Search results for: multiple conditions diagnosis

Authors: Brittany L. Kang

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One familiar theory of the origin of multiple personalities focuses on how symptoms of trauma or abuse are central causes, as seen in paradigmatic examples of the condition. The theory states that multiple personalities constitute a congenital condition, as babies all exhibit multiplicity, and that generally alters only remain separated due to trauma. In more typical cases, the alters converge and become a single identity; only in cases of trauma, according to this account, do the alters remain separated. This theory is misleading in many aspects, the most prominent being that not all multiple personality patients are victims of child abuse or trauma, nor are all cases of multiple personality observed in early childhood. The use of this criterion also causes clinical problems, including an inability to identify multiple personalities through the variety of symptoms and traits seen across observed cases. These issues present a need for revision in the currently applied criterion in order to separate the notion of child abuse and to be able to better understand the origins of multiple personalities itself. Identifying multiplicity through the application of identity theories will improve the current criterion, offering a bridge between identifying existing cases and understanding their origins. We begin by applying arguments from Wiggins, who held that each personality within a multiple was not a whole individual, but rather characters who switch off. Wiggins’ theory is supported by observational evidence of how such characters are differentiated. Alters of older ages are seen to require different prescription lens, in addition to having different handwriting. The alters may also display drastically varying styles of clothing, preferences in food, their gender, sexuality, religious beliefs and more. The definitions of terms such as 'personality' or 'persons' also become more distinguished, leading to greater understanding of who is exactly able to be classified as a patient of multiple personalities. While a more common meaning of personality is a designation of specific characteristics which account for the entirety of a person, this paper argues from Wiggins’ theory that each 'personality' is in fact only partial. Clarification of the concept in question will allow for more successful future clinical applications.

Keywords: identification, multiple personalities, origin, Wiggins' theory

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Shu-Min Tsao, Chung-Ming Lo, Shao-Chun Chen

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Most diabetes patients tend to suffer from its complication of retina diseases. Therefore, early detection and early treatment are important. In clinical examinations, using color fundus image was the most convenient and available examination method. According to the exudates appeared in the retinal image, the status of retina can be confirmed. However, the routine screening of diabetic retinopathy by color fundus images would bring time-consuming tasks to physicians. This study thus proposed a computer-aided exudate diagnosis for the screening of diabetic retinopathy. After removing vessels and optic disc in the retinal image, six quantitative features including region number, region area, and gray-scale values etc… were extracted from the remaining regions for classification. As results, all six features were evaluated to be statistically significant (p-value < 0.001). The accuracy of classifying the retinal images into normal and diabetic retinopathy achieved 82%. Based on this system, the clinical workload could be reduced. The examination procedure may also be improved to be more efficient.

Keywords: computer-aided diagnosis, diabetic retinopathy, exudate, image processing

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Authors: Saeed Pourhassan, Nastaran Maghbouli

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1) Background It has been hypothesized that, in individuals with diabetes mellitus, the peripheral nerve is swollen due to sorbitol over-accumulation. Additionally growing evidence supported electro diagnostic study of diabetes induced neuropathy as a method having some challenges. 2) Objective To examine the performance of sonographic cross-sectional area (CSA) measurements in the diagnosis of diabetic polyneuropathy (DPN). 3) Data Sources Electronic databases, comprising PubMed and EMBASE and Google scholar, were searched for the appropriate studies before Jan 1, 2020. 4) Study Selection Eleven trials comparing different peripheral nerve CSA measurements between participants with and without DPN were included. 5) Data Extraction Study design, participants' demographic characteristics, diagnostic reference of DPN, and evaluated peripheral nerves and methods of CSA measurement. 6) Data Synthesis Among different peripheral nerves, Tibial nerve diagnostic odds ratios pooled from five studies (713 participants) were 4.46 (95% CI, 0.35–8.57) and the largest one with P<0.0001, I²:64%. Median nerve CSA at wrist and mid-arm took second and third place with ORs= 2.82 (1.50-4.15), 2.02(0.26-3.77) respectively. The sensitivities and specificities pooled from two studies for Sural nerve were 0.78 (95% CI, 0.68–0.89), and 0.68 (95% CI, 0.53–0.74). Included studies for other nerves were limited to one study. The largest sensitivity was for Sural nerve and the largest specificity was for Tibial nerve. 7) Conclusions The peripheral nerves CSA measured by ultrasound imaging is useful for the diagnosis of DPN and is most significantly different between patients and participants without DPN at the Tibial nerve. Because the Tibial nerve CSA in healthy participants, at various locations, rarely exceeds 24 mm2, this value can be considered as a cutoff point for diagnosing DPN.

