Search Results (142)

UAVs are essential for forest fire detection due to vast forest areas and inaccessibility of high-risk zones, enabling rapid long-range inspection and detailed close-range surveillance. However, aerial photography faces challenges like multi-scale target recognition and complex scenario adaptation (e.g., deformation, occlusion, lighting variations). RGB-Thermal fusion methods integrate visible-light texture and thermal infrared temperature features effectively, but current approaches are constrained by limited datasets and insufficient exploitation of cross-modal complementary information, ignoring cross-level feature interaction. A time-synchronized multi-scene, multi-angle aerial RGB-Thermal dataset (RGBT-3M) with “Smoke–Fire–Person” annotations and modal alignment via the M-RIFT method was constructed as a way to address the problem of data scarcity in wildfire scenarios. Finally, we propose a CP-YOLOv11-MF fusion detection model based on the advanced YOLOv11 framework, which can learn heterogeneous features complementary to each modality in a progressive manner. Experimental validation proves the superiority of our method, with a precision of 92.5%, a recall of 93.5%, a mAP50 of 96.3%, and a mAP50-95 of 62.9%. The model’s RGB-Thermal fusion capability enhances early fire detection, offering a benchmark dataset and methodological advancement for intelligent forest conservation, with implications for AI-driven ecological protection. Full article

Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article

This study presents the design and validation of an integrated fire safety system that leverages edge AI, hybrid sensing, and precision suppression to overcome the latency and collateral limitations of conventional smoke detection and sprinkler systems. The proposed platform features a dual-mode sensor array for early fire recognition, motorized ventilation units for rapid smoke extraction, and a 360° directional nozzle for targeted agent discharge using a residue-free clean extinguishing agent. Experimental trials demonstrated an average fire detection time of 5.8 s and complete flame suppression within 13.2 s, with 90% smoke clearance achieved in under 95 s. No false positives were recorded during non-fire simulations, and the system remained fully functional under simulated cloud communication failure, confirming its edge-resilient architecture. A probabilistic risk analysis based on ISO 31000 and NFPA 551 frameworks showed risk reductions of 75.6% in life safety, 58.0% in property damage, and 67.1% in business disruption. The system achieved a composite risk reduction of approximately 73%, shifting the operational risk level into the ALARP region. These findings demonstrate the system’s capacity to provide proactive, energy-efficient, and spatially targeted fire response suitable for high-value infrastructure. The modular design and SmartThings Edge integration further support scalable deployment and real-time system intelligence, establishing a strong foundation for future adaptive fire protection frameworks. Full article

Background: Prematurely born newborns with low birth weight constitute a group of patients who require special care from the first days of life. Prematurity and low birth weight affect about 13.4 million infants. Risk factors include placental disorders but also factors related to the mother, such as smoking, alcohol drinking, drug use, malnutrition, or certain diseases. It is imperative to educate women of reproductive age (15–49) about the basic factors influencing embryonic development, such as oral health, diet, medicine intake, and harmful habits. Even though most women are aware of the negative impact of harmful habits on the fetus, still too little attention is paid to oral health in pregnant women. Poor oral health may influence the well-being of the future mother, as well as of the child. Therefore, women of reproductive age and those who are pregnant must have adequate knowledge on this subject. The aim of this study was to assess the knowledge of Polish women of reproductive age (15–49) regarding oral health during pregnancy, including the impact of dental treatment, oral hygiene, and maternal oral conditions on pregnancy outcomes and the health of the newborn. Materials and Methods: This was a cross-sectional study of 508 women, in the reproductive age, whose age ranged from 18 to 49 years old. The surveys were conducted from April 2020 to November 2020. The questionnaire was originally developed based on the available literature and consisted of seven sections: basic information, general health and habits, pregnancy status and dental care, knowledge of treatment options during pregnancy, oral health status and its association with the risk of preterm birth, prematurity and the child’s oral health, and breastfeeding and oral development. Results: After excluding incomplete questionnaires, a total of 499 questionnaires were included in the analysis. Women participating in the study had a fairly good understanding of the impact of oral health on the fetus and the role of breastfeeding in the development of the stomatognathic system (from 50% to 70% correct answers). However, even though most respondents had completed higher education (344/68.94%), their knowledge of oral health, preterm birth, and low birth weight was very limited (including the impact of inflammation on the intrauterine development of the child or bacteria and transfer across the placenta). In these sections, the percentage of correct answers ranged from less than 20% to 50%. When analyzing knowledge by age, education, number of births, and place of residence, the highest levels of knowledge were observed among respondents with higher education, particularly those aged 27–32. Conclusions: Respondents had a fairly good understanding of the general impact of oral health during pregnancy and recognition of the importance of breastfeeding for infants. However, their knowledge about the impact of bacteria and inflammation in the mother’s oral cavity on prematurity and low birth weight was limited. Therefore, educating women of reproductive age and pregnant women on this topic is essential, as it may help reduce the adverse consequences of prematurity. Full article

