Search results for: perceptual metrics

Authors: E. Olmos-Raya, A. Cascales Martínez, N. Minto de Sousa, M. Alcañiz Raya

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The late detection and subjectivity of the assessment of Autism Spectrum Disorder (ASD) imposed a difficulty for the children’s clinical and familiar environment. The results showed in this paper, are part of a research project about the assessment and training of social skills in children with ASD, whose overall goal is the use of virtual environments together with physiological measures in order to find a new model of objective ASD assessment based on implicit brain processes measures. In particular, this work tries to contribute by studying the differences and changes in the Skin Conductance Response (SCR) and Eye Tracking (ET) between a typical development group (TD group) and an ASD group (ASD group) after several combined stimuli using a low cost Immersive Virtual Environment (IVE). Subjects were exposed to a virtual environment that showed natural scenes that stimulated visual, auditory and olfactory perceptual system. By exposing them to the IVE, subjects showed natural behaviors while measuring SCR and ET. This study compared measures of subjects diagnosed with ASD (N = 18) with a control group of subjects with typical development (N=10) when exposed to three different conditions: only visual (V), visual and auditory (VA) and visual, auditory and olfactory (VAO) stimulation. Correlations between SCR and ET measures were also correlated with the Autism Diagnostic Observation Schedule (ADOS) test. SCR measures showed significant differences among the experimental condition between groups. The ASD group presented higher level of SCR while we did not find significant differences between groups regarding DF. We found high significant correlations among all the experimental conditions in SCR measures and the subscale of ADOS test of imagination and symbolic thinking. Regarding the correlation between ET measures and ADOS test, the results showed significant relationship between VA condition and communication scores.

Keywords: autism, electrodermal activity, eye tracking, immersive virtual environment, virtual reality

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Authors: Edgard Barki, Tânia Modesto Veludo-de-Oliveira, Felipe Zambaldi

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The social enterprise sector can be characterized as organizations that aim to solve social problems with financial sustainability and using market mechanisms. This sector has shown an increasing interest worldwide. Despite the growth and relevance of the sector, there is still a gap regarding the assessment of the social impact resulting from the initiatives of the organizations in this field. A number of metrics have been designed worldwide to evaluate the impact of social enterprises (e.g., IRIS, GIIRS, BACO), as well as some ad hoc studies that have been carried out, mainly in the microcredit sector, but there is still a gap to be filled in the development of research in social impact evaluation. Therefore, this research seeks to evaluate the social impact of two social enterprises (Terra Nova and Vivenda) in the area of housing in Brazil. To evaluate these impacts and their dimensions, we conducted an exploratory research, through three focus groups, thirty in-depth interviews and a survey with beneficiaries of both organizations. The results allowed us to evaluate how the two organizations were able to create a deep social impact in the populations served. Terra Nova has a more collective perspective, with a clear benefit of social inclusion and improvement of the community’s infrastructure, while Vivenda has a more individualized perspective, improving self-esteem, sociability and family coexistence.

Keywords: Brazil, housing, social enterprise, social impact evaluation

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Authors: Ozgu Hafizoglu

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An analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with physical, behavioral, principal relations that are essential to learning, discovery, and innovation. The Cognitive Model of Analogy (CMA) leads and creates patterns of pathways to transfer information within and between domains in science, just as happens in the brain. The connectome of the brain shows how the brain operates with mental leaps between domains and mental hops within domains and the way how analogical reasoning mechanism operates. This paper demonstrates the CMA as an evolutionary approach to science, technology, and life. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions in the new era, especially post-pandemic. In this paper, we will reveal how to draw an analogy to scientific research to discover new systems that reveal the fractal schema of analogical reasoning within and between the systems like within and between the brain regions. Distinct phases of the problem-solving processes are divided thusly: stimulus, encoding, mapping, inference, and response. Based on the brain research so far, the system is revealed to be relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain’s mechanism in macro context; brain and spinal cord, and micro context: glia and neurons, relative to matching conditions of analogical reasoning and relational information, encoding, mapping, inference and response processes, and verification of perceptual responses in four-term analogical reasoning. Finally, we will relate all these terminologies with these mental leaps, mental maps, mental hops, and mental loops to make the mental model of CMA clear.