Keywords: diabetes, diagnosis, polyneuropathy, ultrasound

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Nada Slimane, Foued Theljani, Faouzi Bouani

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The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark.

Keywords: clustering, diagnosis, Kalman Filtering, k-means, regularized regression

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Authors: Tawfig Eltaif, Hesham A. Bakarman, N. Alsowaidi, M. R. Mokhtar, Malek Harbawi

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In Commonly, it is primary problem that there is multiple user interference (MUI) noise resulting from the overlapping among the users in optical code-division multiple access (OCDMA) system. In this article, we aim to mitigate this problem by studying an interference cancellation scheme called successive interference cancellation (SIC) scheme. This scheme will be tested on two different detection schemes, spectral amplitude coding (SAC) and direct detection systems (DS), using partial modified prime (PMP) as the signature codes. It was found that SIC scheme based on both SAC and DS methods had a potential to suppress the intensity noise, that is to say, it can mitigate MUI noise. Furthermore, SIC/DS scheme showed much lower bit error rate (BER) performance relative to SIC/SAC scheme for different magnitude of effective power. Hence, many more users can be supported by SIC/DS receiver system.

Keywords: optical code-division multiple access (OCDMA), successive interference cancellation (SIC), multiple user interference (MUI), spectral amplitude coding (SAC), partial modified prime code (PMP)

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Authors: M. I. Youssef, A. E. Emam, M. Abd Elghany

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The performance of wireless communication systems is affected mainly by the environment of its associated channel, which is characterized by dynamic and unpredictable behavior. In this paper, different statistical earth-satellite channel models are studied with emphasize on two main models, first is the Rice-Log normal model, due to its representation for the environment including shadowing and multi-path components that affect the propagated signal along its path, and a three-state model that take into account different fading conditions (clear area, moderate shadow and heavy shadowing). The provided models are based on AWGN, Rician, Rayleigh, and log-normal distributions were their Probability Density Functions (PDFs) are presented. The transmission system Bit Error Rate (BER), Peak-Average-Power Ratio (PAPR), and the channel capacity vs. fading models are measured and analyzed. These simulations are implemented using MATLAB tool, and the results had shown the performance of transmission system over different channel models.

Keywords: fading channels, MIMO communication, RNS scheme, statistical modeling

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Authors: Jae-Hyun Ro, Jong-Kwang Kim, Chang-Hee Kang, Hyoung-Kyu Song

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In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, QR decomposition-M algorithm (QRD-M) has suboptimal error performance. However, the QRD-M has still high complexity due to many calculations at each layer in tree structure. To reduce the complexity of the QRD-M, proposed QRD-M modifies existing tree structure by eliminating unnecessary candidates at almost whole layers. The method of the elimination is discarding the candidates which have accumulated squared Euclidean distances larger than calculated threshold. The simulation results show that the proposed QRD-M has same bit error rate (BER) performance with lower complexity than the conventional QRD-M.

Keywords: complexity, MIMO-OFDM, QRD-M, squared Euclidean distance

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Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

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Authors: Vipin Saini, Sunil Kamboj, Suman Bala, A. Pandurangan

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The present research is aimed at fabrication of multiple-unit controlled-release tablet formulation of aceclofenac by employing acrylic polymers as the release controlling excipients for drug multi-particulates to achieve the desired objectives of maintaining the same controlled release characteristics as that prior to their compression into tablet. Various manufacturers are successfully manufacturing and marketing aceclofenac controlled release tablet by applying directly coating materials on the tablet. The basic idea behind development of such formulations was to employ aqueous acrylics polymers dispersion as an alternative to the existing approaches, wherein the forces of compression may cause twist of drug pellets, but do not have adverse effects on the drug release properties. Thus, the study was undertaken to illustrate manufacturing of controlled release aceclofenac multiple-unit tablet formulation.

