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This paper examines the mediating effect of R&D alliance network dynamics on the relationship between technology convergence and firm innovation quality, it further investigates the moderating effect of degree assortativity in the inventor network. An empirical analysis is conducted based on a panel negative binomial regression model with data of Chinese high‐tech firms listed on the Shanghai and Shenzhen Stock from 2000 to 2018. The results show that novel technology convergence not only contributes to innovation quality directly but also indirectly through ego‐network expansion. The relationship between reinforced technology convergence and innovation quality is inverted U‐shaped, partially mediated by ego‐network stability. In addition, degree assortativity suppresses the effects of novel technology convergence on ego‐network expansion and innovation quality and steepens the inverted U‐shaped relationships of reinforced technology convergence with ego‐network stability and innovation quality. This paper contributes to the relevant literature by revealing the links between technology convergence, ego‐network dynamics, and innovation quality, and emphasizing the importance of inventor collaboration ties formation mechanism in knowledge searching and convergent innovation. Furthermore, the conclusion provides managerial suggestions for high‐tech firms to coordinate internal and external resources to improve technical impacts. [ABSTRACT FROM AUTHOR]

The modern medical system is convergence of cutting-edge technologies and advancements in the healthcare environment. In the modern medical system, the storage of electronic health records is generally leased out to third-party cloud service providers (CSPs). But, CSPs cannot be entirely relied upon due to the potential security and privacy issues. This article presents Multi-Authority and Hierarchical Attribute-Based Encryption Scheme (MH-ABE) scheme to promote secure information sharing and protect patient's privacy. The utilization of CP-ABE in conjunction with multiple Attribute Authorities within the proposed MH-ABE scheme presents a scalable and fine-grained approach to data access control. The proposed MH-ABE scheme incorporates the utilization of a Hierarchical Access Tree to effectively encrypt numerous files concurrently, hence reducing the computational and storage cost. The proposed scheme has also been evaluated using a comparative analysis with existing schemes, emphasizing the assessment of computational and storage costs. The findings of this analysis demonstrate improved performance and efficiency of the proposed ciphertext policy based encryption scheme. The proposed MH-ABE scheme incorporates features such as policy hiding and revocation, and it exhibits resilience against attacks, including collusion resistance, Indistinguishability under chosen-plaintext attack and forward secrecy. [ABSTRACT FROM AUTHOR]

The convergence of information and communication technologies has introduced new and advanced capabilities to Industrial Control Systems. However, concurrently, it has heightened their vulnerability to cyber attacks. Consequently, the imperative for new security methods has emerged as a critical need for these organizations to effectively identify and mitigate potential threats. This paper introduces an innovative approach by proposing a dynamic vulnerability severity calculator. Our methodology encompasses the analysis of environmental topology and the effectiveness of deployed security mechanisms, coupled with the utilization of the Common Vulnerability Scoring System framework to adjust detected vulnerabilities based on the specific environment. Moreover, it evaluates the quantity of vulnerabilities and their interdependencies within each asset. Additionally, our approach integrates these factors into a comprehensive Fuzzy Cognitive Map model, incorporating attack paths to holistically assess the overall vulnerability score. To validate the efficacy of our proposed method, we present a relative case study alongside several modified scenarios, demonstrating its effectiveness in practical applications. [ABSTRACT FROM AUTHOR]

We prove mixing convergence of the least squares estimator of autoregressive parameters for supercritical autoregressive processes of order 2 with Gaussian innovations having real characteristic roots with different absolute values. We use an appropriate random scaling such that the limit distribution is a two-dimensional normal distribution concentrated on a one-dimensional ray determined by the characteristic root having the larger absolute value. [ABSTRACT FROM AUTHOR]

Binary combinatorial optimization plays a crucial role in various scientific and engineering fields. While deterministic approaches have traditionally been used to solve these problems, stochastic methods, particularly metaheuristics, have gained popularity in recent years for efficiently handling large problem instances. Ant Colony Optimization (ACO) is among the most successful metaheuristics and is frequently employed in non‐binary combinatorial problems due to its adaptability. Although for binary combinatorial problems ACO can suffer from issues such as rapid convergence to local minima, its eminently parallel structure means that it can be exploited to solve large and complex problems also in this field. In order to provide a versatile ACO implementation that achieves competitive results across a wide range of binary combinatorial optimization problems, we introduce a parallel multicolony strategy with an improved cooperation scheme to maintain search diversity. We evaluate our proposal (Binary Parallel Cooperative ACO, BiPCACO) using a comprehensive benchmark framework, showcasing its performance and, most importantly, its flexibility as a successful all‐purpose solver for binary combinatorial problems. [ABSTRACT FROM AUTHOR]

Due to their simplicity and global convergence properties, the conjugate gradient (CG) methods are widely used for solving unconstrained optimization problems, especially those of large scale. To establish the global convergence and to obtain better numerical performance in practice, much effort has been devoted to develop new CG methods or even to modify well- known methods. In 2012, Rivaie et al., have proposed a new CG method, called RMIL which has good numerical results and globally convergent under the exact line search. However, in 2016, Dai has pointed out a mistake in the steps of the proof of global convergence of RMIL and hence to guarantee the global convergence he suggested a modified version of RMIL, called RMIL+. In this paper, we present another modified version of RMIL, which is globally convergent via the exact line search. Furthermore, to support the theoretical proof of the global convergence of the modified version in practical computation, a numerical experiment based on comparing it with RMIL, RMIL+, and CG-DESCENT was done. [ABSTRACT FROM AUTHOR]

