Search results for: minimum root mean square (RMS) error matching algorithm

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

Procedia PDF Downloads 447

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: Aid Abdelkrim

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A method of fatigue damage accumulation based upon application of energy parameters of the fatigue process is proposed in the paper. Using this model is simple, it has no parameter to be determined, it requires only the knowledge of the curve W–N (W: strain energy density N: number of cycles at failure) determined from the experimental Wöhler curve. To examine the performance of nonlinear models proposed in the estimation of fatigue damage and fatigue life of components under random loading, a batch of specimens made of 6082 T 6 aluminium alloy has been studied and some of the results are reported in the present paper. The paper describes an algorithm and suggests a fatigue cumulative damage model, especially when random loading is considered. This work contains the results of uni-axial random load fatigue tests with different mean and amplitude values performed on 6082T6 aluminium alloy specimens. The proposed model has been formulated to take into account the damage evolution at different load levels and it allows the effect of the loading sequence to be included by means of a recurrence formula derived for multilevel loading, considering complex load sequences. It is concluded that a ‘damaged stress interaction damage rule’ proposed here allows a better fatigue damage prediction than the widely used Palmgren–Miner rule, and a formula derived in random fatigue could be used to predict the fatigue damage and fatigue lifetime very easily. The results obtained by the model are compared with the experimental results and those calculated by the most fatigue damage model used in fatigue (Miner’s model). The comparison shows that the proposed model, presents a good estimation of the experimental results. Moreover, the error is minimized in comparison to the Miner’s model.

Keywords: damage accumulation, energy model, damage indicator, variable loading, random loading

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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: Muhammad Rafiq Shahid, Ali Hassan, Umm-e- Rubab, Muhammad Nadeem

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Insecticidal property of plant extracts was assessed toward dusky bug of cotton. Plant extracts consisted of bari pata (Ziziphus jajuba), Ak (Calotropis gigantean), Tobacco (Nicotiana tabacum), Bakine (Melia azedarach),Kanar (Nerium oleander),Kurtuma (Mitragyna speciosa) and one Control was also included with distilled water treatment. Forced feeding experiment was used to determine the antibiotic effect of bug plant extracts on dusky bug whereas Multi-choice experiment to determine the antixenosis/ repellent property of botanicals. It is evident from the results that mortality and antibiosis percentage of dusky bug due to the use of botanicals ranged from 15-95% and 20-87.3% respectively that was maximum in tobacoo extract followed by bakain and kurtama, minimum was on Ak, kanair and bakain extract. Non preference ranged from 14.28 to 85.7 where maximum non preference of dusky bug was found on bakain and kurtama followed by ak and kanair however minimum was on Bari pata extract. It was further found that local plant extract possessed insecticidal property toward dusky bug as well as also possesses repellency effect toward dusky bug, thus should be included in integrated pest management program of cotton in order to minimize the ill effects of pesticides it is compulsory to adopt eco-friendly methods of insect pest management.

Keywords: botanical extract, insecticidal and repellency activity, Gossypium hirsutum, oxycarenus laetus

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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: Mahnoosh Aliahmadi, Akbar Esmaeili

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This study synthesized new modified imaging nanocapsules (NCs) of gallium@deferoxamine/folic acid/chitosan/polyaniline/polyvinyl alcohol (Ga@DFA/FA/CS/PANI/PVA) containing Morus nigra extract by selenium nanoparticles prepared from Lactobacillus acidophilus. Se nanoparticles were then deposited on (Ga@DFA/FA/CS/PANI/PVA) using the impregnation method. The modified contrast agents were mixed with M. nigra extract, and their antibacterial activities were investigated by applying them to L929 cell lines. The influence of variable factors including surfactant, solvent, aqueous phase, pH, buffer, minimum Inhibitory concentration (MIC), minimum bactericidal concentration (MBC), cytotoxicity on cancer cells, antibiotic, antibiogram, release and loading, stirring effect, the concentration of nanoparticle, olive oil, and thermotical methods was investigated. The structure and morphology of the synthesized contrast agents were characterized by zeta potential sizer analysis (ZPS), X-Ray diffraction (XRD), Fourier-transform infrared (FT-IR), and energy-dispersive X-ray (EDX), ultraviolet-visible (UV-Vis) spectra, and scanning electron microscope (SEM). The experimental section was conducted and monitored by response surface methods (RSM) and MTT conversion assay. Antibiogram testing of NCs on Pseudomonas aeruginosa bacteria was successful, and the MIC=2 factor was obtained with a less harmful effect.

