Search Results (4,736)

Hazard recognition on construction sites is crucial for ensuring worker safety. Traditional methods widely rely on expert assessments, on-site inspections, and checklists, which can be time-consuming and susceptible to human error. The integration of multimodal Large Language Models (LLMs), such as GPT-based systems, offers a promising opportunity to overcome these limitations. Therefore, this study evaluates the effectiveness of GPT-4o in recognizing workplace hazards from image inputs, with a specific focus on construction sites. The results indicate that the model can serve as a valuable decision-support tool for safety professionals by providing scalable and real-time insights. However, the study also highlights key limitations, including the model’s reliance on general visual features rather than domain-specific safety knowledge, and the continued need for human supervision. Additionally, ethical concerns, including bias in AI-generated hazard assessments, data privacy, and the risk of over-reliance on AI, must be carefully managed to ensure these tools contribute responsibly and effectively to proactive risk management strategies. Full article

Recent studies in Cognitive Translation and Interpreting Studies have spurred a surge in experimental research, particularly in Audio Description (AD) reception studies. However, experimental research has largely focused on the linguistic composition of scripts, leaving the impact of vocal delivery comparatively underexplored. Addressing this gap, the current study investigates the cognitive and emotional effects of narrator voice gender within the complex framework of poetic AD for contemporary dance. Using a within-subjects design, 33 participants with blindness or visual impairments listened to dance performances with ADs voiced by synthetic male and female narrators. A multi-method approach was employed, combining subjective measures (mental effort, valence, arousal, enjoyment, transportation, and recall) with physiological indicators (electrodermal activity and heart rate variability). Results showed that female voices were associated with significantly lower perceived cognitive effort and higher emotional valence and arousal. Physiologically, female voices elicited lower levels of phasic skin conductance, suggesting a calming rather than arousing effect. However, no significant effects were found on enjoyment, transportation, or recall performance. These findings suggest that narrator’s voice modulates the cognitive and emotional experience of poetic AD, particularly at a subjective level. The study contributes to the growing field of inclusive media by highlighting the importance of voice characteristics in shaping accessibility and engagement. Full article

The maturity of cannabis flowers at harvest critically influences cannabinoid yield and product quality. However, conventional assessment methods rely on subjective visual inspection of trichomes and stigmas, making them inherently inconsistent. This research presents an automated framework integrating computer vision and deep learning to objectively evaluate cannabis flower maturity. High-resolution macro images were acquired using low-cost smartphone-based systems under greenhouse and laboratory conditions. A two-stage pipeline was implemented: a fine-tuned Faster R-CNN model detected trichomes (Precision: 0.815; Recall: 0.802), while a YOLOv8 classifier categorized them into clear, milky, or amber classes (Accuracy: 98.6%). In parallel, a YOLOv8 segmentation model delineated stigmas (AP50: 52.2%) to compute color ratios as maturity indicators. Features were aggregated at the flower level and correlated with HPLC-measured cannabinoid concentrations. A dataset of over 14,000 images was collected across multiple imaging sessions to support training, evaluation, and correlation experiments. Results demonstrated that stigma coloration—detectable with low-end devices—provides a robust visual indicator of peak chemical maturity, with the green-to-orange transition aligning with maximum cannabinoid concentration. This work offers a scalable, cost-effective solution for real-time maturity assessment in cannabis cultivation, contributing to improved harvest timing and quality control. Full article

