Search Results (14,622)

Photosynthesis, the main energy source for life on Earth, confronts escalating challenges of high-light–high-temperature stress (HLHT). Our previous study identified a mutation in ATP synthase, F₁-α-C252Y, that significantly enhances the HLHT tolerance of Synechococcus elongatus PCC 7942 (Sye7942), although the underlying mechanism remains obscure. In this study, we found that this mutation led to elevated levels of the b subunit of F_o, F₁ subunits, and the ATP synthase within cells, without affecting ATP synthetic activity, indicating improved intracellular ATP synthesis activity. Additionally, the mutation altered the transcriptome of Sye7942, impacting the expression of genes involved in crucial processes, such as the electron transport chain, carbon fixation, and regulatory factors, which are crucial for cyanobacteria’s adaptation to stresses. Correspondingly, the mutant exhibited enhanced photosynthesis, accelerated growth, and increased glycogen under HLHT conditions, showing improved adaptation. The higher intracellular ATP synthesis activity, along with enhanced photosynthetic activity, suggests increased ATP production in the mutant under HLHT. Enhancing ATP production and remodeling the cellular transcriptome appear to be key strategies employed by the C252Y mutation for Sye7942 acclimating to HLHT. These findings provide valuable insights for enhancing photosynthetic efficiency and stress resilience in cyanobacteria and other photosynthetic organisms facing HLHT challenges. Full article

Uncertainties in generation and dynamic load behavior provide new problems for radial distribution systems (RDS) caused by the growing integration of renewable distributed generators (RDGs), including solar photovoltaic (PV) systems and wind turbines (WTs), as well as electric vehicle charging stations (EVCS). This article offers a thorough techno-economic evaluation of how to best distribute RDG resources (solar PV, wind, and EVCS) inside a 28-bus distribution test system in India, taking into account generation volatility due to the seasons. Optimization of installation and operating costs, enhancing voltage stability, and decreasing active power loss are done all at once using a new Catch Fish Optimization Algorithm (CFOA). Integrating beta and Weibull distributions, respectively, into the probabilistic modeling of solar irradiance and wind speed allows for economic analysis to adhere to recognized approaches from contemporary multi-objective optimization frameworks. The simulation findings confirm that the proposed CFOA-based placement method improves economic efficiency, decreases energy loss, and increases system performance. Full article

Renewable energy sources, such as solar and biomass, represent sustainable alternatives to meet the growing energy demands of the residential sector. This study evaluated the energy, exergy, and environmental performance of two Brayton configurations using supercritical carbon dioxide: a recompression cycle (SRC) and a recompression cycle with intercooling in the main compression (SMC), both coupled to a dual-loop organic Rankine cycle (DORC) and powered by a hybrid solar-biomass thermal system. Mass, energy, and exergy balances were developed, and a life cycle assessment was performed to quantify the environmental impact. The systems were designed to cover a cooling load of 130 kW corresponding to 200 dwellings constructed with Asbestos cement in the Colombian Caribbean region. The results show that both configurations meet the required demand; the SMC-DORC cycle operates at 650 °C, while the SRC-DORC requires 750 °C. The SRC-DORC exhibits higher thermal efficiency (53.24%), while the SMC-DORC achieves a slightly higher exergy efficiency (28.15%). Environmental analysis shows that the construction phase accounts for the majority of the total impact, exceeding 95% of emissions. Overall, both configurations are technically feasible, with the SRC-DORC standing out for its balance between efficiency and environmental impact. Full article

The rapid integration of distributed solar photovoltaic (PV) generation is reshaping low-voltage distribution grids (LVDGs), creating voltage rise, reverse power flow, and congestion challenges for distribution system operators (DSOs). Flexibility in generation and demand, broadly understood as the capability to adjust generation or consumption in response to variability and uncertainty in net load, is increasingly central to cost-effective grid operation under high PV penetration. This review examines flexibility and controllability options in LVDGs, focusing on voltage regulation methods, supply- and demand-side flexibility resources, and market-based coordination mechanisms. The Norwegian Regulation on Quality of Supply (FoL) provides the regulatory context: it enforces 1 min average voltage compliance, stricter than the 10 min averaging window of EN 50160, making short-duration voltage excursions operationally significant and directly influencing the trade-off between curtailment, grid reinforcement, and local flexibility measures. Inverter-based active–reactive power control emerges as the most cost-effective overvoltage mitigation option, complemented by local battery energy storage systems (BESS) and demand response for congestion relief and energy shifting. Key gaps include limited LV observability, insufficient application of quasi-static time series (QSTS) assessment in planning, and underdeveloped DSO-aggregator coordination frameworks. Combined inverter control, feeder-end storage, and demand-side flexibility can defer costly reinforcements, particularly in rural 230 V IT feeders where voltage constraints dominate. Full article

