Search results for: testing benchmark

Authors: Mazoun Nasser Rashid Al Kharusi, Nada Al Siyabi

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Objectives: The main aim of this study is to Find the rate of susceptibility of ESBL E. coli causing UTI to oral antibiotics. Secondary objectives: Prevalence of ESBL E. coli from community urine samples, identify the best empirical oral antibiotics with the least resistance rate for UTI and identify alternative oral antibiotics for testing and utilization. Methods: This study is a retrospective descriptive study of the last three years in five major hospitals in Oman (Khowla Hospital, AN’Nahdha Hospital, Rustaq Hospital, Nizwa Hospital, and Ibri Hospital) equipped with a microbiologist. Inclusion criteria include all eligible outpatient urine culture isolates, excluding isolates from admitted patients with hospital-acquired urinary tract infections. Data was collected through the MOH database. The MOH hospitals are using different types of testing, automated methods like Vitek2 and manual methods. Vitek2 machine uses the principle of the fluorogenic method for organism identification and a turbidimetric method for susceptibility testing. The manual method is done by double disc diffusion for identifying ESBL and the disc diffusion method is for antibiotic susceptibility. All laboratories follow the clinical laboratory science institute (CLSI) guidelines. Analysis was done by SPSS statistical package. Results: Total urine cultures were (23048). E. coli grew in (11637) 49.6% of the urine, whereas (2199) 18.8% of those were confirmed as ESBL. As expected, the resistance rate to amoxicillin and cefuroxime is 100%. Moreover, the susceptibility of those ESBL-producing E. coli to nitrofurantoin, trimethoprim+sulfamethoxazole, ciprofloxacin and amoxicillin-clavulanate is progressing over the years; however, still low. ESBL E. coli was predominating in the female gender and those aged 66-74 years old throughout all the years. Other oral antibiotic options need to be explored and tested so that we add to the pool of oral antibiotics for ESBL E. coli causing UTI in the community. Conclusion: High rate of ESBL E. coli in urine from the community. The high resistance rates to oral antibiotics highlight the need for alternative treatment options for UTIs caused by these bacteria. Further research is needed to identify new and effective treatments for UTIs caused by ESBL-E. Coli.

Keywords: UTI, ESBL, oral antibiotics, E. coli, susceptibility

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Authors: Watchara Songserm, Teeradej Wuttipornpun

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This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average.

Keywords: capacitated MRP, genetic algorithm, linear programming, automotive industries, flow shop, application in industry

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Authors: Anita, Sari Septiani Tangke, Rusdina Bte Ladju, Nasrum Massi

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New diagnostic tools are urgently needed to interrupt the transmission of tuberculosis and multidrug-resistant tuberculosis. The microscopic-observation drug-susceptibility (MODS) assay is a rapid, accurate and simple liquid culture method to detect multidrug-resistant tuberculosis (MDR-TB). MODS were evaluated to determine a lower and same concentration of isoniazid and rifampin for detection of MDR-TB. Direct drug-susceptibility testing was performed with the use of the MODS assay. Drug-sensitive control strains were tested daily. The drug concentrations that used for both isoniazid and rifampin were at the same concentration: 0.16, 0.08 and 0.04μg per milliliter. We tested 56 M. tuberculosis clinical isolates and the control strains M. tuberculosis H37RV. All concentration showed same result. Of 53 M. tuberculosis clinical isolates, 14 were MDR-TB, 38 were susceptible with isoniazid and rifampin, 1 was resistant with isoniazid only. Drug-susceptibility testing was performed with the use of the proportion method using Mycobacteria Growth Indicator Tube (MGIT) system as reference. The result of MODS assay using lower concentration was significance (P<0.001) compare with the reference methods. A lower and same concentration of isoniazid and rifampin can be used to detect MDR-TB. Operational cost and application can be more efficient and easier in resource-limited environments. However, additional studies evaluating the MODS using lower and same concentration of isoniazid and rifampin must be conducted with a larger number of clinical isolates.

Keywords: isoniazid, MODS assay, MDR-TB, rifampin

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Authors: Magdi M. Nabi, Ding-Li Yu

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This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.

