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By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Irrespective of the importance of collective job crafting for team performance, its antecedents have not been fully comprehended. Drawing upon social cognitive theory, this study proposes that sales managers’ charismatic leadership interacts with collective proactive personality in predicting collective job crafting, which in turn influences sales teams’ customer relationship performance and financial performance.

Data were collected from 481 sales employees and 64 sales managers from 64 sales departments of tour companies. These multi-source data were analyzed through structural equation modeling.

The findings revealed that charismatic leadership was positively associated with sales teams’ collective job crafting, which was in turn positively related to sales teams’ customer relationship performance and financial performance. Collective proactive personality negatively moderated the impact of charismatic leadership on collective job crafting.

This study advances the extant knowledge by identifying the role of collective job crafting in translating charismatic leadership into sales teams’ performance.

This study aims to gain a deeper understanding of the industry practice to guide the formation of support tools with a rigorous theoretical backing. Cross-functional teams are an essential component in new product development (NPD) of complex products to promote comprehensive coverage of product design, marketing, sales, support as well as many other activities of business. Efficient use of teams can allow for greater technical competency coverage, increased creativity, reduced development times and greater consideration of ideas from a variety of stakeholders. While academics continually aspire to propose methods for improved team composition, there exists a gap between research directions and applications found within industry practice.

Through interviewing product development managers working across a variety of industries, this paper investigates the common practices of team utilization in an organizational setting. Following these interviews, this paper proposes a conceptual two-dimensional management support model aggregating the primary drivers of team success and providing direction to systematically address features of team management and composition.

Based on this work, product managers are recommended to continually address the positioning of members throughout the entire NPD process. In the early stages, individuals are to be placed to work on project components with explicit consideration toward the perceived complexity of tasks and individual competency. Throughout the development process, individuals’ positions vary based on new information while continued emphasis is placed on maintaining a shared understanding.

Bridging the gap between theory and application within product development teams is a necessary step toward improved product develop. Industrial settings require practical solutions that can be applied economically and efficiently within their organization. Theoretical reflections postulated by academia support improved team design; however, to achieve true success, they must be applicable when considering product development.

The study discusses the professionalization of academic leadership in Israel by analyzing and comparing two different training programs: the Hebrew University of Jerusalem’s (HUJI) program and the CHE-Rothschild program. The HUJI program began in 2016 to train the professoriate to take charge of leadership positions alongside a separate program for administrative staff, while the CHE-Rothschild program was launched in 2019 to train academic leaders, both professors and administrators from universities and colleges nationwide. The analysis reveals two “ideal types” of collegiality: While Model A (exemplified by the HUJI program) bifurcates between the professoriate and administrative staff, Model B (exemplified by the CHE-Rothschild program) binds administrative and academic staff members through course composition, pedagogy, and content. The study suggests a pattern of redefinition of collegiality in academia: we find that while academic hierarchies are maintained (between academic faculty and administrative staff and between universities and colleges), collegiality in academia is being redefined as extending beyond the boundaries of the professoriate and emphasizing a partnership approach to collegial ties.

This paper aims to examine how neighborhood characteristics (income, population composition) and individual building attributes (ownership) affect the recovery period of single-family housing and determine their correlations with property abandonment and changes in residential land use after natural disaster.

This empirical study focuses on Valley Fire, one of the California’s most destructive wildfires in 2015, and uses assessor, community, demographic and sales data to measure recovery of a panel of single-family houses located in Lake County in California between 2012 and 2020. Several regression and correlation models will be developed to test different hypotheses.

This study found that: Recovery period is longer than what expected in most existing literature; ownership status significantly affects recovery period; income level is not a significant factor for shortening the recovery period; and minorities may need more assistance for constant recovery. Findings of this research will help identify at risk communities to avoid uneven housing recovery and lower the rate of property abandonment.

Housing recovery is key to revitalizing communities following major natural disasters. The sociodemographic characteristics of each neighborhood have significant impact on the duration of recovery and possible property abandonment. Understanding how home and neighborhood characteristics affect recovery will help planners prevent long-lasting adverse effects of natural disasters.

Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.

First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.

During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.

In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

Poland generally has a homogeneous society, conservative towards changes and diversity. The corporate culture in Polish companies reflects this mindset, leading to a lack of inclusion on the corporate board. Additionally, many companies may not fully understand the benefits of an inclusive workplace and legal requirements for gender diversity.

The main objective of the study is to provide a better understanding of the attitude of Polish companies towards diversity policies and reveal differences in actual and expected levels of gender diversity in corporate boards. Thus, we examine compliance with the gender diversity guidelines in the corporate governance code.

Using a sample of 367 Polish companies listed on the Warsaw Stock Exchange, we study the composition of the management and supervisory boards to check if they meet the expected gender diversity criteria. We also look at companies' explanations for non-compliance with the main principles regarding diversity policy.

We find that the current composition of corporate boards of stock companies in Poland is male-dominated. Women represent only 12.72% and 17.12% of the management board and supervisory board members, respectively, and 68.94% (42.23%) of companies have no women on their management (supervisory) board. Moreover, only a small percentage of companies comply with the principles related to gender diversity. Qualifications, experience and education are pointed out as the most important criteria for decision-making on board appointments, with only 2% of companies applying gender as an additional criterion. The study suggests that larger companies are more likely to implement diversity policies.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

The purpose of this paper is to perform Shariah review of Brownian motion that is used for prediction of Islamic stock prices and their volatility.

It uses the Shariah compliant development model guidelines to review the Brownian motion and its applications.

The model of Brownian motion does not involve any variable that renders it non-Shariah compliant; neither all applications of Brownian motion are Shariah compliant. Because the model is based on stochastic properties that involve randomness, therefore the issue of gharar takes the utmost important to handle in the applications of the model. The results need to be analyzed strictly in accordance with the Shariah whether they create any element of gharar or uncertainty in case of expected price and volatility estimates.

The research suffers from the limitation that it analyses only one model of physics, i.e. Brownian motion model from Shariah perspective.

The research opens an area for Shariah analysis of results generated from the application of advanced models of physics on matters related to Islamic financial markets.

The originality of this study stems from the fact that to the best of the authors’ knowledge, it is the first study that extends Shariah guidelines into Financial physics for making the foundations of Islamic econophysics.

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.