Search Results (7,815)

This paper addresses the features and limits of the principles and means that provide and support the design of motion control for industrial stationary articulated robots and their involvement in cyber-physical factories as part of the Industry 4.0 concept. The proposed methods are presented herein, from the modelling of kinematics and dynamics considering ideal rigid bodies and principles of classical mechanics, to their application in the design of conventional cascade control and advanced model-based control and use within commercial software tools. The paper demonstrates the modelling principles adapted for control design where a specific novel hierarchical control configuration is outlined. There is an introduction of possible software tools such as Simscape, Robotics Systems Toolbox, RT Toolbox, CIROS and others. It includes the specific aim of the rapid prototyping of robot motion control, which is intended for user development and tuning. In conjunction with conveyor belts, robots-manipulators are essential for cyber-physical factories built on the concept of Industry 4.0. The concept of Industry 4.0 is discussed in respect to the proposed algorithms and software means. Full article

Residential areas are primary functional spaces of urban built-up areas, representing urban social structure externally and influencing urban spatial fabric (SF). Chinese cities have increasingly experienced urban renewal following significant population growth and urban expansion in the last four decades. We selected built-up urban areas of Shanghai as the research scope, considering 6731 residential quarters as research objects, which were identified and classified into six types. Based on complex network theory and analysis methods, an urban residential spatial network (URSN) was constructed in central Shanghai implementing through code. The degree of distribution and network robustness of the URSN was examined, and network “communities” were identified. The findings indicate that URSN stability, like robustness, implies harmonious and smooth social interactions and information transfer, consistent with the SDG 11, where the large-degree node residential quarters play an important role and must be prioritized in urban renewal. Meanwhile, the identification results of the URSN “communities” help us understand territory identity in built-up urban areas. This research provides new concepts and methods for examining SF in urban residential areas that integrate “physical” and “social” spaces, compares this approach to the traditional point-axis structure, and pioneers the study of urban SF from the perspective of complex networks by providing a new way of visualizing the spatial relationship between residential quarters as a network-like structure. Full article

In digital societies, users’ privacy decisions not only affect personal information security and application sustainability, but also profoundly influence the formulation and enforcement of relevant laws. However, existing research mainly adopts a dichotomous perspective of rationality and bounded rationality, lacking an integrated framework to explain the complexity of users’ privacy decisions. Therefore, this study integrates privacy calculus theory and Psychological Distance Theory within the Elaboration Likelihood Model (ELM) framework to explore their mediating and moderating roles in the relationship between perceived privacy relevance and disclosure intention. To achieve the research objectives, we employed the vignette method to design an online survey with a 2 (perceived relevance: low vs. high) × 2 (temporal distance: near vs. distant) between-subjects design, ultimately collecting 293 valid responses. The conclusions are as follows: (1) perceived relevance positively affects users’ privacy disclosure intention, and perceived benefits and perceived risks play a partial mediating role between them; (2) contrary to individuals’ common preference for near temporal distance (immediate rewards), distant temporal distance (delayed rewards) have a more pronounced enhancing effect on the positive relationship between perceived relevance and perceived benefits; (3) the results excluded the influence of control variables such as gender, age, and education level on privacy disclosure intention. This study not only proposes an integrated analytical framework, revealing that users’ privacy decisions are jointly influenced by both rational and irrational factors, but also provides practical insights for application developers and regulatory agencies. Finally, we also discuss the limitations of this study and directions for future research. Full article

In a number of previous publications, scattering theory for N-pole semiconductor quantum devices was developed. In the framework of the Landauer–Büttiker formalism, an S-matrix was constructed with the aid of an R-matrix providing a mapping of the in-going waves onto the out-going waves. These waves include propagating waves and evanescent waves, the latter of which decay exponentially in the leads which are connected to the active region of the N-pole device. In order to formulate the current conservation in the N-pole device, it is necessary to define the current S-matrix schematically as $\tilde{S}=k^{1/2}S{k}^{-1/2}$, where k contains the information about the k-vectors of the mentioned in- and out-going waves. In this paper, we show how the complete current S-matrix is calculated including the coupling between the propagating and evanescent components and coupling to the bound states in the active device region. One then finds a sub-matrix of $\tilde{S}$ which is unitary and which is restricted to the space of the propagating components. We demonstrate that current conservation is associated with the unitarity just of this sub-matrix. Full article

