The Italian textile machinery sector,renowned for its technological excellence and innovative capacity,continues to navigate a complex global market with a strategic emphasis on digitalization,sustainability,and stron...The Italian textile machinery sector,renowned for its technological excellence and innovative capacity,continues to navigate a complex global market with a strategic emphasis on digitalization,sustainability,and strong customer partnerships.Marco Salvade’,President of ACIMIT,provided insights into the industry’s performance,key trends,and future directions.In the first quarter of 2025,Italian textile machinery exports saw a 6%decrease compared to the same period in 2024,totaling€363 million.This dip reflects ongoing geopolitical tensions and a cautious approach among global clients toward new investments.Despite these challenges,Italian manufacturers maintain a strong reputation for technological leadership and resilience.展开更多
Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptab...Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptability of the QFD method and supplier selection process in a mass customization environment and puts forward a supplier selection framework based on the QFD idea.Furthermore,both the objective environment of demand factor analysis and the thinking of the customer representatives participating in the analysis have great uncertainty and fuzziness.Therefore,a demand factor analysis method for supplier selection in the mass customization environment based on language phrases of different granularity is proposed.The proposed method allows the customer representatives participating in the selection to use their preferred language phrase set to represent the importance of demand factors.Finally,the effectiveness and feasibility of the proposed method are verified by an example of a vehicle manufacturer.展开更多
Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV...Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
This study examines the effects of e-banking service quality on customer satisfaction in the Commercial Bank of Ethiopia(CBE)branches in Wolaita Sodo town.Using a causal research design,the study explored the cause-an...This study examines the effects of e-banking service quality on customer satisfaction in the Commercial Bank of Ethiopia(CBE)branches in Wolaita Sodo town.Using a causal research design,the study explored the cause-and-effect relationship between service quality dimensions and customer satisfaction.A sample of 385 customers was selected using convenience sampling,with 365 questionnaires returned.Data were collected through questionnaires and analyzed using SPSS V.21.The Cronbach’s alpha value of 0.72 from a pilot study confirmed reliability.Descriptive and inferential statistics,including multiple linear regression and one-way ANOVA,were employed.Results revealed that three service quality dimensions-responsiveness,reliability,and assurance-were statistically significant and positively influenced customer satisfaction,while two dimensions showed negative associations.The regression model’s coefficient of determination(R²)was 0.621,indicating a moderate explanatory power.Findings suggest that CBE managers and stakeholders should prioritize improving responsiveness,reliability,and assurance by providing prompt,dependable,and trustworthy services.Due to limitations in time and resources,this study was confined to CBE branches in Wolaita Sodo town;future research could expand to a national level or other service sectors.展开更多
To address the challenge of representing key index weights of logistics information systems in a mass customization environment using multi-granularity mixed semantic phrases,a method for determining index weights bas...To address the challenge of representing key index weights of logistics information systems in a mass customization environment using multi-granularity mixed semantic phrases,a method for determining index weights based on complex semantic information is proposed.First,an integrated method processes the multi-granularity mixed semantic variables to obtain initial index weights.Second,the probability of uncertain semantic information is calculated to determine the correction coefficient for key indicators of logistics information systems.Finally,the initial index weights and correction coefficients are synthesized to derive the final index weights.The effectiveness and feasibility of the proposed method are demonstrated using the selection of a logistics information system for a computer company as a case study.展开更多
