Surveillance Research Articles

A systematic review of radar technologies for surveillance of foreign object debris detection on airport runway

A systematic review of radar technologies for surveillance of foreign object debris detection on airport runway

Intersectional trends in poor mental health and health inequities across the US

Though mental distress poses a large and growing threat to population health, our understanding of how its social distribution has changed over time and what these changes imply for mental health equity is limited. To address this, we use data from the Behavioral Risk Factor Surveillance System to non-parametrically describe how age-standardized prevalence of frequent mental distress (FMD) and social inequities in FMD have changed in the United States between 1993 and 2019 for intersectional social groups defined by ethnicity, race, sex, educational attainment, and household poverty status. We find that age-standardized FMD prevalence has increased for almost all social groups, that health inequities between more and less privileged groups have mostly widened in absolute terms but narrowed relatively, and that relying solely on common group FMD summaries masks substantial heterogeneity across intersectional subgroups. Our findings show an urgent need to address the sociopolitical determinants of mental distress, prioritizing policies which would address the growing inequitable burden experienced by those less privileged.

The Burden of Injuries Associated With E-Bikes, Powered Scooters, Hoverboards, and Bicycles in the United States: 2019‒2022.

Objectives. To describe the national burden of injuries associated with e-bikes, bicycles, hoverboards, and powered scooters (micromobility devices) in the United States. Methods. We compared patterns and trends for 1 933 296 estimated injuries associated with micromobility devices from 2019 to 2022 using National Electronic Injury Surveillance System data. Results. The population-based rates of e-bike and powered scooter injuries increased by 293.0% and 88.0%, respectively. When reported, powered scooter injuries had the highest proportion for alcohol use (9.0%) compared with other modes, whereas e-bike injuries had the highest proportion for motor vehicle involvement (35.4%). Internal injuries were more likely among e-bike diagnoses than hoverboard and bicycle (P < .05), but fractures and concussions were more likely among hoverboard diagnoses compared with all other devices (P < .05). When helmet use was identified in clinical notes (20.3%), helmet usage was higher among e-bike injuries (43.8%) compared with powered scooter (34.8%) and hoverboard (30.3%) injuries but lower compared with bicycle injuries (48.7%). Conclusions. The incidence of severe e-bike and powered scooter injuries increased over the 4-year period. Public health stakeholders should focus on improved surveillance and prevention of injuries associated with electric micromobility devices. (Am J Public Health. 2024;114(12):1365-1374. https://doi.org/10.2105/AJPH.2024.307820).

Framework for abnormal event detection and tracking based on effective sparse factorization strategy

The idea of tracking video objects has evolved to facilitate the area of surveillance systems. However, most current research efforts lie in speedy abnormal event detection and tracking of objects of interest tracking. However, the primary challenge is dealing with complex video structures' inherent redundancy. The existing research models for video tracking are more inclined towards improving accuracy. In contrast, the consideration of a more significant proportion of mobile object dynamics, e.g. abnormal events, in motion over the crowded video frame sequence is mainly overlooked, which is essential to study a specific movement pattern of the object of interest appearing in the frame sequence concerning the cost of computation factors. The study thereby introduces a unique strategy of speedy abnormal event detection and tracking, which facilitates video tracking to assess a specific pattern of object of interest movement over complex and crowded video scenes, considering a unique learning-based approach. The extensive simulation outcome further shows that the proposed tracking model accomplishes better tracking accuracy yet retains an optimized computation cost compared to the baseline studies. The computation of video tracking also accomplishes higher detection rates even in the challenging constraints of partial/complete occlusion, illumination variation and background clutter.

Design of an effective multiple objects tracking framework for dynamic video scenes

Nowadays, the applications corresponding to video surveillance systems are getting popular due to their wide range of deployment in various places such as schools, roads, and airports. Despite the continuous evolution and increasing deployment of object-tracking features in video surveillance applications, the loopholes still need to be solved due to the limited functionalities of video-tracking systems. The existing video surveillance systems pose high processing overhead due to the larger size of video files. However, the traditional literature report quite sophisticated schemes which might successfully retain higher object detection accuracy from the video scenes but needs more effectiveness regarding computational complexity under limited computing resources. The study thereby identifies the scope of enhancement in traditional object-tracking functions. Further, it introduces a novel, cost-effective tracking model based on Gaussian mixture model (GMM) and Kalman filter (KF) that can accurately identify numerous mobile objects from a dynamic video scene and ensures computing efficiency. The study's outcome shows that the proposed strategic modelling offers better tracking performance for dynamic objects with cost-effective computation compared to the popular baseline approaches.

