bending, and axial rotation. The maximum change in ROM was observed under flexion motion. Under flexion, ROM in the laminoplasty-pre model increased by 100.2%, 111.8%, and 98.6% compared to the intact model at C3–C4, C4–C5, and C5–C6, respectively. The ROM in laminoplasty-res further increased by 105.2%, 116.8%, and 101.8% compared to the intact model at C3–C4, C4–C5, and C5–C6, respectively. The maximum stress in the annulus/nucleus was observed under left bending at the C4–C5 segment where an increase of 139.5% and 229.6% compared to the intact model was observed for laminoplasty-pre and laminoplasty-res model, respectively. The highest facet contact forces were observed at C4–C5 under axial rotation, where an increase of 500.7% and 500.7% was observed compared to the intact model for laminoplasty-pre and laminoplasty-res, respectively. The posterior ligaments of the cervical spine play a vital role in restoring/stabilizing the cervical spine. When laminoplasty is performed, the surgeon needs to be careful not to injure the posterior soft tissue, including ligaments such as LF.

Description: Understanding the natural resources of native flora in a particular area is essential to be able to identify, record, and update existing records concerning the flora of that area, especially medicinal plants. Until recently, there has been very little scientific documentation on the biological diversity of Aljumum flora. The current study aimed to document medicinal plants among the flora of this region and determine the traditional usages that are documented in the literature. In the flowering season from November 2019 to May 2020, we conducted more than 80 field trips to the study area. The results reported 90 species belonging to 79 genera and 34 families in the Aljumum region, which constitute 82 species of medicinal plants from a total of 2253 known species in Saudi Arabia. The most distributed species were Calotropis procera, Panicum turgidum, and Aerva javanica (5.31%); within four endemic families, we found Fabaceae (32.35%), Poaceae (20.58%), and Asteraceae and Brassicaceae (17.64%). The present study reviews a collection of medicinal plants in Aljumum used in ethnomedicine. Additionally, these natural resources should be preserved, and therefore, conservation programs should be established to protect the natural diversity of the plant species in this region with sustainable environmental management.

Description: Ionic liquids represent a class of highly tunable organic compounds responsible for many applications in the domain of wood-based materials. It has often been emphasized that the use of ionic liquids derives from their high affinity for lignocellulose and their good penetration ability into wood structures. This paper discusses the sorption ability of different types of 1-alkyl-3-methylimidazolium ionic liquids with lateral alkyl chains, ranging from ethyl to hexyl into spruce and beech wood, as a function of their cation molecular mass, anion type (chloride, tetrafluoroborate, acetate), and intrinsic properties (surface tension and kinematic viscosity) at room temperature. All the studied ionic liquids present high relative uptake values at the equilibrium, ranging from 11.2% to 69.7%. The bulk diffusion coefficients of the ionic liquids into the wood range from 2 × 10−3 to 28 × 10−3 mm2/min, being higher in the longitudinal direction for both types of wood. The value of the diffusion coefficients for 1-ethyl-3-methylimidazolium chloride is only 25% lower than that for water, despite the obvious differences in viscosity and surface tension, demonstrating a good penetration ability and the potential for wood industry-related applications (as impregnation compound carriers and preservatives).

Description: The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology.

Description: To secure full benefits without jeopardizing project feasibility, sustainability standards in high-rise building design should be included at all phases of the decision-making process. However, there are limited empirical studies on the influence of building information modeling (BIM) implementation in high-rise buildings. Implementing BIM is a viable technique to improve high-rise building sustainability performance. Therefore, the aim of this research is to explore the influence of BIM implementation in high-rise buildings by integrating the exploratory factor analysis (EFA) and structural equation modeling (SEM) approaches. Following a detailed review of the literature to identify critical success factors (CSFs) for BIM implementation, empirical evidence was gathered through a questionnaire survey with 205 stakeholders in construction projects. The EFA revealed five components, namely, productivity, visualization, coordination, sustainability, and safety improvement, all of which have a significant impact on the long-term construction of high-rise buildings. Moreover, SEM was conducted to develop the model for high-rise buildings. However, it has been revealed that awareness and usage level of BIM technology in high-rise buildings still appears to be limited. This scenario paves the way for future researchers to develop more models in the domain of high-rise buildings in order to improve sustainable development.

Description: “Spot the Difference” is a well-known game where players must find subtle differences between two almost identical pictures. If “Spot the Difference” is designed for videos, what is the difference between videos and pictures? If the performance of videos is measured by an eye tracker, what scan paths will be conducted? In this study, we explored this game using a video to conduct a visual performance evaluation. Twenty-five subjects were recruited in a full-factorial experiment to investigate the effect of background (with background, without background), video type (animation, text), and arrangement (left-to-right, top-to-bottom) on searching, eye tracking performance, and visual fatigue. The results showed that the video type had a significant effect on the accuracy and subjective visual fatigue, with the accuracy and subjective visual fatigue for animation being better than for text. The results also indicated that the arrangement had a significant effect on the number of fixations, where top-to-bottom arrangement brought a higher number of fixations. The background had a significant effect on accuracy and subjective visual fatigue, where the accuracy and subjective visual fatigue without a background was better than with a background. For the analysis of the scan path, a denser scan path was found in text than in animation, in top-to-bottom arrangement than in left-to-right arrangement, and without a background than with a background. In the future, game manufacturers should use the results of this research to design different “Spot the Difference” videos. When designing a simple game, an animation without a background and involving a left-to-right arrangement was recommended. When designing a difficult game, the opposite settings should be used.

Description: Currently, the route planning functions in 2D/3D campus navigation systems in the market are unable to process indoor and outdoor localization information simultaneously, and the UI experiences are not optimal because they are limited by the service platforms. An ARCore-based augmented reality campus navigation system is designed in this paper in order to solve the relevant problems. Firstly, the proposed campus navigation system uses ARCore to enhance reality by presenting 3D information in real scenes. Secondly, a visual inertial ranging algorithm is proposed for real-time locating and map generating in mobile devices. Finally, rich Unity3D scripts are designed in order to enhance users’ autonomy and enjoyment during navigation experience. In this paper, indoor navigation and outdoor navigation experiments are carried out at the Lingang campus of Shanghai University of Electric Power. Compared with the AR outdoor navigation system of Gaode, the proposed AR system can achieve increased precise outdoor localization by deploying the visual inertia odometer on the mobile phone and realizes the augmented reality function of 3D information and real scene, thus enriching the user’s interactive experience. Furthermore, four groups of students have been selected for system testing and evaluation. Compared with traditional systems, such as Gaode map or Internet media, experimental results show that our system could facilitate the effectiveness and usability of learning on campus.

Description: This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.

Description: Collecting data allows researchers to store and analyze important information about activities, events, and situations. Gathering this information can also help us make decisions, control processes, and analyze what happens and when it happens. In fact, a scientific investigation is the way scientists use the scientific method to collect the data and evidence that they plan to analyze. Neuroscience and other related activities are set to collect their own big datasets, but to exploit their full potential, we need ways to standardize, integrate, and synthesize diverse types of data. Although the use of low-cost ElectroEncephaloGraphy (EEG) devices has increased, such as those whose price is below 300 USD, their role in neuroscience research activities has not been well supported; there are weaknesses in collecting the data and information. The primary objective of this paper was to describe a tool for data management and visualization, called MuseStudio, for low-cost devices; specifically, our tool is related to the Muse brain-sensing headband, a personal meditation assistant with additional possibilities. MuseStudio was developed in Python following the best practices in data analysis and is fully compatible with the Brain Imaging Data Structure (BIDS), which specifies how brain data must be managed. Our open-source tool can import and export data from Muse devices and allows viewing real-time brain data, and the BIDS exporting capabilities can be successfully validated following the available guidelines. Moreover, these and other functional and nonfunctional features were validated by involving five experts as validators through the DESMET method, and a latency analysis was also performed and discussed. The results of these validation activities were successful at collecting and managing electroencephalogram data.

Description: The number of people diagnosed with epilepsy as a common brain disease accounts for about 1% of the world’s total population. Seizure prediction is an important study that can improve the lives of patients with epilepsy, and, in recent years, it has attracted more and more attention. In this paper, we propose a novel hybrid deep learning model that combines a Dense Convolutional Network (DenseNet) and Long Short-Term Memory (LSTM) for epileptic seizure prediction using EEG data. The proposed method first converts the EEG data into the time-frequency domain through Discrete Wavelet Transform (DWT) for use in the input of the model. Then, we train the previously transformed image through a hybrid model combining Densenet and LSTM. To evaluate the performance of the proposed method, experiments are conducted for each preictal length of 5, 10, and 15 min using the CHB-MIT scalp EEG dataset. As a result, we obtained a prediction accuracy of 93.28%, a sensitivity of 92.92%, a specificity of 93.65%, a false positive rate of 0.063 per hour, and an F1-score of 0.923 when the preictal length was 5 min. Finally, as the proposed method is compared to previous studies, it is confirmed that the seizure prediction performance was improved significantly.

Description: This paper presents a modified NSGA-II algorithm based on the spatial density (SD) operator, combined with computer graphics-based surface parameterisation methods and computational fluid dynamics (CFD) simulations. This was done to optimise the multi-objective aerodynamic design of a centrifugal impeller for a 100-kW vehicle-mounted fuel cell and improve the multi-conditions aerodynamic performance of the centrifugal impeller of the vehicle-mounted fuel cell (FC). The optimisation objectives are to maximise the isentropic efficiency of the rated and common operating conditions. The optimisation results showed that the efficiency of rated working conditions had an increase of 1.29%, mass flow increase of 8.8%, pressure ratio increase of 0.74% and comprehensive margin increase of 6.2%. The efficiency of common working conditions had an increase of 1.2%, mass flow increase of 9.1%, pressure ratio increase of 0.24% and comprehensive margin increase of 10%. The optimisation effect is obvious under the premise of satisfying the constraints, which proves the optimisation method’s engineering effectiveness and provides technical support and methodological research for the multi-objective aerodynamic design optimisation of centrifugal impellers for vehicle-mounted FCs.

