Search Results (521)

Neighborhood living streets are key everyday public spaces in mixed residential–commercial districts and are an important setting for residents’ mental well-being. Yet many neighborhood evaluations still rely on coarse spatial indicators and provide limited guidance for fine-grained renewal. This study develops a comprehensive, mental-health-relevant, perception-based framework for assessing living-street quality and applies it to Xuesong Road, an aging community street in Wuhan. Five perception dimensions—walkability, safety, comfort, sociability, and pleasure—are operationalized into 18 micro-spatial indicators. Indicator weights are derived from expert judgments using the Analytic Hierarchy Process, and 178 residents’ Likert-scale ratings are synthesized using Fuzzy Comprehensive Evaluation to obtain dimension-level and composite scores. On a five-point scale, the overall score of 3.08 indicates a mid-range level of perceived street quality in relation to mental health. Sociability performs best, followed by walkability, pleasure, and comfort, while safety is the weakest dimension, mainly due to conflicts with non-motorized traffic and inadequate nighttime lighting. The proposed AHP–FCE framework links micro-scale street attributes to perception-based outcomes and provides actionable evidence to inform micro-renewal, with safety-oriented interventions being prioritized to support social sustainability and age-friendly communities. Full article

Soil moisture sensors deployed for long-term monitoring often suffer from prolonged data gaps caused by battery depletion, communication dropouts, or hardware failures. When such gaps overlap with irrigation events, key transient phases are obscured and become difficult for conventional imputers to recover. This study proposes HyDSoil, a hybrid diffusion-based imputation model tailored for event-centered block missingness in multichannel soil moisture time series. HyDSoil is first pretrained on a physically interpretable synthetic generator that mimics the baseline-rise-decay response to irrigation and then fine-tuned on field observations from the Baltimore Ecosystem Study dataset. During reverse diffusion, a mask-guided correction keeps observed values fixed while iteratively denoising missing regions. The denoising backbone integrates one-dimensional convolutions, gated recurrent units, and Transformer components to capture high-frequency event spikes, mid-range temporal dynamics, and long-range cross-depth dependencies, respectively. Experiments on both synthetic and real datasets show that HyDSoil reconstructs irrigation-driven peaks with higher fidelity and achieves consistent improvements over strong baselines in global metrics (MAE and DTW) as well as event-focused metrics (PTE and PAE). Ablation studies further verify the complementary contributions of the convolutional, recurrent, and attention branches, and confirm the benefit of synthetic pretraining for long-duration gaps. Overall, HyDSoil enables more reliable continuous soil moisture monitoring and supports precision irrigation analytics. Full article

Street networks shape urban dynamics. However, at the important meso- and micro-scales, a research limitation remains in systematically linking the spatial logic of streets to the physical configuration of street-level commerce, in particular through an analytical lens that distinguishes between different urban network functions. With a view to overcoming this limitation and extending space syntax theory into the fine-grained analysis of commercial form, this study applies its dual-network logic, contrasting foreground networks and background networks. The spatial patterns of street stores were analyzed across eight street segments in four Chinese cities: Tianjin, Nanjing, Zhengzhou, and Hong Kong. Network types were distinguished using Normalized Angular Choice and patchwork pattern analysis. By using 2019 POI data, Street View imagery, and field surveys, a comparative quantitative analysis was conducted across three metrics: operation methods, functional diversity, and 100-m density. The results indicate differences: chain stores hold a clear advantage in high-value segments of the foreground network, a pattern supported by statistical tests. These segments also exhibit higher functional diversity (mean ENT = 5.12). In contrast, high-value street segments of the background network exhibit a consistently higher prevalence of sole stores. They also have a commercial density approximately 2.6 times greater than that of their foreground counterparts. These findings provide empirical evidence on how foreground and background networks support different kinds of commercial ecologies: one oriented toward micro-economy efficiency and standardized supply, the other toward socio-culturally embedded, high-intensity local exchange. Consequently, by linking specific street spatial configurations to measurable commercial outcomes, this research contributes methodologically by operationalizing the dual-network framework at a novel scale and offering a replicable analytical tool for diagnosing and guiding commercial spatial planning in cities. Full article

