Search results for: depression detection

Authors: Narong Kulnides

Abstract:

This research involved fingerprints on ballistics after shooting. Two objectives of research were as follows; (1) to study the duration of the existence of latent fingerprints on .38, .45, 9 mm and .223 cartridge case after shooting, and (2) to compare the effectiveness of the detection of latent fingerprints by Black Powder, Super Glue, Perma Blue and Gun Bluing. The latent fingerprint appearance were studied on .38, .45, 9 mm. and .223 cartridge cases before and after shooting with Black Powder, Super Glue, Perma Blue and Gun Bluing. The detection times were 3 minute, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78 and 84 hours respectively. As a result of the study, it can be conclude that: (1) Before shooting, the detection of latent fingerprints on 38, .45, and 9 mm. and .223 cartridge cases with Black Powder, Super Glue, Perma Blue and Gun Bluing can detect the fingerprints at all detection times. (2) After shooting, the detection of latent fingerprints on .38, .45, 9 mm. and .223 cartridge cases with Black Powder, Super Glue did not appear. The detection of latent fingerprints on .38, .45, 9 mm. cartridge cases with Perma Blue and Gun Bluing were found 100% of the time and the detection of latent fingerprints on .223 cartridge cases with Perma Blue and Gun Bluing were found 40% and 46.67% of the time, respectively.

Keywords: ballistic, fingerprint, shooting, detection times

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Authors: Shatha Ahmad Alharthi

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Prior research suggests that reduced social interaction can negatively affect well-being and impair mental health (e.g., depression and anxiety), resulting in lower academic performance. The COVID-19 pandemic has significantly limited social interaction among Saudi Arabian school children since the government closed schools and implemented lockdown restrictions to reduce the spread of the disease. These restrictions have resulted in prolonged remote learning for middle school students with unknown consequences for perceived academic performance, mental health, and well-being. This research project explores how middle school Saudi students’ current remote learning practices affect their mental health (e.g., depression and anxiety) and well-being during the lockdown. Furthermore, the study will examine the association between social interaction, mental health, and well-being pertaining to students’ perceptions of their academic achievement. Research findings could lead to a better understanding of the role of lockdown on depression, anxiety, well-being and perceived academic performance. Research findings may also inform policy-makers or practitioners (e.g., teachers and school leaders) about the importance of facilitating increased social interactions in remote learning situations and help to identify important factors to consider when seeking to re-integrate students into a face-to-face classroom setting. Potential implications for future educational research include exploring remote learning interventions targeted at bolstering students’ mental health and academic achievement during periods of remote learning.

Keywords: depression, anxiety, academic performance, social interaction

Procedia PDF Downloads 119

Authors: Moein Izadi, Ali Mohammadzadeh

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Detection of damaged parts of roads after earthquake is essential for coordinating rescuers. In this study, an approach is presented for the semi-automatic detection of damaged roads in a city using pre-event vector maps and both pre- and post-earthquake QuickBird satellite images. Damage is defined in this study as the debris of damaged buildings adjacent to the roads. Some spectral and texture features are considered for SVM classification step to detect damages. Finally, the proposed method is tested on QuickBird pan-sharpened images from the Bam City earthquake and the results show that an overall accuracy of 81% and a kappa coefficient of 0.71 are achieved for the damage detection. The obtained results indicate the efficiency and accuracy of the proposed approach.

