Search results for: Multiple Criteria Fuzzy Optimization

Authors: Yehjune Heo

Abstract:

As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: Anti-spoofing, CNN, fingerprint recognition, loss function, optimizer.

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Authors: Yuki Irie, Shota Ogawa, Hitomi Kosugi, Hiromitsu Shinozaki

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Dropout rates in higher educational institutions pose significant challenges for both students and institutions, with poor mental health (MH) emerging as a key risk factor. Healthcare college students, including medical students, are particularly vulnerable to MH issues due to the demanding academic schedules they face. Poor mental health (MH) would be considered as a key risk factor for dropout from higher educational institutions that pose significant challenges for both students and institutions. And, inadequate food access (FA) has been related to poor MH. Given that targeted students may experience multiple risk factors for poor MH and vulnerable FA, the study aims to clarify the relationship between MH and FA to enhance student well-being. A cross-sectional design was used to explore the association between MH status and FA among 421 students (147 male, 274 female). Participants completed two questionnaires assessing MH and FA during winter 2022. The mean MH score was 6.7 ± 4.6, with higher scores indicating worse MH (max. score 27). While year-round FA showed no significant association with MH, FA during winter was significantly associated with MH (p = 0.01). Although car ownership did not directly impact MH, it was significantly associated with FA (p < 0.01), thus indirectly influencing MH. Our findings underscore the importance of FA in promoting MH, particularly during winter. Adopting a lifestyle that facilitates easier FA may be beneficial for MH, given its indirect association with MH outcomes. These insights emphasize the significance of addressing FA-related challenges to enhance student’s mental well-being.

Keywords: Mental health, food access, co-medical students, lifestyle.

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Authors: Hamza A. Ali, Waleed A. J. Rasheed

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The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values according to the input pattern variations. The new approach is based on splitting the whole network into two subnets; main traditional net and a supportive net. The first deals with the required output of trained patterns with predefined settings, while the second tolerates output generation dynamically with tuning capability for any newly applied input. This tuning comes in the form of an adjustment to the threshold values. Two levels of supportive net were studied; one implements an extended additional layer with adjustable neuronal threshold setting mechanism, while the second implements an auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net that is constructed in dual-layer architecture. Experiment results and analysis of the proposed designs have given quite satisfactory conducts. The supportive layer approach achieved over 90% recognition rate, while the multiple network technique shows more effective and acceptable level of recognition. However, this is achieved at the price of network complexity and computation time. Recognition generalization may be also improved by accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities with the needs of further advanced learning phases.

Keywords: Classification, Recognition, Neural Networks, Pattern Recognition, Generalization.

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Authors: Boon Yih Mah

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Web-based Cognitive Writing Instruction (WeCWI) is a hybrid e-framework for the development of a web-based instruction (WBI), which contributes towards instructional design and language development. WeCWI divides its contribution in instructional design into macro and micro perspectives. In macro perspective, being a 21st century educator by disseminating knowledge and sharing ideas with the in-class and global learners is initiated. By leveraging the virtue of technology, WeCWI aims to transform an educator into an aggregator, curator, publisher, social networker and ultimately, a web-based instructor. Since the most notable contribution of integrating technology is being a tool of teaching as well as a stimulus for learning, WeCWI focuses on the use of contemporary web tools based on the multiple roles played by the 21st century educator. The micro perspective in instructional design draws attention to the pedagogical approaches focusing on three main aspects: reading, discussion, and writing. With the effective use of pedagogical approaches through free reading and enterprises, technology adds new dimensions and expands the boundaries of learning capacity. Lastly, WeCWI also imparts the fundamental theories and models for web-based instructors’ awareness such as interactionist theory, cognitive information processing (CIP) theory, computer-mediated communication (CMC), e-learning interactionalbased model, inquiry models, sensory mind model, and leaning styles model.

Keywords: WeCWI, instructional discovery, technological discovery, pedagogical discovery, theoretical discovery.

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Authors: Zhaoan Wang

Abstract:

Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: Automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation.

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Authors: Yehia A. Eldrainy, Mohammad Nazri Mohd. Jaafar, Tholudin Mat Lazim

