Search Results (4,625)

Rapid urbanization exerts immense pressure on cultivated land. Among these, slope-classified cultivated land (referring to cropland categorized by slope gradients) is especially vulnerable to fragmentation due to its ecological fragility, challenging utilization, and critical role in soil conservation and sustainable agriculture. This study explores the spatiotemporal dynamics and driving mechanisms of slope-classified cultivated land fragmentation (SCLF) in Guangdong Province, China, from 2000 to 2020. Using multi-temporal geospatial data, machine learning interpretation, and socioeconomic datasets, this research quantifies the spatiotemporal changes in SCLF, identifies key drivers and their interactions, and proposes differentiated protection strategies. The results reveal the following: (1) The SCLF decreased in the Pearl River Delta, exhibited “U-shaped” fluctuations in the west and east, and increased steadily in northern Guangdong. (2) The machine learning interpretation highlights significantly amplified synergistic effects among drivers, with socioeconomic factors, particularly agricultural mechanization and non-farm employment rates, exerting dominant influences on fragmentation patterns. (3) A “core–transitional–marginal” protection framework is proposed, intensifying the land use efficiency and ecological resilience in core areas, coupling land consolidation with green infrastructure in transitional zones, and promoting agroecological diversification in marginal regions. This research proposed a novel framework for SCLF, contributing to cultivated land protection and informing differentiated spatial governance in rapidly urbanizing regions. Full article

Skeletal muscle, constituting ~40% of body mass, serves as a primary effector for movement and a key metabolic regulator through myokine secretion. Hereditary myopathies, including dystrophinopathies (DMD/BMD), limb–girdle muscular dystrophies (LGMD), and metabolic disorders like Pompe disease, arise from pathogenic mutations in structural, metabolic, or ion channel genes, leading to progressive weakness and multi-organ dysfunction. Gene therapy has emerged as a transformative strategy, leveraging viral and non-viral vectors to deliver therapeutic nucleic acids. Adeno-associated virus (AAV) vectors dominate clinical applications due to their efficient transduction of post-mitotic myofibers and sustained transgene expression. Innovations in AAV engineering, such as capsid modification (chemical conjugation, rational design, directed evolution), self-complementary genomes, and tissue-specific promoters (e.g., MHCK7), enhance muscle tropism while mitigating immunogenicity and off-target effects. Non-viral vectors (liposomes, polymers, exosomes) offer advantages in cargo capacity (delivering full-length dystrophin), biocompatibility, and scalable production but face challenges in transduction efficiency and endosomal escape. Clinically, AAV-based therapies (e.g., Elevidys^® for DMD, Zolgensma^® for SMA) demonstrate functional improvements, though immune responses and hepatotoxicity remain concerns. Future directions focus on AI-driven vector design, hybrid systems (AAV–exosomes), and standardized manufacturing to achieve “single-dose, lifelong cure” paradigms for muscular disorders. Full article

The nanoporous structure of coal is crucial for the occurrence and development of coalbed methane (CBM). This study, leveraging the combined characterization of atomic force microscopy (AFM) and Gwyddion software, investigated six anthracite samples with varying degrees of metamorphism (R_o = 2.11–3.36%). It revealed the intrinsic relationships between their nanoporous structures, surface morphologies, fractal characteristics, and coalification processes. The research found that as R_o increases, the surface relief of coal decreases significantly, with pore structures evolving from being macropore-dominated to micropore-enriched, and the surface tending towards smoothness. Surface roughness parameters (Ra, Rq) exhibit a negative correlation with R_o. Quantitative data indicate that area porosity, pore count, and shape factor positively correlate with metamorphic grade, while mean pore diameter negatively correlates with it. The fractal dimensions calculated using the variance partition method, cube-counting method, triangular prism measurement method, and power spectrum method all show nonlinear correlations with R_o, moisture (M_ad), ash content (A_ad), and volatile matter (V_daf). Among these, the fractal dimension obtained by the triangular prism measurement method has the highest correlation with R_o, A_ad, and V_daf, while the variance partition method shows the highest correlation with M_ad. This study clarifies the regulatory mechanisms of coalification on the evolution of nanoporous structures and surface properties, providing a crucial theoretical foundation for the precise evaluation and efficient exploitation strategies of CBM reservoirs. Full article

