Search results for: depression detection

Authors: Ujjal Chattaraj, P. C. Dasbebartta, S. Bhuyan

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This paper aims to propose a method to detect the action of the driver’s eyes, using the concept of face detection. There are three major key contributing methods which can rapidly process the framework of the facial image and hence produce results which further can program the reactions of the vehicles as pre-programmed for the traffic safety. This paper compares and analyses the methods on the basis of their reaction time and their ability to deal with fluctuating images of the driver. The program used in this study is simple and efficient, built using the AdaBoost learning algorithm. Through this program, the system would be able to discard background regions and focus on the face-like regions. The results are analyzed on a common computer which makes it feasible for the end users. The application domain of this experiment is quite wide, such as detection of drowsiness or influence of alcohols in drivers or detection for the case of identification.

Keywords: AdaBoost learning algorithm, face detection, framework, traffic safety

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Authors: Samra Naz, Aneeqa Ahmed, Qurat-ul-ain Mubarak, Irum Nausheen

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Driver safety systems protect driver from accidents by sensing signs of drowsiness. The paper proposes a technique which can detect the signs of drowsiness and make corresponding decisions to make the driver alert. This paper presents a technique in which the driver will be continuously monitored by a camera and his eyes, head and mouth movements will be observed. If the drowsiness signs are detected on the basis of these three movements under the predefined criteria, driver will be declared as sleepy and he will get alert with the help of alarms. Three robust techniques of drowsiness detection are combined together to make a robust system that can prevent form accident.

Keywords: drowsiness, eye closure, fatigue detection, yawn detection

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Authors: A. Moawad, K. C. Yao, A. Mansour, R. Gautier

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The detecting of an occupied frequency band is a major issue in cognitive radio systems. The detection process becomes difficult if the signal occupying the band of interest has faded amplitude due to multipath effects. These effects make it hard for an occupying user to be detected. This work mitigates the missed-detection problem in the context of cognitive radio in frequency-selective fading channel by proposing blind channel estimation method that is based on scattering transform. By initially applying conventional energy detection, the missed-detection probability is evaluated, and if it is greater than or equal to 50%, channel estimation is applied on the received signal followed by channel equalization to reduce the channel effects. In the proposed channel estimator, we modify the Morlet wavelet by using its first derivative for better frequency resolution. A mathematical description of the modified function and its frequency resolution is formulated in this work. The improved frequency resolution is required to follow the spectral variation of the channel. The channel estimation error is evaluated in the mean-square sense for different channel settings, and energy detection is applied to the equalized received signal. The simulation results show improvement in reducing the missed-detection probability as compared to the detection based on principal component analysis. This improvement is achieved at the expense of increased estimator complexity, which depends on the number of wavelet filters as related to the channel taps. Also, the detection performance shows an improvement in detection probability for low signal-to-noise scenarios over principal component analysis- based energy detection.

Keywords: channel estimation, cognitive radio, scattering transform, spectrum sensing

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Authors: Zainab Alghali Elsaid Muhammed

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Background : IBS is a gastrointestinal disorder characterized by a variety of symptoms that occur concurrently. It is very common and is associated with high levels of psychiatric comorbidities, all of which have a negative impact on the patient's quality of life. Abdominal pain, diarrhea, constipation, excess gas, and bloating are common symptoms of IBS. Objectives : The purpose of this study is to determine the prevalence of IBS among medical students and intern doctors in Sudan, as well as the risk factors associated with it. Study design: This cross-sectional study was carried out in Sudan from April to July 2022. All participants completed a six-part online questionnaire. The ROME IV criteria questionnaire was used to make an IBS diagnosis. Participants completed the hospital anxiety and depression questionnaire in order to be diagnosed with anxiety and depression. Results : 600 participants filled out the questionnaire. The overall prevalence of IBS was found to be 42%, with females being the most affected. Intern doctors had higher IBS rates (30.0%) than medical students, but this was not statistically significant. Single status (p =0.079), good GPAs (p =0.00), had significant associations with IBS occurrence. Other significantly associated habits were sleeping less than 8 hours (p =0.013), two cups or less of coffee per day (p = 0.109), No smoking (p =0.001), and No exercise (p =0.00, IBS participants were also found to have a significant relationship with abnormal anxiety (p =0.00) and borderline depression (p=0.0156). Conclusion : The high prevalence of IBS in this study suggests that medical students and interns are unable to recognize their symptoms. The main IBS predictors in this study were suffering from anxiety or depression, having an insufficient income, sleeping less than 8 hours per day, working/ studying more than 8 hours per day, and not performing any type of exercise.

