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Pemphigus is an autoimmune blistering disorder characterized by the presence of intraepidermal blisters and erosions, primarily affecting the mucosa and/or skin. There are no established Indian guidelines for the management of pemphigus, and Western guidelines cannot be directly applied due to differences in clinicodemographic profiles, comorbidities, and resource limitations. These guidelines aim to provide Indian dermatologists with evidence-based and consensus-driven recommendations for the management of pemphigus vulgaris (PV) and pemphigus foliaceous (PF), taking into account the unique challenges posed by the Indian healthcare setting. The guidelines focus on the comprehensive management of PV and PF, addressing diagnosis, treatment, monitoring, and follow-up. It is intended for dermatologists working in both outpatient and inpatient settings across India. The first draft of the guidelines was prepared by the writing group and then reviewed by 19 national experts in pemphigus management, including inputs from allied specialties. Areas with limited evidence or anticipated variation in recommendations were subjected to rounds of voting, with responses categorized as "strongly agree," "agree," "neutral," "disagree," or "strongly disagree." Suggestions were incorporated, and statements were revised until a mean agreement score of 4 or higher was reached across 16 key areas after four voting rounds. These guidelines offer a structured approach to managing pemphigus in India, addressing the need for region-specific recommendations that account for unique challenges such as resource constraints and specific comorbidities in Indian patients. They serve as a valuable resource for dermatologists treating pemphigus in a range of clinical settings. [ABSTRACT FROM AUTHOR]

Background: Antibiotic resistance represents a growing concern. A new strategy developed to treat severe infections is represented by ceftazidime/avibactam (CZA/AVI). Despite the promising activities against more pathogens, continuous monitoring is required to identify potential antibiotic resistance in clinical practice settings. Therefore, real-world data from pharmacovigilance databases can help to better define the safety profile. Methods: We analyzed all Individual Case Safety Reports (ICSRs) collected in the EudraVigilance database focusing on ICSRs with at least one adverse event (AE) potentially suggestive of drug resistance (DR) and drug ineffectiveness (DI). Results: A total of 654 ICSRs related to CZA/AVI were retrieved from EudraVigilance, of which N = 378 (57.8%) were related to male and N = 230 (35.1%) to adult patients. A total of 80.2% of all AEs were serious but with a positive outcome. Overall, we found N = 129 (19.7%) cases of potential DR or DI after CZA/AVI administration. The majority of CZA/AVI-induced DR or DI occurred in adult male patients. The most frequently reported AEs were "drug ineffective" and "pathogen resistance". Lastly, CZA/AVI was mostly used for the treatment of "Klebsiella infection" and "Pneumonia". Conclusions: The present study showed how pharmacovigilance could play a key role in generating evidence about the safety profile of CZA/AVI. Further studies are warranted. [ABSTRACT FROM AUTHOR]

Abstract Internet of Things (IoT) technologies present transformative opportunities through connectivity of intelligent devices, environmental sensors, and integrated management systems. This study aims to investigate the benefits and impact of IoT implementation on construction sites by analyzing relationships between key factors and outcomes for safety and efficiency. Hypotheses were developed proposing positive correlations between each factor and effective IoT adoption on construction sites. Structural equation modeling analysis on survey data from construction professionals and site reports strongly validated the research hypotheses. Positive path coefficients and high statistical significance confirmed environmental monitoring (0.38), equipment management (0.343), predictive analytics and maintenance (0.222) and safety monitoring (0.369) as crucial enablers for successful IoT integration leading to safer and more productive construction operations. The findings highlight imperative focus areas and provide actionable insights for construction stakeholders on strategies to effectively leverage IoT capabilities.

Through the innovative combination of CO2 capture in the coal chemical industry and oil reservoir CO2 storage, Yanchang Petroleum has realized multiple benefits such as clean utilization of coal, carbon emission reduction, and crude oil production increase, and explored a new path for low-carbon development of coal chemical industry. The whole process integration technology and field practice of carbon capture, utilization, and storage (CCUS) from Yanchang Petroleum are systematically presented. For CO2 capture, the low-temperature methanol washing and low-cost CO2 capture technology of the coal chemical industry has been formed. The CO2 capture cost is 105 yuan/t, and the capture demonstration scale is 300000 t/a. For CO2 storage, the storage site selection and potential evaluation methods are established. The evaluation results showed that the theoretical CO2 storage capacity of the oilfield is 8.84×108 t, and the suitable storage area is mainly distributed in the west of the oilfield. In addition, the evolution laws of CO2 storage state across the time scale of storage are revealed. Under the water-gas alternation scenario, the proportions of CO2 storage capacity for structure, residual, solubility and mineral trapping corresponding to 10 years and 100 years is 24.38%, 27.19%, 48.38%, and 0.05%; and 15.09%, 38.65%, 45.77% and 0.49%, respectively. Then the sealing evaluation methods of the caprocks are created. It is specified that the Chang 4+5 cap layer in the Huaziping CO2 sequestration test area mainly belongs to type Ⅰ and type Ⅱ. Finally, a “Trinity” CO2 safety monitoring system is established. The results of safety monitoring show that, there is no CO2 geological leakage in the test area and the safety level of CO2 storage is Ⅰ. The safe and effective storage capacity of CO2 is 10.12×104 t in the field application. The technology of CO2 capture in the coal chemical industry and reservoir CO2 storage has achieved good effects of carbon reduction and crude oil production increase in field practice.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the acceleration of urbanization, we are paying more and more attention to the safety and reliability of building water supply and drainage systems. Faced with various limitations of traditional monitoring methods, such as insufficient real-time performance and difficulties in data collection, we propose a new solution that combines BIM (Building Information Modeling) technology and the Internet of Things. This system not only enables real-time monitoring, but also quickly addresses security risks, greatly improving management efficiency and system security. In terms of system stability, the standard deviation of water pressure based on BIM technology is 1.1 kPa and the standard deviation of water flow rate is 0.2 L/s. Moreover, the building water supply and drainage network security monitoring project based on BIM technology can quickly and accurately respond to various security incidents. From the data conclusion, it can be seen that the system based on BIM technology has efficient, accurate, stable, and secure monitoring capabilities. [ABSTRACT FROM AUTHOR]