This paper reports on research into co-creation labs as a tool for exploring environmental communication. Co-creation labs are novel spaces for experimentation, social learning, and trans-disciplinary as well as cross-sectoral collaboration for sustainability transformations. This paper examines the approach taken by researchers to theorising, via a repurposing of the 2×2 scenario matrix, a series of "labs" that represent diverse spaces in which to explore co-creative convergence through transformative learning processes. In addition, examples from work done to date are offered to illustrate progress in implementing these labs and testing the strengths and weaknesses of these co-creative spaces. [ABSTRACT FROM AUTHOR]

To explore whether digital finance can reduce agricultural carbon emissions, promote regional convergence, and foster inclusivity in rural revitalization and shared prosperity, this paper uses the provincial-level index of digital financial inclusion to analyze the impact of digital financial inclusion on the intensity of agricultural carbon emissions and the Degum Gini coefficient (D-Gini coefficient) of regional carbon emission intensity in 30 sample provinces from 2010 to 2020. It examines the mechanism of the impact of digital financial inclusion on both variables to understand the underlying factors better. The main conclusions are as follows: (1) Digital financial inclusion significantly reduces the intensity of agricultural carbon emissions and narrows the gap in carbon emission intensity between regions. (2) The unconditional quantile regression coefficients show that the negative coefficients of the digital financial inclusion index and the three-dimensional indices decrease with increasing quantiles. However, the significant effects vary significantly at different quantiles. (3) Technological progress and the government's ability to allocate financial resources play a significant mediating role, and the income gap between urban and rural areas can be further narrowed, as well as the carbon emission intensity gap between provinces. The empirical results are robust and proven by replacing the econometric analysis method, changing the core variables, and other methods. [ABSTRACT FROM AUTHOR]

Empirical macroeconomics has highlighted the existence of technology clubs in the world. Are such clubs converging in terms of technological progress, and if so, at what speed? And, what is the role of technology convergence in output convergence? To answer these research questions, we here propose a pragmatic nonparametric test. Using a sample of 81 countries over the 1965–2014 period, we find that the 'Advanced' technology club defines the best practice technology in the world, and that the club of the 'Followers' but not the 'Marginalized' one shows technology convergence. Only in the 'Followers' indeed is technology convergence related to output convergence. [ABSTRACT FROM AUTHOR]

Summary: One of the practical preferences of cloud service providers is to use specialized physical hosts. In other words, the goal is to place homogeneous virtual machines (VMs) on the physical host according to performance criteria such as energy consumption, resource wastage, and utilization. virtual machine placement (VMP) falls into NP‐hard knapsack problems. To overcome the time complexity, the use of heuristic and metaheuristic methods has attracted the attention of researchers. In this paper, we use an entropy‐based method for VMP for the first time. The proposed method tries to place the VMs on physical machines by considering the type of VMs to minimize entropy. Entropy is a measurable property that is more associated with disorder, randomness, or uncertainty. We use one of the most common entropy criteria called the Gini coefficient. In summary, among the different placement combinations of VMs, those that can minimize the Gini coefficient are preferred. We then solve the multi‐objective problem with the non‐dominated sorting genetic algorithm (NSGA‐III). We also combine this method with differential evolution methods to improve the quality of solutions. Recent research in other engineering fields has shown that combining metaheuristic methods with differential evolution methods increases the rate of convergence toward the optimal solution. The simulation results on the CloudSim simulator, along with statistical analysis, show that the entropy‐based method has a significant improvement over the state‐of‐the‐art methods in terms of significant performance criteria such as utilization, resource wastage, and energy consumption. [ABSTRACT FROM AUTHOR]

This document is an editorial introducing a special issue on "Systems Engineering and Knowledge Management." It explains that systems engineering is a transdisciplinary approach to developing engineered systems, involving cooperation between business and technical stakeholders. Knowledge management is described as a field that encompasses both technical and organizational management approaches. The editorial then briefly introduces the manuscripts included in the special issue, which cover topics such as success factors in IT service projects, decision-making techniques using fuzzy sets, strategic planning of projects, adapting e-commerce applications to IoT and cloud computing, optimizing residential appliance scheduling, and improving recommender systems using language models. This collection of papers explores the application of advanced technologies in systems engineering across various domains. The papers cover topics such as healthcare, finance, social media analysis, supply chain management, computer vision, cloud computing, smart city development, engineering optimization, genomics, optimization problems, and cybersecurity. The use of technologies like artificial intelligence, machine learning, blockchain, data analytics, bioinformatics, and swarm intelligence is examined to address complex challenges in interconnected systems. The papers also emphasize the importance of knowledge management in effectively utilizing the vast amounts of data generated by these systems. Overall, these papers contribute to interdisciplinary research and innovation in systems engineering to improve the resilience, efficiency, and sustainability of complex systems in the digital age. [Extracted from the article]

Spectral indices developed to extract features from satellite images are simple and fast methods that reduce processing time compared to traditional satellite image classification. In this paper, the effectiveness of the normalized difference building index (NDBI), the normalized difference vegetation index (NDVI), and the Normalized Difference Water Index (NDWI) were evaluated in land cover mapping and detecting its changes in the period from 2013 to 2023 in Fayoum Governorate, Egypt, using Landsat 8-OLI images. The results of supervised classification showed a decrease in green areas between 2013 and 2023 by 76.03 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.8 square kilometers and an increase in built-up areas by 79.83 square kilometers, at the expense of green lands. On the other hand, the results of using spectral indices showed a decrease in green areas between 2013 and 2023 by 91.12 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.35 square kilometers. Meanwhile, it was noticed that, increasing in the built-up areas by 94.47 square kilometers at the expense of green areas. The results showed a general convergence in the changes in the studied classes during the study period, with a great convergence in the change in the area of the water bodies resulted from the classification and from the NDWI and a convergence in the effectiveness of both the NDVI and NDBI. Finally, the effectiveness of the spectral indices in land cover mapping using check points was evaluated. The results showed a high percentage of the number of correctly identified check points for the NDWI index, with this percentage being noticeably low in the case of the NDVI and NDBI. [ABSTRACT FROM AUTHOR]

