Search Results (402)

The global shift toward clean production, like using renewable energy, has significantly decreased the use of synchronous machines (SMs), which help maintain stability and control, causing serious frequency stability issues in power systems with low inertia. Fractional order controller-based virtual synchronous machines (FOC-VSMs) have become a promising option, but they rely on communication networks to work together in real time, causing them to be at risk of cyberattacks, especially from false data injection attacks (FDIAs). This paper suggests a new way to detect FDI attacks using a federated physics-informed long short-term memory (PI-LSTM) network. Each FOC-VSM uses its data to train a PI-LSTM, which keeps the information private but still helps it learn from a common model that understands various operating conditions. The PI-LSTM incorporates physical constraints derived from the FOC-VSM swing equation, facilitating residual-based anomaly detection that is sensitive to minor deviations in control dynamics, such as altered inertia or falsified frequency signals. Unlike traditional LSTMs, the physics-informed architecture minimizes false positives arising from benign disturbances. We assessed the proposed method on an IEEE 9-bus test system featuring two FOC-VSMs. The results show that our method can successfully detect FDI attacks while handling regular changes, proving it could be a strong solution. Full article

Ecological drought in terrestrial systems is a vegetation-functional degradation phenomenon triggered by the long-term imbalance between ecosystem water supply and demand. This process involves nonlinear coupling of multiple climatic factors, ultimately forming a compound ecological stress mechanism characterized by spatiotemporal heterogeneity. Based on meteorological and remote sensing datasets from 1982 to 2022, this study identified the spatial distribution and temporal variability of ecological drought in China, elucidated the dynamic evolution and return periods of typical drought events, unveiled the scale-dependent effects of climatic factors under both univariate dominance and multivariate coupling, as well as deciphered the response mechanisms of ecological drought to meteorological drought. The results demonstrated that (1) terrestrial ecological drought in China exhibited a pronounced intensification trend during the study period, with the standardized ecological water deficit index (SEWDI) reaching its minimum value of −1.21 in February 2020. Notably, the Alpine Vegetation Region (AVR) displayed the most significant deterioration in ecological drought severity (−0.032/10a). (2) A seasonal abrupt change in SEWDI was detected in January 2003 (probability: 99.42%), while the trend component revealed two mutation points in January 2003 (probability: 96.35%) and November 2017 (probability: 43.67%). (3) The drought event with the maximum severity (6.28) occurred from September 2019 to April 2020, exhibiting a return period exceeding the 10-year return level. (4) The mean values of gridded trend eigenvalues ranged from −1.06 in winter to 0.19 in summer; 87.01% of the area exhibited aggravated ecological drought in winter, with the peak period (88.51%) occurring in January. (5) Evapotranspiration (ET) was identified as the dominant univariate driver, contributing a percentage of significant power (POSP) of 18.75%. Under multivariate driving factors, the synergistic effects of ET, soil moisture (SM), and air humidity (AH) exhibited the strongest explanatory power (POSP = 19.21%). (6) The response of ecological drought to meteorological drought exhibited regional asynchrony, with the maximum correlation coefficient averaging 0.48 and lag times spanning 1–6 months. Through systematic analysis of ecological drought dynamics and driving mechanisms, a dynamic assessment framework was constructed. These outcomes strengthen the scientific basis for regional drought risk early-warning systems and spatially tailored adaptive management strategies. Full article

