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The novel coronavirus disease — COVID-19 is a historic catastrophe that has caused many devastating impacts on human life and wellness. Researchers in academia and industry strive to understand the causes of this pandemic disease and find new therapeutics combating it. Consequently, the number of COVID-19 related publications increases rapidly, and it is too difficult for medical researchers and practitioners to keep up with the latest research and development. Literature filtering and categorization, and knowledge discovery can use text mining as a powerful tool. In this paper, we propose a text mining method to explore the categories of COVID-19 related themes and identify the standard methodologies that have been used. We discuss the potential limitations of this preliminary study and present future perspectives related to COVID-19 research. This paper provides an quantitative and qualitative mixed analysis example of using some research papers by data mining method to dig out several hidden information and set up a foundation for data scientists to develop more effective algorithms to deal with COVID-19 related problems. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision. [ABSTRACT FROM AUTHOR]

This paper aims to comprehensively explore the pivotal role of simulation and modeling in the field of Artificial Intelligence (AI). It focuses on elucidating the diverse applications of simulation and modeling in training AI systems, optimizing algorithms, and enhancing decision-making processes. To achieve this objective, we conducted an extensive review of the literature from the Scopus database, employing a well-defined selection process. We utilized keywords such as "simulation," "modeling," "Artificial Intelligence," and related terms to identify relevant papers published within the last 10 years. The selection criteria included assessing the relevance, quality, contribution, and recent citations of the papers. After a rigorous screening process, we selected 40 papers with the highest overall scores for inclusion in our review. The selected papers encompass a wide range of domains where simulation and modeling play a vital role in advancing AI applications. These domains include manufacturing, healthcare, energy consumption prediction, public sector decision-making, education, environmental modeling, and more. Our review highlights how AI leverages simulation and modeling to improve predictive accuracy, optimize resource allocation, and enhance decision-making processes across diverse sectors. We also discuss the potential future directions in the integration of simulation and modeling with AI, emphasizing its significance in various fields. [ABSTRACT FROM AUTHOR]

The International Journal of Semantic Computing has published a special issue on Semantic Computing, which includes five selected papers from the 17th IEEE International Conference on Semantic Computing. Semantic Computing focuses on the derivation, description, integration, and use of semantics for various resources. The papers in the special issue cover topics such as intent detection, constructing probabilistic models from knowledge graphs, unsupervised estimation of subjective content descriptions, the influence of noisy labels on question answering systems, and automatic domain-adaptive sentiment analysis. Each paper presents innovative approaches and findings in their respective areas of research. [Extracted from the article]

Traditional requirement verification methods often fail to fully consider the modeling of time-dependent behaviors during system execution, which may lead to inconsistent system requirements, thereby compromise software reliability and correctness. To address this limitation, this paper proposes the Scenario-Specific Verification Method for System Requirements Consistency via Time Modeling. The Scenario-Specific Verification Method converts use case descriptions and problem graphs into Clock Constraint Specification Language (CCSL) constraints, transformed into clock graphs for verifying requirement consistency. The resulting constraints are solved with the Z3 solver to validate correctness, thereby enhancing system reliability. The novel time modeling approach provides techniques for verifying temporal dependencies in requirements. A case study demonstrates the efficiency and accuracy of the Scenario-Specific Verification Method in detecting inconsistencies. The main contributions include that a technique is demonstrated to formalize use case descriptions as CCSL constraints, improving on natural language and a method is presented utilizing CCSL time modeling and clock graphs to analyze requirements for consistency. Overall, an innovative solution for requirement verification using time modeling is offered by this paper, contributing to the enhancement of system development process reliability and correctness. [ABSTRACT FROM AUTHOR]

Amid the pandemic infection, people are bound to use contactless mobile payment (M-Payment) services. M-Payment is a payment method using an application in a mobile device, such as a mobile phone, and gadget. Owing to the convenience, reliability and contact-free feature of M-Payment, it has been diffusely adopted to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating the stabilization of the social economy. Consequently, it has become one of the day's most important topics. Therefore, the purpose of this study is to provide a systematic literature review (SLR) on the applications of M-payment services in financial strategies, focusing on the pandemic crisis. 19 papers were collected and divided into three groups for further analysis. The results showed that M-Payments applications in financial strategies during the pandemic crisis could help reduce the spread of infection risks by hastening the transition to touchless habits. [ABSTRACT FROM AUTHOR]

In recent years, the concurrent occurrences of the COVID-19 pandemic and the Russian invasion of Ukraine have led to global disruptions in supply chains and a surge in commodity prices. Major advanced economies have experienced an increase in inflation rates and a decline in economic activity. Against this economic backdrop, this paper aims to address the strained condition of supply chains by employing the Global Supply Chain Pressure Index (GSCPI) alongside commodity price indices. Furthermore, it seeks to examine how fluctuations in these variables affect Japan's macroeconomic landscape through local projection and structural Vector Autoregression (VAR). In the local projection analysis, it has been observed that the strain on supply chains induces sustained increases in consumer and producer prices, further contributing to long-term declines in production. Within the structural VAR model framework, the identification of structural shocks is conducted through sign restrictions, affirming the significance of supply chain shocks and energy and food price shocks as crucial factors contributing to recent price increases. This confirmation is supported by variance decomposition and historical decomposition analyses. [ABSTRACT FROM AUTHOR]

Unreliable suppliers may pose a substantial threat to supply chains, especially when they hold private information of their reliability. We consider a dyadic supply chain where the information of supplier reliability (in the form of random production yield) is asymmetric. We propose a new mechanism-design model and derive the buyer's optimal procurement contract menu offered to suppliers with private information. We prove that the contract menu is as simple as offering two different inflated order amounts and setting the procuring price sufficiently low to let the suppliers earn zero reservation profits. These results are derived analytically under uniform distribution. We test them numerically under beta distribution and find them hold as well. However, the informational rent will become positive when the supplier's reservation profit is positive. Positive informational rent is also found when we consider another structure of the supplier's production cost. This paper provides some new insights into supply chain management under asymmetric information of uncertain supply. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including breast cancer image classification, intelligent engineering management with data mining, and identifying optimal methods for power grid error parameters.