Keywords: SVM classifier, disaster management, road damage detection, quickBird images

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Authors: Sarah A. Goodchild, Rachel Gao, Philip N. Bartlett

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A novel detection approach using immobilized DNA probes labeled with Anthraquinone (AQ) as an electrochemically active reporter moiety has been successfully developed as a new, simple, reliable method for the detection of DNA. This method represents a step forward in DNA detection as it can discriminate between multiple nucleotide polymorphisms within target DNA strands without the need for any additional reagents, reporters or processes such as melting of DNA strands. The detection approach utilizes single-stranded DNA probes immobilized on gold surfaces labeled at the distal terminus with AQ. The effective immobilization has been monitored using techniques such as AC impedance and Raman spectroscopy. Simple voltammetry techniques (Differential Pulse Voltammetry, Cyclic Voltammetry) are then used to monitor the reduction potential of the AQ before and after the addition of complementary strand of target DNA. A reliable relationship between the shift in reduction potential and the number of base pair mismatch has been established and can be used to discriminate between DNA from highly related pathogenic organisms of clinical importance. This indicates that this approach may have great potential to be exploited within biosensor kits for detection and diagnosis of pathogenic organisms in Point of Care devices.

Keywords: Anthraquinone, discrimination, DNA detection, electrochemical biosensor

Procedia PDF Downloads 394

Authors: Galit Harel

Abstract:

Deborah was referred for psychoanalytic psychotherapy following a suicide attempt and depression. She began a fascinating journey spanning more than 10 years. During therapy, many questions arose concerning the suicidal episode, which she could not register consciously. The author tried to understand the reasons for her depression and the attempted suicide through the unconscious process in the therapeutic relationship and through the music and poetry that she brought to sessions. In this paper, the author describes the process of listening for the signifiers of semiotic and symbolic language, both metaphoric and metonymic, as revealed in poetry and music according to the theories of Kristeva and Lacan. The poetry enabled the patient to retrieve childhood memories, experience the movement from unconscious to conscious, and mourn through the experience of transference and countertransference in the therapeutic relationship. Also illustrated is the transition from singing the music to more symbolic language, turning the patient’s sensory experience into language, and connecting her personal experience with the culture of her past. The patient’s mourning and the lost love objects are discussed through the prism of classical and object relations theories.

Keywords: depression, lost love object, psychoanalytic psychotherapy, suicide attempt, symbolization of desire

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Authors: L. Sellami, D. Idoughi, P. F. Tiako

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Cloud computing provides infrastructure to the enterprise through the Internet allowing access to cloud services at anytime and anywhere. This pervasive aspect of the services, the distributed nature of data and the wide use of information make cloud computing vulnerable to intrusions that violate the security of the cloud. This requires the use of security mechanisms to detect malicious behavior in network communications and hosts such as intrusion detection systems (IDS). In this article, we focus on the detection of intrusion into the cloud sing IDSs. We base ourselves on client authentication in the computing cloud. This technique allows to detect the abnormal use of ubiquitous service and prevents the intrusion of cloud computing. This is an approach based on client authentication data. Our IDS provides intrusion detection inside and outside cloud computing network. It is a double protection approach: The security user node and the global security cloud computing.

Keywords: cloud computing, intrusion detection system, privacy, trust

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Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

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In this paper approach to incoherent signal detection in multi-element antenna array are researched and modeled. Two types of useful signals with unknown wavefront were considered. First one is deterministic (Barker code), the second one is random (Gaussian distribution). The derivation of the sufficient statistics took into account the linearity of the antenna array. The performance characteristics and detecting curves are modeled and compared for different useful signals parameters and for different number of elements of the antenna array. Results of researches in case of some additional conditions can be applied to a digital communications systems.

Keywords: antenna array, detection curves, performance characteristics, quadrature processing, signal detection

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Authors: Andrey V. Timofeev

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A method is proposed for stable detection of seismoacoustic sources in C-OTDR systems that guarantee given upper bounds for probabilities of type I and type II errors. Properties of the proposed method are rigorously proved. The results of practical applications of the proposed method in a real C-OTDR-system are presented in this.

