Search Results (1,887)

High-temperature stress severely reduces the photosynthetic efficiency of radish (Raphanus sativus L.), a cool-season crop. This study evaluated five nitrogen (N) levels {0 N, 0.5 N, 1 N (234 kg urea ha⁻¹, based on RDA), 2 N, and 4 N} through an open-field experiment under high-temperature stress conditions. Analysis of OJIP transients revealed that high temperatures severely inhibited photosynthetic capacity in the 0 N, 0.5 N, and 4 N treatment groups. These groups exhibited a simultaneous increase in K and J-steps, signifying electron transport bottlenecks and structural damage to the oxygen-evolving complex (OEC). Consequently, energy absorption and trapping decreased, while heat dissipation increased. In contrast, the 2 N treatment maintained superior F_m(maximum fluorescence) and energy flux, demonstrating enhanced photosynthetic resilience. However, despite improved photosynthetic stability, the 2 N group did not show a significant increase in yield compared to the 0.5 N or 1 N treatment groups. These results suggest that photosynthetic protection under heat stress does not necessarily guarantee higher yields, highlighting the need to identify optimal fertilization points for sustainable production. Overall, the findings of this study provide fundamental data for strategic nitrogen management in open-field radish cultivation to mitigate the impacts of increasing climatic instability. Full article

Despite the growing use of tessellated and patterned façades in contemporary architecture, their thermal performance, particularly in cooling-dominated educational buildings, remains insufficiently quantified, with existing studies largely prioritizing daylighting or aesthetic outcomes over energy-driven thermal behavior. This study aims to systematically evaluate how different tessellated façade geometries and perforation ratios influence thermal performance and cooling demand in a Mediterranean climate, and to identify an optimal façade configuration that balances multiple thermal objectives. Three tessellation typologies—nature-inspired (Voronoi), Islamic geometric, and folded origami-based patterns—were parametrically generated and applied as external shading screens to an educational building. Annual thermal simulations were conducted using Climate Studio to assess four performance metrics: solar heat gain, energy use intensity, hours of overheating derived from operative temperature, and peak cooling demand. A post-simulation, data-driven, multi-objective, decision-support approach was applied using Compromise Programming to systematically evaluate and rank discrete façade alternatives based on multiple thermal performance criteria. Results indicate that all tessellated façades reduce solar heat gain and peak cooling demand relative to the unshaded baseline, with performance strongly dependent on both geometry and perforation ratio. Lower perforation ratios (20%) consistently outperform more open configurations, while Voronoi-based façades achieve the most balanced overall thermal performance across all evaluated criteria and emerging as the top-ranked solution. The study’s novelty lies in its comparative, cooling-focused evaluation of fundamentally different tessellation logics using transparent, decision-oriented optimization rather than subjective comfort indices or computationally intensive evolutionary algorithms. Beyond its specific findings, the research provides a transferable methodological framework for integrating geometry-informed façade design into early-stage decision-making, supporting climate-responsive and energy-efficient educational architecture in Mediterranean and similar climates. Full article

Combining Response Surface Methodology (RSM) with Central Composite Design (CCD) is a powerful statistical approach to optimizing materials in energy storage systems. This study presents an open-source Python (v3.8+) framework that replicates and validates the RSM-based optimization of NiCo₂S₄–graphene supercapattery materials. We validated the framework by replicating a 20-experiment CCD analyzing graphene/NCS ratios, hydrothermal time, and S/Ni molar ratios. Advanced optimization using the Differential Evolution algorithm was integrated to efficiently solve the high-dimensional response surface space. The model explained 97.16% of the variance, and comprehensive diagnostic tests confirmed the assumptions of normality and residual independence. This approach provides an open-source methodology that supports reproducible and scalable data-driven material design and facilitates transparent computational materials science studies. Full article

Climate change exerts growing pressure on the built environment, intensifying urban heat stress, altering microclimatic conditions, and increasing energy demand and health risks. Urban areas, characterized by dense construction and extensive soil sealing, are particularly susceptible to thermal anomalies such as Urban Heat Islands (UHIs), making thermal assessment a crucial element in adaptation and mitigation strategies. This research provides an updated and critical review of methodologies for the thermal evaluation of the built environment, with a focus on remote sensing as an emerging and integrative measurement paradigm. The study presents a comprehensive framework of detection systems, including satellite and aerial remote sensing, ground-based monitoring, and hybrid approaches, complemented by analytical and modeling techniques that combine physical and data-driven methods. A comparative assessment of open-access satellite sensors is carried out, analyzing spatial, spectral, and temporal resolutions and their relevance to urban-scale applications. The integration of remote sensing data with artificial intelligence, machine learning, and cloud-based processing is highlighted as a key advancement for improving interpretative, predictive, and decision-support capabilities. The findings indicate that such integration represents a new frontier for multiscale thermal analysis, supporting resilient urban planning, enhanced energy efficiency, and effective climate change mitigation policies. Full article