The diversity, infinity, and nonuniform description of human motion make it challenging for computers to understand human activities. To explore and reuse captured human motion data, this work defines a more comprehensive hierarchical theoretical model of human motion and proposes a standard human posture encoding scheme. We construct a domain knowledge graph (DKG) named the human motion encoding knowledge graph (HME-KG) based on posture codes and action labels. Community detection, similarity analysis, and centrality analysis are used to explore the potential value of motion data. This paper conducts an evaluation and visualization of HME-KG. [ABSTRACT FROM AUTHOR]

Modular robots carry the unique advantage of changing their shape according to different tasks and environments. In order to study the lightweight and flexible combination characteristics of modular robots, this paper presents a two-degree-of-freedom pluggable modular robot and its simulation method, named SnackBot. SnackBot is composed of several independently operating modules. Each module contains a control unit, a micro battery, and two DC motors that drive the module joints. In order to predict robot actions and monitor the state of the robot, the simulation model of SnackBot is built and a virtual environment is established in Ignition Gazebo. The virtual robot and physical robot are synchronously controlled in the environment of the Robot Operating System 2 (ROS2). Finally, experimental results verify the feasibility of the SnackBot. [ABSTRACT FROM AUTHOR]

Milk is considered to be one of the most perfect natural foods for human beings. The analysis of effective components and determination of important components in milk is an important practical work. Based on the previous measurement data of fat volume concentration and refractive index of milk in the market, this paper forms a fitting formula through mathematical fitting, which can solve the corresponding relationship between nonmeasurement points of milk concentration and refractive index. Because the actual measurement points are limited, its practical application is not convenient. However, fitting formulas can calculate the corresponding values of infinitely many points, which can be easily applied. However, judging from the goodness of fit of the fitting formula, a theoretically good fitting formula may produce underfitting and overfitting in practical application. Through the analysis of the first-order to sixth-order fitting formulas of milk concentration and refractive index, this paper tells us that the fitting formula with high goodness of fit is not necessarily a good formula. The specific problems should be analyzed in the application process to make the theory more consistent with the actual situation. From the calculation and analysis in this paper, it can be seen that the fitted sixth-order formula has the best effect when the fat volume concentration of milk is 0–10%. When considering a fat volume concentration of 0–34% in milk, the fitted second-order formula performs better. [ABSTRACT FROM AUTHOR]

This article explores the properties of 2D defective Mo1.33C MXene, a material that shows promise in electromagnetic interference (EMI) shielding. The researchers tested a defect-containing Mo1.33C MXene "paper" and found positive results compared to other MXene and graphene-based materials. The article provides information on the preparation of the MXene and the experimental methods used for characterization and testing. The research suggests that Mo-based defective MXene could have applications in EMI shielding. [Extracted from the article]

In this paper, a new detection method of pin defects based on image segmentation and ResNe-50 is proposed, which realizes the defect detection of faulty pins in many aviation connectors. In this paper, a new dataset image segmentation method is used to segment many aviation connectors in a single image to generate a dataset, which reduces the tedious work of manually labeling the dataset. In the defect detection model, based on ResNet-50, a ResNet-B residual structure is introduced to reduce the loss of features during information extraction; a continuously differentiable CELU is used as the activation function to reduce the neuron death problem of ReLU; a new deformable convolution network (DCN v2) is introduced as the convolution kernel structure of the model to improve the recognition of aviation connectors with prominent geometric deformation pin recognition. The improved model achieved 97.2% and 94.4% accuracy for skewed and missing pins, respectively, in the experiments. The detection accuracy improved by 1.91% to 96.62% compared to the conventional ResNet-50. Compared with the traditional model, the improved model has better generalization ability. [ABSTRACT FROM AUTHOR]

The focus of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimizing the aggregate distance from one pixel to every other pixel in a chosen window. This minimizing operation determines the most confirmative pixel based on its similarity to the chosen window and replaces the central pixel of the window with the determined one. The peer group filters, unlike the vector median filters, determine a set of pixels that are most confirmative to the window and then perform filtering over the determined set. Using a set of pixels in the filtering process rather than one pixel is more helpful as it takes into account the full information of all the pixels that seemingly contribute to the signal. Hence, the peer group filters are found to be more robust to noise. However, the peer group for each pixel is computed deterministically using thresholding schemes. A wrong choice of the threshold will easily impair the filtering performance. In this paper, we propose a peer group filtering approach using principles of Bayesian probability theory and clustering. Here, we present a method to compute the probability that a pixel value is clean (not corrupted by impulse noise) and then apply clustering on the probability measure to determine the peer group. The key benefit of this proposal is that the need for thresholding in peer group filtering is completely avoided. Simulation results show that the proposed method performs better than the conventional vector median and peer group filtering methods in terms of noise reduction and structural similarity, thus validating the proposed approach. [ABSTRACT FROM AUTHOR]