Description: The aim of this study was to determine the basic chemical composition, the content of bioactive compounds and antioxidant activity in currant leaves. The leaves of black, red and white currant shrubs were collected in May, and in the beginning of June, July and August, for two years between 2018 and 2019. The proximate analysis, including dry matter, protein, fat, ash and total carbohydrates, was determined. In addition, the content of the polyphenols and the total antioxidant activity using ABTS, DPPH and FRAP assays were conducted. The highest concentration of protein was detected in the whitecurrant leaves harvested in May in both years, while the highest content of crude fat was found in the blackcurrant leaves harvested in both years, with the exception of the August harvest. Extracts from the blackcurrant leaves collected in June/July 2019 had the highest antioxidant activity that was measured by the ABTS method (about 7000 µmol Trolox/g DM) and confirmed by other methods, while extracts from the whitecurrant leaves produced from the August 2018 collection had the lowest antioxidant activity (1884 µmol Trolox/g DM). Currant leaves are a rich source of bioactive compounds and contain higher amounts of polyphenols as compared with currant fruits. These compounds may play a very important role in the risk reduction and even prevention of the most chronic non-communicable diseases. Therefore, further research is needed to identify currant leaves as a source of bioactives for functional foods and natural health products. The highest antioxidant activity was in the redcurrant leaves from all the harvest times in 2018 as measured by the ABTS and FRAP methods. On the contrary, blackcurrant leaves from all the harvest times in 2019 had the highest antioxidant activity.

Description: With the wide increase in global forestry resources trade, the demand for wood is increasing day by day, especially rare wood. Finding a computer-based method that can identify wood species has strong practical value and very important significance for regulating the wood trade market and protecting the interests of all parties, which is one of the important problems to be solved by the wood industry. This article firstly studies the establishment of wood microscopic images dataset through a combination of traditional image amplification technology and Mix-up technology expansion strategy. Then with the traditional Faster Region-based Convolutional Neural Networks (Faster RCNN) model, the receptive field enhancement Spatial Pyramid Pooling (SPP) module and the multi-scale feature fusion of Feature Pyramid Networks (FPN) module are introduced to construct a microscopic image identification model based on the migration learning fusion model and analyzes the three factors (Mix-up, Enhanced SPP and FPN modules) affecting the wood microscopic image detection model. The experimental results show that the proposed approach can identify 10 kinds of wood microscopic images, and the accuracy rate has increased from 77.8% to 83.8%, which provides convenient conditions for further in-depth study of the microscopic characteristics of wood cells and is of great significance to the field of wood science.

Description: With the increasing integration of non-linear electronic loads, the diagnosis and classification of power quality are becoming crucial for power grid signal management. This paper presents a novel diagnosis strategy based on unsupervised learning, namely residual denoising convolutional auto-encoder (RDCA), which extracts features from the complex power quality disturbances (PQDs) automatically. Firstly, the time–frequency analysis is applied to isolate frequency domain information. Then, the RDCA with a weight residual structure is utilized to extract the useful features in the contaminated PQD data, where the performance is improved using the residual structure. A single-layer convolutional neural network (SCNN) with an added batch normalization layer is proposed to classify the features. Furthermore, combining with RDCA and SCNN, we further propose a classification framework to classify complex PQDs. To provide a reasonable interpretation of the RDCA, visual analysis is employed to gain insight into the model, leading to a better understanding of the features from different layers. The simulation and experimental tests are conducted to verify the practicability and robustness of the RDCA.

Description: Construction projects usually involve signing various contracts with specific billing procedures. In practice, dealing with complex contract structures causes significant problems, especially with regard to timely payment and guaranteed cash flow. Furthermore, a lack of transparency leads to a loss of trust. As a result, late or non-payment is a common problem in the construction industry. This paper presents the concept of implementing smart contracts for automated, transparent, and traceable payment processing for construction projects. Automated billing is achieved by combining Building Information Modeling (BIM) approaches with blockchain-based smart contracts. Thereby, parts of traditional construction contracts are transferred to a smart contract. The smart contract is set up using digital BIM-based tender documents and contains all of the relevant data for financial transactions. Once the contracted construction work has been accepted by the client, payments can be made automatically via authorized financial institutions. This paper describes the framework, referred to as BIMcontracts, the container-based data exchange, and the digital contract management workflow. It discusses the industry-specific requirements for blockchain and data storage and explains which technical and software architectural decisions were made. A case study is used to demonstrate the current implementation of the concept.

Description: The advent of chip calorimetry has enabled an unprecedented extension of the capability of differential scanning calorimetry to explore new domains of materials behavior. In this paper, we highlight some of our recent work: the application of heating and cooling rates above 104 K/s allows for the clear determination of the glass transition temperature, Tg, in systems where Tg and the onset temperature for crystallization, Tx, overlap; the evaluation of the delay time for crystal nucleation; the discovery of new polyamorphous materials; and the in-situ formation of glass in liquid crystals. From these application examples, it is evident that chip calorimetry has the potential to reveal new reaction and transformation behavior and to develop a new understanding.

Description: Optical turbulence, as determined by the widely accepted practice of profiling the temperature structure constant, CT2, via the measurement of ambient atmospheric temperature gradients, can be found to differ quite significantly when characterizing such gradients via thermal-couple differential temperature sensors as compared to doing so with acoustic probes such as those commonly used in sonic anemometry. Similar inconsistencies are observed when comparing optical turbulence strength derived via CT2 as compared to those through direct optical or imaging measurements of small fluctuations of the index of refraction of air (i.e., scintillation). These irregularities are especially apparent in stable atmospheric layers and during diurnal quiescent periods. Our research demonstrates that when care is taken to properly remove large-scale index of refraction gradients, the sonic anemometer-derived velocity structure constant, Cv2, coupled with the similarly derived turbulence-driven index of refraction and vertical wind shear gradients, provides a refractive index structure constant, Cn2, that can more closely match the optical turbulence strengths inferred by more direct means such as scintillometers or differential image motion techniques. The research also illustrates the utility and robustness of quantifying Cn2 from CT2 at a point using a single sonic anemometer and establishes a clear set of equations to calculate volumetric Cn2 data using instrumentation that measures wind velocities with more spatial/temporal fidelity than temperature.

Description: This paper reviews recent progress on different high-speed optical short- and medium-reach transmission systems. Furthermore, a comprehensive tutorial on high-performance, low-cost, and advanced optical transceiver (TRx) paradigms is presented. In this context, recent advances in high-performance digital signal processing algorithms and innovative optoelectronic components are extensively discussed. Moreover, based on the growing increase in the dynamic environment and the heterogeneous nature of different applications and services to be supported by the systems, we discuss the reconfigurable and sliceable TRxs that can be employed. The associated technical challenges of various system algorithms are reviewed, and we proffer viable solutions to address them.

Description: In this paper, a coordinated multipoint joint transmission (CoMP-JT) framework at mmWave for a cyclic prefix (CP)-free multiuser OFDM wireless communication system is developed and analyzed. The aim is to provide high-quality service to cell-edge users; otherwise, the cell-users would suffer from significant signal degradation due to undesired interference. The impact of complex Hadamard transform with block diagonalization channel precoding for multiuser interference reduction and designed subcarrier mapping for out-of-band (OOB) reduction are investigated. In addition, the paper studied the input back-off-aided high-power amplifier for peak-to-average power ratio (PAPR) reduction and forward error correction channel coding for improved bit error rate (BER) for cell-edge users at mmWave frequencies. Moreover, signal-to-interference-noise ratio and ergodic achievable rate are estimated both in the presence and absence of CoMP-JT-based transmission technique to verify their significance in terms of transmitted power. Numerical investigations showed an OOB reduction of 312 dB, PAPR reduction from 17.50 dB to 7.66 dB, and improved BER of 1×10−3 in 16-QAM for a signal-to-noise ratio of −6 dB. Hence, the simulation results demonstrated the effectiveness of the developed system.

Description: Cheese is a worldwide produced and consumed commodity. There are many varieties of cheese from soft to hard, white to yellow, and fresh to aged after ripening. Especially, each category has its own producing technology. Many countries have labeled their most traditional cheese as Protective Designation of Origin (PDO). Moreover, several studies using advanced technologies, such as proteomics, have been performed to enhance labeling. In this review, broadly diffused and marketed, as well as Mediterranean countries’ special interest in Mediterranean diet-related PDO cheeses have been chosen as a reference. The aim of this work was to highlight the use of proteomics methods to examine how cheese proteins and peptides rearrange after ripening and use of starters. Further, we aimed to examine what kind of proteins are produced. Finally, we focused on bioactive molecules in cheeses and distinction of the original product from its counterfeit.

Description: This research deals with determination of the loading of an open container during operating modes. The special feature of this container is its convex walls. This engineering solution increases the useful capacity of a container by 8% in comparison to that of the prototype. The elastic elements in the bearing structure of a container were introduced to decrease the dynamic loads. The dynamic loads in the vertical plane were dumped by means of the dry friction forces between the components of the cross bearers of the container’s base. The dynamic loads in the longitudinal plane were dumped by means of the dry friction forces between the horizontal parts of fittings and fixed lashing components. This study presents the modelling of the dynamic loading of a container in a vertical plane. The dynamic loads of a container in the longitudinal plane were determined with a mathematical model. The authors determined the basic strength characteristics of the bearing structure of a container; and found that the maximum stresses to a container were about 200 MPa, concentrated near the front fittings. The maximum displacements were recorded in the cross bearers of the base and amounted to about 4 mm.

Description: The paper presents the results of a study on improving the safety of navigation, during maneuvers in restricted areas, with the use of an augmented virtuality navigation information display. The augmented virtuality solution has been developed by one of the authors and has been described in a previous paper. A set of simulation scenarios has been proposed to study various maneuvers with different types of ships. Models of existing areas and existing ships have been used so the study can be recreated and reevaluated with different types of interfaces. The study is focused on comparing safe maneuvering areas with different information sources used. The results showed that augmented virtuality solution can indeed decrease the safe maneuvering areas and thus increase the safety of navigation for maneuvers in restricted areas.