Keywords: analogy, analogical reasoning, brain connectome, cognitive model, neurons and glia, mental leaps, mental hops, mental loops

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Authors: Safeer Ahmad, Pratheek Sudhakran, M. Arif Kamal, Tarique Anwar

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In Healthcare facility designing, Efficient MEP services are very crucial because the built environment not only affects patients and family but also Healthcare staff and their outcomes. This paper shall cover the basics of Value engineering and its different phases that can be implemented to the MEP Designing stage for Healthcare facility optimization, also VE can improve the product cost the unnecessary costs associated with healthcare services. This paper explores Healthcare facility services and their Value engineering Job plan for the successful application of the VE technique by conducting a Workshop with end-users, designing team and associate experts shall be carried out using certain concepts, tools, methods and mechanism developed to achieve the purpose of selecting what is actually appropriate and ideal among many value engineering processes and tools that have long proven their ability to enhance the value by following the concept of Total quality management while achieving the most efficient resources allocation to satisfy the key functions and requirements of the project without sacrificing the targeted level of service for all design metrics. Detail study has been discussed with analysis been carried out by this process to achieve a better outcome, Various tools are used for the Analysis of the product at different phases used, at the end the results obtained after implementation of techniques are discussed.

Keywords: value engineering, healthcare facility, design, services

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Authors: Venkateswar Pujari

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This study tries to investigate the complex interplay between control, awareness, and attention in human cognitive processes. The fundamental elements of cognitive functioning that play a significant role in influencing perception, decision-making, and behavior are attention, awareness, and control. Understanding how they interact can help us better understand how our minds work and may even increase our understanding of cognitive science and its therapeutic applications. The study uses an empirical methodology to examine the relationships between attention, awareness, and control by integrating different experimental paradigms and neuropsychological tests. To ensure the generalizability of findings, a wide sample of participants is chosen, including people with various cognitive profiles and ages. The study is structured into four primary parts, each of which focuses on one component of how attention, awareness, and control interact: 1. Evaluation of Attentional Capacity and Selectivity: In this stage, participants complete established attention tests, including the Stroop task and visual search tasks. 2. Evaluation of Awareness Degrees: In the second stage, participants' degrees of conscious and unconscious awareness are assessed using perceptual awareness tasks such as masked priming and binocular rivalry tasks. 3. Investigation of Cognitive Control Mechanisms: In the third phase, reaction inhibition, cognitive flexibility, and working memory capacity are investigated using exercises like the Wisconsin Card Sorting Test and the Go/No-Go paradigm. 4. Results Integration and Analysis: Data from all phases are integrated and analyzed in the final phase. To investigate potential links and prediction correlations between attention, awareness, and control, correlational and regression analyses are carried out. The study's conclusions shed light on the intricate relationships that exist between control, awareness, and attention throughout cognitive function. The findings may have consequences for cognitive psychology, neuroscience, and clinical psychology by providing new understandings of cognitive dysfunctions linked to deficiencies in attention, awareness, and control systems.

Keywords: attention, awareness, control, cognitive functioning, neuropsychological assessment

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Authors: Alexander Marx

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Measuring and mitigating market risk is essential for the stability of enterprises, especially for major banking corporations and investment bank firms. To employ these risk measurement and mitigation processes, the Value at Risk (VaR) is the most commonly used risk metric by practitioners. In the past years, we have seen significant weaknesses in the predictive performance of the VaR in times of financial market crisis. To address this issue, the purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on the stock market indices of the G7 countries (Canada, France, Germany, Italy, Japan, UK, US, Europe). The study employs parametric, non-parametric, and semi-parametric VaR estimation models and is conducted during three different periods which cover the most recent financial market crisis: the overall period (2006–2022), the global financial crisis period (2008–2009), and COVID-19 period (2020–2022). Since the regulatory authorities have introduced and mandated the Conditional Value at Risk (Expected Shortfall) as an additional regulatory risk management metric, the study will analyze and compare both risk metrics on their predictive performance.

