Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian new area,China:A cross-sectional survey-Retraction

In the article titled“Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian New Area,China:A cross-sectional survey”by Zhang Q and Liu L(J Integr Nurs 2021;3(3):117-121.doi:10.4103/jin.jin_14_21),[1]the content and results data of this article was questioned by International database(Web of Science)institution.This article was then investigated by the publisher and Journal of Integrative Nursing(JIN).

The influence of different types of satisfaction on loyalty on C2C online shopping platform:From the perspective of sellers and the platform

With the rise and development of major types of platforms,the competition for resources has become extremely fierce,and the market share of C2C platforms has been seriously threatened by the loss of resources.Therefore,building and maintaining buyers’satisfaction and loyalty to C2C platforms is critical to the survival and sustainability of C2C platforms in China.However,the current knowledge on how platform satisfaction and loyalty are constructed in the C2C e-commerce environment is incomplete.In this study,seller-based satisfaction and platform-based satisfaction are constructed separately.We further distinguish seller-based transaction satisfaction into economic and social satisfaction and explore their antecedents and consequences.To test our research hypotheses,we conduct a survey and collect data from a real online market(Taobao website).The results show that seller-based transaction satisfaction positively affects platform-based overall satisfaction and loyalty,and that perceived product quality,perceived assurance,and perceived price fairness all have a significant effect on economic satisfaction,whereas perceived relationship quality and perceived empathy significantly influence social satisfaction.These findings help us understand the literature related to customer satisfaction in the context of C2C in China and provide inspiration for online sellers and platforms.

Monitoring of Air Quality for Particulate Matter (PM2.5, PM10) and Heavy Metals Proximate to a Cement Factory in Ewekoro, Nigeria

A cement factory nearby communities raise pollution concerns. This study assessed air pollution levels for respirable particulate matter (PM2.5 and PM10) and heavy metals (lead, chromium, nickel, cadmium, zinc and copper) adjacent to a cement factory in Ewekoro and neighbouring communities (Papalantoro, Lapeleko and Itori) in Ogun State, Nigeria. Respirable particulate matter (PM2.5 and PM10) and heavy metals were measured using an ARA N-FRM cassette sampler. Each location sampled was monitored for eight continuous hours daily for 12 days. The PM2.5, PM10 and heavy metals results were compared with different standards, including those of the World Health Organization (WHO), Nigeria’s National Environmental Standard and Regulation Enforcement Agency (NESREA) and Canadian Ambient Air Quality Standards (CAAQS). The PM levels fell within 11 - 19 μg/m3 of the air management level of CAAQS, which signifies continuous actions are needed to improve air quality in the areas monitored but below the NESREA standard. The mean Cd, Cr and Ni concentrations in the cement factory area and the impacted neighbourhoods are higher than the WHO/EU permissible limits, while Zn and Cu were below the WHO/EU permissible limit. A risk assessment hazard quotient (HQ) for Cr was above the WHO/EU safe level (=1) in adults and children throµgh ingestion, inhalation and dermal contact at all the monitoring sites. The HQ for Ni and Cd was higher than the safe level in the cement factory area and Papalantoro, while Zn was at safe levels.

Hybrid evolutionary algorithm for no-wait flow shops to minimize makespan and total flowtime

The NP-hard no-wait flow shop scheduling problems with makespan and total flowtime minimization are considered. Objective increment properties of the problems are analyzed. A non-dominated classification method is introduced to class population individuals into Pareto fronts to improve searching efficiency. Besides investigating the crowding distance and the elitist solution strategy, two effective bi-criteria local search procedures based on objective increments are presented to improve searching effectiveness. Based on the properties and methods, a hybrid evolutionary algorithm is proposed for the considered problems and compared with the best existing algorithms. Experimental results show that the proposed algorithm is effective with high efficiency.

Research on the Landscape Utilization and Renovation of Industrial Wasteland in Panzhihua Steel-casting Factory

Renovation design of wasteland on the original Quarter A of Panzhihua Nongnongping Steelcasting Factory was taken for example in this study, natural conditions and biological landscapes in the study area were investigated. Principles and theories of the renovation design were introduced, it was proposed that protection and landscape renovation of industrial heritage had to be insisted to make regular and unique overall spatial layouts. On this basis, design approaches for the wasteland landscape renovation of the steel-casting factory were proposed as "maintaining original images, realizing the functional substitution; optimizing spatial structure; updating seriously-damaged industrial facilities with insignificant functionality; improving and recovering landscape soil; recovering and reconstructing vegetation".

Infeasibility test algorithm and fast repair algorithm of job shop scheduling problem

To diagnose the feasibility of the solution of a job-shop scheduling problem（JSSP）,a test algorithm based on diagraph and heuristic search is developed and verified through a case study.Meanwhile,a new repair algorithm for modifying an infeasible solution of the JSSP to become a feasible solution is proposed for the general JSSP.The computational complexity of the test algorithm and the repair algorithm is both O（n） under the worst-case scenario,and O（2J＋M） for the repair algorithm under the best-case scenario.The repair algorithm is not limited to specific optimization methods,such as local tabu search,genetic algorithms and shifting bottleneck procedures for job shop scheduling,but applicable to generic infeasible solutions for the JSSP to achieve feasibility.

Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems

The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

China's E-commerce Operators Ready for Double 11 Shopping Festival

With one month until November 11,all major e-commerce platforms have started their preparations for this great event.The"Double 11"shopping festival was first initiated by Alibaba in 2009and is the world's largest online sales gala.This shopping festival is thus named due to the date of November11 and has extended from the original24 hours to several weeks in recent years.The pre-sales stage start from late October.Some new e-commerce companies like TikTok and Pinduoduo have also been involved in the event.

Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

A Windy and Rainy Shanghai

On Suzhou Road in Shanghai,there stood a mercer’s shop.Its shopkeeper was surnamed Yu,but the shop was named Wu’s Silk Shop instead of Yu’s Silk Shop.Yu Jide,the shopkeeper,was born in the town of Yushan,Songjiang Prefecture(a former administrative area of today’s Shanghai).

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems

Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

Microfactory for filter bags developed in Sweden

After an intensive few years of development,ACG Kinna Automatic and ACG Nystrom–members of TMAS,the Swedish textile machinery association–have delivered the first microfactory for the production of fully finished filter bags to a major international filtration industry customer,in cooperation with JUKI Central Europe.

Deep Reinforcement Learning Solves Job-shop Scheduling Problems

To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.

Study on the Genetic and Physiological Toxicity of Wastewater from a Pharmaceutical Factory Using Root Tip Micronucleus Technology of Vicia faba

ObjectiveThe aim was to assess genetic and physiological toxicity of wastewater from a pharmaceutical factory using root tip micronucleus technology of Vicia faba. MethodThe pollution of wastewater from a pharmaceutical factory was detected by using root tip micronucleus technology of Vicia faba, and the genetic and physiological toxicity of the wastewater to Vicia faba was assessed. ResultNon-processed wastewater had an extremely high level of biological toxicity; the cells were unable to live with the wastewater at a high concentration; the cells were able to grow with the wastewater at a low concentration, though the micronucleus ratio was extremely high. The processed wastewater had no significant impact on cell growth, but the micronucleus ratio was extremely high, showing that the processed water also had a high pollution index. ConclusionThe research could provide scientific references for the national treatment of wastewater from a pharmaceutical factory.

The Multi-Agent Modeling and Calculated Analysis of Shop Logistics Scheduling in the Manufacturing Enterprises

In this paper,the multi-agent model about shop logistics is set up. This model has 8 agents: raw materials stock agent,process agent,testing agent,transition agent,production information agent,scheduling agent,process agent and stock agent. The scheduling agent has three subagents: manager agent (MA),resource agent (RA) and part agent (PA). MA,PA and RA are communicating equally that guarantees agility of the whole MAS system. The part tasks pass between MA,RA and PA as an integer,which can guarantee the consistency of the data. We use a detailed example about shop logistics scheduling in a semiconductor company to explain the principle. In this example,we use two scheduling strategies: FCFS and SPT. The result data indicates that the average flow time and lingering ratio are changed using different strategy. It is proves that the multi-agent scheduling is useful.

NEW NONSTANDARD JOB SHOP SCHEDULING ALGORITHM

Considering the complex constraint between operations in nonstandard job shop scheduling problem （NJSSP）, critical path of job manufacturing tree is determined according to priority scheduling function constructed. Operations are divided into dependent operations and independent operations with the idea of subsection, and corresponding scheduling strategy is put forward according to operation characteristic in the segment and the complementarities of identical function machines. Forward greedy rule is adopted mainly for dependent operations to make operations arranged in the right position of machine selected, then each operation can be processed as early as possible, and the total processing time of job can be shortened as much as possible. For independent operations optimum scheduling rule is adopted mainly, the inserting position of operations will be determined according to the gap that the processing time of operations is subtracted from idle time of machine, and the operation will be inserted in the position with minimal gap. Experiments show, under the same conditions, the result that operations are scheduled according to the object function constructed, and the scheduling strategy adopted is better than the result that operations are scheduled according to efficiency scheduling algorithm.

Food safety knowledge and practices of abattoir and butchery shops and the microbial profile of meat in Mekelle City,Ethiopia

Objective:To assess the food safety knowledge and practices in meat handling,and to determine microbial load and pathogenic organisms in meat at Mekelle city.Methods:A descriptive survey design was used to answer questions concerning the current status of food hygiene and sanitation practiced in the abattoir and butcher shops.Workers from the abattoir and butcher shops were interviewed through a structured questionnaire to assess their food safety knowledge.Bacterial load was assessed by serial dilution method and the major bacterial pathogens were isolated by using standard procedures.Results:15.1%of the abattoir workers had no health certificate and there was no hot water,sterilizer and cooling facility in the abattoir.11.3%of the butchers didn’t use protective clothes.There was a food safety knowledge gap within the abattoir and butcher shop workers.The mean values of bacterial load of abattoir meat,butcher shops and street meat sale was found to be 1.1×10~5,5.6×10~5and 4.3×10~6 cfu/g,respectively.The major bacterial pathogens isolated were Escherichia coli,Staphylococcus aureus and Bacillus cereus.Conclusions:The study revealed that there is a reasonable gap on food safely knowledge by abattoir and butcher shop workers.The microbial profile was also higher compared to standards set by World Health Organization.Due attention should be given by the government to improve the food safety knowledge and the quality standard of meat sold in the city.