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This paper presents a cold flow simulation study of a small gas turbine combustor performed using laboratory scale test rig. The main objective of this investigation is to obtain physical insight of the main vortex, responsible for the efficient mixing of fuel and air. Such models are necessary for predictions and optimization of real gas turbine combustors. Air swirler can control the combustor performance by assisting in the fuel-air mixing process and by producing recirculation region which can act as flame holders and influences residence time. Thus, proper selection of a swirler is needed to enhance combustor performance and to reduce NOx emissions. Three different axial air swirlers were used based on their vane angles i.e., 30°, 45°, and 60°. Three-dimensional, viscous, turbulent, isothermal flow characteristics of the combustor model operating at room temperature were simulated via Reynolds- Averaged Navier-Stokes (RANS) code. The model geometry has been created using solid model, and the meshing has been done using GAMBIT preprocessing package. Finally, the solution and analysis were carried out in a FLUENT solver. This serves to demonstrate the capability of the code for design and analysis of real combustor. The effects of swirlers and mass flow rate were examined. Details of the complex flow structure such as vortices and recirculation zones were obtained by the simulation model. The computational model predicts a major recirculation zone in the central region immediately downstream of the fuel nozzle and a second recirculation zone in the upstream corner of the combustion chamber. It is also shown that swirler angles changes have significant effects on the combustor flowfield as well as pressure losses.

Keywords: cold flow, numerical simulation, combustor;turbulence, axial swirler.

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: Time-series, features engineering methods for forecasting, energy demand forecasting, Azure machine learning.

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Authors: K. Aravindhan, Trishit Bandyopadhyay, Mahesh Mehendale, Supriya Kumar De

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The inherent iterative nature of product design and development poses significant challenge to reduce the product design and development time (PD). In order to shorten the time to market, organizations have adopted concurrent development where multiple specialized tasks and design activities are carried out in parallel. Iterative nature of work coupled with the overlap of activities can result in unpredictable time to completion and significant rework. Many of the products have missed the time to market window due to unanticipated or rather unplanned iteration and rework. The iterative and often overlapped processes introduce greater amounts of ambiguity in design and development, where the traditional methods and tools of project management provide less value. In this context, identifying critical metrics to understand the iteration probability is an open research area where significant contribution can be made given that iteration has been the key driver of cost and schedule risk in PD projects. Two important questions that the proposed study attempts to address are: Can we predict and identify the number of iterations in a product development flow? Can we provide managerial insights for a better control over iteration? The proposal introduces the concept of decision points and using this concept intends to develop metrics that can provide managerial insights into iteration predictability. By characterizing the product development flow as a network of decision points, the proposed research intends to delve further into iteration probability and attempts to provide more clarity.

Keywords: Decision Points, Iteration, Product Design, Rework.

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Authors: Kiyohiro Yamazaki

Abstract:

This paper considers a forming process of a single competitive factor in the digital camera industry from the viewpoint of product platform. To make product development easier for companies and to increase product introduction ratios, development efforts concentrate on improving and strengthening certain product attributes, and it is born in the process that the product platform is formed continuously. It is pointed out that the formation of this product platform raises product development efficiency of individual companies, but on the other hand, it has a trade-off relationship of causing unification of competitive factors in the whole industry. This research tries to analyze product specification data which were collected from the web page of digital camera companies. Specifically, this research collected all product specification data released in Japan from 1995 to 2003 and analyzed the composition of image sensor and optical lens; and it identified product platforms shared by multiple products and discussed their application. As a result, this research found that the product platformation was born in the development of the standard product for major market segmentation. Every major company has made product platforms of image sensors and optical lenses, and as a result, this research found that the competitive factors were unified in the entire industry throughout product platformation. In other words, this product platformation brought product development efficiency of individual firms; however, it also caused industrial competition factors to be unified in the industry.

Keywords: Digital camera industry, product evolution trajectory, product platform, unification of competitive factors.

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Authors: Rukayya Ibrahim Muazu, Aiduan Li Borrion, Julia A. Stegemann

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Biomass briquette gasification is regarded as a promising route for efficient briquette use in energy generation, fuels and other useful chemicals. However, previous research has been focused on briquette gasification in fixed bed gasifiers such as updraft and downdraft gasifiers. Fluidised bed gasifier has the potential to be effectively sized to medium or large scale. This study investigated the use of fuel briquettes produced from blends of rice husks and corn cobs biomass, in a bubbling fluidised bed gasifier. The study adopted a combination of numerical equations and Aspen Plus simulation software, to predict the product gas (syngas) composition base on briquette density and biomass composition (blend ratio of rice husks to corn cobs). The Aspen Plus model was based on an experimentally validated model from the literature. The results based on a briquette size 32 mm diameter and relaxed density range of 500 to 650kg/m3, indicated that fluidisation air required in the gasifier increased with increase in briquette density, and the fluidisation air showed to be the controlling factor compared with the actual air required for gasification of the biomass briquettes. The mass flowrate of CO2 in the predicted syngas composition increased with an increase in air flow, in the gasifier, while CO decreased and H2 was almost constant. The ratio of H2 to CO for various blends of rice husks and corn cobs did not significantly change at the designed process air, but a significant difference of 1.0 was observed between 10/90 and 90/10 % blend of rice husks and corn cobs.

Keywords: Briquettes, fluidised bed, gasification, Aspen Plus, syngas.

