In this research Python machine learning module sklearn has been utilized to solve the Markov model. Markov modelling of the COVID-19 dynamics with air quality index (AQI), $\mathrm{PM}_{-2.5}$, $\mathrm{NO}_2$, $\mathrm{PM}_{-10}$, and $\mathrm{O}_3$, respectively. Data of the Chhattisgarh state of India has been analyzed in two phases. In phase-1 the time duration is from March 15, 2020, to May 01, 2020, and for phase-2 it is from Jun 01, 2020, to Jul 15, 2020. It has been noticed that initially change in AQI from 103 to 84.83 changed disease dynamics, and the first cases of COVID-19 reported. In the next two fortnights March 15,2020 , and April 01,2020 , the dynamics are the same, later the AQI change from 84.83 to 63.83 , but no change reported disease dynamics from April 15, 2020, to Jul 15, 2020. In phase 1, a cyclic trend has been observed for changes concerning $\mathrm{PM}_{-2.5}$. The trends for $\mathrm{PM}_{-2.5}$, $\mathrm{NO}_2$, $\mathrm{PM}_{-10}$, and $\mathrm{O}_3$, respectively are same, but for $\mathrm{O}_3$ it is different. COVID-19 reports a negative correlation with AQI, $\mathrm{PM}_{-2.5}$, $\mathrm{NO}_2$, $\mathrm{PM}_{-10}$. Moreover, a positive correlation with $\mathrm{O}_3$. This proves that the lockdown and ban on transport activities improved AQI, $\mathrm{PM}_{-2.5}$, $\mathrm{NO}_2$, $\mathrm{PM}_{-10}$, but not $\mathrm{O}_3$.

A comprehensive analysis of vehicle collision dynamics is presented using a two-mass model that simulates the impact of a vehicle against a rigid barrier. The model incorporates dual springs and dampers to examine the influence of spring stiffness and damping on a mass attached to the vehicle. The equations of motion are solved utilizing state variables, while energy principles are employed to establish correlations between vehicle deformation, impact force, and acceleration. Validation is conducted through comparison with crash test data from a 2023 Honda Accord LX 4-Door Sedan. Average deformation values are used to calculate acceleration, followed by a Monte Carlo simulation to analyze acceleration data recorded by the engine sensor, enabling the determination of vehicle speed through integration. Parametric regression is applied to optimize model parameters, resulting in a high degree of concordance between experimental and theoretical values. The model's accuracy is further verified through the analysis of velocity and deceleration profiles and the integration of the deceleration curve. The findings underscore the model's capability to replicate real-world crash dynamics, highlighting its potential to enhance vehicle safety system design. The innovation of this research lies in its simplified yet effective approach to modeling collision dynamics, offering significant insights into the relationship between vehicle deformation and occupant forces. This work advances the understanding of vehicle collision mechanics and provides a robust tool for the development of advanced safety features. The integration of theoretical and empirical data reinforces the model's reliability, contributing substantively to the field of automotive safety engineering.

Bali is one of the areas in Indonesia prone to water crisis due to the ever-increasing demand for water in line with population growth and economic activity. The cultural values of Tri Hita Karana and Sad Kertih inspire the Balinese perception of water. This study aims to evaluate water management policies in Badung Regency, Bali Province, by incorporating the cultural values of Tri Hita Karana and Sad Kertih. This research used qualitative methods, and the data analysis used document analysis and environmental discourse methods. The results of this study indicate that water governance in Badung is not optimal because it is still centralized and needs to involve cultural roles in the local community. This study concludes that the involvement of local communities, especially customary villages, is essential to realize sustainable water governance in an integrated manner. In addition, it is necessary to strengthen the appreciation of the values of Tri Hita Karana and Sad Kertih, which support the principles of environmental sustainability.

With the expansion of IoT platforms for smart livestock farming applications and their application to agriculture, farmers are becoming more interested. This research paper presents innovative IoT solutions for the economic propagation of animals using Raspberry Pi boards. This research has two main objectives (1) design and build a cooling system that continuously controls the temperature between 8-20 degrees Celsius and controls the oxygen content in the water between 4-8 mg/L. This control uses IOT technology to control the sending and receiving of data with the Raspberry Pi board. (2) The experimental cultivation of Chinese mitten crab in a traditional cold pond was compared to that of a new type of culture pond. The design and creation of the Chinese mitten crab culture pond and the use of IOT technology to control data transmission with Raspberry Pi boards together with temperature and oxygen sensor devices. Culture ponds have been found to cause eddy water and cause sewage and suspensions to be collected in the center of the pond and removed for their intended purpose. The design of the cooling system showed that the temperature and oxygen in the culture pond can be controlled according to its purpose. Similarly, the use of IOT technology to control the operation of temperature and oxygen sensor devices can be controlled with Raspberry Pi boards, which are ready to send and receive data via a Web server in RaspPi, and alarms can be displayed on computers and LINE applications with satisfactory results. Evaluation and experimental cultivation of Chinese mitten crab in a traditional cold pond compared to a new type of culture pond designed and created. Eighty male and 80 female Chinese mitten crabs that were one week old in a culture pond with a 1- to 6-week cycle of traditional culture had an average survival rate of 79.17%, and those in the new type of pond culture had an average survival rate of 89.58%. The evaluation also revealed that male crabs have a higher survival rate than female crabs and have a satisfactory, reliable, and objective growth rate.

