Journal articles: 'Environmental leverages'

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BILETSKYI, Ihor. "CLASSIFICATION OF LEVERAGES OF STATE REGULATION OF THE CONSTRUCTION SECTOR OF RESIDENTIAL REAL ESTATE." Ukrainian Journal of Applied Economics 6, no. 2 (June 30, 2021): 330–37. http://dx.doi.org/10.36887/2415-8453-2021-2-42.

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The construction industry in Ukraine is an important component of social production and a key basis for improving the quality of life and accelerating innovative economic development. State regulation of the construction sector of residential real estate can be carried out with the help of certain leverages. The effectiveness of public policy depends on the correct choice of a leverages or a combination of several tools. State regulation of the construction sector of residential real estate can be carried out with the help of certain leverages. The purpose of the study is scientific and theoretical substantiation of the leverages essence and leverages classification generalization of the construction sector state regulation in residential real estate, considering their impact. According to the results of the study, the peculiarities of the state regulation leverages of the construction sector in residential real estate are given and the concept of «leverages» is clarified, by which they are understood the means of direct and indirect influence of state authorities and local governments within their powers through the prism of the regulatory field in the context of the implementation of state housing policy. The leverages have the following characteristics: 1) the basis for the use of leverages is regulatory and legal support; 2) state regulation leverages are aimed at a specific object, process to achieve a certain result; 3) are used by public authorities and local governments. The prospect of further research may be to determine the impact of environmental factors on the use of leverages of state regulation of the construction sector of residential real estate. Keywords: state regulation, levers of state regulation, construction sector, housing construction, residential real estate.

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Nota, Giancarlo, and Alonso Toro Lazo. "Leveraging the GQM+ Strategy approach and Industry 4.0 technologies for environmental sustainability in manufacturing." Journal of Smart Environments and Green Computing 2, no. 3 (2022): 143–62. http://dx.doi.org/10.20517/jsegc.2022.13.

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Aim: In the last years, sustainability has been identified as an enormous problem, with many facets gaining increasing attention. In this broad scenario, the availability of models for environmental sustainability constitutes a conceptual tool to guide industries towards reducing the environmental impact deriving from production. This work aims to contribute to the research on environmental sustainability in manufacturing by proposing a model that leverages the Goal Question Metrics approach and technologies of Industry 4.0. Methods: The Goal Question Metrics approach and technologies of Industry 4.0 are leveraged by proposing a model that contributes to environmental sustainability in manufacturing. Results: A model is proposed that can be used as a conceptual tool to support improvement programs in environmental sustainability. Conclusion: The application of the Goal Question Metrics+ Strategies to a case study of an automotive industry shows how the approach, combined with the implementation of Industry 4.0 technologies, contributes to the efficient use of natural resources and also reduces the emissions in the atmosphere.

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Charlton, Julie A., Wiktor F. Młynarski, Yoon H. Bai, Ann M. Hermundstad, and Robbe L. T. Goris. "Environmental dynamics shape perceptual decision bias." PLOS Computational Biology 19, no. 6 (June 8, 2023): e1011104. http://dx.doi.org/10.1371/journal.pcbi.1011104.

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To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer’s continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.

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Lamidi, Wasiu Adebayo, Adesola Olufunmilola Oluwatuyi, Tariro Masunda, and Adebayo Olagunju. "An Assessment of the Determinants of Environmental Costs of Listed Deposit Money Banks in Nigeria." International Journal of Business and Management Future 4, no. 1 (February 18, 2020): 12–20. http://dx.doi.org/10.46281/ijbmf.v4i1.483.

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This paper assesses the determinants of environmental costs of Nigerian banking institutions. Looking at the influence of profitability, company size and leverages on environmental cost such as donation, gift and developments, the population of the study are deposit money banks in Nigeria. Using purposive sampling technique, the study selected all fifteen (15) banks listed on the Nigerian Stock Exchange websites as at June 31, 2019 in which their annual reports are available to be extracted. The study utilized archival data i.e the annual report of listed banks from 2014 – 2017 to extract the needed information. Using STATA 14 software, the study conducts diagnostic tests such as heteroskedasticity, multicollinearity and Pearson correlation for data examination. Hence, multiple regression analysis was conducted to investigate the influence of profitability, company size and leverages on environmental costs. The analysis of the study showed that profitability, company size and leverages have positive and significant association with the environmental cost reported by these banks. Therefore, the study recommended that to adapt to the changing system of doing business, accountants ought to be equipped with skills and knowledge about environmental accounting and reporting, and in order to enhance competitiveness, banks must design and apply environmentally friendly strategies.

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Hutsaliuk, Oleksii, Nataliia Havrylova, Oksana Storozhuk, Yana Dovhenko, Snizhana Kovalenko, and Alla Navolokina. "Leverages of financial and environmental management in agricultural sector of the economy." E3S Web of Conferences 558 (2024): 01025. http://dx.doi.org/10.1051/e3sconf/202455801025.

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The article is aimed at determining theoretical views on processes of financial and environmental management of the agricultural sector. It analyzes the financial and environmental management of the agricultural sector of the economy, including the assessment of components of air, water, forest and land resources, crop and livestock production. The authors substantiate the system of ensuring the balanced development of the agricultural sector and examine measures to motivate the organization of eco-friendly agricultural production. The analysis of pollutant emissions into the atmosphere from stationary sources of pollution in Ukraine for 2017-2021 showed that the largest sources of pollution into the atmosphere in Ukraine for 2017-2021 were sulfur dioxide (25.7% in 2021), carbon monoxide into the atmosphere in Ukraine for 2017-2021 (31.4% in 2021) and methane (20.3% in 2021). The article defines the ways of state support for the agricultural sector of the economy and outlines the main principles of the agricultural sector development strategy. The revealed positive impact of the agricultural sector on the environment is ensured by two factors: financial and environmental responsibility manifested through management decision-making measures and financial and environmental motivation manifested through material, social and environmental values of the agricultural producer, consumer and society as a whole.

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Zaharkina, L., and O. Zaitsev. "FINANCIAL LEVERAGE ENVIRONMENTAL SECURITY AS A COMPONENT UKRAINE'S NATIONAL SECURITY." Vìsnik Sumsʹkogo deržavnogo unìversitetu, no. 1 (2019): 19–25. http://dx.doi.org/10.21272/1817-9215.2019.1-3.