The exponential growth and complexity of legal agreements pose significant Big Data challenges and strategic risks for modern organizations, often overwhelming traditional, manual contract management workflows. While AI has enhanced legal research, most current applications require extensive domain-specific fine-tuning or substantial annotated data, and Large Language Models (LLMs) remain susceptible to hallucination risk. This paper presents an AI-based Agreement Management System that addresses this methodological gap and scale. The system integrates a Python 3.1.2/MySQL 9.4.0-backed centralized repository for multi-format document ingestion, a role-based Collaboration and Access Control module, and a core AI Functions module. The core contribution lies in the AI module, which leverages zero-shot learning with OpenAI’s GPT-4o and structured prompt chaining to perform advanced contractual analysis without domain-specific fine-tuning. Key functions include automated metadata extraction, executive summarization, red-flag clause detection, and a novel feature for natural-language contract modification. This approach overcomes the cost and complexity of training proprietary models, democratizing legal insight and significantly reducing operational overhead. The system was validated through real-world testing at a leading industry partner, demonstrating its effectiveness as a scalable and secure foundation for managing the high volume of legal data. This work establishes a robust proof-of-concept for future enterprise-grade enhancements, including workflow automation and predictive analytics. Full article

Monitoring microplastic pollution relies increasingly on bioindicators that integrate environmental exposure across habitats. This review presents animals explicitly proposed as microplastic bioindicators in recent literature and qualitatively evaluates their appropriateness using established biomonitoring criteria encompassing ecological, physiological, and methodological dimensions. In aquatic systems, bivalves (clams and mussels) demonstrate high suitability due to wide distribution, habitat-specific feeding, effective microplastic retention, and well-established analytical protocols. Fish exhibit intermediate suitability, as ecological representativeness and retention vary among species, and standardized methods often require multi-species approaches. Sessile organisms, including barnacles and sea anemones, align strongly with all three dimensions through spatial fidelity, effective retention, and methodological ease. Crustaceans and sponges also exhibit robust ecological relevance and high retention, with sponges uniquely integrating fine particles over time. Terrestrial and aerial indicators, such as carabid beetles and insectivorous birds, provide complementary coverage with moderate physiological integration and feasible ethical sampling. Sea turtles demonstrate exceptional physiological integration and methodological robustness at regional scales, despite non-sedentary behavior. Overall, taxa combining sedentary or spatially faithful ecology, effective microplastic retention, and standardized laboratory applicability, particularly bivalves, sponges, barnacles, sea anemones, and sediment-associated crustaceans, emerge as the most suitable bioindicators. Future research should prioritize harmonized, multi-taxa frameworks to improve standardization, cross-ecosystem comparability, and long-term microplastic monitoring. Full article

Genome-wide association studies (GWAS) have transformed the study of chronic kidney disease (CKD) by identifying hundreds of genetic loci associated with multiple aspects of kidney function, including albuminuria and CKD risk factors, in diverse populations. A major challenge is translating statistically significant signals into causal genes and mechanisms, as most CKD-associated variants lie in non-coding regulatory regions and often act in a cell type- and context-specific manner. In this review, we provide an overview of the current strategies for moving from GWAS signals toward the identification of causal genes for CKD. We discuss advances in four areas: statistical and functional fine-mapping, molecular quantitative trait locus (QTL) mapping, colocalization, and transcriptome-wide associations, highlighting the advantages and disadvantages of each. We further examined how emerging kidney-specific single-cell, single-nucleus, and spatial transcriptomic atlases have enabled the mapping of genetic risk to specific renal cell types and microanatomical niches. By combining these approaches with chromatin interaction data, multi-omics analytics, and clustered regularly interspaced short palindromic repeats (CRISPR)-based studies, the process of generating causal relationships and mechanistic understanding has been further refined. Importantly, this review provides a unifying framework that synthesizes cross-sectional and longitudinal GWAS with kidney-specific functional genomics to distinguish genetic determinants of CKD susceptibility from modifiers of disease progression, thereby highlighting how regulatory variation and disease trajectories inform precision nephrology. As a result, we can provide insights into the role of genetically informed gene prioritization for experimentation, therapeutic target discovery, and the development of a framework for precision nephrology. Together, these advancements highlight how human genetics, in conjunction with functional genomics and experimental biology, can link an association signal to a clinically relevant interpretation of CKD. Full article