Keywords: irritable bowel syndrome, sudan, HADS, rome IV, medical students

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Authors: Abeer Mohammed, Nevein Mustafa Elashery, Mona Hassan Abdel Aal, Ereny Wilson Nagib

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The Aim of the current study is to investigate nutrition role in the management of psychiatric disorders. Research Design: A quasi- experimental research design was utilized for this study. Setting The study was conducted at outpatient clinic at Institute of Psychiatry affiliated to Ain Shams University hospitals, using a convenient sample of 50 psychiatric patients with depression, schizophrenia, bipolar disorders, and obsessive compulsive disorders. Tools: data were collected through; first, an interview questionnaire covering socio-demographic characteristics, second, nutrition assessment tools Third, nutrition risk assessment. Fourth, nutrition management program Results showed that there were highly statistically significant improvements in modified nutritional supplements for patients with depression, schizophrenia, bipolar disorders, and obsessive compulsive disorders' patients after conducting the nutrition management program. Regarding psychiatric patients’ knowledge about healthy food, healthy nutritional habits, and patients’ awareness & readiness for change, there were highly statistically significant improvements. Concerning signs and symptoms of psychiatric disorders, there were highly statistically significant improvements for depression, schizophrenia, bipolar disorders, and obsessive-compulsive patients after conducting the management program. In conclusion, the nutrition management program was effective in improving symptoms associated with, depression, schizophrenia, bipolar disorders, and obsessive compulsive disorders. The study recommended that nurses should have more contribution in counseling psychiatric patients, and their families about healthy diet and healthy habits. Further research should recommend studying the effectiveness of herbs on enhancing mental health for psychiatric patients.

Keywords: nutrition, role, management, psychiatric disorders

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Authors: Muhammad Rosli Abdullah, Noor Hasmiza Harun

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A microorganism detection system has a potential to be used with the advancement in a biosensor development. The detection system requires an optical sensing system, microfluidic device and biological reagent. Although, the biosensors are available in the market, a label free and a lab-on-chip approach will promote a flexible solution. As a preliminary study of microorganism detection, three mechanisms such as Total Internal Reflection (TIR), Micro Fluidic Channel (MFC) and magnetic-electric field propagation were study and simulated. The objective are to identify the TIR angle, MFC parabolic flow and the wavelength for the microorganism detection. The simulation result indicates that evanescent wave is achieved when TIR angle > 42°, the corner and centre of a parabolic velocity are 0.02 m/s and 0.06 m/s respectively, and a higher energy distribution of a perfect electromagnetic scattering with dipole resonance radiation occurs at 500 nm. This simulation is beneficial to determine the components of the microorganism detection system that does not rely on classical microbiological, immunological and genetic methods which are laborious, time-consuming procedures and confined to specialized laboratories with expensive instrumentation equipment.

Keywords: microorganism, microfluidic, total internal reflection, lab on chip

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: Vivian Polak, Lena Verdeli, Wendy Lou, Caroline Lovett

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Postnatal depression (PND) is a common complication of childbirth that increases the risk of future depressive episodes in women, postpartum depression in partners, as well as social, emotional, behavioural, language, and cognitive problems in offspring. Although psychotherapy, and in particular Group Interpersonal Psychotherapy (IPT-G), has been proven effective in treating PND, it remains largely inaccessible. However, research has indicated that online synchronous group therapy can be equally as effective as in-person therapy and is a more affordable and accessible modality of treatment. This study aimed to ascertain whether delivering IPT-G virtually when compared to treatment as usual, could more effectively reduce depressive and anxiety symptoms, enhance mother-infant attachment, improve the couple relationship, augment social support, improve overall functioning, and enhance the quality of life for women in rural and northern Ontario who are suffering from PND. By bridging the gap in access to mental health services during the postpartum period, this study seeks to improve the well-being of mothers and their families in rural and northern Ontario, Canada. A randomized controlled trial was conducted to determine whether virtual IPT-G plus treatment as usual would be more effective than treatment as usual alone in treating women with PND in Ontario, Canada. Preliminary results indicate that women who received virtual IPT-G had a clinically and statistically significant decrease in overall depressive symptoms compared to their counterparts who received only the treatment as usual. As such, providing online synchronous IPT-G in the perinatal period not only has the potential to improve women's outcomes in the present but also to decrease future health costs, reduce the burden on the educational and justice systems, and decrease the number of disability life years lost to postnatal depression.

