Search Results (20,980)

Traditional air conditioning consumes substantial electricity, exacerbates the urban heat island effect, and creates a maladaptive feedback loop, necessitating a shift toward passive-first net-zero pathways. This study takes a typical six-story residential building in Nanjing’s hot summer and cold winter climate zone as a case study. Using EnergyPlus hourly simulations, three progressive passive strategy packages are designed to quantify the impact of building envelope heat exchange on cooling loads, grid stress, and heat resilience. Package A includes external shading and natural ventilation. Package B adds reflective coating and a green roof. Package C further adds night ventilation precooling and high-performance windows. The results show that Package C achieves a 62.5% reduction in peak cooling load and a 63.0% reduction in seasonal cooling load. Daytime peak inward heat gain decreases from 68 W/m² to 22 W/m², while nighttime outward heat dissipation increases from 12 W/m² to 38 W/m². Under an extreme heat day of 41.2 °C with no active cooling, indoor peak temperature drops from 36.8 °C to 29.4 °C, and heat risk hours decrease by 73.6%. Peak-hour power demand is reduced by 70.4%, with a systemic leverage factor of 1.08. Innovations include achieving over 60% load reduction using only mature passive strategies, introducing the systemic leverage factor to quantify urban heat island mitigation benefits, and establishing a vulnerability-to-resilience transformation framework. The passive-first pathway validates building envelope as the first line of defense for net-zero futures. However, the findings are based on a typical six-story residential building in Nanjing and require validation through field measurements or broader application across different climate zones and building typologies before generalization. Full article

Consistent laser cutting quality is one of the problems associated with the nonlinearity of relationships between process parameters and output responses. This problem acquires particular importance when it comes to cutting advanced nanocomposites, which requires precise tuning. Despite the wide adoption of intelligent modelling, few studies have investigated the comparative efficiency of various approaches based on the use of the same dataset. In this research, the effectiveness of three models—Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic System (FLS)—was tested on experimental data related to the CO₂ laser cutting of ABS/CNT nanocomposites. Input parameters included laser power and cutting speed, whereas HAZ width, kerf width, and surface electrical resistivity were used as output data. Data was split into training, testing, and validation datasets; models were created using supervised machine learning. Model performance was evaluated using Root Mean Square Error (RMSE). Analysis of results showed that ANN demonstrated acceptable predictive capabilities, yielding correlation coefficients (R) close to 1 (≈0.99) and RMSE values of 0.2956 for HAZ, 0.2061 for kerf width, and 2.3655 for surface electrical resistivity. Prediction by means of FLS was able to identify general tendencies; however, it produced RMSE values of 0.4741 for HAZ, 0.6297 for kerf width, and 1.9258 for surface electrical resistivity. Finally, the ANFIS model proved to be the most reliable model, yielding the lowest RMSE values for HAZ (0.2784), kerf width (0.0450), and surface electrical resistivity (0.0905). In conclusion, this research shows that ANFIS can be used effectively for building models predicting laser cutting processes; therefore, it represents an approach worth using in future investigations in this field. Full article

Respiratory mortality in Europe is associated with interacting environmental, infrastructural, climatic, and energy-related conditions. This study investigates country–year patterns of respiratory disease mortality by integrating panel-data econometrics, clustering analysis, and machine-learning prediction. The econometric results indicate that agricultural land use and coal-based electricity generation are positively associated with respiratory mortality, while access to electricity and freshwater withdrawals show negative associations. Cooling degree days capture a heat-related environmental-health dimension, although some coefficients become weaker under robust specifications. Sanitation and renewable energy display heterogeneous and specification-sensitive patterns, suggesting that they may partly reflect broader development gradients, infrastructure transitions, and regional heterogeneity rather than direct causal mechanisms. Hierarchical clustering identifies 10 country–year environmental-health profiles, highlighting differentiated combinations of energy systems, land use, infrastructure, climatic exposure, and respiratory mortality. This approach avoids treating countries as fixed homogeneous units and allows environmental-health profiles to vary over time. The selected hierarchical solution provides a balanced and interpretable structure relative to more polarized clustering alternatives. Machine-learning models are used as a complementary predictive exercise rather than as substitutes for econometric inference. Within the adopted validation framework, K-nearest neighbors achieves the strongest predictive performance. Additional stability checks and local additive explanations improve transparency regarding model tuning and prediction behavior, while confirming that machine-learning outputs should be interpreted as predictive rather than causal evidence. Overall, the findings support integrated and region-sensitive policy approaches combining air-quality management, infrastructure resilience, energy transition, climate adaptation, and public-health planning. Full article

