Search Results (1,788)

Accurate exploitation of spatial structures and spectral characteristics is essential for fine-grained crop classification using remote sensing imagery. Although multi-source remote sensing data provide complementary information, most existing methods implicitly assume homogeneous data sources with consistent spatial resolution. In practice, high spatial resolution and rich spectral information are usually provided by different sensors, making cross-source spatial–spectral fusion a non-trivial challenge. To address this issue, we propose SSF-TransUnet, a dual-branch spatial–spectral joint modeling framework for fine crop classification. The proposed network explicitly decouples spatial structure extraction and spectral discriminability learning by jointly utilizing high spatial resolution imagery and multi-spectral observations acquired from different satellite sensors within a unified architecture. To support model training and evaluation, we construct SSCR-Agri, a spatial–spectral complementary resolution agricultural dataset integrating meter-level GF-2 imagery and multi-spectral Sentinel-2 data from five representative agricultural regions in northern China, covering five crop categories including corn, rice, wheat, potato, and others. Extensive experiments demonstrate that SSF-TransUnet consistently outperforms representative CNN-based and hybrid CNN–Transformer models. The proposed method achieves an overall accuracy (OA) of 81.84% and a mean Intersection over Union (mIoU) of 0.6954 in fine-grained crop classification, effectively distinguishing crops. These results highlight the effectiveness of spatial–spectral joint modeling for high-resolution crop mapping and demonstrate its potential for precision agriculture and large-scale agricultural monitoring applications, and shows a promising mechanism when combined with multi-temporal observations. Full article

In low-altitude complex environments, accurately reconstructing the three-dimensional (3D) flight trajectories of small unmanned aerial vehicles (UAV) without onboard positioning modules remains challenging. To address this issue, this paper proposes a multi-view ground camera-based UAV 3D trajectory detection method founded on an optical imaging geometric model. Multiple ground cameras are used to synchronously observe UAV flight, enabling stable 3D trajectory reconstruction without relying on onboard Global Navigation Satellite System (GNSS). At the two-dimensional (2D) observation level, a lightweight object detection model is employed for rapid UAV detection. Foreground segmentation is further introduced to extract accurate UAV contours, and geometric centroids are computed to obtain precise image plane coordinates. At the 3D reconstruction stage, camera extrinsic parameters are estimated using a back intersection method with ground control points, and the UAV spatial position in the world coordinate system is recovered via multi-view forward intersection. Field experiments demonstrate that the proposed method achieves stable 3D trajectory reconstruction in real urban environments, with a median error of 4.93 m and a mean error of 5.83 m. The mean errors along the X, Y, and Z axes are 2.28 m, 4.58 m, and 1.09 m, respectively, confirming its effectiveness for low-cost UAV trajectory monitoring. Full article

Potato is one of the most important food crops worldwide, playing a critical role in global food security and agricultural production. The broken and impurity rates are important indicators for evaluating the harvesting quality of potato combine harvesting operations. To address the difficulty of achieving continuous and online detection using traditional methods, this study investigates an online monitoring approach for potato combine harvesting based on machine vision. Considering the characteristics of large material volume, severe overlap, and similar appearance features under field operating conditions, an online monitoring device suitable for potato combine harvesters was designed, along with a corresponding image acquisition and processing workflow. For the online monitoring device, an improved You Only Look Once version 11 (YOLOv11) detection model, was proposed to meet the requirements of multi-object detection in complex operating scenarios. The model incorporates Multi-Scale Depthwise Convolution (MSDConv), C2PSA_DCA (with Directional Context Attention, DCA), and Directional Selective Attention (DSA) modules, and introduces the Efficient Intersection over Union (EIoU) loss function to enhance recognition capability for broken potatoes and multiple types of impurity targets. While maintaining lightweight characteristics, the improved model demonstrates favorable detection accuracy. Field experiment results show that when the combine harvester operates at a forward speed of 3 km/h, the relative errors for broken and impurity rates are measured as 3.78% and 3.67%, respectively. Under extreme operating conditions with a speed of 4 km/h, the corresponding average relative errors rise to 8.30% and 8.72%, respectively. Overall, the online detection results exhibit satisfactory consistency with manual measurements, providing effective technical support for real-time monitoring of harvesting quality in potato combine harvesting operations. Future research will focus on expanding multi-scenario datasets under diverse soil and illumination conditions, as well as integrating detection results with adaptive control strategies to further enhance intelligent harvesting performance. Full article