A deep learning-based surveillance system for enhancing public safety through internet of things and digital technology using Raspberry Pi

A deep learning-based surveillance system for enhancing public safety through internet of things and digital technology using Raspberry Pi

An Ensemble Framework Augmenting Surveillance Cameras for Detecting Intruder Clusters as Potential Mobs

&lt;p&gt;Many developing nations around the world curtail crimes through video surveillance technology, but the crime rate is still high. This is compounded by short-staffed security operatives and a deficiency of security infrastructure to assist security operatives with knowledge-driven decision support systems in the low-resource constraint environment. In a public environment, it is challenging to detect intruder clusters accurately as potential mobs for early warning. Previous research investigated some classical techniques, but their recommendations were insufficient. This research develops a Machine Learning (ML) 3-tiers Ensemble framework, which integrates Gray Level Co-occurrence Matrices (GLCM) principles to enhance the capabilities of surveillance cameras and security operatives to effectively discern and respond to potential mob formations. The University of California San Diego (UCSD) pedestrian datasets that are publicly available were used for the experiments. With an improved overall average precision of 0.98, recall of 0.98, and accuracy of 98.52% on the UCSD dataset, the suggested framework outperforms the widely used methods for the detection of intruder clusters. The reduction in computational time on processors showcases the framework's significant advancements as a promising solution for robust real-time threat assessment applications.&lt;/p&gt;

Enhancing video anomaly detection for human suspicious behavior through deep hybrid temporal spatial network

Abnormal behavior exhibited by individuals with particular intentions is common, and when such behavior occurs in public places, it can cause physical and mental harm to others. Considering the rise in the automated approach for anomaly detection in videos, accuracy becomes essential. Most existing models follow a deep learning architecture, which faces challenges due to variations in motion. This research work develops a deep learning based hybrid architecture with temporal and spatial features. The hybrid temporal spatial network (HTSNet) consists of two customized architectures: a graph neural network (GNN) and a convolutional neural network (CNN). HTSNet combined with a novel classifier to extract features and classify normal and abnormal behavior. The performance of HTSNet is rigorously evaluated using the University of California, San Diego-Pedestrian 1 (UCSD Ped1) dataset, a benchmark in computer vision research for anomaly detection in video surveillance. The effectiveness of HTSNet is demonstrated through a comparative analysis with current state-of-the-art methods, using the area under the curve (AUC) metric as a standard measure of performance. This paper contributes to the advancement of video surveillance technology, providing a robust framework for enhancing public safety and security in an increasingly digital world.

Unwanted disorders and xenogeneic graft-versus-host disease in experimental immunodeficient mice: How to evaluate and how to report.

Human-derived tumor models are essential for preclinical development of new anticancer drug entities. Generating animal models bearing tumors of human origin, such as patient-derived or cell line-derived xenograft tumors, is dependent on immunodeficient strains. Tumor-bearing immunodeficient mice are susceptible to developing unwanted disorders primarily irrelevant to the tumor nature; and if get involved with such disorders, reliability of the study results will be undermined, inevitably confounding the research in general. Therefore, a rigorous health surveillance and clinical monitoring system, along with the establishment of a strictly controlled barrier facility to maintain a pathogen-free state, are mandatory. Even if all pathogen control and biosafety measures are followed, there are various noninfectious disorders capable of causing tissue and multiorgan damage in immunodeficient animals. Therefore, the researchers should be aware of sentinel signs to carefully monitor and impartially report them. This review discusses clinical signs of common unwanted disorders in experimental immunodeficient mice, and how to examine and report them.

Social Determinants of Health and Health-Related Social Needs Among Adults With Chronic Diseases in the United States, Behavioral Risk Factor Surveillance System, 2022.

