Resource Consumption Efficiency Research Articles

Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection

Detecting small dark targets underwater, such as fishing nets, is critical to the operation of underwater robots. Existing techniques often require more computational resources and operate under harsh underwater imaging conditions when handling such tasks. This study aims to develop a model with low computational resource consumption and high efficiency to improve the detection accuracy of fishing nets for safe and efficient underwater operations. The Light-YOLO model proposed in this paper introduces an attention mechanism based on sparse connectivity and deformable convolution optimized for complex underwater lighting and visual conditions. This novel attention mechanism enhances the detection performance by focusing on the key visual features of fishing nets, while the introduced CoTAttention and SEAM modules further improve the model’s recognition accuracy of fishing nets through deeper feature interactions. The results demonstrate that the proposed Light-YOLO model achieves a precision of 89.3%, a recall of 80.7%, and an mAP@0.5 of 86.7%. Compared to other models, our model has the highest precision for its computational size and is the lightest while maintaining similar accuracy, providing an effective solution for fishing net detection and identification.

Recent Developments in AI and ML for IoT: A Systematic Literature Review on LoRaWAN Energy Efficiency and Performance Optimization.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance.

Analysis of multi-factors affecting resource consumption efficiency in Vietnam

Theprimary objective of this study is todelve into the factors potentially influencing resource utilisationefficiency at the provincial level in Vietnam. We leverage 185 data points gathered from 63 Vietnamese cities or provinces, obtained from two key sources: the General Statistical Office of Vietnam and the Ministry of Information and Communications Vietnam. The collected data was subjected to analysis using an empirical econometric model that incorporates time-series characteristics to explore the connections between these potential determinants of resource consumption efficiency. The analysis revealed a strong correlation between resource utilisationefficiency and the quality of digital transformation, logistics performance, and various aspects of public governance,including accountability to higher authorities, control of corruption, administrative procedures, and public service delivery. Additionally, the provincial competitiveness index, encompassing factors like entry costs, policy bias, and labourpolicy, also demonstrated a significant association. These findings highlight a clear link between effective public governance and efficient resource utilisationin Vietnamese provinces. This suggests that robust public governance systems, combined with advancements in digitalisationand logistics, can play a crucial role in optimisingresource utilisationwithin Vietnamese provinces.

The combination of climate information services in the decision-making process of farmers to reduce climate risks: Application of social cognition theory

One of the main instruments of risk management and climate risk reduction in the agricultural sector is the use of climate information services (CIS). Meteorological information can be useful for society by increasing the efficiency of resource consumption and helping to improve decision-making management. However, there is a gap between the production and implementation of weather forecasts, and that is the lack of practical use of weather forecasts in the agricultural decision-making process. In doing so, this research was conducted with the main objective of identifying components influencing the utilize of CIS by farmers in southwest Iran. The research utilized social cognitive theory (SCT) as the underlying framework, employing a survey method and employing Structural Equation Modeling (SEM) for analysis. The research encompassed all farmers residing in Dezful city within the Khuzestan province, located in the southwest of Iran. The results showed that the variables of SCT, namely outcome expectations, perception of others’ behavior, self-efficacy, socio-structural, and behavioral intentions are very powerful in identifying factors affecting farmers’ use of CIS and can account for 76.4% of the variance of farmers’ behavior. Therefore, these research results can provide essential help to authorities to mitigate the negative impacts of climate change on agriculture.

Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments.

Federated learning (FL) is an emerging distributed learning technique through which models can be trained using the data collected by user devices in resource-constrained situations while protecting user privacy. However, FL has three main limitations: First, the parameter server (PS), which aggregates the local models that are trained using local user data, is typically far from users. The large distance may burden the path links between the PS and local nodes, thereby increasing the consumption of the network and computing resources. Second, user device resources are limited, but this aspect is not considered in the training of the local model and transmission of the model parameters. Third, the PS-side links tend to become highly loaded as the number of participating clients increases. The links become congested owing to the large size of model parameters. In this study, we propose a resource-efficient FL scheme. We follow the Pareto optimality concept with the biased client selection to limit client participation, thereby ensuring efficient resource consumption and rapid model convergence. In addition, we propose a hierarchical structure with location-based clustering for device-to-device communication using k-means clustering. Simulation results show that with prate at 0.75, the proposed scheme effectively reduced transmitted and received network traffic by 75.89% and 78.77%, respectively, compared to the FedAvg method. It also achieves faster model convergence compared to other FL mechanisms, such as FedAvg and D2D-FedAvg.