Keywords: guaranteed detection, C-OTDR systems, change point, interval estimation

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Authors: Ayhan Kucukmanisa, Orhan Akbulut, Oguzhan Urhan

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Nowadays, advanced driver assistance systems (ADAS) have become popular, since they enable safe driving. Lane detection is a vital step for ADAS. The performance of the lane detection process is critical to obtain a high accuracy lane departure warning system (LDWS). Challenging factors such as road cracks, erosion of lane markings, weather conditions might affect the performance of a lane detection system. In this paper, 1-D weighted filter based on row filtering to detect lane marking is proposed. 2-D input image is filtered by 1-D weighted filter considering four-pixel values located symmetrically around the center of candidate pixel. Performance evaluation is carried out by two metrics which are true positive rate (TPR) and false positive rate (FPR). Experimental results demonstrate that the proposed approach provides better lane marking detection accuracy compared to the previous methods while providing real-time processing performance.

Keywords: lane marking filter, lane detection, ADAS, LDWS

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Authors: Junwen Chen, Celina Feleppa, Tingyue Sun, Satoko Sasagawa, Michael Smithson

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School refusal behaviours refer to behaviours to avoid school attendance, chronic lateness in arriving at school, or regular early dismissal. Poor attendance in schools is highly correlated with anxiety, depression, suicide attempts, delinquency, violence, and substance use and abuse. Poor attendance is also a strong indicator of lower achievement in school, as well as problematic social-emotional development. Long-term consequences of school refusal behaviours include fewer opportunities for higher education, employment, and social difficulties, and high risks of later psychiatric illness. Given its negative impacts on youth educational outcomes and well-being, a thorough understanding of factors that are involved in the development of this phenomenon is warranted for developing effective management approaches. This study investigated parental and adolescent factors that may contribute to school refusal behaviours by specifically focusing on the role of parental and adolescents’ anxiety and depression, emotion dysregulation, and parental rearing style. Findings are expected to inform the identification of both parental and adolescents’ factors that may contribute to school refusal behaviours. This knowledge will enable novel and effective approaches that incorporate these factors to managing school refusal behaviours in adolescents, which in turn improve their school and daily functioning. Results are important for an integrative understanding of school refusal behaviours. Furthermore, findings will also provide information for policymakers to weigh the benefits of interventions targeting school refusal behaviours in adolescents. One-hundred-and-six adolescents aged 12-18 years (mean age = 14.79 years old, SD = 1.78, males = 44) and their parents (mean age = 47.49 years old, SD = 5.61, males = 27) completed an online questionnaire measuring both parental and adolescents’ anxiety, depression, emotion dysregulation, parental rearing styles, and adolescents’ school refusal behaviours. Adolescents with school refusal behaviours reported greater anxiety and depression, with their parents showing greater emotion dysregulation. Parental emotion dysregulation and adolescents’ anxiety and depression predicted school refusal behaviours independently. To date, only limited studies have investigated the interplay between parental and youth factors in relation to youth school refusal behaviours. Although parental emotion dysregulation has been investigated in relation to youth emotion dysregulation, little is known about its role in the context of school refusal. This study is one of the very few that investigated both parental and adolescent factors in relation to school refusal behaviours in adolescents. The findings support the theoretical models that emphasise the role of youth and parental psychopathology in school refusal behaviours. Future management of school refusal behaviours should target adolescents’ anxiety and depression while incorporating training for parental emotion regulation skills.

Keywords: adolescents, school refusal behaviors, parental factors, anxiety and depression, emotion dysregulation

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Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

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Authors: Zhengyu Yan, Yan Yu, Jianqiu Chen

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A novel sensing system has been designed for naphthalene detection based on the quenched fluorescence signal of CdS quantum dots. The fluorescence intensity of the system reduced significantly after adding CdS quantum dots to the water pollution model because of the fluorescent static quenching f mechanism. Herein, we have demonstrated the facile methodology can offer a convenient and low analysis cost with the recovery rate as 97.43%-103.2%, which has potential application prospect.

