Challenges and Solutions for Rural Mutual-Support Elderly Care:A Perspective of Embeddedness Theory

In rural China,the trend of population aging and family hollowing is becoming increasingly severe.Traditional elderly care methods are no longer adequate to meet the growing demand for elderly care,leading to the emergence of mutual-support elderly care as a new approach to elderly care.Mutual support has gained widespread practice and has become a vital component of the rural elderly service system.However,rural mutual-support elderly care encounters development bottlenecks,including local cultural resilience,disembedding of social relationships,gaps in policies and regulations,and multiple coordination challenges.Embeddedness theory emphasizes the need for social behavior to be embedded in the network of social connections,providing a valuable theoretical framework for mutual-support elderly care.Drawing on embeddedness theory,this paper investigates the accessibility of mutual-support elderly care in rural areas by examining cognitive embeddedness,relational embeddedness,systemic embeddedness,and structural embeddedness.By addressing the development bottlenecks of mutual-support elderly care in rural China,this paper aims to propose ideas for achieving high-quality development in rural mutual-support elderly care.

Are social embeddedness associated with food risk perception under media coverage?

Traceability system has received wide attention in solving food safety issues, via which food information could be tracked back to producer/farmers. Consumers need to obtain this information from producers or social networks, trust in the information,and consequently assess perceived risks, especially when food scandals are exposed to the media. In this study, we introduce the social embeddedness theory to understand how consumers’ social activities affect their risk perceptions on traceable food. Specifically, we investigate how risk perceptions are predicted by the interpersonal relationships, organizational level and social-level relationships. Results show that the interpersonal relationships were associated with lower levels of risk perceptions, while organizational and social relationships impacted consumer’s risk perceptions at middle and higher levels,respectively. Results also show that the "ripple effect" extended to effect of risk events with negative information, however,did not exist for the group exposed to positive information. Potential food safety implications have been proposed to identify for effective risk mitigation under media coverages.

Socio-cultural Embeddedness of Spatial Agglomeration of Creative Industries in Shanghai

Rapidly emerged creative industries receive increasing attention from a variety of disciplines. However, the space features of creative industries and its association with local socio-cultural contexts have not been fully understood, especially at a micro-city level. This study attempts to understand the agglomeration of creative industries in Shanghai from the sociology perspective. For this study, this paper utilizes primarily a questionnaire survey to explain the space features of creative industries in Shanghai. The results indicate an extensive socio-cultural embeddedness of the agglomeration of creative industries in Shanghai. First, strong emphasis on face-to-face contacts by creative professionals makes geographical agglomeration necessary for creative industries. Second, the reason why inner city of Shanghai is popular among creative professionals and enterprises lies in the diversity of cultures and special environment of the former colonial zones of Shanghai. Additionally, highly concentrated dining and entertainment facilities in the central city of Shanghai offer creative workers social networking places and nightlife venues. Third, as the educational attainment of local citizens and the protection of intellectual property are highly stressed by creative professionals, research and design specialized creative industries are more likely located near universities and research institutes.

Do Personality Variables Predict Job Embeddedness and Proclivity to Be Absent from Work?

The current knowledge base lacks evidence about situational-and surface-level personality variables and their impacts on job embeddedness and proclivity to be absent from work.With this recognition,drawing from the hierarchical personality model and fit theory as well as job embeddedness theory,our paper explores the influences of job resourcefulness(JR)and customer orientation(CO)on job embeddedness and propensity to be absent from work.We tapped time-lagged data gathered from hotel customer-contact employees in the United Arab Emirates to assess the aforementioned linkages via structural equation modeling.CO is a complete mediator between JR and job embeddedness,while job embeddedness completely mediates the linkage between CO and absence intentions.Specifically,hotel employees who can work under a resource-depleted environment are high on CO and therefore display job embeddedness at elevated levels.In addition,customer-oriented hotel employees have higher job embeddedness and therefore exhibit lower absence intentions.

Influencing Factors of Organizational Learning Capability : From the Perspective of Knowledge Embeddedness

Organizational learning capability is an important embodiment of the competitive advantage of enterprises in the era of knowledge economy. Based on the embeddedness theory,six factors are discussed that influence organizational learning capability from the perspective of knowledge embeddedness: employees embeddedness, tools embeddedness, tasks embeddedness,interpersonal relationship embeddedness, organizational culture embeddedness and network environment embeddedness. Combined with the survey data of textile and apparel manufacturing industry,the research proves the important function of knowledge embeddedness in the construction of organizational learning capability, and proposes three research countermeasures for industrial upgrading.

Some Comments on Embeddedness, Knowledge Transfer, Industry Clusters and Global Competitiveness

This paper develops a dynamic theoretical framework for global competitiveness, which describes the relationships among organizations in an industry cluster. The spiral for knowledge transfer, culture variables and embeddedness influence knowledge transfer. Embeddedness and knowledge transfer are the key determinants of industry clusters that lead to global competitiveness. Industry clusters are characterized by external economies, generalized reciprocity and flexible specialization.