Sustainability analysis of irrigated and rainfed wheat production systems under varying levels of nitrogen fertilizer through coupling of emergy accounting and life cycle assessment

Most of the crop production systems have strived for improving crop productivity by increasing various resource inputs; especially water and synthetic nitrogen (N) fertilizer, without considering their environmental consequences. Yet, only few studies have evaluated the sustainability of cropping systems simultaneously using life cycle assessment (LCA) and emergy accounting (EMA) techniques. The former highlights pollutant emissions and environmental impacts, while the latter emphasizes resource consumption and system efficiency. In this three-year field study, the environmental impacts and sustainability of farming systems were estimated under rainfed and irrigated patterns at four N application rates in the Northwest China. Our result indicated that the irrigated pattern enhanced grain yield by 9%, while simultaneously increasing 8% of environmental impact and 12% of emergy input based on per hectare of wheat production. Almost similar results of environmental impact per kg basis of grain production and emergy sustainability index revealed that irrigation practice did not decrease the overall sustainability of a cropping system. Conversely, the higher N levels of 240 and 360 kg N ha−1 did not provide a proportionate yield return and were accompanied by increased environmental impacts of 20% and 69%, respectively on per kg grain production basis, when compared to an N dose of 120 kg N ha−1. Meanwhile, the gross unit emergy value revealed that the production efficiency under 120 kg N ha−1 was enhanced, in comparison with other N levels. Therefore, a moderate N dose of 120 kg N ha−1 under both irrigated and rainfed patterns is highly recommended for adoption due to its overall advantage in terms of yield returns, environmental impact and production efficiency.

Sustainable development practices in small and medium-sized enterprises: multiple case studies

Purpose This study aims to understand the sustainable development of small and medium-sized enterprises (SMEs), analysing their current practices in the social, environmental and economic domain. Design/methodology/approach To fulfil this objective, an exploratory, qualitative approach was adopted, using the multiple case study methodology and focusing on eight cases (SMEs) in Portugal. Data were collected through interviews, since this technique allows proximity and interaction with decision makers and those responsible for firms’ sustainability. Findings From content analysis of the interviews held, the results show that SMEs are aware of and committed to sustainability and that the external context and some of its particularities have a significant impact on their sustainable development. These SMEs undertake various practices of a social, environmental and economic nature, highlighting especially environmental ones such as efficient resource consumption, using more sustainable resources, recycling waste and waste management. Practical implications This study contributes greater knowledge of the phenomenon of SMEs’ sustainable development and identifies practical examples that could increase this firm segment’s awareness of the importance of sustainable practices associated with developing their business. Originality/value In this study, new and innovative sustainability practices are presented in the SMEs. The authors can underline that this study contributes to reinforcing the theory about the topic investigated by adding knowledge about sustainable development in the SME context. It deepens knowledge in this scientific area, which can be spread in the scientific community and among SMEs.

Evaluating the Efficiency of Resource and Energy Consumption of Farmland Systems

Evaluating the Efficiency of Resource and Energy Consumption of Farmland Systems

An empirical assessment of the effect of natural resources and financial technologies on sustainable development in resource abundant developing countries: Evidence using MMQR estimation

The present study aims to estimate the role of natural resources and financial technologies in sustainable development under the STIRPAT framework in seven resource abundant developing countries (Brazil, China, Russia, Saudi Arabia, Botswana, Morocco, and Turkey) over 2000 to 2020 period. To conduct empirical analysis, the study applies Fully Modified OLS (FMOLS), Dynamic OLS (DOLS) and Method of Moments Quantile Regression (MMQR). The findings of FMOLS and DOLS indicate the significant positive effect of natural resources and financial technologies on sustainable development in selected countries. These findings are robust in MMQR estimation (with significance of the coefficients varying at different quantiles). Based on these findings, the study recommends the policy makers and governments in these countries to implement effective policies aimed at the continual use and diffusion of financial technologies and efficient extraction and consumption of natural resources.

