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Found 6273 publications
The nexus between environmental diplomacy, policy stringency and renewable energy in advancing sustainability management across G20 countries
Publication Name: Discover Environment
Publication Date: 2026-12-01
Volume: 4
Issue: 1
Page Range: Unknown
Description:
The growing pace of environmental crisis around the world has aggravated the necessity of more vigorous environmental diplomacy and stringency in policy to develop renewable energy and promote sustainable growth in leading economies. This research study examines the relationship between financial globalization (FG), environmental diplomacy (ED), economic growth (GDP), environmental policy stringency (EPS), urbanization (URB), and renewable energy (RE) and ecological sustainability in G20 countries between 1995 and 2023. Based on the CS-ARDL, FMOLS, and DOLS tests, we use the Load Capacity Factor (LCF) as a holistic sustainability measure and analyze the short- as well as longer-term dynamics. Prolonged outcomes reveal that FG, ED, GDP, and URB adversely affect LCF, which suggests an increase in ecological stress. Nonetheless, RE enhances LCF and EPS moderates the negative consequences of globalization. The positive effect of ED is small in the short-run, whereas EPS will have a high contribution to ecological benefits. The ED-GDP relation indicates a long-term worsening of the environment, which underscores the inefficiencies of diplomatic enforcement. These results confirm the modulating effect of stringent environmental policies and the necessity to develop policy frameworks that would harmonize economic integration and sustainability. Urbanization is a threat to the environment unless controlled with sustainable planning, and renewable energy continues to be a major contributor to ecological health in the long run. The study provides practical recommendations to policymakers to incorporate strict regulation, green investment and environmental diplomacy in the strategies of sustainable development.
Open Access: Yes
Antimicrobial use and Escherichia coli resistance patterns in Hungarian pig farms: a data-driven farm-level analysis
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Antimicrobial resistance (AMR) poses a critical challenge to both human and veterinary medicine, with pig production recognized as one of the major contributor due to intensive antimicrobial usage (AMU). This study aimed to explore the relationship between AMU and AMR patterns of Escherichia coli isolated from commercial pig farms, using data-driven analytical methods. Farm-level records were harmonized with microbiological data from 203 isolates collected in December 2023 across four Hungarian farms. AMU was summarized over 3-, 6-, 9-, and 12-month retrospective windows and expressed in modified population-corrected units, while AMR was quantified as mean minimum inhibitory concentration (MIC) and AMR rate under epidemiological and clinical breakpoints. The results revealed substantial variation in AMU among farms, with amoxicillin predominating across timeframes. Farm-specific comparisons indicated that higher AMU may not always coincide with elevated resistance levels, and data analysis did not consistently identify a direct association between use and resistance at the individual farm level, which warrants further investigation in larger datasets. Correlation analyses identified strong intra-class relationships among β-lactams and fluoroquinolones, as well as a cross-class linking, suggesting concurrent selection pressures. Overall, the integration of AMU and AMR data demonstrated the feasibility of farm-level surveillance for AMR modelling and provides a foundation for future predictive systems to support antimicrobial stewardship in livestock production.
Open Access: Yes
Digital twin-based machine learning framework for predicting nonlinear seismic response of reinforced concrete shear walls using analytical data
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
This study proposes a digital twin (DT) and machine learning (ML) framework to predict nonlinear pushover responses of reinforced concrete (RC) shear walls using analytically derived data. Two hundred SAP2000 layered shell models were analyzed, and monotonic lateral capacity curves were processed via SPO2FRAG for bilinear parameter extraction. Key response features—initial stiffness (K0 ), yield displacement (), and post-yield stiffness ratio ()were identified. Ten input variables including wall geometry, material properties, reinforcement ratios, axial load, and opening ratio were used to train Random Forest regressors for predicting the pushover curve descriptors. Model accuracy was validated using nested cross-validation, yielding mean test R2 values of 0.996 for, 0.995 for, and 0.925 for, while uncertainty measures (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), 95% confidence intervals) supported robustness. The DT surrogate reconstructs pushover curves in under 2 seconds per specimen, supporting rapid parametric analysis and seismic scenario assessments without the need for repetitive finite element simulations. The study also documents model limitations and outlines guidance for extending the approach to shear-dominated walls and experimental validation.