Multi-case Study of Entrepreneurial Path Choice: Based on the Job Embeddedness of Tech-entrepreneur

This study analyzes opportunity recognition and development from the perspective of job embeddedness based on entrepreneurial action of tech-entrepreneur. The impact of job embeddedness to entrepreneurial path choice of tech-entrepreneur is tested by multi-case study. This paper also provides theoretic evidence to enhance the possibility of success of start-ups for entrepreneurs.

Understanding Why People Complain: Using Spatial Embeddedness to Explore Residents' Satisfaction in China's Danwei Neighborhood Renovation

Against the backdrop of China's socio-economic transition, there is a growing imperative to examine neighborhood renovation initiatives in addressing the emerging needs of residents. Developing Granovetter's classic work on embeddedness, this paper proposes a conceptual framework of spatial embeddedness to understand changes in the physical space brought about by neighborhood renovation, in order to explore how it affects residents' satisfaction in a dynamic temporal and spatial process. It presents whether and why residents' real feelings produced from their interaction with neighborhood renovation are(un)different, and how their feelings are shaped based on six months of fieldwork in a danwei neighborhood in Xi'an, China. The paper conceptualizes the relationship between the neighborhood space and embeddedness by adopting spatial embeddedness to capture the interplay of temporal, spatial, and social factors in the process of danwei neighborhood renovation. This framework not only integrates multiple perspectives and scales, but also reflects different levels of residents' satisfaction, trying to establish a connection between the abstract space and the renovation space. It suggests that spatial embeddedness should be considered as a response to the negative social impacts resulting from changes in the physical space in neighborhood renovation.

How Does Focal Firms' Dual Embeddedness in Symbiotic Networks Influence Value Cocreation in Innovation Ecosystems?

Under the current complex and competitive economic environment,more and more firms are embedding themselves into symbiotic networks for value co-creation,since this has become a good strategy to obtain competitive advantages.Thus,it is important to examine the impacts of firms’embeddedness in symbiotic networks on value co-creation in innovation ecosystems.This study analyzes the mechanisms and contextual factors of firms’dual embeddedness(i.e.,relational and knowledge embeddedness)in symbiotic networks and how each influences value co-creation within innovation ecosystems.Using a sample of 1,972 observations,our findings show,firstly,that firms’dual embeddedness in symbiotic networks positively impacts on value co-creation in innovation ecosystems;secondly,that firms’dual embeddedness in symbiotic networks positively impacts on innovation ecosystem resilience;thirdly,that innovation ecosystem resilience mediates the relationships between firms’dual embeddedness in symbiotic networks and value co-creation in innovation ecosystems;and,fourthly,that innovative ecological environments positively moderate the relationship between firms’dual embeddedness and value co-creation in innovation ecosystems.These results not only enrich the theoretical framework concerning value co-creation within innovation ecosystems but also provide managerial suggestions for firms to efficiently enhance the degree of embeddedness in symbiotic networks and build highly resilient innovation ecosystems,thus promoting value co-creation among innovation ecosystem populations.

Mechanism of R&D network formation based on a network embeddedness game model

This paper explains the mechanism of R&D network formation based on a game model of network embeddedness.To describe the game relationship between a new member and an original R&D network,this game model is set up by introducing three factors:R&D efficiency of the new member,asymmetric information,and trust.By solving the game model,this paper analyzes their impact on the level of embeddedness and transaction cost within the Network.Study results show that,during the formation of an R&D network,low efficiency and asymmetric information will do harm to the level of embeddedness and raise the transaction cost,while trust will have a complicated impact on them because of the probability of misplaced-trust.

Intelligent casting:Empowering the future foundry industry

Emerging technological advances are reshaping the casting sector in latest decades.Casting technology is evolving towards intelligent casting paradigm that involves automation,greenization and intelligentization,which attracts more and more attention from the academic and industry communities.In this paper,the main features of casting technology were briefly summarized and forecasted,and the recent developments of key technologies and the innovative efforts made in promoting intelligent casting process were discussed.Moreover,the technical visions of intelligent casting process were also put forward.The key technologies for intelligent casting process comprise 3D printing technologies,intelligent mold technologies and intelligent process control technologies.In future,the intelligent mold that derived from mold with sensors,control devices and actuators will probably incorporate the Internet of Things,online inspection,embedded simulation,decision-making and control system,and other technologies to form intelligent cyber-physical casting system,which may pave the way to realize intelligent casting.It is promising that the intelligent casting process will eventually achieve the goal of real-time process optimization and full-scale control,with the defects,microstructure,performance,and service life of the fabricated castings can be accurately predicted and tailored.

