Search Results (6,719)

This study addresses the multi-objective optimization of characterizing a flat glass processing plant. To assess the operational conditions required for a flat glass processing small and medium-sized enterprise (SME) to become a prosumer compatible with renewable energy community (REC) participation. This work is motivated by the presence of more than 300 SMEs in Italy, like this, where RECs represent one of the few viable strategies for achieving the European Union’s 2050 decarbonization targets. The research is carried out in two scenarios; Scenario-I includes Stage-i and Stage-ii with the mutual goal of forecasting and optimizing. Forecasting is used in Stage-i to optimize the factory load, and in Stage-ii to shift and curtail energy loads based on the forecast, considering the Italian national energy price and the regional price bands (“fasce orarie”) F1, F2, and F3. Forecasting and the indicators of environmental and social performance are the means to ensure the best energy utilization and management, as they prove that the reduction in CO${}_2$ emissions and benefits on the community level can be both obtainable. Subsequently, the techno-economic analysis and evaluation of prosumer-readiness conditions are carried out through the optimization of industrial energy demand: three optimization objectives are assessed in this study (i) energy cost, (ii) carbon emission, and (iii) load curtailment. Four algorithms are put into effect to solve the tri-objective optimization: multi-objective particle swarm optimization (MOPSO), multi-objective ant nesting algorithm (MOANA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimization (MOGWO). The algorithms are validated in Stage-ii to find the desired optimum in the cost of energy, reduce peak formation, and carbon emissions. To achieve this goal, a stochastic approach based on Monte Carlo simulations and VIKOR is used to optimally select the results. The findings show that the NSGA-II, MOPSO, and MOANA are more effective in solving the problem, while the MOGWO algorithm more quickly finds the optimal solution. Based on the defined objectives, a new configuration for the energy community is introduced, together with a community well-being index and an evaluation of the resulting benefits for the factory. In Scenario-II, the PV plants’ installation on the factory is sized, and the excess energy shared with the grid is evaluated. The Scenario-II results show that 497.184 MWh (33.9%) of energy is shared with the grid. Both results suggest how optimized industrial demand profiles improve SME participation in future RECs. Full article

Air pollution negatively impacts human health and environmental sustainability, particularly in areas with high industrial activity. This study comparatively evaluated deep learning-based models for estimating PM₁₀ and SO₂ pollutants in Dilovası and Ereğli (Turkey), industrial areas with high pollutant loads. The study utilized Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), an RNN–GRU stacked hybrid model, an attention-based hybrid model, and the proposed GRU–LSTM dual-path hybrid model. The proposed method consists of four main stages: data conversion into a time-series format, data preprocessing and feature generation, model architecture development, and model training and performance evaluation. The dataset consisted of 365 daily PM₁₀ and SO₂ observations obtained from the Air Monitoring Center for the Dilovası and Ereğli monitoring stations. Model performance was evaluated using the coefficient of determination (R²), training time, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) metrics. The findings showed that the hybrid models provided higher accuracy compared to the single-track models. Specifically, the proposed GRU–LSTM dual-path hybrid model produced the highest R² and lowest error values for both pollutant parameters in both the Dilovası and Ereğli regions. In Dilovası, this model achieved R² = 0.97 for SO₂ and R² = 0.96 for PM₁₀; in Ereğli, it reached R² = 0.92 for SO₂ and R² = 0.98 for PM₁₀. Thus, it has been shown that the GRU–LSTM dual-path hybrid model, which models short-term and long-term temporal dependencies in parallel, is an effective and reliable method for air pollutant forecasting in industrial areas. These findings demonstrate the potential of the proposed model to support air quality monitoring, early warning systems, and environmental decision-making in industrial regions. Full article

Asphalt concrete pavements in many regions suffer from premature deterioration caused by low-temperature cracking and rutting resistance under heavy traffic loads and high summer temperatures. While polymer-modified bitumen is widely used to improve pavement performance, the high cost of commercial polymers restricts its extensive application. This study evaluates the potential of polymer waste as an alternative modifier for asphalt binders to enhance mechanical performance while reducing economic and environmental costs. Experimental results demonstrate that an optimal plastic waste content of 1.0–1.5% significantly improves rutting resistance and increases binder rigidity. The incorporation of 1.5% low-density polyethylene (LDPE) and high-density polyethylene (HDPE) enhances deformation resistance, elastic modulus, and temperature stability. LDPE exhibits better compatibility with bitumen and dissolves more readily, contributing to improved binder homogeneity, whereas HDPE provides higher stiffness and thermal stability. The combined use of polymer waste with styrene–butadiene–styrene (SBS) produces a pronounced synergistic effect, leading to improvements in physical and mechanical properties exceeding 25% compared to Kazakhstan regulatory standards. Increasing polymer waste content further enhances the rigidity of both the binder and asphalt concrete, thereby improving rutting resistance and plastic deformation at elevated temperatures. The proposed approach offers a cost-effective and sustainable solution for road construction, promoting plastic waste recycling, reducing reliance on virgin polymers, and improving pavement durability, particularly under the climatic and traffic conditions of Kazakhstan. Full article