Keywords: CdS quantum dots, modification, detection, naphthalene

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Authors: El Sayed A. Sharara, A. Tsuji, K. Terada

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Call centers have been expanding and they have influence on activation in various markets increasingly. A call center’s work is known as one of the most demanding and stressful jobs. In this paper, we propose the fatigue detection system in order to detect burnout of call center agents in the case of a neck pain and upper back pain. Our proposed system is based on the computer vision technique combined skin color detection with the Viola-Jones object detector. To recognize the gesture of hand poses caused by stress sign, the YCbCr color space is used to detect the skin color region including face and hand poses around the area related to neck ache and upper back pain. A cascade of clarifiers by Viola-Jones is used for face recognition to extract from the skin color region. The detection of hand poses is given by the evaluation of neck pain and upper back pain by using skin color detection and face recognition method. The system performance is evaluated using two groups of dataset created in the laboratory to simulate call center environment. Our call center agent burnout detection system has been implemented by using a web camera and has been processed by MATLAB. From the experimental results, our system achieved 96.3% for upper back pain detection and 94.2% for neck pain detection.

Keywords: call center agents, fatigue, skin color detection, face recognition

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Authors: Preston Nabors, Nasseh Tabrizi

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Portable Document Format (PDF) files have gained significant popularity for sharing and distributing documents due to their universal compatibility. However, the widespread use of PDF files has made them attractive targets for cybercriminals, who exploit vulnerabilities to deliver malware and compromise the security of end-user systems. This paper reviews notable contributions in PDF malware detection, including static, dynamic, signature-based, and hybrid analysis. It presents a comprehensive examination of PDF malware detection techniques, focusing on the emerging threat of adversarial sampling and the need for robust defense mechanisms. The paper highlights the vulnerability of machine learning classifiers to evasion attacks. It explores adversarial sampling techniques in PDF malware detection to produce mimicry and reverse mimicry evasion attacks, which aim to bypass detection systems. Improvements for future research are identified, including accessible methods, applying adversarial sampling techniques to malicious payloads, evaluating other models, evaluating the importance of features to malware, implementing adversarial defense techniques, and conducting comprehensive examination across various scenarios. By addressing these opportunities, researchers can enhance PDF malware detection and develop more resilient defense mechanisms against adversarial attacks.

Keywords: adversarial attacks, adversarial defense, adversarial machine learning, intrusion detection, PDF malware, malware detection, malware detection evasion

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Authors: Muhammad Ali Syed, Arshad Saleem Bhatti, Chen-zhong Li, Habib Ali Bokhari

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Surface plasmon resonance (SPR) biosensors have emerged as a promising technique for bioanalysis as well as microbial detection and identification. Real time, sensitive, cost effective, and label free detection of biomolecules from complex samples is required for early and accurate diagnosis of infectious diseases. Like many other types of optical techniques, SPR biosensors may also be successfully utilized for microbial detection for accurate, point of care, and rapid results. In the present study, we have utilized a commercially available automated SPR biosensor of BI company to study the microbial detection form water samples spiked with different concentration of Staphylococcus aureus bacterial cells. The gold thin film sensor surface was functionalized to react with proteins such as protein G, which was used for directed immobilization of monoclonal antibodies against Staphylococcus aureus. The results of our work reveal that this immunosensor can be used to detect very small number of bacterial cells with higher sensitivity and specificity. In our case 10^3 cells/ml of water have been successfully detected. Therefore, it may be concluded that this technique has a strong potential to be used in microbial detection and identification.

Keywords: surface plasmon resonance (SPR), Staphylococcus aureus, biosensors, microbial detection

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Authors: Sang-Hoon Park, Yeji Ho, Gwang-Moon Eom

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Fast and accurate detection of Freezing of Gait (FOG) is desirable for appropriate application of cueing which has been shown to ameliorate FOG. Utilization of frequency spectrum of leg acceleration to derive the freeze index requires much calculation and it would lead to delayed cueing. We hypothesized that FOG can be reasonably detected from the time domain amplitude of foot acceleration. A time instant was recognized as FOG if the mean amplitude of the acceleration in the time window surrounding the time instant was in the specific FOG range. Parameters required in the FOG detection was optimized by simulated annealing. The suggested time domain methods showed performances comparable to those of frequency domain methods.

