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.

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.

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.

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.

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.

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.

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.

Teaching Reform and Practice of Embedded System Design Based on Outcome-Based Education

Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditional teaching,this paper proposes curriculum teaching reform based on the outcome-based education(OBE)concept,including determining course objectives,reforming teaching modes and methods,and improving the curriculum assessment and evaluation system.After two semesters of practice,this method not only enhances students’learning initiative but also improves teaching quality.

What Drives Migrants Back to Set up Firms?Return-home Entrepreneurial Intention of Rural Migrant Workers in China

The extant literature on international immigrants has discussed migrants’entrepreneurial activities in the context of Western countries but has paid little attention to return-home entrepreneurial intention(RHEI).Rural migrant workers(RMWs)in China,who used to promote rural development by remittances and were characterized by similarities with early international migrants,have gradually returned to their hometowns to initiate entrepreneurial activities.Based on the structured questionnaire conducted in 2015 and 2020in Anhui Province,China,this article combines the concept of mixed embeddedness with the idea of double-layered embeddedness and analyzes the impacts of the social,economic and institutional context in RMWs’hometowns and migration destinations on RMWs’RHEI by using binary logistic regression.The article shows that the social,economic,and institutional environments of RMWs’hometowns and migration destinations have effects on their RHEI.The embeddedness in the economic and informal institutional context in RMWs’RHEI is even more important than personal characteristics.Compared with migration destinations,RMWs’hometowns exert a more influential effect on their RHEI.However,that does not mean that the role of migration destinations can be undervalued.Actually,the better the social,economic,and institutional environments of migration destinations RMWs moved into is,the higher entrepreneurial intention they will have after returning to their hometowns.The article proposes a modified framework in combination of mixed embeddedness with double-layer embeddedness and proves that it is suitable for analyzing RMWs’RHEI.The framework has important implications for strengthening China’s RMWs to return home to start their own businesses.

A Self‑Powered,Highly Embedded and Sensitive Tribo‑Label‑Sensor for the Fast and Stable Label Printer

Label-sensor is an essential component of the label printer which is becoming a most significant tool for the development of Internet of Things(IoT).However,some drawbacks of the traditional infrared label-sensor make the printer fail to realize the high-speed recognition of labels as well as stable printing.Herein,we propose a selfpowered and highly sensitive tribo-label-sensor(TLS)for accurate label identification,positioning and counting by embedding triboelectric nanogenerator into the indispensable roller structure of a label printer.The sensing mechanism,device parameters and deep comparison with infrared sensor are systematically studied both in theory and experiment.As the results,TLS delivers 6 times higher signal magnitude than traditional one.Moreover,TLS is immune to label jitter and temperature variation during fast printing and can also be used for transparent label directly and shows long-term robustness.This work may provide an alternative toolkit with outstanding advantages to improve current label printer and further promote the development of IoT.

Evaluation of the Predicted Particle Properties (P3) Microphysics Scheme in Simulations of Stratiform Clouds with Embedded Convection

To evaluate the ability of the Predicted Particle Properties(P3)scheme in the Weather Research and Forecasting(WRF)model,we simulated a stratiform rainfall event over northern China on 22 May 2017.WRF simulations with two P3 versions,P3-nc and P3-2ice,were evaluated against rain gauge,radar,and aircraft observations.A series of sensitivity experiments were conducted with different collection efficiencies between ice and cloud droplets.The comparison of the precipitation evolution between P3-nc and P3-2ice suggested that both P3 versions overpredicted surface precipitation along the Taihang Mountains but underpredicted precipitation in the localized region on the leeward side.P3-2ice had slightly lower peak precipitation rates and smaller total precipitation amounts than P3-nc,which were closer to the observations.P3-2ice also more realistically reproduced the overall reflectivity structures than P3-nc.A comparison of ice concentrations with observations indicated that P3-nc underestimated aggregation,whereas P3-2ice produced more active aggregation from the self-collection of ice and ice-ice collisions between categories.Efficient aggregation in P3-2ice resulted in lower ice concentrations at heights between 4 and 6 km,which was closer to the observations.In this case,the total precipitation and precipitation pattern were not sensitive to riming.Riming was important in reproducing the location and strength of the embedded convective region through its impact on ice mass flux above the melting level.