Description: Nutraceutical industrial fenugreek seed spent (NIFGS), a relatively low-cost material abundantly available with little toxicity is used in crystal violet (CV) dye remediation from aqueous media and reported in the present study. To access the adsorption capacity, the factors affecting it are kinetics and the equilibrium thermodynamics. All the experiments were designed at approximately pH 7. The adsorption isotherm model proposed by Langmuir fits better than the Freundlich isotherm model. Kinetic studies data confirm the pseudo-second order model. It is evident from thermodynamic parameter values that the process of adsorption is endothermic, physical and dynamic. The process optimization of independent variables that influence adsorption was carried out using response surface methodology (RSM) through bi-level fractional factorial experimental design (FEED). The analysis of variance (ANOVA) was implemented to investigate the combined effect of parameters influencing adsorption. The possibilities of using dye-adsorbed NIFGS (“sludge”) for the fabrication of the composites using plastic waste are suggested.

Description: In their daily activity planning, travelers always considers time and space constraints such as working or education hours and distances to facilities that can restrict the location and time-of-day choices of other activities. In the field of population synthesis, current demand models lack dynamic consistency and often fail to capture the angle of activity choices at different times of the day. This article presents a method for synthetic population generation with a focus on activity-time choice. Activity-time choice consists mainly in the activity’s starting time and its duration, and we consider daily planning with some mandatory home-based activity: the chain of other subsequent activities a traveler can participate in depends on their possible end-time and duration as well as the travel distance from one another and opening hours of commodities. We are interested in a suburban area with sparse data available on population, where a discrete choice model based on utilities cannot be implemented due to the lack of microeconomic data. Our method applies activity-hours distributions extracted from the public census, with a limited corpus, to draw the time of a potential next activity based on the end-time of the previous one, predicted travel times, and the successor activities the agent wants to participate in during the day. We show that our method is able to construct plannings for 126k agents over five municipalities, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, which fit adequately real-world time distributions.

Description: Transfer learning is a growing field that can address the variability of activity recognition problems by reusing the knowledge from previous experiences to recognise activities from different conditions, resulting in the leveraging of resources such as training and labelling efforts. Although integrating ubiquitous sensing technology and transfer learning seem promising, there are some research opportunities that, if addressed, could accelerate the development of activity recognition. This paper presents TL-FmRADLs; a framework that converges the feature fusion strategy with a teacher/learner approach over the active learning technique to automatise the self-training process of the learner models. Evaluation TL-FmRADLs is conducted over InSync; an open access dataset introduced for the first time in this paper. Results show promising effects towards mitigating the insufficiency of labelled data available by enabling the learner model to outperform the teacher’s performance.

Description: Intraoral ultrasonography has been widely employed for the preoperative assessment of tumor margins due to its capability to evaluate depth of invasion (DOI) and tumor thickness (TT). Recently, a novel ultrasonographic technique, ultra-high frequency ultrasound (UHFUS) has been increasingly applied to the study of oral lesions. This study evaluates the potential application of intraoral UHFUS to assess DOI and TT parameters of oral squamous cell carcinoma (OSCC) lesions. Patients clinically suspected of OSCC lesions were enrolled and underwent an intraoral UHFUS examination preoperatively. The parameters of TT, DOI, echogenicity, and vascularization were assessed. The parameters of TT and DOI as evaluated by means of UHFUS were compared to histology, which was set as the benchmark. Ten patients in total were enrolled. UHFUS-based DOI and TT measurements were found to positively correlate with histology (p 〈 0.05), although UHFUS provided a slight overestimation of DOI. No differences were found in terms of echogenicity or vascularization depending on the site of the lesion. According to these preliminary results, UHFUS could support the preoperative assessment of TT and DOI, potentially enhancing the clinical evaluation of OSCC.

Description: The low-power wide-area (LPWA) technologies, which enable cost and energy-efficient wireless connectivity for massive deployments of autonomous machines, have enabled and boosted the development of many new Internet of things (IoT) applications; however, the security of LPWA technologies in general, and specifically those operating in the license-free frequency bands, have received somewhat limited attention so far. This paper focuses specifically on the security and privacy aspects of one of the most popular license-free-band LPWA technologies, which is named LoRaWAN. The paper’s key contributions are the details of the design and experimental validation of a security-focused testbed, based on the combination of software-defined radio (SDR) and GNU Radio software with a standalone LoRaWAN transceiver. By implementing the two practical man-in-the-middle attacks (i.e., the replay and bit-flipping attacks through intercepting the over-the-air activation procedure by an external to the network attacker device), we demonstrate that the developed testbed enables practical experiments for on-air security in real-life conditions. This makes the designed testbed perspective for validating the novel security solutions and approaches and draws attention to some of the relevant security challenges extant in LoRaWAN.

Description: In the current work a new equation for initial damage assessment of limestone based on plane strain theory is proposed. Detailed investigations of the static and dynamic characteristics of limestone with different initial damage degree, using longitudinal wave speed, and static-dynamic compression tests are performed. This study investigated the static and dynamic characteristics of limestone with different initial damage degree, using longitudinal wave speed, and static-dynamic compression tests. Experimental results show that the degree of initial damage decreases with increasing longitudinal wave speed, which reaches the minimum when the longitudinal wave speed is approximately 6000 m/s, and the smaller the longitudinal wave velocity, the greater the degree of initial damage. The static and dynamic compressive strengths of limestone increase with the longitudinal wave velocity and strain rate, but the elastic modulus and Poisson’s ratio do not change significantly. Finally, based on the experimental results, the definitions of damage threshold value and strain softening are proposed, which further verify the influence of strain rate and initial damage on rock compression characteristics. The present study sheds light on the importance of initial damage for the mechanical state of rock in underground engineering.

Description: Capabilities of the attenuated total reflection (ATR) at THz wavelengths for increased sub-surface depth characterisation of (bio-)materials are presented. The penetration depth of a THz evanescent wave in biological samples is dependent on the wavelength and temperature and can reach 0.1–0.5 mm depth, due to the strong refractive index change ∼0.4 of the ice-water transition; this is quite significant and important when studying biological samples. Technical challenges are discussed when using ATR for uneven, heterogeneous, high refractive index samples with the possibility of frustrated total internal reflection (a breakdown of the ATR reflection mode into transmission mode). Local field enhancements at the interface are discussed with numerical/analytical examples. Maxwell’s scaling is used to model the behaviour of absorber–scatterer inside the materials at the interface with the ATR prism for realistic complex refractive indices of bio-materials. The modality of ATR with a polarisation analysis is proposed, and its principle is illustrated, opening an invitation for its experimental validation. The sensitivity of the polarised ATR mode to the refractive index between the sample and ATR prism is numerically modelled and experimentally verified for background (air) spectra. The design principles of polarisation active optical elements and spectral filters are outlined. The results and proposed concepts are based on experimental conditions at the THz beamline of the Australian Synchrotron.

Description: The COVID-19 pandemic has become both a challenge and an opportunity to implement certain changes in the world of education. One of the most important differences has been online evaluation, which had, until now, been marginal in most prestigious universities. This study compared the academic achievement of the last cohort that performed classroom assessment and the first group that was graded for an official degree using synchronous online evaluation. Other variables measured were the self-assessment of students in this second group, in order to understand how it affected their perception of the process using three different indicators: stress, difficulty, and fairness. Nine hundred and nineteen students participated in the study. The results indicate that online assessment resulted in grades that were 10% higher while enjoying the same degree of validity and reliability. In addition, stress and difficulty levels were also in line with the on-site experience, as was the perception that the results were fair. The results allow us to conclude that online evaluation, when proctored, provides the same guarantees as desktop exams, with the added bonus of certain advantages which strongly support their continued use, especially in degrees with many students who may come from many different locations.

Description: The steel–concrete composite structures consist of two different material parts, which are connected with reliable shear connectors to enable the combined action of the steel and concrete members. The shear connectors may experience either one-directional repeated cyclic loadings or fully reversed cyclic loadings depending on the structural functions and acting loadings. It is essential for structural engineers to estimate the residual shear strength of the shear connectors after action of repeated loads. The characteristics of deteriorating shear capacities of Y-type perfobond rib shear connectors under repeated loads were investigated to estimate the energy dissipating capacity as well as the residual shear strength after repeated loads. To perform the repeated load experiments four different intensities of repeated loads were selected based on the monotonic push-out tests which were performed with 15 specimens with five different design variables. The selected load levels range from 35% to 65% of the representative ultimate shear strength under the monotonic load. In total, 12 specimens were tested under five different repeated load types which were applied to observe the energy dissipating characteristics under various load intensities. It was found that the dissipated energy per cycle becomes stable and converges with the increasing number of cycles. A design formula to estimate the residual shear strength after the repeated loads was proposed, which is based on the residual shear strength factor and the nominal ultimate shear strength of the fresh Y-type perfobond rib shear connectors. The design residual shear strength was computed from the number of repeated loads and the energy dissipation amount per cycle. The reduction factor for the design residual shear strength was also proposed considering the target reliability level. The various reduction factors for the design residual shear strength were derived based on the probabilistic characteristics of the residual shear strength as well as the energy dissipation due to repeated loads.

Description: We consider the development of ligand-assisted growth processes for generating shape-anisotropic nanomaterials. Using statistical mechanics, we analyze the conditions under which ligand-assisted growth of shape-anisotropic crystalline nanomaterials from solution can take place. Depending on ligand-facet interaction energy and crystal facet area, molecular ligands can form compact layers on some facets leaving other facets free. The growth process is then restricted to free facets and may result in significant anisotropy in crystal shape. Our study uncovers the conditions for ligand-assisted growth of nanoplatelets and nanowires from isotropic or anisotropic seed nanocrystals of cuboid shape. We show that in contrast to nanoplatelets, ligand-assisted growth of nanowires requires certain anisotropy in the ligand-facet interaction energy.

