Conservation Of Energy Resources Research Articles

Imparting concepts of sustainability and green buildings via a case study on an educational building

Abstract Sustainable building construction is the need of the hour for any developing nation. Sustainable or Green buildings mainly focus on the conservation of energy, water, and other scarce resources. Indian Green Buildings Council (IGBC) and Green Rating for Integrated Habitat Assessment (GRIHA) are two organizations in India which certifies buildings of various categories based on the sustainable practices adopted in the buildings. Certification is given for existing as well as new buildings. This paper attempts to evaluate the sustainable features of an existing school as per IGBC standards. The study revealed that the school under consideration has few sustainable features. Very few points were scored in the most important criteria i.e., water efficiency and energy efficiency, which contributes to more than 50% of the total points. The existing school building can be converted into a green building with certain modifications to the existing school infrastructure. This detailed study on various existing features of the building will help the school authorities to improve the building for a sustainable environment to the school students.

Operative forecasting of fuel gas consumption in gas transportation companies

In the current context of energy resource conservation and increased effi ciency of gas transportation systems, developing approaches to improve the accuracy of fuel gas consumption forecasting at compressor stations is a pressing task. This paper analyzes approaches and algorithms for forecasting the volumes of gas needed for the technological and internal needs of compressor stations during gas compression within the gas transportation organizations of Gazprom PJSC. A classifi cation of gas consumption for technological needs and losses is presented, emphasizing the importance of managing fuel gas consumption to optimize the cost of natural gas transportation.The goal of this study is to develop an approach for operational forecasting of fuel gas consumption at compressor stations of gas transportation organizations, aimed at increasing economic effi ciency and reducing operating costs. To achieve this goal, the following tasks were undertaken: analysis of existing forecasting methods, investigation of data processing techniques for detecting errors and anomalies, and comparison of various regression models to ensure high forecast accuracy.The study employed data cleaning and preprocessing methods, including the Isolation Forest method for anomaly detection, as well as various regression models such as multiple linear regression, RandomForestRegressor, CatBoostRegressor, and XGBoost. Data segmentation was performed using cluster analysis (KMeans), which allowed for improved model accuracy. Forecast accuracy was assessed using t-tests, F-tests, and the mean absolute percentage error (MAPE) metric.The results of the study confi rmed the high accuracy of the proposed approach, demonstrating its potential for optimizing fuel costs in gas transportation organizations.

An energy efficient prediction based protocol for target tracking in wireless sensor networks

Target tracking is one of the most attractive applications of wireless sensor networks, used for estimating the moving target's position accurately. A primary challenge in this domain is achieving precise target path estimation while conserving energy resources. This paper introduces an energy-efficient target tracking protocol in wireless sensor networks, considering both accuracy and reduced energy consumption. The protocol uses the Kalman filter to estimate the target's position and predict the subsequent step of its path. In each step of target tracking, a selected sensor, named the ‘leader’ performs computations for position estimation and path prediction, while two other sensors, known as ‘assistants’ help the leader in the tracking process. Leader selection within the protocol is performed in two phases: an initial phase occurring upon the target's entry to the network and a subsequent phase named the forced handoff phase. The forced handoff phase performs the selection of a new leader when either the target exits the sensing range of the current leader or the leader's energy decreases significantly. Although the proposed protocol is a new work, it can be considered as an improvement of the PPCP protocol by adding several changes and also replacing the binary variational filter with the Kalman filter. The efficiency of the proposed protocol is evaluated through simulations in Matlab. Results demonstrate the protocol's ability to achieve high-precision target tracking while maintaining low energy consumption. Comparative analysis shows its energy efficiency, which significantly increases the network lifetime.