In this paper, the power method is discussed in mathematical detail with respect to the convergence, analysis, numerical computations and multiplicity. Deflation as well as avoiding deflation are described for symmetric positive definite matrices. Variations of the power method are discussed. Projections are applied to some distributions of the spectrum. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência & Saúde Coletiva is the property of Associacao Brasileira de Pos-Graduacao em Saude Coletiva and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The three-dimensional (3D) printing technology has led to transformative shift in prosthodontics. This review summarizes the evolution, processing techniques, materials, integration of digital plan, challenges, clinical applications and future directions of 3D printing in prosthodontics. It appraises from the launch of 3D printing to its current applications in prosthodontics. The convergence of printing technology with digital dentistry has facilitated the creation of accurate, customized prostheses, redefining treatment planning, design, and manufacturing processes. The progression of this technology is from generating models to prosthesis like-fixed dental prosthesis (FDP), implants, and splints. Additionally, it exhibits more wide capabilities. The exploration of materials for 3D printing provides various options like polymers, ceramics, metals, and hybrids, each with distinctive properties that are applicable to different clinical scenarios. The combination of 3D-printing technology and digital workflow simplifies the processes of data transfer, computer-aided design (CAD) design to fabrication, decreasing errors and chairside time. The clinical benefits include enhanced accuracy, comfort, conservative lab procedures, and economics. Challenges in the technology involve significant aspects like initial investment, material availability, and skill requirements. Future trends emphasize on research for improved materials, bioprinting integration, artificial intelligence (AI) application, regularization efforts to ensure safe and common use of the technology. 3D printing offers promise in prosthodontics, addressing challenges through research. The material improvements will promote its broader adoption and revolutionize the future of dental rehabilitation. [ABSTRACT FROM AUTHOR]

This document is a response to a comment made by Robertas Damaševičius regarding an article on info-autopoiesis and artificial general intelligence (AGI). The response addresses specific points raised in the comment and clarifies the author's position. It emphasizes that info-autopoiesis, the self-creation of information, is fundamental only to humans and not achievable by artificial creations. The article argues for the impossibility of achieving AGI using info-autopoiesis and suggests that resources should be directed towards more achievable goals in AI. The response also discusses the relationship between biological aspects of human intelligence and AI research, highlighting the use of neural networks and considering the architecture of AGI. It concludes by stating that info-autopoiesis sets a limit to human knowledge based on fundamental principles and that all human creations are syntactic and incapable of semantic interpretation. The text discusses the concept of info-autopoiesis and its application to AI research. Info-autopoiesis is a framework that aims to explain how living organisms function, and the paper explores its potential implications for AI and AGI development. The author acknowledges the limitations of info-autopoiesis and emphasizes the need for interdisciplinary collaboration and ethical considerations in AI research. The text concludes by suggesting areas for further research and expressing gratitude for the comments and suggestions provided by Robertas Damaševičius. [Extracted from the article]

Against the backdrop of swiftly changing industrial environments, this study aims to investigate the influence of technology convergence on the sustainable innovation of manufacturing enterprises. The purpose of this research is to determine the future competitiveness and expansion potential of industries by evaluating the impact of technological convergence on innovation performance, which serves as a significant metric for assessing the sustainability of corporate innovation practices. Specifically, the relationships among three characteristics of technological convergence and enterprise innovation performance—betweenness, closeness, and clustering—are analyzed. Using the financial, property, and patent data of listed companies in China's automotive manufacturing industry, an empirical study is conducted using a negative binomial regression model. Enhancing all three technology convergence characteristics is found to be conducive to enhancing sustainable innovation. Meanwhile, the corporate knowledge base plays a mediating role in which the effect of knowledge base width on clustering technology integration is more strongly mediated by the effect of knowledge base depth on approaching technology integration. The results of this study are useful for policymakers, corporate strategists, and innovation managers who are looking to enhance sustainable innovation practices within their organizations. By understanding the critical roles of betweenness, closeness, and clustering in technological convergence, stakeholders can better position their firms to leverage these attributes for improved innovation performance and competitive advantage. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

The Fifth Industrial Revolution is ushering in a profound era of technological advancement and societal transformation, prompting a critical examination of the evolving role of libraries in this digital landscape. Traditionally regarded as the custodians of knowledge, libraries must adapt to these changing dynamics of the digital world while preserving their core principles. This study looks into the strategic integration of advanced technologies, aiming to redefine the mission of libraries in the face of complex challenges. The Fifth Industrial Revolution encompasses a convergence of diverse technologies, including the Internet of Things (IoT), cloud computing, biotechnology, nanotechnology, and quantum computing, all underpinned by the currency of data. Artificial intelligence (AI) is a focal point, promising enhancements in user experiences and operational efficiency, however, it also raises critical ethical and privacy concerns that challenge fundamental library principles. This research employs an interpretive content/document analysis approach to examine the evolving roles of libraries, considering their pivotal function in bridging the digital divide, providing equitable access to technology and digital resources, advancing research and innovation, and fostering community engagement. The study underscores the imperative to address challenges related to data privacy, digital inclusion, resource allocation, intellectual property, and sustainability. However, despite these challenges, libraries are poised to remain indispensable pillars of education, research, and community interaction. To do so, they must embrace technology strategically to redefine their roles and expand their influence within evolving knowledge systems. [ABSTRACT FROM AUTHOR]

