Search results for: NKT subset

Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

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This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

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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: Samina Khalid, Shamila Nasreen

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Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.

Keywords: age related macular degeneration, feature selection feature subset selection feature extraction/transformation, FSA’s, relief, correlation based method, PCA, ICA

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Authors: Mikhail Abramyan, Boris Melnikov

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The aim is to study the set of subsets of grids of the Waterloo automaton and the set of covering automata defined by the grid subsets. The study was carried out using the library for working with nondeterministic finite automata NFALib implemented by one of the authors (M. Abramyan) in C#. The results are regularities obtained when considering semilattices of covering automata for the Waterloo automaton. A complete description of the obtained semilattices from the point of view of equivalence of the covering automata to the original Waterloo automaton is given, the criterion of equivalence of the covering automaton to the Waterloo automaton in terms of properties of the subset of grids defining the covering automaton is formulated. The relevance of the subject area under consideration is due to the need to research a set of regular languages and, in particular, a description of their various subclasses. Also relevant are the problems that may arise in some subclasses. This will give, among other things, the possibility of describing new algorithms for the equivalent transformation of nondeterministic finite automata.

Keywords: nondeterministic finite automata, universal automaton, grid, covering automaton, equivalent transformation algorithms, the Waterloo automaton

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Authors: Amanda Coleman, Estefanía Gonzalez

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Empathy is considered an essential skill for engaging with social work clients. Drawing from developments in medical education, researchers will conduct and evaluate a three-part pilot theatre workshop with master level social work students (n ≈ 30) to evaluate the workshop's ability to enhance empathy among participants. Outcomes will be measured using semi-structured post-intervention interviews with a subset of participants (n ≈ 10) as well post-intervention written reflections and pre-and-post intervention quantitative evaluation of empathy using King and Holosko’s 2011 Empathy Scale for Social Workers. The content of the workshop will differ from traditional role plays, which are common in social work education, in that it will draw from role theory and research on creative empathy to emphasize role reversal with clients. Workshops will be held February and March of 2017 with preliminary findings available by April.

Keywords: education, empathy, social work, theatre

Procedia PDF Downloads 243

Authors: Bello Gonzalez T. D. J., Setten Van M., Essen Van A., Brouwer M., Veldman K. T.

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Resistance to fluoroquinolones (FQ) has evolved increasingly over the years, posing a significant challenge for the treatment of human infections, particularly gastrointestinal tract infections caused by zoonotic bacteria transmitted through the food chain and environment. In broiler chickens, a relatively high proportion of FQ resistance has been observed in Escherichia coli indicator, Salmonella and Campylobacter isolates. We hypothesize that flumequine (Flu), used as a secondary choice for the treatment of poultry infections, could potentially be associated with a high proportion of FQ resistance. To evaluate this hypothesis, we used an in vitro fermentation chicken caeca model. Two continuous single-stage fermenters were used to simulate in real time the physiological conditions of the chicken caeca microbial content (temperature, pH, caecal content mixing, and anoxic environment). A pool of chicken caecal content containing FQ-resistant E. coli obtained from chickens at slaughter age was used as inoculum along with a spiked FQ-susceptible Campylobacter jejuni strain isolated from broilers. Flu was added to one of the fermenters (Flu-fermenter) every 24 hours for two days to evaluate the selection and maintenance of FQ resistance over time, while the other served as a control (C-Fermenter). The experiment duration was 5 days. Samples were collected at three different time points: before, during and after Flu administration. Serial dilutions were plated on Butzler culture media with and without Flu (8mg/L) and enrofloxacin (4mg/L) and on MacConkey culture media with and without Flu (4mg/L) and enrofloxacin (1mg/L) to determine the proportion of resistant strains over time. Positive cultures were identified by mass spectrometry and matrix-assisted laser desorption/ionization (MALDI). A subset of the obtained isolates were used for Whole Genome Sequencing analysis. Over time, E. coli exhibited positive growth in both fermenters, while C. jejuni growth was detected up to day 3. The proportion of Flu-resistant E. coli strains recovered remained consistent over time after antibiotic selective pressure, while in the C-fermenter, a decrease was observed at day 5; a similar pattern was observed in the enrofloxacin-resistant E. coli strains. This suggests that Flu might play a role in the selection and persistence of enrofloxacin resistance, compared to C-fermenter, where enrofloxacin-resistant E. coli strains appear at a later time. Furthermore, positive growth was detected from both fermenters only on Butzler plates without antibiotics. A subset of C. jejuni strains from the Flu-fermenter revealed that those strains were susceptible to ciprofloxacin (MIC < 0.12 μg/mL). A selection of E. coli strains from both fermenters revealed the presence of plasmid-mediated quinolone resistance (PMQR) (qnr-B19) in only one strain from the C-fermenter belonging to sequence type (ST) 48, and in all from Flu-fermenter belonged to ST189. Our results showed that Flu selective impact on PMQR-positive E. coli strains, while no effect was observed in C. jejuni. Maintenance of Flu-resistance was correlated with antibiotic selective pressure. Further studies into antibiotic resistance gene transfer among commensal and zoonotic bacteria in the chicken caeca content may help to elucidate the resistance spread mechanisms.

