Search Results (886)

Medical cannabis cultivation requires substantial energy for heating, lighting, and climate control. This study evaluates the economic feasibility of an innovative biomass-fired micro-CHP system in a greenhouse facility for medicinal cannabis cultivation. The system comprises an 80 kW_th boiler retrofitted for biomass and a 7 kW_el ORC engine and is assessed against a diesel-boiler Business-As-Usual (BAU) benchmark. Thermal load simulations for two growing periods (1 March–30 June and 1 September–30 December) estimate an annual heating demand of 91,065.20 kWh_th. The micro-CHP system delivers 8195.87 kWh_el per year, exceeding the greenhouse’s 7839.90 kWh_el consumption. Over a 30-year lifespan at a 7% discount rate, Life Cycle Costing yields EUR 196,421.33 for micro-CHP versus EUR 229,468.46 for BAU, a 14.4% reduction. Under all-equity financing, the project achieves an NPV of EUR 59,591.88, IRR of 27.32%, and a DPBP of 12.1 years; with 70% debt financing, NPV rises to EUR 61,211.39 and DPBP shortens to 10.5 years. Levelized Cost of Energy (LCOE) and Heat (LCOH) are EUR 0.122 per kWh_el and EUR 0.062 per kWh_th, respectively. While the LCOE is below the Greek and EU non-household averages (EUR 0.1578 and EUR 0.1515 per kWh_el), the LCOH exceeds the corresponding heat price benchmarks (EUR 0.0401 and EUR 0.0535 per kWh_th). These results indicate that, in the modeled context, biomass-ORC cogeneration can be a financially attractive and lower-carbon option for medicinal cannabis greenhouse operations. Full article

Post-industrial cities across Europe are undergoing profound transformation, where sustainable building design plays an increasingly strategic role in redefining urban identity and function. The transition toward sustainable urban environments requires innovative construction technologies and performance-driven standards. This study examines the role of sustainable building design in post-industrial urban regeneration, focusing on Katowice, Poland—a city undergoing significant socio-spatial and economic transformation. Through descriptive case studies of selected buildings, the research highlights how high-performance construction techniques, including advanced insulation, energy-efficient ventilation, and integrated daylighting, contribute to prestigious certifications while reducing energy demand for heating, cooling, and lighting. Beyond technical performance, the analyzed projects demonstrate how sustainable buildings can act as catalysts for post-industrial urban renewal, fostering social engagement, environmental responsibility, and architectural innovation. The novelty of this work lies in linking building-scale sustainability interventions with city-scale urban transformation dynamics, offering practical insights for similar post-industrial contexts in Central and Eastern Europe. This research provides the first comparative analysis of certified and non-certified sustainable buildings in the context of post-industrial regeneration in this region. The post-industrial revitalization of Katowice is largely driven by advancements in building energy systems, such as high-efficiency HVAC technologies and other sustainable solutions. The findings demonstrate that sustainable architecture can act as a tangible driver of social, economic, and spatial renewal, providing practical insights for post-industrial regeneration strategies across similar urban contexts. Full article

Due to the large emissions of greenhouse gases from the burning of fossil fuels and people’s demand for green materials and energy, the development of environmentally friendly triboelectric nanogenerators (TENGs) is becoming increasingly significant. Silk fibroin (SF) is considered an ideal biopolymer candidate for fabricating green TENGs due to its biodegradability and renewability. However, its intrinsic brittleness and relatively weak triboelectric performance severely limit its practical applications. In this study, SF was physically blended with poly(ethylenimine) (PEI), a polymer rich in amino groups, to fabricate SF/PEI composite films. The resulting films were employed as tribopositive layers and paired with a poly(tetrafluoroethylene) (PTFE) tribonegative layer to assemble high-performance TENGs. Experimental results revealed that the incorporation of PEI markedly enhanced the flexibility and electron-donating capability of composite films. By optimizing the material composition, the SF/PEI-based TENG achieved an open-circuit voltage as high as 275 V and a short-circuit current of 850 nA, with a maximum output power density of 13.68 μW/cm². Application tests demonstrated that the device could serve as an efficient self-powered energy source, capable of lighting up 66 LEDs effortlessly through simple hand tapping and driving small electronic components such as timers. In addition, the device can function as a highly sensitive self-powered sensor, capable of generating rapid and distinguishable electrical responses to various human motions. This work not only provides an effective strategy to overcome the intrinsic limitations of SF-based materials but also opens up new avenues for the development of high-performance and environmentally friendly technologies for energy harvesting and sensing. Full article