This study investigates the influence of hybrid filler systems comprising carbon black (CB), mica, and surface-modified mica (SM) on the properties of ethylene–propylene–diene monomer (EPDM)/butadiene rubber (PB) composites. To reduce the environmental issues associated with CB, mica was incorporated as a partial substitute, and its compatibility with the rubber matrix was enhanced through surface modification using ureidopropyltrimethoxysilane (URE). The composites with hybrid filler systems and surface modification were evaluated in terms of curing behavior, crosslink density, mechanical and elastic properties, and dynamic viscoelasticity. Rheological analysis revealed that high mica loadings delayed vulcanization due to reduced thermal conductivity and accelerator adsorption, whereas SM composites maintained comparable curing performance. Swelling tests showed a reduction in crosslink density with increased unmodified mica content, while SM-filled samples improved the network density, confirming enhanced interfacial interaction. Mechanical testing demonstrated that the rubber compounds containing SM exhibited average improvements of 17% in tensile strength and 20% in toughness. In particular, the CB20/SM10 formulation achieved a well-balanced enhancement in tensile strength, elongation at break, and toughness, surpassing the performance of the CB-only system. Furthermore, rebound resilience and Tan δ analyses showed that low SM content reduced energy dissipation and improved elasticity, whereas excessive filler loadings led to increased hysteresis. The compression set results supported the thermal stability and recovery capacity of the SM-containing systems. Overall, the results demonstrated that the hybrid filler system incorporating URE-modified mica significantly enhanced filler dispersion and rubber–filler interaction, offering a sustainable and high-performance solution for elastomer composite applications. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

Cement production is a carbon-intensive process that contributes significantly to global greenhouse gas emissions. Approximately 50–60% of these emissions result from limestone calcination, while 30–40% result from fossil fuel combustion in kilns. This study presents a multi-objective optimization (MOO) framework that integrates raw mix design and alternative fuel blending to simultaneously reduce production costs and carbon dioxide (CO₂) emissions while maintaining clinker quality. A hybrid Genetic Algorithm–Linear Programming (GA-LP) model was developed to navigate the balance between economic and environmental objectives under stringent chemical and operational constraints. The approach models the impact of raw materials and fuel ash on critical clinker quality indices: the Lime Saturation Factor (LSF), Silica Modulus (SM), and Alumina Modulus (AM). It incorporates practical constraints such as maximum substitution rates and specific fuel compositions. A case study inspired by a medium-sized African cement plant demonstrates the utility of the model. The results reveal a Pareto front of optimal solutions, highlighting that a 20% reduction in CO₂ emissions from 928 to 740 kg/ton clinker is achievable with only a 24% cost increase. Optimal strategies include 10% fly ash and 30–50% alternative fuels, such as biomass, tire-derived fuel (TDF), and dynamic raw mix adjustments based on fuel ash contributions. Sensitivity analysis further illustrates how biomass cost and LSF targets affect clinker performance, emissions, and fuel shares. The GA-LP hybrid model is validated through process simulation and benchmarked against African case studies. Overall, the findings provide cement producers and policymakers with a robust decision-support tool to evaluate and adopt sustainable production strategies aligned with net-zero targets and emerging carbon regulations. Full article

Manufacturing industries are undergoing a significant transformation toward Smart Manufacturing (SM) to meet the ever-evolving demands for customized products. A major obstacle in this transition is the integration of Computer-Aided Process Planning (CAPP) with Scheduling. This integration poses challenges because of conflicting objectives that must be balanced, resulting in the Integrated Process Planning and Scheduling problem. In response to these challenges, this research introduces a novel hybridized machine learning optimization approach designed to assign and sequence setups in Dynamic Flexible Job Shop environments via dispatching rule mining, accounting for real-time disruptions such as machine breakdowns. This approach connects CAPP and scheduling by considering setups as dispatching units, ultimately reducing makespan and improving manufacturing flexibility. The problem is modeled as a Dynamic Flexible Job Shop problem. It is tackled through a comprehensive methodology that combines mathematical programming, heuristic techniques, and the creation of a robust dataset capturing priority relationships among setups. Empirical results demonstrate that the proposed model achieves a 42.6% reduction in makespan, improves schedule robustness by 35%, and reduces schedule variability by 27% compared to classical dispatching rules. Additionally, the model achieves an average prediction accuracy of 92% on unseen instances, generating rescheduling decisions within seconds, which confirms its suitability for real-time Smart Manufacturing applications. Full article