Keywords: fluoroquinolone-resistance, escherichia coli, campylobacter jejuni, in vitro model

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Authors: Asimina Paparrigopoulou, John Pavlopoulos, Maria Konstantinidou

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Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. By experimenting with data obtained from the Collaborative Database of Dateable Greek Bookhands and the PapPal online collections of objectively dated Greek papyri, this study shows that deep learning dating models, pre-trained on generic images, can achieve accurate chronological estimates for a test subset (67,97% accuracy for book hands and 55,25% for documents). To compare the estimates of these models with those of humans, experts were asked to complete a questionnaire with samples of literary and documentary hands that had to be sorted chronologically by century. The same samples were dated by the models in question. The results are presented and analysed.

Keywords: image classification, papyri images, dating

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Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

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Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

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Authors: Alireza Ghaffari, Hassan Saghi

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Project management is more complex than managing the day-to-day affairs of an organization. When the project dimensions are broad and multiple projects have to be monitored in different locations, the integrated management becomes even more complicated. One of the main concerns of project managers is the integrated project management, which is mainly rooted in the lack of accurate and accessible information from different projects in various locations. The collection of dispersed information from various parts of the network, their integration and finally the selective reporting of this information is among the goals of integrated information systems. It can help resolve the main problem, which is bridging the information gap between executives and senior managers in the organization. Therefore, the main objective of this study is to design and implement an important subset of a project management information system in order to successfully control the cost of construction projects so that its results can be used to design raw software forms and proposed relationships between different project units for the collection of necessary information.

Keywords: financial performance, cost subsystem, PMIS, project management

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Authors: N. W. U. D. Chathurani, Shlomo Geva, Vinod Chandran, Proboda Rajapaksha

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Selecting an appropriate image representation is the most important factor in implementing an effective Content-Based Image Retrieval (CBIR) system. This paper presents a multi-feature fusion approach for efficient CBIR, based on the distance distribution of features and relative feature weights at the time of query processing. It is a simple yet effective approach, which is free from the effect of features' dimensions, ranges, internal feature normalization and the distance measure. This approach can easily be adopted in any feature combination to improve retrieval quality. The proposed approach is empirically evaluated using two benchmark datasets for image classification (a subset of the Corel dataset and Oliva and Torralba) and compared with existing approaches. The performance of the proposed approach is confirmed with the significantly improved performance in comparison with the independently evaluated baseline of the previously proposed feature fusion approaches.

Keywords: feature fusion, image retrieval, membership function, normalization

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

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Authors: Su Mon Saw, Joe Rothnagel