This paper develops a queueing system model to analyze the operations of a hierarchical healthcare system consisting of general hospitals (GHs) and community healthcare centers (CHCs). GHs typically provide a higher level of health care service than CHCs, and thus are preferred choices for many patients' healthcare service needs. Consequently, GHs are often heavily congested and patients often incur excessive waiting time. In contrast, CHCs are often idle and resources are underutilized. To help balance the utilization of resources in GHs and CHCs, a congestion-based information guidance policy is proposed in this paper to inform patients in the GH service queue about the anticipated delay. Upon being informed the delay for GH service, patients may balk, remain in queue for GH service, or switch to receive service at CHCs. This policy is thus expected to relieve the congestion at GHs and promote CHC usage. To study the effects of the proposed policy, a hierarchical healthcare system is modeled as a queueing system with strategic patients. Stationary performance measures of the system are analytically characterized using a Markov chain model. Stochastic and numerical analyses provide insights on how to design information guidance policy that would help improve overall health care service quality under different scenarios. [ABSTRACT FROM AUTHOR]

The leisure agriculture in suburban towns provides important space for citizens to carry out leisure activities. This paper takes Xinkou town in Tianjin city as an example to analyze the development efficiency of leisure agriculture in suburban towns. On the premise of establishing the indicators for evaluating the development efficiency of leisure agriculture in Xinkou town, this paper first uses the data envelopment analysis (DEA) to measure the input and output indicators of leisure agriculture in 18 natural villages in Xinkou town, and calculates the comprehensive technical efficiency, target values, redundancy and returns to scale of leisure agriculture, and then evaluates the development efficiency of leisure agriculture in Xinkou town. Furthermore, by using the high/low clustering tool under the ArcGIS spatial statistics module, this paper conducts a high/low clustering analysis of the development efficiency of leisure agriculture in 18 natural villages of Xinkou town, and figures out the reasons for the high/low clustering of natural villages in Xinkou town spatially. After the research and analysis, the following conclusions are drawn: From the input–output perspective, except for Zhengzhuangzi village, Diliubu village, Yuejiakai village, and Dangcheng village, the development efficiency of leisure agriculture in other villages is relatively low due to their poor resource foundations, or deficiencies in management experience and transportation location. The development of leisure agriculture in Xinkou town is unbalanced, showing a pattern of polarized clusters: Diliubu village and Shuigaozhuang village form the most obvious high-value cluster, while Xiaoshawo village, Bijia village and Xuanjiayuan village form the most obvious low-value cluster, where the southeast corner of Xinkou town registers the lowest development efficiency value of leisure agriculture because it is far away from the town center and the main transportation arteries, and falls short of high-quality pastoral scenery. For the future development of leisure agriculture in Xinkou town, it is supposed to optimize the land use policy, provide active support to the competitive industrial and commercial capital, non-governmental capital, rural collective economic organizations, farmers' cooperatives, and family farms, strengthen the in-depth integration of industries, improve the support system for new-type leisure agriculture, and build leisure agriculture brands with local features. [ABSTRACT FROM AUTHOR]

In view of the problems of high production cost, scarcity and lack of diversity of 3D face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction algorithm with a single 2D face image as input, which only uses 2D face datasets to complete model training. First, the improved ResNet module is introduced to preprocess the input face image. The deep residual neural network has strong feature extraction and characterization ability for the image, which can provide rich high-level semantic feature maps for the subsequent subnetwork. Then, add transformer module completely based on self-attention mechanism to the parameter prediction subnetwork, which can make different parameters of the subnetwork focus on self-related feature map information and avoid interference from invalid feature map information, so as to further improve the parameter prediction accuracy of the subnetwork. Next, training, ablation and comparison experiments were conducted on CelebA, BFM and Photoface datasets, and the combined function of pixel loss function and perceptual loss function was selected as the loss function. The experimental results show that: compared with the historical optimal results of the same network structure, the scale-invariant depth error (SIDE) and mean angle deviation (MAD) are improved by 5.9% and 10.8%, respectively, which strongly proves the effectiveness of the algorithm. Finally, in order to verify the actual effect of the 3D face reconstruction algorithm, examples are selected in this paper for reconstruction. The 3D faces generated by the algorithm all have a good sense of reality, which intuitively and effectively proves the advancement of the algorithm. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive exploration of physics-informed neural networks and their core features. It delves into their role in tackling inverse problems inherent in ordinary differential equation-based models. Within this context, we introduce a two-group epidemiological model, elucidating its fundamental attributes. The central objective of this research is to accurately estimate the model parameters for both groups in the epidemiological model. We offer a detailed exposition of the adopted methodology, providing insights into the algorithm and the techniques employed for its implementation. Through this analysis, we illuminate the complexities of our study, contributing to the growing body of knowledge in this field, which intersects epidemiology and neural network-based parameter estimation for an enriched understanding of infectious disease dynamics. [ABSTRACT FROM AUTHOR]

This research paper develops an algorithm to solve a neutrosophic linear fractional optimization problem, where the cost of the objective functions, the technology coefficients, and the resources are neutrosophic numbers (triangular and trapezoidal numbers). Two defuzzification methods are used here to convert the neutrosophic linear fractional optimization problem into a multi-objective linear fractional optimization problem (MOLFOP). The fractional objective function is defuzzified using the component-wise optimization method, and the linear constraints are defuzzified using the aggregation ranking function strategy. Hence, the MOLFOP is then solved using the fuzzy goal programming method. In order to further explain our suggested algorithm, two numerical examples are solved at the end, and the outcomes obtained by our method are compared with the outcomes obtained using the other algorithm. [ABSTRACT FROM AUTHOR]

