ALBERT

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: Al2O3 and Al2O3-ZrO2 coatings were deposited on stainless steel using atmospheric plasma spraying. The influence of arc current and zirconia addition on the surface morphology of the coating, phase composition and tribological properties under dry sliding conditions were investigated. The addition of zirconia reduced the surface roughness of the coatings. The X-ray diffraction measurements indicated that the Al2O3 coatings were composed of β-Al2O3, α-Al2O3, and γ-Al2O3 phases. The addition of zirconia led to the formation of tetragonal and monoclinic phases of zirconia in the as-sprayed coatings. The friction coefficients of Al2O3 and Al2O3-ZrO2 coatings were similar and varied in the range of 0.72–0.75. The specific wear rates of the as-sprayed coatings were reduced with the increase of arc current. It was obtained that the wear rates of the Al2O3-ZrO2 coatings were at least three times lower compared to Al2O3 coatings.

Beschreibung: 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.

Beschreibung: 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).

Beschreibung: 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.

Beschreibung: This paper reveals the relationship between the Miller plateau voltage and the displacement currents through the gate–drain capacitance (CGD) and the drain–source capacitance (CDS) in the switching process of a power transistor. The corrected turn-on Miller plateau voltage and turn-off Miller plateau voltage are different even with a constant current load. Using the proposed new Miller plateau, the turn-on and turn-off sequences can be more accurately analyzed, and the switching power loss can be more accurately predicted accordingly. Switching loss models based on the new Miller plateau have also been proposed. The experimental test result of the power MOSFET (NCE2030K) verified the relationship between the Miller plateau voltage and the displacement currents through CGD and CDS. A carefully designed verification test bench featuring a power MOSFET written in Verilog-A proved the prediction accuracy of the switching waveform and switching loss with the new proposed Miller plateau. The average relative error of the loss model using the new plateau is reduced to 1/2∼1/4 of the average relative error of the loss model using the old plateau; the proposed loss model using the new plateau, which also takes the gate current’s variation into account, further reduces the error to around 5%.

Beschreibung: The Society of Automotive Engineers (SAE) defines six levels of driving automation, ranging from Level 0 to Level 5. Automated driving systems perform entire dynamic driving tasks for Levels 3–5 automated vehicles. Delegating dynamic driving tasks from driver to automated driving systems can eliminate crashes attributed to driver errors. Sharing status, sharing intent, seeking agreement, or sharing prescriptive information between road users and vehicles dedicated to automated driving systems can further enhance dynamic driving task performance, safety, and traffic operations. Extensive simulation is required to reduce operating costs and achieve an acceptable risk level before testing cooperative automated driving systems in laboratory environments, test tracks, or public roads. Cooperative automated driving systems can be simulated using a vehicle dynamics simulation tool (e.g., CarMaker and CarSim) or a traffic microsimulation tool (e.g., Vissim and Aimsun). Vehicle dynamics simulation tools are mainly used for verification and validation purposes on a small scale, while traffic microsimulation tools are mainly used for verification purposes on a large scale. Vehicle dynamics simulation tools can simulate longitudinal, lateral, and vertical dynamics for only a few vehicles in each scenario (e.g., up to ten vehicles in CarMaker and up to twenty vehicles in CarSim). Conventional traffic microsimulation tools can simulate vehicle-following, lane-changing, and gap-acceptance behaviors for many vehicles in each scenario without simulating vehicle powertrain. Vehicle dynamics simulation tools are more compute-intensive but more accurate than traffic microsimulation tools. Due to software architecture or computing power limitations, simplifying assumptions underlying convectional traffic microsimulation tools may have been a necessary compromise long ago. There is, therefore, a need for a simulation tool to optimize computational complexity and accuracy to simulate many vehicles in each scenario with reasonable accuracy. This research proposes a traffic microsimulation tool that employs a simplified vehicle powertrain model and a model-based fault detection method to simulate many vehicles with reasonable accuracy at each simulation time step under noise and unknown inputs. Our traffic microsimulation tool considers driver characteristics, vehicle model, grade, pavement conditions, operating mode, vehicle-to-vehicle communication vulnerabilities, and traffic conditions to estimate longitudinal control variables with reasonable accuracy at each simulation time step for many conventional vehicles, vehicles dedicated to automated driving systems, and vehicles equipped with cooperative automated driving systems. Proposed vehicle-following model and longitudinal control functions are verified for fourteen vehicle models, operating in manual, automated, and cooperative automated modes over two driving schedules under three malicious fault magnitudes on transmitted accelerations.