Keywords: Chylla-Haase reactor, RBF neural network modelling, model predictive control, semi-batch reactors

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Authors: Hung-Yu Lin

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Background: Parts of researchers assert that external hyperactivity behaviors of ADHD children interfere with their abilities to perform internal cognitive tasks; however, there are still other researchers hold the opposite viewpoint, the external high level of activity may serve as the role of improving internal executive function.Objectives: Thisstudy explored the effects of external motor behavior of ADHD on internal visual attentional performance. Methods: A randomized, two-period crossover design was used in this study, a total of 80 children (aged 6-12) were recruited in this study. 40participants have received ADHD diagnosis, and others are children with typically developing. These children were measured through the visual edition of TOVA (The Test of Variables of Attention) when they wore actigraphy, their testing behavior and movement data werecollected through closely observation and the actigraphies under different research conditions. Result: According to the research result, the author found (1) Higherfrequencyof movement under attentional testing condition was found in children with ADHD, comparing to children with typically developing, and (2) Higher frequency of foot movement showed better attentional performance of the visual attentional test in children with ADHD. However, these results were not showed in children with typically developing. Conclusions: The findings support the functional working memory model, which advocated that a positive relation between gross motor activity and attentional performance within the context of attentive behavior in children with ADHD.

Keywords: ADHD, movement, visual attention, children

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Oloyede John Adebayo

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This paper tested a variant of the monetary model of exchange rate determination, called Frankel’s Capital Account Monetary Model (CAAM) based on Real Interest Rate Differential, on the floating exchange rate experiences of three developing countries of Africa; viz: Ghana, Nigeria and the Gambia. The study adopted the Auto regressive Instrumental Package (AIV) and Almon Polynomial Lag Procedure of regression analysis based on the assumption that the coefficients follow a third-order Polynomial with zero-end constraint. The results found some support for the CAAM hypothesis that exchange rate responds proportionately to changes in money supply, inversely to income and positively to interest rates and expected inflation differentials. On this basis, the study points the attention of monetary authorities and researchers to the relevance and usefulness of CAAM as appropriate tool and useful benchmark for analyzing the exchange rate behaviour of most developing countries.

Keywords: exchange rate, monetary model, interest differentials, capital account

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Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: S. H. Borghei, E. Teymourian, M. Mobin, G. M. Komaki, S. Sheikh

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Imperialist competitive algorithm (ICA) is a recent meta-heuristic method that is inspired by the social evolutions for solving NP-Hard problems. The ICA is a population based algorithm which has achieved a great performance in comparison to other meta-heuristics. This study is about developing enhanced ICA approach to solve the cell formation problem (CFP) using sequence data. In addition to the conventional ICA, an enhanced version of ICA, namely EICA, applies local search techniques to add more intensification aptitude and embed the features of exploration and intensification more successfully. Suitable performance measures are used to compare the proposed algorithms with some other powerful solution approaches in the literature. In the same way, for checking the proficiency of algorithms, forty test problems are presented. Five benchmark problems have sequence data, and other ones are based on 0-1 matrices modified to sequence based problems. Computational results elucidate the efficiency of the EICA in solving CFP problems.

Keywords: cell formation problem, group technology, imperialist competitive algorithm, sequence data

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Authors: Jeugert Kujtila, Kristi Hoxhalli, Ramazan Dalipi, Erjon Cota, Ardit Murati, Erind Bedalli

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Clustering is a versatile instrument in the analysis of collections of data providing insights of the underlying structures of the dataset and enhancing the modeling capabilities. The fuzzy approach to the clustering problem increases the flexibility involving the concept of partial memberships (some value in the continuous interval [0, 1]) of the instances in the clusters. Several fuzzy clustering algorithms have been devised like FCM, Gustafson-Kessel, Gath-Geva, kernel-based FCM, PCM etc. Each of these algorithms has its own advantages and drawbacks, so none of these algorithms would be able to perform superiorly in all datasets. In this paper we will experimentally compare FCM, GK, GG algorithm and a hybrid two-stage fuzzy clustering model combining the FCM and Gath-Geva algorithms. Firstly we will theoretically dis-cuss the advantages and drawbacks for each of these algorithms and we will describe the hybrid clustering model exploiting the advantages and diminishing the drawbacks of each algorithm. Secondly we will experimentally compare the accuracy of the hybrid model by applying it on several benchmark and synthetic datasets.

Keywords: fuzzy clustering, fuzzy c-means algorithm (FCM), Gustafson-Kessel algorithm, hybrid clustering model

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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Authors: Selcuk Aslan, Dervis Karaboga, Celal Ozturk

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Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm.