This research paper aims to study the impact of COVID-19 on the economy, where we have extended the standard epidemiological model, the Susceptible–Infected–Recovered (SIR) model. In the economy's dynamics, we have incorporated aggregate demand and aggregate supply and further set up a system of three-dimensional nonlinear differential equations, which is more likely the Labor–Infected–Quarantine (LIQ) model, an extension of the SIR model. Here, we have derived the steady-state condition and analyzed the model's stability. In addition, we have done a comparative static analysis of parameters with some policy implications. We aim to achieve the twin objective, i.e., controlling the pandemic and reviving the economy under the set of the policy mix. [ABSTRACT FROM AUTHOR]

For the charged-particle dynamics under a strong and constant magnetic field, we consider and analyze exponential integrators in this paper. We derive two kinds of exponential integrators and study their long-time behavior by using the technique of modulated Fourier expansions. It is shown that a symmetric two-stage exponential integrator and some one-stage symplectic exponential integrators approximately conserve the energy and magnetic moment of the charged-particle dynamics over long times. [ABSTRACT FROM AUTHOR]

The new regulation was introduced to the franchise market of South Korea in that an applicant who wants to be a franchiser must set up direct retail stores and run them for at least one year before recruiting franchisees. Considering the purpose of the regulation, it is inferred that once franchisers run their own stores, their franchisees would be better off than otherwise. This paper addresses one reason that the Korea Fair Trade Commission has no choice but to introduce the regulation. Along the line, this paper investigates whether franchisees would have a more likelihood to make profit in case of franchisers operating their own stores. The result demonstrates that this case could be true since the operation of franchiser-owned stores could be helpful in reducing the cost of franchisees. [ABSTRACT FROM AUTHOR]

Large-size electronic-grade polycrystalline silicon is an important material in the semiconductor industry with broad application prospects. However, electronic-grade polycrystalline silicon has extremely high requirements for production technology and currently faces challenges such as carbon impurity breakdown, microstructure and composition nonuniformity and a lack of methods for preparing large-size mirror-like polycrystalline silicon samples. This paper innovatively uses physical methods such as wire cutting, mechanical grinding and ion thinning polishing to prepare large-size polycrystalline silicon samples that are clean, smooth, free from wear and have clear crystal defects. The material was characterized at both macroscopic and microscopic levels using metallographic microscopy, scanning electron microscopy (SEM) with backscattered electron diffraction (EBSD) techniques and scanning transmission electron microscopy (STEM). The crystal structure changes from single crystal silicon core to the surface of the bulk in the large-size polycrystalline silicon samples were revealed, providing a technical basis for optimizing and improving production processes. [ABSTRACT FROM AUTHOR]

This paper focuses on the problems encountered in the production process of electronic-grade polycrystalline silicon. It points out that the characterization of electronic-grade polycrystalline silicon is mainly concentrated at the macroscopic scale, with relatively less research at the mesoscopic and microscopic scales. Therefore, we utilize the method of physical polishing to obtain polysilicon characterization samples and then the paper utilizes metallographic microscopy, scanning electron microscopy-electron backscatter diffraction technology, and aberration-corrected transmission electron microscopy technology to observe and characterize the interface region between silicon core and matrix in the deposition process of electronic-grade polycrystalline silicon, providing a full-scale characterization of the interface morphology, grain structure, and orientation distribution from macro to micro. Finally, the paper illustrates the current uncertainties regarding polycrystalline silicon. [ABSTRACT FROM AUTHOR]

This paper proposes a novel seismic demand hazard model conditional on the separation distance between two adjacent buildings to fully explore the possibility of seismic pounding occurrence between buildings during their design life. The model uses an exponential function with two model parameters estimated through a two-point approach to yield an explicit expression for deriving a Seismic Demand Hazard Curve (SDHC) for adjacent buildings. The resulting SDHC provides a basis for seismic pounding risk assessment of adjacent buildings with a specific separation distance and a design tool for the risk-targeted separation distance between buildings. To verify the model, this paper compares the SDHCs derived for six pairs of adjacent buildings having different heights and natural period combinations with corresponding numerical simulation results. The results show that the SDHCs derived from the model, within the engineering interest range, closely agree with numerical simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for analyzing the vibration characteristics of planar frame structure composed of straight beams with arbitrary shapes. The method is based on the first-order shear theory, which takes into account the shear deformation and cross-sectional rotational inertia of the beams. On the one hand, an improved Fourier series method (IFSM) is employed to approximate the displacement function of the straight beam, and the displacements are determined by using the Rayleigh–Ritz method. On the other hand, the displacements of the curved beam are obtained by using the finite element method (FEM), which are then combined with the IFSM for the analysis of coupled nodes of straight and curved beams. This results in a computational method for calculating the vibration characteristics of complex planar frame structures with concentrated masses, straight and curved beams, arbitrary boundary conditions, and any connection form of nodes. The applicability of the proposed method is demonstrated through numerical examples, including planar frame structure with arbitrary shapes composed of straight beams, different cross-sectional shapes, and combinations of beam with various cross-sectional shapes, as well as planar frame structures with straight and curved beams. The results of the proposed method are validated by comparing them with those obtained from literature or finite element software ABAQUS. The numerical examples show that the proposed method combining the IFSM with FEM has the advantages of high accuracy, good convergence, and strong applicability for calculating the vibration characteristics of planar frame structures. [ABSTRACT FROM AUTHOR]

