Search results for: depression detection

Authors: Dušica Kovačević

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This study explores the influence of destructive groups on the mental health and social integration of high school students in Loznica, Serbia. Despite increasing concerns, there is a significant lack of research on the impact of these groups on adolescents in this region. This qualitative study aims to fill this gap by examining the prevalence of destructive groups, their psychological effects on students, and their broader social implications. Data were collected through surveys and in-depth interviews with high school students, educators, and mental health professionals. The study focuses on key mental health indicators, such as anxiety, depression, and identity formation, alongside social factors, including peer relationships and community engagement. Additionally, it defines coping mechanisms and supporting strategies employed by students affected by these groups. The findings reveal substantial psychological and social challenges faced by students exposed to destructive groups, including increased levels of anxiety and depression, disrupted identity development, and impaired social integration. Insights into the personal experiences of these students provide a detailed understanding of the groups’ impact, underscoring the need for targeted interventions. This research offers evidence-based recommendations for educators, mental health practitioners, and policymakers. It emphasizes the importance of developing effective educational programs and support services to enhance the well-being of high school advocates for proactive measures to protect adolescent mental health and promote healthy social values within educational and community settings.

Keywords: adolescents, mental health, destructive groups, social integration, qualitative study, high school students, Serbia

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Authors: J. Fox, J. Randhawa, M. Chan, L. Campbell, A. Weakely, D. J. Harvey, S. Tomaszewski Farias

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Early identification of individuals at risk for conversion to dementia provides an opportunity for preventative treatment. Many older adults (30-60%) report specific subjective cognitive decline (SCD); however, previous research is inconsistent in terms of what types of complaints predict future cognitive decline. The purpose of this study is to identify which specific complaints from the Everyday Cognition Scales (ECog) scales, a measure of self-reported concerns for everyday abilities across six cognitive domains, are associated with: 1) conversion from a clinical diagnosis of normal to either MCI or dementia (categorical variable) and 2) progressive cognitive decline in memory and executive function (continuous variables). 415 cognitively normal older adults were monitored annually for an average of 5 years. Cox proportional hazards models were used to assess associations between self-reported ECog items and progression to impairment (MCI or dementia). A total of 114 individuals progressed to impairment; the mean time to progression was 4.9 years (SD=3.4 years, range=0.8-13.8). Follow-up models were run controlling for depression. A subset of individuals (n=352) underwent repeat cognitive assessments for an average of 5.3 years. For those individuals, mixed effects models with random intercepts and slopes were used to assess associations between ECog items and change in neuropsychological measures of episodic memory or executive function. Prior to controlling for depression, subjective concerns on five of the eight Everyday Memory items, three of the nine Everyday Language items, one of the seven Everyday Visuospatial items, two of the five Everyday Planning items, and one of the six Everyday Organization items were associated with subsequent diagnostic conversion (HR=1.25 to 1.59, p=0.003 to 0.03). However, after controlling for depression, only two specific complaints of remembering appointments, meetings, and engagements and understanding spoken directions and instructions were associated with subsequent diagnostic conversion. Episodic memory in individuals reporting no concern on ECog items did not significantly change over time (p>0.4). More complaints on seven of the eight Everyday Memory items, three of the nine Everyday Language items, and three of the seven Everyday Visuospatial items were associated with a decline in episodic memory (Interaction estimate=-0.055 to 0.001, p=0.003 to 0.04). Executive function in those reporting no concern on ECog items declined slightly (p <0.001 to 0.06). More complaints on three of the eight Everyday Memory items and three of the nine Everyday Language items were associated with a decline in executive function (Interaction estimate=-0.021 to -0.012, p=0.002 to 0.04). These findings suggest that specific complaints across several cognitive domains are associated with diagnostic conversion. Specific complaints in the domains of Everyday Memory and Language are associated with a decline in both episodic memory and executive function. Increased monitoring and treatment of individuals with these specific SCD may be warranted.

Keywords: alzheimer’s disease, dementia, memory complaints, mild cognitive impairment, risk factors, subjective cognitive decline

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Authors: Pellecchia M. T., Savarese G., Carpinelli L., Calabrese M.