Keywords: imaging contrast agent, nanoparticles, response surface method, Lactobacillus acidophilus, selenium

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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: Amy Y. Wang, Eko L. Yi

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Can vicarious experiences of workplace ostracism affect employees’ willingness to voice? Given the increasingly interdependent nature of the modern workplace in which employees rely on social interactions to fulfill organizational goals, workplace ostracism –the extent to which an individual perceives that he or she is ignored or excluded by others in the workplace– has garnered significant interest from scholars and practitioners alike. Extending beyond conventional studies that largely focus on the perspectives and outcomes of ostracized targets, we address the indirect effects of workplace ostracism on third-party employees embedded in the same social context. Using a social information processing approach, we propose that the ostracism of coworkers acts as political information that influences third-party employees in their decisions to engage in risky and discretionary behaviors such as employee voice. To make sense of and to navigate through experiences of workplace ostracism, we posit that both political understanding and political skill allow third party employees to minimize the risks and uncertainty of voicing. This conceptual model was tested by a study involving 154 supervisor-subordinate dyads of a publicly listed bio-technology firm located in Mainland China. Each supervisor and their direct subordinates composed of a work team; each team had a minimum of two subordinates and a maximum of four subordinates. Human resources used the master list to distribute the ID coded questionnaires to the matching names. All studied constructs were measured using existing scales proved effective in previous literature. Hypotheses were tested using Confirmatory Factor Analysis and Hierarchal Multiple Regression. All three hypotheses were supported which showed that employees were less likely to engage in voice behaviors when their coworkers reported having experienced ostracism in the workplace. Results also showed a significant three-way interaction between political understanding and political skill on the relationship between coworkers’ ostracism and employee voice, indicating that political savviness is a valuable resource in mitigating ostracism’s negative and indirect effects. Our results illustrated that an employee’s coworkers being ostracized indeed adversely impacted his or her own voice behavior. However, not all individuals reacted passively to the social context; rather, we found that politically savvy individuals – possessing both political understanding and political skill – and their voice behaviors were less impacted by ostracism in their work environment. At the same time, we found that having only political understanding or only political skill was significantly less effective in mitigating ostracism’s negative effects, suggesting a necessary duality of political knowledge and political skill in combatting ostracism. Organizational implications, recommendations, and future research ideas are also discussed.

Keywords: employee voice, organizational politics, social information processing, workplace ostracism

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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: 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: Houda Jalali, Hassan Abbassi

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In this numerical work, natural convection and entropy generation of Al₂O₃–water nanofluid in square cavity have been studied. A two-dimensional steady laminar natural convection in a differentially heated square cavity of length L, filled with a nanofluid is investigated numerically. The horizontal walls are considered adiabatic. Vertical walls corresponding to x=0 and x=L are respectively maintained at hot temperature, T_h and cold temperature, T_c. The resolution is performed by the CFD code "FLUENT" in combination with GAMBIT as mesh generator. These simulations are performed by maintaining the Rayleigh numbers varied as 10³ ≤ Ra ≤ 10⁶, while the solid volume fraction varied from 1% to 5%, the particle size is fixed at dp=33 nm and a range of the temperature from 20 to 70 °C. We used models of thermophysical nanofluids properties based on experimental measurements for studying the effect of adding solid particle into water in natural convection heat transfer and entropy generation of nanofluid. Such as models of thermal conductivity and dynamic viscosity which are dependent on solid volume fraction, particle size and temperature. The average Nusselt number is calculated at the hot wall of the cavity in a different solid volume fraction. The most important results is that at low temperatures (less than 40 °C), the addition of nanosolids Al₂O₃ into water leads to a decrease in heat transfer and entropy generation instead of the expected increase, whereas at high temperature, heat transfer and entropy generation increase with the addition of nanosolids. This behavior is due to the contradictory effects of viscosity and thermal conductivity of the nanofluid. These effects are discussed in this work.

Keywords: entropy generation, heat transfer, nanofluid, natural convection

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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: 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: Temsiri Suwan, Penpicha Wanachantararak, Sakornrat Khongkhunthian, Siriporn Okonogi