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Authors: Jeremy Faludi, Zhongyin Hu, Shahd Alrashed, Christopher Braunholz, Suneesh Kaul, Leulekal Kassaye

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Environmental impacts of six 3D printers using various materials were compared to determine if material choice drove sustainability, or if other factors such as machine type, machine size, or machine utilization dominate. Cradle-to-grave life-cycle assessments were performed, comparing a commercial-scale FDM machine printing in ABS plastic, a desktop FDM machine printing in ABS, a desktop FDM machine printing in PET and PLA plastics, a polyjet machine printing in its proprietary polymer, an SLA machine printing in its polymer, and an inkjet machine hacked to print in salt and dextrose. All scenarios were scored using ReCiPe Endpoint H methodology to combine multiple impact categories, comparing environmental impacts per part made for several scenarios per machine. Results showed that most printers’ ecological impacts were dominated by electricity use, not materials, and the changes in electricity use due to different plastics was not significant compared to variation from one machine to another. Variation in machine idle time determined impacts per part most strongly. However, material impacts were quite important for the inkjet printer hacked to print in salt: In its optimal scenario, it had up to 1/38th the impacts coreper part as the worst-performing machine in the same scenario. If salt parts were infused with epoxy to make them more physically robust, then much of this advantage disappeared, and material impacts actually dominated or equaled electricity use. Future studies should also measure DMLS and SLS processes / materials.

Keywords: 3D printing, Additive Manufacturing, Sustainability, Life-cycle assessment, Design for Environment.

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Authors: Abdulla Rahil, Rupert Gammon, Neil Brown

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The rapid growth of renewable energy sources and their integration into the grid have been motivated by the depletion of fossil fuels and environmental issues. Unfortunately, the grid is unable to cope with the predicted growth of renewable energy which would lead to its instability. To solve this problem, energy storage devices could be used. Electrolytic hydrogen production from an electrolyser is considered a promising option since it is a clean energy source (zero emissions). Choosing flexible operation of an electrolyser (producing hydrogen during the off-peak electricity period and stopping at other times) could bring about many benefits like reducing the cost of hydrogen and helping to balance the electric systems. This paper investigates the price of hydrogen during flexible operation compared with continuous operation, while serving the customer (hydrogen filling station) without interruption. The optimization algorithm is applied to investigate the hydrogen station in both cases (flexible and continuous operation). Three different scenarios are tested to see whether the off-peak electricity price could enhance the reduction of the hydrogen cost. These scenarios are: Standard tariff (1 tier system) during the day (assumed 12 p/kWh) while still satisfying the demand for hydrogen; using off-peak electricity at a lower price (assumed 5 p/kWh) and shutting down the electrolyser at other times; using lower price electricity at off-peak times and high price electricity at other times. This study looks at Derna city, which is located on the coast of the Mediterranean Sea (32° 46′ 0 N, 22° 38′ 0 E) with a high potential for wind resource. Hourly wind speed data which were collected over 24½ years from 1990 to 2014 were in addition to data on hourly radiation and hourly electricity demand collected over a one-year period, together with the petrol station data.

Keywords: Hydrogen filling station off-peak electricity, renewable energy, off-peak electricity, electrolytic hydrogen.

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Authors: Han Guan Chew, Keith Yong Ngee Ng, Shan-Wei Fan

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In Singapore, talent retention is one of the most persistent and real issue companies have to grapple with due to the tight labour market. Being resource-scarce, Singapore depends solely on its talented pool of high quality human resource to sustain its competitive advantage in the global economy. But the complex and multifaceted nature of turnover phenomenon makes the prescription of effective talent retention strategies in such a competitive labour market very challenging, especially when it comes to monetary incentives, companies struggle to answer the question of “How much is enough?” By examining the interactive effects of perceived alternative employment opportunities, annual salary and satisfaction with compensation on the turnover intention of 102 Singapore Professionals, Managers, Executives and Technicians (PMET) through correlation analyses and multiple regressions, important insights into the psyche of the Singapore talent pool can be drawn. It is found that annual salary influence turnover intention indirectly through mediation and moderation effects on PMET’s satisfaction on compensation. PMET are also found to be heavily swayed by better external opportunities. This implies that talent retention strategies should not adopt a purely monetary based blanket approach but rather a comprehensive and holistic one that considers the dynamics of prevailing market conditions.

Keywords: Employee Turnover, High Performers, Knowledge Workers, Perceived Alternative Employment Opportunities Salary, Satisfaction on Compensation, Singapore PMET, Talent Retention.

