Search results for: distributed algorithms

Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Newton Muhury, Armando A. Apan, Tek Maraseni

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This study modelled the relationships between vegetation response and available water below the soil surface using the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) generated Normalised Difference Vegetation Index (NDVI) and soil water content (SWC) data. The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001-2010) of monthly streamflow data. The average Nash-Sutcliffe Efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Twenty years (2001-2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet season. For example, the model generated high positive relationships (r=0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the study area against the groundwater flow (GW), soil water content (SWC), and the combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r=0.48) and 13.6% (r=0.63) against GW and SWC, respectively, in the wet season. On the other hand, the model predicted a moderate positive relationship (r=0.63) between shrub vegetation type and soil water content during the dry season, which was reduced by 31.7% (r=0.43) during the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r=0.78, and 0.70) during the dry season. The results of this study indicate the study region is suitable for seasonal crop production in dry season. Moreover, the results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Chiranjit Das, Sanjay Jharkharia

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This study is focused on transport mode selection under lead time variability and emissions constraint. In order to reduce the carbon emissions generation due to transportation, organization has often faced a dilemmatic choice of transport mode selection since logistic cost and emissions reduction are complementary with each other. Another important aspect of transportation decision is lead-time variability which is least considered in transport mode selection problem. Thus, in this study, we provide a comprehensive mathematical based analytical model to decide transport mode selection under emissions constraint. We also extend our work through analysing the effect of lead time variability in the transport mode selection by a sensitivity analysis. In order to account lead time variability into the model, two identically normally distributed random variables are incorporated in this study including unit lead time variability and lead time demand variability. Therefore, in this study, we are addressing following questions: How the decisions of transport mode selection will be affected by lead time variability? How lead time variability will impact on total supply chain cost under carbon emissions? To accomplish these objectives, a total transportation cost function is developed including unit purchasing cost, unit transportation cost, emissions cost, holding cost during lead time, and penalty cost for stock out due to lead time variability. A set of modes is available to transport each node, in this paper, we consider only four transport modes such as air, road, rail, and water. Transportation cost, distance, emissions level for each transport mode is considered as deterministic and static in this paper. Each mode is having different emissions level depending on the distance and product characteristics. Emissions cost is indirectly affected by the lead time variability if there is any switching of transport mode from lower emissions prone transport mode to higher emissions prone transport mode in order to reduce penalty cost. We provide a numerical analysis in order to study the effectiveness of the mathematical model. We found that chances of stock out during lead time will be higher due to the higher variability of lead time and lad time demand. Numerical results show that penalty cost of air transport mode is negative that means chances of stock out zero, but, having higher holding and emissions cost. Therefore, air transport mode is only selected when there is any emergency order to reduce penalty cost, otherwise, rail and road transport is the most preferred mode of transportation. Thus, this paper is contributing to the literature by a novel approach to decide transport mode under emissions cost and lead time variability. This model can be extended by studying the effect of lead time variability under some other strategic transportation issues such as modal split option, full truck load strategy, and demand consolidation strategy etc.

Keywords: carbon emissions, inventory theoretic model, lead time variability, transport mode selection

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Authors: Ahmed Elaksher

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Over the last few years, significant progresses have been made and new approaches have been proposed for efficient collection of 3D spatial data from Unmanned aerial vehicles (UAVs) with reduced costs compared to imagery from satellite or manned aircraft. In these systems, a low-cost GPS unit provides the position, velocity of the vehicle, a low-quality inertial measurement unit (IMU) determines its orientation, and off-the-shelf cameras capture the images. Structure from Motion (SfM) and photogrammetry are the main tools for 3D surface reconstruction from images collected by these systems. Unlike traditional techniques, SfM allows the computation of calibration parameters using point correspondences across images without performing a rigorous laboratory or field calibration process and it is more flexible in that it does not require consistent image overlap or same rotation angles between successive photos. These benefits make SfM ideal for UAVs aerial mapping. In this paper, a direct comparison between SfM Digital Elevation Models (DEM) and those generated through traditional photogrammetric techniques was performed. Data was collected by a 3DR IRIS+ Quadcopter with a Canon PowerShot S100 digital camera. Twenty ground control points were randomly distributed on the ground and surveyed with a total station in a local coordinate system. Images were collected from an altitude of 30 meters with a ground resolution of nine mm/pixel. Data was processed with PhotoScan, VisualSFM, Imagine Photogrammetry, and a photogrammetric algorithm developed by the author. The algorithm starts with performing a laboratory camera calibration then the acquired imagery undergoes an orientation procedure to determine the cameras’ positions and orientations. After the orientation is attained, correlation based image matching is conducted to automatically generate three-dimensional surface models followed by a refining step using sub-pixel image information for high matching accuracy. Tests with different number and configurations of the control points were conducted. Camera calibration parameters estimated from commercial software and those obtained with laboratory procedures were comparable. Exposure station positions were within less than few centimeters and insignificant differences, within less than three seconds, among orientation angles were found. DEM differencing was performed between generated DEMs and few centimeters vertical shifts were found.

Keywords: UAV, photogrammetry, SfM, DEM

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Authors: Lise P. Labéjof, Ivna Mororó, Raquel G. Bastos, Maria Isabel G. Severo, Arno H. de Oliveira

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This work presents an alternative mode of intestine mucosa renewal that may allow to better understand the total loss of villi after irradiation. It was tested a morphological method of 3d reconstruction using micrographs of serial sections of rat duodenum. We used hundreds of sections of each specimen of duodenum placed on glass slides and examined under a light microscope. Those containing the detachment, approximately a dozen, were chosen for observation under a transmission electron microscope (TEM). Each of these sections was glued on a block of epon resin and recut into a hundred of 60 nm-thick sections. Ribbons of these ultrathin sections were distributed on a series of copper grids in the same order of appearance than during the process of microstomia. They were then stained by solutions of uranyl and lead salts and observed under a TEM. The sections were pictured and the electron micrographs showing signs of cells detachment were transferred into two softwares, ImageJ to align the cellular structures and Reconstruct to realize the 3d reconstruction. It has been detected epithelial cells that exhibited all signs of programmed cell death and localized at the villus-crypt junction. Their nucleus was irregular in shape with a condensed chromatin in clumps. Their cytoplasm was darker than that of neighboring cells, containing many swollen mitochondria. In some places of the sections, we could see intercellular spaces enlarged by the presence of shrunk cells which displayed a plasma membrane with an irregular shape in thermowell as if the cell interdigitations would distant from each other. The three-dimensional reconstruction of the crypts has allowed observe gradual loss of intercellular contacts of crypt cells in the longitudinal plan of the duodenal mucosa. In the transverse direction, there was a gradual increase of the intercellular space as if these cells moved away from one another. This observation allows assume that the gradual remoteness of the cells at the villus-crypt junction is the beginning of the mucosa detachment. Thus, the shrinking of cells due to apoptosis is the way that they detach from the mucosa and progressively the villi also. These results are in agreement with our initial hypothesis and thus have demonstrated that the villi become detached from the mucosa at the villus-crypt junction by the programmed cell death process. This type of loss of entire villus helps explain the rapid denudation of the intestinal mucosa in case of irradiation.