Keywords: family wellbeing, group psychotherapy, interpersonal psychotherapy, postnatal depression, virtual psychotherapy

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Authors: Mohd Noah A. Rahman, Afzaal H. Seyal, Norhafilah Bara

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Developing a software application using an interface with computers and peripheral devices using gestures of human body such as hand movements keeps growing in interest. A review on this hand gesture detection and recognition based on computer vision technique remains a very challenging task. This is to provide more natural, innovative and sophisticated way of non-verbal communication, such as sign language, in human computer interaction. Nevertheless, this paper explores hand detection and hand gesture recognition applying a vision based approach. The hand detection and recognition used skin color spaces such as HSV and YCrCb are applied. However, there are limitations that are needed to be considered. Almost all of skin color space models are sensitive to quickly changing or mixed lighting circumstances. There are certain restrictions in order for the hand recognition to give better results such as the distance of user’s hand to the webcam and the posture and size of the hand.

Keywords: hand detection, hand gesture, hand recognition, sign language

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Authors: Elise M. Shauf

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Beryllium can be found naturally in some fruits and vegetables (carrots, garden peas, kidney beans, pears) at very low concentrations, but is typically not clinically significant due to the low-level exposure and limited absorption of beryllium by the stomach and intestines. However, acute or chronic beryllium exposure can result in harmful toxic and carcinogenic biological effects. Beryllium can be both a workplace hazard and an environmental pollutant, therefore determining the presence of beryllium at trace levels can be essential to protect workers as well as the environment. Analysis of gallein, C₂₀H₁₂O₇, to determine if it is usable as a fluorescent dye for beryllium detection. The primary detection method currently in use includes hydroxybenzoquinoline sulfonates (HBQS), for which alternative indicators are desired. Unfortunately, gallein does not have the desired aspects needed as a dye for beryllium detection due to the peak shift properties.

Keywords: beryllium detection, fluorescent, gallein dye, indicator, spectroscopy

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Authors: Junyou Shi, Weiwei Cui

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Testability modeling is a commonly used method in testability design and analysis of system. A dependency matrix will be obtained from testability modeling, and we will give a quantitative evaluation about fault detection and isolation. Based on the dependency matrix, we can obtain the diagnosis tree. The tree provides the procedures of the fault detection and isolation. But the dependency matrix usually includes built-in test (BIT) and manual test in fact. BIT runs the test automatically and is not limited by the procedures. The method above cannot give a more efficient diagnosis and use the advantages of the BIT. A Comprehensive method of fault detection and isolation is proposed. This method combines the advantages of the BIT and Manual test by splitting the matrix. The result of the case study shows that the method is effective.

Keywords: fault detection, fault isolation, testability modeling, BIT

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Authors: Ghorbanali Mohammadi

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Background: Due to the widespread outbreak of the COVID-19 virus, a large number of people become infected with the disease every day and go to hospitals. The acute condition of this disease has caused the death of many people. Since all the stages of treatment for these people happen in the hospitals, nurses are at the forefront of the fight against this virus. This causes nurses to suffer from physical and mental health problems. Methods: Physical and mental problems in nurses were assessed using the Depression, Anxiety and Stress Scale (DASS-42) of Lovibond (1995) and the Nordic Questionnaire. Results: 90 nurses from emergency, intensive care, and coronary care units were examined, and a total of 180 questionnaires were collected and evaluated. It was found that 37.78%, 47.78%, and 21.11% of nurses have symptoms of depression, anxiety, and stress, respectively. 40% of the nurses had physical problems. In total, 65.17% of them were involved in one or more mental or physical illnesses. Conclusions: Of the three units surveyed, the nurses in intensive care, emergency room, and coronary care units worked more than ten hours a day. Examining the interaction of physical and mental health problems indicated that physical problems can aggravate mental problems.

