Prevention System Research Articles

Crime Prevention System and Mechanisms for College Students

In recent years, with the popularization of education and the continuous increase in the national level of education, the number of college students accounts for 10% of the total population. However, at the same time, we have also observed a peculiar phenomenon: the crime rate among college students is increasing year by year, reaching 2.5%. Therefore, we use content analysis and systems theory analysis to analyze the causes of college student crime in recent years and summarize its characteristics among newly added and frequently occurring cases. Through improving traditional legal education and implementing a three-in-one crime prevention model encompassing family, school, and society, we aim to reduce the current crime rate among college students. Simultaneously, we utilize situational crime prevention theory to effectively control criminal opportunities by reducing crime profits, upgrading crime risks, lowering crime incentives, and increasing crime difficulty. This facilitates the establishment of the next step in the prevention system and provides constructive suggestions for the further construction and implementation of the crime prevention system for college students in Huzhou City.

The Unveiling Distress: Harnessing NLP and Deep Learning to Identify Suicidal Signals in Tweets

The rise of social media platforms has provided researchers with unprecedented access to vast amounts of user-generated content, offering a unique opportunity to explore various aspects of human behavior, including mental health. This paper presents a novel approach to identifying suicidal signals in tweets using Natural Language Processing (NLP) techniques and Deep Learning algorithms. We propose a multi-step methodology that involves data collection, preprocessing, feature extraction, and classification. Leveraging state-of-the-art deep learning architectures such as recurrent neural networks (RNNs) and transformer models, our approach aims to accurately detect linguistic patterns indicative of suicidal ideation and distress. We evaluate the effectiveness of our method using a large dataset of annotated tweets and demonstrate promising results in terms of both precision and recall. Furthermore, we discuss the ethical implications and potential applications of our research in suicide prevention and mental health support systems.

Forecasting the Economic Burden of Cardiovascular Disease and Stroke in the United States Through 2050: A Presidential Advisory From the American Heart Association.

Quantifying the economic burden of cardiovascular disease and stroke over the coming decades may inform policy, health system, and community-level interventions for prevention and treatment. We used nationally representative health, economic, and demographic data to project health care costs attributable to key cardiovascular risk factors (hypertension, diabetes, hypercholesterolemia) and conditions (coronary heart disease, stroke, heart failure, atrial fibrillation) through 2050. The human capital approach was used to estimate productivity losses from morbidity and premature mortality due to cardiovascular conditions. One in 3 US adults received care for a cardiovascular risk factor or condition in 2020. Annual inflation-adjusted (2022 US dollars) health care costs of cardiovascular risk factors are projected to triple between 2020 and 2050, from $400 billion to $1344 billion. For cardiovascular conditions, annual health care costs are projected to almost quadruple, from $393 billion to $1490 billion, and productivity losses are projected to increase by 54%, from $234 billion to $361 billion. Stroke is projected to account for the largest absolute increase in costs. Large relative increases among the Asian American population (497%) and Hispanic American population (489%) reflect the projected increases in the size of these populations. The economic burden of cardiovascular risk factors and overt cardiovascular disease in the United States is projected to increase substantially in the coming decades. Development and deployment of cost-effective programs and policies to promote cardiovascular health are urgently needed to rein in costs and to equitably enhance population health.

Characteristics of Injurious Shootings by Police Along the Urban–Rural Continuum

INTRODUCTIONMuch research on shootings by police has focused on urban jurisdictions, but most US law enforcement agencies are not located in cities. Prior research suggests rates of fatal shootings by police are comparable in urban and non-urban areas. Yet, shooting characteristics across the urban-rural continuum are unknown. This study describes and compares fatal and nonfatal injurious shootings by officers in US urban, suburban, and rural areas from 2015-2020. METHODSCharacteristics of fatal and nonfatal injurious shootings by police were abstracted from Gun Violence Archive. In 2023-2024, using zip-code and county-based rurality designations, the national distribution, incidence, and characteristics of injurious shootings by police were compared across urban, suburban, and rural areas of the United States. RESULTSRates of injurious shootings in rural areas approached or exceeded urban rates. As rurality increased, proportionately more injurious shootings involved single responders, sheriffs, or multiple agency types. Across the urban-rural continuum, characteristics of precipitating incidents were similar. Injurious shootings were most frequently preceded by domestic violence incidents, traffic stops, or shots-fired reports; co-occurring behavioral health needs were common. After accounting for local demographic differences, Black, Indigenous, and Hispanic residents were injured at higher rates than white residents in all examined areas. CONCLUSIONSShootings by police represent an overlooked and inequitable source of injury in rural areas. Broadly similar incident characteristics suggest potential for wide-reaching reforms. To prevent injuries, crisis prevention, dispatch, and response systems must assure proportionate rural-area coverage. Additionally, legislative prevention and accountability measures should include sheriffs’ offices for optimal rural-area impact.

