Search Results (111)

Background: Municipal solid waste management is a relevant component of climate and air quality policy, yet published life cycle assessments report inconsistent conclusions on whether sanitary landfilling, waste-to-energy incineration, composting, or anaerobic digestion yields the lowest greenhouse gas and co-pollutant impacts because results depend strongly on methodological choices and local context. Objective: To synthesize and critically evaluate how key life cycle assessment assumptions and boundary decisions influence reported emissions across major waste management pathways, with primary emphasis on the United States and selected comparison to European Union policy frameworks. Methods: Peer-reviewed life cycle assessment studies and supporting technical and regulatory sources were reviewed and compared, focusing on functional unit definition, system boundaries, time horizon, energy substitution and crediting methods, and treatment of methane, nitrous oxide, and air pollutant controls; drivers of variability were identified through structured cross study comparison and sensitivity-focused interpretation. Results: Reported pathway rankings vary primarily with landfill gas collection and utilization assumptions, the carbon intensity of displaced electricity or heat for waste-to-energy systems, and the representation of biological process emissions across active and curing stages; harmonized comparisons reduce variability but do not yield a single consistently superior pathway across all plausible settings. Conclusions: Comparative conclusions are context-dependent and policy-relevant interpretation requires transparent reporting and sensitivity analysis for capturing efficiency, substitution factors, and biological emission controls, along with clear alignment between modeled scenarios and real-world operating conditions. Full article

Background: Evidence on HAIs in Africa is fairly common. Objectives: The main objective was to identify the surveillance tools used for healthcare–associated infections (HAIs) in countries in the WHO African Region. Secondary objectives focused on the organization of surveillance, the pathogens involved, and the frequency of multidrug–resistant species. Inclusion and exclusion criteria: Observational or interventional studies on healthcare–associated infections in humans, published between January 2011 and December 2024, in French or English, were included. However, the following publications were not included: animal studies, healthcare–associated infections not related to healthcare, literature reviews, studies outside the period or geographical area, and studies in languages other than French or English. Sources of information and search date: The databases consulted were PubMed, Web of Science, EMBASE, Cochrane, African Index Medicus, Google Scholar, and AJOL. The search was conducted between January and March 2025. Risk of bias assessment: The risk of bias was assessed using a specific grid (eleven criteria), scored from one (low) to three (high). The studies were classified into three levels of methodological quality. The results of the bias assessment showed that the publications were excellent (strong and moderate) with a cumulative rate of 99.9%. Methods of synthesizing results: Data were extracted using a standardized grid and synthesized narratively. No meta–analysis was performed. Number of studies and characteristics: 95 studies were included, mostly cross–sectional studies (82.1%), cohorts (10.4%), and a few case reports. Most were from West Africa (60.0%), particularly Nigeria (16.8%) and South Africa (14.7%). Main results: • Most common pathogens: Staphylococcus aureus (53.7%), Escherichia coli (43.2%), Klebsiella pneumoniae (32.6%). • Resistance profile: ESBL (27.4%), MRSA (21.1%), multidrug resistance (13.7%). • Sources of HAIs: mainly exogenous (83.2%). • Laboratory methods: phenotypic (70.5%), genotypic or genomic rare (3.1%). • Scope of studies: local (96.8%), national (3.2%). Limitations of evidence: Risk of bias due to underreporting of HAIs, methodological heterogeneity, predominance of cross–sectional studies, low use of molecular methods, lack of modeling, and uneven geographical coverage. Overall interpretation and implications: surveillance of HAIs in Africa remains fragmented and poorly standardized. There is a need to strengthen national systems, integrate molecular methods, train professionals, and promote interventional research. The WHO GLASS program can serve as a framework for harmonizing surveillance. Full article

This manuscript presents a study on the application of blind source separation (BSS) techniques, specifically the Independent Component Analysis (ICA) method, for the detection and identification of localized faults in rolling element bearings. Bearing defects typically manifest as distinct harmonics of characteristic fault frequencies, accompanied by modulation sidebands in the vibration signal spectrum. The accurate extraction and isolation of these components are crucial for reliable fault diagnosis, particularly in systems where multiple vibration sources overlap. In this work, a linear ICA algorithm was applied to vibration signals acquired from a simplified rotating machinery setup designed to emulate common bearing fault conditions. The study investigates the effect of ICA-based signal decomposition on the statistical distribution of selected diagnostic indicators and evaluates its ability to enhance the detectability of fault-related components. The experimental results demonstrate that the application of ICA significantly improves the separation of vibration sources, leading to a more distinct representation of fault signatures. The findings confirm the effectiveness of blind source separation methods in vibration-based diagnostics and highlight the potential of ICA as a complementary tool for improving the accuracy and robustness of bearing fault detection systems in rotating machinery. Full article

