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The study examines health care inequities in viral load testing among hepatitis C (HCV) antibody-positive patients. The analysis predicts whether individual and census tract sociodemographic characteristics impact the likelihood of viral load testing.

This a study of 26,218 HCV antibody-positive patients in Orange County, California, from 2010 to 2020. The case data were matched with the 2017 American Community Survey to help understand the role of neighborhood socioeconomic characteristics in testing for viral load. Multivariable logistic regression was used to predict the probability of ever testing for HCV viral load.

Thirty-six percent of antibody-positive persons were never viral load tested. The results show inequalities in viral load testing by sociodemographic factors. The following groups were less likely to ever test for viral load than their counterparts: (1) individuals under 65 years old, (2) females, (3) residents of census tracts with lower levels of health insurance enrollment, (4) residents of census tracts with lower levels of government health insurance, and (5) residents of census tracts with a higher proportion of non-white residents.

This is a secondary database from public health department reports. Using census tract data raises the issue of the ecological fallacy. Detailed medical records were not available. The results of this study emphasize the social inequality in viral load testing for HCV. These groups are less likely to be treated and cured, and may spread the disease to others.

This chapter is unique as it combines routinely collected public health department data with census tract level data to examine social inequities associated with lower rates of HCV viral load testing.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

The purpose of this study is to present an analysis of the Islamic good governance concept and means known as Al-Siyasah Al-Syar’iyyah as a principle and approach for combating corruption. This literature review aims to synthesize extant literature that discusses the determinants of integrity and how to prevent and combat corruption based on the Al-Siyasah Al-Syar’iyyah perspective.

A systematic search was conducted on a literature review based on Scopus and referred journals from Google Scholar databases. A manual search on Google Scholar was performed to identify additional relevant studies. Studies were selected based on the predetermined criteria. They were thematically examined using content analysis.

The study found that most of the 45 works of the literature, (41 studies and four chapters) suggested that corruption should be considered a sin and that education of Al-Siyasah Al-Syar’iyyah’s perspective against corruption, emphasizing the principle of piety, the institutionalization of justice and accountability, good governance performance with an emphasis on its belief in self-accountability and justice, is the means to combat corruption.

This study is unique in that it focuses on locating material on battling corruption from the standpoint of Al-Siyasah Al-Syar’iyyah. Based on the al-Quran, the Sunnah and the best practices of Muslim rulership, this notion provides an epistemological, ethical and ontological stance in Islam.

I examine patterns of making or deferring strategic repatriations that firms can use to either meet analysts' forecasts or defer to maintain future reported earnings flexibility. First, I examine the extent to which firms repatriate earnings from high foreign tax subsidiaries to decrease US tax expense, resulting in increased net income and lower cash taxes. Using federal tax return information, I find evidence that firms strategically repatriate these earnings to meet or beat current analysts' forecasts. Next, I find evidence that firms that are able to obtain current year tax reductions defer these repatriations in an attempt to build cookie-jar reserves. Lastly, I find that firms do not disclose high foreign tax repatriations (HTRs), even when required by SEC rules. This study contributes to the earnings management, tax avoidance, and disclosure literature by examining a discretionary tax planning strategy.

The purpose of this paper is to provide an insight into the present-day state of bin picking by considering research, technology, products and applications.

Following a short introduction, this first provides examples of recent bin picking research. It then discusses a selection of commercial product developments and applications. Finally, brief conclusions are drawn.

Bin picking has the potential to eliminate repetitive, manual part handling practices in many sectors of the manufacturing and logistics industries. Systems combine robotic gripping and manipulation with machine vision and specialist software and tend to be complex to install and commission. They are produced by robot manufacturers, system integrators, software developers and machine vision specialists and all are constantly developing and improving the technology. These developments are supported by a strong academic research effort, much involving artificial intelligence methods, and while the technology is evolving rapidly, it is yet to reach the point where deployments are routine and widespread.

This provides a timely review of recent bin picking research and commercial developments.

Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.

Given the importance of teacher leadership in influencing, motivating and inspiring student learning engagement and associated learning outcomes, a robust instrument to assess this construct is critical. Although there are some teacher leadership instruments available in existing literature, efforts to adapt robust psychometric instruments to measure teachers' leadership practices in Ethiopian higher education institutions have been limited. Therefore, this study attempted to address this gap by adapting the Teacher Leadership Scale (TLS) based on the Multifactor Leadership Questionnaire (MLQ-5X) and validating its psychometric properties for use in higher education settings.

Using a cross-sectional design, the study involved 409 undergraduate university students who were randomly selected from public universities. Factor analytic methodologies, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), were used to analyze the data collected.

