Search Results (33,335)

Early recognition of crop diseases is essential for ensuring agricultural security and improving yield. However, traditional CNN-based methods often suffer from limited generalization when training data are scarce or when applied to transfer scenarios. To address these challenges, this study adopts the multimodal large model Qwen2.5-VL as the core and targets three major soybean leaf diseases along with healthy samples. We propose a parameter-efficient adaptation framework that integrates cross-architecture hyperparameter transfer and progressive fine-tuning. The framework utilizes a Vision Transformer (ViT) as an auxiliary model, where Bayesian optimization is applied to obtain optimal hyperparameters that are subsequently transferred to Qwen2.5-VL. Combined with existing low-rank adaptation (LoRA) and a multi-stage training strategy, the framework achieves efficient convergence and robust generalization with limited data. To systematically evaluate the model’s multi-scale visual adaptability, experiments were conducted using low-resolution, medium-resolution, and high-resolution inputs. The results demonstrate that Qwen2.5-VL achieves an average zero-shot accuracy of 71.72%. With the proposed cross-architecture hyperparameter transfer and parameter-efficient tuning strategy, accuracy improves to 88.72%, and further increases to 93.82% when progressive fine-tuning is applied. The model also maintains an accuracy of 91.0% under cross-resolution evaluation. Overall, the proposed method exhibits strong performance in recognition accuracy, feature discriminability, and multi-scale robustness, providing an effective reference for adapting multimodal large language models to plant disease identification tasks. Full article

The integration of large models and multimodal foundation models into the low-altitude economy is driving a transformative shift, enabling intelligent, autonomous, and efficient operations for low-altitude vehicles (LAVs). This article provides a comprehensive analysis of the role these large models play within the smart integrated lower airspace system (SILAS), focusing on their applications across the four fundamental networks: facility, information, air route, and service. Our analysis yields several key findings, which pave the way for enhancing the application of large models in the low-altitude economy. By leveraging advanced capabilities in perception, reasoning, and interaction, large models are demonstrated to enhance critical functions such as high-precision remote sensing interpretation, robust meteorological forecasting, reliable visual localization, intelligent path planning, and collaborative multi-agent decision-making. Furthermore, we find that the integration of these models with key enabling technologies, including edge computing, sixth-generation (6G) communication networks, and integrated sensing and communication (ISAC), effectively addresses challenges related to real-time processing, resource constraints, and dynamic operational environments. Significant challenges, including sustainable operation under severe resource limitations, data security, network resilience, and system interoperability, are examined alongside potential solutions. Based on our survey, we discuss future research directions, such as the development of specialized low-altitude models, high-efficiency deployment paradigms, advanced multimodal fusion, and the establishment of trustworthy distributed intelligence frameworks. This survey offers a forward-looking perspective on this rapidly evolving field and underscores the pivotal role of large models in unlocking the full potential of the next-generation low-altitude economy. Full article

This study explores the integration of Georgi Lozanov’s Suggestopedia with Alain Berthoz’s theory of simplexity as a pedagogical paradigm for inclusive and creative educational design. The research, conducted within the specialization courses for educational support at the University of Salerno, involved 230 trainee teachers engaged in a participatory action-research process aimed at translating suggestopedic principles, positive suggestion, music, and relational harmony into didactic planning. Through a combination of theoretical training, laboratory design activities, and reflective evaluation, participants produced 21 interdisciplinary educational projects assessed according to the properties and rules of simplexity. The results show a high degree of methodological coherence, aesthetic quality, and curricular inclusiveness, with music emerging as a key factor in fostering attention, cooperation, and emotional engagement. Data analysis indicates that the fusion of suggestopedic and simplex approaches promotes adaptive, modular, and meaning-oriented design processes that enhance teachers’ creativity and metacognitive awareness. Overall, the findings highlight the educational value of a pedagogy of resonance, in which body, mind, and environment interact harmoniously. The study concludes that the suggestopedic—simplex model represents a regenerative framework for contemporary didactics, capable of transforming complexity into harmony and restoring to education its aesthetic, relational, and human dimension. Full article