The relationship between social determinants of health (SDOH) and health-related social needs (HRSN) and some chronic diseases at the population level is not well known. We sought to determine relationships between SDOH/HRSN and major chronic diseases among US adults by using data from the 2022 Behavioral Risk Factor Surveillance System (BRFSS). We used data from the new Social Determinants and Health Equity (SD/HE) module, conducted in 39 states, the District of Columbia, and 2 territories as part of the 2022 BRFSS. These data yielded a sample of 324,631 adult participants (aged ≥18 y). We examined 12 indicators of SDOH/HRSN and 9 chronic diseases. We calculated weighted prevalence estimates for each SDOH/HRSN measure for each chronic disease and associations between each SDOH/HRSN and each chronic disease. Two-thirds of participants (66.3%) had 1 or more chronic diseases, and 59.4% reported 1 or more adverse SDOH/HRSN. Prevalence estimates for individual SDOH/HRSN measures were generally higher among participants with chronic diseases (except cancer). The more chronic diseases reported, the more likely participants were to have SDOH/HRSN (P < .05 for linear trend). The leading SDOH/HRSN measures associated with each chronic disease varied; however, the most common were mental stress, receiving food stamps or participating in the Supplemental Nutrition Assistance Program, cost as a barrier for needed medical care, and life dissatisfaction. From a treatment and prevention perspective, health care providers should consider the influence of SDOH/HRSN on people with or at risk for chronic diseases. Additionally, human service and public health systems in communities with high rates of chronic disease should consider these findings as they plan to mitigate adverse SDOH.

Incidence of avoidant/restrictive food intake disorder in children and adolescents across the UK and Ireland: a BPSU and CAPSS surveillance study

ObjectiveWe aimed to estimate the incidence of avoidant/restrictive food intake disorder (ARFID) in children and adolescents (CA) presenting to secondary care in the UK and Republic of Ireland (ROI).DesignThis observational...

Enhancing Real-Time Vision Systems: Integrating Dynamic Vision Sensors with Vision Transformers to Increase Computational Efficiency

Abstract. Optimizing and understanding vision systems' computational accuracy is crucial as they become increasingly integrated into daily life. Dynamic Vision Sensors (DVS) and Vision Transformers (ViTs) lead computer vision technology with efficient object recognition and image processing. However, DVS data's high computational complexity poses a problem for its real-time implementations. Merging these technologies can enhance vision system performance in dynamic environments and optimize real-time DVS processing. In our work, we use a ViT architecture to classify the DVS 128 dataset and compare our results with existing works using SNNs. We analyze how our method affects accuracy and loss, experimenting with different DVS-to-ViT input patch sizes. Our results show that the large patch size of 32x32 pixels has better accuracy and smaller loss than the 4x4 pixel patches as epochs increase. Our method also achieved a high 98.4% accuracy and low 0.22 loss within five epochs, significantly outperforming previous works averaging 93.13% accuracy over more epochs. These results highlight ViTs' large potential for real-time DVS data classification in applications that require high accuracy, like autonomous vehicles and surveillance systems.

National and Subnational Trends of Mortality and Years of Life Lost Due to Stroke and Its Subtypes in Young Adults in China, 2005-2020.

The incidence of stroke among young adults was rising globally, but the death burden of stroke in young adults in China is lacking. We aimed to examine the temporal trends in mortality and years of life lost (YLLs) caused by stroke among young adults from 2005 to 2020 across China. Based on the data from the National Mortality Surveillance System in China, we estimated the number and age-standardized rate of mortality and YLLs due to stroke and its subtypes among young adults aged 15-49 years during 2005-2020, for both China and its 31 mainland provinces. During 2005-2020, the age-standardized mortality rate of stroke among young adults aged 15-49 years in China decreased by 21.0%, from 5.9/100,000 to 4.7/100,000, and the YLL rate decreased from 286.9/100,000 to 229.5/100,000. The age-standardized mortality rate among young adults due to intracerebral hemorrhage (ICH) showed a significant downward trend with a decrease of 26.3% while that of ischemic stroke (IS) and subarachnoid hemorrhage (SAH) decreased by 4.5% and 0.6%, respectively. In 2020, the mortality rate of ICH was 3.5 times higher than that of IS (3.3/100,000 vs 0.9/100,000) among young adults in China. The male/female ratio of age-standardized mortality rate of stroke in young adults increased from 2.0 in 2005 to 3.1 in 2020. The age-standardized mortality rate of IS and SAH in young men increased by 11.0% and 2.5%, respectively. In 2020, Tibet (18.4/100,000), Jilin (10.4/100,000), and Qinghai (8.3/100,000) were the top 3 provinces holding the highest age-standardized mortality rate due to stroke among young adults. Tibet was found to have the highest mortality rate due to ICH and SAH while that of IS was higher in northeast China. In China, the death burden caused by ICH among young adults was substantially higher than that of IS. The increasing death burden of IS and SAH among young men requires special attention. Evidence-based intervention strategies are needed to improve the outcomes of stroke and alleviate the death burden due to stroke among young adults in Chinese population.

