Search Results (65,648)

The growing global consumption of concrete is driving up the demand for cement, which has a negative environmental impact due to intensive CO₂ emissions. This impact can be reduced by replacing cement with reactive mineral industrial waste, simultaneously addressing the issue of waste accumulation in landfills. However, to ensure the effective use of such materials, it is essential to comprehensively investigate their influence on concrete durability. This study analyzes glass processing waste (GPW) generated during glass grinding. The waste is removed using water, resulting in the formation of glass processing waste. In the experiment, CEM I 42.5 R cement, GPW, sand, crushed dolomite stone, concrete sludge (CS), chemical admixtures, and water were used. In the tests, cement was replaced with glass processing waste in amounts ranging from 5% to 30%, analyzing a total of seven different compositions. The properties of the sustainable concrete mixture were evaluated, and the mechanical–physical properties of the hardened concrete were determined. Resistance to alkali–silica reaction was tested according to the RILEM AAR-4 methodology, while the environmental impact of glass processing waste was assessed using Life Cycle Assessment (LCA). The results showed that glass processing waste increases the concrete’s resistance to alkali corrosion: as the amount of waste increased, a smaller change in the linear dimensions of the specimens was recorded, and the lowest mass loss was found in the composition where 20% of the cement was replaced by glass processing waste. The environmental impact assessment confirmed a direct correlation—as the amount of glass waste increases, CO₂ emissions decrease proportionally. To produce sustainable concrete, it is recommended to use up to 20% glass processing waste: this allows for the maximum reduction in environmental impact while maintaining mechanical properties and high resistance to alkali–silica reaction. Full article

The overreliance on chemical pesticides has caused severe environmental contamination, health risks, and increasing pest and pathogen resistance, creating an urgent need for greener and more efficient alternatives in sustainable agriculture. Microalgae-mediated green nano-synthesis has emerged as a promising strategy because of its environmental compatibility, cost-effectiveness, and multifunctional potential. This review critically summarizes recent advances in microalgae-derived nanomaterials for agricultural applications. First, we discuss the biochemical basis of nanoparticle biosynthesis, highlighting the roles of microalgal polysaccharides, proteins, photosynthetic pigments, extracellular polymeric substances, and secondary metabolites as reducing, capping, and stabilizing agents. We then summarize intracellular and extracellular synthesis pathways, advanced synthesis strategies, and key reaction parameters, including temperature, pH, and metal precursor concentration, which regulate nanoparticle size, morphology, stability, and yield. Subsequently, major microalgae-derived nanomaterials, including gold, silver, selenium, zinc oxide, bimetallic, and other functional nanoparticles, are discussed in relation to their agricultural applications. These nanomaterials show potential in bacterial, fungal, and viral disease control, biofilm disruption, plant growth promotion, yield enhancement, and abiotic stress mitigation. Their agronomic effects are associated with multiple mechanisms, including reactive oxygen species generation, pathogen membrane disruption, inhibition of biofilm formation, enhanced nutrient bioavailability, antioxidant regulation, and activation of plant systemic resistance. In addition, this review evaluates the phytotoxicity, biocompatibility, soil microbial impacts, and environmental safety of microalgae-derived nanomaterials, emphasizing that green synthesis does not automatically guarantee biosafety. Finally, we discuss their integration into circular agriculture through CO₂ capture and wastewater-derived metal recovery, while highlighting remaining challenges in scale-up, quality control, economic feasibility, regulatory classification, and public acceptance. Overall, microalgae-mediated nanotechnology offers a promising platform for developing safer, more efficient, and circular agricultural inputs. Full article

