Search Results (324)

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have transformed the management of type 2 diabetes mellitus and obesity. However, their sustained effect on delaying gastric emptying raises new challenges in gastrointestinal endoscopy performed under sedation. This narrative review aims to summarize current evidence on the impact of GLP-1 RAs on gastric motility and to propose clinical strategies to mitigate associated procedural risks. Methods: A narrative review was conducted integrating findings from scintigraphy, capsule endoscopy, gastric ultrasound, and existing clinical guidelines. Emphasis was placed on studies reporting residual gastric content (RGC), anesthetic safety outcomes, and procedural feasibility in patients undergoing endoscopy while treated with GLP-1 RAs. Results: GLP-1 RAs significantly increase the prevalence of clinically relevant RGC, despite prolonged fasting, with potential implications for airway protection and sedation safety. Although the risk of pulmonary aspiration remains low (≤0.15%), procedural delays, modifications, or cancellations can occur in up to 30% of cases without adapted protocols. Several professional societies (AGA, ASGE, AASLD) advocate for individualized management based on procedure type, symptomatology, treatment phase, and point-of-care gastric ultrasound (POCUS), in contrast to the systematic discontinuation recommended by the ASA. Conclusions: Effective management requires personalized fasting protocols, risk-based stratification, tailored anesthetic approaches, and interprofessional coordination. We propose a clinical decision algorithm and highlight the need for training in gastrointestinal pharmacology, POCUS, and airway management for endoscopists. Future priorities include prospective validation of clinical algorithms, safety outcome studies, and the development of intersocietal consensus guidelines. Full article

This project aimed to develop personal protective equipment (PPE) that provides full biological protection for the general public without the need for extensive training to use the equipment. With the proposal to develop a device guided by a smartphone monitoring application (to guide the user on the replacement of perishable components, ensuring their safety and biological protection in potentially contaminated places), the embedded electronics of this equipment were built, as well as their control system, including a smartphone app. Thus, a device was successfully developed to monitor and assist individuals in using an advanced PPE device. Full article

Chainsaws are widely used in various occupational settings, including forestry, landscaping, farming, and by homeowners for tasks like tree felling, brush clearing, and firewood cutting. However, the use of chainsaws poses significant risks to operators and bystanders. This research quantified and compared occupational and nonoccupational injuries caused by contact with chainsaws and related objects during the period from 2018 to 2022. The emergency department and OSHA (Occupational Safety and Health Administration) data were used to characterize the cause and nature of the injuries. Results suggest that for this five-year period an estimated 127,944 people were treated in U.S. emergency departments for chainsaw-related injuries. More than 200 non-fatal and 57 fatal occupational chainsaw-involved injuries were found during the same period. Landscaping and forestry were the two industries where most of the occupational victims were employed. Upper and lower extremities were the most likely injured body parts, with open wounds from cuts being the most common injury type. The majority of fatal injuries were caused by falling objects such as trees and tree limbs while using a chainsaw. Our suggestions to reduce injuries include proper training and wearing personal protective equipment, as well as making sure any bystanders are kept in a safety zone away from trees being cut. Full article

Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary surgery planning. The clinical challenges in hepato-biliary surgery arise from heterogeneity of clinical presentations, the need for multiple imaging modalities and highly variable local anatomy. AI-based models have been developed for risk prediction and multi-disciplinary tumor (MDT) board meetings. The future could involve an on-demand and highly accurate AI-powered decision tool for hepato-biliary surgery, assisting the surgeon to make the most informed decision on the treatment plan, conferring the best possible outcome for individual patients. Advances in AI can also be used to automate image interpretation and 3D modelling, enabling fast and accurate 3D reconstructions of patient anatomy. Surgical navigation systems utilizing XR are already in development, showing an early signal towards improved patient outcomes when used for hepato-biliary surgery. Live visualization of hepato-biliary anatomy in the operating theatre is likely to improve operative safety and performance. The technological advances in AI and XR provide new applications in pre-operative planning with potential for patient benefit. Their use in surgical simulation could accelerate learning curves for surgeons in training. Future research must focus on standardization of AI and XR study reporting, robust databases that are ethically and data protection-compliant, and development of inter-disciplinary tools for various healthcare applications and systems. Full article

