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The demand for rubber gloves has significantly increased in both medical and non-medical fields due to the spread of the coronavirus in 2019. It is challenging for rubber glove manufacturing industries to balance the production and demand for the product. Additionally, they must determine techniques to decrease the production costs so as to make rubber gloves more economical for consumers. Generally, natural gas, fossil fuels, and renewable energy sources are used worldwide in the manufacturing of rubber gloves. In addition, Thailand uses biomass energy for rubber glove production, but biomass utilization is not economically friendly. This study used different biomasses as fuel in rubber glove production so as to reduce production costs and make the process more environmentally friendly. Wood chip (WC), palm kernel shells (PKS), and oil palm mesocarp fiber (OPMF) biomass were collected from local regions and used in different ratios. The samples of WC, PKS, and OPMF were prepared in four different ratios, namely, 88:12:0, 85:15:0, 85:13:2, and 85:10:5, for efficient biomass utilization. The 85:10:5 (WC: PKS: OPMF) ratio was found to be the optimal ratio as the annual production costs of rubber gloves significantly decreased to USD 1.64 per 1000 units of gloves. Furthermore, this biomass ratio also showed the best boiler efficiency of 74.87%. Therefore, WC, PKS, and OPMF biomass are recommended as fuel for rubber glove industries to make sustainable and economical production processes.

Bats are important reservoir hosts of emerging viruses. Recent viral outbreaks and pandemics have resulted in an increased research focus on the genetic diversity, population structure, and distribution of bat species. Lyle’s flying fox (Pteropus lylei) is widely distributed throughout central Thailand, with most colonies congregating in temples within proximity to humans. A lack of knowledge regarding the genetic connectivity among different colonies hinders the investigation of zoonotic disease epidemiology and wildlife management. In this study, we hypothesized that genetic material may be exchanged between Lyle’s flying fox colonies that live in proximity. We assessed the mitochondrial displacement loop and cytochrome b nucleotide sequences of samples collected from 94 individuals from ten colonies across different roosting sites and detected limited genetic differentiation but increased nucleotide divergence within colonies. This suggests that genetic connectivity among Lyle’s flying fox colonies has experienced frequent and recent gene flow. These findings indicate that this species has maintained demographic equilibrium in a stable population, with a slight expansion event in certain populations. These data provide insights into the dynamics of bat populations, and the genetic knowledge gained presents opportunities for the improved monitoring of bat population structure.

The Philippines is a developing archipelagic country in Southeast Asia. The country is susceptible to multiple natural disasters, specifically earthquakes. This implies the significance of understanding earthquake evacuation choice in order to design effective planning and management of evacuation to minimize chaos, damage cost, and the loss of lives. This study investigated the determinants of earthquake evacuation and proposed earthquake evacuation planning and management in the Philippines, featuring the case study of Surigao City. The study used the primary dataset of 1055 observations gathered in 2019 and applied the nested logit model (NLM) to investigate the potential factors of earthquake evacuation decisions. We considered three output variables: evacuation choice, evacuation duration, and travel mode choice. We found that residents were more likely to evacuate their homes upon receiving an earthquake warning and move to a public shelter or open space. Additionally, respondents were more inclined to leave their homes when their houses suffered from moderate to severe/complete damage or when electricity and water supply were cut-off. Respondents were most likely to walk to evacuation centers as the majority of residents initially moved to the nearest open space immediately after an earthquake and stayed in an open space for less than 6 hours. No correlation was found between personal and household income factors with evacuation choice and travel mode choice. Furthermore, the study used the Analytical Hierarchy Process (AHP) to determine areas suitable for earthquake evacuation using insights from local government officials and planners. The areas identified for earthquake evacuation were developed to support evacuation planning and management.

Sustainable consumption and production (SCP) is largely influenced by dynamics in the market system. Hence, this study adopts the systems thinking approach as a tool to understand the non-linearity and complexity of sustainable practices. The food delivery business has accelerated the plastic waste problem, especially during COVID-19 where restaurant dine-in was limited. This study aims to identify high leverage points, which contribute to system intervention strategies to improve Thailand’s single-use plastics (SUP) waste situation caused by the food delivery sector. Semi-structured interviews were conducted with relevant stakeholders (n = 14). A qualitative system dynamics model analysis and thematic analysis suggest that the key leverage points include (1) benefit alignment among all stakeholders, (2) cost minimization and profit maximization, (3) laws and regulations, (4) postconsumption waste management systems, and (5) research and development. In addition, to stimulate policy initiatives, this study suggests that system intervention strategies should include behavioral instruments (setting default and eco-labelling), market-based instruments (green-packaging procurement and subsidies), and system and infrastructure provision (waste management systems and deposit return models). Partnerships, research and development, and laws and regulations are proposed as supporting measures.