Keywords: value at risk, financial market risk, banking, quantitative risk management

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Authors: Meghana Shankara, Priyadarshini Natarajan

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Infrared (IR) spectroscopy has revolutionized the way we look at materials around us. Unraveling the pattern in the molecular spectra of materials to analyze the composition and properties of it has been one of the most interesting challenges in modern science. Applications of the IR spectrometry are numerous in the field’s pharmaceuticals, health, food and nutrition, oils, agriculture, construction, polymers, beverage, fabrics and much more limited only by the curiosity of the people. Near Infrared (NIR) spectrometry is applied robustly in analyzing the solids and liquid substances because of its non-destructive analysis method. In this paper, we have reviewed the application of NIR spectrometry in milk quality analysis and have presented the modes of measurement applied in NIRS measurement setup, Design of Experiment (DoE), classification/quantification algorithms used in the case of milk composition prediction like Fat%, Protein%, Lactose%, Solids Not Fat (SNF%) along with different approaches for adulterant identification. We have also discussed the important NIR ranges for the chosen milk parameters. The performance metrics used in the comparison of the various Chemometric approaches include Root Mean Square Error (RMSE), R^2, slope, offset, sensitivity, specificity and accuracy

Keywords: chemometrics, design of experiment, milk quality analysis, NIRS measurement modes

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Authors: Girma Yohannis Bade, Olga Kolesnikova, Grigori Sidorov

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Named entity recognition (NER) is a building block for information extraction. Though the information extraction process has been automated using a variety of techniques to find and extract a piece of relevant information from unstructured documents, the discovery of targeted knowledge still poses a number of research difficulties because of the variability and lack of structure in Web data. NER, a subtask of information extraction (IE), came to exist to smooth such difficulty. It deals with finding the proper names (named entities), such as the name of the person, country, location, organization, dates, and event in a document, and categorizing them as predetermined labels, which is an initial step in IE tasks. This survey paper presents the roles and importance of NER to IE from the perspective of different algorithms and application area domains. Thus, this paper well summarizes how researchers implemented NER in particular application areas like finance, medicine, defense, business, food science, archeology, and so on. It also outlines the three types of sequence labeling algorithms for NER such as feature-based, neural network-based, and rule-based. Finally, the state-of-the-art and evaluation metrics of NER were presented.

Keywords: the role of NER, named entity recognition, information extraction, sequence labeling algorithms, named entity application area

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Authors: Man Wai Lei, Charles Mark Zaroff

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Objective: A majority of studies suggest that people with Autism Spectrum Disorder (ASD) have multisensory integration deficits. However, normal and even supranormal multisensory integration abilities have also been reported. Additionally, little of this work has been undertaken utilizing a dimensional conceptualization of ASD; i.e., a broader autism phenotype. Utilizing methodology that controls for common potential confounds, the current study aimed to examine if deficits in multisensory integration are associated with ASD traits in a non-clinical population. The contribution of affective versus non-affective components of sensory hypersensitivity to multisensory integration was also examined. Methods: Participants were 147 undergraduate university students in Macau, a Special Administrative Region of China, of Chinese ethnicity, aged 16 to 21 (Mean age = 19.13; SD = 1.07). Participants completed the Autism-Spectrum Quotient, the Sensory Perception Quotient, and the Adolescent/Adult Sensory Profile, in order to measure ASD traits, non-affective, and affective aspects of sensory/perceptual hypersensitivity, respectively. In order to explore multisensory integration across visual and haptic domains, participants were asked to judge which one of two equally weighted, but different sized cylinders was heavier, as a means of detecting the presence of the size-weight illusion (SWI). Results: ASD trait level was significantly and negatively correlated with susceptibility to the SWI (p < 0.05); this correlation was not associated with either accuracy in weight discrimination or gender. Examining the top decile of the non-normally distributed SWI scores revealed a significant negative association with sensation avoiding, but not other aspects of effective or non-effective sensory hypersensitivity. Conclusion and Implications: Within the normal population, a greater degree of ASD traits is associated with a lower likelihood of multisensory integration; echoing was often found in individuals with a clinical diagnosis of ASD, and providing further evidence for the dimensional nature of this disorder. This tendency appears to be associated with dysphoric emotional reactions to sensory input.