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Introduction: Parkinson's disease (PD) is characterized by a progressive loss of autonomy which undoubtedly has a significant impact on the quality of life of caregivers, and parents are the main informal caregivers. Caring for a person with PD is associated with an increased risk of psychiatric morbidity and persistent anxiety-depressive distress. The aim of the study is to investigate the burden on caregivers of patients with PD, through the use of multidimensional scales and to identify their personological and environmental determinants. Methods: The study has been approved by the Ethic Committee of the University of Salerno and informed consent for participation to the study was obtained from patients and their caregivers. The study was conducted at the Neurology Department of the A.O.U. "San Giovanni di Dio and Ruggi D’Aragona" of Salerno between September 2020 and May 2021. Materials: The questionnaires used were: a) Caregiver Burden Inventory - CBI a questionnaire of 24 items that allow identifying five sub-categories of burden (objective, psychological, physical, social, emotional); b) Depression Anxiety Stress Scales Short Version - DASS-21 questionnaire consisting of 21 items and valid in examining three distinct but interrelated areas (depression, anxiety and stress); c) Family Strain Questionnaire Short Form - FSQ-SF is a questionnaire of 30 items grouped in areas of increasing psychological risk (OK, R, SR, U); d) Zarit Caregiver Burden Inventory - ZBI, consisting of 22 items based on the analysis of two main factors: personal stress and pressure related to his role; e) Life Satisfaction, a single item that aims to evaluate the degree of life satisfaction in a global way using a 0-100 Likert scale. Findings: N ° 29 caregivers (M age = 55.14, SD = 9.859; 69% F) participated in the study. 20.6% of the sample had severe and severe burden (CBI score = M = 26.31; SD = 22.43) and 13.8% of participants had moderate to severe burden (ZBI). The FSQ-SF highlighted a minority of caregivers who need psychological support, in some cases urgent (Area SR and Area U). The DASS-21 results show a prevalence of stress-related symptoms (M = 10.90, SD = 10.712) compared to anxiety (M = 7.52, SD = 10.752) and depression (M = 8, SD = 10.876). There are significant correlations between some specific variables and mean test scores: retired caregivers report higher ZBI scores (p = 0.423) and lower Life Satisfaction levels (p = -0.460) than working caregivers; years of schooling show a negative linear correlation with the ZBI score (p = -0.491). The T-Test indicates that caregivers of patients with cognitive impairment are at greater risk than those of patients without cognitive impairment. Conclusions: It knows the factors that affect the burden the most would allow for early recognition of risky situations and caregivers who would need adequate support.

Keywords: anxious-depressive axis, caregivers’ burden, Parkinson’ disease, psychological risks

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Authors: Ali Ilter Akdag, Pratima Ray

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Background:This acute gastroenteritis viruses such as rotavirus, astrovirus, and adenovirus are mainly responsible for diarrhea in children below < 5 years old. Molecular detection of these viruses is crucially important to the understand development of the effective cure. This study aimed to determine the prevalence of common these viruses in children < 5 years old presented with diarrhea from Lala Lajpat Rai Memorial Medical College (LLRM) centre (Meerut) North India, India Methods: Total 312 fecal samples were collected from diarrheal children duration 3 years: in year 2014 (n = 118), 2015 (n = 128) and 2016 (n = 66) ,< 5 years of age who presented with acute diarrhea at the Lala Lajpat Rai Memorial Medical College (LLRM) centre(Meerut) North India, India. All samples were the first detection by EIA/RT-PCR for rotaviruses, adenovirus and astrovirus. Results: In 312 samples from children with acute diarrhea in sample viral agent was found, rotavirus A was the most frequent virus identified (57 cases; 18.2%), followed by Astrovirus in 28 cases (8.9%), adenovirus in 21 cases (6.7%). Mixed infections were found in 14 cases, all of which presented with acute diarrhea (14/312; 4.48%). Conclusions: These viruses are a major cause of diarrhea in children <5 years old in North India. Rotavirus A is the most common etiological agent, follow by astrovirus. This surveillance is important to vaccine development of the entire population. There is variation detection of virus year wise due to differences in the season of sampling, method of sampling, hygiene condition, socioeconomic level of the entire people, enrolment criteria, and virus detection methods. It was found Astrovirus higher then Rotavirus in 2015, but overall three years study Rotavirus A is mainly responsible for causing severe diarrhea in children <5 years old in North India. It emphasizes the required for cost-effective diagnostic assays for Rotaviruses which would help to determine the disease burden.

