Search results for: Kruskal algorithm

Authors: Kisan Sarda, Bhavika Shingote

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This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.

Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation

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Authors: Ziad Abdallah, Mohamad Oueidat, Ali El-Zaart

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Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The existing techniques for IMC have two drawbacks: The description of the elementary characteristics from the image and the correlation between labels are not taken into account. In this paper, we present an algorithm (MIML-HOGLPP), which simultaneously handles these limitations. The algorithm uses the histogram of gradients as feature descriptor. It applies the Label Priority Power-set as multi-label transformation to solve the problem of label correlation. The experiment shows that the results of MIML-HOGLPP are better in terms of some of the evaluation metrics comparing with the two existing techniques.

Keywords: data mining, information retrieval system, multi-label, problem transformation, histogram of gradients

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Authors: Seema Vaidya

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Mining association rule is a key issue in data mining. In any case, the standard models ignore the distinction among the exchanges, and the weighted association rule mining does not transform on databases with just binary attributes. This paper proposes a novel continuous example and executes a tree (FP-tree) structure, which is an increased prefix-tree structure for securing compacted, discriminating data about examples, and makes a fit FP-tree-based mining system, FP enhanced capacity algorithm is used, for mining the complete game plan of examples by illustration incessant development. Here, this paper handles the motivation behind making remarkable and weighted item sets, i.e. rare weighted item set mining issue. The two novel brightness measures are proposed for figuring the infrequent weighted item set mining issue. Also, the algorithm are handled which perform IWI which is more insignificant IWI mining. Moreover we utilized the rare item set for choice based structure. The general issue of the start of reliable definite rules is troublesome for the grounds that hypothetically no inciting technique with no other person can promise the rightness of influenced theories. In this way, this framework expects the disorder with the uncommon signs. Usage study demonstrates that proposed algorithm upgrades the structure which is successful and versatile for mining both long and short diagnostics rules. Structure upgrades aftereffects of foreseeing rare diseases of patient.

Keywords: association rule, data mining, IWI mining, infrequent item set, frequent pattern growth

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Authors: L. Chevez, A. Apaza, J. Rodriguez, R. Puga, H. Loro, Juan Z. Davalos

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Virtual Dimension (VD) procedure is used to analyze Hyperspectral Image (HIS) treatment-data in order to estimate the abundance of mineral components of a mining sample. Hyperspectral images coming from reflectance spectra (NIR region) are pre-treated using Standard Normal Variance (SNV) and Minimum Noise Fraction (MNF) methodologies. The endmember components are identified by the Simplex Growing Algorithm (SVG) and after adjusted to the reflectance spectra of reference-databases using Simulated Annealing (SA) methodology. The obtained abundance of minerals of the sample studied is very near to the ones obtained using XRD with a total relative error of 2%.

Keywords: hyperspectral imaging, minimum noise fraction, MNF, simplex growing algorithm, SGA, standard normal variance, SNV, virtual dimension, XRD

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Authors: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin

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MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.

Keywords: computer vision, MediaPipe, adaptive boosting, fast dynamic time warping

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Authors: Kouorsh Zarea, Fatemeh Hassani, Samira Beiranvand, Akram Mohamadi

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Background and Objectives: Medication error in hospitals is a major cause of the errors which disrupt the health care system. The aim of this study was to assess the nurses’ medication errors and related factors. Material and methods: This was a descriptive study on 225 nurses in various hospitals, selected through multistage random sampling. Data was collected by three researcher made tools; demographic, medication error and related factors questionnaires. Data was analyzed by descriptive statistics, Chi-square, Kruskal-Wallis, One-way analysis of variance. Results: Based on the results obtained, the type of medication errors giving drugs to patients later or earlier (55.6%), multiple oral medication together regardless of their interactions (36%) and the postoperative analgesic without a prescription (34.2%), respectively. In addition, factors such as the shortage of nurses to patients’ ratio (57.3%), high load functions (51.1%) and fatigue caused by the extra work (40.4%), were the most important factors affecting the incidence of medication errors. The fear of legal issues (40%) are the most important factor is the lack of reported medication errors. Conclusions: Based on the results, effective management and promotion motivate nurses. Therefore, increasing scientific and clinical expertise in the field of nursing medication orders is recommended to prevent medication errors in various states of nursing intervention. Employing experienced staff in areas with high risk of medication errors and also supervising less-experienced staff through competent personnel are also suggested.

