Search results for: Generalized Cross Validation.

Authors: Mohamed Anwar Hammad, Khaled Mohamed Al Akhali, Yasmin Elsobky

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Board certification is a voluntary process that confirms a pharmacist's capability, competency, education, skills, and proficiency beyond what is essential for licensure. This analysis was intended to investigate the prevalence of board-certified pharmacists in the Arab countries and compare the Board of Pharmacy Specialties (BPS) between Egypt, Saudi Arabia, and Canada. A cross-sectional study was conducted. The data were mined from the BPS website. Data were managed by IBM SPSS Statistics 23.0 and presented as descriptive statistics. Of 36918 Board certified pharmacists (BCPs) until February 2018, only 4038 (10.9%) were from the outside United States of America. From 4038 BCPs, about 1782 (44.1%) were from Arab nations. Egypt has the top prevalence of the BPS among the Arab countries 937 (52.6%) BCPs. However, the Kingdom of Saudi Arabia comes in the second position 442 (24.8%). Pharmacotherapy (BCPS), nutrition support pharmacy (BCNSP), critical care pharmacy (BCCCP) and oncology pharmacy (BCOP) are the highest specialties by 1474 (82.7%), 114 (6.4%), 61 (3.42%) and 60 (3.37%) respectively, while, infectious diseases pharmacy (AQID), cardiology pharmacy (AQCD) and nuclear pharmacy (BCNP) are the lowest prevalence of specialties by 7 (0.4%), 6 (0.3%) and 1 (0.06%) respectively. Added qualifications were canceled and became a new specialty in BPS as the rest of the specialties. Both infectious diseases and cardiology specialties exams are not conducted yet all over the world from the beginning of 2018. Egypt has the second prevalence 937 (2.54%), before Canada 920 (2.49%) and after United States of America 32880 (89.06%) in the worldwide in terms of BCPs. In conclusion the BCPS is the uppermost specialty; however, there is still a need for all the other specialties. In a short period, BCCCP jumped to the third position. Cardiology and infectious disease will be new specialties. Egyptian pharmacists are in the top of Arab countries, and 2nd in worldwide BCPs.

Keywords: AQCD, AQID, Arab Countries, BCACP, BCCCP, BCGP, BCNP, BCNSP, BCOP, BCPPS, BCPS, BCPP, Board of Pharmacy Specialties, BPS, Canada, Egypt, Saudi Arabia, USA.

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Authors: Veronica Diaz Quezada

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In this article, the performance and errors are featured and analysed in the limit problems solving of a real-valued function, in correspondence to competency-based education in engineering careers, in the south of Chile. The methodological component is contextualised in a qualitative research, with a descriptive and explorative design, with elaboration, content validation and application of quantitative instruments, consisting of two parallel forms of open answer tests, based on limit application problems. The mathematical competences and errors made by students from five engineering careers from a public University are identified and characterized. Results show better performance only to solve routine-context problem-solving competence, thus they are oriented towards a rational solution or they use a suitable problem-solving method, achieving the correct solution. Regarding errors, most of them are related to techniques and the incorrect use of theorems and definitions of real-valued function limits of real variable.

Keywords: Engineering education, errors, limits, mathematics competences, problem solving.

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Authors: Chin Wen Cheong

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This study employs a bivariate asymmetric GARCH model to reveal the hidden dynamics price changes and volatility among the emerging markets of Thailand and Malaysian after the Asian financial crisis from January 2001 to December 2008. Our results indicated that the equity markets are sharing the common information (shock) that transmitted among each others. These empirical findings are used to demonstrate the importance of shock and volatility dynamic transmissions in the cross-market hedging and market risk.

Keywords: multivariate ARCH, structural change, value at risk.

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Authors: Parvinder S. Sandhu, Satish Kumar Dhiman, Anmol Goyal

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Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. This paper introduces Genetic Algorithm based software fault prediction models with Object-Oriented metrics. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the classification of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results shows that Genetic algorithm approach can be used for finding the fault proneness in object oriented software components.

