Search results for: Adult dataset

Authors: Kerdsiri Hongthai, Darunee Jongudomkarn, Rutja Phuphaibul

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In Thailand, many adults in rural areas migrate to seek employ¬ment resulting in skipped-generation family where grandparents care for grandchildren with no other adults present. This is a preliminary study using qualitative case study methods, aimed to explore the situations of older adult grandparents' experiences in skipped-generation family in North-East of Thailand. Data were collected by in-depth inter¬views with 6 grandparents living in skipped-generation families; 5 females and 1 males grandparents, aged 62-75, some of them have diabetes mellitus, hypertension, during November to December, 2017. The finding themes are: ‘Caught up in the middle’: the older adults were pleased to have grandchildren but, at the same time, acknowledge the burden that this placed on them, especially when the migrant children failed to send enough money back to support the family. ‘Getting bad health’: they reported to be fatigued and stressed due to burden of caring for their grandchildren without support. This situation can aggravate problems of poor health status and be worsening economic status of the grandparents. In some cases of deprivation, the grandparents feel that having to be the sole care providers of their grandchildren can negative adversely affect their mental status. It is important to find out in other sectors similar to Thailand and lead to more in-depth research to answer the research questions about policy and social support in skipped-generation family in the future.

Keywords: older adult grandparents, experiences, principle care giver, skipped-generation family

Procedia PDF Downloads 144

Authors: Karolina Siejka, Monika Piekarska, Monika Zbroja, Weronika Cyranka, Maryla Kuczynska, Magdalena Grzegorczyk, Malgorzata Nowakowska, Agnieszka Brodzisz, Magdalena Maria Wozniak

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Crohn’s disease is one of chronic inflammatory bowel diseases. It is increasing in prevalence worldwide, especially with young people. The disease usually occurs in the second to the fourth decade of life. Traditionally is diagnosed by clinical indicates, endoscopic, and histological findings. Magnetic Resonance Enterography (MRE) can demonstrate mural and extramural inflammatory signs and complications, which make it a valuable diagnostic modality. The study included 76 adults and 36 children diagnosed with Crohn’s disease. Each patient underwent MRE with intravenous administration of a contrast agent. All the studies were performed using Siemens Aera 1.5T scanner according to a local study protocol. Whenever applicable, MR Enterography findings were verified with endoscopy. Forty adults and all 36 children had an active phase of Crohn’s disease; five adults had a chronic phase of the disease; one adult had both chronic and active inflammatory features. Thirty adults have no sings of pathology. In both adult and pediatric groups the most commonly observed manifestation of active disease was thickened edematous ileum wall (26 adults and 36 children). Adults had Bauhin’s valve edema in 58% cases (n=23) and mesenteric changes in 34% cases (n=9). To compare, 32 children had Bauhin’s valve edema (89%) and, in 23 cases, was found inflammatory infiltration of the peri-intestinal fat (64%). The involvement of the large intestine was more common among children (100%). Complications of Crohn’s disease were found commonly in adults (40% of adults, 22% of children). There were observed 18 fistulas (14 adults, four children) and six abscesses (2 adults, four children). MRE is a reliable method in the evaluation of Crohn’s disease activity, especially of its complications. The lack of radiations makes MRE well-tolerated modality, which can be often repeated, particularly in young patients. The disease had different medical sings depending on age – children often had a more active inflammatory process, but there were more complications in the adult group.

Keywords: Crohn's disease, diagnostics, inflammatory bowel disease, magnetic resonance enterography, MRE

Procedia PDF Downloads 183

Authors: S. Abbasi, M. R. Hadian, M. Abdolvahab, M. Jalili, S. H. Jalaei

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Background: Rehabilitation treatments have significant role in reducing the disabilities of Cerebro Vascular Accident (CVA). Due to great role of upper limb in the function of individuals particularly in Activity of Daily Living and the effect of stability of shoulder girdle on hand function, the aim of this study was to study the effects of Progressive Resistive Exercise on shoulder extensor and abductor muscles isometric strengths in adult hemiplegic. Methods: 17 adult hemiplegics patients (50-70 yrs., mean 60/52, SD7/22); with RT side dominancy and 6 months after stroke, participated in this study. All procedures were approved by ethical committee of TUMS and written consents were also taken. Patients were familiarized with the procedure and shoulder extensor and abductor muscles isometric strengths were measured by dynamometer. Results: according to result to our study, shoulder extensor and abductor muscles isometric strengths showed Significant differences between mean scores of pre and post intervention (P<0/05). Progressive Resistive Exercise improved 34% shoulder extensor muscles isometric strength and 27% shoulder abductor muscle isometric strength. Conclusion: Results of our research showed that progressive resistive exercise approach is a useful method for increasing the isometric strength of shoulder extensor and abductor muscles. Therefore, it might be concluded that improvement of strength of shoulder muscles could result in stability in shoulder girdle and consequently might effect on hand function in hemiplegic patients.