AbstractBias-adjusted three-step latent class (LC) analysis is a popular technique for estimating the relationship between LC membership and distal outcomes. Since it is impossible to randomize LC membership, causal inference techniques are needed to estimate causal effects leveraging observational data. This paper proposes two novel strategies that make use of propensity scores to estimate the causal effect of LC membership on a distal outcome variable. Both strategies modify the bias-adjusted three-step approach by using propensity scores in the last step to control for confounding. The first strategy utilizes inverse propensity weighting (IPW), whereas the second strategy includes the propensity scores as control variables. Classification errors are accounted for using the BCH or ML corrections. We evaluate the performance of these methods in a simulation study by comparing it with three existing approaches that also use propensity scores in a stepwise LC analysis. Both of our newly proposed methods return essentially unbiased parameter estimates outperforming previously proposed methods. However, for smaller sample sizes our IPW based approach shows large variability in the estimates and can be prone to non-convergence. Furthermore, the use of these newly proposed methods is illustrated using data from the LISS panel. [ABSTRACT FROM AUTHOR]

Advancements in technology are currently expediting the learning process for students, prompting an investigation into the integration of technology into student education. This study explores the impact of technological evolution on education. E-learning, a method of education that delivers information to students through modern communication platforms such as computers, networks, and multimedia audio components, is being examined. Whether utilized remotely or in traditional classroom settings, this revolution has made the world increasingly dependent on electronic screens in an era marked by the convergence of media technologies, information, culture, and technology. This paper outlines the development of an e-learning system customized for mobile devices, aimed at improving learning within the university environment. With mobile phones now ubiquitous among students, e-learning has become a potent tool for advancing educational objectives. Furthermore, e-learning aids in elevating student proficiency levels. The research conducted involved implementing and evaluating an application, which produced satisfactory results. As this application becomes integrated into university curricula to support learning, it is anticipated to substantially enhance students’ learning processes and performance, as evidenced by survey results following its implementation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel discrete-time consensus algorithm based on the new min–max criterion for the discrete-time second-order linear agents. To boost the convergence speed, the proposed algorithm allocates three different operating modes to the network agents. Unlike most of the existing consensus algorithms in which the links of the communication graph have specific weights and these weights are required to have positive lower bounds to achieve asymptotic convergence, the communication graph in this paper has unspecific link weights. Accordingly, because of data transmission errors and faults, the weights of the communication links can even go from a positive to a negative value. Thus, the proposed algorithm can bring the agents to consensus under more general conditions. To demonstrate the efficacy of the proposed algorithm, numerical simulations are performed, and their results are compared with those of the minimum consensus algorithm, which shows a significantly faster convergence speed. [ABSTRACT FROM AUTHOR]

We propose a new concept for the regularization and discretization of transfer and Koopman operators in dynamical systems. Our approach is based on the entropically regularized optimal transport between two probability measures. In particular, we use optimal transport plans in order to construct a finite-dimensional approximation of some transfer or Koopman operator which can be analyzed computationally. We prove that the spectrum of the discretized operator converges to the one of the regularized original operator, give a detailed analysis of the relation between the discretized and the original peripheral spectrum for a rotation map on the n -torus and provide code for three numerical experiments, including one based on the raw trajectory data of a small biomolecule from which its dominant conformations are recovered. [ABSTRACT FROM AUTHOR]

The controlled autoregressive autoregressive moving average (CARARMA) models are of popularity to describe the evolution characteristics of dynamical systems. To overcome the identification obstacle resulting from colored noises, this paper studies the identification of the CARARMA models by forming an intermediate correlated noise model. In order to realize the real-time prediction function of the models, the on-line identification scheme is developed by constructing the dynamical objective functions based on the real-time sampled observations. Firstly, a rolling optimization cost function is built based on the observation at a single sampling instant to catch the modal information at a single time point and a generalized extended stochastic gradient (GESG) algorithm is proposed through the stochastic gradient optimization. Secondly, a rolling window cost function is built in accordance with the dynamical batch observations within data window by extending the proposed GESG algorithm and the multi-innovation generalized extended stochastic gradient algorithm is derived. Thirdly, from the perspective of theoretical analysis, the convergence proof of the proposed algorithm is provided based on the stochastic martingale convergence theory. Finally, the simulation analysis and comparison studies are provided to show the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In light of the challenges surrounding the conceptualization and definition of spatial justice within our increasingly data‐driven society, this article commences an inquiry into the convergence of space, justice, and data within human geography literature and related disciplines, focusing notably on the urban field. The paper outlines theories concerning social justice‐based rights to the city, especially emphasizing the significance of existing literature that bridges such theories with recent scholarship on data justice. It supplements these discussions by deriving a theoretical framework for digital spatial justice rooted in other space‐based theories, exploring the more‐than‐human realms of non‐human entities, affect, and information. [ABSTRACT FROM AUTHOR]