Keywords: Genetic Algorithms, Software Fault, Classification, Object Oriented Metrics.

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Authors: M. Khlifa, A. Youssef, A. F. Zaed, A. Kraft, V. Arrighi

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A series of polystyrene (PS) nanoparticles were prepared by grafting polystyrene from both aggregated silica and colloidally dispersed silica nanoparticles using atom-transfer radical polymerisation (ATRP). Cross-linking and macroscopic gelation were minimised by using a miniemulsion system. The thermal and mechanical behaviour of the nanocomposites have been examined by differential scanning calorimetry (DSC) and dynamic mechanical thermal analysis (DMTA).

Keywords: ATRP, nanocomposites, polystyrene, reinforcement.

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Authors: Rampal Singh, S. Balasundaram

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In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations will be obtained using Moore-Penrose generalized pseudo inverse. For the study of the application of the method we consider the time series generated by the Mackey Glass delay differential equation with different time delays, Santa Fe A and UCR heart beat rate ECG time series. For the choice of sigmoid, sin and hardlim activation functions the optimal values for the memory order and the number of hidden neurons which give the best prediction performance in terms of root mean square error are determined. It is observed that the results obtained are in close agreement with the exact solution of the problems considered which clearly shows that ELM is a very promising alternative method for time series prediction.

Keywords: Chaotic time series, Extreme learning machine, Generalization performance.

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Authors: Davide Pierattoni, Ivan Macor, Pier Luca Montessoro

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In the recent years multimedia traffic and in particular VoIP services are growing dramatically. We present a new algorithm to control the resource utilization and to optimize the voice codec selection during SIP call setup on behalf of the traffic condition estimated on the network path. The most suitable methodologies and the tools that perform realtime evaluation of the available bandwidth on a network path have been integrated with our proposed algorithm: this selects the best codec for a VoIP call in function of the instantaneous available bandwidth on the path. The algorithm does not require any explicit feedback from the network, and this makes it easily deployable over the Internet. We have also performed intensive tests on real network scenarios with a software prototype, verifying the algorithm efficiency with different network topologies and traffic patterns between two SIP PBXs. The promising results obtained during the experimental validation of the algorithm are now the basis for the extension towards a larger set of multimedia services and the integration of our methodology with existing PBX appliances.

Keywords: Integrated voice-data communication, computernetwork performance, resource optimization.

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Authors: Jouhra Dad, Mohammed Ouali, Yahia Lebbah

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Urban road network traffic has become one of the most studied research topics in the last decades. This is mainly due to the enlargement of the cities and the growing number of motor vehicles traveling in this road network. One of the most sensitive problems is to verify if the network is congestion-free. Another related problem is the automatic reconfiguration of the network without building new roads to alleviate congestions. These problems require an accurate model of the traffic to determine the steady state of the system. An alternative is to simulate the traffic to see if there are congestions and when and where they occur. One key issue is to find an adequate model for road intersections. Once the model established, either a large scale model is built or the intersection is represented by its performance measures and simulation for analysis. In both cases, it is important to seek the queueing model to represent the road intersection. In this paper, we propose to model the road intersection as a BCMP queueing network and we compare this analytical model against a simulation model for validation.

Keywords: Queueing theory, transportation systems, BCMPqueueing network, performance measures, modeling, simulation

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Authors: Parvinder S. Sandhu, Roma Jaswal, Sandeep Khimta, Shailendra Singh

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.

Keywords: Neural Network, Software faults, Software Metric.

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Authors: Myunggon Yoon, Jung-Ho Moon

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In this paper, we present a transfer function representation of a general one-dimensional combustor. The input of the transfer function is a heat rate perturbation of a burner and the output is a flow velocity perturbation at the burner. This paper considers a general combustor model composed of multiple cans with different cross sectional areas, along with a non-zero flow rate.

Keywords: Thermoacoustics, dynamics, combustor, transfer function.