The rapid rise in energy demand, urban electrification, and the increasing prevalence of Electric Vehicles (EV) have intensified the need for reliable and decentralized energy management solutions. This study proposes an AI-driven centralized control architecture for a community-based photovoltaic–battery energy storage system (PV–BESS) to enhance energy efficiency and self-sufficiency. The framework integrates a central controller which utilizes the Particle Swarm Optimization (PSO) technique which receives the Long Short-Term Memory (LSTM) forecasting output to determine optimal photovoltaic generation, battery charging, and discharging schedules. The proposed system minimizes the grid dependence, reduces the operational costs and a stable power output is ensured under dynamic load conditions by coordinating the renewable resources in the community microgrid. This system highlights that the AI-based Particle Swarm Optimization will reduce the peak load import and it maximizes the energy utilization of the system compared to the conventional optimization techniques. Full article

Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R² from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks. Full article

This study introduces a novel hybrid model for an electromembrane stack, unifying an equivalent electrical circuit model incorporating specific resistance ($R_M,R_s$) and capacitance ($C_{gs},C_{dl}$) parameters with an empirical fouling model in a single framework. The model simplifies the traditional approach by serially connecting N ($N=10$) ion exchange membranes (anionic PC-SA and cationic PC-SK) and is validated using $NaCl$ and $N{a}_{2}S{O}_4$ solutions in comparison with laboratory tests using various voltage signals, including direct current and electrically pulsed reversal operations at frequencies of 2000 and 4000 Hz. The model specifically accounts for the chemical stratification of the cell unit into bulk solution, diffusion, and Stern layers. We also included a calibration method using correction factors (${\alpha }_i$) to fine-tune the electrical current signals induced by voltage stimulation. The empirical component of the model uses experimental data to simulate membrane fouling, ensuring consistency with laboratory-scale desalination processes performed under pulsed reversal operations and achieving a prediction error of less than 10%. In addition, a comparative analysis was used to assess the increase in electrical resistance due to fouling. By integrating electronic and empirical electrochemical data, this hybrid model opens the way to the construction of simple, practical, and reliable models that complement theoretical approaches, signifying an advance for a variety of electromembrane-based technologies. Full article

Individuals across their lifespan may experience hearing loss from medications or chemicals, prompting concern about ototoxic environmental exposures. This study applies computational modeling as a screening-level hazard identification and chemical prioritization approach and is not intended to constitute a human health risk assessment or to estimate exposure- or dose-dependent ototoxic risk. We evaluated in silico drug-induced ototoxicity models on 80 environmental chemicals, excluding 4 with known ototoxicity, and analyzed 76 chemicals using fingerprinting, similarity assessment, and machine learning classification. We compared predicted environmental ototoxicants with ototoxic drugs, paired select polychlorinated biphenyls with the antineoplastic drug mitotane, and used PCB 177 as a case study to construct an ototoxicity pathway. A systems biology framework predicted and compared molecular targets of mitotane and PCB 177 to generate a network-level mechanism. The consensus model (accuracy 0.95 test; 0.90 validation) identified 18 of 76 chemicals as potential ototoxicants within acceptable confidence ranges. Mitotane and PCB 177 were both predicted to disrupt thyroid-stimulating hormone receptor signaling, suggesting thyroid-mediated pathways may contribute to auditory harm; additional targets included AhR, transthyretin, and PXR. Findings indicate overlapping mechanisms involving metabolic, cellular, and inflammatory processes. This work shows that integrated computational modeling can support virtual screening and prioritization for chemical and drug ototoxicity risk assessment. Full article

This article presents the Intelligent Monitoring System (IMS), an AI-assisted, low-latency surveillance platform designed for defense environments. The study addresses the need for real-time autonomous situational awareness by integrating high-speed video transmission with advanced computer vision analytics in constrained network settings. The IMS employs a hybrid transmission architecture based on RTSP for ingestion and WHEP/WebRTC for distribution, orchestrated via MediaMTX, with the objective of achieving end-to-end latencies below one second. The methodology includes a comparative evaluation of video streaming protocols (JPEG-over-WebSocket, HLS, WebRTC, etc.) and AI frameworks, alongside the modular architectural design and prolonged experimental validation. The detection module integrates YOLOv11 models fine-tuned on the VisDrone dataset to optimize performance for small objects, aerial views, and dense scenes. Experimental results, obtained through over 300 h of operational tests using IP cameras and aerial platforms, confirmed the stability and performance of the chosen architecture, maintaining latencies close to 500 ms. The YOLOv11 family was adopted as the primary detection framework, providing an effective trade-off between accuracy and inference performance in real-time scenarios. The YOLOv11n model was trained and validated on a Tesla T4 GPU, and YOLOv11m will be validated on the target platform in subsequent experiments. The findings demonstrate the technical viability and operational relevance of the IMS as a core component for autonomous surveillance systems in defense, satisfying strict requirements for speed, stability, and robust detection of vehicles and pedestrians. Full article