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The article deals with the use of financial leverages to ensure environmental security as a component of Ukraine's national security. Scientific approaches to the concept and constituents of environmental security, peculiarities of its legislative regulation are analyzed. The dynamics of budget revenues using environmental taxation mechanisms have been investigated. The conclusions of its effectiveness are made. Structural analysis of the Consolidated Budget expenditures on environmental protection was also conducted. It is concluded that there is an imbalance in budgetary policy in the field of environmental security. And it is an indicator for decreasing the state's environmental security in the future.

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Selim, Mohamed S., Sherif A. El-Safty, Mohamed A. Shenashen, Shimaa A. Higazy, and Ahmed Elmarakbi. "Progress in biomimetic leverages for marine antifouling using nanocomposite coatings." Journal of Materials Chemistry B 8, no. 17 (2020): 3701–32. http://dx.doi.org/10.1039/c9tb02119a.

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Because of the environmental and economic casualties of biofouling on maritime navigation, modern studies have been devoted toward formulating advanced nanoscale composites in the controlled development of effective marine antifouling self-cleaning surfaces.

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DiSegni, Dafna M., Moshe Huly, and Sagi Akron. "Corporate social responsibility, environmental leadership and financial performance." Social Responsibility Journal 11, no. 1 (March 2, 2015): 131–48. http://dx.doi.org/10.1108/srj-02-2013-0024.

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Purpose – The purpose of this paper is to statistically assess the relationship between corporate characteristics, environmental contribution and financial performance. To this end, the authors compare the financial performance of all US corporations making up the Dow Jones Sustainability Indexes, being the most proactive companies in providing services and goods, while maintaining ethical responsibility and environmental sustainability. Design/methodology/approach – Various performance measures are compared to the mean performance of the related industry, sector and market portfolio. We employ an analysis for several time horizons of the financial measures. Findings – Analysis by the authors suggests that firms that are proactive in supporting social responsibility and environmental sustainability (SRES corporations) are characterized by significantly higher profit measures than the industry and the sector, though not higher than the entire market. They have lower short-term liquidity measures than those of the industry and related sector, and surprisingly, their long-term leverage is significantly higher. Strong SRES corporations are characterized by significantly higher managerial efficiency ratios than the respective industry and sector. Interestingly, however, the per-worker operating efficiency ratios are significantly lower than for all of the benchmarks. Practical implications – The revealed preference of corporations can be extracted from several horizon dependent financial measures. For instance, we could infer the corporate degree of SRES from their long-term capital structure, i.e. their long-term leverages and short-term liquidity measures. Originality/value – These results illustrate the strong relation between social and environmental sustainability, and long-term business plans in respect to the corporate capital structure.

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Fakhrul Yusuf, Muhammad, Hasbullah Ashari, and Mohd Rizal Razalli. "A Study on the Environmental Technological Innovation Strategy of a Malaysian Firm." International Journal of Engineering & Technology 7, no. 4.19 (November 27, 2018): 261–68. http://dx.doi.org/10.14419/ijet.v7i4.19.22064.

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Firms all over the world have pursued sustainable development to be competitive in the marketplace. The purpose of this case study is to examine the factors that enable a firm to pursue the environmental technological innovation (ET-innovation) strategies and how it effectively leverages its resources to achieve the environmental and business targets. This case study also wants to understand how well the firms ET-innovation activities can be explained by stakeholder theory, resource-based view and dynamic of innovation model. This paper uses a qualitative approach and the coding analysis was done on QSR NVivo. The data collection was done through interviews and also compared with other data sources (company brochures, reports, letters, speeches, memos, videos, newspaper articles, trade magazine articles, magazines articles, reports, website articles and technical reports) for triangulation purposes. This research showed that the ET-innovative firm in this case study has leveraged their employee technical abilities to be successful in their respective market. Another factor is the ownerâ€™s high interest in innovation and environmental protection, and involvement in the design and marketing of their eco-product. Despite stakeholdersâ€™ different targets, the case study firm manages to acquire support from their stakeholders through common objective which is to see a successful ET-innovation. Finally, it is proposed for the firm to be aware of the dynamics of eco-product lifecycle while also use current tools to manage their eco-product development especially in the earlier development stage.Â Â

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Leonardo Oliveira Santos de Santana, Jonatas de Oliveira Souza Cavalcante, Gustavo de Souza dos Santos, and Fernando Luiz Pellegrini Pessoa. "Seawater Refinary: A Pathway for Sustainable Metal Recovery and Green Hydrogen Production." JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH 7, no. 3 (December 14, 2024): 312–19. https://doi.org/10.34178/jbth.v7i3.418.

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With the growing environmental challenges of saltwater desalination, seawater mining is emerging as a sustainable alternative to traditional onshore mining. This paper explores the potential of recovering metals from seawater and producing green hydrogen, proposing a "seawater refinery" concept inspired by oil and biorefineries. The refinery leverages advanced separation technologies and "Zero Liquid Discharge" methods to convert brine into valuable products, including green hydrogen (H₂) while minimizing environmental impacts.

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Verfuerth, Caroline, Laura Henn, and Sophia Becker. "Is it up to them? Individual leverages for sufficiency." GAIA - Ecological Perspectives for Science and Society 28, no. 4 (December 19, 2019): 374–80. http://dx.doi.org/10.14512/gaia.28.4.9.

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Sufficiency is one important strategy for sustainable development. At an individual level, we need a better understanding of the relationship between sufficiency attitude and CO2 footprint. In this paper, we analyze sufficiency as a psychological determinant of low-carbon lifestyles and introduce an empirical measurement scale for individual sufficiency attitudes.Sufficiency aims at a total reduction of resource consumption, which is urgently needed to achieve our climate and sustainable development goals. This paper explores individual attitude towards a sufficiency-oriented lifestyle as a driver of a low carbon footprint. Survey data of 310 participants was analyzed to test whether individual sufficiency attitude manifests in people’s carbon footprint. The results provide evidence for this relationship but its strength varies between behavioral domains ‐ that is, heating, electricity, food consumption, everyday mobility, air travel. Potential structural and individual barriers to reducing CO2 emissions are discussed as possible factors that could explain differences between the behavioral domains. We argue that intrapersonal factors matter for sustainable lifestyles but that policy-making and structural change should complement and facilitate voluntary endeavors to achieve low-carbon lifestyles.

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Damodaran, Nedumaran, Sivamani Kalyansundaram, and Vinoth R. "Development And Testing of Arduino Based Autonomous Environmental Monitoring System." IJCRT Journal | UGC Approved Journal | Scopus Indexed Journal Norms 14, no. 4 (November 13, 2024): 1–7. http://dx.doi.org/10.61359/2024050026.