Keywords: aceclofenac, multiple-unit tablets, acrylic polymers, controlled-release

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Authors: E. Sobhani-Tehrani, K. Khorasani, N. Meskin

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This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPE) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. Two NPE structures including series-parallel and parallel are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the NPEs to systems with partial-state measurement.

Keywords: hybrid fault diagnosis, dynamic neural networks, nonlinear systems, fault tolerant observer

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Authors: Ali Assalmi

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This study aimed to compare the yield of two varieties of potato seeds under different water conditions. In the first part of the study, we conducted a literature review to gather academic research published on the two varieties. Based on the literature review, we optimized the water conditions for one variety and tested the other variety under high salinity water conditions. Our findings indicate that the optimized water conditions resulted in a very good yield for one variety of potato seeds. However, under high salinity water conditions, the other variety produced a higher yield in water that was not used due to the high salinity. Overall, our results suggest that the yield of potato seeds can vary significantly based on the water conditions and variety.

Keywords: potatoes, seed varieties, water optimization, high salinity, yield comparison

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Authors: Milena Trojanowska

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Disability is a phenomenon for which meanings and definitions have evolved over the decades. This became the trigger to start a project to answer the question of what disability constitutes in the course of an incurable chronic disease. The chosen research group are people living with multiple sclerosis.The contextual phase of the research was participant observation at the Polish Multiple Sclerosis Society, the largest NGO in Poland supporting people living with MS and their relatives. The research techniques used in the project are (in order of implementation): group interviews with people living with MS and their relatives, narrative interviews, asynchronous technique, participant observation during events organised for people living with MS and their relatives.The researcher is currently conducting follow-up interviews, as inaccuracies in the respondents' narratives were identified during the data analysis. Interviews and supplementary research techniques were used over the four years of the research, and the researcher also benefited from experience gained from 12 years of working with NGOs (diaries, notes). The research was carried out in Poland with the participation of people living in this country only.The research has been based on grounded theory methodology in a constructivist perspectivedeveloped by Kathy Charmaz. The goal was to follow the idea that research must be reliable, original, and useful. The aim was to construct an interpretive theory that assumes temporality and the processualityof social life. TheAtlas.ti software was used to collect research material and analyse it. It is a program from the CAQDAS(Computer-Assisted Qualitative Data Analysis Software) group.Several key factors influencing the construction of a disability identity by people living with multiple sclerosis was identified:-course of interaction with significant relatives,- the expectation of identification with disability (expressed by close relatives),- economic profitability (pension, allowances),- institutional advantages (e.g. parking card),- independence and autonomy (not equated with physical condition, but access to adapted infrastructure and resources to support daily functioning),- the way a person with MS construes the meaning of disability,- physical and mental state,- medical diagnosis of illness.In addition, it has been shown that making an assumption about the experience of disability in the course of MS is a form of cognitive reductionism leading to further phenomenon such as: the expectation of the person with MS to construct a social identity as a person with a disability (e.g. giving up work), the occurrence of institutional inequalities. It can also be a determinant of the choice of a life strategy that limits social and individual functioning, even if this necessity is not influenced by the person's physical or psychological condition.The results of the research are important for the development of knowledge about the phenomenon of disability. It indicates the contextuality and complexity of the disability phenomenon, which in the light of the research is a set of different phenomenon of heterogeneous nature and multifaceted causality. This knowledge can also be useful for institutions and organisations in the non-governmental sector supporting people with disabilities and people living with multiple sclerosis.