The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health ...The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health settings across customs,cultures,and languages in two provinces,on the basis of qualitative interviews with healthcare professionals including doctors,nurses,other healthcare workers,patients,and traditional healers.We emphasize the views of consent with participants of customs,cultural,and languages regarding informed consent.There are factors between peoples of differing circumstances which can greatly alter how they view consent.Some groups would involve people in the decision-making process that may not traditionally be involved in the decision making of a medical decision.Other groups may dislike certain medical procedures as in Papua New Guinea(PNG).And certain people have different views on what should be disclosed of the patient’s condition.Customs,cultures,and languages are common phenomena which continue to affect the daily lives of many thousands of people.It is unclear in PNG about the characteristics of customs,culture,and language on health care because there is no published information on informed consent and issues that affect the making of informed consent.展开更多
Starting from the issues related to the construction and management of customs supervision places for railway and water transportation,this paper systematically analyzes the problems and challenges in the facility lay...Starting from the issues related to the construction and management of customs supervision places for railway and water transportation,this paper systematically analyzes the problems and challenges in the facility layout of customs operation places,the construction of a public platform for customs and port logistics monitoring,the utilization of information and data,and the intensity of technological innovation.Based on in-depth research,this paper attempts to propose improvement strategies and suggestions in terms of scientifically planning the layout of customs supervision places for railway and water transportation,improving the operation of the logistics monitoring public platform,developing and utilizing the information and data system,building a smart customs,and innovating the supervision mode.This aims to further optimize the customs supervision process,improve supervision efficiency and accuracy,and provide a reference for the facilitation and safe development of international trade.展开更多
This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn...This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn in the face of digital disruption.The research uses a quantitative approach focusing on empirical customer data to accurately predict buying trends and adapt marketing techniques.Demand forecasts in the health sector are important,as in every sector.In particular,the material forecast and stock forecasting of the purchasing unit of hospitals are among the areas that receive significant attention.Four classifiers(Random Forest,Logistic Regression,Gradient Boosting and XGBoost)are trained and evaluated using various performance indicators as part of a systematic approach involving Kaggle data collection,preparation and model selection.The results show excellent accuracy in predicting customer attrition,but there are limitations in precision and recall,indicating room for improvement.Confusion matrices provide information about the performance of each classifier,allowing for continuous improvement of predictive analytics techniques.Ethical concerns are rigorously addressed throughout the work process to guarantee appropriate data and machine learning methodologies.The proposals emphasize the proactive use of predictive analytics to identify at-risk customers and implement targeted retention strategies.Incorporating new data sources,improving customer experience,and utilizing collaborative churn management methods are recommended to increase forecast accuracy and business outcomes.Finally,this research provides important insights into the usefulness of predictive analytics for customer churn forecasting as well as practical recommendations for businesses seeking to increase customer retention and reduce churn risk.By leveraging empirical research findings and implementing ethical and rigorous churn control strategies,businesses can achieve long-term success in today’s changing market environment.展开更多
Cross-domain routing in Integrated Heterogeneous Networks(Inte-HetNet)should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements.However,current so...Cross-domain routing in Integrated Heterogeneous Networks(Inte-HetNet)should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements.However,current solutions face numerous challenges in continuously ensuring trustworthy routing,fulfilling diverse requirements,achieving reasonable resource allocation,and safeguarding against malicious behaviors of network operators.We propose CrowdRouting,a novel cross-domain routing scheme based on crowdsourcing,dedicated to establishing sustained trust in cross-domain routing,comprehensively considering and fulfilling various customized routing requirements,while ensuring reasonable resource allocation and effectively curbing malicious behavior of network operators.Concretely,CrowdRouting employs blockchain technology to verify the trustworthiness of border routers in different network domains,thereby establishing sustainable and trustworthy crossdomain routing based on sustained trust in these routers.In addition,CrowdRouting