Keywords: adenovirus, Astrovirus, hospitalized children, Rotavirus

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Authors: Reem El Chakik

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The levels of heavy metals in agricultural lands in Lebanon have been witnessing a noticeable increase in the past few years, due to increased anthropogenic pollution sources. Heavy metals pose a serious threat to the environment for being non-biodegradable and persistent, accumulating thus to dangerous levels in the soil. Besides the traditional laboratory and chemical analysis methods, Hyperspectral Imaging (HSI) has proven its efficiency in the rapid detection of HMs contamination. In Lebanon, a continuous environmental monitoring, including the monitoring of levels of HMs in agricultural soils, is lacking. This is due in part to the high cost of analysis. Hence, this proposed research aims at defining the current national status of HMs contamination in agricultural soil, and to evaluate the effectiveness of using HSI in the detection of HM in contaminated agricultural fields. To achieve the two main objectives of this study, soil samples were collected from different areas throughout the country and were analyzed for HMs using Atomic Absorption Spectrophotometry (AAS). The results were compared to those obtained from the HSI technique that was applied using Hyspex SWIR-384 camera. The results showed that the Lebanese agricultural soils contain high contamination levels of Zn, and that the more clayey the soil is, the lower reflectance it has.

Keywords: agricultural soils in Lebanon, atomic absorption spectrophotometer, hyperspectral imaging., heavy metals contamination

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Authors: Suchana Marahatta, Sabina Bhattarai, Bishnu Hari Paudel, Dilip Thakur

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Background: Every year thousands of patients develop nerve damage and disabilities as a result of leprosy which can be prevented by early detection and treatment. So, early detection and treatment of nerve function impairment is of paramount importance in leprosy. Objectives: To assess the electrophysiological pattern of the peripheral nerves in leprosy patients and to compare it with clinical assessment tools. Materials and Methods: In this comparative cross-sectional study, 74 newly diagnosed leprosy patients without reaction were enrolled. They underwent thorough evaluation for peripheral nerve function impairment using clinical tests [i.e. nerve palpation (NP), monofilament (MF) testing, voluntary muscle testing (VMT)] and nerve conduction study (NCS). Clinical findings were compared with that of NCS using SPSS version 11.5. Results: NCS was impaired in 43.24% of leprosy patient at the baseline. Among them, sensory NCS was impaired in more patients (32.4%) in comparison to motor NCS (20.3%). NP, MF, and VMT were impaired in 58.1%, 25.7%, and 9.4% of the patients, respectively. Maximum concordance of monofilament testing and sensory NCS was found for sural nerve (14.7%). Likewise, the concordance of motor NP and motor NCS was the maximum for ulnar nerve (14.9%). When individual parameters of the NCS were considered, amplitude was found to be the most frequently affected parameter for both sensory and motor NCS. It was impaired in 100% of cases with abnormal NCS findings. Conclusion: Since there was no acceptable concordance between NCS findings and clinical findings, we should consider NCS whenever feasible for early detection of neuropathy in leprosy. The amplitude of both sensory nerve action potential (SNAP) and compound nerve action potential (CAMP) could be important determinants of the abnormal NCS if supported by further studies.

Keywords: leprosy, nerve function impairment, neuropathy, nerve conduction study

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Authors: Jing Zhang, Daniel Nikovski

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We propose an approximation algorithm called LINKUMP to compute the Pan Matrix Profile (PMP) under the unnormalized l∞ distance (useful for value-based similarity search) using double-ended queue and linear interpolation. The algorithm has comparable time/space complexities as the state-of-the-art algorithm for typical PMP computation under the normalized l₂ distance (useful for shape-based similarity search). We validate its efficiency and effectiveness through extensive numerical experiments and a real-world anomaly detection application.

