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As a result of their cell structures, elastomeric foams exhibit high compressibility and are frequently used as buffer cushions in energy absorption. Foam pads between two surfaces typically withstand uniaxial loads. In this paper, we considered the effects of porosity and cell size on the mechanical behavior of random elastomeric foams, and proposed a constitutive model based on an artificial neural network (ANN). Uniform cell size distribution was used to represent monodisperse foam. The constitutive relationship between Cauchy stress and the four input variables of axial stretch λU, lateral stretch λL, porosity φ, and cell size θ was given by con-ANN. The mechanical responses of 500 different foam structures (20% < φ < 60%, 0.1 mm < θ < 0.5 mm) under compression and tension loads (0.4 < λU < 3) were simulated, and a dataset containing 100,000 samples was constructed. We also introduced a pre-ANN to predict lateral stretch to address the issue of missing lateral strain data in practical applications. By combining physical experience, we chose appropriate input forms and activation functions to improve ANN’s extrapolation capability. The results showed that pre-ANN and con-ANN could provide reasonable predictions for λU outside the dataset. We can obtain accurate lateral stretch and axial stress predictions from two ANNs. The porosity affects the stress and λL, while the cell size only affects the stress during foam compression.

Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been widely applied in practical applications, such as monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, etc. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness can be dramatically degraded when conventional approaches are used. Deep learning has been found to have significant impact on a variety of applications, including marine engineering. In this context, we offer a review of deep learning-based underwater marine object detection techniques. Underwater object detection can be performed by different sensors, such as acoustic sonar or optical cameras. In this paper, we focus on vision-based object detection due to several significant advantages. To facilitate a thorough understanding of this subject, we organize research challenges of vision-based underwater object detection into four categories: image quality degradation, small object detection, poor generalization, and real-time detection. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we provide a detailed critical examination of the most extensively used datasets. In addition, we present comparative studies with previous reviews, notably those approaches that leverage artificial intelligence, as well as future trends related to this hot topic.

This paper describes the design and development of an active device that was applied to shoulder rotation rehabilitation. It implements a force control in a similar way to the haptic devices that reflect a force on the user’s hand according to the rehabilitation parameters. This device was tested with patients, combining clinician manipulation and programmed exercises. The results were excellent, given that the range of movement of the shoulder increased in most of the pa-tients. The results of the Usability Scale questionnaire were also promising.

Publisher's version (útgefin grein), This paper is motivated by the notion that coupling systems allows for mitigating the failure of individual ones. We present a novel approach to determining asymptotic stability and robustness of a network consisting of coupled dynamical systems, where individual system dynamics are represented through polynomial or rational functions. The analysis relies on a local analysis; thus, making it computationally implementable. We present an efficient computational method that relies on semidefinite programming. Importantly, for cases where multiple equilibrium points exist, we show how to determine regions around an asymptotically stable equilibrium point that bounds solutions. These regions increase when systems are coupled as we observe when applying the presented analysis framework to a mathematical model of a continuous stirred tank reactor. Importantly, the presented work has implications to other fields as well., "Peer Reviewed"

The use of Electric Vehicles (EVs) is almost inevitable in the near future for the sake of the environment and our plant’s long-term sustainability. The availability of an Electric-Vehicle-Charging Station (EVCS) is the key challenge that owners are worried about. Therefore, we suggest benefiting from individual EVs that have excess energy and are willing to share it with other EVs in order to maximize the availability of EVCSs without the need to rely on the existing charging infrastructure. The Internet of Electric Vehicles (IoEV) is gradually gaining traction, allowing for a more efficient and intelligent transportation system by leveraging these capabilities between EVs. However, the IoEV is considered a trustless environment, with untrustworthy trading partners such as data sellers, buyers, and brokers. Data exchanged between the EV and the Energy AGgregator (EAG) or EV/EV can be used to analyze users’ behavior and compromise their privacy. Thus, a Vehicle-to-Vehicle (V2V)-charging system that is both secure and private must be established. Several V2V-charging systems with security and privacy features have been proposed. However, even if the transmitted communications are entirely anonymous, anonymity alone will not prevent the tracking adversary from reconstructing the target vehicle’s route. These systems frequently fail to find a balance between privacy concerns (e.g., trade traceability to achieve anonymity, and so on) and security measures. In this paper, we propose an efficient privacy-preserving and secure authentication based on Elliptic Curve Qu–Vanstone (ECQV) for a V2V-charging system that fulfils the essential requirements and re-authentication protocol in order to reduce the overhead of future authentication processes. The proposed scheme utilizes the ECQV implicit-certificate mechanism to create credentials and authenticate EVs. The proposed protocols provide efficient security and privacy to EVs, as well as an 88% reduction in computational time through re-authentication, as compared to earlier efforts.

China&rsquo, s increasing attention to patient satisfaction evaluation is part of an international trend of patient-centered healthcare. Patient sociodemographic characteristics are important intrinsic factors that will influence satisfaction. This paper aims to better understand how sociodemographic factors affect Chinese patient satisfaction with tertiary outpatient services using data from the 2017 China National Patient Survey. A total of 28,760 outpatient survey responses were analyzed, spanning 136 tertiary hospitals across 31 provinces. Multilevel logistic regression with fixed hospital effects was used to examine the association of patient satisfaction across multiple healthcare domains with sociodemographic factors. Results show that patients who were of a migrant population, of highest income, most educated, and who had medical aid insurance reported the lowest levels of overall satisfaction. Specifically, increasing age was correlated with decreased satisfaction in process management and affordability domains, while high-income and high-education outpatients reported lower satisfaction scores in the hospital environment domain. Furthermore, migrant patients experienced lower satisfaction across several domains. These intricate findings suggest that hospitals should tailor their services and evaluation metrics to specific patient demographics, and that the government should adopt policies that reduce disparities in healthcare access and affordability to ultimately improve the satisfaction of vulnerable groups.