ingeniously integrates a crowdsourcing mechanism into the auction for routing,achieving fair and impartial allocation of routing rights by flexibly embedding various customized routing requirements into each auction phase.Moreover,CrowdRouting leverages incentive mechanisms and routing settlement to encourage network domains to actively participate in cross-domain routing,thereby promoting optimal resource allocation and efficient utilization.Furthermore,CrowdRouting introduces a supervisory agency(e.g.,undercover agent)to effectively suppress the malicious behavior of network operators through the game and interaction between the agent and the network operators.Through comprehensive experimental evaluations and comparisons with existing works,we demonstrate that CrowdRouting excels in providing trustworthy and fine-grained customized routing services,stimulating active participation in cross-domain routing,inhibiting malicious operator behavior,and maintaining reasonable resource allocation,all of which outperform baseline schemes.展开更多
The jewelry industry faces intense competition,making customer loyalty essential for sustained success.This paper examines customer loyalty through the lens of the ABC attitude model,which encompasses cognitive,affect...The jewelry industry faces intense competition,making customer loyalty essential for sustained success.This paper examines customer loyalty through the lens of the ABC attitude model,which encompasses cognitive,affective,and behavioral dimensions.Cognitive factors,such as perceived quality and brand reputation,establish the foundation of trust,while affective factors,including emotional attachment and trust,strengthen customer relationships.Behavioral factors,such as repeat purchases and advocacy,reflect observable loyalty actions.The study proposes strategies to enhance loyalty,including delivering superior products and services,strengthening customer relationship management,and leveraging word-of-mouth and digital marketing.These approaches provide actionable insights for building long-term customer relationships in a competitive market.Future research could explore emerging technologies and cultural influences to further refine loyalty strategies.This research highlights the multidimensional nature of customer loyalty and offers practical recommendations for jewelry enterprises.展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
1 The Inuit's remarkable ability to thrive(蓬勃发展)in one of Earth's toughest environments hasn't happened by chance.Their complex system of traditions,passed down through countless generations,represents...1 The Inuit's remarkable ability to thrive(蓬勃发展)in one of Earth's toughest environments hasn't happened by chance.Their complex system of traditions,passed down through countless generations,represents far more than mere survival strategies-it's a highly developed blueprint for living in harmony with the Arctic's unforgiving landscape.展开更多
The article Magnetic nanoparticles for single-neuron manipulation to design a customized neural circuit,written by Hongyong Zhang,Lingrui Zhao,Nan Huang,Xiaobo Zhang,Tian Xu,Sumin Bian,and Mohamad Sawan,was originally...The article Magnetic nanoparticles for single-neuron manipulation to design a customized neural circuit,written by Hongyong Zhang,Lingrui Zhao,Nan Huang,Xiaobo Zhang,Tian Xu,Sumin Bian,and Mohamad Sawan,was originally published electronically on the publisher’s internet portal on 22 May 2025 without open access.展开更多
The novel fabrication of multiple components and unique heterostructure can inject infinite vitality into the electromagnetic wave(EMW)attenuation field.Herein,through the self-assembly of polyimide com-plexes and cat...The novel fabrication of multiple components and unique heterostructure can inject infinite vitality into the electromagnetic wave(EMW)attenuation field.Herein,through the self-assembly of polyimide com-plexes and catalytic chemical vapor deposition,porous carbon microflowers were synthesized accompa-nied by carbon nanotubes(CNTs).By regulating the metal ions,the composition and structure of the as-obtained hybrids are modified correspondingly,and thus the adjustable thermal management and EMW absorption capabilities are obtained.In detail,the rich pores and huge specific surface area endow the hierarchical structures with distinguished thermal insulation ability(λ<0.07).The carbon framework and CNTs are beneficial for consuming EMWs via conductive loss and defect polarization loss while reduc-ing the filling ratio and thickness.The doped heteroatoms and abundant heterointerfaces generate ample dipole polarization and interface polarization losses(supported by DFT calculation).The metal nanopar-ticles uniformly embedded in the carbon framework offer optimized impedance matching,proper de-fect polarization,and suitable magnetic loss.Accordingly,the synergy of magnetic-dielectric balance and flower-like superstructure enables FNCFN2 and NNCFN2 to accomplish remarkable microwave absorbing capacity with thin thickness(14 wt.