The COVID-19 pandemic exposed significant vulnerabilities in public-sector administrative capacity, particularly in the implementation of crisis-related state aid schemes. Under conditions of extreme workload, time pressure, and legal constraints, administrative processes became critical determinants of policy effectiveness rather than routine implementation mechanisms. This study examines how such processes perform under crisis conditions and whether process modeling and simulation can identify efficiency gains without undermining procedural control. Using a case study of a COVID-19 state aid scheme administered by the Ministry of Transport of the Slovak Republic, the study combines Business Process Model and Notation (BPMN)-based process modeling, discrete-event simulation, and Monte Carlo analysis, and can identify efficiency gains in crisis-related state aid administration. The methodological approach integrates BPMN-based process modeling, discrete-event simulation, and scenario-based (“what-if”) sensitivity analysis to evaluate process performance under crisis-induced demand surges. Key performance indicators, including processing time, labor costs, and resource utilization, are analyzed using simulation outputs and dashboard-based visualization. Data analysis is conducted through simulation-based evaluation of key performance indicators, including processing time, labor costs, queue length, and resource utilization, under both baseline (AS-IS) and redesigned (TO-BE) process configurations. Scenario-based (“what-if”) and sensitivity analyses are applied to assess the effects of crisis-induced demand surges and capacity constraints on administrative performance. The results show that increased application volume during the crisis led to disproportionate growth in processing times due to queue accumulation and resource contention. Simulation-based process redesign reduced the average process cycle time by up to 12.8% and labor costs per application by up to 8.4% compared to the AS-IS configuration. However, efficiency gains diminished as resource utilization approached capacity limits, indicating structural constraints inherent to public administration. These findings demonstrate that process-oriented simulation provides a robust analytical tool for understanding administrative behavior under crisis conditions and for designing more efficient and resilient state aid mechanisms. The study contributes to public administration research by offering a micro-level, process-based perspective on crisis governance that complements the existing macro-level policy evaluations. Full article

This paper introduces a deep learning-based framework for video watermarking that achieves robust, imperceptible, and fast embedding under a wide range of visual and temporal conditions. The proposed method is organized into seven modules that collaboratively perform frame encoding, semantic region analysis, block selection, watermark transformation, and spatiotemporal injection, followed by decoding and multi-objective optimization. A key component of the framework is its ability to learn a visual importance map, which guides a saliency-based block selection strategy. This allows the model to embed the watermark in perceptually redundant regions while minimizing distortion. To enhance resilience, the watermark is distributed across multiple frames, leveraging temporal redundancy to improve recovery under frame loss, insertion, and reordering. Experimental evaluations conducted on a large-scale video dataset demonstrate that the proposed method achieves high fidelity, while preserving low decoding error rates under compression, noise, and temporal distortions. The proposed method operates processing 38 video frames per second on a standard GPU. Additional ablation studies confirm the contribution of each module to the system’s robustness. This framework offers a promising solution for watermarking in streaming, surveillance, and content verification applications. Full article

Rose leaf diseases severely reduce yield and product quality, and traditional disease monitoring relies on manual visual inspection by experts, which is inefficient for large-scale cultivation. However, deploying accurate and lightweight detectors in field environments remains challenging due to two main obstacles. First, models trained under controlled laboratory conditions suffer performance degradation due to domain shift when deployed in complex field environments. Second, the computational capacity of hardware deployable in the field is often limited. To address these problems, this study proposes a practical knowledge distillation approach based on transferable feature representations from a pre-trained teacher model, rather than on complex distillation architecture. A high-capacity YOLOv12-L teacher, pre-trained on laboratory images, guided the training of a compact YOLOv12-N student using field images. The distilled YOLOv12-N student model achieved an mAP@50 of 81.1% on field test set, representing a 3.5% improvement over the baseline YOLOv12-N model, while maintaining a highly efficient architecture of only 2.56 million parameters and 6.3 GFLOPs. Several ablation studies confirm the core contribution of this work, namely that the performance gains in lightweight detection stem primarily from the transfer of the teacher model’s feature representations, rather than from modifications to the distillation algorithm or student model’s architecture, thus clarifying the importance of high quality feature transfer in cross-domain agricultural vision tasks. This approach provides a generalizable and efficient solution for real-time rose leaf disease detection in precision agriculture. Full article

In response to the challenges of target recognition and misjudgment caused by varying target scales, diverse shapes, and interference such as lake surface reflections in river and lake scenarios, this paper proposes the YOLO v11n-DDH model for fast and detection of spatial targets in river and lake environments. The model builds upon YOLO v11n by introducing the Dynamic Snake Convolution (DySnakeConv) to enhance the ability to extract detailed features. It integrates the Deformable Attention Mechanism (DAttention) to strengthen key features and suppress noise, while combining the improved High-Level Screening Feature Pyramid Network (HSFPN) structure for multi-level feature fusion, thus improving the semantic representation of targets at different scales. Experiments on a self-constructed dataset show that the precision, recall, and mAP of the YOLO v11n-DDH model reached 88.4%, 78.9%, and 83.9%, respectively, with improvements of 3.4, 2.9, and 2.5 percentage points over the original model. Specifically, DySnakeConv increased mAP@50 by 0.6 percentage points, DAttention improved mAP@50 by 0.3 percentage points, and HSFPN contributed to a 0.9 percentage point rise in mAP@50. This patrol system can effectively identify and visualize various pollutants in river and lake areas, such as underwater waste, water quality pollution, illegal swimming and fishing, and the “Four Chaos” issues, providing technical support for intelligent river and lake management. Full article