Description: Medication-related osteonecrosis of the jaw (MRONJ) has been associated with the use of different drugs administered in the treatment of malignant neoplasms or metastases and in antiresorptive therapies. Since 2010, denosumab, a monoclonal antibody whose mechanism of action is to prevent the activation of the RANK receptor on the surface of osteoclasts, has been associated with osteonecrosis of the jaw. Primary intraosseous squamous cell carcinoma (PIOSCC) is a rare neoplasm characterised by the presence of squamous cells within the maxillary or mandibular bone without an initial clinical association with the oral mucosa. We present a case of a PIOSCC in an osteonecrosis of the jaw related to a patient who received antiresorptive treatment with denosumab outlining the clinical and histopathological features of MRONJ and PIOSCC.

Description: We propose a simple and robust HSV color-space-based algorithm that can automatically extract object position information without human intervention or prior knowledge. In manufacturing sites with high variability, it is difficult to recognize products through robot machine vision, especially in terms of extracting object information accurately, owing to various environmental factors such as the noise around objects, shadows, light reflections, and illumination interferences. The proposed algorithm, which does not require users to reset the HSV color threshold value whenever a product is changed, uses ROI referencing method to solve this problem. The algorithm automatically identifies the object’s location by using the HSV color-space-based ROI random sampling, ROI similarity comparison, and ROI merging. The proposed system utilizes an IoT device with several modules for the detection, analysis, control, and management of object data. The experimental results show that the proposed algorithm is very useful for industrial automation applications under complex and highly variable manufacturing environments.

Description: Sky surveys with wide and deep field are the key direction of international astronomy research for the next decades. It is worthwhile to study how to design a sky survey algorithm to obtain the maximum scientific output in a limited period of observation. In this paper, the modeling and simulation of a wide and deep sky survey mission are presented using a Staged Design Algorithm (SDA), which takes into account the inefficient time periods and difficult-to-observe sky areas. In order to ensure the effective completion of the long-time survey observation tasks in large sky areas, a two-stage scheduling algorithm is designed. Firstly, the inefficient time periods and difficult observation areas are scheduled, and then the overall observation is carried out. The prearranged schedule is arranged when inefficient time periods or difficult areas are encountered during the overall arrangement. The simulation results are verified on the basic data of the China Space Station Telescope (CSST), and the obtained simulation result is three years ahead of the target of the telescope design to complete the wide and deep sky survey of 15,000 deg2. The design ideas in this paper not only have good results for sky survey observation but also can be extended to similar satellite Earth observation mission planning.

Description: Downregulation of the ubiquitin-like containing PHD and ring finger 1 (UHRF1) oncogene in cancer cells in response to natural anticancer drugs, including thymoquinone (TQ), is a key event that induces apoptosis. TQ can induce UHRF1 autoubiquitination via the E3 ligase activity of its RING domain, most likely through the downregulation of herpes virus-associated ubiquitin-specific protease (HAUSP). In this study, we evaluated whether HAUSP downregulation and fast ubiquitination of UHRF1 are prerequisites for UHRF1 degradation in response to TQ in cancer cells and whether doxorubicin can mimic the effects of TQ on UHRF1 ubiquitination. RNA sequencing was performed to investigate differentially expressed genes in TQ-treated Jurkat cells. The protein expression of UHRF1, HAUSP and Bcl-2 was detected by means of Western blot analysis. The proliferation of human colon cancer (HCT-116) and Jurkat cells was analyzed via the WST-1 assay. RNA sequencing data revealed that TQ significantly decreased HAUSP expression. TQ triggered UHRF1 to undergo rapid ubiquitination as the first step in its degradation and the inhibition of its cell proliferation. TQ-induced UHRF1 ubiquitination is associated with HAUSP downregulation. Like TQ, doxorubicin induced a similar dose- and time-dependent downregulation of UHRF1 in cancer cells, but UHRF1 did not undergo ubiquitination as detected in response to TQ. Furthermore, TQ decreased Bcl-2 expression without triggering its ubiquitination. A fast UHRF1 ubiquitination is an indispensable event for its degradation in response to TQ but not for its responses to doxorubicin. TQ appears to trigger ubiquitination of UHRF1 but not of the Bcl-2 oncogene, thereby identifying UHRF1 as a specific target of TQ for cancer therapy.

Description: An online corn moisture content measurement device would be a key technology for providing accurate feedback information for industrial drying processes to enable the dynamic tracking and closed-loop control of the process. To overcome the problem of large measurement error caused by the characteristics of the corn flow state and the pore distribution when a parallel plate capacitor is applied to the online moisture content measurement process, in this study, we summarized the constraint conditions of the sensor’s structure parameters by mathematical modeling and calculated the optimal sensor design size. Moreover, the influence of porosity variation on moisture content measurement was studied by using the designed sensor. In addition, a mathematical model for calculating corn moisture content was obtained for the moisture content range of 14.7% to 26.4% w.b., temperature of 5 °C to 35 °C, and porosity of 38.4% to 44.6%. The results indicated that the fluctuation in the online moisture content measurement value was obviously reduced after the porosity compensation. The absolute error of the measured moisture content value was −0.62 to 0.67% w.b., and the average of absolute values of error was 0.32% w.b. The main results provide a theoretical basis and technical support for the development of intelligent industrial grain–drying equipment.

Description: CRISP-DM (cross-industry standard process for data mining) methodology was developed as an intuitive tool for data scientists, to help them with applying Big Data methods in the complex technological environment of Industry 4.0. The review of numerous recent papers and studies uncovered that most of papers focus either on the application of existing methods in case studies, summarizing existing knowledge, or developing new methods for a certain kind of problem. Although all of these types of research are productive and required, we identified a lack of complex best practices for a specific field. Therefore, our goal is to propose best practices for the data analysis in production industry. The foundation of our proposal is based on three main points: the CRISP-DM methodology as the theoretical framework, the literature overview as an expression of current needs and interests in the field of data analysis, and case studies of projects we were directly involved in as a source of real-world experience. The results are presented as lists of the most common problems for selected phases (‘Data Preparation’ and ‘Modelling’), proposal of possible solutions, and diagrams for these phases. These recommendations can help other data scientists avoid certain problems or choose the best way to approach them.

Description: The application of pulsed electric fields (PEF) is an innovative extraction technology promoting cell membrane electroporation, thus allowing for an efficient recovery, from an energy point of view, of antioxidant compounds (chlorophylls, carotenoids, total phenolic compounds, etc.) from microalgae. Due to its selectivity and high extraction yield, the effects of PEF pre-treatment (3 kV/cm, 100 kJ/kg) combined with supplementary extraction at different times (5–180 min) and with different solvents (ethanol (EtOH)/H2O, 50:50, v/v; dimethyl sulfoxide (DMSO)/H2O, 50:50, v/v) were evaluated in order to obtain the optimal conditions for the extraction of different antioxidant compounds and pigments. In addition, the results obtained were compared with those of a conventional treatment (without PEF pre-treatment but with constant shaking). After carrying out the different experiments, the best extraction conditions to recover the different compounds were obtained after applying PEF pre-treatment combined with the binary mixture EtOH/H2O, 50:50, v/v, for 60–120 min. PEF extraction was more efficient throughout the study, especially at short extraction times (5–15 min). In this sense, recovery of 55–60%, 85–90%, and 60–70% was obtained for chlorophylls, carotenoids, and total phenolic compounds, respectively, compared to the maximum total extracted amount. These results show that PEF improves the extraction yield of antioxidant bioactive compounds from microalgae and is a promising technology due to its profitability and environmental sustainability.

Description: After five decades of development, mode-locked lasers have become significant building blocks for many optical systems in scientific research, industry, and biomedicine. Advances in noise measurement and reduction are motivated for both shedding new light on the fundamentals of realizing ultra-low-noise optical frequency combs and their extension to potential applications for standards, metrology, clock comparison, and so on. In this review, the theoretical models of noise in mode-locked lasers are first described. Then, the recent techniques for timing jitter, carrier-envelope phase noise, and comb-line noise measurement and their stabilization are summarized. Finally, the potential of the discussed technology to be fulfilled in novel optical frequency combs, such as electro-optic (EO) modulated combs, microcombs, and quantum cascade laser (QCL) combs, is envisioned.

Description: Traceability, namely the ability to access information about a product and its movement across all stages of the supply chain, has been emerged as a key criterion of a product’s quality and safety. Managing fresh products, such as fruits and vegetables, is a particularly complicated task, since they are perishable with short shelf lives and are vulnerable to environmental conditions. This makes traceability of fresh produce very significant. The present study provides a brief overview of the relative literature on fresh produce traceability systems. It was concluded that the commercially available traceability systems usually neither cover the entire length of the supply chain nor rely on open and transparent interoperability standards. Therefore, a user-friendly open access traceability system is proposed for the development of an integrated solution for traceability and agro-logistics of fresh products, focusing on interoperability and data sharing. Various Internet of Things technologies are incorporated and connected to the web, while an android-based platform enables the monitoring of the quality of fruits and vegetables throughout the whole agri-food supply chain, starting from the field level to the consumer and back to the field. The applicability of the system, named AgroTRACE, is further extended to waste management, which constitutes an important aspect of a circular economy.

Description: Quantum annealing is an emerging new platform for combinatorial optimization, requiring an Ising model formulation for optimization problems. The formulation can be an essential obstacle to the permeation of this innovation into broad areas of everyday life. Our research is aimed at the proposal of a Petri net modeling approach for an Ising model formulation. Although the proposed method requires users to model their optimization problems with Petri nets, this process can be carried out in a relatively straightforward manner if we know the target problem and the simple Petri net modeling rules. With our method, the constraints and objective functions in the target optimization problems are represented as fundamental characteristics of Petri net models, extracted systematically from Petri net models, and then converted into binary quadratic nets, equivalent to Ising models. The proposed method can drastically reduce the difficulty of the Ising model formulation.