%).Therefore,respectable specific reflection loss and specific effec-tive absorption bandwidth are acquired(215.39 dB mm^(-1) and 22.10 GHz mm^(-1),257.23 dB mm^(-1) and 22.12 GHz mm^(-1) respectively),superior to those of certain renowned carbon-based absorbers.The simu-lation results of electric field intensity distributions,power loss density,and radar cross section reduction(maximum value of 36.02 dBm2)also verify the prominent radar stealth capability.Moreover,the cus-tomizable approach can be applied to other metals to obtain fulfilling behaviors.Henceforth,this work provides profound insights into the relationship between structure and performance,and proposes an efficient path for mass-producing multifunctional and high-performance EMW absorbers with excellent thermal properties.展开更多
Custom 465(C465)is a martensitic stainless steel known for its high strength,toughness,and corrosion resistance,widely used in aerospace,automotive,and medical industries.However,limited work has been conducted on its...Custom 465(C465)is a martensitic stainless steel known for its high strength,toughness,and corrosion resistance,widely used in aerospace,automotive,and medical industries.However,limited work has been conducted on its additive manufacturing(AM)and no dedicated heat treatments have been developed for additively manufactured C465 to optimize its strength-ductility trade-off.In this work,the C465 was fabricated via laser powder bed fusion.The effect of hot isostatic pressing,solid solution,cryogenic treatment(−78.5℃),and aging on the composition homogenization,austenite-to-martensite transition,and Ni_(3)Ti precipitation were systemically investigated.The atom probe tomography analysis reveals that Mo atoms accumulate on Ni_(3)Ti precipitate surfaces and inhibits the Ni_(3)Ti growth,con-tributing to the enhanced strength of C465.The modified heat treatment for additively manufactured C465 reaches comparable tensile strength with the wrought counterpart,yielding an ultimate tensile strength of 1773 MPa,yield strength of 1686 MPa,and elongation of 6.5%.A yield strength calculation model was proposed and validated with measured strength under various heat treatments,providing valuable insight for heat treatment design towards diverse industrial applications.展开更多
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
The increasing complexity and size of configuration knowledge bases requres the provisionof advanced methods supporting the development of the actual configuration process and design reuse.A new framework to find a fe...The increasing complexity and size of configuration knowledge bases requres the provisionof advanced methods supporting the development of the actual configuration process and design reuse.A new framework to find a feasible and practical product configuration method is presented in masscustomization. The basic idea of the appoach is to integrate case-based reasoning (CBR) with a con-straint satisfaction problem(CSP). The similarity measure between a crisp and range is also given,which is common in case retrieves. Based on the configuration model, a product platform and customerneeds, case adaptation is carried out with the repair-based algorithm. Lastly, the methodology in theelevator configuration design domain is tested.展开更多
文摘The Italian textile machinery sector,renowned for its technological excellence and innovative capacity,continues to navigate a complex global market with a strategic emphasis on digitalization,sustainability,and strong customer partnerships.Marco Salvade’,President of ACIMIT,provided insights into the industry’s performance,key trends,and future directions.In the first quarter of 2025,Italian textile machinery exports saw a 6%decrease compared to the same period in 2024,totaling€363 million.This dip reflects ongoing geopolitical tensions and a cautious approach among global clients toward new investments.Despite these challenges,Italian manufacturers maintain a strong reputation for technological leadership and resilience.
文摘Supplier selection in a mass customization environment is a systematic engineering,and Quality Function Deployment(QFD)based on customer demand is a systematic product development method.This paper studies the adaptability of the QFD method and supplier selection process in a mass customization environment and puts forward a supplier selection framework based on the QFD idea.Furthermore,both the objective environment of demand factor analysis and the thinking of the customer representatives participating in the analysis have great uncertainty and fuzziness.Therefore,a demand factor analysis method for supplier selection in the mass customization environment based on language phrases of different granularity is proposed.The proposed method allows the customer representatives participating in the selection to use their preferred language phrase set to represent the importance of demand factors.Finally,the effectiveness and feasibility of the proposed method are verified by an example of a vehicle manufacturer.