Keywords: shoulder extensor muscles isometric strength, shoulder abductor muscles isometric strength, hemiplegic, physical therapy

Procedia PDF Downloads 317

Authors: Rida Nasir, Waseem Iqbal

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Due to the constant revolution in technology and the increase in anti-forensic techniques used by attackers to remove their traces, professionals often struggle to choose the best tool to be used in digital forensic investigations. This paper compares two of the most well-known and widely used licensed commercial tools, i.e., Encase & FTK. The comparison was drawn on various parameters and features to provide an authentic evaluation of licensed versions of these well-known commercial tools against various real-world scenarios. In order to discover the popularity of these tools within the digital forensic community, a survey was conducted publicly to determine the preferred choice. The dataset used is the Computer Forensics Reference Dataset (CFReDS). A total of 70 features were selected from various categories. Upon comparison, both FTK and EnCase produce remarkable results. However, each tool has some limitations, and none of the tools is declared best. The comparison drawn is completely unbiased, based on factual data.

Keywords: digital forensics, commercial tools, investigation, forensic evaluation

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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: Esther Mafunda, Simbarashe Muparangi

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The present paper tests the applicability of the Regression Hypothesis on the pathological language dissolution of a Shona male adult with Broca’s aphasia. It particularly assesses the prediction of the Regression Hypothesis, which states that the process according to which language is forgotten will be the reversal of the process according to which it will be acquired. The main aim of the paper is to find out whether mirror symmetries between L1 acquisition and L1 dissolution of tense in Shona and, if so, what might cause these regression patterns. The paper also sought to highlight the practical contributions that Linguistic theory can make to solving language-related problems. Data was collected from a 46-year-old male adult with Broca’s aphasia who was receiving speech therapy at St Giles Rehabilitation Centre in Harare, Zimbabwe. The primary data elicitation method was experimental, using the probe technique. The TART (Test for Assessing Reference Time) Shona version in the form of sequencing pictures was used to access tense by Broca’s aphasic and 3.5-year-old child. Using the SPSS (Statistical Package for Social Studies) and Excel analysis, it was established that the use of the future tense was impaired in Shona Broca’s aphasic whilst the present and past tense was intact. However, though the past tense was intact in the male adult with Broca’s aphasic, a reference to the remote past was made. The use of the future tense was also found to be difficult for the 3,5-year-old speaking child. No difficulties were encountered in using the present and past tenses. This means that mirror symmetries were found between L1 acquisition and L1 dissolution of tense in Shona. On the basis of the results of this research, it can be concluded that the use of tense in a Shona adult with Broca’s aphasia supports the Regression Hypothesis. The findings of this study are important in terms of speech therapy in the context of Zimbabwe. The study also contributes to Bantu linguistics in general and to Shona linguistics in particular. Further studies could also be done focusing on the rest of the Bantu language varieties in terms of aphasia.

Keywords: Broca’s Aphasia, regression hypothesis, Shona, language dissolution

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Authors: Nouf Al Harthi

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Venous thromboembolism (VTE) is one of the most common causes of hospital-related deaths in critically ill patients. Guidelines recommended VTE prophylaxis with standardized, fixed doses for most patients. The underweight population has limited data to guide the appropriate drug and dosing regimen. The aim of this study was to describe the pattern of VTE prophylaxis dose regimens for underweighted critically ill adult patients and the prevalence of associated VTE and bleeding. This study is a retrospective cohort study, conducted in King Abdulaziz Medical City, Saudi Arabia. It included all critical patients admitted to the intensive care units and were above 14 years old with weight less than 50 kg or BMI of 18.5 kg/m2 or less and were on heparin as VTE prophylaxis for more than 72 hours from January 2016 until January 2020. After screening 270 patients, only 40 patients were included in this study according to our inclusion and exclusion criteria. Only 6 patients (15%) received VTE prophylaxis as an adjusted dose of heparin 2500 U Q12, while the rest of the patients were taking standard dosing of heparin, 5000 U Q12 was given to 21 (52.50%) patients and 5000 U Q8 was given to 13 (32.50%) patients. None of the adjusted doses developed any complications such as VTE or bleeding. There was no significant difference compared with the standard dose group. This study focused on describing the pattern of heparin doses as VTE prophylaxis in underweight patients. We also compared the standard dosing and adjusted dosage of VTE prophylaxis on underweight patients and any complications. There was no significant difference in the complication’s outcome or benefits between the two groups.