Objectives: Based on social cognitive theory, this study aims to explore the associated factors of and whether and how health information literacy was correlated to health behavior and glycemic control among individuals with type 2 diabetes and metabolic syndrome. Methods: Following convenient sampling, this cross-sectional, correlational study was conducted among 225 patients with type 2 diabetes and metabolic syndrome from an outpatient clinic in a suburban area of Beijing, China. Hierarchical multiple regression and mediation analysis were performed to explore the effect of health information literacy on self-management practice and hemoglobin A1c in this sample. The STROBE guidelines for cross-sectional studies were followed. Results: The findings showed incompetent health information literacy, inadequate self-management behavior, and suboptimal glycemic control in a sample of patients with type 2 diabetes and metabolic syndrome. Based on social cognitive theory, the results of regression analysis indicated that self-management attitude, health problem-solving, and chronic illness resources were correlated with self-management practice, and health problem-solving and health information evaluation were correlated with hemoglobin A1c. Mediation analysis revealed that self-management attitude, health problem-solving, and chronic disease resources fully mediated the effect of health information literacy on self-management practice. There was an indirect effect of health information literacy on hemoglobin A1c through health problem-solving. Conclusions: The findings demonstrated that health information literacy has significant indirect and direct effects on self-management behavior and glycemic control through self-management attitude, health problem-solving, and chronic disease resources in a sample of patients with type 2 diabetes and metabolic syndrome. Full article

Background. The SARS-CoV-2 pandemic had a significant impact on sexual health and human behavior, revealing a widespread decline in sexual function and behaviors. Objective. To summarize these findings and highlight their importance for public health, this article discusses the changes observed in sexual function and behavior during the pandemic, as well as potential explanations for these trends. Methods. This study followed the PRISMA-ScR guidelines, using the keyword search commands: “sexual function” AND (“SARS-CoV-2” OR “COVID-19” OR coronavirus) and “sexual behavior*” AND (“SARS-CoV-2” OR “COVID-19” OR coronavirus) in the Scopus and PubMed databases. The search was conducted on 10 March 2024, including articles published from January 2019 to March 2024. Inclusion criteria required studies focusing on sexual health/function during the SARS-CoV-2 pandemic, excluding non-English articles and non-adult populations. Studies were screened based on relevance, methodological rigor, and sample size, with data extraction focusing on sexual behavior/function metrics. Results were synthesized to identify trends and propose explanatory models. Results. While some individuals experienced reductions in sexual desire and activities, others reported increases, indicating varied individual responses to stressors such as a pandemic. Two hypotheses are presented to explain these changes: terror management theory and the dual control model of sexual response. The critical role of public health in addressing sexual health and well-being needs during a health crisis is discussed, emphasizing the importance of providing clear information, ensuring access to remote sexual health services, and reducing stigma. The need to integrate sexual health into the global response to future health crises is highlighted to ensure a comprehensive approach to human well-being. Conclusions. This review shows the multifaceted impact of the pandemic and social distancing in people’s sexual function and behaviors, underscoring the importance of considering sexual health as an integral part of the emergency health planning and response, to promote the physical and mental well-being of the population during crises such as the SARS-CoV-2 pandemic. Full article

Structural Health Monitoring relies on accurate modal identification and effective damage detection to assess structural performance and safety. However, traditional sensor placement methods struggle to balance modal identification uncertainty, which arises from limited sensor coverage and measurement noise and damage detection sensitivity, which requires sensors to be optimally positioned to capture structural stiffness variations. To address this challenge, this study proposes a multi-objective sensor placement optimization method based on the Non-Dominated Sorting Genetic Algorithm. The method introduces two key objective functions: minimizing modal identification uncertainty by leveraging Bayesian modal identification theory and information entropy and maximizing damage detection sensitivity by incorporating an entropy-based measure to quantify the uncertainty in stiffness variation estimation. By formulating the problem as Pareto-based multi-objective optimization, the method efficiently explores a trade-off between the two competing objectives and provides a diverse set of optimal sensor placement solutions. The proposed approach is validated through numerical experiments on a simply supported beam and a benchmark bridge structure, demonstrating that different optimization objectives lead to distinct sensor placement patterns. The results show that solutions prioritizing modal identification distribute sensors across the structure to improve global response estimation, while solutions favoring damage detection concentrate sensors in critical areas to enhance sensitivity. The proposed method significantly improves sensor placement strategies by offering a systematic and flexible framework for SHM applications, enabling engineers to tailor monitoring strategies based on specific structural assessment needs. Full article

