Safety Monitoring System Research Articles

Evolving CRISPR/Cas system for food safety monitoring across the food supply chain

Evolving CRISPR/Cas system for food safety monitoring across the food supply chain

Optimization Analysis of Tennis Player Training Mode Based on 3D Simulation Model

This paper is based on 3D simulation technology and deeply analyzes the optimization strategy of tennis player training mode. The research focuses on two core systems: one is a remote motion monitoring system based on architecture, which captures and analyzes athletes’ motion data in real time, providing coaches with accurate motion feedback and training effectiveness evaluation; The second is a 3D-based safety monitoring system, which uses 3D simulation technology to simulate the training environment, monitor and warn potential safety risks in real time, and ensure the safety of athletes during training. These two systems are designed for different demand scenarios in tennis training, with complementary functions, and together constitute an intelligent solution for optimizing the training mode of tennis players. By continuously improving algorithms, optimizing data storage and computation methods, and improving the accuracy of data analysis and mapping, this study aims to enhance the scientific, efficient, and safe training of tennis players, providing strong support for the comprehensive improvement of their skills and physical fitness.

Advancement of Finite Element Method Solver Used in Dam Safety Monitoring System by Interpolation of Pore Pressure and Temperature Values

In this paper, we focused on the advancement of Dam Monitoring Software that incorporates the Finite Element Method (FEM), as these large infrastructure constructions are crucial for ensuring a dependable water supply, irrigation, flood control, renewable electric energy generation, and safe operation, which is of utmost importance to any country. However, the material properties and geotechnical environments of dams can change (deteriorate) over time, while the standards and legal norms that govern them become more and more rigorous, so in order to accurately assess the state of a dam and detect any concerning behavior, the software must be updated as well. The custom-developed FEM solver, unlike many commercial alternatives, is adaptable and can be reconfigured to function within a Dam Monitoring System. In this paper, we present the procedure for interpolating numerical values at measurement points, when the position of the measurement point does not align with the node of the element, allowing for additional instrument locations to be added to the monitored system without the need for remeshing the numerical model. This procedure is used to compare the actual pore pressures and temperature values of the concrete dam structure with the prediction of the numerical model, and the agreement is much greater with the new interpolation algorithm in comparison to the nearest nodal values, with the average relative difference for pore pressure reduced from 8.89% to 8.10%, justifying this implementation.

A Survey on Women Safety and Health Monitoring System

Women safety is a very big issue even in this modern world running with advanced technologies. After a history of times, women got a freedom of equality at the workplace, asset rights, family law, and education. Even Feminist Movement, claimed during 20th Century, Women are not safe anywhere and violence against women and girl child’s happening everywhere during the lonely travelling on roads, picnic spots, work and deserted places. Though we have many armies and super forces to protect everybody, the day to day crime rate against women has not been reduced. There are several safety devices available to protect the women during the violence, but need more human intervention such as entering the data, or shaking the device roughly. We propose a solution for a secure and peaceful environment for women with handbag safety hand-held devices with the aim to provide false proof women safety devices by overcoming the disadvantages in existing system. When we are talking more about Women empowerment, Women achievements it is also very important to think about women safety since a huge numbers (848) of women’s are Indian Women Are Harassed, Raped, Killed Every Day. The proposed work aims at IoT based women safety device by hardware controller attached to the handbag, android application and Bluetooth connectivity in Smartphone. By pressing the controller button, the device alerts the first holder, relatives stored in the database and police when a woman is not safe. The main advantage of the work is, the device works without internet connectivity. Additional features such as protecting the valuable things to be stolen by thieves in crowded places or buses by a separate alarming system, heartbeat sensor setup to monitor heart rate, fingerprint scanner for effecting accessing of the devices and also the mobile android application provide the victim’s location to reach the women and safeguard the women from any harassment at the right time. The main thought of this paper is that it will not only protect the women from physical harassment during odd hours but also safe the women health when it is found abnormal, as a single device comparatively.

