Search Results (59,144)

This paper presents a day-ahead forecasting approach for discharge profiles of a 0.5 MW battery energy storage system connected to the power grid, utilizing historical daily discharge profiles collected over one year to capture key operational patterns and variability. Two forecasting techniques are employed: a Kalman filter for dynamic state estimation and Holt’s exponential smoothing method enhanced with adaptive alpha to capture trend changes more responsively. These methods are applied to generate next-day discharge forecasts, aiming to support better battery scheduling, improve grid interaction, and enhance overall energy management. The accuracy and robustness of the forecasts are evaluated against real operational data. The results confirm that combining model-based and statistical techniques offers a reliable and flexible solution for short-term battery discharge prediction in real-world grid applications. Full article

Organic manure and grass mulching are widely recognized as modifiers of soil microbial communities and nutrient dynamics; however, the combined effects of these practices on nitrogen fractionation and microbial functionality in orchard ecosystems remain poorly understood. This study conducted a comprehensive evaluation of soil nitrogen fractions, enzymatic activity, microbial diversity and functional traits in walnut orchards under three management practices: organic manure (OM), grass mulching combined with manure (GM), and chemical fertilization (CF) in China’s Loess Plateau. The results revealed that OM and GM significantly enhanced soil nutrient pools, with GM elevating total nitrogen by 1.96-fold, soil organic carbon by 97.79%, ammonium nitrogen by 128%, and nitrate nitrogen by 54.56% relative to CF. Furthermore, the OM significantly increased the contents of total hydrolysable nitrogen, amino sugar nitrogen, amino acid nitrogen, ammonia nitrogen, hydrolysable unidentified nitrogen, non-acid-hydrolyzable nitrogen compared to the CF and GM treatments. Meanwhile, ASN and AN had significant effects on mineral and total nitrogen. The OM and GM had higher activities of leucine aminopeptidase enzymes (LAP), α-glucosidase enzyme, β-glucosidase enzyme (βG), and N-acetyl-β-D-glucosidase enzyme (NAG). Microbial community analysis revealed distinct responses to different treatments: OM and GM enhanced bacterial Shannon index, while suppressing fungal diversity, promoting the relative abundance of copiotrophic bacterial phyla such as Proteobacteria and Chloroflexi. Moreover, GM favored the enrichment of lignocellulose-degrading Ascomycota fungi. Functional annotation indicated that Chemoheterotrophy (43.54%) and Aerobic chemoheterotrophy (42.09%) were the dominant bacterial metabolic pathways. The OM significantly enhanced the abundance of fermentation-related genes. Additionally, fungal communities under the OM and GM showed an increased relative abundance of saprotrophic taxa, and a decrease in the relative abundances of potential animal and plant pathogenic taxa. The Random forest model further confirmed that βG, LAP, and NAG, as well as Basidiomycota, Mortierellomycota, and Ascomycota served as pivotal mediators of soil organic nitrogen fraction. Our findings demonstrated that combined organic amendments and grass mulching can enhance soil N retention capacity, microbial functional redundancy, and ecosystem stability in semi-arid orchards. These insights support the implementation of integrated organic management as a sustainable approach to enhance nutrient cycling and minimize environmental trade-offs in perennial fruit production systems. Full article

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m²/year for GE and 0.73 g/m²/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m²/year for GE and 0.29 g/m²/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies. Full article