This paper first presents six (6) types of benchmark amplitude design specifications, including variable low-pass specification, variable high-pass specification, variable bandpass specification, variable bandstop specification, variable notch-frequency specification, and variable notch-width specification. The design specifications can be taken as typical benchmark specifications targeted at testing as well as validating the effectiveness of various newly developed algorithms for designing various typical variable filters (VFs). Then, the paper provides a novel unity-bounded (UB) function developed for ensuring the stability of those VFs with recursive structures. Finally, a wide variety of simulation results are included for validating the design performance as well as the assured stability by using both the benchmark design specifications and the novel UB function. [ABSTRACT FROM AUTHOR]

This paper presents a survey of machine learning (ML) methods used in applications dedicated to the building and construction industry. A building information modeling (BIM) model, being a database system for civil engineering data, is presented. A representative selection of methods and applications is described. The aim of this paper is to facilitate the continuation of research efforts and to encourage bigger participation of database system researchers in the field of civil engineering. [ABSTRACT FROM AUTHOR]

The Π -shaped open section girder is a very common section, which has been widely used in the construction of modern bridges. However, compared with a streamlined box girder, it is easy to cause vortex-induced vibration (VIV) in bridges due to its bluff section shape and poor aerodynamic stability. In this paper, a typical open section with a height–width ratio of 10:1 is taken as the research object. Through wind tunnel tests and numerical simulation technology, the time-history data of VIV of the Π -shaped section are obtained. By selecting three different neural network models to establish the relationship between external parameters (wind attack angle, wind speed, turbulence) and structural response parameters (the onset wind speed of VIV, the length of the lock-in interval). After comparing different machine learning methods, the most suitable machine learning model is selected to predict the VIV response characteristics of the main girder. The research results show that the typical open section has a serious VIV phenomenon. The BP neural network model has the best performance in predicting the VIV performance of the open section in terms of calculation speed, prediction accuracy and solving nonlinear adaptability. At the same time, it is found that different characteristic parameters of the incoming wind field have different influences on the prediction results of the VIV response. Among them, the wind attack angle is the key parameter that affects the onset wind speed of VIV, but the turbulence intensity has a greater impact on the maximum amplitude of VIV. At the same time, the optimized BP model can predict the characteristic parameters of VIV response under unknown wind fields. This paper provides a new idea to study the effect of different incoming flow parameters on the performance of section VIV and further improves people's understanding of VIV system. [ABSTRACT FROM AUTHOR]

The labor force participation rate of women in India has decreased despite economic growth, which has limited the advancement of their rights unlike in most other countries. This paper examines the determinants of labor force participation by gender in rural and urban areas using a probit model with data from the India Human Development Survey (IHDS) for 2011–2012 to analyze the factors that restrict workforce participation. The estimation results are presented below. First, the marital status of males, both rural and urban, has a significant impact on their participation in the workforce due to family support. In contrast, females face multiple restrictions based on marital, caste and religion, demonstrating that sociocultural factors have limited their participation regardless of regional factors. Second, in rural areas, the educational years of female workers negatively affect labor participation, but the constraints of marriage are weaker than in urban areas. Women in lower castes participate more in the rural labor force, which is interpreted as a result of their livelihood in the industrial structure consisting of agriculture. On the other hand, the caste system negatively affects female participation in urban areas. Finally, these results indicate that the low-labor participation of females is the result of a complicated process influenced by various factors, including regional effects and patriarchal cultural values in India. Therefore, it is necessary to provide employment opportunities and encourage economic incentives for more women to enter the labor market spontaneously, considering regional effects in policies, in order for women to emerge as one group of social leaders. [ABSTRACT FROM AUTHOR]

The focus of this paper is on COVID-19, the December 2019 dated respiratory epidemic or infection that has ravaged most, if not all, of the world's 253 "countries" (consisting of 194 independent nation-states, 55 dependent-states, Antarctica, and 3 other territories). Readers of this paper should be aware of at least three facts. First, as of January 2022, the submission date of this paper, COVID-19 continues to impact the world, although to a much lesser extent, given the protection afforded by the mRNA vaccines, the boosters, and the antiviral medications (e.g., oseltamivir, penciclovir, acyclovir, Paxlovid, etc.). Second, the paper should be regarded as being inconclusive in both its outlook and its list of references; it provides, at best, an intermediary account of the continuing COVID-19 pandemic. Third, like the Spanish Flu of 1918, it is speculated that the COVID-19 pandemic will not endure beyond three years and will conclude as an endemic problem by, hopefully, early 2023. Therefore, the paper is partitioned into three sections which, respectively, address the infection's three phases: epidemic (initial infection), pandemic (worldwide infection) and endemic (pervasive but nonlife-threatening infection) phases. The lessons learned from the range of decisions made throughout the COVID-19 phases should help to inform and better prepare the world for future pathogens and deadly diseases. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a popular neurological disorder affecting a critical part of the world's population. Its early diagnosis is extremely imperative for enhancing the quality of patients' lives. Recently, improved technologies like image processing, artificial intelligence involving machine learning, deep learning, and transfer learning have been introduced for detecting AD. This review describes the contribution of image processing, feature extraction, optimization, and classification approach in AD recognition. It deeply investigates different methods adopted for multiclass diagnosis of AD. The paper further presents a brief comparison of existing AD studies in terms of techniques adopted, performance measures, classification accuracy, publication year, and datasets. It then summarizes the important technical barriers in reviewed works. This paper allows the readers to gain profound knowledge regarding AD diagnosis for promoting extensive research in this field. [ABSTRACT FROM AUTHOR]

In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China's A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets. [ABSTRACT FROM AUTHOR]