Aluminum alloy frame is a novel structure system developed in recent years. In this article, the load-bearing performance of the beam-beam joint of the aluminum alloy frame is studied through numerical analysis and experiment and a safety monitoring method is developed. The impacts of the arch angle and bolt diameter on the beam-beam joint mechanical characteristics are explored through experiments under vertical load. When the diameter of the bolt was increased, the load-bearing performance of such joint displayed a pattern of first increasing and then decreasing. As the arch angle increased, the load-bearing performance on the joint gradually improved. Based on the experiments, numerical analysis models varying in arch angle were established, and the impacts of arch angles on the stiffness during the yield stage, ultimate load, and elastic stiffness of the aluminum alloy portal frame (AAPF) beam-beam joints were further explored through numerical simulation of the structure under vertical and horizontal loads. When the arching angle was increased, the elastic stiffness and yield stiffness of the beam-beam joint under vertical load showed a pattern of first increasing and then decreasing. When the arching angle was increased, the elastic stiffness and yield stiffness of the beam-beam joint under horizontal load significantly decreased. Based on the mechanical properties, a safety monitoring method for AAPF beam-beam joints based on displacement monitoring and frequency monitoring is proposed. [ABSTRACT FROM AUTHOR]

With the rapid development of underground space, the issue of safety in construction processes is becoming more and more significant. The purpose of this paper is to solve the problem of the existing underground space monitoring technology not being centralized and unified. In view of the problems related to large underground spaces in the process of constructing complex structures, with the introduction of Internet of Things technology and digital twins, we put forward an application of an intelligent safety-monitoring digital twin system in the construction of a large underground space structure, and at the same time, explore the Internet and digital integration mechanism of the twin system. The twin system uses BIM technology to establish the corresponding physical construction model, and collects multi-source heterogeneous monitoring data in real time through Internet of Things technology to achieve the exchange of information between the virtual construction model and the physical construction model. The twin system uses the multi-source heterogeneous data for real-time security analysis, and obtains the security status of the structure and feeds it back to the application service layer. The effectiveness and practicability of the twin system in large underground spaces are verified by an example project. Aiming at the safe performance of the orthogonal arch, the mapping relationship of various parameter indexes is obtained, and reasonable control measures are given. This study provides a new solution for improving the safety of construction projects and risk prevention and control, and has important theoretical and practical value for the safety management of underground space construction processes. [ABSTRACT FROM AUTHOR]

Comprehensive perception and real-time connectivity are fundamental functional elements of intelligent coal mines. Currently, coal mining faces suffer from insufficient overall environmental perception capabilities. Limitations include the small number of monitoring points for perception devices, inadequate terminal wireless network coverage, and a lack of high-precision positioning services. These shortcomings result in inadequate data sample sizes required for comprehensive perception of mines and coal mining faces, low information transparency, and reduced accuracy in hazard identification and safety warnings. To address these issues, this study investigated coal mining faces as the application scenario and CH4 as the monitoring target, exploring key technologies and monitoring models for large-scale data perception of coal mine environmental parameters. By investigating low-power wireless CH4 sensing and self-calibration technologies, the study enabled the deployment of numerous CH4 sensors in coal mining faces for comprehensive perception, resolving technical challenges associated with calibration-free maintenance. The study also addressed the difficulties of identifying the identities and locations of numerous sensors by developing device encoding and positioning technologies for sensing devices. Additionally, the study proposed high-speed wireless data transmission technologies suitable for the linear space of mines, along with autonomous routing discovery, network fault detection, timely isolation of fault nodes, and self-recovery for wireless backbone link nodes. These advancements solved the real-time data transmission and maintenance challenges arising from the deployment of large numbers of CH4 sensors and the mobility of coal mining faces. Furthermore, a continuous monitoring model for large-scale data based on edge computing was developed. This model processed the collected CH4 sensor data using spatial digital cloud mapping technology to achieve continuous monitoring and comprehensive perception of CH4 across the entire coal mining face, as well as hierarchical data processing at operational sites. The key technologies and monitoring model for large-scale data perception of CH4 in coal mining faces accumulate foundational technical knowledge for comprehensive perception studies of other mine environmental parameters.

There is a need to transition from conventional (on-site) clinical trials (CTs) to trials conducted within the comfort of a patient’s home or community (decentralized CT) through e-consent, remote data monitoring, and telemedicine consults. This shift in trial procedures will positively impact recruitment rates, compliance and participant retention, protocol deviations, and delays or missed visits. Home nursing in CTs (HNCTs) will be an important component of this decentralization effort. A few limitations may impact the implementation of HNCT in India. In this regard, the workstream conducted semi-structured qualitative interviews with experts from diverse domains of CT conduct (researchers from academia and industry, clinicians, investigators, nursing staff, patient research advocates, institutional ethics committee, or institutional review board members, legal experts, and trial participants) to collect their understanding, perspectives, and the ground realities about HNCTs in India. The current review puts forth the key areas that would facilitate the establishment of HNCTs in India and provides recommendations for the same.