Energy intensity convergence can be used to assess the effectiveness of policies in reducing energy intensity. This study analyzes the energy intensity convergence in Indonesia based on panel data of 33 provinces from 2010 to 2018. Spatial econometrics techniques are used in the estimation of beta convergence to measure the spatial dependence of energy intensity. Empirical results show that there is evidence of both absolute and conditional beta convergences with no evidence of sigma convergence. From the results, this study found that other variables, such as provincial income, the role of manufacturing industries, the role of international trade, FDI, and population density, might encourage energy intensity convergence. From the estimation results, several policy recommendations are derived to increase energy efficiency: First, using a more efficient industrial technology. Second, attracting foreign investment to non-industrial sectors. Third, developing exports from sectors that use less energy to increase energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive rejection of multiple narrow-band disturbances with time-varying frequencies is investigated. A systematic control approach based on a resonant generalized extended state observer (RGESO) is proposed. The frequency response of the RGESO is shaped into a desired form to reject the total disturbance, including the wide-band uncertainties and the narrow-band disturbances. The state-space realization of the RGESO is also provided to estimate both the states and the total disturbance. To handle the time-varying frequencies of the narrow-band disturbances, a direct frequency estimation approach based on the Levenberg–Marquardt method and an online supervision strategy is presented to improve convergence and reliability, and the estimations are then used to adapt the RGESO parameters. Parameter tuning and stability robustness analysis methods are also developed for the proposed scheme to facilitate its application. Finally, extensive simulations are conducted to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The realm of implantable bioelectronics represents a frontier in medical science, merging technology, biology, and medicine to innovate treatments that enhance, restore, or monitor physiological functions. This field has yielded devices like cochlear implants, cardiac pacemakers, deep brain stimulators, and vagus nerve stimulators, each designed to address a specific health condition, ranging from sensorineural hearing loss to chronic pain, neurological disorders, and heart rhythm irregularities. Such devices underscore the potential of bioelectronics to significantly improve patient outcomes and quality of life. Recent technological breakthroughs in materials science, nanotechnology, and microfabrication have enabled the development of more sophisticated, smaller, and biocompatible bioelectronic devices. However, the field also encounters challenges, particularly in extending the capabilities of devices such as retinal prostheses, which aim to restore vision but currently offer limited visual acuity. Research in implantable bioelectronics is highly timely, driven by an aging global population with a growing prevalence of chronic diseases that could benefit from these technologies. The convergence of societal health needs, advancing technological capabilities, and a supportive ecosystem for innovation marks this era as pivotal for bioelectronic research. [ABSTRACT FROM AUTHOR]

Federated learning has gained significant attention due to its groundbreaking ability to enable distributed learning while maintaining privacy constraints. However, as a consequence of data heterogeneity among decentralized devices, it inherently experiences significant learning degradation and slow convergence speed. Therefore, it is natural to employ the concept of clustering homogeneous clients into the same group, allowing only the model weights within each group to be aggregated. While most existing clustered federated learning methods employ either model gradients or inference outputs as metrics for client partitioning to group similar devices together, heterogeneity may still exist within each cluster. Moreover, there is a scarcity of research exploring the underlying reasons for determining the appropriate timing for clustering, resulting in the common practice of assigning each client to its own individual cluster, particularly in the context of highly non-independent and identically distributed (Non-IID) data. In this paper, we introduce a two-stage decoupling federated learning algorithm with adaptive personalization layers named FedTSDP, where client clustering is performed twice according to inference outputs and model weights, respectively. Hopkins amended sampling is adopted to determine the appropriate timing for clustering and the sampling weight of public unlabeled data. In addition, a simple yet effective approach is developed to adaptively adjust the personalization layers based on varying degrees of data skew. Experimental results show that our proposed method has reliable performance on both IID and non-IID scenarios. [ABSTRACT FROM AUTHOR]

In the context of increasing data scale, contemporary optimization algorithms struggle with cost and complexity in addressing the feature selection (FS) problem. This paper introduces a Harris hawks optimization (HHO) variant, enhanced with a multi-strategy augmentation (CXSHHO), for FS. The CXSHHO incorporates a communication and collaboration strategy (CC) into the baseline HHO, facilitating better information exchange among individuals, thereby expediting algorithmic convergence. Additionally, a directional crossover (DX) component refines the algorithm's ability to thoroughly explore the feature space. Furthermore, the soft-rime strategy (SR) broadens population diversity, enabling stochastic exploration of an extensive decision space and reducing the risk of local optima entrapment. The CXSHHO's global optimization efficacy is demonstrated through experiments on 30 functions from CEC2017, where it outperforms 15 established algorithms. Moreover, the paper presents a novel FS method based on CXSHHO, validated across 18 varied datasets from UCI. The results confirm CXSHHO's effectiveness in identifying subsets of features conducive to classification tasks. [ABSTRACT FROM AUTHOR]

This study aimed to create a foundation for the integration of AI in technology education. The framework of this study was based on the important problem-solving learning process in technology education. The developed framework structures the problem-solving steps of such types of problems into problem solving, design, development, and evaluation. Each technological problem and problem-solving step area can be implemented using Intelligent Tutoring System (ITS), Dialogue-Based Tutoring System (DBTS), and Exploratory Learning Environment (ELE) of AI convergence education. Technology education supports learners develop Technological knowledge and develop critical and creative thinking when attempting to resolve real-life technological glitches. Since the process of solving technological problems is complex in itself, it was necessary to categorize technological problems in real life and organize the procedure for resolving technical issues into a series for formal education in schools. In this process of technological problemsolving learning, explicit knowledge may be necessary, and interpretive or procedural knowledge may be necessary. Methods of utilizing AI convergence education vary depending on the target knowledge. Therefore, structuring and presenting a plan to apply various AI convergence educations to the process of solving highly complex technological problems is meaningful in that it suggests the basis for educational direction. Based on the framework developed in this study, expected that AI convergence education methods appropriate for each problem-solving process of various technological problems will be systematically researched and implemented in the future. [ABSTRACT FROM AUTHOR]