This paper presents a method for identifying causal relationships between strategic objectives within a strategy map of a Balanced Scorecard. Strategy maps are modeled as a network of strategic objectives (nodes) and causal relationships (directed arcs). The nodes are also grouped into clusters that represent the perspectives of a Balanced Scorecard: (a) Finances, Clients, Internal Processes and Growth and Learning. The method uses the Analytic Network Process (ANP) to establish the importance of every relationship and uses a multi-objective integer linear programming model to select the relationships to be included within a strategy map of a company. The method provides a method that optimizes the selection of the relationships to be included in a strategy map. An illustration of the application of the method in a manufacturing company is presented. [ABSTRACT FROM AUTHOR]

In this paper, we consider multi-criteria decision-making problems of selecting the best decision alternative in which the preferences of the decision maker are given in the form of quasi-orders. If the decision maker's preferences are modeled by a family of complete quasi-orders (in particular, by a parametric family of additive value functions) then a common approach to solving such problems is based on the notion of potential optimality. In this paper, we consider the case in which the decision maker's preferences are modeled by a family of incomplete quasi-orders. We suggest a new approach to selecting decision alternatives by introducing the notion of potential nondominated alternatives. Such alternatives can be regarded as candidates for the best alternative. We investigate properties of the sets of potential nondominated alternatives and, for the case of a parametric family of partial quasi-orders, we develop a method of their identification. Our development is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

To achieve a dynamic comprehensive risk assessment of road transport of hazardous chemicals, this paper first collected 213 road transport accidents involving hazardous chemicals that occurred between 2015 and 2020 to build a case library. Then, it established a risk assessment system (including 9 primary indicators and 28 secondary indicators) based on the frequencies of reasons for accident cases and explored the quantification methods for indicators based on their definitions. Subsequently, this paper calculated the weights of indicators based on the backpropagation neural network algorithm and the data in the case library and formulated a model for dynamic risk assessment of road transport of hazardous chemicals. The model takes into account the impact of comprehensive indicators such as drivers, vehicles, goods, roads, and the environment. The accuracy of the model calculation results is 93%. Eventually, the study verified the model using simulation data in real scenarios. The validation results show that the model could achieve a dynamic comprehensive assessment of risks in road transport of hazardous chemicals. [ABSTRACT FROM AUTHOR]

This paper is devoted to the study of multiobjective semi-infinite programming problems on Hadamard manifolds. We consider a class of multiobjective semi-infinite programming problems (abbreviated as MSIP) on Hadamard manifolds. We use the concepts of second-order Karush–Kuhn–Tucker stationary point and second-order Karush–Kuhn–Tucker geodesic pseudoconvexity of the considered problem to derive necessary and sufficient second-order conditions of efficiency, weak efficiency and proper efficiency for MSIP along with certain generalized geodesic convexity assumptions. Moreover, we formulate the second-order Mond–Weir-type dual problem related to MSIP and deduce weak and strong duality theorems relating MSIP and the dual problem. The significance of our results is demonstrated with the help of non-trivial examples. To the best of our knowledge, this is the first time that second-order optimality conditions for MSIP have been studied in Hadamard manifold setting. [ABSTRACT FROM AUTHOR]

In this paper, we address single-machine scheduling with sum of logarithm processing times-based learning effect. Under exponential past-sequence-dependent delivery times, we proved that the makespan and total completion time minimizations can be solved by the smallest processing time (SPT) first rule. Under the agreeable weight condition and agreeable due date condition, we show that the total weighted completion time minimization and maximum tardiness minimization can be solved in polynomial time, respectively. [ABSTRACT FROM AUTHOR]

As the number of long-span bridges continues to grow, wind-induced vibration issues have become increasingly intensified. Among them, flutter is a serious wind-induced vibration that can lead to bridge collapse. To address this problem, this paper proposes inerter-based dampers as a solution for flutter control and compares their performance to traditional tuned mass dampers (TMDs). Firstly, eight distinct inerter-based dampers were proposed, based on various combinations of mass-inerter-spring-damper (MISD) components. Secondly, the equations of motion of the bridge-MISD system were derived based on the two-mode coupled flutter theory and the mechanical impedance analysis method. Thirdly, the flutter control performance of the eight proposed MISDs is examined using a simply supported beam, with the six better-performing dampers further evaluated using the Jiangyin Bridge. The results show that inerter-based dampers can effectively improve the critical flutter wind speed of bridges, with performance sensitive to frequency. Additionally, incorporating an inerter decreases the static displacement of the mass block, while increasing the mass of MISD and the distance between the damper and the section center improves flutter control performance. The optimal spring stiffness ratios for the six MISDs are identified, largely dependent on the damper's inertance and mass. In conclusion, this paper offers a promising solution to addressing flutter in long-span bridges and provides insights into the design and optimization of inerter-based dampers. [ABSTRACT FROM AUTHOR]

Based on the decoupling theory, this paper analyzes the relationship between the embodied carbon productivity and the embodied value added in China's industrial sectors, evaluates the development mode of the sectors, and explores a reasonable path to promote the green and low-carbon transformation of the sectors based on the panel multinomial logit model. The empirical conclusions are as follows: (i) Except for the decrease in 2002–2005, the embodied carbon productivity achieved rapid growth in other time periods; during the period of 2002–2020, the embodied value added increased by 339.59 percentage points, with an average annual growth rate of 8.57%, and the embodied carbon productivity increased by 70.59 percentage points, with an average annual growth rate of 3.01%. (ii) As the embodied value added of the 28 industrial subsectors expanded, the changes in their embodied carbon productivity were different. This process can be roughly classified into three main phases: The first phase is the extensive growth period of 2002–2005, in which the embodied carbon productivity of most industrial subsectors declined while the embodied value added expanded; the second phase is the low-carbon transformation and development period of 2005–2012, during which the recessive positive-efficiency emission reduction became the dominant mode; and the third phase is the low-carbon development period of 2012–2020, in which the high-efficiency emission reduction became the dominant mode. (iii) When the industrial sectors are in an expansive state, increasing the substitution of capital for energy factors will make the transformation of industrial sectors more inclined to expansive high-efficiency emission reduction mode, and when the industrial sectors fall into recession, optimizing the energy structure will produce the same effect. [ABSTRACT FROM AUTHOR]