Keywords: 3dr, transmission electron microscopy, ionizing radiations, rat small intestine, apoptosis

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Authors: Uraib Sharaha, Ahmad Salman, Eladio Rodriguez-Diaz, Elad Shufan, Klaris Riesenberg, Irving J. Bigio, Mahmoud Huleihel

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Antimicrobial drugs have an important role in controlling illness associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing like disk diffusion are time-consuming and other method including E-test, genotyping are relatively expensive. Fourier transform infrared (FTIR) microscopy is rapid, safe, and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 550 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 85% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

Keywords: antibiotics, E. coli, FTIR, multivariate analysis, susceptibility

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Authors: Valentina Marutyan

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Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

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Authors: Jillian M. Hagel, Joseph E. Tucker, Peter J. Facchini

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With the aim of establishing a holistic approach for the treatment of central nervous system (CNS) disorders, we are pursuing a drug development program rapidly progressing through discovery and characterization phases. The drug candidates identified in this program are referred to as neuroplastogens owing to their ability to mediate neuroplasticity, which can be beneficial to patients suffering from anxiety, depression, or posttraumatic stress disorder. These and other related neuropsychiatric conditions are associated with the onset of neuronal atrophy, which is defined as a reduction in the number and/or productivity of neurons. The stimulation of neuroplasticity results in an increase in the connectivity between neurons and promotes the restoration of healthy brain function. We have synthesized a substantial catalogue of proprietary indolethylamine derivatives based on the general structures of serotonin (5-hydroxytryptamine) and psychedelic molecules such as N,N-dimethyltryptamine (DMT) and psilocin (4-hydroxy-DMT) that function as neuroplastogens. A primary objective in our screening protocol is the identification of derivatives associated with a significant reduction in hallucination, which will allow administration of the drug at a dose that induces neuroplasticity and triggers other efficacious outcomes in the treatment of targeted CNS disorders but which does not cause a psychedelic response in the patient. Both neuroplasticity and hallucination are associated with engagement of the 5HT2A receptor, requiring drug candidates differentially coupled to these two outcomes at a molecular level. We use novel and proprietary artificial intelligence algorithms to predict the mode of binding to the 5HT2A receptor, which has been shown to correlate with the hallucinogenic response. Hallucination is tested using the mouse head-twitch response model, whereas mouse marble-burying and sucrose preference assays are used to evaluate anxiolytic and anti-depressive potential. Neuroplasticity is assays using dendritic outgrowth assays and cell-based ELISA analysis. Pharmacokinetics and additional receptor-binding analyses also contribute the selection of lead candidates. A summary of the program is presented.

Keywords: neuroplastogen, non-hallucinogenic, drug development, anxiety, depression, PTSD, indolethylamine derivatives, psychedelic-inspired, 5-HT2A receptor, computational chemistry, head-twitch response behavioural model, neurite outgrowth assay

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Authors: Yonit Nissim, Eyal Weissblueth

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This research is the continuation of a process that began in 2010 with the goal of redesigning the training program for future early childhood teachers at the Ohalo College, to integrate technology and provide 21st-century skills. The article focuses on a study of the processes involved in developing a special educational unit which challenged students with the task of designing, planning and building an internet site for kindergartens. This project was part of their second-year studies in the early childhood track of an interdisciplinary course entitled 'Educating for the Future.' The goal: enabling students to gain experience in developing an internet site specifically for kindergartens, and gain familiarity with Google platforms, the acquisition and use of innovative skills and the integration of technology in pedagogy. Research questions examined how students handled the task of building an internet site. The study explored whether the guided process of building a site helped them develop proficiency in creativity, teamwork, evaluation and learning appropriate to the 21st century. The research tool was a questionnaire constructed by the researchers and distributed online to the students. Answers were collected from 50-course participants. Analysis of the participants’ responses showed that, along with the significant experience and benefits that students gained from building a website for kindergarten, ambivalence was shown toward the use of new, unfamiliar and complex technology. This attitude was characterized by unease and initial emotional distress triggered by the departure from routine training to an island of uncertainty. A gradual change took place toward the adoption of innovation with the help of empathy, training, and guidance from the instructors, leading to the students’ success in carrying out the task. Initial success led to further successes, resulting in a quality product and a feeling of personal competency among the students. A clear and extreme emotional shift was observed on the spectrum from a sense of difficulty and dissatisfaction to feelings of satisfaction, joy, competency and cognitive understanding of the importance of facing a challenge and succeeding. The findings of this study can contribute to increased understanding of the complex training process of future kindergarten teachers, coping with a changing world, and pedagogy that is supported by technology.

Keywords: early childhood teachers, educating for the future, emotions, kindergarten website

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Authors: Qinke Sun, Liang Zhou

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The ‘Dragon-Elephant Debate’ between China and India is an important manifestation of global multipolarity in the 21st century. The two rising powers have carried out economic reforms one after another in the interval of more than ten years, becoming the fastest growing developing country and emerging economy in the world. At the same time, the development differences between China and India have gradually attracted wide attention of scholars. Based on the continuous annual night light data (DMSP-OLS) from 1992 to 2012, this paper systematically compares and analyses the regional development differences between China and India by Gini coefficient, coefficient of variation, comprehensive night light index (CNLI) and hot spot analysis. The results show that: (1) China's overall expansion from 1992 to 2012 is 1.84 times that of India, in which China's change is 2.6 times and India's change is 2 times. The percentage of lights in unlighted areas in China dropped from 92% to 82%, while that in India from 71% to 50%. (2) China's new growth-oriented cities appear in Hohhot, Inner Mongolia, Ordos, and Urumqi in the west, and the declining cities are concentrated in Liaoning Province and Jilin Province in the northeast; India's new growth-oriented cities are concentrated in Chhattisgarh in the north, while the declining areas are distributed in Uttar Pradesh. (3) China's differences on different scales are lower than India's, and regional inequality of development is gradually narrowing. Gini coefficients at the regional and provincial levels have decreased from 0.29, 0.44 to 0.24 and 0.38, respectively, while regional inequality in India has slowly improved and regional differences are gradually widening, with Gini coefficients rising from 0.28 to 0.32. The provincial Gini coefficient decreased slightly from 0.64 to 0.63. (4) The spatial pattern of China's regional development is mainly east-west difference, which shows the difference between coastal and inland areas; while the spatial pattern of India's regional development is mainly north-south difference, but because the southern states are sea-dependent, it also reflects the coastal inland difference to a certain extent. (5) Beijing and Shanghai present a multi-core outward expansion model, with an average annual CNLI higher than 0.01, while New Delhi and Mumbai present the main core enhancement expansion model, with an average annual CNLI lower than 0.01, of which the average annual CNLI in Shanghai is about five times that in Mumbai.

