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An infrastructure based on multiple heterogeneous access networks is one of the leading enablers for the emerging paradigm of pervasive computing. The optimal management of diverse networking resources is a challenging problem. This paper aims to present a context‐aware policy mechanism with related end‐to‐end (E2E) evaluation algorithm for adaptive connectivity management in multi‐access wireless networks.

A policy is used to express the criteria for adaptive selection of the best local and remote network interfaces. The best connection can then be used for the establishment of a channel as well as for the maintenance of on‐going data transmission. Rich context information is considered in the policy representation with respect to user profile and preference, application characteristics, device capability, and network quality of service conditions. The decision of the best access networks to be used is made on the basis of an E2E evaluation process. The decision can be made in both master–slave and peer‐to‐peer modes, according to the decision matrixes generated in both ends. The paper focuses on the policy representation and connection evaluation algorithm. A case study is presented to show the usability of the proposed policy mechanism and decision‐making algorithm in the adaptiv management of heterogeneous networking resources.

The proposed policy mechanism is for the adaptive decision of connection selection in channel establishment and vertical handoff between heterogeneous access networks. A policy is represented as a four‐tuple, including the direction and the class of traffic, requirement expression, and concrete evaluation items. Three steps are involved in the evaluation process, namely policy traverse, decision matrix calculation, and decision‐making.

The policy mechanism can be easily extended to include adaptive selection of multiple user devices in addition to multiple connections.

The purpose of this paper is to explore the effects of three managerial ideologies on the degree of employment contract breach perceived in connection with a downsizing.

Surveys were used to collect data from southwest China. Multiple regression analyses were conducted to explore the impact of three managerial ideologies on the perceived employment contract breach in connection with downsizing.

Results suggest that a strong belief in the ideology of market competition reduces an individual's perception that downsizing constitutes a breach of the employment contract between employer and employee. By contrast, a belief in employee worth has the opposite effect, strengthening the believer's perception that downsizing constitutes an employment contract breach. Belief in the third ideology, the ideology of shareholder interest, appears to have no influence on whether respondents perceived downsizing as an employment contract breach.

The results are important for understanding the way employees interpret common business practices like downsizing. Given the accumulation of enough confirmatory results, findings from studies like this paper might be used to inform the practice of management, which might result in a more satisfied and better performing workforce.

This paper contributes to the literatures on organizational downsizing and business ideologies. Specifically, it investigates ideological beliefs and their effects on perceptions of downsizing in a new arena – a country that is not used to the concepts of market competition and shareholder interest, and one that has only experienced large‐scale layoffs in very recent times. The view of the western business concepts such as psychological contract within the context of traditional Chinese philosophies and value systems provides in‐depth understanding of the challenges facing today's transitional economies such as China.

This paper aims to examine current trends in business accreditation by describing and comparing the major international business accreditation agencies (Association to Advance Collegiate Schools of Business, European Quality Improvement System, Association of MBAs, Association of Collegiate Business Schools and Programs and International Assembly for Collegiate Business Education), and analyze their recent market expansion strategies (development and penetration using Ansoff model) as they compete for the schools seeking initial or continuing accreditation.

This is a comparative study of the business accreditation agencies and their competitive strategies, using publically available data such as lists of accredited schools published by the agencies as main data collection method.

Business accreditation agencies have utilized the market penetration and market development strategies to expand their market share in recent years. The key growth areas are international schools, regional teaching-oriented institutions, two-year institutions and for-profit institutions.

This study is based on publically available data published by accreditation agencies. More in-depth analysis with survey method could be utilized in future study to identify more specific strategies and their impact on business schools seeking accreditation.

Accreditation is no longer a luxury but a requirement for business schools, but they have to make an informed decision on which agency to pursue to assure an appropriate fit.

The public needs to understand the value and the requirements of accreditation. Multiple agencies provide different options to fit the missions of the different types of schools.

This study is valuable to business school stakeholders for understanding accreditation, the need for accreditation and the options they have available.

This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.

A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.

The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.

The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.

A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.