Search

During randomized controlled trials, it is critical to remain vigilant in safety monitoring. A common approach is to present information over time, such as frequency tables and graphs, when analyzing adverse events. Nevertheless, there is still a need for developing statistical methods for analyzing safety data of a dynamic nature. The process is typically challenging due to small sample sizes, a lack of observational data sources, difficulties in false-positive control, and the necessity for early detection of serious adverse events. In this article, we propose a simple and effective framework called Bayesian Efficient sAfety Monitoring (BEAM) to analyze evidence aggregation of potentially serious adverse events that may arise during the trial, as well as a timeline for when concrete evidence for safety concerns of unlikely outcomes becomes available. BEAM can be easily tabulated and visualized before the trial starts, making evaluations transparent and easy to use in practice, while maintaining flexibility when the underlying adverse event rate varies. Simulation studies have shown that BEAM supports continuous monitoring, can incorporate external information, and demonstrates good operating characteristics across various scenarios. In most practical situations, it has a reasonable likelihood of detecting elevated risks and identifying safety signals early on when safety concerns arise regarding the investigational drug. [ABSTRACT FROM AUTHOR]

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]

Copyright of International Journal of Engineering Transactions C: Aspects is the property of International Journal of Engineering (IJE) 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 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.)

Offshore wind turbines (OWTs) have complex operating conditions and are prone to accidents. Timely and accurate vibration prediction can reduce the probability of failure of OWTs. To address this issue, this study proposed a vibration prediction model based on long short-term memory (LSTM) network. The proposed prediction model was applied to an OWT in an offshore wind farm in China, and three evaluation indicators, i.e. mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE), were used to verify the vibration prediction performance of LSTM. The results demonstrated that the prediction accuracy of LSTM was better than that of back propagation (BP) neural network, and LSTM had optimum engineering practicability. The prediction accuracy of LSTM and BP neural networks decreased with an increase in prediction durations. However, the prediction accuracy of LSTM was higher. This study improves the online safety monitoring of OWTs. [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.

The safety monitoring for nonlinear dynamical systems with embedded neural network components is addressed in this paper. The interval-observer-based safety monitor is developed consisting of two auxiliary neural networks derived from the neural network components of the dynamical system. Due to the presence of nonlinear activation functions in neural networks, we use quadratic constraints on the global sector to abstract the nonlinear activation functions in neural networks. By combining a quadratic constraint approach for the activation function with Lyapunov theory, the interval observer design problem is transformed into a series of quadratic and linear programming feasibility problems to make the interval observer operate with the ability to correctly estimate the system state with estimation errors within acceptable limits. The applicability of the proposed method is verified by simulation of the lateral vehicle control system. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering 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.)

In recent years, the rapid advancement of technology has paved the way for innovative solutions aimed at enhancing personal safety and security. Among these, wearable Internet of Things (IoT) devices have emerged as a significant development, particularly in safeguarding vulnerable groups such as women and children. This paper introduces a smart security solution that leverages wearable IoT systems to provide real-time monitoring and protection. The increasing incidence of crimes against women and children highlights the urgent need for effective safety measures. Traditional security approaches often fall short in offering immediate assistance or preventive measures. However, wearable IoT devices, equipped with sensors and connectivity features, offer a proactive approach to security. These devices can monitor various physiological and environmental parameters, detect potential threats, and trigger timely alerts to guardians or authorities Our proposed smart security solution integrates advanced IoT technologies with user-friendly wearable devices designed specifically for women and children. This system encompasses several critical components, including GPS tracking, real-time communication, health monitoring, and emergency alert mechanisms. By harnessing the power of IoT, this solution aims to provide continuous protection, enhance situational awareness, and facilitate rapid response in case of emergencies. In this paper, we will explore the design, functionality, and potential impact of wearable IoT devices in improving the safety and security of women and children. We will also discuss the challenges and considerations in implementing such systems, including privacy concerns, data security, and the need for reliable connectivity. Through this comprehensive examination, we aim to demonstrate the viability and importance of IoT-based wearable technology in fostering a safer environment for vulnerable populations. [ABSTRACT FROM AUTHOR]