Keywords: Artificial Bee Colony algorithm, GPU computing, swarm intelligence, parallelization

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Authors: Sakul Jariyachansit

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The purposes of this research were to investigate and to compare the factors affecting the decision for Tourism in Thailand by ASEAN Tourists and among ASEAN community tourists. Samples in this research were 400 ASEAN Community Tourists who travel in Thailand at Suvarnabhumi Airport during November 2016 - February 2016. The researchers determined the sample size by using the formula Taro Yamane at 95% confidence level tolerances 0.05. The English questionnaire, research instrument, was distributed by convenience sampling, for gathering data. Descriptive statistics was applied to analyze percentages, mean and standard deviation and used for hypothesis testing. The statistical analysis by multiple regression analysis (Multiple Regression) was employed to prove the relationship hypotheses at the significant level of 0.01. The results showed that majority of the respondents indicated the factors affecting the decision for Tourism in Thailand by ASEAN Tourists, in general there were a moderate effects and the mean of each side is moderate. Transportation was the most influential factor for tourism in Thailand. Therefore, the mode of transport, information, infrastructure and personnel are very important to factor affecting decision making for tourism in Thailand by ASEAN tourists. From the hypothesis testing, it can be predicted that the decision for choosing Tourism in Thailand is at R2 = 0.449. The predictive equation is decision for choosing Tourism in Thailand = 1.195 (constant value) + 0.425 (tourist attraction) +0.217 (information received) and transportation factors, tourist attraction, information, human resource and infrastructure at the significant level of 0.01.

Keywords: factor, decision making, ASEAN tourists, tourism in Thailand

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Authors: Jalal M. Abdallah

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There is a great problem in testing and investigations the reliability of different type of transformers insulation materials. It summarized in how to create and simulate the real conditions of working transformer and testing its insulation materials for Partial Discharge PD, typically as in the working mode. A lot of tests may give untrue results as the physical behavior of the insulation material differs under tests from its working condition. In this work, the real working conditions were simulated, and a large number of specimens have been tested. The investigations first stage, begin with choosing samples of different types of insulation materials (papers, pressboards, etc.). The second stage, the samples were dried in ovens at 105 C⁰and 0.01bar for 48 hours, and then impregnated with dried and gasless oil (the water content less than 6 ppm.) at 105 C⁰and 0.01bar for 48 hours, after so specimen cooling at room pressure and temperature for 24 hours. The third stage is investigating PD for the samples using ICM PD measuring device. After that, a continuous test on oil-impregnated insulation materials (paper, pressboards) was developed, and the phase resolved partial discharge pattern of PD signals was measured. The important of this work in providing the industrial sector with trusted high accurate measuring results based on real simulated working conditions. All the PD patterns (results) associated with a discharge produced in well-controlled laboratory condition. They compared with other previous and other laboratory results. In addition, the influence of different temperatures condition on the partial discharge activities was studied.

Keywords: transformers, insulation materials, voids, partial discharge

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Authors: Muhammad Ajmair

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This empirical study followed structural vector auto regression (SVAR) approach proposed by the so-called AB-model of Amisano and Giannini (1997) to check the impact of relevant macroeconomic determinants on economic growth in Pakistan. Before that auto regressive distributive lag (ARDL) bound testing technique and time varying parametric approach along with general to specific approach was employed to find out relevant significant determinants of economic growth. To our best knowledge, no author made such a study that employed auto regressive distributive lag (ARDL) bound testing and time varying parametric approach with general to specific approach in empirical literature, but current study will bridge this gap. Annual data was taken from World Development Indicators (2014) during period 1976-2014. The widely-used Schwarz information criterion and Akaike information criterion were considered for the lag length in each estimated equation. Main findings of the study are that remittances received, gross national expenditures and inflation are found to be the best relevant positive and significant determinants of economic growth. Based on these empirical findings, we conclude that government should focus on overall economic growth augmenting factors while formulating any policy relevant to the concerned sector.