The result confirmed a set of 36 items arranged in nine factors, which have a theoretically supported factor structure, excellent model fit and robust evidence for validity, and reliability and measurement invariance. These results demonstrate that the scale is a strong psychometric tool for measuring the leadership profile and practice of higher education teachers.

It can be concluded that the TLS can assist stakeholders in several ways. Researchers can benefit from the scale to measure teachers' leadership practices and predict their influence on student learning outcomes. In addition, the scale can help practitioners and policymakers collect relevant data to rethink teacher professional development initiatives, leadership training programs and other practices aimed at improving teacher leadership effectiveness.

With the rapid development of the sharing economy in manufacturing industries, manufacturers and the equipment suppliers frequently share capacity through the third-party platform. This paper aims to study influences of manufacturers sharing capacity on the supplier and to analyze whether the supplier shares capacity as well as its influences.

This paper deals with conditions that the supplier and manufacturers share capacity through the third-party platform, and the third-party platform competes with the supplier in equipment sales. Considering the heterogeneity of the manufacturer's earning of unit capacity usage and the production efficiency of manufacturer's usage strategies, this paper constructs capacity sharing game models. Then, model equilibrium results under different sharing scenarios are compared.

The results show that when the production or maintenance cost is high, manufacturers sharing capacity simultaneously benefits the supplier, the third-party platform and manufacturers with high earnings of unit capacity usage. When both the rental efficiency and the production cost are low, or both the rental efficiency and the production cost are high, the supplier simultaneously sells equipment and shares capacity. The supplier only sells equipment in other cases. When both the rental efficiency and the production cost are low, the supplier’s sharing capacity realizes the win-win-win situation for the supplier, the third-party platform and manufacturers with moderate earnings of unit capacity usage.

This paper innovatively examines supplier's selling and sharing decisions considering manufacturers sharing capacity. It extends the research on capacity sharing and is important to supplier's operational decisions.

In a kitting supply system, the occurrence of material-handling errors is unavoidable and will cause serious production losses to an assembly line. To minimize production losses, this paper aims to present a dynamic scheduling problem of automotive assembly line considering material-handling mistakes by integrating abnormal disturbance into the material distribution problem of mixed-model assembly lines (MMALs).

A multi-phase dynamic scheduling (MPDS) algorithm is proposed based on the characteristics and properties of the dynamic scheduling problem. In the first phase, the static material distribution scheduling problem is decomposed into three optimization sub-problems, and the dynamic programming algorithm is used to jointly optimize the sub-problems to obtain the optimal initial scheduling plan. In the second phase, a two-stage rescheduling algorithm incorporating removing rules and adding rules was designed according to the status update mechanism of material demand and multi-load AGVs.

Through comparative experiments with the periodic distribution strategy (PD) and the direct insertion method (DI), the superiority of the proposed dynamic scheduling strategy and algorithm is verified.

To the best of the authors’ knowledge, this study is the first to consider the impact of material-handling errors on the material distribution scheduling problem when using a kitting strategy. By designing an MPDS algorithm, this paper aims to maximize the absorption of the disturbance caused by material-handling errors and reduce the production losses of the assembly line as well as the total cost of the material transportation.

The study aims to investigate the impact of financial inclusion (FI) on environmental quality and the mediating role of industrialization (IZ). In addition, these relationships among the counties with different levels of income and carbon emissions were also analyzed.

This paper used the International Monetary Fund database for indicators of FI. The environmental indicators were obtained from the World Bank database for a panel of worldwide countries from 2004 to 2019. Separate indices of environmental sustainability (ES) and environmental degradation (ED) were created by using principal component analysis . The generalized method of moments regression was applied to examine the relationship between variables.

The study found full mediation of IZ between FI and ES, whereas partial mediation between FI and environmental degradation. The results were found robust against alternative measures of carbon emissions. Furthermore, the study also bifurcated the sample according to the level of income and carbon emission. It was found that FI plays a positive role in the betterment of environmental quality for high-income countries, while a negative role in upper-middle-income, lower-middle-income and low-income countries. Besides, FI has a negative role in the ES of the countries having higher or lower carbon emission levels.

Empirically this study contributes by creating two different novel measures of ES and environmental degradation, in contrast to other studies that solely relied on carbon emission. Contrary to previous studies, this study suggests that FI is not solely responsible for environmental damages, and IZ is the key channel by which FI shifts its impact on ES. Moreover, for environmental degradation, there are some other channels involved that need to be investigated further. This study has also noted that the relationship between FI and ES is context-dependent. Theoretically, this paper contributes to the literature by using ecological modernization theory in the nexus of FI, IZ and environmental quality.