In the last decade, the interdisciplinary field of neuroarchitecture has grown significantly, revealing measurable links between architectural features and human neural processing. This review synthesizes current research at the nexus of neuroscience and architecture, with a focus on how emerging virtual reality (VR) and artificial intelligence (AI) technologies are being utilized to understand and enhance human spatial experience. We systematically reviewed literature from 2015 to 2025, identifying key empirical studies and categorizing advances into three themes: core components of neuroarchitectural research; the use of physiological sensors (e.g., EEG, heart rate variability) and virtual reality to gather data on occupant responses; and the integration of neuroscience with AI-driven analysis. Findings indicate that built environment elements (e.g., geometry, curvature, lighting) influence brain activity in regions governing emotion, stress, and cognition. VR-based experiments combined with neuroimaging and physiological measures enable ecologically valid, fine-grained analysis of these effects, while AI techniques facilitate real-time emotion recognition and large-scale pattern discovery, bridging design features with occupant emotional responses. However, the current evidence base remains nascent, limited by small, homogeneous samples and fragmented data. We propose a four-domain framework (somatic, psychological, emotional, cognitive-“SPEC”) to guide future research. By consolidating methodological advances in VR experimentation, physiological sensing, and AI-based analytics, this review provides an integrative roadmap for replicable and scalable neuroarchitectural studies. Intensified interdisciplinary efforts leveraging AI and VR are needed to build robust, diverse datasets and develop neuro-informed design tools. Such progress will pave the way for evidence-based design practices that promote human well-being and cognitive health in built environments. Full article

Background: To overcome the gastrointestinal and hepatic toxicity of oral pirfenidone (PFD) in the treatment of idiopathic pulmonary fibrosis (IPF), this study systematically constructed a minimal-component, buffer-free pirfenidone aerosol inhalation solution (PFD-AIS), achieving lung-targeted delivery, reduced systemic exposure, and maintained antifibrotic efficacy. Methods: Analytical methods for PFD-AIS, covering content, related substances, aerodynamic particle size distribution (APSD), and delivered dose uniformity, were established. The prescription and preparation process of the formulation was optimized by evaluating its key quality attributes. Pharmacodynamic and pharmacokinetic evaluations of PFD-AIS were performed in a mouse lung-fibrosis model and SD rats. Results: The final specification of PFD-AIS was set to 40 mg:4 mL, containing 40 mg of PFD, 28 mg of sodium chloride, and 4 mL of injection water with a preparation process of 40 °C for 60 min and a pH range of 4–8. The PFD-AIS exhibited a fine particle fraction (FPF) of 56.1%, meeting the requirements for deep lung deposition. The delivered dose and delivery rate were 17.52 mg and 2.48 mg/min, respectively, both complying with inhalation formulation standards. In the bleomycin-induced IPF mouse model, the PFD-AIS markedly improved pulmonary fibrosis pathology, reduced the lung coefficient, and significantly lowered serum ALT/AST levels, indicating hepatic protection. In the SD rats, compared with oral dosing, PFD-AIS administration resulted in significantly lower AUC_0−t (−63%) and AUC_0–∞ (−67%) values, demonstrating a substantial reduction in systemic drug exposure. Conclusion: This work presents a complete, systematic chain—from formulation, process, and quality control to pharmacodynamics and pharmacokinetics—of a PFD-AIS. The PFD-AIS is effective and feasible, featuring a stable preparation process and controllable quality. Lung-directed drug delivery enhances PFD’s therapeutic efficacy, reduces systemic exposure and liver toxicity, and offers significant clinical advantages. Full article