Leccinum is an ecologically significant and taxonomically complex genus of ectomycorrhizal fungi widely distributed across boreal, temperate, Mediterranean, and selected tropical regions. Despite its ecological, nutritional, and applied importance, no comprehensive review has previously synthesized global knowledge on this genus. This work provides the first integrative assessment of Leccinum research, combining a bibliometric analysis of 293 peer-reviewed publications with an in-depth qualitative synthesis of ecological, biochemical, and environmental findings. Bibliometric results show increasing scientific attention since the mid-20th century, with major contributions from Europe, Asia, and North America, and dominant research themes spanning taxonomy, ecology, chemistry, and environmental sciences. The literature review highlights substantial advances in phylogenetic understanding, species diversity, and host specificity. Leccinum forms ectomycorrhizal associations with over 60 woody host genera, underscoring its functional importance in forest ecosystems. Nutritionally, Leccinum species are rich in proteins, carbohydrates, minerals, bioactive polysaccharides, phenolic compounds, and umami-related peptides, with demonstrated antioxidant, immunomodulatory, and antitumor activities. At the same time, the genus exhibits notable bioaccumulation capacity for heavy metals (particularly Hg, Cd, and Pb) and radionuclides, making it both a valuable food source and a sensitive environmental bioindicator. Applications in biotechnology, environmental remediation, forest restoration, and functional food development are emerging but remain insufficiently explored. Identified research gaps include the need for global-scale phylogenomic frameworks, expanded geographic sampling, standardized biochemical analyses, and deeper investigation into physiological mechanisms and applied uses. This review provides the first holistic synthesis of Leccinum, offering an integrated perspective on its taxonomy, ecology, nutritional composition, environmental significance, and practical applications. The findings serve as a foundation for future mycological, ecological, and biotechnological research on this diverse and understudied fungal genus. Full article

Constructed wetlands (CWs) are signified as green, self-sustaining systems for wastewater treatment. To date, their conventional designs struggle with slow kinetics and poor removal of refractory pollutants. This review redefines CWs as photo-reactive engineered systems, integrating near-neutral Fenton and photo-Fenton processes and in-situ oxidant generation to overcome diffusion limits, acid dosing, and sludge formation. By coupling catalytic fillers, solar utilization, and plant–microbe–radical (ROS) synergies, the approach enables intensified pollutant degradation while preserving the low-energy nature of CWs. Bibliometric trends indicate a sharp rise in studies linking CWs with advanced oxidation and renewable energy integration, confirming the emergence of a circular treatment paradigm. A decision framework is proposed that aligns material selection, reactor hydrodynamics, and solar light management with sustainability indicators such as energy efficiency, Fe-leach budget, and ROS-to-photon yield. This synthesis bridges environmental biotechnology with solar-driven catalysis, paving the way for next-generation eco-engineered wetlands capable of operating efficiently beyond the classical Fenton constraints. This work introduces the concept of “Constructed Wetlands Beyond the Fenton Limit”, where CWs are reimagined as photo-reactive circular systems that unify catalytic, biological, and solar processes under near-neutral conditions. It provides the first integrated decision matrix and performance metrics connecting catalyst design, ROS efficiency, and circular sustainability that offers a scalable blueprint for real-world hybrid wetland applications. Full article

Human Capital Agility is increasingly recognized as a critical capability for achieving sustainable project success in the highly dynamic construction sector, yet an original and empirically testable Human Capital Agility model rooted in Human Capital theory is still lacking. This study aims to develop and validate a Human Capital Agility framework that integrates Leadership Agility and Knowledge Management and to construct a hierarchical roadmap for the gradual development of Human Capital Agility. Using a multi-method design, survey data from 141 construction professionals were analyzed with Partial Least Squares Structural Equation Modeling to test the structural relationships among Knowledge Management, Leadership Agility, Human Capital Agility, Sustainable Project Success, and the moderating role of Firm Size, while expert judgments from nine practitioners were modeled using Modified Total Interpretive Structural Modeling to derive the internal hierarchy of Human Capital Agility components. The results show that Leadership Agility is a dominant driver of Human Capital Agility and that Human Capital Agility significantly enhances Sustainable Project Success, whereas the direct effect of tacit knowledge on Leadership Agility is not supported. The hierarchical model maps nine key components of Human Capital Agility into six levels, separating foundational drivers such as attitudes and predisposition from higher-level outcome capabilities such as generative behavior, responsiveness, adaptability, and resilience. These findings provide an integrated and empirically grounded Human Capital Agility model that offers both a causal explanation and a practical roadmap for strengthening human capital capabilities in construction projects. Full article