Keywords: disability, multiple sclerosis, grounded theory, poland

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Authors: Dwitya Elvira, Raveinal Masri, Rohayat Bilmahdi

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Human Immunodeficiency Virus (HIV) pandemic increased significantly worldwide. The rise in cases of HIV/AIDS was also followed by an increase in the incidence of opportunistic infection, with tuberculosis being the most opportunistic infection found in HIV/AIDS and the main cause of mortality in HIV/AIDS patients. Diagnosis of tuberculosis in HIV/AIDS patients is often difficult because of the uncommon symptom in HIV/AIDS patients compared to those without the disease. Thus, diagnostic tools are required that are more effective and efficient to diagnose tuberculosis in HIV/AIDS. CXCL-10/IP-10 is a chemokine that binds to the CXCR3 receptor found in HIV/AIDS patients with a weakened immune system. Tuberculosis infection in HIV/AIDS activates chemokine IP-10 in urine, which is used as a marker for diagnosis of infection. The aim of this study was to prove whether IP-10 urine can be a biomarker diagnosis of active lung tuberculosis in HIV-AIDS patients. Design of this study is a cross sectional study involving HIV/AIDS patients with lung tuberculosis as the subject of this study. Forty-seven HIV/AIDS patients with tuberculosis based on clinical and biochemical laboratory were asked to collect urine samples and IP-10/CXCL-10 urine being measured using ELISA method with 18 healthy human urine samples as control. Forty-seven patients diagnosed as HIV/AIDS were included as a subject of this study. HIV/AIDS were more common in male than in women with the percentage in male 85.1% vs. 14.5% of women. In this study, most diagnosed patients were aged 31-40 years old, followed by those 21-30 years, and > 40 years old, with one case diagnosed at age less than 20 years of age. From the result of the urine IP-10 using ELISA method, there was significant increase of the mean value of IP-10 urine in patients with TB-HIV/AIDS co-infection compared to the healthy control with mean 61.05 pg/mL ± 78.01 pg/mL vs. mean 17.2 pg/mL. Based on this research, there was significant increase of urine IP-10/CXCL-10 in active lung tuberculosis with HIV/AIDS compared to the healthy control. From this finding, it is necessary to conduct further research into whether urine IP-10/CXCL-10 plays a significant role in TB-HIV/AIDS co-infection, which can also be used as a biomarker in the early diagnosis of TB-HIV.

Keywords: chemokine, HIV/AIDS, IP-10 urine, tuberculosis

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Authors: Vladimir Veremey

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The paper describes the design and simulation of dual-polarization antennas that use the resonance and radiating properties of the H₀₀ mode of metal open waveguides. The proposed antennas are formed by two orthogonal slots in a finite conducting ground plane. The slots are backed by metal screens connected to the ground plane forming open waveguides. It has been shown that the antenna designs can be efficiently used in mm-wave bands. The antenna single mode operational bandwidth is higher than 10%. The antenna designs are very simple and low-cost. They allow flush installation and can be efficiently used in various communication and remote sensing devices on fast moving carriers. Mutual coupling between antennas of the proposed design is very low. Thus, multiple antenna structures with proposed antennas can be efficiently employed in multi-band and in multiple-input-multiple-output (MIMO) systems.

Keywords: antenna, antenna arrays, Multiple-Input-Multiple-Output (MIMO), millimeter wave bands, slot antenna, flush installation, directivity, open waveguide, conformal antennas

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: I. Dumand, C. Clesse, M. Decker, C. Savini, J. Lighezzolo-Alnot

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In the landscape of French psychiatry, long-term institutionalization of patients with severe and disabling chronics disorders is common. Faced with the failures of classical reinsertion, sometimes these users are hurriedly considered as 'insortables'. However, this representation is often swayed by the current behavior of the patient observed through the clinical observation. Unfortunately, it seems that this way of proceeding can not integrate the potentialities of the institutionalized patients and their possible evolution. Therefore, in order not to make hasty conclusions about the life perspectives of these individuals, it seems essential to associate with clinical observation a psycho social rehabilitation diagnosis. Multidisciplinary, it combine all the aspects that make up the life of the subject (the life aspirations, psycho social determinants, family support, cognitive potential, symptoms ...). In this paper, we will rank these different aspects necessary prerequisites to the realization of a psycho social rehabilitation diagnosis. Then, we will specifically speak of the issue of psychological evaluation. By adopting an integrative approach combining neuro psychological tools (Grober and Buschke, Stroop, WCST, AIPSS, WAIS, Eyes test ...) and projective tools interpreted under a psycho dynamic angle (Rorschach, TAT ..) we think that we can grasp the patient in his globality. Thus, during this process we will justify the interest of combining a cognitive and a psycho affective approach, we will identify the different items assessed and their future implications on the everyday life of the users. Finally, we show that this diagnosis can give a chance to reintegration to 30% of patients considered as ''insortables''. In conclusion, we will highlight the importance of this process dear to the community psychology emphasizing in the same time the interests of this approach in terms of empowerment, recovery and quality of life.