This paper presents a numerical simulation study evaluating carbon dioxide capture and storage (CCS) feasibility for the Turceni Power Plant in Oltenia, Romania, using the nearby depleted Bibești-Bulbuceni gas reservoir. A comprehensive reservoir model was developed using Petrel software, integrating geological and reservoir engineering data for the formations of the Bibești-Bulbuceni structure, which is part of the western Moesian Platform. The static model incorporated realistic petrophysical inputs for the Meotian reservoirs. Dynamic simulations were performed using Eclipse compositional simulator with Peng–Robinson equation of state for a CH4-CO₂ system. The model was initialized with natural gas initially in place at 149 bar reservoir pressure, then produced through depletion to 20.85 bar final pressure, achieving 80% recovery factor. CO₂ injection simulations modeled a phased 19-well injection program over 25 years, with individual well constraints of 100 bar bottom-hole pressure and 200,000 Sm³/day injection rates. Results demonstrate successful injection of a 60 Mt CO₂, with final reservoir pressure reaching 101 bar. The modeling framework validates the technical feasibility of transforming Turceni’s power generation into a net-zero process through CCS implementation. Key limitations include simplified geochemical interactions and relying on historical data with associated uncertainties. This study provides quantitative evidence for CCS viability in depleted hydrocarbon reservoirs, supporting industrial decarbonization strategies. The strategy not only aligns with the EU’s climate-neutral policy but also enhances local energy security by repurposing existing geological resources. The findings highlight the potential of CCS to bridge the gap between current energy systems and a sustainable, climate-neutral future. Full article

Intercropping has been widely proven to boost agricultural yields and control diseases. This study examined the impact of sugarcane monoculture (SM) and sugarcane–pumpkin intercropping (IP) systems on soil physicochemical characteristics and microbial community dynamics. Compared to monoculture, intercropping significantly increased soil pH by 8.82% and total potassium (TK) by 17.92%, while reducing soil organic matter (SOM) by 25.56%. Bacterial communities under intercropping exhibited significantly higher alpha and beta diversity, whereas fungal community diversity remained unaffected. Notably, the relative abundances of certain taxa with known roles in plant growth promotion and pathogen suppression—such as Anaeromyxobacter, Nitrospira, and Massilia—were enriched. Canonical correlation analysis revealed that bacterial community composition was strongly associated with TK, while fungal community structure correlated with variations in soil available nitrogen (AN). These findings indicate that sugarcane–pumpkin intercropping reshapes soil microbial communities and contributes to some improvement in soil nutrient availability. Full article

Background: Chiari-like malformation (CM) and syringomyelia (SM) are commonly observed conditions in Pomeranian dogs. Affected dogs may develop clinical signs that significantly impact quality of life. Therefore, it is crucial to select only unaffected dogs for breeding. However, the progression of CM/SM has not been fully elucidated. Dogs that are unaffected or mildly affected may progress to severe SM over time. The primary aim of this study is to investigate the progression of CM/SM through repeated MRI scans. A secondary objective is to evaluate the effect of furosemide treatment on syrinx sizes, given its frequent prescription. Methods: Pomeranians that underwent two CM/SM screenings between 2015 and 2025 were included. CM/SM classifications were assessed, and quantitative syrinx measurements were conducted. Maximum syrinx diameter (MSD) and maximum syrinx-to-spinal cord diameter ratio (MSD/SCD-r) were measured and documented. Dogs were classified based on the progression of SM. Furosemide treatment was documented, and its effect on syrinx size was compared with that in dogs not receiving furosemide. Results: At the time of the second MRI, 39.6% of dogs either developed SM or showed substantial progression, whereas 12.5% demonstrated partial recovery. Of the dogs initially classified as free from SM, 20.7% had developed the condition. A significant increase was observed in both MSD (p = 0.0058) and MSD/SCD-r (p = 0.0038) between MRI1 and MRI2. Notably, the change in MSD between MRI1 and MRI2 was statistically significantly smaller in dogs treated with furosemide compared to untreated dogs (p = 0.030). Conclusions: These findings indicate that syrinx dimensions are dynamic and may fluctuate over time, although a general trend toward progression is observed. Furthermore, furosemide may mitigate the progression of SM. Full article

