The influence of CEO characteristics on corporate environmental performance of SMEs: Evidence from Vietnamese SMEs

Drawing on upper echelon theory, this study investigates the impact of CEOs’ (chief executive officers) demographic characteristics on corporate environmental performance (CEP) in small and medium-sized enterprises (SMEs). We hypothesized that CEO characteristics, including gender, age, basic educational level, professional educational level, political connection, and ethnicity, affect SMEs’ environmental performance. Using the cross-sectional data analysis of 810 Vietnamese SMEs, this study provides evidence that female CEOs and CEOs’ educational level (both basic and professional) are positively related to the probability of CEP. We also find that based on the role of institutional environment on CEP, political connections had a negative effect on CEP in the context of Vietnam. Another finding is that SMEs with chief executives from ethnic minority groups show a higher level of the probability of corporate environmental performance than companies operated by Kinh chief executives. Since CEP is an essential dimension of corporate social responsibility, a strategic decision for SMEs, it is crucial for the company to select appropriate CEOs based on their demographic characteristic

THE IMPACT OF PROTESTANTISM ON THE ECONOMY OF THE COHO CHIL PEOPLE IN LAM DONG PROVINCE

This study examines the relationship between Protestantism and the Coho Chil economy using qualitative methods to analyze data from fieldwork in the Coho Chil Protestant communities of Lam Dong Province. The research shows that Protestantism has a positive impact on the economy of the Coho Chil community. This impact is manifested in casting out superstitious and magical practices and in applying economic rationalization. The Protestant doctrine and sermons by clergy have influenced the thinking of the people and their economic performance. Protestantism creates a social network and social capital that not only influences their economic life, but also provides timely material support for Christians in need. This paper serves as an additional resource for research related to the relationship between religion and the economy – an intriguing topic that is not found in much previous research in Vietnam.This study examines the relationship between Protestantism and the Coho Chil economy using qualitative methods to analyze data from fieldwork in the Coho Chil Protestant communities of Lam Dong Province. The research shows that Protestantism has a positive impact on the economy of the Coho Chil community. This impact is manifested in casting out superstitious and magical practices and in applying economic rationalization. The Protestant doctrine and sermons by clergy have influenced the thinking of the people and their economic performance. Protestantism creates a social network and social capital that not only influences their economic life, but also provides timely material support for Christians in need. This paper serves as an additional resource for research related to the relationship between religion and the economy – an intriguing topic that is not found in much previous research in Vietnam

Identifying target organ location of Radix Achyranthis Bidentatae: a bioinformatics approach on active compounds and genes

Background: Herbal medicines traditionally target organs for treatment based on medicinal properties, and this theory is widely used for prescriptions. However, the scientific evidence explaining how herbs act on specific organs by biological methods has been still limited. This study used bioinformatic tools to identify the target organ locations of Radix Achyranthis Bidentatae (RAB), a blood-activating herb that nourishes the liver and kidney, strengthens bones, and directs prescription to the lower body.Methods: RAB’s active compounds and targets were collected and predicted using databases such as TCMSP, HIT2.0, and BATMAN-TCM. Next, the RAB’s target list was analyzed based on two approaches to obtain target organ locations. DAVID and Gene ORGANizer enrichment-based approaches were used to enrich an entire gene list, and the BioGPS and HPA gene expression-based approaches were used to analyze the expression of core genes.Results: RAB’s targets were found to be involved in whole blood, blood components, and lymphatic organs across all four tools. Each tool indicated a particular aspect of RAB’s target organ locations: DAVID-enriched genes showed a predominance in blood, liver, and kidneys; Gene ORGANizer showed the effect on low body parts as well as bones and joints; BioGPS and HPA showed high gene expression in bone marrow, lymphoid tissue, and smooth muscle.Conclusion: Our bioinformatics-based target organ location prediction can serve as a modern interpretation tool for the target organ location theory of traditional medicine. Future studies should predict therapeutic target organ locations in complex prescriptions rather than single herbs and conduct experiments to verify predictions

Monitoring Heart Rate Variability Based on Self-powered ECG Sensor Tag

This paper proposes a batteryless sensing and computational device to collect and process electrocardiography (ECG) signals for monitoring heart rate variability (HRV). The proposed system comprises of a passive UHF radio frequency identification (RFID) tag, an extreme low power microcontroller, a low-power ECG circuit, and a radio frequency (RF) energy harvester. The microcontroller and ECG circuits consume less power of only ~30 µA and ~3 mA, respectively. Therefore, the proposed RF harvester operating at frequency band of 902 MHz ~ 928 MHz can sufficiently collect available energy from the RFID reader to supply power to the system within a maximum distance of ~2 m. To extract R-peak of the ECG signal, a robust algorithm that consumes less time processing is also developed. The information of R-peaks is stored into an Electronic Product Code (EPC) Class 1st Generation 1st compliant ID of the tag and read by the reader. This reader is functioned to collected the R-peak data with sampling rate of 100ms; therefore, the user application can monitor fully range of HRV. The performance of the proposed system shows that this study can provide a good solution in paving the way to new classes of healthcare applications

Differentiable Physics-based Greenhouse Simulation

We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data. The physics-based simulation model is fully interpretable and is able to do state prediction for both climate and crop dynamics in the greenhouse over very a long time horizon. The model works by constructing a system of linear differential equations and solving them to obtain the next state. We propose a procedure to solve the differential equations, handle the problem of missing unobservable states in the data, and train the model efficiently. Our experiment shows the procedure is effective. The model improves significantly after training and can simulate a greenhouse that grows cucumbers accurately.Comment: Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022. 7 pages, 2 figure

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