Hurricanes create compound hazards such as storm surge, flooding, and wind-driven debris that can degrade roadway capacity, fragment network connectivity, and hinder evacuation and shelter operations. From a sustainability perspective, improving evacuation planning is essential for reducing disaster-related losses, protecting vulnerable populations, and strengthening the resilience of coastal communities facing intensifying climate-driven hazards. This paper develops a micro-scale, agent-based evacuation modeling framework to assess evacuation performance under baseline and compound-hazard conditions, with emphasis on municipal decision support. The framework is demonstrated for Westbrook, Connecticut, at the census block-group scale in AnyLogic by integrating household locations, vehicle availability, road-network connectivity, and shelter capacities from publicly available datasets. Evacuation propensity and destination choice are parameterized using survey data, enabling empirically grounded decisions for in-town versus out-of-town evacuation among household-vehicle agents. Compound disruptions are represented through flood-related road closures derived from SLOSH storm-surge outputs and stochastic wind-related disruptions that dynamically constrain accessibility during the simulation. Scenarios are evaluated for Saffir–Simpson Category 1–2 and Category 3–4 hurricanes under baseline and compound conditions. Model outputs quantify normalized evacuation time, congestion and critical intersections, shelter demand and unmet capacity, evacuation failure, and spatial heterogeneity across block groups. Results indicate that compound flooding substantially increases evacuation times and failure rates, with the largest performance degradation concentrated in higher-vulnerability areas. Optimization experiments further compare the effectiveness of behavioral shifts, shelter-capacity expansion, and earlier departure timing in reducing delays and unmet shelter demand. Overall, the proposed framework provides transparent, reproducible, and scalable analytics that town engineers and emergency planners can use to evaluate evacuation readiness under compound hurricane impacts. Full article

Background: Recurrent vulvovaginal candidiasis (RVVC) is a chronic inflammatory disease primarily caused by Candida albicans (C. albicans). Its pathogenesis remains incompletely understood, and clinical management is challenged by recurrence and drug resistance. Ferroptosis, an iron-dependent form of programmed cell death driven by lipid peroxidation, has been implicated in various infectious and inflammatory diseases. However, its role in RVVC remains unclear, with a particular lack of evidence from clinical samples and animal experiments. Objective: This study aimed to investigate the association between RVVC and ferroptosis. First, we analyzed high-throughput sequencing data from human RVVC samples in the Gene Expression Omnibus (GEO) database to identify the expression profile of ferroptosis-related genes. Second, using an established murine model of chronic vulvovaginal candidiasis (CVVC), we validated changes in ferroptosis-related markers in vaginal tissues in vivo. Furthermore, an in vitro model of C. albicans-infected bone marrow-derived macrophages (BMDMs) was employed to explore the underlying mechanisms. This study provides experimental evidence for elucidating the pathogenesis of RVVC and exploring novel therapeutic strategies. Methods: The RVVC-related gene expression dataset GSE278036 was obtained from the GEO database. Differentially expressed genes (DEGs) were screened using the DESeq2 algorithm and intersected with ferroptosis-related genes from the FerrDb database to identify key targets. A protein–protein interaction (PPI) network was constructed using the STRING database and Cytoscape software, and hub genes were identified via the Betweenness centrality algorithm. Functional and pathway analyses, including gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways, were performed. Immune infiltration analysis characterized the immune microenvironment in RVVC patients. A CVVC mouse model was established in vivo, and a C. albicans-BMDMs infection model was established in vitro. The ferroptosis inhibitor ferrostatin-1 (Fer-1) was administered to investigate the pathological function and regulatory mechanisms of ferroptosis in RVVC at the molecular, cellular, and tissue levels. Results: Differential analysis identified 3132 DEGs in RVVC, which intersected with ferroptosis-related genes to yield 194 key targets. Among them, 20 hub genes were identified, including ferroptosis regulators and inflammatory factors. Functional enrichment analysis confirmed that these shared targets regulate RVVC pathology through a “ferroptosis-inflammation-immunity” multi-pathway network. Immune infiltration analysis revealed a specific immune disorder in RVVC patients characterized by “activation of the pro-inflammatory innate immune axis and suppression of the adaptive immune axis,” which was closely associated with ferroptosis-related genes. In vivo and in vitro experiments confirmed that C. albicans infection induced ferroptosis in vaginal tissues and macrophages, as manifested by lipid ROS accumulation, Fe²⁺ overload, GSH depletion, downregulation of GPX4 and SLC7A11, upregulation of ACSL4, 4-HNE, and MDA, and mitochondrial structural damage. Macrophages were identified as key target cells for ferroptosis, and their ferroptosis led to impaired antifungal function. Fer-1 treatment significantly inhibited ferroptosis, reduced vaginal histopathological damage and inflammatory cell infiltration, decreased fungal burden, downregulated abnormally elevated inflammatory factors, and restored Th1/Th2 immune balance. Furthermore, Fer-1 preserved macrophage viability and enhanced their antifungal killing capacity. Conclusions: This study provides the first evidence linking RVVC to ferroptosis through a combination of clinical data analysis and experiments, suggesting that ferroptosis is involved in its pathological process. These findings offer a new perspective for elucidating RVVC pathogenesis and developing targeted therapeutic strategies. Full article

