Search results for: twinning variant selection

Authors: Geetanjali Panda, Suvrakanti Chakraborty

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In this paper, Newton-like descent methods are proposed for unconstrained optimization problems, which use q-derivatives of the gradient of an objective function. First, a local scheme is developed with alternative sufficient optimality condition, and then the method is extended to a global scheme. Moreover, a variant of practical Newton scheme is also developed introducing a real sequence. Global convergence of these schemes is proved under some mild conditions. Numerical experiments and graphical illustrations are provided. Finally, the performance profiles on a test set show that the proposed schemes are competitive to the existing first-order schemes for optimization problems.

Keywords: Descent algorithm, line search method, q calculus, Quasi Newton method

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Authors: H. Akhloufi, M. Hulscher, J. M. Prins, I. H. Van Der Sijs, D. Melles, A. Verbon

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Background: A timely switch from intravenous to oral antibiotic therapy has many advantages, such as reduced incidence of IV-line related infections, a decreased hospital length of stay and less workload for healthcare professionals with equivalent patient safety. Additionally, numerous studies have demonstrated significant decreases in costs of a timely intravenous to oral antibiotic therapy switch, while maintaining efficacy and safety. However, a considerable variation in iv to oral antibiotic switch therapy criteria has been described in literature. Here, we report the development of a set of iv to oral switch criteria that are generally applicable in all hospitals. Material/methods: A RAND-modified Delphi procedure, which was composed of 3 rounds, was used. This Delphi procedure is a widely used structured process to develop consensus using multiple rounds of questionnaires within a qualified panel of selected experts. The international expert panel was multidisciplinary and composed out of clinical microbiologists, infectious disease consultants and clinical pharmacists. This panel of 19 experts appraised 6 major intravenous to oral antibiotic switch therapy criteria and operationalized these criteria using 41 measurable conditions extracted from the literature. The procedure to select a concise set of iv to oral switch criteria included 2 questionnaire rounds and a face-to-face meeting. Results: The procedure resulted in the selection of 16 measurable conditions, which operationalize 6 major intravenous to oral antibiotic switch therapy criteria. The following 6 major switch therapy criteria were selected: (1) Vital signs should be good or improving when bad. (2) Signs and symptoms related to the infection have to be resolved or improved. (3) The gastrointestinal tract has to be intact and functioning. (4) The oral route should not be compromised. (5) Absence of contra-indicated infections. (6) An oral variant of the antibiotic with good bioavailability has to exist. Conclusions: This systematic stepwise method which combined evidence and expert opinion resulted in a feasible set of 6 major intravenous to oral antibiotic switch therapy criteria operationalized by 16 measurable conditions. This set of early antibiotic iv to oral switch criteria can be used in daily practice in all adult hospital patients. Future use in audits and as rules in computer assisted decision support systems will lead to improvement of antimicrobial steward ship programs.

Keywords: antibiotic resistance, antibiotic stewardship, intravenous to oral, switch therapy

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Zarine Avetisyan

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Concepts are part and parcel of everyday text and talk. Their ubiquity predetermines the topicality of the given research which aims at the semantic decomposition of concepts in general and the concept of joy in particular, as well as the study of lexico-semantic variants as means of realization of a certain concept in different “semantic settings”, namely in a certain context. To achieve the stated aim, the given research departs from the methods of componential and contextual analysis, studying lexico-semantic variants /LSVs/ of the concept of joy and the semantic signs embedded in those LSVs, such as the semantic sign of intensity, supporting emotions, etc. in the context of Modern English fiction.

Keywords: concept, context, lexico-semantic variant, semantic sign

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Authors: Shiva Kumar, G. S. Vijay, Srinivas Pai P., Shrinivasa Rao B. R.

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In the present study RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tech and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: radial basis function networks, emissions, performance parameters, fuzzy c means

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Cigdem Safak Saglam

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Most thermal power plants use steam as working fluid in their power cycle. Therefore, in addition to fuel, water is the other main input for thermal plants. Water and steam must be highly pure in order to protect the systems from corrosion, scaling and biofouling. Pure process water is produced in water treatment plants having many several treatment methods. Treatment plant design is selected depending on raw water source and required water quality. Although working principle of fossil-fuel fired thermal power plants are same, there is no standard design and equipment arrangement valid for all thermal power plant utility systems. Besides that, there are many other technology evaluation and selection criteria for designing the most optimal water systems meeting the requirements such as local conditions, environmental restrictions, electricity and other consumables availability and transport, process water sources and scarcity, land use constraints etc. Aim of this study is explaining the adopted methodology for technology selection for process water preparation and industrial waste water treatment plant in a thermal power plant project located in Tufanbeyli, Adana Province in Turkey. Thermal power plant is fired with indigenous lignite coal extracted from adjacent lignite reserves. This paper addresses all above-mentioned factors affecting the thermal power plant water treatment facilities (demineralization + waste water treatment) design and describes the ultimate design of Tufanbeyli Thermal Power Plant Water Treatment Plant.

