Search results for: mental image

Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Anuruddha Jagoda, Samiddhi Samarakoon, Anil Jasinghe

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In an emergency department ‘where every second count for patient’s management’ timely availability of X- rays play a vital role in early diagnosis and management of patients. Trauma care centers rely heavily on timely radiologic imaging for patient care and radiology plays a crucial role in the emergency department (ED) operations. A research study was carried out to assess timeliness of availability of X-rays and total turnaround time at the Accident Service of National Hospital of Sri Lanka which is the premier trauma center in the country. Digital Radiology system was implemented as an intervention to improve the timeliness of availability of X-rays. Post-implementation assessment was carried out to assess the effectiveness of the intervention. Reduction in all three aspects of waiting times namely waiting for initial examination by doctors, waiting until X –ray is performed and waiting for image availability was observed after implementation of the intervention. However, the most significant improvement was seen in waiting time for image availability and reduction in time for image availability had indirect impact on reducing waiting time for initial examination by doctors and waiting until X –ray is performed. The most significant reduction in time for image availability was observed when performing 4-5 X rays with DR system. The least improvement in timeliness was seen in patients who are categorized as critical.

Keywords: emergency department, digital radilogy, timeliness, trauma care

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Radoslaw Domke

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The social history of Poland in the years 1945-1990 has already been thoroughly researched based on the so-called Classical sources. Many types of archival and press sources, diaries, memoirs, and literature on the subject were analyzed. It turns out, however, that the fictional film material remains an unknown source. In the paper, the author intends to focus on the image of Polish society that emerges from the analysis of cinematography produced by the Polish People's Republic. The conclusions presented in the paper can be the basis for further research on the visual history of post-war societies.

Keywords: visual history, history of Poland, social history, cinematography

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Authors: Luo Jin China, Jin Fang

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Countryside has been generally recognized and regarded as a characteristic symbol which presents in human memory for a long time. As a result of the change of times, because of it’s failure to meet the growing needs of the growing life and mental decline, the vast rural area began to decline. But their history feature image which accumulated by the ancient tradition provides people with the origins of existence on the spiritual level, such as "identity" and "belonging", makes people closer to the others in the spiritual and psychological aspects of a common experience about the past, thus the sense of a lack of culture caused by the losing of memory symbols is weakened. So, in the modernization process, how to repair its vitality and transform and planning it in a sustainable way has become a hot topics in architectural and urban planning. This paper aims to break the constraints of disciplines, from the perspective of interdiscipline, using the research methods of systems science to analyze and discuss the theories and methods of rural form factors, which based on the viewpoint of memory in psychology. So, we can find a right way to transform the Rural to give full play to the role of the countryside in the actual use and the shape of history spirits.

Keywords: rural, sustainable renovation, restoration, psychology, memory

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Authors: Chiara Cosentino, Carlo Pruneti, Carla Merisio, Domenico Sgromo

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Introduction – Psychoneuroimmunology as approach clearly showed how psychological features can influence health through specific physiological pathways linked to the stress reaction. This can be true also in cancer, in its latter conceptualization seen as a chronic disease. Therefore, it is still not clear how the psychological features can combine with a physiological specific path, for a better adjustment to cancer. The aim of this study is identifying how in Italian survivors, perceived social support, body image, coping and quality of life correlate with or influence Heart Rate Variability (HRV), the physiological parameter that can mirror a condition of chronic stress or a good relaxing capability. Method - The study had an exploratory transversal design. The final sample was made of 38 ovarian cancer survivors aged from 29 to 80 (M= 56,08; SD=12,76) following a program for Ovarian Cancer at the Oncological Clinic, University Hospital of Parma, Italy. Participants were asked to fill: Multidimensional Scale of Perceived Social Support (MSPSS); Derridford Appearance Scale-59 (DAS-59); Mental Adjustment to Cancer (MAC); Quality of Life Questionnaire (EORTC). For each participant was recorded Short-Term HRV (5 minutes) using emWavePro. Results– Data showed many interesting correlations within the psychological features. EORTC scores have a significant correlation with DAS-59 (r =-.327 p <.05), MSPSS (r =.411 p<.05), and MAC scores, in particular with the strategy Fatalism (r =.364 p<.05). A good social support improves HRV (F(1,33)= 4.27 p<.05). Perceiving themselves as effective in their environment, preserving a good role functioning (EORTC), positively affects HRV (F(1,33)=9.810 p<.001). Women admitting concerns towards body image seem prone to emotive disclosure, reducing emotional distress and improving HRV (β=.453); emotional avoidance worsens HRV (β=-.391). Discussion and conclusion - Results showed a strong relationship between body image and Quality of Life. These data suggest that higher concerns on body image, in particular, the negative self-concept linked to appearance, was linked to the worst functioning in everyday life. The relation between the negative self-concept and a reduction in emotional functioning is understandable in terms of possible distress deriving from the perception of body appearance. The relationship between a high perceived social support and a better functioning in everyday life was also confirmed. In this sample fatalism, was associated with a better physical, role and emotional functioning. In these women, the presence of a good support may activate the physiological Social Engagement System improving their HRV. Perceiving themselves effective in their environment, preserving a good role functioning, also positively affects HRV, probably following the same physiological pathway. A higher presence of concerns about appearance contributes to a higher HRV. Probably women admitting more body concerns are prone to a better emotive disclosure. This could reduce emotional distress improving HRV and global health. This study reached preliminary demonstration of an ‘Integrated Model of Defense’ in these cancer survivors. In these model, psychological features interact building a better quality of life and a condition of psychological well-being that is associated and influence HRV, then the physiological condition.