Intelligent alarm system for river embankment seepage based on BILSTM

Currently, the alarm functions of existing levee seepage monitoring systems are limited to single-parameter monitoring and lack rate-of-change alarms and correlation alarms. This can lead to false alarms, missed alarms, equipment failures, or unnecessary downtime. To enhance the intelligence of levee safety monitoring and seepage alarms, a levee seepage intelligent alarm system based on a Bidirectional Long Short-Term Memory (BILSTM) network model was designed and implemented. Firstly, data cleaning and preprocessing are carried out on the engineering safety monitoring operation data to reduce the influence of dirty data such as outliers and repetitive values on the accuracy of alarms. Secondly, for the correlation between the piezometric tube water levels of the levee and the Yangtze River water levels, a correlation analysis based on Mutual Information (MI) theory was conducted to minimize the effect of piezometric tube water level change delays on correlation. Finally, the BILSTM model was used to predict trends in these potentially abnormal data intervals. Based on engineering application requirements, alarm thresholds were established, and a multi-level alarm module was developed. Field operation test results show that the proposed method can accurately predict the piezometric tube water levels of levees, achieving intelligent alarms within the engineering safety monitoring system.

Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM

This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target’s posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring.

Evaluating the impact of HIV pre-exposure prophylaxis on pregnancy, infant, and maternal health outcomes in Malawi: PrIMO study protocol

BackgroundIncident HIV during the perinatal period significantly impedes elimination of Mother-to-Child HIV Transmission (eMTCT) efforts. Pre-Exposure Prophylaxis (PrEP) effectively reduces HIV acquisition, and new agents like injectable Cabotegravir (CAB-LA) offer potential advantages for pregnant and breastfeeding women. The Pregnancy, Infant, and Maternal health Outcomes (PrIMO) study will compare rates of composite adverse pregnancy outcomes, and infant adverse events, growth and neurodevelopment between mother-infant dyads receiving CAB-LA and those receiving oral PrEP in Malawi.MethodsPrIMO is an observational cohort study involving: (1) the development of a PrEP Pregnancy Registry for longitudinal surveillance of pregnant women on PrEP in Malawi; and (2) the enrolment of a prospective safety cohort of 621 pregnant women initiating oral PrEP or CAB-LA and their subsequent infants. The registry will include all women continuing or initiating PrEP during pregnancy across targeted sites in Lilongwe and Blantyre districts. The safety cohort will enrol a subset of those women and their infants from Bwaila District Hospital in Lilongwe, Malawi. We hypothesize that CAB-LA’s safety will be comparable to daily oral PrEP regarding adverse pregnancy outcomes, maternal/infant adverse events, and infant development. Participants in the cohort will choose either oral PrEP or CAB-LA and will be followed until 52 weeks post-delivery. Safety data will be collected from all mother-infant pairs and qualitative interviews will be conducted with a subset of purposively selected women (n = 50) to assess the acceptability of each PrEP modality.DiscussionThe PrIMO study will provide critical data on the safety of CAB-LA in pregnant and breastfeeding women and their infants. Results will guide clinical recommendations as the Malawi Ministry of Health prepares for the rollout of CAB-LA to this population. Evaluation of Registry implementation will inform its expansion to a nationwide safety monitoring system for PrEP use during pregnancy, with implications for similar systems in the region.Trial Registration numberNCT06158126. The study was prospectively registered (5 December 2023) in ClinicalTrials.gov.

IoT Based Smart Helmet for Coal Mining and Safety Monitoring System

IoT Based Smart Helmet for Coal Mining and Safety Monitoring System

Exposure of Cattle Breeding Herds to Naturally Co-Contaminated Zearalenone and Deoxynivalenol: The Relevance of a Urinary Mycotoxin Monitoring System for Herd Health and Food Safety.