Keywords: spatial pattern, spatial difference, DMSP-OLS, China, India

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Authors: Sandra Smart-Akande, Chaminda Hewage, Imtiaz Khan, Thanuja Mallikarachchi

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According to UK police-recorded data, there has been a substantial increase in knife-related crime and youth violence in the UK since 2014 particularly in the London boroughs. These crime rates are disproportionally distributed across London with the majority of these crimes occurring in the highly deprived areas of London and among young people aged 11 to 24 with large discrepancies across ethnicity, age, gender and borough of residence. Comprehensive studies and literature have identified risk factors associated with a knife carrying among youth to be Adverse Childhood Experience (ACEs), poor mental health, school or social exclusion, drug dealing, drug using, victim of violent crime, bullying, peer pressure or gang involvement, just to mention a few. ACEs are potentially traumatic events that occur in childhood, this can be experiences or stressful events in the early life of a child and can lead to an increased risk of damaging health or social outcomes in the latter life of the individual. Research has shown that children or youths involved in youth violence have had childhood experience characterised by disproportionate adverse childhood experiences and substantial literature link ACEs to be associated with criminal or delinquent behavior. ACEs are commonly grouped by researchers into: Abuse (Physical, Verbal, Sexual), Neglect (Physical, Emotional) and Household adversities (Mental Illness, Incarcerated relative, Domestic violence, Parental Separation or Bereavement). To the author's best knowledge, no study to date has investigated how household mental health (mental health of a parent or mental health of a child) and domestic violence (domestic violence on a parent or domestic violence on a child) is related to knife homicides across the local authorities areas of London. This study seeks to address the gap by examining a large sample of data from the London Metropolitan Police Force and Characteristics of Children in Need data from the UK Department for Education. The aim of this review is to identify and synthesise evidence from data and a range of literature to identify the relationship between adverse childhood experiences and youth violence in the UK. Understanding the link between ACEs and future outcomes can support preventative action.

Keywords: adverse childhood experiences, domestic violence, mental health, youth violence, prediction analysis, London knife crime

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Authors: Ahmed Al Ansari, Khalid Al Khalifa

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Objective: Multisource feedback (MSF), also called the 360-Degree evaluation is an evaluation process by which questionnaires are distributed amongst medical peers and colleagues to assess physician performance from different sources other than the attending or the supervising physicians. The aim of this study was to design, implement, and evaluate a 360-Degree process in assessing emergency physicians trainee in the Kingdom of Bahrain. Method: The study was undertaken in Bahrain Defense Force Hospital which is a military teaching hospital in the Kingdom of Bahrain. Thirty emergency physicians (who represent the total population of the emergency physicians in our hospital) were assessed in this study. We developed an instrument modified from the Physician achievement review instrument PAR which was used to assess Physician in Alberta. We focused in our instrument to assess professionalism, communication skills and collaboration only. To achieve face and content validity, table of specification was constructed and a working group was involved in constructing the instrument. Expert opinion was considered as well. The instrument consisted of 39 items; were 15 items to assess professionalism, 13 items to assess communication skills, and 11 items to assess collaboration. Each emergency physicians was evaluated with 3 groups of raters, 4 Medical colleague emergency physicians, 4 medical colleague who are considered referral physicians from different departments, and 4 Coworkers from the emergency department. Independent administrative team was formed to carry on the responsibility of distributing the instruments and collecting them in closed envelopes. Each envelope was consisted of that instrument and a guide for the implementation of the MSF and the purpose of the study. Results: A total of 30 emergency physicians 16 males and 14 females who represent the total number of the emergency physicians in our hospital were assessed. The total collected forms is 269, were 105 surveys from coworkers working in emergency department, 93 surveys from medical colleague emergency physicians, and 116 surveys from referral physicians from different departments. The total mean response rates were 71.2%. The whole instrument was found to be suitable for factor analysis (KMO = 0.967; Bartlett test significant, p<0.00). Factor analysis showed that the data on the questionnaire decomposed into three factors which counted for 72.6% of the total variance: professionalism, collaboration, and communication. Reliability analysis indicated that the instrument full scale had high internal consistency (Cronbach’s α 0.98). The generalizability coefficients (Ep2) were 0.71 for the surveys. Conclusions: Based on the present results, the current instruments and procedures have high reliability, validity, and feasibility in assessing emergency physicians trainee in the emergency room.

Keywords: MSF system, emergency, validity, generalizability

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Authors: Sharifullah Ahmed, Sarwar Jahan Md. Yasin

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Shallow foundations on unimproved soft natural soils can undergo a high consolidation and secondary settlement. For low and medium rise building projects on such soil condition, pile foundation may not be cost effective. In such cases an alternative to pile foundations may be shallow strip footings placed on a double layered improved soil system soil. The upper layer of this system is untreated or cement treated compacted sand and underlying layer is natural soft clay. This system will reduce the settlement to an allowable limit. The current research has been conducted with the settlement of a rigid plane-strain strip footing of 2.5 m width placed on the surface of a soil consisting of an untreated or cement treated sand layer overlying a bed of homogeneous soft clay. The settlement of the mentioned shallow foundation has been studied considering both cases with the thicknesses of the sand layer are 0.3 to 0.9 times the width of footing. The response of the clay layer is assumed as undrained for plastic loading stages and drained during consolidation stages. The response of the sand layer is drained during all loading stages. FEM analysis was done using PLAXIS 2D Version 8.0. A natural clay deposit of 15 m thickness and 18 m width has been modeled using Hardening Soil Model, Soft Soil Model, Soft Soil Creep Model, and upper improvement layer has been modeled using only Hardening Soil Model. The groundwater level is at the top level of the clay deposit that made the system fully saturated. Parametric study has been conducted to determine the effect of thickness, density, cementation of the sand mat and density, shear strength of the soft clay layer on the settlement of strip foundation under the uniformly distributed vertical load of varying value. A set of the chart has been established for designing shallow strip footing on the sand mat over thick, soft clay deposit through obtaining the particular thickness of sand mat for particular subsoil parameter to ensure no punching shear failure and no settlement beyond allowable level. Design guideline in the form of non-dimensional charts has been developed for footing pressure equivalent to medium-rise residential or commercial building foundation with strip footing on soft inorganic Normally Consolidated (NC) soil of Bangladesh having void ratio from 1.0 to 1.45.