Workplace harassment of transgender employees remains pervasive and understudied. In this narrative review of 63 studies over the past 25 years, we summarize the literature on transgender workplace harassment. We focus on its prevalence and forms. Individual, organizational and cultural factors contribute to its occurrence; psychological and occupational outcomes; and strategies to reduce or prevent harassment. We find that harassment often extends beyond traditional definitions; includes misgendering, deadnaming, and the questioning or outright denial of one’s gender identity; and is particularly pervasive in masculinity contest cultures. These experiences are associated with both negative well-being of transgender employees and less effectiveness of the organizations that employ them, though more causal evidence is needed. We also highlight critical conceptual and methodological gaps to guide future research. Much of the existing research on LGBTQ+ employees in the workplace has focused primarily on sexual minorities, leaving the unique experiences of gender minorities invisible. Further, an intersectional lens is needed, as harassment experiences of trans women, trans men, and nonbinary people may differ in significant ways. Finally, we identify strategies to improve workplace climate including both top-down formal policy and bottom-up interpersonal behaviors. Full article

Background: Individuals with intellectual and developmental disabilities (IDD) experience persistent health disparities that result in poorer health outcomes, reduced quality of life, and inequitable access to healthcare. Objective: This narrative review synthesized existing literature to identify key barriers to health equity and contributors to health disparities among individuals with IDD. Method: This study was a narrative (non-systematic) review that adopted a qualitative synthesis approach. A literature review was conducted across PubMed, CINAHL, PsycINFO, Medline, and Google Scholar to identify peer-reviewed articles published between 2010 and 2025 that address health disparities, health inequities, healthcare barriers, and social determinants of health among individuals with IDD. Thematic analysis was employed to synthesize the included studies and identify recurring patterns and themes. Results: A total of 88 articles were included. Two overarching domains shaping health disparities were identified: barriers to health equity and contributing factors. Seven barrier categories emerged: attitudinal, communication, policy, programmatic, social, physical, and transportation. Five key contributors were also identified: limited access to healthcare, comorbid conditions, low health literacy, adverse social determinants of health, and caregiver burden. Conclusions: Health disparities among individuals with IDD are driven by intersecting social, structural, and healthcare system barriers rather than individual limitations alone. This review informs policymakers, public health professionals, and interventionists on how to advance health equity for individuals with IDD through targeted, person-centered interventions. Full article

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

Medieval literature often seems to be a remote, irrelevant, incomprehensible world of narrative texts lost in heroic, religious, or courtly themes, limited to stories about King Arthur, courtly lovers, military heroes, and religious martyrs, saints, and prophets. In reality, as any expert can easily confirm, when we turn our full attention to pre-modern literature from across Europe (and also other parts of the world), we can often recognize the true extent to which poets utilized their narratives for spiritual, philosophical, religious, scientific, and medical explorations that have much to tell us today and prove to be deeply meaningful in a timeless manner. One key aspect, which was shared among virtually all medieval artists, poets, and theologians, consisted of the unique experience by an individual who is entitled through a physical opening to see into the depth or the height of all existence and can thus discover a wholly different world. Through this motif of the gaze, an entire epiphanic realization can set in, which thus quickly transforms the purely entertaining narrative medium into a narrative catalyst of profound spiritual experiences, helping the individual to gain inspiration from the Godhead (e.g., mysticism). Indeed, numerous times, medieval poets employed the motif of the visionary gaze, developed in very concrete terms, to trace and explain the process of perspicuity and accompanying acuity which ultimately leads to new intellectual, emotional, and religious understandings and experiences. While many intellectuals already embraced this notion of a visionary concept of spiritual comprehension, it might come as a surprise that secular and religious poets also operated quite intentionally with the concept of a hole in the wall or some other opening as a springboard for intellectual and spiritual experiences, directly drawing from the concepts of the optical sciences as understood at that time. Oddly but highly significantly, Christian and pagan notions tend to intersect in those narrative moments, particularly in late medieval literature, merging the visionary experience with the monstrous within human society, associating the gaze with the erotic and religious dimension. Full article

