Search results for: H₂-optimal model reduction

Authors: Esther Chiramal, Kelvin Soh Boon Kai

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Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human-interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text attack uses word substitution, data augmentation techniques, and gradient-based attacks on powerful pre-trained Bidirectional Encoder Representations from Transformers (BERT) variants to generate adversarial sentences. These attacks are generally white-box in nature and not practical as they can be easily detected by humans e.g., Changing the word from “Poor” to “Rich”. We proposed a simple yet effective Grey-box cum Black-box approach that does not require the knowledge of the model while using a set of surrogate Transformer/BERT models to perform the attack using Explainable AI techniques. As Transformers are the current state-of-the-art models for almost all Natural Language Processing (NLP) tasks, an attack generated from BERT1 is transferable to BERT2. This transferability is made possible due to the attention mechanism in the transformer that allows the model to capture long-range dependencies in a sequence. Using the power of BERT generalisation via attention, we attempt to exploit how transformers learn by attacking a few surrogate transformer variants which are all based on a different architecture. We demonstrate that this approach is highly effective to generate semantically good sentences by changing as little as one word that is not detectable by humans while still fooling other BERT models.

Keywords: BERT, explainable AI, Grey-box text attack, transformer

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Authors: Marwa Ahmed Abd El Hamied

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In this paper In this paper, a new technique based on Artificial Immunity System (AIS) technique has been developed to track Maximum Power Point (MPP). AIS system is implemented in a photovoltaic system that is subjected to variable temperature and insulation condition. The proposed novel is simulated using Mat Lab program. The results of simulation have been compared to those who are generated from Observation Controller. The proposed model shows promising results as it provide better accuracy comparing to classical model.

Keywords: component, artificial immunity technique, solar energy, perturbation and observation, power based methods

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Authors: Changmin Jiang

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In this paper, we build a theoretical model to investigate the relationship between two recent trends in airline frequent-flyer programs (FFPs): the adoption of the “coalition” business model with other commercial partners, and the separation from airlines’ operations. We show that commercial partners benefit from teaming up with FFP, while increasing the number of commercial partners will increase the total profit; it reduces the average profit of the parties involved. Furthermore, we show that the number of commercial partners of an FFP is negatively related with the benefit to keep the FFP in-house.

Keywords: frequent flyer program, coalition, commercial partners, spin-off

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Authors: H.Karnezi, K. Tierney

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Childhood fears that persist over time and interfere with the children’s normal functioning may have detrimental effects on their social and emotional development. Cognitive behavior therapy is considered highly effective in treating fears and anxieties. However, given that many childhood fears are based on fantasy, the applicability of CBT may be hindered by cognitive immaturity. Furthermore, a lack of motivation to engage in therapy is another commonly encountered obstacle. The purpose of this study was to introduce and evaluate a more developmentally appropriate intervention model, specifically designed to provide phobic children with the motivation to overcome their fears. To this end, principles and techniques from cognitive and behavior therapies are incorporated into the ‘Drama in Education’ model. The Cognitive Behaviour Drama (CBD) method involves using the phobic children’s creativity to involve them in the therapeutic process. The children are invited to engage in exciting fictional scenarios tailored around their strengths and special interests. Once their commitment to the drama is established, a problem that they will feel motivated to solve is introduced. To resolve it, the children will have to overcome a number of obstacles culminating in an in vivo confrontation with the fear stimulus. The study examined the application of the CBD model in three single cases. Results in all three cases shown complete elimination of all fear-related symptoms. Preliminary results justify further evaluation of the Cognitive Behaviour Drama model. It is time and cost-effective, ensuring the clients' immediate engagement in the therapeutic process.