Beschreibung: In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.

Beschreibung: 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.

Beschreibung: “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.

Beschreibung: With the rapid development of deep learning, computer vision has assisted in solving a variety of problems in engineering construction. However, very few computer vision-based approaches have been proposed on work productivity’s evaluation. Therefore, taking a super high-rise project as a research case, using the detected object information obtained by a deep learning algorithm, a computer vision-based method for evaluating the productivity of assembling reinforcement is proposed. Firstly, a detector that can accurately distinguish various entities related to assembling reinforcement based on CenterNet is established. DLA34 is selected as the backbone. The mAP reaches 0.9682, and the speed of detecting a single image can be as low as 0.076 s. Secondly, the trained detector is used to detect the video frames, and images with detected boxes and documents with coordinates can be obtained. The position relationship between the detected work objects and detected workers is used to determine how many workers (N) have participated in the task. The time (T) to perform the process can be obtained from the change of coordinates of the work object. Finally, the productivity is evaluated according to N and T. The authors use four actual construction videos for validation, and the results show that the productivity evaluation is generally consistent with the actual conditions. The contribution of this research to construction management is twofold: On the one hand, without affecting the normal behavior of workers, a connection between construction individuals and work object is established, and the work productivity evaluation is realized. On the other hand, the proposed method has a positive effect on improving the efficiency of construction management.

Beschreibung: 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.

Beschreibung: In this work, a single-layer TiO2–ZrO2 thin film is deposited on the AISI 316L austenitic stainless steel by the sol–gel process and the dip coating method to improve its corrosion resistance properties. For the sol preparation, titanium isopropoxide and zirconium butoxide are used as the precursors, yttrium acetate hydrate is used for the ZrO2 stabilization, i-propanol as the solvent, nitric acid as the catalyst, acetylacetone as the chelating agent, and the distilled water for the hydrolysis. The deposited films are annealed at 400 °C or 600 °C. Morphology and phase composition of the sol–gel TiO2–ZrO2 films and powders are analyzed by scanning electron microscopy (SEM) equipped with EDX detector and X-ray diffraction (XRD), respectively. The thickness of the sol–gel TiO2–ZrO2 films deposited on the stainless steel is determined by glow discharge optical emission spectrometry (GD-OES). The corrosion behavior of the stainless steel, coated by amorphous films, is evaluated in 3 wt% NaCl and 0.5 mol dm−3 HCl by potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques. It is found that the sol–gel TiO2–ZrO2 films with the amorphous structure, deposited by the sol–gel process, and calcined at 400 °C significantly enhance the corrosion properties of AISI 316L in both chloride media.

Beschreibung: 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%.

Beschreibung: Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: TiAlN coatings with different Al ratios were deposited by the cathodic arc ion plating (AIP) method, and the relationship between solid particle erosion resistance and structural, mechanical properties was investigated by a micro slurry-jet erosion (MSE) test. The crystal structure of TiAlN coating changes depending on the Al ratio. The coating shows a B1 single cubic phase between the Al ratio of 0 and 0.58; above this ratio, formation of a B4 hexagonal phase is observed. The mechanical properties such as hardness and Young’s modulus of the TiAlN coating also depend on the Al ratio and the crystal structure. The erosion rate decreases by increasing the Al ratio up to 0.58, as the coating is a cubic single phase. The TiAlN coating shows the lowest erosion rate at an Al ratio of 0.58. The erosion rate increases drastically as the crystalline phase changes from the B1 cubic to B4 hexagonal phase at the Al ratio of more than 0.58. The change in erosion rate is also discussed in connection with mechanical properties such as erodent particle hardness to coating hardness ratio and coating hardness to Young’s modulus ratio.