This study investigates the sustainability practices employed by cattle farmers in Indonesia, applying the Sustainability Assessment of Food and Agriculture Systems (SAFA) framework. As global concerns surrounding environmental degradation and resource depletion intensify, understanding sustainable agricultural practices, particularly in livestock farming, has become imperative. A qualitative approach was employed, gathering data through interviews and field observations with seven cattle farmers from Boyolali and Salatiga, two districts recognized for their significant cattle farming activities. The analysis focused on four key dimensions of sustainability: environmental integrity, social well-being, economic resilience, and good governance. It was found that while farmers implement various sustainable practices, such as crop rotation and the use of organic fertilizers, significant challenges remain. These include limited access to environmentally friendly technologies, inadequate financial resources, and insufficient government policy support. The selection of participants was based on their ability to provide in-depth insights into sustainability practices in cattle farming, complementing the qualitative data collected. The findings highlight the necessity of improving technological adoption and enhancing community engagement to drive more sustainable outcomes in the sector. Additionally, the study underscores the role of policymakers in fostering more supportive environments for sustainable agriculture. This research fills a critical gap in the literature on the sustainability of cattle farming in Indonesia, offering practical recommendations to stakeholders, including policymakers, to promote more resilient and environmentally sustainable farming practices. By detailing the current practices and challenges encountered by farmers, the study contributes to the development of informed agricultural policies aimed at ensuring long-term sustainability within the cattle farming sector in Indonesia.

Amid increasing global awareness of the urgency of climate change and limited energy resources, designing strategies to reduce energy consumption and carbon emissions are crucial, especially in developing countries like Indonesia. With its growing per capita energy use and significant carbon emission burden, Indonesia faces a dual challenge: meeting its growing energy needs while minimizing environmental impacts. This study integrates Knowledge of Energy-Saving Labels (KEL) into the Theory of Planned Behavior (TPB) to explore the purchasing behavior of Energy-Efficient Home Appliances (EHAs) on Java Island, which is the region with the highest domestic energy consumption in Indonesia. Data from 239 valid questionnaires were collected and analyzed using the Partial Least Squares (PLS) approach through Smart-PLS version 4 software. The findings show that consumer attitudes (CA), perceived behavioral control (PBC), and subjective norms (SN) significantly influence the intention to purchase EHAs. Likewise, KEL significantly influences CA, PBC, and SN. This research not only confirms the applicability of the TPB in analyzing the behavior of Indonesian consumers towards EHAs but also provides practical insights for policymakers and industry to formulate more effective strategies to increase awareness and adoption of energy-efficient household products.

This study investigates perceptions of greenwashing within Indonesia's burgeoning fintech sector from the viewpoints of consumers and industry professionals. The research employs a stratified purposive sampling technique to ensure representation across diverse demographics familiar with fintech services. Purposive sampling identified and selected 18 consumers and 24 industry professionals with specific expertise relevant to fintech. Both groups participated in Likert-scale surveys designed to gauge their perceptions of greenwashing across various dimensions: product transparency, social responsibility, environmental impact, ethical investment options, and green marketing practices. Findings reveal generally positive consumer views towards product transparency (4.0), social responsibility (4.2), and green marketing practices (4.5), with more tempered ratings for environmental impact (3.5) and ethical investment options (3.8). Similarly, industry professionals rated product transparency (4.2), social responsibility (4.1), and green marketing practices (4.3) positively, with slightly higher ratings for environmental impact (3.9) and comparable ratings for ethical investment options (3.7). Hypothesis testing indicates significant disparities between consumer and professional perceptions, particularly concerning trust in fintech claims and perceived sustainability impacts. The study underscores the need for fintech firms to enhance transparency and ethical standards to bolster consumer trust and align with industry expectations. Ultimately, this research contributes to a deeper understanding of greenwashing within fintech, offering insights for industry stakeholders and policymakers to foster sustainable practices.