Keywords: phobias, autism, intervention, drama

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Authors: Babak Nemat

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Background and Purpose: Non-alcoholic fatty liver is one of the most common liver disorders, which, as the most important cause of death from liver disease, has unpleasant consequences and complications. The aim of this study was to investigate the effect of an educational intervention based on a health belief model to prevent non-alcoholic fatty liver among women. Materials and Methods: This experimental study was performed among 110 women referring to comprehensive health service centers in Malayer City, west of Iran, in 2023. Using the available sampling method, 110 participants were divided into experimental and control groups. The data collection tool included demographic characteristics and a questionnaire based on the health belief model. In the experimental group, three one-hour training sessions were conducted in the form of pamphlets, lectures, and group discussions. Data were analyzed using SPSS software version 21, by correlation tests, paired t-tests, and independent t-tests. Results: The mean age of participants was 38.07±6.28 years, and most of the participants were middle-aged, married, housewives with academic education, middle-income, and overweight. After the educational intervention, the mean scores of the constructs include perceived sensitivity (p=0.01), perceived severity (p=0.01), perceived benefits (p=0.01), guidance for internal (p=0.01), and external action (p=0.01), and perceived self-efficacy (p=0.01) in the experimental group were significantly higher than the control group. The score of perceived barriers in the experimental group decreased after training. The perceived obstacles score in the test group decreased after the training (15.2 ± 3.9 v.s 11.2 ± 3.3, (p<0.01). Conclusion: The findings of the study showed that the design and implementation of educational programs based on the constructs of the health belief model can be effective in preventing women from developing higher levels of non-alcoholic fatty liver.

Keywords: non-alcoholic fatty liver, health belief model, education, women

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Authors: Mohammad Rajabalinejad

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Safety is often seen as a requirement or a performance indicator through the design process, and this does not always result in optimally safe products or systems. This paper suggests integrating the best safety practices with the design process to enrich the exploration experience for designers and add extra values for customers. For this purpose, the commonly practiced safety standards and design methods have been reviewed and their common blocks have been merged forming Safety Cube. Safety Cube combines common blocks for design, hazard identification, risk assessment and risk reduction through an integral approach. An example application presents the use of Safety Cube for design of machinery.

Keywords: safety, safety cube, product, system, machinery, design

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Yishu Gong, Liangliang Yang, Jianyu Zhang, Zhengyu Chen, Sihong He, Xusheng Zhang, Wei Zhang

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and is characterized by cognitive decline and behavioral changes. People living with Alzheimer’s disease often find it hard to complete routine tasks. However, there are limited objective assessments that aim to quantify the difficulty of certain tasks for AD patients compared to non-AD people. In this study, we propose to use speech emotion recognition (SER), especially the frustration level, as a potential biomarker for quantifying the difficulty patients experience when describing a picture. We build an SER model using data from the IEMOCAP dataset and apply the model to the DementiaBank data to detect the AD/non-AD group difference and perform longitudinal analysis to track the AD disease progression. Our results show that the frustration level detected from the SER model can possibly be used as a cost-effective tool for objective tracking of AD progression in addition to the Mini-Mental State Examination (MMSE) score.

Keywords: Alzheimer’s disease, speech emotion recognition, longitudinal biomarker, machine learning

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Authors: Xhevrie Mamaqi

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Organization capacity learning is a process referring to the sum total of individual and collective learning through training programs, experience and experimentation, among others. Today, in-business ongoing training is one of the most important strategies for human capital development and it is crucial to sustain and improve workers’ knowledge and skills. Many organizations, firms and business are adopting a strategy of continuous learning, encouraging employees to learn new skills continually to be innovative and to try new processes and work in order to achieve a competitive advantage and superior business results. This paper uses the Resource Based View and Capacities (RBV) approach to construct a hypothetical relationships model between training and business results. The test of the model is applied on transversal data. A sample of 266 business of Spanish sector service has been selected. A Structural Equation Model (SEM) is used to estimate the relationship between ongoing training, represented by two latent dimension denominated Human and Social Capital resources and economic business results. The coefficients estimated have shown the efficient of some training aspects explaining the variation in business results.

Keywords: business results, human and social capital resources, training, RBV theory, SEM