Keywords: design charts, ground improvement, PLAXIS 2D, primary and secondary settlement, sand mat, soft clay

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Authors: Misuk Kim, Seokho Kang, Jehyuk Lee, Hyunchang Cho, Sungzoon Cho

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In semiconductor manufacturing, virtual metrology (VM) refers to methods to predict properties of a wafer based on machine parameters and sensor data of the production equipment, without performing the (costly) physical measurement of the wafer properties (Wikipedia). Additional benefits include avoidance of human bias and identification of important factors affecting the quality of the process which allow improving the process quality in the future. It is however rare to find VM applied to other areas of manufacturing. In this work, we propose to use VM to copper clad laminate (CCL) manufacturing. CCL is a core element of a printed circuit board (PCB) which is used in smartphones, tablets, digital cameras, and laptop computers. The manufacturing of CCL consists of three processes: Treating, lay-up, and pressing. Treating, the most important process among the three, puts resin on glass cloth, heat up in a drying oven, then produces prepreg for lay-up process. In this process, three important quality factors are inspected: Treated weight (T/W), Minimum Viscosity (M/V), and Gel Time (G/T). They are manually inspected, incurring heavy cost in terms of time and money, which makes it a good candidate for VM application. We developed prediction models of the three quality factors T/W, M/V, and G/T, respectively, with process variables, raw material, and environment variables. The actual process data was obtained from a CCL manufacturer. A variety of variable selection methods and learning algorithms were employed to find the best prediction model. We obtained prediction models of M/V and G/T with a high enough accuracy. They also provided us with information on “important” predictor variables, some of which the process engineers had been already aware and the rest of which they had not. They were quite excited to find new insights that the model revealed and set out to do further analysis on them to gain process control implications. T/W did not turn out to be possible to predict with a reasonable accuracy with given factors. The very fact indicates that the factors currently monitored may not affect T/W, thus an effort has to be made to find other factors which are not currently monitored in order to understand the process better and improve the quality of it. In conclusion, VM application to CCL’s treating process was quite successful. The newly built quality prediction model allowed one to reduce the cost associated with actual metrology as well as reveal some insights on the factors affecting the important quality factors and on the level of our less than perfect understanding of the treating process.

Keywords: copper clad laminate, predictive modeling, quality control, virtual metrology

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Authors: C. Román, M. Guevara, P. Salas, D. Duclap, J. Houenou, C. Poupon, J. F. Mangin, P. Guevara

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Diffusion Magnetic Resonance Imaging (dMRI) allows the non-invasive study of human brain white matter. From diffusion data, it is possible to reconstruct fiber trajectories using tractography algorithms. Our previous work consists in an automatic method for the identification of short association bundles of the superficial white matter (SWM), based on a whole brain inter-subject hierarchical clustering applied to a HARDI database. The method finds representative clusters of similar fibers, belonging to a group of subjects, according to a distance measure between fibers, using a non-linear registration (DTI-TK). The algorithm performs an automatic labeling based on the anatomy, defined by a cortex mesh parcelated with FreeSurfer software. The clustering was applied to two independent groups of 37 subjects. The clusters resulting from both groups were compared using a restrictive threshold of mean distance between each pair of bundles from different groups, in order to keep reproducible connections. In the left hemisphere, 48 reproducible bundles were found, while 43 bundles where found in the right hemisphere. An inter-hemispheric bundle correspondence was then applied. The symmetric horizontal reflection of the right bundles was calculated, in order to obtain the position of them in the left hemisphere. Next, the intersection between similar bundles was calculated. The pairs of bundles with a fiber intersection percentage higher than 50% were considered similar. The similar bundles between both hemispheres were fused and symmetrized. We obtained 30 common bundles between hemispheres. An atlas was created with the resulting bundles and used to segment 78 new subjects from another HARDI database, using a distance threshold between 6-8 mm according to the bundle length. Finally, a laterality index was calculated based on the bundle volume. Seven bundles of the atlas presented right laterality (IP_SP_1i, LO_LO_1i, Op_Tr_0i, PoC_PoC_0i, PoC_PreC_2i, PreC_SM_0i, y RoMF_RoMF_0i) and one presented left laterality (IP_SP_2i), there is no tendency of lateralization according to the brain region. Many factors can affect the results, like tractography artifacts, subject registration, and bundle segmentation. Further studies are necessary in order to establish the influence of these factors and evaluate SWM laterality.

Keywords: dMRI, hierarchical clustering, lateralization index, tractography

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Authors: Javed Mohammed

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Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

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Authors: Mujahid Muhammad, Paul Kearney, Adel Aneiba

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The ability for vehicles to communicate with other vehicles (V2V), the physical (V2I) and network (V2N) infrastructures, pedestrians (V2P), etc. – collectively known as V2X (Vehicle to Everything) – will enable a broad and growing set of applications and services within the intelligent transport domain for improving road safety, alleviate traffic congestion and support autonomous driving. The telecommunication research and industry communities and standardization bodies (notably 3GPP) has finally approved in Release 14, cellular communications connectivity to support V2X communication (known as LTE – V2X). LTE – V2X system will combine simultaneous connectivity across existing LTE network infrastructures via LTE-Uu interface and direct device-to-device (D2D) communications. In order for V2X services to function effectively, a robust security mechanism is needed to ensure legal and safe interaction among authenticated V2X entities in the LTE-based V2X architecture. The characteristics of vehicular networks, and the nature of most V2X applications, which involve human safety makes it significant to protect V2X messages from attacks that can result in catastrophically wrong decisions/actions include ones affecting road safety. Attack vectors include impersonation attacks, modification, masquerading, replay, MiM attacks, and Sybil attacks. In this paper, we focus our attention on LTE-based V2X security and access control mechanisms. The current LTE-A security framework provides its own access authentication scheme, the AKA protocol for mutual authentication and other essential cryptographic operations between UEs and the network. V2N systems can leverage this protocol to achieve mutual authentication between vehicles and the mobile core network. However, this protocol experiences technical challenges, such as high signaling overhead, lack of synchronization, handover delay and potential control plane signaling overloads, as well as privacy preservation issues, which cannot satisfy the adequate security requirements for majority of LTE-based V2X services. This paper examines these challenges and points to possible ways by which they can be addressed. One possible solution, is the implementation of the distributed peer-to-peer LTE security mechanism based on the Bitcoin/Namecoin framework, to allow for security operations with minimal overhead cost, which is desirable for V2X services. The proposed architecture can ensure fast, secure and robust V2X services under LTE network while meeting V2X security requirements.