CO_(2)flooding in shale oil reservoir with radial borehole fracturing for CO_(2)storage and enhanced oil recovery

This study introduces a novel method integrating CO_(2)flooding with radial borehole fracturing for enhanced oil recovery and CO_(2)underground storage,a solution to the limited vertical stimulation reservoir volume in horizontal well fracturing.A numerical model is established to investigate the production rate,reservoir pressure field,and CO_(2)saturation distribution corresponding to changing time of CO_(2)flooding with radial borehole fracturing.A sensitivity analysis on the influence of CO_(2)injection location,layer spacing,pressure difference,borehole number,and hydraulic fractures on oil production and CO_(2)storage is conducted.The CO_(2)flooding process is divided into four stages.Reductions in layer spacing will significantly improve oil production rate and gas storage capacity.However,serious gas channeling can occur when the spacing is lower than 20 m.Increasing the pressure difference between the producer and injector,the borehole number,the hydraulic fracture height,and the fracture width can also increase the oil production rate and gas storage rate.Sensitivity analysis shows that layer spacing and fracture height greatly influence gas storage and oil production.Research outcomes are expected to provide a theoretical basis for the efficient development of shale oil reservoirs in the vertical direction.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

Ice-Induced Vibrational Response of Single-Pile Offshore Wind-Turbine Foundations

Important challenges must be addressed to make wind turbines sustainable renewable energy sources.A typical problem concerns the design of the foundation.If the pile diameter is larger than that of the jacket platform,traditional mechanical models cannot be used.In this study,relying on the seabed soil data of an offshore wind farm,the m-method and the equivalent embedded method are used to address the single-pile wind turbine foundation problem for different pile diameters.An approach to determine the equivalent pile length is also proposed accordingly.The results provide evidence for the effectiveness and reliability of the model based on the equivalent embedded method.

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

Functional Optical Fiber Sensors Detecting Imperceptible Physical/Chemical Changes for Smart Batteries

The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist.Now the battery is still a“black box”,thus requiring a deep understanding of its internal state.The battery should“sense its internal physical/chemical conditions”,which puts strict requirements on embedded sensing parts.This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed,focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano-micro-level battery material structural phase transition,electrolyte degradation,electrode-electrolyte interface dynamics to three-dimensional macro-safety evolution.The paper contributes to understanding how to use optical fiber sensors to achieve“real”and“embedded”monitoring.Through the inherent advantages of the advanced optical fiber sensor,it helps clarify the battery internal state and reaction mechanism,aiding in the establishment of more detailed models.These advancements can promote the development of smart batteries,with significant importance lying in essentially promoting the improvement of system consistency.Furthermore,with the help of smart batteries in the future,the importance of consistency can be weakened or even eliminated.The application of advanced optical fiber sensors helps comprehensively improve the battery quality,reliability,and life.

High-Precision Flow Numerical Simulation and Productivity Evaluation of Shale Oil Considering Stress Sensitivity

Continental shale oil reservoirs,characterized by numerous bedding planes and micro-nano scale pores,feature significantly higher stress sensitivity compared to other types of reservoirs.However,research on suitable stress sensitivity characterization models is still limited.In this study,three commonly used stress sensitivity models for shale oil reservoirs were considered,and experiments on representative core samples were conducted.By fitting and comparing the data,the“exponential model”was identified as a characterization model that accurately represents stress sensitivity in continental shale oil reservoirs.To validate the accuracy of the model,a two-phase seepage mathematical model for shale oil reservoirs coupled with the exponential model was introduced.The model was discretely solved using the finite volume method,and its accuracy was verified through the commercial simulator CMG.The study evaluated the productivity of a typical horizontal well under different engineering,geological,and fracture conditions.The results indicate that considering stress sensitivity leads to a 13.57%reduction in production for the same matrix permeability.Additionally,as the fracture half-length and the number of fractures increase,and the bottomhole flowing pressure decreases,the reservoir stress sensitivity becomes higher.

“Instruction-Category” Approach of Test Suite Construction for Oven Embedded System

Being different from testing for popular GUI software, the “instruction-category” approach is proposed for testing embedded system. This approach is constructed by three steps including refining items, drawing instruction-brief and instruction-category, and constructing test suite. Consequently, this approach is adopted to test oven embedded system, and detail process is deeply discussed. As a result, the factual result indicates that the “instruction-category” approach can be effectively applied in embedded system testing as a black-box method for conformity testing.

Analyzing the Impact of Feature Selection on Crop Yield Prediction

In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques allow us to remove the extraneous and noisy features from the original feature set. The feature selection techniques help the model to focus only on the important features of the data, thus reducing execution time and improving efficiency of the model. The aim of this study is to determine relevant subset features for achieving high predictive performance by using different feature selection techniques like Filter methods, Wrapper methods and embedded methods. In this work, different feature selection techniques like Rank-based feature selection technique, weighted feature selection technique and Hybrid Feature Selection Technique have been applied to the agricultural data. The optimal feature set returned by different feature selection techniques is used for yield prediction using Linear regression, Random Forest, and Decision Tree Regressor. The accuracy of prediction obtained using the above three methods has been analyzed by using different evaluation parameters. This study helps in increasing predictive accuracy with the minimum number of features.