Reimagining Green Human Resource Management for Sustainable Performance: Towards an Integrative Processual Framework

Despite different attempts to explain the direct and indirect impacts of green practices on Human Resource Management issues like performance and sustainability, an integrative framework showing how GHRM can be used as a vehicle for sustainable performance is missing. To address this void, we conducted a systematic literature review of over 117 papers on GHRM which led to the identification of fourteen organisation and people-oriented areas. We clustered the most frequently occurring into five categories namely Ability-Motivation-Opportunity, Resource Based View, Social Identity, Social Exchange and Stakeholder Theory and justified why the remaining nine were left out on the basis of non-frequent occurrence in the literature. Based on these results, an integrative processual framework on five organisational and people-oriented aspects has been developed for the first time in GHRM research. These are then further developed into 5 steps to show how they can be implemented. It was later found necessary to add a sixth step to emphasise how GHRM can be reconfigured and practically implemented as a force for sustainable individual and organisational performance. We contribute theoretically to Social Identity and Social Engagement Theory by highlighting the significant role of Deci and Ryan’s ‘Self-Determination’ and ‘Organismic Integration Theories’ in enhancing GHRM implementation and individual and organizational reputational benefits. Our managerial contributions are anchored on the need for prioritising mechanisms, policy initiatives and Human Resource Practices for a greener social, environmental and economic performance. This new triple bottom line is referred to as Sustainable GHRM and Performance (SGHRMP). We discuss the value and implications of our contributions and provide a future research agenda for GHRM

Preliminary analyses of cultured Symbiodinium isolated from sand in the oceanic Ogasawara Islands, Japan

Abstract The dinoflagellate genus Symbiodinium is generally found in many tropical and subtropical marine invertebrates. Recently, reports have focused on free-living types. We examined free-living Symbiodinium from the Ogasawara (Bonin) Islands, a group of oceanic islands south of Japan. Examining sand samples, seven of eight initial isolates were successfully cultured. Genetic analyses of 18S, 28S and internal transcribed spacer (ITS) ribosomal DNA regions reveal that one isolate cultured with only IMK was identical to clade A isolated from coral reef sand in Okinawa, and four additional isolates cultured with only IMK comprised a new clade A lineage. Additionally, two isolates cultured with IMK and soil extract were closely related to a little-known divergent lineage within clade D. Our results demonstrate some free-living Symbiodinium types may have very wide distributions, and that utilizing different culturing techniques will further discovery of unique Symbiodinium lineages from environmental samples

Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images

Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation. Unsupervised disease detection methods, such as anomaly detection, can significantly reduce human effort in these scenarios. While anomaly detection typically focuses on learning from images of healthy subjects only, real-world situations often present unannotated datasets with a mixture of healthy and diseased subjects. Recent studies have demonstrated that utilizing such unannotated images can improve unsupervised disease and anomaly detection. However, these methods do not utilize knowledge specific to registered neuroimages, resulting in a subpar performance in neurologic disease detection. To address this limitation, we propose Brainomaly, a GAN-based image-to-image translation method specifically designed for neurologic disease detection. Brainomaly not only offers tailored image-to-image translation suitable for neuroimages but also leverages unannotated mixed images to achieve superior neurologic disease detection. Additionally, we address the issue of model selection for inference without annotated samples by proposing a pseudo-AUC metric, further enhancing Brainomaly's detection performance. Extensive experiments and ablation studies demonstrate that Brainomaly outperforms existing state-of-the-art unsupervised disease and anomaly detection methods by significant margins in Alzheimer's disease detection using a publicly available dataset and headache detection using an institutional dataset. The code is available from https://github.com/mahfuzmohammad/Brainomaly.Comment: Accepted in WACV 202

Two dinoflagellate taxa, Durinskia (Peridiniales, Dinophyceae) and Goniodoma (Gonyaulacales, Dinophyceae) from Okinawa, Japan

琉球大学21世紀COEプログラム「サンゴ礁島嶼系の生物多様性の総合解析」平成20年度成果発表会（平成21年3月14日開催）　講演・特別講演会場：理系複号棟102号室，ポスター発表会場：琉球大学50周年記念館1

A Systematic Literature Review of GHRM: Organizational Sustainable Performance Reimagined Using a New Holistic Framework

Despite the plethora of explications of the direct and indirect impacts of green people management practices on different dimensions of individual and organizational performance and sustainability, a holistic model demonstrating the constituent aspects and impacts of such sustainability on organizational, individual, and team performance is missing. The objective of this study is to address this gap/void through a review of 127 papers on green human resource management (GHRM) following a systematic literature review approach. Based on the systematic review, this study used a thematic analysis, which identified twenty-four disparate people and organizational aspects and grouped the most used ones into five theoretical lenses, including AMO = ability–motivation–opportunity, RBV = resource-based view, SHT = stakeholder theory, SET =social exchange theory, and SIT = social identity theory. These five sets of results were used to develop the first-of-its-kind holistic framework showing how GHRM works in a cyclical process to fill the missing gap in how to sustainably improve individual, group, and organizational performance for multiple organizational stakeholders. Second, this article contributes theoretically to the social engagement and social identity theories, thereby extending Deci and Ryan’s organismic integration and self-determination theories to show how GHRM practices can be implemented for sustainable organizational performance. Third, this study also proposed a new and more sustainable bottom line for business organizations seeking to improve their performance, and this contribution is referred to as sustainable GHRM-organizational performance (SGHRM-OrgP). Finally, this study proposes a research agenda highlighting where more research areas are needed. Despite the potential that such a model offers for organizational sustainability, the authors recognize the next research step of applying its constituent parts in practically optimizing performance

Carbon‐Based Textile‐Structured Triboelectric Nanogenerators for Smart Wearables

Recent advances in wearable electronics have been propelled by the rapid growth of microelectronics and Internet of Things. The proliferation of electronic devices and sensors relies heavily on power sources, predominantly batteries, with significant implications for the environment. To address this concern and to reduce carbon emissions, there is a growing emphasis on renewable energy harvesting technologies, among which textile-based triboelectric nanogenerators (T-TENGs) stand out as an innovative and sustainable solution due to having the interesting characteristics like large contact area, lightweight design, flexibility, comfort, scalability, and breathability. T-TENGs can harness mechanical energy from human movement and convert it into electric energy. However, one of the challenges is low electric power output, which can be addressed by meticulous selection of material pairs with significant differences in work function and optimizing contact areas. The incorporation of carbon-based nanomaterials, such as carbon nanotubes and graphene, emerges as a key strategy to enhance output. This review delineates recent progress in T-TENGs incorporating carbonaceous nanofillers, comprehensively addressing fundamental classification, operational mode, structural design, working performance, and potential challenges that are hindering commercialization. By doing this, this review aims to stimulate future investigations into sustainable, high-performance smart wearables integrated with T-TENGs