Simultaneous multi-material embedded printing for 3D heterogeneous structures

In order to mimic the natural heterogeneity of native tissue and provide a better microenvironment for cell culturing,multi-material bioprinting has become a common solution to construct tissue models in vitro.With the embedded printing method,complex 3D structure can be printed using soft biomaterials with reasonable shape fidelity.However,the current sequential multi-material embedded printing method faces a major challenge,which is the inevitable trade-off between the printed structural integrity and printing precision.Here,we propose a simultaneous multi-material embedded printing method.With this method,we can easily print firmly attached and high-precision multilayer structures.With multiple individually controlled nozzles,different biomaterials can be precisely deposited into a single crevasse,minimizing uncontrolled squeezing and guarantees no contamination of embedding medium within the structure.We analyse the dynamics of the extruded bioink in the embedding medium both analytically and experimentally,and quantitatively evaluate the effects of printing parameters including printing speed and rheology of embedding medium,on the 3D morphology of the printed filament.We demonstrate the printing of double-layer thin-walled structures,each layer less than 200μm,as well as intestine and liver models with 5%gelatin methacryloyl that are crosslinked and extracted from the embedding medium without significant impairment or delamination.The peeling test further proves that the proposed method offers better structural integrity than conventional sequential printing methods.The proposed simultaneous multi-material embedded printing method can serve as a powerful tool to support the complex heterogeneous structure fabrication and open unique prospects for personalized medicine.

Considering explosive charge shape and embedded depth in the design of concrete shelter thickness

Cost and safety are important considerations when designing the thickness of a protective reinforced concrete shelter.The blast perforation limit(BPL)is the minimum concrete shelter thickness that resists perforation under blast loading.To investigate the influence of the depth of embedment(DOE)and length-to-diameter ratio(L/D)of an explosive charge on the BPL,the results of an explosion test using a slender explosive partially embedded in a reinforced concrete slab were used to validate a refined finite element model.This model was then applied to conduct more than 300 simulations with strictly controlled variables,obtaining the BPLs for various concrete slabs subjected to charge DOEs ranging from0 to∞and L/D values ranging from 0.89 to 6.87.The numerical results were compared with the experimental results from published literature,further verifying the reliability of the simulation.The findings indicate that for the same explosive charge mass and L/D,the greater the DOE,the larger the critical residual thickness(Rc,defined as the difference between the BPL and DOE)up to a certain constant value;for the same explosive charge mass and DOE,the greater the L/D,the smaller the Rc.Thus,corresponding DOE and shape coefficients were introduced to derive a new equation for the BPL,providing a theoretical approach to the design and safety assessment of protective structures.

Efficient production optimization for naturally fractured reservoir using EDFM

Naturally fractured reservoirs make important contributions to global oil and gas reserves and production.The modeling and simulation of naturally fractured reservoirs are different from conventional reservoirs as the existence of natural fractures.To address the development optimization problem of naturally fractured reservoirs,we propose an optimization workflow by coupling the optimization methods with the embedded discrete fracture model(EDFM).Firstly,the effective and superior performance of the workflow is verified based on the conceptual model.The stochastic simplex approximate gradient(StoSAG)algorithm,the ensemble optimization(EnOpt)algorithm,and the particle swarm optimization(PSO)algorithm are implemented for the production optimization of naturally fractured reservoirs based on the improved versions of the Egg model and the PUNQ-S3 model.The results of the two cases demonstrate the effectiveness of this optimization workflow by finding the optimal well controls which yield the maximum net present value(NPV).Compared to the initial well control guess,the final NPV obtained from the production optimization of fractured reservoirs based on all three optimization algorithms is significantly enhanced.Compared with the optimization results of the PSO algorithm,StoSAG and EnOpt have significant advantages in terms of final NPV and computational efficiency.The results also show that fractures have a significant impact on reservoir production.The economic efficiency of fractured reservoir development can be significantly improved by the optimization workflow.