Intelligent ventilation systems can optimize airflow regulation to enhance mining safety and reduce energy consumption, driving green development in mineral resource extraction. This paper systematically elaborates on the overall architecture, cutting-edge advances, and core technologies of current intelligent mining ventilation. Building upon this foundation, a comprehensive intelligent mine ventilation solution encompassing the entire process of ventilation design, optimization, and operation is constructed based on a five-layer architecture, integrating key technologies such as intelligent sensing, real-time solving, airflow regulation, and remote control, providing an overarching framework for smart mine ventilation development. To address the computational efficiency bottleneck of traditional methods, an improved loop-solving method based on minimal independent closed loops is realized, achieving near real-time analysis of ventilation networks. Furthermore, a multi-level airflow regulation strategy is realized, including the methods of optimization control based on mixed integer linear programming and equipment-driven demand-based regulation, effectively resolving the challenges of calculating nonlinear programming models. Case studies indicate that the intelligent ventilation system significantly enhances mine safety and efficiency, leading to approximately 10–20% energy saving, a 40–60% quicker emergency response, and an average increase of about 20% in the utilization of fresh air at working faces through its remote and real-time control capabilities. Full article

This study investigates sustainability-oriented design and production practices in Slovenia, focusing on brand-led approaches grounded in local innovation, cultural heritage and community engagement. Through mapping of Slovenian fashion enterprises, the research identifies and analyzes core sustainability and circularity strategies including zero- and low-waste design, recycling, upcycling and the development of adaptable, long-lasting garments. Further attention is given to participatory design methods involving consumers, the strategic social media use for community building and service-based circular economy models such as lifetime garment repair. Technological and production innovations, localized supply chains and small-scale production models are assessed for their role in reducing environmental impact and advancing sustainable supply chain management. The study also analyzes initiatives to shorten the fashion loop, including dematerialization and production minimization, as pathways to reduce resource consumption. Methodologically, the study combines empirical fieldwork, participant observation and literature review to deliver a comprehensive analysis of Slovenia’s sustainable fashion sector. The findings contribute to the global discourse on regional and place-based sustainability in fashion demonstrating how design-driven, small- and medium-sized enterprises can integrate circular economy principles, cultural continuity and collaborative innovation to foster environmentally responsible and socially embedded fashion. Full article

This study examines the ethical problems of using artificial intelligence (AI) applications in higher education, focusing on activities performed by university educators. Drawing on Slovak legislation that defines educators’ responsibilities, the study classifies their activities into three categories: teaching, scientific research, and other (academic management and self-directed professional development). From standpoint of methodology, a thematic review of 42 open-access, peer-reviewed articles published between 2022 and 2025 was conducted across the Web of Science and Scopus databases. Relevant AI applications and their associated ethical issues were identified and thematically categorized. Results of this study show that AI applications are extensively used across all analysed areas of university educators’ activities. Most notably used are applications that are generative language models, editing and paraphrasing tools, learning and assessment software, management and search tools, visualizing and design tools, and analysis and management systems. Their adoption raises ethical concerns which can be thematically grouped into six categories: privacy and data protection, bias and fairness, transparency and accountability, autonomy and oversight, governance gaps, and integrity and plagiarism. The results provide universities with a structured analytical framework to assess and address ethical risks related to AI use in specific academic activities. Although the study is limited to open-access literature, it offers a conceptual foundation for future empirical research and the development of ethical, institutionally grounded AI policies in higher education. Full article

Wild orchids, valued for their beauty and economic importance, are facing the challenges of distribution contraction and range shifts from climate change. The rare Cymbidium cyperifolium (class II in the List of National Key Protected Wild Plants in China, Vulnerable on the China Biodiversity Red List) remains understudied regarding its responses to climate variability. Utilizing an enhanced MaxEnt model, we predicted suitable habitats under diverse climate scenarios, revealing a potential distribution of 52.37 × 10⁴ km², concentrated in eastern Yunnan, western Guangxi, the Guizhou border, and southern Hainan. Cymbidium cyperifolium is sensitive to climate change, and temperature annual range (Bio 7) contributes a significant 77.42% of the distribution probability (i.e., habitat suitability), highlighting temperature’s pivotal influence on its distribution. Although the overall potential distribution area and low-suitability regions in China are predicted to decrease, medium and high-suitability areas are expected to expand. The center of mass of the high-altitude habitat is concentrated in southeastern Yunnan Province, migrating just slightly, yet tending westward and northeastward. Based on these findings, we recommend the expansion of existing protected areas or the establishment of new ones for C. cyperifolium, particularly in eastern Yunnan and western Guangxi. Additionally, our research can serve as a reference for the ex situ conservation of C. cyperifolium and other orchids with similar ecological habits, underscoring the broader implications in biodiversity preservation efforts. Full article