The underground powerhouse of the Shuangjiangkou hydropower station is one of the largest caverns under construction in China, and its stability during construction is crucial for safe construction. To study the stability of the surrounding rock during excavation, the displacement and stress of the surrounding rock were monitored by multi-point displacement meters and bolt stress meters. Based on the monitoring data, the elastic modulus, Poisson's ratio, friction angle, and cohesion of surrounding rock were inversely analyzed by the PSO-BP algorithm. Then, the back-analyzed parameters were used to simulate the subsequent excavations and predict the stability of surrounding rock during the following construction. The analysis results show that the surrounding rocks were generally stable during the initial four stages of excavation, and the main factors affecting their stability were blasts and unfavorable geological structures, including the lamprophyre vein and the F1 fault. These unfavorable geological structures also significantly decrease the mechanical parameters of surrounding rock as demonstrated by back analysis, and the stability prediction results show that the omnibus bar cave and the tailrace tunnel were at the greatest risk of instability during the subsequent excavations. This study provides a practical analysis for engineering excavation of the underground caverns. [ABSTRACT FROM AUTHOR]

Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 μ ε ), pressure (0.14 bar), and refractive index (6 × 10−5 RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/ μ ε , −0.3 pm/bar, and −18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development. [ABSTRACT FROM AUTHOR]

There is a need to transition from conventional (on-site) clinical trials (CTs) to trials conducted within the comfort of a patient's home or community (decentralized CT) through e-consent, remote data monitoring, and telemedicine consults. This shift in trial procedures will positively impact recruitment rates, compliance and participant retention, protocol deviations, and delays or missed visits. Home nursing in CTs (HNCTs) will be an important component of this decentralization effort. A few limitations may impact the implementation of HNCT in India. In this regard, the workstream conducted semi-structured qualitative interviews with experts from diverse domains of CT conduct (researchers from academia and industry, clinicians, investigators, nursing staff, patient research advocates, institutional ethics committee, or institutional review board members, legal experts, and trial participants) to collect their understanding, perspectives, and the ground realities about HNCTs in India. The current review puts forth the key areas that would facilitate the establishment of HNCTs in India and provides recommendations for the same. [ABSTRACT FROM AUTHOR]

Continuous medical and safety monitoring of subject data during a clinical trial is a critical part of evaluating the safety of trial participants and as such is governed by protocol procedures and regulatory guidelines to meet the trial's intended objectives. We present an open-source validated graphical tool (clinDataReview R package) which provides access to the trial data with drill-down to individual patient profiles. The tool incorporates functionalities that facilitate detection of error and data inconsistencies requiring follow-up. It supports regular medical monitoring and oversight as well as safety monitoring committees with interactive tables and listings alongside graphical visualizations of the primary safety data in reports. An implementation example is given where the tool is used to deliver validated outputs following FDA/EMA guidelines. As such, this tool enables a more efficient, interactive, and reproducible review of safety data collected during an ongoing clinical trial.

The purpose of this study is to design a system that can monitor the displacement changes of engineering structures in real time and provide timely alarms when abnormal displacement is detected, in order to provide efficient safety monitoring and early warning functions and reduce the safety risks of engineering structures. The study adopted a combination of intelligent algorithms and the Global Satellite Navigation System (Beidou). By installing high-precision displacement sensors, monitor the displacement changes of the engineering structure. Then, intelligent algorithms are used to analyze and process the data collected by the sensors in real time, in order to extract the characteristics of displacement changes. At the same time, combined with the positioning data of the BeiDou navigation system, high-precision displacement monitoring is achieved. When the monitoring results exceed the set threshold, the system will automatically trigger an alarm mechanism to promptly notify relevant personnel for processing. Through experiments and comparative analysis, the Beidou high-precision displacement safety monitoring and 3D spatial alarm system based on intelligent algorithms designed in this study can effectively monitor and alarm displacement changes. The system has the characteristics of high accuracy, real-time performance, and reliability, which can timely detect displacement anomalies and provide reliable safety monitoring and warning functions.

Computer vision has emerged as a promising tool for improving safety at construction sites through automatic scene recognition. However, traditional approaches require significant labor-intensive and time-consuming efforts for annotations. Although weakly supervised learning has the advantage of localizing objects without location information, the conventional class activation map (CAM) technique struggles with small and linear object localization and background noise at construction sites. For effective scene recognition related to construction safety, the spatial relationships between precisely localized objects are crucial. Due to the limitations of traditional CAM techniques in localizing small and linear objects, using CAM for construction safety monitoring remains inaccurate. Therefore, this study proposes a weakly supervised learning approach with an improved CAM for enhancing object detection in construction sites. The improved CAM localizes objects of various scales and is robust against background interference. Spatial relationships between localized objects are employed to determine the status of scenes for construction safety monitoring. Experiment using datasets associated with falls from ladders (FFL) demonstrates that the improved CAM surpasses the traditional CAM in mIoU (mean intersection over union) for the object localization performance as well as in the accuracy and F1-Score for recognizing unsafe scenes. This demonstrates the robust potential of employing CAM as a weakly supervised learning strategy, underlining its substantial feasibility for preventing hazards in construction sites. The proposed framework can minimize annotation efforts, demonstrating the potential of CAM as a viable computer vision technique for efficiently detecting hazards at construction sites.