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Authors: Maryam Ziaee

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This research proposes a new approach and provides an opportunity for manufacturing companies to produce large amounts of products that meet their prospective customers’ tastes, needs, and expectations and simultaneously enable manufacturers to increase their profit. Mass customization is the production of products or services to meet each individual customer’s desires to the greatest possible extent in high quantities and at reasonable prices. This process takes place at different levels such as the customization of goods’ design, assembly, sale, and delivery status, and classifies in several categories. The main focus of this study is on one class of mass customization, called optional customization, in which companies try to provide their customers with as many options as possible to customize their products. These options could range from the design phase to the manufacturing phase, or even methods of delivery. Mass customization values customers’ tastes, but it is only one side of clients’ satisfaction; on the other side is companies’ fast responsiveness delivery. It brings the concept of agility, which is the ability of a company to respond rapidly to changes in volatile markets in terms of volume and variety. Indeed, mass customization is not effectively feasible without integrating the concept of agility. To gain the customers’ satisfaction, the companies need to be quick in responding to their customers’ demands, thus highlighting the significance of agility. This research offers a different method that successfully integrates mass customization and fast production in manufacturing industries. This research is built upon the hypothesis that the success key to being agile in mass customization is to forecast demand, cooperate with suppliers, and control inventory. Therefore, the significance of the supply chain (SC) is more pertinent when it comes to this stage. Since SC behavior is dynamic and its behavior changes constantly, companies have to apply one of the predicting techniques to identify the changes associated with SC behavior to be able to respond properly to any unwelcome events. System dynamics utilized in this research is a simulation approach to provide a mathematical model among different variables to understand, control, and forecast SC behavior. The final stage is delayed differentiation, the production strategy considered in this research. In this approach, the main platform of products is produced and stocked and when the company receives an order from a customer, a specific customized feature is assigned to this platform and the customized products will be created. The main research question is to what extent applying system dynamics for the prediction of SC behavior improves the agility of mass customization. This research is built upon a qualitative approach to bring about richer, deeper, and more revealing results. The data is collected through interviews and is analyzed through NVivo software. This proposed model offers numerous benefits such as reduction in the number of product inventories and their storage costs, improvement in the resilience of companies’ responses to their clients’ needs and tastes, the increase of profits, and the optimization of productivity with the minimum level of lost sales.

Keywords: agility, manufacturing, resilience, supply chain

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Authors: Mehdi Khaki, Ehsan Forootan, Joseph Awange, Michael Kuhn

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Terrestrial water storage, as a source of freshwater, plays an important role in human lives. Hydrological models offer important tools for simulating and predicting water storages at global and regional scales. However, their comparisons with 'reality' are imperfect mainly due to a high level of uncertainty in input data and limitations in accounting for all complex water cycle processes, uncertainties of (unknown) empirical model parameters, as well as the absence of high resolution (both spatially and temporally) data. Data assimilation can mitigate this drawback by incorporating new sets of observations into models. In this effort, we use multi-mission satellite-derived remotely sensed observations to improve the performance of World-Wide Water Resources Assessment system (W3RA) hydrological model for estimating terrestrial water storages. For this purpose, we assimilate total water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) and surface soil moisture data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) into W3RA. This is done to (i) improve model estimations of water stored in ground and soil moisture, and (ii) assess the impacts of each satellite of data (from GRACE and AMSR-E) and their combination on the final terrestrial water storage estimations. These data are assimilated into W3RA using the Ensemble Square-Root Filter (EnSRF) filtering technique over Mississippi Basin (the United States) and Murray-Darling Basin (Australia) between 2002 and 2013. In order to evaluate the results, independent ground-based groundwater and soil moisture measurements within each basin are used.

Keywords: data assimilation, GRACE, AMSR-E, hydrological model, EnSRF

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Authors: Navneet Kaur, S. D. Tiwari

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Superparamagnetism is an interesting phenomenon and observed in small particles of magnetic materials. It arises due to a reduction in particle size. In the superparamagnetic state, as the thermal energy overcomes magnetic anisotropy energy, the magnetic moment vector of particles flip their magnetization direction between states of minimum energy. Superparamagnetic nanoparticles have been attracting the researchers due to many applications such as information storage, magnetic resonance imaging, biomedical applications, and sensors. For information storage, thermal fluctuations lead to loss of data. So that nanoparticles should have high blocking temperature. And to achieve this, nanoparticles should have a higher magnetic moment and magnetic anisotropy constant. In this work, the magnetic anisotropy constant of the antiferromagnetic nanoparticles system is determined. Magnetic studies on nanoparticles of NiO (nickel oxide) are reported well. This antiferromagnetic nanoparticle system has high blocking temperature and magnetic anisotropy constant of order 105 J/m3. The magnetic study of NiO nanoparticles in the superparamagnetic region is presented. NiO particles of two different sizes, i.e., 6 and 8 nm, are synthesized using the chemical route. These particles are characterized by an x-ray diffractometer, transmission electron microscope, and superconducting quantum interference device magnetometry. The magnetization vs. applied magnetic field and temperature data for both samples confirm their superparamagnetic nature. The blocking temperature for 6 and 8 nm particles is found to be 200 and 172 K, respectively. Magnetization vs. applied magnetic field data of NiO is fitted to an appropriate magnetic expression using a non-linear least square fit method. The role of particle size distribution and magnetic anisotropy is taken in to account in magnetization expression. The source code is written in Python programming language. This fitting provides us the magnetic anisotropy constant for NiO and other magnetic fit parameters. The particle size distribution estimated matches well with the transmission electron micrograph. The value of magnetic anisotropy constants for 6 and 8 nm particles is found to be 1.42 X 105 and 1.20 X 105 J/m3, respectively. The obtained magnetic fit parameters are verified using the Neel model. It is concluded that the effect of magnetic anisotropy should not be ignored while studying the magnetization process of nanoparticles.