Keywords: venous thromboembolism prophylaxis, heparin, underweight patients, adult, critical care units

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Authors: Afsoon Danesh Afrooz, Sohrab Imani, Ali Ahadiyat, Aref Maroof, Yahya Ostadi

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This study has been investigated for finding alternative and safe botanical pesticides instead of chemical insecticides. The effects of plant extracts of Eucalyptus camaldulensis and Thymus daenensis were tested against adult of Callosobrochus maculatus F. Experiments were carried out at 27±1°C and 60 ± 5% R. H. under dark condition with adopting a complete randomized block design. Three replicates were set up for five concentrations of each plants extract. LC50 values were determined by SPSS 16.0 software. LC50 values indicated that plant extract of Thymus daenensis with 1.708 (µl/l air) against adult was more effective than the plant extract of Eucalyptus camaldulensis with LC50 12.755 (µl/l air). It was found that plant extract of Thymus daenensis in comparison with extract of Eucalyptus camaldulensis could be used as a pesticide for control store pests.

Keywords: callosobruchus maculatus, Eucalyptus camaldulensis, insecticidal effects, Thymus daenensis

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Lise Retat, Maria Carmen Huerta, Laura Webber, Franco Sassi

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Background: Intra-generational social mobility (ISM) can be defined as the extent to which individuals change their socio-economic position over a period of time or during their entire life course. The relationship between poor health and ISM is established. Therefore, quantifying the impact that potential health policies have on ISM now and into the future would provide evidence for how social inequality could be reduced. This paper takes the condition of overweight and obesity as an example and estimates the mean earning change per individual if the UK were to introduce policies to effectively reduce overweight and obesity. Methods: The HealthLumen individual-based model was used to estimate the impact of obesity on social mobility measures, such as earnings, occupation, and wealth. The HL tool models each individual's probability of experiencing downward ISM as a result of their overweight and obesity status. For example, one outcome of interest was the cumulative mean earning per person of implementing a policy which would reduce adult overweight and obesity by 1% each year between 2020 and 2030 in the UK. Results: Preliminary analysis showed that by reducing adult overweight and obesity by 1% each year between 2020 and 2030, the cumulative additional mean earnings would be ~1,000 Euro per adult by 2030. Additional analysis will include other social mobility indicators. Conclusions: These projections are important for illustrating the role of health in social mobility and for providing evidence for how health policy can make a difference to social mobility outcomes and, in turn, help to reduce inequality.

Keywords: modelling, social mobility, obesity, health

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Authors: A. Y. Tsidulko, T. M. Pankova, E. V. Grigorieva