Keywords: authentication, long term evolution, security, vehicle-to-everything

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Authors: Prince Vivek, Vijay K. Bharti, Manishi Mukesh, Ankita Sharma, Om Prakash Chaurasia, Bhuvnesh Kumar

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High performance of endurance in equid requires adaptive changes involving physio-biochemical, and molecular responses in an attempt to regain homeostasis. We hypothesized that the identification of the suitable reference genes might be considered for assessing of endurance related traits in pony at high altitude and may ensure for individuals struggling to potent endurance trait in ponies at high altitude. A total of 12 mares of ponies, Zanskar breed, were divided into three groups, group-A (without load), group-B, (60 Kg) and group-C (80 Kg) on backpack loads were subjected to a load carry protocol, on a steep climb of 4 km uphill, and of gravel, uneven rocky surface track at an altitude of 3292 m to 3500 m (endpoint). Blood was collected before and immediately after the load carry on sodium heparin anticoagulant, and the peripheral blood mononuclear cell was separated for total RNA isolation and thereafter cDNA synthesis. Real time-PCR reactions were carried out to evaluate the mRNAs expression profile of a panel of putative internal control genes (ICGs), related to different functional classes, namely glyceraldehyde 3-phosphate dehydrogenase (GAPDH), β₂ microglobulin (β₂M), β-actin (ACTB), ribosomal protein 18 (RS18), hypoxanthine-guanine phosophoribosyltransferase (HPRT), ubiquitin B (UBB), ribosomal protein L32 (RPL32), transferrin receptor protein (TFRC), succinate dehydrogenase complex subunit A (SDHA) for normalizing the real-time quantitative polymerase chain reaction (qPCR) data of native pony’s. Three different algorithms, geNorm, NormFinder, and BestKeeper software, were used to evaluate the stability of reference genes. The result showed that GAPDH was best stable gene and stability value for the best combination of two genes was observed TFRC and β₂M. In conclusion, the geometric mean of GAPDH, TFRC and β₂M might be used for accurate normalization of transcriptional data for assessing endurance related traits in Zanskar ponies during load carrying.

Keywords: endurance exercise, ubiquitin B (UBB), β₂ microglobulin (β₂M), high altitude, Zanskar ponies, reference gene

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Authors: Jerry Fiave, Dacosta Aboagye, Stephen Ayisi-Addo, Mabel Kissiwah Asafo, Felix Osei-Sarpong, Ebenezer Kye-Mensah, Renee Opare-Otoo

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Background: Acceptability and preference of social and behaviour change (SBC) materials by target audiences is an important determinant of effective health communication outcomes. In Ghana, however, pre-test and post-test studies on acceptability and preference of specific SBC materials for specific audiences are rare. The aim of this study was therefore to assess the acceptability and preference of printed posters on COVID-19 related stigma as suitable SBC materials for health workers to influence behaviours that promote uptake of HIV-focused services. Methods: A total of 218 health workers who provide HIV-focused services were purposively sampled in 16 polyclinics where the posters were distributed in the Greater Accra region of Ghana. Data was collected in March 2021 using an adapted self-administered questionnaire in Google forms deployed via WhatsApp to participants. The data were imported into SPSS version 27 where chi-square test and regression analyses were performed to establish association as well as strength of association between variables respectively. Results: A total of 142 participants (physicians, nurses, midwives, lab scientists, health promoters, diseases control officers) made up of 85(60%) females and 57(40%) males responded to the questionnaire, giving a response rate of 65.14%. Only 88 (61.97%) of the respondents were exposed to the posters. The majority of those exposed said the posters were informative [82(93.18%)], relevant [85(96.59%)] and attractive [83(94.32%)]. They [82(93.20%)] also rated the material as acceptable with no statistically significant association between category of health worker and acceptability of the posters (X =1.631, df=5, p=0.898). However, participants’ most preferred forms of material on COVID-19 related stigma were social media [38(26.76%)], television [33(23.24%)], SMS [19(13.38%)], and radio [18(12.70%)]. Clinical health workers were 4.88 times more likely to prefer online or electronic versions of SBC materials than nonclinical health workers [AOR= 4.88 (95% CI= 0.31-0.98), p=0.034]. Conclusions: Printed posters on COVID-19 related stigma are acceptable SBC materials in communicating behaviour change messages that target health workers in promoting uptake of HIV-focused services. Posters are however, not among the most preferred materials for health workers. It is therefore recommended that material assessment studies are conducted to inform the development of acceptable and preferred materials for target audiences.

Keywords: acceptability, AIDS, HIV, posters, preference, SBC, stigma, social and behaviour change communication

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Authors: Susana Gonçalves, Joana Lencart, Anabela Gregório Dias

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Introduction: In combination with chemotherapy, Total Body Irradiation (TBI) is most used as part of the conditioning regimen prior to allogeneic hematopoietic stem cell transplantation. Conventional TBI techniques have a long application time but non-conformality of beam-application with the inability to individually spare organs at risk. Our institution’s intention is to start using Volumetric Modulated Arc Therapy (VMAT) techniques to increase homogeneity of delivered radiation. As a first approach, a dosimetric plan was performed on a computed tomography (CT) scan of a Rando Alderson antropomorfic phantom (head and torso), using a set of six arcs distributed along the phantom. However, a full body anthropomorphic phantom is essential to carry out technique validation and implementation. Our aim is to define the physical and chemical characteristics and the ideal manufacturing procedure of upper and lower limbs to our anthropomorphic phantom, for later validate TBI using VMAT. Materials and Methods: To study the better fit between our phantom and limbs, a CT scan of Rando Alderson anthropomorphic phantom was acquired. CT was performed on GE Healthcare equipment (model Optima CT580 W), with slice thickness of 2.5 mm. This CT was also used to access the electronic density of soft tissue and bone through Hounsfield units (HU) analysis. Results: CT images were analyzed and measures were made for the ideal upper and lower limbs. Upper limbs should be build under the following measures: 43cm length and 7cm diameter (next to the shoulder section). Lower limbs should be build under the following measures: 79cm length and 16.5cm diameter (next to the thigh section). As expected, soft tissue and bone have very different electronic density. This is important to choose and analyze different materials to better represent soft tissue and bone characteristics. The approximate HU values of the soft tissue and for bone shall be 35HU and 250HU, respectively. Conclusion: At the moment, several compounds are being developed based on different types of resins and additives in order to be able to control and mimic the various constituent densities of the tissues. Concurrently, several manufacturing techniques are being explored to make it possible to produce the upper and lower limbs in a simple and non-expensive way, in order to finally carry out a systematic and appropriate study of the total body irradiation. This preliminary study was a good starting point to demonstrate the feasibility of TBI with VMAT.