Keywords: pan matrix profile, unnormalized euclidean distance, double-ended queue, discord discovery, anomaly detection

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Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

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This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

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Authors: Aishrith Rao

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COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

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Authors: Ian Omung'a

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Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

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Authors: Yulan Wu

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The spread of fake news on social media has posed significant societal harm to the public and the nation, with its threats spanning various domains, including politics, economics, health, and more. News on social media often covers multiple domains, and existing models studied by researchers and relevant organizations often perform well on datasets from a single domain. However, when these methods are applied to social platforms with news spanning multiple domains, their performance significantly deteriorates. Existing research has attempted to enhance the detection performance of multi-domain datasets by adding single-domain labels to the data. However, these methods overlook the fact that a news article typically belongs to multiple domains, leading to the loss of domain knowledge information contained within the news text. To address this issue, research has found that news records in different domains often use different vocabularies to describe their content. In this paper, we propose a fake news detection framework that combines domain knowledge and expert knowledge. Firstly, it utilizes an unsupervised domain discovery module to generate a low-dimensional vector for each news article, representing domain embeddings, which can retain multi-domain knowledge of the news content. Then, a feature extraction module uses the domain embeddings discovered through unsupervised domain knowledge to guide multiple experts in extracting news knowledge for the total feature representation. Finally, a classifier is used to determine whether the news is fake or not. Experiments show that this approach can improve multi-domain fake news detection performance while reducing the cost of manually labeling domain labels.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Nicholas V. Scott, Jack McCarthy

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Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

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Authors: Jennifer Leach, Umashanger Thayasivam

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The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

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Authors: Cherifi Abdelhamid

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In optical communication systems, OFDM has received increased attention as a means to overcome various limitations of optical transmission systems such as modal dispersion, relative intensity noise, chromatic dispersion, polarization mode dispersion and self-phase modulation. The multipath dispersion limits the maximum transmission data rates. In this paper we investigate OFDM system where multipath induced intersymbol interference (ISI) is reduced and we increase the number of users by combining OFDM system with OCDMA system using direct detection Incorporate OOC (orthogonal optical code) for minimize a bit error rate.

Keywords: OFDM, OCDMA, OOC (orthogonal optical code), (ISI), prim codes (Pc)

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Authors: Islam Khalil, Amr El-Kadi

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With the increased use of web technologies, microservices, and Application Programming Interface (API) for integration between systems, and with the development of containerization of services on the operating system level as a method of isolating system execution and for easing the deployment and scaling of systems, there is a growing need as well as opportunities for providing platforms that improve the security of such services. In our work, we propose an architecture for a containerization platform that utilizes various concepts derived from the human immune system. The goal of the proposed containerization platform is to introduce the concept of slowing down or throttling suspected malicious digital pathogens (intrusions) to reduce their damage footprint while providing more opportunities for forensic inspection of suspected pathogens in addition to the ability to snapshot, rollback, and recover from possible damage. The proposed platform also leverages existing intrusion detection algorithms by integrating and orchestrating their cooperative operation for more effective intrusion detection. We show how this model reduces the damage footprint of intrusions and gives a greater time window for forensic investigation. Moreover, during our experiments, our proposed platform was able to uncover unintentional system design flaws that resulted in internal DDoS-like attacks by submodules of the system itself rather than external intrusions.