Beschreibung: Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: Soil stabilization, through either physical or chemical techniques of coating-cementation development, is a commonly adopted practice in geotechnical and transportation engineering projects used to strengthen soils and mitigate negative geo-environmental impacts. This can be particularly important in enhancing the mechanical properties of soils which are prone to degradation of their response because of freezing and thawing actions. It was attempted in this research study to examine the effect of lime–zeolite stabilization on the behavior of a silt-sized natural soil of low plasticity, by performing standard compaction tests as well as unconfined compressive experiments evaluating the strength of the composite samples. The natural soil mixed with various contents of lime and zeolite partial replacement was subjected to consecutive freezing and thawing cycles. The results from the study showed significant improvement of the mechanical performance of the treated soil when mixed with lime partially replaced with zeolite in terms of strength and durability and, based on scanning electron microscopic images, additional insights were attempted to be obtained on the microstructure of the specimens subjected to temperature changes.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: Low pressure fluid transport (1) applications often require low and precise volumetric flow rates (2) including low leakage to reduce additional costly and complex sensors. A peristaltic pump design (3) was realized, with the fluid’s flexible transport channel formed by a solid cavity and a wobbling plate comprising a rigid and a soft layer (4). In operation, the wobbling plate is driven externally by an electric motor, hence, the soft layer is contracted and unloaded (5) during pump-cycles transporting fluid from low to high pressure sides. A thorough characterization of the pump system is required to design and dimension the components of the peristaltic pump. To capture all these parameters and their dependencies on various operation-states, often complex and long-lasting dynamic 3D FE-simulations are required. We present, here, a holistic design methodology (6) including analytical as well as numerical calculations, and experimental validations for a peristaltic pump with certain specifications of flow-rate range, maximum pressures, and temperatures. An experimental material selection process is established and material data of candidate materials (7) (liquid silicone rubber, acrylonitrile rubber, thermoplastic-elastomer) are directly applied to predict the required drive torque. For the prediction, a semi-physical, analytical model was derived and validated by characterizing the pump prototype.

Beschreibung: 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.

Beschreibung: Biomaterial surface modification represents an important approach to obtain a better integration of the material in surrounding tissues. Different techniques are focused on improving cell support as well as avoiding efficiently the development of infections, such as by modifying the biomaterial surface with amine groups (–NH2). Previous studies showed that –NH2 groups could promote cell adhesion and proliferation. Moreover, these chemical functionalities may be used to facilitate the attachment of molecules such as proteins or to endow antimicrobial properties. This mini-review gives an overview of different techniques which have been used to obtain amine-rich coatings such as plasma methods and adsorption of biomolecules. In fact, different plasma treatment methods are commonly used with ammonia gas or by polymerization of precursors such as allylamine, as well as coatings of proteins (for example, collagen) or polymers containing –NH2 groups (for example, polyethyleneimine). Moreover, this mini-review will present the methods used to characterize such coatings and, in particular, quantify the –NH2 groups present on the surface by using dyes or chemical derivatization methods.

Beschreibung: The aim of this research project is to analyze support panels that are based on aramid fabrics which are reinforced with polybenzoxazine/urethane (poly(BA-a/PU)) composites and contain multiwalled carbon nanotubes (MWCNTs). Through the measurement of mechanical properties and a series of ballistic-impact tests that used 7.62 × 51 mm2 projectiles (National Institute of Justice (NIJ), level III), the incorporated MWCNTs were found to enhance the energy-absorption (EAbs) property of the composites, improve ballistic performance, and reduce damage. The perforation process and the ballistic limit (V50) of the composite were also studied via numerical simulation, and the calculated damage patterns were correlated with the experimental results. The result indicated hard armor based on polybenzoxazine nanocomposites could completely protect the perforation of a 7.62 × 51 mm2 projectile at impact velocity range of 847 ± 9.1 m/s. The results revealed the potential for using the poly(BA-a/PU) nanocomposites as energy-absorption panels for hard armor.

Beschreibung: The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance.

Beschreibung: 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.