基金supported in part by National Natural Science Foundation of China(62271096,U20A20157)Natural Science Foundation of Chongqing,China(cstc2020jcyj-zdxmX0024,CSTB2022NSCQMSX0600)+5 种基金University Innovation Research Group of Chongqing(CXQT20017)Program for Innovation Team Building at Institutions of Higher Education in Chongqing(CXTDX201601020)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000626Chongqing Municipal Technology Innovation and Application Development Special Key Project(cstc2020jscx-dxwtBX0053)。
文摘Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
文摘This study examines the effects of e-banking service quality on customer satisfaction in the Commercial Bank of Ethiopia(CBE)branches in Wolaita Sodo town.Using a causal research design,the study explored the cause-and-effect relationship between service quality dimensions and customer satisfaction.A sample of 385 customers was selected using convenience sampling,with 365 questionnaires returned.Data were collected through questionnaires and analyzed using SPSS V.21.The Cronbach’s alpha value of 0.72 from a pilot study confirmed reliability.Descriptive and inferential statistics,including multiple linear regression and one-way ANOVA,were employed.Results revealed that three service quality dimensions-responsiveness,reliability,and assurance-were statistically significant and positively influenced customer satisfaction,while two dimensions showed negative associations.The regression model’s coefficient of determination(R²)was 0.621,indicating a moderate explanatory power.Findings suggest that CBE managers and stakeholders should prioritize improving responsiveness,reliability,and assurance by providing prompt,dependable,and trustworthy services.Due to limitations in time and resources,this study was confined to CBE branches in Wolaita Sodo town;future research could expand to a national level or other service sectors.
文摘To address the challenge of representing key index weights of logistics information systems in a mass customization environment using multi-granularity mixed semantic phrases,a method for determining index weights based on complex semantic information is proposed.First,an integrated method processes the multi-granularity mixed semantic variables to obtain initial index weights.Second,the probability of uncertain semantic information is calculated to determine the correction coefficient for key indicators of logistics information systems.Finally,the initial index weights and correction coefficients are synthesized to derive the final index weights.The effectiveness and feasibility of the proposed method are demonstrated using the selection of a logistics information system for a computer company as a case study.
文摘The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health settings across customs,cultures,and languages in two provinces,on the basis of qualitative interviews with healthcare professionals including doctors,nurses,other healthcare workers,patients,and traditional healers.We emphasize the views of consent with participants of customs,cultural,and languages regarding informed consent.There are factors between peoples of differing circumstances which can greatly alter how they view consent.Some groups would involve people in the decision-making process that may not traditionally be involved in the decision making of a medical decision.Other groups may dislike certain medical procedures as in Papua New Guinea(PNG).And certain people have different views on what should be disclosed of the patient’s condition.Customs,cultures,and languages are common phenomena which continue to affect the daily lives of many thousands of people.It is unclear in PNG about the characteristics of customs,culture,and language on health care because there is no published information on informed consent and issues that affect the making of informed consent.
文摘Starting from the issues related to the construction and management of customs supervision places for railway and water transportation,this paper systematically analyzes the problems and challenges in the facility layout of customs operation places,the construction of a public platform for customs and port logistics monitoring,the utilization of information and data,and the intensity of technological innovation.Based on in-depth research,this paper attempts to propose improvement strategies and suggestions in terms of scientifically planning the layout of customs supervision places for railway and water transportation,improving the operation of the logistics monitoring public platform,developing and utilizing the information and data system,building a smart customs,and innovating the supervision mode.This aims to further optimize the customs supervision process,improve supervision efficiency and accuracy,and provide a reference for the facilitation and safe development of international trade.
文摘This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn in the face of digital disruption.The research uses a quantitative approach focusing on empirical customer data to accurately predict buying trends and adapt marketing techniques.Demand forecasts in the health sector are important,as in every sector.In particular,the material forecast and stock forecasting of the purchasing unit of hospitals are among the areas that receive significant attention.Four classifiers(Random Forest,Logistic Regression,Gradient Boosting and XGBoost)are trained and evaluated using various performance indicators as part of a systematic approach involving Kaggle data collection,preparation and model selection.The results show excellent accuracy in predicting customer attrition,but there are limitations in precision and recall,indicating room for improvement.Confusion matrices provide information about the performance of each classifier,allowing for continuous improvement of predictive analytics techniques.Ethical concerns are rigorously addressed throughout the work process to guarantee appropriate data and machine learning methodologies.The proposals emphasize the proactive use of predictive analytics to identify at-risk customers and implement targeted retention strategies.Incorporating new data sources,improving customer experience,and utilizing collaborative churn management methods are recommended to increase forecast accuracy and business outcomes.Finally,this research provides important insights into the usefulness of predictive analytics for customer churn forecasting as well as practical recommendations for businesses seeking to increase customer retention and reduce churn risk.By leveraging empirical research findings and implementing ethical and rigorous churn control strategies,businesses can achieve long-term success in today’s changing market environment.