Description: Fire is an abnormal event that can cause significant damage to lives and property. Deep learning approach has made large progress in vision-based fire detection. However, there is still the problem of false detections due to the objects which have similar fire-like visual properties such as colors or textures. In the previous video-based approach, Faster Region-based Convolutional Neural Network (R-CNN) is used to detect the suspected regions of fire (SRoFs), and long short-term memory (LSTM) accumulates the local features within the bounding boxes to decide a fire in a short-term period. Then, majority voting of the short-term decisions is taken to make the decision reliable in a long-term period. To ensure that the final fire decision is more robust, however, this paper proposes to use a Bayesian network to fuse various types of information. Because there are so many types of Bayesian network according to the situations or domains where the fire detection is needed, we construct a simple Bayesian network as an example which combines environmental information (e.g., humidity) with visual information including the results of location recognition and smoke detection, and long-term video-based majority voting. Our experiments show that the Bayesian network successfully improves the fire detection accuracy when compared against the previous video-based method and the state of art performance has been achieved with a public dataset. The proposed method also reduces the latency for perfect fire decisions, as compared with the previous video-based method.

Description: Despite the wide application of organic light-emitting diodes (OLEDs), the performance of OLED devices is sometimes limited by their reliabilities. In this paper, we report the transient degradation behaviors of fluorescent blue OLEDs, where both the current and luminance initially increase under electrical stress within a short stress time. We analyze the degradation mechanism in terms of the carrier recombination and transport. From the comprehensive analyses of electrical and optical characteristics, it is suggested that the electron transport is responsible for the initial transient behavior of the device.

Description: Digital transformation and artificial intelligence are creating an opportunity for innovation across all levels of industry and are transforming the world of work by enabling factories to embrace cutting edge Information Technologies (ITs) into their manufacturing processes. Manufacturing Execution Systems (MESs) are abandoning their traditional role of legacy executing middle-ware for embracing the much wider vision of functional interoperability enablers among autonomous, distributed, and collaborative Cyber-Physical Production System (CPPS). In this paper, we propose a basic methodology for universally modeling, digitalizing, and integrating services offered by a variety of isolated workcells into a single, standardized, and augmented production system. The result is a reliable, reconfigurable, and interoperable manufacturing architecture, which privileges Open Platform Communications Unified Architecture (OPC UA) and its rich possibilities for information modeling at a higher level of the common service interoperability, along with Message Queuing Telemetry Transport (MQTT) lightweight protocols at lower levels of data exchange. The proposed MES architecture has been demonstrated and validated in several use-cases at a research manufacturing laboratory of excellence for industrial testbeds.

Description: In-cylinder oxygen concentration (ICOC) is critical for advanced combustion control of internal combustion engines, and is hard to be accessed in commercial measurements. In existing research, ICOC is predicted by conventional dynamical model based on mass/energy conservation, which suffers from uncertainties such as inaccuracy of volumetric efficiency or the error of orifice geometry. In this paper, we enhance the ICOC estimation by implementing two vital strategies. Firstly, we introduce a method called virtual measurement to resist the conventional model uncertainties, in this method we modeling the ICOC as a function of ignition delay which can be obtained by measuring the in-cylinder pressure. Secondly, we apply Kalman filter to fuse the ICOC results from the conventional dynamical model and the virtual measurement. The data fusion algorithm turns the estimation to a predictor-corrector fashion, which further improves the overall accuracy and robustness. The proposed approach is validated through a calibrated GT-Power engine model. The results show that the estimation error can be achieved form at worst 0.03 to at best 0.01 on steady state.

Description: The aim of this study is to investigate the impact of CT acquisition parameter setting on organ dose and its influence on image quality metrics in pediatric phantom during CT examination. The study was performed on 64-slice multidetector CT scanner (MDCT) Siemens Definition AS (Siemens Sector Healthcare, Forchheim, Germany) using various CT CAP protocols (P1–P9). Tube potential for P1, P2, and P3 protocols were fixed at 100 kVp while P4, P5, and P6 were fixed at 80 kVp with used of various reference noise values. P7, P8, and P9 were the modification of P1 with changes on slice collimation, pitch factor, and tube current modulation (TCM), respectively. TLD-100 chips were inserted into the phantom slab number 7, 9, 10, 12, 13, and 14 to represent thyroid, lung, liver, stomach, gonads, and skin, respectively. The image quality metrics, signal to noise ratio (SNR) and contrast to noise ratio (CNR) values were obtained from the CT console. As a result, this study indicates a potential reduction in the absorbed dose up to 20% to 50% along with reducing tube voltage, tube current, and increasing the slice collimation. There is no significant difference (p 〉 0.05) observed between the protocols and image metrics.

Description: In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.

Description: Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the ugly” optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society’s movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer’s performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms.

Description: Health improvement is an important social development goal for every country. By using a geographical weighted regression (GWR) model on the 5th and 6th censuses data, this paper analyzes the spatially varied influencing factors of the change in life expectancy of residents in Chinses cities. The results indicate that: (1) The initial level of life expectancy may have a negative correlation with its increase, indicating that life expectancy in different areas may eventually converge to a higher level; moreover, the degree of convergence of life expectancy in cities with different economic development levels is variant. (2) Results of geographically weighted regression model demonstrate significant spatial heterogeneity in the effects of the level of economic development, medical conditions, demographic structure, and natural environment on health improvement. Natural conditions, such as topography, dictate the change in life expectancy in most cities in the middle eastern region of China. Change of educational level is the leading factor in the vast western region while the change in birth rate is the most critical in Xinjiang. Thus, local-based strategies are critical for solving health problems, especially with a focus on promoting health conditions in middle-income and low-income areas.

Description: The 360 Virtual Reality (VR) images generated by several core technologies such as feature extraction, feature mapping, warping, bundle adjustment, seam finding, and blending have arbitrarily shaped holes because the warped and translated pictures cannot cover the entire VR image. Various inpainting algorithms can be considered as solutions to conceal the holes, but a variety of degradations still remain in the concealed VR pictures because they have been invented for non-VR images. This paper proposes an efficient algorithm to conceal the holes that are generated in stereo 360 VR images, where the camera parameters are analyzed and used to derive the positions of pixelsto fill the holes. Simulation results showed that the proposed algorithm outperformed conventional methods in subjective and objective evaluations.

Description: Although Cyber-Physical Systems (CPS) provides a paradigm to accommodate frequent changes in manufacturing sector, modeling and managing operations of CPS are challenging issues due to the complex interactions between entities in the system. Development of an effective context-aware workflow management system to guide the entities in the system is a critical factor to attain the potential benefits of CPS. In this paper, we will address the issue on the design of context-aware workflow management systems for CPS in IoT-enabled manufacturing environment. A CPS consists two parts, the Physical World and the Cyber World. To achieve the goal to design a context-aware information system for CPS, the Cyber World models of the entities in the system are constructed based on discrete timed Petri nets (DTPN) and a multi-agent system architecture in which each entity in the system is modeled as an agent to capture the interactions of entities in CPS. To develop context-aware workflow management systems for CPS, a Configuration/Scheduling Feasibility Problem and a Context Generation Problem in CPS are formulated. A condition for configuration/scheduling feasibility based on transformation of the Cyber World Models is established to develop an algorithm to generate contextual information to guide the operation of CPS. The proposed method is illustrated by examples. A series of experiments have been conducted to demonstrate the practicality of the proposed method in terms of computation time and response time. The results indicate that the computation time and total response time increase polynomially with respect to problem size parameters and show that the proposed method is effective in solving real problems.

Description: Emotion plays a powerful role in human interaction with robots. In order to express more human-friendly emotions, robots need the capability of contextual appraisal that expresses the emotional relevance of various targets in the spatiotemporal situation. In this paper, an emotional appraisal methodology is proposed to cope with such contexts. Specifically, the Ortony, Clore, and Collins model is abstracted and simplified to approximate an emotional appraisal model in the form of a sentence-based cognitive system. The contextual emotion appraisal is modeled by formulating the emotional relationships among multiple targets and the emotional transition with events and time passing. To verify the proposed robotic system’s feasibility, simulations were conducted for scenarios where it interacts with humans manipulating liked or disliked objects on a table. This experiment demonstrated that the robot’s emotion could change over time like humans by using a proposed formula for emotional valence, which is moderated by emotion appraisal of occurring events.

Description: Innovations within the medical device sector are constantly and rapidly emerging due to increasing demand, especially for orthosis systems, which usually constitute built rigids with low comfort, due mainly to the standardized production process. This article reports the design process of a plantar orthosis system from the application of Theory of Inventive Problem Solving (TRIZ) tools, known as generic parameters, matrix of contradictions, and inventive principles. The final orthosis is integrated by four modules or components (1 central and 3 movables) and customers can buy only the required ones, reducing cost in unnecessary parts. The plantar orthosis was defined based on three engineering parameters that delimited the design work by developing a customizable system that is capable of performing simultaneous functions and whose manufacture could be standardized. We identified the existence of a technical contradiction between the engineering parameters, customization, and standardization, which was solved by the inventive principles of segmentation, inversion, transition to a new dimension, and porous materials. A modular design with four components was accomplished, molds are built for each component in a machining center and injected using granulated ethylene vinyl acetate copolymer. The positions of the movable components are configured through a bolt-hole assembly mechanism to the central component, which is a flat perforated plantar base. The novelty in the design here presented is elated to supports that constitute the orthosis, which are interchangeable and adjustable to the pathological and morphological needs of each patient.

Description: This paper proposes the use of a regularized mixture of linear experts (MoLE) for predictive modeling in multimode-multiphase industrial processes. For this purpose, different regularized MoLE were evaluated, namely, through the elastic net (EN), Lasso, and ridge regression (RR) penalties. Their performances were compared when trained with different numbers of samples, and in comparison to other nonlinear predictive models. The models were evaluated on real multiphase polymerization process data. The Lasso penalty provided the best performance among all regularizers for MoLE, even when trained with a small number of samples.