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High-grade gliomas are the most frequent and most aggressive brain tumors which are characterized by active invasion of tumor cells into the surrounding brain tissue, where the extracellular matrix (ECM) plays a crucial role. Disruption of ECM can be involved in anticancer drugs effectiveness, side-effects and also in tumor relapses. The anti-inflammatory agent dexamethasone is a common drug used during high-grade glioma treatment for alleviating cerebral edema. Although dexamethasone is widely used in the clinic, its effects on normal brain tissue ECM remain poorly investigated. It is known that proteoglycans (PGs) are a major component of the extracellular matrix in the central nervous system. In our work, we studied the effects of dexamethasone on the ECM proteoglycans (syndecan-1, glypican-1, perlecan, versican, brevican, NG2, decorin, biglican, lumican) using RT-PCR in the experimental animal model. It was shown that proteoglycans in rat brain have age-specific expression patterns. In early post-natal rat brain (8 days old rat pups) overall PGs expression was quite high and mainly expressed PGs were biglycan, decorin, and syndecan-1. The overall transcriptional activity of PGs in adult rat brain is 1.5-fold decreased compared to post-natal brain. The expression pattern was changed as well with biglycan, decorin, syndecan-1, glypican-1 and brevican becoming almost equally expressed. PGs expression patterns create a specific tissue microenvironment that differs in developing and adult brain. Dexamethasone regimen close to the one used in the clinic during high-grade glioma treatment significantly affects proteoglycans expression. It was shown that overall PGs transcription activity is 1.5-2-folds increased after dexamethasone treatment. The most up-regulated PGs were biglycan, decorin, and lumican. The PGs expression pattern in adult brain changed after treatment becoming quite close to the expression pattern in developing brain. It is known that microenvironment in developing tissues promotes cells proliferation while in adult tissues proliferation is usually suppressed. The changes occurring in the adult brain after dexamethasone treatment may lead to re-activation of cell proliferation due to signals from changed microenvironment. Taken together obtained data show that dexamethasone treatment significantly affects the normal brain ECM, creating the appropriate microenvironment for tumor cells proliferation and thus can reduce the effectiveness of anticancer treatment and promote tumor relapses. This work has been supported by a Russian Science Foundation (RSF Grant 16-15-10243)

Keywords: dexamthasone, extracellular matrix, glioma, proteoglycan

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

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Authors: Yoseph Halala Handiso, Zewdi Gebregziabher

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Background: Malnutrition in hospitalized patients remains a major public health problem in both developed and developing countries. Despite the fact that malnourished patients are more prone to stay longer in hospital, there is limited data regarding the magnitude of malnutrition and its effect on length of stay among surgical patients in Ethiopia, while nutritional assessment is also often a neglected component of the health service practice. Objective: This study aimed to assess the prevalence of malnutrition at admission and its effect on the length of hospital stay among adult surgical patients in Wolaita Sodo University Comprehensive Specialized Hospital, South Ethiopia, 2022. Methods: A facility-based prospective cohort study was conducted among 398 adult surgical patients admitted to the hospital. Participants in the study were chosen using a convenient sampling technique. Subjective global assessment was used to determine the nutritional status of patients with a minimum stay of 24 hours within 48 hours after admission (SGA). Data were collected using the open data kit (ODK) version 2022.3.3 software, while Stata version 14.1 software was employed for statistical analysis. The Cox regression model was used to determine the effect of malnutrition on the length of hospital stay (LOS) after adjusting for several potential confounders taken at admission. Adjusted hazard ratio (HR) with a 95% confidence interval was used to show the effect of malnutrition. Results: The prevalence of hospital malnutrition at admission was 64.32% (95% CI: 59%-69%) according to the SGA classification. Adult surgical patients who were malnourished at admission had higher median LOS (12 days: 95% CI: 11-13) as compared to well-nourished patients (8 days: 95% CI: 8-9), means adult surgical patients who were malnourished at admission were at higher risk of reduced chance of discharge with improvement (prolonged LOS) (AHR: 0.37, 95% CI: 0.29-0.47) as compared to well-nourished patients. Presence of comorbidity (AHR: 0.68, 95% CI: 0.50-90), poly medication (AHR: 0.69, 95% CI: 0.55-0.86), and history of admission (AHR: 0.70, 95% CI: 0.55-0.87) within the previous five years were found to be the significant covariates of the length of hospital stay (LOS). Conclusion: The magnitude of hospital malnutrition at admission was found to be high. Malnourished patients at admission had a higher risk of prolonged length of hospital stay as compared to well-nourished patients. The presence of comorbidity, polymedication, and history of admission were found to be the significant covariates of LOS. All stakeholders should give attention to reducing the magnitude of malnutrition and its covariates to improve the burden of LOS.

Keywords: effect of malnutrition, length of hospital stay, surgical patients, Ethiopia

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Authors: Gabriele Borg, Alexei De Bono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale.

Keywords: Big Data, vehicular traffic, traffic management, mobile data patterns

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Somar Boubou, Tatsuo Narikiyo, Michihiro Kawanishi

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In this work, we address the problem of 3D object recognition with depth sensors such as Kinect or Structure sensor. Compared with traditional approaches based on local descriptors, which depends on local information around the object key points, we propose a global features based descriptor. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed particularly to capture the surface geometric characteristics of the 3D objects represented by depth images. We describe the 3D surface of an object in each frame using a 2D spatial histogram capturing the normalized distribution of differential angles of the surface normal vectors. The object recognition experiments on the benchmark RGB-D object dataset and a self-collected dataset show that our proposed descriptor outperforms two others descriptors based on spin-images and histogram of normal vectors with linear-SVM classifier.

