Search Results (31,964)

Fibres play a crucial role in diverse biomedical applications, ranging from tissue engineering to drug delivery. Electrospinning has emerged as a simple and versatile technique for producing ultrafine fibres at micro- to nanoscale dimensions. Synthetic biopolymers are effective cues to replace damaged tissue in the biomedical field, both in vitro and in vivo applications. Among them, poly (L-lactic acid) (PLLA) is a renewable, environmentally friendly biopolymer material. Polycaprolactone (PCL) is a synthetic polymer with good biocompatibility and biodegradation characteristics. However, both electrospun PLLA and PCL fibres have their limitations. To overcome these shortcomings, electrospinning PLLA/PCL blend fibres has been the subject of many studies. This review discusses the different parameters for the electrospinning of PLLA/PCL-based fibres for biomedical applications. Furthermore, we also discuss how electrospun PLLA/PCL-based scaffolds can be modified or combined with other biomaterials, such as natural polymers and bioceramics, and examine their in vitro and in vivo applications in various tissue repair strategies. Full article

Pluripotent stem cells (PSCs) exhibit remarkable self-renewal capacity and differentiation potential, necessitating tight regulation of gene expression at both transcriptional and post-transcriptional levels. Among post-transcriptional mechanisms, RNA turnover and degradation together play pivotal roles in maintaining transcriptome homeostasis and controlling RNA stability. RNA degradation plays a pivotal role in determining transcript stability for both messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs), thereby influencing cell identity and fate transitions. The core RNA decay machinery, which includes exonucleases, decapping complexes, RNA helicases, and the RNA exosome, ensures timely and selective decay of transcripts. In addition, RNA modifications such as 5′ capping and N6-methyladenosine (m⁶A) further modulate RNA stability, contributing to the fine-tuning of gene regulatory networks essential for maintaining PSC states. Recent single-cell and multi-omics studies have revealed that RNA degradation exhibits heterogeneous and dynamic kinetics during cell fate transitions, highlighting its role in preserving transcriptome homeostasis. Conversely, disruption of RNA decay pathways has been implicated in developmental defects and disease, underscoring their potential as therapeutic targets. Collectively, RNA degradation emerges as a central regulator of PSC biology, integrating the decay of both mRNAs and ncRNAs to orchestrate pluripotency maintenance, lineage commitment, and disease susceptibility. Full article

Lignin-derived hard carbon (LHC) has emerged as a highly promising anode material for sodium-ion batteries (SIBs), owing to its renewable nature, structural tunability, and notable electrochemical properties. Although considerable advancements have been made in the development of LHCs in recent years, the absence of a comprehensive and critical review continues to impede further innovation in the field. To address this deficiency, the present review begins by examining the intrinsic characteristics of lignin and hard carbon (HC) to elucidate the underlying mechanisms of LHC microstructure formation. It then systematically categorizes the synthesis strategies, structural attributes, and performance influences of various LHCs, focusing particularly on how feedstock characteristics and fabrication parameters dictate final material behavior. Furthermore, optimization methodologies such as feedstock pretreatment, controlled processing, and post-synthesis modifications are explored in detail to provide a practical framework for performance enhancement. Finally, informed recommendations and future research directions are proposed to facilitate the integration of LHCs into next-generation SIB systems. This review aspires to deepen scientific understanding and guide rational design for improved LHC applications in energy storage. Full article

This paper proposes a Light Gradient Boosting Machine (LightGBM) model for medium-term photovoltaic (PV) power forecasting by integrating meteorological features with historical generation data. This approach addresses prediction biases that often arise when relying solely on a single meteorological data source. Historical power output and meteorological variables (irradiance, temperature, humidity, etc.) were collected from a PV station and preprocessed through data cleaning, standardization, and temporal alignment to construct a multivariate prediction framework. A comprehensive feature set was then built, including meteorological, temporal, interaction, and lag features. Feature importance analysis and Recursive Feature Elimination (RFE) were employed for input optimization, while feature-layer concatenation was applied for data fusion. Finally, the LightGBM (Version 2.3.1) framework, combined with Bayesian optimization and time-series cross-validation, was used to enhance generalization and predictive robustness. Experimental results confirm that the model achieved an MAE of 37.49, RMSE of 64.67, and R² of 0.89. The model effectively captured high-dimensional nonlinear relationships, thereby improving the accuracy of medium-term photovoltaic forecasts and providing reliable decision support for power system scheduling and renewable energy integration. Full article