African Vulture Optimization-Based Decision Tree (AVO-DT): An Innovative Method for Malware Identification and Evaluation through the Application of Meta-Heuristic Optimization Algorithm

Abstract Malware remains a big threat to cyber security, calling for machine learning-based malware detection. Malware variations exhibit common behavioral patterns indicative of their source and intended use to enhance the existing framework’s usefulness. Here we present a novel model, i.e., African Vulture Optimization-based Decision Tree (AVO-DT) to increase the overall optimization. The datasets from Android apps and malware software train the AVO-DT model. After training, the datasets are pre-processed by removing training errors. The DT algorithm is used by the developed AVO model to carry out the detection procedure and predict malware activity. To detect malware activities and improve accuracy, such an AVO-DT model technique employs both static and dynamic methodologies. The other measurements on Android applications might be either malicious or benign. Here we also developed malware prevention and detection systems to address ambiguous search spaces in multidimensionality difficulties and resolve optimization challenges.

Case report: three ways to mitigate the risk of embolization during left atrial appendage closure in a patient with a massive and proximal left atrial appendage thrombus

Abstract Background Left atrial appendage (LAA) thrombus is a contraindication for LAA closure (LAAC). However, in selected cases, oral anticoagulants are strictly contraindicated because of a history of life-threatening bleeding, and LAAC remains the only possible therapy to avoid systemic and especially cerebral embolization. Case summary We report a case of LAAC despite a massive proximal thrombus in a patient who had an absolute contraindication to anticoagulant therapy, with thorough pre-planning using CT scan, device modelling and thrombus trapping techniques to reduce the risk of systemic embolic events and perform LAAC safely. Discussion Although LAAC remains at high risk in this setting, the use of cautious techniques and tools, from pre-procedure planning to systemic embolization prevention systems associated to a precise transoesopheageal echocardiography guiding throughout the procedure, allows it to be performed as safely as possible when no other option is available.

Combating Evolving Threats: A Signature-Anomaly Based Hybrid Intrusion Detection System for Smart Homes with False Positive Mitigation

Abstract: As people are looking for a more comfortable life, IoT applications are coming to play. Smart home system is one of the most popular IoT applications in the last decade. A smart home network is crucial to function smart home system properly. Cyber attacks on a smart home network can damage a lot. Network intrusion detection and prevention system (NIDPS) is a good solution to protect against Cyber threat in smart home network. This research will implement hybrid NIDPS in smart home network by combining signature based and anomaly-detection based NIDPS. This hybrid NIDPS will prevent known known attack from public internet, local internet and zero-day attack. Also, this system will be able to reduce false positive result and improve signature based NIDPS rules accurately by manual inspection.

Intelligent Helmet System for Prevention of Accidents

Abstract: India, like, has a huge number of road accidents almost every year. The accidents may be due to like many reasons like by drinking and driving, driving like rashly, exceeding the speed limit, etc., you know. Sometimes, the person who like gets injured might not be like responsible for the Accident, you know what I mean. It might be the fault of some other vehicle rider, maybe. But overall, both riders will get like affected, kind of. Due to a lack, you know, of first aid and emergency medical services on time, the riders may like die, which is kind of sad, you know. Some deaths are occurred due to the ambulance not reaching the desired location on time, which is, like, bad, you know. In case of an accident, to save time and inform the concerned person, a system is proposed like which can made sure that the rider gets the required attention in a short time, you know. In, like, India, many people use two-wheeler vehicles as compared to four-wheeler vehicles because of its, you know, low which is kind, like, interesting, you know what I mean. In many accidents, the rider gets injured mainly on the head, kind of, you know. A helmet kind of plays a very important role in saving the life of the ridden, you know what I mean. So to encourage people to wear helmets and to avoid accidents, a design is proposed that synchronizes the, you know, module present in the bike, which is kind of cool.