The increasing integration of renewable generation introduces highly stochastic operating conditions, substantially enlarging the operating space and posing severe computational challenges for traditional iterative power flow solvers. To address this, we propose a Physics-Informed Graph Attention Network (PI-GAT) for fast and physically consistent power flow assessment in power systems with high renewable penetration. PI-GAT represents buses and branches as graph-structured inputs and employs edge-aware multi-head attention to adaptively capture electrical interactions between connected nodes. By embedding AC power flow equations as residuals in the training loss, PI-GAT promotes physical consistency, improving nodal power balance consistency even under high renewable variability and N−1 contingency scenarios. Experimental results on IEEE 30-bus and 118-bus systems demonstrate that PI-GAT reduces active and reactive power mismatches by up to approximately 62% across the two benchmark systems relative to the edge-aware GAT baseline. This improvement in physical consistency is accompanied by a modest increase in point-wise voltage and phase-angle errors. Moreover, PI-GAT achieves substantial inference-time speedups over conventional numerical solvers, especially under batched multi-scenario inference. These findings indicate that PI-GAT provides a reliable and efficient surrogate model for real-time security assessment and contingency screening in power systems with high penetration of renewable generation. 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

Optical performance monitoring is essential for adaptive and intelligent coherent optical communication systems. In this paper, a Transformer-based multi-task meta-learning framework is proposed for joint modulation format identification and electrical signal-to-noise ratio (ESNR) estimation from original received waveforms. A simulated coherent optical communication system is established to generate QPSK, 16QAM, and 32QAM signals under different launch-power conditions. The received I/Q waveforms are directly used as model inputs, avoiding handcrafted feature extraction or constellation-image conversion. The proposed model employs a shared one-dimensional Transformer encoder to extract temporal waveform representations. A prototypical classification branch is used for few-shot modulation format identification, while an ESNR regression branch is introduced for continuous signal-quality estimation. The two tasks are jointly optimized under an episodic support-query training mechanism. Experimental results show that the proposed method achieves 99.99% modulation identification accuracy on the test episodes. For ESNR estimation, the model obtains an MAE of 0.1194 dB, an RMSE of 0.1738 dB, and an R² value of 99.83%. These results demonstrate that the proposed framework can simultaneously provide accurate modulation decisions and reliable ESNR estimation, showing its potential for waveform-based optical performance monitoring. Full article

This study presents a comparative life cycle assessment (LCA) of a conventional wet flue gas desulfurization (FGD) process and two carbonate-melt-based FGD configurations (CMFGD-H and CMFGD-T), based on a functional unit of 1 kg SO₂ removed. Process-level life cycle inventory (LCI) data were generated using process simulation to ensure consistency and comparability across all systems. The results indicate that both CMFGD configurations significantly reduce environmental impacts in terms of global warming potential (GWP), fine particulate matter formation (PM), and terrestrial acidification (TA) compared to the conventional FGD process. Specifically, GWP decreased from 177.75 kg CO₂ eq to 37.47 and 35.68 kg CO₂ eq for CMFGD-H and CMFGD-T, respectively. Similar reductions were observed for PM and TA, primarily due to the elimination of limestone consumption, the absence of gypsum waste generation, and reduced direct process emissions. Hotspot analysis revealed that direct CO₂ emissions dominate GWP across all configurations, whereas PM and TA are influenced by both direct emissions and upstream energy supply. In the CMFGD systems, environmental burdens shift from direct emissions toward upstream processes, particularly electricity and hydrogen production, highlighting the importance of energy system characteristics. However, a clear trade-off was identified in fossil resource scarcity (FRC), which increased significantly for CMFGD configurations (1.858–1.976 kg oil eq) compared to the conventional process (0.128 kg oil eq). This increase is primarily attributed to greater dependence on upstream energy supply chains, including fossil-based electricity, fuel, and hydrogen production. Sensitivity analysis further indicates that FRC is configuration-dependent, with hydrogen consumption dominating in CMFGD-H and CO utilization playing a more significant role in CMFGD-T. Nevertheless, even with reductions in these key parameters, FRC remains substantially higher than that of the conventional process, indicating that this impact is fundamentally governed by upstream energy dependency rather than individual process variables. The results demonstrate that CMFGD technologies offer substantial environmental benefits in terms of emission-related impacts but may increase resource depletion. These findings highlight that achieving sustainable CMFGD systems requires an integrated approach that combines process optimization with low-carbon and resource-efficient energy supply. Full article