Segmenting individual timber logs in robotic grasping scenarios poses significant challenges due to cluttered arrangements, overlapping geometries, and visually uniform textures, requiring instance segmentation models that balance accuracy and computational efficiency. In this work, we study the integration of the EfficientViT-SAM backbone into the MP-Former framework to analyze its impact on segmentation accuracy, inference speed, and cross-dataset generalization in autonomous forestry applications. Our contributions are threefold: (1) we benchmark Mask2Former and MP-Former with different variants of Swin Transformer as backbones on the TimberSeg 1.0 dataset, (2) we study the use of the EfficientViT-SAM-XL architecture as an alternative encoder backbone to analyze its impact on inference speed and segmentation accuracy, and (3) we use an In-house dataset as a hold-out test set, comprising 113 images and 923 annotations in the annotated subset and 50 images in the unannotated subset, for evaluating model generalization under real-world deployment scenarios. On the TimberSeg 1.0 dataset, our top-performing model, EfficientViT-SAM-XL1 MP-Former, achieves an mAP of 61.05, outperforming the Swin-B Mask2Former of the TimberSeg 1.0 paper by +3.52 mAP, while running at 12 FPS (+3.53 FPS gain). When tested on our In-house dataset, the model attains an mAP of 67.06. Notably, it matches the memory efficiency of TimberSeg’s strongest baseline, despite having nearly double the number of parameters, demonstrating its practical viability for robotic applications in forestry environments. Full article

Intentional high-power electromagnetic (EM) interference poses a serious threat to sensitive electronic systems and often manifests as ultra-wideband (UWB) sub- and nanosecond pulses. Metallic shielding enclosures with technological apertures are commonly used for protection; however, apertures enable electromagnetic coupling into the enclosure and limit shielding performance. While most existing studies focus on transient disturbances with durations exceeding the enclosure transit time, this work addresses an ultrashort high-power subnanosecond UWB plane-wave pulse whose duration is significantly shorter than the enclosure transit time, a regime that remains insufficiently explored. A time-domain numerical analysis is performed for a low-profile rectangular metallic enclosure with a front-wall aperture, focusing on internal EM field evolution, internal pulse formation, and polarization-dependent shielding effectiveness. Three-dimensional full-wave simulations were carried out using CST Microwave Studio over a 90 ns observation window. The results show that the incident pulse excites primary subnanosecond EM waves inside the enclosure, which subsequently generate secondary waves through multiple reflections from the enclosure walls. Their interaction produces complex, long-lasting, time-varying internal field patterns. Although attenuated, the resulting internal subnanosecond pulses repeatedly traverse the enclosure interior, forming a pulse train-like sequence that may pose a cumulative electromagnetic threat to internal electronics. A key contribution of this work is the quantification of time-dependent local shielding effectiveness for both electric and magnetic fields, derived directly from the internal pulse train-like series obtained in the time domain. The concept of local, time-dependent shielding effectiveness provides physical insight that cannot be obtained from a single globally averaged SE value. In the case of ultrashort electromagnetic pulse excitation, the internal field response of an enclosure is strongly non-stationary and highly non-uniform in space, with local field maxima occurring at specific times and locations despite good average shielding performance. Time-dependent local SE enables identification of worst-case temporal conditions, repeated high-amplitude internal exposures, and critical regions inside the enclosure where shielding is significantly weaker than suggested by global metrics. Therefore, while conventional SE remains useful as a summary measurand, local time-dependent SE is essential for assessing the actual electromagnetic risk to sensitive electronics under ultrashort pulse disturbances. In addition, a global shielding effectiveness metric mapped over selected enclosure cross-sections is introduced to enable rapid visual assessment of shielding performance. The analysis demonstrates a strong dependence of internal wave propagation, internal pulse formation, and both local and global shielding effectiveness on the polarization of the incident subnanosecond EM pulse. These findings provide new physical insight into aperture coupling and shielding behavior in the ultrashort-pulse regime and offer practical guidance for the assessment and design of compact shielding enclosures exposed to high-power UWB EM threats. Full article