The transition to renewable energy is crucial in order to attain sustainable development, lower greenhouse gas emissions, and secure long-term energy security. This study examines spatial–temporal trends in electricity generation (both renewable and non-renewable) across EU-28 countries using monthly Eurostat data (2008–2025) at the NUTS0 level. Two harmonized Space–Time Cubes (STCs) were constructed for renewable and non-renewable electricity covering the fully comparable 2017–2024 interval, while 2008–2016 data were used for descriptive validation, and 2025 data were used for one-step-ahead forecasting. In this paper, the authors present a novel multi-method approach to energy transition dynamics in Europe, integrating forecasting (ESF), hot-spot detection (EHSA), and clustering (TSC) with the help of a new spatial–temporal modeling framework. The methodology is a step forward in the development of methodological literature, since it regards predictive and exploratory GIS analytics as comparative energy transition evaluation. The paper uses Exponential Smoothing Forecast (ESF) and Emerging Hot Spot Analysis (EHSA) in a GIS-based analysis to uncover the dynamics in the region and the possible production pattern. The ESF also reported strong predictive performance in the form of the mean Root Mean Square Errors (RMSE) of renewable and non-renewable electricity generation of 422.5 GWh and 438.8 GWh, respectively. Of the EU-28 countries, seasonality was statistically significant in 78.6 per cent of locations that relied on hydropower, and 35.7 per cent of locations exhibited structural outliers associated with energy-transition asymmetries. EHSA identified short-lived localized spikes in renewable electricity production in a few Western and Northern European countries: Portugal, Spain, France, Denmark, and Sweden, termed as sporadic renewable hot spots. There were no cases of persistent or increase-based hot spots in any country; therefore, renewable growth is temporally and spatially inhomogeneous in the EU-28. In the case of non-renewable sources, a hot spot was evident in France, with an intermittent hot spot in Spain and sporadic increases over time, but otherwise, there was no statistically significant activity of hot or cold spots in the rest of Europe, indicating structural stagnation in the generation of fossil-based electricity. Time Series Clustering (TSC) determined 10 temporal clusters in the generation of renewable and non-renewable electricity. All renewable clusters were statistically significantly increasing (p < 0.001), with the most substantial increase in Cluster 4 (statistic = 9.95), observed in Poland, Finland, Portugal, and the Netherlands, indicating a transregional phase acceleration of renewable electricity production in northern, western, and eastern Europe. Conversely, all non-renewable clusters showed declining trends (p < 0.001), with Cluster 5 (statistic = −8.58) showing a concerted reduction in the use of fossil-based electricity, in line with EU decarbonization policies. The results contribute to an improved understanding of the spatial dynamics of the European energy transition and its potential to support energy security, reduce fossil fuel dependency, and foster balanced regional development. These insights are crucial to harmonize policy measures with the objectives of the European Green Deal and the United Nations Sustainable Development Goals (especially Goals 7, 11, and 13). Full article

Agriculture is a major source of greenhouse gas emissions, yet predicting temperature increases associated with specific CO₂ sources remains challenging due to the heterogeneity of agri-environmental systems. In response, this study presents a machine learning framework that adopts an agri-food system boundary (production to retail) and combines systematic model benchmarking, interpretability, and a multi-scale perspective. Seven regression models, including tree ensembles and deep learning architectures, are evaluated on a harmonized dataset covering 236 countries over the 1990–2020 period to forecast annual temperature increases. Results show that gradient-boosted decision trees consistently outperform deep learning models in predictive accuracy and offer more stable feature attributions. Interpretability analysis reveals that spatio-temporal variables are the dominant drivers of global temperature variation, while environmental and sector-specific factors play more localized roles. A country-level case study on Iran illustrates how the framework captures national deviations from global patterns, highlighting intensive rice cultivation and on-farm energy use as key influential factors. By integrating high-performance predictions with interpretable insights, the proposed framework supports the design of both global and country-specific climate mitigation strategies. Full article

This study investigates the vibration and noise characteristics of oil-immersed power transformers, with a particular focus on the influence of transformer oil on structural dynamics and acoustic emission. The research integrates multi-physics modelling, finite-element simulation, and field measurements to analyze the vibration transmission paths from the core and windings to the tank wall. A fluid–structure interaction (FSI) model is developed to account for the damping effect of insulating oil, and a correction factor is introduced to adjust modal parameters. Simulation results reveal that oil significantly enhances vibration propagation, especially in the vertical direction, while structural ribs and clamping configurations affect local vibration intensity. Noise simulations show that magnetostriction is the dominant source of audible sound, with harmonic components sensitive to load and voltage variations. Experimental validation using a portable sound level meter confirms the simulation trends and highlights the spatial variability of acoustic pressure. The findings provide a theoretical and practical basis for optimizing sensor placement and developing voiceprint-based diagnostic tools for transformer condition monitoring. Full article