Urban industrial parks have high electricity demand, and rooftop photovoltaic (PV)-battery systems can help reduce grid dependence and carbon emissions. However, battery degradation affects battery replacement timing and long-term economic performance, which should be considered in capacity sizing. This study proposes a degradation-aware techno-economic sizing method for rooftop PV-battery systems in urban industrial parks. GIS-based rooftop assessment, EnergyPlus load modeling, TRNSYS system simulation, battery SOH tracking, and NPV evaluation were integrated into one framework. A case study was conducted for an urban industrial park in Shanghai, China. The usable rooftop area was estimated as 113,208 m², corresponding to a PV capacity of approximately 18,765 kWp. The annual PV generation was 24.7 GWh, accounting for 24.7% of the park’s annual electricity demand. Battery capacities from 5000 to 40,000 kWh were evaluated. The results show that increasing battery capacity improves load shifting and reduces direct grid supply, but the marginal benefit gradually decreases. The maximum NPV is obtained at 30,000 kWh, with an NPV of 128.36 million CNY, a simple payback period of 4.6 years, and a discounted payback period of 6.0 years. The rooftop PV system achieves a 25-year CO₂ emission reduction of approximately 335,967 tCO₂ after considering PV degradation. Sensitivity analyses show that BES cost, tariff spread, and discount rate are key factors affecting the recommended capacity. 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

Microbial fuel cells (MFCs) are emerging as biomimetic bioelectrochemical systems that emulate naturally occurring microbial electron-transfer pathways for stimulus bioenergy generation and wastewater remediation. In this study, food–vegetable leachate (FVL) and sugarcane bagasse-derived biol were evaluated in combination with carbon fiber (CF) and biochar-modified carbon fiber (BCF) electrodes used as membrane components in MFCs. Four configurations, in duplicate, were constructed by coupling two substrates (biol or FVL) with two membrane types (CF and BCF). All systems exhibited progressive anodic acidification and up to a 55% increase in electrical conductivity. The highest voltage output was achieved in MFC-BL-2 (404.59 mV), followed by MFC-FL-1, driven by synergistic interactions between the substrate and biochar-enhanced conductive networks. MFC-FL-1 also demonstrated superior contaminant removal performance, achieving 60% COD reduction, 36% BOD reduction, and 50% NH₄⁺–N removal. SEM–EDS analysis confirmed that biochar-modified electrodes developed a porous structure and substantially enhanced microbial adhesion. FVL-fed systems formed dispersed electroactive biofilms that facilitated electron transfer, whereas biol-fed systems developed compact biofilms that constrained electron flux. By integrating waste-derived lignocellulosic materials with electroactive microbial consortia, this work advances a biomimetic circular bioengineering platform for sustainable bioelectrochemical recovery and wastewater remediation. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article

Copper azides are promising energetic materials for miniaturized pyrotechnic devices and micro explosive trains owing to their short detonation growth distance and high initiation energy. However, controllable preparation of copper nanoparticle precursors and their in situ conversion to copper azides under mild conditions remains challenging. In this study, copper nanoparticles were synthesized via a coordination-assisted aqueous reduction method at room temperature under air atmosphere using nitrilotriacetic acid disodium salt (NTA·H·2Na) as the complexing agent. The resulting nanoparticles were pressed into polyester rings to construct confined precursor structures, and copper azide micro-charges were prepared through in situ gas–solid reaction with HN₃ gas generated from NaN₃ and concentrated phosphoric acid at 60 °C. SEM characterization revealed that the morphological evolution of copper azides followed a three-stage pattern: “product island nucleation, branch/block coalescence growth, and continuous product layer formation and structural reconstruction”. Detonation velocity tests using the electrical probe method showed an average value of (5.10 ± 0.07) × 10³ m/s. Flyer impact initiation tests demonstrated that, with a charge thickness of 1.00 mm, both a 30 μm polyimide flyer and a 40 μm titanium flyer could successfully initiate a HNS–IV explosive. The preparation methodology and performance characterization established in this work provide an experimental basis for the application of copper azides in micro-initiation systems. Full article

