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Found 6278 publications
Experimental investigation and finite element analysis of varying bitumen content in asphalt mixtures
Publication Name: Discover Applied Sciences
Publication Date: 2026-02-01
Volume: 8
Issue: 2
Page Range: Unknown
Description:
The percentage of bitumen in asphalt mixtures plays a crucial role in determining pavement performance throughout its service life. This study investigates the effect of varying bitumen contents on the mechanical behaviour and durability of asphalt mixtures. Three mixtures containing 4.7%, 5.1%, and 5.5% bitumen binder were evaluated through a comprehensive set of laboratory tests, including Marshall stability and flow, semi-circular bending, pressure aging vessel, wheel rutting, dynamic modulus, creep compliance, and fatigue performance tests, supported by finite element modeling. The nonlinear plastic behaviour and damage evolution were analyzed using the Perzyna-type viscoplastic model and Lemaitre’s isotropic damage model. Results indicate that mixtures with lower bitumen content (4.7%) exhibit earlier fatigue damage, while higher bitumen content (5.5%) leads to increased rutting and creep compliance. The 5.1% bitumen mixture demonstrated the most balanced performance, showing 40% less induced plastic strain damage than the 4.7% mixture and 27% less than the 5.5% mixture.
Open Access: Yes
According to Whose Morals? The Decision-Making Algorithms of Self-Driving Cars and the Limits of the Law
Publication Name: Future Transportation
Publication Date: 2026-02-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
The emergence of self-driving vehicles raises not only technological challenges, but also profound moral and legal challenges, especially when the decisions made by these vehicles can affect human lives. The aim of this study is to examine the moral and legal dimensions of algorithmic decision-making and their codifiability, approaching the issue from the perspective of the classic trolley dilemma and the principle of double effect. Using a normative-analytical method, it explores the moral models behind decision-making algorithms, the possibilities and limitations of legal regulation, and the technological and ethical dilemmas of artificial intelligence development. One of the main theses of the study is that in the case of self-driving cars, the programming of moral decisions is not merely a theoretical problem, but also a question requiring legal and social legitimacy. The analysis concludes that, given the nature of this borderline area between law and ethics, it is not always possible to avoid such dilemmas, and therefore it is necessary to develop a public, collective, principle-based normative framework that establishes the social acceptability of algorithmic decision-making.
Open Access: Yes
Asbestos Poverty as a New Paradigm for Multidimensional Urban Sustainability
Publication Name: Journal of Urban Health
Publication Date: 2026-02-01
Volume: 103
Issue: 1
Page Range: 214-228
Description:
The popularity of asbestos-containing products stemmed from their fire resistance, thermal insulation properties, and mechanical strength. However, their well-documented adverse health effects led to the prohibition of their use in many countries. This research aims to conduct a comprehensive examination of the often-overlooked social dimensions associated with asbestos, with a specific focus on the affected population’s circumstances and the potential solutions accessible to them. Its analysis encompasses legal regulations concerning asbestos, societal awareness, and the economic implications of asbestos removal from the perspective of those impacted. The findings highlight that the remediation of asbestos-containing products is often contingent on the financial and social conditions of the affected population, posing significant challenges for the economic sector and environmental protection efforts. This research contributes to the development of integrated approaches that address social, economic, and environmental dimensions in tandem. Its originality lies in situating the concepts of social sustainability and socially oriented environmental development within the context of asbestos-related policies. The findings suggest that achieving asbestos-free environments is feasible only through the integration of social dimensions, taking into account the economic and social conditions of the affected communities.
Open Access: Yes
What diseases and risks cause health losses in Hungary?
Publication Name: Orvosi Hetilap
Publication Date: 2026-02-01
Volume: 167
Issue: 6
Page Range: 232-242
Description:
Introduction: Using Global Burden of Disease 2023 data, this study examines the structure of health losses in Hungary, focusing on diseases, risk factors, and international comparisons. Objective: To identify which diseases and risk factors contribute most to Hungary’s health burden, how these relate to disability and premature mortality, and how patterns differ by gender and in comparison, with Central European countries. Method: Age-standardized values per 100,000 inhabitants, broken down by gender and disease/risk category, were analyzed for Hungary and compared with Austria, the Czech Republic, Poland, and Slovakia. Results: Cardiovascular diseases, cancers, and musculoskeletal disorders caused the largest losses. High blood pressure was the leading risk factor. Premature mortality was substantially higher in Hungary; men showed especially elevated levels due to smoking, diet, and hypertension. Morbidity-related losses were dominated by musculoskeletal and mental disorders. Discussion: Hungary’s burden stems not only from mortality but also from chronic disabling conditions. The mortality component is particularly unfavourable in international comparison. Conclusion: Improving treatment quality, timely care, and early diagnosis is essential, while reducing morbidity requires stronger long-term care and rehabilitation. Effective policy should complement lifestyle-focused prevention with better access to high-quality curative care and gender-responsive interventions. Consistent use of objective burden-of-disease data can support decision-making. A systemic approach – combining prevention, supportive environments, and a strengthened healthcare system – is needed to reduce health losses in Hungary. Orv Hetil. 2026; 167(6): 232–242.