Keywords: containers, human immunity, intrusion detection, security, web services

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Authors: Sarah P. Wuebbolt, Ashlee N. Sawyer-Mays

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Non-medical prescription and other drug (NMPOD) use has been a significant public health issue for the last few decades, with problematic use increasing among university students more recently. The current study focused on associations between NMPOD use and mental health, well-being, and world beliefs among young adults. Young adults (N=513) completed online questionnaires assessing stress, demographic characteristics, self-esteem, NMPOD use, coping mechanisms, and anxiety. A substantial portion of participants reported using cannabis (48.5%, n=249), while smaller portions of participants reported using stimulants (26.7%, n = 137), sedatives (17.2%, n=88), opioids (10.8%, n=55), and hallucinogens (14.4%, n=74). Five hierarchical logistic regressions were performed to determine the independent relationships between mental health, well-being, and world belief factors and NMPOD use for the five classes of substances. After controlling for demographic factors (age, gender, race/ethnicity, sexual orientation, and religious affiliation), depression was associated with increased non-medical stimulant, opioid, and cannabis use; coping self-efficacy was associated with increased hallucinogen use, and attendance of worship services was associated with decreased non-medical cannabis and hallucinogen use. Results suggest that depression was strongly associated with non-medical stimulant, opioid, and cannabis use, and attendance of worship services was protective against cannabis and hallucinogen use. To the best of our knowledge, this is one of the first studies to investigate the relationships between mental health, well-being, world beliefs, and NMPOD use among young adults. The present study illuminates future targets for intervention, such as increased access to mental health diagnosis and treatment and the exploration of the roles of religion and shared community in the prevention of drug use among young adults.

Keywords: cannabis, mental health, non-medical prescription and other drug use, world beliefs

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Authors: Nadjah Chergui, Narhimene Boustia

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Intrusion Detection Systems are an essential tool for network security infrastructure. However, IDSs have a serious problem which is the generating of massive number of alerts, most of them are false positive ones which can hide true alerts and make the analyst confused to analyze the right alerts for report the true attacks. The purpose behind this paper is to present a formalism model to perform correlation engine by the reduction of false positive alerts basing on vulnerability contextual information. For that, we propose a formalism model based on non-monotonic JClassicδє description logic augmented with a default (δ) and an exception (є) operator that allows a dynamic inference according to contextual information.

Keywords: context, default, exception, vulnerability

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Authors: Fatima Mokeddem

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The phonocardiogram signal (PCG) is a physiological signal that reflects heart mechanical activity, is a promising tool for curious researchers in this field because it is full of indications and useful information for medical diagnosis. PCG segmentation is a basic step to benefit from this signal. Therefore, this paper presents an algorithm that serves the separation of heart sounds and heart murmurs in case they exist in order to use them in several applications and heart sounds analysis. The separation process presents here is founded on three essential steps filtering, envelope detection, and heart sounds segmentation. The algorithm separates the PCG signal into S1 and S2 and extract cardiac murmurs.

Keywords: phonocardiogram signal, filtering, Envelope, Detection, murmurs, heart sounds

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Authors: Gwendy Darras, Mattias Desmet

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Previous research showed that psychological distress has a negative impact on the disease course of viral infections. For COVID-19, the same association was observed in small samples of specific segments of the population (e.g. health care workers). The present study presents a more refined analysis of this association, measuring a broader spectrum of psychological distress in a large sample (n=3084) of the general Flemish population. Several types of psychological distress (state, trait and health anxiety, depression, intra-, and interpersonal stress) are registered throughout three periods: one year before the contamination, one week before the contamination, and during the contamination. In doing so, validated scales such as DASS-21, IIP-32, and FCV-19S are used. Furthermore, the course of COVID-19 is registered in several ways: number of symptoms, number of days sick leave due to COVID-19, and number of days the symptoms have lasted. Also, different control variables such as vaccination status, medical and psychological history are taken into account. Statistical analysis shows that all types of psychological distress are positively correlated with the severity of the COVID-19 disease course. Anxiety during the contamination shows the strongest correlation, but psychological distress one year before the onset of COVID-19 was still significantly associated with the worsening of the disease course. As the assessment of the latter type of distress happened before the onset of the COVID-19 disease course, retrospective bias resulting in artificial associations between self-reported stress and COVID-19 severity is unlikely to have impacted the observations. In view of possible future pandemics, it is important to focus on general stress and anxiety reduction in the general population as soon as possible. It is also advisable to minimize the use of stress-inducing messages to encourage the population to adhere to the measures issued during a pandemic.