The proliferation of sensor networks in critical infrastructure, healthcare monitoring, and smart city applications demands robust privacy-preserving mechanisms for data verification. Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) offer a promising cryptographic primitive that enables data integrity verification without revealing sensitive sensor readings. However, the practical feasibility of deploying zk-SNARKs in resource-constrained sensor network environments remains insufficiently characterized. This paper presents a systematic benchmarking study of the Groth16 zk-SNARK protocol across eight representative circuit types spanning six orders of magnitude in computational complexity, from basic arithmetic operations (1 constraint) to ECDSA signature verification (1,510,185 constraints). Using an automated open-source benchmarking framework built on the Circom-snarkjs toolchain, we conducted 160 statistically controlled measurements (20 iterations per circuit) with cold/warm separation, collecting proof generation time, verification time, proof size, memory consumption, and witness generation overhead. Our results demonstrate that Groth16 proofs maintain a constant size of $804.7\pm 1.7$ bytes and near-constant verification time of $0.662\pm 0.032$ s regardless of circuit complexity, with coefficients of variation below 5% across all circuit types. Proof generation time exhibits sub-linear scaling ($\alpha =0.256$, $R^2=0.608$), with statistically significant differences between circuit categories confirmed by one-way ANOVA ($F=355.0$, $p<{10}^{-79}$, ${\eta }^2=0.94$). We identify three operational deployment tiers for sensor network architectures and estimate energy budgets for battery-powered devices. These findings provide actionable guidance for the design of privacy-preserving data verification systems in next-generation sensor networks. Full article

Global climate change has brought issues of pollution and environmental protection to the forefront of public attention. The energy consumption and carbon emissions of buildings have become critical issues in energy conservation and emission reduction, and are important for environmental protection. This article focuses on typical residential buildings in Wuhan, a representative of regions with hot summers and cold winters, to study the impacts of different layout design parameters on energy consumption and carbon emission intensity of building complexes. VirVil-HTB2 was used for modeling and simulating building complex layouts, while SPSS was used for data analysis. This study shows that solar radiation is an important indicator for predicting building energy efficiency, directly affecting energy consumption and carbon emissions. We also examined the impact of building orientation, building spacing, staggered spacing, and the layout of open spaces between buildings on heating energy consumption, cooling energy consumption, and carbon emissions. Building spacing was positively correlated with cooling energy consumption and negatively correlated with heating energy consumption and carbon emissions. The effect of staggered spacing on energy consumption is greater in the south–north direction than in the west–east direction. Additionally, setting the building orientation to 135° results in the lowest carbon emissions. Under the idealized simulation conditions of this study, the west–east dispersed open-space layout is a preferable configuration for reducing carbon emissions from residential neighborhood buildings. This study explores the impact of layout design parameters on energy consumption and carbon emissions of building complexes in hot summer and cold winter regions, providing references for energy optimization and environmental sustainability research. Full article