Keywords: thermal power plant, lignite coal, pretreatment, demineralization, electrodialysis, recycling, ash dampening

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Authors: Juliet Udoudom

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Inappropriate complement selection constitutes one of the major features of non-standard complementation in the Nigerian users of English output of sentence construction. This paper investigates complement clause choice in Written Educated Nigerian English (ENE) and offers some results. It aims at determining preferred and dispreferred patterns of complement clause selection in respect of verb heads in English by selected Nigerian users of English. The complementation data analyzed in this investigation were obtained from experimental tasks designed to elicit complement categories of Verb – Noun -, Adjective – and Prepositional – heads in English. Insights from the Government – Binding relations were employed in analyzing data, which comprised responses obtained from one hundred subjects to a picture elicitation exercise, a grammaticality judgement test, and a free composition task. The findings indicate a general tendency for clausal complements (CPs) introduced by the complementizer that to be preferred by the subjects studied. Of the 235 tokens of clausal complements which occurred in our corpus, 128 of them representing 54.46% were CPs headed by that, while whether – and if-clauses recorded 31.07% and 8.94%, respectively. The complement clause-type which recorded the lowest incidence of choice was the CP headed by the Complementiser, for with a 5.53% incident of occurrence. Further findings from the study indicate that semantic features of relevant embedding verb heads were not taken into consideration in the choice of complementisers which introduce the respective complement clauses, hence the that-clause was chosen to complement verbs like prefer. In addition, the dispreferred choice of the for-clause is explicable in terms of the fact that the respondents studied regard ‘for’ as a preposition, and not a complementiser.

Keywords: complement, complement clause complement selection, complementisers, government-binding

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Authors: B. Falah, N. E. Abakouy

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Due to its high computational cost, mutation testing has been neglected by researchers. Recently, many cost and mutants’ reduction techniques have been developed, improved, and experimented, but few of them has relied the possibility of reducing the cost of mutation testing on the program type of the application under test. This paper is a comparative study between four operators’ selection techniques (mutants sampling, class level operators, method level operators, and all operators’ selection) based on the program code type of each application under test. It aims at finding an alternative approach to reveal the effect of code type on mutation testing score. The result of our experiment shows that the program code type can affect the mutation score and that the programs using polymorphism are best suited to be tested with mutation testing.

Keywords: equivalent mutant, killed mutant, mutation score, mutation testing, program code type, software testing

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: P. Aarumugam, N. Krishnamoorthy, K. Gunasekaran

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The mosquitoes with loss of minimum three legs especially the hind legs have the negative impact on the survival hood of mosquitoes. Three days old unfed adult female laboratory strain was selected in each generation against sublethal dosages (0.004%, 0.005%, 0.007% and 0.01%) of deltamethrin upto 40 generations. Impregnated papers with acetone were used for control. Every fourth generation, survived mosquitoes were observed for functional mortality. Hind legs lost were significantly (P< 0.05) higher in treated than the controls up to generation 24, thereafter no significant lost. In contrary, no significant forelegs lost among exposed mosquitoes. Middle legs lost were also not significant in the exposed mosquitoes except first generation (F1). The field strain (Chennai) did not show any significant loss of legs (fore or mid or hind) compared to the control. The selection pressure on mosquito population influences strong natural selection to develop various adaptive mechanisms.