Improved artificial fish swarm based optimize rapidly-exploring random trees multi-robot exploration algorithm

To solve the problems of high storage resource consumption and low efficiency of the RRT exploration algorithm in the late stage of exploration, this paper proposes an Improved Artificial Fish Swarm based Optimize Rapidly-exploring Random Trees multi-robot Exploration Algorithm. Firstly, the efficiency of a single robot’s exploration of nearby unknown regions is improved by dynamically adjusting the step size of the RRT tree.Secondly, the improved artificial fish swarm algorithm is used to delete the redundant nodes in the RRT tree and optimize the node state in the RRT tree, which reduces the occupation of memory resources and improves the exploration efficiency of the RRT tree in the narrow environment.Results from comparative experiments in simulation environments with different degrees of openness show that the optimized exploration algorithm can save significant storage resources and show better exploration performance in narrow environments compared to the original RRT exploration algorithm.

Promoting mineral resources consumption efficiency: Evidence from technology of big data

Scholars hold differing views on the topic of mineral resource efficiency. This paper explores the application of big data to enhance mineral resource efficiency within the context of 500 Chinese companies listed on the Shanghai Stock Exchange between 2010 and 2019. The research methodology employs panel data, consisting of 5000 observations, and employs the Autoregressive Distributed Lag approach for estimation. The study reveals that the incorporation of big data has a positive, enduring impact on the mineral resource efficiency of the selected Chinese firms. Specifically, a 1% increase in big data utilization corresponds to a 0.43% enhancement in mineral resource efficiency. Furthermore, stock prices, environmental investments, and transparency indices play pivotal roles in advancing the objective of improving mineral resource consumption efficiency among these Chinese enterprises. On the contrary, the tax burden has an adverse influence on their resource consumption efficiency. To promote mineral resource efficiency through big data technology, practical policies are recommended. These include fostering sustainable ICT education, developing IT-driven resource management solutions, and supporting the advancement of environmentally-friendly technologies within the mining industry.

The effect of natural resource utilization efficency, financial development for the economic development In Asian Countries

Understanding the factors that influence economic development has drawn a lot of interest in light of the continuously changing economic environment of Asian nations. Using a Generalized Method of Moments (GMM) model, this research examines the link between natural resource use effectiveness, financial development, and economic growth in selected Asian nations from 2010 to 2021. The empirical research shows a positive and statistically significant association between natural resource consumption efficiency and economic development, highlighting the importance of resource management. Financial development also positively correlates with economic growth, highlighting the need for well-functioning financial systems. Economic growth is boosted by natural resource efficiency and financial development, indicating a synergistic effect. To promote economic development, policymakers in Asian countries should concentrate on improving resource management procedures.

Green economic development by increasing efficiency of natural resource consumption

The present study aims to assess the implications of fossil fuels resource efficiency on sustainable development across two distinct cohorts comprising 26 oil-importing economies and 19 oil-exporting economies, spanning the time period from 2000 to 2020. Employing empirical estimations through the Pooled Mean Group (PMG) technique, the research reveals that the sustainable development index in oil-exporting countries exhibits higher sensitivity to the efficiency of fossil fuels resources and the poverty rate, compared to oil-importing nations. Moreover, the impact of carbon dioxide emissions on the sustainable development index is notably more pronounced in oil-importing countries than in their oil-exporting counterparts. Additionally, the sustainable development index in oil-exporting economies demonstrates heightened sensitivity to temperature fluctuations, exceeding that observed in oil-importing nations. In pursuit of fossil fuels resources efficiency and sustainable development, the implementation of strategic measures such as carbon taxation, Fintech-driven initiatives, and the integration of green cryptocurrencies should be encouraged in both country groups.

