Search Results (57,588)

Evaluating resource and environmental carrying capacity (RECC) serves as a fundamental approach for assessing regional environmental baselines and is widely applied in territorial spatial planning. Focusing on Daye City—a characteristic resource-exhausted city in Hubei Province—this study developed a comprehensive RECC evaluation system. By integrating the obstacle degree model, hotspot analysis, and Geodetector, we investigated the spatial differentiation mechanisms of RECC and the resulting production–living–ecological (PLE) spatial conflicts, ultimately proposing targeted optimization pathways. The core findings are as follows: (1) The RECC of Daye City exhibits pronounced spatial polarization and a distinct north–south gradient. (2) The spatial stress of industrial/mining land emerges as the primary obstacle (36.47%). Together with geological hazard risk and soil erosion sensitivity, it forms a core constraint chain. The highly significant hotspots of these factors strongly overlap in the north-central mining districts. (3) Geodetector analysis reveals robust bivariate and nonlinear enhancement effects among these core obstacle factors. This indicates that the cascading vicious cycle of mining disturbance, ecological degradation, and declining carrying capacity fundamentally underlies the constrained RECC in mining regions. (4) PLE spatial conflicts across the study area are dominated by production–ecological conflicts (47.73%), presenting a spatial pattern that heavily couples with the polarized obstacle zones. Based on these findings, this study proposes differentiated regulation strategies centered on mitigating mining-induced stress and interrupting the cascading transmission of disaster risks. These strategies aim to restructure and optimize the territorial spatial pattern, providing robust quantitative decision support for the sustainable transformation of similar resource-exhausted cities. Full article

Bioenergy crops are widely regarded as a promising approach to support renewable energy production, diversify farm income, and enhance land-use efficiency. Despite these potential benefits, adoption rates remain low, and empirical understanding of landowners’ decision-making processes is still emerging. This study examines landowners’ interest in and likelihood of adopting bioenergy crops, explicitly differentiating between early-stage interest and near-term adoption intentions. Survey data from 207 landowners are analyzed using a bivariate probit model to identify key factors influencing both outcomes. The results reveal a marked disparity between expressed interest and adoption likelihood, with a significantly greater proportion of landowners indicating interest than those willing to adopt in the near term. Economic orientation increases adoption interest by 9.5 percentage points, while identity orientation increases adoption likelihood by 6.6 percentage points. Determinants such as increased awareness, land size, experience, and participation in conservation programs exert varying influences across different decision stages. These findings suggest that stated interest and stated near-term adoption likelihood represent related but distinct dimensions of adoption readiness, shaped by different economic, identity-based, and institutional factors. Effective promotion of bioenergy crops requires more than general awareness campaigns. Policies should combine financial incentives, technical assistance, market development support, and outreach strategies that present bioenergy crops as compatible with landowners’ economic goals, stewardship values, recreational uses, and long-term attachment to their land. Full article

Low-field benchtop nuclear magnetic resonance (NMR) spectroscopy has emerged as an accessible analytical platform for rapid, routine, and application-oriented analysis. However, its broader analytical adoption remains constrained by intrinsic limitations, including reduced spectral resolution, severe signal overlap, and lower sensitivity compared with conventional high-field instruments. To address these limitations, artificial intelligence (AI), including machine learning and deep learning approaches, has increasingly been explored alongside conventional chemometric strategies to enhance information extraction from low-field spectral data. This review examines recent developments in AI-assisted benchtop NMR across three major application domains: classification and authentication, quantitative analysis, and spectral processing or automated interpretation. Current evidence suggests that classification and authentication currently represent the most mature application area, whereas quantitative analysis shows promising but often condition-dependent performance. In contrast, spectral reconstruction and automated interpretation remain comparatively early-stage and exploratory, despite their potential long-term relevance for addressing intrinsic information limitations. Key challenges, including limited dataset diversity, poor model transferability, validation pitfalls, limited interpretability, and the lack of benchmarking and standardized workflows, are critically discussed. Future progress will likely depend not only on advances in AI algorithms, but also on the development of robust, reproducible, and analytically meaningful workflows. Overall, AI-assisted benchtop NMR is evolving from proof-of-concept applications toward a more structured analytical framework for extracting chemically meaningful information from spectrally constrained low-field data. Full article