Keywords: economic growth, gross national expenditures, inflation, remittances

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Authors: Wanwisa Deenin, Abdulhadee Yakoh, Chahya Kreangkaiwal, Orawon Chailapakul, Kanitha Patarakul, Sudkate Chaiyo

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LipL32 is an outer membrane protein present only on pathogenic Leptospira species, which are the causative agent of leptospirosis. Leptospirosis symptoms are often misdiagnosed with other febrile illnesses as the clinical manifestations are non-specific. Therefore, an accurate diagnostic tool for leptospirosis is indeed critical for proper and prompt treatment. Typical diagnosis via serological assays is generally performed to assess the antibodies produced against Leptospira. However, their delayed antibody response and complicated procedure are undoubtedly limited the practical utilization especially in primary care setting. Here, we demonstrate for the first time an early-stage detection of LipL32 by an integrated lateral-flow immunoassay with electrochemical readout (eLFIA). A ferrocene trace tag was monitored via differential pulse voltammetry operated on a smartphone-based device, thus allowing for on-field testing. Superior performance in terms of the lowest detectable limit of detection (LOD) of 8.53 pg/mL and broad linear dynamic range (5 orders of magnitude) among other sensors available thus far was established. Additionally, the developed test strip provided a straightforward yet sensitive approach for diagnosis of leptospirosis using the collected human sera from patients, in which the results were comparable to the real-time polymerase chain reaction technique.

Keywords: leptospirosis, electrochemical detection, lateral flow immunosensor, point-of-care testing, early-stage detection

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Authors: Ebrahim Farahmand, Ali Mahani

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Wireless sensor networks (WSNs) can facilitate continuous monitoring of patients and increase early detection of emergency conditions and diseases. High density WSNs helps us to accurately monitor a remote environment by intelligently combining the data from the individual nodes. Due to energy capacity limitation of sensors, enhancing the lifetime and the reliability of WSNs are important factors in designing of these networks. The clustering strategies are verified as effective and practical algorithms for reducing energy consumption in WSNs and can tackle WSNs limitations. In this paper, an Energy-efficient weight-based Clustering Protocol (EWCP) is presented. Artificial retina is selected as a case study of WSNs applied in body sensors. Cluster heads’ (CHs) selection is equipped with energy efficient parameters. Moreover, cluster members are selected based on their distance to the selected CHs. Comparing with the other benchmark protocols, the lifetime of EWCP is improved significantly.

Keywords: WSN, healthcare monitoring, weighted based clustering, lifetime

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Authors: Sakinah Idris, Gabrine Jagersma, Bjorn Jaime Van Pelt, Kirstin Greaves-Lord

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Background: The PEERS (Program for the Education and Enrichment of Relational Skills) intervention can be considered a well-established, evidence-based intervention in the USA. However, testing the efficacy of cultural adaptations of PEERS is still ongoing. More and more, the involvement of all stakeholders in the development and evaluation of interventions is acknowledged as crucial for the longer term implementation of interventions across settings. Therefore, in the current project, teens with ASD (Autism Spectrum Disorder), their neurotypical peers, parents, teachers, as well as clinicians were involved in the development and evaluation of the Dutch version of PEERS. Objectives: The current presentation covers (1) the formative phase and (2) the preliminary adaptation test phase of the cultural adaptation of evidence-based interventions. In the formative phase, we aim to describe the process of adaptation of the PEERS program to the Dutch culture and care system. In the preliminary adaptation phase, we will present results from the preliminary adaptation test among 32 adolescents with ASD. Methods: In phase 1, a group discussion on common vocabulary was conducted among 70 teenagers (and their teachers) from special and regular education aged 12-18 years old. This inventory concerned 14 key constructs from PEERS, e.g., areas of interests, locations for making friends, common peer groups and crowds inside and outside of school, activities with friends, commonly used ways for electronic communication, ways for handling disagreements, and common teasing comebacks. Also, 15 clinicians were involved in the translation and cultural adaptation process. The translation and cultural adaptation process were guided by the research team, and who included input and feedback from all stakeholders through an iterative feedback incorporation procedure. In phase 2, The parent-reported Social Responsiveness Scale (SRS), the Test of Adolescent Social Skills Knowledge (TASSK), and the Quality of Socialization Questionnaire (QSQ) were assessed pre- and post-intervention to evaluate potential treatment outcome. Results: The most striking cultural adaptation - reflecting the standpoints of all stakeholders - concerned the strategies for handling rumors and gossip, which were suggested to be taught using a similar approach as the teasing comebacks, more in line with ‘down-to-earth’ Dutch standards. The preliminary testing of this adapted version indicated that the adolescents with ASD significantly improved their social knowledge (TASSK; t₃₁ = -10.9, p < .01), social experience (QSQ-Parent; t₃₁ = -4.2, p < .01 and QSQ-Adolescent; t₃₂ = -3.8, p < .01), and in parent-reported social responsiveness (SRS; t₃₃ = 3.9, p < .01). In addition, subjective evaluations of teens with ASD, their parents and clinicians were positive. Conclusions: In order to further scrutinize the effectiveness of the Dutch version of the PEERS intervention, we recommended performing a larger scale randomized control trial (RCT) design, for which we provide several methodological considerations.