Keywords: medication error, nurse, clinical care, drug errors

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Authors: Zayed Freah Zeadat

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The wellbeing of urban dwellers is strongly associated with the quality and quantity of green infrastructure. Nevertheless, urban green infrastructure is still lagging in many Arab cities, and Jordan is no exception. The capital city of Jordan, Amman, is becoming more urban dense with limited green spaces. The unplanned urban growth in Amman has caused several environmental problems such as urban heat islands, air pollution, and lack of green spaces. This study aims to investigate the most suitable drivers to leverage the implementation of urban green infrastructure in Jordan through qualitative and quantitative analysis. The qualitative research includes an extensive literature review to discuss the most common drivers used internationally to promote urban green infrastructure implementation in the literature. The quantitative study employs a questionnaire survey to rank the suitability of each driver. Consultants, contractors, and policymakers were invited to fill the research questionnaire according to their judgments and opinions. Relative Importance Index has been used to calculate the weighted average of all drivers and the Kruskal-Wallis test to check the degree of agreement among groups. This study finds that research participants agreed that indirect financial incentives (i.e., tax reductions, reduction in stormwater utility fee, reduction of interest rate, density bonus, etc.) are the most effective incentive policy whilst granting sustainability certificate policy is the least effective driver to ensure widespread of UGI is elements in Jordan.

Keywords: urban green infrastructure, relative importance index, sustainable urban development, urban Jordan

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Authors: Huang Xiaoling, Liu Lufeng

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In this paper, we seek to determine one reasonable local hub port and optimal routes for a containership fleet, performing pick-ups and deliveries, between the hub and spoke ports in a same region. The relationship between a hub port, and traffic in feeder lines is analyzed. A new network planning method is proposed, an integrated hub port location and route design, a capacitated vehicle routing problem with pick-ups, deliveries and time deadlines are formulated and solved using an improved genetic algorithm for positioning the hub port and establishing routes for a containership fleet. Results on the performance of the algorithm and the feasibility of the approach show that a relatively small fleet of containerships could provide efficient services within deadlines.

Keywords: route planning, hub port location, container feeder service, regional transportation network

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Authors: Pallavi Bhogaram, Xiaolong Wu, Min He, Onyedikachi Okenwa

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A transportation network is a realization of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include road networks, railways, air routes, pipelines, and many more. The transportation network plays a vital role in maintaining the vigor of the nation’s economy. Hence, ensuring the network stays resilient all the time, especially in the face of challenges such as heavy traffic loads and large scale natural disasters, is of utmost importance. In this paper, we used the Neo4j application to develop the graph. Neo4j is the world's leading open-source, NoSQL, a native graph database that implements an ACID-compliant transactional backend to applications. The Southern California network model is developed using the Neo4j application and obtained the most critical and optimal nodes and paths in the network using centrality algorithms. The edge betweenness centrality algorithm calculates the critical or optimal paths using Yen's k-shortest paths algorithm, and the node betweenness centrality algorithm calculates the amount of influence a node has over the network. The preliminary study results confirm that the Neo4j application can be a suitable tool to study the important nodes and the critical paths for the major congested metropolitan area.

Keywords: critical path, transportation network, connectivity reliability, network model, Neo4j application, edge betweenness centrality index

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Authors: Egor E. Nuzhin, Nikolay V. Brilliantov

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We propose an approach for addressing Reinforcement Learning (RL) problems. It combines the ideas of umbrella sampling, borrowed from Monte Carlo technique of computational physics and chemistry, with optimal control methods, and is realized on the base of neural networks. This results in a powerful algorithm, designed to solve hard RL problems – the problems, with long-time delayed reward, state-traps sticking and a lack of terminal states. It outperforms the prominent algorithms, such as PPO, RND, iLQR and VI, which are among the most efficient for the hard problems. The new algorithm deals with a continuous ensemble of agents and expected return, that includes the ensemble entropy. This results in a quick and efficient search of the optimal policy in terms of ”exploration-exploitation trade-off” in the state-action space.

