Search Results (6,150)

Although numerous studies have examined the impact of high-speed rail (HSR) on regional economic development, few have explored this relationship from a network perspective—a research gap this paper seeks to fill. Specifically, this paper aims to clarify the theoretical mechanism through which the HSR network affects the spatial evolution of regional industries, focusing on the new industry entry. We improve the local spread model by incorporating the HSR network as a key component and perform empirical analyses using the Spatial Durbin Model (SDM) and spatial mediation effect model, drawing on data from Chinese A-share-listed companies. The findings indicate that China’s regional industries underwent spatial evolution characterized by “diffusive agglomeration”. In terms of direct effects, connectivity ranks as the most influential HSR network indicator; however, when both direct and spillover effects are taken into account, accessibility becomes the primary factor, underscoring its vital role in reshaping the spatial distribution of industries. Additionally, the HSR network exerts a slightly stronger impact on industrial spatial diffusion (fueled by knowledge spillovers) than on industrial agglomeration (driven by market size), and its attraction to new industry entry is notably greater in peripheral regions than in core regions. These results demonstrate that HSR, characterized by “transporting people rather than goods”, mainly facilitates the exchange of knowledge, technology and information instead of reducing freight costs, offering valuable insights for optimizing regional industrial layouts. Full article

Fibrosis is a hallmark of the tumor microenvironment in many solid cancers, driving tumor progression, immune evasion, and treatment resistance; however, the molecular and cellular mechanisms underlying fibrogenesis—particularly stromal–immune crosstalk across organs—remain incompletely understood, compounded by organ-specific heterogeneity and a lack of reliable immune-related biomarkers. To address this, we performed an integrative single-cell RNA sequencing (scRNA-seq) analysis of fibrotic tissues from four major organs—liver, lung, heart, and kidney—alongside non-fibrotic controls, applying unsupervised clustering, trajectory inference, cell–cell communication modeling, and gene set variation analysis (GSVA) to map the fibro-immune landscape. Our analysis revealed both conserved and organ-specific features: fibroblasts were the dominant extracellular matrix (ECM)-producing cells in liver and lung, whereas endothelial-derived stromal populations prevailed in heart and kidney. Immune profiling uncovered distinct infiltration patterns—macrophages displayed organ-specific polarization states; T cells were enriched for tissue-resident subsets in lung and mucosal-associated invariant T (MAIT) cells in liver; and B cells exhibited marked heterogeneity, including a pathogenic interferon-responsive subset prominent in pulmonary fibrosis. GSVA further identified divergent signaling programs across organs and lineages, including TGF-β/TNF-α in the heart, NOTCH/mTOR in the kidney, glycolysis/ROS in the lung, and KRAS/interferon pathways in the liver. Cell–cell communication analysis highlighted robust crosstalk between macrophages, T/B cells, and stromal cells mediated by collagen, laminin, and CXCL signaling axes. Together, this cross-organ atlas delineates a highly heterogeneous fibro-immune ecosystem in human fibrotic diseases, revealing shared mechanisms alongside organ-specific regulatory networks, with immediate translational implications for precision anti-fibrotic therapy, immunomodulatory drug repurposing, and the development of context-specific biomarkers for clinical stratification and therapeutic monitoring. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article

Background: The growing recognition of shared molecular pathways and molecular signatures between cardiovascular diseases and cancer has motivated interest in exploring antihypertensive-associated chemical space for oncological applications. Concurrently, artificial intelligence (AI)-driven molecular generation has enabled the rapid creation of virtual lead candidates for specific therapeutic indications, although their broader biological interaction profiles often remain unexplored. Methods: In this paper, we explore the computational screening of a library of AI-generated antihypertensive virtual lead compounds to evaluate their polypharmacological anticancer potential. The compounds were originally designed and prioritized for modulating $\mathsf{\beta }$-adrenergic receptors but are here re-evaluated in a cancer-focused context using a multi-stage in silico approach. We chose five (5) known cancer target proteins and performed compound profiling for drug-likeness, pharmacokinetic suitability, and safety. Docking simulations, binding free energy estimates, molecular interaction mapping, and pharmacophore modeling were used to evaluate the molecules’ interactions with the cancer-linked protein targets. We employed the binding free energy estimates of the ligand–protein complexes to determine compounds with polypharmacological anticancer potential. In addition, molecular dynamics simulations of some of the compounds with polypharmacological anticancer potential were employed to evaluate binding stability and dynamic behavior of selected ligand–target complexes. Results: Several compounds showed good docking scores, physicochemical characteristics, and pharmacokinetic profiles. Also, the results reveal that several AI-generated antihypertensive virtual leads exhibit favorable multi-target binding profiles, with consistent docking affinities and stable interaction networks across multiple cancer-related targets. Conclusions: Our findings suggest that several of the hypothetically evaluated compounds exhibit favorable physicochemical properties, acceptable predicted pharmacokinetic and safety profiles, and consistent predicted binding affinities across multiple cancer-relevant targets. Full article