In the context of neural network optimization, this study explores the performance and computational efficiency of learning rate adjustment strategies applied with Adam and SGD optimizers. Methods evaluated include exponential annealing, step decay, and SHAP-informed adjustments across three datasets: Breast Cancer, Diabetes, and California Housing. The SHAP-informed adjustments integrate feature importance metrics derived from cooperative game theory, either scaling the global learning rate or directly modifying gradients of first-layer parameters. A comprehensive grid search was conducted to optimize the hyperparameters, and performance was assessed using metrics such as test loss, RMSE, R² score, accuracy, and training time. Results revealed that while step decay consistently delivered strong performance across datasets, SHAP-informed methods often demonstrated even higher accuracy and generalization, such as SHAP achieving the lowest test loss and RMSE on the California Housing dataset. However, the computational overhead of SHAP-based approaches was significant, particularly in targeted gradient adjustments. This study highlights the potential of SHAP-informed methods to guide optimization processes through feature-level insights, offering advantages in data with complex feature interactions. Despite computational challenges, these methods provide a foundation for exploring how feature importance can inform neural network training, presenting promising directions for future research on scalable and efficient optimization techniques. Full article

The impact of social media use on student academic achievement is complex and varies across studies, likely due to diverse usage motives and mediating factors. This study investigates the mediating role of daily time usage on the relationship between social media use motives and GPA among 301 Vietnamese university students, guided by the uses and gratifications theory. Following a rigorous validation process, we confirmed the applicability of the Social Networking Usage questionnaire within the Vietnamese context. Our findings revealed a complex relationship between social networking motives and GPA. While entertainment motives demonstrated a directly positive influence on GPA, information-seeking motives showed no significant relationship, either directly or indirectly. Critically, our results suggest that daily time spent on social networking acts as a key mediator in the interplay between academic and socialization motives and GPA. Specifically, we observed opposing effects: while academic motives indirectly benefitted GPA, socialization motives negatively impacted GPA. However, these opposing effects were channeled through daily time usage, suggesting that increasing time spent on social networking, regardless of the initial motive, ultimately detracts from academic performance. Our findings suggest that students need to be mindful of how their SNS usage motives influence their time allocation and, consequently, their academic performance. Universities may employ time management training and encourage academic uses of SNSs while advising students to limit non-academic SNS use, particularly during study periods, to minimize distractions and maximize learning time. Full article

The formal study of computer malware was initiated in the seminal work of Fred Cohen in the mid-80s, who applied elements of Computation Theory in the investigation of the theoretical limits of using the Turing Machine formal model of computation in detecting viruses. Cohen gave a simple but realistic formal definition of the characteristic actions of a computer virus as a Turing Machine that replicates itself and proved that detecting this behaviour, in general, is an undecidable problem. In this paper, we complement Cohen’s approach by providing a simple generalization of his definition of a computer virus so as to model any type of malware behaviour and showing that the malware/non-malware classification problem is, again, undecidable. Most importantly, beyond Cohen’s work, our work provides a generic theoretical framework for studying anti-malware applications and identifying, at an early stage, before their deployment, several of their inherent vulnerabilities which may lead to the construction of zero-day exploits and malware strains with stealth properties. To this end, we show that for any given formal system, which can be seen as an anti-malware formal model, there are infinitely many, effectively constructible programs for which no proof can be produced by the formal system that they are either malware or non-malware programs. Moreover, infinitely many of these programs are, indeed, malware programs which evade the detection powers of the given formal system. Full article

Considering the characteristics of power quality signals and denoising requirements, a denoising method of a power quality signal based on parameter coordination of membership function in fuzzy logic theory is proposed in this paper. First of all, for the signal sequence of power quality, seven masks are designed to make the best use of the signal sequence information. Secondly, based on fuzzy logic theory, the corresponding membership degrees are calculated for these seven masks, and the average value of all points in these masks is used as the input of fuzzy logic theory. Then, according to the membership function of the input and output variable, the boundary parameters are designed harmoniously to obtain the best denoising effect. Finally, based on the experimental simulation results, it is proved that the proposed method can not only smooth the noise well, but also keep the information for the abrupt point in its entirety, which is more suitable for denoising power quality signals compared with the traditional filtering method. Full article