Recognizing the limitations of traditional vehicle routing models in urban environments, this work presents the Time-Dependent Truck-Drone Green Vehicle Routing Problem with Pickup and Delivery (TDTDGVRPPD) to simultaneously optimize environmental impact and operational efficiency. We first develop a truck fuel consumption and carbon emission model that accounts for the effects of time-varying speeds and real-time loads during delivery. A nonlinear energy consumption model is then proposed for drones, considering payload weight. Based on these models, a mathematical formulation is developed to minimize the total operational cost, including truck and drone usage costs, truck fuel and carbon emission costs, drone energy consumption costs, truck–drone coordination time costs, and time-window violation penalties. The model also incorporates truck no-entry zones, time-varying speeds, and customers’ simultaneous pickup and delivery demands. An Improved Whale Optimization Algorithm (IWOA) hybridized with Variable Neighborhood Search (VNS) is developed to solve the problem. Simulation results show that the proposed model and algorithm effectively optimize truck departure times to avoid traffic congestion, reduce truck–drone coordination time, and lower total logistics costs and energy consumption, thereby contributing to energy conservation and emission reduction in logistics operations. Full article

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

To ensure the operational safety of the OCTABUOY platform used for offshore wind turbine installation in shallow waters, an eight-point symmetric mooring system was designed based on its octagonal structural configuration. The system provides high horizontal stiffness and balanced load distribution, enhancing stability under complex environmental conditions. Physical model tests were conducted under combined wind, wave, and current loading, considering multiple wave directions, environmental cases, and five draft conditions. The mooring tensions and six-degree-of-freedom motions were systematically analyzed to evaluate system performance and safety. Results show that the proposed mooring system effectively limits platform motions and maintains stable load-sharing characteristics. The minimum safety factor under the most unfavorable condition exceeds the design requirement. In addition, the system demonstrates good redundancy: after single-line failure, remaining mooring lines redistribute loads without progressive collapse. Draft and wave incident angle significantly influence peak tensions and motion responses, with smaller drafts and oblique wave directions producing relatively higher loads. The experimental results confirm the reliability and safety margin of the eight-point mooring system and provide practical guidance for the engineering application and operational assessment of the OCTABUOY platform in shallow-water wind installation projects. Full article

Lead (Pb) induces oxidative stress, inflammation, and hepatorenal injury. We evaluated whether fenugreek (Trigonella foenum-graecum) seed powder (200 mg/kg) protects against subchronic Pb-acetate exposure in male albino mice. Sixty mice were randomized to six groups (n = 10): control (G1), fenugreek-only (G2), Pb 150 mg/kg (G3), and three co-exposure groups receiving fenugreek with Pb at 50, 100, and 150 mg/kg (G4–G6), gavaged daily for 8 weeks. LC–DAD–ESI–MS/MS of the seed batch tentatively identified 32 metabolites, dominated by flavonoid C-glycosides, luteolin dihydrogalloyl-glucosyl-pentosyl glucoside (15.90%), vicenin-3 (14.46%), vicenin-2 (9.66%), vicenin-1 (8.80%), kaempferol 7-O-rhamnosyl-glucoside (8.71%), with additional acylated phenolic conjugates. Pb exposure (G3) significantly reduced growth and intake, elevated serum ALT, AST, ALP, urea, and creatinine, raised blood Pb, and produced hepatic necrosis, vacuolation, and inflammation. Molecularly, Pb upregulated Nrf2, HO-1, SCD-1, TNF-α, and IL-6 and suppressed SOD-3. Fenugreek co-treatment attenuated all these changes across the three Pb doses, with greatest effect at the lowest Pb load (G4). Notably, fenugreek co-treatment reduced rather than further increased Nrf2 and HO-1 expression relative to Pb alone, a pattern most consistent with lowering the upstream oxidative stimulus rather than direct induction of these pathways. The seed’s polyphenolic profile—rich in vicenin-type C-glycosides and luteolin and kaempferol derivatives—offers a plausible chemical basis for the antioxidant, anti-inflammatory, and modest Pb-lowering effects observed; however, because whole seed powder was administered and metabolite identifications are tentative, these structure–activity relationships are presented as hypotheses for future bioactivity-guided fractionation rather than as demonstrated mechanisms. These preclinical findings support further investigation of fenugreek as a candidate dietary adjunct against environmental Pb exposure, contingent on protein-level validation, pharmacokinetic characterization, benchmarking against a standard chelator, and bioactivity-guided fractionation. Full article