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In the present study, silver nanoparticles (AgNPs) were synthesized by green synthesis approach using Moringa oleifera aqueous extract (ME) as a reducing agent and silver nitrate as a precursor. The obtained AgNPs were characterized using UV-Vis spectroscopy (UV-Vis), dynamic light scattering (DLS), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffractometry (XRD). The results from UV-Vis revealed that the maximum absorption of AgNPs was at 430 nm and the EDX spectrum confirmed Ag element. The results from DLS indicated that the amount of ME played an important role in particle size, size distribution, and zeta potential of the obtained AgNPs. The smallest size (62.4 ± 1.8 nm) with narrow distribution (0.18 ± 0.02) of AgNPs was obtained after using 1% w/v of ME. This system gave high negative zeta potential of -36.5 ± 2.8 mV. SEM results indicated that the obtained AgNPs were spherical in shape. Antibacterial activity using dilution method revealed that the minimum inhibitory and minimum bactericidal concentrations of the obtained AgNPs against Streptococcus mutans were 0.025 and 0.1 mg/mL, respectively. Cytotoxicity test of AgNPs on adenocarcinomic human alveolar basal epithelial cells (A549) indicated that the particles impacted against A549 cells. The percentage of cell growth inhibition was 87.5 ± 3.6 % when only 0.1 mg/mL AgNPs was used. These results suggest that ME is the potential reducing agent for green synthesis of AgNPs.

Keywords: antibacterial activity, Moringa oleifera extract, reducing agent, silver nanoparticles

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Authors: Md Masihuddin Siddiqui, Abdul Haakim Mohammed

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Earthquakes are the most unpredictable and devastating of all natural disasters. The behaviour of a building during an earthquake depends on several factors such as stiffness, adequate lateral strength, ductility, and configurations. The experience from the performance of buildings during past earthquakes has shown that the buildings with regular geometry, uniformly distributed mass and stiffness in plan as well as in elevation suffer much less damage compared to irregular configurations. The three centers namely- centre of mass, centre of strength, centre of stiffness are the torsional parameters which contribute to the strength of the building in case of an earthquake. Inertial forces and resistive forces in a structural system act through the center of mass and center of rigidity respectively which together oppose the forces that are produced during seismic excitation. So these centers of a structural system should be positioned where the structural system is the strongest so that the effects produced due to the earthquake may have a minimal effect on the structure. In this paper, the effects of variation of strength eccentricity and stiffness eccentricity in reducing the torsional responses of the asymmetrical buildings by using pushover analysis are studied. The maximum reduction of base torsion was observed in the case of minimum strength eccentricity, and the least reduction was observed in the case of minimum stiffness eccentricity.

Keywords: strength eccentricity, stiffness eccentricity, asymmetric structure, base torsion, push over analysis

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Authors: Sergey Kopysov, Nikita Nedozhogin, Leonid Tonkov

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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

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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

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Authors: R. M. Noah, N. M. Nasir, M. R. Jais, M. S. S. Wahab, M. H. Abdullah, A. S. S. Raj

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Infectious diseases are getting more difficult to treat due to the resistant strains of bacteria. Current generations of antibiotics are most likely ineffective against multi-drug resistant strains bacteria. Thus, there is an urgent need in search of natural antibiotics in particular from medicinal plants. One of the common medicinal plants, Melaleuca cajuputi, has been reported to possess antibacterial properties. The study was conducted to evaluate and justify the presence of antibacterial activity of Melaleuca cajuputi essential oil (EO) against the multi-drug resistant bacteria. Clinical isolates obtained from the teaching hospital were re-assessed to confirm the exact identity of the bacteria to be tested, namely methicillin-resistant staphylococcus aureus (MRSA), carbapenem-resistant enterobacteriaceae (CRE), and extended-spectrum beta-lactamases producer (ESBLs). A well diffusion method was done to observe the inhibition zones of the essential oil against the bacteria. Minimum inhibitory concentration (MIC) was determined using the microdilution method in 96-well flat microplate. The absorbance was measured using a microplate reader. Minimum bactericidal concentration (MBC) was performed using the agar medium method. The zones of inhibition produced by the EO against MRSA, CRE, and ESBL were comparable to that of generic antibiotics used, gentamicin and augmentin. The MIC and MBC results highlighted the antimicrobial efficacy of the EO. The outcome of this study indicated that the EO of Melaleuca cajuputi had antibacterial activity on the multi-drug resistant bacteria. This finding was eventually substantiated by electron microscopy work.

Keywords: melaleuca cajuputi, antibacterial, resistant bacteria, essential oil

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

Abstract:

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

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

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Authors: Hassan Mahmoudzadeh

Abstract:

The bacterium of Agrobacterium vitis causes crown and root gall disease, an important disease of grapevine, Vitis vinifera L. Also, Phylloxera is one of the most important pests in viticulture. Grapevine rootstocks were developed to provide increased resistance to soil-borne pests and diseases, but rootstock effects on some traits remain unclear. The interaction between rootstock, scion and environment can induce different responses to the grapevine physiology. 'Sultsna' (Vitis vinifera L.) is one of the most valuable raisin grape cultivars in Iran. Thus, the aim of this study was to determine the rootstock effect on the growth characteristics and yield components and quality of 'Sultana' grapevine grown in the Urmia viticulture region. The experimental design was completely randomized blocks, with four treatments, four replicates and 10 vines per plot. The results show that all variables evaluated were significantly affected by the rootstock. The Sultana/110R and Sultana/Nazmieh were among other combinations influenced by the year and had a higher significant yield/vine (13.25 and 12.14, respectively). Indeed, they were higher than that of Sultana/5BB (10.56 kg/vine) and Sultana/Spota (10.25 kg/vine). The number of clusters per burst bud and per vine and the weight of clusters were affected by the rootstock as well. Pruning weight/vine, yield/pruning weight, leaf area/vine and leaf area index are variables related to the physiology of grapevine, which was also affected by the rootstocks. In general, rootstocks had adapted well to the environment where the experiment was carried out, giving vigor and high yield to Sultana grapevine, which means that they may be used by grape growers in this region. In sum, the study found the best rootstocks for 'Sultana' to be Nazmieh and 110R in terms of root and shoot growth. However, the choice of the right rootstock depends on various aspects, such as those related to soil characteristics, climate conditions, grape varieties, and even clones, and production purposes.

Keywords: grafting, vineyards, grapevine, succeptability

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Authors: Fares Hedayatit, Arnauld Joly, Panagiotis Papadimitriou

Abstract:

Many machine learning tasks such as text annotation usually require training over very big datasets, e.g., millions of web documents, that can be represented in a sparse input space. State-of the-art tree-based ensemble algorithms cannot scale to such datasets, since they include operations whose running time is a function of the input space size rather than a function of the non-zero input elements. In this paper, we propose an efficient splitting algorithm to leverage input sparsity within decision tree methods. Our algorithm improves training time over sparse datasets by more than two orders of magnitude and it has been incorporated in the current version of scikit-learn.org, the most popular open source Python machine learning library.

Keywords: big data, sparsely representable data, tree-based models, scalable learning

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Authors: Bruno Trstenjak, Dzenana Donko

Abstract:

Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: G. K. Viswanadh, B. Rajasekhar, G. Venkata Ramana

Abstract:

Water distribution networks play a vital role in ensuring a reliable supply of clean water to urban areas. However, they face several challenges, including pressure control, pump speed optimization, and burst event detection. This paper combines insights from two studies to address these critical issues in Water distribution networks, focusing on the specific context of Kapra Municipality, India. The first part of this research concentrates on optimizing pressure control and pump speed in complex Water distribution networks. It utilizes the EPANET- MATLAB Toolkit to integrate EPANET functionalities into the MATLAB environment, offering a comprehensive approach to network analysis. By optimizing Pressure Reduce Valves (PRVs) and variable speed pumps (VSPs), this study achieves remarkable results. In the Benchmark Water Distribution System (WDS), the proposed PRV optimization algorithm reduces average leakage by 20.64%, surpassing the previous achievement of 16.07%. When applied to the South-Central and East zone WDS of Kapra Municipality, it identifies PRV locations that were previously missed by existing algorithms, resulting in average leakage reductions of 22.04% and 10.47%. These reductions translate to significant daily Water savings, enhancing Water supply reliability and reducing energy consumption. The second part of this research addresses the pressing issue of burst event detection and localization within the Water Distribution System. Burst events are a major contributor to Water losses and repair expenses. The study employs wireless sensor technology to monitor pressure and flow rate in real time, enabling the detection of pipeline abnormalities, particularly burst events. The methodology relies on transient analysis of pressure signals, utilizing Cumulative Sum and Wavelet analysis techniques to robustly identify burst occurrences. To enhance precision, burst event localization is achieved through meticulous analysis of time differentials in the arrival of negative pressure waveforms across distinct pressure sensing points, aided by nodal matrix analysis. To evaluate the effectiveness of this methodology, a PVC Water pipeline test bed is employed, demonstrating the algorithm's success in detecting pipeline burst events at flow rates of 2-3 l/s. Remarkably, the algorithm achieves a localization error of merely 3 meters, outperforming previously established algorithms. This research presents a significant advancement in efficient burst event detection and localization within Water pipelines, holding the potential to markedly curtail Water losses and the concomitant financial implications. In conclusion, this combined research addresses critical challenges in Water distribution networks, offering solutions for optimizing pressure control, pump speed, burst event detection, and localization. These findings contribute to the enhancement of Water Distribution System, resulting in improved Water supply reliability, reduced Water losses, and substantial cost savings. The integrated approach presented in this paper holds promise for municipalities and utilities seeking to improve the efficiency and sustainability of their Water distribution networks.

Keywords: pressure reduce valve, complex networks, variable speed pump, wavelet transform, burst detection, CUSUM (Cumulative Sum), water pipeline monitoring

Procedia PDF Downloads 87