Keywords: depression anxiety and stress scale of Lovibond, nordic questionnaire, mental health of nurses, physical health problems in nurses

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Authors: Olena Mayboroda, Angel Gonzalez Benito, Jonathan Sabate Del Rio, Marketa Svobodova, Sandra Julich, Herbert Tomaso, Ciara K. O'Sullivan, Ioanis Katakis

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DNA amplification is required for most molecular diagnostic applications but conventional PCR has disadvantages for field testing. Isothermal amplification techniques are being developed to respond to this problem. One of them is the Recombinase Polymerase Amplification (RPA) that operates at isothermal conditions without sacrificing specificity and sensitivity in easy-to-use formats. In this work RPA was used for the optical detection of solid-phase amplification of the potential biowarfare agent Yersinia pestis. Thiolated forward primers were immobilized on the surface of maleimide-activated microtitre plates for the quantitative detection of synthetic and genomic DNA, with elongation occurring only in the presence of the specific template DNA and solution phase reverse primers. Quantitative detection was achieved via the use of biotinylated reverse primers and post-amplification addition of streptavidin-HRP conjugate. The overall time of amplification and detection was less than 1 hour at a constant temperature of 37oC. Single-stranded and double-stranded DNA sequences were detected achieving detection limits of 4.04*10-13 M and 3.14*10-16 M, respectively. The system demonstrated high specificity with negligible responses to non-specific targets.

Keywords: recombinase polymerase amplification, Yersinia pestis, solid-phase detection, ELONA

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Authors: Komal Babbar

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Multiuser Detection is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference. The authors have presented the Bit-error rate (BER) achieved by linear multi-user detectors: Matched filter (which treats the MAI as AWGN), Decorrelating and MMSE. In this work, authors investigate the bit error probability analysis for Matched filter, decorrelating, and MMSE. This problem arises in several practical CDMA applications where the receiver may not have full knowledge of the number of active users and their signature sequences. In particular, the behavior of MAI at the output of the Multi-user detectors (MUD) is examined under various asymptotic conditions including large signal to noise ratio; large near-far ratios; and a large number of users. In the last section Authors also shows Matlab Simulation results for Multiuser detection techniques i.e., Matched filter, Decorrelating, MMSE for 2 users and 10 users.

Keywords: code division multiple access, decorrelating, matched filter, minimum mean square detection (MMSE) detection, multiple access interference (MAI), multiuser detection (MUD)

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Authors: Junling Li, Fang Meng, Yichun Zhang

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Image saliency detection has been long studied, while several challenging problems are still unsolved, such as detecting saliency inaccurately in complex scenes or suppressing salient objects in the image borders. In this paper, we propose a new saliency detection algorithm in order to solving these problems. We represent the image as a graph with superixels as nodes. By considering appearance similarity between the boundary and the background, the proposed method chooses non-saliency boundary nodes as background priors to construct the background probability model. The probability that each node belongs to the model is computed, which measures its similarity with backgrounds. Thus we can calculate saliency by the transformed probability as a metric. We compare our algorithm with ten-state-of-the-art salient detection methods on the public database. Experimental results show that our simple and effective approach can attack those challenging problems that had been baffling in image saliency detection.

Keywords: visual saliency, background probability, boundary knowledge, background priors