Keywords: vision in control, robotics, histogram, differential histogram of normal vectors

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Authors: Chaima Babi, Said Gadri

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The rapid increase in fake news continues to grow at a very fast rate; this requires implementing efficient techniques that allow testing the re-liability of online content. For that, the current research strives to illuminate the fake news problem using deep learning DL and machine learning ML ap-proaches. We have developed the traditional LSTM (Long short-term memory), and the bidirectional BiLSTM model. A such process is to perform a training task on almost of samples of the dataset, validate the model on a subset called the test set to provide an unbiased evaluation of the final model fit on the training dataset, then compute the accuracy of detecting classifica-tion and comparing the results. For the programming stage, we used Tensor-Flow and Keras libraries on Python to support Graphical Processing Units (GPUs) that are being used for developing deep learning applications.

Keywords: machine learning, deep learning, natural language, fake news, Bi-LSTM, LSTM, multiclass classification

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

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Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno

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Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.

Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index

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Authors: Nashwa A. Mohamed

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Introduction: Omeprazole is a proton pump inhibitor used commonly in the treatment of acid-peptic disorders. Although omeprazole is generally well tolerated, serious adverse effects such as renal failure have been reported. Ginger is an antioxidant that could play a protective role in models of experimentally induced nephropathies. Aim of the work: The aim of this work was to study the possible histological changes induced by omeprazole on renal cortex and evaluate the possible protective effect of ginger on omeprazole-induced renal damage in adult male albino rats. Materials and methods: Twenty-four adult male albino rats divided into four groups (six rats each) were used in this study. Group I served as the control group. Rats of group II received only an aqueous extract of ginger daily for 3 months through a gastric tube. Rats of group III were received omeprazole orally through a gastric tube for 3 months. Rats of group IV were given both ginger and omeprazole at the same doses and through the same routes as the previous two groups. At the end of the experiment, the rats were sacrificed. Renal tissue samples were processed for light, immunohistochemical and electron microscopic examination. The obtained results were analysed morphometrically and statistically. Results: Omeprazole caused several histological changes in the form of loss of normal appearance of renal cortex with degenerative changes in the renal corpuscle and tubules. Cellular infilteration was also observed. The filteration barrier was markedly affected. Ginger ameliorated the omeprazole-induced histological changes. Conclusion: Omeprazole induced injurious effects on renal cortex. Coadministration of ginger can ameliorate the histological changes induced by omeprazole.

Keywords: ginger, kidney, omeprazole, rat

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Authors: Walid Moudani, Ahmad Shahin

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This research presents a study to handle the recruitment services system. It aims to enhance a business intelligence system by embedding data mining in its core engine and to facilitate the link between job searchers and recruiters companies. The purpose of this study is to present an intelligent management system for supporting recruitment services based on data mining methods. It consists to apply segmentation on the extracted job postings offered by the different recruiters. The details of the job postings are associated to a set of relevant features that are extracted from the web and which are based on critical criterion in order to define consistent clusters. Thereafter, we assign the job searchers to the best cluster while providing a ranking according to the job postings of the selected cluster. The performance of the proposed model used is analyzed, based on a real case study, with the clustered job postings dataset and classified job searchers dataset by using some metrics.

Keywords: job postings, job searchers, clustering, genetic algorithms, business intelligence

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Authors: Regina Connolly, Justin Connolly

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Despite the benefits which they confer, Information & Communication Technologies (ICT) also have the potential to be used negatively. This paper focuses on one of those negative social effects - adolescent cyberbullying. Although early research in this field has pointed to the fact that the successful intervention and resolution of bullying incidents is to a large degree dependent on such incidents being reported to an adult caregiver, the literature consistently shows that adolescents who have been bullied tend not to inform others of their experiences. However, the reasons underlying such reluctance to seek adult intervention remain undetermined. Similarly, the degree to which gender, age or other variables apply in the case of adolescents’ resistance to report cyberbullying experiences has yet to be established. Understanding the factors that influence this resistance to communicate on the part of adolescents will assist caregivers, teachers and those involved in the formulation of school anti-bullying policies in their attempts to counter the cyberbullying phenomenon.