Open Access: Yes
Uncovering the energy infrastructure in Europe: Data-driven digital twin for policy analysis and interpretation via multi-way analysis
Publication Name: Energy
Publication Date: 2026-02-01
Volume: 344
Issue: Unknown
Page Range: Unknown
Description:
With the adoption of the European Green Deal, the target to reduce net greenhouse gas emissions by 55 % by 2030, compared to 1990 levels, requires a higher renewable energy fraction and better energy efficiency. This requires a comprehensive re-evaluation of the power infrastructure within the European Union (EU). To achieve this, a EU-focused digital twin has been constructed, focusing on the European region and neighboring countries. The twin uses annual and 30-min resolution data from 113 main stations representing 40 countries, with a focus on EU member states. Multi-way analysis (PARAFAC2) is used to align interpretation for both data and high-resolution data, prioritizing regional energy infrastructure features. An automated graph-theory (P-graph) approach is used to construct a large-scale multi-time-sliced energy-balanced model as a digital twin model. This novel integration of macro-level trend analysis (via PARAFAC2), time-resolved optimization, and equity-based constraints enables a data-driven exploration of diverse policy scenarios. This study shows that effective EU energy policy should balance renewable diversification, equity in energy access, and regional cooperation, as policy shifts significantly affect energy flows and trade dynamics. While resilient infrastructure may require high investment, trade-off analysis reveals cost-effective, balanced pathways that optimize both sustainability and security objectives. The work demonstrates the potential for data-driven policy making for regional or international infrastructure, focusing on optimization of energy transfer activities, promotion of renewable sources, and systematic planning.
Open Access: Yes
Introducing LEAF: LLM Edge Assessment Framework for Generative AI on the Edge
Publication Name: Machine Learning and Knowledge Extraction
Publication Date: 2026-02-01
Volume: 8
Issue: 2
Page Range: Unknown
Description:
The transition of Large Language Models (LLMs) from centralized clouds to edge environments is critical for addressing privacy concerns, latency bottlenecks, and operational costs. However, existing edge benchmarking frameworks remain tailored to discriminative Deep Learning tasks (e.g., object detection), failing to capture the multidimensional challenges of generative AI, specifically the trade-offs between token generation speed, semantic accuracy, and hardware sustainability. To address this gap, we introduce LEAF (LLM Edge Assessment Framework), a novel evaluation methodology that integrates Circular Economy principles directly into performance metrics. LEAF assesses edge deployments across five synergistic pillars: Circular Economy Score, Energy Efficiency (Joules/Token), Performance Speed (Tokens/Second), semantic accuracy (BERTScore), and End-to-End Latency. We validate LEAF through an extensive experimental analysis of five distinct hardware classes, ranging from embedded IoT devices (Raspberry Pi 4 and 5, NVIDIA Jetson Nano) to professional edge servers (NVIDIA T400) and repurposed legacy workstations (NVIDIA GTX 1050 Ti). Utilizing 4-bit quantized models via the Ollama runtime, our results reveal a counterintuitive insight: repurposed consumer hardware significantly outperforms modern purpose-built edge SoCs. The legacy GTX 1050 Ti achieved a 20× speedup over the Raspberry Pi 4 and maintained superior energy-per-task efficiency compared to low-power ARM architectures by minimizing active runtime. These findings challenge the prevailing narrative that newer silicon is essential for Edge AI, demonstrating that sustainable, high-performance inference can be achieved by extending the lifecycle of existing hardware. LEAF thus provides a blueprint for a “Green Edge” ecosystem that balances computational capability with environmental responsibility.