Description: The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

Description: The optimal treatment for hepatocellular carcinoma (HCC) should be selected based on tumor conditions, liver functional reserve, and performance status. Surgical treatment, such as liver resection and liver transplantation, is the most favorable treatment method; however, its indication criteria differ according to each country’s guidelines. In Western countries, liver resection is indicated only for early-stage HCC patients with Barcelona-Clinic Liver Cancer staging classification (BCLC) 0/A. While in Asian countries, liver resection is one of the treatment options for advanced HCC, such as BCLC B/C. Recently, the treatment of HCC is about to enter a drastic transitional period. It started with the widespread use of minimally invasive surgery for HCC, followed by a high rate of hepatitis C virus eradication with the advent of direct acting antivirals and developing a multidisciplinary treatment for highly advanced HCC. As a result, the importance of liver resection for HCC is increasing, and it is time to reconsider the criteria for selecting treatment methods for HCC patients. This article outlines current topics in the surgical treatment of HCC.

Description: Distant supervised relation extraction (DSRE) is widely used to extract novel relational facts from plain text, so as to improve the knowledge graph. However, distant supervision inevitably suffers from the noisy labeling problem that will severely damage the performance of relation extraction. Currently, most DSRE methods are mainly focused on reducing the weights of noisy sentences, ignoring the bag-level noise where all sentences in a bag are wrongly labeled. In this paper, we present a novel noise detection-based relation extraction approach (NDRE) to automatically detect noisy labels with entity information and dynamically correct them, which can alleviate both instance-level and bag-level noisy problems. By this means, we can extend the dataset from the Web tables without introducing more noise. In this approach, to embed the semantics of sentences from corpus and web tables, we firstly propose a powerful sentence coder that employs an internal multi-head self-attention mechanism between the piecewise max-pooling convolutional neural network. Second, we adopt a noise detection strategy, which is expected to dynamically detect and correct the original noisy label according to the similarity between sentence representation and entity-aware embeddings. Then, we aggregate the information from corpus and web tables to make the final relation prediction. Experimental results on a public benchmark dataset demonstrate that our proposed approach achieves significant improvements over the state-of-the-art baselines and can effectively reduce the noisy labeling problem.

Description: In this paper, a novel method for the effective extraction of the light stripes in rail images is proposed. First, a preprocessing procedure that includes self-adaptive threshold segmentation and brightness enhancement is adopted to improve the quality of the rail image. Secondly, center of mass is utilized to detect the center point of each row of the image. Then, to speed up the procedure of centerline optimization, the detected center-points are segmented into several parts based on the geometry of the rail profile. Finally, piecewise fitting is adopted to obtain a smooth and robust centerline. The performance of this method is analyzed in detail, and experimental results show that the proposed method works well for rail images.

Description: An identification of strengths, weakness, opportunities, and threats (SWOT) factors remains imperative for enabling a successful Smart Campus transition. The absence of a structured approach for analyzing the relationships between these SWOT factors and the influence thereof on Smart Campus transitions negate effective implementation. This study leverages a systems thinking approach to bridge this gap. Data were collected through a stakeholder workshop within a University of Technology case study and analyzed using qualitative content analysis (QCA). This resulted in the establishment of SWOT factors affecting Smart Campus transitions. Systems thinking was utilized to analyze the relationships between these SWOT factors resulting in a causal loop diagram (CLD) highlighting extant interrelationships. A panel of experts drawn from the United Kingdom, New Zealand, and South Africa validated the relationships between the SWOT factors as elucidated in the CLD. Subsequently, a Smart Campus transition framework predicated on the CLD archetypes was developed. The framework provided a holistic approach to understanding the interrelationships between various SWOT factors influencing Smart Campus transitions. This framework remains a valuable tool for facilitating optimal strategic planning and management approaches by policy makers, academics, and implementers within the global Higher Education Institution (HEI) landscape for managing successful Smart Campus transition at the South African University of Technology (SAUoT) and beyond.

Description: Monte Carlo Tree Search is one of the main search methods studied presently. It has demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan and other different problems. There are several optimizations of Monte Carlo, but most of them need heuristics or some domain language at some point, making very difficult its application to other problems. We propose a general and optimized implementation of Monte Carlo Tree Search using neural networks without extra knowledge of the problem. As an example of our proposal, we made use of the Dots and Boxes game. We tested it against other Monte Carlo system which implements specific knowledge for this problem. Our approach improves accuracy, reaching a winning rate of 81% over previous research but the generalization penalizes performance.

Description: By the end of 2025 European ports are required to provide (Directive 2014/94/EU) facilities to ensure the Liquefied Natural Gas (LNG) use and on-shore electricity supply for vessels (Cold Ironing—CI). Even though this involves considerable port investment, many uncertainties about CI and LNG performance exist because their application depends on vessel operators’ willingness. Additionally, lag times for CI connection/disconnection along with methane emissions from LNG undermine their feasibility for Short Sea Shipping (SSS). Since, among the SSS aims are the reduction in berthing times and its effectiveness for inter-islands’ traffic where, land electricity grids are frequently dependent on the fuel burning generation by penalizing the CI performance. This paper introduces a calculation method to evaluate the pollution savings in monetary terms by CI and LNG use in SSS. The method is applied to three European routes by testing the environmental performance of two fleets: feeder and Ro-Pax vessels. The results show that feeders reach higher environmental improvements by using port mitigation than Ro-Pax vessels. Additionally, the need for ensuring the sustainability of on-shore grids before the CI implementation was evinced, especially in insularity frameworks, where the environmental benefits from LNG use proved to be more effective.

Description: The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’s disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author’s personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility.

Description: Acoustic surveys were used to locate coastal Pacific herring (Clupea pallasii) spawning grounds, and the spatial and temporal patterns of their spawning in Jinhae Bay, Korea were examined. We deployed mooring with a newly designed autonomous echosounder for ~70 days during the Pacific herring spawning season in Jinhae Bay, from January to April 2018. At the same time, ship-based acoustic surveys were conducted to identify the spatial distribution twice, at 38 and 120 kHz, onboard the fishing vessel in January and April 2018 in the bay. Fish school signals, including those from adult Pacific herring, are often detected through ship-based acoustic surveys in January, from outside the bay. In the spring, weak scattering signals from fish larvae and zooplankton were continuously detected inside the bay. Backscatter at the mooring in the center of Jinhae Bay was low from mid-January to early March, gradually increasing to higher levels until the end of March. The backscatter observed from the mooring correlated well with ship-based acoustic surveys in the center of the bay. This study proposes that the mooring type acoustic echosounder is a valuable tool for temporal abundance information and other aspects of fish behavior.

Description: This work numerically evaluates the role of advancing velocity on the water entry of rigid wedges, highlighting its influence on the development of underpressure at the fluid–structure interface, which can eventually lead to fluid detachment or cavity formation, depending on the geometry. A coupled FEM–SPH numerical model is implemented within LS-DYNA, and three types of asymmetric impacts are treated: (I) symmetric wedges with horizontal velocity component, (II) asymmetric wedges with a pure vertical velocity component, and (III) asymmetric wedges with a horizontal velocity component. Particular attention is given to the evolution of the pressure at the fluid–structure interface and the onset of fluid detachment at the wedge tip and their effect on the rigid body dynamics. Results concerning the tilting moment generated during the water entry are presented, varying entry depth, asymmetry, and entry velocity. The presented results are important for the evaluation of the stability of the body during asymmetric slamming events.

Description: Landing gear is a key load-bearing structure of aircraft during ground operation, and the landing capacity of landing gear is determined by the performance of buffer. To solve the problem of buffer failure caused by insufficient static sealing of a rubber ring at groove side, a new structure of a butterfly rubber ring is proposed by analyzing the factors affecting sealing performance of the rubber ring. First, the constitutive equation of rubber material is derived based on the theory of hyper-elasticity, and the material parameters are obtained by fitting the experimental data. Then, by analyzing the simulation method of installation mode and installation stroke, the simulation method suitable for calculating the sealing performance of the rubber ring is established. The linear fitting formulas with Pearson coefficient greater than 0.92 are used to discuss the influence of pre-compression rate and hydraulic pressure on the sealing performance of the rubber ring. Compared with O-ring, the contact pressure of butterfly-ring is increased by 30% in assembly state and 14% in working state. The results show that the butterfly rubber ring has excellent static sealing performance. It is concluded that improving the configuration of the sealing ring can solve the insufficient unilateral sealing of the hydraulic buffer.

Description: A hardware-based spiking neural network (SNN) has attracted many researcher’s attention due to its energy-efficiency. When implementing the hardware-based SNN, offline training is most commonly used by which trained weights by a software-based artificial neural network (ANN) are transferred to synaptic devices. However, it is time-consuming to map all the synaptic weights as the scale of the neural network increases. In this paper, we propose a method for quantized weight transfer using spike-timing-dependent plasticity (STDP) for hardware-based SNN. STDP is an online learning algorithm for SNN, but we utilize it as the weight transfer method. Firstly, we train SNN using the Modified National Institute of Standards and Technology (MNIST) dataset and perform weight quantization. Next, the quantized weights are mapped to the synaptic devices using STDP, by which all the synaptic weights connected to a neuron are transferred simultaneously, reducing the number of pulse steps. The performance of the proposed method is confirmed, and it is demonstrated that there is little reduction in the accuracy at more than a certain level of quantization, but the number of pulse steps for weight transfer substantially decreased. In addition, the effect of the device variation is verified.

Description: Miniaturization of different measurement processes and a scaled-down approach open the possibility for rapid measurements with the small amounts of samples and reagents into a compact platform with integrated sensors and different measuring components. In this paper, we report a microfluidic approach for measurements of salivary pH, dissolved O2, and CO2 during chewing xylitol gum. The study was done with the samples of 30 healthy volunteers who were chewing a xylitol gum, and the measurements were performed in the microfluidic (MF) chip with integrated commercial PreSens sensors. Xylitol exhibited a significant effect on the pH of saliva in terms of its initial drop, which was the most significant between the 5th and 10th minutes. The effect of xylitol on the amount of oxygen and carbon dioxide in saliva cannot be confirmed. The employed microfluidic platform was shown to be applicable and effective in the analysis of salivary biomarkers relevant to caries development.