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Short open reading frames (sORFs) located in 5’UTR of mRNAs are known as uORFs. Characterization of uORF-encoded peptides (uPEPs) i.e., a subset of short open reading frame encoded peptides (sPEPs) and their translation regulation lead to understanding of causes of genetic disease, proteome complexity and development of treatments. Existence of uORFs within cellular proteome could be detected by LC-MS/MS. The ability of uORF to be translated into uPEP and achievement of uPEP identification will allow uPEP’s characterization, structures, functions, subcellular localization, evolutionary maintenance (conservation in human and other species) and abundance in cells. It is hypothesized that a subset of sORFs are translatable and that their encoded sPEPs are functional and are endogenously expressed contributing to the eukaryotic cellular proteome complexity. This project aimed to investigate whether sORFs encode functional peptides. Liquid chromatography-mass spectrometry (LC-MS) and bioinformatics were thus employed. Due to probable low abundance of sPEPs and small in sizes, the need for efficient peptide enrichment strategies for enriching small proteins and depleting the sub-proteome of large and abundant proteins is crucial for identifying sPEPs. Low molecular weight proteins were extracted using SDS-PAGE from Human Embryonic Kidney (HEK293) cells and Strong Cation Exchange Chromatography (SCX) from secreted HEK293 cells. Extracted proteins were digested by trypsin to peptides, which were detected by LC-MS/MS. The MS/MS data obtained was searched against Swiss-Prot using MASCOT version 2.4 to filter out known proteins, and all unmatched spectra were re-searched against human RefSeq database. ProteinPilot v5.0.1 was used to identify sPEPs by searching against human RefSeq, Vanderperre and Human Alternative Open Reading Frame (HaltORF) databases. Potential sPEPs were analyzed by bioinformatics. Since SDS PAGE electrophoresis could not separate proteins <20kDa, this could not identify sPEPs. All MASCOT-identified peptide fragments were parts of main open reading frame (mORF) by ORF Finder search and blastp search. No sPEP was detected and existence of sPEPs could not be identified in this study. 13 translated sORFs in HEK293 cells by mass spectrometry in previous studies were characterized by bioinformatics. Identified sPEPs from previous studies were <100 amino acids and <15 kDa. Bioinformatics results showed that sORFs are translated to sPEPs and contribute to proteome complexity. uPEP translated from uORF of SLC35A4 was strongly conserved in human and mouse while uPEP translated from uORF of MKKS was strongly conserved in human and Rhesus monkey. Cross-species conserved uORFs in association with protein translation strongly suggest evolutionary maintenance of coding sequence and indicate probable functional expression of peptides encoded within these uORFs. Translation of sORFs was confirmed by mass spectrometry and sPEPs were characterized with bioinformatics.

Keywords: bioinformatics, HEK293 cells, liquid chromatography-mass spectrometry, ProteinPilot, Strong Cation Exchange Chromatography, SDS-PAGE, sPEPs

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Lauren Zammerilla Westcott, Ronald C. Jones, James W. Fleshman

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The surgical treatment of breast cancer has rapidly evolved over the past 50 years, progressing from Halsted’s radical mastectomy to a public campaign of surgical options, aesthetic reconstruction, and patient empowerment. This article examines the happenings that led to the transition of breast surgery as a subset of general surgery to its own specialized field. Sparked by the research of Dr. Bernard Fisher and the first National Surgical Adjuvant Breast and Bowel Project trial in 1971, the field of breast surgery underwent significant growth over the next several decades, enabling general surgeons to limit their practices to the breast. High surgical volumes eventually led to the development of the first formal breast surgical oncology fellowship in a large community-based hospital at Baylor University Medical Center in 1982. The establishment of the American Society of Breast Surgeons, as well several landmark clinical trials and public campaign efforts, further contributed to the advancement of breast surgery, making it the specialized field of the current era.

Keywords: breast cancer, breast fellowship, breast surgery, surgical history

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Authors: Johannes J. Schneider, Mathias S. Weyland, Peter Eggenberger Hotz, William D. Jamieson, Oliver Castell, Alessia Faggian, Rudolf M. Füchslin

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In the description of the origin of life, there are still some open gaps, e.g., the formation of macromolecules cannot be fully explained so far. The Kauffman model proposes the existence of autocatalytic sets of macromolecules which mutually catalyze reactions leading to each other’s formation. Usually, this model is simulated in one well-stirred pot only, with a continuous inflow of small building blocks, from which larger molecules are created by a set of catalyzed ligation and cleavage reactions. This approach represents the picture of the primordial soup. However, the conditions on the early Earth must have differed geographically, leading to spatially different outcomes whether a specific reaction could be performed or not. Guided by this picture, the Kauffman model is simulated in a large number of containers in parallel, with neighboring containers being connected by diffusion. In each container, only a subset of the overall reaction set can be performed. Under specific conditions, this approach leads to a larger probability for the existence of an autocatalytic metabolism than in the original Kauffman model.

Keywords: agglomeration, autocatalytic set, differential equation, Kauffman model

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Authors: Margaret Ryan, Linda Cupples, Lyndsey Nickels, Paul Sowman

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The current investigation explored the thematic roles of the nouns specified in the lexical entries of experiencer verbs. While prior experimental research assumes experiencer and theme roles for both subject-experiencer (SE) and object-experiencer (OE) verbs, syntactic theorists have posited additional agent and causer roles. Experiment 1 provided evidence for an agent as participants assigned a high degree of intentionality to the logical subject of a subset of SE and OE actives and passives. Experiment 2 provided evidence for a causer as participants assigned high levels of causality to the logical subjects of experiencer sentences generally. However, the presence of an agent, but not a causer, coincided with processing ease. Causality may be an aspect rather than a thematic role. The varying thematic roles amongst experiencer-verb sentences have important implications for stimulus selection because we cannot presume processing is similar across differing sentence subtypes.