Keywords: anisotropy, superparamagnetic, nanoparticle, magnetization

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets.

Keywords: discriminative LMA features, features reduction, human motion recognition, random forest

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Authors: A. Bekbaev, M. Dzhamanbaev, R. Abitaeva, A. Karbozova, G. Nabyeva

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In this paper, the regression dependence of dancing intensity from wind speed and length of span was established due to the statistic data obtained from multi-year observations on line wires dancing accumulated by power systems of Kazakhstan and the Russian Federation. The lower and upper limitations of the equations parameters were estimated, as well as the adequacy of the regression model. The constructed model will be used in research of dancing phenomena for the development of methods and means of protection against dancing and for zoning plan of the territories of line wire dancing.

Keywords: power lines, line wire dancing, dancing intensity, regression equation, dancing area intensity

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Authors: A. Sgobba, C. Meskell

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The feasibility of on-site electricity production from solar and wind and the resulting load management for a specific manufacturing plant in Ireland are assessed. The industry sector accounts directly and indirectly for a high percentage of electricity consumption and global greenhouse gas emissions; therefore, it will play a key role in emission reduction and control. Manufacturing plants, in particular, are often located in non-residential areas since they require open spaces for production machinery, parking facilities for the employees, appropriate routes for supply and delivery, special connections to the national grid and other environmental impacts. Since they have larger spaces compared to commercial sites in urban areas, they represent an appropriate case study for evaluating the technical and economic viability of energy system integration with low power density technologies, such as solar and wind, for on-site electricity generation. The available open space surrounding the analysed manufacturing plant can be efficiently used to produce a discrete quantity of energy, instantaneously and locally consumed. Therefore, transmission and distribution losses can be reduced. The usage of storage is not required due to the high and almost constant electricity consumption profile. The energy load of the plant is identified through the analysis of gas and electricity consumption, both internally monitored and reported on the bills. These data are not often recorded and available to third parties since manufacturing companies usually keep track only of the overall energy expenditures. The solar potential is modelled for a period of 21 years based on global horizontal irradiation data; the hourly direct and diffuse radiation and the energy produced by the system at the optimum pitch angle are calculated. The model is validated using PVWatts and SAM tools. Wind speed data are available for the same period within one-hour step at a height of 10m. Since the hub of a typical wind turbine reaches a higher altitude, complementary data for a different location at 50m have been compared, and a model for the estimate of wind speed at the required height in the right location is defined. Weibull Statistical Distribution is used to evaluate the wind energy potential of the site. The results show that solar and wind energy are, as expected, generally decoupled. Based on the real case study, the percentage of load covered every hour by on-site generation (Level of Autonomy LA) and the resulting electricity bought from the grid (Expected Energy Not Supplied EENS) are calculated. The economic viability of the project is assessed through Net Present Value, and the influence the main technical and economic parameters have on NPV is presented. Since the results show that the analysed renewable sources can not provide enough electricity, the integration with a cogeneration technology is studied. Finally, the benefit to energy system integration of wind, solar and a cogeneration technology is evaluated and discussed.