Bacillus thuringiensis Cry toxins are well known for their high insecticidal activity against Lepidoptera, Diptera, and Coleoptera and have been widely used in Bt transgenic crops. However, their activity against Hemipteran aphids remains relatively low. Identifying novel Cry proteins and elucidating their action mechanisms can facilitate the development of effective aphid control strategies. In this study, we found that ingestion of Cry2Ab12 did not kill Myzus persicae adults but significantly reduced their offspring number and exerted a lethal effect on M. persicae nymphs. After identifying Cry2Ab12 toxin-binding proteins in M. persicae, we further characterized the interaction with Obg-like ATPase 1 (OLA1), a conserved protein involved in growth regulation. Bio-layer interferometry (BLI), ELISA, and enzyme activity assays revealed that Cry2Ab12 and OLA1 do not interact directly. Interestingly, heat shock protein 60 (HSP60) was shown to mediate the interaction among Cry2Ab12, HSP60, and OLA1, leading to inhibition of OLA1 enzymatic activity. Based on these findings and bioinformatics simulations, we proposed a mechanistic model for Cry2Ab12 toxicity against M. persicae: upon ingestion of a sufficient amount of Cry2Ab12, the formation of the Cry2Ab12–HSP60–OLA1 complex impairs the cellular stress response, disrupts normal OLA1 expression, and ultimately restricts larval growth and development, resulting in lethality. This study provides new insights into the action of Cry toxins in aphids and offers a basis for developing enhanced aphid biocontrol strategies. Full article

In the field of urban traffic management, optimizing traffic signals at intersections is crucial for enhancing traffic flow efficiency. Despite advances in intelligent traffic signal control strategies through deep reinforcement learning (DRL), practical deployment challenges persist, such as abrupt changes in signal phases and significant hardware costs. This paper proposes a novel Traffic Characteristics-Guided Progressive optimization (TCGP) method that builds on classical fixed-time traffic signals. It is based on the classic fixed-time and quickly optimizes the green time ratio of intersection traffic lights by integrating the relationship between green light duration and traffic flow. Then, it efficiently explores the traffic signal cycle duration of a single intersection. Using a progressive optimization strategy, TCGP addresses the “curse of dimensionality” problem caused by a large number of intersections. TCGP ensures compatibility with traditional control methods and offers performance comparable to state-of-the-art DRL approaches, with competitive stability and computational efficiency. Evaluations with public datasets and real traffic data from Zhengzhou, Henan, China, confirm TCGP’s competitive performance and adaptability. This contributes fresh perspectives to the modernization of urban traffic systems. Full article

Objectives: Neuroinflammation is recognized as a significant characteristic of Alzheimer’s disease (AD). Currently, there is a notable absence of effective pharmacological agents to prevent or treat neuroinflammatory processes associated with AD. Heat shock protein 70 (HSP70) is pivotal in the progression of neuroinflammation. In this study, we explored the potential of maltol, a Maillard reaction product derived from red ginseng, as a therapeutic agent for neuroinflammation. Methods: In vitro, HMC3 microglial cell models were developed to examine the regulatory effects of gradient concentrations of maltol (12.5, 25, 50 μM) on the TLR4/MyD88/NF-κB p65 signaling pathway, neuroinflammation, and pyroptosis. Analyses of the GEO database and Gene Set Enrichment Analysis (GSEA) were performed to identify the core targets of maltol, followed by HSP70 gene silencing experiments to validate the targeted regulatory mechanism. Results: Maltol significantly mitigated LPS-induced neuronal damage and cognitive deficits in mice. It effectively suppressed microglia-mediated neuroinflammation and pyroptosis, reversed oxidative stress-induced neuronal ferroptosis, and inhibited neuronal apoptosis. In vitro experiments demonstrated that maltol obstructed TLR4/MyD88 binding, thereby inhibiting NF-κB p65-mediated neuroinflammation and pyroptosis, while also alleviating excessive ROS accumulation to enhance oxidative stress and ferroptosis. Bioinformatics analysis identified HSP70 as a crucial target for the anti-inflammatory and antioxidant effects of maltol. Subsequent gene silencing experiments confirmed that maltol exerted its inhibitory effects on LPS-induced neuroinflammation and pyroptosis in an HSP70-dependent manner. Conclusions: Maltol exhibits significant protective effects against Alzheimer’s disease-related neuroinflammation, oxidative stress, pyroptosis, and ferroptosis through the targeting of HSP70. This study elucidates the molecular mechanisms by which maltol improves neuroinflammatory injury and provides a novel theoretical foundation and therapeutic strategy for the intervention of Alzheimer’s disease neuroinflammation using traditional Chinese medicine. Full article