This paper addresses an action that, from a Deleuzian perspective, is capable of modifying the despairing current social situation in which we are immersed, through the creation of political Ideas. Even though Deleuze conceives social Ideas as vast civilizing structures, we propose to bring into the political domain the logic of other acts of creation, such as the artistic or the philosophical, where the monumental coexists with minor figures that are nonetheless capable of introducing novelty into the world. The politician is the figure of those who are capable of having an Idea that allows to break the habits that perpetuate the current situation, and gives consistency to the intensive forms of life that continually create and dissolve themselves in the flow of becoming. Thus, macro- and micro-politics do not oppose each other, but offer in their immanence an alternative to social nihilism.

In 2019, the National Archives of Korea (NAK) developed a blockchain recordkeeping platform to conduct R&amp, D on recordkeeping approaches. This paper introduces two types of R&amp, D studies that have been conducted thus far. The first is the use of blockchain transaction audit trail technology to ensure the authenticity of audiovisual archives, i.e., the application of blockchain to a new system. The second uses blockchain technology to verify whether the datasets of numerous information systems built by government agencies are managed without forgery or tampering, i.e., the application of blockchain to an existing system. Government work environments globally are rapidly shifting from paper records to digital. However, the traditional recordkeeping methodology has not adequately kept up with these digital changes. Despite the importance of responding to digital changes by incorporating innovative technologies such as blockchain in recordkeeping practices, it is not easy for most archives to invest funds in experiments on future technologies. Owing to the Korean government’s policy of investing in digital transformation, NAK’s blockchain recordkeeping platform has been developed, and several R&amp, D tasks are underway. Hopefully, the findings of this study will be shared with archivists around the world who are focusing on the future of recordkeeping.

Research on ethnic entrepreneurship has generated substantial literature on the development of ethnic businesses among different immigrant groups in North America. Such studies tend to focus on the emergence of immigrant entrepreneurship among earlier immigrant groups by highlighting either the group characteristics or the opportunity structure. Existing studies also tend to overlook the importance of innovation in immigrants’ small businesses due to the marginality of immigrant businesses. Thus, there is a dearth of research on new immigrant communities in the USA that illuminates immigrants’ innovative practices. Drawing on the experiences of 50 Bangladeshi entrepreneurs in New York, this research examined how this emerging immigrant group transformed into immigrant entrepreneurs through the investigation of their innovative practices in small business. This paper determined that immigrant entrepreneurs are embedded within the dynamics of the immigration trajectory and the broader context of American society. Although these new immigrants were driven towards the lower end of the economy, this study found that innovations have expanded the breadth and depth of their businesses and made their businesses different and rewarding.

Power quality and stability have become the most important issues in power system operations, Micro Grids, and Smart Grids. Sensitive equipment can be seriously damaged when exposed to unstable power swing conditions. An unstable system may cause serious damage to Micro Grid System elements such as generators, transformers, transmission lines, and so forth. Therefore, out-of-step detection is essential for the safe operation of a Micro Grid system. In general, Equal Area Criterion (EAC) is a method for evaluating the stability of Smart Grid systems. However, EAC can be performed only if it is possible to analyze the active power and generator angle. This paper presents an analysis of the trajectory of complex power using a mathematical model. The variation of complex power is analyzed using a mathematical method, and then the relationship between complex power and EAC is presented, and a simulation performed. Later, in part II, a novel out-of-step detection algorithm based on part I will be presented and tested.

Green innovation has become an important support to promote the strategic goals of peak carbon dioxide emission and high-quality economic development. This paper systematically examines whether internal control can effectively drive enterprise green innovation and achieve the “win–win” result of enterprise economic development and environmental protection. We found that internal control can significantly improve the green innovation level of enterprises, which is mainly reflected in the mandatory disclosure stage of internal control information and the other four elements of internal control, except the control environment. The mechanism test found that internal control can promote green innovation by reducing enterprise risks, alleviating agency conflicts, relieving financing constraints, and improving the rationality of innovation investment. The heterogeneity test found that the promotion effect of internal control on green innovation is more significant in large enterprises and private enterprises; internal control can effectively supervise the maintenance of relationship resources, and relationship resources positively regulate the promotion effect of internal control on green innovation; excessive executive power will weaken the internal control supervision and punishment of executives, and weaken the positive effect between internal control and green innovation; internal control can improve the utilization rate of government subsidies, and government subsidies positively regulate the positive effect of internal control on green innovation. The research conclusions enrich the market-oriented research on the driving factors of enterprise green innovation, and provide empirical evidence for enhancing the competitiveness of enterprise green innovation and achieving carbon neutrality.

Electrochemical sensors play a significant role in detecting chemical ions, molecules, and pathogens in water and other applications. These sensors are sensitive, portable, fast, inexpensive, and suitable for online and in-situ measurements compared to other methods. They can provide the detection for any compound that can undergo certain transformations within a potential window. It enables applications in multiple ion detection, mainly since these sensors are primarily non-specific. In this paper, we provide a survey of electrochemical sensors for the detection of water contaminants, i.e., pesticides, nitrate, nitrite, phosphorus, water hardeners, disinfectant, and other emergent contaminants (phenol, estrogen, gallic acid etc.). We focus on the influence of surface modification of the working electrodes by carbon nanomaterials, metallic nanostructures, imprinted polymers and evaluate the corresponding sensing performance. Especially for pesticides, which are challenging and need special care, we highlight biosensors, such as enzymatic sensors, immunobiosensor, aptasensors, and biomimetic sensors. We discuss the sensors’ overall performance, especially concerning real-sample performance and the capability for actual field application.