Multiple objects tracking in a video sequence can be performed by detecting and distinguishing the objects that appear in the sequence. In the context of computer vision, the robust multi-object tracking problem is a difficult problem to solve. Visual tracking of multiple objects is a vital part of an autonomous driving vehicle's vision technology. Wide-area video surveillance is increasingly using advanced imaging devices with increased megapixel resolution and increased frame rates. As a result, there is a huge increase in demand for high-performance computation system of video surveillance systems for real-time processing of high-resolution videos. As a result, in this paper, we used a single stage framework to solve the MOT problem. We proposed a novel architecture in this paper that allows for the efficient use of one and multiple GPUs are used to process Full High Definition video in real time. For high-resolution video and images, the suggested approach is real-time multi-object detection based on Enhanced Yolov5-7S on Multi-GPU Vertex. We added one more layer at the top in backbone to increase the resolution of feature extracted image to detect small object and increase the accuracy of model. In terms of speed and accuracy, our proposed approach outperforms the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Making predictions bounded by uncertainty is a crucial component of modern decision support systems. Knowledge graphs provide a structured, semantic-oriented approach to data integration, storage and retrieval; however, they represent data in an absolute way and do not natively support reasoning under uncertain future conditions. In this paper, we present a novel technique for building a probabilistic model from a knowledge graph using a Bayesian network, which is a step towards enabling probabilistic reasoning under uncertainty within a knowledge graph. Our approach supports mixing of continuously and discretely-distributed variables, which is necessary for jointly processing real-world data, but has not typically been supported in prior work. As an interface to the probabilistic model, we propose an extension of the SPARQL query language called Orion DSL, which is currently a working prototype. We also define a custom probabilistic ontology in order to store outputs of the model directly in the knowledge graph alongside the original data, which we refer to as a Probabilistic Knowledge Graph (PKG). The evaluation shows that: (1) the dependencies and distributions of data in a synthetically-generated knowledge graph were accurately captured by the Bayesian model, and (2) an SPARQL query against the PKG to retrieve computed probability distributions was orders of magnitude more performant than a similarly-intentioned query against the base knowledge graph. We anticipate that the models generated by this system will have applications in the areas of predicting missing data, approximate query processing, and utility-based optimization. Future work will involve more detailed explorations of each of these topics as we work towards a PKG-based decision support system. [ABSTRACT FROM AUTHOR]

Medical cancer rehabilitation healthcare center data maintenance is a global challenge with increased mortality risk. The Internet of Things (IoT)-based applications in healthcare were implemented through sensors and various connecting devices. The main problem of this procedure is the privacy of data, which is the biggest challenge with IoT, as all the connected devices transfer data in real time, the integration of multiple and other protocols can be hacked by the end-to-end connection, and it is not secure, security issues may crop up due to handling of such massive data in real time. Recent studies showed that a more structured risk assessment is needed to secure the medical cancer rehabilitation healthcare center data maintenance. In this respect, collaborative learning frameworks, such as Deep Federated Collaborative Learning (DFCL), are implemented for the study of medical cancer rehabilitation healthcare center data maintenance based on IoT-based systems and are proposed with smart short-term Bayesian convolution network systems for data analysis. This DFCL approach has been preferred in this context, strengthening privacy by allowing sensitive data to be retained. Experiments on benchmark datasets demonstrate that the federated model balances fairness, privacy, and accuracy. In this paper, we analyze administrative data count by medical stages taken from 2016 to 2022, the administrative data include data for routine operations. It is frequently used to assess by achieving an accuracy range of 19.8%. The leading diagnoses taken as per the patient's cost and stay count identifying a disease, illness, or problem by examining the unusual combination of symptoms made an accurate diagnosis which is 26% more efficient than the leading diagnosis. The hospital dictionary analysis is based on dictionary analysis count and data visualization summary; accuracy is 50% higher than the existing data visualization summary. By comparing the hospital dictionary, home health care analysis shows a 44.5% efficient analysis rate for patient data maintenance. Moreover, the adult day-care centers analyzed 88.6% efficient analysis rate for patient data maintenance with 750 patients. [ABSTRACT FROM AUTHOR]