Surface ozone is a pollutant linked to higher risks of cardiopulmonary diseases with long-term exposure. Timely forecasting of ozone levels helps authorities implement preventive measures to protect public health and safety. However, few studies have been able to reliably provide long-term hourly ozone forecasts due to the complexity of ozone’s diurnal variations. To address this issue, this study constructs a hybrid prediction model integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bi-directional long short-term memory neural network (BiLSTM), and the persistence model to forecast the hourly ozone concentrations for the next continuous 36 h. The model is trained and tested at the Wanshouxigong site in Beijing. The ICEEMDAN method decomposes the ozone time series data to extract trends and obtain intrinsic mode functions (IMFs) and a residual (Res). Fourier period analysis is employed to elucidate the periodicity of the IMFs, which serves as the basis for selecting the prediction model (BiLSTM or persistence model) for different IMFs. Extensive experiments have shown that a hybrid model of ICEEMDAN, BiLSTM, and persistence model is able to achieve a good performance, with a prediction accuracy of R² = 0.86 and RMSE = 18.70 µg/m³ for the 36th hour, outperforming other models. Full article

Series elastic actuators (SEAs) represent a key technological solution to enhance safety, performance, and adaptability in robotic devices for physical training. Their ability to decouple the rigid actuator’s mechanical impedance from the load, combined with passive absorption of external disturbances, makes them particularly suitable for pediatric applications. In children aged 2 to 5 years—where motor control is still developing and movements can be unpredictable or unstructured—SEAs provide a compliant mechanical response that ensures user protection and enables safe physical interaction. This study explores the role of SEAs as a central component for imparting compliance and backdrivability in robotic systems designed for upper-limb training. A dynamic model is proposed, incorporating interaction with the user’s limb, along with a computed torque control strategy featuring integral action. The system’s performance is validated through simulations and experimental tests, demonstrating stable trajectory tracking, disturbance absorption, and effective impedance decoupling. The results support the use of SEAs as a foundational technology for developing safe adaptive robotic solutions in pediatric contexts capable of responding flexibly to user variability and promoting secure interaction in early motor development environments. Full article

Background: Informal welders in Pretoria West face growing occupational safety risks due to hazardous working environments and limited regulatory oversight. Despite the high-risk nature of their work, many remain unaware of relevant safety legislation and inconsistently use personal protective equipment (PPE). This study aimed to investigate the occupational safety risks, challenges, and levels of compliance with safety practices among informal welders in Pretoria West, South Africa. Methods: A cross-sectional mixed-methods approach was employed, incorporating both qualitative and quantitative designs. Data were collected from 40 male welders (aged 20–55 years) using structured questionnaires, observational checklists, and semi-structured interviews. Descriptive statistics were generated using Microsoft Excel, while thematic content analysis was applied to the qualitative data. Results: Eighty-five percent (85%) of welders reported using gas welding, and more than half had received training in welding and PPE use; however, 47.5% had no formal training. A high prevalence of work-related injuries was reported, including burns, cuts, and eye damage. Common safety concerns identified were burns (42.5%), electric shocks (35%), and malfunctioning equipment. Observational data revealed inconsistent PPE use, particularly with flame-resistant overalls and eye protection. Qualitative insights highlighted challenges such as demanding client expectations, hazardous physical environments, and inadequate equipment maintenance. Many sites lacked compliance with occupational safety standards. Conclusion: The study reveals critical gaps in safety knowledge, training, and PPE compliance among informal welders. These deficiencies significantly elevate the risk of occupational injuries. Strengthening occupational health and safety regulations, improving access to PPE, and delivering targeted training interventions are essential to safeguard the well-being of welders and those in their surrounding communities. Full article

Utilizing deep learning models to detect malicious anomalies within the traffic of application layer J1939 protocol networks, found on heavy-duty commercial vehicles, is becoming a critical area of research in platform protection. At the physical layer, the controller area network (CAN) bus is the backbone network for most vehicles. The CAN bus is highly efficient and dependable, which makes it a suitable networking solution for automobiles where reaction time and speed are of the essence due to safety considerations. Much recent research has been conducted on securing the CAN bus explicitly; however, the importance of protecting the J1939 protocol is becoming apparent. Our research utilizes long short-term memory models to predict the next binary data sequence of a J1939 packet. Our primary objective is to compare the performance of our J1939 detection system trained on data sub-fields against a published CAN system trained on the full data payload. We conducted a series of experiments to evaluate both detection systems by utilizing a simulated attack representation to generate anomalies. We show that both detection systems outperform one another on a case-by-case basis and determine that there is a clear requirement for a multifaceted security approach for vehicular networks. Full article