Enhancing the ampacity of existing overhead transmission conductors through surface heat-dissipation regulation is important for grid capacity expansion. Herein, a superhydrophobic radiative heat-dissipating conductor was fabricated by combining phosphoric acid anodization with low-surface-energy modification. Porous anodic aluminum oxide (AAO) layers were in situ constructed on ACSR conductors under different anodizing current densities and oxidation times, followed by modification with hexadecyltrimethoxysilane or 1H,1H,2H,2H-perfluorodecyltrimethoxysilane to obtain H-AAO and F-AAO conductors, respectively. The surface morphology, optical properties, wettability, electrical resistance, current-induced temperature rise, and aging stability were systematically evaluated. The porous AAO layer enhanced the broadband infrared emissivity of the conductor surface while maintaining relatively high solar-band reflectance. The F-AAO conductor exhibited a water contact angle of 164.9° and a sliding angle of 1.8°, confirming excellent super-hydrophobicity. At 450 A, the steady-state temperature of the F-AAO conductor decreased from 106.85 °C for the Bare conductor to 75.34 °C. Under a 70 °C temperature limit, the allowable current increased from 343.58 to 431.57 A, corresponding to a 25.6% enhancement. Moreover, the F-AAO conductor retained stable heat-dissipation performance after 28 days of thermal aging. These findings demonstrate that anodization-assisted surface engineering is a feasible strategy for improving radiative heat dissipation, environmental adaptability, and current-carrying performance of overhead transmission conductors. Full article

This paper proposes an integrated formation control framework with a finite-time nonlinear disturbance observer (FT-NDO) for automated guided vehicles (AGVs) operating in port environments, where constrained workspace, narrow formation spacing, and complex external disturbances pose significant challenges. An adaptive leader–follower formation strategy with dynamic inter-vehicle spacing is developed to enhance maneuverability during turning. Within a hierarchical control structure that decouples lateral and longitudinal dynamics, two sliding mode controllers (SMCs) are designed: (a) a lateral SMC that prioritizes heading accuracy, limiting yaw angle error to within ±2°; and (b) a nonsingular terminal SMC (NTSMC) for longitudinal control, improving error convergence speed compared to conventional SMC. An FT-NDO is further incorporated into both control loops to estimate and compensate for external disturbances in real time, achieving a disturbance estimation accuracy of over 95% and significantly attenuating the impact of environmental disturbances. Validation through simulation and physical experiment of a dual-AGV formation in a realistic port scenario demonstrates that the proposed approach restricts formation deviation to 0.015 m and maintains stable operation under various disturbance conditions. This study provides a practical solution for dual-AGV collaborative transportation in spatially constrained and dynamically disturbed environments, with direct implications for improving operational efficiency and safety in port logistics. Full article

Background/Objectives: Oral cancer is one of the most common types of cancer, with high mortality rates if not detected early. Traditional diagnostic methods based on clinical examination rely on experience, leading to delays in early and reliable diagnosis. In recent years, medical imaging and AI-based computer-aided diagnostic systems have shown promising results in the automated identification of oral cancer. In particular, the efficient management of high-dimensional feature spaces in machine learning and deep learning approaches directly impacts classification performance. In this context, metaheuristic-based feature selection technics is a critical component because of eliminating redundant and irrelevant features. To address these challenges, this study proposes a metaheuristic-based feature selection method to reduce feature dimensionality and enhance the classification performance of oral cancer detection. Methods: This study proposes an improved Honey Badger Algorithm-based feature selection approach for the automated detection of oral cancer. In the proposed method, the distance vector used in the HBA method has been redefined to improve the balance between exploration and exploitation. Additionally, a new Cauchy mutation-based migration strategy was integrated into the proposed method to increase diversity in the search space and avoid getting stuck in local minima. The continuous-valued iHBA method was discretized with a modified sin–cos transfer function for feature selection. Oral cancer images were filtered using the CLAHE method, and after extracting deep features with the ResNet50 architecture, the proposed metaheuristic-based method was used to select discriminative features. Results: The proposed method was first tested for reliability and limitations through repeated runs on problems with different characteristics, such as unimodal and multimodal classical test functions. Then, the method was applied to extract significant features for oral cancer detection using a Histopathological Imaging Database containing 1224 histopathological oral tissue images at 100× and 400× magnification levels from 230 patients. The proposed approach was assessed in terms of accuracy, precision, recall, F1-score, and convergence curves in comparison with various classical feature selection techniques, such as wrapper-based, filter-based, and embedded-based methods, as well as other metaheuristic-based methods. The experimental results demonstrated that the suggested strategy outperformed both traditional feature selection techniques and alternative metaheuristic approaches. Conclusions: The effectiveness of the proposed method in improving diagnostic accuracy was evaluated through comprehensive experimental analyses. The obtained findings show that the proposed iHBA-based feature selection approach can reduce feature dimensionality, eliminate redundant and irrelevant features, and improve the classification performance of oral cancer detection. Therefore, the proposed method provides an effective and competitive computer-aided diagnostic framework for the automated classification of histopathological oral cancer images. Full article