基金supported in part by the National Natural Science Foundation of China under Grant U23A20300 and 62072351in part by the Key Research Project of Shaanxi Natural Science Foundation under Grant 2023-JC-ZD-35+1 种基金in part by the Concept Verification Funding of Hangzhou Institute of Technology of Xidian University under Grant GNYZ2024XX007in part by the 111 Project under Grant B16037.
文摘Cross-domain routing in Integrated Heterogeneous Networks(Inte-HetNet)should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements.However,current solutions face numerous challenges in continuously ensuring trustworthy routing,fulfilling diverse requirements,achieving reasonable resource allocation,and safeguarding against malicious behaviors of network operators.We propose CrowdRouting,a novel cross-domain routing scheme based on crowdsourcing,dedicated to establishing sustained trust in cross-domain routing,comprehensively considering and fulfilling various customized routing requirements,while ensuring reasonable resource allocation and effectively curbing malicious behavior of network operators.Concretely,CrowdRouting employs blockchain technology to verify the trustworthiness of border routers in different network domains,thereby establishing sustainable and trustworthy crossdomain routing based on sustained trust in these routers.In addition,CrowdRouting ingeniously integrates a crowdsourcing mechanism into the auction for routing,achieving fair and impartial allocation of routing rights by flexibly embedding various customized routing requirements into each auction phase.Moreover,CrowdRouting leverages incentive mechanisms and routing settlement to encourage network domains to actively participate in cross-domain routing,thereby promoting optimal resource allocation and efficient utilization.Furthermore,CrowdRouting introduces a supervisory agency(e.g.,undercover agent)to effectively suppress the malicious behavior of network operators through the game and interaction between the agent and the network operators.Through comprehensive experimental evaluations and comparisons with existing works,we demonstrate that CrowdRouting excels in providing trustworthy and fine-grained customized routing services,stimulating active participation in cross-domain routing,inhibiting malicious operator behavior,and maintaining reasonable resource allocation,all of which outperform baseline schemes.
文摘The jewelry industry faces intense competition,making customer loyalty essential for sustained success.This paper examines customer loyalty through the lens of the ABC attitude model,which encompasses cognitive,affective,and behavioral dimensions.Cognitive factors,such as perceived quality and brand reputation,establish the foundation of trust,while affective factors,including emotional attachment and trust,strengthen customer relationships.Behavioral factors,such as repeat purchases and advocacy,reflect observable loyalty actions.The study proposes strategies to enhance loyalty,including delivering superior products and services,strengthening customer relationship management,and leveraging word-of-mouth and digital marketing.These approaches provide actionable insights for building long-term customer relationships in a competitive market.Future research could explore emerging technologies and cultural influences to further refine loyalty strategies.This research highlights the multidimensional nature of customer loyalty and offers practical recommendations for jewelry enterprises.
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
文摘1 The Inuit's remarkable ability to thrive(蓬勃发展)in one of Earth's toughest environments hasn't happened by chance.Their complex system of traditions,passed down through countless generations,represents far more than mere survival strategies-it's a highly developed blueprint for living in harmony with the Arctic's unforgiving landscape.
文摘The article Magnetic nanoparticles for single-neuron manipulation to design a customized neural circuit,written by Hongyong Zhang,Lingrui Zhao,Nan Huang,Xiaobo Zhang,Tian Xu,Sumin Bian,and Mohamad Sawan,was originally published electronically on the publisher’s internet portal on 22 May 2025 without open access.
基金supported by the Natural Science Foundation of Shandong Province(Nos.ZR2021ME194,2022TSGC2448,and 2023TSGC0545)the Key Technology Research and Development Program of Shandong Province(No.2021ZLGX01).