My precious: development and validation of the conservation of human energy resources index (CHERI)

ABSTRACT Conserving energy resources is crucial for sustaining effort and preventing exhaustion, especially in demanding work environments. Yet, a reliable and valid measure for assessing individuals’ motivation to conserve energy is currently lacking. To fill this gap, we introduce a concise five-item scale, the Conservation of Human Energy Resources Index (CHERI), available in both German (CHERI-G) and English (CHERI-E) versions. In four comprehensive studies (N = 1,814), we evaluated the structure and psychometric properties of both language versions. Our results reveal that the CHERI measures a one-dimensional, homogeneous construct characterized by a high internal consistency (Studies 1, 2, and 4). Our findings also emphasize the CHERI’s sensitivity to change (Study 3) and its meaningful associations with measures from the nomological network of motivation, energy, and effort (Studies 2 and 4). Additionally, we demonstrate the cross-cultural equivalence of the CHERI-G and CHERI-E as well as their equivalence across situational contexts, encompassing both workplace and academic settings (Study 4). Taken together, the present research provides preliminary evidence for the psychometric properties of the CHERI – a measure that can be used to enhance our understanding of energy conservation behaviours and to open new perspectives for advancing research on energy management and recovery.

Blockchain-based concept for total site heat integration: A pinch-based smart contract energy management in industrial symbiosis

Industrial symbiosis has gained prominence in pursuing sustainable industrial practices, aiming to optimise resource utilisation and reduce environmental impacts. A critical aspect of this endeavour is the efficient management of energy resources within an industrial ecosystem. This paper presents a novel approach to enhance Total Site Heat Integration (TSHI) implementations by employing Blockchain as a facilitator for decentralised energy management. TSHI is an efficient and widely applied method for industrial symbiosis concerning energy flows, which employs the steam mains in site utility systems as the platform for exchanging heat between industrial processes at various temperature levels. It is shown that the synergy of Pinch Analysis and Smart Contract technologies is capable of facilitating energy integration of processes belonging to independent market actors, compared with the currently dominant integration inside a single company. The proposed framework leverages Blockchain as a distributed ledger to enable secure, transparent, and automated energy management across multiple industrial entities in an industrial symbiotic network. The integration of Pinch Analysis principles ensures that the Heat Integration process is optimised to improve the overall energy efficiency. Smart contracts enable automatic negotiation and execution of energy transactions based on predefined rules, minimising the time lag for concluding deals on energy resource exchange and conservation. This paper examines several scenarios to illustrate the implementation of the proposed Blockchain-based TSHI concept within an industrial symbiosis network. It is demonstrated that up to 16 % cost savings are possible by simply enabling transparency via Blockchain. The results could drive innovative development to revolutionise decentralised energy management in a complex industrial ecosystem, especially by synchronising energy exchanges in time.

Enhancing Energy Management in New Energy Vehicles and Energy Storage Systems Through Advanced Data Analysis and Machine Learning

This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS.

Enhancing Energy Management in New Energy Vehicles and Energy Storage Systems Through Advanced Data Analysis and Machine Learning

This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS.

Optimizing energy-efficient cluster head selection in wireless sensor networks using a binarized spiking neural network and honey badger algorithm

Wireless Sensor Networks (WSNs) play a vitalparton some applications, like environmental monitoring, healthcare, industrial automation. Optimizing the performance of WSN is essential to ensure efficient data delivery while conserving energy resources. This manuscript addresses the challenges of Multi-Objective Cluster Head Selection and Energy-Aware Routing in WSNs through the development of the Binarized Spiking Neural Network optimized with the Honey Badger Algorithm (EACHS-BzSpNNHBA-WSN). The appropriate cluster heads selection is vital for the overall presentation of WSNs, influencing data aggregation, routing, and energy consumption. Conventional approaches often struggle to balance the conflicting objectives of maximizing network lifetime, minimizing energy consumption, and ensuring timely data delivery. Therefore, there is a pressing need for innovative solutions capable of addressing these multi-objective optimization challenges. In this study, the EACHS-BzSpNNHBA-WSN framework is proposed, leveraging the computational power of Binarized Spiking Neural Networks and the optimization capabilities of the Honey Badger Algorithm to identify optimal cluster heads in WSNs. By formulating the cluster head selection as a multi-objective optimization problem, the aim is to achieve robust data delivery while minimizing energy consumption and extending the network lifetime. The proposed framework is implemented in MATLAB platform. The performance assessment is conducted using various metrics, including the number of active nodes, packet drop rate, energy consumption, network lifetime, delay, throughput, packet delivery ratio. Comparative analysis with existing models demonstrates the effectiveness of the EACHS-BzSpNNHBA-WSN approach.