Keywords: Autism Spectrum Disorder, dimensional, multisensory integration, size-weight illusion

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Authors: Toshitaka Higashino, Naoki Wakamiya

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Human beings perform a task by perceiving information from outside, recognizing them, and responding them. There have been various attempts to analyze and understand internal processes behind the reaction to a given stimulus by conducting psychological experiments and analysis from multiple perspectives. Among these, we focused on Model Human Processor (MHP). However, it was built based on psychological experiments and thus the relation with brain activity was unclear so far. To verify the validity of the MHP and propose our model from a viewpoint of neuroscience, EEG (Electroencephalography) measurements are performed during experiments in this study. More specifically, first, experiments were conducted where Latin alphabet characters were used as visual stimuli. In addition to response time, ERPs (event-related potentials) such as N100 and P300 were measured by using EEG. By comparing cycle time predicted by the MHP and latency of ERPs, it was found that N100, related to perception of stimuli, appeared at the end of the perceptual processor. Furthermore, by conducting an additional experiment, it was revealed that P300, related to decision making, appeared during the response decision process, not at the end. Second, by experiments using Japanese Hiragana characters, i.e. Japan's own phonetic symbols, those findings were confirmed. Finally, Japanese Kanji characters were used as more complicated visual stimuli. A Kanji character usually has several readings and several meanings. Despite the difference, a reading-related task and a meaning-related task exhibited similar results, meaning that they involved similar information processing processes of the brain. Based on those results, our model was proposed which reflects response time and ERP latency. It consists of three processors: the perception processor from an input of a stimulus to appearance of N100, the cognitive processor from N100 to P300, and the decision-action processor from P300 to response. Using our model, an application system which reflects brain activity can be established.

Keywords: brain activity, EEG, information processing model, model human processor

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Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

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In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

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Authors: Melissa James Maceachern

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This study examines a financial aid program that is designed to “keep residents at home” to attend higher education by providing financial aid as an incentive or discount in their first year of university following high school graduation. This study offers insight into financial matters for higher education students that can assist in providing policy direction for student financing. In particular, this study found that students appeared to value the bursary but none of the key metrics related to participation or conversion to the home institution indicated that the bursary impacted enrolment or participation. One key metric, student loans received by direct entry high school students did indicate a decline in the number of recipients. This study also identified accessibility issues to higher education that are of importance when considering the declining youth populations, future labour market needs and the need to sustain higher education institutions. This is undoubtedly a challenging period of time given the changing social and demographic forces within Canada. A comprehensive examination of the policy and programs to address these forces needs to be undertaken. This study highlights the importance of utilizing financial aid in combination with other policy to assist students in accessing higher education.

Keywords: accessibility, participation, financing, government

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Authors: R. Gautam, P. Sastha Kanagasabai, G. N. Rathna

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The proposed healthcare system enables quadriplegic patients, people with severe motor disabilities to send commands to electronic devices and monitor their vitals. The growth of Brain-Computer-Interface (BCI) has led to rapid development in 'assistive systems' for the disabled called 'assistive domotics'. Brain-Computer-Interface is capable of reading the brainwaves of an individual and analyse it to obtain some meaningful data. This processed data can be used to assist people having speech disorders and sometimes people with limited locomotion to communicate. In this Project, Emotiv EPOC Headset is used to obtain the electroencephalogram (EEG). The obtained data is processed to communicate pre-defined commands over the internet to the desired mobile phone user. Other Vital Information like the heartbeat, blood pressure, ECG and body temperature are monitored and uploaded to the server. Data analytics enables physicians to scan databases for a specific illness. The Data is processed in Intel Edison, system on chip (SoC). Patient metrics are displayed via Intel IoT Analytics cloud service.

Keywords: brain computer interface, Intel Edison, Emotiv EPOC, IoT analytics, electroencephalogram

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Abderrazek Ben Maatoug, Ibrahim Fatnassi, Wassim Ben Ayed

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In this paper we investigate the issue of market risk quantification for Middle East and North Africa (MENA) Islamic market equity. We use Value-at-Risk (VaR) as a measure of potential risk in Islamic stock market, for long and short position, based on Riskmetrics model and the conditional parametric ARCH class model volatility with normal, student and skewed student distribution. The sample consist of daily data for the 2006-2014 of 11 Islamic stock markets indices. We conduct Kupiec and Engle and Manganelli tests to evaluate the performance for each model. The main finding of our empirical results show that (i) the superior performance of VaR models based on the Student and skewed Student distribution, for the significance level of α=1% , for all Islamic stock market indices, and for both long and short trading positions (ii) Risk Metrics model, and VaR model based on conditional volatility with normal distribution provides the best accurate VaR estimations for both long and short trading positions for a significance level of α=5%.