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This paper presents the development of an Autonomous Environmental Monitoring System (AEMS) designed to track various environmental parameters. With the growing demand for autonomous monitoring systems in today’s modern era, we propose an Arduino-based real-time hardware solution to measure parameters such as flame, temperature, humidity, and distance. The system leverages advanced and cost-effective electronic sensors and hardware to monitor these environmental factors. We developed individual sensor modules for each parameter and integrated them into a unified multi-sensor system through sensor fusion. The collected data is then displayed on a 16 × 2 LCD module. The result is a fully functional autonomous monitoring system, tested under laboratory environmental conditions using a compact, handheld device.

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Perri, Saverio, and Amilcare Porporato. "Environmental concentrations as ratios of random variables." Environmental Research Letters 17, no. 2 (January 26, 2022): 024011. http://dx.doi.org/10.1088/1748-9326/ac4a9f.

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Abstract Human-induced environmental change increasingly threatens the stability of socio-ecological systems. Careful statistical characterization of environmental concentrations is critical to quantify and predict the consequences of such changes on human and ecosystems conditions. However, while concentrations are naturally defined as the ratio between solute mass and solvent volume, they have rarely been treated as such, typically limiting the analysis to familiar distributions generically used for any other environmental variable. To address this gap, we propose a more general framework that leverages their definition explicitly as ratios of random variables. We show that the resulting models accurately describe the behavior of nitrate plus nitrite in US rivers and salt concentration in estuaries in the Everglades by accounting for heavy tails potentially emerging when the water volume fluctuates around low values. Models that preclude the presence of heavy tails and the related high probability of extreme concentrations could significantly undermine the accuracy of diagnostic frameworks and the effectiveness of mitigation interventions, especially for soil contamination characterized by a water volume (i.e. soil moisture) frequently approaching zero.

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Yue, Mingda. "Harnessing Regression Analysis and Machine Learning to Unveil the Market Dynamics and Environmental Impact of New Energy Vehicles." Highlights in Science, Engineering and Technology 98 (May 16, 2024): 412–18. http://dx.doi.org/10.54097/k2ggwv44.

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This study leverages regression analysis and machine learning algorithms to systematically investigate the growth patterns of the global New Energy Vehicle (NEV) market, evaluate the effects of international policy frameworks on NEVs, and quantify the environmental advantages of NEVs in urban settings. Through the analysis of worldwide market data, case studies on international policies, and urban air quality metrics, this research delineates the pivotal contributions of NEVs to mitigating urban pollution and advancing environmental sustainability. The findings provide empirical evidence and strategic recommendations for policymakers, the automotive industry, and environmental organizations, aimed at promoting the sustainable development of the global NEV market.

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Petter, Fabiano A., and Beata E. Madari. "Biochar: Agronomic and environmental potential in Brazilian savannah soils." Revista Brasileira de Engenharia Agrícola e Ambiental 16, no. 7 (July 2012): 761–68. http://dx.doi.org/10.1590/s1415-43662012000700009.

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Due to the high activity of microorganisms, the loss of soil organic matter is high in tropical regions. This loss becomes even greater if the soil is managed improperly or when there is no technology that leverages the permanence of the soil carbon by maintaining appropriate levels of organic matter, providing chemical, physical and biological soil improvements and contributing to reduce CO2 emissions to the atmosphere. Due to its aromatic structure, biochar is a highly stable form of carbon in the soil that may contribute to the reduction of greenhouse gas emissions, such as CO2, N2O and CH4, and act as a soil conditioner, improving the physical and chemical properties of the soil. Biochar may also result in increased productivity due to the improvement of soil attributes or a possible electrophysiological effect. Research over the past decades has demonstrated the potential of biochar as a soil conditioner, improving fertility and nutrient-use efficiency, in addition to maximising the productivity of crops, such as soybean and rice.

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Sun, George, and Yi-Hui Zhou. "Predicting Microbiome Growth Dynamics under Environmental Perturbations." Applied Microbiology 4, no. 2 (June 10, 2024): 948–58. http://dx.doi.org/10.3390/applmicrobiol4020064.

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MicroGrowthPredictor is a model that leverages Long Short-Term Memory (LSTM) networks to predict dynamic changes in microbiome growth in response to varying environmental perturbations. In this article, we present the innovative capabilities of MicroGrowthPredictor, which include the integration of LSTM modeling with a novel confidence interval estimation technique. The LSTM network captures the complex temporal dynamics of microbiome systems, while the novel confidence intervals provide a robust measure of prediction uncertainty. We include two examples—one illustrating the human gut microbiota composition and diversity due to recurrent antibiotic treatment and the other demonstrating the application of MicroGrowthPredictor on an artificial gut dataset. The results demonstrate the enhanced accuracy and reliability of the LSTM-based predictions facilitated by MicroGrowthPredictor. The inclusion of specific metrics, such as the mean square error, validates the model’s predictive performance. Our model holds immense potential for applications in environmental sciences, healthcare, and biotechnology, fostering advancements in microbiome research and analysis. Moreover, it is noteworthy that MicroGrowthPredictor is applicable to real data with small sample sizes and temporal observations under environmental perturbations, thus ensuring its practical utility across various domains.

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Kirkole, Advika, and Dr Jitendra M Bakliwal. "Botanic Monitoring of Plant Using Raspberry Pi." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (September 1, 2024): 1–7. http://dx.doi.org/10.55041/ijsrem37301.

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The project leverages the Raspberry Pi, a cost- effective board computer, to cultivate an all-encompassing botanic monitoring and control system. By amalgamating sensors, cameras, and actuators with the Raspberry Pi's computing power, real-time environmental data can be gleaned, refined, and tapped remotely via web or cell phone interface. This metaphysical marks the Raspberry Pi's purpose in establishing a diverse, cost-effective panacea for botanic monitoring and control, with benefits including precision, mechanization, and accessibility over agriculture and environmental research. Keywords— Nutrition Detection, Sensor Network, Wireless Communication, Precision agriculture, Design, Plant monitoring.

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Salgado, Suleima, Gregory Felzien, and Jared Brumbeloe. "Georgia Leverages Telehealth to Expand HIV Care Management in Underserved Areas." American Journal of Preventive Medicine 61, no. 5 (November 2021): S55—S59. http://dx.doi.org/10.1016/j.amepre.2021.07.001.

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Campbell, Carianne. "An Approach for Creating Site-Specific Planting Palettes to Support Pollinators in the Sky Islands." Air, Soil and Water Research 13 (January 2020): 117862212095026. http://dx.doi.org/10.1177/1178622120950269.