Global Impact of COVID-19 Pandemic on Gastrointestinal Infections: A Scoping Review.

During the COVID-19 pandemic, nonpharmaceutical public health interventions (NPIs) were implemented worldwide to control the spread of severe acute respiratory syndrome coronavirus 2. However, the incidence of other pathogens, including gastrointestinal (GI) pathogens, was also affected. Here, we reviewed studies assessing the impact of NPIs during the COVID-19 pandemic on the incidence of GI infections, particularly foodborne infections. A systems literature search was conducted in May 2023, using Living Evidence on COVID-19 (COAP) and Scopus. Articles were identified and selected through a screening process with inclusion and exclusion criteria based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. Data were extracted from each full-text article included in the review. Parameters included were GI viruses, GI bacteria, NPIs against the COVID-19 pandemic, and the associated impact of NPIs on GI pathogens. A total of 42 articles were included in the review, representing 18 countries. Overall, a larger reduction was observed for viral GI infections compared with bacterial GI infections during the COVID-19 pandemic, particularly for norovirus. For bacterial GI infections, Campylobacter and nontyphoidal Salmonella were the most frequently detected pathogens in the majority of the studies, with the largest reduction observed for Shigella and Shiga toxin-producing Escherichia coli infections. The sharp decrease in GI viral infections in most of the included countries is suggested to be related to the disruption of person-to-person transmission due to several implemented interventions (e.g., social distancing and hand hygiene). GI bacterial pathogens, more commonly transmitted via the foodborne route, were least impacted, and their reduction is associated with closure of food-providing settings and travel restrictions. However, the observed changes appear to be multifactorial; alterations in health-care-seeking behaviors and in routinary diagnostic testing have undeniably played a significant role, affecting national surveillance systems. Therefore, although NPIs likely had a substantial impact on the burden of GI infectious diseases, the extent of the true change cannot be fully assessed.

Road traffic accidents and injuries, 2018: a multi-centric epidemiological study from India

Objective: Road traffic injuries (RTIs) have been recognized globally as an important public health problem. Effective road safety initiatives should address the traffic system as a whole to find a solution and look at the traffic system as a collective to discuss interactions between vehicles, road users and road infrastructure. Therefore, epidemiological data from different geographies of the country should be available. Hence, an electronic-based comprehensive and integrated RTI surveillance system was established in five centres located across the country to assess the burden of RTIs, including the outcome. Methods: This paper is a cross-sectional multi-centric study conducted using an electronic-based comprehensive and integrated RTI surveillance system. Results: A total of 15,319 participants were enrolled under the surveillance of road traffic events for a period of one year. Self-fall/skid was the most common (34.87%) type of accident, followed by a crash between two vehicles (25.77%) and a crash with a pedestrian (16.59%). Among them, 88.94% were the injured, who were alive with or without rehabilitation, and 673 (4.43%) were dead. Mostly, two-wheelers (geared or non-geared) were involved in the accident as they shared a significant portion (75.54%) of the total accidents. Conclusion: The study highlights the epidemiological issues related to road accidents and RTIs that need to be addressed in order to find appropriate solutions for reducing the RTI burden. It gives an understanding of the manner of trauma, the pattern of injuries, and the outcome of road traffic accidents required to adopt efficient preventive and comprehensive trauma care.

A Purely Biomanufactured System for Delivering Nanoparticles and STING Agonists.

Nanovaccines have significantly contributed in the prevention and treatment of diseases. However, most of these technologies rely on chemical or hybrid semibiological synthesis methods, which limit the manufacturing performance advantages and improved inoculation outcomes compared with traditional vaccines. Herein, a universal and purely biological nanovaccine system is reported. This system integrates three modules: (1) self-assembling nanoparticles, (2) self-catalyzed synthesis of small-molecule stimulator of interferon gene (STING) agonists, and (3) delivery vectors that target the cytosolic surveillance system. Various nanoparticles are efficiently self-assembled using this system. After confirming the excellent immunostimulatory and lymph node targeting of this system, its broad-spectrum antiviral efficacy is further demonstrated. By leveraging the comprehensive biosynthetic capabilities of bacterial cells, this system can efficiently combine various adjuvant-active modular components and antigenic cargo, thereby providing a highly diversified and potent vaccine platform.