Background: Keratoconus (KC) is the most common ectatic corneal disorder, causing progressive corneal deformation, visual impairment, and reduced quality of life. Although KC pathogenesis is multifactorial, the contribution of systemic factors, including hormonal regulation, remains incompletely understood. This study aimed to investigate the role of sex hormones and gonadotropins in KC in a predominantly Greek population. Methods: We recruited 105 KC patients and 71 healthy controls (HC). Plasma levels of luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol (E2), prolactin (PRL), testosterone (TES), dehydroepiandrosterone sulfate (DHEA-S), and progesterone (PRG) were measured and analyzed in relation to corneal tomographic and biomechanical parameters, as well as treatment modality. Results: LH showed positive correlations with corneal biomechanical parameters. KC patients who underwent penetrating keratoplasty exhibited higher FSH levels and a reduced LH/FSH ratio compared with those treated with corneal cross-linking. E2 levels were increased in women over 46 years of age, while PRL correlated with Kmax and Q-value. Men with KC demonstrated reduced TES associated with corneal morphology and biomechanics, increased PRG levels, and reduced DHEA-S in keratoplasty-treated patients. Conclusions: These findings suggest that sex hormones and gonadotropins may contribute to KC pathophysiology, supporting a systemic hormonal component in disease progression. Full article

Photodynamic therapy (PDT) is a technique based on the use of photosensitizers activated by light to destroy cancer cells in the presence of oxygen. This enables localized cancer treatment and, in some settings, fluorescence-guided visualization. However, the efficacy and clinical translation of PDT have been limited by the low specificity of traditional photosensitizers. The aim of the study is to create a ligand-guided PDT approach for pancreatic ductal adenocarcinoma (PDAC) using a peptide-conjugated photosensitizer binding to integrin αvβ6, which is a receptor linked to tumor growth and prevalent in PDAC cells. Current treatment options for this tumor are limited, with surgical resection and chemotherapy only effective when the tumor is detected early. Given the limited treatment options for PDAC, PDT via αvβ6 offers a new pathway for precision treatment. The cyclic peptide cyclo[FRGDLAFp(NMe)K], recognized for its high affinity to αvβ6, was chosen to guide a phthalocyanine-class photosensitizer toward αvβ6-expressing PDAC models. The PDT approach was further refined by developing 3D spheroid models and in vivo BxPc3 xenograft models in NOD/SCID mice, where its therapeutic efficacy was assessed. In the absence of a non-targeted control photosensitizer, a contribution from non-specific accumulation and EPR effects in the in vivo setting cannot be fully ruled out. This study highlights the potential of a peptide-guided photosensitizer, demonstrating uptake and photodynamic activity in spheroids, with moderate in vivo results addressing tumor microenvironment challenges. Optimization of PDT dosing, laser precision, and preclinical models, such as patient-derived xenografts, are crucial to enhance clinical translation. Full article

While light-based external human–machine interfaces (eHMIs) on automated vehicles (AVs) are increasingly studied to mediate pedestrian–vehicle conflicts, gaps persist in understanding how specific dynamic features of the AV’s headlights influence pedestrians’ prediction of its yielding intention and their crossing behavior. This study systematically investigates the effects of dynamic elements of vehicle lighting—including animation patterns, animation speed, and light-emitting area—on pedestrians’ objective and subjective evaluations. A factorial design framework was employed, where participants viewed video simulations of an approaching AV displaying headlight designs combining multiple dynamic features. For different vehicle motion states, the vehicle–pedestrian distance was integrated as a variable to examine its interaction effect with lighting features. Objective measures of cueing effects were complemented by subjective ratings and user preference study via questionnaires. Results showed that there were more crossing behaviors of the pedestrian when presenting higher animation speed of dynamic light eHMIs. Animation pattern and light-emitting area does not play an important role in pedestrian decision-making, but proper design of these two features can evoke higher visual attention. When the vehicle–pedestrian distance is longer, the dynamic features of lighting will more affect people’s willingness to cross. The effects of light eHMIs seemed more significant for the AV travelling in constant speed. Our findings advance preliminary suggestions for selecting light-based eHMIs in the appropriate scenarios and can contribute actionable insights for designing intuitive, human-centric AV–pedestrian negotiation strategies. Full article