The utilization of oil-based drilling fluids is a significant technical approach for drilling in ultra-deep, unconventional, and other complex hydrocarbon reservoirs. However, these fluids present notable disadvantages, including high preparation costs and environmental pollution. There is an urgent need to develop an eco-friendly, high-performance water-based drilling fluid system suitable for complex geological conditions to support the exploration and development of oil and gas under deep, challenging, and unconventional conditions. Addressing the current issue where polymer filtrate reducers cannot simultaneously achieve temperature resistance, salt resistance, and environmental performance, a novel organic/inorganic composite micro-nano filtrate reducer (MNFR) was developed using inverse emulsion polymerization. The MNFR has a D₅₀ particle size of 1.313μm, withstands temperatures up to 200℃, resists saturated NaCl brine, and exhibits an EC₅₀ biotoxicity value of 86700 mg/L. Furthermore, a high-temperature-resistant (up to 200℃) eco-friendly high-performance drilling fluid system (HBHP) was constructed, demonstrating excellent rheological and filtration properties, with a high temperature and high pressure (HTHP) filtration volume of only 7.6mL and an EC₅₀ biotoxicity value of 54300mg/L. It also shows outstanding plugging, anti-collapse, and hydration inhibition properties. The HBHP system has been applied in three wells in the Shengli oilfield, with no complex situations related to wellbore stability occurring during field operations, thus providing technical support for the green development of complex hydrocarbon reservoirs such as deep, ultra-deep, offshore deepwater, and unconventional formations.

The recognition of abnormal events in surveillance video streams plays a crucial role in modern security systems, yet conventional techniques such as Support Vector Machines (SVMs) and decision trees (DTs) exhibit limitations in terms of accuracy and efficiency. These traditional models are often hindered by high false alarm rates and struggle to adapt to dynamic environments with variable conditions, thus reducing their practical applicability. In response to these challenges, an innovative approach, termed Adaptive Regression for Event Recognition (ARER), has been developed, leveraging advanced regression algorithms tailored for video data analysis. The ARER model integrates deep learning techniques, allowing for more precise temporal and contextual analysis of video footage. This methodology is structured through a multi-layered architecture that progresses from basic motion detection to complex anomaly identification. Trained on an extensive dataset covering a range of environmental and situational variables, ARER demonstrates enhanced robustness and adaptability. Evaluation results indicate that the ARER model achieves a 0.35% improvement in detection accuracy and a 0.40% reduction in false positives when compared to SVMs. Additionally, system throughput is increased by 0.25%, and detection latency is reduced by 0.30% in comparison to DTs. These advancements highlight the ARER approach as a superior alternative for real-time monitoring, offering significant improvements in both reliability and performance for surveillance applications.

Human Artificial Intelligence Teaming (HAIT) is a significant topic that is dominating different research domains. One of these domains is the automotive industry, whereby automation is suggested to certain aspects of driving, while the driver can intervene and be aware of the decisions. Trust is a major issue; hence the AI collaborates with the human towards making a decision regarding different aspects of driving. The Internet of Vehicles (IoV) is a topic that can use HAIT in many of its applications. A major point of the HAIT application is the increase in the transparency of the AI process and trust is being built between the two teammates. In this paper, the goal is to offer a comprehensive review of HAIT and its significance, going deep into various representations to facilitate the development of automated vehicles systems. HAIT seeks to promote trust in automated automotive systems, particularly regarding data sourced from vehicle sensors. The human roles 'in,' 'on,' and 'over' the loop within HAIT is provided, elucidating their pivotal contributions. Furthermore, ongoing academic contributions are reviewed integrating HAIT into the automotive sector, emphasizing the symbiosis between IoV and AI to forge unified solutions. The solutions have been separated according to their functionality and models used comprising Reinforcement Learning, Hidden Markov Models, Deep Learning and experiments as well as simulation based methods. The use of HAIT in automotive applications will pave the way to its utilisation in other disciplines such as aviation and maritime.

This research focused on reducing emissions from petrol engines to mitigate greenhouse gases. Experiments aimed to decrease pollutants from petrol engines and enhance efficiency at full load using hydrogen as a secondary fuel, injecting it for 2 milliseconds and 2.5 milliseconds. The study comprised two phases: one using petrol alone at all loads, and the other combining petrol with hydrogen injections at 216 gm/hour and 270 gm/hour. Performance, pollutants, brake, and mechanical efficiencies were compared between phases. Efficiency gradually improved with load for the 2ms injection. Efficiency improved in all timing cases with hydrogen compared to running on petrol alone. The highest efficiencies occurred with 2.5ms hydrogen injection, reducing pollutants at full load, making it the optimal interval. Injecting hydrogen in petrol engines improves efficiency by reducing emissions. Injecting hydrogen at 270 gm/hour at full load increased brake and indicated thermal efficiency by 9%, with no change in mechanical efficiency compared to pure petrol, which was slightly higher. Emissions of NO, CO₂, and HC were reduced by 16.5%, 15%, and 17.2% respectively. Oxygen percentage by volume increased by 10.43%, supporting complete combustion.