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Restoration practitioners are challenged to continually modify and adapt their approaches to restoration by considering a greater diversity of restoration techniques and broader suites of plant species. Presented herein is a pollinator planting palette design approach that leverages botanical species richness to assist pollinator conservation efforts in the uniquely biodiverse Sky Island region.

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Tsai, Wen-Hsien, Hsiang-Ching Chen, Shuo-Chieh Chang, and Kai-Cheng Chan. "Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity-Based Costing, and the Theory of Constraints." Processes 12, no. 11 (October 22, 2024): 2311. http://dx.doi.org/10.3390/pr12112311.

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The textile industry, a cornerstone of daily life and a highly globalized sector, faces significant environmental challenges due to its high water and energy consumption and extensive chemical usage. This study proposes a comprehensive green production planning and control model integrating Industry 4.0 concepts, activity-based costing (ABC), and the theory of constraints (TOC). The model utilizes mathematical programming to optimize product mix, maximize profitability, and minimize environmental impact. It leverages real-time sensing technologies and ERP systems to facilitate waste recovery, reduce carbon emissions, and achieve energy savings. Various carbon emission cost models, including continuous and discontinuous tax functions, are explored to balance corporate profitability with environmental sustainability. The findings demonstrate the model’s potential in optimizing resource utilization, reducing the environmental footprint, and enhancing profitability.

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Maleki Minbashrazgah, Morteza, and Atefeh Shabani. "Eco-capability role in healthcare facility’s performance." Management of Environmental Quality: An International Journal 30, no. 1 (January 14, 2019): 137–56. http://dx.doi.org/10.1108/meq-07-2017-0073.

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PurposeWith the exponential growth of problems linked to the natural environment, healthcare facilities are increasingly faced with ecological challenges in their operations. In this regard, they progressively need to improve their environmental performance like other industries in recent years. Applying the natural-resource-based view (NRBV) and dynamic capabilities literature, the purpose of this paper is to investigate this eco-capability which fully leverages human, relationship, and technology resources.Design/methodology/approachApplying the NRBV and dynamic capabilities literature, this paper investigates this eco-capability which fully leverages human, relationship, and technology resources. Using survey data from staffs across 15 hospitals in north of Iran, researchers estimated a latent moderated structural model that provides support for these three resource components.FindingsResults obtained from structural equation analysis revealed that human, technology, and relationship resources have positive impact on eco-capability. Also eco-capability has positive impact on market and financial performance and the perceived quality of the hospital’s offering.Research limitations/implicationsAs the model consist just of three kinds of resources, future studies could include examining the effect of additional resources and other internal and external factors on eco-capability.Practical implicationsThis paper includes suggestions for facility’s managers to develop environmental strategy in three areas: human, technology, and relationship resources.Originality/valueThe role of eco-resources has been used only rarely to investigate organization capability and performance. This paper includes suggestions for facility’s managers to develop environmental strategy in three areas: human, technology, and relationship resources.

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Patil, Amruta V. "Sign Detection Model for the Deaf People using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (June 21, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem35998.

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Sign detection is a crucial task in various computer vision applications, including autonomous driving, robotics, and augmented reality. This paper proposes a novel sign detection model based on deep learning techniques. The proposed model leverages convolutional neural networks (CNNs) to automatically learn discriminative features from sign images, enabling robust detection performance across diverse environmental conditions. Keywords: sign detection, deep learning, convolution neural networks, computer vision, autonomous driving, robotics, augmented reality.

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Tatiraju, Tatiraju, K. Dhineshkumar, Haritima Mishra, and Chandra Sekar P. "Machine Learning-Enhanced Wireless Sensor Networks for Real-Time Environmental Monitoring." International Journal of BIM and Engineering Science 10, no. 1 (2025): 18–25. http://dx.doi.org/10.54216/ijbes.100103.

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Wireless Sensor Networks (WSNs) are pivotal for real-time environmental monitoring, providing valuable data on variables like temperature, humidity, and pollution levels. However, ensuring timely and accurate data transmission and analysis remains a challenge due to resource constraints in WSNs. This study introduces a machine learning-enhanced WSN framework that leverages predictive algorithms for efficient data processing and anomaly detection in real time. By integrating machine learning models, the system can predict environmental trends, detect sensor faults, and identify unusual events, improving data reliability and reducing network load. Experimental evaluations in a simulated environment show a 40% improvement in anomaly detection accuracy and a 35% reduction in data redundancy. Furthermore, this framework achieved a 25% increase in energy efficiency, enhancing network longevity. This machine learning-optimized WSN framework provides an effective solution for continuous environmental monitoring in applications such as wildlife tracking, pollution control, and smart agriculture.

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Son, Yeonguk, Taeyong Lee, Bo Wen, Jiyoung Ma, Changshin Jo, Yoon-Gyo Cho, Adam Boies, Jaephil Cho, and Michael De Volder. "High energy density anodes using hybrid Li intercalation and plating mechanisms on natural graphite." Energy & Environmental Science 13, no. 10 (2020): 3723–31. http://dx.doi.org/10.1039/d0ee02230f.

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Galanis, Ioannis, Iraklis Anagnostopoulos, Priyaa Gurunathan, and Dona Burkard. "Environmental-Based Speed Recommendation for Future Smart Cars." Future Internet 11, no. 3 (March 24, 2019): 78. http://dx.doi.org/10.3390/fi11030078.

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Modern vehicles are enhanced with increased computation, communication and sensing capabilities, providing a variety of new features that pave the way for the deployment of more sophisticated services. Specifically, smart cars employ hundreds of sensors and electronic systems in order to obtain situational and environmental information. This rapid growth of on-vehicle multi-sensor inputs along with off-vehicle data streams introduce the smart car era. Thus, systematic techniques for combining information provided by on- and off-vehicle car connectivity are of remarkable importance for the availability and robustness of the overall system. This paper presents a new method to employ service oriented agents that cohesively align on- and off-vehicle information in order to estimate the current status of the car. In particular, this work combines, integrates, and evaluates multiple information sources targeting future smart cars. Specifically, the proposed methodology leverages weather-based, on-route, and on-vehicle information. As a use case, the presented work informs the driver about the recommended speed that the car should adapt to, based on the current status of the car. It also validates the proposed speed with real-time vehicular measurements.

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Biradar, Dr Rajkumar L. "A Comprehensive Study on Detection of Water Bodies using Satellite Imagery." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 970–77. http://dx.doi.org/10.22214/ijraset.2024.61706.

