How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective

The German manufacturing industry has to withstand an increasing global competition on product quality and production costs. As labor costs are high, several industries have suffered severely under the relocation of production facilities towards aspiring countries, which have managed to close the productivity and quality gap substantially. Established manufacturing companies have recognized that customers are not willing to pay large price premiums for incremental quality improvements. As a consequence, many companies from the German manufacturing industry adjust their production focusing on customized products and fast time to market. Leveraging the advantages of novel production strategies such as Agile Manufacturing and Mass Customization, manufacturing companies transform into integrated networks, in which companies unite their core competencies. Hereby, virtualization of the process- and supply-chain ensures smooth inter-company operations providing real-time access to relevant product and production information for all participating entities. Boundaries of companies deteriorate, as autonomous systems exchange data, gained by embedded systems throughout the entire value chain. By including Cyber-Physical-Systems, advanced communication between machines is tantamount to their dialogue with humans. The increasing utilization of information and communication technology allows digital engineering of products and production processes alike. Modular simulation and modeling techniques allow decentralized units to flexibly alter products and thereby enable rapid product innovation. The present article describes the developments of Industry 4.0 within the literature and reviews the associated research streams. Hereby, we analyze eight scientific journals with regards to the following research fields: Individualized production, end-to-end engineering in a virtual process chain and production networks. We employ cluster analysis to assign sub-topics into the respective research field. To assess the practical implications, we conducted face-to-face interviews with managers from the industry as well as from the consulting business using a structured interview guideline. The results reveal reasons for the adaption and refusal of Industry 4.0 practices from a managerial point of view. Our findings contribute to the upcoming research stream of Industry 4.0 and support decision-makers to assess their need for transformation towards Industry 4.0 practices. 

Phylogenetic Characterization of Atrazine-Degrading Bacteria Isolated from Agricultural Soil in Eastern Thailand

In this study sugarcane field soils with a long history of atrazine application in Chachoengsao and Chonburi provinces have been explored for their potential of atrazine biodegradation. For the atrazine degrading bacteria isolation, the soils used in this study named ACS and ACB were inoculated in MS-medium containing atrazine. Six short rod and gram-negative bacterial isolates, which were able to use this herbicide as a sole source of nitrogen, were isolated and named as ACS1, ACB1, ACB3, ACB4, ACB5 and ACB6. From the 16S rDNA nucleotide sequence analysis, the isolated bacteria ACS1 and ACB4 were identified as Rhizobium sp. with 89.1-98.7% nucleotide identity, ACB1 and ACB5 were identified as Stenotrophomonas sp. with 91.0-92.8% nucleotide identity, whereas ACB3 and ACB6 were Klebsiella sp. with 97.4-97.8% nucleotide identity.

Retaining Users in a Commercially-Supported Social Network

A commercially-supported social network has become an emerging channel for an organization to communicate with and provide services to customers. The success of the commercially-supported social network depends on the ability of the organization to keep the customers in participating in the network. Drawing from the theories of information adoption, information systems continuance, and web usability, the author develops a model to explore how a commercially-supported social network can encourage customers to continue participating and using the information in the network. The theoretical model will be proved through an online survey of customers using the commercially-supported social networking sites of several high technology companies operating in the same sector. The result will be compared with previous studies to learn about the explanatory power of the research model, and to identify the main factors determining users’ intention to continue using a commercially-supported social network. Theoretical and practical implications and limitations are discussed.

Magnetohydrodynamic Free Convection in a Square Cavity Heated from Below and Cooled from Other Walls

Magnetohydrodynamic free convection fluid flow and heat transfer in a square cavity filled with an electric conductive fluid with Prandtl number of 0.7 has been investigated numerically. The horizontal bottom wall of the cavity was kept at Th while the side and the top walls of the cavity were maintained at a constant temperature Tc with Th>Tc. The governing equations written in terms of the primitive variables were solved numerically using the finite volume method while the SIMPLER algorithm was used to couple the velocity and pressure fields. Using the developed code, a parametric study was performed, and the effects of the Rayleigh number and the Hartman number on the fluid flow and heat transfer inside the cavity were investigated. The obtained results showed that temperature distribution and flow pattern inside the cavity depended on both strength of the magnetic field and Rayleigh number. For all cases two counter rotating eddies were formed inside the cavity. The magnetic field decreased the intensity of free convection and flow velocity. Also it was found that for higher Rayleigh numbers a relatively stronger magnetic field was needed to decrease the heat transfer through free convection.