Open Access: Yes
Trends and insights from bibliometric analysis for mapping artificial intelligence and machine learning in sustainable development
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
Rapid population growth, environmental degradation and persistent urgency of climate change have intensified the global search for sustainable development solutions. Governments, researchers and institutions alike face the challenge of balancing economic progress with social equity and environmental protection. In response, recent scholarships have increasingly turned to digital technologies as potential enablers of sustainable transformation. This study addresses the need to understand how artificial intelligence (AI) and machine learning (ML) are being incorporated into sustainable development strategies, with a particular focus on mapping knowledge trends and research patterns. Using bibliometric analysis of SCOPUS data spanning 2015 to 2024, the study uncovers the evolution of research topics, highlights influential authors and institutions, and traces the diffusion of ideas across disciplines. The findings reveal that AI and ML are emerging as key drivers of sustainability, with strong applications in energy and emission management, environmental monitoring, climate change mitigation, precision agriculture and water resource management. Research in this area has grown rapidly over the past decade, shifting from theory to real applications. It also highlights that China's and the United States dual dominance in both publication volume and citation impact, while also recognizing the contributions of other countries like India, the United Kingdom and Australia in shaping global research landscapes. Three main implications arise from these results. For policymakers, the evidence underscores the urgency of designing inclusive policies, investing in digital infrastructure, and fostering global cooperation to ensure the equitable distribution of technological benefits. For the research community, the study points to opportunities for cross-disciplinary collaborations that link technological innovation with real-world sustainability challenges. From a broader societal perspective, the findings emphasize the importance of knowledge sharing and technology transfer, enabling both developed and developing countries to advance collectively toward achieving the Sustainable Development Goals.
Open Access: Yes
Enhancing heat and mass transfer in hybrid nanofluid with gyrotactic microbes and local thermal non equlibrium effects using artificial neural network
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
This study analyzes the impact of local thermal non-equilibrium on the bioconvection flow of hybrid nanofluid across a slender extending sheet containing gyrotactic bacteria using artificial neural networks trained using a Bayesian regularization backpropagation approach (ANN-BRS). The effects of magnetic fields, thermal radiation, and Hall current are all things related to fluid flow. The suggested model has particular applicability in microscale drug delivery systems, where gyrotactic microorganisms and hybrid nanofluid can be employed to control nutrition and medication dispersion under non-equilibrium temperature circumstances. It can be used in lab-on-chip and organ-on-chip technologies to improve bio-mixing and accurate heat control. The model also applies to micro-solar collectors and porous micro-heat exchangers, which use hybrid nanoparticles to boost thermal efficiency. It can also be used in bioreactors and biomedical cooling systems, where local thermal non-equilibrium effects and ANN-based prediction allow for precise control of heat, mass, and microbe transfer, resulting in optimal performance. Similarity transformations are used to convert the original nonlinear PDEs into non-dimensional ODEs and the bvp4c program is applied to numerically resolve the resulting boundary-value problem. The training, testing, and validation processes yield the expected outcomes for every scenario based on the chosen data points. Regression analysis, histograms of error, and mean square error (MSE) metrics are employed to assess the ANN-BRS model's outcome. The liquid phase heat thermal profile increases as the interphase heat transfer parameter values rise, while the solid phase thermal profile decreases.
Open Access: Yes
The effect of mixed fatigue on knee biomechanics and muscle activation during sidestep cutting in elite soccer players
Publication Name: BMC Sports Science Medicine and Rehabilitation
Publication Date: 2026-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Background: Football is one of the most popular sports in the world, and it is also a sport with a high rate of injury. The study aims to investigate the effects of physical and mental mixed fatigue (PMF) on knee biomechanics during sidestep cutting maneuvers in elite male soccer players, thereby assessing the potential mechanisms underlying non-contact knee injuries. Methods: Thirty-six elite male soccer players were recruited (age: 21.61 ± 1.22 years; body mass: 75.16 ± 6.34 kg; height: 175.8 ± 3.53 cm; shoe size: 41–44 EUR). Following a targeted fatigue induction protocol, key lower limb biomechanical data were acquired during anticipated sidestep cutting maneuvers both pre- and post-PMF. Statistical analyses were performed utilizing paired sample t-tests and one-dimensional Statistical Parametric Mapping (SPM1d). Results: Following PMF, knee valgus increased at initial contact (P = 0.022). Kinetic analysis, supported by SPM1d, revealed a marked transition from an extensor-dominant to a flexor-dominant pattern in sagittal knee moments (P = 0.007), alongside elevated knee valgus moments (P = 0.039). Neuromuscularly, quadriceps and lateral gastrocnemius activation (iEMG/RMS) significantly decreased, whereas compensatory increases were observed in the hamstrings and medial gastrocnemius (all P < 0.001). Conclusion: While PMF preserved most kinematics, the statistically significant increase in knee valgus, though small in magnitude, suggests an impaired frontal-plane control that may elevate Anterior Cruciate Ligament (ACL) strain. The shift from quadriceps to hamstring dominance reflects a compensatory neuromuscular strategy. These findings emphasize the importance of incorporating cognitive load into injury-prevention programs and monitoring mental fatigue to reduce non-contact knee injury risks.