As the scale of distributed resources continues to expand, decentralization and multi-agent characteristics bring significant challenges to low-carbon dispatching and market participation of power grids. To this end, this paper proposes a collaborative optimization scheduling framework with distributed resource aggregators (DRAs) as the main body, innovatively coupling carbon Emission trading (CET) with electric vehicle carbon quota participation, and the renewable energy quota (RPS) with tradable green certificate (TGC) transaction as the carrier, as well as constructing the connection path between the two to realize the integrated utilization of environmental rights and interests. Based on the ε-constraint method, a bi-objective optimization model of economic cost minimization and carbon emission minimization is established, and a multi-dimensional evaluation system, covering the internal and overall operation performance of the aggregator, is designed. The example shows that, under the proposed CET-RPS coupling mechanism, the total cost of DRA is about 23.4% lower than that of the existing mechanism. When the carbon emission constraint is relaxed from 2700 t to 3000 t, the total cost decreases from CNY 2537.32 to CNY 2487.74, indicating that the carbon constraint has a significant impact on the marginal cost. This study provides a feasible path for the large-scale participation of distributed resources in low-carbon power systems. Full article

Electrolysis utilizing renewable electricity is an environmentally friendly, non-polluting, and sustainable method of hydrogen production. Seawater is the most desirable and inexpensive electrolyte for this process to achieve commercial acceptance compared to competing hydrogen production technologies. We reviewed metal–organic frameworks as possible electrocatalysts for hydrogen production by seawater electrolysis. Metal–organic frameworks are interesting for seawater electrolysis due to their large surface area, tunable permeability, and ease of functional processing, which makes them extremely suitable for obtaining modifiable electrode structures. Here we discussed the development of metal–organic framework-based electrocatalysts as multifunctional materials with applications for alkaline, PEM, and direct seawater electrolysis for hydrogen production. Their advantages and disadvantages were examined in search of a pathway to a successful and sustainable technology for developing electrode materials to produce hydrogen from seawater. Full article

The increasing global energy demand is driving the deployment of renewable energy in the electrical power infrastructure, which emphasizes the critical importance of grid-connected inverters (GCIs). As the power injected into the utility grid increases, GCIs commonly operate in parallel. However, interactions between multiple GCIs and the presence of LC grid impedance pose significant challenges to the stable operation of GCIs. Existing control strategies to deal with multiple GCIs often neglect the capacitive component of grid impedance, which results in instability and deteriorated power quality in a complex grid condition. To overcome these problems, this study proposes a current control scheme and stability assessment of multiple GCIs. To effectively mitigate high-frequency resonance, the proposed method is achieved by an incomplete state feedback control which eliminates the feedback control terms for unmeasurable states. Furthermore, resonant and integral control terms are incorporated to reduce steady-state error as well as to improve harmonic compensation induced by the PCC voltages. A full-state observer is employed to reduce sensing requirements and simplify system complexity. Multiple-GCI behavior is comprehensively analyzed under complex grid environments. A comprehensive stability assessment is also conducted to evaluate the interactions of multiple GCI systems with LC grid impedance. The effectiveness of the designed controller in enhancing power quality and guaranteeing system stability is validated by theoretical analysis, PSIM simulations, and experimental tests on a DSP-controlled 2 kW prototype system. Full article

Climate change exacerbates challenges for old urban parks in high-density cores, intensifying urban heat islands and overcrowding hazards and causing limited extreme weather resilience. These parks face climate maladaptation, urban health risks, and reduced adaptive capacity. This study applies performance-based urban design through an “environmental analysis Overlay method,” integrating space syntax, CFD-Phoenics wind simulation, and solar analysis to translate climate adaptation, urban health, and urban resilience dimensions into measurable indicators including ventilation efficiency, crowd dispersion comfort, and flexible space capacity. Using Chengzhong Park in Wuxi as a case study, the method employs a diagnosis–optimization–validation process. Results demonstrate substantial improvements: (1) Climate: Problematic wind areas (>4 m/s or <0.5 m/s (stagnant)) decreased from 30% to 11%, while comfortable wind zones (0.5–1 m/s) increased to over 30%, achieving optimal microclimate conditions 89% of the park; (2) Health: Pedestrian circulation capacity increased by 25%, and activity areas with under 3 h of winter sunlight reduced from 26% to 19%; (3) Resilience: Spatial units consolidated from 155 to 115, with global-local integration improving from R² = 0.39 to 0.64, significantly enhancing network coherence and adaptive functionality. The findings confirm that this method provides a scientifically rigorous, replicable pathway for climate-adaptive renewal of old urban parks, supporting urban resilience agendas. Full article