Optimization of Propulsion in Flapping Micro Air Vehicles Using Genetic Algorithm Method

In this paper the kinematic parameters of a regular Flapping Micro Air Vehicle (FMAV) is investigated. The optimization is done using multi-objective Genetic algorithm method. It is shown that the maximum propulsive efficiency is occurred on the Strouhal number of 0.2-0.3 and foil-pitch amplitude of 15°-30°. Furthermore, increasing pitch amplitude with respect to power optimization increases the thrust slightly until pitch amplitude around 30°, and then the trust is increased notably with increasing of pitch amplitude. Additionally, the maximum mean thrust coefficient is computed of 2.67 and propulsive efficiency for this value is 42%. Based on the thrust optimization, the maximum propulsive efficiency is acquired 54% while the mean thrust coefficient is 2.18 at the same propulsive efficiency. Consequently, the maximum propulsive efficiency is obtained 77% and the appropriate Strouhal number, pitch amplitude and phase difference between heaving and pitching are calculated of 0.27, 31° and 77°, respectively.

Modeling and Optimization of Part Type Selection and Loading Problem in Flexible Manufacturing System Using Real Coded Genetic Algorithms

 This paper deals with modeling and optimization of two NP-hard problems in production planning of flexible manufacturing system (FMS), part type selection problem and loading problem. The part type selection problem and the loading problem are strongly related and heavily influence the system’s efficiency and productivity. These problems have been modeled and solved simultaneously by using real coded genetic algorithms (RCGA) which uses an array of real numbers as chromosome representation. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.

Opinion Mining Framework in the Education Domain

The internet is growing larger and becoming the most popular platform for the people to share their opinion in different interests. We choose the education domain specifically comparing some Malaysian universities against each other. This comparison produces benchmark based on different criteria shared by the online users in various online resources including Twitter, Facebook and web pages. The comparison is accomplished using opinion mining framework to extract, process the unstructured text and classify the result to positive, negative or neutral (polarity). Hence, we divide our framework to three main stages; opinion collection (extraction), unstructured text processing and polarity classification. The extraction stage includes web crawling, HTML parsing, Sentence segmentation for punctuation classification, Part of Speech (POS) tagging, the second stage processes the unstructured text with stemming and stop words removal and finally prepare the raw text for classification using Named Entity Recognition (NER). Last phase is to classify the polarity and present overall result for the comparison among the Malaysian universities. The final result is useful for those who are interested to study in Malaysia, in which our final output declares clear winners based on the public opinions all over the web.

Modelling of Induction Motor Including Skew Effect Using MWFA for Performance Improvement

This paper deals with the modelling and simulation of the squirrel cage induction motor by taking into account all space harmonic components as well as the introduction of the bars skew in the calculation of the linear evolution of the magnetomotive force (MMF) between the slots extremities. The model used is based on multiple coupled circuits and the modified winding function approach (MWFA). The effect of skewing is included in the calculation of motors inductances with an axial asymmetry in the rotor. The simulation results in both time and spectral domains show the effectiveness and merits of the model and the error that may be caused if the skew of the bars are neglected.