The widespread presence of Fusarium mycotoxins in animal feed is a global issue, not only for the health of livestock but also for ensure the safety of food as an end product. High concentrations of zearalenone (ZEN) and deoxynivalenol (DON) have been detected in the diets of Japanese Black (JB) and Holstein Friesian (HF) breeding herds. Consequently, we monitored serum biochemical parameters over a long time in both herds, focusing on anti-Müllerian hormone (AMH) levels and acute-phase inflammation. Additionally, urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) and progesterone levels were measured in the HF herd. The JB herd, a ZEN-dominant model with low DON contamination, demonstrated ZEN levels that exceeded the Japanese limit in the purchased total mixed rations (TMR). Conversely, the HF herd, which primary consumes DON-dominant feed with low ZEN contamination, had high DON levels in the dent corn silage. Specifically, the JB herd's TMR contained 1.79 mg/kg ZEN and 0.58 mg/kg DON, whereas the HF herd's silage had 15.3 mg/kg DON (dried sample) and 0.1 mg/kg ZEN. Enzyme-linked immunoassay were used to measure urinary ZEN-DON levels following confirmation through liquid chromatography-tandem mass spectrometry. Urinary ZEN-DON levels measured were significantly correlated (p < 0.05, r > 0.6) in both herds. In the HF herd, AMH levels increased (p = 0.01) and serum amyloid A (SAA) levels decreased (p = 0.02) when contaminated and at the end of the monitoring period. Additionally, urinary ZEN and DON levels were significantly correlated with SAA levels (ZEN: p = 0.00, r = 0.46; DON: p = 0.03, r = 0.33), with an increase in ZEN and DON levels resulting in higher SAA levels. The JB herd showed no significant differences. Additionally, in the HF herd, 8-OHdG/Cre levels increased significantly during major contamination periods (p < 0.05). Clinical data from the HF herd indicated an increase in mastitis cases and treatment rates during periods of major contamination. Abortion rates in the HF herd decreased from 22.9% (before monitoring) to 8.9% (during the high contamination period) and finally to 1% (at the end of the monitoring period), with corresponding increases in progesterone levels. ZEN-DON contamination adversely affects breeding cattle's productivity, reproductive performance, and health. Therefore, monitoring urinary ZEN-DON is valuable for detecting contaminants and ensuring the safety of food products.

Evaluation of Drug and Herbal Medicinal Promotions on Social Media During the COVID-19 Pandemic in Relation to World Health Organization Ethical Criteria and South African Health Products Regulatory Authority Guidelines in South Africa: Cross-Sectional Content Analysis.

Consideration of ethics in the promotion of medications is essential to safeguard the health of consumers, particularly during health crises. The World Health Organization (WHO) and the South African Health Products Regulatory Authority (SAHPRA) have established stringent standards to ensure the integrity of pharmaceutical promotions and safeguard public health, including advertisements on the internet and social media platforms. However, the dynamic nature of online advertising poses challenges for monitoring and enforcing ethical standards. The study aimed (1) to examine the COVID-19 drug and medicinal promotions across online platforms and social media from 2020 to 2022 in South Africa and (2) to ensure that drug promotions adhere to ethical guidelines outlined by the WHO and SAPHRA. A cross-sectional content analysis was conducted to assess drug and medicinal advertisements across various internet and social media platforms. A systematic approach was used to identify and analyze promotional content, focusing on adherence to ethical guidelines outlined by WHO and SAPHRA. Data were collected and analyzed to determine the extent of compliance and identify any potential violations or areas for improvement. A total of 14 online drug advertisements were included in this analysis. Our findings show that most of the drugs advertised did not meet the regulations and guidelines provided by WHO and SAHPRA. There were omissions about active ingredients, proprietary names, adverse drug responses, precautions, and overdosage and adverse drug reactions. Traditional medicines were not fully consistent with the approved WHO ethical criteria data sheet. Our analysis highlights the critical importance of ensuring compliance with ethical guidelines in drug promotions on the internet and social media platforms. There is a need for continued vigilance and enforcement efforts to uphold ethical standards and protect the health of the public. Ongoing monitoring and collaboration between national drug regulatory agencies, pharmaceutical companies, and online platforms will be essential for promoting responsible advertising. In addition, safety monitoring and pharmacovigilance systems for herbal medicinal products are yet to be established.

Study of urease activity in semi-finished meat products

Meat production is one of the fastest growing sectors of global agriculture, and poultry meat is the most accessible and expanding source of protein for people of all income levels. In connection with recent world events, such as the coronavirus pandemic, and considering its consequences, the issue of safety of "accessible" foodstuffs, among which poultry meat occupies a worthy place, is acute. For Kazakhstan, as well as for all countries, it is important to expand the range of food products that can significantly increase the immunity of the population to successfully overcome the consequences of the COVID 19 pandemic. Development of local poultry farming and import substitution are the main solutions to ensure product safety in any state. For effective development of poultry farming it is necessary to overcome one of the limiting factors - lack of modern system of safety monitoring throughout the food chain. The purpose of this work is to study the urease activity in meat semi-finished products. In this study urease activity was measured in raw and cooked meat products. As a result of the study the dependence of heat treatment with the pH index was determined. The indicator of urease activity during production allows for more detailed safety and quality assurance of meat semi-finished products.