Keywords: assessment, potentiality, psychosocial rehabilitation diagnosis, tools

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Authors: Ligia Florio, João Mauricio Castaldelli-Maia

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Background: Food addiction (FA) emerged in the 1990s as a possible contributor to the increasing prevalence of obesity and overweight, in conjunction with changing food environments and mental health conditions. However, FA is not yet listed as one of the disorders in the DSM-5 and/or the ICD-11. Although there are controversies and debates in the literature about the classification and construct of FA, the most common approach to access it is the use of a research tool - the Yale Food Addiction Scale (YFAS) - which approximates the concept of FA to the concept diagnosis of dependence on psychoactive substances. There is a need to explore the dimensional phenotypes accessed by YFAS in different population groups for a better understanding and scientific support of FA diagnoses. Methods: The primary objective of this project was to investigate the construct validity of the FA concept by mYFAS 2.0 in individuals who underwent bariatric surgery (n = 100) at the Hospital Estadual Mário Covas since 2011. Statistical analyzes were conducted using the STATA software. In this sense, structural or factor validity was the type of construct validity investigated using exploratory factor analysis (EFA) and item response theory (IRT) techniques. Results: EFA showed that the one-dimensional model was the most parsimonious. The IRT showed that all criteria contributed to the latent structure, presenting discrimination values greater than 0.5, with most presenting values greater than 2. Conclusion: This study reinforces a FA dimension in patients who underwent bariatric surgery. Within this dimension, we identified the most severe and discriminating criteria for the diagnosis of FA.

Keywords: obesity, food addiction, bariatric surgery, regain

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Authors: Tetsiki Taniguchi, Yoshio Karasawa

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This paper describes a subarray based low computational design method of multiuser massive multiple input multiple output (MIMO) system. In our previous works, use of large array is assumed only in transmitter, but this study considers the case both of transmitter and receiver sides are equipped with large array antennas. For this aim, receive arrays are also divided into several subarrays, and the former proposed method is modified for the synthesis of a large array from subarrays in both ends. Through computer simulations, it is verified that the performance of the proposed method is degraded compared with the original approach, but it can achieve the improvement in the aspect of complexity, namely, significant reduction of the computational load to the practical level.

Keywords: large array, massive multiple input multiple output (MIMO), multiuser, singular value decomposition, subarray, zero forcing

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Authors: Amir Moslehi, Gholamreza Raisali

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Microdosimetric detectors based on multiple-thick gas electron multiplier (multiple-THGEM) configurations are being used in various fields of radiation protection and dosimetry. In the present work, microdosimetric response of these detectors to fast neutrons has been investigated by Monte Carlo method. Three similar microdosimeters made of A-150 and rexolite as the wall materials are designed; the first based on single-THGEM, the second based on double-THGEM and the third is based on triple-THGEM. Sensitive volume of the three microdosimeters is a right cylinder of 5 mm height and diameter which is filled with the propane-based tissue-equivalent (TE) gas. The TE gas with 0.11 atm pressure at the room temperature simulates 1 µm of tissue. Lineal energy distributions for several neutron energies from 10 keV to 14 MeV including 241Am-Be neutrons are calculated by the Geant4 simulation toolkit. Also, mean quality factor and dose-equivalent value for any neutron energy has been determined by these distributions. Obtained data derived from the three microdosimeters are in agreement. Therefore, we conclude that the multiple-THGEM structures present similar microdosimetric responses to fast neutrons.