The novel engines nowadays with high efficiency are operated under the superpressure, supercritical, and supersonic extreme conditions that are situated in the broken reaction zone regime. In this article, the propagation and heat/radical diffusion physics of a high-pressure dimethyl ether (DME)/air turbulent lean-premixed flame are investigated numerically by direct numerical simulation (DNS). A wide range of statistical and diagnostic methods, including Lagrangian fluid tracking, Joint Probability Density Distribution (JPDF), and chemical explosive mode analysis (CEMA), are applied to reveal the local combustion modes and dynamics evolution, as well as the roles of heat/mass transport and cool/hot flame interaction in the turbulent combustion, which would be beneficial to the design of novel engines with high performances. It is found that the three-staged combustion, including cool-flame, warm-flame, and hot-flame fronts, is a unique behavior of DME flame under the elevated-pressure, lean-premixed condition. In the broken reaction zone regime, the reaction zone thickness increases remarkably, and the heat release rate (HRR) and fuel consumption rate in the cool-flame zone are increased by 16% and 19%, respectively. The diffusion effect not only enhances flame propagation, but also suppresses the local HRR or fuel consumption. The strong turbulence interplaying with diffusive transports is the underlying physics for the enhancements in cool- and hot-flame fronts. The dominating diffusive sub-processes are revealed by the aid of the diffusion index. Full article

The rapid expansion of urbanization and inadequate planning have triggered a water balance crisis in many cities, manifesting as both the need for artificial lake supplementation and frequent urban flooding. Using the Xuanwu Lake watershed in Nanjing as a case study, this research aims to optimize the Blue–Green Infrastructure (BGI) network to maximize rainfall utilization within the watershed. The ultimate goal is to restore natural water balance processes and reduce reliance on artificial supplementation while mitigating urban flood risks. First, the Soil Conservation Service Curve Number (SCS–CN) model is employed to estimate the maximum potential of natural convergent flow within the watershed. Second, drawing on landscape connectivity theory, a multi-level BGI network optimization model is developed by integrating the Minimum Cumulative Resistance (MCR) model and the gravity model, incorporating both hydrological connectivity and flood safety considerations. Third, a water balance model based on the Storm Water Management Model (SWMM) framework and empirical formulas is constructed and coupled with the network optimization model to simulate and evaluate water budget performance under optimized scenarios. The results indicate that the optimized scheme can reduce artificial supplementation to Xuanwu Lake by 62.2% in June, while also ensuring effective supplementation throughout the year. Annual runoff entering the lake reaches 13.25 million cubic meters, meeting approximately 13% of the current annual supplementation demand. Moreover, under a 100-year return period flood scenario, the optimized network reduces total watershed flood volume by 35% compared to pre-optimization conditions, with flood-prone units experiencing reductions exceeding 50%. These findings underscore the optimized BGI network scheme’s capacity to reallocate rainwater resources efficiently, promoting a transition in urban water governance from an “engineering-dominated” approach to an “ecology-oriented and self-regulating” paradigm. Full article

This study presents a novel bivariate-output deep learning framework based on LeNet-5 for the simultaneous prediction of urban flood susceptibility and inundation depth in Seoul, South Korea. Unlike previous studies that relied on single-output models, the proposed approach jointly learns classification and regression targets through a shared feature extraction structure, enhancing consistency and generalization. Among six tested architectures, the Le5SD_CBAM model—integrating a Convolutional Block Attention Module (CBAM)—achieved the best performance, with 83% accuracy, an Area Under the ROC Curve (AUC) of 0.91 for flood susceptibility classification, and a mean absolute error (MAE) of 0.12 m and root mean squared error (RMSE) of 0.18 m for depth estimation. The model’s spatial predictions aligned well with hydrological principles and past flood records, accurately identifying low-lying flood-prone zones and capturing localized inundation patterns influenced by infrastructure and micro-topography. Importantly, it detected spatial mismatches between susceptibility and depth, demonstrating the benefit of joint modeling. Variable importance analysis highlighted elevation as the dominant predictor, while distances to roads, rivers, and drainage systems were also key contributors. In contrast, secondary terrain attributes had limited influence, indicating that urban infrastructure has significantly altered natural flood flow dynamics. Although the model lacks dynamic forcings such as rainfall and upstream inflows, it remains a valuable tool for flood risk mapping in data-scarce settings. The bivariate-output framework improves computational efficiency and internal coherence compared to separate single-task models, supporting its integration into urban flood management and planning systems. Full article