This paper addresses the trajectory tracking control problem of a six-degree-of-freedom Gough–Stewart Platform (GSP) by proposing a control strategy that combines a sliding mode (SM) controller with a rapid forward kinematics solution algorithm. The study first develops an efficient forward kinematics method that integrates Support Vector Regression (SVR) with the Levenberg–Marquardt algorithm, effectively resolving issues related to multiple solutions and local optima encountered in traditional iterative approaches. Subsequently, a SM controller based on the GSP’s dynamic model is designed to achieve high-precision trajectory tracking. The proposed control strategy’s robustness and effectiveness are validated through simulation experiments, demonstrating superior performance in the presence of model discrepancies and external disturbances. Comparative analysis with traditional PD controllers and linear SM controllers shows that the proposed method offers significant advantages in both tracking accuracy and control response speed. This research provides a novel solution for high-precision control in GSP applications. Full article

The integration of forestry and agriculture has promoted edible fungi cultivation in forest understory spaces. However, the impact of spent mushroom substrates on forest soils remains unclear. This study explored the use of seafood mushroom spent substrates (SMS) and grass substrates to cultivate Dictyophora indusiata. After cultivation, soil pH stabilized, organic carbon increased by 34.02–62.24%, total nitrogen rose 1.1–1.9-fold, while soil catalase activity increased by 43.78–100.41% and laccase activity surged 3.3–11.2-fold. The 49% Cenchrus fungigraminus and 49% SMS treatment yielded the highest 4-coumaric acid levels in the soil, while all treatments reduced maslinic and pantothenic acid content. SMS as padding material with C. fungigraminus enhanced soil bacterial diversity in the first and following years. Environmental factors and organic acids influenced the recruitment of genus of Latescibacterota, Acidothermus, Rokubacteriales, Candidatus solibacter, and Bacillus, altering organic acid composition. In conclusion, cultivating D. indusiata understory enhanced environmental characteristics, microbial dynamics, and organic acid profiles in forests’ soil in short time. Full article

Soil moisture (SM) is a key variable in agricultural ecosystems and is crucial for drought prevention and control management. However, SM is influenced by underlying surface and meteorological conditions, and it changes rapidly in time and space. To capture the changes in SM and improve the accuracy of short-term and medium-to-long-term predictions on a daily scale, an LSTMseq2seq model driven by both observational data and mechanism models was constructed. This framework combines historical meteorological elements and SM, as well as the SM change characteristics output by the VIC model, to predict SM over a 90-day period. The model was validated using SMAP SM. The proposed model can accurately predict the spatiotemporal variations in SM in Jiangxi Province. Compared with classical machine learning (ML) models, traditional LSTM models, and advanced transformer models, the LSTMseq2seq model achieved R² values of 0.949, 0.9322, 0.8839, 0.8042, and 0.7451 for the prediction of surface SM over 3 days, 7 days, 30 days, 60 days, and 90 days, respectively. The mean absolute error (MAE) ranged from 0.0118 m³/m³ to 0.0285 m³/m³. This study also analyzed the contributions of meteorological features and simulated future SM state changes to SM prediction from two perspectives: time importance and feature importance. The results indicated that meteorological and SM changes within a certain time range prior to the prediction have an impact on SM prediction. The dual-driven LSTMseq2seq model has unique advantages in predicting SM and is conducive to the integration of physical mechanism models with data-driven models for handling input features of different lengths, providing support for daily-scale SM time series prediction and drought dynamics prediction. Full article