Higher Education is facing a transformation of the teaching profession due to the confluence of active methodologies, learning management systems, and artificial intelligence. However, existing research tends to address these elements in isolation, lacking integrative analyses that examine their combined impact on the teaching role in higher education. Through a narrative review and thematic analysis of 49 articles, opportunities and challenges in this intersection are identified. The results indicate that the teacher is not merely a content transmitter but a designer of formative experiences, a critical guide, and an ethical reference in the use of AI. More than a replacement, technology proposes a teacher profile as an architect of critical and adaptive learning that combines pedagogy, technology, and ethics. Full article

Segmenting non-rigid objects such as smoke in video requires effective utilization of temporal information, which remains challenging due to their irregular deformation and complex appearance variations. Based on our previously proposed DeffNet for industrial fumes video segmentation, this letter presents a novel adaptive frame selection algorithm that employs fuzzy logic control to dynamically optimize the temporal processing step size for the specific task of industrial smoke video segmentation. Our method quantifies inter-frame variation using the Structural Similarity Index (SSIM) and Normalized Cross-Correlation (NCC) as inputs to a fuzzy inference system. Gaussian membership functions, shaped via K-means clustering, and a five-rule fuzzy system are designed to determine the optimal step size, maximizing informative dynamic feature extraction while minimizing redundant computation. As a lightweight front-end module, the algorithm integrates seamlessly into the existing DeffNet segmentation framework without reconstructing new network architecture. Extensive experiments on a dedicated industrial smoke video dataset demonstrate that our approach effectively improves the segmentation performance of DeffNet, achieving 84.27% Intersection over Union (IoU) while maintaining a high inference speed of 39.71 FPS. This work provides an efficient and scene-specific solution for temporal modeling in industrial smoke non-rigid object segmentation and offers a practical improved strategy for DeffNet in real-time industrial smoke monitoring. Full article

The clinical renaissance of psychedelic medicine has highlighted the therapeutic potential of rapid-acting neuroplastogens, or “psychoplastogens,” for psychiatric disorders. However, the widespread application of classical psychedelics—such as psilocybin and LSD—and the atypical dissociative ibogaine is severely limited by their hallucinogenic properties and, particularly in the case of ibogaine, life-threatening cardiotoxicity. Addressing these limitations, Tabernanthalog (TBG) has emerged as a frontrunner in the field. This non-hallucinogenic analog of ibogaine was rationally designed to eliminate interactions with the human ether-à-go-go-related gene (hERG, KCNH2) potassium channel, thereby mitigating cardiotoxic risks. While initially characterized for its anti-addictive and antidepressant-like properties, recent data from 2024–2025 have significantly expanded its therapeutic horizon. TBG demonstrates robust efficacy in preclinical models of neuropathic and visceral pain, as well as in the rescue of cognitive deficits associated with cancer-related cognitive impairment (CRCI). TBG has shown efficacy in reversing cognitive impairments induced directly by the presence of a tumor in preclinical models, rather than by chemotherapy-specific neurotoxicity. Crucially, emerging evidence suggests that TBG’s mechanism extends beyond simple 5-HT2A receptor agonism. New findings point to a multi-target profile involving the inhibition of nicotinic acetylcholine receptors (nAChRs), positive modulation of NMDA receptors, and functional crosstalk with mGlu2 receptors. Furthermore, TBG appears to induce structural neuroplasticity without the widespread induction of immediate early genes (IEGs) seen with classical hallucinogens, suggesting a decoupling of therapeutic rewiring from the subjective psychedelic experience. This review synthesizes current preclinical evidence to discuss TBG as a promising chemical scaffold for next-generation neurotherapeutics targeting the intersection of psychiatry and neurology. Full article