Keywords: information and Communication technologies, technology-enabled bullying, cyberbullying

Procedia PDF Downloads 265

Authors: T. N. Girish, B. E. Pradeep, Ravi Parkash

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Insects from tropical wet or dry seasons are likely to cope starvation stress through seasonal phenotypic plasticity in energy metabolites. Accordingly, we analyzed such plastic changes in Zaprionus indianus flies reared under wet or dry season-specific conditions; and also after adult acclimation at 32℃ for 1 to 6 days; and to low (40% RH) or high (70% RH) humidity. Both thermal or humidity acclimation revealed significant accumulation of body lipids for wet season flies but low humidity acclimation did not change the level of body lipids in dry season flies. Developmental and adult acclimation showed sex specific differences i.e., starvation resistance and body lipids were higher in the males of dry season but in females of wet season. We found seasonal and sex specific differences in the relative level for body lipids at death; and in the rates of accumulation or utilization of energy metabolites (body lipids, carbohydrates and proteins). Body lipids constitute the preferred energy source under starvation for flies of both the seasons. However, utilization of carbohydrates (~20% to 30%) and proteins (~20% to 25%) was evident only in dry season flies. Higher starvation resistance after thermal or humidity acclimation is achieved by increased accumulation of lipids. Adult acclimation of wet or dry season flies revealed plastic changes in mean daily fecundity despite reduction in fecundity under starvation. Thus, thermal or humidity induced plastic responses in body lipids support starvation resistance under wet or dry seasons.

Keywords: heat or humidity acclimation, plastic changes in body lipids and starvation resistance, tropical drosophilid, Wet- or Dry seasons, Zaprionus indianus

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Authors: Halima Zaman

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This paper identifies real-life challenges faced by non-English-speaking learners. The author focuses on challenges both inside and outside the classroom. A qualitative approach has been applied to conduct the study with two different groups of ESOL (English to Speakers of Other Languages) learners. The author pays attention to the reasons behind the difficulties in controlling the learners’ focus within the classroom. Learners’ lifestyles, motivations, and previous educational backgrounds have been considered while determining the challenges they face within the classroom. Some existing challenges of teaching English to adults have been discussed in this paper; however, the primary focus is to observe those two groups of learners to identify their challenges. In this paper, the author has applied the academic knowledge of her Master of Arts in English Language teaching program to support and strengthen the observation of this case study. The paper ends with a number of recommendations that can be beneficial for newcomers to ESOL teaching and a scope of further exploratory research.

Keywords: ESOL, challenges, classroom, motivation, adult learners, teaching

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Authors: Hidir S. Nogay

Abstract:

In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: cascaded neural network, internal temperature, inverter, three-phase induction motor

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Authors: Doaa M. Elghannam, Nashwa Khayrat Abousamra, Doaa A. Shahin, Enas F. Goda, Hanan Azzam, Emad Azmy, Manal Salah El-Din

Abstract:

Background: The pathogenesis of acute myeloid leukemia (AML) involves the cooperation of mutations promoting proliferation/survival and those impairing differentiation. Point mutations of the NRAS gene are the most frequent somatic mutations causing aberrant signal-transduction in acute myeloid leukemia (AML). Aim: The present work was conducted to study the frequency and prognostic significance of NRAS gene mutations (NRASmut) in de novo Egyptian adult AML. Material and methods: Bone marrow specimens from 150 patients with de novo acute myeloid leukemia and controls were analyzed by genomic PCR-SSCP at codons 12, 13 (exon 1), and 61 (exon 2) for NRAS mutations. Results: NRAS gene mutations was found in 19/150 (12.7%) AML cases, represented more frequently in the FAB subtype M4eo (P = 0.028), and at codon 12, 13 (14of 19; 73.7%). Patients with NRASmut had a significant lower peripheral marrow blasts (P = 0.004, P=0.03) and non significant improved clinical outcome than patients without the mutation. Complete remission rate was (63.2% vs 56.5%; p=0.46), resistant disease (15.8% vs 23.6%; p=0.51), three years overall survival (44% vs 42%; P = 0.85) and disease free survival (42.1% vs 38.9%, P = 0.74). Multivariate analysis showed that age was the strongest unfavorable factor for overall survival (relative risk [RR], 1.9; P = .002), followed by cytogenetics (P = .004). FAB types, NRAS mutation, and leukocytosis were less important. Conclusions: NRAS gene mutation frequency and spectrum differ between biologically distinct subtypes of AML but do not significantly influence prognosis and clinical outcome.