The issue of the formation of wood dust particles in the work environment is still an actual topic in terms of its impact on employee health and the risk of fire or explosion in a woodworking operation. This article deals with the characteristics of spruce dust (Picea abies Karst. (L.)), which was taken from several types of wood technology. Experimental samples of spruce dust were taken from four types of sawing technologies, including grinding, briquetting and from the suction device container. The physical parameters of the samples taken were monitored and the particle size analysis was determined. The granulometric composition of the samples is significantly different. The sample of spruce wood dust from sawing has the most numerous fraction (250 µm), while the sample from grinding has the most numerous fraction 63–250 µm (87%).The aim of the paper was to monitor the minimum ignition temperature of the settled spruce dust layer and to look for a significant dependence of the minimum ignition temperature and ignition time on the type of spruce dust sample. A significant dependence was not confirmed. Significant moisture dependence of the samples was confirmed; the highest humidity was observed in the container, the lowest in sawing.

Optical microfiber-based temperature sensors have been proposed for many applications in a variety of industrial uses, including biomedical, geological, automotive, and defense applications. This increasing demand for these micrometric devices is attributed to their large dynamic range, high sensitivity, fast-response, compactness and robustness. Additionally, they can perform in-situ measurements remotely and in harsh environments. This paper presents an overview of optical microfibers, with a focus on their applications in temperature sensing. This review broadly divides microfiber-based temperature sensors into two categories: resonant and non-resonant microfiber sensors. While the former includes microfiber loop, knot and coil resonators, the latter comprises sensors based on functionally coated/doped microfibers, microfiber couplers, optical gratings and interferometers. In the conclusions, a summary of reported performances is presented.

This paper proposes a reciprocal-selection-based &lsquo, Win&ndash, Win&rsquo, overlay spectrum-sharing scheme for device-to-Device-enabled cellular networks to address the resource sharing between Device-to-Device devices and the cellular users by using an overlay approach. Based on the proposed scheme, the cell edge users intend to lease part of its spectrum resource to Device-to-Device transmission pairs. However, the Device-to-Device users have to provide the cooperative transmission assistance for the cell edge users in order to improve the Quality of Service of the uplink transmission from the cell edge users to the base station. Compared to the underlay spectrum-sharing scheme, overlay spectrum-sharing scheme may reduce spectrum efficiency. Hence, Non-Orthogonal Multiple Access technology is invoked at the Device-to-Device transmitter in order to improve the spectrum efficiency. The Stackelberg game is exploited to model the behaviours of the cell edge users and Device-to-Device devices. Moreover, based on matching theory, the cell edge users and Device-to-Device pairs form one-to-one matching and the stability of matching is analysed. The simulation results show that the proposed reciprocal-selection-based &lsquo, overlay spectrum-sharing scheme is capable of providing considerable rate improvements for both EUs and D2D pairs and reducing transmit power dissipated by the D2D transmitter to forward data for the EU compared with the existing methods.

“Wasan” is the collective name given to a set of mathematical texts written in Japan in the Edo period (1603–1867). These documents represent a unique type of mathematics and amalgamate the mathematical knowledge of a time and place where major advances where reached. Due to these facts, Wasan documents are considered to be of great historical and cultural significance. This paper presents a fully automatic algorithmic process to first detect the kanji characters in Wasan documents and subsequently classify them using deep learning networks. We pay special attention to the results concerning one particular kanji character, the “ima” kanji, as it is of special importance for the interpretation of Wasan documents. As our database is made up of manual scans of real historical documents, it presents scanning artifacts in the form of image noise and page misalignment. First, we use two preprocessing steps to ameliorate these artifacts. Then we use three different blob detector algorithms to determine what parts of each image belong to kanji Characters. Finally, we use five deep learning networks to classify the detected kanji. All the steps of the pipeline are thoroughly evaluated, and several options are compared for the kanji detection and classification steps. As ancient kanji database are rare and often include relatively few images, we explore the possibility of using modern kanji databases for kanji classification. Experiments are run on a dataset containing 100 Wasan book pages. We compare the performance of three blob detector algorithms for kanji detection obtaining 79.60% success rate with 7.88% false positive detections. Furthermore, we study the performance of five well-known deep learning networks and obtain 99.75% classification accuracy for modern kanji and 90.4% for classical kanji. Finally, our full pipeline obtains 95% correct detection and classification of the “ima” kanji with 3% False positives.

The evolution of information technology and the great advances in artificial intelligence are leading to a level of automation that has never been reached before. A large part of this level of automation is due to the use of robotics, which in turn ends up both hindering and accelerating the process of Industry 4.0. Industry 4.0 is driven by innovative technologies that have an effect on production systems and business models. Although technologies are the driving motors of production within Industry 4.0, many production systems require collaboration between robotics and humans, and safety is required for both parties. Given the need for robots to collaborate with humans simultaneously or in parallel, a new generation of robots, called cobots, “Collaborative Robots”, are gaining prominence to face these challenges. With cobots, it is possible to overcome security barriers and envisage working safely side-by-side with humans. This paper presents the development and testing of a low-cost, within standards, 6-axis collaborative robot that can be used for educational purposes in different task-specific applications. The development of this collaborative robot involves the design and 3D printing of the structure (connections and parts), sizing and selection of circuits and/or electronic components, programming, and control. Furthermore, this study considers the development of a user interface application with the robotic arm. Thus, the application of technological solutions, as well as of the scientific and educational approaches used in the development of cobots can foster the wide implementation of Industry 4.0.

To improve the generation quality of image style transfer, this paper proposes a light progressive attention adaptive instance normalization (LPAdaIN) model that combines the adaptive instance normalization (AdaIN) layer and the convolutional block attention module (CBAM). In the construction of the model structure, first, a lightweight autoencoder is built to reduce the information loss in the encoding process by reducing the number of network layers and to alleviate the distortion of the stylized image structure. Second, each AdaIN layer is progressively applied after the three relu layers in the encoder to obtain the fine-grained stylized feature maps. Third, the CBAM is added between the last AdaIN layer and the decoder, ensuring that the main objects in the stylized image are clearly visible. In the model optimization, a reconstruction loss is designed to improve the decoder’s ability to decode stylized images with more precise constraints and refine the structure of the stylized images. Compared with five classical style transfer models, the LPAdaIN is visually shown to more finely apply the texture of the style image to the content image, in order to obtain a stylized image, in which the main objects are clearly visible and the structure can be maintained. In terms of quantitative metrics, the LPAdaIN achieved good results in running speed and structural similarity.

