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The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

This research paper aims to explore asymmetric market efficiency of the 13 Euro countries, i.e. Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Netherland, Portugal, Slovakia, Slovenia and Spain, concerning the period before global financial crisis (GFC), after GFC and period of COVID-19 pandemic.

Multifractal detrended fluctuation analysis (MF-DFA) is applied to examine the persistence and anti-persistency. It also discusses the random walk behavior hypothesis of these 13 countries non-stationary time series. Additionally, generalized Hurst exponents are applied to estimate the relative efficiency between short- and long-run horizons and small and large fluctuations.

The current study results suggest that most countries' markets are multifractal and exhibit long-term persistence in the short and long run. Moreover, the results with respect to full sample confirm that Portugal is the most efficient country in short run and Austria is the least efficient country. However, in long run, Austria appeared to be highly efficient, and Slovakia is the least efficient. In the pre-GFC period, Greece is said to be the relatively most efficient market in the short run, whereas Austria is the most efficient market in the long run. In the case of Post-GFC, Netherland and Ireland are the most efficient markets in short and long run, respectively. Lastly, COVID-19 results indicate that Finland's stock market is the most efficient in short run. Whereas, in the long run, the high efficiency is illustrated by Germany. In contrast, the most affected stock market due to COVID-19 is Belgium.

This study will add value to the present knowledge on efficient market hypothesis (EMH) with the MF-DFA approach. Also, with the MF-DFA approach, potential investors will be capable of ranking the stock markets of Eurozone countries based on their efficiency in the period before and after GFC and then specifically in the period of COVID-19.

本研究旨在探討13個歐元區國家在環球金融危機前後, 以及2019新型冠狀病毒病肆虐時期之不對稱市場效率; 這13個國家包括: 奧地利、比利時、芬蘭、法國、德國、希臘、愛爾蘭、義大利、荷蘭、葡萄牙、斯洛伐克、斯洛維尼亞和西班牙。

研究人員使用多重分形去趨勢波動分析法、來探討持續性與反持續性。這分析法也用來討論正在研究中的13個國家的非平穩時間序列的隨機漫步假說; 而且, 廣義赫斯特指數被用來估算長期/短期投資與大/小波動之間的相對效率。

研究結果間接表明了大部份國家的市場都是多重分形的; 而且, 它們無論以短期抑或以長期來審視觀察, 均能展示持久性。再者, 就整體樣本而言, 研究結果確認了在短期來看, 葡萄牙是效率最高的國家, 而奧地利則效率最低。唯以長期來審視觀察, 奧地利則似乎效率很高, 而效率最低的則是斯洛伐克。在環球金融危機爆發前, 就短期而言, 希臘被認為是相對效率最高的市場, 而長期而言, 效率最高的則是奧地利。至於在環球金融危機爆發後, 就短期而言, 荷蘭是效率最高的市場, 而就長期而言, 效率最高的則是愛爾蘭。最後, 2019新型冠狀病毒病的結果顯示, 就短期而言, 荷蘭的股票市場是效率最高的, 而長期而言, 德國則展示了其高效率性。而受疫情影響最大的股票市場則是比利時。

研究採用了多重分形去趨勢波動分析法、來探討股票市場的效率, 並以此分析法來討論有關國家的非平穩時間序列的隨機漫步假說, 這使我們對效率市場假說有進一步的認識; 就此而言, 本研究為有關的探討增添價值; 而且, 有意投資者在使用多重分形去趨勢波動分析法下, 能夠基於歐元區國家的股票市場在環球金融危機前後, 以及更明確地在2019新型冠狀病毒病肆虐時期的效率, 來把這些股票市場分等級。

環球金融危機、2019新型冠狀病毒病、效率市場假說、多重分形去趨勢波動分析.

Facing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.

Based on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.

Using the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.

With the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.

This study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.

Based on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.

The findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.

The study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.

The aim of this research is to assess the influence of the underlying service quality variable, usually related to university students' perception of the educational experience. Another aspect analysed in this work is the development of a procedure to determine which variables are more significant to assess students' satisfaction.

In order to achieve both goals, a twofold methodology was approached. In the first phase of research, an assessment of the service quality was performed with data gathered from 580 students in a process involving the adaptation of the SERVQUAL scale through a multi-objective optimization methodology. In the second phase of research, results obtained from students were compared with those obtained from the teaching staff at the university.

Results from the analysis revealed the most significant service quality dimensions from the students' viewpoint according to the scores that they provided. Comparison of the results with the teaching staff showed noticeable differences when assessing academic quality.

Significant conclusions can be drawn from the theoretical review of the empirical evidences obtained through this study helping with the practical design and implementation of quality strategies in higher education especially in regard to university education.

The purpose is to explore how the configurations resulting from the interplay of last mile logistics practices and firm characteristics are associated with firm performance in an omni-channel context.