In the coal industry, miner safety is increasingly challenged by growing mining depths and complex environments. The failure to wear Personal Protective Equipment (PPE) is a frequent issue in accidents, threatening lives and reducing operational efficiency. Additionally, existing PPE datasets are inadequate for model training due to their small size, lack of diversity, and poor labeling. Current methods often struggle with the complexity of multi-scenario and multi-type PPE detection, especially under varying environmental conditions and with limited training data. In this paper, we propose a novel minersPPE dataset and an improved algorithm based on YOLOv8, enhanced with Dilated-CBAM (Dilated Convolutional Block Attention Module) and DBB (Diverse Branch Block) Detection Block (YOLOv8-DCDB), to address these challenges. The minersPPE dataset constructed in this paper includes 14 categories of protective equipment needed for various body parts of miners. To improve detection performance under complex lighting conditions and with varying PPE features, the algorithm incorporates the Dilated-CBAM module. Additionally, a multi-branch structured detection head is employed to effectively capture multi-scale features, especially enhancing the detection of small targets. To mitigate the class imbalance issue caused by the long-tail distribution in the dataset, we adopt a K-fold cross-validation strategy, optimizing the detection results. Compared to standard YOLOv8-based models, experiments on the minersPPE dataset demonstrate an 18.9% improvement in detection precision, verifying the effectiveness of the proposed YOLOv8-DCDB model in multi-scenario, multi-type PPE detection tasks. Full article

Pipe-insulating joints are common cathodic protection devices in long-distance oil and gas pipeline infrastructures. To ensure safety, they are often designed too conservatively, resulting in large dimensions, high self-weight, and substantial costs. This study analyzed an insulating joint under the most unfavorable conditions to identify the component of the maximum stress in the insulating joint, which is the right flange. Then, using parameterized finite element calculations, five independent dimensions of the right flange were combined and arranged to obtain a dataset of the right flange dimensions and their maximum stress. Subsequently, four different fitting algorithms were trained with this dataset, and the ridge regression algorithm, which showed the best predictive performance, was used to establish a surrogate model for calculating the maximum stress of the right flange. Finally, the surrogate model was combined with a genetic algorithm to determine the optimal design dimensions of the right flange. This study also provides examples verifying the accuracy and reliability of the surrogate model and genetic algorithm. In these examples, the maximum stress under the design dimensions given by the optimization algorithm has a maximum error of 8.98% and an average error of 4.63% compared to the preset maximum stress target, while the stress predicted by the surrogate model has a maximum error of 9.65% and an average error of 5.33% compared to the actual stress. This improves the computational efficiency of the optimization algorithm by establishing a surrogate model, which can be used to optimize the dimensions of insulation joints. Full article

Ensuring that electrical workers use personal protective equipment (PPE) correctly is critical to electrical safety, but existing detection methods face significant limitations when applied in the electrical industry. This paper introduces MRC-DETR (Multi-Scale Re-calibration Detection Transformer), a novel framework for detecting Power Engineering Personal Protective Equipment (PEPPE) in complex electrical operating environments. Our method introduces two technical innovations: a Multi-Scale Enhanced Boundary Attention (MEBA) module, which significantly improves the detection of small and occluded targets through optimized feature representation, and a knowledge distillation strategy that enables efficient deployment on edge devices. We further contribute a dedicated PEPPE dataset to address the lack of domain-specific training data. Experimental results demonstrate superior performance compared to existing methods, particularly in challenging power industry scenarios. Full article

In 2020, Directive (EU) 2020/2184 was published and subsequently transposed into Italian legislation via Legislative Decree 18/23 (Lgs.D. 18/23). The Directive aims to protect public health through a proactive approach based on a site-specific risk analysis along the entire water supply chain (Water Safety Plan, WSP). Between February and November 2024, a survey was conducted in Italy to assess both hospitals’ knowledge of Lgs.D. 18/23 and the application of the WSP in these facilities. A total of 300 hospitals were asked to complete an anonymous questionnaire containing 60 questions about the characteristics of the facility and the management of the water network, including the chemical–physical and microbiological monitoring of Legionella and other microbiological parameters. A total of 102 questionnaires were sent out (response rate: 34%), but only 72 were properly completed and analyzed. The results of the chemical–physical monitoring are not presented in this manuscript. Overall, 52.8% of the hospitals were built before 2000, and most are aware of Directive (EU) 2020/2184, Lgs.D.18/23 (80.6%), in particular, Article 9 on the risk assessment and management of internal water systems and the guidelines for its implementation (77.8%). All hospitals perform annual microbiological water testing, including Legionella analysis, with a detection rate of <50%. National guidelines for the implementation of WSPs are known in 75% of the hospitals, but only 38.9% have started planning to implement them, and 13.9% organize staff training on the subject. The questionnaire responses highlight the need to train hospital staff in water system risk management and WSP planning, which will be mandatory by 2029. Full article