Magnetic resonance sounding (MRS) technology is the only geophysical means to directly and quantitatively detect groundwater and has achieved good results in hydrogeological prospecting applications. In recent years, researchers have conducted considerable research on the efficiency of a single instrument, yielding certain results. However, the overall work efficiency of this method has not been effectively determined in its application to a large-scale survey. Hence, we propose both a joint detection method for MRS that determines the minimum working distance when multiple systems operate simultaneously and a collaborative measurement method of dual systems operating simultaneously in a fixed range of work areas. The cooperative working mode of the instruments is tested in the detection area, and the working mode proposed in this paper is shown to effectively avoid measurement interference between systems. Compared with the working mode of a single set of instruments, the measurement efficiency is more than doubled. Through this research, the feasibility of multiple MRS instruments working together in the same work area is verified, which provides effective technical support for the rapid and high-efficiency utilization of MRS over a wide range of measurement areas.

In the last few years, numerous research efforts have been conducted to merge the Internet of Things (IoT) with smart city environments. The goal to make a city “smart” is arising as a possible solution to lessen the issues caused by the urban population growth and fast urbanization. Attention also has focused on the pedestrian crossings because they are one of the most dangerous places in the transport field. Information and Communications Technologies (ICT) can undoubtedly be an excellent support in developing infrastructures that can best manage pedestrian crossing. For this reason, this paper introduces a fuzzy logic-based solution able to manage dynamically the traffic lights’ phases in signalized pedestrian crossings. The proposed approach provides the possibility to change the phases of the traffic light taking into account the time of the day and the number of pedestrians about to cross the road. The paper presents a thorough description of the fuzzy logic controller configuration, an in-depth analysis of the application scenario and simulative assessments obtained through Vissim simulations.

Social sciences and humanities (SSH) research is divided across a wide array of disciplines, sub-disciplines and languages. While this specialization makes it possible to investigate the extensive variety of SSH topics, it also leads to a fragmentation that prevents SSH research from reaching its full potential. The TRIPLE project brings answers to these issues by developing an innovative discovery platform for SSH data, researchers&rsquo, projects and profiles. Having started in October 2019, the project has already three main achievements that are presented in this paper: (1) the definition of main features of the GOTRIPLE platform, (2) its interoperability, (3) its multilingual, multicultural and interdisciplinary vocation. These results have been achieved thanks to different methodologies such as a co-design process, market analysis and benchmarking, monitoring and co-building. These preliminary results highlight the need for respecting diversity of practices and communities through coordination and harmonization.

As people pay more and more attention to a healthy diet, it has become a consensus to eat more coarse grains. The development of its edible value is of great significance for a healthy human diet and has attracted the attention of many scholars and food processing companies. However, due to the differences in protein composition and structure between corn flour and wheat protein, it is difficult to form a network structure during processing, and the viscoelasticity and flexibility are poor. Based on this, this paper proposes a machine vision-based noodle positioning monitoring method to achieve noodle alignment monitoring in the noodle processing process. First, the images are captured by binocular cameras and preprocessed. Further, feature detection and matching algorithms are used to recover the pose information between binocular cameras, and then the recognition targets are matched. Finally, noodle alignment monitoring during noodle processing is achieved. Experiments show that the detection accuracy of the method proposed in this paper is much higher than the traditional manual detection, which can improve the noodle quality and reduce labor costs.

WebShell is a common network backdoor attack that is characterized by high concealment and great harm. However, conventional WebShell detection methods can no longer cope with complex and flexible variations of WebShell attacks. Therefore, this paper proposes a deep super learner for attack detection. First, the collected data are deduplicated to prevent the influence of duplicate data on the result. Second, to detect the results of the algorithm, static and dynamic feature are taken as the feature of the algorithm to construct a comprehensive feature set. We then use the Word2Vec algorithm to vectorize the features. During this period, to prevent the outbreak of the number of features, we use a genetic algorithm to extract the validity of the feature dimension. Finally, we use a deep super learner to detect WebShell. The experimental results show that this algorithm can effectively detect WebShell, and its accuracy and recall are greatly improved.

As an important part of the urban environment, trees have certain risks while living in harmony with humans. For example, the failure of trees in extreme weather may cause casualties and damage to public and private; the decline and death of old and valuable trees can have an impact on the diversity and cultural value of trees. This paper outlines the theories related to tree risk and the development of tree risk assessment, evaluates the advantages and disadvantages of various tree risk assessment methods in existing studies, and explains some factors affecting the bearing capacity and related applications using knowledge of tree mechanics. Approaches in modern probing techniques are applied to study the response and loading of tree crowns and branches under wind loads, the application of different non-destructive testing techniques in visual assessment for detecting internal defects and root distribution of trees, and the role and impact of objective quantitative test results on tree risk assessment. Finally, the future development direction of tree risk assessment is predicted, which provides an important reference for research on tree risk assessment.

Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.

The existing physical layer security technology based on fountain codes needs to ensure that the legal channel is superior to the eavesdropping channel; when the quality of the legal channel and the eavesdropping channel are close, the information security cannot be guaranteed. Aiming at this problem, this paper proposes a shifted Luby transform (SLT) code security scheme for partial information encryption, which is mainly divided into two stages, partial information encryption transfer and degree distribution adjustment. The main idea is that the source randomly extracts part of the information symbols, and performs XOR encryption with the random sequence containing the main channel noise sent by the legitimate receiver. Afterward, the degree distribution is adjusted using the number of transfer information symbols received by the legitimate receiver to improve the average degree of the encoded codewords. Since the eavesdropper can only obtain fewer information symbols in the initial stage, it is difficult to decode the generated coded symbols after the degree distribution adjustment, thereby ensuring the safe transmission of information. The experimental results show that, compared with other LT anti-eavesdropping schemes, even if the legitimate channel is not dominant, the proposed scheme still has better security performance and less decoding overhead.