Terahertz (THz) sub-millimeter wave imaging offers unique capabilities for stand-off biometrics through concealment, yet it suffers from severe sparsity, low resolution, and high noise. To address these limitations, we introduce a novel unified Multi-Task Learning (MTL) network centered on a custom shared U-Net-like THz data encoder. This network is designed to simultaneously solve three distinct critical tasks on concealed THz facial data, given a limited dataset of approximately 1400 THz facial images of 20 different identities. The tasks include concealed face verification, facial posture classification, and a generative reconstruction of unconcealed faces from concealed ones. While providing highly successful MTL results as a standalone solution on the very challenging dataset, we further studied the expansion of this architecture via a cross-modal teacher-student approach. During training, a privileged visible-spectrum teacher fuses limited visible features with THz data to guide the THz-only student. This distillation process yields a student network that relies solely on THz inputs at inference. The cross-modal trained student achieves better latent space in terms of inter-class separability compared to the single-modality baseline, but with reduced intra-class compactness, while maintaining a similar success in the task performances. Both THz-only and distilled models preserve high unconcealed face generative fidelity. Full article

Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance frameworks. This review provides a system-level synthesis of recent research on the integration of autonomous and connected autonomous vehicles in mixed traffic environments. Following PRISMA 2020 guidelines, 51 peer-reviewed studies published between 2016 and 2025 were systematically reviewed and thematically analyzed. The review addresses technological foundations, safety impacts, traffic flow and network performance, mixed traffic dynamics, infrastructure and urban systems, and policy and governance challenges. The findings indicate that AV impacts are highly non-linear and sensitive to market penetration rates, control strategies, and human behavioral adaptation. While high levels of automation and connectivity can improve safety, capacity, and traffic stability, early-stage deployment may temporarily increase delays and traffic conflicts. Policy measures—such as pricing, shared mobility integration, and regulatory oversight—are therefore critical to ensuring that AV deployment delivers sustainable and equitable mobility outcomes. Full article

Background and Objectives: Palmoplantar pustulosis (PPPust) and palmoplantar psoriasis (PPso) are distinct palm/sole dermatoses that have historically shared the abbreviation “PPP”. Though the two—since the advent of advanced biotechnology—are now deemed separate diagnoses, each still falls under the ‘palmoplantar spectrum’. It is important to note that PPso and PPPust are each distinct from generalized pustular psoriasis (GPP), a condition that is outside the scope of our study. We quantified the relative efficacy of biologic and small-molecule monotherapies on the palmoplantar spectrum using Bayesian network meta-analyses (NMAs). Materials and Methods: On 6 November 2025, we searched PubMed, Scopus, ClinicalTrials.gov, and citations (i.e., citation mining) for randomized trials of monotherapy reporting PPP Area and Severity Index (PPPASI) outcomes at 12 or 16 weeks; we secondarily investigated fresh pustule-related outcomes at 4 weeks. We ran Bayesian NMAs with uniform priors; nodes were defined by dose and timepoint. Interventions’ Surface Under the Cumulative Ranking Curve (SUCRA) values were computed; pairwise effects with 95% credible intervals were also estimated. Sensitivity analyses adjusted for diagnosis (pustulosis vs. psoriasis) via network meta-regression. Results: Twenty trials (n = 2030) with 23 active comparators provided data for 10 endpoints (fresh pustules at 4 weeks; PPPASI-50/75 and mean percentage and absolute PPPASI change at 12 and 16 weeks). Conclusions: The NMA indicates efficacy of ixekizumab and brodalumab (IL-17 inhibitors), guselkumab (IL-23 inhibitor), and spesolimab (IL-36 inhibitor) in managing palmoplantar pustulosis. Full article

This article proposes a parallel current-summing LCC multilevel inverter (MLI) to suppress harmonic distortion of radiated EMI for wireless power transfer. Traditionally, ZVS has been an issue for staircase voltage output multilevel inverters because a shared current output became faster than some of the voltage transitions in staircase voltage output. The other common problem was capacitor voltage imbalance and resultant output voltage distortion if a sophisticated voltage balancing function is not used. The proposed LCC MLI ensures ZVS by separating each voltage transition into multiple bridge legs. Each bridge leg outputs different phases of currents for each voltage transition. The individual output currents are summed at the matching network of wireless power transfer, generating a near-sinusoid output current to suppress harmonic distortions. In this way, each leg achieves ZVS even though the summed output current at the LCC network is faster than some of the voltage transitions. To avoid the capacitor voltage imbalance issue, the proposed MLI eliminated the flying capacitor. Instead, the four parallel legs are supplied by a shared DC input link. Therefore, the four legs can output identical voltages without using a typical DC flying capacitor. The necessity of multiple input voltage sources is, therefore, also eliminated. Measurement demonstrates that the proposed method effectively reduces radiated harmonic EMI by up to 14 dB. Full article