The objective of this study was to investigate electron beam hardening (EBH) technology and compare its performance with laser beam hardening (LBH) in the context of manufacturing components such as gears, which increasingly employ submicron bainitic steels. Given the stringent demands for durability and fatigue resistance of gear teeth, identifying an optimal surface hardening method is essential for extending service life. Comprehensive analyses, including light and electron microscopy, hardness testing, tribocorrosion testing, and X-ray diffraction for phase composition, were conducted. The EBH-treated layer exhibited a slightly higher hardness (by 26 HV) compared to the LBH-treated layer (average 654 HV), while the base material measured 393 HV. The EBH process produced a uniform hardness distribution with a subsurface zone of reduced hardness. In contrast, LBH resulted in a surface oxide layer absent in EBH due to its vacuum environment. Both techniques reduced the residual austenite content in the surface layer from 22.5% to approximately 1.3%–1.4%. Notably, EBH achieved comparable hardening effects with nearly half the energy input of LBH, demonstrating superior energy efficiency and industrial feasibility. Application of the developed EBH process to an actual gear component confirmed its practical potential for modern gear manufacturing. Full article

Understory vegetation plays a pivotal role in enhancing forest biodiversity, and its restoration is crucial for sustainable forest development, energy flow, and nutrient cycling. However, the dynamics of the biomass, diversity, and species composition of understory vegetation in plantations in south China, along with their key drivers, remain poorly understood. This study investigated four mature plantation types (Pinus massoniana, Pinus caribaea, Cunninghamia lanceolata, and mixed Chinese fir–broadleaf forests) in south China through plot surveys, environmental factor measurements, and structural equation modeling (SEM) to explore the diversity, biomass allocation patterns, and driving mechanisms of understory vegetation. The results demonstrated the following. (1) The introduced Caribbean pine forests exhibited higher shrub biomass than native Masson pine forests, which was driven by their high canopy openness favoring light-demanding species (e.g., Melicope pteleifolia, IV = 33.93%), but their low mingling degree limited herb diversity. (2) Masson pine forests showed superior shrub diversity due to their random spatial distribution and higher soil total potassium (TK) content. (3) Mixed Chinese fir–broadleaf forests achieved 24.50–66.06% higher herb biomass compared to coniferous monocultures, supported by high mingling degree, random spatial configuration, and phosphorus-potassium-enriched soil, with concurrently improved herb diversity. SEM revealed that stand structure (DBH, density, mingling degree) directly drove shrub diversity by regulating light availability, while herb biomass was primarily governed by soil total phosphorus (TP) and pH. Canopy-induced light suppression negatively affected herb diversity. We recommend optimizing stand density and canopy structure through thinning and pruning to enhance light heterogeneity alongside supplementing slow-release P fertilizers in P-deficient stands. This study provides theoretical support for the multi-objective management of south China plantations, emphasizing the synergistic necessity of stand structure optimization and soil amendment. Full article

The transition from traditional power systems to smart grids demands advanced methods for detecting and classifying Power Quality Disturbances (PQDs)—variations in voltage, current, or frequency that disrupt device performance. The rise of renewable energy and nonlinear loads, such as LED lighting, has increased PQD occurrences. While deep learning models can effectively analyze data from grid sensors to detect PQD occurrences, privacy concerns often prevent operators from sharing raw data which is necessary to train the models. To address this, Federated Learning (FL) enables collaborative model training without exposing sensitive information. However, FL’s decentralized design introduces new risks, particularly data poisoning attacks, where malicious clients corrupt model updates to degrade the global model accuracy. Despite these risks, PQD classification under FL and its vulnerability to such attacks remain largely unexplored. In this work, we develop FL-based classifiers for PQD detection and compare their performance to traditionally trained, centralized models. As expected from prior FL research, we observed a slight drop in performance: the model’s accuracy decreased from 97% (centralized) to 96% (FL), while the false alarm rate increased from 0.19% to 4%. We also emulate five poisoning scenarios, including indiscriminate attacks aimed at degrading model accuracy and class-specific attacks intended to hide particular disturbance types. Our experimental results show that the attacks are very successful in reducing the accuracy of the classifier. Furthermore, we implement a detection mechanism designed to identify and isolate corrupted client updates, preventing them from influencing the global model. Experimental results reveal that our defense substantially curtails the performance degradation induced by poisoned updates, thereby preserving the robustness of the global model against adversarial influence. Full article

