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The purpose of this paper is to examine Asian Americans' perceptions of the police, specifically how they construct support. Although such literature has been growing in recent years, research on Asian American interactions with the police remains limited. Additionally, this paper is situated within the theoretical framework of system justification theory to account for Asian Americans' views of the police.

This study relies on interview data collected from 20 Asian Americans residing in mid-Atlantic states. Participants were either recruited directly by the researchers or through the snowball-sampling method.

Police support is influenced by perception of neighborhood safety, personal police contact and empathetic feelings toward the police. Specifically, regarding the latter component, humanizing or empathizing with police officers is a form of rationalizing individual police misconduct that reinforced police legitimacy. Most participants had similar characteristics and displayed police justification. Additional research is needed regarding what characteristics or patterns are likely to lead to lower levels of police justification.

This article's findings improve our understanding of system justification among Asian Americans, particularly as it relates to policing.

This study aims to develop a conceptual model to understand how customer knowledge management (CKM) affects fitness club membership renewal through the mediation of relationship quality.

Data were collected outside of fitness clubs using a systematic sampling method. A total of 224 valid responses were collected. Structural equation modelling was used to evaluate the relationship between the constructs of the research model.

The results indicate that both knowledge from customers and knowledge for customers have a positive influence on customer satisfaction and customer trust. Among them, knowledge for customers has a stronger influence on customer satisfaction while knowledge from customers has a greater influence on customer trust. Additionally, three dimensions of relationship quality (customer satisfaction, customer trust and customer commitment) positively influence membership renewal intention with customer commitment exhibiting the greatest influence on it.

This study combines the theories of CKM and relationship quality management to explain why members will renew their service contracts. By using fitness clubs as an example, this research extends the authors' understanding of how knowledge from and for customers can influence customers' attitudes and behavioural intentions towards service companies.

While travelling, tourists like to use mobile technology to share their travel experiences. This study aims to understand how the social value gained by tourists from sharing a travel experience with mobile technology affects their satisfaction with the travel experience through onsite mobile sharing behaviour.

A second-order hierarchical model is constructed to examine the moderated mediating role of onsite mobile sharing behaviour in improving tourists’ travel satisfaction. Through systematic sampling, 304 responses were collected at ten attraction points in Guangzhou and Shenzhen, China.

The results show that, compared with self-centred values (self-presentation and self-identification), other-centred values (building social connection and reciprocity) contribute more to forming social values of sharing. In addition, onsite mobile sharing behaviour partially mediates and moderates the effect of social values on travel satisfaction.

This study applies the social capital theory to identify the value gained by sharing travel experiences and empirically evaluates the impact of these values on the overall value of sharing travel experiences. This study also contributes to tourism research by examining the moderated mediating role of onsite mobile sharing behaviour in improving travel satisfaction. This study helps destination marketing to make strategies to motivate tourists to use mobile technology to share their travel experiences while travelling.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

The role of corporate social responsibility (CSR) fulfillment is critical when building resilience of project-based organizations (PBOs). However, fulfilling CSR to build a highly resilient PBO remains a black box problem. This study explores the different CSR combinations that enhance PBO resilience.

This study defines CSR in terms of shareholder, employee, and social CSR, and analyzes corporate characteristics in terms of corporate scale and nature. Data are collected from Hexun.com and the China Stock Market and Accounting Research Database (CSMAR). The qualitative comparative analysis (QCA) method is used to analyze 48 listed construction and engineering companies from China to explore the CSR configurations for PBOs in enhancing organizational resilience.

A large firm size is a necessary condition for high organizational resilience. We find six paths to build high and non-high resilience in PBOs, and the driving mechanisms of high and non-high resilience exhibit an asymmetric relationship.

This study cracks the black box of CSR fulfillment and PBO resilience. It reveals the CSR configurations that enhance or inhibit the resilience of PBOs. It also provides scientific basis for PBOs in their fulfillment of CSR in response to crises, and the enhancement of organizational resilience. Future research can be expanded to other industries, as the study sample is only limited to civil engineering construction companies. Since this study uses cross-sectional data, time series can be introduced in the future to further explore the relationship between CSR and organizational resilience.

This study provides targeted suggestions that can help decision-makers of construction companies to determine how they can fulfill CSR to enhance organizational resilience. At the same time, it can provide intellectual support for PBOs to cope with systemic crises and promote the fulfillment of CSR.

In terms of theoretical value, on the one hand, this study verifies the relationship between CSR fulfillment and PBO resilience, revealing its mechanism of action and multiple paths; on the other hand, it provides a new way of thinking for management research methods and enriches the theoretical study of organizational resilience.

This research is aimed to mainly be applicable to expediting engineering projects, uses the method of inverse optimization and the double-layer nested genetic algorithm combined with nonlinear programming algorithm, study how to schedule the number of labor in each process at the minimum cost to achieve an extremely short construction period goal.

The method of inverse optimization is mainly used in this study. In the first phase, establish a positive optimization model, according to the existing labor constraints, aiming at the shortest construction period. In the second phase, under the condition that the expected shortest construction period is known, on the basis of the positive optimization model, the inverse optimization method is used to establish the inverse optimization model aiming at the minimum change of the number of workers, and finally the optimal labor allocation scheme that meets the conditions is obtained. Finally, use algorithm to solve and prove with a case.

The case study shows that this method can effectively achieve the extremely short duration goal of the engineering project at the minimum cost, and provide the basis for the decision-making of the engineering project.

The contribution of this paper to the existing knowledge is to carry out a preliminary study on the relatively blank field of the current engineering project with a very short construction period, and provide a path for the vast number of engineering projects with strict requirements on the construction period to achieve a very short construction period, and apply the inverse optimization method to the engineering field. Furthermore, a double-nested genetic algorithm and nonlinear programming algorithm are designed. It can effectively solve various optimization problems.