In this study, we introduce a robust and affordable safety monitoring system using an object-detection algorithm trained using CutMix-based synthetic data. The image-processing algorithm, which is mainly used to improve productivity in the manufacturing industry, has recently emerged as a solution to safety problems at manufacturing sites. With deep learning, various applications have been developed to accurately monitor operator posture, wear safety equipment, and enter and exit hazardous areas. However, there are still limitations in terms of cost and time when using these applications in actual manufacturing sites, especially for small- and medium-sized enterprises. To address these challenges, our system offers the following optimal approaches: 1) leveraging domain-specific data-augmentation methods for efficient hand-monitoring systems, 2) implementing the entire system with accessible hardware and software, and 3) on-site validation at an actual small- and medium-sized enterprise, demonstrating superior performance over traditional augmentation methods. Our training method achieved 98.5% accuracy using just a minute of data, outperforming traditional methods by 28.7%. The system maintains an inference rate of over eight frames per second and can be configured for less than ${\$}200$ . Notably, our system allows rapid training and deployment in diverse environments and has a significant impact on manufacturing safety management.

Copyright of Geology & Exploration is the property of Geology & Exploration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aluminum alloy frame is a novel structure system developed in recent years. In this article, the load-bearing performance of the beam-beam joint of the aluminum alloy frame is studied through numerical analysis and experiment and a safety monitoring method is developed. The impacts of the arch angle and bolt diameter on the beam-beam joint mechanical characteristics are explored through experiments under vertical load. When the diameter of the bolt was increased, the load-bearing performance of such joint displayed a pattern of first increasing and then decreasing. As the arch angle increased, the load-bearing performance on the joint gradually improved. Based on the experiments, numerical analysis models varying in arch angle were established, and the impacts of arch angles on the stiffness during the yield stage, ultimate load, and elastic stiffness of the aluminum alloy portal frame (AAPF) beam-beam joints were further explored through numerical simulation of the structure under vertical and horizontal loads. When the arching angle was increased, the elastic stiffness and yield stiffness of the beam-beam joint under vertical load showed a pattern of first increasing and then decreasing. When the arching angle was increased, the elastic stiffness and yield stiffness of the beam-beam joint under horizontal load significantly decreased. Based on the mechanical properties, a safety monitoring method for AAPF beam-beam joints based on displacement monitoring and frequency monitoring is proposed.

A simple, direct method for the determination residual hexane content in refined oils was developed, which makes use of commercial Semiconducting Metal Oxides (SMOX) sensors and is proposed as an alternative to the currently used standards (ISO 9832:2002, ISO 2719:2016). The main advantages are related to the direct measurement of the headspace of oil samples. The measurements are performed at an oil sample temperature of 30 °C and by spiking the samples with hexane in the 8–132 mg·kg−1 range, which is in line with the requirements of current standard for the maximum residue limit set by European Union regulation. Using separate measurements performed with the help of a computer-controlled gas mixing system it is possible to determine the relationship between the concentration of hexane in oil and in the headspace. [ABSTRACT FROM AUTHOR]

Topical and systemic retinoids have long been used in the treatment of ichthyoses and other disorders of cornification. Due to the need for long-term use of retinoids for these disorders, often beginning in childhood, numerous clinical concerns must be considered. Systemic retinoids have known side effects involving bone and eye. Additionally, potential psychiatric and cardiovascular effects need to be considered. Contraceptive concerns, as well as the additive cardiovascular and bone effects of systemic retinoid use with hormonal contraception must also be deliberated for patients of childbearing potential. The Pediatric Dermatology Research Alliance (PeDRA) Use of Retinoids in Ichthyosis Work Group was formed to address these issues and to establish best practices regarding the use of retinoids in ichthyoses based on available evidence and expert opinion.

Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance. [ABSTRACT FROM AUTHOR]

Dynamic deflection deformation is a crucial index which can reflect the healthy operation of bridges; due to their limitations, monitoring technologies (e.g., sensors, automatic total stations, and GPS) cannot perform high-frequency and whole-process non-contact dynamic deformation monitoring. Therefore, taking the Jinan Fenghuangshan Road Bridge as an example, this paper developed key low-cost and high-precision close-range photogrammetry technology and combined this with GB-RAR technology to remotely monitor bridge dynamic deformation. The results indicate that the measurement accuracy pertaining to the technology, which is based on an isometric virtual surface, is approximately 0.5 mm when the monitoring distance is 250 m, which can meet the accuracy requirements of bridge deformation monitoring. A vehicle's dynamic load is transmitted in the form of a stress wave inside the bridge, and with regard to span, the wave is most significant in the 1/2 to 1/4 range. This study observes that the maximum deflection deformation of the bridge is 47.2 mm, which is within the allowable deformation range of the General Specifications for Design of Highway Bridges and Culverts. The research results herein provide technical support and data reference for safety monitoring, and for the reinforcement and maintenance of bridges such as the Fenghuangshan Road Bridge. [ABSTRACT FROM AUTHOR]

The development of food industry technologies and increasing the sustainability and effectiveness of processing comprise some of the relevant objectives of EU policy. Furthermore, advances in the development of innovative non-thermal technologies can meet consumers' demand for high-quality, safe, nutritious, and minimally processed foods. Acoustic technology is characterized as environmentally friendly and is considered an alternative method due to its sustainability and economic efficiency. This technology provides advantages such as the intensification of processes, increasing the efficiency of processes and eliminating inefficient ones, improving product quality, maintaining the product's texture, organoleptic properties, and nutritional value, and ensuring the microbiological safety of the product. This review summarizes some important applications of acoustic technology in food processing, from monitoring the safety of raw materials and products, intensifying bioprocesses, increasing the effectiveness of the extraction of valuable food components, modifying food polymers' texture and technological properties, to developing biodegradable biopolymer-based composites and materials for food packaging, along with the advantages and challenges of this technology. [ABSTRACT FROM AUTHOR]