Drawing on configuration theory (CT), the authors use fuzzy-set qualitative comparative analysis (fsQCA) to analyze data on 72 Swedish omni-channel retailers.

Four configurations are identified—store-oriented small and medium-sized enterprises (SME's), online-oriented SME's, large store-oriented retailers and large online-oriented retailers. The results show that while offering a wide range of delivery options is necessary to achieve high performance, it is not sufficient, and that returns and fulfilment should be simultaneously considered. For instance, large high-performers leverage their stores and warehouses for fulfilment and returns in an integrated way irrespective of sales channel-mix. However, SME's appear to focus on fulfilment simplicity with less-costly delivery alternatives, where store-oriented SME's leverage stores and the online-oriented counterparts leverage warehouses. Consequently, the authors develop a configurational taxonomy and discuss a set of recipes which provide insights for researchers and practitioners.

The study provides a more comprehensive understanding of the pathways to success, and potential pitfalls, in the last mile logistics context.

This study applies a novel methodology in the field, namely fsQCA, to explore the paths to competitive advantage. It covers a wide range of stages in the LM including back-end fulfilment, delivery and returns. It also provides insight into the logistics practices of both SME's and large omni-channel retailers.

This study examines the relationship between partners' locus of control and their spouses' domains of job satisfaction (job satisfaction and its domains, personal income and promotion) among Australian couples.

Data were obtained from the Household, Income and Labor Dynamics of Australia (HILDA) Survey. Various estimation strategies including ordinary least squares (OLS), Mundlak approach and instrumental variable (IV) method are used to reveal the relationship between spouse's locus of control and domains of job satisfaction.

To reduce sex heterogeneity, the analysis used in this study is disaggregated by sex. In particular, the findings of this study show that wives' locus of control positively influences husbands' satisfaction with pay and working hours, while there is no relationship between husbands' locus of control and wives' domains of job satisfaction.

The study's findings emphasize the importance of locus of control in couples. A good work–life balance and a healthy marital relationship potentially facilitate positive effects of characteristics from the partner on employees' job satisfaction. Thus, on the organizational level, employers may consider creating a working environment that promotes a healthy marital relationship for their staff, including flexible working schedules, work from home options, family days or family-extended staff events.

This study is the first to reveal the relationship between spousal locus of control and domains of job satisfaction, enriching the current literature on this topic.

Entrepreneurship education (EE) is critical for developing the skills of tomorrow's entrepreneurs and leaders. While significant research examines the content, student learning processes and outcomes, less studied are the entrepreneurship educators and their pedagogical preferences. Following a cognitive process model of decision-making, this study explores how self-efficacy, philosophy of teaching, entrepreneurship training and teaching experience influence entrepreneurship educator preferences to follow either a teacher-centric or a student-centric approach. This study also includes gender in a secondary analysis of the relationships.

The data were collected from 289 entrepreneurship educators in 2021, and fuzzy-set comparative qualitative analysis (fsQCA) was used to obtain configurations of conditions (causal recipes) that lead to teacher-centric or student-centric model. A secondary analysis explores whether there are different configurations of conditions when gender is added to the analysis.

The results of our fsQCA analysis reveal multiple configurations of conditions (causal recipes) that result in a preference for either a teacher-centric or student-centric approach to teaching entrepreneurship. The authors find that teaching experience is the main condition for the teacher-centric model, while self-efficacy and entrepreneurship training are the main conditions for the pathways leading to student-centric model. The fsQCA results also show that the configurations are affected when gender is taken into account in the analysis.

This study, one of the first of its kind, uses a configurational approach to examine pathways that contribute to the teaching preferences of entrepreneurship educators. This paper uses self-efficacy, teaching philosophy, teaching experience and entrepreneurship training as conditions to identify multiple unique pathways that result in either a teacher-centric or student-centric pedagogical model in EE. Notably, differences by gender are also found in this study.

The COVID-19 pandemic and recent disruptive events are affecting firms’ operations and supply chain networks on a large scale, causing disturbances in supply, demand, production and logistics activities. Although supply chain resilience (SCR) research has received large attention in recent years, the purpose of this paper is to offer an original contribution by exploring how complex configurations and interactions between SCR strategies and capacities can lead to resilience.

This study investigates the configurations of SCR strategies and capacities using a fuzzy-set qualitative comparative analysis.

First, the findings reveal different SCR strategy configurations through the lens of absorptive, reactive and restorative capacities to achieve resilience. Second, this study applies the contingent resource-based view (CRBV) perspective to interpret how organizations can achieve resilience before, during and after a disruptive event. Third, it offers an analysis of different groups of organizations, based on the adoption of different SCR strategies and capacities.

This study identifies a set of equifinal SCR strategies and capacity configurations that can be implemented to cope with a disruptive event and lead to resilience. It also enriches the research addressing the consecutive phases of SCR investments, developing the CRBV perspective. In our results, five solutions describe organizations that invest in absorptive capacities, representing an ex ante readiness.