Keywords: TBI, VMAT, anthropomorphic phantom, tissue equivalent materials

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Authors: Byeongcheol Kang, Junyi Kim, Hyungsik Jung, Hyungjun Yang, Jaewoo An, Jonggeun Choe

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It is important to figure out reservoir properties for better production management. Due to the limited information, there are geological uncertainties on very heterogeneous or channel reservoir. One of the solutions is to generate multiple equi-probable realizations using geostatistical methods. However, some models have wrong properties, which need to be excluded for simulation efficiency and reliability. We propose a novel method of model selection scheme, based on distance-based clustering for reliable application of production optimization algorithm. Distance is defined as a degree of dissimilarity between the data. We calculate Hausdorff distance to classify the models based on their similarity. Hausdorff distance is useful for shape matching of the reservoir models. We use multi-dimensional scaling (MDS) to describe the models on two dimensional space and group them by K-means clustering. Rather than simulating all models, we choose one representative model from each cluster and find out the best model, which has the similar production rates with the true values. From the process, we can select good reservoir models near the best model with high confidence. We make 100 channel reservoir models using single normal equation simulation (SNESIM). Since oil and gas prefer to flow through the sand facies, it is critical to characterize pattern and connectivity of the channels in the reservoir. After calculating Hausdorff distances and projecting the models by MDS, we can see that the models assemble depending on their channel patterns. These channel distributions affect operation controls of each production well so that the model selection scheme improves management optimization process. We use one of useful global search algorithms, particle swarm optimization (PSO), for our production optimization. PSO is good to find global optimum of objective function, but it takes too much time due to its usage of many particles and iterations. In addition, if we use multiple reservoir models, the simulation time for PSO will be soared. By using the proposed method, we can select good and reliable models that already matches production data. Considering geological uncertainty of the reservoir, we can get well-optimized production controls for maximum net present value. The proposed method shows one of novel solutions to select good cases among the various probabilities. The model selection schemes can be applied to not only production optimization but also history matching or other ensemble-based methods for efficient simulations.

Keywords: distance-based clustering, geological uncertainty, particle swarm optimization (PSO), production optimization

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Authors: Uwizera Egide

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Introduction: Dermatological conditions are one of the most burdensome diseases in our health system. Global studies suggest that around 1 in 3 people gets a skin disease at a certain point in their life, though this does not necessarily guarantee the urge to consult. For a high-ranking disease, it is surprising how there is not enough data to support its effect on the economy and the general health system impact. It is for that reason that this study’s aim is to identify the burden of dermatological conditions in Rwanda so as to have a general picture of what our population is going through in regards to dermatological conditions. Methods: We used a cross sectional retrospective study. Data were obtained from patient’s information recorded in an open clinic at CHUB in a period of six months from July to December of the year 2021. Results: The study had a total of 4600 patients who attended dermatology service in a period of six months from July to December of the year 2021. We found a list of 102 dermatological diseases that presented at variable rates. The most prevalent disease was atopic dermatitis, at a rate of 23%. About 90% of presented conditions had only one choice of treatment from the hospital pharmacy. Most patients who presented were between 18-35 years old and with a predominance of the female gender; the level of education was either secondary or University Degree in our study, 65.4% of patients who presented were female; the majority, around 45% were between 18-35 years old, mostly being single 56%. The majority came from Southern province as it is the location of the hospital. The insurance mostly used was community-based health insurance with 63.8%, followed by RSSB with 18.5%, MS/UR, and other private insurances. The frequency of group drugs prescribed among all dermatological medications, steroids were the most commonly given medications at a rate of 39%, followed by emollients, antibiotics, and antifungal. The drugs prescribed were mostly available in the pharmacy of CHUB, with 60% and 40% being found in pharmacies outside the hospital. Conclusion: Dermatological conditions are prevalent in all age groups and distributed through all socioeconomic classes. About 9.2% of patient who consulted CHUB in 2021 presented one Dermatological condition of which 40 % of prescribed medications is never found in Hospital urging a need to buy medication in private pharmacies with more expenses and a risk of not complying on prescribed medication if in case they can’t afford paying them outside the CHUB. This finding urges a need to avail all essential dermatological drugs in hospital pharmacies to allow our patients to get them for the proper compliance of prescribed drugs in the management of skin diseases.

Keywords: atopic dermatitis, CHUB (centre hopitalier univerisitaire de butare), dermatological condition, fungal infections

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Authors: Hoda Rahmani, Gary Weckman

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The body of research examining the integration of drones into various industries is expanding rapidly. Despite progress made in addressing the cybersecurity concerns for commercial drones, knowledge deficits remain in determining potential occupational hazards and risks of drone use to employees’ well-being and health in the workplace. This creates difficulty in identifying key approaches to risk mitigation strategies and thus reflects the need for raising awareness among employers, safety professionals, and policymakers about workplace drone-related accidents. The purpose of this study is to investigate the prevalence of and possible risk factors for drone-related mishaps by comparing the application of drones in construction with manufacturing industries. The chief reason for considering these specific sectors is to ascertain whether there exists any significant difference between indoor and outdoor flights since most construction sites use drones outside and vice versa. Therefore, the current research seeks to examine the causes and patterns of workplace drone-related mishaps and suggest possible ergonomic interventions through data collection. Potential ergonomic practices to mitigate hazards associated with flying drones could include providing operators with professional pieces of training, conducting a risk analysis, and promoting the use of personal protective equipment. For the purpose of data analysis, two data mining techniques, the random forest and association rule mining algorithms, will be performed to find meaningful associations and trends in data as well as influential features that have an impact on the occurrence of drone-related accidents in construction and manufacturing sectors. In addition, Spearman’s correlation and chi-square tests will be used to measure the possible correlation between different variables. Indeed, by recognizing risks and hazards, occupational safety stakeholders will be able to pursue data-driven and evidence-based policy change with the aim of reducing drone mishaps, increasing productivity, creating a safer work environment, and extending human performance in safe and fulfilling ways. This research study was supported by the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant #T42OH008432.