Keywords: cultural adaptation, PEERS, preliminary testing, translation

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Authors: Zainal Abu Zatim, Hafizah Omar Zaki

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Substantial research had been done on refining employee’s customer service performance. Thus, there were very limited empirical studies that are engage in an inter-organizational climate study in assessing employee’s customer service performance. With the current economic situation as well as emerging needs and requirements, all businesses either from public or private sector serving customers put greater attention on fulfilling those needs and requirements. In this state of affairs, the act of polishing its employees’ skills, knowledge, teamwork and passion is very important in ensuring better performance deliverance. A study conducted in one of the telecommunication service provider company in Malaysia had been done to test its inter-organizational climate study. The Internal Climate Study was done to benchmark opinions and perceptions of its employees. The study had provided baseline information about perceptions that exist in the internal environment and ways forward to improve customer service performance. The approach used is through the use of focus group and qualitative interview.

Keywords: employees, Customer Service Performance, inter-organizational climate study, public and private sector

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Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su

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Deep reinforcement learning has been applied to address various problems in robotics, such as autonomous driving and unmanned aerial vehicle. However, because of the sparse reward penalty for a collision with obstacles during the navigation mission, the agent fails to learn the optimal policy or requires a long time for convergence. Therefore, using obstacles and enemy agents, in this paper, we present a curriculum-based boost learning method to effectively train compound skills during multi-agent reinforcement learning. First, to enable the agents to solve challenging tasks, we gradually increased learning difficulties by adjusting reward shaping instead of constructing different learning environments. Then, in a benchmark environment with static obstacles and moving enemy agents, the experimental results showed that the proposed curriculum learning strategy enhanced cooperative navigation and compound collision avoidance skills in uncertain environments while improving learning efficiency.

Keywords: curriculum learning, hard exploration, multi-agent reinforcement learning, robotic navigation, sparse reward

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: Zhongmin Wang, Lin Wei, Hengshan Zhang, Tianhua Chen, Yimin Zhou

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The assembly line balancing problems are signficant in mass production systems. In order to deal with the uncertainties that practically exist but barely mentioned in the literature, this paper develops a mathematic model with an optimisation algorithm to solve the assembly line balancing problem with uncertainty operation time. The developed model is able to work with a variable number of workstations under the uncertain environment, aiming to obtain the minimal number of workstation and minimal idle time for each workstation. In particular, the proposed approach first introduces the concept of protection time that closely works with the uncertain operation time. Four dominance rules and the mechanism of determining up and low bounds are subsequently put forward, which serve as the basis for the proposed branch and bound algorithm. Experimental results show that the proposed work verified on a benchmark data set is able to solve the uncertainties efficiently.

Keywords: assembly lines, SALBP-UOT, uncertain operation time, branch and bound algorithm.

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Authors: Shirin jalili, Hadi Shirzad, Samaneh Nabavi, Somayeh Khanjani

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Biosensors in forensic analysis are ideal biological tools that can be used for rapid and sensitive initial screening and testing to detect of suspicious components like biological and chemical agent in crime scenes. The wide use of different biomolecules such as proteins, nucleic acids, microorganisms, antibodies and enzymes makes it possible. These biosensors have great advantages such as rapidity, little sample manipulation and high sensitivity, also Because of their stability, specificity and low cost they have become a very important tool to Forensic analysis and detection of crime. In crime scenes different substances such as rape samples, Semen, saliva fingerprints and blood samples, act as a detecting elements for biosensors. On the other hand, successful fluid recovery via biosensor has the propensity to yield a highly valuable source of genetic material, which is important in finding the suspect. Although current biological fluid testing techniques are impaired for identification of body fluids. But these methods have disadvantages. For example if they are to be used simultaneously, Often give false positive result. These limitations can negatively result the output of a case through missed or misinterpreted evidence. The use of biosensor enable criminal researchers the highly sensitive and non-destructive detection of biological fluid through interaction with several fluid-endogenous and other biological and chemical contamination at the crime scene. For this reason, using of the biosensors for detecting the biological fluid found at the crime scenes which play an important role in identifying the suspect and solving the criminal.