Projects integrating Science, Technology, Engineering, and Mathematics (STEM) are essential to interdisciplinary research. This study presents a STEM (Science, Technology, Engineering, and Mathematics) methodology with the primary objective of designing, constructing, and validating a functional cannabinoid extraction device. To inform the device’s drying parameters, the dehydration kinetics of female hemp buds or flowering buds (FHB) were first analyzed using infrared drying at 100 °C for different durations. The plants were cultivated and harvested in accordance with good agricultural practices using Dinamed CBD Autoflowering seeds. The FHB were harvested and prepared by manually separating them from the stems and leaves. Six 5 g samples were prepared, each with a slab geometry of varying surface area and thickness. Two of these samples were ground: one into a fine powder and the other into a coarse powder. Mathematical fits were obtained for each resulting curve using either an exponential decay model or the logarithmic equation $y\left(t\right)=A\left(e^{-kt}\right)+y_0$ calculate the equilibrium moisture (m_E). The Moisture Rate (MR) was calculated, and by modelling with the logarithmic equation, the constant k and the effective diffusivity (Deff) were determined with the analytical solution of Fick’s second law. The Deff values (ranging from 10⁻⁷ to 10⁻⁵) were higher than previously reported. The coarsely ground powder sample yielded the highest k and Deff values and was selected for oil extraction. The device was then designed using Quality Function Deployment (QFD), specifically the House of Quality (HoQ) matrix, to systematically translate user requirements into technical specifications. A 200 g sample of coarsely ground, dehydrated FHB was prepared for ethanol extraction. Chemical results obtained by Liquid Chromatography coupled with Photodiode Array Detection (LC-PDA) revealed the presence of THC, CBN, CBC, and CBG. The extraction device design was validated using previous results showing the presence of CBD and CBDA. The constructed device successfully extracted cannabinoids, including Δ9-THC, CBG, CBC, and CBN, from coarsely ground FHB, validating the integrated STEM approach. This work demonstrates a practical framework for developing accessible agro-technical devices through interdisciplinary collaboration. Full article

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches. Full article

Artificial intelligence is reshaping approaches to architectural heritage conservation by enabling a deeper understanding of how people perceive and experience historic built environments. This study employs deep learning and large language models (LLMs) to explore public perceptions of the Qinghefang Historical and Cultural District in Hangzhou, illustrating how AI-driven analytics can inform intelligent heritage management and architectural revitalization. Large-scale public online reviews were processed through BERTopic-based clustering to extract thematic structures of experience, while interpretive synthesis was refined using an LLM to identify core perceptual dimensions including Hangzhou Housing & Residential Choice, Hangzhou Urban Tourism & Culture, Hangzhou Food & Dining, and Qinghefang Culture & Creative. Sentiment polarity and emotional intensity were quantified using a fine-tuned BERT model, revealing distinct affective and perceptual patterns across the district’s architectural and cultural spaces. The results demonstrate that AI-based textual analytics can effectively decode human–heritage interactions, offering actionable insights for data-informed conservation, visitors’ experience optimization, and sustainable management of historic districts. This research contributes to the emerging field of AI-driven innovation in architectural heritage by bridging computational intelligence and heritage conservation practice. Full article

