Reviewer Acknowledgements

Studies in Engineering and Technology (SET) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether SET publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 8, Number 1Girish Upreti, Methodist University, USAHala Abd El Megeed, National Institute for Standards, EgyptHassan Shaaban, Egyption Atomic Authority (EAEA), EgyptHosny Abbas Abouzeid, Higher Technological Institute, EgyptHossam Zaqoot, Ministry of Environmental Affairs, GazaHsienyu Lee, National Taiwan University, USAJose Hernandez, Chilean Nuclear Energy Commission, ChileMarco A Ruano, Economics Department Universidad Carlos III de Madrid, SpainMohammad Reza Barati, Flinders University, AustraliaSimona Rainis, Regional Agency for Rural Development of Autonomous Region of Friuli Venezia Giulia, Italy Andrew WalterEditorial AssistantStudies in Engineering and Technology-------------------------------------------Redfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USATel: 1-503-828-0536 ext. 504Fax: 1-503-828-0537E-mail: [email protected]: http://set.redfame.com

Business Case Mining and E-R Modeling Optimization

Business case mining and business rule discovery are at the center for entity relationship (E-R) modeling and database design to obtain E-R models. How to transform business cases through business rules into E-R models is a fundamental issue for database design. This article addresses this issue by exploring business case mining and E-R modeling optimization. Business case mining is business rule discovery from a business case. This article reviews case-based reasoning, explores business case-based reasoning, and presents a unified approach to business case mining for business rule discovery. The approach includes people-centered entity/business rule discovery and function-centered entity/business rule discovery. E-R modeling optimization aims to improve the E-R modeling process to get a better E-R diagram that reflects the business case properly. This article proposes a unified optimal method for E-R modeling. The unified optimal method includes people-centered E-R modeling, function-centered E-R modeling, and hierarchical E-R modeling. The approach proposed in this research will facilitate the research and development of E-R modeling, database design, data science, and big data analytics.

Recovery of Coal Values from Middling and Rejects by Froth Flotation and Mozley Mineral Separation

The recovery of coals values from Middling and Rejects carries out by using Froth flotation and Mozley Mineral Separation. The middling and rejects are the waste products from gravity beneficiation process, it has been noted that most of washery plants are selling this product at low cost because they have less values.The independent variables selected for Mozley Mineral Separator and their ranges were indicated in the parentheses as follow, water flow rates (400, 600, 800ml/s), amplitude (1.25, 1.5, 1.75inch) and collection time (30, 40, 60 s) while the independent variables for froth flotation were; Pulp density (10, 12.5, 15 %), collector dosage (39.3, 44.4, 49.5 g/t) and frother dosage (61.8, 65.3, 68.8 g/t). The number of experimental runs and regression equation determined by using Design Expert softwareThe d80 for middling and rejects samples were 10.5mm and 12.89mm respectively. The ash contents for the middling sample treated by froth flotation decrease from 37% to 15.85% at the reagent concentration of 49.5g/t collector, 65.3g/t frother and pulp density of 10%. The froth flotation results of middling sample shown to have a great reduction of ash contents. The overall optimum middling recovery and yield for washery grade I and II attain at reagent concentration and pulp density of 47.703g/t, 68.568g/t and 13.2% for collector, frother and pulp density respectively. The feed of reject coal was 71% and the ash contents reduced to 28.87% with the recovery of 0.85%. The analysis through Mozley mineral separator did not show significant changes in the reduction of ash from both middling and rejects. The ash contents achieved were above the scope of the studies for recovering of coal values. The experiments for middling and reject by froth flotation and Mozley mineral separator may be carried out by varying other parameters as well as the type of methods.

Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application.

Determination of the Undrained Shear Strength of Sensitive Clay Using Some Laboratory Soil Data

The undrained shear strength is a paramount parameter in determining the consistency and the ultimate bearing capacity of a clay layer. This resistance can be determined by in-situ tests, such as the field vane test or by laboratory tests, including the portable vane test, the triaxial, the simple compression test, and the consistency penetrometer test (i.e. the Swedish cone). However, the field vane test and the Swedish cone are the most commonly test used by geotechnical experts. In this paper, relationships between the field undrained shear strength of sensitive clay and some laboratory soil properties were developed. The soil properties consisted of the percentage of fine particles (less than 2 µm), the moisture content and the Atterberg limits. Furthermore, a correlation was proposed associating between the undrained shear strength of sensitive clay as obtained by the field vane test and the laboratory cone penetration test (Swedish cone). In addition, some applications of the proposed correlation on some geotechnical problems were included, such as the determination of the consistency and the bearing capacity of a clay layer. Comparison of the results of the developed correlations with the experimental results of the present investigation and the results reported in the literature show acceptable agreement.