Keywords: commercial drones, ergonomic interventions, occupational safety, pattern recognition

Procedia PDF Downloads 207

Authors: Faezeh Mosallat, Eric L. Bibeau, Tarek El Mekkawy

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Availability of a wide variety of renewable resources, such as large reserves of hydro, biomass, solar and wind in Canada provides significant potential to improve the sustainability of energy uses. As buildings represent a considerable portion of energy use in Canada, application of distributed solar energy systems for heating and cooling may increase the amount of renewable energy use. Parabolic solar trough systems have seen limited deployments in cold northern climates as they are more suitable for electricity production in southern latitudes. Heat production by concentrating solar rays using parabolic troughs can overcome the poor efficiencies of flat panels and evacuated tubes in cold climates. A numerical dynamic model is developed to simulate an installed parabolic solar trough facility in Winnipeg. The results of the numerical model are validated using the experimental data obtained from this system. The model is developed in Simulink and will be utilized to simulate a tri-generation system for heating, cooling and electricity generation in remote northern communities. The main objective of this simulation is to obtain operational data of solar troughs in cold climates as this is lacking in the literature. In this paper, the validated Simulink model is applied to simulate a solar assisted absorption cooling system along with electricity generation using organic Rankine cycle (ORC) and thermal storage. A control strategy is employed to distribute the heated oil from solar collectors among the above three systems considering the temperature requirements. This modeling provides dynamic performance results using real time minutely meteorological data which are collected at the same location the solar system is installed. This is a big step ahead of the current models by accurately calculating the available solar energy at each time step considering the solar radiation fluctuations due to passing clouds. The solar absorption cooling is modeled to use the generated heat from the solar trough system and provide cooling in summer for a greenhouse which is located next to the solar field. A natural gas water heater provides the required excess heat for the absorption cooling at low or no solar radiation periods. The results of the simulation are presented for a summer month in Winnipeg which includes the amount of generated electric power from ORC and contribution of solar energy in the cooling load provision

Keywords: absorption cooling, parabolic solar trough, remote community, validated model

Procedia PDF Downloads 214

Authors: Jane Ebele Iloanya

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The introduction of the learning outcome approach in contemporary curriculum design and instruction, has brought student–centered education to the fore. In teacher –centered teaching and learning, the teacher transfers knowledge to the students, who are always at the receiving end. The teacher is assumed to know it all and hardly trusts the knowledge of the students. Teacher-centered education places emphasis on the supremacy of the teacher over the students who should ideally, be able to dialogue with the teacher. The paper seeks to examine the issue of democratisation of the teaching and learning process in Institutions of Higher Learning in Botswana. Botswana is a landlocked country in Southern Africa, with a total population of about two million people. In 1977, Botswana’s First National Policy on Education was unveiled. This came eleven years after the country gained independence from Great Britain. The philosophy which informed the 1977 Education Policy was “Social Harmony”. The philosophy of social harmony has four main principles: Unity, Development, Democracy and Self- Reliance. These principles were meant to permeate all aspects of lives of the people of Botswana, including, the issue of how teaching and learning is conducted in Botswana’s institutions of higher learning. This paper will examine the practicalisation of the principle of democracy in teaching and learning at higher education level in Botswana. It will in particular, discuss the issue of students’ participation and engagement in the teaching and learning process. The following questions will be addressed: 1.Are students involved in planning the curriculum? 2.How engaged are the students in the teaching and learning process? 3.How democratic are the teachers in terms of students’ rights and privileges? A mixed–method approach will be adopted in this study. Questionnaires will be distributed to the students to elicit their views on the practicalisation of the principle of democracy at the higher education level. Semi-structured interview questions will be administered in order to collect information from the lecturers on the issue of democratisation of teaching and learning at the higher education level in Botswana. In addition, relevant and related literature will be reviewed to augment collected data. The study will focus on three tertiary institutions in Gaborone, the capital city of Botswana. Currently, there are ten tertiary institutions in Gaborone; both privately and government owned. The outcome of this study will add to the existing body of knowledge on the issue of the practicalisation of democracy at the higher education level in Botswana. This research is therefore relevant in helping to find out if democratisation of teaching and learning has been realised in Botswana’s Institutions of higher learning. It is important to examine Botswana’s national policy on education in this way to ascertain if it has been effective in giving the country’s education system that democratic element, which is essential for a student-centered approach to the teaching and learning process.

Keywords: democratisation, higher education, learning, teaching

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Authors: Virupakshi Hiremata, Ratnakar M. Shet, Raghavendra Gunnaiah, Prashantha A.

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Muskmelon is a health-beneficial and refreshing dessert vegetable with a low shelf life. Mangalore melon, a genetic homeologue of muskmelon, has a shelf life of more than six months and is mostly used for culinary purposes. Understanding the genetics of shelf life, yield and yield-related traits and identification of markers linked to such traits is helpful in transfer of extended shelf life from Mangalore melon to the muskmelon through intra-specific hybridization. For QTL mapping, 276 F2 mapping population derived from the cross Arka Siri × SS-17 was genotyped with 40 polymorphic markers distributed across 12 chromosomes. The same population was also phenotyped for yield, shelf life and fruit quality traits. One major QTL (R2 >10) and fourteen minor QTLs (R2 <10) localized on four linkage groups, governing different traits were mapped in F2 mapping population developed from the intraspecific cross with a LOD > 5.5. The phenotypic varience explained by each locus varied from 3.63 to 10.97 %. One QTL was linked to shelf-life (qSHL-3-1), five QTLs were linked to TSS (qTSS-1-1, qTSS-3-3, qTSS-3-1, qTSS-3-2 and qTSS-1-2), two QTLs for flesh thickness (qFT-3-1, and qFT-3-2) and seven QTLs for fruit yield per vine (qFYV-3-1, qFYV-1-1, qFYV-3-1, qFYV1-1, qFYV-1-3, qFYV2-1 and qFYV6-1). QTL flanking markers may be used for marker assisted introgression of shelf life into muskmelon. Important QTL will be further fine-mapped for identifying candidate genes by QTLseq and RNAseq analysis. Fine-mapping of Important Quantitative Trait Loci (QTL) holds immense promise in elucidating the genetic basis of complex traits. Leveraging advanced techniques like QTLseq and RNA sequencing (RNA seq) is crucial for this endeavor. QTLseq combines next-generation sequencing with traditional QTL mapping, enabling precise identification of genomic regions associated with traits of interest. Through high-throughput sequencing, QTLseq provides a detailed map of genetic variations linked to phenotypic variations, facilitating targeted investigations. Moreover, RNA seq analysis offers a comprehensive view of gene expression patterns in response to specific traits or conditions. By comparing transcriptomes between contrasting phenotypes, RNA seq aids in pinpointing candidate genes underlying QTL regions. Integrating QTLseq with RNA seq allows for a multi-dimensional approach, coupling genetic variation with gene expression dynamics.