Description: When it concerns dissemination, the relationship between cultural heritage and technology has not always been easy. On the one hand, the emotions involved in knowing, enjoying, and feeling a real heritage remain should not be eclipsed by the technological experience. On the other hand, technology cannot be relegated to the point where its use is so superficial as to make it irrelevant. Hence, in the search of good practices in the dissemination of cultural heritage, it is essential that interdisciplinary work teams, composed of humanists and technicians, design new experiences that try to achieve a balance between what must be shown and the way in which it is done. This paper aims to show the potential of augmented reality (AR) for the dissemination of cultural heritage, since it allows the perceiving of real remains while offering virtual reconstructions and complementary information, sounds, images, etc. A bibliometric analysis focusing on its use for the preservation of the memory of vernacular architecture is made. Then, from the understanding and interpretation of the ideas exposed in literature, a decalogue of good practices in the use of AR for architectural heritage is established considering the holistic perspectives that any dissemination initiative must have. These initiatives can attract people to rural areas and contribute to the improvement of their social and economic situation. As an example, a proof of concept of an AR experience is presented at the end of the paper.

Description: To improve the ductility and seismic performance of a double coupling beam, the authors applied a polyethylene terephthalate (PET) sheet and steel tube to form fiber-reinforced polymer (FRP)–concrete–steel double-skin tubular (DST) composite coupling beams. A low-cyclic reversed experimental program was carried out which factored in the member form, steel tube diameter, and construction methods. The results indicate that the ductility and energy dissipation performance of double coupling beams—whether wrapped with a PET-FRP sheet or surrounded by an FRP–concrete–steel DST composite system—is a substantial improvement over the traditional reinforced-concrete double coupling beam (RC-DCB). The ductility coefficient and accumulated energy dissipation of the DST-DCB members improved above 170% and 2300%, respectively. These percentages compare to the RC-DCB and are based on the rupture of a PET-FRP sheet. The results are similar to those of the large rupture strain double coupling beam (LRS-DCB). Meanwhile, the external wrapped PET-FRP sheet does not affect the initial stiffness and peak strength of the RC-DCB. Relatively, the inner steel tube will improve the initial stiffness, yielding strength, and peak strength. DST-DCB members still have considerable deformability after 85% of peak strength since the external PET-FRP sheet provided an effective constraint effect on the core concrete and the inner steel tube could bear excellent shear deformation.

Description: This study aims to investigate the pressure changes, bubble dynamics, and flow physics inside the U- and C-shaped pipes with four different gravitational directions. The simulation is performed using a 1D centerline-based mesh generation technique along with a two-fluid model in the open-source software, OpenFOAM v.6. The continuity and momentum equations of the two-fluid model are discretized using the pressure-implicit method for the pressure-linked equation algorithm. The static and hydrostatic pressures in the two-phase flow were consistent with those of single-phase flow. The dynamic pressure in the two-phase flow was strongly influenced by the effect of the buoyancy force. In particular, if the direction of buoyancy force is the same as the flow direction, the dynamic pressure of the air phase increases, and that of the water phase decreases to satisfy the law of conservation of mass. Dean flows are observed on the transverse plane of the curve regions in both C-shaped and U-shaped pipes. The turbulent kinetic energy is stronger in a two-phase flow than in a single-phase flow. Using the 1D centerline-based mesh generation technique, we demonstrate the changes in pressure and the turbulent kinetic energy of the single- and two-phase flows, which could be observed in curve pipes.

Description: Previously, numerous creep studies on wood materials have been conducted in various coupon-scale tests. None had conducted research on creep properties of full-scale wooden cross-arms under actual environment and working load conditions. Hence, this research established findings on effect of braced arms on the creep behaviors of Virgin Balau (Shorea dipterocarpaceae) wood timber cross-arm in 132 kV latticed tower. In this research, creep properties of the main members of both current and braced wooden cross-arm designs were evaluated under actual working load conditions at 1000 h. The wooden cross-arm was assembled on a custom-made creep test rig at an outdoor area to simulate its long-term mechanical behaviours under actual environment of tropical climate conditions. Further creep numerical analyses were also performed by using Findley and Burger models in order to elaborate the transient creep, elastic and viscoelastic moduli of both wooden cross-arm configurations. The findings display that the reinforcement of braced arms in cross-arm structure significantly reduced its creep strain. The inclusion of bracing system in cross-arm structure enhanced transient creep and stress independent material exponent of the wooden structure. The addition of braced arms also improved elastic and viscoelastic moduli of wooden cross-arm structure. Thus, the outcomes suggested that the installation of bracing system in wooden cross-arm could extend the structure’s service life. Subsequently, this effort would ease maintenance and reduce cost for long-term applications in transmission towers.

Description: The mechanical properties of cement-based composites reinforced with glass textile are strongly affected by the low ability of the inorganic matrix to penetrate within the single filaments, and by the low durability of the fibers in an alkaline environment. Over the last decades, different studies have investigated the possibility to improve the mechanical properties and the durability of this class of composites by modifying the surface of the reinforcement, mainly using different types of organic or inorganic coatings. Although different solutions have been proposed, the feasibility of applying these techniques strictly depend on the final application of the composite. This review aims to give an overview of the different methods that have been proposed in the literature and to highlight advantages and drawbacks.

Description: Bicycles with electrically powered pedal assistance (PA) show great potential as ecological alternatives for engine-based vehicles. There is plenty of research available about the influence of various bicycle parameters on cycling technique. Though, to the best of the authors’ knowledge, there is none about the influence of PA. In this study, a recreational bicycle is equipped with PA and unique instrumentation to measure the user-induced loads on seat, steer and pedals. Joint loading is derived in the sagittal plane from inverse dynamics and muscle activity of the lower limbs is recorded with an electromyography system integrated in cycling pants. An experiment is set up, in which volunteers cycle on an athletics track, with a varying level of PA and a varying seat height. An ANOVA is conducted to determine significant differences due to the level of PA and seat height and to analyze the interaction effect. No interaction effect was found and only differences due to the level of PA were significant. Knowledge about the influence of PA provides insights into (i) electric bicycle design; (ii) the usage of electric bicycle for physically challenged people; (iii) the usage of electric bicycles as a rehabilitation tool.

Description: Using natural language processing tools, we investigate the semantic differences in medical guidelines for three decision problems: breast cancer screening, lower back pain and hypertension management. The recommendation differences may cause undue variability in patient treatments and outcomes. Therefore, having a better understanding of their causes can contribute to a discussion on possible remedies. We show that these differences in recommendations are highly correlated with the knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors. While this article is a case study using three sets of guidelines, the proposed methodology is broadly applicable. Technically, our method combines word embeddings and a novel graph-based similarity model for comparing collections of documents. For our main case study, we use the CDC summaries of the recommendations (very short documents) and full (long) texts of guidelines represented as bags of concepts. For the other case studies, we compare the full text of guidelines with their abstracts and tables, summarizing the differences between recommendations. The proposed approach is evaluated using different language models and different distance measures. In all the experiments, the results are highly statistically significant. We discuss the significance of the results, their possible extensions, and connections to other domains of knowledge. We conclude that automated methods, although not perfect, can be applicable to conceptual comparisons of different medical guidelines and can enable their analysis at scale.

Description: In contrary to what has traditionally been believed, bone formation can occur through two different types of osteogenesis: static (SO) and dynamic (DO) osteogenesis, which are thus named because the former is characterized by pluristratified cords of unexpectedly stationary osteoblasts which differentiate at a fairly constant distance from the blood capillaries and transform into osteocytes without moving from the onset site, while the latter is distinguished by the well-known typical monostratified laminae of movable osteoblasts. The two types of osteogenesis differ in multiple aspects from both structural and functional viewpoints. Besides osteoblast arrangement, polarization, and motion, SO and DO differ in terms of time of occurrence (first SO and later DO), conditioning factors to which they are sensitive (endothelial-derived cytokines or mechanical loading, respectively), distribution of osteocytes to which they give rise (haphazard or ordered in planes, respectively), the collagen texture resulting from the different deposition types (woven or lamellar, respectively), the mechanical properties of the bone they form (poor for SO due to the high cellularity and woven texture and good for DO since osteocytes are located in more suitable conditions to perceive loading), and finally the functions of each, i.e., SO provides a preliminary rigid scaffold on which DO can take place, while DO produces bone tissue according to mechanical/metabolic needs..

Description: In machine learning applications, classification schemes have been widely used for prediction tasks. Typically, to develop a prediction model, the given dataset is divided into training and test sets; the training set is used to build the model and the test set is used to evaluate the model. Furthermore, random sampling is traditionally used to divide datasets. The problem, however, is that the performance of the model is evaluated differently depending on how we divide the training and test sets. Therefore, in this study, we proposed an improved sampling method for the accurate evaluation of a classification model. We first generated numerous candidate cases of train/test sets using the R-value-based sampling method. We evaluated the similarity of distributions of the candidate cases with the whole dataset, and the case with the smallest distribution–difference was selected as the final train/test set. Histograms and feature importance were used to evaluate the similarity of distributions. The proposed method produces more proper training and test sets than previous sampling methods, including random and non-random sampling.

Description: A combination of factors exposes musicians to neuro-musculoskeletal disorders, which lead to pain and damage. These involve overuse due to long playing hours, containing repetitive movements under stressful conditions, usually performed in an unnatural posture. Although the evoked disorders are usually non-traumatic, they may often lead to prolonged or even permanent damage. For instance, in upper string players, these include bursitis and tendinopathies of the shoulder muscles, tendonitis of the rotator cuff, injury at the tendon sheaths, medial or lateral epicondylitis (also known as tennis elbow), myofascial pain, and wrist tendonitis (also known as carpal tunnel syndrome, or De Quervein’s syndrome). In cases of intensive performance, a traumatic injury may result, requiring drastic means of intervention such as surgery. It should be pointed out that the upper body and upper extremities are the most commonly affected sites of playing musicians. This review provides a description of the playing-related motor disorders in performing musicians, and of the methodologies used to identify and evaluate these disorders, particularly for violinists and other upper string players.