SARS-CoV-2 variants have demonstrated distinct epidemiological patterns and clinical presentations throughout the COVID-19 pandemic. Understanding variant-specific differences at the respiratory epithelium is crucial for understanding their pathogenesis. Here, we utilized human nasal organoid air–liquid interface (HNO-ALI) cell cultures to compare the viral replication kinetics, innate immune response, and epithelial damage of six different strains of SARS-CoV-2 (B.1.2, WA, Alpha, Beta, Delta, and Omicron). All variants replicated efficiently in HNO-ALIs, but with distinct replication kinetic patterns. The Delta variant exhibited delayed replication kinetics, achieving a steady state at 6 days post-infection compared to 3 days for other variants. Cytokine analysis revealed robust pro-inflammatory and chemoattractant responses (IL-6, IL-8, IP-10, CXCL9, and CXCL11) in WA1, Alpha, Beta, and Omicron infections, while Delta significantly dampened the innate immune response, with no significant induction of IL-6, IP-10, CXCL9, or CXCL11. Immunofluorescence and H&E analysis showed that all variants caused significant ciliary damage, though WA1 and Delta demonstrated less destruction at early time points (3 days post-infection). Together, these data show that, in our HNO-ALI model, the Delta variant employs a distinct “stealth” strategy characterized by delayed replication kinetics and epithelial cell innate immune evasion when compared to other variants of SARS-CoV-2, potentially explaining a mechanism that the Delta variant can use for its enhanced transmissibility and virulence observed clinically. Our findings demonstrate that variant-specific differences at the respiratory epithelium could explain some of the distinct clinical presentations and highlight the utility of the HNO-ALI system for the rapid assessment of emerging variants. Full article

Fracture systems critically govern coal seam permeability, influencing hydrocarbon migration pathways and well placement strategies. We established a predictive framework for fracture characterization in thin-interbedded coal reservoirs by integrating seismic response analysis with multi-domain validation. Utilizing borehole log statistics and staggered-grid wave equation modeling, we first decode azimuthal amplitude anisotropy patterns in fractured coal seams under varying lithological contexts. Key findings reveal that (1) isotropic thick surrounding rocks yield distinct fracture symmetry axis alignment (ellipse long-axis orientation shifts with layer velocity), while (2) anisotropic thin-interbedded host strata amplify azimuthal anisotropy ratios at mid–far offsets but induce prediction ambiguity under comparable fracture intensities. By applying azimuthally partitioned OVT data with optimized mid–long offset stacking, our amplitude ellipse fitting method demonstrates unique fracture solutions validated against structural, logging, and production data. This workflow resolves the multi-solution challenges in thin-layered systems, enabling precise fracture parameter prediction to optimize coalbed methane development in geologically complex basins. Full article

Land use/land cover (LULC) change is a key driving factor influencing the dynamics of terrestrial ecosystem carbon storage. In high-groundwater-level coal resource-based cities (HGCRBCs), the interplay of urban expansion, mining disturbances, and land reclamation makes the carbon storage evolution process more complex. This study takes Jining, Zaozhuang, and Heze cities in Shandong Province as the research area and constructs a coupled analytical framework of “mining–reclamation–carbon storage” by integrating the Patch-generating Land Use Simulation (PLUS), Probability Integral Method (PIM), InVEST, and Grey Multi-Objective Programming (GMOP) models. It systematically evaluates the spatiotemporal characteristics of carbon storage changes from 2000 to 2020 and simulates the carbon storage responses under different development scenarios in 2030. The results show that: (1) From 2000 to 2020, the total carbon storage in the region decreased by 31.53 Tg, with cropland conversion to construction land and water bodies being the primary carbon loss pathways, contributing up to 89.86% of the total carbon loss. (2) Among the 16 major LULC transition paths identified, single-process drivers dominated carbon storage changes. Specifically, urban expansion and mining activities individually accounted for nearly 70% and 8.65% of the carbon loss, respectively. Although the reclamation path contributed to a recovery of 1.72 Tg of carbon storage, it could not fully offset the loss caused by mining. (3) Future scenario simulations indicate that the ecological conservation scenario yields the highest carbon storage, while the economic development scenario results in the lowest. Mining activities generally lead to approximately 3.5 Tg of carbon loss, while post-mining reclamation can restore about 72% of the loss. Full article