A Comprehensive Analysis of various Covid-19 Detection and Pandemic Prevention Systems and Methods Using Data Science

Abstract: The paper proposes a technique for early Covid-19 test prediction using AI and data science. The study analyzes various data sources to understand the patterns and trends of the pandemic and the impact of various interventions on the spread of the virus. The proposed system integrates data from multiple sources to identify potential cases and can also track the spread of the virus in real-time. The system has high accuracy in predicting positive cases and has the potential to improve our ability to detect and predict Covid-19 cases, ultimately saving lives and reducing the economic impact of the pandemic. The proposed technique for early Covid-19 test prediction using AI and data science can be a game- changer in our fight against the pandemic. With the integration of multiple data sources, including individual health records, social media, and geographic data, the system can identify potential cases even before symptoms appear. This early detection can help individuals get timely medical attention and prevent the spread of the virus to others.

How can Artificial Intelligence Techniques Effectively Enhance Credit Card Fraud Detection Systems?

The boom in Artificial Intelligence (AI) revolution is significantly transforming our everyday lives. Every aspect of technology, process, and system implements AI to provide a superior user experience, enhanced machine capabilities and advanced problem-solving and research capabilities. However, due to this technological revolution, rise in fraud has become an enormous challenge in the digital economy. This research paper aims to test different Machine Learning (ML) models to explore real-time fraud detection capabilities accurately, particularly for credit card fraud prevention systems. Technologies like online bank transfers and smartphone payments based on credit accounts are major contributors to fraudulent transactions. AI/ML models have proven to be industry-disruptors, robust, significantly faster and produce more accurate results. These advantages have led to launch of successful companies like OpenAI. This paper uses model metrics such as Supervised, Unsupervised, and Ensemble methods to improve the detection of unauthorized transactions for fraud detection. Models will be ranked for performance using accuracy metrics, F-1, and Area Under Curve (AUC) scores. While zero false rates are not yet achievable, this study aims to reach a reasonably low level by selecting an appropriate model.

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

Out-of-Hospital Cardiac Arrest in the Paediatric Patient: An Observational Study in the Context of National Regulations.

Introduction: Cardiac arrest results in a high death rate if cardiopulmonary resuscitation and early defibrillation are not performed. Mortality is strongly linked to regulations, in terms of prevention and emergency-urgency system organization. In Italy, training of lay rescuers and the presence of defibrillators were recently made mandatory in schools. Our analysis aims to analyze Out-of-Hospital Cardiac Arrest (OHCA) events in pediatric patients (under 18 years old), to understand the epidemiology of this phenomenon and provide helpful evidence for policy-making. Methods: A retrospective observational analysis was conducted on the emergency databases of Lombardy Region, considering all pediatric OHCAs managed between 1 January 2016, and 31 December 2019. The demographics of the patients and the logistics of the events were statistically analyzed. Results: The incidence in pediatric subjects is 4.5 (95% CI 3.6-5.6) per 100,000 of the population. School buildings and sports facilities have relatively few events (1.9% and 4.4%, respectively), while 39.4% of OHCAs are preventable, being due to violent accidents or trauma, mainly occurring on the streets (23.2%). Conclusions: Limiting violent events is necessary to reduce OHCA mortality in children. Raising awareness and giving practical training to citizens is a priority in general but specifically in schools.

An Investigation in the Control Effectiveness of the Driving Wheel Slip Prevention System of a Diesel Engine Dump Truck

<div>This article proposes the structure and algorithm to design a PID controller for the driving wheel slip prevention system (DWSPs) of a dump truck using a diesel engine, which is equipped just only with a traditional high-pressure pump (HPP) under low-adhesion coefficient conditions. First, a longitudinal dynamic model, and a dynamic model of the wheel and powertrain of a dump truck are, respectively, established, and an experiment in the torque determination of a diesel engine is set up to investigate longitudinal vehicle dynamics as well. Then, a control system structure of the DWSPs for a dump truck using a diesel engine with a high-pressure inline fuel pump is proposed. Finally, based on performance analysis of other types of controllers, a PID controller is selected to control actual load level of a diesel engine. The criteria representing the vehicle’s acceleration such as the vehicle speed, vehicle acceleration, total slip time, and time to reach vehicle speed are selected to examine the effectiveness of the proposed controller when vehicles are being operated under the road surface conditions with low values of adhesion coefficient. The obtained results show that the proposed controller has greatly limited the driving wheel slipping phenomenon and increased the acceleration of the original dump truck. In addition, these findings can be used as a theoretical basis for the improvement of the wheel slip controllers in trucks using diesel engines.</div>

VEHICAL ACCIDENT PREVENTION SYSTEM FOR MOUNTAIN ROADS

Vehicle accident prevention system can be a major step in accident safety on mountain roads. In past 100s of accident and death on mountain roads, some even fall from the cliff which are not traced after that have been noticed. Such accidents not only cause loss of human life but also major loss financially not just to the individual but also the government. Mountain roads are narrow and any such accidents can force it to be closed for hours which can be a huge issue. This project addresses the pressing issue of mountain road safety through the deployment of two strategically positioned ultrasonic sensors in U curves. The primary objective is to detect oncoming vehicles, precisely measure distances, and promptly activate warnings on the opposite side. By leveraging real-time data and proactive measures, the system aims to significantly reduce accidents in challenging terrains. The integration of red LED lights and a buzzer enhances visibility and auditory awareness. This innovative approach reflects a crucial step forward in accident prevention, particularly in mountainous regions, where traditional safety measures fall short.