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Authors: Brenden M. Shutt, Lubjana Beshaj, Paul L. Goethals, Ambrose Kam

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Communication security is of particular interest to military data networks. A relatively novel approach to network security is blockchain, a cryptographically secured distribution ledger with a decentralized consensus mechanism for data transaction processing. Recent advances in blockchain technology have proposed new techniques for both data validation and trust management, as well as different frameworks for managing dataflow. The purpose of this work is to test the feasibility of different blockchain architectures as applied to military command and control networks. Various architectures are tested through discrete-event simulation and the feasibility is determined based upon a blockchain design’s ability to maintain long-term stable performance at industry standards of throughput, network latency, and security. This work proposes a consortium blockchain architecture with a computationally inexpensive consensus mechanism, one that leverages a Proof-of-Identity (PoI) concept and a reputation management mechanism.

Keywords: Blockchain, command & control network, discrete-event simulation, reputation management.

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Authors: Nidal F. Shilbayeh, Reem A. Al-Saidi, Ahmed H. Alsswey

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In this paper, we presented an evaluation and analysis of E-Voting Authentication Preparation Scheme (EV-APS). EV-APS applies some modified security aspects that enhance the security measures and adds a strong wall of protection, confidentiality, non-repudiation and authentication requirements. Some of these modified security aspects are Kerberos authentication protocol, PVID scheme, responder certificate validation, and the converted Ferguson e-cash protocol. Authentication and privacy requirements have been evaluated and proved. Authentication guaranteed only eligible and authorized voters were permitted to vote. Also, the privacy guaranteed that all votes will be kept secret. Evaluation and analysis of some of these security requirements have been given. These modified aspects will help in filtering the counter buffer from unauthorized votes by ensuring that only authorized voters are permitted to vote.

Keywords: E-Voting preparation stage, blind signature protocol, nonce based authentication scheme, Kerberos authentication protocol, pseudo voter identity scheme PVID.

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Authors: Chien-Min Chu, Bor-Wen Tsai, Kang-Tsung Chang

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Landslide susceptibility map delineates the potential zones for landslide occurrence. Previous works have applied multivariate methods and neural networks for mapping landslide susceptibility. This study proposed a new approach to integrate decision tree model and spatial cluster statistic for assessing landslide susceptibility spatially. A total of 2057 landslide cells were digitized for developing the landslide decision tree model. The relationships of landslides and instability factors were explicitly represented by using tree graphs in the model. The local Getis-Ord statistics were used to cluster cells with high landslide probability. The analytic result from the local Getis-Ord statistics was classed to create a map of landslide susceptibility zones. The map was validated using new landslide data with 482 cells. Results of validation show an accuracy rate of 86.1% in predicting new landslide occurrence. This indicates that the proposed approach is useful for improving landslide susceptibility mapping.

Keywords: Landslide susceptibility Zonation, Decision treemodel, Spatial cluster, Local Getis-Ord statistics.

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Authors: Zakari Yaou, Lirong Cui

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In a product development process, understanding the functional behavior of the system, the role of components in achieving functions and failure modes if components/subsystem fails its required function will help develop appropriate design validation and verification program for reliability assessment. The integration of these three issues will help design and reliability engineers in identifying weak spots in design and planning future actions and testing program. This case study demonstrate the advantage of unascertained theory described in the subjective cognition uncertainty, and then applies blind number (BN) theory in describing the uncertainty of the mechanical system failure process and the same time used the same theory in bringing out another mechanical reliability system model. The practical calculations shows the BN Model embodied the characters of simply, small account of calculation but betterforecasting capability, which had the value of macroscopic discussion to some extent.

Keywords: Mechanical structure Design, time-dependent stochastic process, unascertained information, blind number theory.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

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Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.

Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.

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Authors: Indra Budi, Rizal Fathoni Aji, Agus Widodo

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Prediction of future research topics by using time series analysis either statistical or machine learning has been conducted previously by several researchers. Several methods have been proposed to combine the forecasting results into single forecast. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed to select the forecasting methods, in which every point to forecast is calculated by using the best methods used by similar validation dataset. The dataset used in the experiment is time series derived from research report in Garuda, which is an online sites belongs to the Ministry of Education in Indonesia, over the past 20 years. The experimental result demonstrates that the proposed method may perform better compared to the fix combination of predictors. In addition, based on the prediction result, we can forecast emerging research topics for the next few years.