Background: Faricimab is one of the most potent anti-vascular endothelial growth factors used in the management of diabetic macular edema (DME). However, real-world benefits regarding its timing and efficacy are still being explored. Methods: This retrospective non-randomized pilot study aimed to evaluate the efficacy of intravitreal faricimab in the treatment of DME. Eyes initially treatment-naïve for DME with a follow-up of 1 year were grouped as: group 1, where faricimab was introduced within the first six months after the start of treatment; group 2, where it was initiated six or more months after treatment with other drugs. Study parameters included changes in best corrected visual acuity (BCVA) and optical coherence tomography based structural parameters within the 6 × 6 mm optical coherence tomography (OCT) scan regions. Results: Forty-two eyes from 26 patients were analyzed. No statistically significant differences were observed between the groups in cluster-weighted proportions of intra- or sub-retinal fluid, retinal thickness or volume parameters, although group 1 showed modest numerical benefits. SRF showed a trend towards qualitative reduction in group 1, although IRF showed persistence in both groups. Adjusted linear mixed-effects modeling demonstrated no significant impact of early faricimab initiation on functional and anatomical outcomes, which appeared to be influenced by the baseline BCVA, glycemic control, and the number of injections, nullifying the benefits. Conclusions: Faricimab demonstrated modest anatomical improvements with earlier treatment in eyes initially treatment-naïve for DME. Further prospective studies are indicated to assess the treatment strategy and the timing of introduction with faricimab in such eyes. Full article

Reliable and rapid forecasting of wind turbine output power is vital for operators, particularly day-ahead and intraday market scheduling and reserve allocation. However, the inherent unpredictability, intermittency, and volatility of wind turbine output make forecasting processes difficult. To address this challenge, this study proposes a Sinusoidal Representation Network (SIREN)-based forecasting model for high-accuracy, rapid direct multi-horizon forecasting of wind turbine output power. SIREN is selected due to the periodic and symmetrical mathematical structure of its sinusoidal activation function, which allows the model to represent both low-frequency trends and high-frequency sudden changes in wind energy data. To improve data quality, compensate for asymmetric fluctuations in wind data, and provide more suitable inputs for SIREN training. Several preprocessing steps are utilized before feeding the data into the model. The proposed preprocessing step includes a moving median filter, robust scaling based on median and interquartile range, Winsorizing clipping, and a Hampel filter to reduce the effects of instantaneous noise, outliers, and local peaks without disrupting temporal continuity. Subsequently, a Savitzky–Golay smoothing is applied to attenuate high-frequency measurement noise while preserving curvature, local peaks, and physically meaningful short-term dynamics in the data. The sliding-window approach is used to formulate the multi-horizon forecasting problem directly, and a direct h-step-ahead forecasting architecture is designed, preserving structural symmetry in the time series. The SIREN is trained and tested using MATLAB with the help of two different datasets: Dataset-1 has a 10 min resolution for 1 year, and Dataset-2 has a 1 h resolution for 15 years. The forecast horizon parameter h is considered separately for each step, and the proposed SIREN is independently trained, validated, and tested for each target horizon while maintaining chronological order. The results demonstrate that the proposed model is able to yield high forecast performance for a wide spectrum of horizons ranging from 10 min to 15 days. The accuracy of the proposed model for Dataset-1 is R² of 99.6%, MSE of 0.085%, MAE of 1.7%, and MAPE of 12%, while for Dataset-2, the accuracy is R² of 98.8%, MSE of 0.3%, MAE of 3.6%, and MAPE of 23%. Ablation and sensitivity analyses are conducted to evaluate the impact of the basic components used in the proposed model on forecasting performance. In addition, combative experiments are performed using traditional time series, ML, and DL forecasting techniques to better assess the contribution of the model. The obtained results show that the SIREN-based direct forecasting approach provides strong learning capability, as well as high forecasting accuracy, for both high-resolution and low-resolution wind power data. Overall, its ability to capture the symmetric and periodic characteristics inherent in wind turbine power data makes it a promising alternative for multi-horizon wind power forecasting applications. Full article