Keywords: Anopheles stephensi, deltamethrin, functional mortality, synthetic pyrethroids

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Authors: Dembele Dembele, Philippe

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Several Second Language Acquisition (SLA) studies have stressed the benefits of Content and Language Integrated Learning (CLIL) and shown how CLIL students outperformed their non-CLIL counterparts in many L2 skills. However, numerous experimental CLIL programs seem to have mainly targeted above-average and rather highly motivated language learners. The need to understand the impact of the student’s language proficiency on code-switching in CLIL instruction motivated this study. Therefore, determining the implications of the students’ low-language proficiency for CLIL methodology, as well as the frequency with which CLIL teachers use the main pedagogical functions of code-switching, seemed crucial for a Spanish CLIL instruction on a large scale. In the mixed-method approach adopted, ten face-to-face interviews were conducted in nine Valencian public secondary education schools, while over 30 CLIL teachers also contributed with their experience in two online survey questionnaires. The results showed the crucial role language proficiency plays in the Valencian CLIL/Plurilingual selection criteria. The presence of a substantial number of low-language proficient students in CLIL groups, which in turn implied important methodological consequences, was another finding of the study. Indeed, though the pedagogical use of L1 was confirmed as an extended practice among CLIL teachers, more than half of the participants perceived that code-switching impaired attaining their CLIL lesson objectives. Therein, the dissertation highlights the need for more extensive empirical research on how code-switching could prove beneficial in CLIL instruction involving low-language proficient students while maintaining the maximum possible exposure to the target language.

Keywords: CLIL methodology, low language proficiency, code switching, selection criteria, code-switching functions

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Authors: Vincent Liu

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Machine learning algorithms (ML) can achieve high accuracy in predicting outcomes compared to classical models. Metaheuristic, nature-inspired algorithms can enhance traditional ML algorithms by optimizing them such as by performing feature selection. We compare ten ML algorithms to predict 30-day hospital readmission rates for diabetes patients in the US using a dataset from UCI Machine Learning Repository with feature selection performed by Greywolf nature-inspired algorithm. The baseline accuracy for the initial random forest model was 65%. After performing feature engineering, SMOTE for class balancing, and Greywolf optimization, the machine learning algorithms showed better metrics, including F1 scores, accuracy, and confusion matrix with improvements ranging in 10%-30%, and a best model of XGBoost with an accuracy of 95%. Applying machine learning this way can improve patient outcomes as unnecessary rehospitalizations can be prevented by focusing on patients that are at a higher risk of readmission.

Keywords: diabetes, machine learning, 30-day readmission, metaheuristic

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Authors: Yoftahe Nigussie Worku

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This research focuses on advancing fire tube boiler technology by systematically optimizing design parameters to achieve efficient saturated steam production. The main objective is to design a high-performance boiler with a production capacity of 2000kg/h at a 12-bar design pressure while minimizing costs. The methodology employs iterative analysis, utilizing relevant formulas, and considers material selection and production methods. The study successfully results in a boiler operating at 85.25% efficiency, with a fuel consumption rate of 140.37kg/hr and a heat output of 1610kW. Theoretical importance lies in balancing efficiency, safety considerations, and cost minimization. The research addresses key questions on parameter optimization, material choices, and safety-efficiency balance, contributing valuable insights to fire tube boiler design.

Keywords: safety consideration, efficiency, production methods, material selection

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Authors: J. E. O. Hernandez

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In the next 20 years, energy production by burning fuels will increase and so will the atmospheric concentration of CO2 and its well-known threats to life on Earth. The technologies available for capturing CO2 are still dubious and this keeps fostering an interest in bio-inspired approaches. The leading one is the application of carbonic anhydrase (CA) –a superfast biocatalyst able to convert up to one million molecules of CO2 into carbonates in water. However, natural CA underperforms when applied to real smoky CO2 in chimneys and, so far, the efforts to create superior CAs in the lab rely on screening methods running under pristine conditions at the micro level, which are far from resembling those in chimneys. For the evolution of man-made enzymes, selection rather than screening would be ideal but this is challenging because of the need for a suitable artificial environment that is also sustainable for our society. Herein we present the stepwise design and construction of a bioprocess (from bench-scale to semi-pilot) for evolutionary selection experiments. In this bioprocess, reaction and adsorption took place simultaneously at atmospheric pressure in a spray tower. The scrubbing solution was fed countercurrently by reusing municipal pressure and it was mainly prepared with water, carbonic anhydrase and calcium chloride. This bioprocess allowed for the enzymatic carbonation of smoky CO2; the reuse of process water and the recovery of solid carbonates without cooling of smoke, pretreatments, solvent amines and compression of CO2. The average yield of solid carbonates was 0.54 g min-1 or 12-fold the amount produced in serum bottles at lab bench scale. This bioprocess could be used as a tailor-made environment for driving the selection of superior CAs. The bioprocess and its match CA could be sustainably used to reduce global warming by CO2 emissions from exhausts.