Congestion Management in a Deregulated Power System with Micro Grid

This paper presents congestion management in deregulated power systems. In a deregulated environment, every buyer wants to buy power from the cheapest generator available, irrespective of relative geographical location of buyer and seller. As a consequence of this, the transmission corridors evacuating the power of cheaper generators would get overloaded if all such transactions are approved. Congestion management is a mechanism to prioritize the transactions and commit to such a schedule which would not overload the network. The congestions in the transmission lines are determined by Optimal Power Flow (OPF) solution, which is carried by primal liner programming method. Congestion in the transmission lines are alleviated by connected Distributed Generation (DG) of micro grid at load bus. A method to determine the optimal location of DG unit has been suggested based on transmission line relief sensitivity based approach. The effectiveness of proposed method has been demonstrated on modified IEEE-14 and 30 bus test systems.

Novel Nanomagnetic Beads Based - Latex Agglutination Assay for Rapid Diagnosis of Human Schistosomiasis Haematobium

The objective of the present study was to evaluate the novel nanomagnetic beads based–latex agglutination assay (NMB-LAT) as a simple test for diagnosis of S. haematobium as well as standardize the novel nanomagnetic beads based –ELISA (NMB-ELISA). According to urine examination this study included 85 S. haematobium infected patients, 30 other parasites infected patients and 25 negative control samples. The sensitivity of novel NMB-LAT was 82.4% versus 96.5% and 88.2% for NMB-ELISA and currently used sandwich ELISA respectively. The specificity of NMB-LAT was 83.6% versus 96.3% and 87.3% for NMB-ELISA and currently used sandwich ELISA respectively. In conclusion, the novel NMB-ELISA is a valuable applicable diagnostic technique for diagnosis of human schistosomiasis haematobium. The novel NMB-ELISA assay is a suitable applicable diagnostic method in field survey especially when followed by ELISA as a confirmatory test in query false negative results. Trials are required to increase the sensitivity and specificity of NMB-ELISA assay.

The Use Support Vector Machine and Back Propagation Neural Network for Prediction of Daily Tidal Levels along the Jeddah Coast, Saudi Arabia

Sea level rise threatens to increase the impact of future  storms and hurricanes on coastal communities. Accurate sea level  change prediction and supplement is an important task in determining  constructions and human activities in coastal and oceanic areas. In  this study, support vector machines (SVM) is proposed to predict  daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal  parameter values of kernel function are determined using a genetic  algorithm. The SVM results are compared with the field data and  with back propagation (BP). Among the models, the SVM is superior  to BPNN and has better generalization performance.  

Thinned Elliptical Cylindrical Antenna Array Synthesis Using Particle Swarm Optimization

This paper describes optimal thinning of an Elliptical  Cylindrical Array (ECA) of uniformly excited isotropic antennas  which can generate directive beam with minimum relative Side Lobe  Level (SLL). The Particle Swarm Optimization (PSO) method, which  represents a new approach for optimization problems in  electromagnetic, is used in the optimization process. The PSO is used  to determine the optimal set of ‘ON-OFF’ elements that provides a  radiation pattern with maximum SLL reduction. Optimization is done  without prefixing the value of First Null Beam Width (FNBW). The  variation of SLL with element spacing of thinned array is also  reported. Simulation results show that the number of array elements  can be reduced by more than 50% of the total number of elements in  the array with a simultaneous reduction in SLL to less than -27dB.  

Epidemiology of Bone Hydatidosis in Eastern Libya from 1995 to 2013

Bone hydatidosis is an infection in worldwide distribution. Although there is no evidence in literature on Bone Hydatid disease in Libya, we tried to present the first Epidemiological study of this disease in Eastern Libya through retrospective study from 1995 to 2013. Our data were collected from 3 hospitals in Eastern Libya particularly the sheep-raising areas with total number of musculoskeletal infection cases of two thousand one hundred ninety four (2,194). There were five (5) five cases of bone infection, four (4) of it have been diagnosed after more than three (3) months.  Our study is comparable to other international study but this type of bone infection need further studies for effective control strategies for all dogs to avoid serious complications that might happened from the delay in diagnosing this type of disease.