Climate change and soil degradation pose a challenge to grape quality, motivating the development of integrated monitoring approaches combining soil analysis with remote sensing techniques. However, harmonized information addressing this multidisciplinary challenge remains scarce. Therefore, this systematic review synthesizes the scientific literature published since 2020 with the aim of (i) identifying key soil properties and techniques applied, (ii) evaluating remote sensing approaches and their integration with soil data, and (iii) highlighting knowledge gaps and challenges for sustainable precision viticulture. A search in Scopus yielded 197 full-text articles classified into three thematic groups and analyzed using a standardized extraction protocol. Our synthesis reveals that pH, electrical conductivity, soil organic matter, and cation exchange capacity are the most consistently reported physicochemical parameters across the reviewed studies, while next-generation sequencing and multi-omics approaches are increasingly adopted in microbiological research to characterize rhizosphere communities and their links to terroir expression. In remote sensing, multispectral UAV platforms and satellite missions (Sentinel-2, Landsat) combined with vegetation indices, principally NDVI, dominate the toolset for monitoring vine vigor and water status. Nevertheless, genuine integration of remote-sensing outputs with root-zone soil measurements remains uncommon, with most studies treating both data streams independently. The principal knowledge gaps identified concern the absence of standardized sustainability assessment frameworks, limited cross-terroir transferability of predictive models, and insufficient long-term multi-site datasets to underpin climate change adaptation in vineyard management. Full article

In modern power systems such as new energy generation and smart grids, inverters serve as core equipment for electrical energy conversion and transmission. Their operational reliability directly impacts system power supply quality and safety stability. Currently, research on inverter fault diagnosis technology primarily focuses on linear load conditions, with diagnostic method design and validation based on linear load characteristics. However, with the rapid advancement of power electronics technology, power electronic loads such as variable frequency drives, charging stations, and distributed power sources are increasingly prevalent in power systems. These loads exhibit nonlinear and time-varying characteristics under complex operating conditions, leading to a growing variety of inverter faults with significantly diversified and complex fault signatures. Traditional diagnostic methods fail to adapt to the unique characteristics of power electronic loads, making it difficult to accurately identify various faults. Consequently, they no longer meet the diagnostic demands of practical engineering scenarios. In addition, current diagnostic methods for open-circuit power transistors, intermittent faults, and sensor faults often employ different approaches, which consume significant controller resources and are prone to mutual interference, leading to false triggers. This paper takes a three-phase four-wire inverter as the research subject. Targeting the challenge of fault diagnosis under power electronic load conditions, it proposes a comprehensive diagnostic method capable of simultaneously diagnosing power switch open circuits, intermittent faults, and current sensor faults. First, the characteristics of various faults are analyzed. Subsequently, fault diagnosis variables are constructed using the actual arm voltage of the inverter and the ideal arm voltage. Logical rules for each type of fault are established, and diagnosis is performed through fuzzy logic inference. Finally, experiments validated the effectiveness of this fault diagnosis scheme, with open-circuit faults detected in less than 2 ms, intermittent faults in less than 0.5 ms, and sensor faults in less than 3 ms. Full article

Voltage-gated ion channels (VGICs) are traditionally associated with electrically excitable cells; however, increasing evidence indicates that they are also expressed in non-excitable cells, including vascular endothelial cells. This review aims to summarize the current knowledge on the expression, regulation, and functional role of VGICs in the vascular endothelium, and to highlight their potential contribution to endothelial signaling. We examined the molecular structure, biophysical properties, and functional roles of voltage-gated Na⁺ (Na_V), Ca²⁺ (Ca_V), and K⁺ (K_V) channels in vascular endothelial cells. Particular attention was given to studies investigating VGIC activity in native endothelium and to emerging mechanisms regulating their activation. Endothelial cells express multiple VGIC subtypes at low densities, which are insufficient to generate action potentials but can modulate membrane potential (V_M) and Ca²⁺-dependent signaling. The dynamic regulation of the endothelial V_M, through the interplay between hyperpolarizing and depolarizing conductances, emerges as a key determinant of VGIC availability and activation. VGICs contribute to essential endothelial functions, including angiogenesis, vasomotor responses, blood–brain barrier permeability, and inflammation. Dysregulated VGIC expression and/or activity may be implicated in several pathological conditions, such as atherosclerosis, calcific aortic stenosis, and tumor vascularization. VGICs represent an unexpected but functionally relevant component of endothelial signaling. Elucidating their role in native vascular beds and disease contexts may uncover novel mechanisms of endothelial regulation and identify new therapeutic targets in cardiovascular and cancer biology. Full article