Coal-accompanying gas is an essential resource, with numerous mining methods. The practice has proved that injecting high-pressure air into the coal seam can replace and flush the gas in the coal seam, effectively solving the problem of inadequate single gas drainage in soft and low permeability coal seams. This paper uses the finite element method to solve the model, simulate and study the gas drainage by high-pressure air injection in the bedding drilling, and establish a fluid-structure coupling model for gas drainage by high-pressure air injection. The competitive adsorption of N2, O2, and CH4, diffusion and migration of CH4 in coal matrix and fissure, matrix deformation caused by CH4 adsorption, and desorption and control of coal deformation by applied stress are considered in the model. When the fixed extraction time is 600 days (d), the optimal spacing between the extraction hole and injection hole is 12.5 m. The safe extraction effect and minimum drilling amount can be ensured. It provides a basis for guiding gas drainage by injecting high-pressure air on-site.

This invited paper demonstrated an ultra-highly sensitive ammonia (NH3) sensor based on the light-induced thermoelastic spectroscopy (LITES) technique for the first time. A quartz tuning fork (QTF) with a resonance frequency of 32.768 kHz was employed as a detector. A fiber-coupled, continuous wave (CW), distributed feedback (DFB) diode laser emitting at 1530.33 nm was chosen as the excitation source. Wavelength modulation spectroscopy (WMS) and second-harmonic (2f) detection techniques were applied to reduce the background noise. In a one scan period, a 2f signal of the two absorption lines located at 6534.6 cm−1 and 6533.4 cm−1 were acquired simultaneously. The 2f signal amplitude at the two absorption lines was proved to be proportional to the concentration, respectively, by changing the concentration of NH3 in the analyte. The calculated R-square values of the linear fit are equal to ~0.99. The wavelength modulation depth was optimized to be 13.38 mA, and a minimum detection limit (MDL) of ~5.85 ppm was achieved for the reported NH3 sensor.

This paper tracked hepatitis B patients from Medan, Indonesia to Penang, Malaysia under transnational medical care and has an understanding of their medical history and socioeconomic status. The condition of these patients improved as a result of good compliance with medical treatment, including lifestyle adjustment and regular medication. Under the influence of the marketization of healthcare, transnational medical patients in the social structure, based on their economic ability and socioeconomic status, may be expected to experience health inequalities. People with unhealthy medical distribution and weak socioeconomic status are easily prone to diseases due to environmental and social conditions; it is easier for such patients to delay or give up their medical treatment. After continuous tracking and increasing patient exposure to medical knowledge and self-care management opportunities, increasing awareness, screening, care, and treatment, the transmission of hepatitis B can be reduced to enable them to gain upward mobility by their capacities and thus improve their health. Social mobility is deemed the main approach to reduce social inequality. There have been limited medical clinical observations and tracking confirming this theory. This paper, which uses medical observation, confirmed that social mobility is considered as the principal key to reducing inequalities in health.

This paper reviews the progress of AlGaN-based deep-ultraviolet (DUV) light emitting diodes (LEDs), mainly focusing in the work of the authors’ group. The background to the development of the current device structure on sapphire is described and the reason for using a (0001) sapphire with a miscut angle of 1.0° relative to the m-axis is clarified. Our LEDs incorporate uneven quantum wells (QWs) grown on an AlN template with dense macrosteps. Due to the low threading dislocation density of AlGaN and AlN templates of about 5 × 108/cm2, the number of nonradiative recombination centers is decreased. In addition, the uneven QW show high external quantum efficiency (EQE) and wall-plug efficiency, which are considered to be boosted by the increased internal quantum efficiency (IQE) by enhancing carrier localization adjacent to macrosteps. The achieved LED performance is considered to be sufficient for practical applications. The advantage of the uneven QW is discussed in terms of the EQE and IQE. A DUV-LED die with an output of over 100 mW at 280–300 nm is considered feasible by applying techniques including the encapsulation. In addition, the fundamental achievements of various groups are reviewed for the future improvements of AlGaN-based DUV-LEDs. Finally, the applications of DUV-LEDs are described from an industrial viewpoint. The demonstrations of W/cm2-class irradiation modules are shown for UV curing.

In recent years, dam construction has become more complex, requiring an effective project management method. Building Information Modeling (BIM) affects how construction projects are planned, designed, executed, and operated. Therefore, reducing execution time, cost, and risk and increasing quality are the primary goals of organizations. In this paper, first, the time and cost of the project were obtained via the BIM process. Subsequently, optimization between the components of the survival pyramid (time, cost, quality, and risk) in construction projects was completed in a case study of the Ghocham storage dam in five different modes, including contractor’s offers, BIM, actual, and two other modes based on the expert’s opinions. For this aim, five different meta-heuristic optimization algorithms were utilized, including two classical algorithms (Genetic and Simulated Annealing) and three novel algorithms (Black Widow Optimization, Battle Royale Optimization, and Black Hole Mechanics Optimization). In four cases, once each element of the survival pyramid was optimized separately, all four cases were traded off simultaneously. Moreover, the results were obtained from all the mentioned algorithms in five scenarios based on the number of function evaluation (Nfe), Standard Deviation (SD), Computation Time (CT), and Best Cost (BC). MATLAB software completed the coding related to the objective functions and optimization algorithms. The results indicated the appropriate performance of GA and BHMO algorithms in some scenarios. However, only the GAs should be considered effective algorithms in a dam construction projects’ time–cost–quality–risk (TCQR) tradeoff.