Diesel engines power much of the heavy-duty equipment used in underground mines, where exhaust emissions pose acute environmental and occupational health challenges. However, predicting the amount of air required to dilute these emissions is difficult because exhaust mass flow and pollutant concentrations vary nonlinearly with multiple operating parameters. We apply deep learning to predict the total exhaust mass flow and carbon monoxide (CO) concentration of a six-cylinder gas–diesel (dual-fuel) turbocharged KAMAZ 910.12-450 engine under controlled operating conditions. We trained artificial neural networks on the preprocessed experimental dataset to capture nonlinear relationships between engine inputs and exhaust responses. Model interpretation with Shapley additive explanations (SHAP) identifies torque, speed, and boost pressure as dominant drivers of exhaust mass flow, and catalyst pressure, EGR rate, and boost pressure as primary contributors to CO concentration. In addition, symbolic regression yields an interpretable analytical expression for exhaust mass flow, facilitating interpretation and potential integration into control. The results indicate that deep learning enables accurate and interpretable prediction of key exhaust parameters in dual-fuel engines, supporting emission assessment and mitigation strategies relevant to underground mining operations. These findings support future integration with ventilation models and real-time monitoring frameworks. Full article

Accurately estimating the time of concentration (Tc) is critical for hydrological modelling, flood forecasting, and hydraulic infrastructure design. However, conventional methods often overlook the combined effects of rainfall intensity and antecedent soil moisture, thereby limiting their applicability under changing climates. This study presents a novel approach that integrates data-driven techniques with remote sensing data to improve Tc estimation. This method was successfully applied in the Kalu River Basin, Sri Lanka, demonstrating its performance in a tropical catchment. While an overall inverse relationship between rainfall intensity and Tc was observed, deviations in several events underscored the influence of initial soil moisture conditions on catchment response times. To address this, a modified kinematic wave-based equation incorporating both rainfall intensity and soil moisture was developed and calibrated, achieving high predictive accuracy (calibration: R² = 0.97, RMSE = 1.1 h; validation: R² = 0.96, RMSE = 0.01 h). A hydrological model was developed to assess the impacts of Tc uncertainties on design hydrographs. Results revealed that underestimating Tc led to substantially shorter lag times and significantly increased peak flows, highlighting the sensitivity of flood simulations to Tc variability. This study highlights the need for improved $T_C$ estimation and presents a robust, transferable methodology for enhancing hydrological predictions and climate-resilient infrastructure planning. Full article