Keywords: cancer survivors, heart rate variability, ovarian cancer, psychophysiological adjustment

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Authors: Janusz Bobulski, Kamila Pasternak

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Despite many years of effort and research, the problem of waste management is still current. So far, no fully effective waste management system has been developed. Many programs and projects improve statistics on the percentage of waste recycled every year. In these efforts, it is worth using modern Computer Vision techniques supported by artificial intelligence. In the article, we present a method of identifying plastic waste based on the asymmetry analysis of the histogram of the image containing the waste. The method is simple but effective (94%), which allows it to be implemented on devices with low computing power, in particular on microcomputers. Such de-vices will be used both at home and in waste sorting plants.

Keywords: waste management, environmental protection, image processing, computer vision

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Authors: Saumya Srivastava, Rina Maiti

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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

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Authors: Yousif Mohamed Y. Abdallah, Razan Manofely, Rajab M. Ben Yousef

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X-Ray images are very popular as a first tool for diagnosis. Automating the process of analysis of such images is important in order to help physician procedures. In this practice, teeth segmentation from the radiographic images and feature extraction are essential steps. The main objective of this study was to study correction preprocessing of x-rays images using local adaptive filters in order to evaluate contrast enhancement pattern in different x-rays images such as grey color and to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in x-rays images. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were contrast enhancement filtering and deblurring images using the blind deconvolution algorithm. In this paper, prominent constraints are firstly preservation of image's overall look; secondly, preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image.

Keywords: enhancement, x-rays, pixel intensity values, MatLab

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Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

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Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

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Authors: Tran Tran

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Mental health issues are prevalent among Vietnamese undergraduate students, and they are greatly exacerbated during the COVID-19 pandemic for this population. While there is empirical evidence supporting the effectiveness and efficiency of therapy on mental health issues among college students, the rates of Vietnamese college students seeking professional mental health services were alarmingly low. Multiple factors can prevent those in need from finding support. The Internalized Stigma Model posits that public stigma directly affects intentions to seek psychological help via self-stigma and attitudes toward seeking help. However, little research has focused on what factors can predict public stigma toward seeking professional psychological support, especially among this population. A potential predictor is academic majors since academic majors can influence undergraduate students' perceptions, attitudes, and intentions. A study suggested that students who have completed two or more psychology courses have a more positive attitude toward seeking care for mental health issues and reduced stigma, which might be attributed to increased mental health literacy. In addition, research has shown that women are more likely to utilize mental health services and have lower stigma than men. Finally, studies have also suggested that experience of mental health services can increase endorsement of perceived need and lower stigma. Thus, it is expected that perceived helpfulness from past service uses can reduce stigma. This study aims to address this gap in the literature and investigate which factors can predict public stigma, specifically academic major, gender, and perceived helpfulness, potentially suggesting an avenue of prevention and ultimately improving the well-being of Vietnamese college students. The sample includes 408 undergraduate students (Mage = 20.44; 80.88% female) Hanoi city, Vietnam. Participants completed a pen-and-paper questionnaire. Students completed the Stigma Scale for Receiving Psychological Help, which yielded a mean public stigma score. Participants also completed a measurement assessing their perceived helpfulness of their university’s counseling center, which included eight subscales: future self-development, learning issues, career counseling, medical and health issues, mental health issues, conflicts between teachers and students, conflicts between parents and students, and interpersonal relationships. Items were summed to create a composite perceived helpfulness score. Finally, participants provided demographic information. This included gender, which was dichotomized between female and other. Additionally, it included academic major, which was also similarly dichotomized between psychology and other (e.g., natural science, social science, and pedagogy & social work). Linear relationships between public stigma and gender, academic major, and perceived helpfulness were analyzed individually with a regression model. Findings suggested that academic major, gender, and perceived counseling center's helpfulness predicted stigma against seeking professional psychological help. Specifically, being a psychology major predicted lower levels of public stigma (β = -.25, p < .001). Additionally, gender female predicted lower levels of public stigma (β = -.11, p < .05). Lastly, higher levels of perceived helpfulness of the counseling center also predicted lower levels of public stigma (β = -.16, p < .01). The study’s results offer potential intervention avenues to help reduce stigma and increase well-being for Vietnamese college students.