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Abstract: Satellite imagery plays a crucial role in overseeing and safeguarding natural resources, particularly water bodies like lakes, rivers, and oceans, which hold significant environmental and human value. Detecting water body pixels in satellite images is a key aspect of environmental management and planning. Recent advancements in deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offer promising solutions for tasks like object detection and segmentation in image processing. This study leverages CNNs and RNNs to precisely identify water body pixels in satellite images, aiming to develop a model capable of accurately distinguishing between water and non-water pixels. Such a model holds practical significance for environmental monitoring and management efforts

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Kila, Ramy, and Feruza Amirkulova. "Active attenuation of free field noise in a spatial domain using neural networks inferred controller." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A183. http://dx.doi.org/10.1121/10.0023202.

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In this talk, we explore the application of deep neural networks (DNNs) to infer the decoupling of an incoming acoustic array, distinguishing between the signal of interest and noise components. The objective is to leverage this decoupling information to develop an effective controller that can attenuate the noise, thereby improving the quality of the desired signal. The decoupled noise is then fed into a specially designed controller to attenuate the noise while preserving the desired signal. The controller leverages the DNN's predictions to adaptively adjust its parameters, allowing it to adapt to changing noise conditions and improve noise reduction performance. The approach is evaluated using simulations and real-world acoustic array measurements. The results demonstrate the performance of the DNN-based decoupling and noise attenuation system in reducing noise while preserving the quality of the desired signal. The proposed system offers effective adaptive solutions for enhancing the quality of desired signals in the presence of noise, with potential applications in areas such as speech recognition, audio communication systems, and environmental noise suppression.

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Chu, De-Tian, Lin-Yuan Bai, Jia-Nuo Huang, Zhen-Long Fang, Peng Zhang, Wei Kang, and Hai-Feng Ling. "Enhanced Safety in Autonomous Driving: Integrating a Latent State Diffusion Model for End-to-End Navigation." Sensors 24, no. 17 (August 26, 2024): 5514. http://dx.doi.org/10.3390/s24175514.

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Ensuring safety in autonomous driving is crucial for effective motion planning and navigation. However, most end-to-end planning methodologies lack sufficient safety measures. This study tackles this issue by formulating the control optimization problem in autonomous driving as Constrained Markov Decision Processes (CMDPs). We introduce an innovative, model-based approach for policy optimization, employing a conditional Value-at-Risk (VaR)-based soft actor-critic (SAC) to handle constraints in complex, high-dimensional state spaces. Our method features a worst-case actor to ensure strict compliance with safety requirements, even in unpredictable scenarios. The policy optimization leverages the augmented Lagrangian method and leverages latent diffusion models to forecast and simulate future trajectories. This dual strategy ensures safe navigation through environments and enhances policy performance by incorporating distribution modeling to address environmental uncertainties. Empirical evaluations conducted in both simulated and real environments demonstrate that our approach surpasses existing methods in terms of safety, efficiency, and decision-making capabilities.

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Andhella, Sylviana, Hamfri Djajadikerta, and Martinus Yuwana Marjuka. "Technopreneurship in Pro-Environmental Behavior for Sustainable Carbon Emission Reduction in Central Kalimantan." Aptisi Transactions on Technopreneurship (ATT) 6, no. 2 (July 19, 2024): 254–69. http://dx.doi.org/10.34306/att.v6i2.400.

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This study examines the integration of technopreneurship with pro-environmental behavior to achieve sustainable carbon emission reduction in Central Kalimantan. Focusing on the Dayak-Banjar communities, the research aims to develop a model that leverages local wisdom and technopreneurial activities to promote environmental conservation. The methodology includes qualitative research through interviews and questionnaires distributed in Baung, Muara Dua, and Kuala Pembuang villages. Findings reveal that local communities, deeply affected by environmental degradation, are willing to adopt pro-environmental behaviors. Key factors influencing this change include attitudes, subjective norms, perceived behavioral control, and knowledge. The study highlights the potential of technopreneurship in promoting sustainable practices and provides a foundation for designing policies that support pro-environmental behavior through technopreneurship. By fostering pro-environmental behavior within local communities, this research demonstrates that it is possible to balance ecological conservation with economic growth, aligning with SDG 13, SDG 15, and SDG 8.

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Dimitris Koutras, Panayiotis Kotzanikolaou, Evangelos Paklatzis, Christos Grigoriadis, and Christos Douligeris. "A framework for automating environmental vulnerability analysis of network services." ITU Journal on Future and Evolving Technologies 5, no. 1 (March 12, 2024): 104–19. http://dx.doi.org/10.52953/tbfn5500.

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The primary objective of this paper is to introduce a comprehensive framework designed to automate the assessment of environmental vulnerability status of communication protocols and networked services, within operational contexts. The proposed algorithm leverages the Common Vulnerability Scoring System version 3 (CVSS 3) metrics in conjunction with network security data. The initial step involves the establishment of a network security ontology, which serves to model the environmental attributes associated with the current security posture of communication protocol channels available within an infrastructure. The process commences with the identification and enumeration of all active communication services through a combination of diverse information gathering tools. Subsequently, active network services undergo assessment using a blend of passive scanning and active security analysis tools, which produce the environmental security score. This score can be integrated into vulnerability scoring systems such as CVSS, facilitating the fine-tuning of base CVSS scores, as well as vulnerability mitigation in real-world environments. To validate the proposed framework, we conducted testing across various networks and communication protocols within a controlled environment, thereby offering tangible illustrations for widely-utilized communication protocols.

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Chiolerio, Alessandro. "Liquid computing and cybernetics for robotics and computer science." Open Access Government 45, no. 1 (January 14, 2025): 214–15. https://doi.org/10.56367/oag-045-11865.

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Liquid computing and cybernetics for robotics and computer science Recent advancements show that liquid memories and liquid computing devices bring a transformative approach to robotics and computer science. Developing artificial intelligence (AI) leverages computing systems whose desired features include adaptability, robustness, and resource efficiency. Central to these developments is achieving intrinsic plasticity – an innate self-adaptation to diverse tasks and environmental conditions that enables learning – while maintaining fault tolerance, essential for continuous operation under unpredictable or extreme conditions.

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Kazi,, Ayaan. "Auto-Adjusting Rear View Mirror: Enhancing Safety and Driving Experience." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (February 1, 2025): 1–9. https://doi.org/10.55041/ijsrem41230.

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This paper explores the development of an auto-adjusting rear-view mirror system designed to enhance driving safety by automating mirror adjustments based on real-time data from various sensors. The system leverages face detection and environmental data to provide optimal visibility, reduce blind spots, and improve driver convenience. Future expansions include integration with advanced driver-assistance systems (ADAS) and personalization features. Keywords--- Auto-adjusting mirror, face detection, servo motor control, Raspberry Pi, driving safety, ADAS.