Industrial emissions are a central environmental concern, particularly with respect to the spatial distribution of major enterprises and the identification of key determinants. Traditional research has largely focused on characterizing the current status of these enterprises, but this approach exhibits several notable shortcomings. These include a lack of regional statistical analysis, an absence of a comprehensive industrial typology, inadequate cross-evaluation of enterprise scale and pollution emissions, and insufficient exploration of socioeconomic correlations. We introduce a multifaceted evaluation framework for Key Environmental Supervision Units (KESUs), focusing on key industrial classifications and their underlying development drivers. The analysis utilized a comprehensive dataset covering 153,107 individual KESUs across six categories from 2020 to 2024, incorporating distribution patterns across 31 provincial-level regions, 28 industrial classifications of national economic activities, and 18 socioeconomic impact factors. The results showed that KESUs in East China accounted for 41.7% of the total, with the highest concentrations in industrialized cities and economically developed zones. Manufacturing was identified as the dominant industrial classification, with chemical raw materials and products comprising the largest subcategory (13.0% of total KESUs in 2024). Atmosphere KESUs and water KESUs represented the largest proportions, accounting for 29.7% and 25.3% of single-type KESUs, respectively. This study provides a systematic analysis of KESUs, offering a detailed mapping of distribution patterns, emission characteristics, and control challenges for major pollution sources. The findings can provide critical insights to support decision-making aimed at improving regional pollution source management and advancing environmental protection practices. Full article

Background/Objectives: Polypharmacy is common in elderly nursing home residents (NHR), due to the high prevalence of chronic diseases. This practice increases the risk of clinically significant drug–drug interactions (DDIs) with serious consequences for patient health and safety. The objective of this study was to evaluate the prevalence of DDIs using the UpToDate Drug–Drug Interaction Checker, potentially inappropriate medication (PIM, as defined by the STOPP-START criteria), and their association with major clinical outcomes. Methods: Demographic data, clinical history and detailed medication records of 275 patients from Romania were collected. Potentially inappropriate medications were identified using 16 selected criteria from the 2023 STOPP/START guidelines. Statistical analysis was performed using GraphPad, R, and Python, involving Chi-squared and Fisher’s exact tests with Benjamini–Hochberg correction, linear regression, and drug-interaction network analysis to characterise interaction frequency and severity. Results: Detailed medical histories over the past year were available for 76 patients. The mean number of drugs prescribed was 9.61 ± 4.47 drugs, with an average of 10.68 ± 10.54 potential interactions per patient. The primary clinical outcome was associated with not respecting certain STOPP-START recommendations (p < 0.01). Overall, 33.1% of NHRs utilised herbal supplements, resulting in a total of 76 potential herb–drug interactions. Conclusions: The results suggest a potential impact of DDIs on clinical outcomes, underscoring the need for further studies to clarify causality. The use of STOPP/START recommendations and deprescribing could lead to better tolerability and smaller drug-related burden in the institutionalised, elderly population. Full article

The complex genus Achillea L. comprises more than 140 species distributed widely throughout the Northern Hemisphere. Several species are widely used in traditional medicine for their therapeutic properties, yet few studies have correlated their biological properties with the plant’s phytochemical composition. Among these, Achillea setacea Waldst. & Kit. is a perennial species traditionally used to treat digestive and inflammatory disorders. In this study, the essential oil of A. setacea, collected wild in North Macedonia, was analyzed spectrometrically and spectroscopically by GC-MS and NMR, respectively. A total of nineteen compounds were identified, with camphor (31.3%), 4-terpineol (11.3%), and eucalyptol (10.6%) being the main constituents. Furthermore, the biological activities of pure oil were evaluated, showing notable antioxidant properties, as well as antimicrobial effects against a panel of clinically relevant microorganisms, including Gram-positive and Gram-negative bacteria. Furthermore, its impact on human intestinal epithelial (Caco-2) cells was assessed, highlighting its potential relevance for gastrointestinal applications, in agreement with the traditional use of Achillea species for digestive disorders. Full article