Keywords: umbrella sampling, reinforcement learning, policy gradient, dynamic programming

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Authors: Zekang Lan, Yan Xu, Yingkun Huang, Dian Huang, Shengzhong Feng

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Jobs on high-performance computing (HPC) clusters can suffer significant performance degradation due to inter-job network interference. Topology-aware job allocation problem (TJAP) is such a problem that decides how to dedicate nodes to specific applications to mitigate inter-job network interference. In this paper, we study the window-based TJAP on a fat-tree network aiming at minimizing the cost of communication hop, a defined inter-job interference metric. The window-based approach for scheduling repeats periodically, taking the jobs in the queue and solving an assignment problem that maps jobs to the available nodes. Two special allocation strategies are considered, i.e., static continuity assignment strategy (SCAS) and dynamic continuity assignment strategy (DCAS). For the SCAS, a 0-1 integer programming is developed. For the DCAS, an approach called neural simulated algorithm (NSA), which is an extension to simulated algorithm (SA) that learns a repair operator and employs them in a guided heuristic search, is proposed. The efficacy of NSA is demonstrated with a computational study against SA and SCIP. The results of numerical experiments indicate that both the model and algorithm proposed in this paper are effective.

Keywords: high-performance computing, job allocation, neural simulated annealing, topology-aware

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Authors: Muhammad A. Khan, Waseem Shahzad

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Analysis of protein sequences is carried out for the purpose to discover their structural and ancestry relationship. Sequence similarity determines similar protein structures, similar function, and homology detection. Biological sequences composed of amino acid residues or nucleotides provide significant information through sequence alignment. In this paper, we present a new similarity/dissimilarity measure to sequence alignment based on the primary structure of a protein. The approach finds the distance between the two given sequences using the novel sequence alignment algorithm and a mathematical model. The algorithm runs at a time complexity of O(n²). A distance matrix is generated to construct a phylogenetic tree of different species. The new similarity/dissimilarity measure outperforms other existing methods.

Keywords: alignment, distance, homology, mathematical model, phylogenetic tree

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Authors: Ladan Maijama’a, Jibril D. Jiya, Ejike C. Anene

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This paper presents Differential Evolution Algorithm (DEA) based Variable Structure Position Control (VSPC) of Laboratory DC servomotor (LDCSM). DEA is employed for the optimal tuning of Variable Structure Control (VSC) parameters for position control of a DC servomotor. The VSC combines the techniques of Sliding Mode Control (SMC) that gives the advantages of small overshoot, improved step response characteristics, faster dynamic response and adaptability to plant parameter variations, suppressed influences of disturbances and uncertainties in system behavior. The results of the simulation responses of the VSC parameters adjustment by DEA were performed in Matlab Version 2010a platform and yield better dynamic performance compared with the untuned VSC designed.

Keywords: differential evolution algorithm, laboratory DC servomotor, sliding mode control, variable structure control

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Authors: A. Seddiki, M. Djebbouri, D. Guerchi

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Medical imaging produces human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rate but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in region of interest (ROI). This paper discusses a contribution to the lossless compression in the region of interest of Scintigraphic images based on SPIHT algorithm and global transform thresholding using Huffman coding.

Keywords: global thresholding transform, huffman coding, region of interest, SPIHT coding, scintigraphic images