Keywords: freezing of gait, detection, Parkinson's disease, time-domain method

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

Procedia PDF Downloads 241

Authors: V. Pentland, S. Spilsbury, A. Biswas, M. F. Mottola, S. Paplinskie, M. S. Mitchell

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Postpartum depression (PPD) is a form of major depressive disorder that afflicts 10–22% of mothers worldwide. Rising demands for traditional PPD treatment options (e.g., psychiatry), especially in the context of the COVID-19 pandemic, are increasingly difficult to meet. More accessible treatment options (e.g., walking) are needed. The objective of this review is to determine the impact of walking on PPD severity. A structured search of seven electronic databases for randomised controlled trials published between 2000 and July 29, 2021, was completed. Studies were included if walking was the sole or primary aerobic exercise modality. A random-effects meta-analysis was conducted for studies reporting PPD symptoms measured using a clinically validated tool. A simple count of positive/null effect studies was undertaken as part of a narrative summary. Five studies involving 242 participants were included (mean age=~28.9 years; 100% with mild-to-moderate depression). Interventions were 12 (n=4) and 24 (n=1) weeks long. Each assessed PPD severity using the Edinburgh Postnatal Depression Scale (EPDS) and was included in the meta-analysis. The pooled effect estimate suggests that relative to controls, walking yielded clinically significant decreases in mean EPDS scores from baseline to intervention end (pooled MD=-4.01; 95% CI:-7.18 to -0.84, I2=86%). The narrative summary provides preliminary evidence that walking-only, supervised, and group-based interventions, including 90-120+ minutes/week of moderate-intensity walking, may produce greater EPDS reductions. While limited by a relatively small number of included studies, pooled effect estimates suggest walking may help mothers manage PPD. This is the first time walking as a treatment for PPD, an exercise modality that uniquely addresses many barriers faced by mothers has been summarized in a systematic way. Trial registration: PROSPERO (CRD42020197521) on August 16th, 2020

Keywords: postpartum, exercise, depression, walking

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Authors: Prathama Guha Chaudhuri, Arunima Datta, Ashis Mukhopadhyay

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Background: Visualization (guided imagery) is a set of techniques which induce relaxation and help people create positive mental images in order to reduce stress.It is relatively inexpensive and can even be practised by bed bound people. Studies have shown visualization to be an effective tool to improve cancer patients’ anxiety, depression and quality of life. The common images used with cancer patients in the developed world are those involving the individual’s body and its strengths. Since breast cancer patients in India are more family oriented and often their main concerns are the stigma of having cancer and subsequent isolation of their families, including their children, we figured that positive images involving acceptance and integration within family and society would be more effective for them. Method: Data was collected from 119 breast cancer patients on chemotherapy willing to undergo psychotherapy, with no history of past psychiatric illness. Their baseline stress, anxiety, depression and quality of life were assessed using validated tools. The participants were then randomly divided into three groups: a) those who received visualization therapy with standard imageries involving the body and its strengths (sVT), b) those who received visualization therapy using indigenous family oriented imageries (mVT) and c) a control group who received supportive therapy. There were six sessions spread over two months for each group. The psychological outcome variables were measured post intervention. Appropriate statistical analyses were done. Results:Both forms of visualization therapy were more effective than supportive therapy alone in reducing patients’ depression, anxiety and quality of life.Modified VT proved to be significantly more effective in improving patients’ anxiety and quality of life. Conclusion: Visualization is a valuable therapeutic option for reduction of psychological distress and improving quality of life of breast cancer patients.In order to be more effective, the images used need to be modified according to the sociocultural background and individual needs of the patients.