Revisiting environmental Kuznets curve: The effects of foreign aid on air quality in Africa

The Environmental Kuznets Curve hypothesis suggests that an inverted U relationship exists between economic development and environmental pollution. However, this study shows that Chinese aid, particularly in the form of economic infrastructure in Africa, improved the local air quality and economic development simultaneously, even though Africa is still at the early stage of urbanization and industrialization. A battery of robustness checks, including instrumental variable estimation and permutation test with randomly assigned aid projects, further supports our findings. Therefore, aids in advanced economic infrastructure that improve resource consumption efficiency can mitigate the dilemma between growth and environment for emerging economies. (JEL: F35, O18, O55, Q53).

Industry 4.0 Technologies Promote Micro-Level Circular Economy but Neglect Strong Sustainability in Textile Industry

Large textile industries are deploying Industry 4.0 Technologies (I4.0T) aiming to develop a micro-level circular economy (CECP), considering cleaner production practices as a strategic tool. However, due to the environmental impact generated by the textile industry, it has not yet achieved Strong Sustainability (SS), denoting an important path to be pursued by managers and shareholders in view of meeting the 2030 agenda. With this, the objective of this study is to evaluate whether the adoption of I4.0T promotes CECP-driving SS actions in large textile industries located in Brazil. A survey was the research method adopted, and for data analysis, structural equation modeling was used to test the hypotheses and Pearson correlation between variables. It was concluded that the adoption of I4.0T promotes CECP. However, they neglect SS actions. Facilitators for CECP practices were big data, augmented reality, and autonomous robots. In terms of SS, I4.0T has a low impact on increasing resource consumption efficiency, reusing waste as input in other processes, and increasing access to commodities. This research contributes to the linking I4.0T, CECP, and SS theory. Moreover, with the dissemination of knowledge to managers about the I4.0T that generates the CECP, it is possible to develop sustainable strategies in operations. However, to move in this direction, it is essential that there is a collective effort of the government, companies, and society, starting by raising awareness about the importance of SS in the textile sector.

Nexus of financial decentralization and institutional resource consumption efficiency for a carbon neutral society: Policy implication of China

The Chinese government's use of fiscal policy, which includes fiscal income and spending, is crucial in its fight against environmental pollution and climate change. Innovation‐driven has arisen as a new alternative to reconcile economic success with environmental conservation in light of tighter resource restrictions and the suggestion of the Chinese High‐quality Development policy. The paper uses an econometric model to examine the connection between institutional resource consumption efficiency and carbon neutrality, using a data set consisting of Chinese inter‐provincial panel data from 2007 to 2020 as a research sample; fiscal decentralization is also discussed as a potential moderating factor. Collaborative innovation positively boosts ecological efficiency and produces geographical spillovers, and the findings suggest so long as the economic distance is considered. When it comes to increasing ecological efficiency, collaborative innovation plays a more important role than financial decentralization alone. Among these is the improvement of carbon neutrality, which is best achieved via the joint promotion of ecological efficiency and industrial organizations. By reducing the intensity of the positive effect of innovative cooperation on ecological efficiency, the moderating effect suggests that fiscal decentralization is a critical factor in this relationship. Promoting China's ecological efficiency necessitates a fair distribution of local budgetary spending. China should also actively encourage the degree of synergy between institutional resource consumption efficiency and associated institutions to improve eco‐efficiency.

Partner selection strategies of SMEs for reaching the Sustainable Development Goals

The paper aims to investigate the characteristics of SMEs’ partners that can better contribute to the achievement of the SDGs. Given the explorative nature of this research, a qualitative methodology is adopted, with a longitudinal case study employed as the research method. The Wråd case study confirmed that SMEs can contribute to the achievement of the SDGs by developing partnerships with multiple stakeholders. In addition, this research revealed that the characteristics of the SMEs’ partners can be referred to the four following dimensions: economic, environmental, social and purpose. Furthermore, it proposed that quality utility value, technique level, profitability are the subdimensions of the economic dimension and that resource consumption efficiency, pollution production, energy efficiency, environment management system and environment equipment and facilities are the subdimensions of the environmental dimension. This research expressed also that public health, employee engagement, brand reputation and local communities’ influence are the subdimensions of the social dimension. Finally, it underlined that sustainability-oriented awareness development, sustainable orientation, sustainable collective culture, proactive managerial skills, organisational learning and proactive and innovative leadership are the subdimensions of the purpose dimension. In line with previous studies underlining the importance of increasing the TBL dimensions, this study suggests a further extension of this theory with the ‘purpose’ dimension. This study highlights the importance of developing a vision supported by a social awareness of the problems that need to be addressed and that transcend the fashion sector. Further researches could explore a larger sample of SMEs operating in different businesses and the point of view of the SME’s partners.