Keywords: Combination, emerging topics, ensemble, forecasting, machine learning, prediction, research topics, similarity measure, time series.

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Authors: Mohammad Taghi Taghavifard, Danial Mirheydari

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This paper suggests an algorithm for the evaluation and selection of suppliers. At the beginning, all the needed materials and services used by the organization were identified and categorized with regard to their nature by ABC method. Afterwards, in order to reduce risk factors and maximize the organization's profit, purchase strategies were determined. Then, appropriate criteria were identified for primary evaluation of suppliers applying to the organization. The output of this stage was a list of suppliers qualified by the organization to participate in its tenders. Subsequently, considering a material in particular, appropriate criteria on the ordering of the mentioned material were determined, taking into account the particular materials' specifications as well as the organization's needs. Finally, for the purpose of validation and verification of the proposed model, it was applied to Mobarakeh Steel Company (MSC), the qualified suppliers of this Company are ranked by the means of a Hierarchical Fuzzy TOPSIS method. The obtained results show that the proposed algorithm is quite effective, efficient and easy to apply.

Keywords: ABC analysis, Hierarchical Fuzzy TOPSIS, Primary supplier evaluation, Purchasing strategy, supplier selection

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Authors: Norziana Aminudin, Titik Khawa Abdul Rahman, Ismail Musirin

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One of the factors to maintain system survivability is the adequate reactive power support to the system. Lack of reactive power support may cause undesirable voltage decay leading to total system instability. Thus, appropriate reactive power support scheme should be arranged in order to maintain system stability. The strength of a system capacity is normally denoted as system loadability. This paper presents the enhancement of system loadability through optimal reactive power planning technique using a newly developed optimization technique, termed as Multiagent Immune Evolutionary Programming (MAIEP). The concept of MAIEP is developed based on the combination of Multiagent System (MAS), Artificial Immune System (AIS) and Evolutionary Programming (EP). In realizing the effectiveness of the proposed technique, validation is conducted on the IEEE-26-Bus Reliability Test System. The results obtained from pre-optimization and post-optimization process were compared which eventually revealed the merit of MAIEP.

Keywords: Load margin, MAIEP, Maximum loading point, ORPP.

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Authors: Xiaoqing Wang, Fang Youyuan, Zheng Yanxing, Gu Tianyang, Zong Jianjian, Tong Jinrong

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Compared with traditional distributed environment, the net-centric environment brings on more demanding challenges for information sharing with the characteristics of ultra-large scale and strong distribution, dynamic, autonomy, heterogeneity, redundancy. This paper realizes an information sharing model and a series of core services, through which provides an open, flexible and scalable information sharing platform.

Keywords: Net-centric environment, Information sharing, Metadata registry and catalog, Cross-domain data access control.

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Authors: S. Anna Durai, E. Anna Saro

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Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are directed towards achieving quick convergence of the network without loss of quality of the restored image. In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Back-propagation Network, it takes longer time to converge. The reason for this is, the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbors with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative distribution function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used, the Back-propagation Neural Network yields high compression ratio as well as it converges quickly.

Keywords: Back-propagation Neural Network, Cumulative Distribution Function, Correlation, Convergence.

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Authors: Andrejs Krasnikovs, Olga Kononova, Amjad Khabbaz, Edgar Machanovsky, Artur Machanovsky

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Fracture process in mechanically loaded steel fiber reinforced high-strength (SFRHSC) concrete is characterized by fibers bridging the crack providing resistance to its opening. Structural SFRHSC fracture model was created; material fracture process was modeled, based on single fiber pull-out laws, which were determined experimentally (for straight fibers, fibers with end hooks (Dramix), and corrugated fibers (Tabix)) as well as obtained numerically ( using FEM simulations). For this purpose experimental program was realized and pull-out force versus pull-out fiber length was obtained (for fibers embedded into concrete at different depth and under different angle). Model predictions were validated by 15x15x60cm prisms 4 point bending tests. Fracture surfaces analysis was realized for broken prisms with the goal to improve elaborated model assumptions. Optimal SFRHSC structures were recognized.