Sentiment analysis (SA) has become an essential tool for analyzing social media content in order to monitor public opinion and support digital analytics. Although transformer-based SA models exhibit remarkable performance, they lack mechanisms to mitigate hallucinated sentiment, which refers to the generation of unsupported or overconfident predictions without explicit linguistic evidence. To address this limitation, this study presents a hallucination-aware SA model by incorporating semantic grounding, interpretability-congruent supervision, and neuro-symbolic reasoning within a unified architecture. The proposed model is based on a fine-tuned Open Pre-trained Transformer (OPT) model, using three fundamental mechanisms: a Sentiment Integrity Filter (SIF), a SHapley Additive exPlanations (SHAP)-guided regularization technique, and a confidence-based lexicon-deep fusion module. The experimental analysis was conducted on two multi-class sentiment datasets that contain Twitter (now X) and Reddit posts. In Dataset 1, the suggested model achieved an average accuracy of 97.6% and a hallucination rate of 2.3%, outperforming the current transformer-based and hybrid sentiment models. With Dataset 2, the framework demonstrated strong external generalization with an accuracy of 95.8%, and a hallucination rate of 3.4%, which is significantly lower than state-of-the-art methods. These findings indicate that it is possible to include hallucination mitigation into transformer optimization without any performance degradation, offering a deployable, interpretable, and linguistically complex social media SA framework, which will enhance the reliability of neural systems of language understanding. 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

Industrial control systems (ICSs) are increasingly interconnected with enterprise IT networks and remote services, which expands the attack surface of operational technology (OT) environments. However, collecting sufficient attack traffic from real OT/ICS networks is difficult, and the resulting scarcity and class imbalance of malicious data hinder the development of intrusion detection systems (IDSs). At the same time, large language models (LLMs) have shown promise for security analytics when system events are expressed in natural language. This study investigates an LLM-based network IDS for a smart-factory OT/ICS environment and proposes a synthetic data generation method that injects domain knowledge into attack samples. Using the ICSSIM simulator, we construct a bottle-filling smart factory, implement six MITRE ATT&CK for ICS-based attack scenarios, capture Modbus/TCP traffic, and convert each request–response pair into a natural-language description of network behavior. We then generate synthetic attack descriptions with GPT by combining (1) statistical properties of normal traffic, (2) MITRE ATT&CK for ICS tactics and techniques, and (3) expert knowledge obtained from executing the attacks in ICSSIM. The Llama 3.1 8B Instruct model is fine-tuned with QLoRA on a seven-class classification task (Benign vs. six attack types) and evaluated on a test set composed exclusively of real ICSSIM traffic. Experimental results show that synthetic data generated only from statistical information, or from statistics plus MITRE descriptions, yield limited performance, whereas incorporating environment-specific expert knowledge is associated with substantially higher performance on our ICSSIM-based expanded test set (100% accuracy in binary detection and 96.49% accuracy with a macro F1-score of 0.958 in attack-type classification). Overall, these findings suggest that domain-knowledge-infused synthetic data and natural-language traffic representations can support LLM-based IDSs in OT/ICS smart-factory settings; however, further validation on larger and more diverse datasets is needed to confirm generality. Full article

This study takes a further step forward in the analytical treatment of Langmuir–Hinshelwood (LH) kinetics for heterogeneous catalysis by deriving its closed-form solution. Unlike previous studies, we present a general solution that does not impose severe restrictions on the experimental conditions. This solution not only recovers the typical first- and zeroth-order regimes but also enables the simultaneous determination of the reaction rate constant and absorption–desorption equilibrium constant, unlike the traditional approaches to this equation, which needed additional isotherm experiments. The final solution requires a fine mathematical treatment for its numerical implementation, but enhanced approximations of the closed-form solution overcome this problem without losing the main advantage of calculating both constants at the same time. A parameter called “critical time” has been introduced, whose calculation allows us to distinguish quantitatively between kinetic regimes. Finally, the validation of these approximations has been carried out with experiments on zinc oxide and anatase (TiO₂) under different conditions. Anatase experiments undoubtedly show a first-order tendency, regardless the quantity of powder. On the other hand, the degradation regime of the ZnO case cannot be easily ascribed to the zeroth or first order by simple inspection, but the model can mathematically rule out the zeroth order and confirm that it undergoes first-order degradation. Full article