Introduction: The evaluation of memory is a crucial aspect of both cognitive research and clinical applications, as it offers valuable insights into an individual's cognitive wellbeing and performance. Conventional neuropsychological assessments represent the established method for assessing different aspects of memory. Recent technological advancements, specifically in the field of virtual reality (VR), have introduced novel methods for evaluating memory. Objective: This systematic review aims to examine the current state of memory assessment using VR technologies, assessing the degree of convergence and divergence between VR-based memory assessments and conventional neuropsychological tests. Method: A systematic review of the literature was conducted searching PubMed, PsycINFO, Web of Science databases, leading to the incorporation of 24 studies. Studies were grouped according to the examined memory domain (episodic, prospective, spatial domain). Convergence and divergence validity were examined for each, and information on software and hardware features was collected. Results: This review demonstrates a notable alignment between VR-based memory assessments and conventional neuropsychological tests. Moreover, VR tasks have shown to exhibit associations with executive functions and overall cognitive performance. The inclusion of various ecological contexts, such as residential environments, commercial establishments, and simulated scenarios, serves to augment the ecological validity of memory evaluations conducted in VR. Discussion: The findings indicate that VR assessments demonstrate a functional perspective by effectively capturing the dynamic relationship between memory, executive functions, and overall cognitive performance. Nevertheless, it is imperative to acknowledge and tackle certain constraints that may hinder the widespread adoption and utilization of VR tasks. These limitations encompass factors such as restricted accessibility to VR tasks and the presence of heterogeneity in VR hardware and software. The dynamic and ever-changing nature of VR technology presents a range of potential avenues for future investigation and utilization in the domain of memory evaluation. [ABSTRACT FROM AUTHOR]

With the development of China's economy entering a new stage, the quality of life, which centers on the well-being of residents, provides an essential hand in promoting the transformation of the regional economy from high-speed development to high-quality development. Based on a panel threshold regression model, we examine in this paper whether quality of life helps regional economies realize developmental convergence. The research shows that: (1) The quality of life overall can promote regional economic development and passes the series test with relatively robust results. (2) The quality of life has a non-linear effect on regional economic growth, which is mainly manifested in the fact that the impact is more significant in regions with higher levels of quality of life and weaker in regions with lagging quality of life and may widen the gap between regions at the same time. (3) We categorize the study regions to test further regional heterogeneity based on regional location and development status. At the Quality of Life Level-I regions, their influence on economic development has a more substantial pulling effect. Therefore, each region should seize the strategic opportunity to improve the quality of life, focus on the balanced development of the quality of life, strengthen policy support and social security, and strive to promote the coordinated development of China's regional economy. [ABSTRACT FROM AUTHOR]

The Kaczmarz algorithm is widely used in image reconstruction. In this paper, we propose an enhanced version of the Kaczmarz algorithm by integrating Nesterov momentum into the Kaczmarz algorithm, denoted as the Kaczmarz algorithm with momentum. Our innovation offers a novel approach to accelerate the convergence rate of the Kaczmarz algorithm significantly. Based on greedy rules for selecting work row, we prove that the Kaczmarz method with momentum can achieve global linear convergence rates. Furthermore, we extend a constrained version of the Kaczmarz algorithm with Nesterov momentum for improved image reconstruction and prove its convergence. Numerical experiments show that the proposed algorithm outperforms the original Kaczmarz algorithm in terms of efficiency for image reconstruction. [ABSTRACT FROM AUTHOR]

Physical fitness training, an important way to improve physical fitness, is the basic guarantee for forming combat effectiveness. At present, the evaluation types of physical fitness training are mostly conducted manually. It has problems such as low efficiency, high consumption of human and material resources, and subjective factors affecting the evaluation results. "Internet+" has greatly expanded the traditional network from the perspective of technological convergence and network coverage objects. It has expedited and promoted the rapid development of Internet of Things (IoT) technology and its applications. The IoT with many sensor nodes shows the characteristics of acquisition information redundancy, node energy sensitivity, network distribution openness, data demand reliability, etc. Thus, the research on data security fusion method of the IoT has important theoretical significance and application prospects. In order to ensure the authenticity and reliability of the fusion results of physical fitness training data, the security characteristics and performance of the IoT are analyzed, and the basic requirements for the security fusion of IoT sensory data are identified. An improved cluster-based data fusion model is proposed to address the shortcomings of the cluster-based data fusion model, and a security fusion method of physical fitness training data is studied. Finally, this article conducts a large number of simulation experiments. The experimental results show that the improved clusterbased data fusion model has better performance, further improving the security of physical fitness training data fusion based on the IoT. Finally, the article provides a security performance analysis. [ABSTRACT FROM AUTHOR]

Drawing on Erving Goffman's microsociology, this article explores the networking of music streaming technologies and their convergence with social media. Acts of privatized music listening that were once seamlessly secluded in back regions like the home and therefore removed from the view of others can now become presented more widely in front region contexts. Reporting on in-depth qualitative interviews with users of music streaming and how they perceive their musical listening has been altered, I investigate some of the affordances of streaming as it contributes to an unravelling or collapsing of demarcations between front and back region activity. As a result, users of streaming services describe how they become mindful of how they undertake their music listening and how these technologies consequently require careful management. [ABSTRACT FROM AUTHOR]

Under the emerging paradigm of mutual promotion between domestic and international dual circulation, the key to accelerating quality change, building a strong trading country, and achieving high-quality economic development hinge on harnessing China's expansive domestic market advantage, fully leveraging high-quality domestic resources, and facilitating the enhancement of export product quality. Technological convergence naturally arises from the rapid evolution of industrial technology and the proliferation of innovation-driven strategies. Yet, scant exploration has been conducted on the factors shaping export product quality through the lens of technological convergence, particularly regarding its mechanisms on enterprise product quality. This study employs data from Chinese industrial enterprises spanning 2000 to 2013 to systematically investigate the impact and mechanisms of technological convergence on export product quality. The findings demonstrate that deepening technological convergence substantially boosts export product quality,which is supported by rigorous robustness and endogeneity assessments. Heterogeneity analysis reveals a more pronounced enhancement in export product quality among non-state-owned, non-foreign-funded, and coastal enterprises. Mechanism inquiries reveal that intensified technological convergence heightens total factor productivity and the efficiency of fixed-cost investments, ultimately leading to an upgrade in export product quality. Moreover, marketization levels and governmental support positively regulate export product quality through technological convergence. These research conclusions provide both theoretical insights and empirical implications for the role of technological convergence in augmenting export product quality. This paper's contributions are twofold: First, it supplements extant literature on technological convergence by constructing enterprise-year level indicators using databases from Chinese industrial enterprises, customs, and patents. It systematically explores the impact and mechanisms of technological convergence on export product quality, elucidating the regulatory roles of market forces and government interventions. Second, it delves into the significant influence of the widespread and wide-ranging technological convergence on export product quality, a facet often overlooked in prior research. The study partially unveils the intricate nexus between technological convergence and export product quality for enterprises, offering guidance to government agencies in crafting targeted policies to foster technological convergence within innovation-driven frameworks. By bolstering the national unified market and fostering independent technological innovation, such as through technological convergence, Chinese enterprises can seamlessly integrate into the global manufacturing ecosystem, thereby enhancing and optimizing export product quality. [ABSTRACT FROM AUTHOR]