The integration of microalgae cultivation in the treatment of aquaculture wastewater (AWW) offers a sustainable solution for the recovery of nutrients and the valorisation of biomass. In this study, the potential of Chlorella vulgaris for growth in raw AWW and its variants was investigated and the efficiency of nutrient removal, biochemical composition of biomass, biodiesel potential by FAME analysis, and biogas production were evaluated. C. vulgaris was cultivated in three media: raw AWW, microelement-enriched AWW, and a synthetic base medium. Raw AWW allowed for the highest biomass production (2.4 g VS/L) and nutrient removal efficiency (ammonia: 100%, phosphate: 93.7%, nitrate: 37.8%). The addition of microelements did not significantly improve growth or nutrient uptake. The biomass grown on AWW showed a favourable lipid profile for biodiesel, dominated by C16:0 and C18:1. The highest biogas and methane yields were recorded for biomass from raw AWW as 358 ± 11 L/kg VS and 216 ± 7 L/kg VS, respectively. The results confirm that AWW is a suitable medium for the cultivation of C. vulgaris, enabling efficient wastewater treatment and the production of high-quality biomass. Full article

Combustion instability poses a significant challenge in aerospace propulsion systems, particularly in afterburners that employ bluff-body flame stabilizers. The flame transfer function (FTF) is essential for characterizing the dynamic response of flames to perturbations, which is critical for predicting and controlling these instabilities. This study experimentally investigates the effect of varying the number of fuel injection holes (N = 3, 4, 5, 6) on the FTF and flame dynamics in a model afterburner combustor. Using acoustic excitations, the FTF was measured across a range of frequencies, with flame behavior analyzed via high-speed imaging and chemiluminescence techniques. Results reveal that the FTF gain exhibits dual-peak characteristics, initially decreasing and then increasing with higher N values. The frequencies of these gain peaks shift to higher values as N increases, while the time delay between velocity and heat release rate fluctuations decreases, indicating a faster flame response. Flame morphology analysis shows that higher N leads to shorter, taller flames due to enhanced fuel distribution and mixing. Detailed examination of flame dynamics indicates that different pulsation modes dominate at various frequencies, elucidating the observed FTF behavior. This research provides novel insights into the optimization of fuel injection configurations to enhance combustion stability in afterburners, advancing the development of more reliable and efficient aerospace propulsion systems. Full article

Efficient and fast water uptake by seeds, facilitated by optimal soil moisture, plays a critical role in timely germination and early seedling vigor for foxtail millet production in arid and semi-arid regions. The husk, as a unique structure through which the seed contacts the soil, plays an important role in water uptake and germination. Many foxtail millet germplasm accessions have papillae on the epidermis of their husks, yet the role of this trait in water uptake and germination, as well as the genetic basis and regulatory mechanism related to this trait, remain unknown. In this study, we demonstrated that the water uptake by the seeds from accessions with papillae was significantly higher than that of accessions without papillae two hours and four hours after sowing during a 10 h experiment, resulting in faster germination. Analysis of segregating ratios from two F2 populations derived from crossing between accessions with and without papillae indicated that husk papilla density was of monogenic dominance. Bulked Segregant Analysis Sequencing (BSA-Seq) showed that candidate regions on chromosome 5 were significantly associated with husk papilla density. The mapped region overlapped by the two BSA populations for papilla density included 72 genes. In combination with the expression profiles of these genes, five candidate genes were identified, encoding aquaporins, fructose transporter, and glycoside hydrolase. This study elucidated the role of husk papillae in enhancing water uptake and germination in foxtail millet, provided genetic insights into the trait, and laid the foundation for further study on the mechanism of husk papilla differentiation. Full article