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Authors: Richard Molyneux, Christopher Parrott, Kirill Horoshenkov

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This paper aims to demonstrate how various algorithms can be implemented within swarms of autonomous robots to provide continuous inspection within underground pipeline networks. Current methods of fault detection within pipes are costly, time consuming and inefficient. As such, solutions tend toward a more reactive approach, repairing faults, as opposed to proactively seeking leaks and blockages. The paper presents an efficient inspection method, showing that autonomous swarm robotics is a viable way of monitoring underground infrastructure. Tailored adaptations of various Vehicle Routing Problems (VRP) and path-planning algorithms provide a customised inspection procedure for complicated networks of underground pipes. The performance of multiple algorithms is compared to determine their effectiveness and feasibility. Notable inspirations come from ant colonies and stigmergy, graph theory, the k-Chinese Postman Problem ( -CPP) and traffic theory. Unlike most swarm behaviours which rely on fast communication between agents, underground pipe networks are a highly challenging communication environment with extremely limited communication ranges. This is due to the extreme variability in the pipe conditions and relatively high attenuation of acoustic and radio waves with which robots would usually communicate. This paper illustrates how to optimise the inspection process and how to increase the frequency with which the robots pass each other, without compromising the routes they are able to take to cover the whole network.

Keywords: Autonomous inspection, buried pipes, stigmergy, swarm intelligence, vehicle routing problem.

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Authors: J-B. Clavel, N. Thiollière, B. Mouginot

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The presented work is motivated by a French law regarding nuclear waste management. A new conceptual Accelerator Driven System (ADS) designed for the Minor Actinides (MA) transmutation has been assessed by numerical simulation. The MUltiple Spallation Target (MUST) ADS combines high thermal power (up to 1.4 GWth) and high specific power. A 30 mA and 1 GeV proton beam is divided into three secondary beams transmitted on three liquid lead-bismuth spallation targets. Neutron and thermalhydraulic simulations have been performed with the code MURE, based on the Monte-Carlo transport code MCNPX. A methodology has been developed to define characteristic of the MUST ADS concept according to a specific transmutation scenario. The reference scenario is based on a MA flux (neptunium, americium and curium) providing from European Fast Reactor (EPR) and a plutonium multireprocessing strategy is accounted for. The MUST ADS reference concept is a sodium cooled fast reactor. The MA fuel at equilibrium is mixed with MgO inert matrix to limit the core reactivity and improve the fuel thermal conductivity. The fuel is irradiated over five years. Five years of cooling and two years for the fuel fabrication are taken into account. The MUST ADS reference concept burns about 50% of the initial MA inventory during a complete cycle. In term of mass, up to 570 kg/year are transmuted in one concept. The methodology to design the MUST ADS and to calculate fuel composition at equilibrium is precisely described in the paper. A detailed fuel evolution analysis is performed and the reference scenario is compared to a scenario where only americium transmutation is performed.

Keywords: Accelerator Driven System, double strata scenario, minor actinides, MUST, transmutation.

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Authors: Louiza C. Duncker, Duarte Gonçalves

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Wildlife crime is a complex problem with many interconnected facets, which are generally responded to in parts or fragments in efforts to “break down” the complexity into manageable components. However, fragmentation increases complexity as coherence and cooperation become diluted. A whole-of-society approach has been developed towards finding a common goal and integrated approach to preventing wildlife crime. As part of this development, research was conducted in rural communities adjacent to conservation areas in South Africa to define and comprehend the challenges faced by them, and to understand their perceptions of wildlife crime. The results of the research showed that the perceptions of community members varied - most were in favor of conservation and of protecting rhinos, only if they derive adequate benefit from it. Regardless of gender, income level, education level, or access to services, conservation was perceived to be good and bad by the same people. Even though people in the communities are poor, a willingness to stop rhino poaching does exist amongst them, but their perception of parks not caring about people triggered an attitude of not being willing to stop, prevent or report poaching. Understanding the nuances, the history, the interests and values of community members, and the drivers behind poaching mind-sets (intrinsic or driven by transnational organized crime) is imperative to create sustainable and resilient communities on multiple levels that make a substantial positive impact on people’s lives, but also conserve wildlife for posterity.

Keywords: Conservation, community perceptions, wildlife crime, rhino poaching, interest and value creation, whole-of-society approach.

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Authors: Wanida Suwunniponth

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The objectives of this research paper were to study the expectation and satisfaction of tourists in five tourism service quality dimensions, namely, website quality, service ability, trust ability, customer empathy, and responsiveness to customer and also to study the influences of satisfaction affecting loyalty toward quality service of the online tourism enterprises located in Bangkok Thailand. This research utilized both quantitative and qualitative research methods. In terms of quantitative method, a questionnaire was used as a tool to collect data from 400 tourists who were using in online travel services. Statistics analysis included descriptive statistics, t-test and Multiple Regression Analysis. In terms of qualitative analysis, an in-depth interview and content analysis were used along with 10 individual management levels of e-commerce enterprises.