Open Access: Yes
DOI: 10.3390/make8020048
Does Green Energy and Technological Innovations Induce Agriculture and Land Sustainability: Contextual Evidence From Climate Resilient Practices
Publication Name: Land Degradation and Development
Publication Date: 2026-01-30
Volume: 37
Issue: 2
Page Range: 790-805
Description:
With the growing environmental concerns, the existing literature mostly highlights the industrial pollution while neglecting the factors associated with the agriculture-related greenhouse gas emissions. Regarding this, the study explores the impacts of green energy, tech development, and urbanization on agriculture's greenhouse gas emissions. The prime objective of the current research is to unveil the green energy, tech innovations, and environmental sustainability nexus to draw novel implications in the context of Sustainable Development Goals (SDGs). In doing so, the authors employ the quarterly data of China from 1990Q1 to 2020Q4. For the long-run empirical analysis, the authors utilize various time-series estimating approaches, such as Quantile regression, which performs better in testing the nexus at different quantiles. However, the Fully Modified OLS, Dynamic OLS, and Canonical Cointegration Regression methods are used as robustness tools to authenticate the estimate of the primary approach. The results suggest that greener energy and technological innovations significantly reduce agriculture sector emissions. Furthermore, the presence of green energy transforms its negative influence into a positive one. Contrastingly, the use of traditional fossil fuel energy, urbanization, and financial development are significant drivers of emissions. This study's findings support SDGs, particularly SDG-2, which supports the stance of sustainable agriculture and encourages green energy use. Overall, the study discourses policy-related suggestions in the sustainability's context.
Open Access: Yes
DOI: 10.1002/ldr.70219
Automated multi-stream spiral-wound heat exchanger design and optimization
Publication Name: Applied Thermal Engineering
Publication Date: 2026-01-30
Volume: 284
Issue: Unknown
Page Range: Unknown
Description:
Spiral-wound heat exchangers (SWHEs) offer high heat transfer efficiency and compact design advantages, making them well-suited for services in process industries. Accelerating the application of SWHEs demands design methodologies that avoid extensive user manipulations and complex solution procedures. This study develops a novel incremental-based heat transfer framework for the automated design of single-phase SWHEs, which simultaneously optimizes multi-stream allocation across activated tube layers and exchanger geometries. At each increment, energy balances are enforced for all streams using local heat transfer coefficients and areas. On the tube-side, flow distribution is optimized by permitting variable split heat capacities and mass flow rates within tube layers while ensuring pressure balance for each stream at the bundle outlet. New correlations for shell-side flow regimes are introduced into the proposed sizing model to link discrete tube-layer selections with their corresponding cross-sectional areas throughout the optimization process. The capability of the proposed framework is demonstrated through four case studies, including model validation, two-stream and multi-stream SWHE design, and application to an industrial-scale heat exchanger network (HEN). Rigorous Aspen EDR-CoilWound simulations validate the proposed model and design results, with the HEN case exhibiting only a 2.95 % deviation from the target duty. In Case Study 2, SWHE results in a 24.29 % reduction in required heat transfer area. Case Studies 3 and 4 demonstrate that SWHE configurations can achieve 31.8 %–40.7 % reductions in exchanger volume, attributable to their superior compactness relative to conventional shell-and-tube heat exchangers (STHEs). Benchmarking against detailed STHE designs further clarifies optimal deployment strategies and highlights residual limitations of SWHE technology.
Open Access: Yes
Combining interbasin water replenishment and solar capacities for sustainable energy and water management in the catchment of Lake Velence
Publication Name: Advances in Geosciences
Publication Date: 2026-01-22
Volume: 67
Issue: Unknown
Page Range: 129-136
Description:
Climate change exerts substantial adverse effects on water resources within the catchment area of Lake Velence in Hungary, intensifying conflicts among stakeholders and diverse water users. This region, characterized by rapid urbanization and economic expansion, also exhibits ecological heterogeneity, including significant wetland areas, some designated as Ramsar sites. At the same time, population growth and modern real estate development have led to a high density of solar panel installations, resulting in above-average per-property renewable energy production capacity across the country. This study proposes an inter-basin water transfer system to mitigate the hydrological impacts of climate change, leveraging the area’s topography and solar energy production potential by integrating pumped hydro storage reservoirs and surplus solar energy to transfer water from the adjacent Váli-víz watershed is considered. The ecological flow requirements of the donor area are also considered to protect its ecosystem. The objective is to design a sustainable, low-carbon water replenishment system that addresses the region’s economic, social, and ecological requirements. By synchronizing excess solar energy production with pumped hydro storage systems, the approach ensures dual functionality: renewable energy storage and strategic water supply enhancement for Lake Velence, thus increasing the system and the area’s resilience under climate stress.
Open Access: Yes