Aluminium is one of the most carbon-intensive structural materials, making the direct reuse of aluminium members a highly effective strategy for reducing environmental impacts by avoiding primary production. Despite this potential, the reuse of aluminium structural members has received far less attention than steel reuse. This study addresses that gap through two complementary contributions. First, it develops a reuse pathway for aluminium structural members based on existing steel reuse frameworks while addressing aluminium-specific technical challenges. Second, it evaluates the environmental implications of this approach through a life cycle assessment of an aluminium portal frame tent structure in accordance with EN 15804+A2 and the EF 3.1 method, covering Modules A1–A5, C1–C4, and D. Three end-of-life scenarios are considered: a cut-off baseline, a recycling scenario, and a reuse scenario. Aluminium production accounts for 37.6% of the cradle-to-gate impact while representing only about 3.3% of the mass. Direct reuse lowers the net global warming potential by about 22% relative to recycling and is the lowest-impact option across all 16 impact categories. The results identify direct reuse as the environmentally preferable end-of-life route, although wider implementation depends on design for disassembly and a dedicated technical framework for reclaimed aluminium. Full article

Immersive solutions are becoming a trending technology for decision support across fields such as transportation, healthcare, and urban planning. Despite their role, the mechanism by which they affect decision-making is unclear. Our study examines the design primitives in immersive technology that are manipulated to influence decision-making and synthesizes how they operate to shape decision outcomes. We follow PRISMA guidelines to search. A total of 198 studies were included. Eight primitive families were identified, including perceptual realism, environmental structure, interactivity, temporal simulation, embodiment, social presence, multisensory integration, and other contextual manipulations. Mechanisms through which they impacted decision-making were classified into cognitive, perceptual, affective, motivational, social-influence, and behavioral-heuristic mechanisms. Perceptual realism, environmental structure, and interactivity emerged as the most frequently investigated primitives, while presence, risk perception, spatial cognition, engagement, and social influence were among the most reported mechanisms. Our results suggest that immersive technologies function as decision-shaping systems that alter how users perceive uncertainty, risks, consequences, and alternatives, highlighting the need for theory-driven research and evaluation in high-stakes decision contexts. Full article

Multitasking is pervasive in multimodal interaction, particularly within safety-critical domains like driving. Evaluating the impact of In-Vehicle Multimodal Interaction (IVMI) on drivers is critical, yet existing methods predominantly rely on post hoc subjective surveys or coarse unimodal monitoring. Grounded in Multiple Resource Theory and following a Research through Design methodology, we operationalized this theory into a non-intrusive tool chain that evaluates IVMI impact from multimodal behavioral signals (visual, touch, and driving) and supports real-time, objective evaluation in both simulated and real-world domains. To mitigate the Sim-to-Real gap, the method combines real-world multimodal data acquisition with a modality-decoupled cross-domain calibration. Its feasibility was evaluated through a simulator study ($n=27$) and a small-nscale real-world on-road pilot study ($n=3$). The results suggest that the tool chain effectively acquires high-fidelity data to support the previously developed evaluation model (Quadratic Weighted Kappa = 0.916) and achieves a preliminary calibration of cross-domain latent feature spaces. As its reference labels are behaviorally derived and share a common basis with the model inputs, this agreement indicates internal consistency rather than independent construct validation. Crucially, while multimodal interaction behaviors (visual and touch) exhibited relatively high cross-domain consistency, real-world driving behaviors showed systematic magnitude suppression. This finding is tentatively interpreted, as a hypothesis to be tested in future work, through the lens of Risk Homeostasis Theory, and highlights the necessity of monitoring multimodal interaction behaviors rather than relying solely on vehicle telemetry. Overall, this research develops and provides preliminary feasibility evidence for a theory-driven cross-domain tool chain, indicating its potential to objectively quantify multimodal interaction impacts in real-world multitasking contexts. Given the small, homogeneous on-road sample, these pilot-stage results should be read as feasibility evidence and a methodological basis for future large-scale, demographically diverse validation. Full article