СУЧАСНІ ТЕНДЕНЦІЇ РОЗВИТКУ СИСТЕМ КОМПЕНСАЦІЇ РЕАКТИВНОЇ ПОТУЖНОСТІ

This paper surveys current trends and developments in reactive power compensation systems, elucidating their significance in enhancing system capacity and flexibility. By analyzing the capacity factor enhancement enabled by reactive power compensation, the study underscores its role in optimizing electricity delivery, thus conserving energy resources and facilitating the integration of renewable energy sources. It explores the key models and concepts driving advancements in reactive power compensation within power grids. As modern electrical systems grow in complexity, ongoing research into improving compensation methods becomes imperative to ensure the reliability and efficiency of industrial power grids. Future endeavors will involve developing a model utilizing real-world enterprise data to investigate the effects of technological variations in filter parameters on the performance of complex reactive power compensation schemes, encompassing both filters and capacitor banks. Keywords: reactive power, compensation systems, filter-compensating circuit, industrial power grid, electric power. Context. The paper considers the current trends used in the field of reactive power compensation, and the main directions of development of modern methods of reactive power compensation. Objective. The purpose of the work is to overview of current trends in the development of reactive power compensation systems, as well as an overview of possible ways of their effective application in various industrial networks. Methods. The paper presents a key aspect of reactive power compensation is its ability to increase system capacity and flexibility. By improving the capacity factor, utilities can deliver electricity more efficiently, reducing the need for excess capacity and thus conserving energy resources. In addition, this process plays an important role in supporting the integration of renewable energy sources such as wind and solar power, which often introduce volatility into electricity generation schemes. Results. The paper presents the main models and concepts in the field of reactive power compensation in power grids. Conclusions. Given the growing complexity of modern electrical systems, it is important to continue research into improving methods and means of reactive power compensation to ensure the reliability and efficiency of industrial power grids. In further research, it is planned to develop a model using a real enterprise as an example and investigate the impact of technological deviations of filter parameters on the steady-state and transient performance of complex reactive power compensation schemes, including not only filters but also capacitor banks.

Energy Budgeting and Efficiency Analysis of Organic Cotton: A DEA Approach

Background: Cotton (Gossypium hirsutum L.) is crop of economic importance in global and national scale. India is the greatest producer of quality cotton, the third largest exporter and the second largest consumer. Conventional cotton farming creates environmental pressures and negative impact on public health. Organic farming is the sustainability option in cotton production. Cotton is the energy intensive crop, which uses high amount of input energy to produce seed cotton. Hence, energy budgeting is highly essential for better energy utilization and resource conservation and also measuring the input use efficiency of a farm/agronomic practices is vitally important in present day Indian agriculture. Methods: Sensing the economic importance of cotton, ten number of eco-friendly, ecologically safe organic nutrient management (ONM) practices were framed and field experiment was conducted in split plot design with two main plots (M) and five subplots (S) and replicated thrice. The direct and indirect energy used in different ONM practices were computed and energy coefficients were computed from energy equivalents and that has been used as inputs to generate efficiency coefficients by using an input oriented DEA approach. Result: Energy budgeting on field operation basis reported that, the field preparation and irrigation operation uses most of the energy (in non-renewable forms) which needs attention and there is the scope to find alternative energy conservation systems. Results on input use efficiency reported that the ONM practice, double green manuring followed by cotton and application of well decomposed poultry manure and foliar application of fermented fish extract at 5% concentration at 25 and 35 DAS are 100% efficient in terms of technical CRS, technical VRS, scale, allocative and cost efficiencies. This organic nutrient management practice would produce optimum output from the least amount of input and would be ideal for sustainable cotton production.