Keywords: biological carbon capture and sequestration, carbonic anhydrase, directed evolution, global warming

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Authors: Belinda Brunner

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Objectives: Communicate common test constructs of well-known higher education admissions tests. Discuss influences on admissions test construct definition and design and discuss research on related to factors influencing success in academic study. Discuss how admissions tests can be used to identify relevant talent. Examine how admissions test can be used to facilitate educational mobility and inform selection decisions when the prerequisite curricula is not standardized Observations: Generally speaking, constructs of admissions tests can be placed along a continuum from curriculum-related knowledge to more general reasoning abilities. For example, subject-specific achievement tests are more closely aligned to a prescribed curriculum, while reasoning tests are typically not associated with a specific curriculum. This session will draw reference from the test-constructs of well-known international higher education admissions tests, such as the UK clinical aptitude test (UKCAT) which is used for medicine and dentistry admissions. Conclusions: The purpose of academic admissions testing is to identify potential students with the prerequisite skills set needed to succeed in the academic environment, but how can the test construct help achieve this goal? Determination of the appropriate test construct for tests used in the admissions selection decisions should be influenced by a number of factors, including the preceding academic curricula, other criteria influencing the admissions decision, and the principal purpose for testing. Attendees of this session will learn the types of aptitudes and knowledge that are assessed higher education admissions tests and will have the opportunity to gain insight into how careful and deliberate consideration of the desired test constructs can aid in identifying potential students with the greatest likelihood of success in medical school.

Keywords: admissions, measuring success, selection, identify skills

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Authors: Bo Wang

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The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically.

Keywords: multi-source image geometric process, high precision geometric positioning, quotient space of multi-geometric features, optimized weight selection

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

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Authors: Hecson Christian, Joel Macwan

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Landfill site selection in an urban area is a critical issue in the planning process. With the growth of the urbanization, it has a mammoth impact on the economy, ecology, and environmental health of the region. Outsized amount of wastes are produced and the problem gets soared every day. Hence, selection of ideal site for sanitary landfill is a challenge for urban planners and solid waste managers. Disposal site is a function of many parameters. Among all, Geotechnical parameters are very vital as the same is related to surrounding open land. Moreover, the accessible safe and acceptable land is also scarce. Therefore, in this paper geotechnical parameters are used to develop a GIS model to identify an ideal location for landfill purpose. Metropolitan city of Surat is highly populated and fastest growing urban area in India. The research objectives are to conduct field experiments to collect data and to transfer the facts in GIS platform to evolve a model, to find ideal location. Planners’ preferences were obtained to use analytical hierarchical process (AHP) to find weights of each parameter. Integration of GIS and Multi-Criteria Decision Analysis (MCDA) techniques are applied to improve decision-making. It augments an environment for transformation and combination of geographical data and planners’ preferences. GIS performs deterministic overlay and buffer operations. MCDA methods evaluate alternatives based on the decision makers’ subjective values and priorities. Research results have shown many alternative locations. Economic analysis of selected site from actual operations point of view is not included in this research.

Keywords: GIS, AHP, MCDA, Geo-technical

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Authors: Ronak Khosravi, Mahmood Abbasi Layegh, Siamak Haghipour, Avin Esmaili

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In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.

Keywords: coefficient features, relevance vector machines, spectral features, support vector machines, temporal features

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Authors: Agnieszka H. Ludwig-Slomczynska, Michal T. Seweryn, Przemyslaw Kapusta, Ewelina Pitera, Katarzyna Cyganek, Urszula Mantaj, Lucja Dobrucka, Ewa Wender-Ozegowska, Maciej T. Malecki, Pawel Wolkow