Keywords: value-at-risk, risk management, islamic finance, GARCH models

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Authors: Latha Subbiah, Dhanalakshmi Samiappan

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In this paper, we propose denoising Common Carotid Artery (CCA) B mode ultrasound images by a decomposition approach to curvelet thresholding and automatic segmentation of the intima media thickness and adventitia boundary. By decomposition, the local geometry of the image, its direction of gradients are well preserved. The components are combined into a single vector valued function, thus removes noise patches. Double threshold is applied to inherently remove speckle noise in the image. The denoised image is segmented by active contour without specifying seed points. Combined with level set theory, they provide sub regions with continuous boundaries. The deformable contours match to the shapes and motion of objects in the images. A curve or a surface under constraints is developed from the image with the goal that it is pulled into the necessary features of the image. Region based and boundary based information are integrated to achieve the contour. The method treats the multiplicative speckle noise in objective and subjective quality measurements and thus leads to better-segmented results. The proposed denoising method gives better performance metrics compared with other state of art denoising algorithms.

Keywords: curvelet, decomposition, levelset, ultrasound

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Authors: Abebe Mulugojam Negash, Yongbin Yu, Ekong Favour, Bekalu Nigus Dawit, Molla Woretaw Teshome, Aynalem Birtukan Yirga

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Breast cancer remains a significant health concern, necessitating advancements in diagnostic methodologies. Addressing this, our paper confronts the notable challenges in breast cancer classification, particularly the imbalance in datasets and the constraints in the accuracy and interpretability of prevailing deep learning approaches. We proposed an attention-based residual neural network (ResNet), which effectively combines the robust features of ResNet with an advanced attention mechanism. Enhanced through strategic data augmentation and positive weight adjustments, this approach specifically targets the issue of data imbalance. The proposed model is tested on the BreakHis dataset and achieved accuracies of 99.00%, 99.04%, 98.67%, and 98.08% in different magnifications (40X, 100X, 200X, and 400X), respectively. We evaluated the performance by using different evaluation metrics such as precision, recall, and F1-Score and made comparisons with other state-of-the-art methods. Our experiments demonstrate that the proposed model outperforms existing approaches, achieving higher accuracy in breast cancer classification.

Keywords: residual neural network, attention mechanism, positive weight, data augmentation

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Authors: Yichao Ma, Chengsiong Chin, Wailok Woo

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Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and three-dimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who is the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: hdd noise, jury test, neural network model, psychoacoustic annoyance

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Authors: Zahra Hatami, Hesham Ali, David Volkman

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Selecting a portfolio for investing is a crucial decision for individuals and legal entities. In the last two decades, with economic globalization, a stream of financial innovations has rushed to the aid of financial institutions. The importance of selecting stocks for the portfolio is always a challenging task for investors. This study aims to create a financial network to identify optimal portfolios using network centralities metrics. This research presents a community detection technique of superior stocks that can be described as an optimal stock portfolio to be used by investors. By using the advantages of a network and its property in extracted communities, a group of stocks was selected for each of the various time periods. The performance of the optimal portfolios compared to the famous index. Their Sharpe ratio was calculated in a timely manner to evaluate their profit for making decisions. The analysis shows that the selected potential portfolio from stocks with low centrality measurement can outperform the market; however, they have a lower Sharpe ratio than stocks with high centrality scores. In other words, stocks with low centralities could outperform the S&P500 yet have a lower Sharpe ratio than high central stocks.

Keywords: portfolio management performance, network analysis, centrality measurements, Sharpe ratio

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Authors: Mário Ernesto Sitoe, Orlando Zacarias

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University education is influenced by several factors that range from the adoption of strategies to strengthen the whole process to the academic performance improvement of the students themselves. This work uses data mining techniques to develop a predictive model to identify students with a tendency to evasion and retention. To this end, a database of real students’ data from the Department of University Admission (DAU) and the Department of Mathematics and Informatics (DMI) was used. The data comprised 388 undergraduate students admitted in the years 2009 to 2014. The Weka tool was used for model building, using three different techniques, namely: K-nearest neighbor, random forest, and logistic regression. To allow for training on multiple train-test splits, a cross-validation approach was employed with a varying number of folds. To reduce bias variance and improve the performance of the models, ensemble methods of Bagging and Stacking were used. After comparing the results obtained by the three classifiers, Logistic Regression using Bagging with seven folds obtained the best performance, showing results above 90% in all evaluated metrics: accuracy, rate of true positives, and precision. Retention is the most common tendency.