Keywords: stigma, vietnamese college students, counseling services, help-seeking

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Authors: Poornima Khadanga, Gaurav Pawar, Madhavi Rairikar

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Background: The COVID 19 pandemic, with its unparalleled mental health repercussions, has impacted people globally and has catalyzed a Mental Health pandemic among the youth. The detrimental effects on mental health needs to be pondered at the earliest. Aims: To study the behavioral problems among children and adolescents visiting Psychiatry Outpatient Department Tertiary Health Care during COVID pandemic and its correlation with socio-demographic profiles. Methods: A cross sectional study was conducted by interviewing 120 participants between 4 to 17 years of age and their parents, visiting Psychiatry OPD. Behavioral problems were assessed using the Strength and Difficulties Questionnaire and diagnosed by DSM-5. Statistical analysis was done by SPSS-21. Results: Male participants showed significant association with conduct (t=2.36, p=0.02) and hyperactive problems (t=5.07, p<0.05). Increase in screen time showed a positive correlation with conduct problems (r=0.22. p=0.02). Attention Deficit Hyperkinetic Disorder (18.3%) was the most commonly diagnosed psychiatric illness. Total difficulty score was significantly associated with difficult temperament (F=68.69, p<0.05). Conclusion: The study brings to light the pattern of behavioral problems that emerged during recent times of uncertainties among the young ones, including those with special needs. The increase in disruptive behaviors with increase screen time needs to be addressed at the earliest.

Keywords: behavioral problems, pandemic, screen time, temperament

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Authors: Steven M. Sanders, April T. Berry, David W. Hollingsworth

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Previous research has shown that veterans have higher rates of mental health concerns compared to non-veteran populations. A potential risk factor for the development of mental health concerns (i.e., depression & anxiety), particularly in Black veterans, is psychological inflexibility. Psychological inflexibility, a component of Acceptance & Commitment Therapy (ACT), is a process by which behavior is expressed in ways that attempt to control emotional and psychological reactions to uncomfortable stimuli and situations rather than by direct contingencies or personal values. The present study explored the relationship between psychological inflexibility, symptoms of depression, and symptoms of anxiety in a sample of 131 Black veterans. Results demonstrated that Black veterans who endorsed psychological inflexibility also endorsed higher levels of both depression and anxiety symptomology. These findings indicate the deleterious consequences of experiencing psychological inflexibility, which could be treated through ACT.

Keywords: psychological flexibility, veteran, black, psychological distress

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Authors: Muhammad Abubakar Alhassan, Diane Pennington

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Social networking sites have become a space for people to discuss public health issues such as non-suicidal self-injury (NSSI). There are thousands of tweets containing self-harm and self-injury hashtags on Twitter. It is difficult to distinguish between different users who participate in self-injury discussions on Twitter and how their opinions change over time. Also, it is challenging to understand the topics surrounding NSSI discussions on Twitter. We retrieved tweets using #selfham and #selfinjury hashtags and investigated those from the United kingdom. We applied inductive coding and grouped tweeters into different categories. This study used the Latent Dirichlet Allocation (LDA) algorithm to infer the optimum number of topics that describes our corpus. Our findings revealed that many of those participating in NSSI discussions are non-professional users as opposed to medical experts and academics. Support organisations, medical teams, and academics were campaigning positively on rais-ing self-injury awareness and recovery. Using LDAvis visualisation technique, we selected the top 20 most relevant terms from each topic and interpreted the topics as; children and youth well-being, self-harm misjudgement, mental health awareness, school and mental health support and, suicide and mental-health issues. More than 50% of these topics were discussed in England compared to Scotland, Wales, Ireland and Northern Ireland. Our findings highlight the advantages of using the Twitter social network in tackling the problem of self-injury through awareness. There is a need to study the potential risks associated with the use of social networks among self-injurers.