The results revealed that the respondents had higher expectations than their level of satisfaction in all five categories. However, the respondents were more satisfied with online travel services than without online service. The demographic factors such as gender and age had no influence on the level of satisfaction whereas the demographic factors of education, occupation, and income had influenced the level of satisfaction. The test results also indicated that the level of satisfaction from responsiveness to customer had the highest influence on the loyalty of tourists who used online travel. The level of satisfaction from customer empathy had the highest influence on the tourists to recommend others to use online travel services. Also, the level of satisfaction from service ability had the highest influence on tourists to take an actual trip.

Keywords: Satisfaction, Loyalty, Service Quality, Online Tourism Enterprises.

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Authors: Sarabjeet Kaur Kochhar

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With a surge of stream processing applications novel techniques are required for generation and analysis of association rules in streams. The traditional rule mining solutions cannot handle streams because they generally require multiple passes over the data and do not guarantee the results in a predictable, small time. Though researchers have been proposing algorithms for generation of rules from streams, there has not been much focus on their analysis. We propose Association rule profiling, a user centric process for analyzing association rules and attaching suitable profiles to them depending on their changing frequency behavior over a previous snapshot of time in a data stream. Association rule profiles provide insights into the changing nature of associations and can be used to characterize the associations. We discuss importance of characteristics such as predictability of linkages present in the data and propose metric to quantify it. We also show how association rule profiles can aid in generation of user specific, more understandable and actionable rules. The framework is implemented as SUPAR: System for Usercentric Profiling of Association Rules in streaming data. The proposed system offers following capabilities: i) Continuous monitoring of frequency of streaming item-sets and detection of significant changes therein for association rule profiling. ii) Computation of metrics for quantifying predictability of associations present in the data. iii) User-centric control of the characterization process: user can control the framework through a) constraint specification and b) non-interesting rule elimination.

Keywords: Data Streams, User subjectivity, Change detection, Association rule profiles, Predictability.

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Authors: Maliheh Raji, Hossein Abolghasemi, Jaber Safdari, Ali Kargari

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Samarium as a rare-earth element is playing a growing important role in high technology. Traditional methods for extraction of rare earth metals such as ion exchange and solvent extraction have disadvantages of high investment and high energy consumption. Emulsion liquid membrane (ELM) as an improved solvent extraction technique is an effective transport method for separation of various compounds from aqueous solutions. In this work, the extraction of samarium from aqueous solutions by ELM was investigated using response surface methodology (RSM). The organic membrane phase of the ELM was a nanofluid consisted of multiwalled carbon nanotubes (MWCNT), Span80 as surfactant, Cyanex 272 as mobile carrier, and kerosene as base fluid. 1 M nitric acid solution was used as internal aqueous phase. The effects of the important process parameters on samarium extraction were investigated, and the values of these parameters were optimized using the Central Composition Design (CCD) of RSM. These parameters were the concentration of MWCNT in nanofluid, the carrier concentration, and the volume ratio of organic membrane phase to internal phase (Roi). The three-dimensional (3D) response surfaces of samarium extraction efficiency were obtained to visualize the individual and interactive effects of the process variables. A regression model for % extraction was developed, and its adequacy was evaluated. The result shows that % extraction improves by using MWCNT nanofluid in organic membrane phase and extraction efficiency of 98.92% can be achieved under the optimum conditions. In addition, demulsification was successfully performed and the recycled membrane phase was proved to be effective in the optimum condition.

Keywords: Cyanex 272, emulsion liquid membrane, multiwalled carbon nanotubes, nanofluid, response surface methodology, Samarium.

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Authors: Syahril Ridzuan Ab Rahim, Ibrahim Ahmad, Mohd Azizi Chik, Ahmad Zafir Md. Rejab, and U. Hashim

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This research presents the development of simulation modeling for WIP management in semiconductor fabrication. Manufacturing simulation modeling is needed for productivity optimization analysis due to the complex process flows involved more than 35 percent re-entrance processing steps more than 15 times at same equipment. Furthermore, semiconductor fabrication required to produce high product mixed with total processing steps varies from 300 to 800 steps and cycle time between 30 to 70 days. Besides the complexity, expansive wafer cost that potentially impact the company profits margin once miss due date is another motivation to explore options to experiment any analysis using simulation modeling. In this paper, the simulation model is developed using existing commercial software platform AutoSched AP, with customized integration with Manufacturing Execution Systems (MES) and Advanced Productivity Family (APF) for data collections used to configure the model parameters and data source. Model parameters such as processing steps cycle time, equipment performance, handling time, efficiency of operator are collected through this customization. Once the parameters are validated, few customizations are made to ensure the prior model is executed. The accuracy for the simulation model is validated with the actual output per day for all equipments. The comparison analysis from result of the simulation model compared to actual for achieved 95 percent accuracy for 30 days. This model later was used to perform various what if analysis to understand impacts on cycle time and overall output. By using this simulation model, complex manufacturing environment like semiconductor fabrication (fab) now have alternative source of validation for any new requirements impact analysis.