Black chokeberry (Aronia melanocarpa L.) pomace (BCP), a major by-product of juice production, is an underutilized source of polyphenols and anthocyanins with strong antioxidant properties. This study aimed to optimize and compare three green extraction techniques—pressurized liquid extraction (PLE), microwave-assisted extraction (MAE), and ultrasound-assisted extraction (UAE)—for recovering total polyphenols (TP) and total monomeric anthocyanins (TMA) from BCP, with reflux extraction as a benchmark. The effects of temperature, extraction time, and solid–solvent ratio were evaluated, and cryogrinding was assessed as a pre-treatment. PLE achieved the highest TP yields at elevated temperatures but reduced anthocyanin recovery, while MAE offered a balance of high TP and TMA, with strong antioxidant capacity. Cryogrinding enhanced TP extraction, with only 1 min of cryogrinding maximizing yield. UPLC-MS/MS analysis of optimized MAE extract confirmed cyanidin-3-glucoside and cyanidin-3-galactoside as dominant anthocyanins, alongside notable flavonols and phenolic acids, validating the rich phenolic profile. Overall, MAE combined with 1 min of cryogrinding proved to be the most effective approach for preserving heat-sensitive compounds while achieving high yields. These findings demonstrate that optimized green extraction can efficiently valorize BCP, supporting sustainable food processing and waste reduction in line with circular economy principles. Full article

While supply chain competition increasingly occurs at the network level, most research remains limited to dyadic or triadic relationships, failing to capture the full complexity of multi-tier supply networks. This research investigates the influence of four distinct types of network ties—contractual, transactional, professional, and personal—on supply chain performance, evaluated across five dimensions: cost, quality, delivery, flexibility, and innovation. The analysis draws on data gathered from 153 component-level supply networks, encompassing a total of 1852 entities within South Korea’s automotive and electronics manufacturing sectors. We employed social network analysis with a directed-valued network approach to capture asymmetric relationships. Results reveal that network architecture affects performance dimensions differently: centralized professional knowledge sharing enhances delivery performance, while concentrated personal ties prove detrimental; for innovation, dense network connections and dominant transactional hubs unexpectedly hinder performance by fostering conformity; cost performance shows mixed effects, with transactional centralization impeding efficiency while professional and personal leadership facilitates cost reduction. The influence of the original equipment manufacturer on supplier selection moderates these relationships, particularly mitigating negative impacts of personal tie centralization. These findings challenge conventional assumptions about network density benefits and demonstrate that supply network competence—the ability to configure and leverage network architecture—requires careful consideration of multiple tie types and their distinct effects on different performance outcomes. Full article

This paper presents a novel integration of bioclimatic-passive architectural elements—Trombe walls, pergolas, and deciduous climbers—in the context of residential buildings in Eastern and Central Europe, a combination that remains largely underexplored in the current literature. The innovativeness of the proposed concept is reflected in the combined use of the following building elements: three types of passive Trombe wall (single-glazed, double-glazed, and triple-glazed), pergolas, and four types of deciduous climbers (V. coignetiae, H. lupulus, W. sinensis, and A. macrophylla). By using meteorological data for the towns Kragujevac and Kielce, the influence of location parameters for two dominant European climate zones (moderate continental and continental) is also included in this investigation. The initial single-family building models were created following the Serbian and Polish rulebooks on energy efficiency for new buildings and equipped with the same thermo-technical systems and people occupancy conditions. Based on the conducted simulations (using Google SketchUp 8 and EnergyPlus 7.1) and obtained results on the annual level, the following main conclusions can be drawn: (1) a moderate continental climate is more suitable for implementing the proposed concept; (2) a single-glazed passive Trombe wall is not energy or environmentally justified; (3) the energy, environmental, and economic benefits for both selected locations are greatest in the case of the combined use of pergolas, V. coignetiae, and triple-glazed passive Trombe wall; and (4) before the wider commercial application of the proposed concept in the future, efforts should be made to explore economic opportunities, which, among other things, involve a focus on market stability and accessibility. Full article