Keywords: crack, fiber concrete, fiber pull-out, strength.

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% @ 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes that have been designed, three were conventional concretes for three grades under discussion and fifteen were HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days, and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave-One-Out Validation (LOOV) methods.

Keywords: ANN, concrete mixes, compressive strength, fly ash, high performance concrete, linear regression, strength prediction models.

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Authors: Andrey A. Chernousov, Ben Y. B. Chan

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The thermal behavior of a large-scale, phase change material (PCM) enhanced building envelope system was studied in regard to the need for pre-fabricated construction in subtropical regions. The proposed large-scale envelope consists of a reinforced aluminum skin, insulation core, phase change material and reinforced gypsum board. The PCM impact on an energy efficiency of an enveloped room was resolved by validation of the EnergyPlus numerical scheme and optimization of a smart material location in the core. The PCM location was optimized by a minimization method of a cooling energy demand. It has been shown that there is good agreement between the test and simulation results. The optimal location of the PCM layer in Hong Kong summer conditions has been then recomputed for core thicknesses of 40, 60 and 80 mm. A non-dimensional value of the optimal PCM location was obtained to be same for all the studied cases and the considered external and internal conditions.

Keywords: Thermal performance, phase change material, energy efficiency, PCM optimization.

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Authors: Apirak Sombat, Teerapol Saleewong, Poom Kumam, Parin Chaipunya, Wiyada Kumam, Anantachai Padcharoen, Yeol Je Cho, Thana Sutthibutpong

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This research is aimed to study a two-step iteration process defined over a finite family of σ-asymptotically quasi-nonexpansive nonself-mappings. The strong convergence is guaranteed under the framework of Banach spaces with some additional structural properties including strict and uniform convexity, reflexivity, and smoothness assumptions. With similar projection technique for nonself-mapping in Hilbert spaces, we hereby use the generalized projection to construct a point within the corresponding domain. Moreover, we have to introduce the use of duality mapping and its inverse to overcome the unavailability of duality representation that is exploit by Hilbert space theorists. We then apply our results for σ-asymptotically quasi-nonexpansive nonself-mappings to solve for ideal efficiency of vector optimization problems composed of finitely many objective functions. We also showed that the obtained solution from our process is the closest to the origin. Moreover, we also give an illustrative numerical example to support our results.

Keywords: σ-asymptotically quasi-nonexpansive nonselfmapping, strong convergence, fixed point, uniformly convex and uniformly smooth Banach space.

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Authors: S. Ben Saad, F. Ayad, H. Damotte

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This study deals with Computational Fluid Dynamics (CFD) studies of the interactions between the air flow and louvered fins which equipped the automotive heat exchangers. 3D numerical simulation results are obtained by using the ANSYS Fluent 13.0 code and compared to experimental data. The paper studies the effect of louver angle and louver pitch geometrical parameters, on overall thermal hydraulic performances of louvered fins. The comparison between CFD simulations and experimental data show that established 3-D CFD model gives a good agreement. The validation agrees, with about 7% of deviation respectively of friction and Colburn factors to experimental results. As first, it is found that the louver angle has a strong influence on the heat transfer rate. Then, louver angle and louver pitch variation of the louvers and their effects on thermal hydraulic performances are studied. In addition to this study, it is shown that the second half of the fin takes has a significant contribution on pressure drop increase without any increase in heat transfer.

Keywords: CFD simulations, automotive heat exchanger, performances.

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Authors: Abdulrahman Aloufi

Abstract:

E-commerce has evolved to become a functional and mainstream tool of global trading, including in the Kingdom of Saudi Arabia. Consequently, online consumers need protection just as much as consumers in the offline world. In 2019, the Ministry of Commerce in Saudi Arabia established a so-called ‘e-commerce law’; however, this law does not cover the court enforcement of contracts entered into by international vendors, so it is not applicable in cross-border situations. The purpose of this paper is to identify the gaps present in this new e-commerce law in Saudi Arabia.