Around 1.3 million people worldwide die each year because of road traffic crashes. There are many reasons which cause accidents, and driving in the wrong direction is one of them. In our research, we developed an autonomous framework called WrongWay-LVDC that detects wrong-way driving vehicles from closed-circuit television (CCTV) videos. The proposed WrongWay-LVDC provides several helpful features such as lane detection, correct direction validation, detecting wrong-way driving vehicles, and image capturing features. In this work, we proposed three main contributions: first, the improved algorithm for road lane boundary detection on CCTV (called improved RLB-CCTV) using the image processing technique. Second is the Distance-Based Direction Detection (DBDD) algorithm that uses the deep learning method, where the system validates and detects wrong-driving vehicles. Lastly, the Inside Boundary Image (IBI) capturing feature algorithm captures the most precise shot of the wrong-way-of-driving vehicles. As a result, the framework can run continuously and output the reports for vehicles’ driving behaviors in each area. The accuracy of our framework is 95.23%, as we tested with several CCTV videos. Moreover, the framework can be implemented on edge devices with real-time speed for functional implementation and detection in various areas.

The importance of creativity for working life and in organizations has increased in recent years. At the same time, the theme of sustainability has been intensely debated in research, society, and organizations. Together, creativity and sustainability have sometimes been described as a contradictory phenomenon: they are described in ways that place them in opposition to each other. To better understand creativity and sustainability and their differences from the perspective of people in different positions, we take advantage of a sociocultural approach in which we do not focus only on creative individuals but also on the impact of creativity on both organizational stakeholders and society at large. We aim to explore manager and employee descriptions of creativity and its relationship with sustainability at work in the Finnish technology sector, with a particular focus on how they relate to the sustainability of the creative processes and to workplace activities more generally. Based on a thematic analysis of 56 interviews, we found that the managers and employees in Finnish technology organizations described creativity in different ways, looking at the phenomenon from the viewpoints of clients, businesses, society, or colleagues, and had different perspectives on what it means to create, with the former treating creativity as huge innovations and the latter as daily problem-solving. We also found that sustainability in relation to creativity appears either as applying old solutions and thus recycling previous ideas or outcomes or as destroying old products and replacing them with the new (creative destruction). We discuss these partly conflicting discourses at the end of the article and present suggestions for future research.

This paper presents a comprehensive study on the optimization of electric vehicle (EV) battery management using Q-learning, a powerful reinforcement learning technique. As the demand for electric vehicles continues to grow, there is an increasing need for efficient battery-management strategies to extend battery life, enhance performance, and minimize operating costs. The primary objective of this research is to develop and assess a Q-learning-based approach to address the intricate challenges associated with EV battery management. This paper starts by elucidating the key challenges inherent in EV battery management and discusses the potential advantages of incorporating Q-learning into the optimization process. Leveraging Q-learning’s capacity to make dynamic decisions based on past experiences, we introduce a framework that considers state-of-charge, state-of-health, charging infrastructure, and driving patterns as critical state variables. The methodology is detailed, encompassing the selection of state, action, reward, and policy, with the training process informed by real-world data. Our experimental results underscore the efficacy of the Q-learning approach in optimizing battery management. Through the utilization of Q-learning, we achieve substantial enhancements in battery performance, energy efficiency, and overall EV sustainability. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach, demonstrating compelling results. Our Q-learning-based method achieves a significant 15% improvement in energy efficiency compared to conventional methods, translating into substantial savings in operational costs and reduced environmental impact. Moreover, we observe a remarkable 20% increase in battery lifespan, showcasing the effectiveness of our approach in enhancing long-term sustainability and user satisfaction. This paper significantly enriches the body of knowledge on EV battery management by introducing an innovative, data-driven approach. It provides a comprehensive comparative analysis and applies novel methodologies for practical implementation. The implications of this research extend beyond the academic sphere to practical applications, fostering the broader adoption of electric vehicles and contributing to a reduction in environmental impact while enhancing user satisfaction.

To confirm the capability and mechanisms of Sr(II) and Cs(I) adsorption from the aqueous phase using basil seed (BS), virgin BS, calcined BS (BS500 and BS1000), and enzymatically treated BS, namely Mannanase BGM (M-BS), Pectinase G (P-BS), Hemicellulase (H-BS), and Cellulase A (C-BS) was evaluated. The adsorption capabilities of Sr(II) and Cs(I) of various BS adsorbents were also evaluated. The quantity of Sr(II) and Cs(I) adsorbed onto BS was greater than that of BS500 or BS1000, suggesting that the physicochemical characteristics of the BS surface affected Sr(II) and Cs(I) removal from the aqueous phase. Furthermore, the quantity of Sr(II) and Cs(I) adsorbed onto virgin BS was greater than that of enzymatically treated BS, indicating that glucomannan or (1,4)-xylan in the cellulosic hydrocolloid of the BS strongly affected the adsorption capability of Cs(I) or Sr(II) (except for M-BS in Sr(II) adsorption). Our obtained results indicate that, as an adsorbent, BS was capable of removing Sr(II) and Cs(I) from the aqueous solution.