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Obesity results from an imbalance between energy intake and its expenditure. Genome-Wide Association Study (GWAS) analyses have led to discovery of only about 100 variants influencing body mass index (BMI), which explain only a small portion of genetic variability. Analysis of gene epistasis gives a chance to discover another part. Since it was shown that interaction and communication between nuclear and mitochondrial genome are indispensable for normal cell function, we have looked for epistatic interactions between the two genomes to find their correlation with BMI. Methods: The analysis was performed on 366 T1DM patients using Illumina Infinium OmniExpressExome-8 chip and followed by imputation on Michigan Imputation Server. Only genes which influence mitochondrial functioning (listed in Human MitoCarta 2.0) were included in the analysis – variants of nuclear origin (MAF > 5%) in 1140 genes and 42 mitochondrial variants (MAF > 1%). Gene expression analysis was performed on GTex data. Association analysis between genetic variants and BMI was performed with the use of Linear Mixed Models as implemented in the package 'GENESIS' in R. Analysis of association between mRNA expression and BMI was performed with the use of linear models and standard significance tests in R. Results: Among variants involved in epistasis between mitochondria and nucleus we have identified one in mitochondrial transcription factor, TFB2M (rs6701836). It interacted with mitochondrial variants localized to MT-RNR1 (p=0.0004, MAF=15%), MT-ND2 (p=0.07, MAF=5%) and MT-ND4 (p=0.01, MAF=1.1%). Analysis of the interaction between nuclear variant rs6701836 (nuc) and rs3021088 localized to MT-ND2 mitochondrial gene (mito) has shown that the combination of the two led to BMI decrease (p=0.024). Each of the variants on its own does not correlate with higher BMI [p(nuc)=0.856, p(mito)=0.116)]. Although rs6701836 is intronic, it influences gene expression in the thyroid (p=0.000037). rs3021088 is a missense variant that leads to alanine to threonine substitution in the MT-ND2 gene which belongs to complex I of the electron transport chain. The analysis of the influence of genetic variants on gene expression has confirmed the trend explained above – the interaction of the two genes leads to BMI decrease (p=0.0308). Each of the mRNAs on its own is associated with higher BMI (p(mito)=0.0244 and p(nuc)=0.0269). Conclusıons: Our results show that nuclear-mitochondrial epistasis can influence BMI in T1DM patients. The correlation between transcription factor expression and mitochondrial genetic variants will be subject to further analysis.

Keywords: body mass index, epistasis, mitochondria, type 1 diabetes

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Authors: M. F. Yilmaz, B. Ö. Çağlayan

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Fragility curve is an effective common used tool to determine the earthquake performance of structural and nonstructural components. Also, it is used to determine the nonlinear behavior of bridges. There are many historical bridges in the Turkish railway network; the earthquake performances of these bridges are needed to be investigated. To derive fragility curve Intensity measures (IMs) and Engineering demand parameters (EDP) are needed to be determined. And the relation between IMs and EDP are needed to be derived. In this study, a typical simply supported steel girder riveted railway bridge is studied. Fragility curves of this bridge are derived by two parameters lognormal distribution. Time history analyses are done for selected 60 real earthquake data to determine the relation between IMs and EDP. Moreover, efficiency, practicality, and sufficiency of three different IMs are discussed. PGA, Sa(0.2s) and Sa(1s), the most common used IMs parameters for fragility curve in the literature, are taken into consideration in terms of efficiency, practicality and sufficiency.

Keywords: railway bridges, earthquake performance, fragility analyses, selection of intensity measures

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Authors: Steven Robinson, Adriana Dager, Param Patel

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Wandering spleen is a rare variant where there is abnormal development of the ligamentous peritoneal attachments of the spleen which normally anchor it in the left upper quadrant of the abdomen. Ligamentous abnormalities can be congenital, or acquired through pregnancy, injury, or iatrogenic causes. Absence or laxity of these ligaments allows migration of the spleen into ectopic portions of the abdomen, which is also associated with an elongated vascular pedicle. Incidence of wandering spleen is reported at less than 0.25% with a female to male ratio of approximately 6:1. The most common complication of a wandering spleen is torsion around its vascular pedicle which can lead to thrombosis and infarction. Torsion of a wandering spleen is a rare but important cause of an acute abdomen. Imaging, and specifically CT or ultrasound, is crucial in the diagnosis. We present a case of a torsed wandering spleen which was treated with splenectomy.

Keywords: Wandering Spleen, Torsion, Splenic Torsion, Spleen

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Authors: Radhwan Yousif Sedik Al-Jawadi

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Optimization is necessary for finding appropriate solutions to a range of real-life problems. In particular, genetic (or more generally, evolutionary) algorithms have proved very useful in solving many problems for which analytical solutions are not available. In this paper, we present an optimization algorithm called Dynamic Schema with Dissimilarity and Similarity of Chromosomes (DSDSC) which is a variant of the classical genetic algorithm. This approach constructs new chromosomes from a schema and pairs of existing ones by exploring their dissimilarities and similarities. To show the effectiveness of the algorithm, it is tested and compared with the classical GA, on 15 two-dimensional optimization problems taken from literature. We have found that, in most cases, our method is better than the classical genetic algorithm.