A hatékony operátori tevékenység kulcstényezői

Aim: The aim of the study is to explore the role and significance of human factors and cognitive processes in the operator activities of surveillance systems. Methodology: The authors discuss important human factors related to effective video surveillance and the environmental conditions necessary for efficient work, drawing observations from various research studies and standards. Findings: The quality of operator activities is strongly influenced by the operator's sensory, perceptual, and attentional abilities. The efficient operation of surveillance systems largely depends on the operators' intuition, expertise, and the appropriate work environment. Value: The study contributes to a better understanding of the role of human factors in video surveillance systems and allows us to formulate guidelines and recommendations that can improve the efficiency of operator activities.

Key Factors of Efficient Operator Activities

Aim: The study aims to explore the role and significance of human factors and cognitive processes in the operator activities of surveillance systems. Methodology: The authors discuss important human factors related to effective video surveillance and the environmental conditions necessary for efficient work, drawing observations from various research studies and standards. Findings: The sensory, perceptual, and attentional abilities of the operator have a significant impact on the quality of their activities. The efficient operation of surveillance systems largely depends on the operators’ intuition, expertise, and the appropriate work environment. Value: The study contributes to a better understanding of the role of human factors in video surveillance systems and allows us to formulate guidelines and recommendations that can improve the efficiency of operator activities.

The gendered forest: Digital surveillance technologies for conservation and gender-environment relationships

Forests are critical spaces that shape and enable gendered subjectivities in culturally and historically specific ways. However, scholarly work on forest or biodiversity conservation continues to take a very perfunctory view on gender–environment relationships. Many projects remain gender blind or view everyday practices of forest resource collection by women through a transactional or economic lens. Research has shown that forests are spaces wherein identities of women are entwined with their everyday activities in the forest. In this article, we demonstrate the gendered nature of forests of the Corbett Tiger Reserve (CTR) in India, and their different socio-cultural framings. We reveal how the forest spaces of the CTR are used by women for a wide variety of cultural and livelihood needs. We further show how biodiversity conservation practice in such forest spaces alters the activities of women in a myriad of ways. The increasing use of digital technologies in biodiversity conservation shapes how the forest space is observed and governed. We argue that the use of digital technologies for forest governance such as camera traps and drones tends to transform these forests into masculinized spaces that extend the patriarchal gaze of society to the forest. Finally, we reflect on how the use of digital technologies for biodiversity conservation is easily co-opted for purposes beyond conservation that reinforce patriarchal norms and propagate gendered structural violence.

A Nationwide Evaluation of Antibiotic Consumption in Kazakhstan from 2019 to 2023

Background/Objectives: There has been a lack of a holistic approach to evaluating antibiotic consumption in Kazakhstan over the past few years using an internationally recognized methodology. Therefore, this study aimed to provide a nationwide evaluation of antibiotic consumption in Kazakhstan during the period 2019–2023. Methods: Defined daily doses per 1000 inhabitants per day (DIDs) were calculated for systemic antibiotics (J01 code of the Anatomical Therapeutic Chemical Classification System (ATC)) following the methodology established by the Global Antimicrobial Resistance and Use Surveillance System (GLASS-AMC). The average annual percent change (AAPC) was computed for each chemical agent, pharmacological group, and J01 in general to evaluate past trends in antibiotic consumption. Results: The consumption of J01 antibiotics ranged between 10.869 DIDs (2022) and 14.470 DIDs (2020). There was an increase in antibiotic consumption during 2020 and 2021, but the overall trend was declining, with an AAPC of −2.45%. Azithromycin was the most consumed systemic antibiotic, followed by ceftriaxone and ciprofloxacin. The consumption of “Watch” group antibiotics prevailed in Kazakhstan during the study period, with the number of people consuming the top five “Watch” group antibiotics rising from 72,578 in 2019 to 94,617 in 2023. Conclusions: The findings of this study are crucial for the reorganization of the national antimicrobial stewardship program.