Keywords: Advanced Productivity Family (APF), Complementary Metal Oxide Semiconductor (CMOS), Manufacturing Execution Systems (MES), Work In Progress (WIP).

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Authors: H. B. Kekre, Sudeep D. Thepade

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The automatic construction of large, high-resolution image vistas (mosaics) is an active area of research in the fields of photogrammetry [1,2], computer vision [1,4], medical image processing [4], computer graphics [3] and biometrics [8]. Image stitching is one of the possible options to get image mosaics. Vista Creation in image processing is used to construct an image with a large field of view than that could be obtained with a single photograph. It refers to transforming and stitching multiple images into a new aggregate image without any visible seam or distortion in the overlapping areas. Vista creation process aligns two partial images over each other and blends them together. Image mosaics allow one to compensate for differences in viewing geometry. Thus they can be used to simplify tasks by simulating the condition in which the scene is viewed from a fixed position with single camera. While obtaining partial images the geometric anomalies like rotation, scaling are bound to happen. To nullify effect of rotation of partial images on process of vista creation, we are proposing rotation invariant vista creation algorithm in this paper. Rotation of partial image parts in the proposed method of vista creation may introduce some missing region in the vista. To correct this error, that is to fill the missing region further we have used image inpainting method on the created vista. This missing view regeneration method also overcomes the problem of missing view [31] in vista due to cropping, irregular boundaries of partial image parts and errors in digitization [35]. The method of missing view regeneration generates the missing view of vista using the information present in vista itself.

Keywords: Vista, Overlap Estimation, Rotation Invariance, Missing View Regeneration.

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Authors: A. Kumar, V. Devadas

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Energy is the basic element for any country’s economic development. India is one of the most populated countries, and is dependent on fossil fuel and nuclear-based energy generation. The energy sector faces huge challenges and is dependent on the import of energy from neighboring countries to fulfill the gap in demand and supply. India has huge setbacks for efficient energy generation, distribution, and consumption, therefore they consume more quantity of energy to produce the same amount of Gross Domestic Product (GDP) compared to the developed countries. Technology and technique use, availability, and affordability in the various sectors are varying according to their economic status. In this paper, an attempt is made to quantify the domestic electrical energy consumption in Jodhpur, India. Survey research methods have been employed and stratified sampling technique-based households were chosen for conducting the investigation. Pre-tested survey schedules are used to investigate the grassroots level study. The collected data are analyzed by employing statistical techniques. Thereafter, a multiple regression model is developed to understand the functions of total electricity consumption in the domestic sector corresponding to other independent variables including electrical appliances, age of the building, household size, education, etc. The study resulted in identifying the governing variable in energy consumption at the household level and their relationship with the efficiency of household-based electrical and energy appliances. The analysis is concluded with the recommendation for optimizing the gap in peak electrical demand and supply in the domestic sector.

Keywords: Appliance, consumption, electricity, households.

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Authors: S. Aberkane, N. Djabali, S. Fafi, A. Baghezza

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Chronic illnesses affect many Algerians. It is possible to investigate the impact of illness representations and coping on quality of life and whether illness representations are indirectly associated with quality of life through their influence on coping. This study aims at investigating the relationship between illness perception, coping strategies and quality of life with chronic illness. Illness perceptions are indirectly associated with the quality of life through their influence on coping mediation. A sample of 316 participants with chronic illness living in the region of Batna, Algeria, has been adopted in this study. A correlation statistical analysis is used to determine the relationship between illness perception, coping strategies, and quality of life. Multiple regression analysis was employed to highlight the predictive ability of the dimensions of illness perception and coping strategies on the dependent variables of quality of life, where mediation analysis is considered in the exploration of the indirect effect significance of the mediator. This study provides insights about the relationship between illness perception, coping strategies and quality of life in the considered sample (r = 0.39, p < 0.01). Therefore, it proves that there is an effect of illness identity perception, external and medical attributions related to emotional role, physical functioning, and mental health perceived, and these were fully mediated by the asking for assistance (c’= 0.04, p < 0.05), the guarding (c’= 0.00, p < 0.05), and the task persistence strategy (c’= 0.05, p < 0.05). The findings imply partial support for the common-sense model of illness representations in a chronic illness population. Directions for future research are highlighted, as well as implications for psychotherapeutic interventions which target unhelpful beliefs and maladaptive coping strategies (e.g., cognitive behavioral therapy).

Keywords: Chronic illness, coping, illness perception, quality of life, self-regulation model.