Keywords: chromosome injection, dynamic schema, genetic algorithm, similarity and dissimilarity

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Authors: Chen Chih-Cheng, Chen I-Cheng, Lee Yung-Tan, Kuo Yen-Whea, Yu Chin-Hung

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The purpose of this study is to construct a model for best spike player selection in a top volleyball tournament of the world. Data consisted of the records of 2013 World Grand Prix declared by International Volleyball Federation (FIVB). Simple Multiple-Attribute Rating Technique (SMART) was used for optimal decision-making model on the best spike player selection. The research results showed that the best spike player ranking by SMART is different than the ranking by FIVB. The results demonstrated the effectiveness and feasibility of the proposed model.

Keywords: simple multiple-attribute rating technique, World Grand Prix, best spike player, International Volleyball Federation

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Authors: Christian Doppler, Gernot Hirschl, Gerhard Zsiga

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Starting from 2020, an EU-wide CO2-limitation of 95g/km is scheduled for the average of an OEMs passenger car fleet. Considering that, further measures of optimization on the diesel cycle will be necessary in order to reduce fuel consumption and emissions while keeping performance values adequate at the least. The present article deals with charge air cooling (CAC) on the basis of a diesel passenger car model in a 0D/1D-working process calculation environment. The considered engine is a 2.4 litre EURO VI diesel engine with variable geometry turbocharger (VGT) and low-pressure exhaust gas recirculation (LP EGR). The object of study was the impact of charge air cooling on the engine working process at constant boundary conditions which could have been conducted with an available and validated engine model in AVL BOOST. Part load was realized with constant power and NOx-emissions, whereas full load was accomplished with a lambda control in order to obtain maximum engine performance. The informative results were used to implement a simulation model in Matlab/Simulink which is further integrated into a full vehicle simulation environment via coupling with ICOS (Independent Co-Simulation Platform). Next, the dynamic engine behavior was validated and modified with load steps taken from the engine test bed. Due to the modular setup in the Co-Simulation, different CAC-models have been simulated quickly with their different influences on the working process. In doing so, a new cooler variation isn’t needed to be reproduced and implemented into the primary simulation model environment, but is implemented quickly and easily as an independent component into the simulation entity. By means of the association of the engine model, longitudinal dynamics vehicle model and different CAC models (air/air & water/air variants) in both steady state and transient operational modes, statements are gained regarding fuel consumption, NOx-emissions and power behavior. The fact that there is no more need of a complex engine model is very advantageous for the overall simulation volume. Beside of the simulation with the mentioned demonstrator engine, there have also been conducted several experimental investigations on the engine test bench. Here the comparison of a standard CAC with an intake-manifold-integrated CAC was executed in particular. Simulative as well as experimental tests showed benefits for the water/air CAC variant (on test bed especially the intake manifold integrated variant). The benefits are illustrated by a reduced pressure loss and a gain in air efficiency and CAC efficiency, those who all lead to minimized emission and fuel consumption for stationary and transient operation.

Keywords: air/water-charge air cooler, co-simulation, diesel working process, EURO VI fuel consumption

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Authors: Abdullah Bilal Ozturk, Nil Acarali, Hediye Irem Ozgunduz, Hava Gizem Kandilci, Hanifi Sarac

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In this study, zeolite, shellac and different boron chemicals were used as additive to dye and effects were comprehensively investigated. Considering previous studies additive materials that had not used before were determined for produce dye with physical properties. Literature research about the materials provides determining easily sufficient amount of additive materials. Accessible of additives or yearly production amounts are become important issue at selection of materials. Zeolite and boron chemicals are suitable selection in that easy access and has large amount of production in our country. Previous research about boron chemicals shows they have flame retardant effect on textile materials besides numerous usage areas. Also, from previous research, shellac was used widely for protection and insulation of metallic materials. Zeolite added to dye to increase adhesive effect of dye. In this study, corrosion tests were applied to find out if there are positive effects of zeolite, shellac, and boron chemicals to dye’s physical properties.

Keywords: dye, corrosion, zeolite, shellac, boron

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Authors: Olanrewaju Kafayat, O., Salman Kabir, K.