The rapid growth of residential photovoltaic (PV) installations has increased interest in electrical storage units (ESUs) as a means of enhancing self-consumption and reducing surplus electricity fed into the grid. However, in temperate climates characterized by strong seasonal variability in solar generation, the economic viability of residential battery storage remains uncertain. This study examines whether ESUs provide measurable financial benefits under such climatic conditions, particularly after the transition from net-metering to net-billing schemes. The analysis combines empirical household electricity consumption data with simulation-based modeling of PV–battery operation. Periods of surplus energy production during high solar generation were taken into account, as well as periods of increased energy demand in the winter season and technical limitations related to energy storage, including the difference between actual and nominal capacity of energy storage systems. The results indicate that although battery storage increases self-consumption and reduces grid injection during peak generation periods, its economic performance is limited by the seasonal mismatch between electricity production and demand. Consequently, under net-billing conditions, residential ESUs do not automatically ensure economic profitability in temperate climates. Full article

Aiming at the problems of uneven radiation distribution and difficult regulation in double-film multi-span greenhouses in the Gobi Desert, a spatiotemporal simulation model of the radiation environment based on the coupling of Rhino–Grasshopper and Radiance was constructed in this study. Parametric simulation and multi-objective optimization were adopted to significantly improve the solar radiation capture and distribution uniformity inside the greenhouse, providing a scientific basis for greenhouse design in the Gobi area. The results show that the model has high accuracy (R² > 0.98), and the radiation inside the greenhouse presents a distribution pattern of “higher in the northeast, lower in the southwest, higher in the upper layer and lower in the lower layer”. The optimal orientation is 1° west of south, and the optimal configuration is 8 m span, 5 m eave height, and 30° roof slope. This study can provide quantitative support for the structural design, planting layout and energy-saving regulation of double-film multi-span greenhouses in arid desert areas, and has important practical value for promoting the efficient and sustainable development of facility agriculture in the Gobi Desert. Full article

Accurate and interpretable learning diagnosis is increasingly required in AI-enabled educational assessment. Existing cognitive diagnostic models typically represent item attributes with a binary Q-matrix and infer mastered or not mastered knowledge states. Although polytomous extensions allow graded mastery, item attributes rarely encode theory-aligned cognitive-process demands, which limits pedagogical interpretation of diagnosed profiles. This study aims to operationalize revised Bloom’s taxonomy at the exercise–knowledge level by constructing an Exercise–Knowledge–Cognition tensor and to develop RLDM-EKC as a DINA-type cognitive diagnosis model that infers ordered knowledge–cognition profiles. The model defines EKC-based ideal responses, estimates slip and guess parameters with an Expectation–Maximization procedure, and derives learner profiles using Maximum A Posteriori inference with uncertainty summaries. We validate the approach on synthetic data and on TIMSS 2007 Grade 4 mathematics data, comparing against classical CDMs including DINA, PA-DINA, and pG-DINA. In simulation, RLDM-EKC attains a PMR of 81.7% and an AAMR of 91.6%, and in empirical data, it yields theory-aligned multi-level cognitive profiles with transparent uncertainty reporting. These properties support actionable, human-in-the-loop feedback for teachers and learners under realistic deployment constraints. Full article

For offshore renewable energy planning and intelligent power management, access to long-term, high-resolution, and physically consistent meteorological and power generation records is essential. Such data supports a wide range of tasks, including resource assessment, hybrid system capacity sizing, grid operation planning, and data-driven forecasting model development. This article presents the construction of a 10-year continuous hourly dataset for 16 deep-sea grid sites in the Beibu Gulf, China, spanning from January 2016 to December 2025. The raw meteorological variables, including 10 m wind speed, wind direction, solar irradiance, and 2 m air temperature, were retrieved from the NASA POWER satellite database and subsequently cleaned using a 24 h periodic substitution algorithm designed to preserve the physical integrity of daily weather cycles. The dataset is organized into two sub-datasets, the Historical Weather Dataset and the Normalized Power Yield Dataset, with the latter providing normalized wind and solar power outputs on a 1.0 per-unit (p.u.) basis derived from a wind turbine power curve model and a PV thermodynamic model. All 32 CSV files are freely accessible online with UTF-8 encoding. The utility of the dataset is illustrated through two representative application cases including offshore site selection with hybrid capacity sizing and physics-informed deep learning forecasting, demonstrating its suitability for both engineering analysis and machine learning model development. Full article