In the Depths of Wash Water: Isolation of Opportunistic Bacteria from Fresh-Cut Processing Plants.

The fruit and vegetable industry in post-harvest processing plants is characterized by a substantial consumption of water resources. Wash waters may serve as an environment for the periodic or permanent habitation of microorganisms, particularly if biofilm forms on the inner walls of tanks and flushing channels. Despite the implementation of integrated food safety monitoring systems in numerous countries, foodborne pathogens remain a global public health and food safety concern, particularly for minimally processed food products such as vegetables and fruits. This necessitates the importance of studies that will explore wash water quality to safeguard minimally processed food against foodborne pathogen contamination. Therefore, the current study aimed to isolate and identify bacteria contaminating the wash waters of four fresh-cut processing plants (Poland) and to evaluate the phenotypic antibiotic resistance profiles in selected species. Bacteria were isolated using membrane filtration and identified through mass spectrometry, followed by antibiotic susceptibility testing according to EUCAST guidelines. The results revealed that the level of contamination with total aerobic bacteria in the water ranged from 1.30 × 106 cfu/mL to 2.54 × 108 cfu/mL. Among the isolates, opportunistic pathogens including Enterococcus faecalis, Pseudomonas aeruginosa, Klebsiella oxytoca, Klebsiella pneumoniae, Serratia marcescens, and Proteus vulgaris strains were identified. An especially noteworthy result was the identification of cefepime-resistant K. oxytoca isolates. These findings highlight the importance of monitoring the microbial microflora in minimally processed foods and the need for appropriate sanitary control procedures to minimize the risk of pathogen contamination, ensuring that products remain safe and of high quality throughout the supply chain.

Development of Safety Monitoring System Based on Visual Intelligence Technology

Development of Safety Monitoring System Based on Visual Intelligence Technology

Internet of things and ensemble learning-based mental and physical fatigue monitoring for smart construction sites

The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks.

A LoRa-based safety monitoring system for hydrogen compressed natural gas (HCNG)

Abstract Hydrogen compressed with natural gas pipeline for gas mixture delivery is an effective way to realize large-scale, low-cost delivery of hydrogen. The Internet of Things (IoT) technology is one of the important supporting technologies for smart energy transformation, and the low power wide area network (LPWAN) represented by LoRa will play a key role. In this paper, a LoRa-based hydrogen compressed natural gas safety monitoring system is designed, which can collect, transmit, and analyze multiple types of data, such as ambient temperature and humidity, gas concentration, pipeline and equipment vibration, etc., and store the data and configure the alarm thresholds through the cloud server, and finally complete the real-time safety monitoring in the application layer. The system is characterized by high efficiency, high precision, low power consumption, and intelligence, which will have a positive effect on the safe transportation of hydrogen.

Analysis of Child Safety Monitoring System using the SPSS Method

A wearing children's safety system functions as a smart gadget. Accurate and exact real-time location can be used to identify a child's surroundings. This system includes an ambient temperature display, an SOS light, and a distress buzzer. It aids them in locating their child. The system is simple to design. The programmer is designed to establish an approach that is user-friendly on both sides. GPS-equipped smart phones should be available to both parents and kids. The child's location, call history, message history, and communication via their smartphone are all tracked using this software. Safety monitoring systems use a person, rather than a mechanical system (safety monitor), to warn when roofs are six feet or more below the lowest level and are in danger of falling. A safety supervisor must be a competent person to recognize fall hazards and warn workers about them. The device has IoT tracking and a GSM module that allows you to monitor the child at all times. It has countless sensors connected to a CPU and is used to detect the right signals like heart rate, temperature and other dangers and alert the parents. A programmer called system monitoring software keeps track of, logs, and examines all levels of the IT ecosystem's system resources, from operating systems and electronics to the underlying system applications and services, to running user software. At the top Today, 80% of all child abuse cases reported around the globe. The remaining 26% were boys, with 74% being girls. In this world, a daughter goes missing every 40 seconds. Children If one's children's future is the foundation of one's nation, is affected, it affects overall development Nation. Due to abuses, children's emotional and mental stability is affected Destroying and future endeavors. The things that happen to these defenseless kids are not their fault. Parents are therefore accountable for raising their own children. But, since their children's future and career are not considered, parents are compelled to want for money. So, it is challenging to understand them. There are kids everywhere. We offer a setting in our system where this issue is able to be resolved effectively. It makes it simple for parents to follow their kids in real time as if they were close to them and they focus on their own business without any manual intervention. Ratio studies are statistical analyses of data from appraisals and property valuations. Nearly all states utilise them to produce quantitative measure of the proportion of current market price about which individually estimated taxable property is appraised as well as to offer assessment performance indicators. Evaluation parameters taken as Temperature sensor, Pulse sensor, GPS, GSM and Web camera. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Software Engineering Defect Prediction the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.