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The role of organic agriculture in providing food and income is now gaining wider recognition (Van Elzakker et al 2007). The increasing public concerns about food safety issues on the use of fertilizers, pesticide residues, growth hormones, GM organisms, and increasing awareness of environmental quality issues have led to an expanding demand for environmentally friendly products (Thompson, 1998; Rimal et al., 2005). As a result national governments are concerned about diet and health, and there has been renewed recognition of the role of public policy in promoting healthy diets, thus to provide healthier, safer, more confident citizens (Poole et al., 2007), With these benefits, a study into organic vegetables is very vital to all the major stakeholders. This study analyzed the willingness of consumers to pay for organic vegetables in Oyo state, Nigeria. Primary data was collected with the aid of structured questionnaire administered to 168 respondents. These were selected using multistage random sampling. The first stage involved the selection two (2) ADP zones out of the three (3) ADP zones in Oyo state, The second stage involved the random selection of two (2) local government areas each out of the two (2) ADP zones which are; Ibadan South West and Ogbomoso North and random selection of 4 wards each from the local government areas. The third stage involved random selection of 42 household each from of the local government areas. Descriptive statistics, the principal component analysis, and the logistic regression were used to analyze the data. Results showed 55 percent of the respondents were female while 80 percent were  50 years. 74 percent of the respondents agreed that organic vegetables are of better quality. 31 percent of the respondents were aware of organic vegetables as against 69 percent who were not aware. From the logistic model, educational attainment, amount spent on organic vegetables monthly, better quality of organic vegetables and accessibility to organic vegetables were significant and had a positive relationship on willingness to pay for organic vegetable. The variables that were significant and had a negative relationship with WTP are less attractiveness of organic vegetables and household size of the respondents. This study concludes that consumers with higher level of education were more likely to be aware and willing to pay for organic vegetables than those with low levels of education, the study therefore recommends creation of awareness on the relevance of consuming organic vegetables through effective marketing and educational campaigns.

Keywords: consumers awareness, willingness to pay, organic vegetables, Oyo State

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Authors: H. Zidoum, A. AlShareedah, S. Al Sawafi, A. Al-Ansari, B. Al Lawati

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Systemic lupus erythematosus (SLE) is an autoimmune disease with genetic and environmental components. SLE is characterized by a wide variability of clinical manifestations and a course frequently subject to unpredictable flares. Despite recent progress in classification tools, the early diagnosis of SLE is still an unmet need for many patients. This study proposes an interpretable disease classification model that combines the high and efficient predictive performance of CatBoost and the model-agnostic interpretation tools of Shapley Additive exPlanations (SHAP). The CatBoost model was trained on a local cohort of 219 Omani patients with SLE as well as other control diseases. Furthermore, the SHAP library was used to generate individual explanations of the model's decisions as well as rank clinical features by contribution. Overall, we achieved an AUC score of 0.945, F1-score of 0.92 and identified four clinical features (alopecia, renal disorders, cutaneous lupus, and hemolytic anemia) along with the patient's age that was shown to have the greatest contribution on the prediction.

Keywords: feature selection, classification, systemic lupus erythematosus, model interpretation, SHAP, Catboost

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Authors: A. Robbin

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Language selection can be an efficient negotiation strategy employed by both service or product providers and their customers to achieve transactional success. The transactional scenario in Gbagi Market, Ibadan, Nigeria provides an appropriate setting for the exploration of the Nigerian multilingual situation with its own interesting linguistic peculiarities which questions the functionality of the ‘Lingua Franca’ in trade situations. This study examined English Language proficiency among Yoruba Traders in Gbagi Market, Ibadan and its use as determinants of transactional success during service encounters. Randomly selected Yoruba-English bilingual traders and customers were administered questionnaires and the data subjected to statistical and descriptive analysis using Giles Communication Accommodation Theory. Findings reveal that only fifty percent of the traders used for the study were proficient in speaking English language. Traders with minimal proficiency in Standard English, however, resulted in the use of the Nigerian Pidgin English. Both traders and customers select the Mother Tongue, which is the Yoruba Language during service encounters but are quick to converge to the other’s preferred language as the transactional exchange demands. The English language selection is not so much for the prestige or lingua franca status of the language as it is for its functions, which include ease of communication, negotiation, and increased sales. The use of English during service encounters is mostly determined by customer’s linguistic preference which the trader accommodates to for better negotiation and never as a first choice. This convergence is found to be beneficial as it ensures sales and return patronage. Although the English language is not a preferred code choice in Gbagi Market, it serves a functional trade strategy for transactional success during service encounters in the market.

Keywords: communication accommodation theory, language selection, proficiency, service encounter, transaction

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