Applicable recommendations play a key role in improving training and procedures used in civil protection. Since 1 January 2025, the Law on Civil Protection and Civil Defense has been in force in Poland. It responds to the experience of current threats, including the war in Ukraine, the 2024 floods in Western Poland, the COVID-19 pandemic, and other crises. The Act systemically regulates the problem of building social resilience, which must be developed and applied regarding today’s modern threats. The primary actor in civil protection is the fire brigade system, in which volunteer firefighters are recruited from local communities and act for their benefit. In this context, it is interesting to ask whether and what solutions should be applied in order to improve the effectiveness of the training and exercise system of volunteer fire brigades (TSOs) in the field of civil protection and crisis management. The aim of this investigation was to develop evaluations and applicable recommendations to improve the effectiveness of the training system for volunteer firefighters based on a survey of volunteer firefighters in the Cracow Poviat. Two survey diagnostic techniques were used: expert interviews and questionnaire research. The findings were compared with the results of an analysis of source documents obtained in TSO units. The expert interviews covered all chief fire officers of the municipalities in the Cracow Poviat. The paper begins with an introduction and a systematic literature review. The conclusions consist of the proposal of applicable changes in the scope of basic, specialist, and additional training. Areas of missing training are also identified. The firefighters’ knowledge of crisis management procedures is verified, deficiencies are identified, and applicable changes in the organization of field exercises are proposed. Full article

In this study, we propose a rehabilitation training system that incorporates active and passive rehabilitation modes to enhance the convenience, efficacy, and safety of rehabilitation training for patients with upper limb hemiplegia. This system facilitates elbow flexion and extension as well as wrist and palm flexion and extension. The experimental results demonstrate that the exoskeleton robot on the affected limb exhibits a rapid response and maintains a highly synchronized movement with the unaffected upper limb equipped with an angle sensor, preserving stability and coordination throughout the movement process without significant delay affecting the overall motion. When the movement of the unaffected upper limb exceeds the predetermined angle threshold, the affected limb promptly initiates a protective mechanism to maintain its current posture. Upon equalization of the angles between the two limbs, the affected limb resumes synchronized movement, thereby ensuring the safety of the rehabilitation training. This research provides some insights into the functional improvements of safe and reliable upper limb exoskeleton rehabilitation training systems. Full article

Background: The rapid integration of artificial intelligence (AI) technologies into healthcare systems presents new opportunities and challenges, particularly regarding legal and ethical implications. In Saudi Arabia, the lack of legal awareness could hinder safe implementation of AI tools. Methods: A sequential explanatory mixed-methods design was employed. In Phase One, a structured electronic survey was administered to 357 clinicians across public and private healthcare institutions in Saudi Arabia, assessing legal awareness, liability concerns, data privacy, and trust in AI. In Phase Two, a qualitative expert panel involving health law specialists, digital health advisors, and clinicians was conducted to interpret survey findings and identify key regulatory needs. Results: Only 7% of clinicians reported high familiarity with AI legal implications, and 89% had no formal legal training. Confidence in AI compliance with data laws was low (mean score: 1.40/3). Statistically significant associations were found between professional role and legal familiarity (χ² = 18.6, p < 0.01), and between legal training and confidence in AI compliance (t ≈ 6.1, p < 0.001). Qualitative findings highlighted six core legal barriers including lack of training, unclear liability, and gaps in regulatory alignment with national laws like the Personal Data Protection Law (PDPL). Conclusions: The study highlights a major gap in legal readiness among Saudi clinicians, which affects patient safety, liability, and trust in AI. Although clinicians are open to using AI, unclear regulations pose barriers to safe adoption. Experts call for national legal standards, mandatory training, and informed consent protocols. A clear legal framework and clinician education are crucial for the ethical and effective use of AI in healthcare. Full article