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This paper examines the growth trajectory, documents, journals, worldwide distribution authors, scientific production and thematic focus in the field of sustainable HRM.

A total of 765 publications (between 1982 and 2023) were chosen from the Scopus database that were diligently examined to reach insightful results. To aid the investigation, the Biblioshiny tool was used.

Through thematic mapping, the study finds that sustainable HRM is still an emerging and contemporary concept. Moreover, the themes of sustainable HRM are underdeveloped and need conceptual clarity. Additionally, these themes have evolved internally which have made a modest contribution to the advancement of the discipline. Furthermore, topic trending, word frequency and document citations indicate that a growing body of literature on sustainable HRM focuses extensively on environmental issues, demonstrating that HRM should be given greater attention to roles related to sustainability in the workplace.

Sustainable HRM should be a top priority for businesses, with an emphasis on environmental and sustainability concerns. To be in line with global sustainability standards, HR training needs to be updated. Companies should develop HR policies that put sustainability first and spend money on analytical tools. Academic and industrial cooperation can improve the field more quickly. Best practices can be standardized by participation in forums. It is crucial to take a holistic approach that balances environmental, social and economic factors.

This study distinguishes itself by adding highlights on bibliometric insights and thematic emphasis on sustainable HRM, adding to the progress of the field's knowledge and offering insightful pathways for future research.

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

Corporate social responsibility (CSR) aims at upholding socio-economic condition of deprived communities prevailing in society as well as bringing profitability for corporate companies themselves. This study investigates to what extent the CSR initiatives of financial institutions in Bangladesh have been able to reach out to deprived communities and support various dimensions of financial inclusion (FI) in the country.

In this study, both supply-side and demand-side data are included and discussed rigorously as tools to cross-check the result. Both qualitative and quantitative data are incorporated, and graphs and tables are used to represent the critical information better. A triangulation of in-depth interviews with secondary data is used to ensure rigor and trustworthiness. The triangulation approach works as a method to verify secondary data by interviewees in this study.

The result shows a positive geographical and demographical penetration of CSR activities, but it does not necessarily bring FI in all cases. This study identifies some key issues that prevail in the current context of Bangladesh, such as usage, distance, quality and cost of financial products.

This study makes use of both supply-side and demand-side information and thus explain the reasons behind the involuntary exclusion of financial services.

Historically, investments in innovation are perceived as one of the paramount decisions businesses opt to thrive and the impact of such investments on businesses' market performance is well documented in the literature. However, the environmental aspects of making such investments are yet to be addressed by the firms, which in turn, present considerable damage to the environment. Coupling with the natural resource-based view (NRBV) and the stakeholder theory of the firm, this research builds on an earlier work of Khalil and Nimmanunta (2021) in an attempt to examine the link between innovation and firms' environmental and financial value. The authors extend their analysis and document a more consistent approach to measuring environmental innovation which allows the authors to investigate the firms from three additional economies with respect to firms' investments in both traditional and environmental innovations.

The underlying models are tested using the time fixed-effects panel regression by utilizing information from publicly traded companies of ten Asian economies, including Japan, Hong Kong, Taiwan, Thailand, Turkey, Malaysia, Singapore, India, Indonesia, and Saudi Arabia. The reported sample covers annual firm-level ESG data obtained from Thomson Reuters' Datastream and Refinitiv Eikon during the 2015–2019 period.

This research offers support to the conventional wisdom that innovation is advantageous to the firms' market value. The authors further decompose innovation into traditional innovation and environmental innovation. The findings of this research suggest that traditional innovation is favorable only for the firms' market valuation and traditional innovation is strongly ineffectual for the environment – traditional innovation produces sizeable environmental distress by contributing substantially to carbon emissions. In contrast, the resultant effects of investments in environmental innovation are evident to be instrumental for both firms' financial performance and the environment.

This research has primarily focused on only two components of a company's environmental performance: reduction in carbon emissions (CO2) and corporate social responsibility (CSR). Given the complexity of firms' environmental strategies and the multidimensionality of the variable, which encompasses a wide range of corporate behavior in terms of relationships with communities, suppliers, consumers, and broader environmental responsibilities broadening the scope of the study by including other important aspects of environmental sustainability is, therefore, critical.

The findings of this research signify environmental innovation as one of the vital investment approaches as firms can exploit benefits related to the market from firms' sustainable practices, developing eco-friendly processes by introducing steady yet systematic chains of green products and services. Such products and services may have a feature of enhanced functionality with a better layout in terms of improved product life with better recycling options, and lower consumption and exploitation of energy and natural resources. These sustainable practices would be advantageous for the firms regarding the possibility of setting prices above the standard level through establishing green brands and gaining market share of environmentally anxious consumers. For those companies that are striving to take the leading role in the green industry and longing to seek superior returns on the companies' environmental investments, these benefits, in particular, are exceptionally critical to them.

The linkage between firms' financial and environmental performance in the context of simultaneous inclusion of both green and traditional innovations remains unclear and is yet to be investigated by researchers. Thus, this research shed light on the role of environmental innovation and traditional innovation on firms' environmental performance and financial performance. The authors utilize a novel dataset with a clear indication of measuring different elements of innovation that allows us to develop a more robust approach to corporates' environmental, social and governance (ESG) performance metrics having the slightest biases related to transparency and firm size.

This preliminary small-scale research aims to achieve an insight into drug use offenders’ rehabilitation experiences both in and out of prison, as well as considering how perceptions of the public may inhibit their successful reintegration into society.

A mixed-methods approach was used, with five semi-structured interviews conducted alongside the distribution of the Attitude towards prisoners questionnaire among the general public (n = 106) in the North-West region of England.

Participants recalled a mixture of experiences, highlighting the presence of contraband in prisons to be an inhibiting factor of successful rehabilitation. Additionally, participants expressed the need for more support when preparing for release such as confirmation of accommodation and possible employment. Questionnaires also indicated the public to perceive offenders negatively (M = 76, SD = 16.99), thus presenting a further challenge in the resettlement of drug use offenders.

Findings highlight that improving practices to decrease the presence of contraband in prisons, monitoring an individual’s use of methadone, preparing an individual for their release from prison and educating the public would reduce some of the obstacles experienced by drug use offenders.

This study outlines some of the obstacles that drug use offenders experience when attempting to end their drug use activity and criminal engagement.