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Authors: Klea Ramaj

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Objectives: There have recently been increasing calls to better understand the intergenerational transmission of adverse childhood experiences (ACEs). In particular, little is known about the links between maternal (ACEs), maternal stress, maternal depression, and child abuse against toddlers in countries in South-East Europe. This paper, therefore, aims to present new descriptive data on the epidemiology of maternal mental well-being and maternal ACEs in the capital of Albania, Tirana. It also aims to advance our understanding of the overlap between maternal stress, maternal depression, maternal exposure to ACEs, and child abuse toward two-to-three-year-old. Methods: This is a cross-sectional study conducted with a representative sample of 328 mothers of two-to-three-year-olds, recruited through public nurseries located in 8 diverse socio-economic and geographical areas in Tirana, Albania. Maternal stress was measured through the perceived stress scale (α = 0.78); maternal depression was measured via the patient health questionnaire (α = 0.77); maternal exposure to ACEs was captured via the ACEs international questionnaire (α = 0.77); and child maltreatment was captured via ISPCAN ICAST-P (α = 0.66). The main outcome examined here will be child maltreatment. The paper will first present estimates of maternal stress, depression, and child maltreatment by demographic groups. It will then use multiple regression to examine associations between child maltreatment and risk factors in the domains of maternal stress, maternal depression, and maternal ACEs. Results: Mothers' mean age was 32.3 (SD = 4.24), 87.5% were married, 51% had one child, and 83.5% had completed higher education. Analyses show high levels of stress and exposure to childhood adversity among mothers in Tirana. 97.5% of mothers perceived stress during the last month, and 89% had experienced at least one childhood adversity as measured by the ACE questionnaire, with 20.2% having experienced 4+ ACEs. Analyses show significant positive associations between maternal ACEs and maternal stress r(325) = 0.25, p = 0.00. Mothers with a high number of ACEs were more likely to abuse their children r(327) = .43, p = 0.00. 32% of mothers have used physical discipline with their 2–3-year-old, 84% have used psychological discipline, and 35% have neglected their toddler at least once or twice. The mothers’ depression levels were also positively and significantly associated with child maltreatment r(327) = .34, p = 0.00. Conclusions: This study provides cross-sectional data on the link between maternal exposure to early adversity, maternal mental well-being, and child maltreatment within the context of Tirana, Albania. The results highlight the importance of establishing policies that encourage maternal support, positive parenting, and family well-being in order to help break the cycle of transgenerational violence.

Keywords: child maltreatment, maternal mental well-being, intergenerational abuse, Tirana, Albania

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Authors: Helen Coronel, Will Brewer, Peggy Bergeron, Clarissa Hall, Victoria Casson

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Simulation-based training provides the nurse practitioner (NP) student with a safe and controlled environment in which they can practice a real-life scenario. This type of learning fosters critical thinking skills essential to practice. The expectation of this study was that students would have an increase in their competency and confidence after performing the simulation. Approximately 8.4% of Americans suffer from depression. The state of Alabama is ranked 47th out of 50 for access to mental health care. As a result of this significant shortage of mental health providers, primary care providers are frequently put in the position of screening for and treating mental health conditions, such as depression. Family nurse practitioners are often utilized as primary care providers, making their ability to assess, diagnose and treat these disorders a necessary skill. The expected outcome of this simulation is an increase in confidence, competency and the empowerment of the nurse practitioner student’s ability to assess, diagnose and treat a common mood disorder they may encounter in practice. The Kirkpatrick Module was applied for this study. A non-experimental design using descriptive statistical analysis was utilized. The simulation was based on a common psychiatric mood disorder frequently observed in primary care and mental health clinics. Students were asked to watch a voiceover power point presentation prior to their on-campus simulation. The presentation included training on the assessment, diagnosis, and treatment of a patient with depression. Prior to the simulation, the students completed a pre-test, then participated in the simulation, and completed a post-test when done. Apple iPads were utilized to access a simulated health record. Major findings of the study support an increase in students’ competency and confidence when assessing, diagnosing, and treating an adult patient with depression.

Keywords: advanced practice, nurse practitioner, simulation, primary care, depression

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Authors: Yeongyu Choi, Ju H. Park, S. M. Lee, Ho-Youl Jung

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In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object’s motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values.

Keywords: corner detection, optical flow, epipolar geometry, RANSAC

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Authors: Michael Dole, Pierre Ninin, Denis Raffourt

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Most of the CERN’s facilities hosting particle accelerators are large, underground and radioactive areas. All fire detection systems installed in such areas, shall be carefully studied to cope with the particularities of this stringent environment. The detection equipment usually chosen by CERN to secure these underground facilities are based on air sampling technology. The electronic equipment is located in non-radioactive areas whereas air sampling networks are deployed in radioactive areas where fire detection is required. The air sampling technology provides very good detection performances and prevent the "radiation-to-electronic" effects. In addition, it reduces the exposure to radiations of maintenance workers and is permanently available during accelerator operation. In order to protect the Super Proton Synchrotron and its 7 km tunnels, a specific long distance aspirating smoke detector has been developed to detect smoke at up to 700 meters between electronic equipment and the last air sampling hole. This paper describes the architecture, performances and return of experience of the long distance fire detection system developed and installed to secure the CERN Super Proton Synchrotron tunnels.

Keywords: air sampling, fire detection, long distance, radioactive areas

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Authors: Dilip K. Prasad, C. Krishna Prasath, Deepu Rajan, Lily Rachmawati, Eshan Rajabally, Chai Quek

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This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here.