The main obstacles to migrant workers starting businesses when they return to their hometowns are their own lack of financial literacy and aptitude, a lack of sufficient venture capital, and inadequate financing options. This paper conducts an analysis of the opportunities and challenges under the rural revitalization environment in conjunction with the Internet of Things Technology, through which a migrant labor entrepreneurship analysis platform is built. Data crawling technology is applied to capture internet data resources related to innovative entrepreneurship in the data collection module, and the entrepreneurship data is processed by the Internet of Things Technology. Based on the research, the approach suggested in this paper has its benefits for analyzing the opportunities and difficulties that migrant workers encounter in a rural revitalization context, in order to finally provide some theoretical support for pertinent research. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning technology, object detection algorithms have made significant breakthroughs in the field of computer vision. However, due to the complexity and computational requirements of deep Convolutional Neural Network (CNN), these models face many challenges in practical applications, especially on resource-constrained edge devices. To address this problem, researchers have proposed many lightweight methods that aim to reduce the model size and computational complexity while maintaining high performance. The popularity of mobile devices and embedded systems has led to an increasing demand for lightweight models. However, existing lightweight methods often lead to accuracy loss, limiting their feasibility in practical applications. Therefore, how to realize the light weight of the model while maintaining high accuracy has become an urgent problem to be solved. To address this challenge, this paper proposes a lightweight YOLOv7 method based on PConv, Squeeze-and-Excitation (SE) attention mechanism and Wise-IoU (WIoU), which we refer to as YOLOv7-PSW. PConv can effectively reduce the number of parameters and computational complexity. The SE can help the model focus on important feature information, thereby improving performance. WIoU is introduced to measure the similarity between the detection box and the Ground Truth, so that the model can effectively reduce the False Positive rate. By applying these advanced techniques to the YOLOv7, we achieve a lightweight model while maintaining a high detection accuracy. Experimental results on PASCAL VOC dataset show that YOLOv7-PSW performs better than the original YOLOv7 on object detection tasks. The number of parameters is reduced by 12.3%, FLOPs is reduced by 18.86%, and the accuracy is improved by about 0.5%. While the detection accuracy is not decreased or even slightly improved, the number of FLOPs and parameters is greatly reduced, which realizes lightweight to a certain extent. The proposed method can provide new ideas and directions for the subsequent research on lightweight object detection, and is expected to promote its application on edge devices. Meanwhile, YOLOv7-PSW can also be applied to other computer vision tasks to improve its performance and efficiency. In summary, the proposed YOLOv7-PSW lightweight method realizes the light weight of the model while maintaining high accuracy. This is of great significance for promoting the application of object detection algorithms on edge devices. [ABSTRACT FROM AUTHOR]

Orchid is a type of plant that grows on land. It is highly valued for its beauty and is cherished by many because of its graceful flower shape, delicate fragrance, vibrant colors, and noble symbolism. Although there are various types of orchids, some of them look similar in appearance and color, making it challenging for people to distinguish them quickly and accurately. The existing methods for classifying orchid species face issues with accuracy due to the similarities between different species and the differences within the same species. This affects their practical use. To address these challenges, this paper introduces an efficient method for classifying orchid species using transfer learning. The main achievement of this study is the successful utilization of transfer learning to achieve accurate orchid species classification. This approach reduces the need for large datasets, minimizes overfitting, cuts down on training time and costs, and enhances classification accuracy. Specifically, the proposed approach involves four phases. First, we gathered a collection of 12 orchid image sets, totaling 12,227 images, through a combination of network sources and field photography. Next, we analyzed the distinctive features present in the collected orchid image sets. We identified certain connections between the acquired orchid datasets and other datasets. Finally, we employed transfer learning technology to create an efficient classification function for orchid species based on these relationships. As a result, our proposed method effectively addresses the challenges highlighted. Experimental results demonstrate that our classification algorithm, which utilizes transfer learning, achieves a classification accuracy rate of 96.16% compared to not using the transfer learning method. This substantial improvement in accuracy greatly enhances the efficiency of orchid classification. [ABSTRACT FROM AUTHOR]

The nested tube system is a cost-efficient energy absorbing device that is particularly crucial for applications with constrained squeeze zones and space. Using the triple continuous nested tube (TCNT) method for nesting is quite useful. The single-stage mode and two-stage mode are its two distinctive force–displacement curve forms. In this study, finite element models suitable for TCNT structures were developed using Abaqus software, and several control groups were constructed according to different initial collapse loads. Through an in-depth analysis of each component of the TCNT, it is discovered that the stiffness competition between the two inner rings is the primary factor contributing to the step-like mechanical response. The TCNT system comprehensively outperforms the conventional single ring design for specific energy absorption (SEA). The prediction equation regarding the initial collapse load of this system is also discussed in this paper. Since the relative errors of the theoretical and numerical solutions are more concentrated, the accuracy can be increased by the amplification factor. The final polynomial fit equation obtained gives a glimpse of the full force–displacement curve. [ABSTRACT FROM AUTHOR]

This paper focuses on the computation issue of portfolio optimization with scenario-based mean-average value at Risk (AVaR) in uncertain environment. The portfolio optimization problem is designed in two cases: risk-taker and risk-averse models. The main idea is to replace the portfolio selection models with linear programming (LP) problems. Since the computing time required for solving LP greatly depends on the dimension and the structure of the problem, the conventional numerical methods are usually less effective in real-time applications. One promising approach to handle online applications is to employ recurrent neural networks based on circuit implementation. Hence, according to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving the LP problems related to portfolio selection problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Some illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a semi-analytical technique based on Taylor's series method namely DTM has been used to solve the differential equation which governs the motion of three types of annular FGM plates. The differential equation has been obtained using Hamilton principle and classical plate theory. The mechanical properties of the plate (Young's modulus and density) are considered to be graded in thickness direction and vary following the power-law. The behaviour of volume fraction index and radii ratio has been investigated onto first three modes of frequency parameter for all three plates. Moreover, the novelty of this paper is the application of the versatile technique DTM to study the effect of radii ratio and volume fraction index on three different types of annular FGM plates. A comparison has been made between the obtained numerical results and the results are available in the literature. A good agreement of the results verifies the accuracy of the present technique. Three-dimensional mode shapes for all three plates are also presented. [ABSTRACT FROM AUTHOR]