Simulation and modeling of deep adversarial probabilistic neural network-based intrusion prevention system in cloud computing for smart grid cyber physical systems

Smart Grid Cyber Physical Systems (SG-CPS) have a substantial impact on power grid infrastructure upgrading. Nonetheless, due to the sophisticated nature of the infrastructure and the critical demand for resilient intrusion prevention systems, the task of protecting its security against data integrity attacks is a significant challenge. The simulation and assessment of security performance in SG-CPS present substantial hurdles in real-world power grid systems, owing mostly to experimental constraints. This necessitates the development of novel ways to improve distribution chain security. This research introduces a novel approach, employing a Deep Adversarial Probabilistic Neural Network (DAPNN)-based Intrusion prevention system in a cloud environment. Combining Bayesian Probabilistic Neural Networks (BEPNNs) with adversarial training and rule-based decision-making enhances precision and resilience. The major goal of this research is to detect and counteract false data injection (FDI) attacks that have the potential to compromise the integrity of power grid data. This paper proposes a novel methodology for intrusion detection in SG-CPS that combines BEPNNs with adversarial training. The addition of rule-based decision-making improves the system’s precision and resilience. The IEEE 24-bus system provides the foundation for providing data points relevant to normal operating conditions, contingency scenarios, and intentional attacks. The training procedure includes the use of a BEPNN for feature extraction, as well as the use of adversarial training approaches. The intrusion detection system has decision-making logic based on rules. The cloud infrastructure solution used in the study is Microsoft Azure. The results show that the DAPNN-based Intrusion Prevention System is effective in detecting and mitigating FDI attacks in SG-CPS. The system outperforms in terms of accuracy, precision, recall and F-measure, hence improving the security of the power grid infrastructure.

Behavioral counseling for cardiovascular disease prevention in 36 low-income and middle-income countries

BackgroundGiven the substantial prevalence of cardiovascular disease (CVD) in low-income and middle-income countries (LMICs), evaluation of behavioral counseling for prevention of CVD is important. MethodsWe pooled nationally representative cross-sectional surveys from 36 LMICs between 2013 and 2020. The population was divided into three groups according to CVD risk: the potential risk group, the risk group and the CVD group. We estimated the prevalence of six types of behavioral counseling among the three groups separately: smoking, salt reduction, fruit and vegetable intake, dietary fat reduction, physical activity and body weight. ResultsThere were 16,057 (25.4%) in the potential risk group, 43,113 (49.9%) in the risk group, and 7796 (8.6%) in the CVD group. The prevalence of receiving at least four types of counseling in the three groups was 15.6% (95% CI 13.9 to 17.5), 14.9% (95% CI 14.0 to 15.9), and 19.8% (95% CI 17.7 to 22.2), respectively. The lowest prevalence was for tobacco use counseling: 24.5% (95% CI 22.5 to 26.4), 23.2% (95% CI 22.1 to 24.3), and 32.1% (95% CI 29.5 to 34.8), respectively. The prevalence of counseling was higher in upper-middle-income countries than in lower-middle-income countries. Women, older people, those with more education, and those living in urban areas were more likely to receive counseling. ConclusionThe prevalence of behavioral counseling for CVD is low in LMICs, especially among potentially at-risk populations and in low-income countries. These findings highlight the current urgent need to improve CVD prevention and management systems to enhance behavioral counseling and intervention.