The integration of grid-forming wind turbines introduces forced oscillations and harmonic/inter-harmonic interference, which degrades the accuracy of traditional energy-flow-based source localization methods. To address this issue, this paper proposes a novel method based on improved complete ensemble empirical mode decomposition with Adaptive Noise (ICEEMDAN) and an improved Teager energy operator (ITEO). The proposed method first employs ICEEMDAN to adaptively decompose wide-area measurement signals, effectively suppressing mode mixing and noise. Then, ITEO is utilized to extract the dominant oscillation components. By incorporating an adjustable computation window, ITEO enhances frequency selectivity, amplifying force oscillations while suppressing high-frequency noise, leading to robust energy estimation. Following this, the dissipative modal energy flow is calculated from the reconstructed time-domain waveforms. Ultimately, the disturbance source is precisely identified based on the dissipative energy flow theory. The method is validated through extensive simulations on a multi-bus test system with grid-forming wind turbines, considering disturbances from both synchronous generator excitations and wind turbine internal controls, as well as in high-noise environments. Additional validation using a real-world oscillation event from the ISO New England system confirms that the proposed method achieves superior accuracy and robustness compared to conventional methods. Full article

This paper presents a terahertz (THz) dual-band transmitter for a wideband sparse synthetic bandwidth radar. The transmitter employs an innovative single-path-reuse dual-band architecture. This architecture utilizes a proposed quad-transformer-coupled voltage-controlled oscillator (VCO) as an on-chip local oscillator source. It also incorporates an innovative dual-harmonic generator and a dual-band antenna, which work together within the single signal path to generate both the fundamental frequency and its second harmonic, thereby creating the dual bands required for a sparse synthetic bandwidth radar. Fabricated in a TSMC 40 nm CMOS technology, measurement results show that the transmitter achieves a peak equivalent isotropically radiated power (EIRP) of −7.95 dBm in the low-frequency band (121.34∼126.85 GHz) and −7.86 dBm in the high-frequency band (242.68∼253.7 GHz), validating the proposed architecture’s capability to generate dual-band signals simultaneously. The entire chip occupies a compact area of only 0.54 × 0.62 ${\mathrm{m}\mathrm{m}}^2$ and consumes 136 mW of DC power. Full article

This review analyzes how recent electric-vehicle LCAs have been carried out, emphasizing goals and scope, functional units, system boundaries (cradle-to-grave and well-to-wheel), and attributional versus consequential modeling rather than reporting outcomes. Using a systematic search of studies mainly from 2018–2025, it maps common tools and data sources (Ecoinvent, GREET, GaBi, and regional inventories) and summarizes LCIA practices, underscoring the need to report versions, regionalization, and assumptions transparently for comparability. Uncertainty studies are uneven: sensitivity and scenario analyses are common, while probabilistic approaches (e.g., Monte Carlo) are less used, indicating room for more consistent, multi-parameter uncertainty analysis. The results show that outcomes are context-dependent: BEVs deliver the largest life-cycle GHG cuts on low-carbon grids with improved battery production and end-of-life management; PHEVs and HEVs act as transitional options shaped by real-world use; and FCEV benefits depend on low-carbon hydrogen. Vehicle-integrated photovoltaics and solar-powered vehicles are promising yet under-studied, with performance tied to local irradiance, design, and grid evolution. Future research suggests harmonized reporting, more regionalized and time-aware modeling, broader probabilistic uncertainty, and comprehensive LCAs of VIPV/SPV and circular pathways to support policy-ready, comparable results. Full article

Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems. Full article

Tidal wetlands are essential for coastal resilience, biodiversity, and carbon storage; yet, many are increasingly vulnerable to sea-level rise due to insufficient sediment supply. This study presents a national-scale, GIS-based model that quantifies riverine inorganic sediment contributions to tidal wetland accretion across over 700,000 coastal catchments in the contiguous United States. By integrating datasets from USGS, NOAA, and USFWS, the model calculates sediment yield, thickness, and accretion balance, enabling comparison with current sea-level rise projections. Results reveal significant regional disparities: the Northeast and Midwest exhibit higher sediment accumulation, while the Pacific and Southeast show widespread sediment deficits. Spatial statistical analyses identified clusters of high and low sediment supply, highlighting areas of resilience and vulnerability. A total of 93 field sites confirmed the model’s ability to distinguish between riverine-dominated and mixed-source sedimentation regimes. These findings underscore the importance of riverine sediment in sustaining wetland elevation and inform where non-riverine sources may be critical. The model’s outputs have been shared with coastal planners and stakeholders to support local decision-making, conservation prioritization, and adaptation strategies. This work demonstrates both the challenges and fruitfulness of harmonizing disparate national datasets into a unified framework for assessing wetland vulnerability and provides a scalable tool for guiding coastal resilience planning in the face of accelerating sea-level rise. Full article