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Serhan, Michael, David Jackemeyer, Karam Abi Karam, Kasyap Chakravadhanula, Mark Sprowls, Pinar Cay-Durgun, and Erica Forzani. "A novel vertical flow assay for point of care measurement of iron from whole blood." Analyst 146, no. 5 (2021): 1633–41. http://dx.doi.org/10.1039/d0an02351e.

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We present a novel vertical flow assay to measure iron levels in whole blood from a finger-prick sample. The system leverages a sensor strip, detection chamber and in-house app that detects color change and outputs iron concentration in five minutes.

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Pettinaroli, Andrea, Stefano Susani, Riccardo Castellanza, Elena Maria Collina, Matteo Pierani, Riccardo Paoli, and Francesco Romagnoli. "The Impact of a Permeation Grouting Technique Quantitatively Assessed Through a Process-Focused Life Cycle Assessment." Environmental and Climate Technologies 27, no. 1 (January 1, 2023): 797–812. http://dx.doi.org/10.2478/rtuect-2023-0058.

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Abstract Permeation grouting treatments can be considered a well-established ground improvement strategy in urban built environments, where an accurate fine-tuning of its components can lead to tailored and efficient interventions. However, how to improve its overall environmental impact remains an open question. Using the Life Cycle Assessment (LCA) approach emphasizing the construction phase, this research highlights the leverages that can improve the environmental performance of this geotechnical construction process. The alternative approaches in terms of materials and processes are identified, quantified, and compared using the standard output of the LCA analysis and represent the ideal input for the three-phased sustainability assessment method for geotechnical infrastructure developed by the authors.

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Wu, Hsin-Te, Jie-Xin Li, and Mu-Yen Chen. "Building a Sustainable Development Education System for Large Organizations Based on Artificial Intelligence of Things." Journal of Organizational and End User Computing 36, no. 1 (November 29, 2024): 1–19. https://doi.org/10.4018/joeuc.361716.

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This paper proposes building a sustainable development computing system for large organizations based on AI and IoT. The system leverages AI to calculate the carbon emissions caused by large organizations' activities and utilizes IoT devices to monitor and compute environmental coefficients. The system also employs automated devices to achieve net-zero carbon emissions. By integrating weather forecast information from meteorological agencies to understand external environmental conditions, and by consulting a knowledge database to devise appropriate response strategies, the AI system can activate relevant equipment to improve both the organization's living environment and carbon emission processes. The feasibility and practical application of this system will be demonstrated through actual simulations to enhance its viability and effectiveness.

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Wang, Xiangyang, Zhiyi Liu, Jiamin Li, and Xuefei Lei. "How organizational unlearning leverages digital process innovation to improve performance: The moderating effects of smart technologies and environmental turbulence." Technology in Society 75 (November 2023): 102395. http://dx.doi.org/10.1016/j.techsoc.2023.102395.

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Kloch, Kristoffer, and Jimmi Normann Kristiansen. "The economic and environmental viability of green and autonomous ships in inland shipping ecosystems." Journal of Physics: Conference Series 2867, no. 1 (October 1, 2024): 012030. http://dx.doi.org/10.1088/1742-6596/2867/1/012030.

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Abstract This paper explores the potential of using green, autonomous ships in revitalising inland shipping in Europe against the backdrop of declining market share and the dominance of “economy-of-scale” in waterborne freight transportation. It assesses the economic and environmental viability of converting freight from road- to waterborne modalities in broader business ecosystems, specifically along the Rotterdam-Ghent corridor. The analysis leverages operational and commercial insights from logistics firms, ports and terminal operators, combined with data on European goods flows by road, and accounts for operational, financial and environmental variables including realistic scenario building and ecosystem implications. Findings indicate that inland shipping in general and green, autonomous shipping in particular offer both economically and environmentally viable alternatives to road transport. The study calls for further research into green, autonomous ships from an ecosystem perspective as a potential solution to current challenges in sustainable freight transportation.

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J, Srushti M., Ravindra S, Spoorthi K. S, T. S. Chandana, and Indu Priya Shirisha. "Parking Slot Vacancy Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (December 31, 2024): 1187–91. https://doi.org/10.22214/ijraset.2024.66006.

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Abstract: Parking congestion is a persistent issue in urban environments, leading to wasted time, fuel, and environmental impact. This paper introduces a 'Parking Slot Vacancy Detection System' that leverages real-time video feeds and machine learning algorithms, specifically Mask- RCNN, to accurately detect vacant and occupied parking slots. Predefined slot mapping, vehicle detection, and real- time processing are combined to provide efficient and scalable solutions. The proposed system minimizes errors caused by lighting variations or overlapping objects, aiming to enhance parking lot utilization and reduce driver frustration

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Ikechukwu, Chukwurah. "Addressing Food Security and Environmental Concerns through Bioplastic Innovation in Food Packaging." Global Journal of Environmental Science and Sustainability 1, no. 1 (December 18, 2024): 53–68. https://doi.org/10.69798/49264347.

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The food packaging industry faces a significant challenge: reconcile the need for food security with the imperative to reduce environmental pollution. In response, this study suggests sustainable food packaging materials that address both concerns. It presents a novel approach that leverages renewable resources, bioplastics, and edible packaging to create innovative solutions. Our study reveals that these sustainable materials exhibit superior performance, including enhanced shelf life, improved food safety, and reduced waste generation. This study contributes to the development of a circular economy in the food packaging sector, aligning with the United Nations’ Sustainable Development Goals’. The findings have far-reaching implications for policymakers, industry stakeholders and consumers, highlighting the potential for sustainable food packaging to mitigate environmental pollution while ensuring food security. By advancing sustainable food packaging solutions, this research paves the way for a more resilient and environmentally conscious food system, ultimately benefiting human health, the environment, and the economy. The article presents a compelling case for the adoption of sustainable food packaging materials, poised to transform the industry and contribute meaningfully to a more sustainable future.

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Nagaraju, Dr V. Siva, and N. Ramya Sri. "IMAGE ANALYSIS FRAMEWORK FOR CARBON FOOTPRINT ESTIMATION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (December 23, 2024): 1–6. https://doi.org/10.55041/ijsrem39945.