Aboveground biomass (AGB) is a key indicator of forest structure and carbon sequestration, yet its dynamics under concurrent anthropogenic disturbances remain poorly understood. This study investigates the spatiotemporal dynamics and driving mechanisms of AGB in the Jianfengling tropical lowland rainforest (JFLTLR) within Hainan Tropical Rainforest National Park (NRHTR) from 2015 to 2023. Six machine learning models—Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF)—were evaluated, with RF achieving the highest accuracy (R² = 0.83). Therefore, RF was employed to generate high-resolution annual AGB maps based on Sentinel-1/2 data fusion, field surveys, socio-economic indicators, and topographic variables. Human pressure was quantified using the Human Influence Index (HII). Threshold analysis revealed a critical breakpoint at ΔHII ≈ 0.1712: below this level, AGB remained relatively stable, whereas beyond it, biomass declined sharply (≈−2.65 mg·ha⁻¹ per 0.01 ΔHII). Partial least squares structural equation modeling (PLS-SEM) identified plantation forests as the dominant negative driver, while GDP (−0.91) and road (−1.04) exerted strong indirect effects through HII, peaking in 2019 before weakening under ecological restoration policies. Spatially, biomass remained resilient within central core zones but declined in peripheral regions associated with road expansion. Temporally, AGB exhibited a trajectory of decline, partial recovery, and renewed loss, resulting in a net reduction of ≈ 0.0393 × 10⁶ mg. These findings underscore the urgent need for a “core stabilization–peripheral containment” strategy integrating disturbance early-warning systems, transportation planning that minimizes impacts on high-AGB corridors, and the strengthening of ecological corridors to maintain carbon-sink capacity and guide differentiated rainforest conservation. Full article

Ammonia is an ideal candidate for clean energy in the future, and its large-scale production has long relied on the Haber–Bosch process, which operates at a high temperature and pressure. However, this process faces significant challenges due to the growing demand for ammonia and the increasing need for environmental protection. The high energy consumption and substantial CO₂ emissions associated with the Haber–Bosch method have greatly limited its application. Consequently, increasing research efforts have been devoted to developing green ammonia synthesis technologies. Among these, the electrocatalytic nitrogen reduction reaction (NRR), which uses water and nitrogen as raw materials to synthesize NH₃ under mild conditions, has emerged as a promising alternative. This method offers the potential for carbon neutrality and decentralized production when coupled with renewable electricity. However, it is important to note that the current energy efficiency and ammonia production rates of NRR under ambient aqueous conditions generally lag behind those of modern Haber–Bosch processes integrated with green hydrogen (H₂). As the core of the NRR process, the performance of electrocatalysts directly impacts the efficiency, energy consumption, and product selectivity of the entire reaction. To date, significant efforts have been made to identify the most suitable electrocatalysts. In this paper, we focus on the current research status of metal catalysts—including both precious and non-precious metals—as well as non-metal catalysts. We systematically review important advances in performance optimization, innovative design strategies, and mechanistic analyses of various catalysts. We clarify innovative optimization strategies for different catalysts and summarize and compare the catalytic effects of various catalyst types. Finally, we discuss the challenges facing electrocatalysis research and propose possible future development directions. Through this paper, we aim to provide guidance for the preparation of high-efficiency NRR catalysts and the future industrial application of electrochemical ammonia synthesis. Full article

As wind power penetration continues to increase, the sub-synchronous control interaction (SSCI) problem caused by the interaction between doubly fed induction generators (DFIGs) and series-compensated transmission lines has become increasingly prominent, posing a serious threat to power system stability. To address this problem, this research proposes a centralized sub-synchronous oscillation damping controller (CSODC) for wind farms. First, a DFIG impedance model was constructed based on multi-operating-point impedance scanning and a Taylor series expansion, achieving impedance prediction with an error of less than 2% under various power conditions. Subsequently, a CSODC comprising a sub-synchronous damping calculator (SSDC) and a power electronic converter is designed. By optimizing feedback signals, phase shift angles, gain parameters, and filter parameters, dynamic adjustment of controllable impedance in the sub-synchronous frequency band is achieved. Frequency-domain impedance analysis demonstrates that the CSODC significantly enhances the system’s equivalent resistance, reversing it from negative to positive at the resonance frequency point. Time-domain simulations validated the CSODC’s effectiveness in scenarios involving series capacitor switching and wind speed disturbances, demonstrating rapid sub-synchronous current decay. The results confirm that the proposed strategy effectively suppresses sub-synchronous oscillations across multiple scenarios, offering an economical and efficient solution to stability challenges in high-penetration renewable energy grids. Full article