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Authors: Barun K. De

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Acquired immunodeficiency syndrome (AIDS) is caused by two types of human immunodeficiency viruses, collectively designated HIV. HIV infection is spreading globally particularly in developing countries. Before an individual is diagnosed with HIV, the disease goes through different phases. First there is an acute early phase that is followed by an established or chronic phase. Subsequently, there is a latency period after which the individual becomes immunodeficient. It is in the acute phase that an individual is highly infectious due to a high viral load. Presently, HIV diagnosis involves use of tests that do not detect the acute phase infection during which both the viral RNA and p24 antigen are expressed. Instead, these less sensitive tests detect antibodies to viral antigens which are typically sero-converted later in the disease process following acute infection. These antibodies are detected in both asymptomatic HIV-infected individuals as well as AIDS patients. Studies indicate that early diagnosis and treatment of HIV infection can reduce medical costs, improve survival, and reduce spreading of infection to new uninfected partners. Newer 4th generation combination antigen/antibody tests are highly sensitive and specific for detection of acute and established HIV infection (HIV1 and HIV2) enabling immediate linkage to care. The CDC (Center of Disease Control, USA) recently recommended an algorithm involving three different tests to screen and diagnose acute and established infections of HIV-1 and HIV-2 in a general population. Initially a 4th generation combo test detects a viral antigen p24 and specific antibodies against HIV -1 and HIV-2 envelope proteins. If the test is positive it is followed by a second test known as a differentiation assay which detects antibodies against specific HIV-1 and HIV-2 envelope proteins confirming established infection of HIV-1 or HIV-2. However if it is negative then another test is performed that measures viral load confirming an acute HIV-1 infection. Screening results of a Phoenix area population detected 0.3% new HIV infections among which 32.4% were acute cases. Studies in the U.S. indicate that this algorithm effectively reduces HIV infection through immediate treatment and education following diagnosis.

Keywords: new algorithm, HIV, diagnosis, infection

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Authors: Jie Xu, Zengshan Tian, Ze Li

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Previous indoor ranging or localization systems achieving high accuracy time of flight (ToF) estimation relied on two key points. One is to do strict time and frequency synchronization between the transmitter and receiver to eliminate equipment asynchronous errors such as carrier frequency offset (CFO), but this is difficult to achieve in a practical communication system. The other one is to extend the total bandwidth of the communication because the accuracy of ToF estimation is proportional to the bandwidth, and the larger the total bandwidth, the higher the accuracy of ToF estimation obtained. For example, ultra-wideband (UWB) technology is implemented based on this theory, but high precision ToF estimation is difficult to achieve in common WiFi or Bluetooth systems with lower bandwidth compared to UWB. Therefore, it is meaningful to study how to achieve high-precision ranging with lower bandwidth when the transmitter and receiver are asynchronous. To tackle the above problems, we propose a two-way channel error elimination theory and a frequency hopping-based carrier phase ranging algorithm to achieve high accuracy ranging under asynchronous conditions. The two-way channel error elimination theory uses the symmetry property of the two-way channel to solve the asynchronous phase error caused by the asynchronous transmitter and receiver, and we also study the effect of the two-way channel generation time difference on the phase according to the characteristics of different hardware devices. The frequency hopping-based carrier phase ranging algorithm uses frequency hopping to extend the equivalent bandwidth and incorporates a carrier phase ranging algorithm with multipath resolution to achieve a ranging accuracy comparable to that of UWB at 400 MHz bandwidth in the typical 80 MHz bandwidth of commercial WiFi. Finally, to verify the validity of the algorithm, we implement this theory using a software radio platform, and the actual experimental results show that the method proposed in this paper has a median ranging error of 5.4 cm in the 5 m range, 7 cm in the 10 m range, and 10.8 cm in the 20 m range for a total bandwidth of 80 MHz.

Keywords: frequency hopping, phase error elimination, carrier phase, ranging

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Authors: Hameed Rukayat Opeyemi, Pericles Pilidis, Pagone Emanuele

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An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects.

Keywords: gas turbine, low carbon technology, pareto optimal, multi-objective optimization

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

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Authors: Kainat Affrin, P. Reshma, G. Narendra Kumar

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Optimization of timetable is the need of the day for the rescheduling and routing of trains in real time. Trains are scheduled in parallel with the road transport vehicles to the same destination. As the number of trains is restricted due to single track, customers usually opt for road transport to use frequently. The air pollution increases as the density of vehicles on road transport is increased. Use of an alternate mode of transport like train helps in reducing air-pollution. This paper mainly aims at attracting the passengers to Train transport by proper rescheduling of trains using hybrid of stop-skip algorithm and iterative convex programming algorithm. Rescheduling of train bi-directionally is achieved on a single track with dynamic dual time and varying stops. Introduction of more trains attract customers to use rail transport frequently, thereby decreasing the pollution. The results are simulated using Network Simulator (NS-2).