Multisystem inflammatory syndrome in children (MIS-c) is an uncommon, but serious, inflammatory response that occurs after SARS-CoV-2 infection. As time went by, MIS-c was also reported as a potential adverse event following COVID-19 vaccination. A descriptive analysis was performed of Individual Case Safety Reports (ICSRs) associated with anti COVID-19 vaccines and related to the pediatric population from 2020 to 2022. The present pharmacovigilance study aimed to describe cases of MIS-c following COVID-19 vaccination, stratified by sex, reported in the Vaccine Adverse Events Reporting System (VAERS) and meeting the Brighton Collaboration criteria for case definition. We assessed all suspected cases through the case definition and classification of the Brighton Collaboration Group, and only definitive, probable, and possible cases were included in the analysis. The Reporting Odds Ratio (ROR) with 95% Confidence Interval (CI) was computed to assess if males have a lower/higher probability of reporting ICSRs with MIS-c compared with females. Overall, we found 79 cases of potentially reported MIS-c following vaccination. This study demonstrated that MIS-c following vaccination was more commonly reported for male subjects with a median age of 10 years (IQR 10.0–11.4), especially after the first dose of anti COVID-19 vaccines with a median time to onset of 27 days. Even so, the rate of occurrence of MIS-c following anti COVID-19 vaccines is lower (0.12/100,000 vaccinated subjects; 95% CI, 0.12–0.13). Overall, all ICSRs were serious and caused or prolonged hospitalization. Finally, disproportionality analysis showed that males had a higher reporting probability of MIS-c compared with females following immunization with mRNA COVID-19 vaccines. Since only a few years of marketing are available, further data from real-life contexts are needed. [ABSTRACT FROM AUTHOR]

Detecting intrusion in hazardous areas is one of the priorities and duties of safety enhancement. With the emergence of vision intelligence technology, hazardous-area-detection algorithms can support safety managers in predicting potential hazards and making decisions. However, because of the dynamic and complex nature of the jobsite, high-risk zones have a different geometry and can be changed following the schedule and workspace of activity. This leads to hazardous areas being annotated manually. Thus, this study proposes a computer vision and a 4D BIM-based approach for intrusion detection in hazardous areas, called IDC4D. IDC4D comprises three modules: (1) the 4D BIM-based safety planning (4BSP) module, which analyzes the hazardous area; (2) the hazardous area registration (HAR) module, which delivers the hazardous area from the BIM model to the camera's first frame image; and (3) the hazardous-area-intrusion-detection module (HAID), which applies the computer vision algorithm to identify the correlation between workers and hazardous areas. The efficiency of the IDC4D approach is validated by testing a maintenance project on the construction site. IDC4D supports the planner in choosing the plan and detecting the event of workers entering hazardous areas while working. It showed an average precision of 93% and 94% in phase 1 and phase 2, respectively. These findings provide insight into how varying geometries of diverse hazard areas can be handled for enhancing intrusion detection. [ABSTRACT FROM AUTHOR]

Background and Objectives: Non-steroidal anti-inflammatory drugs (NSAIDs), which have anti-inflammatory and analgesic properties, are commonly used in the treatment of various, particularly frequent, as well as chronic, conditions in older patients. Due to common polypragmasia in these patients and a high risk of adverse drug reactions (ADRs) and drug interactions, pain management poses a therapeutic challenge. This study describes the importance of ADR reports in the identification of polypharmacy and the ensuing interactions. Materials and Methods: Both healthcare professionals (HPs) and non-healthcare professionals (non-HPs) reports collected in the EudraVigilance database of NSAIDs, including most commonly co-reported medications and reported reactions, were analysed and differences between HPs and non-HPs reports were identified. Results: In the analysed period and group, non-HPs reported more reactions but indicated fewer drugs as suspect or concomitant. The outcomes of our analysis indicate more HP engagement and more detailed reports of serious ADRs when compared to non-serious individual case safety reports (ICSRs) by non-HPs, which appeared more detailed. Such reactions as kidney failure and increased risk of bleeding are known adverse reactions to NSAIDs and common symptoms of their interactions, which were described in the available literature. They were much more frequently reported by HPs than by non-HPs. Non-HPs more frequently reported reactions that may have been considered less significant by HPs. Conclusions: The differences between healthcare professionals' (HPs) and non-healthcare professionals' (non-HPs) reports may result from the fact that the reports from patients and their caregivers require a professional medical diagnosis based on symptoms described by the patient or additional diagnostic tests. This means that when appropriately classified, medically verified, and statistically analysed, the data may provide new evidence for the risks of medication use or drug interactions. [ABSTRACT FROM AUTHOR]

The crowd sensing technology can realize the sensing and computing of people, machines, and environment in smart industrial IoT-based coal mine, which provides a solution for safety monitoring through distributed intelligence optimization. However, due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines, the accuracy of human body position prediction and pressure value prediction is not high. To solve these problems, this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine. First, we propose a Particle Swarm Optimization-Elman Neural Network (PE) algorithm for the mobile human position prediction. Second, we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them, our proposed PE algorithm has the lowest average cumulative prediction error, and the trajectory fit rate is improved by 24.1%, 13.9% and 8.7% compared with Kalman filtering, Elman and Kalman plus Elman algorithms, respectively. Meanwhile, compared with single-input ARIMA, RNN, LSTM, and GRU, the RMSE values of our proposed ADI-LSTM are reduced by 36.6%, 52%, 32%, and 13.7%, respectively; and the MAPE values are reduced by 0.0003%, 0.9482%, 1.1844%, and 0.3620%, respectively.© 2015 Published by Elsevier Ltd.