Green hydrogen offers a promising yet underexplored pathway for agricultural decarbonisation, requiring technological readiness and coordinated action from policymakers, industry, and farmers. This paper integrates techno-economic modelling with stakeholder engagement (semi-structured interviews and an expert workshop) to assess its potential. Analyses were conducted for farms of 123 hectares and clusters of 10 farms, complemented by seven interviews and a workshop with nine sector experts. Findings show both opportunities and barriers. While on-farm hydrogen production is technically feasible, it remains economically uncompetitive due to high levelised costs, shaped by seasonal demand variability and low utilisation of electrolysers and storage. Pooling demand across multiple users is essential to improve cost-effectiveness. Stakeholders identified three potential business models: fertiliser production via ammonia synthesis, cooperative-based models, and local refuelling stations. Of these, cooperative hydrogen hubs emerged as the most promising, enabling clusters of farms to jointly invest in renewable-powered electrolysers, storage, and refuelling facilities, thereby reducing costs, extending participation to smaller farms, and mitigating risks through collective investment. By linking techno-economic feasibility with stakeholder perspectives and business model considerations, the results contribute to socio-technical transition theory by showing how technological, institutional, and social factors interact in shaping hydrogen adoption in agriculture. With appropriate policy support, cooperative hubs could lower costs, ease concerns over affordability and complexity, and position hydrogen as a practical driver of agricultural decarbonisation and rural resilience. Full article

Against the backdrop of global energy transition, geothermal energy has emerged as a critical renewable resource, yet its exploration remains challenging due to uneven subsurface distribution and complex surface conditions. This study pioneers a novel framework integrating the Normalized Shaded Vegetation Index (NSVI) with radiative transfer-based land surface temperature inversion to detect geothermal anomalies in the Gonghe Basin, Qinghai Province. Using multi-source remote sensing data (GF5 B AHSI, ZY1–02D/E AHSI, and Landsat 9 TIRS), we first constructed NSVI, achieving 97.74% classification accuracy for shadowed vegetation/water bodies (Kappa = 0.9656). This effectively resolved spectral mixing issues in oblique terrain, enhancing emissivity calculations for land surface temperature retrieval. The radiative transfer equation method combined with NSVI-derived parameters yielded high-precision land surface temperature estimates (RMSE = 2.91 °C; R² = 0.963 against Landsat 9 products), revealing distinct thermal stratification: bright vegetation (41.31 °C) > shadowed vegetation (38.43 °C) > water (33.56 °C). Geothermal anomalies were identified by integrating temperature thresholds (>45.80 °C), 7 km fault buffers, and concealed Triassic granite constraints, pinpointing high-potential zones covering 0.12% of the basin. These zones are concentrated in central Gonghe, northern Guinan, and central-northern Guide counties. The framework provides a replicable solution for geothermal prospecting in topographically complex regions, with implications for optimizing exploration across the Gonghe Basin. Full article

The accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV power forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified classic adapted to daily generation patterns, Holt’s linear trend method, and the Holt–Winters seasonal method. The models were implemented in Microsoft Excel (Microsoft 365, version 2408) using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the Solver Add-in. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt–Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). These results pertain to a one-step-ahead evaluation on a single plant and an extended validation window; accuracy is dependent on meteorological conditions, with larger errors during rapid cloud transi. The study identifies forecasting models that combine high accuracy with structural simplicity, intuitive implementation, and minimal parameter tuning—features that make them well-suited for integration into lightweight real-time energy control systems, despite not being evaluated in terms of runtime or memory usage. The modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools. Full article

The peculiarity of the functioning of modern electric power systems, caused by the presence of renewable energy sources, flexible control devices based on power electronics, and the reduction of the reserve of the transmission capacity of the electric network, increases the relevance of identifying and damping low-frequency oscillations (LFOs) of the electrical mode. This paper presents a comparative analysis of methods for estimating the parameters of low-frequency oscillations. Their applicability limits are shown as well as their peculiarity associated with low adaptability, and time costs in assessing the parameters of the electrical mode with low-frequency oscillations are revealed. A method for the accelerated evaluation of low-frequency oscillation parameters is proposed, the delay of which is ¼ of the oscillation cycle. The method was tested on both synthetic and physical signals. In the first case, the source of data was a four-machine mathematical model of a power system. In the second case, signals of transient processes occurring in a real power system were used as physical data. The accuracy of the proposed method was obtained by calculating the difference between the original and reconstructed signals. As a result, calculated error values were obtained, describing the accuracy and efficiency of the proposed method. The proposed algorithm for estimating LFO parameters displayed an error value not exceeding 0.8% for both synthetic and physical data. Full article