China’s retail industry faces unique challenges in supply chain financing, particularly for small and medium-sized enterprises (SMEs) that often struggle to secure loans due to insufficient credit ratings and collateral in the business environment of China. This paper presents a groundbreaking approach that integrates real-time data elements into financing models, addressing the critical issue of information asymmetry between financial institutions and retail SMEs. By leveraging dynamic data such as orders, receivables, and project progress, our novel framework moves beyond the limitations of traditional asset-based lending, employing advanced data analytics for enhanced credit assessment and risk management. Applying the Stackelberg game theory, we explore the strategic interactions between suppliers and purchasers in the retail supply chain, identifying optimal financing strategies that improve capital flow efficiency and reduce overall costs. Our comprehensive data-driven model incorporates various scenarios, including the traditional supply chain financing model (Model T) and the innovative data-element secured financing model (Model G). The latter further considers risk assessment, risk appetite, volume, and schedule factors, providing a holistic approach to financial decision-making. Through rigorous mathematical modeling and numerical analysis, we demonstrate the effectiveness of our proposed framework in optimizing supply chain financing strategies. The results highlight the potential for data-driven approaches to unlock new financing opportunities for SMEs, fostering a more collaborative and efficient ecosystem within the retail industry. This study presents comprehensive data-driven strategies that unlock new financing opportunities for SMEs, providing a practical roadmap for stakeholders to foster a more collaborative and efficient supply chain financing ecosystem. The significance of studying supply chain finance for small and medium-sized enterprises (SMEs) lies in optimizing financing models to address the financing difficulties faced by SMEs. This helps improve their market competitiveness and promotes resource sharing and collaboration among all parties in the supply chain, thereby achieving sustainable economic development. Full article

In the last decade, a growing number of schools have begun implementing dual language education (DLE), and studies have shown evidence of the benefits of DLE for elementary education students. However, existing research syntheses do not focus on DLE in the early years (pre-Kindergarten and Kindergarten), considering young bilingual children’s development and learning characteristics. In this paper, a novel conceptual framework is used to explore the extant literature on DLE in the early years moving beyond Bronfenbrenner’s ecological systems theory to consider additional characteristics relating to bilingual children’s development and learning. A systematic literature review was conducted following a rigorous procedure, resulting in nine studies that met the inclusion criteria. Information about each study was coded and analyzed. The results describe the studies’ sample characteristics, research design, and findings organized by students’ academic skills (i.e., language, literacy, and mathematics), dual language classroom practices, and parents’ perceptions of DLE. This paper highlights current knowledge of DLE programs in the early years, identifies gaps, and offers recommendations for future research, policy, and practice. Full article

The accelerating processes of globalization and modernization have imposed unprecedented anthropogenic pressures on the cultural diversity of historic districts, leading to the physical degradation of historical heritage and the fragmentation of cultural transmission chains. To address this challenge, this study establishes an innovative spatial-gene theoretical framework that seeks to balance heritage protection with urban development by integrating landscape characteristics and cultural connotations, thereby enhancing the conservation of cultural diversity in historic districts. Focusing on the historic Small Wild Goose Pagoda district as a case study, we developed a comprehensive methodology integrating field research, historical induction, spatial analysis, and place-making. Through this operational framework, we systematically identified four constitutive spatial genes: the mountain–water pattern, the urban-axis, the li-fang, and the architectural courtyard. These genetic elements inform a dual-regeneration strategy that promotes synergy and dialogue between old and new: (1) place-making guided by historical morphological grammar rules and (2) activity organization that reconfigures the value system of “openness and inclusiveness”. This research not only advances spatial-gene theory but also provides a replicable model for regenerating historic districts oriented toward cultural diversity, effectively combining historical authenticity with contemporary functionality to promote sustainable urban development. Full article

With the rapid advancement of artificial intelligence (AI), chatbots represent a transformative tool in digital customer engagement, reshaping customer–brand relationships. This paper explores AI chatbots on customer–brand interactions by analyzing key features, such as interaction, perceived enjoyment, customization, and problem-solving. Based on the Technology Acceptance Model (TAM), the research investigates how these attributes influence perceived ease of use, perceived usefulness, customer attitudes, and ultimately, customer–brand relationships. Adopting a mixed-methods approach, this study begins with qualitative interviews to identify key engagement factors, which then inform the design of a structured quantitative survey. The findings reveal that AI chatbot features significantly enhance customer perceptions, with ease of use and usefulness in shaping positive attitudes and strengthening brand connections. The research further underscores the role of AI-driven personalization in delivering sustainable customer engagement by optimizing digital interactions, reducing resource-intensive human support, and promoting long-term brand loyalty. By integrating TAM with customer–brand relationship theories, this study contributes to AI and sustainability research by highlighting how intelligent chatbots can facilitate responsible business practices, enhance operational efficiency, and promote digital sustainability through automation and resource optimization. The findings provide strategic insights for businesses seeking to design AI-driven chatbot systems that improve customer experience and align with sustainable digital transformation efforts. Full article