The growing demand of the cereal market, which demands quality products at low cost, has driven the development of new, more accessible wheat varieties. This study evaluated the technological quality of flours obtained from three new wheat varieties produced in Andahuaylas: Espigón de Oro (EOVF), the Gavilón (GVF), and the Andino (AVF) varieties, comparing them with a widely used plain flour (PF). Their proximate parameters, rheological, thermal, and structural properties, elemental composition, and functional groups were analyzed. The local flours (EOVF, GVF, and AVF) presented similar carbohydrate and fat contents, but higher ash, and lower moisture and protein content than plain flour. The rheology and thermal stability showed limitations associated with a less consistent dough and a more fragile structure, indicating lower gluten quality. Differential scanning calorimetry found gelatinization temperatures between 53.42 °C and 57.12 °C, with energy requirements (ΔH) of 1.08 to 1.23 J/g, while thermographic analysis revealed that component degradation began at 150 °C. Scanning electron microscopy micrographs revealed starch granules with varied shapes and a trimodal distribution. Elemental analysis showed a good energy contribution, with 47.9–54.6% carbon and 45.2–51.5% OH. The FT-IR spectra showed similar functional profiles among all the flours. These results suggest that flours from new wheat varieties have a low energy requirement for cooking, making them ideal for extrusion processes and for products with a soft and light texture. They also represent an excellent alternative to commercial flour for developing functional, infant, and easily digestible foods. Full article

Greenhouse horticulture is an energy-intensive production system that requires innovative solutions to reduce energy demand without compromising crop yield or quality. Functional greenhouse covers are particularly promising, as they regulate solar radiation while integrating energy-harvesting technologies. In this study, six nanostructured glass coatings incorporating semiconductor-based quantum dots (QDs) and quantum wires (QWs) of Ge and TiN are developed using magnetron sputtering—an industrially scalable technique widely applied in smart window and energy-efficient glass manufacturing. The coatings’ optical properties are characterized in the laboratory, and their agronomic performance is evaluated in greenhouse trials with lamb’s lettuce (Valerianella locusta) and radish (Raphanus sativus). Plant growth, yield, and leaf color (CIELAB parameters) are analyzed in relation to spectral transmission and the daily light integral (DLI). Although uncoated horticultural glass achieves the highest yields, several Ge-QD coatings provide favorable compromises by selectively absorbing non-photosynthetically active radiation (non-PAR) while maintaining acceptable crop performance. These results demonstrate that nanostructured coatings can simultaneously sustain crop growth and enable solar energy conversion, offering a practical pathway toward energy-efficient and climate-smart greenhouse systems. Full article

This study presents an advanced, interpretability-focused machine learning framework for forecasting electricity consumption in Turkey over the period 2016–2024. The proposed approach is based on a high-dimensional dataset that incorporates a diverse set of variables, including sector-specific electricity usage (residential, industrial, lighting, agricultural, and commercial), electricity production, trade metrics (imports and exports in USD), and macroeconomic indicators such as the Industrial Production Index (IPI). A comprehensive set of eight state-of-the-art regression algorithms—including ensemble models such as CatBoost, LightGBM, Random Forest, and Bagging Regressor—were developed and rigorously evaluated. Among these, CatBoost emerged as the most accurate model, achieving R² values of 0.9144 for electricity production and 0.8247 for electricity consumption. Random Forest and LightGBM followed closely, further confirming the effectiveness of tree-based ensemble methods in capturing nonlinear relationships in complex datasets. To enhance model interpretability, SHAP (SHapley Additive exPlanations) and traditional feature importance analyses were applied, revealing that residential electricity consumption was the dominant predictor across all models, accounting for more than 70% of the variance explained in consumption forecasts. In contrast, macroeconomic indicators and temporal variables showed marginal contributions, suggesting that electricity demand in Turkey is predominantly driven by internal sectoral consumption trends rather than external economic or seasonal dynamics. In addition to historical evaluation, scenario-based forecasting was conducted for the 2025–2030 period, incorporating varying assumptions about economic growth and population trends. These scenarios demonstrated the model’s robustness and adaptability to different future trajectories, offering valuable foresight for strategic energy planning. The methodological contributions of this study lie in its integration of high-dimensional, multivariate data with transparent, interpretable machine learning models, making it a robust and scalable decision-support tool for policymakers, energy authorities, and infrastructure planners aiming to enhance national energy resilience and policy responsiveness. Full article