Keywords: breast cancer, visualization therapy, quality of life, anxiety, depression

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Authors: Evija Silina, Maris Taube, Maksims Zolovs

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Background: Type 1 diabetes mellitus (T1D) is the most common chronic endocrine pathology in children. The management of type 1 diabetes requires a strong diet, physical activity, lifelong insulin therapy, and proper self-monitoring of blood glucose and is usually complicated and, therefore, may result in a variety of psychosocial problems for children, adolescents, and their families. Metabolic control of the disease is determined by glycated haemoglobin (HbA1c), the main criterion for diabetes compensation. A correlation was observed between anxiety and depression levels and glycaemic control in many previous studies. It is assumed that anxiety and depression symptoms negatively affect glycaemic control. Parental psychological distress was associated with higher child self-report of stress and depressive symptoms, and it had negative effects on diabetes management. Objective: The main objective of this paper is to evaluate the relationship between parental mental health conditions (depression and anxiety) and metabolic control of their adolescents with T1DM. Methods: This cross-sectional study recruited adolescents with T1D (N=251) and their parents (N=251). The respondents completed questionnaires. The 7-item Generalized Anxiety Disorder (GAD-7) scale measured anxiety level; The Patient Health Questionnaire – 9 (PHQ-9) measured depressive symptoms. Glycaemic control of patients was assessed using the last glycated haemoglobin (HbA1c) values. GLM mediation analysis was performed to determine the potential mediating effect of the parent’s mental health conditions (depression and anxiety) on the relationship between the mental health conditions (depression and anxiety) of a child on the level of glycated hemoglobin (HbA1c). To test the significance of the mediated effect (ME) for non-normally distributed data, bootstrapping procedures (10,000 bootstrapped samples) were used. Results: 502 respondents were eligible for screening to detect anxiety and depression symptoms. Mediation analysis was performed to assess the mediating role of parent GAD-7 on the linkage between a dependent variable (HbA1c) and independent variables (child GAD-7 un child PHQ-9). The results revealed that the total effect of child GAD-7 (B = 0.479, z = 4.30, p < 0.001) on HbA1c was significant but the total effect of child PHQ-9 (B = 0.166, z = 1.49, p = 0.135) was not significant. With the inclusion of the mediating variable (parent GAD-7), the impact of child GAD-7 on HbA1c was found insignificant (B = 0.113, z=0.98, p = 0.326), the impact of child PHQ-9 on HbA1c was found also insignificant (B = 0.068, z=0.74, p = 0.458). The indirect effect of child GAD-7 on HbA1c through parent GAD-7 was found significant (B = 0.366, z = 4.31, p < 0.001) and the indirect effect of child PHQ-9 on HbA1c through parent GAD-7 was found also significant (B = 0.098, z = 2.56, p = 0.010). This indicates that the relationship between a dependent variable (HbA1c) and independent variables (child GAD-7 un child PHQ-9) is fully mediated by parent GAD-7. Conclusion: The main result suggests that glycated haemoglobin in adolescents with Type 1 diabetes is related to adolescents’ mental health via parents’ anxiety. It means that parents’ anxiety plays a more significant role in the level of glycated haemoglobin in adolescents than depression and anxiety in the adolescent.

Keywords: type 1 diabetes, adolescents, parental diabetes-specific mental health conditions, glycated haemoglobin, anxiety, depression

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Authors: Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan

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Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.

Keywords: color overlapping windows, image stitching, leukocyte detection, white blood cell detection

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Authors: Moustapha H. Ibrahim, Dahir Abdourahman

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This document presents a short-circuit fault detection process in a photovoltaic (PV) system. The proposed method is developed in MATLAB/Simulink. It determines whatever the size of the installation number of the short circuit module. The proposed algorithm indicates the presence or absence of an abnormality on the power of the PV system through measures of hourly global irradiation, power output, and ambient temperature. In case a fault is detected, it displays the number of modules in a short circuit. This fault detection method has been successfully tested on two different PV installations.