One of the fundamental problems in the field of technological studies is to clarify the drivers and dynamics of technological evolution for sustaining industrial and economic change. This study confronts the problem by analyzing the converging technologies to explain effects on the evolutionary dynamics over time. This paper focuses on technological interaction between artificial intelligence and quantum technologies using a technometric model of technological evolution based on scientific and technological information (publications and patents). Findings show that quantum technology has a growth rate of 1.07, artificial intelligence technology has a rate of growth of 1.37, whereas the technological interaction of converging quantum and artificial intelligence technologies has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings suggest that technological interaction is one of the fundamental determinants in the rapid evolution of path-breaking technologies and disruptive innovations. The deductive implications of results about the effects of converging technologies are: (a) accelerated evolutionary growth; (b) a disproportionate (allometric) growth of patents driven by publications supporting a fast technological evolution. Our results support policy and managerial implications for the decision making of policymakers, technology analysts, and R&D managers that can direct R&D investments towards fruitful inter-relationships between radical technologies to foster scientific and technological change with positive societal and economic impcats. [ABSTRACT FROM AUTHOR]

Bacteria have distinctive properties that make them ideal for biomedical applications. They can self‐propel, sense their surroundings, and be externally detected. Using bacteria as medical therapeutic agents or delivery platforms opens new possibilities for advanced diagnosis and therapies. Nano‐drug delivery platforms have numerous advantages over traditional ones, such as high loading capacity, controlled drug release, and adaptable functionalities. Combining bacteria and nanotechnologies to create therapeutic agents or delivery platforms has gained increasing attention in recent years and shows promise for improved diagnosis and treatment of diseases. In this review, design principles of integrating nanoparticles with bacteria, bacteria‐derived nano‐sized vesicles, and their applications and future in advanced diagnosis and therapeutics are summarized. [ABSTRACT FROM AUTHOR]

The technological innovation landscape is rapidly evolving based on the convergence of knowledge and artificial intelligence. This creates unprecedented opportunities and challenges for managing innovative projects. The object of this study is the system of syncretic management of innovative projects in the era of the artificial intelligence explosion. The problem addressed is related to the application of principles, models, and methods of syncretic management of innovative projects in the context of integrating various elements, including interdisciplinary collaboration, artificial intelligence technologies, and adaptive methodologies, to optimize project outcomes. The result of the research is a system of syncretic management of innovative projects that encompasses various aspects of management, innovation, and integration with artificial intelligence systems. The essence of the results outlines the stages of managing the life cycles of innovative projects, emphasizing resource allocation, risk assessment, and adaptive strategies. In the field of innovation management, the model includes methodologies for idea generation, technological scouting, and open innovation, recognizing the role of artificial intelligence in shaping the innovation environment. A crucial aspect of the model is the integration of artificial intelligence technologies throughout the project. The syncretic approach emphasizes cross-functional collaboration, creating an environment where different disciplines contribute to project success seamlessly. The importance of the proposed approach is associated with the integration of syncretic control with artificial intelligence systems based on additional competencies. The effectiveness of the practical application of systems of integrated syncretic management of innovative projects was evaluated in the process of analyzing the situation and preparing solutions many times faster, with a quality that exceeds existing systems [ABSTRACT FROM AUTHOR]

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today. [ABSTRACT FROM AUTHOR]

This article presents a bibliometric analysis conducted on the implementation of sensor-extracted data in the context of Historical Building Information Modeling (HBIM) with a focus on the conservation of heritage buildings. The study utilized recognized databases, namely, Web of Science (WoS) and Scopus. A bibliometric analysis methodology was applied, employing the VOSviewer 1.6.19 software. The results obtained provide a robust overview of the current state and trends in HBIM and sensor research and enabled the identification of emerging trends in the field of preventive conservation. Three promising trends were identified, establishing connections between this field and 4D spatial data management, energy performance, and artificial intelligence. Additionally, relations with climate change were also detected. These trends underscore a significant convergence of technologies and methodologies in the realm of heritage building conservation. The conclusions serve as a valuable contribution to guiding future research in the field of built heritage conservation. [ABSTRACT FROM AUTHOR]

The healthcare sector is continuously evolving, and it is anticipated that the next healthcare model would be information-focused. Cloud computing technology can help the sector handle heterogeneous sources of data and the effective convergence of data. Due to security and privacy considerations, it is challenging to transfer a medical record from multiple hospital databases. Consequently, the article proposed a blockchain-based patient information management system by using the Blockchain-based Privacy-Preserving and Robust Healthcare data (BPPRH). This transfers the medical data to the patient through the cloud more securely. The cloud is modelled with the hypervisor and VMs. During the data transmission or storage in the cloud, there are several attacks like ransomware, malicious insiders, Man-in-the-middle attack, malware injection attacks, and Denial-of-Service attacks (DoS) occur in the network. Modified Fuzzy Particle Swarm Optimization (MFPSO) is presented to detect these threats. After detecting the attacks, Edge-Cloud-based Collaborative Systems (ECCS) prevent the data from attackers with the use of Regularized Maximum Likelihood Estimation, Inverse Network, and Shadow Model Reconstruction models. The results showed that the system provides better security with high accuracy of 99.32% respectively. The proposed Blockchain-based architecture is designed to contribute to the healthcare management systems' robustness and to avoid recorded security limitations in commonly used systems for smart healthcare respectively. [ABSTRACT FROM AUTHOR]