In this paper, we introduce a new class of robust model selection criteria. These criteria are defined by estimators of the expected overall discrepancy using pseudodistances and the minimum pseudodistance principle. Theoretical properties of these criteria are proved, namely asymptotic unbiasedness, robustness, consistency, as well as the limit laws. The case of the linear regression models is studied and a specific pseudodistance based criterion is proposed. Monte Carlo simulations and applications for real data are presented in order to exemplify the performance of the new methodology. These examples show that the new selection criterion for regression models is a good competitor of some well known criteria and may have superior performance, especially in the case of small and contaminated samples.

In this paper, a new class of impulsive neural networks with fractional-like derivatives is defined, and the practical stability properties of the solutions are investigated. The stability analysis exploits a new type of Lyapunov-like functions and their derivatives. Furthermore, the obtained results are applied to a bidirectional associative memory (BAM) neural network model with fractional-like derivatives. Some new results for the introduced neural network models with uncertain values of the parameters are also obtained.

In this paper, the inference on location parameter for the skew-normal population is considered when the scale parameter and skewness parameter are unknown. Firstly, the Bootstrap test statistics and Bootstrap confidence intervals for location parameter of single population are constructed based on the methods of moment estimation and maximum likelihood estimation, respectively. Secondly, the Behrens-Fisher type and interval estimation problems of two skew-normal populations are discussed. Thirdly, by the Monte Carlo simulation, the proposed Bootstrap approaches provide the satisfactory performances under the senses of Type I error probability and power in most cases regardless of the moment estimator or ML estimator. Further, the Bootstrap test based on the moment estimator is better than that based on the ML estimator in most situations. Finally, the above approaches are applied to the real data examples of leaf area index, carbon fibers’ strength and red blood cell count in athletes to verify the reasonableness and effectiveness of the proposed approaches.

Detection methods for monitoring infectious pathogens has never been more important given the need to contain the spread of the COVID-19 pandemic. Herein we propose a highly sensitive magnetic-focus-enhanced lateral flow assay (mLFA) for the detection of SARS-CoV-2. The proposed mLFA is simple and requires only lateral flow strips and a reusable magnet to detect very low concentrations of the virus particles. The magnetic focus enhancement is achieved by focusing the SARS-CoV-2 conjugated magnetic probes in the sample placed in the lateral flow (LF) strips for improved capture efficiency, while horseradish peroxidase (HRP) was used to catalyze the colorimetric reaction for the amplification of the colorimetric signal. With the magnetic focus enhancement and HRP-based amplification, the mLFA could yield a highly sensitive technology for the recognition of SARS-CoV-2. The developed methods could detect as low as 400 PFU/mL of SARS-CoV-2 in PBS buffer based on the visible blue dots on the LF strips. The mLFA could recognize 1200 PFU/mL of SARS-CoV-2 in saliva samples. With clinical nasal swab samples, the proposed mLFA could achieve 66.7% sensitivity and 100% specificity.

The reuse and repurposing of lithium-ion batteries for transportation in stationary energy systems improve the economic value of batteries. A precise suitability test at the beginning of the second life is therefore necessary. Common methods such as electrochemical impedance spectroscopy (EIS) and current interrupt (CI) analysis, as well as capacity analysis, can be used for testing. In this paper, these methods are studied from the aspects of test duration, sensitivity and acquisition costs of the measuring instruments. For this purpose, tests are carried out on battery modules, which were used for transportation. It is shown that subtle differences are better detected with EIS and less accurately with the CI method. The test duration is fastest with the CI method, followed by EIS and the capacity test. Strongly aged modules are reliably detected with all methods.

Metaphors are an integral and important part of human communication and greatly impact the way our thinking is formed and how we understand the world. The theory of the conceptual metaphor has shifted the focus of research from words to thinking, and also influenced research of the linguistic metaphor, which deals with the issue of how metaphors are expressed in language or speech. With the development of natural language processing over the past few decades, new methods and approaches to metaphor identification have been developed. The aim of the paper is to map the methods of annotating and identifying metaphors in the field of natural language processing and to give a systematic overview of how relevant linguistic theories and natural language processing intersect. The paper provides an outline of cognitive linguistic metaphor theory and an overview of relevant methods of annotating linguistic and conceptual metaphors as well as publicly available datasets. Identification methods are presented chronologically, from early approaches and hand-coded knowledge to statistical methods of machine learning and contemporary methods of using neural networks and contextual word embeddings.

An alternative energy source that has appeared beyond expectations and has seen a lot of progress is the fuel cell. A proton exchange membrane (PEM) fuel cell is chosen for analysis and requires a DC-DC boost converter as an interface between the fuel cell and the load to provide a high-gain regulated voltage. Although great effort towards developing different converter topologies has been made during recent decades, less attention has been devoted to the reliability and thermal performance assessment of the present converters. In this paper, five non-isolated DC-DC converters are analyzed in terms of both thermal behavior and reliability. The temperature estimation of semiconductor devices as a critical part of the thermal analysis has been made via a detailed thermal model and the reliability is evaluated by means of a power cycling test. Finally, a performance score has been attributed using the TOPSIS ranking methodology and considering all the criteria (e.g., the number of components and cost) at the same time. The results indicated that the floating interleaved boost converter is always at the top of the list, even if the weight of the indicators is changed. When the weight of the cost criterion is higher than the reliability criterion, the multi-switch boost converter will be in second place. If the weight of the reliability criterion is greater than cost, the interleaved and multi-switch converter are ranked second and third, respectively. Additionally, the Cuk converter with a closeness coefficient of zero is always associated with the most unfavorable performance. The work is (partially) supported by the energy transition funds project “BREGILAB” organized by the FPS economy, S.M.E.s, and Self-Employed and Energy (Met de steun van het Energietransitiefonds)."