Robotic pipe welding represents a key and rapidly evolving technology for the automation of pipe and pipe-joint welding processes with standard, intersecting, and complex geometries. This review analyses 84 studies published over the past three decades, categorising them into four primary research areas: general pipe welding, intersecting pipes, boiler and tube-to-tubesheet welding, and control and modelling. Two separate comparative analyses were conducted: one within intersecting pipe research and another within the control and modelling category. The aggregated findings reveal consistent, complementary patterns: simulation and laboratory experiments clearly dominate validation methods, while industrial-scale evaluations remain scarce. The results further demonstrate that control strategies, sensor integration, and validation levels are strongly interconnected, collectively determining system performance, reliability, and practical applicability. Despite significant progress, challenges remain, including system integration complexity, limited robustness in variable industrial environments, insufficient real-time adaptive control, and inconsistent quantitative performance evaluation. Further research should prioritise the development of digital twins, human–robot collaboration, multi-sensor fusion, reinforcement learning-based adaptive control, and scalable industrial deployment. This review provides an overview of current progress and outlines key directions for developing intelligent and reliable robotic pipe welding systems. Full article

To achieve precision and lightweight design for pavement distress detection in complex scenarios, an improved YOLOv8n model, named YOLOv8n-Ultra, is constructed. The Coordinate Attention (CA) module is embedded into the C2f layer of the backbone network to empower the feature extraction of the neural network to focus on specific semantic information related to distress. The Ghost module is introduced to realize lightweight design of the model, and the Wise Intersection over Union (WIoU) loss function is adopted to dynamically optimize the precision of bounding box regression, enabling the model to pay more attention to hard-to-detect objects. Ablation experiments are designed to test the impact of different improvement methods on the detection performance of the model. Verified by three repeated experiments, the results show that compared with the YOLOv8n model, the YOLOv8n-Ultra model improves the precision (P) from 78.5% to 79.4%, increases the recall (R) from 74.0% to 78.7%, and enhances the mAP0.5 by 3.8 percentage points to 82.7%. It only increases the parameter count by 65.1% to 4.97 M, which is still substantially lower than that of traditional models such as YOLOv3 (61.92 M) and Faster Region-based Convolutional Neural Network (Faster-RCNN, 107.5 M), while maintaining an FPS of 202.4 f/s when tested on the experimental hardware (NVIDIA GeForce RTX 2060 SUPER GPU) specified in Section “Experimental Environment and Parameter Settings”. A paired t-test (p < 0.05) confirms that the improvement effect is statistically significant and the model exhibits good stability. In summary, the YOLOv8n-Ultra model provides a technical reference for pavement distress detection with balanced precision and lightweight characteristics. Full article

While school bullying has received substantial academic attention, the specific roles of teachers as (co-)perpetrators or bystanders in (cyber-)bullying dynamics remain markedly underexplored—particularly in the Austrian context. This article foregrounds pupils’ perception of teachers’ involvement in (cyber-)bullying. Drawing on feminist perspectives and insights from digital and gender(-queer) geographies, as well as interdisciplinary (cyber-)bullying research, it explores how pupils perceive teachers’ involvement in bullying dynamics and how they believe it shapes the perceived severity, trajectories, and outcomes of (cyber-)bullying. In doing so, the article contributes a specific but underexplored perspective on power and violence in schools. The analysis is based on 41 written narratives produced by young people attending upper secondary vocational colleges in Austria. The findings reveal that pupils subjectively perceive teachers as taking on various roles in (cyber-)bullying dynamics, including preventers, (silent) accomplices, defenders, outsiders, and (co-)perpetrators. In these accounts, teacher involvement in bullying reinforces power hierarchies, intensifies victimisation, and intersects with peer bullying dynamics, creating a complex system of interrelated influences. The study highlights the intersectional nature of discrimination and bullying, showing how pupils’ identities are entangled with their embodied experiences of both teacher- and peer-perpetrated bullying. These findings suggest an urgent need for spatially and structurally informed reforms in school policies and teacher training programmes to address teacher-perpetrated bullying, raise awareness of teachers’ responsibility in peer bullying dynamics, and foster safer, more inclusive learning spaces for pupils in Austria. Full article