With the advent of a new era and continuous improvement of the technological level, AI technology has gradually come to the people's vision. In the context of a new era, with the continuous promotion of digital construction of oil and gas surface engineers and the rapid development of information technology, oil and gas surface engineers have begun to enter the stage of intelligent construction. An important research topic is the application of artificial intelligence technology in the safety supervision of oil and gas surface technology. In response to the frequent occurrence of safety accidents in oil and gas surface construction and the low level of intelligence of existing safety oversight systems, an AI-based safety oversight system for oil and gas surface construction has been developed, the greatest feature of which is to apply AI technology throughout the process of safety oversight of oil and gas surface construction. Through the continuous research, improvement and application of new technologies, a gradually built information and intelligent management system for oil and gas surface construction has been established. The ultimate goal of intelligent construction is to achieve panoramic visualization, real-time perception, intelligent analysis, automatic control and potential prediction. Artificial intelligence technology provides targeted research and practical construction ideas for intelligent exploration of oil and gas surface technology, through systematic analysis of management and control problems such as fragile ecological environment, frequent geological disasters and severe corrosion. Based on the actual situation of surface technology in specific areas, professional technology is applied to improve the oil and gas surface technology integrity management information system, which can achieve database accuracy, standardized data models and control processes. The three main objectives of system analysis to reduce manual dependence provide important technical support for the safe operation of oil and gas surface design. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The construction of a subway deep foundation pit is complex and risky, thus multiple safety risk factors bring great challenges to evaluating the safety status accurately. Advanced monitoring technology equipment could obtain a large number of monitoring data, and how integrating complex and diversified monitoring data to assess the safety risk of foundation pits has become a new problem. Therefore, an intelligent multi-source fusion assessment model is proposed. This model is mainly used for solving risk probability distribution, deep learning, and intelligent prediction of monitoring indicators, and then evaluating safety status by fusing various parameters of multiple indicators. Thus, based on the data of deep learning and the measured multivariate data, the dynamic risk during foundation pit construction can be obtained. Moreover, a typical case study was performed through monitoring and carrying out the risk assessment which is located at the Martyrs' Lingyuan Station of Jinnan Metro Line R2, China. In this case, the PSO-SVM and LSTM models are used to predict the deformation trend, and the monitoring data is reliable with high precision. After multi-index fusion model calculation, the results show that the foundation pit structure is in a safe state, and the evaluation situation is basically consistent with the site. Consequently, the prediction of the new multi-source intelligent fusion risk assessment method is convincing. [ABSTRACT FROM AUTHOR]

The mechanical characteristics and mechanisms of rock failure involve complex rock mass mechanics problems involving parameters such as energy concentration, storage, dissipation, and release. Therefore, it is important to select appropriate monitoring technologies to carry out relevant research. Fortunately, infrared thermal imaging monitoring technology has obvious advantages in the experimental study of rock failure processes and energy dissipation and release characteristics under load damage. Therefore, it is necessary to establish the theoretical relationship between the strain energy and infrared radiation information of sandstone and to reveal its fracture energy dissipation and disaster mechanism. In this study, an MTS electro-hydraulic servo press was used to carry out uniaxial loading experiments on sandstone. The characteristics of dissipated energy, elastic energy, and infrared radiation during the damage process of sandstone were studied using infrared thermal imaging technology. The results show that (1) the transition of sandstone loading from one stable state to another occurs in the form of an abrupt change. This sudden change is characterized by the simultaneous occurrence of elastic energy release, dissipative energy surging, and infrared radiation count (IRC) surging, and it has the characteristics of a short duration and large amplitude variation. (2) With the increase in the elastic energy variation, the surge in the IRC of sandstone samples presents three different development stages, namely fluctuation (stage Ⅰ), steady rise (stage Ⅱ), and rapid rise (stage Ⅲ). (3) The more obvious the surge in the IRC, the greater the degree of local damage of the sandstone and the greater the range of the corresponding elastic energy change (or dissipation energy change). (4) A method of sandstone microcrack location and propagation pattern recognition based on infrared thermal imaging technology is proposed. This method can dynamically generate the distribution nephograph of tension-shear microcracks of the bearing rock and accurately evaluate the real-time process of rock damage evolution. Finally, this study can provide a theoretical basis for rock stability, safety monitoring, and early warning. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in a variety of fields due to their high energy density, high power density, long service life, and environmental friendliness. However, safety accidents with lithium-ion batteries occur frequently. The real-time safety monitoring of lithium-ion batteries is particularly important during their use. The fiber Bragg grating (FBG) sensors have some additional advantages over conventional electrochemical sensors, such as low invasiveness, electromagnetic anti-interference, and insulating properties. This paper reviews lithium-ion battery safety monitoring based on FBG sensors. The principles and sensing performance of FBG sensors are described. The single-parameter monitoring and dual-parameter monitoring of lithium-ion batteries based on FBG sensors are reviewed. The current application state of the monitored data in lithium-ion batteries is summarized. We also present a brief overview of the recent developments in FBG sensors used in lithium-ion batteries. Finally, we discuss future trends in lithium-ion battery safety monitoring based on FBG sensors. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Safety occurrence reports can contain valuable information on how incidents occur, revealing knowledge that can assist safety practitioners. This paper presents and discusses a literature review exploring how Natural Language Processing (NLP) has been applied to occurrence reports within safety-critical industries, informing further research on the topic and highlighting common challenges. Some of the uses of NLP include the ability for occurrence reports to be automatically classified against categories, and entities such as causes and consequences to be extracted from the text as well as the semantic searching of occurrence databases. The review revealed that machine learning models form the dominant method when applying NLP, although rule-based algorithms still provide a viable option for some entity extraction tasks. Recent advances in deep learning models such as Bidirectional Transformers for Language Understanding are now achieving a high accuracy while eliminating the need to substantially pre-process text. The construction of safety-themed datasets would be of benefit for the application of NLP to occurrence reporting, as this would allow the fine-tuning of current language models to safety tasks. An interesting approach is the use of topic modelling, which represents a shift away from the prescriptive classification taxonomies, splitting data into "topics". Where many papers focus on the computational accuracy of models, they would also benefit from real-world trials to further inform usefulness. It is anticipated that NLP will soon become a mainstream tool used by safety practitioners to efficiently process and gain knowledge from safety-related text. [ABSTRACT FROM AUTHOR]