Monitoring public perceptions of contaminated sites based on social media

Contaminated sites have a negative impact on human health and the ecological environment, which can potentially lead to major pollution incidents, and frequently receive complaints and reports from the public. Therefore, monitoring public perceptions of contaminated sites is crucial for risk management. However, methods based on traditional questionnaire surveys are limited in terms of time, cost, and target audience size. The purpose of this study was to establish a method using social media to monitor public perceptions of contaminated sites in the Yangtze River Delta urban agglomeration. Thus, 6802 public opinions were collected from social media (mainly microblogs in China) and topic modeling and pollution and spatial mining techniques were employed. The topic modeling results indicated that public perceptions of contaminated sites mainly focused on the construction of prevention and control systems, law enforcement work, developments and challenges applicable to the coal industry, environmental public interest litigation (pertaining to pollution), pollution inspection and rectification, and green development and ecological governance, with intensities of 7.9%, 7.8%, 7.1%, 6.1%, 5.9%, and 4.8%, respectively. Three communities resulting from public opinions in the study area included “environment and pollution,” “enterprises,” and “environmental protection,” with proportions of 37.68%, 34.78%, and 27.54%, respectively. The field investigation results indicated that approximately 90% of the tweets in three typical cities (i.e., Taizhou City in Zhejiang Province and Wuxi and Changzhou Cities in Jiangsu Province) involved key industrial enterprises or contaminated sites located within 1 km of the surrounding areas. The emotional analysis indicated that >3401 tweets dealt with a pollution probability (i.e., the possibility of potentially contaminated sites mentioned in social media becoming contaminated sites) exceeding 0.90 for the period 2011–2021. This finding suggests that the pollution probability for the sites involved in these tweets was high. Our study provides methodological references for monitoring public perceptions of contaminated sites for large-scale, long-term, and high-resolution studies.

Epidemiological characteristics of local visceral leishmaniasis in Beijing Municipality from 2021 to 2023

To investigate the epidemiological characteristics of local visceral leishmaniasis in Beijing Municipality from 2021 to 2023, so as to provide insights into formulation of the visceral leishmaniasis control strategy. Epidemiological data of visceral leishmaniasis cases reported in Beijing Municipality from 2021 to 2023 were collected from the National Health Informatization Disease Prevention and Control Information System, and the epidemiological characteristics of local visceral leishmaniasis cases were analyzed using a descriptive epidemiological method. In November 2021 and 2023, 50 to 100 permanent residents were voluntarily selected within a 100 meter radius of sites where patients lived or acquired Leishmania infections, and venous blood was sampled for anti-Leishmania antibody testing. Venous blood was sampled from dogs for anti-Leishmania antibody testing in natural villages where patients lived or acquired Leishmania infections, or in districts where Leishmania infected dogs were reported. In addition, sandflies were captured with CO2 mosquito traps and fine mesh nets in natural villages where patients lived or acquired Leishmania infections from May to September, 2021 and 2023, for sandfly species identification. A total of 4 local visceral leishmaniasis cases were reported in Beijing Municipality from 2021 to 2023, with ages of 2 to 77 years, and acquiring Leishmania infections in Mentougou District (2 cases), Changping District (1 case), and Yanqing District (1 case). The anti-Leishmania antibody testing was all negative in 73 human blood samples and the sero-prevalence of anti-Leishmania antibody was 25.00% in 36 venous blood samples from domestic dogs in 2021, with a total of 4 520 Phlebotomus chinensis captured. The sero-prevalence of anti-Leishmania antibody was 0.51% in 198 human blood samples and 13.58% in 243 venous blood samples from domestic dogs in 2023, with 16.10%, 25.00%, 17.78% and 3.13% sero-prevalence in dogs sampled from Mentougou District, Changping District, Yanqing District and Haidian District, respectively (P = 0.011), while a total of 1 712 Ph. chinensis were captured, including 1 421 female sandflies (86.54%). The prevalence of local visceral leishmaniasis was low in Beijing Municipality from 2021 to 2023; however, there is a risk of further spread in the epidemic foci. Intensified visceral leishmaniasis surveillance and control is recommended.

Design of an Internet of Things (IoT) based Early Fire Prevention System Due to LPG Gas Leakage Using NodeMCU ESP8266 Module

Design of an Internet of Things (IoT) based Early Fire Prevention System Due to LPG Gas Leakage Using NodeMCU ESP8266 Module

Discussion on the prevention and control strategies of chronic hepatitis B: experience from AIDS prevention and control in China

Hepatitis B is a major infectious disease that seriously endangers the health of the people of China. Patients with hepatitis B have a large base in our country, and the core indicators such as detection and antiviral treatment ratio are far from the real goal of eliminating the public health threat of uiral hepatitis.Notably, the chronic hepatitis B prevention and control system lacks a wide targeted strategies. This paper systematically analyzes our country's main successful experience with AIDS prevention and control and, on that basis, proposes the ideas and strategic paths for the construction of a chronic hepatitis B prevention and control system, analyzes and discusses the current difficulties and problems in prevention and control, and looks forward to future prevention and control efforts.