Surface, terrain, or even atmosphere analysis using images or their fragments is important due to the possibilities of further processing. In particular, attention is necessary for satellite and/or drone images. Analyzing image elements by classifying the given classes is important for obtaining information about space for autonomous systems, identifying landscape elements, or monitoring and maintaining the infrastructure and environment. Hence, in this paper, we propose a neural classifier architecture that analyzes different features by the parallel processing of information in the network and combines them with a feature fusion mechanism. The neural architecture model takes into account different types of features by extracting them by focusing on spatial, local patterns and multi-scale representation. In addition, the classifier is guided by an attention mechanism for focusing more on different channels, spatial information, and even feature pyramid mechanisms. Atrous convolutional operators were also used in such an architecture as better context feature extractors. The proposed classifier architecture is the main element of the modeled framework for satellite data analysis, which is based on the possibility of training depending on the client’s desire. The proposed methodology was evaluated on three publicly available classification datasets for remote sensing: satellite images, Visual Terrain Recognition, and USTC SmokeRS, where the proposed model achieved accuracy scores of 97.8%, 100.0%, and 92.4%, respectively. The obtained results indicate the effectiveness of the proposed attention mechanisms across different remote sensing challenges. Full article

Wildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target morphologies, and the difficulty of detecting small-scale smoke and flame objects. To address these issues, this paper proposed an improved Oriented R-CNN model enhanced with metric learning for wildfire detection in power transmission corridors. Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. Experimental results showed that the introduction of the MCM-Loss module increased the average precision (AP) for smoke targets by 2.7%. In addition, the group convolution-based network ResNeXt was adopted to replace the original backbone network ResNet, broadening the channel dimensions of the feature extraction network and enhancing the model’s capability to detect flame and smoke targets with diverse morphologies. This substitution led to a 0.6% improvement in mean average precision (mAP). Furthermore, an FPN-CARAFE module was designed by incorporating the content-aware up-sampling operator CARAFE, which improved multi-scale feature representation and significantly boosted performance in detecting small targets. In particular, the proposed FPN-CARAFE module improved the AP for fire targets by 8.1%. Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors. Full article

Purpose: The aim of this retrospective study was to analyse the compensation procedures concerning patients presenting with work-related lung cancer (LC), hospitalised in a French university hospital, and to assess the benefit of systematic specialised occupational disease (OD) consultations in improving procedures for reporting and recognising OD. Methods: Patient exposure to occupational lung carcinogens was assessed via an analysis of a standardised questionnaire, completed between 1 January 2009 and 24 April 2023. Among the 2024 patients who completed the questionnaire, 621 patients with probable exposure to occupational lung carcinogens were included. Among these patients, two groups were compiled: group 1, consisting of the 392 subjects who did not benefit from specialised OD consultations, and group 2, consisting of the 229 subjects who benefited from such consultations since 2014 and to whom a medical certificate to claim for compensation was issued by a physician. During the second phase of our study, we determined the outcome of the compensation procedure for OD. Uni- and multivariate logistic regressions were performed according to descending logistic regression methods. Results: Multivariate analyses, including smoking status, sex, age and claim for compensation, confirm the significant relationship between specialised OD consultation and claim for compensation (OR 18.13, 95% CI [11.47–28.64]). Furthermore, the rate of occupational disease recognition has multiplied by 1.5 since 2014. Conclusion: This study confirms the importance of specialised OD consultations in helping patients with LC to obtain compensation and to reduce under-recognition. Full article

Objective: The aim of this study is to investigate uterine prolapse (UP) among women attending a semi-urban health center for routine gynecological examinations. Specifically, the study explores the potential association between UP and various established or suspected risk factors, including age, menopausal status, number and mode of deliveries, birth weight, smoking habits, and body mass index (BMI). Furthermore, it examines the relationship between the presence or severity of UP and the scores of specific questionnaires and their subscales. Finally, the study seeks to develop a predictive model for the likelihood of UP based on questionnaire responses. Methods: A quantitative study was conducted at the gynecological department of a health center in Greece from January 2021 to October 2022. A total of 134 women were recruited using convenience sampling during routine gynecological visits. The degree of prolapse was classified according to the International Continence Society (ICS) Pelvic Organ Prolapse Quantification (POP-Q) classification system. Data collection also included the use of validated instruments: the Australian Pelvic Floor Questionnaire (APFQ), the Urogenital Distress Inventory-6 (UDI-6), the Pelvic Floor Distress Inventory-20 (PFDI-20), and the Pelvic Floor Impact Questionnaire-7 (PFIQ-7). The data were processed with the Statistical Package for the Social Sciences (SPSS) v25. Results: Of the 134 participants, 21 (15.7%) aged 21 to 82 showed signs of UP, while 113 women (84.3%) did not. The average age of the women with UP was 55 years. Fourteen (10.4%) of these women were diagnosed with UP stage I, three of them (2.2%) with stage II, and four of them (3%) with stage III UP. There were no stage IV UP incidents. The risk factors associated with the disease include age, mode of delivery, parity, and duration of menopause. Regarding parity, every subsequent birth after the first one increases the likelihood of a UP incident by approximately 125%. Conclusions: Most women with UP did not exhibit severe symptoms, as UP typically does not manifest symptoms until it reaches a final stage. Considering the population aging and the increase in morbidity, a regular pelvic organ prolapse (POP) checkup should be established to facilitate early recognition, prevention, and treatment of symptoms. This study offers a potential tool for non-invasive screening to facilitate identifying UP in women early, which has not been previously reported. Full article