文摘The novel fabrication of multiple components and unique heterostructure can inject infinite vitality into the electromagnetic wave(EMW)attenuation field.Herein,through the self-assembly of polyimide com-plexes and catalytic chemical vapor deposition,porous carbon microflowers were synthesized accompa-nied by carbon nanotubes(CNTs).By regulating the metal ions,the composition and structure of the as-obtained hybrids are modified correspondingly,and thus the adjustable thermal management and EMW absorption capabilities are obtained.In detail,the rich pores and huge specific surface area endow the hierarchical structures with distinguished thermal insulation ability(λ<0.07).The carbon framework and CNTs are beneficial for consuming EMWs via conductive loss and defect polarization loss while reduc-ing the filling ratio and thickness.The doped heteroatoms and abundant heterointerfaces generate ample dipole polarization and interface polarization losses(supported by DFT calculation).The metal nanopar-ticles uniformly embedded in the carbon framework offer optimized impedance matching,proper de-fect polarization,and suitable magnetic loss.Accordingly,the synergy of magnetic-dielectric balance and flower-like superstructure enables FNCFN2 and NNCFN2 to accomplish remarkable microwave absorbing capacity with thin thickness(14 wt.%).Therefore,respectable specific reflection loss and specific effec-tive absorption bandwidth are acquired(215.39 dB mm^(-1) and 22.10 GHz mm^(-1),257.23 dB mm^(-1) and 22.12 GHz mm^(-1) respectively),superior to those of certain renowned carbon-based absorbers.The simu-lation results of electric field intensity distributions,power loss density,and radar cross section reduction(maximum value of 36.02 dBm2)also verify the prominent radar stealth capability.Moreover,the cus-tomizable approach can be applied to other metals to obtain fulfilling behaviors.Henceforth,this work provides profound insights into the relationship between structure and performance,and proposes an efficient path for mass-producing multifunctional and high-performance EMW absorbers with excellent thermal properties.
基金supported by the National Key Research and Development Program of China(No.2022YFB4600302)the Fundamental Research Funds for the Central Universities,China(No.FRF-IDRY-23-011).
文摘Custom 465(C465)is a martensitic stainless steel known for its high strength,toughness,and corrosion resistance,widely used in aerospace,automotive,and medical industries.However,limited work has been conducted on its additive manufacturing(AM)and no dedicated heat treatments have been developed for additively manufactured C465 to optimize its strength-ductility trade-off.In this work,the C465 was fabricated via laser powder bed fusion.The effect of hot isostatic pressing,solid solution,cryogenic treatment(−78.5℃),and aging on the composition homogenization,austenite-to-martensite transition,and Ni_(3)Ti precipitation were systemically investigated.The atom probe tomography analysis reveals that Mo atoms accumulate on Ni_(3)Ti precipitate surfaces and inhibits the Ni_(3)Ti growth,con-tributing to the enhanced strength of C465.The modified heat treatment for additively manufactured C465 reaches comparable tensile strength with the wrought counterpart,yielding an ultimate tensile strength of 1773 MPa,yield strength of 1686 MPa,and elongation of 6.5%.A yield strength calculation model was proposed and validated with measured strength under various heat treatments,providing valuable insight for heat treatment design towards diverse industrial applications.
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
基金This project is supported by National Natural Science Foundation of China(No.50275133) and China Hi-tech Program CIMS Topic (No.2003-China(No.50275133) and China Hi-tech Program CIMS Topic (No.2003-AA411320). Received July 22, 2003
文摘The increasing complexity and size of configuration knowledge bases requres the provisionof advanced methods supporting the development of the actual configuration process and design reuse.A new framework to find a feasible and practical product configuration method is presented in masscustomization. The basic idea of the appoach is to integrate case-based reasoning (CBR) with a con-straint satisfaction problem(CSP). The similarity measure between a crisp and range is also given,which is common in case retrieves. Based on the configuration model, a product platform and customerneeds, case adaptation is carried out with the repair-based algorithm. Lastly, the methodology in theelevator configuration design domain is tested.