Keywords: anxiety, COVID-19, depression, psychoneuroimmunology, psychological distress, stress

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Authors: Somia Bouzid, Messaoud Ramdani

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The success of any diagnosis strategy critically depends on the sensors measuring process variables. This paper presents a detection and diagnosis sensor faults method based on a Bottleneck Neural Network (BNN). The BNN approach is used as a statistical process control tool for drinking water distribution (DWD) systems to detect and isolate the sensor faults. Variable reconstruction approach is very useful for sensor fault isolation, this method is validated in simulation on a nonlinear system: actual drinking water distribution system. Several results are presented.

Keywords: fault detection, localization, PCA, NLPCA, auto-associative neural network

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Authors: Christine Maleesha Withanachchi, Eithne Heffernan, Derek Hoare

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Background: Social isolation (SI} is a major consequence of hearing loss (HL}. Isolation can lead to serious health problems (e.g., dementia and depression). Hearing Aids (HA) is the primary intervention for HL. However, these are less effective in social situations. Interventions are needed for SI in adults with hearing loss (AHL). Objectives: Investigated the relationship between HL and SI. Explored the views of AHL and hearing healthcare professionals (HHP) towards interventions for isolation. Methods: Individual and group semi-structured interviews were conducted. Interviews were conducted at the Nottingham Institute of Health Research (NIHR) Biomedical Research Centre (BRC). Six AHL and seven HHP were recruited via maximum variation sampling. The interview transcripts were analyzed using inductive thematic analysis. Results: Social impacts of HL: Most participants described that HL hurt them. This was in the form of social withdrawal, strain on relationships, and identity loss. Downstream effects of HL: Most audiologists acknowledged that isolation from HL could lead to depression. HL can also lead to exhaustion and unemployment. Impact of stigma: There are negative connotations around HL and HA (e.g. old age) and there is difficulty talking about isolation. The complexity of SI: There can be difficulty separating SI due to HL from SI due to other contributing factors (e.g. comorbidities). Potential intervention for isolation: Participants were unfamiliar with interventions for isolation and few, if any, were targeted for AHL specifically. Most participants thought an intervention should be patient-centered and run by an AHL in the community. Opinions differed regarding whether it should hear specific or generic. Implementation of intervention: Challenges to the implementation of an intervention for SI exist due to the sensitivity of the subject. Conclusions: This study demonstrated that SI is a major consequence of HL and uncovered novel findings related to its interventions. Uptake of interventions offered to AHL to reduce loneliness and social isolation is expected to be better if led by AHL in the community as opposed to HHP led interventions in the hospital or clinic settings.

Keywords: adults with hearing loss, hearing aids, interventions, social isolation

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Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Eunsong Lee, Gae Baik Kim, Young Pil Kim

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Bisphenol A (BPA) is one of the endocrine disruptors (EDCs), which have been suspected to be associated with reproductive dysfunction and physiological abnormality in human. Since the BPA has been widely used to make plastics and epoxy resins, the leach of BPA from the lining of plastic products has been of major concern, due to its environmental or human exposure issues. The simple detection of BPA based on the self-assembly of aptamer-mediated gold nanoparticles (AuNPs) has been reported elsewhere, yet the detection sensitivity still remains challenging. Here we demonstrate an improved AuNP-based sensor of BPA by using fluorescence-combined AuNP colorimetry in order to overcome the drawback of traditional AuNP sensors. While the anti-BPA aptamer (full length or truncated ssDNA) triggered the self-assembly of unmodified AuNP (citrate-stabilized AuNP) in the presence of BPA at high salt concentrations, no fluorescence signal was observed by the subsequent addition of SYBR Green, due to a small amount of free anti-BPA aptamer. In contrast, the absence of BPA did not cause the self-assembly of AuNPs (no color change by salt-bridged surface stabilization) and high fluorescence signal by SYBP Green, which was due to a large amount of free anti-BPA aptamer. As a result, the quantitative analysis of BPA was achieved using the combination of absorption of AuNP with fluorescence intensity of SYBR green as a function of BPA concentration, which represented more improved detection sensitivity (as low as 1 ppb) than did in the AuNP colorimetric analysis. This method also enabled to detect high BPA in water-soluble extracts from thermal papers with high specificity against BPS and BPF. We suggest that this approach will be alternative for traditional AuNP colorimetric assays in the field of aptamer-based molecular diagnosis.