Urban Water Systems (UWS) are complex infrastructures that interact with energy, food, ecosystems and socio-political systems, and are under growing pressure from climate change and resource depletion. Planning circular interventions in this context requires system-level analysis to avoid fragmented, siloed decisions. This paper develops the Hydrosocial Resource Urban Nexus (HRUN) framework that integrates hydrosocial thinking with the Water–Energy–Food–Ecosystems (WEFE) nexus to guide UWS design. We conduct a structured literature review and analyse different configurations of circular interventions, mapping their synergies and trade-offs across socioeconomic and environmental functions of hydrosocial systems. The framework is operationalised through a typology of circular interventions based on their circularity purpose (water reuse, resource recovery and reuse, or water-cycle restoration) and management scale (from on-site to centralised), while greening degree (from grey to green infrastructure) and digitalisation (integration of sensors and control systems) are treated as transversal strategies that shape their operational profile. Building on this typology, we construct cause–effect matrices for each intervention type, linking recurring operational patterns to hydrosocial functionalities and revealing associated synergies and trade-offs. Overall, the study advances understanding of how circular interventions with different configurations can strengthen or weaken system resilience and sustainability outcomes. The framework provides a basis for integrated planning and for quantitative and participatory tools that can assess trade-offs and governance effects of different circular design choices, thereby supporting the transition to more resilient and just water systems. Full article

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R² of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R² ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R² ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation. Full article

The architecture of spas and recreational complexes is increasingly being analyzed not only through the prism of its formal diversity but also through its functional, technical, and esthetic responses to evolving societal expectations. This article descriptively examines the context of evolving user needs and select examples representing new architectural forms integrated into the landscape, responding to the growing demand for beauty (understood subjectively), experiences, and emotional value in 21st-century tourism and recreation. The most diverse and characteristic examples were selected and described in order to maintain a broad context of analysis and illustrate contemporary changes as faithfully as possible. The descriptive approach enables a systematic and comprehensive representation of phenomena, identifying recurring patterns, spatial trends, and contextual relationships. Rather than being limited to numerical data, it provides a structured analytical framework that supports the objective documentation of architectural and urban processes. The aim of this study is to systematize selected design trends that reflect contemporary cultural aspirations and environmental concerns, and to illustrate the evolving relationship between architecture, nature, and users. The results indicate a consistent shift toward landscape-integrated, experiential, and esthetically driven architectural solutions, demonstrating that contemporary tourism facilities increasingly prioritize atmosphere, immersion in nature, and sensory engagement over traditional utilitarian design. This study concludes that beauty, understood as subjective esthetic experience, has become a key determinant in shaping new architectural forms, reinforcing the role of architecture as both a cultural expression and a tool for enhancing well-being in tourism and leisure environments. Full article

Landslide dams, typically composed of newly deposited, loose, and heterogeneous materials, are highly susceptible to failure induced by overtopping and seepage, particularly under extreme hydrological conditions. Accurate prediction of such breaching processes is essential for flood risk management and emergency response, yet existing models generally consider only a single failure mechanism. This study develops a mathematical model to simulate landslide dam breaching under the coupled action of overtopping and seepage erosion. The model integrates surface erosion and internal erosion processes within a unified framework and employs a stable time-stepping numerical scheme. Application to three real-world landslide dam cases demonstrates that the model successfully reproduces key breaching characteristics across overtopping-only, seepage-only, and coupled erosion scenarios. The simulated breach hydrographs, reservoir water levels, and breach geometries show good agreement with field observations, with peak outflow and breach timing predicted with errors generally within approximately 5%. Sensitivity analysis further indicates that the model is robust to geometric uncertainties, as variations in breach outcomes remain smaller than the imposed parameter perturbations. These results confirm that explicitly accounting for the coupled interaction between overtopping and seepage significantly improves the representation of complex breaching processes. The proposed model therefore provides a reliable computational tool for analyzing landslide dam failures and supports more accurate hazard assessment under multi-mechanism erosion conditions. Full article