Keywords: autonomous maritime vehicle, object detection, situation awareness, tracking

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Authors: Yuhui Liu, Lydia Speyer, Jasmin Wertz, Ingrid Obsuth

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Childhood adversities refer to situations where a child's basic needs for safety and support are compromised, leading to substantial disruptions in their emotional, cognitive, social, or neurobiological development. Given the prevalence of adversities (8%-39%), their impact on developmental outcomes is challenging to completely avoid. Delinquency is an important consequence of childhood adversities, given its potential causing violence and other forms of victimisation, influencing victims, delinquents, their families, and the whole of society. Studying mediators helps explain the link between childhood adversity and delinquency, which aids in designing effective intervention programs that target explanatory variables to disrupt the path and mitigate the effects of childhood adversities on delinquency. The Dimensional Model of Adversity and Psychopathology suggests that threat-based adversities influence outcomes through emotion processing, while deprivation-based adversities do so through cognitive mechanisms. Thus, considering a wide range of threat-based and deprivation-based adversities and their co-occurrence and their associations with delinquency through cognitive and emotional mechanisms is essential. This study employs the Millennium Cohort Study, tracking the development of approximately 19,000 individuals born across England, Scotland, Wales and Northern Ireland, representing a nationally representative sample. Parallel mediation models compare the mediating roles of self-esteem (cognitive) and depression (affective) in the associations between childhood adversities and delinquency. Eleven types of childhood adversities were assessed both individually and through latent class analysis, considering adversity experiences from birth to early adolescence. This approach aimed to capture how threat-based, deprived-based, or combined threat and deprived-based adversities are associated with delinquency. Eight latent classes were identified: three classes (low adversity, especially direct and indirect violence; low childhood and moderate adolescent adversities; and persistent poverty with declining bullying victimisation) were negatively associated with delinquency. In contrast, three classes (high parental alcohol misuse, overall high adversities, especially regarding household instability, and high adversity) were positively associated with delinquency. When mediators were included, all classes showed a significant association with delinquency through depression, but not through self-esteem. Among the eleven single adversities, seven were positively associated with delinquency, with five linked through depression and none through self-esteem. The results imply the importance of affective variables, not just for threat-based but also deprivation-based adversities. Academically, this suggests exploring other mechanisms linking adversities and delinquency since some adversities are linked through neither depression nor self-esteem. Clinically, intervention programs should focus on affective variables like depression to mitigate the effects of childhood adversities on delinquency.

Keywords: childhood adversity, delinquency, depression, self-esteem

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Authors: Muziwandile Luthuli

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Although numerous factors predicting adherence to antiretroviral therapy (ART) among people living with HIV/AIDS (PLWHA) have been broadly studied on both regional and global level, up-to-date adherence of patients to ART remains an overarching, dynamic and multifaceted problem that needs to be investigated over time and across various contexts. There is a rarity of empirical data in the literature on interactive mechanisms by which psychosocial factors influence adherence to ART among PLWHA within the South African context. Therefore, this study was designed to investigate the relationship between depression, HIV stigma, and adherence to ART among adult patients living with HIV at a tertiary hospital in Durban, South Africa, and the mediating roles of self-efficacy and social support. The health locus of control theory and the social support theory were the underlying theoretical frameworks for this study. Using a cross-sectional research design, a total of 201 male and female adult patients aged between 18-75 years receiving ART at a tertiary hospital in Durban, KwaZulu-Natal were sampled, using time location sampling (TLS). A self-administered questionnaire was employed to collect the data in this study. Data were analysed through SPSS version 27. Several statistical analyses were conducted in this study, namely univariate statistical analysis, correlational analysis, Pearson’s chi-square analysis, cross-tabulation analysis, binary logistic regression analysis, and mediational analysis. Univariate analysis indicated that the sample mean age was 39.28 years (SD=12.115), while most participants were females 71.0% (n=142), never married 74.2% (n=147), and most were also secondary school educated 48.3% (n=97), as well as unemployed 65.7% (n=132). The prevalence rate of participants who had high adherence to ART was 53.7% (n=108), and 46.3% (n=93) of participants had low adherence to ART. Chi-square analysis revealed that employment status was the only statistically significant socio-demographic influence of adherence to ART in this study (χ2 (3) = 8.745; p < .033). Chi-square analysis showed that there was a statistically significant difference found between depression and adherence to ART (χ2 (4) = 16.140; p < .003), while between HIV stigma and adherence to ART, no statistically significant difference was found (χ2 (1) = .323; p >.570). Binary logistic regression indicated that depression was statistically associated with adherence to ART (OR= .853; 95% CI, .789–.922, P < 001), while the association between self-efficacy and adherence to ART was statistically significant (OR= 1.04; 95% CI, 1.001– 1.078, P < .045) after controlling for the effect of depression. However, the findings showed that the effect of depression on adherence to ART was not significantly mediated by self-efficacy (Sobel test for indirect effect, Z= 1.01, P > 0.31). Binary logistic regression showed that the effect of HIV stigma on adherence to ART was not statistically significant (OR= .980; 95% CI, .937– 1.025, P > .374), but the effect of social support on adherence to ART was statistically significant, only after the effect of HIV stigma was controlled for (OR= 1.017; 95% CI, 1.000– 1.035, P < .046). This study promotes behavioral and social change effected through evidence-based interventions by emphasizing the need for additional research that investigates the interactive mechanisms by which psychosocial factors influence adherence to ART. Depression is a significant predictor of adherence to ART. Thus, to alleviate the psychosocial impact of depression on adherence to ART, effective interventions must be devised, along with special consideration of self-efficacy and social support. Therefore, this study is helpful in informing and effecting change in health policy and healthcare services through its findings