The efficiency of natural resource consumption and government administration concerning green economic growth in Asian countries

This paper seeks the relationship between natural resources, public administration, and green economic growth in 30 Asian economies divided into two different income level groups over 2010–2021 by the FMOLS technique. The preliminary results confirmed that the efficiency of natural resource consumption and government administration in Asian countries has a significant positive effect. Moreover, fossil fuel consumption in both groups of countries has a negative and significant effect on economic growth. Furthermore, the green finance market and patent applications have a significant positive effect only in high-income Asian countries. In high-income Asian economies, there are bi-directional causality linkages among green economic growth, natural resource utilization efficiency, and public administration quality. In contrast, green economic growth in low-income economies cannot cause natural resource consumption efficiency and more suitable quality of public administration. Green multilateralism and establishing a green value chain are recommended as practical policies.

Evaluation of Agricultural Eco-Efficiency and Its Spatiotemporal Differentiation in China, Considering Green Water Consumption and Carbon Emissions Based on Undesired Dynamic SBM-DEA

Both agricultural output and environmental pollution in China show certain characteristics of spatiotemporal variation due to the evolution and inter-provincial disparities of resource endowment, economic development level, and production mode. This paper considered the green water consumption, carbon emissions in agricultural production, and the persistent role of fixed asset investment, constructed an undesired dynamic SBM-DEA model, and evaluated the agricultural eco-efficiency (AEE) of 31 provinces in China from 2007 to 2018, analyzing the spatiotemporal differentiation. The results show that, during 2007–2018, the following can be concluded: (1) The AEE of 31 provinces in China showed the characteristics of an overall stable rise from 0.64 to 0.70 but uneven development among regions from 2007 to 2018. (2) The averages of either the agricultural resource consumption efficiency index or pollution emission efficiency index in 31 provinces slightly increased over time, while the average value of the agricultural fixed asset investment efficiency index showed a decline in volatility. The spatial discrepancy of any index mainly stems from the index disparity between groups with a high AEE and groups with a low one, with contribution rates of more than 85%. (3) It is recommended to emphasize strengthening communication and cooperation between provinces with high and low AEE and implement distinct regional strategies to improve AEE.

Improving Lithium-Ion Battery Supply Chain Information Security by User Behavior Monitoring Algorithm Incorporated in Cloud Enterprise Resource Planning

Cloud enterprise resource planning (Cloud ERP) provides an efficient big data management solution for lithium-ion battery (LiB) enterprises. However, in the open ecological environment, Cloud ERP makes the LiB supply chain face multi-user and multi-subject interactions, which can generate sensitive data and privacy data security issues (such as user override access behavior). In this study, we take the value and information interaction into account to examine the user behaviors of the diverse stakeholders in the LiB supply chain. Therefore, a user behavior monitoring algorithm (UBMA), different from the mainstream supervised algorithms and unsupervised learning algorithms, is proposed to monitor the unsafe behaviors that may threaten data privacy in Cloud ERP. The results show that the UBMA can accurately search out the user behavior sequence where the unsafe behavior is located from a large amount of user behavior information, which reduces the complexity of directly identifying the unsafe behavior. In addition, compared with the recursive unsupervised binary classification method, the UBMA model has a lower resource consumption and higher efficiency. In addition, the UBMA has great flexibility. The UBMA can be further updated and extended by re-establishing the statistical characteristics of the standard user behavior fields to quickly adapt to user changes and function upgrades in the LiB supply chain.