Keywords: bisphenol A, colorimetric, fluoroscence, gold-aptamer nanobiosensor

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Authors: Suzanne Zazycki, Bamidele Osamika, Heather Craska, Kaelyn Conaway, Reena Murphy, Stephanie Spence

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Invasive Plant Species (IPS) are an important component of effective preservation and conservation of natural lands management. IPS are non-native plants which can aggressively encroach upon native species and pose a significant threat to the ecology, public health, and social welfare of a community. The presence of IPS in U.S. nature preserves has caused economic costs, which has estimated to exceed $26 billion a year. While different methods have been identified to control IPS, few methods have been recognized for early detection of IPS. This study examined identified methods for early detection of IPS in Hueston Woods State Nature Preserve. Mixed methods research design was adopted in this four-phased study. The first phase entailed data gathering, the phase described the characteristics and qualities of IPS and the importance of early detection (ED). The second phase explored ED methods, Geographic Information Systems (GIS) and Citizen Science were discovered as ED methods for IPS. The third phase of the study involved the creation of hotspot maps to identify likely areas for IPS growth. While the fourth phase involved testing and evaluating mobile applications that can support the efforts of citizen scientists in IPS detection. Literature reviews were conducted on IPS and ED methods, and four regional experts from ODNR and Miami University were interviewed. A questionnaire was used to gather information about ED methods used across the state. The findings revealed that geospatial methods, including Unmanned Aerial Vehicles (UAVs), Multispectral Satellites (MSS), and Normalized Difference Vegetation Index (NDVI), are not feasible for early detection of IPS, as they require GIS expertise, are still an emerging technology, and are not suitable for every habitat for the ED of IPS. Therefore, Other ED methods options were explored, which include predicting areas where IPS will grow, which can be done through monitoring areas that are like the species’ native habitat. Through literature review and interviews, IPS are known to grow in frequently disturbed areas such as along trails, shorelines, and streambanks. The research team called these areas “hotspots” and created maps of these hotspots specifically for HW NP to support and narrow the efforts of citizen scientists and staff in the ED of IPS. The results further showed that utilizing citizen scientists in the ED of IPS is feasible, especially through single day events or passive monitoring challenges. The study concluded that the creation of hotspot maps to direct the efforts of citizen scientists are effective for the early detection of IPS. Several recommendations were made, among which is the creation of hotspot maps to narrow the ED efforts as citizen scientists continues to work in the preserves and utilize citizen science volunteers to identify and record emerging IPS.

Keywords: early detection, hueston woods state nature preserve, invasive plant species, hotspots

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: Chandra Prayaga, Lakshmi Prayaga, Aaron Wade, Kyle Rank, Gopi Shankar Mallu, Sri Satya, Harsha Pola

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In today's tech-driven world, where connectivity shapes our daily lives, maintaining physical and emotional health is crucial. Athletic trainers play a vital role in optimizing athletes' performance and preventing injuries. However, a shortage of trainers impacts the quality of care. This study introduces a vision-based exercise monitoring system leveraging Google's MediaPipe library for precise tracking of bicep curl exercises and simultaneous posture monitoring. We propose a three-stage methodology: landmark detection, side detection, and angle computation. Our system calculates angles at the elbow, wrist, neck, and torso to assess exercise form. Experimental results demonstrate the system's effectiveness in distinguishing between good and partial repetitions and evaluating body posture during exercises, providing real-time feedback for precise fitness monitoring.