Keywords: evasion and retention, cross-validation, bagging, stacking

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Authors: Afshan Channa, Sameeha Aleem, Harim Mohsin

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First rank symptoms (FRS) are considered to be pathognomic for Schizophrenia. However, FRS is not a distinctive feature of Schizophrenia. It has also been noticed in affective disorder, albeit not inclusive in diagnostic criteria. The presence of FRS in Mania leads to misdiagnosis of psychotic illness, further complicating the management and delay of appropriate treatment. FRS in Mania is associated with poor clinical and functional outcome. Its existence in the first episode of bipolar disorder may be a predictor of poor short-term outcome and decompensating course of illness. FRS in Mania is studied in west. However, the cultural divergence and detriments make it pertinent to study the frequency of FRS in affective disorder independently in Pakistan. Objective: The frequency of first rank symptoms in manic patients, who were under treatment at psychiatric services of tertiary care hospital. Method: The cross sectional study was done at psychiatric services of Aga Khan University Hospital, Karachi, Pakistan. One hundred and twenty manic patients were recruited from November 2014 to May 2015. The patients who were unable to comprehend Urdu or had comorbid psychiatric or organic disorder were excluded. FRS was assessed by administration of validated Urdu version of Present State Examination (PSE) tool. Result: The mean age of the patients was 37.62 + 12.51. The mean number of previous manic episode was 2.17 + 2.23. 11.2% males and 30.6% females had FRS. This association of first rank symptoms with gender in patients of mania was found to be significant with a p-value of 0.008. All-inclusive, 19.2% exhibited FRS in their course of illness. 43.5% had thought broadcasting, made feeling, impulses, action and somatic passivity. 39.1% had thought insertion, 30.4% had auditory perceptual distortion, and 17.4% had thought withdrawal. However, none displayed delusional perception. Conclusion: The study confirms the presence of FRS in mania in both male and female, irrespective of the duration of current manic illness or previous number of manic episodes. A substantial difference was established between both the genders. Being married had no protective effect on the presence of FRS.

Keywords: first rank symptoms, Mania, psychosis, present state examination

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Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

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The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

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Authors: Ajeni Ari

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In addressing gender and transport, considerations of mobility disparities amongst users are important. Public transport (PT) policy and design do not efficiently account for the varied mobility practices between men and women, with literature only recently showing a movement towards gender inclusion in transport. Arrantly, transport policy and designs remain gender-blind to the variation of mobility needs. The global movement towards sustainability highlights the need for expeditious strategies that could mitigate biases within the existing system. At the forefront of such plan of action may, in part, be mandated inclusive infrastructural designs that stimulate user engagement with the transport system. Fundamentally access requires a means or an opportunity to entity, which for PT is an establishment of its physical environment and/or infrastructural design. Its practicality may be utilised with knowledge of shortcomings in tangible or intangible aspects of the service offerings allowing access to opportunities. To inform on existing biases in PT planning and design, this study analyses qualitative data to examine the opinions and lived experiences among transport user in Ireland. Findings show that infrastructural design plays a significant role in users’ engagement with the service. Paramount to accessibility are service provisions that cater to both user interactions and those of their dependents. Apprehension to use the service is more so evident with women in comparison to men, particularly while carrying out household duties and caring responsibilities at peak times or dark hours. Furthermore, limitations are apparent with infrastructural service offerings that do not accommodate the physical (dis)ability of users, especially universal design. There are intersecting factors that impinge on accessibility, e.g., safety and security, yet essentially, infrastructural design is an important influencing parameter to user perceptual conditioning. Additionally, data discloses the need for user intricacies to be factored in transport planning geared towards gender inclusivity, including mobility practices, travel purpose, transit time or location, and system integration.

Keywords: public transport, accessibility, women, transport infrastructure

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Authors: Rania Khadim, Mohammed Erritali, Abdelhakim Maaden

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Nowadays Wireless Sensor Networks have attracted worldwide research and industrial interest, because they can be applied in various areas. Geographic routing protocols are very suitable to those networks because they use location information when they need to route packets. Obviously, location information is maintained by Location-Based Services provided by network nodes in a distributed way. In this paper we choose to evaluate the performance of two hierarchical rendezvous location based-services, GLS (Grid Location Service) and HLS (Hierarchical Location Service) coupled to the GPSR routing protocol (Greedy Perimeter Stateless Routing) for Wireless Sensor Network. The simulations were performed using NS2 simulator to evaluate the performance and power of the two services in term of location overhead, the request travel time (RTT) and the query Success ratio (QSR). This work presents also a new scalability performance study of both GLS and HLS, specifically, what happens if the number of nodes N increases. The study will focus on three qualitative metrics: The location maintenance cost, the location query cost and the storage cost.