Keywords: NRAS Gene, egyptian adult, acute myeloid leukemia, cytogenetics

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Authors: Salahud Din, Saima Masood, Hafsa Zaneb, Habib Ur Rehman, Saima Ashraf, Imad Khan, Muqader Shah

Abstract:

The objective of this study was (1) to study gross morphological, osteometric and clinical important landmarks in the skull of adult Chinkara to obtain baseline data and (2) to study sexual dimorphism in male and female adult Chinkara through osteometry. For this purpose, after performing postmortem examination, the carcass of adult Chinkara of known sex and age was buried in the locality of the Manglot Wildlife Park and Ungulate Breeding Centre, Nizampur, Pakistan; after a specific period of time, the bones were unearthed. Gross morphological features and various osteometric parameters of the skull were studied in the University of Veterinary and Animal Sciences, Lahore, Pakistan. The shape of the Chinkara skull was elongated and had thirty-two bones. The skull was comprised of the cranial and the facial part. The facial region of the skull was formed by maxilla, incisive, palatine, vomar, pterygoid, frontal, parietal, nasal, incisive, turbinates, mandible and hyoid apparatus. The bony region of the cranium of Chinkara was comprised of occipital, ethmoid, sphenoid, interparietal, parietal, temporal, and frontal bone. The foramina identified in the facial region of the skull of Chinkara were infraorbital, supraorbital foramen, lacrimal, sphenopalatine, maxillary and caudal palatine foramina. The foramina of the cranium of the skull of the Chinkara were the internal acoustic meatus, external acoustic meatus, hypoglossal canal, transverse canal, sphenorbital fissure, carotid canal, foramen magnum, stylomastoid foramen, foramen rotundum, foramen ovale and jugular foramen, and the rostral and the caudal foramina that formed the pterygoid canal. The measured craniometric parameters did not show statistically significant differences (p > 0.05) between male and female adult Chinkara except Palatine bone, OI, DO, IOCDE, OCT, ICW, IPCW, and PCPL were significantly higher (p > 0.05) in male than female Chinkara and mean values of the mandibular parameters except b and h were significantly (p < 0.5) higher in male Chinkara than female Chinkara. Sexual dimorphism exists in some of the orbital and foramen magnum parameters, while high levels of sexual dimorphism identified in mandible. In conclusion, morphocraniometric studies of Chinkara skull made it possible to identify species-specific skull and use clinical measurements during practical application.

Keywords: Chinkara, skull, morphology, morphometrics, sexual dimorphism

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

Abstract:

Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Kevin Yeo, Christine Hall, Babinchak Tim

Abstract:

BAT® [Botulism Antitoxin Heptavalent (A,B,C,D,E,F,G)-(Equine)] anti-toxin is a mixture of equine immune globulin fragments indicated for the treatment of symptomatic botulism in adult and pediatric patients. The effectiveness of BAT anti-toxin is based on efficacy studies conducted in animal models. A general explanation of the pivotal animal studies, post market surveillance and outcomes of an observational patient registry for patients treated with BAT product distributed in the USA is briefly discussed. Overall it took 20 animal studies for two well-designed and appropriately powered pivotal efficacy studies – one in which the effectiveness of BAT was assessed against all 7 serotypes in the guinea pig, and the other where efficacy is confirmed in the Rhesus macaque using Serotype A. Clinical Experience for BAT to date involves approximately 600 adult and pediatric patients with suspected botulism. In pre-licensure, patient data was recorded under the US CDC expanded access program (259 adult and pediatric patients between 10 days to 88 years of age). In post licensure, greater than 350 patients to date have received BAT and been followed up by enhanced expanded access program. The analysis of the post market surveillance data provided a unique opportunity to demonstrate clinical benefit in the field study required by the animal rule. While the animal rule is applied because human efficacy studies are not ethical or feasible, a post-marketing requirement is to conduct a study to evaluate safety and clinical benefit when circumstances arise and demonstrate the favourable benefit-risk profile that supported licensure.

Keywords: botulism, threat, clinical benefit, observational patient registry

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

Abstract:

In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

Procedia PDF Downloads 68