Keywords: self-harm, non-suicidal self-injury, Twitter, social networks

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Authors: Nazeema Ahmed

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Mental health in higher education has received increasing attention over the past few decades. The high academic demands of the engineering degree, coupled with students’ mental health challenges, have led to higher education institutions offering psychotherapeutic services to students. This paper discusses an evaluation of the psychotherapy service at the University of Cape Town. The aim was to determine (i) the efficacy of the service; and (ii) the impact of race, gender, and language of the therapist on the students’ therapeutic process. An online survey was sent to 109 students who attended psychotherapy. The majority expressed favorable experiences of psychotherapy, with reports of increased capacity to engage with their academic work. Most students did not experience the gender, race, or language of the psychologists to be barriers to their therapy. The findings point to a need for ongoing psychological support for students.

Keywords: psychotherapy, efficacy, engineering, education

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Authors: Faraz Tahir, M. Usman Akram, Albab Ahmad Khan, Mujahid Abbass, Ahmad Tariq, Nuzhat Qaiser

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The objective of this study was to evaluate the Degree of anemia by taking the picture of the palpebral conjunctiva using Smartphone Camera. We have first localized the region of interest from the image and then extracted certain features from that Region of interest and trained SVM classifier on those features and then, as a result, our system classifies the image in real-time on their level of hemoglobin. The proposed system has given an accuracy of 70%. We have trained our classifier on a locally gathered dataset of 30 patients.

Keywords: anemia, palpebral conjunctiva, SVM, smartphone

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Jesús A. Pérez, Sam Coppieters, Dimitri Debruyne

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In the past decade, the use of digital image correlation (DIC) techniques has increased significantly in the area of experimental mechanics, especially for materials behavior characterization. This non-contact tool enables full field displacement and strain measurements over a complete region of interest. The DIC algorithm requires a random contrast pattern on the surface of the specimen in order to perform properly. To create this pattern, the specimen is usually first coated using a white matt paint. Next, a black random speckle pattern is applied using any suitable method. If the applied paint coating is too thick, its top surface may not be able to exactly follow the deformation of the specimen, and consequently, the strain measurement might be underestimated. In the present article, a study of the influence of the paint thickness on the strain underestimation is performed for different strain levels. The results are then compared to typical paint coating thicknesses applied by experienced DIC users. A slight strain underestimation was observed for paint coatings thicker than about 30μm. On the other hand, this value was found to be uncommonly high compared to coating thicknesses applied by DIC users.

Keywords: digital image correlation, paint coating thickness, strain

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Authors: Selen Muftuoglu, Dilara Kefeli

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The present study was conducted by using the cross-sectional study design and to determine the relationship between body image, eating behavior and nutritional status in 80 female athletes who were basketball, volleyball, flag football, indoor soccer, and ice hockey players. This study demonstrated that 70.0% of the female athletes had skipped meal. Also, female athletes had a normal body mass index (BMI), but 65.0% of them indicated that want to be thinner. On the other hand, we analyzed that their daily nutrients intake, so we observed that 43.4% of the energy was from the fatty acids, especially saturated fatty acids, and they had lower fiber, calcium and iron intake. Also, we found that BMI, waist circumference, waist to hip ratio were negatively correlated with Multidimensional Body-Self Relations Questionnaire and The Dutch Eating Behavior Questionnaire score and they were lower in who had meal skipped or not received diet therapy. As a conclusion, nutrition education is frequently neglected in sports programs. There is a paucity of nutrition education interventions among different sports.