Keywords: PV system, short-circuit, fault detection, modelling, MATLAB-Simulink

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Authors: Jaime Martín-de-Nicolás, David Mata-Moya, Nerea del-Rey-Maestre, Pedro Gómez-del-Hoyo, María-Pilar Jarabo-Amores

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Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions and sea state can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. A fast CFAR ship detector based on a robust statistical modeling of sea clutter with respect to sea states in SAR images is used. In this paper, the minimum SNR required to obtain a given detection probability with a given false alarm rate for any sea state is determined. A Gaussian target model using real SAR data is considered. Results show that SNR does not depend heavily on the class considered. Provided there is some variation in the backscattering of targets in SAR imagery, the detection probability is limited and a post-processing stage based on morphology would be suitable.

Keywords: SAR, generalized gamma distribution, detection curves, radar detection

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Authors: M. Khouil, N. Saber, M. Mestari

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In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: collision identification, fixed time, convex polyhedra, neural network, AMAXNET

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Authors: Sriraman Kothuri, Komal Teja Mattupalli

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In this paper we proposed a novel algorithm for recognizing hulls in a hand written digits. This is an extension to the work on “Digit Recognition Using Freeman Chain code”. In order to find out the hulls in a user given digit it is necessary to follow three steps. Those are pre-processing, Boundary Extraction and at last apply the Hull Detection system in a way to attain the better results. The detection of Hull Regions is mainly intended to increase the machine learning capability in detection of characters or digits. This can also extend this in order to get the hull regions and their intensities in Black Holes in Space Exploration.

Keywords: chain code, machine learning, hull regions, hull recognition system, SASK algorithm

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Authors: Li Yuan

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Objective: The combination of group psychology and Sertraline was used to explore the impact on the mental health status of adolescent patients with first-episode depression. Methods: A total of 118 adolescent depressed patients admitted to Yan'an University Hospital from October 2023 to August 2024 were divided into control group and observation group by random single blind method with 59 patients in each group. The two groups were treated with Sertraline, the control group received usual care, and the observation group used the usual care. The scores of mental health status and sleep quality index were compared between the two groups. Results: In intra-group comparison, the mental health status and sleep quality of the observation and control groups were better than the pre-intervention scores, and the difference was statistically significant (P <0.05). Post-intervention comparison: HAMA and HAMD scores were (12.36 ± 2.13) and (11.78 ± 2.02), significantly lower than (16.52 ± 2.09) and (15.79 ± 2.46), respectively (all P <0.05); PSQI score was (7.66 ± 1.05) and significantly lower (9.88 ± 3.01), with statistically significant difference (P <0.05). Conclusion: Self-regulation can improve their mental health and sleep quality.

Keywords: group psychotherapy, Sertraline, adolescent, depression, mental health status

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Authors: Je Young Lee, Hee Sung Kim, Kwang Ho Choi, Joonhoo Lim, Sebum Chun, Hyung Keun Lee

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A purpose of this study is to develop efficient detection method for Global Navigation Satellite Systems (GNSS) applications based on adaptive estimation. Due to dependence of radio frequency signals, GNSS measurements are dominated by systematic errors in receiver’s operating environment. Thus, to utilize GNSS for aerospace or ground vehicles requiring high level of safety, unhealthy measurements should be considered seriously. For the reason, this paper proposes adaptive fault detection method to deal with unhealthy measurements in various harsh environments. By the proposed method, the test statistics for fault detection is generated by estimated measurement noise. Pseudorange and carrier-phase measurement noise are obtained at time propagations and measurement updates in process of Carrier-Smoothed Code (CSC) filtering, respectively. Performance of the proposed method was evaluated by field-collected GNSS measurements. To evaluate the fault detection capability, intentional faults were added to measurements. The experimental result shows that the proposed detection method is efficient in detecting unhealthy measurements and improves the accuracy of GNSS positioning under fault occurrence.

Keywords: adaptive estimation, fault detection, GNSS, residual

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Authors: Zdenek Silar, Martin Dobrovolny

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This article deals with the obstacle detection on a railway crossing (clearance detection). Detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. For classification of passing vehicles is used optical flow direction determination. The optical flow estimation is based on a modified Lucas-Kanade method.