Keywords: QTL, shelf life, TSS, muskmelon and Mangalore melon

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Authors: Sara De Pelsmaeker, Catarina Ferreira da Silva, Janne Prawit

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Recent studies show that enzymatically hydrolysed collagen is absorbed and distributed to joint tissues, where it has analgesic and active anti-inflammatory properties. Reviews of the associated relevant literature also support this theory. However, these studies are all using hydrolyzed collagen from animal hide or skin. This study looks into the effect of daily supplementation of hydrolyzed cartilage collagen (HCC), which has a different composition. A consumer study was set up using a double-blind placebo-controlled design with a control group using twice a day 0.5gr of maltodextrin and an experimental group using twice 0.5g of HCC, over a trial period of 12 weeks. A follow-up phase of 4 weeks without supplementation was taken into the experiment to investigate the ‘wash-out’ phase. As this consumer study was conducted during the lockdown periods, a specific app was designed to follow up with the participants. The app had the advantage that in this way, the motivation of the participants was enhanced and the drop-out range of participants was lower than normally seen in consumer studies. Participants were recruited via various sports and health clubs across the UK as we targeted a general population of people that considered themselves in good health. Exclusion criteria were ‘not experiencing any medical conditions’ and ‘not taking any prescribed medication’. A minimum requirement was that they regularly engaged in some level of physical activity. The participants had to log the type of activity that they conducted and the duration of the activity. Weekly, participants were providing feedback on their joint health and subjective pain using the validated pain measuring instrument Visual Analogue Scale (VAS). The weekly repoAbstract Public Health and Wellbeing Conferencerting section in the app was designed with simplicity and based on the accuracy demonstrated in previous similar studies to track subjective pain measures of participants. At the beginning of the trial, each participant indicated their baseline on joint pain. The results of this consumer study indicated that HCC significantly improved joint health and subjective pain scores compared to the placebo group. No significant differences were found between different demographic groups (age or gender). The level of activity, going from high intensive training to regular walking, did not significantly influence the effect of the HCC. The results of the wash-out phase indicated that when the participants stopped the HCC supplementation, their subjective pain scores increased again to the baseline. In conclusion, the results gave a positive indication that the daily supplementation of HCC can contribute to the overall mobility and wellbeing of a general active population

Keywords: VAS-score, food supplement, mobility, joint health

Procedia PDF Downloads 162

Authors: Lahcen Daoudi, Fatima Zahra Omdi, Abldelali Gourfi

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In Morocco, as in most Mediterranean countries, water scarcity is a common situation because of low and unevenly distributed rainfall. The expansions of irrigated lands, as well as the growth of urban and industrial areas and tourist resorts, contribute to an increase of water demand. Therefore in the 1960s Morocco embarked on an ambitious program to increase the number of dams to boost water retention capacity. However, the decrease in the capacity of these reservoirs caused by sedimentation is a major problem; it is estimated at 75 million m3/year. Dams and reservoirs became unusable for their intended purposes due to sedimentation in large rivers that result from soil erosion. Soil erosion presents an important driving force in the process affecting the landscape. It has become one of the most serious environmental problems that raised much interest throughout the world. Monitoring soil erosion risk is an important part of soil conservation practices. The estimation of soil loss risk is the first step for a successful control of water erosion. The aim of this study is to estimate the soil loss risk and its spatial distribution in the different fields of Morocco and to prioritize areas for soil conservation interventions. The approach followed is the Revised Universal Soil Loss Equation (RUSLE) using remote sensing and GIS, which is the most popular empirically based model used globally for erosion prediction and control. This model has been tested in many agricultural watersheds in the world, particularly for large-scale basins due to the simplicity of the model formulation and easy availability of the dataset. The spatial distribution of the annual soil loss was elaborated by the combination of several factors: rainfall erosivity, soil erodability, topography, and land cover. The average annual soil loss estimated in several basins watershed of Morocco varies from 0 to 50t/ha/year. Watersheds characterized by high-erosion-vulnerability are located in the North (Rif Mountains) and more particularly in the Central part of Morocco (High Atlas Mountains). This variation of vulnerability is highly correlated to slope variation which indicates that the topography factor is the main agent of soil erosion within these basin catchments. These results could be helpful for the planning of natural resources management and for implementing sustainable long-term management strategies which are necessary for soil conservation and for increasing over the projected economic life of the dam implemented.

Keywords: soil loss, RUSLE, GIS-remote sensing, watershed, Morocco

Procedia PDF Downloads 460

Authors: B. Bernabela, P. Bles, A. Bloecker, D. DeRock, M. van Es, M. Gerritse, T. de Jongh, W. Lansing, M. Martinot, J. van de Wetering

Abstract:

A heated debate has been taking place concerning research and teaching at Dutch universities for the last few years. The ministry of science and education has published reports on its strategy to improve university curricula and position the Netherlands as a globally competitive knowledge economy. These reports have provoked an uproar of responses from think tanks, concerned academics, and the media. At the center of the debate is disagreement over who should determine the Dutch university curricula and how these curricula should look. Many stakeholders in the higher education system have voiced their opinion, and some have not been heard. The result is that the diversity of visions is ignored or taken for granted in the official reports. Recognizing this gap in stakeholder analysis, the aim of this paper is to bring attention to the wide range of perspectives on who should be responsible for designing higher education curricula. Based on a previous analysis by the Rathenau Institute, we distinguish five different groups of stakeholders: government, business sector, university faculty and administration, students, and the societal sector. We conducted semi-structured, in-depth interviews with representatives from each stakeholder group, and distributed quantitative questionnaires to people in the societal sector (i.e. people not directly affiliated with universities or graduates). Preliminary data suggests that the stakeholders have different target points concerning the university curricula. Representatives from the governmental sector tend to place special emphasis on the link between research and education, while representatives from the business sector rather focus on greater opportunities for students to obtain practical experience in the job market. Responses from students reflect a belief that they should be able to influence the curriculum in order to compete with other students on the international job market. On the other hand, university faculty expresses concern that focusing on the labor market puts undue pressure on students and compromises the quality of education. Interestingly, the opinions of members of ‘society’ seem to be relatively unchanged by political and economic shifts. Following a comprehensive analysis of the data, we believe that our results will make a significant contribution to the debate on university education in the Netherlands. These results should be regarded as a foundation for further research concerning the direction of Dutch higher education, for only if we take into account the different opinions and views of the various stakeholders can we decide which steps to take. Moreover, the Dutch experience offers lessons to other countries as well. As the internationalization of higher education is occurring faster than ever before, universities throughout Europe and globally are experiencing many of the same pressures.

Keywords: Dutch University curriculum, higher education, participants’ opinions, stakeholder perspectives

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