Keywords: body image, eating behavior, eating disorders, female athletes, nutritional status

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Authors: Michael McTighe

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Background: The COVID-19 pandemic caused the cancellation of many large-scale in-person sporting events, which led to an increase in the availability of virtual ultra-marathons. This study intended to assess how participation in virtual long distances races relates to levels of physical activity for an extended period of time. Moreover, traditional ultra-marathons are known for being not only physically demanding, but also mentally and emotionally challenging. A second component of this study was to assess how psychological contructs related to emotion regulation and mental toughness predict overall performance in the sport. Method: 83 virtual runners participating in a four-month 1000-kilometer race with the option to exceed 1000 kilometers completed a questionnaire exploring demographics, their performance, and experience in the virtual race. Participants also completed the Difficulties in Emotions Regulation Scale (DERS) and the Sports Mental Toughness Questionnaire (SMTQ). Logistics regressions assessed these constructs’ utility in predicting completion of the 1000-kilometer distance in the time allotted. Multiple regression was employed to predict the total distance traversed during the fourmonth race beyond 1000-kilometers. Result: Neither mental toughness nor emotional regulation was a significant predictor of completing the virtual race’s basic 1000-kilometer finish. However, both variables included together were marginally significant predictors of total miles traversed over the entire event beyond 1000 K (p = .051). Additionally, participation in the event promoted an increase in healthy activity with participants running and walking significantly more in the four months during the event than the four months leading up to it. Discussion: This research intended to explore how psychological constructs relate to performance in a virtual type of endurance event, and how involvement in these types of events related to levels of activity. Higher levels of mental toughness and lower levels in difficulties in emotion regulation were associated with greater performance, and participation in the event promoted an increase in athletic involvement. Future psychological skill training aimed at improving emotion regulation and mental toughness may be used to enhance athletic performance in these sports, and future investigations into these events could explore how general participation may influence these constructs over time. Finally, these results suggest that participation in this logistically accessible, and affordable type of sport can promote greater involvement in healthy activities related to running and walking.

Keywords: virtual races, emotion regulation, mental toughness, ultra-marathon, predictors in performance

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Authors: S. Sushma, S. Balasubramanian, K. C. Latha, R. Sridhar

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Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and lacunarity contribute to assess breast cancer risk. Fractal Dimension represents the complexity while the lacunarity characterize the gap of a fractal dimension. In this paper, we present our result confirming that the lacunarity value resulted in algorithm using mammogram images states that level of lacunarity will be low when the Fractal Dimension value will be high.

Keywords: breast cancer, fractal dimension, image analysis, lacunarity, mammogram

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Authors: Zin Mar Lwin

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Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods.

Keywords: BCI, EEG, ICA, SVM

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Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu

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The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.

Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG

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Authors: Mohamad Shariff Nurasikin, Paul Crawford, Nicola Wright

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People with any mental disorder can get benefit from the spiritual aspects of life for recovery, particularly in searching for the meaning of life and engaging in meaningful activities. However, little is known about such effects in the population of bipolar disorder. The concepts of spirituality are highly contestable, as they are too broad and removed from the original religious understanding. The concepts are more notable as encompassing multi-dimensional aspects of people’s lives such as social, emotional, and psychological. Viewing that Western or secular worldview dominates most of the literature in spirituality, it is time to explore the concept of spirituality from the Eastern and religious worldview, such as the Malaysian view. Thus, the aim of this study is to provide a conceptual understanding of people with bipolar disorder with a religious affiliation in Malaysia. This study employs a Grounded Theory and explores the narratives from the interviews of 25 participants. The narratives strongly suggest the salient resources or can be referred to as various forms of capital, as in the capital theory, namely, religious, social, psychological, and medicinal. More important is how these capitals are the enablers for recovery in mental health and well-being, where the participants in the sample engage in a more meaningful life and positive adaptations. This study also extends the Bourdieusian spiritual capital, in which the salient resources are termed as the capital bundle. More significant is how the capital bundles are working contiguously in building and accumulating the spiritual capital. This process is conducive to recovery within the social life of people with bipolar disorder or perhaps other mental disorders.

Keywords: bipolar, Bourdeau, recovery, spiritual

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Authors: Urwah Ali

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The aim of this study is to carry out a meta-analysis in order to establish an overall international figure and to summarize the evidence relating to the possible relationship between child sexual abuse and subsequent mental and physical health outcomes. A systematic review was conducted using the HEC Digital Library, Pub Med, PsycINFO and SAHIL databases published after 2010 containing empirical data pertaining to CSA. Out of 124 articles assessed for eligibility, 32 studies provided evidence of a relationship between sexual child maltreatment and various health outcomes for use in subsequent meta-analyses. Statistical significance associations were observed between childhood sexual victimization and psychological problems in their adulthood [odds ratio (OR) = 1.5; 95%Cl 3.07–4.43]. For most studies included for meta-analysis, the odds ratio falls above 1.00, indicating that patients having history of childhood sexual victimization were more likely to develop psychological disorders.