Keywords: biosensors, forensic analysis, biological fluid, crime detection

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Authors: Erjola Reufi, Thomas Beer

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Plain, unreinforced concrete is a brittle material, with a low tensile strength, limited ductility and little resistance to cracking. In order to improve the inherent tensile strength of concrete there is a need of multi directional and closely spaced reinforcement, which can be provided in the form of randomly distributed fibers. Fiber reinforced concrete (FRC) is a composite material consisting of cement, sand, coarse aggregate, water and fibers. In this composite material, short discrete fibers are randomly distributed throughout the concrete mass. The behavioral efficiency of this composite material is far superior to that of plain concrete and many other construction materials of equal cost. The present experimental study considers the effect of steel fibers and polypropylene fiber on the modulus of elasticity of concrete. Hook end steel fibers of length 5 cm and 3 cm at volume fraction of 0.25%, 0.5% and 1.% were used. Also polypropylene fiber of length 12, 6, 3 mm at volume fraction 0.1, 0.25, and 0.4 % were used. Fifteen mixtures has been prepared to evaluate the effect of fiber on modulus of elasticity of concrete. Ultrasonic pulse velocity (UPV) and resonant frequency methods which are two non-destructive testing techniques have been used to measure the elastic properties of fiber reinforced concrete. This study found that ultrasonic wave propagation is the most reliable, easy and cost effective testing technique to use in the determination of the elastic properties of the FRC mix used in this study.

Keywords: fiber reinforced concrete(FRC), polypropylene fiber, resonance, ultrasonic pulse velocity, steel fiber

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Authors: Jim J. Catina, Jackee M. Gwynn, Jin S. Kang

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Additive manufacturing (AM) for hybrid rocket engines is becoming increasingly attractive due to its ability to create complex grain configurations with improved regression rates when compared to cast grains. However, the presence of microvoids in parts produced through the additive manufacturing method of Fused Deposition Modeling (FDM) results in a lower fuel density and is believed to cause a decrease in regression rate compared to ideal performance. In this experiment, FDM was used to create hybrid rocket fuel grains with a star configuration composed of acrylonitrile butadiene styrene (ABS). Testing was completed to determine the effect of heat treatment as a post-processing method to improve the combustion performance of hybrid rocket fuel grains manufactured by FDM. For control, three ABS star configuration grains were printed using FDM and hot fired using gaseous oxygen (GOX) as the oxidizer. Parameters such as thrust and mass flow rate were measured. Three identical grains were then heat treated to varying degrees and hot fired under the same conditions as the control grains. This paper will quantitatively describe the amount of improvement in engine performance as a result of heat treatment of the AM hybrid fuel grain. Engine performance is measured in this paper by specific impulse, which is determined from the thrust measurements collected in testing.

Keywords: acrylonitrile butadiene styrene, additive manufacturing, fused deposition modeling, heat treatment

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Authors: Chrysanthi Nega, Fotini-Sonia Apergi

Abstract:

Child sexual abuse (CSA) in Greece is a highly prevalent phenomenon and yet remains largely underreported. CSA can negatively impact cognitive, emotional and psychosocial development, as well as personality formation and capacity for initiation and maintenance of healthy interpersonal relationships. It is particularly important for school-based prevention programs to be implemented early in elementary school, as they are reportedly effective in lowering abuse incidences and providing knowledge for coping in threatening environments. The purpose of the current study was to test the effectiveness of a school-based CSA prevention program (Safe-Touches) on Greek elementary school students (grades 1-3, N=272) and explore the effect of age and time of testing (academic term). There was a significant effect of age in the knowledge of Inappropriate Touch, when comparing pre and post-intervention assessments, with third graders showing greatest gains in knowledge, followed by second and first graders. Time of testing during the academic year also had a significant effect, as first graders tested later in the school year, scored higher on knowledge of Inappropriate Touch. The findings of the current study provide insight into the optimal timing to implement CSA prevention programs. Exposure to such programs and incorporation in the school curricula could largely benefit children of the Greek community in terms of safety and awareness.