Keywords: fast neutrons, geant4, multiple-thick gas electron multiplier, microdosimeter

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Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

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One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

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Authors: M. Zamurad Shah, M. Kemal Ozgoren, Raza Samar

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This paper presents a 3D guidance scheme for Unmanned Aerial Vehicles (UAVs). The proposed guidance scheme is based on the sliding mode approach using nonlinear sliding manifolds. Generalized 3D kinematic equations are considered here during the design process to cater for the coupling between longitudinal and lateral motions. Sliding mode based guidance scheme is then derived for the multiple-input multiple-output (MIMO) system using the proposed nonlinear manifolds. Instead of traditional sliding surfaces, nonlinear sliding surfaces are proposed here for performance and stability in all flight conditions. In the reaching phase control inputs, the bang-bang terms with signum functions are accompanied with proportional terms in order to reduce the chattering amplitudes. The Proposed 3D guidance scheme is implemented on a 6-degrees-of-freedom (6-dof) simulation of a UAV and simulation results are presented here for different 3D trajectories with and without disturbances.

Keywords: unmanned aerial vehicles, sliding mode control, 3D guidance, nonlinear sliding manifolds

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Authors: Rana F. Obeidat, Suzanne S. Dickerson, Gregory G. Homish, Nesreen M. Alqaissi, Robin M. Lally

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Background: Despite the fact that breast cancer is the most prevalent cancer among Jordanian women, practically nothing is known about their perceptions of early stage breast cancer and surgical treatment. Objective: To gain understanding of the diagnosis and surgical treatment experience of Jordanian women diagnosed with early stage breast cancer. Methods: An interpretive phenomenological approach was used for this study. A purposive sample of 28 Jordanian women who were surgically treated for early stage breast cancer within 6 months of the interview was recruited. Data were collected using individual interviews and analyzed using Heideggerian hermeneutical methodology. Results: Fear had a profound effect on Jordanian women’s stories of diagnosis and surgical treatment of early stage breast cancer. Women’s experience with breast cancer and its treatment was shaped by their pre-existing fear of breast cancer, the disparity in the quality of care at various health care institutions, and sociodemographic factors (e.g., education, age). Conclusions: Early after the diagnosis, fear was very strong and women lost perspective of the fact that this disease was treatable and potentially curable. To control their fears, women unconditionally trusted God, the health care system, surgeons, family, friends, and/or neighbors, and often accepted treatment offered by their surgeons without questioning. Implications for practice: Jordanian healthcare providers have a responsibility to listen to their patients, explore meanings they ascribe to their illness, and provide women with proper education and support necessary to help them cope with their illness.

Keywords: breast cancer, early stage, Jordanian, experience, phenomenology

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Authors: Viacheslav Sushko, Viktor Sushko

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Pre-cancerous breast diseases are pathological changes that precede the appearance of adenocarcinoma. The most common benign breast disease is mastopathy. We examined the life and disease history of 114 women aged 58-69 who were diagnosed with adenocarcinoma of the breast at different stages of development. They filled out the Reeder Scale to determine the level of stress. The results of the study revealed that 62 of them had mastopathy at the age of 30-45 years old. These women refused surgical treatment for mastopathy. Five to six years before their diagnosis of adenocarcinoma of the mammary gland, 84 women had experienced severe stress (death of a beloved close relative, torture accompanied by rape, prolonged stay in extreme conditions (under bombardment and bombardment). In the assessment of data from completed Reeder scales, 114 women had a high level of mental stress, with a score from 1-1.72. The 84 women who suffered from severe stress showed overeating or a significant decrease in food intake, insomnia, apathy, increased irritability and restlessness, loss of interest in sexual relationships, forgetfulness, difficulty in performing routine work, prolonged uncontrollable headaches, unexplained fatigue, heart pain, reduced capacity for work. In conclusion, it is important to provide psychotherapy for breast cancer patients as the diagnosis, and the different stages of treatment are very stressful. It is also advisable to see a psychiatrist at an early stage and prevent distress and treat precancerous breast disease.

Keywords: breast cancer, distress, mastopathy, severe stress

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Authors: Prateek Rastogi, Jenash Acharya

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Histopathology examination has been a boon to forensic experts all around the world since its implication in autopsy cases. Whenever a case of sudden death is encountered, forensic experts clandestinely focus on cardiovascular, respiratory, gastrointestinal or cranio-cerebral causes. After ruling out poisoning or trauma, they are left with the only option available, histopathology examination. Besides preserving thoracic and abdominal organs, brain tissues are very less frequently subjected for the analysis. Based on provisional diagnosis documented on hospital treatment record files, one hemisphere of grossly unremarkable cerebrum was confirmatively diagnosed by histopathology examination to be a case of cerebral toxoplasmosis.