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This study has been undertaken to develop an image analysis framework for estimating the carbon footprint of consumer products using advanced computational techniques. The framework leverages image recognition models alongside a dataset of product-specific carbon emission metrics to provide accurate assessments. Streamlit serves as the primary interface for user interaction, while Python, HTML, and CSS facilitate backend computation and frontend design. The analytical framework integrates data preprocessing, feature extraction, and environmental impact modelling to ensure robust analysis. Publicly available datasets on carbon footprints have been utilized to train and validate the system. The proposed system aims to bridge the gap between academia and sustainable practices, offering an innovative tool for researchers, educators, and policymakers to assess environmental impacts effectively Key Words: Image Analysis, carbon footprint estimation, environmental impact, Streamlit framework, Python programming, sustainable development, machine learning.

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Yildiz, Bilal, Murat Aydemir, Canan Hisarkaya Kurnaz, Hatip Arslan, and Kasim Öz. "How IGA Istanbul Airport leverages IoT and automation for improved efficiency, safety and passenger experience." Journal of Airport Management 18, no. 4 (September 1, 2024): 410. http://dx.doi.org/10.69554/pqif1520.

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iGA Istanbul Airport is the first and only airport established with a wireless Internet of Things (IoT). IoT systems offer an effective solution for receiving data from many points with advantages such as fast implementation, low installation costs and long lifetime. In addition to standard products, special wireless IoT sensors designed for specific needs are also used in iGA Istanbul Airport. This infrastructure provides data for the execution, improvement and development of airport processes. The big data and analysis application created with the data collected from IoT devices and system integrations manages efficiency and predictive maintenance activities. With the effective use of IoT systems, iGA Istanbul Airport has achieved significant gains in increasing customer satisfaction, managing predictive maintenance from a more proactive perspective, reducing environmental impacts and increasing operational efficiency. This paper discusses the use of the IoT infrastructure at iGA Istanbul Airport and the benefits it provides.

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Bai, Jie, and Ajmera Mohan Singh. "Exploring Computer-Aided Environmental Art Design: A Course Overview." Data and Metadata 4 (January 1, 2025): 488. https://doi.org/10.56294/dm2025488.

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Computer aided education is transforming with the integration of technology. In the context of advancing art education, there is a pressing need for innovation to enhance student engagement and learning outcomes. This study introduces an innovative approach by employing an Adaptive Kookaburra Optimized Dynamic Recurrent Neural Network (AKO-DRNN) with the framework of computer-aided environmental art design courses. The traditional methods of teaching art are being complemented by computer-aided tools and intelligent systems. This research explores the application of AKO-DRNN in revolutionizing art education, focusing on environmental art design. The primary goal is to develop an instructional system that leverages advanced algorithms to offer personalized, accurate aesthetic guidance, enhance creative exploration, and elevate students' practical skills in environmental art design. This study integrates AKO-DRNN into the course structure, which combines deep learning (DL) models with environmental design principles. The AKO-DRNN model utilizes dynamic recurrent networks optimized by a Kookaburra-inspired optimization algorithm to effectively analyze and predict artistic styles and features. This model provides real-time feedback and adaptive learning paths tailored to individual student needs. Implementation of the suggested model has demonstrated significant improvements in students’ design quality, creativity, and skill acquisition. The adaptive nature of the model enhances learning outcomes and engagement. The established framework offers a robust solution for modernizing art education in environmental design, fostering greater innovation and practical skills among students.

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Gajbhiye, Amar M. "Design and Development of Solar Powered Automated Agriculture Bot." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (June 30, 2024): 503–6. http://dx.doi.org/10.22214/ijraset.2024.63167.

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Abstract: The development of a solar-powered seed sowing machine with an integrated pesticide tank aims to address the challenges of traditional farming by enhancing efficiency and sustainability. This innovative machine leverages solar energy to power its components, reducing reliance on fossil fuels and minimizing environmental impact. The machine is equipped with a high-pressure diaphragm pump for precise pesticide application, ensuring optimal pest control while conserving resources. The machine is powered by a 12-volt battery charged by solar panels, ensuring uninterrupted operation even in remote areas without access to the electrical grid.

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Seaborn, Paul, Tricia D. Olsen, and Jason Howell. "Is Insider Control Good for Environmental Performance? Evidence From Dual-Class Firms." Business & Society 59, no. 4 (January 30, 2018): 716–48. http://dx.doi.org/10.1177/0007650317749221.

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Corporate environmental performance has become a key focus of business leaders, policy makers, and scholars alike. Today, scholarship on environmental practice increasingly highlights how various aspects of corporate governance can influence environmental performance. However, the prior literature is inconclusive as to whether ownership by insiders (officers and directors) will have positive or negative environmental effects and whether insider voting control or equity control is more salient to environmental outcomes. This article leverages a unique empirical data set of dual-class firms, where insiders have voting rights disproportionate to their equity rights, to shed light on this question. We find that, on average, dual-class firms underperform their single-class peers on environmental measures and that the discrepancy comes from dual-class firms where insiders have more voting control, relative to their equity stake. While small increases in voting control are associated with improved environmental performance, too much (relative to insiders’ equity stake) worsens firms’ environmental performance. Insider equity control alone has no impact on environmental outcomes. Our findings have important implications for agency theory and environmental scholarship by identifying contingencies on the impact of voting and equity-based incentives. This research casts doubt on the idea that providing insiders with significant voting control will aid environmental performance.

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Saha, Utsha, Binita Saha, and Md Ashique Imran. "A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection." Sensors 24, no. 23 (December 8, 2024): 7841. https://doi.org/10.3390/s24237841.

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Automatic License Plate Recognition (ALPR) systems are essential for Intelligent Transport Systems (ITS), effective transportation management, security, law enforcement, etc. However, the performance of ALPR systems can be significantly affected by environmental conditions such as heavy rain, fog, and pollution. This paper introduces a weather-adaptive Convolutional Neural Network (CNN) framework that leverages the YOLOv10 model that is designed to enhance license plate detection in adverse weather conditions. By incorporating weather-specific data augmentation techniques, our framework improves the robustness of ALPR systems under diverse environmental scenarios. We evaluate the effectiveness of this approach using metrics such as precision, recall, F1, mAP50, and mAP50-95 score across various model configurations and augmentation strategies. The results demonstrate a significant improvement in overall detection performance, particularly in challenging weather conditions. This study provides a promising solution for deploying resilient ALPR systems in regions with similar environmental complexities.

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Oshun, Jasper, Kristina Keating, Margaret Lang, and Yojana Miraya Oscco. "Interdisciplinary Water Development in the Peruvian Highlands: The Case for Including the Coproduction of Knowledge in Socio-Hydrology." Hydrology 8, no. 3 (August 2, 2021): 112. http://dx.doi.org/10.3390/hydrology8030112.