Transitioning to sustainable energy systems demands the creation of innovative methods that deliver dependable and effective renewable energy technologies. CSP systems that integrate parabolic trough designs with thermal energy storage (TES) systems provide essential solutions to overcome energy intermittency challenges. Molten salts serve dual functions as heat transfer fluids (HTFs) and thermal energy storage (TES) media, making them critical to CSP system performance improvements. The study introduces a hybrid MCDM framework that combines the CRITIC method for objective weighting with the SWARA approach for expert-adjusted weighting and utilizes an enhanced Lexicographic Goal Programming to evaluate molten salt options for off-grid parabolic trough systems. The evaluation process considered melting point alongside thermal stability while also assessing cost-effectiveness, recyclability, and safety requirements. The use of Pareto front analysis helped identify non-dominated salts, which then underwent a tiered optimization process emphasizing safety, performance, and sustainability features. Results confirm that the ternary nitrate composition Ca(NO₃)₂:NaNO₃:KNO₃ offers the best overall performance across all tested policy scenarios, driven by its superior thermophysical properties. Solar Salt (NaNO₃-KNO₃) consistently ranks as a robust second choice, excelling in economic and sustainability metrics. The proposed approach provides a flexible, policy-sensitive framework for material selection tailored to enhance the efficiency and sustainability of off-grid CSP systems and support the renewable energy objectives. Full article

Perovskite solar cells (PSCs) have emerged as a promising contender in photovoltaics, owing to their rapidly advancing power conversion efficiencies (PCEs) and compatibility with low-temperature solution processing techniques. Single-junction architectures reveal inherent limitations imposed by the Shockley–Queisser (SQ) limit, motivating adoption of a dual-absorber structure comprising Cs₄CuSb₂Cl₁₂ (CCSC) and Cs₂TiI₆ (CTI)—lead-free perovskite derivatives valued for environmental benignity and intrinsic stability. Comprehensive theoretical screening of 26 electron/hole transport layer (ETL/HTL) candidates identified SrTiO₃ (STO) and CuSCN as optimal charge transport materials, producing an initial simulated PCE of 16.27%. Subsequent theoretical optimization of key parameters—including bulk and interface defect densities, band gap, layer thickness, and electrode materials—culminated in a simulated PCE of 30.86%. Incorporating quantifiable practical constraints, including radiative recombination, resistance, and FTO reflection, revised simulated efficiency to 26.60%, while qualitative analysis of additional factors follows later. Furthermore, comparing multiple algorithms within this theoretical framework demonstrated eXtreme Gradient Boosting (XGBoost) possesses superior predictive capability, identifying CTI defect density as the dominant impact on PCE—thereby underscoring its critical role in analogous architectures and offering optimization guidance for experimental studies. Collectively, this theoretical research delineates a viable pathway toward developing stable, environmentally sustainable PSCs with high properties. Full article

As information and sensor technologies advance swiftly, data-driven approaches have emerged as a dominant paradigm in scientific research. In the petroleum industry, precise forecasting of patterns of two-phase flow involving oil and water is essential for enhancing production efficiency and ensuring safety. This study investigates the application of Kolmogorov–Arnold Networks (KAN) for predicting patterns of two-phase flow involving oil and water and compares it with the conventional Multi-Layer Perceptron (MLP) neural network. To obtain real physical data, we conducted the experimental section to simulate the patterns of two-phase flow involving oil and water under various well angles, flow rates, and water cuts at the Key Laboratory of Oil and Gas Resources Exploration Technology of the Ministry of Education, Yangtze University. These data were standardized and used to train both KAN and MLP models. The findings indicate that KAN outperforms the MLP network, achieving 50% faster convergence and 22.2% higher accuracy in prediction. Moreover, the KAN model features a more streamlined structure and requires fewer neurons to attain comparable or superior performance to MLP. This research offers a highly effective and dependable method for predicting patterns of two-phase flow involving oil and water in the dynamic monitoring of production wells. It highlights the potential of KAN to boost the performance of energy systems, particularly in the context of intelligent transformation. Full article