Greening human resource management and employee commitment towards the environment: An interaction model

In response to a greater environmental awareness, organizations are concerned more and more about the “greening” human resource management (GHRM). Although the literature on GHRM has been extending, published studies have paid little attention to the research of GHRM and its contribution to employee commitment towards the environment, especially the interactions of GHRM practices, so far. Thus, to bridge this research gap, this study extends the Ability-Motivation-Opportunity and the Social exchange theories in the green context by investigating a new conceptual framework, which explores the indirect and interactive effects of GHRM practices (training, reward, and organizational culture) on employee environmental commitment. A quantitative study is conducted through a survey involving 209 respondents. Findings suggest that: (1) three GHRM practices are important tools in stimulating directly employees to commit to the environmental activities, (2) a two-way interaction of green training and green organizational culture can unlock employee commitment for the environment, especially at the high and average levels of green organizational culture, (3) the commitment is also increased significantly through a three-way interaction, the two strongest effects are recognized with the conditions of high-green organizational culture and the average-and high-green reward, whereas (4) the interacting between green training and green reward is an unimportant factor in encouraging employee environmental attachment. © 2019 The Author(s).Internal Grant Agency of FaME TBU [IGA/FaME/2018/009]; La Trobe Business School, Australi

How to drive brand engagement and EWOM intention in social commerce: A competitive strategy for the emerging market

Brand engagement and eWOM intention have been found to be critical factors driving competitive advantage for companies, as the evolution of social networking sites has changed the perspective of how companies engage with customers. Based on social exchange theory, the current research proposes an empirical model that emphasizes (1) the unique role of social commerce characteristics, including personalization, socialization, and information availability, in enhancing consumer-brand engagement, (2) the connection between consumer-brand engagement and eWOM intention, and (3) the moderating influence of trust towards such connection. A survey of 248 Facebook users with online shopping experience was employed. By using PLS-graph 3.0, structural equation modelling, the findings demonstrate that personalization and socialization positively influence brand engagement, which in turn leads to eWOM intention. Furthermore, trust moderates the brand engagement-eWOM intention relationship. Unexpectedly, information availability has shown no significant effect on brand engagement. The study encompasses the knowledge of social exchange theory into the social commerce environment by investigating the linkage between the social commerce environment and brand engagement. It contributes value to marketing theories by describing the moderating role of trust from the viewpoint of Gen Y. In addition, the study's findings may shed light on how firms in emerging markets can increase competitiveness by stimulating brand engagement and eWOM intention, as well as enhancing consumer trust in the comments regarding the products/services within the social commerce environment. © 2020 Tomas Bata University in Zlín. All rights reserved.Internal Grant Agency of the Faculty of Management and Economics, Tomas Bata University in Zlin [IGA/FaME/2018/015

Greening human resource management and employee commitment toward the environment: An interaction model

In response to a greater environmental awareness, organizations are concerned more and more about the “greening” human resource management (GHRM). Although the literature on GHRM has been extending, published studies have paid little attention to the research of GHRM and its contribution to employee commitment toward the environment, especially the interactions of GHRM practices, so far. Thus, to bridge this research gap, this study extends the Ability-Motivation-Opportunity and the Social exchange theories in the green context by investigating a new conceptual framework, which explores the indirect and interactive effects of GHRM practices (training, reward, and organizational culture) on employee environmental commitment. A quantitative study is conducted through a survey involving 209 respondents. Findings suggest that: (1) three GHRM practices are important tools in stimulating directly employees to commit to the environmental activities, (2) a two-way interaction of green training and green organizational culture can unlock employee commitment for the environment, especially at the high and average levels of green organizational culture, (3) the commitment is also increased significantly through a three-way interaction, the two strongest effects are recognized with the conditions of high-green organizational culture and the average- and high-green reward, whereas (4) the interacting between green training and green reward is an unimportant factor in encouraging employee environmental attachment

INVESTIGATION ON HYDROLOGIC PERFORMANCE OF PERVIOUS CONCRETE PAVEMENT BY FINITE ELEMENT ANALYSIS

Pervious concrete pavement has been used widely as an effective practice for water management in low-impact development techniques. The hydrologic performance of pervious concrete pavement depends significantly on the rainfall intensity and the designed slope. This study assessed the hydrologic performance of pervious concrete pavement by evaluating the time for surface ponding via finite element analysis. A series of simulations were carried out to explore the relationship between hydrologic performance and pervious concrete pavement by the Hydrus 2D program. The research’s results showed that as the slope increased, the time of surface ponding also increased. The data indicated that the slope variable had a low impact on the water level in pervious concrete pavement under a constant rainfall intensity. Observation of the effect of rainfall intensity showed that when the rainfall intensity increased twofold, the time for surface ponding dropped about two times. Furthermore, when surface ponding appeared, pervious concrete pavement at higher rainfall intensity had lower water content. The rainfall intensity also significantly affects the hydrologic performance of the pervious concrete pavement. This study only assessed the hydrologic performance by using the time for surface ponding via finite element analysis. Further experimental studies should be conducted to examine the relationship of other factors to the hydrologic performance of pervious concrete pavement