面向异构集成应用的“片上”嵌入式冷却技术的研究进展

The electronics packaging community strongly believes that Moore’s law will continue for another few years due to recent technological efforts to build heterogeneously integrated packages.Heterogeneous integration(HI)can be at the chip level(a single chip with multiple hotspots),in multi-chip modules,or in vertically stacked three-dimensional(3D)integrated circuits.Flux values have increased exponentially with a simultaneous reduction in chip size and a significant increase in performance,leading to increased heat dissipation.The electronics industry and the academic research community have examined various solutions to tackle skyrocketing thermal-management challenges.Embedded cooling eliminates most sequential conduction resistance from the chip to the ambient,unlike separable cold plates/heat sinks.Although embedding the cooling solution onto an electronic chip results in a high heat transfer potential,technological risks and complexity are still associated with the implementation of these technologies and with uncertainty regarding which technologies will be adopted.This manuscript discusses recent advances in embedded cooling,fluid selection considerations,and conventional,immersion,and additive manufacturing-based embedded cooling technologies.

Optimization of operational strategies for rich gas enhanced oil recovery based on a pilot test in the Bakken tight oil reservoir

Horizontal well drilling and multistage hydraulic fracturing have been demonstrated as effective approaches for stimulating oil production in the Bakken tight oil reservoir.However,after multiple years of production,primary oil recovery in the Bakken is generally less than 10%of the estimated original oil in place.Gas huff‘n’puff(HnP)has been tested in the Bakken Formation as an enhanced oil recovery(EOR)method;however,most field pilot test results showed no significant incremental oil production.One of the factors affecting HnP EOR performance is premature gas breakthrough,which is one of the most critical issues observed in the field because of the presence of interwell fractures.Consequently,injected gas rapidly reaches adjacent production wells without contacting reservoir rock and increasing oil recovery.Proper conformance control is therefore needed to avoid early gas breakthrough and improve EOR performance.In this study,a rich gas EOR pilot in the Bakken was carefully analyzed to collect the essential reservoir and operational data.A simulation model with 16 wells was then developed to reproduce the production history and predict the EOR performance with and without conformance control.EOR operational strategies,including single-and multiple-well HnP,with different gas injection constraints were investigated.The simulation results of single-well HnP without conformance control showed that a rich gas injection rate of at least 10 MMscfd was needed to yield meaningful incremental oil production.The strategy of conformance control via water injection could significantly improve oil production in the HnP well,but injecting an excessive amount of water also leads to water breakthrough and loss of oil production in the offset wells.By analyzing the production performance of the wells individually,the arrangement of wells was optimized for multiple-well HnP EOR.The multiwell results showed that rich gas EOR could improve oil production up to 7.4%by employing conformance control strategies.Furthermore,replacing rich gas with propane as the injection gas could result in 14%of incremental oil production.

Thermoelectric energy harvesting for internet of things devices using machine learning:A review

Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)networks.As a key pillar of fifth generation(5G)and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025.Thermoelectric generators(TEGs)are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy.These devices are able to recover lost thermal energy,produce energy in extreme environments,generate electric power in remote areas,and power micro‐sensors.Applying the state of the art,the authorspresent a comprehensive review of machine learning(ML)approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy.The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment,biological structures,machines,and other technologies are summarised.Based on detailed research of the state of the art in TEG‐powered devices,the authors investigated the research challenges,applied algorithms and application areas of this technology.The aims of the research were to devise new energy prediction and energy management systems based on ML methods,create supervised algorithms which better estimate incoming energy,and develop unsupervised and semi‐supervised ap-proaches which provide adaptive and dynamic operation.The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability,usability in ubiquitous temperature difference scenarios,and long oper-ating lifetime.However,TEGs also have low energy efficiency(around 10%)and require a relatively constant heat source.