Search Results (2,426)

The efficiency of anaerobic digestion (AD) of lignocellulosic biomass is strongly determined by biomass yield, chemical composition, and bioavailability, all of which undergo substantial seasonal variation. However, integrated analyses linking these factors with AD performance, process kinetics, and energy-economic efficiency remain limited. This study aimed to evaluate the effect of seasonal variability in the chemical composition of Helianthus annuus biomass on AD efficiency from a technological and economic perspective. The novelty of this study lies in integrating seasonal changes in biomass composition with AD kinetics, CH₄ productivity per hectare, and CHP techno-economic performance to identify the optimal harvest window for Helianthus annuus. The experiments were conducted using biomass harvested from June to December. The results showed significant (p < 0.05) variability in biomass properties, including a progressive increase in lignocellulosic fractions over the growing season, with neutral detergent fiber (NDF) increasing from 30.58 ± 1.8 to 66.58 ± 3.1% TS and acid detergent lignin (ADL) from 5.13 ± 0.5 to 10.35 ± 0.9% TS, accompanied by a decline in substrate bioavailability. The maximum CH₄ yield of 258 ± 13 mL/g VS was obtained in August, with a process rate of 29.0 ± 3.4 mL/g VS·d and the highest utilization of methane potential, reaching 62.5 ± 3.8% (BMP_CH4/TBMP). Correlation and regression analyses indicated that ADL and NDF were the strongest empirical predictors of AD performance within the analyzed dataset, showing a negative association with both CH₄ production yield and kinetics (R² up to 0.86), whereas reducing sugars had a stimulatory effect. Multiple regression models showed high predictive performance, with R² = 0.889 for BMP_CH4. The highest energy and economic efficiency was achieved in summer. In August, CH₄ production reached 3214 ± 596 m³/ha, corresponding to 11.2 ± 2.1 MWh/ha of electricity and a net result of 1559 ± 417 EUR/ha. Increased lignification in the later part of the season led to reduced process efficiency and a deterioration of the economic balance. From a practical perspective, these results demonstrate that harvest scheduling should be based on the trade-off between biomass quantity and biodegradability rather than on biomass yield alone. Full article

Recent advances in chiplet architectures, heterogeneous integration, 2.5D/3D packaging, high-performance computing, and RF applications have increased the demand for high-density vertical interconnects and low-loss packaging platforms. Glass substrates have attracted considerable attention for next-generation advanced packaging because of their low dielectric loss, high dimensional stability, smooth surface, and compatibility with large-area panel-level processing. Through-glass vias (TGVs) are essential vertical interconnect structures that enable the electrical integration of glass substrates. This focused review summarizes TGV technologies for glass-based advanced packaging from the perspectives of via formation, seed layer deposition, metallization, Cu filling, defect formation, reliability, and plugging-based alternative architectures. Representative TGV formation methods, including laser drilling, selective laser etching, laser-induced deep etching, wet/dry etching, and photosensitive glass processing, are compared. Metallization approaches based on sputtering, electroless plating, ALD/CVD, and hybrid processes are discussed together with Cu electroplating strategies such as conformal plating, bottom-up filling, pulse or pulse-reverse plating, and engineered-geometry filling. Key defects, including voids, seams, pinch-off, seed discontinuity, Cu/glass interfacial delamination, glass cracking, and Cu protrusion, are reviewed in relation to thermomechanical reliability. Finally, polymer/dielectric plugging, plugging/re-drilling, conductive paste plugging, and hybrid Cu/plugging structures are discussed as application-specific alternatives for balancing electrical performance, reliability, manufacturability, yield, and cost. Full article

Road freight logistics is one of the most difficult transport segments to decarbonize. In recent years, battery electric trucks and hydrogen fuel cell electric trucks have emerged as the most promising alternatives to conventional heavy-duty vehicles. However, their integration cannot be reduced to a question of vehicle substitution, as it depends on a broader system of conditions. This paper aims to identify and structure the system-determining parameters that influence the use of battery electric trucks and hydrogen fuel cell electric trucks in road freight logistics. To this end, the study applies a systematic literature review, yielding a final sample of 42 publications. The review shows that drive type suitability depends on parameters across four categories: economic, ecological, performance-related, and external. Accordingly, no single factor determines suitability; rather, outcomes emerge from the interaction of multiple conditions. The reviewed literature does not support a universally superior drive technology. Instead, the suitability of battery electric trucks and hydrogen fuel cell electric trucks depends on the specific configuration of the surrounding system. The paper thus provides a structured framework for future comparative assessments in sustainable road freight logistics. The study is embedded in the Research Campus Mobility2Grid, which provides a practice-oriented context for assessing alternative drive technologies in relation to fleet, depot, energy, and logistics-system requirements. Full article