Beschreibung: Defective hard candies are usually produced due to inadequate feeding or insufficient cooling during the candy production process. The human-based inspection strategy needs to be brought up to date with the rapid developments in the confectionery industry. In this paper, a detection and classification method for defective hard candies based on convolutional neural networks (CNNs) is proposed. First, the threshold_li method is used to distinguish between hard candy and background. Second, a segmentation algorithm based on concave point detection and ellipse fitting is used to split the adhesive hard candies. Finally, a classification model based on CNNs is constructed for defective hard candies. According to the types of defective hard candies, 2552 hard candies samples were collected; 70% were used for model training, 15% were used for validation, and 15% were used for testing. Defective hard candy classification models based on CNNs (Alexnet, Googlenet, VGG16, Resnet-18, Resnet34, Resnet50, MobileNetV2, and MnasNet0_5) were constructed and tested. The results show that the classification performances of these deep learning models are similar except MnasNet0_5 with the classification accuracy of 84.28%, and the Resnet50-based classification model is the best (98.71%). This research has certain theoretical reference significance for the intelligent classification of granular products.

Beschreibung: In this study, a femtosecond laser was used to pretreat the surface of the Al–Li alloy, the surface micromorphology, roughness, contact angle, and surface wettability of which were adjusted by changing the laser scanning speed, and the sample was bonded into a single joint with polyether ether ketone (PEEK) adhesive. The mechanism of the laser surface treatment affecting the bonding strength of the Al–Li alloy was explored through tensile and shear experiments. The results indicated that optimizing the laser surface treatment parameters could change the surface roughness and surface micromorphology of the Al–Li alloy, so as to change its surface free energy and bonding strength. Compared with the untreated sample, the bonding strength of the Al–Li alloy increased by 81%, 95%, 107%, 91%, and 78% under the treatment of laser scanning at 25, 20, 15, 10, and 5 mm/s, respectively. As a whole, femtosecond laser etching of the Al–Li alloy surface had an important influence on its wettability and bonding performance.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: Handover usually deals with the mobility of the end users in a mobile network to assure about the ongoing session of a user. It is observed that frequent handover results in call dropping due to latency. In order to overcome this issue, a fuzzy based handover decision scheme for mobile devices using a predictive model is proposed. First, an MFNN (Multi-layer Feed Forward Network) is used to determine the next cell of the user along with best hand off time. To obtain the best access network, multiple-attribute Access Network Selection Function (ANSF) is used. The fuzzy rule is applied by considering the parameter data rate, reliability, signal strength, battery power and mobility as input and the output obtained is the optimal network. The proposed scheme selects the best access network and enhances the quality of services.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: In this paper, we construct novel numerical algorithms to solve the heat or diffusion equation. We start with 105 different leapfrog-hopscotch algorithm combinations and narrow this selection down to five during subsequent tests. We demonstrate the performance of these top five methods in the case of large systems with random parameters and discontinuous initial conditions, by comparing them with other methods. We verify the methods by reproducing an analytical solution using a non-equidistant mesh. Then, we construct a new nontrivial analytical solution containing the Kummer functions for the heat equation with time-dependent coefficients, and also reproduce this solution. The new methods are then applied to the nonlinear Fisher equation. Finally, we analytically prove that the order of accuracy of the methods is two, and present evidence that they are unconditionally stable.

Beschreibung: IP fragmentation is still prevalent on the Internet. Defragmented traffic is a prerequisite for many network processing algorithms. This work focuses on the size and organization of a flow table, which is an essential ingredient of the hardware IP defragmentation block. Previous research suggests that fragmented IP traffic is highly local, and a relatively small flow table (on the order of a thousand entries) can process most of the traffic. Samples of IP traffic were obtained from public data sources and used for a statistical analysis, revealing the key factors in achieving design goals. The findings were backed by an extensive design space exploration of the software defragmentation model, which resulted in the efficiency estimates. To provide a robust score of the simulation model, a new validation technique is employed that helps to overcome the limitations of the samples.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: This study evaluates the effects of gelatin coating enriched with ethanolic propolis extract (PE) at 1%, 2% or 3% (w/v) on the quality parameters of pork meat during storage at 2 °C. Physical (pH, weight loss, color) and chemical parameters (percentage contents of metmyoglobin (MetMb), along with thiobarbituric acid reactive substances (TBARS)) were measured, and microbiological (total aerobic plate count (TAPC)) analysis, as well as consumer evaluation, was carried out every four days during the storage period of twelve days. The results indicated that the proposed treatments affected (p 〈 0.05) the quality characteristics of meat samples. The high prevention of physicochemical alterations and maximum inhibition of microorganisms was obtained for samples stored in gelatin coatings containing 2% and 3% PE. Additionally, despite a slight deterioration in odor on Day 4 in the P3 group, no negative changes in overall acceptability of the P2 and P3 groups compared to uncoated samples were observed. The obtained results indicate a significant role of propolis extract incorporation into gelatin packaging to extend the shelf life of stored pork.

