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The purpose of this research is to identify major barriers to the implementation of reverse logistics (RL). Also, the study addresses best practices among reuse, remanufacture, recycling, refurbishment and repair as alternatives for RL processes.

This study targets supply chain management experts for their opinions regarding the identification of critical barriers and alternatives for RL implementation. Their opinions were extracted through a Web questionnaire based on 14 criteria with 5 alternatives. The tools of multi-criteria decision-making are used for analysis, i.e. fuzzy VIKOR and fuzzy TOPSIS.

The results indicate that lack of recognition of competitive advantage to be gained through RL practice is the most critical barrier to RL implementation. The least barrier or major facilitator for RL is “supportive initiative for end-of-life products.” The top-ranked alternative in this study is reuse followed by remanufacturing. The least important alternative is “repair” in the case of Pakistan. These alternatives are ranked based on “q values” derived through fuzzy VIKOR.

The results of this study can only be generalized for the manufacturing sector of Pakistan during the period of the study.

The findings of this study will assist managers in deploying the best practices concerning RL.

Fuzzy VIKOR and fuzzy TOPSIS have not been applied to RL alternatives in previous research.

The purpose of this paper is to understand local perceptions on climate change and its impacts on biodiversity, rangeland, agriculture and human health.

A household survey with 300 interviewees and focus group discussions with key stakeholders were conducted and validated at two steps, using the climate data from the nearest weather stations and reviewing literatures, to correlate the local perceptions on climate change and its impacts.

Majority of the respondents reported an increase in temperature and change in the precipitation pattern with increased hazardous incidences such as floods, avalanches and landslides. Climate change directly impacted plant distribution, species composition, disease and pest infestation, forage availability, agricultural productivity and human health risks related to infectious vector-borne diseases.

Because of the remoteness and difficult terrain, there are insufficient local weather stations in the mountains providing inadequate scientific data, thus requiring extrapolation from nearest stations for long-term climate data monitoring.

The research findings recommend taking immediate actions to develop local climate change adaptation strategies through a participatory approach that would enable local communities to strengthen their adaptive capacity and resilience.

Local knowledge-based perceptions on climate change and its impacts on social, ecological and economic sectors could help scientists, practitioners and policymakers to understand the ground reality and respond accordingly through effective planning and implementing adaptive measures including policy formulation.

This research focuses on combining local knowledge-based perceptions and climate science to elaborate the impacts of climate change in a localised context in Rakaposhi Valley in Karakoram Mountains of Pakistan.

Health and safety in small construction firms is often neglected by owners leading to poor health and safety performance and unacceptably high fatality and injury rates. A body of knowledge has established significant links between the motivational behaviours of operatives towards health and safety. Motivation is also considered as a key tool for improving operative productivity as when operatives experience safe worksites, they can carry out their work in a more productive manner. The purpose of this research is to develop a framework to examine the motivational factors that affect operative health and safety in small construction firms.

A critical review and synthesis of the body of knowledge incorporating motivational theory, health and safety literature and the factors which characterise small firms, is used to develop the framework.

Key components of the framework include the presence of intrinsic and extrinsic components, appropriate health and safety policies and procedures, the type of work environment, the operatives (i.e. attitude, experience and training) as well as the presence of appropriate management and supervision. The study revealed that operatives in small firms are less likely to be extrinsically motivated due to the absence of training, management commitment, policies and the wider working environment

Failure of motivational support can result in increased danger and risk in exposing operatives to injury in the small firm environment. In this context, the damage caused to operative's health and safety in small construction firms is dependent mainly on the extrinsic factors.

The framework provides a basis for improving our understanding of how to motivate operatives to act safely and will help to improve the health and safety performance of small firms. It is therefore vital to emphasise enhancement efforts on these extrinsic strategies in the small firms' environment especially in the initial stages of the project (or activity), so that the health and safety of operatives in small firms can be improved.

This study proposes a contribution in developing an understanding of the motivational factors and their influence on the health and safety of operatives in small construction firms. The study revealed that operatives in small firms are less likely to be extrinsically motivated and have only intrinsically motivated elements in their workplace. The study proposes an indirect link between the extrinsic and intrinsic factors that affect motivation.

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color (fabric color).

By using the fabric weave patterns image identification system, this study analyzed the fabric image based on the Hierarchical-MAX (HMAX) model of computer vision, to extract feature values related to texture of fabric. Red Green Blue (RGB) color descriptor based on opponent color channels simulating the single opponent and double opponent neuronal function of the brain is incorporated in to the texture descriptor to extract yarn color feature values. Finally, support vector machine classifier is used to train and test the algorithm.

This two-stage processing architecture can be used to construct a system based on computer vision to recognize fabric texture and to increase the system reliability and accuracy. Using this method, the stability and fault tolerance (invariance) was improved.

Traditionally, fabric texture recognition is performed manually by visual inspection. Recent studies have proposed automatic fabric texture identification based on computer vision. In the identification process, the fabric weave patterns are recognized by the warp and weft floats. However, due to the optical environments and the appearance differences of fabric and yarn, the stability and fault tolerance (invariance) of the computer vision method are yet to be improved. By using our method, the stability and fault tolerance (invariance) was improved.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

Ecological degradation is causing various medical hazards worldwide. Modern globalization is enhancing several polluting factors that cause ecosystem deterioration. This situation leads to the increasing significance of implementing green environmental practices. Previous studies emphasize various green concepts, mainly in finance, encouraging investors to make ethical and responsible decisions to promote clean ecological practices. But still, emerging concepts like socially responsible investment (SRI) require more understanding and acknowledgment, particularly in developing economies.