Research Progress on Nonlinear Modeling of Electro-Hydraulic Servo Systems and Innovative Applications of Control Strategies

Abstract: In various specific application domains, electro-hydraulic servo systems are widely adopted due to their high power-to-volume ratio, superior dynamic response performance, and extremely high control accuracy. However, the inevitable presence of nonlinear factors in the system poses a series of challenges for precise mathematical modeling and the design and application of different forms of control strategies. This article aims to explore how various control strategies are specifically implemented to address the nonlinear characteristics of electro-hydraulic servo systems, building upon the research progress in modeling these systems considering nonlinear factors. Subsequently, the article summarizes their innovative applications in electro-hydraulic servo systems. Simultaneously, relevant patents and technologies were reviewed and summarized. The paper provides a detailed comparison and analysis of various nonlinear modeling methods, with a particular focus on the significance of neural networks and fuzzy logic control in enhancing the performance of electro-hydraulic servo systems. The article reveals significant advancements in modern control strategies in dealing with uncertainties and disturbances within the system, offering new perspectives and ideas for improving system efficiency and achieving energy resource conservation with technological progress. The review provides a series of practical recommendations aimed at optimizing the performance of electro-hydraulic servo systems. Despite progress in multiple research endeavors, future research should focus on further enhancing system performance and applying these strategies to a broader range of scenarios.

How does economic growth impact environmental quality in Ethiopia? A non‐linear approach

AbstractFueled by economic growth, among other factors, environmental degradation is one of the most serious challenges facing humanity in the twenty‐first century. Although extensive research has been conducted on the relationship between economic growth and environmental quality, the findings are inconclusive. This study investigated the relationship between economic growth and environmental quality in Ethiopia from 1981 to 2016 using non‐linear autoregressive distributive lag and two‐step least squares models. First, the study finds that the relationship between economic growth and environmental quality is asymmetric. Second, economic growth negatively affects environmental quality. Third, the positive environmental effect of a negative shock on economic growth is stronger than that of a positive shock. Fourth, energy consumption and population growth negatively affect environmental quality, whereas the effects of renewable energy consumption, natural resources, trade openness and human capital are positive. Fifth, an inverted N‐shaped relationship exists between economic growth and environmental quality, where GDP per capita greater than $686.770 contributes to environmental quality. Therefore, high and sustainable economic growth, renewable energy consumption, population control, resource conservation, trade openness and human capital accumulation are required to improve the environmental quality of Ethiopia. The limitation of this study is that it examines the direct impact of economic growth on environmental quality. It would be better to examine the potential indirect impact of economic growth via variables, such as institutional quality and human capital.

Recent Advances and Perspectives of Nanomaterials

Nanomaterials have tremendous potential to contribute to sustainability in various sectors, including environmental remediation, energy storage and conversion, and agriculture. By harnessing their unique properties, nanomaterials offer innovative solutions that can address pressing global challenges while minimizing harmful impacts on the environment. The objective of this paper is to provide an overview of nanomaterials and their potential applications in promoting sustainability. The paper aims to explore the various ways in which nanomaterials can contribute to sustainable development, including their use in renewable energy production, resource conservation, and environmental remediation. Through a comprehensive review of relevant literature and case studies, the paper seeks to highlight the advantages and challenges associated with the integration of nanomaterials in sustainable technologies and practices. By presenting evidence-based arguments, the paper intends to demonstrate the significant role that nanomaterials can play in addressing global sustainability challenges and fostering a more environmentally friendly and socially responsible future.

Comprehensive Utilisation and Performance Optimisation of Agro-electricity and Agro-photovoltaic Complementary Systems in Agricultural Production

Agroelectricity agro-photovoltaic (APV) complementary systems are increasingly attracting attention in the field of agricultural production as a way of integrating and utilising renewable energy resources. The aim of this study is to investigate the integrated utilisation and performance optimisation of agro-electricity agro-photovoltaic (AEPV) systems in agricultural production in order to improve the efficiency of agricultural production, conserve energy resources, and protect the environment. The integrated utilisation section explores the application of agroelectricity agro-photovoltaic systems in different types of agricultural production, emphasising the importance of integrated energy utilisation strategies to improve energy and economic efficiency. The Performance Optimisation Methods and Strategies section describes optimisation methods for system operation, equipment performance, energy dispatch and storage to improve system performance and reliability. The section on sustainability and environmental impacts emphasises the environmental friendliness and sustainability of agropower agro-photovoltaic systems, and discusses related environmental protection strategies and social responsibility. The research and application of agro-power agricultural and photovoltaic complementary systems are expected to bring more sustainable and cost-effective solutions to agricultural production.