Keywords: location based-services, routing protocols, scalability, wireless sensor networks

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Authors: Rajan Vohra, Ravinder Singh Sawhney, Kunwar Partap Singh

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Electrical charge transport through two basic strands thymine and adenine of DNA have been investigated and analyzed using the jellium model approach. The FFT-2D computations have been performed for semi-empirical Extended Huckel Theory using atomistic tool kit to contemplate the charge transport metrics like current and conductance. The envisaged data is further evaluated in terms of transmission spectrum, HOMO-LUMO Gap and number of electrons. We have scrutinized the behavior of the devices in the range of -2V to 2V for a step size of 0.2V. We observe that both thymine and adenine can act as molecular devices when sandwiched between two gold probes. A prominent observation is a drop in HLGs of adenine and thymine when working as a device as compared to their intrinsic values and this is comparative more visible in case of adenine. The current in the thymine based device exhibit linear increase with voltage in spite of having low conductance. Further, the broader transmission peaks represent the strong coupling of electrodes to the scattering molecule (thymine). Moreover, the observed current in case of thymine is almost 3-4 times than that of observed for adenine. The NDR effect has been perceived in case of adenine based device for higher bias voltages and can be utilized in various future electronics applications.

Keywords: adenine, DNA, extended Huckel, thymine, transmission spectra

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Authors: Anthony Bawa, Konstantinos Banitsas

Abstract:

Rheumatism is a muscular disorder that affects the muscles of the upper and lower limbs. This condition could potentially progress to impair the movement of patients. This study aims to investigate the hip muscular imbalance in patients with chronic rheumatism. A clinical trial involving a total of 15 participants, made up of 10 patients and 5 control subjects, took place in KATH Hospital between August and September. Participants recruited for the study were of age 54 ± 8years, weight 65± 8kg, and height 176 ± 8cm. Muscle signals were recorded from the rectus femoris, and vastus lateralis on the right and left hip of participants. The parameters used in determining the hip muscular imbalances were the maximum voluntary contraction (MVC%), the mean difference, and hip muscle fatigue levels. The mean signals were compared using a t-test, and the metrics for muscle fatigue assessment were based on the root mean square (RMS), mean absolute value (MAV) and mean frequency (MEF), which were computed between the hip muscles of participants. The results indicated that there were significant imbalances in the muscle coactivity between the right and left hip muscles of patients. The patients’ MVC values were observed to be above 10% when compared with control subjects. Furthermore, the mean difference was seen to be higher with p > 0.002 among patients, which indicated clear differences in the hip muscle contraction activities. The findings indicate significant hip muscular imbalances for patients with rheumatism compared with control subjects. Information about the imbalances among patients will be useful for clinicians in designing therapeutic muscle-strengthening exercises.

Keywords: muscular, imbalances, rheumatism, Hip

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Authors: Victor Kang, Sirene Bellahnid, Amy Al-Simaani

Abstract:

Efficient documentation and completion of clerical tasks are pillars of efficient patient-centered care in acute settings such as the emergency department (ED). Medical scribes aid physicians with documentation, navigation of electronic health records, results gathering, and communication coordination with other healthcare teams. However, the use of medical scribes is not widespread, with some hospitals even continuing to discontinue their programs. One reason for this could be the lack of studies that have outlined concrete improvements in efficiency and patient and provider satisfaction in emergency departments before and after incorporating scribes. Methods: We conducted a review of the literature concerning the implementation of a medical scribe program and emergency department performance. For this review, a narrative synthesis accompanied by textual commentaries was chosen to present the selected papers. PubMed was searched exclusively. Initially, no date limits were set, but seeing as the electronic medical record was officially implemented in Canada in 2013, studies published after this date were preferred as they provided insight into the interplay between its implementation and scribes on quality improvement. Results: Throughput, efficiency, and cost-effectiveness were the most commonly used parameters in evaluating scribes in the Emergency Department. Important throughput metrics, specifically door-to-doctor and disposition time, were significantly decreased in emergency departments that utilized scribes. Of note, this was shown to be the case in community hospitals, where the burden of documentation and clerical tasks would fall directly upon the attending physician. Academic centers differ in that they rely heavily on residents and students; so the implementation of scribes has been shown to have limited effect on these metrics. However, unique to academic centers was the provider’s perception of incrased time for teaching was unique to academic centers. Consequently, providers express increased work satisfaction in relation to time spent with patients and in teaching. Patients, on the other hand, did not demonstrate a decrease in satisfaction in regards to the care that was provided, but there was no significant increase observed either. Of the studies we reviewed, one of the biggest limitations was the lack of significance in the data. While many individual studies reported that medical scribes in emergency rooms improved relative value units, patient satisfaction, provider satisfaction, and increased number of patients seen, there was no statistically significant improvement in the above criteria when compiled in a systematic review. There is also a clear publication bias; very few studies with negative results were published. To prove significance, data from more emergency rooms with scribe programs would need to be compiled which also includes emergency rooms who did not report noticeable benefits. Furthermore, most data sets focused only on scribes in academic centers. Conclusion: Ultimately, the literature suggests that while emergency room physicians who have access to medical scribes report higher satisfaction due to lower clerical burdens and can see more patients per shift, there is still variability in terms of patient and provider satisfaction. Whether or not this variability exists due to differences in training (in-house trainees versus contractors), population profile (adult versus pediatric), setting (academic versus community), or which shifts scribe work cannot be determined based on the studies that exist. Ultimately, more scribe programs need to be evaluated to determine whether these variables affect outcomes and prove whether scribes significantly improve emergency room efficiency.

Keywords: emergency medicine, medical scribe, scribe, documentation

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Authors: Stanley Fore

Abstract:

The case study company is one of the leading newsprint companies in South Africa. The company has achieved this status through operational expansion, diversification and investing in cutting-edge technology. They have a reputation for the highest quality and personalised service that transcends borders and industries. The company offers a wide variety of small and large scales printing services. The company is faced with the challenge of significant waste production during normal operations. The company generates 1200 kg of plastic waste and 60 – 70 tonnes of paper waste per month. The company operates a waste management process currently, whereby waste paper is sold, at low cost, to recycling firms for further processing. Having considered the quantity of waste being generated, the company has embarked on a venture to find a more profitable solution to its current waste production. As waste management and recycling is not the company’s core business, the aim of the venture is to implement a secondary profitable waste process business. The venture will be expedited as a strategic project. This research aims to estimate the financial feasibility of a selected solution as well as the impact of non-financial considerations thereof. The financial feasibility is analysed using metrics such as Payback period; internal rate of return and net present value.

Keywords: waste, printing industry, up-cycling, management

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Authors: Nurdan Yildirim, Arif Hepbasli

Abstract:

Heating, cooling and lighting appliances in buildings account for more than one third of the world’s primary energy demand. Therefore, main components of the building heating systems play an essential role in terms of energy consumption. In this context, efficient energy and exergy utilization in HVAC-R systems has been very essential, especially in developing energy policies towards increasing efficiencies. The main objective of the present study is to assess the performance of a family house with a volume of 326.7 m3 and a net floor area of 121 m2, located in the city of Izmir, Turkey in terms of energetic, exergetic and sustainability aspects. The indoor and exterior air temperatures are taken as 20°C and 1°C, respectively. In the analysis and assessment, various metrics (indices or indicators) such as exergetic efficiency, exergy flexibility ratio and sustainability index are utilized. Two heating options (Case 1: condensing boiler and Case 2: air heat pump) are considered for comparison purposes. The total heat loss rate of the family house is determined to be 3770.72 W. The overall energy efficiencies of the studied cases are calculated to be 49.4% for Case 1 and 54.7% for Case 2. The overall exergy efficiencies, the flexibility factor and the sustainability index of Cases 1 and 2 are computed to be around 3.3%, 0.17 and 1.034, respectively.

Keywords: buildings, exergy, low exergy, sustainability, efficiency, heating, renewable energy

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Authors: Fei Gao, Rodolfo C. Raga Jr.

Abstract:

This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

Procedia PDF Downloads 45