Moyamoya angiopathy (MMA) is a cerebrovascular disease determining chronic progressive steno-occlusion of the supraclinoid internal carotid arteries and their main branches. The pathogenesis of MMA remains largely unknown. Multiple sclerosis (MS) is a chronic, inflammatory, demyelinating disease of the central nervous system characterized by the progressive accumulation of focal demyelinating lesions, whose pathophysiology has been theorized but still incompletely understood. Beyond misdiagnoses due to mimicking features among the two disorders, MS coexisting with MMA have been previously, rarely, reported. Herein, we present two other cases of patients with MMA with a concomitant, previously missed, diagnosis of MS and discuss their overlapping features as a hint for a potentially shared pathophysiology. The finding of typical angiographic features enables MMA diagnosis, yet it does not allow us to rule out other potentially concomitant disorders affecting the CNS. The association may be easily missed if the clinical/neuroradiological picture is not carefully assessed. Cerebral spinal fluid analysis and spine neuroimaging should be suggested in all MMA patients with atypical MRI lesions. Full article

In recent years, accelerated global climate change has precipitated an increased frequency of wildfire events, with their devastating impacts on ecological systems and human populations becoming increasingly significant. Satellite remote sensing technology, leveraging its extensive spatial coverage and real-time monitoring capabilities, has emerged as a pivotal approach for wildfire early warning and comprehensive disaster assessment. To effectively detect subtle smoke signatures while minimizing background interference in remote sensing imagery, this paper introduces a novel dual-branch attention framework (CSFAttention) that synergistically integrates channel–spatial refinement with frequency spectral analysis to aggregate smoke features in remote sensing images. The channel–spatial branch implements an innovative triple-pooling strategy (incorporating average, maximum, and standard deviation pooling) across both channel and spatial dimensions to generate complementary descriptors that enhance distinct statistical properties of smoke representations. Concurrently, the frequency branch explicitly enhances high-frequency edge patterns, which are critical for distinguishing subtle textural variations characteristic of smoke plumes. The outputs from these complementary branches are fused through element-wise summation, yielding a refined feature representation that optimizes channel dependencies, spatial saliency, and spectral discriminability. The CSFAttention module is strategically integrated into the bottleneck structures of the ResNet architecture, forming a specialized deep network specifically designed for robust smoke recognition. Experimental validation on the USTC_SmokeRS dataset demonstrates that the proposed CSFResNet achieves recognition accuracy of 96.84%, surpassing existing deep networks for RS smoke recognition. Full article

Fires pose significant threats to human safety, health, and property. Traditional methods, with their inefficient use of features, struggle to meet the demands of fire detection. You Only Look Once (YOLO), as an efficient deep learning object detection framework, can rapidly locate and identify fire and smoke objects in visual images. However, research utilizing the latest YOLO11 for fire and smoke detection remains sparse, and addressing the scale variability of fire and smoke objects as well as the practicality of detection models continues to be a research focus. This study first compares YOLO11 with classic models in the YOLO series to analyze its advantages in fire and smoke detection tasks. Then, to tackle the challenges of scale variability and model practicality, we propose a Multi-Scale Convolutional Attention (MSCA) mechanism, integrating it into YOLO11 to create YOLO11s-MSCA. Experimental results show that YOLO11 outperforms other YOLO models by balancing accuracy, speed, and practicality. The YOLO11s-MSCA model performs exceptionally well on the D-Fire dataset, improving overall detection accuracy by 2.6% and smoke recognition accuracy by 2.8%. The model demonstrates a stronger ability to identify small fire and smoke objects. Although challenges remain in handling occluded targets and complex backgrounds, the model exhibits strong robustness and generalization capabilities, maintaining efficient detection performance in complicated environments. Full article