Keywords: air pollution, AODV, re-scheduling, WSNs

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Authors: Mehmet Erdem Uzun, Hande Şirin, Amela Kojic Ateş, Utku Beyazıt, Aynur Bütün Ayhan

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The aim of this study was to examine the health literacy of parents with children diagnosed with ADHD. The study group consisted of 394 parents with children diagnosed with ADHD who applied to the child and adolescent psychiatry outpatient clinic of a public hospital in Bursa, Turkey. 339 mothers and 52 fathers participated in the study. The parents were administered a questionnaire prepared by the researchers in addition to the European Health Literacy Scale-Short Form. Prior to the onset of the analyses, a normality test was performed, and it was determined that the data did not show normal distribution. In this regard, Mann-Whitney U and Kruskal Wallis tests were employed in the analyses. According to the results obtained, it was determined that the health literacy of the mothers (x2=21.015, p<.001) and fathers (x2=7.462, p<.05) differed according to their education levels; that is, the health literacy level of parents who graduated from primary school was lower than the other parents. In addition, it was determined that the level of health literacy differed according to the income level of the family (x2=14.308, p<.05), and the health literacy level of the parents in the low-income group was lower than the other parents. On the other hand, it was seen that the health literacy levels of mothers and fathers did not differ according to the variables of age, whether they had social security, whether the child diagnosed with ADHD was taking medication, and if so, how long the child had been taking medication; age and gender of the child; whether there were other individuals diagnosed with ADHD in the family, and whether the child or parents had a chronic disease (p>.05). The results obtained were discussed in the light of the literature findings.

Keywords: health literacy, parents, children, ADHD

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Authors: Ramesh Nagarajappa, Upasana Mohapatra

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Considered a modern phobia, Nomophobia (NO MObile PHOne PhoBIA) is a term that describes the irrational fear or anxiety of being unable to access one’s own mobile phone. Objectives: To develop and validate the nomophobia questionnaire, administering it to a sample of adolescents representing undergraduate dental students. To assess the prevalence of Nomophobia, determine the usage pattern of mobile phones, and evaluate the impact due to lack of access to mobile phones among undergraduate dental students. Methodology: A cross-sectional study was conducted on 302 undergraduate students at Bhubaneswar through a self-administered questionnaire via Google Forms consisting of 19 items evaluating the pattern and anxiety related to usage of mobile phones. Responses were recorded on a 5-point Likert scale. Kruskal Wallis, Mann-Whitney U, and Chi-square tests were used for statistical analysis. Results: Test-Retest reliability showed kappa of k=0.86 and Internal consistency Chronbach’s-Alpha to be α=0.82. Prevalence of nomophobia (score ≥ 58) was 32.1%, and students at risk of being nomophobic (score 39-57) was 61.9%. It was highest in males (32.6%) and amongst the interns (41.9%) and lowest (25.5%) amongst the second-year students. Participants felt nervous/insecure if their phones were away from them because of the fear that somebody might have accessed their data (3.07±1.93) and or tried to contact them (3.09±1.13), which were not statistically significant (p>0.05). Conclusions: Effect of mobile phone on dental students and the fear of not having their phones with them is increasing elaborately, that needs to be controlled, which if not achieved, would negatively hamper their academic performance and their being in the society.

Keywords: addiction, dental students, mobile phone, nomophobia

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Authors: Loai Abdallah, Malik Yousef

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A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance.

Keywords: ensemble clustering, decision trees, classification, K nearest neighbors

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Authors: Richard Nyanzi, Maupi E. Letsoalo, Jacobus N. Eloff, Piet J. Jooste

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Candida albicans naturally occurs in the gastrointestinal tract (GIT) of more than 50% of humans. Overgrowth of the fungus causes several forms of candidiasis including oral thrush. Overgrowth tends to occur in immunocompromised humans such as diabetic, cancer and HIV patients. Antifungal treatment is available, but not without shortcomings. In this study, inhibitory activity of five probiotic Lactobacillus strains was demonstrated against the growth of seven clinical strains of Candida albicans by co-culturing of the organisms in a maize gruel (MG) medium. Phenotypic tests, molecular techniques and phylogenetic analysis have enabled precise identification of the organisms used in the study. The quantitative pour plate technique was used to enumerate colonies of the yeasts and the lactobacilli and the Kruskal-Wallis test and ANOVA tests were employed to compare the distributions of the colonies of the organisms. The cereal medium, containing added carbon sources, was inoculated with a Candida and a Lactobacillus strain in combination and incubated at 37 °C for 168 h. Aliquots were regularly taken and subjected to pH determination and colony enumeration. Certain Lactobacillus strains proved to be inhibitory and also lethal to some Candida albicans strains. A low pH due to Lactobacillus acid production resulted in significant low Candida colony counts. Higher Lactobacillus colony counts did not necessarily result in lower Candida counts suggesting that inhibitory factors besides low pH and competitive growth by lactobacilli contributed to the reduction in Candida counts. Such anti-Candida efficacy however needs to be confirmed by in vivo studies.