Beschreibung: Organic–inorganic nanoparticles, which can improve and modify the mechanical and chemical properties of polymers, have been used as fillers to prepare high-performance hybrid nanocomposite membranes. In this study, we explored whether the incorporation of organic nanofillers (graphene (G), graphene oxide (GO), carbon nanotubes (CNTs), or oxidized carbon nanotubes (CNTOxi)) into polysulfone (PSF) and montmorillonite (MMt)-modified PSF membranes could enhance membrane performance for the removal of heavy metal ions from contaminated solutions. These hybrid membranes were prepared by a phase inversion method using chloroform as the solvent. The surface morphologies of the membranes revealed good dispersibility of the organoclay and carbon nanomaterials in the PSF matrix. The hybrid nanocomposite membranes showed significantly improved thermal stability and mechanical properties as compared to the pristine PSF and PSF/MMt membranes. The adsorption efficiencies of these hybrid adsorptive membranes for Hg(II), Pb(II), Sr(II), Fe(III), Zn(II), Ni(II), Al(III), Co(II), Y(III), and Cr(III) were investigated. The PSF/MMt/CNTOxi and PSF/MMt/GO membranes exhibited the highest adsorption efficiencies. In particular, these adsorptive membranes showed selectivity toward Hg(II), and the Hg(II) extraction percentage was maximized at pH 2. The maximum Hg(II) adsorption capacities of PSF/MMt/CNTOxi and PSF/MMt/GO were 151.36 and 144.89 mg/g, respectively, and the adsorption isotherm was in approval with the Langmuir model. These hybrid nanocomposites can be used in water purification application.

Beschreibung: 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.

Beschreibung: The computing resources of today’s smartphones are underutilized most of the time. Using these resources could be highly beneficial in edge computing and fog computing contexts, for example, to support urban services for citizens. However, new challenges, especially regarding job scheduling, arise. Smartphones may form ad hoc networks, but individual devices highly differ in computational capabilities and (tolerable) energy usage. We take into account these particularities to validate a task execution scheme that relies on the computing power that clusters of mobile devices could provide. In this paper, we expand the study of several practical heuristics for job scheduling including execution scenarios with state-of-the-art smartphones. With the results of new simulated scenarios, we confirm previous findings and better comprehend the baseline approaches already proposed for the problem. This study also sheds some light on the capabilities of small-sized clusters comprising mid-range and low-end smartphones when the objective is to achieve real-time stream processing using Tensorflow object recognition models as edge jobs. Ultimately, we strive for industry applications to improve task scheduling for dew computing contexts. Heuristics such as ours plus supporting dew middleware could improve citizen participation by allowing a much wider use of dew computing resources, especially in urban contexts in order to help build smart cities.

Beschreibung: Friction nonlinear disturbance is one of the main factors affecting the control performance of servo tracking system. In this paper, an improved Active Disturbance Rejection Control (ADRC) scheme of dual-axis servo turntable is researched to achieve accurate tracking. Firstly, the mathematical dynamics model of dual-axis servo tracking turntable system is established. The Elastoplastic model is used to describe nonlinear friction, in which the immeasurable part is extended to be a new state. Secondly, considering the smooth and monotonic increasing property of hyperbolic tangent function, an improved tracking differentiator is introduced, which can provide better noise attenuation performance. Thirdly, based on adjustable parameter systematic pole placement method, the fuzzy control algorithm is applied to realize the intelligent tuning of the improved Extended State Observer (ESO) gains, in which the input of the fuzzy controller is the estimation error, while the output is the observer bandwidth. Finally, the improved ADRC system is transformed into a Lurie system, then the extended circle criteria are adopted to analyze the absolute stability of the proposed system. Simulation and experimental verification of the improved ADRC method for the dual-axis turntable tracking servo system are conducted. Results illustrate the effectiveness and robustness of the proposed controller.