Zhenwu (Perfected Warrior), one of the most influential Daoist martial deities, was historically shaped by the northern celestial emblem Xuanwu and later personified and integrated into the Daoist pantheon. While scholarship on Zhenwu has largely relied on textual sources, cliff sculptures provide a material setting in which doctrine, ritual space, and iconography can be examined together. Taking the Zhenwu niche (No. 1) at Nanshan, Dazu (Chongqing) as a case study, this article first situates the niche within the spatial program of the Nanshan Daoist carvings and describes its architectural design, composition, and inscriptional evidence of worship. It then revisits key motifs associated with Zhenwu—such as the sword, bare feet, and the turtle–snake pair—through Daoist and inner-alchemical (neidan) textual traditions. Rather than positing a direct or exclusive link between the Nanshan sculpture and inner-alchemical practice, the article argues that the niche mobilizes an established iconographic repertoire that could have resonated with late imperial discourses of self-cultivation, and that its northern placement within the Nanshan ensemble reinforces these cosmological associations. By combining site-based analysis with a cautious reading of Daozang and neidan texts, the study contributes to scholarship on Daoist visual culture and offers a framework for comparing Zhenwu images across regions and media. Full article

Background/Objectives: Endoscopic endonasal transsphenoidal surgery (ETSS) has undergone substantial evolution driven by continuous technological innovations and is increasingly established as a minimally invasive and highly precise approach for the treatment of pituitary neuroendocrine tumors (PitNETs) and selected parasellar lesions. The objective of this review is to summarize the historical development of ETSS and to provide an integrated overview of recent advances shaping contemporary neuroendoscopic surgery. Methods: A narrative review of the literature was conducted focusing on key technological and conceptual developments in ETSS, including advances in endoscopic visualization systems, artificial intelligence (AI)-based image analysis, intraoperative navigation, educational support frameworks, and skull base reconstruction techniques. Representative clinical studies and review articles were examined to contextualize current applications and limitations. Results: Recent innovations have expanded the functional capabilities of ETSS beyond pituitary surgery alone. Progress in visualization, navigation, and reconstruction techniques has contributed to improved anatomical understanding, surgical safety, and outcome optimization. Furthermore, accumulating clinical evidence supports the selective extension of ETSS indications to complex midline skull base pathologies, including craniopharyngiomas, meningiomas, and chordomas, while emphasizing the importance of appropriate patient selection. Conclusions: ETSS has evolved from a single operative technique into an integrated surgical platform supported by technological convergence. Ongoing refinement of visualization, digital assistance, and reconstructive strategies is expected to further enhance safety and precision. This review highlights current trends in ETSS and outlines future directions for innovation and clinical application in neuroendoscopic skull base surgery. Full article

This study proposes an integrated façade design framework that harmonizes the creative divergence of Generative AI with the economic efficiency of Design for Manufacturing and Assembly (DfMA). To address low productivity in the construction industry, a stepwise pipeline is developed, synthesizing image generation via Midjourney, automated coding using ChatGPT, and quantitative optimization. Central to this process is the Hamming Distance algorithm, which evaluates image similarity to implement core DfMA principles: standardization and simplification. The study introduces a multidimensional decision-making model utilizing Grid Size (GS), Replacement Rate (RR), and Hamming Threshold (HT) indices to visualize the trade-off between component minimization and design fidelity. This process transforms abstract 2D patterns into manufacturable geometric panels, bridging the gap between conceptual design and constructability. The results demonstrate that algorithmic optimization significantly reduces component count, contributing to potential cost savings and schedule reduction. Ultimately, this research establishes a collaborative model where architects’ qualitative insights complement AI’s quantitative analysis, enabling designers to regain agency over digital tools and realize creative visions within technical constraints. Full article