The working conditions and environment of coal mine shafts are intricate and special. Currently, manual inspections or fixed-point monitoring is generally applied for daily safety monitoring, and intelligent and automated inspection equipment and its supporting technologies are not available. Starting from the technical requirements of the electromagnetic adsorption device of the wall-climbing robot for safety monitoring of the coal mine shaft, based on the structural characteristics and chemical composition of the composite shaft lining of the coal mine, the fundamental structure of the electromagnetic array and the electromagnetic unit are clarified, and a multi-layer matrix simulation point overlap mapping analysis method is proposed. Based on the system modeling and simulation calculations in MATLAB software, the number and distribution law of effective mapping points between the endpoints of the electromagnetic array and the reinforced frame in the shaft lining are inferred, which leads to the establishment of a calculation model of the equivalent adsorption area. The NSGA-II algorithm, a non-dominant elite strategy based on a genetic algorithm, is used to calculate the optimum combination scheme of various genetic parameters of individual electromagnetic units. Through the statistical analysis of the optimal individual data of each generation in the iterative process, the accuracy of the algorithm process and constraints, as well as the fitness function, are verified. Based on the research results of this paper, the electromagnetic adsorption issue of the mine shaft wall-climbing robot on the composite shaft lining structure has been effectively solved, which has theoretical significance and practical value for improving the autonomous ability and monitoring level of coal mine shaft safety monitoring. [ABSTRACT FROM AUTHOR]

With the rapid development of underground space, the issue of safety in construction processes is becoming more and more significant. The purpose of this paper is to solve the problem of the existing underground space monitoring technology not being centralized and unified. In view of the problems related to large underground spaces in the process of constructing complex structures, with the introduction of Internet of Things technology and digital twins, we put forward an application of an intelligent safety-monitoring digital twin system in the construction of a large underground space structure, and at the same time, explore the Internet and digital integration mechanism of the twin system. The twin system uses BIM technology to establish the corresponding physical construction model, and collects multi-source heterogeneous monitoring data in real time through Internet of Things technology to achieve the exchange of information between the virtual construction model and the physical construction model. The twin system uses the multi-source heterogeneous data for real-time security analysis, and obtains the security status of the structure and feeds it back to the application service layer. The effectiveness and practicability of the twin system in large underground spaces are verified by an example project. Aiming at the safe performance of the orthogonal arch, the mapping relationship of various parameter indexes is obtained, and reasonable control measures are given. This study provides a new solution for improving the safety of construction projects and risk prevention and control, and has important theoretical and practical value for the safety management of underground space construction processes.

ObjectiveTo determine the reporting rate of adverse reaction of Haemophilus influenzae type b （Hib） containing vaccine in Songjiang District from 2016 to 2020， so as to provide evidence for the inclusion of Hib vaccine in children's immunization planning and safety monitoring.MethodsThe adverse reaction data of Hib vaccination were collected through the Chinese Disease Prevention and Control Information System and presented by descriptive methods.ResultsFrom 2016 to 2020， the reporting rate of adverse reaction of various Hib-containing vaccines in Songjiang District was determined to be 747.76/105 doses， among which the rates of general reaction and abnormal reaction were 734.31/105 doses and 13.45/105 doses， respectively. The reporting rate of general reaction was significantly higher than that of abnormal reaction （χ2=1 400.18，P

Simple Summary: Gastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms arising in the gastrointestinal tract, especially in the stomach (60–65%). The development of most cases of GISTs is associated with activating mutations in the receptor tyrosine kinase genes KIT and PDGFRA. The discovery of mutations leads to the approval of tyrosine kinase inhibitors (TKIs) for GISTs. Considering the recent approval of the last two TKIs and given their increased use in clinical practice, a spontaneous reporting system (SRS) database, the main tool in pharmacovigilance, takes a remarkable role in promptly characterizing the benefit/risk profile of these new drugs and consequently improve the quality of life of patients affected by GISTs. Tyrosine kinase inhibitors (TKIs) are widely used in gastrointestinal stromal tumors (GISTs). The aim of this study is to evaluate the reporting frequency of neuropsychiatric adverse drug reactions (ADRs) for TKIs through the analysis of European individual case safety reports (ICSRs). All ICSRs collected in EudraVigilance up to 31 December 2021 with one TKI having GISTs as an indication (imatinib (IM), sunitinib (SU), avapritinib (AVA), regorafenib (REG), and ripretinib (RIP)) were included. A disproportionality analysis was performed to assess the frequency of reporting for each TKI compared to all other TKIs. The number of analyzed ICSRs was 8512, of which 57.9% were related to IM. Neuropsychiatric ADRs were reported at least once in 1511 ICSRs (17.8%). A higher reporting probability of neuropsychiatric ADRs was shown for AVA. Most neuropsychiatric ADRs were known, except for a higher frequency of lumbar spinal cord and nerve root disorders (reporting odds ratio, ROR 4.46; confidence interval, CI 95% 1.58–12.54), olfactory nerve disorders (8.02; 2.44–26.33), and hallucinations (22.96; 8.45–62.36) for AVA. The analyses of European ICSRs largely confirmed the safety profiles of TKIs in GISTs, but some ADRs are worthy of discussion. Further studies are needed to increase the knowledge of the neuropsychiatric disorders of newly approved TKIs. [ABSTRACT FROM AUTHOR]