Managing vegetative vigor is a critical challenge for apple production in subtropical regions, where high water availability often promotes excessive canopy growth. This study evaluated the effects of split applications of prohexadione–calcium (ProCa) combined with naphthaleneacetic acid (NAA) and ethephon on the vegetative growth and yield performance of ‘Maxi Gala’ and ‘Fuji Suprema’ apples during the 2022/23 and 2023/24 growing seasons. The experimental design consisted of six plant growth regulator (PGR) protocols: a commercial standard (Control) with two applications, and five protocols based on six split applications initiated when fruit diameter reached ~8 mm, with 10-day intervals. The treatments included ProCa; ProCa + NAA; ProCa + ethephon; ProCa + NAA + ethephon; and ethephon + NAA. The ProCa + NAA protocol demonstrated the highest efficiency in vigor control, reducing shoot growth by up to 38% in ‘Maxi Gala’ and 65% in ‘Fuji Suprema’ relative to Control. Furthermore, this treatment enhanced fruit skin coloration, increased the proportion of Category 1 fruit, and improved return bloom and overall yield. These findings suggest that split applications of ProCa associated with NAA provide a robust strategy to optimize apple orchard productivity under the edaphoclimatic conditions of southern Brazil. Full article

Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

Background: Conventional transdermal drug delivery systems are often limited by poor skin permeability and low drug loading efficiency, necessitating the development of advanced delivery platforms. Objectives: This study aimed to develop and optimize photopolymerizable bioinks (PBs) for liquid crystal display (LCD)-based 3D printing of crosslinked hydrogel microneedles (cHMNs) to enhance transdermal delivery of estradiol valerate (E2V). Methods: A Box–Behnken design (BBD) was used to optimize the effects of Gantrez™ S-97, Jurymer™, and polyvinyl alcohol (PVA) on viscosity, exposure time, hardness, and elasticity, with strong predictive performance (R² = 0.9702–0.9907). Results: Estradiol valerate-loaded nanoparticles (E2V-NPs) were prepared via ionotropic gelation, exhibiting a particle size of 698.33 (0.78) nm, PDI of 0.50 (0.06), zeta potential of −39.09 (7.32) mV, and high encapsulation efficiency (86.87 (0.78)%). The optimized PBs enabled fabrication of uniform cHMNs (~800 µm height) with adequate mechanical strength (hardness 20.45 (1.23) N; elasticity 2.97 (0.49) MPa) and effective insertion capability. The E2V-NPs-loaded cHMNs exhibited sustained drug release over 12 days (~56.92 (4.27)%). Skin permeation studies showed a significantly enhanced flux (10.81 (4.55) µg/cm²/h) and cumulative permeation (12.94 (2.06) µg/cm²) compared to topical E2V-NPs and suspension, along with increased skin accumulation (38.55 (0.10) µg). Cytotoxicity studies confirmed that E2V and E2V-NPs were biocompatible (>80% viability), while PBs showed concentration-dependent cytotoxicity. Conclusions: Overall, this integrated platform combining design of experiment, nanoparticles, microneedles, and LCD 3D printing offered a promising strategy for enhancing transdermal drug delivery efficiency and reproducibility. Full article

Respiratory syncytial virus (RSV) remains a major cause of severe respiratory disease worldwide, particularly affecting young children and immunocompromised individuals, highlighting the need for additional therapeutic strategies. In this study, the antiviral activity of the Petasites hybridus extract Ze 339 was investigated in RSV-infected cell-culture models. Antiviral efficacy was assessed using plaque reduction assays, reporter virus analyses, and proteomic profiling to elucidate potential mechanisms of action. Ze 339 potently reduced the infectivity of both RSVA and RSVB in vitro and retained antiviral activity when administered up to six hours post-infection, resulting in markedly reduced plaque formation and viral protein biosynthesis without inducing cytotoxicity. Proteomic analyses revealed that Ze 339 modulates host cell pathways associated with reduced cell proliferation, attenuated immune signaling, and enhanced cholesterol and lipid metabolism. These changes were more pronounced in infected than in uninfected cells and coincided with a marked downregulation of viral proteins. The observed proteomic signature suggests a host-directed antiviral effect and identifies altered lipid metabolism and cell-cycle-associated pathways as potential contributors to reduced RSV replication. Taken together, these findings demonstrate the antiviral activity of Ze 339 against RSV and support the hypothesis that modulation of host cell pathways contributes to its antiviral effects, providing a rationale for further evaluation of Ze 339 as a repurposed therapeutic candidate for RSV infection. Full article