Urban freight externalities are increasingly addressed through regulation policies targeting both vehicle access and loading zones management. While urban vehicle access regulations and loading and unloading zone management are widely applied, existing research has largely regarded them as separate policy domains, overlooking their potential interdependence within urban freight governance. This study develops an exploratory comparative typology of UVARs and loading zone management across selected global cities. A hierarchical clustering method was applied to a harmonized set of indicators to identify distinct urban freight governance typologies. The UVAR clustering analysis was conducted on 39 cities with freight-related UVARs, while the loading zone clustering analysis was conducted on 39 cities with formal loading management zones. The cross-analysis suggests some co-occurrence patterns between UVARs and loading zone typologies. But the chi-square test does not provide statistical evidence of dependence. Therefore, this study can be interpreted as an exploratory mapping of regulatory configurations. The findings provide a comparative basis for future research linking urban freight regulatory typologies with environmental, operational, economic, and social performance indicators. Full article

Pollen constitutes the primary source of proteins, amino acids, lipids, sterols, vitamins, and minerals for honey bees. However, not all pollen types provide the same resources or have the same biological value. Its chemical composition changes according to botanical origin, geographic location, and environmental conditions. This variability can influence metabolism, the immune system, oxidative balance, and the ability to resist or tolerate infections. This article examines the available evidence on the relationship between pollen chemical quality and the dynamics of Deformed Wing Virus (DWV) infection in Apis mellifera. The analysis is approached from molecular, physiological, ecological, and seasonal perspectives. Current findings suggest that more diverse and higher-quality pollen diets are generally associated with greater colony survival and improved health status, although their effects on viral load are more heterogeneous and context-dependent. In some studies, pollen intake is linked to a reduction in DWV, whereas in others viral loads remain stable or even increase despite improvements in survival, physiological condition, or colony performance. These differences suggest that pollen may act not only by enhancing resistance to the virus but also by increasing tolerance to infection-associated damage. The potential role of pollen bioactive compounds, particularly flavonoids and phenolic acids, is also discussed. Nevertheless, evidence of direct antiviral action of these compounds in bees remains limited, as many proposed mechanisms derive from other organisms. This synthesis provides an integrative perspective on pollen nutrition and its relevance for colony resilience against viral infections. Full article

Seasonal infertility remains a major challenge in pig production and is influenced by environmental conditions and sow parity. This study aimed to identify in-barn heat load (HL), light variability (LV), and parity as farm-specific predictors of farrowing success (FS) and litter size (LS) in purebred Large White sows raised under subtropical and tropical conditions in Brazil. Reproductive records from 2021 were obtained from two commercial farms, and in-barn temperature and illuminance were recorded using data loggers installed in gestation units. Services between March and August 2021 were analyzed using logistic regression for FS and linear regression for LS, resulting in 732 service records from Farm 1 and 233 from Farm 2. In the combined analysis, parity was associated with higher odds of FS (OR = 1.20; p = 0.003) and greater LS (β = 0.47; p < 0.001). HL was not associated with FS but showed a small positive association with LS (β = 0.28; p = 0.031). In Farm 2, LV was negatively associated with FS (OR = 0.72; p = 0.028) and LS (β = −0.59; p = 0.020). Overall, parity structure strongly shaped reproductive performance, and in-barn environmental effects were context-dependent. Full article

This study addresses the technical, environmental, economic, and systemic role of multi-apartment residential buildings as hydrogen consumption nodes within urban energy systems. A representative five-story building comprising 30 apartments and 2400–2800 m² of heated floor area, located in a cold European climate, was modelled with an annual heat demand of approximately 185,000 kWh. Four heating configurations were assessed: a conventional natural gas/biomethane boiler (baseline), a hydrogen boiler, a hydrogen-fuel-cell combined heat and power (CHP) system, and a hybrid heat-pump–hydrogen solution. Dynamic simulations indicate that all hydrogen-based systems can fully satisfy space heating and domestic hot water demand without modifications to the internal hydronic distribution network. The fuel cell CHP achieved an overall efficiency of 93%. It generated approximately 54,000 kWh/year of on-site electricity, while the hybrid configuration reached a seasonal efficiency of 108% and the highest primary energy reduction (46%). Operational CO₂ emissions decreased from 37,800 kg/year (gas baseline) to 1900 kg/year (green hydrogen boiler), 1200 kg/year (fuel cell CHP), and 900 kg/year (hybrid system), corresponding to reductions of up to 98%. Peak-load analysis demonstrated improved operational stability in CHP and hybrid systems, characterised by reduced cycling frequency and enhanced thermal resilience through hydrogen storage integration. Capital expenditure (CAPEX) ranged from 41,000 EUR (gas baseline) to 101,000 EUR (fuel cell CHP), reflecting additional storage, safety, and control requirements. Over a 20-year lifecycle (5% discount rate), the hybrid system achieved the lowest levelized cost of heat (0.076 EUR/kWh), followed by fuel cell CHP (0.081 EUR/kWh), compared to 0.087 EUR/kWh for gas. Payback periods ranged between 9 and 13 years, depending on configuration and hydrogen pricing assumptions. Sensitivity analysis identified a break-even hydrogen price of approximately 0.085 EUR/kWh, while carbon pricing above 100 EUR/t CO₂ significantly improves economic competitiveness. District-scale aggregation modelling suggests that hydrogen-equipped multi-apartment buildings can reduce grid electricity imports by 30–40% through on-site generation and seasonal storage. The findings confirm that multi-apartment buildings offer structural and economic advantages for early hydrogen deployment compared to dispersed housing typologies. By combining high demand density, centralised infrastructure, and compatibility with sector-coupling strategies, such buildings can function as distributed energy hubs within decarbonized urban systems. Full article