Keywords: ART adherence, depression, HIV/AIDS, PLWHA

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Authors: Laura Bickel

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This paper aims to investigate to what extent Merleau-Ponty’s philosophy of body memory serves as a viable resource for the enactive approach to cognitive science and its first-person experience-based research on ‘recurrent depressive disorder’ coded F33 in ICD-10. In pursuit of this goal, the analysis begins by revisiting the neuroreductive paradigm. This paradigm serves biological psychiatry to explain the condition of vital contact in terms of underlying neurophysiological mechanisms. It is demonstrated that the neuroreductive model cannot sufficiently account for the depressed person’s episodical withdrawal in causal terms. The analysis of the irregular loss of vital resonance requires integrating the body as the subject of experience and its phenomenological time. Then, it is shown that the enactive approach to depression as disordered sense-making is a promising alternative. The enactive model of perception implies that living beings do not register pre-existing meaning ‘out there’ but unfold ‘sense’ in their action-oriented response to the world. For the enactive approach, Husserl’s passive synthesis of inner time consciousness is fundamental for what becomes perceptually present for action. It seems intuitive to bring together the enactive approach to depression with the long-standing view in phenomenological psychopathology that explains the loss of vital contact by appealing to the disruption of the temporal structure of consciousness. However, this paper argues that the disruption of the temporal structure is not justified conceptually. Instead, one may integrate Merleau-Ponty’s concept of the past as the unconscious into the enactive approach to depression. From this perspective, the living being’s experiential and biological past inserts itself in the form of habit and bodily skills and ensures action-oriented responses to the environment. Finally, it is concluded that the depressed person’s withdrawal indicates the impairment of this application process. The person suffering from F33 cannot actualize sedimented meaning to respond to the valences and tasks of a given situation.

Keywords: depression, enactivism, neuroreductionsim, phenomenology, temporality

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Authors: Saleh Alshehri

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Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases.

Keywords: image databases, image processing, pattern recognition, neural networks

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: Laicheng Cao, Xiangqian Su, Youxiao Wu

Abstract:

Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system.

Keywords: intrusion detection, feature selection, iwo, tabu search

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Authors: Alireza Mirrashid, Mohammad Khoshbin, Ali Atghaei, Hassan Shahbazi

Abstract:

In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.

