The relationship between corporate social responsibility as perceived by employees and job satisfaction based on Nanjing Coherence Supply Chain Company in China

Abstract: The study investigates the link between corporate social responsibility (CSR), as perceived by employees, and job satisfaction in Nanjing Coherence Supply Chain Company. In order to determine how CSR, as perceived by employees, influences internal job satisfaction, hierarchical regression analysis is used to probe their potential relationship. Specifically, CSR is split up into three dimensions: CSR to non-social stakeholders, CSR to employees and CSR to customers. These dimensions are each addressed by particular items in a questionnaire. The analysis is solely based on a sample of 121 responses of employees to the questionnaire in a survey conducted in a Nanjing Coherence Supply Chain Company. From the results of regression analysis, the hypotheses are supported

Cooperative Cell-Free ISAC Networks: Joint BS Mode Selection and Beamforming Design

Owing to the promising ability of saving hardware cost and spectrum resources, integrated sensing and communication (ISAC) is regarded as a revolutionary technology for future sixth-generation (6G) networks. The mono-static ISAC systems considered in most of existing works can only achieve limited sensing performance due to the single observation angle and easily blocked transmission links, which motivates researchers to investigate cooperative ISAC networks. In order to further improve the degrees of freedom (DoFs) of cooperative ISAC networks, the transmitter-receiver selection, i.e., base station (BS) mode selection problem, is meaningful to be studied. However, to our best knowledge, this crucial problem has not been extensively studied in existing works. In this paper, we consider the joint BS mode selection, transmit beamforming, and receive filter design for cooperative cell-free ISAC networks, where multi-BSs cooperatively serve communication users and detect targets. We aim to maximize the sum of sensing signal-to-interference-plus-noise ratio (SINR) under the communication SINR requirements, total power budget, and constraints on the numbers of transmit/receive BSs. An efficient joint beamforming design algorithm and three different heuristic BS mode selection methods are proposed to solve this non-convex NP-hard problem. Simulation results demonstrates the advantages of cooperative ISAC networks, the importance of BS mode selection, and the effectiveness of our proposed algorithms

Integrating Social Circles and Network Representation Learning for Item Recommendation

With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users’ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms

in KKAy mice

and mechanisms of resveratrol on the amelioration of oxidative stress and hepatic steatosi

Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable Energy Review

MicroRNA-212-5p Prevents Dopaminergic Neuron Death by Inhibiting SIRT2 in MPTP-Induced Mouse Model of Parkinson’s Disease

Recently, emerging evidences show that sirtuins (SIRTs) modulate aging progress and affect neurodegenerative diseases. For example, inhibition of SIRT2 has been recognized to exert neuroprotective effects in Parkinson’s disease (PD). However, current SIRT2 inhibitors are lack of selective property distinguished from its homolog. In this study, we found that SIRT2 protein level was highly increased in PD model, which was negatively regulated by miR-212-5p. In detail, miR-212-5p transfection reduced SIRT2 expression and inhibited SIRT2 activity. In vivo study, miR-212-5p treatment prevented dopaminergic neuron loss and DAT reduction by targeting SIRT2, which means miR-212-5p shows neuroprotective effect in PD. Mechanismly, we found nuclear acetylated p53 was up-regulation according to p53 is a major deacetylation substrate of SIRT2. Furthermore, decreased cytoplasmic p53 promoted autophagy in PD model, which was showed as autophagosomes, autophagic flux, LC3 B and p62 expression. Meanwhile, we also found miR-212-5p treatment somehow alleviated apoptosis in PD model, which might have some underlying mechanisms. In conclusions, our study provides a direct link between miR-212-5p and SIRT2-mediated p53-dependent programmed cell death in the pathogenesis of PD. These findings will give us an insight into the development of highly specifically SIRT2 inhibitor of opening up novel therapeutic avenues for PD

Intention Understanding in Human-Robot Interaction Based on Visual-NLP Semantics

With the rapid development of robotic and AI technology in recent years, human-robot interaction has made great advancement, making practical social impact. Verbal commands are one of the most direct and frequently used means for human-robot interaction. Currently, such technology can enable robots to execute pre-defined tasks based on simple and direct and explicit language instructions, e.g., certain keywords must be used and detected. However, that is not the natural way for human to communicate. In this paper, we propose a novel task-based framework to enable the robot to comprehend human intentions using visual semantics information, such that the robot is able to satisfy human intentions based on natural language instructions (total three types, namely clear, vague, and feeling, are defined and tested). The proposed framework includes a language semantics module to extract the keywords despite the explicitly of the command instruction, a visual object recognition module to identify the objects in front of the robot, and a similarity computation algorithm to infer the intention based on the given task. The task is then translated into the commands for the robot accordingly. Experiments are performed and validated on a humanoid robot with a defined task: to pick the desired item out of multiple objects on the table, and hand over to one desired user out of multiple human participants. The results show that our algorithm can interact with different types of instructions, even with unseen sentence structures