Keywords: physical health, athletic trainers, fitness monitoring, technology driven solutions, Google’s MediaPipe, landmark detection, angle computation, real-time feedback

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Authors: Nabin Prasad Joshi

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A new variant of the coronavirus family found in the Wuhan city market of China is causing serious harm to human beings. After the WHO decided COVID-19 was a pandemic situation, everyone started to measure the prevention of infectious diseases according to WHO guidelines. It includes social distancing, isolation, quarantine, lockdown, sanitation, and masking, respectively. During this time, the researcher has observed the difficulties of cultivating the new normal in people in Nepal. People have perceived the single coronavirus differently; common populations and frontline workers have different perceptions of coronavirus. The researcher started to measure the psychosocial impact of the COVID-19 pandemic on frontline workers and general populations in Kathmandu valley. The total number of sample units for this research is 82; it includes 52 general populations and 30 frontline workers. These sample units are selected through convenient sampling and purposive sampling, respectively. This research is based on descriptive and exploratory design. DASS-21 of the Nepali version is a comprehensive data collection tool for depression, anxiety, and stress measurement in this research, and simultaneously the psychosocial checklist, key-informant interview, and case study have been done. Quantitative data are analyzed with the help of excel, and qualitative data are through thematic analysis. The study has shown that the occurrence of psychosocial issues among frontline workers is greater than in general populations. It is found that the informants with higher education status have greater psychosocial issues in comparison to low education status. In the context of a pandemic, family/friends’ support can function as a protective factor when at adequate levels.

Keywords: anxiety, depression, isolation, lockdown

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Authors: Endang Lukitaningsih, Fathul Jannah, Arief R. Hakim, Ratna D. Puspita, Zullies Ikawati

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Rifampicin is a semisynthetic antibiotic derivative of rifamycin B produced by Streptomyces mediterranei. RIF has been used worldwide as first line drug-prescribed throughout tuberculosis therapy. This study aims to develop and to validate an HPLC method couple with a UV detection for determination of rifampicin in spiked human plasma and its application for bioequivalence study. The chromatographic separation was achieved on an RP-C18 column (LachromHitachi, 250 x 4.6 mm., 5μm), utilizing a mobile phase of phosphate buffer/acetonitrile (55:45, v/v, pH 6.8 ± 0.1) at a flow of 1.5 mL/min. Detection was carried out at 337 nm by using spectrophotometer. The developed method was statistically validated for the linearity, accuracy, limit of detection, limit of quantitation, precise and specifity. The specifity of the method was ascertained by comparing chromatograms of blank plasma and plasma containing rifampicin; the matrix and rifampicin were well separated. The limit of detection and limit of quantification were 0.7 µg/mL and 2.3 µg/mL, respectively. The regression curve of standard was linear (r > 0.999) over a range concentration of 20.0 – 100.0 µg/mL. The mean recovery of the method was 96.68 ± 8.06 %. Both intraday and interday precision data showed reproducibility (R.S.D. 2.98% and 1.13 %, respectively). Therefore, the method can be used for routine analysis of rifampicin in human plasma and in bioequivalence study. The validated method was successfully applied in pharmacokinetic and bioequivalence study of rifampicin tablet in a limited number of subjects (under an Ethical Clearance No. KE/FK/6201/EC/2015). The mean values of Cmax, Tmax, AUC(0-24) and AUC(o-∞) for the test formulation of rifampicin were 5.81 ± 0.88 µg/mL, 1.25 hour, 29.16 ± 4.05 µg/mL. h. and 29.41 ± 4.07 µg/mL. h., respectively. Meanwhile for the reference formulation, the values were 5.04 ± 0.54 µg/mL, 1.31 hour, 27.20 ± 3.98 µg/mL.h. and 27.49 ± 4.01 µg/mL.h. From bioequivalence study, the 90% CIs for the test formulation/reference formulation ratio for the logarithmic transformations of Cmax and AUC(0-24) were 97.96-129.48% and 99.13-120.02%, respectively. According to the bioequivamence test guidelines of the European Commission-European Medicines Agency, it can be concluded that the test formulation of rifampicin is bioequivalence with the reference formulation.

Keywords: validation, HPLC, plasma, bioequivalence

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Authors: Subhajit Das, Nirjhar Dhang

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Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

Procedia PDF Downloads 215

Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

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Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

Procedia PDF Downloads 68