Keywords: attention, fire detection, smoke detection, spatio-temporal

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Authors: Abe D. Desta

Abstract:

This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: A. Gazdzinska, P. Jagielski, E. Kaniewska, S. P. Gazdzinski, M. Wylezol

Abstract:

Background: In the multi-factor etiology of obesity, an increasing degree of importance is attributed to behavioral factors. Lifestyle and health-oriented behaviors heavily influence the treatment of multiple diseases, including obesity. However, only a few studies evaluated health-related behaviors exhibited by patients qualified for bariatric surgery. None of them was performed in Polish population. Aim: Assessment of health behaviors of obese patients according to the degree of mood disorders. Method: The study involved 93 patients (66 females) who were qualified for bariatric surgery in the Department of Surgery of the Military Institute of Aviation Medicine in Warsaw. Diagnostic instrument was the Juczynski’s Inventory of Health Behavior (HBI), which evaluates health behavior in four categories, i.e. proper nutrition habits (PNH), preventive behavior (PH), health practices (HP) and positive mental attitude (PMA). The average HBI falls in the range between 24 and 120 points, for each category of health behaviors fall between 1 and 5 (higher score means higher severity declared healthy behaviors). The depressive symptoms in patients were assessed with Beck Depression Inventory (BDI). All analyses were conducted using STATISTICA 12. Results: The average age was 44.2 ± 11.5 years, mean BMI was 44.3 ± 10.5 kg/m2 and 46.8 ± 7.6 kg/m2, in females and males respectively. According to BDI, 32% patients had mild level of depression, 10% moderate and 14% severe depression. BDI scores were not different between females and males. Low results with regard to the health behaviors declared were obtained by 35.5 % of patients, medium by 44.0%, while high ones by only 20.5%. On average, patients gained 3.28 points in PNH, 3.37 points in PH, 3.29 points in HP, while 3.42 in the PMA category, showing average intensity of these behaviors. These health behaviors were practiced significantly more often by women (p = 0.04). The average HBI was 80.2; with average score of 81.5 for females and 76.6 for males, respectively (p = 0.03). Women were better in the PNH category (p = 0.02). A positive correlation was found between age and all categories of health behaviors, in particular PNH (R = 0.38; p = 0.001), PH (R = 0.26; p = 0.01), HP (R = 0.27; p = 0.01) and PMA (R = 0.24; p = 0.02), independent of gender. The severity of depression had a significant impact only on the behaviors associated with proper eating habits, which saw a negative correlation between BDI scores and the PNH (R = -0.21; p = 0.04). Conclusions: Majority of morbidly obese patients qualified for bariatric surgery obtained low to average scores in health behavior questionnaire. However, these results are similar in comparison with the Polish adult population. In accordance to these results, it seems that healthy behaviors, among them eating behaviors, do not appear to be a cause of obesity epidemic or they might be acquired when the disease is already underway. Female gender and age had a positive effect, and depression had a negative effect on the level of health behaviors among patients qualified for bariatric surgery.

Keywords: depression, habits, health behaviours, obesity

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Authors: Telma Fontao de Castro, Kylee Miller, Maria Xavier Araújo, Isabel Brandao, Sandra Torres

Abstract:

Objective: There is a dearth of research investigating the long-term emotional functioning of individuals recovered from anorexia nervosa (AN). This 15-year longitudinal study aimed to examine whether difficulties in cognitive processing of emotions persisted after long-term AN recovery and its link to anxiety and depression. Method: Twenty-four females, who were tested longitudinally during their acute and recovered AN phases, and 24 healthy control (HC) women, were screened for anxiety, depression, alexithymia, and emotion regulation difficulties (ER; only assessed in recovery phase). Results: Anxiety, depression, and alexithymia levels decreased significantly with AN recovery. However, scores on anxiety and difficulty in identifying feelings (alexithymia factor) remained high when compared to the HC group. Scores on emotion regulation difficulties were also lower in HC group. The abovementioned differences between AN recovered group and HC group in difficulties in identifying and accepting feelings and lack of emotional clarity were no longer present when the effect of anxiety and depression was controlled. Conclusions: Findings suggest that emotional dysfunction tends to decrease in AN recovered phase. However, using an HC group as a reference, we conclude that several emotional difficulties are still increased after long-term AN recovery, in particular, limited access to emotion regulation strategies, and difficulty controlling impulses and engaging in goal-directed behavior, thus suggesting to be a trait vulnerability. In turn, competencies related to emotional clarity and acceptance of emotional responses seem to be state-dependent phenomena linked to anxiety and depression. In sum, managing emotions remains a challenge for individuals recovered from AN. Under this circumstance, maladaptive eating behavior can serve as an affect regulatory function, increasing the risk of relapse. Emotional education and stabilization of depressive and anxious symptomatology after recovery emerge as an important avenue to protect from long-term AN relapse.

Keywords: alexithymia, anorexia nervosa, emotion recognition, emotion regulation

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