The synergistic and mutually reinforcing relationship between the development of the new energy industry and comprehensive land consolidation is crucial for integrating ecologically fragile areas into the national “dual carbon” goals and supporting regional high-quality development. Based on a systematic literature review, field investigations in typical regions, and multi-case comparative analysis, this paper analyzes the challenges and opportunities for the new energy industry in ecologically fragile areas as well as the mutually reinforcing mechanisms between new energy industry development and land consolidation. On this basis, it explores pathways for comprehensive land consolidation in coordination with new energy development. Building on local practices, it further identifies five typical models. The results show the following: (1) The development of the new energy industry in ecologically fragile areas faces multiple challenges, including a fragile ecological environment, inadequate infrastructure, a mismatch between resource supply and demand, and land use conflicts. Against the backdrop of the energy transition, breakthroughs in key technologies, and the guidance of territorial spatial planning, the value of wind and solar resources in these areas are becoming increasingly prominent, offering broad prospects for the new energy industry. (2) The development of the new energy industry and comprehensive land consolidation in ecologically fragile areas are mutually reinforcing. Factors such as resource endowment, ecological constraints, new quality productive forces, and investment and financing mechanisms interact and integrate with each other, resulting in diversified synergistic pathways. (3) Based on the priorities of new energy industry development and the primary objectives of consolidation, five models are identified: Ecological Restoration-led Model, Resource Development-led Model, Industrial Collaboration-led Model, Technological Innovation-led Model and Integrated Development Model. Each model has distinct priorities and applicable scenarios. This study will provide a reference for new energy development and sustainable development in ecologically fragile areas, including desertified and Gobi desert areas, coal mining subsidence areas, and areas rich in wind, solar, and hydropower resources. Full article

This study addresses the sustainable supply chain network design (SSCND) problem by integrating economic and environmental dimensions through a multi-objective, multi-echelon stochastic mathematical model. The proposed model focuses on simultaneously optimizing total cost, carbon emissions, water footprint, and renewable energy utilization. Strategic solar energy investment alongside facility location and sizing decisions are considered under uncertain conditions. Initially, a multi-product stochastic model is developed and solved utilizing the augmented epsilon constraint (AUGMECON2) method to obtain Pareto-optimal solutions for small-scale instances. For validation purposes, the exact solutions obtained using AUGMECON2 were used as the benchmark for the small-scale instance, while the proposed hybrid NSGA-II algorithm generated near-optimal solutions with deviations of 0.30%, 1.53%, 0.03%, and 0.0006% for total cost, carbon emissions, renewable energy use, and water footprint, respectively. Compared with the cost-oriented solution, the renewable energy-focused solution increased total cost by 76.33% while reducing the water footprint by 6.36% and carbon emissions by 3.57%. For medium- and large-scale instances, where exact solutions became computationally impractical, the hybrid NSGA-II algorithm remained applicable and generated feasible Pareto solutions within 59.05 s and 309.62 s, respectively. Overall, the presented framework provides a scalable decision-support tool for sustainable supply chain planning under uncertainty. Full article

Thermoacoustic power generation holds significant promise for applications such as solar thermal utilization, industrial waste heat recovery, and distributed energy systems, owing to its high efficiency and reliability. Conventional standing-wave and traveling-wave thermoacoustic generators, however, are often limited by bulky resonators and substantial acoustic power dissipation. Replacing the resonator with a linear alternator (LA) offers an effective means to improve system compactness and output performance. Nonetheless, under standing-wave acoustic conditions, the LA’s large piston swept volume increases the device size, thereby constraining overall compactness. To address this limitation, a novel moving-magnet LA with electromagnetic components integrated into the moving piston is proposed. Compared to conventional configurations, this design significantly reduces the size and weight of the alternator. Furthermore, the influence of different magnetic circuit configurations on output performance is systematically investigated, enabling optimization of the alternator design. Results demonstrate that the proposed alternator achieves a more compact structure while delivering output performance comparable to that of conventional external magnetic-circuit designs, thereby validating the feasibility of the proposed approach. Full article