Keywords: abuse, sexual abuse, childhood sexual abuse, mental health

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Authors: Charles Q. Yu, Victoria H. Fan, Ahmed F. Al-Qahtani, Ibraim Viera

Abstract:

Introduction: Over 12 million people are blind due to opacity of the cornea, the clear tissue forming the front of the eye. Current methods use plastic implants to produce a clear optical pathway into the eye but are limited by a high rate of complications. New implants utilizing completely inside-the-eye projection technology can overcome blindness due to scarring of the eye by producing images on the retina without need for a clear optical pathway into the eye and may be free of the complications of traditional treatments. However, the interior of the eye is a challenging location for the design of optical focusing systems which can produce a sufficiently high quality image. No optical focusing systems have previously been characterized for this purpose. Methods: 3 optical focusing systems for intraocular (inside the eye) projection were designed and then modeled with ray tracing software, including a pinhole system, a planoconvex, and an achromatic system. These were then constructed using off-the-shelf components and tested in the laboratory. Weight, size, magnification, depth of focus, image quality and brightness were characterized. Results: Image quality increased with complexity of system design, as did weight and size. A dual achromatic doublet optical system produced the highest image quality. The visual acuity equivalent achieved with this system was better than 20/200. Its weight was less than that of the natural human crystalline lens. Conclusions: We demonstrate for the first time that high quality images can be produced by optical systems sufficiently small and light to be implanted within the eye.

Keywords: focusing, projection, blindness, cornea , achromatic, pinhole

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Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda

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This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.

Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure

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Authors: Zoubida Mahi

Abstract:

A successful satellite mission in space requires a reliable attitude and orbit control system to command, control and position the satellite in appropriate orbits. Several sensors are used for attitude control, such as magnetic sensors, earth sensors, horizon sensors, gyroscopes, and solar sensors. The star tracker is the most accurate sensor compared to other sensors, and it is able to offer high-accuracy attitude control without the need for prior attitude information. There are mainly three approaches in star sensor research: digital simulation, hardware in the loop simulation, and field test of star observation. In the digital simulation approach, all of the processes are done in software, including star image simulation. Hence, it is necessary to develop star image simulation software that could simulate real space environments and various star sensor configurations. In this paper, we present a new stellar image simulation tool that is used to test and validate the stellar sensor algorithms; the developed tool allows to simulate of stellar images with several types of noise, such as background noise, gaussian noise, Poisson noise, multiplicative noise, and several scenarios that exist in space such as the presence of the moon, the presence of optical system problem, illumination and false objects. On the other hand, we present in this paper a new star extraction algorithm based on a new centroid calculation method. We compared our algorithm with other star extraction algorithms from the literature, and the results obtained show the star extraction capability of the proposed algorithm.

Keywords: star tracker, star simulation, star detection, centroid, noise, scenario

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Authors: Sandro Nikolaishvili

Abstract:

The end of the eleventh and the beginning of the twelfth century was a transitional period for the Byzantine empire as well as for the Caucasus. The empire was struggling for its survival under Alexios I Komnenos while Medieval Georgia was emerging as a dominant player in the Caucasus under Davit IV the Builder. The reigns of these two rulers were periods of renewal and transformation. I aim to compare the imperial image of Alexios I Komnenos with the renewed kingship ideology under Davit IV. I will hypothesize about the possible translation of the Byzantine political culture into the Medieval Georgia.

Keywords: Byzantium, Georgia, imperial, image

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Authors: Aynur Bütün Ayhan, Utku Beyazıt

Abstract:

Adolescents can be heavily influenced by social media content as they develop their identities and body images. Therefore, the intensive use of social media platforms may have important effects on their body image beliefs. In this context, the objective of the present study was to assess the relationship between adolescents' social media use and their body image concerns. The study included 265 adolescents (133 girls and 132 boys) between the ages of 15 and 17 who were attending a high school in Ankara, Türkiye. In the study, the adolescents were administered the Social Media Addiction Scale to assess their level of social media use and the Social Appearance Anxiety Scale to assess their social appearance anxiety. Prior to analysis, a normality test was applied, and it was determined that the data displayed a non-parametric distribution. As a result, a significant positive relationship (r=.322, p<.01) was found between adolescents' level of social use and social appearance anxiety. It was also determined that social media addiction and social appearance anxiety significantly differed (p<.05) according to adolescents' opinions about their own bodies, being influenced by body images they see on social media and weight perceptions. The findings suggest that social media use should be managed carefully for adolescents to develop a healthy body image.

Keywords: social media, adolescent, social appearence, anxiety

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