In response to the increasing demands for cage strength and operational stability of ball bearings in new energy vehicle motors operating under high-speed and light-load conditions, this paper focuses on the 6207 deep groove ball bearing as the research subject. It systematically analyzes the influence of various structural parameters of the crown-type cage, including profile radius, side beam thickness, claw length, and claw radius, on its eccentricity. Furthermore, the paper explores the mechanism by which eccentricity affects the dynamic performance of the cage. By establishing a rigid–flexible coupled dynamics model and conducting simulation analyses, the results indicate that the claw ends of the crown-type cage pockets are the regions of maximum deformation, while the pocket bottom experiences the highest equivalent stress, identifying it as a critical location for fracture failure. The research demonstrates that the impact of eccentricity on performance is non-monotonic: a reduction in eccentricity can significantly diminish the collision force between the balls and the cage, decrease vibration amplitude, and lower equivalent stress; concurrently, the maximum cage deformation and vibration acceleration level increase correspondingly. Additionally, the centrifugal force acting on the cage itself significantly elevates the equivalent stress. Therefore, the optimal design of the crown-type cage necessitates a comprehensive trade-off among multiple objectives, including strength and stability. It is essential to avoid inappropriate eccentricity design that may arise from the pursuit of a single performance indicator (such as friction reduction or weight reduction), thereby providing a theoretical foundation for the refined design of high-performance bearing cages. Full article

There is a global need to reduce greenhouse gases, and industrial applications are one of the hardest-to-abate sectors. These energy-intensive industries require high-temperature heat which predominantly comes from fossil fuels. The United Kingdom National Nuclear Laboratory has developed a model to forecast the demand of both electricity and heat up to the year 2050, therefore providing an estimated demand that nuclear energy could help fulfil. This article uses the model to investigate the market potential for light water reactors and high-temperature gas-cooled reactors to determine the applicable heat markets globally. The analysis shows that the demand will be up to 1257 TWh for light water reactors and up to 2123 TWh for high-temperature gas-cooled reactors by 2050. Full article

High-efficiency power management is essential for silicon photomultiplier (SiPM)-based sensing systems, especially in portable radiation detectors that demand long battery life and stable operation. Conventional fixed-frequency, voltage-mode boost converters face two critical issues: efficiency degradation at light load due to dominant switching losses, and narrow loop bandwidth in discontinuous conduction mode (DCM), which limits transient response. This work proposes a boost converter IC that integrates a pulse-skipped switching (PSS) scheme with a Gm-boosted compensator to address these challenges. The PSS method adaptively suppresses unnecessary switching events, significantly improving light-load efficiency, while the Gm-boosted compensator enhances loop gain, expanding the bandwidth and enabling faster recovery under dynamic conditions. Implemented in a 250 nm BCD process, the converter provides up to 30 V output from a 3.3–5 V supply with load currents up to 10 mA. Simulation results show a peak efficiency of 86.3% at 1 mA and a loop bandwidth increase of more than 14 times compared with a conventional fixed-frequency, voltage-mode design. Beyond radiation applications, the proposed converter is broadly applicable to battery-powered IoT, medical monitoring, and portable energy systems requiring efficient high-voltage generation. Full article