This research introduces a novel methodology that combines Building Information Modelling (BIM) and Economic Multi-Criteria Decision-Making (EMCDM) with Neural Networks to optimize hybrid renewable energy systems in small communities. Its core aim is to improve sustainability, technical performance, and financial vokiability through integrated modelling and decision-making. The approach is applied to a hydropower site, evaluating five Scenarios (IDs 1–5) under a Community and Industry model. Financial benchmarks include a 10% Minimum Required Return and a 7-year payback period. ID3—hydropower, solar, and wind—proves most effective, with ANPV of €10,905 (wet) and €4501 (dry), and ROI of 155%/64%. Its ROIA/MRA Index peaks at 539%, and Payback/N ratios remain within acceptable limits (55%/96%). LCOE stays stable in average conditions (0.042–0.046 €/kWh), rising in dry years (0.07–0.10 €/kWh). Profitability differences primarily stem from demand and curtailment, rather than production costs. The NARX neural network reliably models SS% values from renewable inputs with low error across scenarios. The integrated BIM–EMCDM framework ensures transparent, sustainable, and risk-balanced energy system decisions for long-term autonomy. Full article

The rational design of oxygen carriers (OCs) is critical for enhancing biomass chemical looping gasification (BCLG) performance. This work systematically investigated the effects of different supports (Al₂O₃, ZrO₂, TiO₂, SiO₂) on the performance of NiFe-based OCs with oxidation and catalytic reforming functions. The gasification reactivity and support-active phase interaction of OCs in both oxidized and reduced states were evaluated. XRD, H₂-TPR, XPS, and SEM techniques were employed to characterize the phase composition, synergistic interactions, and surface morphology. The results showed that NiFeAl exhibited the optimal gasification performance in both oxidized and reduced states, achieving a syngas (H₂ + CO) yield of approximately 1.4 m³/kg (dry walnut shell). NiFeAl featured a higher Fe binding energy, abundant cavity structures, and the uniform dispersion of Ni and Fe on Al₂O₃, which confirm the formation of an appropriately strong Ni-Fe-Al ternary system. In contrast, NiFeZr suffered from the higher CO₂ yield, attributed to the over-oxidation caused by the weak interactions. NiFeTi and NiFeSi had lower syngas yields due to their poor reducibility induced by excessively strong interactions. This work verifies that moderate support-active phase interactions in OCs are optimal for BCLG. Full article

Accurate measurement of streamflow is fundamental for water resources management, ecological conservation, flash flood early warning, and climate change impact studies. This study presents a proof of concept on the usage of Internet of Things (IoT) for automatic streamflow measurements using commercial off-the-shelf (COTS) hardware. The system is designed, implemented, and experimentally evaluated as a low-cost, solar-powered IoT device tailored to small-order streams and headwater tributaries. At its core is the Hall-effect YF-S201 flow sensor. Although primarily designed for closed-conduit applications, the sensor was tested in a controlled setup where stream water was diverted into a short pipe section, enabling continuous monitoring and calibration. This paper provides details on the design and validation of a low-cost (approximately 24 Euros), solar-powered streamflow measurement system based on a water flow sensor, using wireless communications, and cloud storage based on an ESP32 board, PostgreSQL, and a web interface. The device was tested in a simulated environment. Results indicate the proposed device reliably tracks flow variability, while offering portability, energy autonomy, and cost efficiency, and may serve as a feasible alternative for low-infrastructure, temporary deployments. Full article

Metallurgical-grade silicon (MG-Si) is an important source material for many industrial applications, including the manufacture of alloys, solar photovoltaics, and electronics. The process to refine raw materials into MG-Si is energy-intensive, with the predominant method of submerged-arc furnaces requiring energy consumption of approximately 11–13 kWh/kg Si. Recent research has discussed promising methods for reducing the energy required for the silicon production process, including the use of molten-salt electrolysis (MSE), a technique that offers potential savings in energy consumption without requiring carbon inputs for the process. This paper presents a techno-economic study of a potential industrial-scale MSE plant for MG-Si production to evaluate the trade-offs between capital and operating costs of the system. Capital costs are sourced from recent MG-Si plants and an existing cost model developed for MSE processes that includes the size of the plant and the operating temperature among its inputs. The results show that MSE technology has the potential to be an economically cost-competitive option for MG-Si production if the technology successfully scales to industrial production and matures enough to allow for financing costs similar to that of a comparably sized submerged-arc furnace plant. Full article