This study presents a comprehensive life cycle assessment (LCA) of the modernization of an existing 460 MW coal-fired power unit into a hybrid system incorporating a high-temperature gas-cooled reactor (HTGR). The analysis was conducted from a cradle-to-grave perspective using a functional unit of 1 MWh of net electricity, based on the ecoinvent 3.9 database and the ReCiPe 2016 Midpoint method. The results indicate that the modernized system achieves a global warming potential (GWP) of 18.2 g CO₂-eq/kWh, representing a 93.5% reduction compared to a supercritical coal-fired unit. The largest contribution to the total environmental burden is associated with the upstream uranium supply chain, accounting for approximately 42% of GWP. In contrast, the operational phase exhibits a negative contribution due to the application of environmental credits resulting from the avoidance of emissions related to coal combustion. The findings also confirm a significant improvement in resource efficiency, including reduced primary energy demand and waste generation compared to the reference system. Sensitivity analysis demonstrated the robustness of the results with respect to variations in key economic and thermodynamic parameters, particularly CAPEX (capital expenditures) and operating temperature. Overall, the results suggest that hybrid retrofitting of coal-fired power plants with HTGR technology may serve as a viable transitional pathway supporting the decarbonization of the Polish energy sector. Full article

Low-dimensional (0D/1D/2D) nanomaterials exhibit unique physical and chemical properties different from general bulk materials due to enhanced surface and interface contributions and quantum confinement effects, which strongly modulate electronic structures. Boron, with atomic number 5, can form multicenter bonds and enables the construction of structurally diverse nanomaterials across different dimensionalities. In this review, boron-based low-dimensional materials are systematically organized from 0D clusters to 1D nanostructures and 2D sheets, and their optical and electrical properties are discussed in relation to structural factors such as dimensionality. This review provides an integrated perspective on how dimensional expansion and structural design govern the optical and electrical properties of boron-based nanomaterials. Full article

With the continuous growth of urban building density and elevator deployment, the reliability, maintenance, and degradation risk warning of elevator systems have attracted increasing attention. Conventional monitoring methods based on fixed thresholds or rule logic are easy to implement, but they often fail to identify progressive degradation and are sensitive to complex operating conditions and measurement noise. This paper proposes a physics-informed neural network (PINN)-based method for elevator health monitoring and early warning. First, multi-sensor data are processed through time alignment and feature reconstruction, and a dual-path acceleration estimation method is introduced to improve the stability of dynamic state calculation. Second, a simplified traction elevator dynamic model considering load variation, motor drive, and mechanical resistance is embedded into PINN training to identify hidden parameters. Electrical and dynamic residual indicators are then constructed to characterise system condition from different physical perspectives. Finally, a time-accumulated risk model combining anomaly magnitude and persistence duration is developed to detect progressive degradation trends. Results show stable parameter convergence and effective condition assessment. The proposed approach detects degradation trends earlier than conventional threshold-based monitoring methods and reduces false alarms caused by transient disturbances. It provides an interpretable and practical solution for predictive maintenance and intelligent operation of elevator systems. Full article

As urban energy demand increases and available roof space remains limited, balcony photovoltaic (PV) systems have emerged as a promising distributed renewable energy solution. This study aims to evaluate the multidimensional benefits of these systems in urban residential applications from a dual-carbon perspective. A combination of experimental tests and numerical simulations was used to investigate the effects of installation tilt angles and vertical self-shading in high-rise buildings. A comprehensive assessment model was constructed, integrating technical power generation gains, economic returns, and environmental carbon reduction benefits. The results demonstrate that when comprehensively balancing generation gains, economic viability, and structural safety, the practical optimal installation tilt angle for balcony PV systems is around 30°. The Levelized Cost of Electricity (LCOE) is calculated at 0.050–0.061 USD/kWh. Furthermore, a standard 800 W system operating under Beijing’s climate conditions can reduce carbon emissions by approximately 12.68 tons over its 25-year lifecycle. Therefore, balcony PV systems deliver significant technical, economic, and environmental benefits, serving as a highly feasible strategy to promote low-carbon and sustainable development in high-density cities. Full article