Keywords: background estimation, direction of optical flow, K-means clustering, objects detection, railway crossing monitoring, velocity vectors

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Authors: Ma Lei-Lei, Zhou You

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Aiming at the problems of low detection accuracy of deep learning algorithm in traffic sign detection, this paper proposes improved EfficientDet based traffic sign detection algorithm. Multi-head self-attention is introduced in the minimum resolution layer of the backbone of EfficientDet to achieve effective aggregation of local and global depth information, and this study proposes an improved feature fusion pyramid with increased vertical cross-layer connections, which improves the performance of the model while introducing a small amount of complexity, the Balanced L1 Loss is introduced to replace the original regression loss function Smooth L1 Loss, which solves the problem of balance in the loss function. Experimental results show, the algorithm proposed in this study is suitable for the task of traffic sign detection. Compared with other models, the improved EfficientDet has the best detection accuracy. Although the test speed is not completely dominant, it still meets the real-time requirement.

Keywords: convolutional neural network, transformer, feature pyramid networks, loss function

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Authors: Amy Claridge, Tishra Beeson

Abstract:

Background: Maternal depression is a serious health concern impacting between 10-16% of birthing persons. It is associated with difficulty in emotional interaction and the formation of attachment bonds between parent and infant. Longitudinally, maternal depression can have severe, lasting impacts on both parent and child, increasing the risk for mental, social, and physical health issues. Rates of prenatal depression have been higher among individuals who were pregnant during the first year of the COVID-19 pandemic. Pregnant persons are considered a high-risk group for poor clinical outcomes from COVID-19 infection and may also have faced or continue to face additional stressors such as financial burdens, loss of income or employment, and the benefits accompanying employment, especially among those in the United States (U.S.). It is less clear whether individuals who gave birth during the pandemic continue to experience high levels of depressive symptoms or whether symptoms have been reduced as a pandemic response has shifted. The current study examined longitudinal reports of depressive symptoms among individuals in the U.S. who gave birth between March 2020 and September 2021. Methods: This mixed-method study involved surveys and interviews with birthing persons (18-45 years old) in their third trimester of pregnancy and at 8 weeks postpartum. Participants also completed a follow-up survey at 12-18 months postpartum. Participants were recruited using convenience methods via an online survey. Survey participants included 242 U.S. women who self-reported depressive symptoms (10-item Edinburgh Postnatal Depression Scale) at each data collection wave. A subset of 23 women participated in semi-structured prenatal and 8-week postpartum qualitative interviews. Follow-up interviews are currently underway and will be integrated into the presentation. Preliminary Results: Prenatal depressive symptoms were significantly positively correlated to 8-week and 12-18-month postpartum depressive symptoms. Participants who reported clinical levels of depression prenatally were 3.29 times (SE = .32, p < .001) more likely to report clinical levels of depression at 18 months postpartum. Those who reported clinical depression at 8-weeks postpartum were 6.52 times (SE = .41, p < .001) more likely to report clinical levels of depression at 18 months postpartum. Participants who gave birth earlier in the pandemic reported significantly higher prenatal (t(103) = 2.84, p < .01) and 8-week postpartum depressive symptoms (t(126) = 3.31, p < .001). Data from qualitative interviews contextualize the findings. Participants reported negative emotions during pregnancy, including sadness, grief, and anxiety. They attributed this in part to their experiences of pregnancy during the pandemic and uncertainty related to the birth experience and postpartum period. Postpartum interviews revealed some stressors specific to childbirth during the COVID-19 pandemic; however, most women reflected on positive experiences of birth and postpartum. Conclusions: Taken together, findings reveal a pattern of persistent depressive symptoms among U.S. parents who gave birth during the pandemic. Depressive symptoms are of significant concern for the health of parents and children, and the findings of this study suggest a need for continued mental health intervention for parents who gave birth during the pandemic. Policy and practice implications will be discussed.

Keywords: maternal mental health, perinatal depression, postpartum depression, covid-19 pandemic

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