Open Access: Yes
Decentralized finance and sustainability analysis of global research patterns and emerging themes
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
Decentralized finance (DeFi) is rapidly transforming financial systems, yet its environmental, social, and economic sustainability implications remain underexplored. To address this gap, we conducted a structured review of peer-reviewed literature published between 2022 and 2025, drawing on 239 records retrieved from Scopus and Web of Science and screened through the PRISMA 2020 protocol in Covidence. The review combined bibliometric analysis, thematic mapping, and a systematic review to synthesize patterns, clusters, and critical insights. Bibliometric results show a sharp post-2023 rise in outputs, with China leading in publication volume and Switzerland achieving the highest citation impact, although collaboration networks remain fragmented and weakly connected. Thematic analysis reveals three dominant clusters: blockchain-driven financial innovation, AI and fintech applications for sustainability, and green economy transitions, highlighting DeFi’s dual role as a driver of transparency and inclusion but also a source of energy inefficiency and systemic risk. The systematic review further identifies regulatory gaps, particularly around Maximal Extractable Value (MEV), and emphasizes the need for energy-efficient consensus mechanisms, standardized ESG metrics for tokenized assets, and inclusive platform designs to bridge digital divides. By aligning DeFi’s disruptive potential with sustainability objectives, the study proposes hybrid governance models and interdisciplinary collaboration to foster a resilient, equitable, and low-carbon financial ecosystem, underscoring the urgency of balancing technological innovation with planetary boundaries to realize DeFi’s promise as a catalyst for sustainable development.
Open Access: Yes
Ensemble deep learning approach for traffic video analytics in edge computing
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Video analytics is the new era of computer vision in identifying and classifying objects. Traffic surveillance videos can be analysed to using computer vision to comprehend the road traffic. Monitoring the real-time road traffic is essential to control them. Computer vision helps in identifying the vehicles on the road, but the present techniques either perform the video analysis on the cloud platform or the edge platform. The former introduces more delay in processing while controlling is needed in real-time, the latter is not accurate in estimating the current road traffic. YOLO algorithms are the most notable ones for efficient real-time object detection. To make such object detections feasible in lightweight environments, its tinier version called Tiny YOLO is used. Edge computing is the efficient framework to have its computation done on the edge of the physical layer without the need to move data into the cloud to reduce latency. A novel hybrid model of vehicle detection and classification using Tiny YOLO and YOLOR is constructed at the edge layer. This hybrid model processes the video frames at a higher rate and produces the traffic estimate. The numerical traffic volume is sent to Ensemble Learning in Traffic Video Analytics (ELITVA) which uses F-RNN to make decisions in reducing the traffic flow seamlessly. The experimental results performed on drone dataset captured at road signals show an increase in precision by 13.8%, accuracy by 4.8%, recall by 17.4%, F1 score by 19.9%, and frame rate processing by 12.8% compared to other existing traffic surveillance systems and efficient controlling of road traffic.
Open Access: Yes
Hybrid ML and metaheuristic optimization of slag-fly ash-gypsum modified solidified sludge for construction
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Conventional sludge disposal, including incineration and landfilling, is unsustainable and can cause secondary pollution; thus, sludge solidification is emerging as a sustainable alternative. This study aims to combine machine learning (ML) and metaheuristic optimization to maximize the unconfined compressive strength (UCS) of municipal sludge modified with slag, desulfurized gypsum, and fly ash. A total of 190 specimens were tested, and predictive models based on Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), LightGBM, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Histogram Gradient Boosting (HistGBoost) were coupled with the Whale Optimization Algorithm (WOA). In addition, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gazelle optimization algorithm (GOA), Octopus Optimization Algorithm (OOA), Hiking Optimization Algorithm (HOA), and Young’s double-slit experiment optimizer (YDSE) were applied for comparison. Sensitivity analysis identified optimal WOA–ML parameter settings. The results demonstrated that the WOA–RF model outperformed all metaheuristic and other WOA–ML approaches by achieving the highest predicted UCS (8.29851 MPa). The WOA-ML models yielded an average optimal mix comprising sludge (44.2%), gypsum (19%), slag (18.7%), fly ash (16%), and NaOH (2.1%). Among the metaheuristic algorithms, PSO, GOA, OOA, TJO, DOA, GA, and YDSE demonstrated competitive performance. GWO achieved the highest UCS (8.226109 MPa), while HOA yielded the lowest (5.15366 MPa). The optimal mix averaged 38.9% sludge, 23.7% gypsum, 21.6% fly ash, 13.4% slag, and 2.5% NaOH. Partial dependence analysis confirmed the nonlinear effects of these parameters, while SHAP sensitivity analysis validated the optimization results. RSM validation further confirmed that both WOA–ML and metaheuristic approaches reliably predict the optimal UCS of modified sludge.
Open Access: Yes