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Authors: Romeo A. Botes, Imelda Smit

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The demands of an ever changing and complex higher education environment, along with the profile of modern learners challenge current approaches to assessment and feedback. More learners enter the education system every year. The younger generation expects immediate feedback. At the same time, feedback should be meaningful. The assessment of practical activities in programming poses a particular problem, since both lecturers and learners in the information and computer science discipline acknowledge that paper-based assessment for programming subjects lacks meaningful real-life testing. At the same time, feedback lacks promptness, consistency, comprehensiveness and individualisation. Most of these aspects may be addressed by modern, technology-assisted assessment. The focus of this paper is the continuous development of an artefact that is used to assist the lecturer in the assessment and feedback of practical programming activities in a senior database programming class. The artefact was developed using three Design Science Research cycles. The first implementation allowed one programming activity submission per assessment intervention. This pilot provided valuable insight into the obstacles regarding the implementation of this type of assessment tool. A second implementation improved the initial version to allow multiple programming activity submissions per assessment. The focus of this version is on providing scaffold feedback to the learner – allowing improvement with each subsequent submission. It also has a built-in capability to provide the lecturer with information regarding the key problem areas of each assessment intervention.

Keywords: Programming, computer-aided assessment, technology-assisted assessment, programming assessment software, design science research, mixed-method.

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Authors: Amah Nnachi Lofty, Oyefeso Olufemi, Ibiam Udu Ama

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New and more powerful communications technologies continue to emerge at a rapid pace and their uses in education are widespread and the impact remarkable in the developing societies. This study investigates Mobile Collaboration Learning Technique (MCLT) on learners’ outcome among students in tertiary institutions of developing nations (a case of Nigeria students). It examines the significance of retention achievement scores of students taught using mobile collaboration and conventional method. The sample consisted of 120 students using Stratified random sampling method. Five research questions and hypotheses were formulated, and tested at 0.05 level of significance. A student achievement test (SAT) was made of 40 items of multiple-choice objective type, developed and validated for data collection by professionals. The SAT was administered to students as pre-test and post-test. The data were analyzed using t-test statistic to test the hypotheses. The result indicated that students taught using MCLT performed significantly better than their counterparts using the conventional method of instruction. Also, there was no significant difference in the post-test performance scores of male and female students taught using MCLT. Based on the findings, the following submissions was made that: Mobile collaboration system be encouraged in the institutions to boost knowledge sharing among learners, workshop and training should be organized to train teachers on the use of this technique, schools and government should consistently align curriculum standard to trends of technological dictates and formulate policies and procedures towards responsible use of MCLT.

Keywords: Education, communication, learning, mobile collaboration, technology.

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Authors: Guarry Montrose, Ted Soubdhan

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In the fight against climate change, the energy consuming building sector must also be taken into account to solve this problem. In this case thermal insulation of buildings using bio-based materials is an interesting solution. Therefore, the thermal performance of some materials of this type has been studied. The advantages of these natural materials of plant origin are multiple, biodegradable, low economic cost, renewable and readily available. The use of biobased materials is widespread in the building sector in order to replace conventional insulation materials with natural materials. Vegetable fibers are very important because they have good thermal behaviour and good insulating properties. The aim of using bio-sourced materials is in line with the logic of energy control and environmental protection, the approach is to make the inhabitants of the houses comfortable and reduce their energy consumption (energy efficiency). In this research we will present the results of studies carried out on the thermal conductivity of banana leaves, latan leaves, vetivers fibers, palm kernel fibers, sargassum, coconut leaves, sawdust and bulk sugarcane leaves. The study on thermal conductivity was carried out in two ways, on the one hand using the flash method, and on the other hand a so-called hot box experiment was carried out. We will discuss and highlight a number of influential factors such as moisture and air pockets present in the samples on the thermophysical properties of these materials, in particular thermal conductivity. Finally, the result of a thermal performance test of banana leaves on a roof in Haiti will also be presented in this work.

Keywords: Buildings, insulating properties, natural materials of plant origin, thermal performance.

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Authors: M. P. Valles-González, A. González Meije, A. Pastor Muro, M. García-Martínez, B. González Caballero

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This paper studies a failure case of a fuel pressure supply tube from an aircraft engine. Multiple fracture cases of the fuel pressure control tube from aircraft engines have been reported. The studied set was composed by the mentioned tube, a welded connecting pipe, where the fracture has been produced, and a union nut. The fracture has been produced in one of the most critical zones of the tube, in a region next to the supporting body of the union nut to the connector. The tube material was X6CrNiTi18-10, an austenitic stainless steel. Chemical composition was determined using an X-Ray fluorescence spectrometer (XRF) and combustion equipment. Furthermore, the material was characterized mechanically, by a hardness test, and microstructurally using a stereo microscope and an optical microscope. The results confirmed that the material was within specifications. To determine the macrofractographic features, a visual examination and an observation using a stereo microscope of the tube fracture surface were carried out. The results revealed a tube plastic macrodeformation, surface damaged and signs of a possible corrosion process. Fracture surface was also inspected by scanning electron microscopy (FE-SEM), equipped with an energy-dispersive X-ray microanalysis system (EDX), to determine the microfractographic features in order to find out the failure mechanism involved in the fracture. Fatigue striations, which are typical from a progressive fracture by a fatigue mechanism, were observed. The origin of the fracture was placed in defects located on the outer wall of the tube, leading to a final overload fracture.