요양병원 입원환자 환자안전사고 위해정도에 영향을 미치는 요인

Purpose: This study aimed to identify the characteristics of patient safety incidents among patients admitted to long-term care hospitals and the factors affecting the severity of these incidents, and provide essential data for developing strategies to prevent and manage patient safety incidents in long-term care hospitals. Method: The study utilized 7,594 patient safety report data collected from 2017 to 2022, based on Patient Safety Reporting and Learning System. Descriptive statistics and cross-analysis were conducted to identify the characteristics of patient safety incidents among inpatients of long-term care hospitals. Multinomial logistic regression analysis was performed to analyze the related factors. All statistical analyses were carried out using the SPSS statistical program. Results: Factors identified as affecting the severity of patient safety incidents in long-term care hospital inpatients included gender, age, primary diagnosis, the impact of COVID-19, the number of beds, the location of the incident, and the type of incident. Conclusion: As the aging population leads to an increase in patient safety incidents in long-term care hospitals, it is crucial to prevent these incidents and ensure their severity does not escalate. This requires systematic safety education for all healthcare workers and efforts such as implementing patient safety incident prevention programs and monitoring systems within long-term care hospitals.

Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras

Despite advancements in technology, safety equipment, and training within the construction industry over recent decades, the prevalence of fatal and nonfatal injuries and accidents remains a significant concern among construction workers. Hard hats and safety vests are crucial safety gear known to mitigate severe head trauma and other injuries. However, adherence to safety protocols, including the use of such gear, is often inadequate, posing potential risks to workers. Moreover, current manual safety monitoring systems are laborious and time-consuming. To address these challenges and enhance workplace safety, there is a pressing need to automate safety monitoring processes economically, with reduced processing times. This research proposes a deep learning-based pipeline for real-time identification of non-compliance with wearing hard hats and safety vests, enabling safety officers to preempt hazards and mitigate risks at construction sites. We evaluate various neural networks for edge deployment and find that the Single Shot Multibox Detector (SSD) MobileNet V2 model excels in computational efficiency, making it particularly suitable for this application-oriented task. The experiments and comparative analyses demonstrate the pipeline’s effectiveness in accurately identifying instances of non-compliance across different scenarios, underscoring its potential for improving safety outcomes.

Intelligent Video Monitoring and Analysis System for Power Grid Construction Site Safety Using Wireless Power Transfer

Power grid construction significantly enhances power grid management and risk control. Inconsistent operations at construction sites can jeopardize grid stability and crew safety. Traditional power lines are less favored due to mobility limitations, while batteries add burden and impracticality. To address this, a wireless power transfer and video monitoring system is developed using RF technology and Yolo V3 model. This enables continuous monitoring and employee safety analysis. The system's detection performance is optimized using HPSO, surpassing existing methods in accuracy and speed. It ensures real-time monitoring and improves the detection of potential risk sources, crucial for construction site safety.

Overview of global monitoring systems for the side effects and adverse events associated with medicinal cannabis use: a scoping review using a systematic approach

ObjectivesThe use of cannabis-based medicine (CBM) as a therapeutic has surged in Australia over the past 5 years. Historically, the United Nations Single Convention on Narcotic Drugs (1961) prohibited cannabis...