The increasing accumulation of glass-fiber-reinforced polymer (GFRP) waste poses significant environmental challenges, calling for effective and scalable recycling strategies. In this work, a solvent-assisted cold mixing process was employed to incorporate very high amounts of GFRP (up to 75 wt%) into recycled expanded polystyrene (ePS). The composites were deeply characterized, with particular attention to the role of particle size distribution and filler content. The results demonstrated that GFRP granulometry played a key role in determining composite performance. Intermediate particle sizes (0.25 mm) provided the best balance between dispersion, interfacial interaction, and mechanical properties, whereas excessively fine fractions introduced defects and reduced impact resistance (from 0.7 to 2.0 kJ/m² going from dust to 0.25 mm at 75 wt%). Notably, the solvent-assisted approach has been widely recognized as an effective strategy to ensure homogeneous dispersion even at high filler contents, allowing subsequent melt processing without re-agglomeration. Recycled composites retained most of their chemical and mechanical properties after reprocessing, with only moderate performance losses mainly related to fiber fragmentation. Overall, this study demonstrates an effective and sustainable route for the simultaneous valorization of ePS and GFRP waste, enabling the production of highly loaded composites with preserved functionality and improved resource efficiency. Full article

Water loss and significant problems in the operation of water distribution networks caused by pipe failures are a global problem that needs immediate attention. This study is based on the experience-based assumption that the probability of water main breaks occurring is highest within a short time and a short distance from a previous (considered initial or base) break. The dataset used includes the historical pipe breaks recorded from 2007 to 2020 in the city of Larisa, Greece. A Geographic Information System (GIS) application is used for better data visualization, but also for effective operation and management of the developed water network database. Cluster analysis and Near and Spatial Join geoprocessing tools are the main tools used to detect and analyze trends in data related to space and time. In addition, the study attempts to identify relations between pipe attributes (material, age), environmental stressors (traffic load, soil type), and spatiotemporal clustering patterns. Finally, a machine learning-based water pipe failure Prediction Model is developed to serve as the computational engine of a Decision Support System (DSS) designed to optimize pipe replacement prioritization. Full article

Patients presenting with conditions attributed to environmental exposures face complex, multi-toxicant burdens, yet the stability of multivariate toxicant patterns under demographic adjustment remains poorly evaluated. This study assessed the sensitivity of principal component analysis (PCA) structures to demographic confounding and variable composition in 551 patients (aged <1–86 years) with environmentally related conditions. Nine urinary biomarkers were analysed using PCA with Varimax and Promax rotation on both raw data and residuals adjusted for age and sex. Among the identified patterns, the solvent/industrial cluster (xylene and styrene metabolites) was the most stable, persisting across both raw and residual analyses regardless of rotation method. However, overall component structures were sensitive to preprocessing: the clustering pattern of the herbicide 2,4-dichlorophenoxyacetic acid shifted markedly after demographic adjustment, illustrating indirect confounding whereby demographic effects on co-variables altered apparent biomarker associations. Notably, inclusion of an endogenous mitochondrial marker (tiglylglycine) alongside exogenous toxicant biomarkers produced Heywood cases (loadings > 1), violating factor analysis assumptions and indicating that mixing exposure and response variables destabilises the model. Cumulative variance explained was modest, consistent with the weak inter-biomarker correlations observed (KMO ≈ 0.52). These findings do not support the identification of robust, demographically stable clustering patterns in this cohort. Instead, they demonstrate that PCA-derived structures in heterogeneous clinical data are vulnerable to demographic confounding, variable selection and marker type, and caution against interpreting transient clustering patterns as definitive exposure signatures without rigorous validation. It should be noted that the absence of a non-clinical matched control group means that whether the identified co-exposure signatures are distinctive to symptomatic patients or reflective of general population exposure patterns cannot be determined; future studies should incorporate matched controls to address this directly. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article