Keywords: sentence comprehension, lexicon, canonicity, processing, thematic roles, syntax

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

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The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

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Authors: Neil Morgan

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The notion of Threshold Concepts has offered a fertile new perspective on the transformative effects of mastery of particular concepts on student understanding of subject matter and their developing identities as inductees into disciplinary discourse communities. Only by successfully traversing key knowledge thresholds, it is claimed, can neophytes gain access to the more sophisticated understandings of subject matter possessed by mature members of a discipline. This paper uses thematic analysis of disciplinary guiding principles to identify nine candidate Threshold Concepts that appear to underpin effective TESOL practice. The relationship between these candidate TESOL Threshold Concepts, TESOL principles, and TESOL instructional techniques appears to be amenable to a schematic representation based on superordinate categories of TESOL practitioner concern and, as such, offers an alternative to the view of Threshold Concepts as a privileged subset of disciplinary core concepts. The paper concludes by exploring the potential of a Threshold Concepts framework to productively inform TESOL initial teacher education (ITE) and in-service education and training (INSET).

Keywords: TESOL, threshold concepts, TESOL principles, TESOL ITE/INSET, community of practice

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: A. Lambert-Mogiliansky

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In this paper, we investigate the properties of simple rules for reappointment aimed at holding a public official accountable and monitor his activity. The public official allocates budget resources to various activities which results in the delivery of public services to citizens. He has discretion over the use of resource so he can divert some of them for private ends. Because of a liability constraint, zero diversion can never be secured in all states. The optimal reappointment mechanism under complete information is shown to exhibit some leniency thus departing from the zero tolerance principle. Under asymmetric information (about the state), a rule with random verification in a pre-announced subset is shown to be optimal in a class of common rules. Surprisingly, those common rules make little use of hard information about service delivery when available. Similarly, PO's claim about his record is of no value to improve the performance of the examined rules. In contrast requesting that the PO defends his records publicly can be very useful if the service users are given the chance to refute false claims with cheap talk complaints: the first best complete information outcome can be approached in the absence of any observation by the manager of the accountability mechanism.

Keywords: accountability, corruption, persuasion, debate

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Authors: Neda Mehdipour, Mohammad Hasan Gerami, Reza Rahnama, Ali Hamzehpour, Hanieh Nemati

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Laurencia caspica (red macroalgae) Enteromorpha intestinalis and Cladophora glomerata (green macroalgae) are three major macroalgae that grow along the southern coasts of the Caspian Sea. We investigated spatial and temporal variation of these three macroalgal species on hard substrates and their relation with environmental factors in 2014. Sampling was done seasonally from spring to winter 2014 from eight sites. Results indicated that of these three species had heterogeneity distribution along southern parts of the Caspian Sea. In addition, C. glomerata was dominant taxa in all stations and had maximum contribution in dissimilarities between sampling sites. According to BIO-ENV salinity, pH and Silicate were the best subset variables for explaining changes in the abundance over time of the hard-substrates macroalgae fauna under study. However, the position of species in Redundancy Analysis (RDA) plot revealed that L. caspica associated with temperature, E. intestinalis with pH and C. glomerata associated with phosphate and silicate.

Keywords: macroalgae, distribution, environmental factors, Caspian Sea

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Authors: Javier E. García-Gallo, Nelson J. Fonseca-Ruiz, John F. Duitama-Munoz