Assessment of AI-Based Robust Model Predictive Control Application in Large-Scale Photovoltaic-Based Controlled Environment Agriculture for Urban Agriculture

This research conducts an in-depth evaluation of integrating photovoltaic-based controlled environment agriculture (PV-based CEA) within urban centers, focusing on the advantages of implementing AI-based robust model predictive control (AI-based RMPC) as opposed to traditional control strategies in such settings. The study explores the impact of these advanced control techniques on energy usage, resource conservation, economic viability, and environmental effects in urban PV-based CEA. A large-scale assessment through simulations conducted year-round in 10 significant U.S. cities will test the effectiveness and feasibility of this approach. Results from these comprehensive simulations suggest considerable improvements, with energy and resource efficiencies showing a 15.4% and 12.3% enhancement, respectively. Furthermore, economic gains are projected at 7.5%, alongside a notable reduction in environmental pollutants, with water pollution dropping by 8.7% and light pollution by 3.6%.

Promoting plant reliability and safety through effective process automation and control engineering practices

In the realm of industrial operations, enhancing plant reliability and safety is paramount for achieving optimal performance and minimizing risks. Effective process automation and control engineering practices play a crucial role in this endeavor. By leveraging advanced technologies and methodologies, organizations can streamline operations, reduce human error, and ensure consistent output quality. Process automation involves the use of control systems, such as Programmable Logic Controllers (PLCs), Distributed Control Systems (DCS), and advanced sensors, to monitor and manage industrial processes. Implementing these technologies enhances operational efficiency by allowing for real-time data collection and analysis, enabling timely decision-making. Additionally, automation reduces the reliance on manual interventions, thereby decreasing the likelihood of operational errors that can lead to safety incidents. Control engineering practices are essential for designing and optimizing control systems that maintain desired process variables within specified limits. Utilizing techniques such as feedback control, predictive control, and model-based control ensures that processes operate smoothly, even in the face of disturbances. These practices not only improve process stability but also contribute to energy efficiency and resource conservation, aligning with sustainability goals. Safety is a critical consideration in process automation. By integrating safety systems with automation technologies, organizations can establish a robust framework for risk management. Safety Instrumented Systems (SIS) and Emergency Shutdown Systems (ESD) are examples of controls that help mitigate hazardous situations, providing an additional layer of protection for both personnel and equipment. Furthermore, promoting a culture of continuous improvement through regular training and audits ensures that automation systems remain effective and up to date with industry standards. By fostering collaboration among engineering, operations, and safety teams, organizations can enhance communication and promote shared responsibility for plant reliability. In conclusion, promoting plant reliability and safety through effective process automation and control engineering practices is essential for modern industrial operations. By harnessing advanced technologies and fostering a culture of safety, organizations can achieve higher operational efficiency, reduce risks, and ensure sustainable growth.

Characterization and Separation of Damage Mechanisms of Extruded Case-Hardening Steel AISI 5115 under Cyclic Axial-Torsional Loading

Due to the emerging relevance of topics such as climate change or scarcity of resources, the requirements for energy efficiency, emissions and resource conservation are increasing. In this context, components manufactured by metal forming offer a high potential for lightweight construction, cost effectiveness and resource efficiency. The defects resulting from forming processes e.g. in form of micropores and their growth are currently not taken into account. A commercial design is usually based on mechanical material properties and additional safety factors. The knowledge of the ductile forming-induced damage in the component design enables an improved design. In this study, the influence of different forming process parameters during full forward rod extrusion on the structural damage and the fatigue properties were investigated for the case-hardened steel AISI 5115 (16MnCrS5, 1.7131). The intention was to compare the fatigue properties of different damage states under cyclic axial and axial-torsional loading including the identification and separation of underlying damage mechanisms. A significant effect of superimposed cyclic torsional loading on cyclic axial properties and mechanisms was found, which was associated with a decrease of 38 % in the lifetime. Axial-torsional fatigue tests were conducted at various test temperatures to determine the effect of forming-induced damage and test temperature on the fatigue strength. In addition, differences in microstructure as a result of forming-induced and fatigue-induced damages were validated by using scanning electron microscopy.