Keywords: candida albicans, oral thrush, candidiasis, lactobacillus, probiotics

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Authors: Mircea Ivanescu, Nirvana Popescu, Decebal Popescu, Dorin Popescu

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This paper addresses the control problem of a class of hyper-redundant arms. In order to avoid discrepancy between the mathematical model and the actual dynamics, the dynamic model with uncertain parameters of this class of manipulators is inferred. A procedure to design a feedback controller which stabilizes the uncertain system has been proposed. A PD boundary control algorithm is used in order to control the desired position of the manipulator. This controller is easy to implement from the point of view of measuring techniques and actuation. Numerical simulations verify the effectiveness of the presented methods. In order to verify the suitability of the control algorithm, a platform with a 3D flexible manipulator has been employed for testing. Experimental tests on this platform illustrate the applications of the techniques developed in the paper.

Keywords: distributed model, flexible manipulator, observer, robot control

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: Bruna Luisa Ramos Prado Vasques, Mariane Rembold Petraglia, Antonio Petraglia

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This paper investigates MIMO (Multiple-Input Multiple-Output) adaptive filtering techniques for the application of supervised source separation in the context of convolutive mixtures. From the observation that there is correlation among the signals of the different mixtures, an improvement in the NSAF (Normalized Subband Adaptive Filter) algorithm is proposed in order to accelerate its convergence rate. Simulation results with mixtures of speech signals in reverberant environments show the superior performance of the proposed algorithm with respect to the performances of the NLMS (Normalized Least-Mean-Square) and conventional NSAF, considering both the convergence speed and SIR (Signal-to-Interference Ratio) after convergence.

Keywords: adaptive filtering, multi-rate processing, normalized subband adaptive filter, source separation

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Authors: M. Nemer, E. I. Konukseven

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In this paper, a motion generation algorithm for a six Degrees of Freedom (DoF) robotic hand in a static environment is presented. The purpose of developing this method is to be used in the path generation of the end-effector for edge finishing and inspection processes by utilizing the CAD model of the considered workpiece. Nonetheless, the proposed algorithm may be extended to be applicable for other similar manufacturing processes. A software package programmed in the application programming interface (API) of SolidWorks generates tool path data for the robot. The proposed method significantly simplifies the given problem, resulting in a reduction in the CPU time needed to generate the path, and offers an efficient overall solution. The ABB IRB2000 robot is chosen for executing the generated tool path.

Keywords: CAD-based tools, edge deburring, edge scanning, offline programming, path generation

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Authors: Naziru Suleiman

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The ever increasing cases of financial frauds and corporate accounting scandals in recent years have raised more concern on the operation of internal control mechanisms and performance of the internal audit departments in organizations. In most cases the seeming presence of both the internal control system and internal audit in organizations do not prove useful as frauds errors and irregularities are being perpetuated. The aim of this study, therefore, is to assess the role of internal audit in achieving the objectives of internal control system of federal hospitals in Kano State from the perception of the respondents. The study used survey research design and generated data from primary source by means of questionnaire. A total number of 100 copies of questionnaire were administered out of which 68 were duly completed and returned. Cronbach’s alpha was used to test the internal validity of the various items in the constructs. Descriptive statistics, chi-square test, Mann Whitney U test and Kruskal Wallis ANOVA were employed for the analysis of data. The study finds that from the perception of the respondents, internal audit departments in Federal Hospitals in Kano State are effective and that they contribute positively to the overall attainment of the objectives of internal control system of these hospitals. There is no significant difference found on the views of the respondents from the three hospitals. Hence, the study concludes that strong and functional internal audit department is a basic requirement for effectiveness of operations of the internal control system. In the light of the findings, it is recommended that internal audit should continue to ensure that the objectives of internal control system of these hospitals are achieved through proper and adequate evaluation and review of the system.