Urban rooftop photovoltaic systems represent a substantial yet still underutilized renewable energy resource, particularly in high-density residential environments. Accurate large-scale assessment of rooftop solar potential, however, remains challenging due to the complex geometry of urban morphology and the limited availability of high-resolution geospatial data. This study presents a large-scale methodological framework for estimating the theoretical photovoltaic potential of urban rooftop spaces using Unmanned Aerial System (UAS)-based digital photogrammetry and GIS-based spatial analysis. The approach integrates centimeter-resolution Digital Surface Models (DSMs) and orthophotos derived from fixed-wing UAS surveys with detailed rooftop vectorization and solar radiation modeling implemented in a GIS environment. The methodology accounts for rooftop geometry, surface orientation, slope, shading effects, and rooftop-mounted obstacles. The methodology consists of data collection of high-resolution RGB imagery suitable for detailed three-dimensional reconstruction. The images are captured with a UAS equipped with a S.O.D.A. 3D photogrammetric camera, creating a dense, georeferenced three-dimensional point cloud based on UAS imagery. Based on the point cloud, a high-resolution Digital Surface Model (DSM) was produced. Rooftop boundaries and rooftop-mounted structures were digitized on the basis of an orthophoto created from UAS imagery. The analysis workflow consists of solar modeling using ArcGIS Pro, including calculating the solar radiation. The next methodological step is to filter low radiation rooftops, steep slopes, and northern-oriented rooftops. Finally, we calculate the potential electricity production. The framework was applied to high-density residential districts in Sofia, Bulgaria, dominated by prefabricated panel buildings with predominantly flat rooftops. Drone applications in such studies are typically restricted to modeling individual roofs, which severely limits their scalability for district-wide evaluations. To overcome this, the study employs a specialized fixed-wing UAS uniquely certified for legal operations over densely populated urban environments. This platform rapidly maps large territories, ensuring consistent lighting and shading conditions that significantly enhance the accuracy of subsequent rooftop digitization. Furthermore, the resulting centimeter-level precision enables the exact vectorization of micro-rooftop obstacles. Capturing these intricate details is a critical innovation that effectively prevents the overestimation of solar energy potential commonly observed in conventional large-scale models. Solar radiation was modeled at the pixel level for a full annual cycle and filtered using photovoltaic suitability criteria, including minimum annual radiation thresholds, slope, and aspect constraints. Theoretical electricity production was subsequently estimated using zonal statistics and system performance parameters representative of contemporary photovoltaic installations. The results indicate a total theoretical annual electricity potential of approximately 76.7 GWh for the analyzed rooftop spaces, with an average production of about 34 MWh per rooftop and pronounced spatial variability driven by rooftop geometry and exposure conditions. The findings demonstrate the significant renewable energy potential embedded in existing urban rooftop infrastructure and highlight the applicability of UAS-based photogrammetry for high-resolution, large-area solar potential assessments. The proposed framework provides actionable information for urban energy planning, municipal solar cadaster development, and the strategic integration of photovoltaic systems into dense urban environments, particularly in regions lacking open-access high-resolution geospatial datasets. Full article

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

Vehicle-to-grid (V2G) technology has emerged as a key enabler for coupling large-scale electric vehicle (EV) deployment with the operation of smart energy systems. By allowing bidirectional power and information exchange between EVs and the grid, V2G transforms EVs from passive loads into distributed energy resources capable of supporting grid flexibility, reliability, and renewable energy integration. However, the practical realization of V2G remains challenged by technical complexity, system coordination, user participation, and regulatory constraints. This paper presents a comprehensive review of V2G integration from a system-level perspective. Rather than focusing solely on individual technologies, the review examines how V2G is embedded within smart energy systems, emphasizing the interactions among EVs, aggregators, grid operators, energy markets, and end users. Key enabling technologies, including bidirectional charging, aggregation mechanisms, communication frameworks, and data-driven control strategies, are discussed in relation to their system-level roles and limitations. The impacts of V2G on grid operation, energy management, and market participation are analyzed, with particular attention to reliability, battery lifetime, and user trust. Furthermore, this review identifies critical open issues that hinder large-scale deployment, spanning infrastructure readiness, standardization, economic incentives, and cybersecurity. Emerging application scenarios, such as building-integrated V2G, fleet-based services, and artificial intelligence (AI) supported coordination, are also discussed to illustrate potential evolution pathways. By synthesizing technological developments with system-level impacts and unresolved challenges, this paper aims to provide a structured reference for researchers, system planners, and policymakers seeking to advance the integration of V2G into future smart energy systems. Full article

In recent decades, the Ucayali region, the main territory of the Asheninka communities, has experienced increasing socio-environmental pressures associated with climate change, educational inequality, and territorial vulnerability in rural and indigenous contexts. In response, this research proposes the design of a sustainable agricultural school for the Asheninka community, conceived as an educational building that integrates biophilic strategies to enhance environmental performance and spatial quality. The methodological approach comprises a literature review, site-specific environmental analysis based on hydrometeorological data, and the development of an architectural proposal focused on sustainable building design. Digital tools such as Revit and SketchUp were employed alongside official climatic data sources to support design decision-making. The proposal includes twelve biophilic agricultural classrooms incorporating passive design strategies, rainwater harvesting systems with a capacity of 22.5 m³ per day per classroom, and photovoltaic-powered public lighting systems. Results indicate that the integration of natural ventilation, green infrastructure, and locally sourced materials contributes to significant improvements in thermal comfort, humidity control, and energy autonomy within the educational facilities. The architectural complex is complemented by green corridors and collective open spaces that reinforce environmental performance at the site scale. This study demonstrates that sustainable educational buildings adapted to local ecosystems and climatic conditions can function as effective infrastructures for environmental mitigation and resilient rural development, contributing to more sustainable forms of urban and rural living. Full article