Beschreibung: Conventionally, public key certificates bind one subject with one static public key so that the subject can facilitate the services of the public key infrastructure (PKI). In PKI, certificates need to be renewed (or revoked) for several practical reasons, including certificate expiration, private key breaches, condition changes, and possible risk reduction. The certificate renewal process is very costly, especially for those environments where online authorities are not available or the connection is not reliable. A dynamic public key certificate (DPKC) facilitates the dynamic changeover of the current public–private key pairs without renewing the certificate authority (CA). This paper extends the previous study in several aspects: (1) we formally define the DPKC; (2) we formally define the security properties; (3) we propose another implementation of the Krawczyk–Rabin chameleon-hash-based DPKC; (4) we propose two variants of DPKC, using the Ateniese–Medeiros key-exposure-free chameleon hash; (5) we detail two application scenarios.

Beschreibung: In modulation identification issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature characteristics. Feature weighting boosts the performance of machine learning algorithms, particularly the class of instance-based learning algorithms such as the Minimum Distance (MD) classifier, in which the distance measure is highly sensitive to the magnitude of features. In this paper, we propose an improved version of the Salp Swarm optimization Algorithm (SSA), called ISSA, that will be applied to optimize feature weights for an MD classifier. The aim is to improve the performance of a blind digital modulation detection approach in the context of multiple-antenna systems. The improvements introduced to SSA mainly rely on the opposition-based learning technique. Computer simulations show that the ISSA outperforms the SSA as well as the algorithms that derive from it. The ISSA also exhibits the best performance once it is applied for feature weighting in the above context.

Beschreibung: The purpose of this study was to investigate the feasibility of a time-of-flight (TOF) brain positron emission tomography (PET) providing high-quality images. It consisted of 30 detector blocks arranged in a ring with a diameter of 257 mm and an axial field of view of 52.2 mm. Each detector block was composed of two detector modules and two application-specific integrated circuit (ASIC) chips. The detector module was composed of an 8 × 8 array of 3 × 3 mm2 multi-pixel photon counters and an 8 × 8 array of 3.11 × 3.11 × 15 mm3 lutetium yttrium oxyorthosilicate scintillators. The 64-channel individual readout ASIC was used to acquire the position, energy, and time information of a detected gamma ray. A coincidence timing resolution of 187 ps full width at half maximum (FWHM) was achieved using a pair of channels of two detector modules. The energy resolution and spatial resolution were 6.6 ± 0.6% FWHM (without energy nonlinearity correction) and 2.5 mm FWHM, respectively. The results of this study demonstrate that the developed TOF brain PET could provide excellent performance, allowing for a reduction in radiation dose or scanning time for brain imaging due to improved sensitivity and signal-to-noise ratio.

Beschreibung: 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.

Beschreibung: Detecting trace amounts of explosives to ensure personal safety is important, and this is possible by using laser-based spectroscopy techniques. We performed surface-enhanced Raman scattering (SERS) using plasmonic nanogap substrates for the solution phase detection of some nitro-based compounds, taking advantage of the hot spot at the nanogap. An excitation wavelength of 785 nm with an incident power of as low as ≈0.1 mW was used to excite the nanogap substrates. Since both RDX and PETN cannot be dissolved in water, acetone was used as a solvent. TNT was dissolved in water as well as in hexane. The main SERS peaks of TNT, RDX, and PETN were clearly observed down to the order of picomolar concentration. The variations in SERS spectra observed from different explosives can be useful in distinguishing and identifying different nitro-based compounds. This result indicates that our nanogap substrates offer an effective approach for explosives identification.