Keywords: internal audit, internal control, federal hospitals, financial frauds

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Authors: Sergey Kopysov, Nikita Nedozhogin, Leonid Tonkov

Abstract:

The article presents a parallel iterative solver for large sparse linear systems which can be used on a heterogeneous platform. Traditionally, the problem of solving linear systems does not scale well on multi-CPU/multi-GPUs clusters. For example, most of the attempts to implement the classical conjugate gradient method were at best counted in the same amount of time as the problem was enlarged. The paper proposes the pipelined variant of the conjugate gradient method (PCG), a formulation that is potentially better suited for hybrid CPU/GPU computing since it requires only one synchronization point per one iteration instead of two for standard CG. The standard and pipelined CG methods need the vector entries generated by the current GPU and other GPUs for matrix-vector products. So the communication between GPUs becomes a major performance bottleneck on multi GPU cluster. The article presents an approach to minimize the communications between parallel parts of algorithms. Additionally, computation and communication can be overlapped to reduce the impact of data exchange. Using the pipelined version of the CG method with one synchronization point, the possibility of asynchronous calculations and communications, load balancing between the CPU and GPU for solving the large linear systems allows for scalability. The algorithm is implemented with the combined use of technologies: MPI, OpenMP, and CUDA. We show that almost optimum speed up on 8-CPU/2GPU may be reached (relatively to a one GPU execution). The parallelized solver achieves a speedup of up to 5.49 times on 16 NVIDIA Tesla GPUs, as compared to one GPU.

Keywords: conjugate gradient, GPU, parallel programming, pipelined algorithm

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Authors: Nan Xu

Abstract:

In airlines, the crew scheduling problem is usually decomposed into two stages: crew pairing and crew rostering. In the crew pairing stage, pairings are generated such that each flight is covered by exactly one pairing and the overall cost is minimized. In the crew rostering stage, the pairings generated in the crew pairing stage are combined with off days, training and other breaks to create individual work schedules. The paper focuses on cabin crew rostering problem, which is challenging due to the extremely large size and the complex working rules involved. In our approach, the objective of rostering consists of two major components. The first is to minimize the number of unassigned pairings and the second is to ensure the fairness to crew members. There are two measures of fairness to crew members, the number of overnight duties and the total fly-hour over a given period. Pairings should be assigned to each crew member so that their actual overnight duties and fly hours are as close to the expected average as possible. Deviations from the expected average are penalized in the objective function. Since several small deviations are preferred than a large deviation, the penalization is quadratic. Our model of the airline crew rostering problem is based on column generation. The problem is decomposed into a master problem and subproblems. The mater problem is modeled as a set partition problem and exactly one roster for each crew is picked up such that the pairings are covered. The restricted linear master problem (RLMP) is considered. The current subproblem tries to find columns with negative reduced costs and add them to the RLMP for the next iteration. When no column with negative reduced cost can be found or a stop criteria is met, the procedure ends. The subproblem is to generate feasible crew rosters for each crew member. A separate acyclic weighted graph is constructed for each crew member and the subproblem is modeled as resource constrained shortest path problems in the graph. Labeling algorithm is used to solve it. Since the penalization is quadratic, a method to deal with non-additive shortest path problem using labeling algorithm is proposed and corresponding domination condition is defined. The major contribution of our model is: 1) We propose a method to deal with non-additive shortest path problem; 2) Operation to allow relaxing some soft rules is allowed in our algorithm, which can improve the coverage rate; 3) Multi-thread techniques are used to improve the efficiency of the algorithm when generating Line-of-Work for crew members. Here a column generation based algorithm for the airline cabin crew rostering problem is proposed. The objective is to assign a personalized roster to crew member which minimize the number of unassigned pairings and ensure the fairness to crew members. The algorithm we propose in this paper has been put into production in a major airline in China and numerical experiments show that it has a good performance.

Keywords: aircrew rostering, aircrew scheduling, column generation, SPPRC

Procedia PDF Downloads 148