Keywords: Consumer protection, e-commerce law, Saudi consumer, international vendor.

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Authors: Francesco Maiorana

Abstract:

In this paper a one-dimension Self Organizing Map algorithm (SOM) to perform feature selection is presented. The algorithm is based on a first classification of the input dataset on a similarity space. From this classification for each class a set of positive and negative features is computed. This set of features is selected as result of the procedure. The procedure is evaluated on an in-house dataset from a Knowledge Discovery from Text (KDT) application and on a set of publicly available datasets used in international feature selection competitions. These datasets come from KDT applications, drug discovery as well as other applications. The knowledge of the correct classification available for the training and validation datasets is used to optimize the parameters for positive and negative feature extractions. The process becomes feasible for large and sparse datasets, as the ones obtained in KDT applications, by using both compression techniques to store the similarity matrix and speed up techniques of the Kohonen algorithm that take advantage of the sparsity of the input matrix. These improvements make it feasible, by using the grid, the application of the methodology to massive datasets.

Keywords: Clustering algorithm, Data mining, Feature selection, Grid, Kohonen Self Organizing Map.

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Authors: Paul W. Murray

Abstract:

This research explorers the relationship between leadership style and continuous improvement (CI) teams. CI teams have several features that are not always found in other types of teams, including multi-functional members, short time period for performance, positive and actionable results, and exposure to senior leadership. There is no one best style of leadership for these teams. Instead, it is important to select the best leadership style for the situation. The leader must have the flexibility to change styles and the skill to use the chosen style effectively in order to ensure the team’s success.

Keywords: Leadership style, Lean Manufacturing, Teams, Cross-functional.

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Authors: Ionel Zagan, Vasile Gheorghita Gaitan

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Minimizing the response time to asynchronous events in a real-time system is an important factor in increasing the speed of response and an interesting concept in designing equipment fast enough for the most demanding applications. The present article will present the results regarding the validation of the nMPRA (Multi Pipeline Register Architecture) architecture using the FPGA Virtex-7 circuit. The nMPRA concept is a hardware processor with the scheduler implemented at the processor level; this is done without affecting a possible bus communication, as is the case with the other CPU solutions. The implementation of static or dynamic scheduling operations in hardware and the improvement of handling interrupts and events by the real-time executive described in the present article represent a key solution for eliminating the overhead of the operating system functions. The nMPRA processor is capable of executing a preemptive scheduling, using various algorithms without a software scheduler. Therefore, we have also presented various scheduling methods and algorithms used in scheduling the real-time tasks.

Keywords: nMPRA architecture, pipeline processor, preemptive scheduling, real-time system.

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Authors: Shashank.B. Thakre, L.B. Bhuyar, Samir.J. Deshmukh

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The value of overall oxygen transfer Coefficient (KLa), which is the best measure of oxygen transfer in water through aeration, is obtained by a simple approach, which sufficiently explains the utility of the method to eliminate the discrepancies due to inaccurate assumption of saturation dissolved oxygen concentration. The rate of oxygen transfer depends on number of factors like intensity of turbulence, which in turns depends on the speed of rotation, size, and number of blades, diameter and immersion depth of the rotor, and size and shape of aeration tank, as well as on physical, chemical, and biological characteristic of water. An attempt is made in this paper to correlate the overall oxygen transfer Coefficient (KLa), as an independent parameter with other influencing parameters mentioned above. It has been estimated that the simulation equation developed predicts the values of KLa and power with an average standard error of estimation of 0.0164 and 7.66 respectively and with R2 values of 0.979 and 0.989 respectively, when compared with experimentally determined values. The comparison of this model is done with the model generated using Computational fluid dynamics (CFD) and both the models were found to be in good agreement with each other.

Keywords: CFD Model, Overall oxygen transfer coefficient, Power, Mathematical Model, Validation.

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