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Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

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.

Due to the influence of complex and intersecting factors, self-governed public open spaces (POSs) (managed by local communities) are subject to collective action dilemmas such as tragedy of the commons (overexploitation), free-riding, underinvestment and mismanagement. This review paper adopts a multi-dimensional and multi-tier social-ecological system (SES) framework proposed by McGinnis and Ostrom, drawing on collective action theory to explore the key institutional-social-ecological factors that impact POS self-governance.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was utilized to systematically screen and review the relevant literature for the period from 2000 to 2023 in three databases: Web of Science, Scopus and Google Scholar. A total of 57 papers were chosen for in-depth analysis.

The literature review identified and categorized several variables associated with the self-organizing system of POS; consequently, an SES-based POS management framework was developed for the first time, consisting of 114 institutional-social-ecological sub-variables from different dimensions and three levels. Compared to ecological factors, among others, governance organizations, property-rights systems, socioeconomic attributes and actors' knowledge of SES have been commonly and primarily studied.

There is still room for the refinement of the conceptual SES-based POS collective action framework over the time (by adding in new factors), and indefinitely empirical research validating those identified factors is also worth to be undertaken, particularly testing how SES factors and interaction variables affect the POS quality (collective action).

The findings of this study can provide local policy insights and POS management strategies based on the identification of specific SES factors for relevant managers. Moreover, this research makes significant theoretical contributions to the integration of the SES framework and collective action theory with POS governance studies.

First-generation college students in the United States are a unique demographic that is often characterized by the institutions that serve them with a risk-laden and deficit-based model. However, our analysis of the transcripts of open-ended, semi-structured interviews with 22 “first-gen” respondents suggests they are actively deft, agentic, self-determining parties to processes of identity construction that are both externally imposed and potentially stigmatizing, as well as exemplars of survivance and determination. We deploy a grounded theory approach to an open-coding process, modeled after the extended case method, while viewing our data through a novel synthesis of the dual theoretical lenses of structural and radical/structural symbolic interactionism and intersectional/standpoint feminist traditions, in order to reveal the complex, unfolding, active strategies students used to make sense of their obstacles, successes, co-created identities, and distinctive institutional encounters. We find that contrary to the dictates of prevailing paradigms, identity-building among first-gens is an incremental and bidirectional process through which students actively perceive and engage existing power structures to persist and even thrive amid incredibly trying, challenging, distressing, and even traumatic circumstances. Our findings suggest that successful institutional interventional strategies designed to serve this functionally unique student population (and particularly those tailored to the COVID-moment) would do well to listen deeply to their voices, consider the secondary consequences of “protectionary” policies as potentially more harmful than helpful, and fundamentally, to reexamine the presumption that such students present just institutional risk and vulnerability, but also present a valuable addition to university environments, due to the unique perspective and broader scale of vision their experiences afford them.

Racial stigma and racial criminalization have been centralizing pillars of the construction of Blackness in the United States. Taking such systemic injustice and racism as a given, then question then becomes how these macro-level arrangements are reflected in micro-level processes. This work uses radical interactionism and stigma theory to explore the potential implications for racialized identity construction and the development of “criminalized subjectivity” among Black undergraduate students at a predominately white university in the Midwest. I use semistructured interviews to explore the implications of racial stigma and criminalization on micro-level identity construction and how understandings of these issues can change across space and over the course of one's life. Findings demonstrate that Black university students are keenly aware of this particular stigma and its consequences in increasingly complex ways from the time they are school-aged children. They were aware of this stigma as a social fact but did not internalize it as a true reflection of themselves; said internalization was thwarted through strong self-concept and racial socialization. This increasingly complex awareness is also informed by an intersectional lens for some interviewees. I argue not only that the concept of stigma must be explicitly placed within these larger systems but also that understanding and identity-building are both rooted in ever-evolving processes of interaction and meaning-making. This research contributes to scholarship that applies a critical lens to Goffmanian stigma rooted in Black sociology and criminology and from the perspectives of the stigmatized themselves.

The purpose of this study is to determine the competency characteristics required for the employment of master’s degree students in educational technology.

A combined qualitative and quantitative method was used to consult multiple experts through a modified Delphi method. Competency characteristics were extracted from Chinese recruitment apps, national recruitment websites and university training programs. Ten senior teacher experts who teach educational technology master’s students were consulted through a questionnaire consultation to validate the proposed competency model. The weights of competency characteristics were determined through a combination of the analytic hierarchy process and entropy method.

The results show that when recruiting educational technology master’s students, more emphasis is placed on operational skills. The majority of companies tend to assess practical abilities rather than theoretical knowledge. Relevant knowledge of educational technology, psychology, computer science and education is considered to be the basic knowledge components of educational technology master’s students, while professional skills are the core skills required for their positions. Therefore, universities need to focus on training, educational technology graduate students in these areas of competence. The study also found that professional qualities (such as physical and mental fitness) and personality traits (interpersonal communication and interaction) receive more attention from companies and are essential competencies for educational technology master’s students.

A competence model for educational technology master’s students is proposed, which includes aspects such as knowledge, personal skills/abilities, professional qualities and personality traits. The competence elements included in this model can serve as reference indicators for universities to cultivate the competence of educational technology master’s students, as well as reference points for recruiting units to help them select talents. This represents a new dimension in research related to the employment of educational technology master’s students. The study enriches the research objects and competence dictionary in the field of competence research.

This performance autoethnography may or may not be interpreted as a continuation of a conversation regarding the experiences of those with devalued statuses in academic settings. The authors rely on “strange accounting” to consider their experiences in the academy from various standpoints: before and after promotion, before and after leaving academia. While reflecting on our past experiences, we introduce the concept of “everyday precariousness” as a way of explaining the normalization of instability, insecurity, and negative affect that is part of everyday life for those with devalued statuses in academic settings and beyond. Everyday precariousness is an embodied experience for those in vulnerable positions. Normalized exposure to risks, such as discrimination, harassment, bullying, or structural instability, produces an undercurrent of threat that permeates academic culture. Our stories of everyday precariousness span race, ethnicity, class, academic roles, and gender boundaries (among many others). Analyzing these experiences furthers previous work on the uses of strange accounting as well as the dynamics of status silencing. In the final analysis, unresisted and unabated, everyday precariousness and status silencing can lead to institutional failure and resonance disasters.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

This study is the maiden attempt to understand the effect of specific human resource practices (HRPs) on employee retention (ER) with the mediation of job satisfaction (JS) and moderation of work experience (WE) and job hopping (JH) in the context of the textile industry.

This study adopted a quantitative methodology and applied quota sampling to gather data from employees (n = 365) of leading textile companies in India. The conceptual model and hypotheses were tested with the help of Partial Least Squares-Structural Equation Modelling (PLS-SEM).

The findings of a path analysis revealed that compensation and performance appraisal (CPA) have the highest impact on JS followed by employee work participation (EWP). On the other hand, EWP had the highest impact on ER followed by grievance handling (GRH). The study revealed that JS significantly mediates between HRPs like CPA and ER. During Multi-group analysis (MGA) it was found that the importance of EWP and health and safety (HAS) was more in employee groups with higher WE, but it was the opposite in the case of CPA. In the case of JH behavior, the study observed that EWP leads to JS in loyal employees. Similarly, JS led to ER, and the effect was more pronounced for loyal employees.

In the context of the Indian textile industry, this work is the first attempt to comprehend how HRPs affect ER. Secondly, it confirmed that JS is not a guaranteed mediator between HRPs and ER, it could act as an insignificant, partial or full mediator. Additionally, this study establishes the moderating effects of WE and JH in the model through multigroup analysis.