This study proposes a rigorous methodology for the optimal placement of voltage regulators in the Guayacanes feeder of the Buena Fe substation, Ecuador, by integrating electrical feeder modeling, exhaustive search, and multi-criteria decision-making. The feeder was modeled in detail by incorporating its radial topology, nodal electrical parameters, and representative operating conditions under minimum- and maximum-load scenarios. Based on this model, a set of technical evaluation criteria was established to quantify the impact of regulator installation, including active power losses, reactive power losses, global voltage deviation, average voltage variation, and voltage imbalance. An exhaustive search strategy was then implemented to evaluate all feasible regulator-location alternatives over the candidate nodes, thereby ensuring a complete exploration of the solution space. The resulting alternatives were ranked using the Weighted Sum Method (WSM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), allowing the comparison of candidate locations from a multi-criteria perspective. The results indicate that node MTS 108932 provides the most technically favorable overall solution, achieving the greatest improvement in voltage profile quality and the most significant reduction in electrical losses. In addition, a sensitivity analysis was conducted by varying the weighting structure of the decision criteria, confirming the robustness of the selected alternative under different decision-maker preference scenarios. The proposed framework provides a technically sound decision-support methodology for voltage regulation planning in real radial distribution systems. Full article

Short-term wind power forecasting is a key enabling technology for wind farm operation optimization, power grid dispatch, and electricity market decision-making. However, existing studies often lack unified standards in data partitioning, input feature construction, and hyperparameter tuning, making fair and reproducible comparisons across models difficult to achieve. To address this issue, this study focuses on day-ahead wind power forecasting for a single wind farm and establishes a benchmarking framework with strict chronological splitting, a shared feature information set, and a consistent hyperparameter tuning budget. Within this framework, six representative models, namely Ridge, XGBoost, LightGBM, DLinear, Transformer, and PatchTST, are systematically evaluated. A two-level evaluation protocol combining a fixed hold-out split and expanding-window rolling validation is adopted to compare model performance from multiple perspectives, including overall accuracy, sensitivity to hyperparameter tuning, robustness across rolling windows, and performance under typical operating scenarios. The results show that model rankings are not fully consistent between the hold-out evaluation and the rolling-validation setting. Under the fixed hold-out split, LightGBM achieved the lowest NRMSE of 10.2326%, while Transformer obtained the lowest NMAE of 6.9944%. In contrast, under the 8-fold expanding-window rolling validation, Transformer achieved the lowest average NRMSE of 8.1684%, followed by LightGBM with 8.7344%. These results indicate that the best performance on a single test split does not necessarily imply the strongest robustness across multiple time windows. In addition, strong tree-based models remain highly competitive in this single-wind-farm forecasting task, whereas more complex deep temporal models do not always deliver stable advantages. Meanwhile, the performance gains brought by hyperparameter optimization vary substantially across models, suggesting that conclusions drawn from default-parameter comparisons are of limited reliability. These findings demonstrate that systematic benchmarking under strict temporal constraints and fair tuning conditions is essential for clarifying the comparative performance, robustness, and engineering applicability of different model families. The study can therefore provide practical guidance for model selection and deployment in short-term wind power forecasting for single wind farms. Full article