Towards secure IoT system from a smart city perspective: An optimized algorithm and implementation

AbstractSecuring Internet of Things (IoT) systems is crucial for maintaining the resilience and trustworthiness of smart cities. In this context, the routing protocol's efficiency is pivotal for large‐scale IoT applications. However, achieving dependable and energy‐efficient routing in wireless sensor networks (WSNs) poses significant challenges due to limited and non‐rechargeable node energy resources. Also, traditional approaches often fell short of ensuring robust security and efficient data transmission. Hence, the purpose of this research was to address these shortcomings by introducing the Blowfish Honey‐Improved Spotted Hyena Optimization (BONY‐ISHO) protocol aimed at bolstering the security and efficiency of clustering and data routing in WSNs. Firstly, it introduces blockchain technology to establish secure and authenticated clustering, ensuring that only legitimate cluster heads and member nodes can participate. This innovation significantly enhances network security. Secondly, our protocol incorporates a hybrid Blowfish‐Honey cryptographic technique for data protection within the Area of Interest (AOI). This dual‐layer security approach, comprising Blowfish encryption and Honey encryption for key management, guarantees robust privacy and security. Thirdly, we have optimized data routing through the Improved Spotted Hyena Optimization (ISHO) algorithm, extending the network's lifetime and conserving energy resources. Performance evaluations confirm the superiority of the BONY‐ISHO protocol and existing solutions. It achieves a higher packet delivery ratio (99.8%), increased throughput (45 000 kbps), reduced energy consumption (1.68 joules), and minimized latency (0.068 sec) when compared to traditional methods. These performance enhancements make the BONY‐ISHO protocol a compelling choice for securing and optimizing IoT‐based WSNs. Our study concludes that the BONY‐ISHO protocol is a highly effective solution for addressing security and efficiency challenges in IoT‐based WSNs. By combining blockchain‐based authentication, hybrid cryptography, and intelligent routing algorithms, we have achieved a comprehensive enhancement of network security and data transmission efficiency. The protocol's ability to thwart malicious nodes and optimize routing pathways ensures the integrity and reliability of IoT‐based WSNs.

BIM in green building: Enhancing sustainability in the small construction project

The growing importance of green building practices in addressing environmental concerns, with a focus on energy efficiency, resource conservation, and occupant well-being, necessitates innovative solutions. Building Information Modeling (BIM), a sophisticated digital platform facilitating real-time collaboration and construction process visualization, emerges as a promising tool to enhance efficiency in construction projects. This study explores the role of BIM in advancing sustainability in smaller-scale construction projects. The methodology encompasses a comprehensive literature review and a quantitative analysis, including Exploratory Factor Analysis (EFA) and Structural Equation Modeling (SEM), with a case study centered in Perak, Malaysia. The research aims to assess the impact of BIM on resource efficiency, energy performance, waste reduction, and collaborative decision-making in small-scale green buildings. The findings reveal significant positive correlations between BIM adoption and early-stage design optimization, energy efficiency analysis, material selection, life cycle assessment, waste reduction, and prefabrication. This study underscores the vital role of integrating BIM in smaller-scale construction to promote environmentally responsible practices and achieve sustainable outcomes. Future research avenues may explore strategies for optimized BIM integration and deeper insights into the financial aspects of BIM adoption.

Development of glossy and UV-resistant urushi coatings by thermal polymerization

The use and development of natural biomass-based plastics are attracting attention with the aim of limiting environmental pollution and conserving energy resources. Urushi, a natural curing resin traditionally used in numerous applications such as decorative arts, furniture, and tableware, has great potential as an environment-friendly alternative to petroleum-based plastics. In this study, we produced urushi coatings in a short time using a thermal polymerization method, which is more convenient than the conventional oxidative polymerization method. The thermal polymerization method requires less time and energy than conventional oxidative polymerization and hence is more environmentally friendly. The urushi coatings were exposed to UV-A irradiation for 0–30 days, and the effects of irradiation on the surface were evaluated using Fourier-transform infrared spectroscopy, water contact angle measurements, scanning electron microscopy, gloss measurements, and pencil hardness tests. As a result, we developed functional high-gloss urushi-coated films with excellent hardness using thermal polymerization; these films retain these properties even after prolonged UV-A irradiation.