Study Objectives: This review aims to explore the epidemiology, pathophysiology, risk factors, and diagnostic approaches of obstructive sleep apnea syndrome and small airway disease, emphasizing their interrelationship and implications for clinical management. Methods: A comprehensive analysis of the literature was conducted to examine shared and distinct characteristics of obstructive sleep apnea syndrome and small airway disease. Risk factors, clinical presentations, diagnostic tools, and management strategies were reviewed to identify potential areas for improvement in care. Results: Obstructive sleep apnea syndrome, characterized by intermittent upper airway obstruction during sleep, contributes to fragmented sleep and systemic diseases. Small airway disease involves inflammation and obstruction of the small airways, impairing airflow and gas exchange. Shared risk factors, such as obesity, smoking, and age, were identified as contributors to the development and progression of both conditions. The co-occurrence of obstructive sleep apnea syndrome and small airway disease exacerbates respiratory symptoms and increases the risk of comorbidities, such as pulmonary hypertension, heart failure, and respiratory failure. Recognition of their interplay highlights the need for integrated diagnostic and therapeutic strategies. Conclusions: The interrelationship between obstructive sleep apnea syndrome and small airway disease underscores the importance of integrated management approaches to improve patient outcomes. Addressing shared risk factors and understanding the interplay between these conditions are crucial for optimizing care. This review identifies key knowledge gaps, including the need for precise diagnostic tools and targeted therapies, which are essential for advancing personalized treatment strategies for individuals with obstructive sleep apnea syndrome and small airway disease. Full article

In the aluminum electrolysis production workshop, heavy-load overhead cranes equipped with multi-functional operation terminals are responsible for critical tasks such as anode replacement, shell breaking, slag removal, and material feeding. The real-time monitoring of these four types of operation terminals is of the utmost importance for ensuring production safety. High-resolution cameras are used to capture dynamic scenes of operation. However, the terminals undergo morphological changes and rotations in three-dimensional space according to task requirements during operations, lacking rotational invariance. This factor complicates the detection and recognition of multi-form targets in 3D environment. Additionally, operations like striking and material feeding generate significant dust, often visually obscuring the terminal targets. The challenge of real-time multi-form object detection in high-resolution images affected by smoke and dust environments demands detection and dehazing algorithms. To address these issues, we propose the YOLOv8n-Al-Dehazing method, which achieves the precise detection of multi-functional material handling terminals in aluminum electrolysis workshops. To overcome the heavy computational costs associated with processing high-resolution images by using YOLOv8n, our method refines YOLOv8n through component substitution and integrates real-time dehazing preprocessing for high-resolution images, thereby reducing the image processing time. We collected on-site data to construct a dataset for experimental validation. Compared with the YOLOv8n method, our method approach increases inference speed by 15.54%, achieving 120.4 frames per second, which meets the requirements for real-time detection on site. Furthermore, compared with state-of-the-art detection methods and variants of YOLO, YOLOv8n-Al-Dehazing demonstrates superior performance, attaining an accuracy rate of 91.0%. Full article

Background/Objective: Condyloma acuminatum, also known as genital warts, results from infections of the basal epithelium or mucous membranes by human papillomavirus (HPV). These lesions can significantly impact patients’ quality of life. Recent advances in laser technology, particularly ablative lasers such as CO₂ and Erbium–YAG (Er:YAG), have introduced new treatment opportunities. The Er:YAG laser has gained recognition as a safe and effective treatment for viral warts. This study aimed to evaluate the efficacy of Er:YAG laser treatment of male genital warts and to identify risk factors that might influence its effectiveness. Methods: A retrospective chart review of 102 patients who underwent Er:YAG laser wart removal between January 2019 and April 2024 was conducted. Results: Of the 102 patients, 61 (60%) achieved complete response by the 12-month follow-up visit. The response rates were significantly lower when there was a high number of sessions required for complete response, long duration between wart onset and laser treatment, high number of warts treated, positive smoking status, concurrent immunosuppressed state, or active metabolic disease. Conclusions: The Er:YAG laser is an effective method for treating recalcitrant warts. Various factors were shown to influence its efficacy. Full article

Computer vision-based state recognition is widely employed in substations, but conventional video monitoring systems often encounter challenges during emergency situations, such as smoke from fires. In such scenarios, LiDAR emerges as an appealing alternative, capable of capturing the depth information of the target. However, when smoke concentration is high, the quality of collected point cloud data deteriorates, impacting the assessment of the disconnector open–close status. This paper delves into the impact of a smoky environment on point cloud data and introduces a two-stage discrimination process. Firstly, a feature extraction method using sliced point clouds is employed to construct edge features of the conductive arm. Building upon this foundation, an open–close position identification method based on edge pre-processing is employed to obtain the final measurement results. Field experiments demonstrate that the proposed method effectively mitigates smoke interference and accurately determines the disconnector’s open–close status with high reliability and precision. This approach could serve as a reference for the development of continuous disconnector closing state monitoring technology. Full article