In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) reanalysis, TEMP and precipitation (PRE) from Climate Research Unit (CRU), and soil moisture (SM) and evapotranspiration (ET) from the Global Land Evaporation Amsterdam Model (GLEAM). In addition, four teleconnection indices were considered: El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). This study used the Mann–Kendall test and Sen’s slope estimator to analyze trends, alongside multiple linear regression to investigate the relationships between TEMP, RH, and key climatic variables—namely evapotranspiration (ET), soil moisture (SM), and precipitation (PRE)—as well as large-scale teleconnection indices (e.g., IOD, ENSO, PDO, and NAO) on annual and seasonal scales. The key findings are as follows: (1) mean annual TEMP exceeding 30 °C and RH less than 30% were concentrated in arid regions of the Sahelian–Sudano belt in West Africa (WAF), Central Africa (CAF) and North East Africa (NEAF). Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade⁻¹). Winter (0.27 °C decade⁻¹) and spring (0.20 °C decade⁻¹) exhibited the strongest warming, followed by autumn (0.18 °C decade⁻¹) and summer (0.10 °C decade⁻¹). (4) RH trends showed stronger seasonal decline compared to annual changes, with reduction ranging from 5 to 10% per decade in certain seasons, and about 2% per decade annually. (5) Pearson correlation analysis demonstrated a strong negative relationship between TEMP and RH with a correlation coefficient of r = − 0.60. (6) Significant associations were also observed between TEMP/RH and both climatic variables (ET, SM, PRE) and large scale-teleconnection indices (ENSO, IOD, PDO, NAO), indicating that surface conditions may reflect a combination of local response and remote climate influences. However, further analysis is needed to distinguish the extent to which local variability is independently driven versus being a response to large-scale forcing. Overall, this research highlights the physical mechanism linking TEMP and RH trends and their climatic drivers, offering insights into how these changes may impact different ecological and socio-economic sectors. Full article

Room-temperature rotary swaging was conducted on microalloyed high-ductility Mg-0.7Sm-0.3Zr alloy rods to investigate microstructural and mechanical variations across different swaging passes and radial positions. The results indicate that following room-temperature rotary swaging, the alloy rods exhibit a large number of tensile twins and low-angle grain boundaries, leading to significant grain refinement. After two swaging passes, the microstructure exhibits a pronounced radial gradient, characterized by progressively finer grain sizes from the core to the edge regions, with a hardness difference of 3.8 HV between the edge and the core. After five swaging passes, the grain size was refined from an initial 4.37 μm to 2.02 μm. The yield strength and ultimate tensile strength of the alloy increased from 157 MPa and 210 MPa in the extruded state to 292 MPa and 302 MPa, respectively. This observed strengthening is primarily attributed to grain refinement, dislocation hardening, and texture strengthening, with grain refinement playing the dominant role. The grain refinement process during rotary swaging can be divided into two stages: in the initial stage, coarse grains are subdivided by tensile twinning; in the later stage, high-stress-induced grain boundary bulging leads to new dynamic recrystallization, further refining the microstructure. Full article

Balancing mechanisms require the minimization of both the Shaking Moment ($ShM$) and Shaking Force ($\mathbf{ShF}$), a complex multi-criteria challenge often tackled using single-objective algorithms. However, these methods face difficulties in navigating competing objectives. In contrast, multi-objective algorithms provide a more efficient and adaptable framework, while Fully Cartesian Coordinates ($FCC$) simplify the balancing equations compared to conventional Cartesian formulations. This study focuses on optimizing the dynamic balance of a six-bar Watt linkage using $FCC$. A wide set of optimization methods is analyzed and compared, and among them, the S-Metric Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) demonstrates superior performance. This algorithm achieves the most significant hypervolume value in only 10.44 min of execution. The results indicate that multi-objective algorithms outperform single-objective approaches, offering faster and more diverse optimization solutions. Additionally, this study introduces an analytical method that enables the straightforward identification of removable counterweights, achieving an equally effective balance while minimizing the number of counterweights required. Full article