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The victim’s rights movement and restorative justice (RJ) have gained momentum around the world. More laws and policies have focused on crime victims and their families. Western literature suggests that the victim’s family suffers physical, emotional, and financial tolls and that the power of the victim’s family in pursuing justice for their loved ones remains limited. This is particularly concerning within the political and legal context of the abolitionist movement, innocence project, and human rights groups’ campaigns against police torture. Grounded in the perspectives of RJ and Chinese legal culture, this study examines the victim’s family, represented by Ding and senior Yu, of the Nian Bin capital murder case. Drawing on published reports and using the thematic content analysis method, this study examines the following aspects of victim’s family in a death penalty case: 1) victim family’s physical, emotional, and financial tolls; 2) victims’ family and the criminal justice system; 3) victims’ family and the media; and 4) the relationship between the victims’ and the accused’s families. This study concludes with discussions of the competing goals of families impacted by a crime and RJ practices that would help mitigate the loss of the victim’s family and enhance their confidence in the criminal justice system.

This chapter uses the historian’s method of micro-history to rethink the significance of the Supreme Court decision Muller v. Oregon (1908). Muller is typically considered a labor law decision permitting the regulation of women’s work hours. However, this chapter argues that through particular attention to the specific context in which the labor dispute took place – the laundry industry in Portland, Oregon – the Muller decision and underlying conflict should be understood as not only about sex-based labor rights but also about how the labor of laundry specifically involved race-based discrimination. This chapter investigates the most important conflicts behind the Muller decision, namely the entangled histories of white laundresses’ labor and labor activism in Portland, as well as the labor of their competitors – Chinese laundrymen. In so doing, this chapter offers an intersectional reading of Muller that incorporates regulations on Chinese laundries and places the decision in conversation with a long line of anti-Chinese laundry legislation on the West Coast, including that at issue in Yick Wo v. Hopkins (1886).

Plastic pollution is a widespread problem around the world. However, the problem is more severe and ever increasing in developing countries. The literature suggests that the majority of the work and the solutions that have been proposed to address plastic littering have been undertaken in developed countries. Thus, there is a need to explore the problem in developing countries to better understand the issue and to develop context-specific solutions. We explored the norms perspective using ‘The Focus Theory of Normative Conduct’, individual ethical position and place attachment factors. Interviews were conducted in Pakistan with 16 people who were recently at a beach at which there was a large amount of plastic litter. Results showed that people are influenced by the pro-littering social norms of their friends and neighbours. However, people with strong anti-littering personal norms are not influenced by pro-littering social norms. We also found that people have varying moral position, and their lack of attachment with the public place also influences their littering behaviour. Moreover, people believe that other people litter due to lack of education and awareness, lack of garbage bins and a general level of carelessness. Future research needs to focus on activating an individual's idealist moral position and an individuals' attachment with the place to enhance the activation of anti-littering personal norms which will help reduce littering behaviour. Further still, government needs to set up campaigns at public places to create awareness among people about the impact of littering, and government also needs to improve the effectiveness and efficiency of waste management. Businesses can also play a role by providing waste bins which may be used as a source of promoting their support for reducing litter.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

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.

This chapter reviews marketing scholarship on environmental sustainability. The literature covers several themes of both consumer behavior and firm-level topics. Consumer issues include their assessment of efficacy and the extent to which they are aware and sensitive to environmental issues. Numerous interventions and marketing appeals for modifying attitudes and behaviors have been tested and are reported. Consumers and business managers have both been queried regarding attitudes of recycling and waste. Firm-level phenomena are reflected, including how brand managers can signal their green efforts to their customers, whether doing so is beneficial, all in conjunction with macro pressures or constraints from industry or governmental agencies. This chapter closes with a reflection on the research.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.

Reverse supply chain (RSC) is an extension of the traditional supply chain (TSC) motivated by environmental requirements and economic incentives. TSC management deals with planning, executing, monitoring, and controlling a collection of organizations, activities, resources, people, technology, and information as the materials and products move from manufacturers to the consumers. Except for a short warranty period, TSC excludes most of the responsibilities toward the product beyond the point of sale. However, because of growing environmental awareness and regulations (e.g. product stewardship statute), TSC alone is no longer an adequate industrial practice. New regulations and public awareness have forced manufacturers to take responsibilities of products when they reach their end of lives. This has necessitated the creation of an infrastructure, known as RSC, which includes collection, transportation, and management of end-of-life products (EOLPs). The advantages of implementing RSC include the reduction in the use of virgin resources, the decrease in the materials sent to landfills and the cost savings stemming from the reuse of EOLPs, disassembled components, and recycled materials. TSC and RSC together represent a closed loop of materials flow. The whole system of organizations, activities, resources, people, technology, and information flowing in this closed loop is known as the closed-loop supply chain (CLSC).

In RSC, the management of EOLPs includes cleaning, disassembly, sorting, inspecting, and recovery or disposal. The recovery could take several forms depending on the condition of EOLPs, namely, product recovery (refurbishing, remanufacturing, repairing), component recovery (cannibalization), and material recovery (recycling). However, neither the quality nor the quantity of returning EOLPs is predictable. This unpredictable nature of RSC is what makes its management challenging and necessitates innovative management science solutions to control it.

In this chapter, we address the order-driven component and product recovery (ODCPR) problem for sensor-embedded products (SEPs) in an RSC. SEPs contain sensors and radio-frequency identification tags implanted in them at the time of their production to monitor their critical components throughout their lives. By facilitating data collection during product usage, these embedded sensors enable one to predict product/component failures and estimate the remaining life of components as the products reach their end of lives. In an ODCPR system, EOLPs are either cannibalized or refurbished. Refurbishment activities are carried out to meet the demand for products and may require reusable components. The purpose of cannibalization is to recover a limited number of reusable components for customers and internal use. Internal component demand stems from the component requirements in the refurbishment operation. It is assumed that the customers have specific remaining-life requirements on components and products. Therefore, the problem is to find the optimal subset and sequence of the EOLPs to cannibalize and refurbish so that (1) the remaining-life-based demands are satisfied while making sure that the necessary reusable components are extracted before attempting to refurbish an EOLP and (2) the total system cost is minimized. We show that the problem could be formulated as an integer nonlinear program. We then develop a hybrid genetic algorithm to solve the problem that is shown to provide excellent results. A numerical example is presented to illustrate the methodology.

This chapter aims to explore representations of information literacy and media literacy in Singapore’s educational discourse as part of its 21st century skills framework. Currently, information literacy and media literacy co-exist in Singapore’s education discourse but there is no related work attempting to clarify these two concepts in Singapore or to bridge them to propose an overarching framework. In what ways are these two terminologies identical or different in the local education context? We try to answer this question through reviewing relevant official documents. We start with a review the literature on the global scale regarding information literacy and media literacy. Then, we focus on Singapore to explore how various governmental agencies defining information literacy and media literacy. This chapter, in other words, is a result from a pilot study to understand how information literacy and media literacy is defined and understood in Singapore’s education system.