The existing trade-off between agricultural production and ecosystem services is widening with the intensification of agricultural systems. In this context, diversification can play a crucial role in enhancing agroecosystem multifunctionality, and in orchard systems it can also be achieved through the management of inter-row spaces via the ecological functions provided by sown and resident vegetation. Nowadays its contribution is not recognised. The study aims to compare the temporal dynamics of four cover crop mixes present in the inter-row spaces and assess their ecological functions using the Vegetation-based Ecological Functions Sustainability Index, and evaluate the relative influence of management, season, and year on cover crop performance. Total and relative coverage and species number were collected from 2022 to 2024. Non-metric multidimensional scaling analysis showed that vegetation composition varied across treatments, and cover crop management shaped plant community structure. It highlighted that the strongest difference was the contrast between grass cover (r = −0.95) and bare soil (r = 0.91). Permutational Multivariate Analysis of Variance on ecological functions indicated that sampling year (R² = 0.1822, F = 8.4874, p < 0.001). and season (R² = 0.1280, F = 5.9659, p < 0.001) had a significant impact on vegetation cover. Moreover, biodiversity effects hinge primarily on the ecological functions performed by species, rather than on their number. Overall, these findings highlight that the ecological contribution of inter-row vegetation depends more on the functional traits expressed by plant communities than on species richness alone. Furthermore, year and season strongly influence the dynamics of cover crops and resident vegetation, making multi-year monitoring essential to determine their persistence and the ecological functions they perform. Full article

Background/Objectives: Special populations, including pediatric, geriatric, and organ-impaired patients, are consistently underrepresented in randomized controlled trials (RCTs), resulting in limited evidence for safe and effective dosing. Off-label use is common, and variability in drug exposure and response increases the risk of adverse drug reactions (ADRs). This review aims to examine how integrating pharmacometrics (PMX) with real-world data (RWD) can address evidence gaps by supporting dose optimization, population expansion, and safety evaluation in these vulnerable groups. Methods: A narrative literature review was conducted using PubMed, Embase, and Web of Science (January 2000–November 2025). Using Boolean combinations of PMX and RWD-related search terms, approximately 200–300 records were identified across the three databases; approximately 30 full-text articles were reviewed, and representative case studies were selected based on population diversity, methodological variation, and regulatory or clinical impact. Results: RWD–PMX integration has been applied across three domains: (i) dosing optimization through therapeutic drug monitoring (TDM)-informed PopPK modeling and model external validation in pediatric and neonatal populations; (ii) population expansion supporting dose extrapolation and regulatory decision-making for unapproved groups; and (iii) safety evaluation enabling identification of exposure–toxicity risk factors in vulnerable cohorts. Conclusions: Integrating PMX with RWD provides a practical and mechanistically grounded framework for evaluating dosing, treatment eligibility, and safety in populations insufficiently represented in clinical trials. Accumulating evidence indicates that RWD–PMX methodologies can complement traditional clinical research and inform regulatory decision-making. Continued refinement of data quality standards, validation practices, and guidance frameworks will be essential for broader adoption. Full article

The growing importance of embodied carbon (EC) in building decarbonisation requires transparent, context-specific Life Cycle Assessment (LCA) approaches. This study develops a practical framework for quantifying cradle-to-site EC (A1–A4), combining detailed post-construction material quantification with a structured data selection methodology. Carbon factors (CFs) are primarily sourced from geographically representative Environmental Product Declarations (EPDs) and evaluated through a reliability framework that incorporates material similarity, geographical proximity, and data completeness. An Analytic Hierarchy Process (AHP) is further applied to select representative values for key materials such as ready-mix concrete. The application of this framework highlights the critical influence of data representativeness on EC results and demonstrates a transparent and reproducible approach for reducing uncertainty in early-stage assessments. The case study yields a total EC of 228 kg CO₂e/m², with structural materials identified as the main carbon hotspots: ready-mix concrete accounts for approximately 40% of total impacts, reinforcing steel for around 11%, while masonry systems, infill, and levelling layers contribute a significant additional share. Together, these materials represent slightly more than 75% of total embodied emissions. Beyond the numerical results, the study shows that a limited number of material categories dominate the carbon footprint, enabling targeted decarbonisation strategies. The proposed framework is designed to be transferable to similar building contexts and supports more robust, data-driven decision-making in the Portuguese construction sector and beyond. It is particularly relevant in regions where locally representative environmental data are not necessarily sufficient, as it provides a structured approach for developing embodied carbon assessments under such condition. Full article