Description: Peer review is the driving force behind the development of a journal, and it is the reviewers who ensure that the high standard of the papers published in Plasma is maintained [...]

Description: Rock art is one of the most fragile and relevant cultural phenomena in world history, carried out in shelters or the walls and ceilings of caves with mineral and organic substances. The fact it has been preserved until now can be considered as fortunate since both anthropogenic and natural factors can cause its disappearance or deterioration. This is the reason why rock art needs special conservation and protection measures. The emergence of digital technologies has made a wide range of tools and programs available to the community for a more comprehensive documentation of rock art in both 2D and 3D. This paper shows a workflow that makes use of visible and near-infrared hyperspectral technology to manage, monitor and preserve this appreciated cultural heritage. Hyperspectral imaging is proven to be an efficient tool for the recognition of figures, coloring matter, and state of conservation of such valuable art.

Description: Modal analysis provides the dynamic behavior of an object or structure, and is often undertaken using the Finite Element Method (FEM) due to its ability to deal with arbitrary geometries. This article investigates the use of Augmented Reality (AR) to provide the in situ visualization of a modal analysis for an aluminum impeller. Finite Element Analysis (FEA) software packages regularly use heat maps and shape deformation to visualize the outcomes of a given simulation. AR allows the superimposition of digital information on a view of the real-world environment, and provides the opportunity to overlay such simulation results onto real-world objects and environments. The presented modal analysis undertaken herein provides natural frequencies and the corresponding deformation of an aluminum impeller. The results indicate the ability for the design part and finite element analysis results to be viewed on the physical part. A mobile AR-FEA-based system was developed for Modal Analysis result visualization. This study offers designers and engineers a new way to visualize such simulation results.

Description: This study aims to improve knowledge on fermented beef and fallow deer sausages and the effect of nitrite elimination and the addition of freeze dried acid whey on the fatty acid profile and volatile compounds. Three different formulations within each of the two product groups, made of beef and fallow deer meat, respectively, were prepared: control sample with sodium nitrite, sample without nitrite, and sample without nitrite and with the addition of freeze-dried acid whey powder (0.7%). After production, the sausages were subjected to analysis including proximate chemical composition, pH and water activity, Thiobarbituric Acid Reactive Substance (TBARS), fatty acid profile, and volatile compound determination. The fermented sausages were characterized by an average pH and water activity in the range of 5.23–5.79 and 0.910–0.918, respectively. Fallow deer sausages were characterized by a higher content of saturated and polyunsaturated fatty acids in comparison to beef sausages. The elimination of nitrite did not significantly affect the amount of volatile compounds in fermented sausages. However, the effect of the freeze-dried acid whey powder addition on the amount of some volatile compounds in uncured sausages was observed, in particular, that derived from bacterial metabolism.

Description: The positive effects of interest on different aspects, e.g., learning and education, economy, psychological well-being, and social relations, have been widely addressed by many psychological and physiological studies in the last two decades. While the psychological work has investigated this impact of interest theoretically, the physiological studies have focused more on the modulatory effects. However, some studies have addressed both sides of the effects. In this work, we conduct a comprehensive review of physiological studies on interest detection, from different perspectives carried out between 2003 and 2019. A lack of connection between the psychological and physiological studies was identified. Therefore, this paper aims to integrate the unique psychological and physiological aspects and characteristics of interest to form a base for future research by considering the pros and cons of the included studies. For example, considering the two types of interest (situational and individual) the detected interest in learning, gaming, and advertisement’s physiological experiments could be referring specifically to situational interest. Hence, bridging the gap between both physiological and psychological studies is essential for improving the research on interest. Furthermore, we propose several suggestions for future work direction.

Description: Rice germ is an abundant source of ferulic acid, which is known for its anti-oxidant activity. This study aimed to evaluate the regulatory effects of fermented rice germ extracts on hepatic glucose metabolism in C57BL/KsJ-db/db mice. Rice germ was fermented with Lactobacillus plantarum and extracted with 30% ethanol (RG_30E) or 50% ethanol (RG_50E). Mice were fed modified AIN-93 diets containing fermented rice germ extracts and ferulic acid for 8 weeks. RG_50E significantly reduced food intake as well as liver weight and RG_30E and RG_50E improved glucose homeostasis, as indicated by fasting blood glucose levels and glucose tolerance. Hepatic triglyceride and total cholesterol levels were significantly decreased in db/db mice fed RG_30E and RG_50E. The antioxidant capacity of RG_30E and RG_50E was confirmed by a decrease in malondialdehyde levels and an increase in hepatic superoxide dismutase activity. The expression of genes related to glycolysis and gluconeogenesis was significantly regulated by RG_30E and RG_50E. These results suggest that fermented rice germ extracts have the potential to regulate hypoglycemia and hepatic glucose metabolism in type 2 diabetes db/db mice.

Description: Grazing incidence small-angle neutron scattering (GISANS) provides access to interfacial properties, e.g., in soft matter on polymers adsorbed at a solid substrate. Simulations in the frame of the distorted wave Born approximation using the BornAgain software allow to understand and quantify the scattering pattern above and below the sample horizon, in reflection and transmission, respectively. The small-angle scattering from the interfacial layer, visible around the transmitted beam, which might contribute also on the side of the reflected beam, can be understood in this way and be included into the analysis. Background reduction by optimized sample cell design is supported by simulations, paving the way for an optimized GISANS cell.

Description: The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.

Description: Current pallet design methodology frequently underestimates the load capacity of the pallet by assuming the payload is uniformly distributed and flexible. By considering the effect of payload characteristics and their interactions during pallet design, the structure of the pallets can be optimized, and raw material consumption reduced. The objective of this study was to develop and validate a finite element model capable of simulating the bending of a generic pallet while supporting a payload made of corrugated boxes and stored on a warehouse load beam rack. The model was generalized in order to maximize its applicability in unit load design. Using a two-dimensional, nonlinear, implicit dynamic model, it allowed for the evaluation of the effect of different payload configurations on the pallet bending response. The model accurately predicted the deflection of the pallet segment and the movement of the packages for a unit load segment with three or four columns of boxes supported in a warehouse rack support. Further refinement of the model would be required to predict the behavior of unit loads carrying larger boxes. The model presented provides an efficient solution to the study of the affecting factors to ultimately optimize pallet design. Such a model has not been previously developed. The model successfully acts as a tool to study and predict the load bridging performance of unit loads requiring only widely available input data, therefore providing a general solution.

Description: To provide a more practical method of controlling the frequency and tie-line power flow of a multi-area interconnected power system (MAIPS), a state observer based on sliding mode control (SOboSMC) acting under a second-order time derivative is proposed. The proposed design is used to study load frequency control against load disturbance, matched and mismatched uncertainty and parameter measurement difficulties of power systems that exist in the modern power plant, such as multi-area systems integrated with wind plants. Firstly, the state observer is designed to optimally estimate system state variables. The estimated states are applied to construct the model of the MAIPS. Secondly, a SOboSMC is designed with an integral switching surface acting on the second-order time derivative to forcefully drive the dynamic errors to zero and eliminate chattering, which can occur in the first-order approach to sliding mode control. In addition, the stability of the total power system is demonstrated with the Lyapunov stability theory based on a new linear matrix inequality (LMI) technique. To extend the validation of the proposed design control for practical purposes, it was tested in a New England system with 39 bus power against random load disturbances. The simulation results confirm the superiority of the proposed SOboSMC over other recent controllers with respect to overshoot and settling time.

Description: Current developments in information technology and increased inclination towards smart cities have led to the initiation of a plethora of features by technology-oriented companies (i.e., car manufacturers) to improve users’ privacy and comfort. The invention of smart vehicle technology paved the way for the excessive use of machine-to-machine technologies. Moreover, third-party sharing of financial services are also introduced that support machine-to-machine (M2M) communication. These monetary systems’ prime focus is on improving reliability and security; however, they overlook aspects like behaviors and users’ need. For instance, people often hand over their bank cards or share their credentials with their colleagues to withdraw money on their behalf. Such behaviors may originate issues about privacy and security that can have severe losses for the card owner. This paper presents a novel blockchain-based strategy for payment of fueling of smart cars without any human interaction while maintaining transparency, privacy, and trust. The proposed system is capable of data sharing among the users of the system while securing sensitive information. Moreover, we also provide a blockchain-based secure privacy-preserving strategy for payment of fueling among the fuel seller and buyer without human intervention. Furthermore, we have also analytically evaluated several experiments to determine the proposed blockchain platform’s usability and efficiency. Lastly, we harness Hyperledger Caliper to assess the proposed system’s performance in terms of transaction latency, transactions per second, and resource consumption.

Description: A novel method named residual-variational mode decomposition (RVMD) is proposed in this study to extract bearing fault features accurately. RVMD can determine the number of modes and the balance parameter adaptively, and it has two stages. In the first stage, the signal is decomposed into a series of modes until the correlation coefficient between the raw signal and the decomposition results reaches the threshold. A redefined kurtosis, which can resist the interferences from aperiodic impulse efficiency, is applied to rebuild the ensemble kurtosis index. The mode that has the largest rebuild-ensemble kurtosis, and its neighbors, are kept. By putting the residual signal into the second stage, an iteration process is applied to determine the optimal parameters for variational mode decomposition (VMD). VMD is re-run with the optimal parameters, and the sub-mode filtered with the larger rebuild-ensemble kurtosis is examined by the envelope analysis technology to observe the fault feature. The effectiveness of RVMD is verified by the simulation signal and three experiment signals. Its superiority is shown by comparing it with some existing methods.