The growing volatility and complexity of global food supply chains have intensified the need for integrated analytical frameworks capable of supporting anticipatory and data-driven decision-making. This article examines how logistics sector observatories can function as strategic intelligence infrastructures for identifying structural tensions and supporting resilience in cold-chain logistics systems. The article introduces the concept of logistics sector observatories as strategic intelligence infrastructures and examines its empirical relevance through a longitudinal analysis of the Spanish cold-chain logistics sector. Empirically, the research draws on a multi-source dataset constructed through the ALDEFE Observatory in collaboration with industry stakeholders over the core study period 2021–2025, encompassing storage capacity, consumption dynamics, energy costs, international logistics indices, and macroeconomic variables. Complementary energy benchmark data for 2019–2025 are used to contextualize electricity cost volatility. Methodologically, the study combines qualitative insights from stakeholder interviews with exploratory quantitative longitudinal analysis. The results suggest severe structural tensions driven by the interaction between rigid capacity constraints and energy cost volatility. The analysis identifies a pattern of persistently high storage occupancy despite substantial energy-price fluctuations. This finding is consistent with the structural inelasticity of cold-chain demand, which reduces operational slack and affects system resilience. Beyond operational resilience, the study highlights the potential contribution of sector observatories to the energy sustainability transition through future sector-level indicators related to energy intensity, refrigeration efficiency, and carbon performance. The study contributes a sector-level, data-driven perspective on visibility, coordination, and anticipatory governance in complex logistics environments. Full article

As a significant branch of nanotechnology, carbon-based nanomaterials (CNMs) have garnered extensive attention for their broad application potential in agriculture, attributed to their unique structural and physicochemical properties. They are considered one of the important tools for promoting sustainable agricultural development. Among them, carbon nanotubes (CNTs), owing to their excellent mechanical properties, electrical characteristics, and high specific surface area, have recently attracted considerable interest in plant growth regulation and the development of agricultural inputs. This article systematically reviews the research progress of CNMs, especially CNTs, in agriculture. Firstly, it outlines the structural characteristics and physicochemical properties of different types of CNMs. Subsequently, from a plant physiological perspective, it focuses on analyzing their mechanisms of action in nutrient uptake, photosynthesis regulation, and antioxidant defense. Based on this, it summarizes the application progress of CNMs in plant growth promotion, nano-pesticide and fertilizer delivery, and precision agriculture sensing. Furthermore, this article emphasizes the dose-dependent biphasic effect (hormesis) of CNMs on plants: at relatively low, system-specific doses, they can promote growth and enhance stress resistance, whereas at higher or supra-optimal doses, they may induce oxidative stress, cellular damage, and photosynthesis inhibition. However, significant variations in responses exist depending on the material type, physicochemical properties, and plant species, and a unified understanding of the underlying mechanisms has not yet been established. Finally, this article discusses green synthesis strategies for CNMs and their potential ecological risks and points out that future research should focus on key issues such as precise dose regulation, long-term environmental behavior, and multi-scale mechanism analysis. This review aims to provide a systematic reference for understanding CNM–plant interactions and their safe application in agriculture. Full article

Energy-intensive industries deploying hybrid renewable energy systems require performance monitoring frameworks that evolve with phased system implementation. This paper introduces the performance and sustainability framework, a simulation-grounded evolution of the sustainability balanced scorecard for longitudinal assessment of renewable energy infrastructure. The framework requires that key performance indicators derive from validated techno-economic simulations, that assessment is repeated at temporal checkpoints corresponding to physical system changes, and that each balanced scorecard perspective includes at least one environmental or circular-economy indicator. The framework is demonstrated in a cheese manufacturing facility in Crete, Greece, where a 38 kW cheese whey biomass generator, 72.2 kW photovoltaic system, and 10 kW wind turbine are deployed over five years. Annual HOMER Pro re-simulations are combined with weighted SWOT scoring to track technical, economic, environmental, and organisational performance. By Year 5, the system achieves an 88.7% electrical renewable fraction, 60.0% gross-operational CO₂-eq reduction, 0.1148 EUR/kWh levelised cost of energy, and 22.3% internal rate of return. The longitudinal trajectory also reveals declining delivered thermal renewable contribution and cheese whey utilisation, exposing operational trade-offs that single-point scorecard assessments would miss. Applicability of the PSF to community-scale governance under ISO 37101:2016 and to renewable energy communities under Directive (EU) 2018/2001 is examined exclusively as a conceptual scaling framework for future research. The present empirical demonstration is restricted to a single-facility case study, and no community-level stakeholder data are collected or analysed. Full article