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Sepsis is a syndrome that occurs with physiological and biochemical abnormalities induced by severe infection and carries a high mortality and morbidity, therefore the severity of its condition must be interpreted quickly. After patient admission in an intensive care unit (ICU), it is necessary to synthesize the large volume of information that is collected from patients in a value that represents the severity of their condition. Traditional severity of illness scores seeks to be applicable to all patient populations, and usually assess in-hospital mortality. However, the use of machine learning techniques and the data of a population that shares a common characteristic could lead to the development of customized mortality prediction scores with better performance. This study presents the development of a score for the one-year mortality prediction of the patients that are admitted to an ICU with a sepsis diagnosis. 5650 ICU admissions extracted from the MIMICIII database were evaluated, divided into two groups: 70% to develop the score and 30% to validate it. Comorbidities, demographics and clinical information of the first 24 hours after the ICU admission were used to develop a mortality prediction score. LASSO (least absolute shrinkage and selection operator) and SGB (Stochastic Gradient Boosting) variable importance methodologies were used to select the set of variables that make up the developed score; each of this variables was dichotomized and a cut-off point that divides the population into two groups with different mean mortalities was found; if the patient is in the group that presents a higher mortality a one is assigned to the particular variable, otherwise a zero is assigned. These binary variables are used in a logistic regression (LR) model, and its coefficients were rounded to the nearest integer. The resulting integers are the point values that make up the score when multiplied with each binary variables and summed. The one-year mortality probability was estimated using the score as the only variable in a LR model. Predictive power of the score, was evaluated using the 1695 admissions of the validation subset obtaining an area under the receiver operating characteristic curve of 0.7528, which outperforms the results obtained with Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS) and Simplified Acute Physiology Score II (SAPSII) scores on the same validation subset. Observed and predicted mortality rates within estimated probabilities deciles were compared graphically and found to be similar, indicating that the risk estimate obtained with the score is close to the observed mortality, it is also observed that the number of events (deaths) is indeed increasing as the outcome go from the decile with the lowest probabilities to the decile with the highest probabilities. Sepsis is a syndrome that carries a high mortality, 43.3% for the patients included in this study; therefore, tools that help clinicians to quickly and accurately predict a worse prognosis are needed. This work demonstrates the importance of customization of mortality prediction scores since the developed score provides better performance than traditional scoring systems.

Keywords: intensive care, logistic regression model, mortality prediction, sepsis, severity of illness, stochastic gradient boosting

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Authors: Mohaned G. Abed

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Attention Deficit Hyperactivity Disorder (ADHD) is considered one of the most frequently diagnosed psychiatric childhood disorders. It has an effect on 3–5% of school-aged children, and brings about difficulties in academic and social interaction. This study explored the knowledge and beliefs of parents in Saudi Arabia about children with ADHD. The Knowledge about Attention Deficit Disorder Questionnaire (KADD-Q) was administered to a sample of parents, followed by interviews with a subset of the total respondents. The results indicated that the parents knew more about the characteristics of ADHD than they knew about its related causes and treatment. Overall, the findings indicated that these parents had some knowledge about general characteristics of ADHD, but they had little understanding of causes and possible interventions. These results suggest an important need for more formal parents training regarding all aspects of ADHD in school age children.

Keywords: attention deficit hyperactivity disorder, childhood disorders, school-aged children, difficulties in academic, social interaction

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Authors: F. N. U. Abdullah, A. M. H. Ibrahim, Shuyu Liu

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Hybrid wheat root system is the major plant organ for water and nutrient acquisition. An initial wheat root study with Winrhizo scanner showed that entries with high root surface area but narrow root angle are associated with past drought tolerance in Texas, while those with wide angle can perform best under normal growing conditions. In a hybrid field experiment, commercial heterosis up to 8.3% in grain yield was obtained from diverse parents selected from male and female diverse groups. These hybrids showed promising yield at Greenville and McGregor, Texas, with grain yield up to 4412 Kg ha⁻¹ as compared to best performing commercial varieties ‘TAM-304’ (4075 Kg ha⁻¹) and ‘Gallagher’ (3981 Kg ha⁻¹). Among 130 hybrids produced, a subset of 50 better-performing hybrids and parents was subjected to one-month-old plant root studies scanned with Winrhizo. The results showed a significant positive correlation of grain yield with initial root angle and a negative correlation with root length.

Keywords: hybrids, root studies, heterosis, wheat breeding

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Authors: Saba Latif

Abstract:

The use of social media among university students is a topic of ongoing debate, with conflicting views on its impact on academic achievement. This study aimed to explore the relationship between social media use and academic achievement among university students and to identify factors that may contribute to positive or negative effects. The study used a mixed-methods design, including a survey of 500 university students and qualitative interviews with a subset of participants. The survey results showed that social media use was prevalent among students, with Facebook and Instagram are the most commonly used platforms. The findings also indicated a positive relationship between social media use and academic achievement, with students who reported higher levels of social media use also reporting higher GPAs. However, the qualitative interviews revealed that excessive use of social media could be a distraction that hinders academic performance, especially when students use it to procrastinate or to stay up late at night. Overall, the findings suggest that social media use can have both positive and negative effects on academic achievement among university students. Responsible and balanced use of social media, such as setting limits on usage and avoiding procrastination, may help students maximize the benefits while minimizing the risks.