Mechanical System Topology Optimization for better Maintenance

For a given mechanical equipment, knowing its modular topology has the advantage of facilitating its maintenance. Indeed, during a maintenance problem, we will not act on the whole product except on the failed module (product subsystem) and we would also gain time to detect, diagnose and compensate for the observed failure. On the other hand, the clustering algorithm, which has served as a reference for several works has several limits. It generates much more complex and more expensive modules in terms of coupling costs, which could require more resources, more intervention time and more maintenance work. This has worse consequences for product maintenance, because the more complex the product modules are, the more expensive the maintenance is. We therefore propose an improved clustering algorithm which has the advantage of reducing maintenance costs by reducing the coupling and decoupling costs (Disassembly and reassembly costs) of the modules, generated by the reference algorithm for good maintainability (dis-assemblability). The application is made on a soy roaster. The approach followed in the proposed algorithm consists first of all in defining a DSM (Design Structure Matrix) which will make it possible to define the correction coefficients of the coupling cost, then in formulating an objective function to reduce the coupling costs, and finally to take into account the integrating elements to reduce the size of the modules. The result achieved is the proposal for a modular topology (modular architecture) leading to a significant reduction in maintenance costs. The developed algorithm also allows an economy of scale in reducing the complexity of the modules, promoting good maintainability.

Reviewer Acknowledgements

Studies in Engineering and Technology (SET) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether SET publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 7, Number 1Alexander Medvedev, Transport and Telecommunication Institute (TTI), LatviaAlexander Pisarevskiy, Bauman Moscow State Tecnnical University, RussiaArnaud Duchosal, University of Montpellier, FranceGirish Upreti, Methodist University, USAHala Abd El Megeed, National Institute for Standards, EgyptHassan Shaaban, Egyption Atomic Authority (EAEA), EgyptHossam Zaqoot, Ministry of Environmental Affairs, GazaHossein Moayedi, Universiti Teknologi Malaysia, MalaysiaJose Hernandez, Chilean Nuclear Energy Commission, ChileMahdieh Zabihimayvan, Wright State University, USAMarco A Ruano, Economics Department Universidad Carlos III de Madrid, SpainMohammad Reza Barati, Flinders University, AustraliaPau Redon, Fundación Hospital General de Valencia, SpainSimona Rainis, RARDARFVG, ItalyTangming Yuan, University of York, UKTony di Feo, Natural Resources Canadanior Engineer, CanadaWael Salah, Palestine Technical University - Kadoorie, PalestineYao Liu, University Malaysia Pahang, MalaysiaYi Zheng, National Institute for Occupational Safety and Health (NIOSH), USA Andrew WalterEditorial AssistantStudies in Engineering and Technology-------------------------------------------Redfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USATel: 1-503-828-0536 ext. 504Fax: 1-503-828-0537E-mail: [email protected]: http://set.redfame.com

A Proposed Scheme for Fault Discovery and Extraction Using ANFIS: Application to Train Braking System

This paper showcases the use of model oriented techniques for real time fault discovery and extraction on train track unit. An analytical system model is constructed and simulated in Mathlab to showcase the fair and unfair status of the system. The discovery and extraction phases are centered on a hybrid adaptive neuro-fuzzy inference feature extraction and segregated module. Output module interprites zero (0) as a good status of the traintrack unit and one (1) as an unpleasant status. Final results showcase the robustness and ability to discover and extract multitude of unpleasant scenarios that hinder the smooth operations of train track units due to its high selectivity and sensitivity quality.

Clinical Decision-Making: Developing a 4 C Model Using Graph Theoretic Approach

The purpose of this paper is to propose a graph-theoretic mathematical model to measure how conducive the environment of a hospital is for decision-making. We propose a 4-C model, developed from four interacting factors: confidence, complexity, capability, and customer. In this graph-theoretic model, abstract information regarding the system is represented by the directed edges of a graph (or digraph), which together depict how one factor affects another. The digraph yields a matrix model useful for computer processing. The net effect of different factors and their interdependencies on the hospital's decision-making environment is quantified and a single numerical index is generated. This paper categorizes all the major factors that influence clinical decision-making and attempts to provide a tool to study and measure their interactions with each other. Each factor and each interaction among factors are to be quantified by healthcare experts according to their best judgment of the magnitude of its effect in a local hospital environment.A hospital case study is used to demonstrate how the 4-C model works. The graph-theoretic approach allows for the inclusion of new factors and generation of alternative environments by a combination of both qualitative and quantitative modeling. The 4-C model can be used to create both a database and a simple numerical scale that help a hospital set customized guidelines, ranging from patient admittance procedures to diagnostic and treatment processes, according to its specific situation. Implementing this methodology systematically can allow a hospital to identify factors that will lead to improved decision-making as well as identifying operational factors that present roadblocks.

Optimization of “Deoxidation Alloying” Batching Scheme

In this paper, a mathematical model was established to predict the deoxidation alloying and to optimize the type and quantity of input alloys. Firstly, the GCA method was used to obtain the main factors affecting the alloy yield of carbon and manganese based on the historical data. Secondly, the alloy yield was predicted by the stepwise MRA, the BP neural network and the regression SVM models, respectively. The conclusion is that the regression SVM model has the highest prediction accuracy and the maximum deviation between the test set prediction result and the real value was only 0.0682 and 0.0554. Thirdly, in order to reduce the manufacturer's production cost, the genetic algorithm was used to calculate the production cost mathematical programming model. Finally, sensitivity analysis was performed on the prediction model and the cost optimization model. The unit price of 20% of the alloy raw materials was increased by 20%, and the total cost change rate was 0.7155%, the lowest was -0.4297%, which proved that the mathematical model established presented strong robustness and could be certain reference value for the current production of iron and steel enterprises.