Keywords: cerebral toxoplasmosis, sudden death, health information, histopathology

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Authors: Nayer Mofidtabatabaei

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Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

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Authors: Deena Moustafa

Abstract:

The purpose of this paper is to describe the sources of parenting stress in mothers of Dual Diagnosis children (n =60) and examine the relationship between parenting stress and maternal psychological status (depression and well-being), also examine if there is any difference between the previous variables in different disabilities associated with Autism. A descriptive correlational design was used. Data were collected via online questionnaires. The study finds that there was no significant relationship between Autism Parenting Stress Index (APSI) scores and types of disability which associated with Autism, although Mothers with deaf autistic reported more parenting stress, Similar findings were found regarding Depressive Symptoms, as there was no significant relationship between (CESD-R) scores and types of disability which associated with Autism, also study finds that there was a significant correlation of the (APSI) with the (CESD-R) Mothers with higher overall parenting stress reported more depressive symptoms. Likewise, there was also a significant correlation between the (APSI) and the (RPWB) Mothers reporting more parenting stress also reported lower levels of well-being.

Keywords: parenting stress, maternal psychological statues, mothers of dual diagnosis, autism

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Authors: Manahil Alfuraydan, Jodie Croxall, Lisa Hurt, Mike Kerr, Sinead Brophy

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Autism Spectrum Disorder (ASD) is a developmental condition characterised by impairment in terms of social communication, social interaction, and a repetitive or restricted pattern of interest, behaviour, and activity. There is a significant delay between seeking help and a confirmed diagnosis of ASD. This may result in delay in receiving early intervention services, which are critical for positive outcomes. The long wait times also cause stress for the individuals and their families. Telehealth potentially offers a way of improving the diagnostic pathway for ASD. This review of the literature aims to examine which telehealth approaches have been used in the diagnosis and assessment of autism in children and adults, whether they are feasible and acceptable, and how they compare with face-to-face diagnosis and assessment methods. A comprehensive search of following databases- MEDLINE, CINAHL Plus with Full text, Business Sources Complete, Web of Science, Scopus, PsycINFO and trail and systematic review databases including Cochrane Library, Health Technology Assessment, Database of Abstracts and Reviews of Effectiveness and NHS Economic Evaluation was conducted, combining the terms of autism and telehealth from 2000 to 2018. A total of 10 studies were identified for inclusion in the review. This review of the literature found there to be two methods of using telehealth: (a) video conferencing to enable teams in different areas to consult with the families and to assess the child/adult in real time and (b) a video upload to a web portal that enables the clinical assessment of behaviours in the family home. The findings were positive, finding there to be high agreement in terms of the diagnosis between remote methods and face to face methods and with high levels of satisfaction among the families and clinicians. This field is in the very early stages, and so only studies with small sample size were identified, but the findings suggest that there is potential for telehealth methods to improve assessment and diagnosis of autism used in conjunction with existing methods, especially for those with clear autism traits and adults with autism. Larger randomised controlled trials of this technology are warranted.

Keywords: assessment, autism spectrum disorder, diagnosis, telehealth

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Authors: T. Damak, S. Houidi, M. A. Ben Ayed, N. Masmoudi

Abstract:

The Versatile Video Coding standard (VVC) is actually under development by the Joint Video Exploration Team (or JVET). An Adaptive Multiple Transforms (AMT) approach was announced. It is based on different transform modules that provided an efficient coding. However, the AMT solution raises several issues especially regarding the complexity of the selected set of transforms. This can be an important issue, particularly for a future industrial adoption. This paper proposed an efficient hardware implementation of the most used transform in AMT approach: the DCT II. The developed circuit is adapted to different block sizes and can reach a minimum frequency of 192 MHz allowing an optimized execution time.

Keywords: adaptive multiple transforms, AMT, DCT II, hardware, transform, versatile video coding, VVC

Procedia PDF Downloads 121