This paper proposes a harmonic source localization method for power systems, combining voltage difference features with a random forest classifier. The method captures harmonic propagation patterns and optimizes network topology handling to ensure accurate and efficient identification across various configurations. Validated on IEEE standard transmission networks, it achieves high accuracy and scalability. While effective in transmission systems, distribution networks pose challenges due to complex topologies and high impedance. Future enhancements will focus on advanced feature engineering, data augmentation, and real-time processing to improve adaptability in diverse power system environments. Full article

Nanophotonics, the study of light–matter interactions at the nanometer scale, has emerged as a transformative field that bridges photonics and nanotechnology. Using engineered nanomaterials—including plasmonic metals, high-index dielectrics, two-dimensional (2D) materials, and hybrid systems—nanophotonics enables light manipulation beyond the diffraction limit, unlocking novel applications in sensing, imaging, and quantum technologies. This review provides a comprehensive overview of recent advances (post-2020) in nanophotonic materials, fabrication methods, and their cutting-edge applications. We first discuss the fundamental principles governing nanophotonic phenomena, such as localized surface plasmon resonances (LSPRs), Mie resonances, and exciton–polariton coupling, highlighting their roles in enhancing light–matter interactions. Next, we examine state-of-the-art fabrication techniques, including top-down (e.g., electron beam lithography and nanoimprinting) and bottom-up (e.g., chemical vapor deposition and colloidal synthesis) approaches, as well as hybrid strategies that combine scalability with nanoscale precision. We then explore emerging applications across diverse domains: quantum photonics (single-photon sources, entangled light generation), biosensing (ultrasensitive detection of viruses and biomarkers), nonlinear optics (high-harmonic generation and wave mixing), and integrated photonic circuits. Special attention is given to active and tunable nanophotonic systems, such as reconfigurable metasurfaces and hybrid graphene–dielectric devices. Despite rapid progress, challenges remain, including optical losses, thermal management, and scalable integration. We conclude by outlining future directions, such as machine learning-assisted design, programmable photonics, and quantum-enhanced sensing, and offering insights into the next generation of nanophotonic technologies. This review serves as a timely resource for researchers in photonics, materials science, and nanotechnology. Full article

M-edge X-ray absorption spectroscopy (XAS), which probes 3p→3d transitions in first-row transition metals, provides detailed insights into oxidation states, spin-states, and local electronic structure with high element and orbital specificity. Operating in the extreme ultraviolet (XUV) region, this technique provides sharp multiplet-resolved features with high sensitivity to ligand field and covalency effects. Compared to K- and L-edge XAS, M-edge spectra exhibit significantly narrower full widths at half maximum (typically 0.3–0.5 eV versus >1 eV at the L-edge and >1.5–2 eV at the K-edge), owing to longer 3p core-hole lifetimes. M-edge measurements are also more surface-sensitive due to the lower photon energy range, making them particularly well-suited for probing thin films, interfaces, and surface-bound species. The advent of tabletop high-harmonic generation (HHG) sources has enabled femtosecond time-resolved M-edge measurements, allowing direct observation of ultrafast photoinduced processes such as charge transfer and spin crossover dynamics. This review presents an overview of the fundamental principles, experimental advances, and current theoretical approaches for interpreting M-edge spectra. We further discuss a range of applications in catalysis, materials science, and coordination chemistry, highlighting the technique’s growing impact and potential for future studies. Full article

This paper details the development and implementation of an intelligent energy efficiency system for an electrical grid that incorporates renewable energy sources, specifically photovoltaic systems. The system is applied in a small locality of approximately 8000 inhabitants and aims to optimize energy consumption in public institutions by scheduling electrical appliances during periods of surplus PV energy production. The proposed solution employs a hybrid neuro-fuzzy approach combined with scheduling techniques to intelligently shift loads and maximize the use of locally generated green energy. This enables appliances, particularly schedulable and schedulable non-interruptible ones, to operate during peak PV production hours, thereby minimizing reliance on the national grid and improving overall energy efficiency. This directly reduces the cost of electricity consumption from the national grid. Furthermore, a comprehensive power quality analysis covering variables including harmonic distortion and voltage stability is proposed. The results indicate that while photovoltaic systems, being switching devices, can introduce some harmonic distortion, particularly during peak inverter operation or transient operating regimes, and flicker can exceed standard limits during certain periods, the overall voltage quality is maintained if proper inverter controls and grid parameters are adhered to. The system also demonstrates potential for scalability and integration with energy storage systems for enhanced future performance. Full article