In this paper, it is rigorously proven that since observational data (i.e., numerical values of physical quantities) are rational numbers only due to inevitably nonzero measurements errors, the conclusion about whether Nature at the smallest scales is discrete or continuous, random and chaotic, or strictly deterministic, solely depends on experimentalist’s free choice of the metrics (real or p-adic) he chooses to process the observational data. The main mathematical tools are p-adic 1-Lipschitz maps (which therefore are continuous with respect to the p-adic metric). The maps are exactly the ones defined by sequential Mealy machines (rather than by cellular automata) and therefore are causal functions over discrete time. A wide class of the maps can naturally be expanded to continuous real functions, so the maps may serve as mathematical models of open physical systems both over discrete and over continuous time. For these models, wave functions are constructed, entropic uncertainty relation is proven, and no hidden parameters are assumed. The paper is motivated by the ideas of I. Volovich on p-adic mathematical physics, by G. ‘t Hooft’s cellular automaton interpretation of quantum mechanics, and to some extent, by recent papers on superdeterminism by J. Hance, S. Hossenfelder, and T. Palmer.

Currently, urbanization driven by global capital flows entails a main trend in many large cites in China, while global capital investment in small inland cities especially in western China is extremely scarce, where their globalization characters the powerful nationalization power and market activation. Dunhuang, a small inland city in western China, has transformed successfully from an agricultural county to an international tourist city, a platform for worldwide cultural communication, and a node city in the Belt and Road region because of its unique and brilliant resources: Mogao Grottoes and Dunhuangology. Therefore, this paper develops a conceptual framework of the multiple cooperative mechanisms and globalization path (MCMGP) of Dunhuang, elaborating the process of industrial transformation, urban globalization, and multiple cooperative mechanisms between government and market actors based on interviewing records and statistics. Findings show that the MCMGP features government-led intervention, resource orientation, and centralization that embodies the driver of state-owned enterprises (SOEs). Also, the MCM in Dunhuang’s globalization contains the mechanism of enrolment, mobilization and action, governance and global marketing, distributed in the two phases. Equally important, in response to the Belt and Road Initiative (BRI) and Silk Road (Dunhuang) International Cultural Expo (SRDICE) from the state, the city government has significantly reinvested and refined cultural tourism via governance mechanisms, carving out a key node city in the Silk Road and elevating an international tourist city. Environmentally, Dunhuang’s tourism internationalization enhances the process of the development of a sustainable shared mobility industry. Furthermore, its tourism development and social–ecology system maintain the synergistic relationships which international tourism promotes such as urban ecosystem and public welfare and in turn, social–ecological enhancement serve Dunhuang’s international tourism well. Practical implications of how Dunhuang’s experience may have lessons for others are discussed in China’s peculiar socialist market economy discourse.

In Africa, many urban farmers apply cropping systems from rural backgrounds into their urban setting. This paper explores the possibility that &ldquo, upgrading&rdquo, cropping systems in African cities could boost economic empowerment for impoverished urban farmers. To these ends, the author conducted a case study of cropping systems in Cape Town, identifying the strengths and weaknesses of the predominant cropping system. Data collection consisted of in-depth interviews and focus-group discussions with a selection of 59 urban farmers as well as interviews with key informants from non-governmental organizations, and local government. The findings are interpreted using an asset-based community development lens, which suggests that local networks and locally sourced inputs, utensils, and infrastructure are fundamental to resilient urban agriculture in this context. A limitation of the case study method is in the generalisability of the findings to other contexts. This study may, however, be used as a guideline for conducting similar case studies in other contexts.

The clover-grass ratio is an important factor in composing feed ratios for livestock. Cameras in the field allow the user to estimate the clover-grass ratio using image analysis; however, current methods assume the total dry matter is known. This paper presents the preliminary results of an image analysis method for non-destructively estimating the total dry matter of clover-grass. The presented method includes three steps: (1) classification of image illumination using a histogram of the difference in excess green and excess red; (2) segmentation of clover and grass using edge detection and morphology; and (3) estimation of total dry matter using grass coverage derived from the segmentation and climate parameters. The method was developed and evaluated on images captured in a clover-grass plot experiment during the spring growing season. The preliminary results are promising and show a high correlation between the image-based total dry matter estimate and the harvested dry matter ( R 2 = 0.93 ) with an RMSE of 210 kg ha − 1 .

This paper proposes a method for automatic discrimination of two mackerel species: Scomber japonicus (chub mackerel) and Scomber australasicus (blue mackerel). Because S. japonicus has a much higher market price than S. australasicus, the two species must be properly sorted before shipment, but their similar appearance makes discrimination difficult. These species can be effectively distinguished using the ratio of the base length between the dorsal fin&rsquo, s first and ninth spines to the fork length. However, manual measurement of this ratio is time-consuming and reduces fish freshness. The proposed technique instead uses image processing to measure these lengths. We were able to successfully discriminate between the two species using the ratio as a geometric feature, in combination with several texture features. We then quantitatively verified the effectiveness of the proposed method and demonstrated that it is highly accurate in classifying mackerel.

Changes in the world’s situation and the need to be competitive and less dependent on the global economy lead to improvements in many organisations. Together with business model transformation, using the latest technologies such as augmented reality, autonomous robots, mobile technologies, big data, cloud computing, and others known as an Industry 4.0 pillars, enterprises are changing the industry status quo. These technologies are widely described and studied in the literature, mainly on the example of large production companies. In order to fill the gap, this article had two objectives. The first objective was to investigate the usage of Industry 4.0 technologies in the time of the COVID-19 pandemic. The second one was to verify the strength of the impact of selected barriers on the level of implementation of individual I4.0 technologies and influence of cooperation during technology implementation. To achieve the objectives of the article, quantitative research was conducted. Data from 125 anonymous survey participants were collected in October 2021 and analysed using a statistical tool. The study results revealed that the term Industry 4.0 is known among industry employees. Surveyed organisations are using I4.0 technologies and still planning to implement them in a wider range despite the pandemic situation. Moreover, the most often-cited barriers in the literature on the subject are costs or employees’ acceptations, too-individualised customer requirements, or insufficient standardisation, which affect the implementation of Industry 4.0 solutions with strengths varying from low to moderate intensity. The information in this paper may be of use to Industry 4.0 solution providers or collaborative and partnership organisations.