This paper aims to interpret the connotation of high-quality development of tourism economy (HQTE) from the perspective of the new development concepts of innovation, coordination, green, openness and sharing, and then to evaluate the spatial differentiation of China's HQTE based on provincial panel data from 2009 to 2018. Specifically, we employ the spatial convergence model to explore the absolute and conditional β convergence trends of HQTE in the whole country and the eastern, central and western regions of China. Our empirical results reveal that: 1) within the decade, from 2009 to 2018, regions of China with the highest HQTE index is its eastern region followed by the central region and then the western region, but the fastest growing one is the western region of China followed by the central region and then the eastern region. 2) Whether or not the spatial effect is included, there are absolute and conditional β convergence in HQTE in the whole country and aforementioned three regions. 3) The degree of government attention as well as the level of economic development and location accessibility are the positive driving factors for the convergence of HQTE in the whole country and the three regions. The degree of marketization and human capital have not passed the significance test either in the whole country or in the three regions. The above conclusions could deepen the understanding of the regional imbalance and spatial convergence characteristics of HQTE, clarify the primary development objects, and accomplish the goal of China's HQTE. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of the import of convergence arguments in theory assessment. A first part is devoted to making the point of the different types of strategies based on convergence, providing new distinctions with respect to the existing literature. Specific attention is devoted to robustness vs consilience arguments and one representative example for each category is then discussed in some detail. These are: (a) Perrin's famous robustness argument on behalf of the atomic hypothesis on the grounds of the concordance of thirteen different procedures to the same result for the Avogadro number; (b) the consilience argument motivating the trust in the viability of the extra-dimension conjecture in the context of early string theory. These two cases are expressly chosen in order to highlight possible differences, also including whether the convergence obtains in terms of empirical or theoretical procedures. Notwithstanding these various differences, in both cases the evaluation of the assessment strategy similarly depends, in a significant way, on how the convergence argument is interpreted, as shown in the final part of the paper. [ABSTRACT FROM AUTHOR]

This article presents an investigation into the value-based affordances of volition as an integral philosophical component of technology education, specifically in relation to design methodology. As the central aspect of technology education, design has a prominent position in curricula all over the world, not just in subjects named Design and Technology, but also in most technology and engineering subjects in schools. In philosophy, it has been asserted that design volition (axiology) has a strong relationship with and in many ways forms the basis of design as a methodological stance. The primary philosophical frameworks used as the foundation for the philosophical analysis presented in this article are the ones introduced by Carl Mitcham in his Thinking through Technology (1994) and Andrew Feenberg's critical theory of technology. We perform a narrative review of relevant literature. Based on this review, we attempt a clearer definition of the lucid concept of volition/axiology in the literature, as well as explicate the relationship between axiology and methodology. In particular, we investigate this relationship in terms of values, intentionality, and sustainability in design. We review design as a societal and value-laden phenomenon, prone to strong and weak intentionality where expert and lay designers converge or diverge on sustainable values depending on the amount of interpretative flexibility. Concerning implications, school teaching needs to be founded on design volition which does not reduce technology education to technical education. Technology teachers and students need to be explicit about the values involved at all levels of technology and to clarify, justify, and debate their choices. Students must also be exposed to the way in which "sustainable values" might decrease interpretative flexibility and increase the opportunities for a technology to be sustainable in the medium to long term. [ABSTRACT FROM AUTHOR]

In academia, it is rare for an event or issue to foster the extensive participation of multiple disciplines. Research related to COVID-19 has undeniably yielded a wealth of valuable insights and impetus for the progress of interdisciplinary research, encompassing concepts, methodologies, intellectual approaches, theories, frameworks, data integration and analysis, and pertinent considerations. In the academic community, there is a widespread expectation that as science and technology continue to progress, the convergence of medicine with various other fields will gain momentum. Fields like computer science are anticipated to see expanded applications in domains such as medicine, vaccine research, disease diagnosis, and more. This study aims to examine interdisciplinary approaches in health-related research, particularly in the context of COVID-19. The goal is to analyze and comprehend the involvement and collaboration patterns of various disciplines in pandemic research, with a specific emphasis on the role and integration level of computer science. This study analyzed 240,509 COVID-19 related articles published from December 2019 to September 2022 using methods such as chord diagrams, modularity analysis, and eigenvector centrality analysis in Social Networking Analysis (SNA). The findings revealed an emerging trend of integration trend between Humanities & Social Sciences and Natural Sciences. Expectations that computer science would prominently feature in pandemic research during this technology-driven era haven't materialized. While it maintains links with engineering, it hasn't formed strong connections with medicine. This indicates a gap between computer science and core medical research in large-scale health crises, where COVID-19 research remains centered on medicine with varying interdisciplinary collaboration, and high-tech disciplines like computer science have not achieved their expected influence in these studies. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and investigate the concept of quasi statistical convergence of complex uncertain sequences. We also establish the relationship of the notion with statistical convergence of complex uncertain sequences. Furthermore, we investigate some interrelationships between quasi statistical convergence almost surely, quasi statistical convergence in measure, quasi statistical convergence in mean, and quasi statistical convergence in distribution. [ABSTRACT FROM AUTHOR]