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Agrarian communities in the Peruvian Andes depend on local water resources that are threatened by both a changing climate and changes in the socio-politics of water allocation. A community’s local autonomy over water resources and its capacity to plan for a sustainable and secure water future depends, in part, on integrated local environmental knowledge (ILEK), which leverages and blends traditional and western scientific approaches to knowledge production. Over the course of a two-year collaborative water development project with the agrarian district of Zurite, we designed and implemented an applied model of socio-hydrology focused on the coproduction of knowledge among scientists, local knowledge-holders and students. Our approach leveraged knowledge across academic disciplines and cultures, trained students to be valued producers of knowledge, and, most importantly, integrated the needs and concerns of the community. The result is a community-based ILEK that informs sustainable land and water management and has the potential to increase local autonomy over water resources. Furthermore, the direct link between interdisciplinary water science and community benefits empowered students to pursue careers in water development. The long-term benefits of our approach support the inclusion of knowledge coproduction among scholars, students and, in particular, community members, in applied studies of socio-hydrology.

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MacKay, J. Morag, and Mark Andrew Ryan. "Human rights-based approach to unintentional injury prevention." Injury Prevention 24, Suppl 1 (April 25, 2018): i67—i73. http://dx.doi.org/10.1136/injuryprev-2017-042692.

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Unintentional injury remains an important global public health issue, and efforts to address it are often hampered by a lack of visibility, leadership, funding, infrastructure, capacity and evidence of effective solutions. The growing support for a socioecological model and a systems approach to prevention—along with the acknowledgement that injury prevention can be a byproduct of salutogenic design and activities—has increased opportunities to integrate unintentional injury prevention into other health promotion and disease prevention agendas. It has also helped to integrate it into the broader human development agenda through the Sustainable Development Goals. This growing support provides new opportunities to use a human rights-based approach to address the issue. The human rights-based approach is based on the idea that all members of society have social, economic and cultural rights and that governments are responsible and accountable for upholding those rights. It incorporates a systems approach, addresses inequity and places an emphasis on the most vulnerable corners of humanity. It also leverages legal statutes and provides organisations with the opportunity to build existing international goals and benchmarks into their monitoring efforts. This paper describes the approach and highlights how it can leverage attention and investment to address current challenges for unintentional injury.

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Zhang, Yawen, Carrie Wall, J. Michael Jech, and Qin Lv. "Spatio-temporal contrastive learning for acoustic data." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A241. http://dx.doi.org/10.1121/10.0011193.

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Large volumes of water column sonar data have been generated from acoustic surveys of living marine resources. They provide valuable information about marine ecosystems. To leverage them for acoustic target identification, scarcity of annotations is usually an issue. While some annotations have been generated by scientists via manual scrutiny or limited automation, they are limited to certain species. To fill in this gap, we propose a spatio-temporal contrastive learning approach for acoustic data. This unsupervised deep learning technique leverages both acoustic and non-acoustic (spatial and temporal) information for acoustic target classification. We firstly employ the Simple Linear Iterative Clustering (SLIC) algorithm to extract superpixels from acoustic data. Then the spatial and temporal similarity between two superpixels is computed, and the proposed spatio-temporal contrastive learning approach is applied to learn a semantically meaningful representation for superpixels. Finally, the learned representations of superpixels are used in acoustic target classification. We demonstrate that this approach outperforms previous methods with acoustic data collected by the NOAA National Marine Fisheries Service in the Gulf of Maine and Georges Bank region over 10 years. This dataset is archived at NOAA National Centers for Environmental Information and accessed for free through AWS and NOAA Big Data Program.

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Susatyo, R. Wahyu Indra, Eko Indrajit, and Erick Dazki. "Enterprise Architecture in the Construction Management Software using the Business Model Canvas." sinkron 8, no. 3 (July 31, 2024): 1953–66. http://dx.doi.org/10.33395/sinkron.v8i3.13877.

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Indonesia's construction industry has boomed over the past decade, acting as a powerful engine for the nation's economic growth. However, this success story comes with a growing list of challenges. Construction projects are becoming increasingly intricate, demanding not only efficiency and quality but also enhanced safety and a minimized environmental footprint. To tackle these complexities, the industry is undergoing a crucial transformation towards "smart construction." This approach leverages the power of information and communication technology (ICT) throughout the entire project lifecycle, from planning and design to execution and maintenance. By integrating ICT tools like Building Information Modeling (BIM) and cloud-based project management platforms, smart construction streamlines processes, minimizes errors, and optimizes resource allocation, ultimately leading to improved efficiency, enhanced worker safety, and a reduction in environmental impact. A key pillar of smart construction lies in the integration of enterprise architecture (EA) within construction management software. EA provides a structured framework for aligning IT systems with construction businesses' specific goals and objectives. This ensures that software development and management are adaptable to evolving industry needs and foster continuous innovation. This research delves into the application of EA within the construction management software industry, specifically focusing on the TOGAF Architecture Development Method (ADM). By exploring EA through this established framework, the research aims to shed light on how the construction industry in Indonesia can leverage technology to its fullest potential. This will ensure continued growth within the sector and contribute to a lasting positive impact on the nation's economy.

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Lim, Kok-Tiong, and Kim-Leng Goh. "Does Environmental Factor Influence the Rating of Creditworthiness? A Comparative Analysis of Developed versus Developing Countries." Malaysian Journal of Economic Studies 61, no. 2 (December 9, 2024): 215–43. http://dx.doi.org/10.22452/mjes.vol61no2.2.

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This paper examines the influence of environmental factor in the determination of a country’s creditworthiness given the world’s agenda to contain the rise in global temperature. This paper leverages on two environmental factor proxies, CO2 emissions per capita and renewable energy per capita, to assess whether the environmental factor plays a significant role in determining the sovereign credit ratings (SCRs) issued by three leading credit rating agencies (CRAs), i.e., Moody’s, S&P and Fitch for 49 countries spanning the period of 2000 to 2021. The empirical results show that the environmental factor is being considered by the CRAs. Since the signing of the Paris Agreement in 2015, the environmental factor has turned significant for the determination of the SCRs of developing countries, but not for the developed countries. The creditworthiness of developing countries is subjected to a penalty for CO2 emissions. While the level of renewable energy adoption is higher amongst the developed countries, the evidence does not show that their level of CO2 emissions is lower. This paper recommends the CRAs to explicitly state the environmental factor criteria and update their SCR methodologies to ensure uniformity in application.

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Journal articles on the topic 'Environmental leverages'

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