Keywords: child sexual abuse, Safe-Touches, school-based prevention, schooling

Procedia PDF Downloads 303

Authors: Katalin Gombos, Bence Gálik, Krisztina Ildikó Kalács, Krisztina Gödöny, Ákos Várnagy, József Bódis, Attila Gyenesei, Gábor L. Kovács

Abstract:

Although non-invasive pre-implantation genetic testing for aneuploidy (NIPGT-A) is potentially appropriate to assess chromosomal ploidy of the embryo, practical application of it in a routine IVF center has not been started in the absence of a recommendation. We developed a comprehensive workflow for a clinically applicable strategy for NIPGT-A based on next-generation sequencing (NGS) technology. We performed MALBAC whole genome amplification and NGS on spent blastocyst culture media of Day 3 embryos fertilized with intra-cytoplasmic sperm injection (ICSI). Spent embryonic culture media of morphologically good quality score embryos were enrolled in further analysis with the blank culture media as background control. Chromosomal abnormalities were identified by an optimized bioinformatics pipeline applying a copy number variation (CNV) detecting algorithm. We demonstrate a comprehensive workflow covering both wet- and dry-lab procedures supporting a clinically applicable strategy for NIPGT-A. It can be carried out within 48 h which is critical for the same-cycle blastocyst transfer, but also suitable for “freeze all” and “elective frozen embryo” strategies. The described integrated approach of non-invasive evaluation of embryonic DNA content of the culture media can potentially supplement existing pre-implantation genetic screening methods.

Keywords: next generation sequencing, in vitro fertilization, embryo assessment, non-invasive pre-implantation genetic testing

Procedia PDF Downloads 141

Authors: Hina Iqbal, Mustansar Ali Ghazanfar, Sandor Szedmak

Abstract:

Recommender systems uses artificial intelligence practices for filtering obscure information and can predict if a user likes a specified item. Kernel mapping Recommender systems have been proposed which are accurate and state-of-the-art algorithms and resolve recommender system’s design objectives such as; long tail, cold-start, and sparsity. The aim of research is to propose hybrid framework that can efficiently integrate different versions— namely item-based and user-based KMR— of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results (in terms of accuracy, precision, recall, F1 measure and ROC metrics) reveal that the proposed algorithm is quite accurate especially under cold-start and sparse scenarios.

Keywords: Kernel Mapping Recommender Systems, hybrid recommender systems, cold start, sparsity, long tail

Procedia PDF Downloads 319

Authors: Hamad Albadr

Abstract:

Joint Commission International (JCI) accreditation process intent to improve the safety and quality of care in the international community through the provision of education, publications, consultation, and evaluation services. These standards apply to the entire organization as well as to each department, unit, or service within the organization. Medical Technology that contains both medical equipment and devices, is an essential part of health care. Appropriate management of equipment maintenance for ensuring medical technology safe, the equipment life is maximized, and the total costs are minimized. JCI medical technology evaluation and accreditation use standards, intents, and measurable elements. The paper focuses on evaluation of JCI standards for medical technology in Saudi Arabian hospitals: a Study Case of PSMMC that define the performance expectation, structures, or functions that must be in place for a hospital to be accredited by JCI through measurable elements that indicate a score during the survey process that identify the requirements for full compliance with the standard specially through Facility Management and Safety (FMS) section that require the hospital establishes and implements a program for inspecting, testing, and maintaining medical technology and documenting the results, to ensure that medical technology is available for use and functioning properly, the hospital performs and documents; an inventory of medical technology; regular inspections of medical technology; testing of medical technology according to its use and manufacturers’ requirements; and performance of preventive maintenance.

Keywords: joint commission international (JCI) accreditation, medical technology, Saudi Arabia, Saudi Arabian hospitals

Procedia PDF Downloads 539

Authors: M. Yusuf Ansari, Asad Abbas

Abstract:

Proton exchange membrane (PEM) fuel cell is a very efficient and promising energy conversion device. Although Nafion® is considered as benchmark materials for membrane used in PEM fuel cell, it has limitations that restrict its uses. Alternative materials for the membrane is always a challenging field for researchers. Sulfonated poly(ether ether ketone) (SPEEK) is one of the promising material for membrane due to its chemical and mechanical stability and lower cost. In this work, SPEEK is synthesized, and property booster such as silica nanoparticles and polyvinyl alcohol (PVA) are also added to analyse changes in properties such as water uptake, IEC, and conductivity. It has been found that adding PVA support high water uptake and proton conductivity but at large amount of PVA reduces the proton conductivity due to very high water uptake. Adding silica enhances water uptake and proton conductivity.

Keywords: PEM Membrane, sulfonated poly (ether ether ketone) (SPEEK), silica fumes (SiO2), polyvinyl alcohol (PVA)

Procedia PDF Downloads 262