This study analyzes the long-run relationships between digitalization, investment, innovation, and economic growth in connection with the energy transition in the SPRING-F group (Spain, Poland, Romania, Italy, the Netherlands, Germany, and France) using annual data for the period of 2000–2024. The analysis starts from the premise that digitalization affects economic performance not only directly, but also through structural transmission mechanisms linked to investment and the energy transition. To capture these dynamics, this study employs three complementary panel ARDL models. The first model explains economic growth (GDP per capita) as a function of digitalization, capital accumulation, R&D expenditure, renewable energy consumption, trade openness, and foreign direct investment. The second model estimates gross capital formation (GCF) in order to assess the investment transmission channel. The third model explains renewable energy consumption (RNEC) in order to capture the sustainability dimension. The results show that trade openness and capital accumulation are the strongest long-run drivers of economic growth in the SPRING-F group. Internet use, R&D expenditure, and FDI also display positive long-run associations with GDP per capita, whereas fixed broadband subscriptions and renewable energy consumption enter the growth equation with negative coefficients, suggesting that digital infrastructure and the green transition do not automatically generate immediate growth gains. The GCF model confirms that investment acts as an important transmission mechanism, especially through the robust GDP–GCF linkage. The RNEC model indicates that the energy transition is positively associated with investment, innovation, and trade openness, while GDP and digital infrastructure remain negatively associated with the renewable energy share. Overall, the findings point to a conditional and nonlinear relationship between growth, digitalization, investment, and sustainability, with the sustainability channel remaining more specification-sensitive than the growth and investment equations. The long-run results for the GDP equation should also be interpreted with additional caution, given the comparatively weaker cointegration evidence for Model 1. Full article

This study evaluates the rooftop solar photovoltaic (PV) potential at the building level in the tourism-rich districts of Northern Khyber Pakhtunkhwa (KPK), Pakistan, using advanced geospatial analysis to support renewable energy planning. By combining the Area Solar Radiation tool with detailed building footprint data, the study identified solar energy potential and prioritized areas for PV system installations. Results show that approximately 35% of the 1.29 million buildings analyzed are suitable for solar panels, with energy generation capacity varying by building size and district. Spatial analysis further highlighted Union Councils (UCs) where over 50% of buildings are solar-suitable, enabling precise targeting of renewable energy initiatives. The study underscores the importance of integrating local geographical and socio-economic data to enhance the feasibility and scalability of solar energy solutions in rural and urban settings and can be used to guide policy prioritization and funding decisions. This research demonstrates how geospatial analysis and open data can drive localized clean energy adoption, directly contributing to Sustainable Development Goal 7 by advancing affordable and sustainable energy solutions. Full article

A replicable methodology for testing the size and placement of Battery Energy Storage Systems connected to high-voltage transmission networks is presented in this study. The proposed approach involves the power flow analysis inside a Renewable Energy Zone, namely a high-renewable area prone to grid congestion during peak generation periods, based on time-series hourly analysis over a critical month. The model includes detailed operational descriptions such as lines ampacity, battery state of charge limits, round-trip efficiency, self-discharge behavior, and ramp rate restrictions. The methodology distinguishes itself by its simplicity, flexibility, and use of open-source tools, making it a valuable asset for supporting future transmission planning in high-renewable-energy scenarios. The model was developed in Python (version 3.12) using the open-source Pandapower library, introducing an innovative constraint management criterion, and validated against real data provided by the national Transmission System Operator. The approach was then applied to a portion of the Sicilian grid with massive wind and solar penetration. Results show that strategic allocation of batteries leads to a significant reduction in line overloads (up to 13 GWh mitigated in one month), improves the dispatch of renewable energy generated within the Renewable Energy Zone and allows a more sustainable exercise of the power system. Full article

Experimental investigations and datasets in the open literature remain scarce for the fast transient response of air systems induced by sudden internal structural failures, hindering rigorous experimental validation of the governing trends associated with multiple influencing factors. To address this gap, we establish a fast transient air-system test platform and develop a step boundary simulation device based on mechanical energy storage, enabling rapid and repeatable boundary transients. The experiments demonstrate that the minimum boundary-change time is less than 6 ms, satisfying the simulation requirement for boundary transients associated with typical sudden structural failures (≤10 ms). Guided by a dimensionless analysis, we conduct fast transient cavity-venting experiments under varying outlet areas, cavity geometric parameters, and initial pressure ratios, thereby obtaining the transient response data of the cavity pressure. In parallel, we simulate the test process using a three-dimensional numerical approach validated against the experiments; by combining experimental and numerical results, we systematically analyze the effects of key factors on the fast transient response during cavity venting and elucidate the underlying mechanisms. This paper provides experimentally validated data and a reliable experimental methodology for studying fast transient response processes in air systems, and it supports the passive safety design of aero-engines. Full article