Safety monitoring identifies and evaluates potential side effects of the treatment in order to prevent the occurrence of future ones by improving the patient problem-solving abilities related to their safety. The use of biologic disease-modifying antirheumatic drugs is associated with recommendations for close disease monitoring. To ensure their safe implementation, the existing recommendations for the management of chronic inflammatory arthritis require monitoring of related safety issues, which can be overcome through patient education. Safety monitoring includes various aspects: to patients, on the one hand, and to specialized rheumatology team on the other (most commonly a physician, health care professional - nurse, etc.), as well as active involvement of primary care health professionals. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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]

Hydrogen leakage diagnosis has attracted more and more attention, especially in the high-pressure hydrogen system of fuel cell vehicles, where the chance of deflagration is quite high. Traditional methods for detecting gas leaks rely on stationary sensor measurements and manual inspections. Manual inspections are time and resource-consuming, and it is challenging to achieve real-time, comprehensive inspections in large spaces. In this study, a new Adaptive Stream Stripe-Background Oriented Schlieren (ASS-BOS) method for hydrogen jet visualization is proposed. Three different background patterns, namely random scatter background, checkerboard background, and stripe background, are designed for multiple experiments. The results show that when observing hydrogen jets in the far-field region (low-density gradient), ASS-BOS has higher accuracy and resolution, detects larger background displacements, and shows more turbulence details. In addition, the concentration distribution of the hydrogen jet is reconstructed using ASS-BOS. We compare the concentration of the hydrogen jet obtained from the inversion of the ASS-BOS method with the experimental results measured by the sensor. We find that the average error is 3.45%, indicating that ASS-BOS can accurately reconstruct the concentration distribution of the hydrogen jet. • A new ASS-BOS method is proposed to reconstruct the radial concentration distribution of hydrogen jets. • A series of background pattern was designed based on the characteristics of hydrogen jets. • The visualization of the hydrogen jet was tested comprehensively using different background images. • The ASS-BOS method has better resolution and accuracy for large-area hydrogen jet observation. [ABSTRACT FROM AUTHOR]

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy 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.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering 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.)

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.

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]

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy 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.)

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.

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering 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.)

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]

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]

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]

Mechanoluminescence (ML) materials are widely used in information storage, stress sensing, and structural health monitoring due to their simplicity, nondestructiveness, and real‐time sensing characteristics. However, the currently reported ML films suffer from high‐temperature decomposition, opacity, and unstable ML properties, which seriously limits their applications. Herein, a transparent TbBO3 glass‐ceramic (GCs) with significant ML performance is explored. A large number of Si‐O and B‐O tetrahedra in the glass matrix greatly improves the structural stability. Meanwhile, the disordered atomic arrangement reduces the obstacles in the light propagation process, making it highly transparent. Furthermore, femtosecond laser irradiation produces an ultra‐deep trap of 517 K, which makes its ML performance highly stable. Based on this material, an aircraft stress safety monitoring system that can accurately pinpoint where the aircraft window is hit is designed. Therefore, this work provides a new strategy for the design of highly stable and transparent ML materials. [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.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering 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.)

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]

Gender -affirming hormone therapy (GAHT) is the inevitable and most important way for transgender people to undergo gender transition, while the safety of hormone therapy needs more attention. For transgender men, the use of androgen (male hormone) will undoubtedly affect their physiological indicators, and even lead to some adverse reactions. For transgender men, intramuscular injection of testosterone undecanoate (TU) 1 000 mg every 12 weeks is currently the mainstream GAHT program in the world, with the best safety, effectiveness, economy and convenience. The GAHT of transgender men should be monitored for hematocrit, liver function, blood lipids, blood pressure, lean body mass, body mass index, bone mineral density, etc., to avoid the possible adverse events caused by excess androgen. This paper reviewed the GAHT of transgender men and its safety monitoring, and provided a reference for safe and rational use of GAHT for Chinese transgender men. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)