Homeodomain-Leucine Zipper (HD-Zip) constitutes a distinct class of plant-specific transcription factors (TFs) that serves an essential function in mediating plant responses to environmental cues, with the HD-Zip II subfamily recognized as a major regulator of light-intensity adaptation and other environmental responses. However, the involvement of HD-Zip genes in regulating the light response of Juglans mandshurica Maxim. is largely unexplored. In this study, a genome-wide identification, classification, and expression analysis of the HD-Zip gene family in J. mandshurica was conducted. Furthermore, transcriptomic profiling under varying light-intensity conditions was performed to investigate the transcriptional regulation and potential functional networks of differentially expressed HD-Zip genes. The results showed that a total of 57 HD-Zip family genes were identified in J. mandshurica (named as JmHD-Zip) and classified into four subfamilies (HD-Zip I, HD-Zip II, HD-Zip III and HD-Zip IV). Gene structure and phylogenetic analyses indicated that members within the same subfamily exhibited analogous structural characteristics and shared strong homology with closely related species such as Juglans sigillata Dode and Populus trichocarpa. Torr. & A.Gray ex Hook. Promoter cis-acting element analysis revealed that the promoter regions of JmHD-Zip genes were enriched with multiple regulatory motifs associated with light responsiveness, hormone signaling, and stress regulation. Protein–protein interaction network analysis identified JmHDZ57 and JmHDZ43 as the central genes of the differentially expressed HD-Zip genes. Through validation of gene functions, JmHDZ43 promotes plant growth by coordinating shade-responsive morphogenesis via integration of light and hormone signaling pathways. This study offers a theoretical foundation and candidate gene resources for breeding initiatives and molecular investigations of light adaptation in J. mandshurica and potentially other woody species. Full article

University–employer alliances have expanded as a strategy to foster innovation, employability, and knowledge transfer; however, their growth often results in instrumental arrangements oriented toward short-term metrics (agreements, hours, deliverables) that weaken curricular transformation and Social Responsibility. This article proposes a governance architecture to design and audit sustainable Collaborative Education, understood as a technologically mediated multi-actor network organized by a shared principle of Social Responsibility. The method operates in two moves: (i) a conceptual ordering that uses the substance–accidents distinction and a formative telos to subordinate organizational and technological means to the educational purpose; and (ii) the translation of concepts into decision domains (who decides, with what evidence, under what risks, and with what safeguards), positioning Technological Mediation as governance infrastructure rather than a neutral support. The proposal delivers three managerial outputs: (a) a hierarchy of seven support entities (metaphysical question, Social Responsibility, projects and strategies, institutional management, institutional development, stakeholders, and benefits); (b) governance principles (primacy of purpose, multi-actor accountability, justifiable distribution of benefits and risks, and deliberative traceability); and (c) a compact matrix and checklist applicable through document auditing and platform design review, without requiring field data collection. Taken together, the framework shows how employer-side corporate governance can align incentives, rules of evidence, and data use to enable co-responsibility and avoid capture, strengthening the sustainability of collaboration over time across organizational contexts. Full article

Visible-infrared image fusion is crucial for applications like autonomous driving and nighttime surveillance, yet it remains challenging due to the inherent limitations of existing deep learning models. Convolutional Neural Networks (CNNs) are constrained by their local receptive fields, while Transformers suffer from quadratic computational complexity. To address these issues, this paper investigates the application of the Mamba model—a novel State Space Model (SSM) with linear-complexity global modeling and selective scanning capabilities—to the task of visible-infrared image fusion. Building upon Mamba, we propose a novel fusion framework featuring two key designs: (1) A Multi-Path Mamba (MPMamba) module that orchestrates parallel Mamba blocks with convolutional streams to extract multi-scale, modality-specific features; and (2) a Dual-path Mamba Attention Fusion (DMAF) module that explicitly decouples and processes shared and complementary features via dual Mamba paths, followed by dynamic calibration with a Convolutional Block Attention Module (CBAM). Extensive experiments on the MSRS benchmark demonstrate that our framework achieves state-of-the-art performance, outperforming strong baselines such as U2Fusion and SwinFusion across key metrics including Information Entropy (EN), Spatial Frequency (SF), Mutual Information (MI), and edge-based fusion quality (Qabf). Visual results confirm its ability to produce fused images that saliently preserve thermal targets while retaining rich texture details. Full article