Beschreibung: Volatile fatty acids obtained from the fermentation of the organic fraction of municipal solid waste can be used as raw materials for non-toxic ethyl ester (EE) synthesis as well as feedstock for the production of polyhydroxyalkanoates (PHAs). Taking advantage of the concept of an integrated process of a bio-refinery, in the present paper, a systematic investigation on the extraction of intracellular poly(3-hydroxybutyrate-co-3-hydroxyvalerate), produced by mixed microbial culture by using EEs was reported. Among the tested EEs, ethyl acetate (EA) was the best solvent, dissolving the copolymer at the lowest temperature. Then, extraction experiments were carried out by EA at different temperatures on two biomass samples containing PHAs with different average molecular weights. The parallel characterization of the extracted and non-extracted PHAs evidenced that at the lower temperature (100 °C) EA solubilizes preferentially the polymer fractions richer in 3HV comonomers and with the lower molecular weight. By increasing the extraction temperature from 100 °C to 125 °C, an increase of recovery from about 50 to 80 wt% and a molecular weight reduction from 48% to 65% was observed. The results highlighted that the extracted polymer purity is always above 90 wt% and that it is possible to choose the proper extraction condition to maximize the recovery yield at the expense of polymer fractionation and degradation at high temperatures or use milder conditions to maintain the original properties of a polymer.

Beschreibung: 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.

Beschreibung: We constructed a capacitively coupled plasma (CCP) source and installed various diagnostic tools to perform process diagnosis using a plasma process gas (CxFy). We obtained the energy and mass distributions of the ions and radicals from Ar, C4F8/Ar, and C4F6/Ar plasmas. The energy distribution of the ions incident on the substrate was controlled using the self-bias voltage, and the ion energy was found to be inversely proportional to the mass. The measured species and density of the ions and radicals can help understand plasma process results as they provide information about the ions and radicals incident on the substrate.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: Android apps are daily installed by billions of users worldwide, who grant access to an extensive set of sensitive personal data. Different techniques have been developed over the years to understand how apps protect or harm their users’ privacy. However, these results have been produced in different research domains and addressing privacy from different perspectives, resulting in a growing but scattered body of knowledge. To bridge this gap, we have carried out a systematic mapping study to provide practitioners and researchers with an overview of the state-of-the-art technique, published between 2016 and 2020, to assess privacy in Android apps. In this paper, we highlight the most relevant findings, identify and analyse the most pressing gaps, and discuss the promising research directions.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: A conventional interdigital bandpass filter (BPF) is characterized by coupled and tapped lines and affords low insertion loss (IL) and easy fractional bandwidth (FBW) adjustment. However, the maximum FBW of the filter is limited to 30%, beyond that, its gap size increases, thereby rendering filter fabrication impractical on a standard printed circuit board. In addition, the filter size cannot be changed because it dictates the operational frequency of the filter. Hence, in this study, we propose a compact interdigital BPF based on a spiral and folded stepped impedance resonator (SIR), which affords low IL and excellent group delay. The spiral, folded structure facilitates drastic FBW adjustment: the center frequency and adjustable range of the FBW of the designed BPF are 800 MHz and 80 to 180%, respectively. Additionally, the proposed BPF can adjust the FBW by k-factor which can adjust from 80 to 180%. The insertion and return losses of the proposed filter are 0.043 dB and 17.1 dB, respectively, and the group delay is 0.098 ns. The total filter size is only 13.8 mm × 5.98 mm, which corresponds to a size reduction by factors of 〉2/8 relative to a conventional filter and 2.1 relative to the latest BPF design. The group delay difference between the BPF and other filters is 0.15 ns. In addition, the range of adjustable FBW for the filter is 1.36 times different than for other filters.

Beschreibung: A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

Beschreibung: This paper presents a dual-mode low-density parity-check (LDPC) decoding architecture that has excellent error-correcting capability and a high parallelism design for fifth-generation (5G) new-radio (NR) applications. We adopted a high parallelism design using a layered decoding schedule to meet the high throughput requirement of 5G NR systems. Although the increase in parallelism can efficiently enhance the throughput, the hardware implementation required to support high parallelism is a significant hardware burden. To efficiently reduce the hardware burden, we used a grouping search rather than a sorter, which was used in the minimum finder with decoding performance loss. Additionally, we proposed a compensation scheme to improve the decoding performance loss by revising the probabilistic second minimum of a grouping search. The post-layout implementation of the proposed dual-mode LDPC decoder is based on the Taiwan Semiconductor Manufacturing Company (TSMC) 40 nm complementary metal-oxide-semiconductor (CMOS) technology, using a compensation scheme of grouping comparison for 5G communication systems with a working frequency of 294.1 MHz. The decoding throughput achieved was at least 10.86 Gb/s without evaluating early termination, and the decoding power consumption was 313.3 mW.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.