Aurelia coerulea is a bloom-forming scyphozoan that recurs in Masan Bay, a semi-enclosed embayment on the southern coast of Korea. To quantify its population-level predation impact on mesozooplankton, medusae and zooplankton were sampled monthly at six stations from May to September 2013, except for August. Individual ingestion rates were estimated from gut-content analysis combined with temperature-dependent digestion time, and population ingestion rates were evaluated relative to mesozooplankton biomass and production. Medusa abundance peaked in May (2.11 medusae m⁻³) and declined sharply thereafter, whereas the individual body size and the total population biomass increased. Oithona similis was the dominant prey, indicating positive selectivity from June to September despite its low biomass in the ambient zooplankton community. Mesozooplankton abundance, biomass, and production ranged from 4300 to 26,900 ind. m⁻³, 6.9 to 20.6 mg C m⁻³, and 2.26 to 5.82 mg C m⁻³ d⁻¹, respectively. Individual ingestion rates of the jellyfish on the mesozooplankton ranged from 0.31 to 5.31 mg C medusa⁻¹ d⁻¹. Population ingestion rates ranged from 0.06 to 0.66 mg C m⁻³ d⁻¹, equivalent to 0.3–6.6% and 1.1–11.8% of the biomass and production of the mesozooplankton, respectively. Predation impact of A. coerulea on mesozooplankton in Masan Bay varied seasonally and depended primarily on medusa abundance, because population ingestion rates decreased after May despite increasing individual ingestion rates associated with medusa growth. A modified calculation of the population ingestion rate, considering jellyfish distribution only in the upper layer under water-stratification conditions in summer, was also discussed. Full article

Nuclear fusion represents a transformative solution for global energy systems, offering a carbon-free, inherently safe, and virtually inexhaustible power source. As the field transitions from experimental reactors like ITER to demonstration power plants (DEMO) capable of delivering net electricity to the grid (300–500 MW), the computational demands for plasma control have escalated. Modern fusion diagnostics, particularly soft X-ray (SXR) systems, generate massive data volumes that require high-throughput processing to ensure plasma stability and optimize energy gain. Recent breakthroughs in record-breaking plasma durations have further exposed the critical latency bottlenecks in traditional analytical workflows. This work addresses these challenges by introducing advanced computational strategies optimized towards next-generation reactors. Firstly, we present new data-processing algorithms in C++ and CUDA, achieving significant reductions in computation time. This allowed for more efficient analysis of collected experimental data for plasma confinement studies. Secondly, we discuss hardware architectures that will allow, in the future, up-scaling and parallel runtime processing of data with a feedback signal to the reactor control systems. We present a detailed analysis of the computational workflows underlying soft X-ray diagnostics, followed by a presentation of the proposed optimized algorithms. Their impact on prospective hardware system designs is then evaluated in terms of scalability, latency, and throughput. Performance evaluations demonstrated substantial speedups of both the sequential CPU-based and the parallel GPU-based algorithms, highlighting the potential of these methods for future real-time plasma control for energetically stable and efficient fusion power generation. The sequential and parallel algorithms were 18.8 and 89.1 times faster, respectively, versus the baseline implementation. The processing rate was increased from 31.8 MiB/s to 4.32 GiB/s. The results show the effectiveness of massively parallel computation for plasma diagnostics and pave the way towards further research to produce a cluster-based distributed system. The demand for such high-performance, real-time data processing methodologies extends beyond the plasma confinement domain and is expected to grow across energy systems as they become increasingly complex and data-driven. Full article