This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational safety. The primary objective of the study is to evaluate the synergy between renewable energy sources (solar and wind energy) and emerging propulsion technologies in aviation (hydrogen and electrification) from the perspective of safety and operational stability. The methodology is based on a systematic review of 78 scientific studies identified in the Scopus and Web of Science databases. The analysis identifies critical technical and operational barriers, including electromagnetic interference caused by wind turbines, optical hazards associated with photovoltaic systems, and stability challenges in airport microgrids under peak loads resulting from the charging of electric aircraft. Particular attention is given to the safety of hydrogen infrastructure, where findings from the literature indicate the need to revise separation distances and highlight the potential reduction of airport stand capacity by 5% to 16%. The study synthesizes these findings into a strategic framework for “Smart Green Airports”, proposing solutions such as adaptive infrastructure design, the deployment of predictive models based on artificial intelligence, and the implementation of inherently safe energy storage systems. The paper concludes that achieving airport energy self-sufficiency while maintaining the integrity of flight operations is feasible only through the holistic integration of technical measures, simulation-based planning, and strict compliance with updated safety regulations. Full article

Renewable energy sources, such as solar thermal and photovoltaic, geothermal, biomass, hydropower, and wind, offer significant sustainability advantages. Yet the sector still faces difficulties in several areas that tend to reduce the efficiency of these new energy forms. Some of these challenges include inconsistent electricity supply, the diffuse nature of renewable energy sources, which makes them difficult to exploit, and the inconsistent and unpredictable nature of electricity supply, which has repercussions for renewable energy markets. Although Industry 4.0 is inherently energy-intensive, its positive contribution to renewable energy systems may outweigh its costs. Consequently, this study conducts a scoping review on the role of digital technologies in renewable energy systems. It focuses on open-access conference papers, journal articles, and book chapters published between 2020 and 2026, selected from scientific platforms and databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. A multi-stage screening process and a summary sheet for a set of 89 selected articles were produced to extract the necessary information. The results show that Industry 4.0 influences renewable energy systems at the design and installation stage in predictive maintenance, efficient management, and energy security. Meanwhile, Industry 4.0 in renewable energy systems still faces negative externalities that can be categorised as political, financial, infrastructural, environmental, human, security, and technological. To address these challenges, which tend to become entangled in cycles of negative reinforcement, the paper suggests defining standardised, clear, strict, and stable frameworks at the political, legal, regulatory, and environmental levels to overcome most challenges associated with the digital transformation of renewable energy. The study also recommends flexible, inclusive strategic planning that accounts for the digital maturity of the renewable energy system. From these perspectives, the study contributes to the literature by addressing the role of Industry 4.0 technologies in renewable energy systems from a strategic and coordinated perspective, from both human and technological standpoints. It also offers managerial and policy implications by supporting innovation in renewable energy systems on the one hand and contributing to policy and regulatory decision-making that favour their growth on the other. Full article

Genetically encoded voltage indicators (GEVIs) have long promised optical access to membrane potential, yet their adoption has lagged significantly behind genetically encoded calcium indicators. A central but underappreciated reason is that the metrics used to evaluate and compare GEVIs—fractional fluorescence change (ΔF/F), kinetics, and signal-to-noise ratio—rest on an assumption that is frequently violated: that GEVI fluorescence reflects a single underlying process. In this perspective, we argue that GEVI signals are composite optical measurements, arising from the superposition of voltage-dependent fluorescence, intracellular and nonresponsive signal, background, and contributions from neighboring cells. Under these conditions, ΔF/F is not a measure of sensor sensitivity but a contrast metric whose value depends on baseline fluorescence composition, optical sampling, and imaging configuration. This reinterpretation has two key consequences. First, it explains a substantial source of variability in GEVI performance that is currently attributed to noise or experimental inconsistency. Second, and more importantly, it reveals that the complexity of GEVI signals is not a limitation to be minimized but a resource to be exploited. By resolving composite signal components, GEVIs can report multiplexed physiological variables, expose hidden conformational states of voltage-sensing domains, probe membrane organization, and reveal intracellular and intercellular electrical coupling. We propose that realizing the full potential of GEVIs requires treating ΔF/F not as a gold standard for sensor performance, but as one interpretable component of a richer optical measurement whose structure encodes multiple layers of cellular physiology. Full article