This paper presents a novel method for extracting the land vertical displacement rates (VDRs) in Shanghai (China) using the multi-platform SAR images collected between 2009 and 2010, i.e., the ascending/descending COSMO-SkyMed (CSK) X-band images and the descending TerraSAR (TSX) X-band images. Different from the conventional approach the proposed method estimates the VDRs by multi-temporal interferometric SAR processing (i.e., temporarily coherent point InSAR, TCPInSAR) and the localized least square (LS) solution, through which the horizontal displacement rates (HDRs) can be also derived as the by-product. The three types of displacement rates in the radar line of sight (LOS) are first estimated by TCPInSAR with use of the ascending/descending CSK and descending TSX data. Both the VDRs and HDRs are then estimated by a localized LS method on the basis of the three types of LOS displacement rates derived by TCPInSAR. For comparison purposes, the VDRs for the single-platform SAR data are also obtained through dividing the LOS displacement rates by the cosine of the radar incidence angle. The analysis with the aid of GPS data shows the horizontal motion due to the geological settings in Shanghai cannot be ignored, and the VDRs derived from the single-platform data are not accurate and reliable without taking into account the horizontal motion. The experimental results indicate the TCPInSAR-LS method can be used to efficiently improve the accuracy in the VDRs by integrating the multi-platform SAR data and considering both horizontal and vertical motion, thus overcoming the disadvantages in the conventional approach. The improved VDRs in Shanghai range between −22.8 and 9.6 mm/year, while the relative east-west motion rates range between −7.2 and 6.2 mm/year, and the relative north–south motion is subtle and can be ignored. Analysis of the improved VDRs shows that a slightly uplifting area of about 70 km2 appears in the west downtown of Shanghai, and a subsiding bowl appears in the Hongkou District of Shanghai. The validation indicates that the uplifting trend can be ascribed to the measures conducted by the Shanghai municipal government, i.e., decreasing the groundwater pumping volume and increasing the groundwater recharge volume.

Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices, deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances, other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.

Integrated fiber-wireless (FiWi) should be regarded as a promising access network architecture in future 5G networks, and beyond, this due to its seamless combination of flexibility, ubiquity, mobility of the wireless mesh network (WMN) frontend with a large capacity, high bandwidth, strong robustness in time, and a wavelength-division multiplexed passive optical network (TWDM-PON) backhaul. However, the key issue in both traditional human-to-human (H2H) traffic and emerging Tactile Internet is the energy conservation network operation. Therefore, a power-saving method should be instrumental in the wireless retransmission-enabled architecture design. Toward this end, this paper firstly proposes a novel energy-supply paradigm of the FiWi converged network infrastructure, i.e., the emerging power over fiber (PoF) technology instead of an external power supply. Then, the existing time-division multiplexing access (TDMA) scheme and PoF technology are leveraged to carry out joint dynamic bandwidth allocation (DBA) and provide enough power for the sleep schedule in each integrated optical network unit mesh portal point (ONU-MPP) branch. Additionally, the correlation between the transmitted optical power of the optical line terminal (OLT) and the quality of experience (QoE) guarantee caused by multiple hops in the wireless frontend is taken into consideration in detail. The research results prove that the envisioned paradigm can significantly reduce the energy consumption of the whole FiWi system while satisfying the average delay constraints, thus providing enough survivability for multimode optical fiber.

The adoption of green building technology has become significant for ensuring sustainable development; it has become the main step to a sustainable future. The designs for green buildings include finding a balance between comfortable home construction and a sustainable environment. Moreover, the application of emerging technology is also used to supplement existing methods in the development of greener buildings to preserve a sustainable built environment. The main problem of this research is how to tackle the environmental parameters balance based on new techniques that are being used for green building optimization. To mitigate the cumulative effect of the constructed climate on human wellbeing and the regular ecosystem, the most popular goals for green buildings should be planned. This can be achieved by efficient use of natural resources such as energy, water, and other resources and minimizing waste. This will contribute to the security of occupant health, enhancement of work performance, emissions control, and improvement of the environment. In the construction of green buildings, several criteria that may contradict, interrelated indistinct and of qualitative and/or quantitative environment are broadened to utilize. This paper provides a detailed state of the art analysis on improving existing practices in green architecture/building using analytical hierarchy process (AHP) techniques to tackle the environmental balancing values based on optimal strategies and designs by green solutions to help make the best possible option from numerous options.

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

The fast and accurate classification of surrounding rock mass is the basis for tunnel design and construction and has significant value in engineering applications. Therefore, this paper proposes a method for classifying and predicting surrounding rock mass based on particle swarm optimization (PSO)–least squares support vector machine (LSSVM). The premise of the research is that the data acquired from digital drilling technology are divided into a training group and a test group; the training group continuously optimizes the algorithm for the particle swarm optimization least squares support vector machine, and then the test group is used for verification. Moreover, the fast searching abilities of the particle swarm significantly accelerate the computational power and computational accuracy of the least squares support vector machine, making it a high-speed analog search tool. Taking the Jiaozhou Bay undersea tunnel in China as an example, a comparison of the evaluation results of PSO-LSSVM and QGA-RBF (quantum genetic algorithm-radical basis function neural network) is undertaken. The results show that PSO-LSSVM matches well with the field-measured surrounding rock grade. Applying the method in an engineering context proves that it has good self-learning abilities, even when the sample size is small and the prediction accuracy is high; as such, it meets the engineering requirements. The technique has the advantages of small sample prediction, pattern recognition, and nonlinear prediction.