Hybrid AC/DC microgrids have emerged as a promising solution for integrating diverse renewable energy sources, enhancing efficiency, and strengthening resilience in modern power systems. However, existing control schemes exhibit critical shortcomings that limit their practical effectiveness. Traditional linear controllers, designed around nominal operating points, often fail to maintain stability under large load and generation fluctuations. Optimization-based methods are highly sensitive to model inaccuracies and parameter uncertainties, reducing their reliability in dynamic environments. Intelligent approaches, such as fuzzy logic and ML-based controllers, provide adaptability but suffer from high computational demands, limited interpretability, and challenges in real-time deployment. These limitations highlight the need for robust control strategies that can guarantee reliable operation despite disturbances, uncertainties, and varying operating conditions. Numerical performance indices demonstrate that the reviewed robust control strategies outperform conventional linear, optimization-based, and intelligent controllers in terms of system stability, voltage and current regulation, and dynamic response. This paper provides a comprehensive review of recent robust control strategies for hybrid AC/DC microgrids, systematically categorizing classical model-based, intelligent, and adaptive approaches. Key research gaps are identified, including the lack of unified benchmarking, limited experimental validation, and challenges in integrating decentralized frameworks. Unlike prior surveys that broadly cover microgrid types, this work focuses exclusively on hybrid AC/DC systems, emphasizing hierarchical control architectures and outlining future directions for scalable and certifiable robust controllers. Also, comparative results demonstrate that state of the art robust controllers—including H∞-based, sliding mode, and hybrid intelligent controllers—can achieve performance improvements for metrics such as voltage overshoot, frequency settling time, and THD compared to conventional PID and droop controllers. By synthesizing recent advancements and identifying critical research gaps, this work lays the groundwork for developing robust control strategies capable of ensuring stability and adaptability in future hybrid AC/DC microgrids. Full article

The high-strength aluminum alloy 7B04 used in aircraft structures poses challenges in welding. In this study, 7075 aluminum alloy powder is used as an interlayer to strengthen the vacuum diffusion bonding (DB) joint of 7B04 aluminum alloy. Surface treatments with plasma activation before DB can effectively increase the bonding rate and lap shear strength (LSS) of the joint. The effects of DB temperature, pressure, and holding time on the joint LSS were analyzed by developing a quadratic regression model based on the response surface method (RSM). The model’s determination coefficient reached 99.52%, with a relative error of about 5%, making it suitable for 7B04 aluminum alloy DB process parameters optimization and joint performance prediction. Two sets of process parameters (505 °C-5.7 h-4.5 MPa and 515 °C-7.5 h-4.4 MPa) were acquired using the satisfaction function optimization method. Experimental results confirmed that the error between measured and predicted LSS is approximately 5%, and a higher LSS of 174 MPa was achieved at 515 °C-7.5 h-4.4 MPa. Full article

Artificial intelligence is rapidly reshaping skill expectations across media, marketing, and journalism, however, university curricula are not evolving at a comparable speed. To quantify the resulting curriculum–skill gap in communication-related programs, two synchronous corpora were assembled for the period July 2024–June 2025: 66 course descriptions from six leading UK universities and 107 graduate-to-mid-level job advertisements in communications, digital media, advertising, and public relations. Alignment around AI, datafication, and platform governance was assessed through a three-stage natural-language-processing workflow: a dual-tier AI-keyword index, comparative TF–IDF salience, and latent Dirichlet allocation topic modeling with bootstrap uncertainty. Curricula devoted 6.0% of their vocabulary to AI plus data/platform terms, whereas job ads allocated only 2.3% (χ² = 314.4, p < 0.001), indicating a conceptual-critical emphasis on ethics, power, and societal impact in the academy versus an operational focus on SEO, multichannel analytics, and campaign performance in recruitment discourse. Topic modeling corroborated this divergence: universities foregrounded themes labelled “Politics, Power & Governance”, while advertisers concentrated on “Campaign Execution & Performance”. Environmental and social externalities of AI—central to the Special Issue theme—were foregrounded in curricula but remained virtually absent from job advertisements. The findings are interpreted as an extension of technology-biased-skill-change theory to communication disciplines, and it is suggested that studio-based micro-credentials in automation workflows, dashboard visualization, and sustainable AI practice be embedded without relinquishing critical reflexivity, thereby narrowing the curriculum–skill gap and fostering environmentally, socially, and economically responsible media innovation. With respect to the novelty of this research, it constitutes the first large-scale, data-driven corpus analysis that empirically assessed the AI-related curriculum–skill gap in communication disciplines, thereby extending technology-biased-skill-change theory into this field. Full article