Keywords: Aircraft Engine, microstructure, fatigue, FE-SEM, fractography, fracture, fuel tube, stainless steel.

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Authors: A. A. Aiyeloja, A.T. Oladele, O. Tumulo

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Raphia wine is an important forest product with cultural significance besides its use as medicine and food in southern Nigeria. This work aims to evaluate the profitability of Raphia wine production and marketing in Sapele Local Government Area, Nigeria. Four communities (Sapele, Ogiede, Okuoke and Elume) were randomly selected for data collection via questionnaires among producers and marketers. A total of 50 producers and 34 marketers were randomly selected for interview. Data was analyzed using descriptive statistics, profit margin, multiple regression and rate of returns on investment (RORI). Annual average profit was highest in Okuoke (Producers – N90, 000.00, Marketers - N70, 000.00) and least in Sapele (Producers N50, 000.00, Marketers – N45, 000.00). Calculated RORI for marketers were Elume (40.0%), Okuoke (25.0%), Ogiede (33.3%) and Sapele (50.0%). Regression results showed that location has significant effects (0.000, ρ ≤ 0.05) on profit margins. Male (58.8%) and female (41.2%) invest in Raphia wine marketing, while males (100.0%) dominate production. Results showed that Raphia wine has potentials to generate household income, enhance food security and improve quality of life in rural, semi-urban and urban communities. Improved marketing channels, storage facilities and credit facilities via cooperative groups are recommended for producers and marketers by concerned agencies.

Keywords: Raphia wine, Profit margin, RORI, Livelihood, Nigeria.

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Authors: Stamatios A. Theocharis, George A. Tsihrintzis

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One of the most important factors for the success of e-government is training and preparing the workforce of the public sector. As changes and innovation in the public sector progress at a very slow pace and more slowly than in the private sector, issues related to human resources require special care. This is because the workforce will eventually seize the opportunities of the technological solutions used in e-Government. Thus, the central administration should provide employees with continuous and focused training not only on new technologies but also on a wide range of subjects and also improve interdepartmental interaction.

To achieve all this, new methods and training tools need to be implemented in addition to assessment of the employees. In this spirit, we propose the development of an educational platform with user personalization features. We propose the development of this platform using Moodle as the basic tool. Incorporating a personalization mechanism is very important since different employees have different backgrounds, education levels, computer skills, or different capability to develop further. Key features of the proposed platform include, besides typical e-learning tools, communities organized in order to exchange experiences and knowledge, groups of users based on certain criteria, automatic evaluation of users and potential self-education and self-assessment. In its fully developed form, this platform can be part of a more comprehensive knowledge management system for the public sector.

Keywords: e-Government, civil employees education, education technologies.

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Authors: M. Govindarajan

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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In decision tree approach is most useful in classification problem. With this technique, tree is constructed to model the classification process. There are two basic steps in the technique: building the tree and applying the tree to the database. This paper describes a proposed C5.0 classifier that performs rulesets, cross validation and boosting for original C5.0 in order to reduce the optimization of error ratio. The feasibility and the benefits of the proposed approach are demonstrated by means of medial data set like hypothyroid. It is shown that, the performance of a classifier on the training cases from which it was constructed gives a poor estimate by sampling or using a separate test file, either way, the classifier is evaluated on cases that were not used to build and evaluate the classifier are both are large. If the cases in hypothyroid.data and hypothyroid.test were to be shuffled and divided into a new 2772 case training set and a 1000 case test set, C5.0 might construct a different classifier with a lower or higher error rate on the test cases. An important feature of see5 is its ability to classifiers called rulesets. The ruleset has an error rate 0.5 % on the test cases. The standard errors of the means provide an estimate of the variability of results. One way to get a more reliable estimate of predictive is by f-fold –cross- validation. The error rate of a classifier produced from all the cases is estimated as the ratio of the total number of errors on the hold-out cases to the total number of cases. The Boost option with x trials instructs See5 to construct up to x classifiers in this manner. Trials over numerous datasets, large and small, show that on average 10-classifier boosting reduces the error rate for test cases by about 25%.

Keywords: C5.0, Error Ratio, text mining, training data, test data.

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