M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation

Polyp segmentation has recently garnered significant attention, and multiple methods have been formulated to achieve commendable outcomes. However, these techniques often confront difficulty when working with the complex polyp foreground and their surrounding regions because of the nature of convolution operation. Besides, most existing methods forget to exploit the potential information from multiple decoder stages. To address this challenge, we suggest combining MetaFormer, introduced as a baseline for integrating CNN and Transformer, with UNet framework and incorporating our Multi-scale Upsampling block (MU). This simple module makes it possible to combine multi-level information by exploring multiple receptive field paths of the shallow decoder stage and then adding with the higher stage to aggregate better feature representation, which is essential in medical image segmentation. Taken all together, we propose MetaFormer Multi-scale Upsampling Network (M$^2$UNet) for the polyp segmentation task. Extensive experiments on five benchmark datasets demonstrate that our method achieved competitive performance compared with several previous methods

Unbiased Random Number Generation using Injection-Locked Spin-Torque Nano-Oscillators

Unbiased sources of true randomness are critical for the successful deployment of stochastic unconventional computing schemes and encryption applications in hardware. Leveraging nanoscale thermal magnetization fluctuations provides an efficient and almost cost-free means of generating truly random bitstreams, distinguishing them from predictable pseudo-random sequences. However, existing approaches that aim to achieve randomness often suffer from bias, leading to significant deviations from equal fractions of 0 and 1 in the bitstreams and compromising their inherent unpredictability. This study presents a hardware approach that capitalizes on the intrinsic balance of phase noise in an oscillator injection locked at twice its natural frequency, leveraging the stability of this naturally balanced physical system. We demonstrate the successful generation of unbiased and truly random bitstreams through extensive experimentation. Our numerical simulations exhibit excellent agreement with the experimental results, confirming the robustness and viability of our approach.Comment: 13 pages, 8 figure

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model

Security-Reliability Tradeoffs for Satellite-Terrestrial Relay Networks with a Friendly Jammer and Imperfect CSI

peer reviewedThis article proposes and analyzes the reliability and security tradeoff for a satellite-terrestrial (SatTer) relay system. Herein, a satellite sends confidential information to multiple ground users with the help of a relay base station (BS) in the presence of multiple eavesdroppers trying to wiretap the information. In particular, a friendly jammer is deployed near the relay BS to improve secure transmissions. Moreover, the nonidentical Rayleigh fading channels and imperfect channel state information are adopted for a general system model. Then, we consider both amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies to give a full picture of the benefits of each method. In this context, we derive the closed-form expressions of the outage probability and intercept probability corresponding to AF- and DF-based relaying schemes, which is a high challenge and has not been investigated before. Then, Monte-Carlo simulations are conducted to evaluate the correctness of the mathematical analysis and the effectiveness of the proposed methods. Furthermore, the security and reliability trade-off of the SatTer system and the influences of various system parameters (e.g., satellite's transmit power, channel estimation errors, relay's transmit power, fading severity parameter, the average power of light-of-sight, and satellite's multipath components) on the system performance are shown

Isolation and identification of triterpenoid compounds from Couroupita guianensis Aubl.

In this report, the extracts from the fruit and leaves of Couroupita guianensis were isolated using chromatographic methods and investigated for chemical composition. Four triterpenoid compounds were isolated and identified as betulinic acid, oleanolic acid, β-amyrin and friedelin. Their chemical structures were interpreted based on modern spectra such as MS, NMR and compared with previously published spectral data

Performance analysis of multihop full-duplex NOMA systems with imperfect interference cancellation and near-field path-loss

Outage probability (OP) and potential throughput (PT) of multihop full-duplex (FD) nonorthogonal multiple access (NOMA) systems are addressed in the present paper. More precisely, two metrics are derived in the closed-form expressions under the impact of both imperfect successive interference cancellation (SIC) and imperfect self-interference cancellation. Moreover, to model short transmission distance from the transmit and receive antennae at relays, the near-field path-loss is taken into consideration. Additionally, the impact of the total transmit power on the performance of these metrics is rigorously derived. Furthermore, the mathematical framework of the baseline systems is provided too. Computer-based simulations via the Monte Carlo method are given to verify the accuracy of the proposed framework, confirm our findings, and highlight the benefits of the proposed systems compared with the baseline one.Web of Science231art. no. 52