Keywords: social media, university, achievement, effective, learning

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Aliasghar Arab

Abstract:

Autonomous robots interacting with humans, as safety-critical nonlinear control systems, are complex closed-loop cyber-physical dynamical machines. Keeping these intelligent yet complicated systems safe and smooth during their operations is challenging. The aim of the safe predictive output feedback linearization control synthesis is to design a novel controller for smooth trajectory following while unsafe situations must be avoided. The controller design should obtain a linearized output for smoothness and invariance to a safety subset. Inspired by finite-horizon nonlinear model predictive control, the problem is formulated as constrained nonlinear dynamic programming. The safety constraints can be defined as control barrier functions. Avoiding unsafe maneuvers and performing smooth motions increases the predictability of the robot’s movement for humans when robots and people are working together. Our results demonstrate the proposed output linearization method obeys the safety constraints and, compared to existing safety-guaranteed methods, is smoother and performs better.

Keywords: robotics, collaborative robots, safety, autonomous robots

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Authors: Fatemeh Valizadeh Gamchi, Edward L. Boone

Abstract:

This research focuses on Lyme disease, a widespread infectious condition in the United States caused by the bacterium Borrelia burgdorferi sensu stricto. It is critical to identify environmental and economic elements that are contributing to the spread of the disease. This study examined data from Virginia to identify a subset of explanatory variables significant for Lyme disease case numbers. To identify relevant variables and avoid overfitting, linear poisson, and regularization regression methods such as a ridge, lasso, and elastic net penalty were employed. Cross-validation was performed to acquire tuning parameters. The methods proposed can automatically identify relevant disease count covariates. The efficacy of the techniques was assessed using four criteria on three simulated datasets. Finally, using the Virginia Department of Health’s Lyme disease data set, the study successfully identified key factors, and the results were consistent with previous studies.

Keywords: lyme disease, Poisson generalized linear model, ridge regression, lasso regression, elastic net regression

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Authors: Mariem Kchaou, Wiem Khlif, Faiez Gargouri

Abstract:

Modeling business processes is an essential task when evaluating, improving, or documenting existing business processes. To be efficient in such tasks, a business process model (BPM) must have high structural quality and high performance. Evidently, evaluating the performance of a business process model is a necessary step to reduce time, cost, while assessing the structural quality aims to improve the understandability and the modifiability of the BPMN model. To achieve these objectives, a set of structural and performance measures have been proposed. Since the diversity of measures, we propose a framework that integrates both structural and performance aspects for classifying them. Our measure classification is based on business process model perspectives (e.g., informational, functional, organizational, behavioral, and temporal), and the elements (activity, event, actor, etc.) involved in computing the measures. Then, we implement this framework in a tool assisting the structural quality and the performance of a business process. The tool helps the designers to select an appropriate subset of measures associated with the corresponding perspective and to calculate and interpret their values in order to improve the structural quality and the performance of the model.

Keywords: performance, structural quality, perspectives, tool, classification framework, measures

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Authors: Lobna Cherif, Valerie Wood

Abstract:

The importance of both character and resilience for military members has been emphasized at the highest levels of military leadership. Initial research suggests that the presence of character strengths might be relevant in predicting success and well-being for some military populations (e.g., recruits). In this presentation, we will first review our research investigating the perceived importance of character strengths for Canadian military cadet (N = 134) success, the top strengths endorsed by cadets, and, in a subset of cadets (n = 94), the relationships among core strengths and resilience. Participants first completed a survey comprised of a resilience measure and demographic items, then one month later completed a Values in Action (VIA) character strengths profile, questions related to character strengths (their personal top-five character strengths, and strengths they believed were important for military-related stressors and leadership, academic success, resilience, and completion of the military challenge). Findings indicated that military cadets consider (among others), perseverance, judgment, and teamwork to be most critical for bouncing back from stressors. However, the most frequently endorsed strengths that characterized cadets were bravery, honesty, and perseverance. Finally, perseverance, bravery, and humor were positively correlated with cadet resilience, while endorsement of love was negatively correlated with resilience.

Keywords: character strengths, leadership, positive psychology, resilience

Procedia PDF Downloads 163