Diagnosis of Static, Dynamic and Mixed Eccentricity in Line Start Permanent Magnet Synchronous Motor by Using FEM

In Line start permanent magnet synchronous motor,  eccentricity is a common fault that can make it necessary to remove  the motor from the production line. However, because the motor may  be inaccessible, diagnosing the fault is not easy. This paper presents  an FEM that identifies different models, static eccentricity, dynamic  eccentricity, and mixed eccentricity, at no load and full load. The  method overcomes the difficulty of applying FEMs to transient  behavior. It simulates motor speed, torque and flux density  distribution along the air gap for SE,DE, and ME. This paper  represents the various effects of different eccentricitiestypes on the  transient performance.

Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water

In this research, the capability of neural networks in  modeling and learning complicated and nonlinear relations has been  used to develop a model for the prediction of changes in the diameter  of bubbles in pool boiling distilled water. The input parameters used  in the development of this network include element temperature, heat  flux, and retention time of bubbles. The test data obtained from the  experiment of the pool boiling of distilled water, and the  measurement of the bubbles form on the cylindrical element. The  model was developed based on training algorithm, which is  typologically of back-propagation type. Considering the correlation  coefficient obtained from this model is 0.9633. This shows that this  model can be trusted for the simulation and modeling of the size of  bubble and thermal transfer of boiling.

Modification of Rk Equation of State for Liquid and Vapor of Ammonia by Genetic Algorithm

Cubic equations of state like Redlich–Kwong (RK)  EOS have been proved to be very reliable tools in the prediction of  phase behavior. Despite their good performance in compositional  calculations, they usually suffer from weaknesses in the predictions  of saturated liquid density. In this research, RK equation was  modified. The result of this study show that modified equation has  good agreement with experimental data.  

Compressive Strength Evaluation of Underwater Concrete Structures Integrating the Combination of Rebound Hardness and Ultrasonic Pulse Velocity Methods with Artificial Neural Networks

In this study, two kinds of nondestructive evaluation  (NDE) techniques (rebound hardness and ultrasonic pulse velocity  methods) are investigated for the effective maintenance of underwater  concrete structures. A new methodology to estimate the underwater  concrete strengths more effectively, named “artificial neural network  (ANN) – based concrete strength estimation with the combination of  rebound hardness and ultrasonic pulse velocity methods” is proposed  and verified throughout a series of experimental works.  

Realization of Autonomous Guidance Service by Integrating Information from NFC and MEMS

In this paper, we present an autonomous guidance service by combinating the position information from NFC and the orientation information from 6 a 6 axis acceleration and terrestrial magnetism sensor. We developed an algorithm to calculate the device orientation  based on the data from acceleration and terrestrial magnetism sensor.With this function, a autonomous guidance service can be provided, according the visitors's position and orientation. This service may be convient for old people or disables or children.

Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks

PH, temperature and time of extraction of each stage,  agitation speed and delay time between stages effect on efficiency of  zinc extraction from concentrate. In this research, efficiency of zinc  extraction was predicted as a function of mentioned variable by  artificial neural networks (ANN). ANN with different layer was  employed and the result show that the networks with 8 neurons in  hidden layer has good agreement with experimental data.  

Simulation of “Net” Nutrients Removal by Green Mussel (Perna viridis) in Estuarine and Coastal Areas

Green mussels (Perna viridis) can effectively remove  nutrients from seawater through their filtration process. This study  aims to estimate “net” nutrient removal rate by green mussel through  calculation of nutrient uptake and release. Nutrients (carbon, nitrogen  and phosphorus) uptake was calculated based on the mussel filtration  rate. Nutrient release was evaluated from carbon, nitrogen and  phosphorus released as mussel faeces. By subtracting nutrient release  from nutrient uptake, net nutrient removal by green mussel can be  found as 3302, 380 and 124 mg/year/indv. Mass balance model was  employed to simulate nutrient removal in actual green mussel  farming conditions. Mussels farm area, seawater flow rate, and  amount of mussels were considered in the model. Results show that  although larger quantity of green mussel farms lead to higher nutrient  removal rate, the maximum green mussel cultivation should be taken  into consideration as nutrients released through mussel excretion can  strongly affect marine ecosystem.