To address the needs of an increasingly ageing population, the World Health Organisation (WHO) has established the Age-Friendly Cities and Communities network. This initiative aims to support interested municipalities in promoting active ageing by improving environments and services from a municipal perspective. A notable example is the city of Jaén (Spain), a municipality in southern Spain with just over 100,000 inhabitants that is working to join this network. As part of this process, an assessment was carried out to identify the specific needs and demands of older people. The methodology used follows the recommendations of the WHO, using the guidelines of the Vancouver Protocol, which is based on a participatory approach and shared diagnosis. The study involved 132 informants, including older people, service providers, and carers of older people. Based on the Age-Friendly Cities (AFC), this study analyses the needs and demands of older people across its eight domains. While particular attention is given to social relationships and participation, the findings also highlight significant challenges related to the physical and built environment, especially public spaces and transportation. In terms of social relations, older people in Jaén show a strong sense of belonging, valuing the closeness of their social environment, especially their neighbourhood and family members. However, there are differences between districts, as well as some concern that this closeness may weaken in the future. In terms of participation in leisure activities, there is a demand for more attractive and accessible options. With regard to participation in associations or politics, the former is more common, although older people still consider it to be insufficient. Full article

Decarbonisation of urban areas is essential to reaching climate neutrality, as cities house half the global population and account for over 70% of carbon emissions. However, applying innovative approaches, such as establishing positive energy districts (PEDs), remains challenging due to stakeholder engagement and funding constraints, largely driven by knowledge gaps and a lack of best practices. This study examines barriers, facilitators and lessons learnt from six case studies in Europe, Canada and Singapore through a mixed-methods approach, including stakeholder interviews, grey literature analysis and a semi-structured review. Findings highlight district heating networks, heat pumps and photovoltaics as key technologies, with regional variations. While Mediterranean regions prioritise solar energy, northern climates employ a diverse range of solutions, including geothermal and seasonal storage. Political commitment and funding enable progress, whereas regulatory gaps and stakeholder misalignment hinder it. The study underscores the need for sharing best practices to enable PED implementation. Full article

Gestational diabetes mellitus (GDM) develops silently during early pregnancy, yet its earliest circulating molecular signatures remain poorly defined. In this exploratory biomarker study, we characterized first-trimester circulating microRNA (miRNAs) associated with later GDM using a pool-based small RNA sequencing approach. Using a systematic and unbiased sequencing strategy with locus-level miRNA resolution, we profiled the first-trimester plasma miRNome and prioritized a set of 18 mature miRNAs from among 255 detected species. Set-level functional enrichment analyses based on curated and predicted miRNA–target interactions derived primarily from cellular and tissue-based studies showed annotation-based convergence on pathways related to Ca²⁺ homeostasis, glucagon–insulin regulatory circuits, and PI3K–AKT signaling. Network analysis indicated coordinated associations among these miRNAs and shared target pathways involved in insulin secretion and insulin sensitivity. Key contributors—including miR-29a-3p, miR-29c-3p, miR-146a-5p, let-7a-5p, and miR-182-5p—were linked, through in silico target annotation, to central metabolic regulators such as PTEN, PIK3R1, AKT1, AKT2, and components of Ca²⁺ signaling (ATP2A2, CALM1/3, ITPR1, RYR2). These circulating miRNAs should be interpreted primarily as biomarkers reflecting coordinated metabolic states rather than as direct causal mediators. Most identified miRNAs have not been previously reported in the context of first-trimester GDM, supporting the exploratory and hypothesis-generating nature of this circulating miRNA signature in early gestational metabolic research. Full article

We propose a novel Expert-Driven Conditional Auxiliary Classifier Generative Adversarial Network (AC-GAN) framework tailored for heterogeneous multi-modal federated learning at edge AI devices such as the NVIDIA Jetson Orin Nano. Unlike prior works that assume idealized distributions or rely on centralized data, our approach jointly addresses statistical non-IID data, model heterogeneity, privacy protection, and resource constraints through an expert-guided training pipeline and hierarchical model updates. Specifically, we introduce a collaborative synthesis and aggregation mechanism where local experts guide conditional data generation, enabling realistic data augmentation on resource-constrained edge nodes and enhancing global model generalization without sharing raw data. Through hierarchical updates between client and server levels, our method mitigates bias from skewed local distributions and significantly reduces communication overhead compared to classical federated averaging baselines. We demonstrate that while “perfect precision” is theoretically unattainable under non-IID and real-world conditions, our framework achieves substantially improved precision and false positive trade-offs (e.g., precision 0.89) relative to benchmarks, validating robustness in practical multi-modal settings. Extensive experiments across synthetic and real datasets show that the proposed AC-GAN approach consistently outperforms federated baselines in accuracy, convergence stability, and privacy preservation. Our results suggest that expert-guided conditional generative modeling is a promising direction for scalable, privacy-aware edge intelligence. Full article