Vaccine development, in most cases, is a long, complex process, often lasting years and involving a combination of public and private stakeholders. Particularly, the vaccine clinical development process is highly regulated by several guidelines, regulatory pathways and science-based recommendations from experts. Designing and executing a successful clinical development plan for any candidate vaccine requires a solid scientific, medical, operational and regulatory knowledge and expertise, to comply with regulations and assure adequate benefit-risk balance for the product to be used in mass vaccination of healthy populations.The purpose of this study was to assess the approaches and practices related to Clinical Development functions, and related activities among vaccine manufacturers based in emerging countries, and to identify industry needs in terms of organizational development and training needs. A structured questionnaire designed specifically for assessing indicators of clinical activities, in the last five years, comprised of four sections aimed to collect information on (1) the organizational structure and the activities conducted by the clinical functions; (2) the clinical trial design ability and the management of clinical trial documents; (3) the clinical trial management and monitoring activities; (4) the quality aspects of clinical activities. The results suggest that the great majority of respondents is engaged in intense clinical development activities, as indicated by the high number of licensed vaccines available and supplied in the national markets or in foreign markets, including vaccines with WHO prequalification status.Areas to further strengthen the clinical activities and medical research preparedness were identified. Greater engagements of stakeholders’ and investments will be required to expand the clinical basis in vaccine R&D, and to support achieving a high level of preparedness in emerging countries, for development of new vaccines against future regional epidemics and global pandemics.

This study presents a deep neural network (DNN)-based safety monitoring method. Nonstationary objects such as moving workers, heavy equipment, and pallets were detected, and their trajectories were tracked. Time-varying safety zones (SZs) of moving objects were estimated based on their trajectories, velocities, proceeding directions, and formations. SZ violations are defined by set operations with sets of points in the estimated SZs and the object trajectories. The proposed methods were tested using images acquired by CCTV cameras and virtual cameras in 3D simulations in plants and on loading docks. DNN-based detection and tracking provided accurate online estimation of time-varying SZs that were adequate for safety monitoring in the workplace. The set operation-based SZ violation definitions were flexible enough to monitor various violation scenarios that are currently monitored in workplaces. The proposed methods can be incorporated into existing site monitoring systems with single-view CCTV cameras at vantage points.

Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance.

Detecting intrusion in hazardous areas is one of the priorities and duties of safety enhancement. With the emergence of vision intelligence technology, hazardous-area-detection algorithms can support safety managers in predicting potential hazards and making decisions. However, because of the dynamic and complex nature of the jobsite, high-risk zones have a different geometry and can be changed following the schedule and workspace of activity. This leads to hazardous areas being annotated manually. Thus, this study proposes a computer vision and a 4D BIM-based approach for intrusion detection in hazardous areas, called IDC4D. IDC4D comprises three modules: (1) the 4D BIM-based safety planning (4BSP) module, which analyzes the hazardous area; (2) the hazardous area registration (HAR) module, which delivers the hazardous area from the BIM model to the camera’s first frame image; and (3) the hazardous-area-intrusion-detection module (HAID), which applies the computer vision algorithm to identify the correlation between workers and hazardous areas. The efficiency of the IDC4D approach is validated by testing a maintenance project on the construction site. IDC4D supports the planner in choosing the plan and detecting the event of workers entering hazardous areas while working. It showed an average precision of 93% and 94% in phase 1 and phase 2, respectively. These findings provide insight into how varying geometries of diverse hazard areas can be handled for enhancing intrusion detection.

The development of food industry technologies and increasing the sustainability and effectiveness of processing comprise some of the relevant objectives of EU policy. Furthermore, advances in the development of innovative non-thermal technologies can meet consumers’ demand for high-quality, safe, nutritious, and minimally processed foods. Acoustic technology is characterized as environmentally friendly and is considered an alternative method due to its sustainability and economic efficiency. This technology provides advantages such as the intensification of processes, increasing the efficiency of processes and eliminating inefficient ones, improving product quality, maintaining the product’s texture, organoleptic properties, and nutritional value, and ensuring the microbiological safety of the product. This review summarizes some important applications of acoustic technology in food processing, from monitoring the safety of raw materials and products, intensifying bioprocesses, increasing the effectiveness of the extraction of valuable food components, modifying food polymers’ texture and technological properties, to developing biodegradable biopolymer-based composites and materials for food packaging, along with the advantages and challenges of this technology.

Dynamic deflection deformation is a crucial index which can reflect the healthy operation of bridges; due to their limitations, monitoring technologies (e.g., sensors, automatic total stations, and GPS) cannot perform high-frequency and whole-process non-contact dynamic deformation monitoring. Therefore, taking the Jinan Fenghuangshan Road Bridge as an example, this paper developed key low-cost and high-precision close-range photogrammetry technology and combined this with GB-RAR technology to remotely monitor bridge dynamic deformation. The results indicate that the measurement accuracy pertaining to the technology, which is based on an isometric virtual surface, is approximately 0.5 mm when the monitoring distance is 250 m, which can meet the accuracy requirements of bridge deformation monitoring. A vehicle’s dynamic load is transmitted in the form of a stress wave inside the bridge, and with regard to span, the wave is most significant in the 1/2 to 1/4 range. This study observes that the maximum deflection deformation of the bridge is 47.2 mm, which is within the allowable deformation range of the General Specifications for Design of Highway Bridges and Culverts. The research results herein provide technical support and data reference for safety monitoring, and for the reinforcement and maintenance of bridges such as the Fenghuangshan Road Bridge.