Confronted with increasing global food demands, diminishing arable land, and climate volatility, controlled-environment agriculture with advanced red and far-red LED lighting can enhance photosynthesis and optimize plant growth. This investigation reports the generation of a Mn⁴⁺/Nd³⁺ co-doped Y₂SiO₅ phosphor with a Nd³⁺ concentration ranging from 0.1 to 2.5 mol% via a solid-state synthesis method, aiming to enhance red and far-red emission for plant cultivation LEDs. For the Y₂SiO₅:Mn⁴⁺ (1 mol%), Nd³⁺ (2 mol%) phosphor, the phase integrity, nanostructured morphology, elemental mapping, and vibrational characteristics were examined using XRD, Rietveld analysis, FTIR, SEM, and EDX. Nd³⁺ ions act as near-infrared excitation mediators, ensuring efficient Nd³⁺ → Mn⁴⁺ energy transfer upon 808 nm excitation, and this leads to pronounced red photoluminescence from Mn⁴⁺ ions that covers the range of 640–710 nm, exhibiting strong emission peaks centered at 650nm, 663nm, and 685nm, coinciding with the absorption band of phytochromes and chlorophyll. The optimal emission intensity was accomplished for a Nd³⁺ doping concentration of 2 mol%, beyond which concentration quenching occurred. The material produced a strong, concentrated deep red emission with CIE coordinates near (0.73, 0.27) and a high color purity of 98.96%, making it well-suited for photosynthetic activation. A phosphor-integrated red pc-LED was fabricated, and Tulsi plants were grown under this LED during the winter in Meghalaya, a period critical for plant growth due to the low ambient light. Over a 30-day period, the plants exhibited enhanced height and leaf development, demonstrating the practical potential of Mn⁴⁺/Nd³⁺ co-doped Y₂SiO₅ for energy-efficient, wavelength-optimized horticultural lighting. Full article

The current transformation of global energy systems has been the subject of a multi-faceted scientific discourse for years. Researchers focus on technical and technological aspects, seeking new and improved alternatives to current solutions. They also analyse formal and legal frameworks of the changes and evaluate their economic aspects or environmental effects. The public’s attitude towards the changes in light of demanding environmental conditions is investigated the least. In particular, little heed is paid to the opinions of rural populations, especially in Poland. In light of the above, this paper aims to analyse the issue of Poland’s energy transition and the public’s perception of the challenges of environmental protection and the resulting need to improve energy solutions to promote the dissemination of renewable energy sources. The research area was Poland, and detailed research was conducted in five districts (Małopolska region), where the age of the respondents was taken as the differentiating feature. The study was based on a literature review and, at a detailed level, on a diagnostic survey among residents of Wadowicki, Miechowski, Krakowski, Limanowski, and Tarnowski Districts. The 2024 CAPI (Computer Assisted Personal Interviewing) survey involved 300 randomly selected interviewees. The study employed a qualitative and quantitative approach, utilising statistical tools such as Spearman’s rank correlation coefficient analysis, the Kruskal–Wallis rank test, and the nonparametric Mann–Whitney U test. The statistical analysis was supported by IBM’s SPSS v.25. The results show that the majority of the population understand and agree with the need for an energy transition in Poland towards renewable energy. Indications of no opinion or in favour of non-renewable energy in the Polish energy system are distinct. This class of indications is determined by the interviewees’ age and suggests potential for improving public awareness of the matter in the group of mature respondents. Full article

The building sector accounts for nearly 40% of total energy consumption in Europe, with heritage buildings posing a critical challenge due to conservation constraints. This study investigates two protected heritage sites—Palazzo Ruspoli in Cerveteri and Palazzo Vitelleschi in Tarquinia—to identify effective energy retrofit strategies integrating high-efficiency windows, HVAC and lighting systems, and photovoltaic (PV) solutions for both on-site and virtual self-consumption within Renewable Energy Communities (RECs). Energy surveys, modeling, and simulations were performed to evaluate technical, environmental, and economic impacts. The results show contrasting outcomes between the two cases: at Palazzo Vitelleschi, the combination of efficient systems and rooftop PV reduced non-renewable primary energy demand and CO₂ emissions by 73.5%, with a 10.7-year payback period; at Palazzo Ruspoli, REC-based virtual self-consumption achieved net-negative carbon emissions (−240%), a 95% reduction in non-renewable energy demand, and a 19.4-year payback period. These findings demonstrate that heritage buildings can move beyond carbon neutrality and actively offset emissions through shared renewable generation. The proposed simulation-based framework provides a replicable method to balance conservation and sustainability, supporting the decarbonization of the historical built environment. Full article