This study has focused on exploring the impediments to SRI-adopting practices. It is conducted in two phases. Initially, a systematic literature review was conducted to identify the hurdles in promoting SRI. Later, open-ended interviews from the active investors of Pakistan Stock Exchange Limited were executed to explore the barriers to implementing the SRI system. The responses were transcribed and tested through NVivo software.

The information extracted from the recorded statements was further classified into three themes: initial, subordinate and cluster, which provides an understanding of the identified factors. The findings suggest that the significant complications hindering SRI are a lack of regulatory framework, inadequate conceptual knowledge and limited resources.

The findings state that the identified impediments can help in developing a framework for successfully implementing SRI practices in emerging economies. It can strengthen the stakeholders' knowledge and suggest a guideline for investment decisions, providing them with socially, ethically and financially positive returns. Therefore, this study will inspire active and potential investors to adopt SRI practices, making the economic uplift certain.

This study will add value to the existing body of knowledge related to SRI and highlight the importance of SRI by suggesting it as a source to ensure sustainable green efficiency, particularly in the postpandemic era. It emphasizes the urgent need for a policy framework for effective investment decisions in emerging economies.

This study investigated the effect of green marketing mix on consumer repurchase intention in Ghana. The study focusses on the interaction effect of green knowledge on green marketing mix and consumer repurchase in Ghana.

A quantitative approach to research was employed. In all, 371 participants were chosen using the purposive sampling technique. Data analysis was conducted using the SPSS software.

The findings showed that green price, green place and green promotion had a positive significant effect on repurchase intention. However, green product insignificantly influenced repurchase intention. The findings further showed that green knowledge moderated the relationship between green price and green place, on repurchase intention. Green knowledge was not found to moderate the relationship between green product, green promotion and repurchase intention.

The study advances our knowledge on green marketing mix, green knowledge and repurchase intention within the beverage sector. It reveals the positive implication of green marketing mix on a firm’s customers using the marketing mix theory.

This study aims to investigate the impact of organizational mistreatments, such as workplace bullying and ostracism, on task performance. Additionally, it explores the mediating role of cyber loafing and the moderating role of negative reciprocity beliefs.

Adopting a positivism research philosophy and a deductive approach, this study focuses on the petroleum sector-related organizations where enterprise resource planning requires active Internet use, potentially leading to cyber loafing. Data were collected from employees using purposive sampling techniques, resulting in a sample size of 248. The data analysis was conducted using AMOS and SPSS software.

The results indicate that cyber loafing fully mediates the relationship between workplace bullying and ostracism on task performance. Additionally, negative reciprocity beliefs significantly moderate the relationship between workplace bullying, ostracism and cyber loafing.

This study presents a moderated-mediation model of organizational mistreatments and task performance, elucidating the mechanisms through the mediating role of cyber loafing and the moderating role of negative reciprocity beliefs.

This study aims to assess the effects of green supply chain management (GSCM) strategies on health-care organizations’ environmental performance (EP). It also assesses that green innovation (GI), including green technology innovation (GTI) and green management innovation (GMI), plays mediating roles in the connection between GSCM and EP.

In total, 375 health-care sector employees in the Chattogram division of Bangladesh provided opinions in the questionnaire survey. PLS-SEM was used to verify the data’s validity and reliability and assess the hypotheses.

The findings reveal that GSCM practices significantly positively impact GI and EP. Moreover, GTI and GMI mediate the relationship between GSCM and EP in Bangladesh’s health-care sector.

The current findings could promote environmental sustainability in Bangladesh’s health-care industry by fostering environmental collaboration.

The detection of the mediation effect of GTI and GMI on GSCM and EP is a unique contribution of this study that enriches the present GSCM and GI literature, particularly in the Bangladeshi health-care sector.

Altering the microstructure and developing the surface metal matrix composites (MMCs) in a solid-state by friction stir processing (FSP) has been on trend for the past decade. The microstructural modification increases the material properties, which are structure sensitive. The microstructural evolution is highly influenced by the selection of process parameters in FSP. In this study, the effect of process parameters on the microstructure evolution and microhardness of the fabrication of surface MMCs of newly commercialized Mg-ZE41 alloy by the incorporation of different reinforcement particles such as ZrO₂, CeO₂ and Al₂O₃ is investigated.

By making use of Taguchi’s design of experimentation, which recognizes the crucial factors and ascertain their effect on the properties of the material, the optimization of process parameters for this study was done using MATLAB-14 software. The parameters were adopted along with the levels throughout the FSP for the fabrication of different surface MMCs. For each reinforcement particle, two factors at three levels each had L9 factorial design were used to analyse the effect of these factors on the processing result (microstructure, grain refinement and hardness). The two different factors used in this study are tool rotational speed (TRS) and tool traverse speed (TTS) as a part of the full factorial design matrix for different surface MMCs.

Among all combinations, TRS of 1500 rpm and TTS of 20 mm/min. for ZE41-ZrO₂ MMCs and ZE41-CeO₂ MMCs were observed as optimum to produce defect-free processed zone along with the high level of grain refinement and hardness, whereas for ZE41-Al₂O₃ the same was obtained at 1500 rpm TRS and 10 mm/min TTS.

In this paper, the role of process parameters in the development of surface MMCs on newly commercialized Mg-ZE41 alloy by FSP is investigated. The effect of TRS and TTS on microstructure evolution, grain refinement and microhardness is analysed. Hence, in this study, the optimum parameters for the fabrication of surface MMCs of Mg-ZE41 alloy have thus been established.