Keywords: demand, energy system integration, load, manufacturing, national grid, renewable energy sources

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Authors: B. X. Tchomeni, A. A. Alugongo, L. M. Masu

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This paper presents a 4-DOF nonlinear model of a cracked of Laval rotor established based on Energy Principles. The model has been used to simulate coupled torsional-lateral response of the cracked rotor stator-system with multiple parametric excitations, namely, rotor-stator-rub, a breathing transverse crack, unbalanced mass, and an axial force. Nonlinearity due to a “breathing” crack is incorporated by considering a simple hinge model which is suitable for small breathing crack. The vibration response of a cracked rotor passing through its critical speed with rotor-stator interaction is analyzed, and an attempt for crack detection and monitoring explored. Effects of unbalanced eccentricity with phase and acceleration are investigated. By solving the motion equations, steady-state vibration response is obtained in presence of several rotor faults. The presence of a crack is observable in the power spectrum despite the excitation by the axial force and rotor-stator rub impact. Presented results are consistent with existing literature and could be adopted into rotor condition monitoring strategies

Keywords: rotor, crack, rubbing, axial force, non linear

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Authors: Gabriel Sunday Ayayia

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Food waste remains a critical global challenge with significant environmental, economic, and ethical implications. In an era where food waste and food insecurity coexist, innovative technology-driven solutions have emerged, aiming to bridge the gap between surplus food and those in need. Simultaneously, disparities in food access persist, exacerbating issues of hunger and malnutrition. Emerging food-sharing apps offer a promising avenue to mitigate these problems but require further examination within the context of the Ubuntu sharing economy. This study seeks to understand the impact of food-sharing apps, guided by the principles of Ubuntu, on reducing food waste and enhancing food access. The study examines how specific food-sharing apps within the Ubuntu sharing economy could contribute to fostering community resilience and reducing food waste. Ubuntu underscores the idea that we are all responsible for the well-being of our community members. In the context of food waste, this means that individuals and businesses have a collective responsibility to ensure that surplus food is shared rather than wasted. Food-sharing apps align with this principle by facilitating the sharing of excess food with those in need, transforming waste into a communal resource. This research employs a mixed-methods approach of both quantitative analysis and qualitative inquiry. Large-scale surveys will be conducted to assess user behavior, attitudes, and experiences with food-sharing apps, focusing on the frequency of use, motivations, and perceived impacts. Qualitative interviews with app users, community organizers, and stakeholders will explore the Ubuntu-inspired aspects of food-sharing apps and their influence on reducing food waste and improving food access. Quantitative data will be analyzed using statistical techniques, while qualitative data will undergo thematic analysis to identify key patterns and insights. This research addresses a critical gap in the literature by examining the role of food-sharing apps in reducing food waste and enhancing food access, particularly within the Ubuntu sharing economy framework. Findings will offer valuable insights for policymakers, technology developers, and communities seeking to leverage technology to create a more just and sustainable food system.

Keywords: sharing economy, food waste reduction, technology, community- based approach

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

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In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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Authors: Joane Adeclas, Yoonyoung Kim, Taekyun Hur

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This study investigates individual’s intrinsic motivation to practice their leisure activities, as well as, how the cultural environment may influence their motivation to practice their activities. Focused on the positive psychology, the present study proposed redefinition of leisure activities considering two factors. First, leisure activities could be as any activities that provide pleasure or meaning to individuals. Second, they can be practiced alone or in groups. In fact, based on this definition, a four-dimensional model of leisure activities was developed, to measure individual’s perception of their leisure experience, based on four factors that are: personal pleasure, social pleasure, personal meaning and social meaning. Furthermore, recent studies have argued that leisure activities can be interpreted and understood differently across cultures. Therefore, the present study proposed to examine the possible role of the cultural context of individual’s leisure practices. To do so, two cultural groups (Koreans vs. French) were compared in terms of the four-dimensional model of leisure activities. Three hundred Koreans and three hundred French participants were asked to answer an online survey about their leisure activities. Participants had to respond to questions related to several aspects of leisure practices as followed: the reason why their practice their leisure activities, the reason why they fail to practice their leisure, and their obsession relate to their leisure activities. Factor analyses based on participant’s responses proposed a moderate fit of the four-dimensional model of leisure activities. Furthermore, significant cultural differences were also found. As a result, the cultural context seems to influence the reason why individuals practice their leisure activities based on our model. In fact, Koreans explained more than French, the practice of their leisure activities with social-pleasurable reasons. At a contrary, French explained more than Koreans, the practice of their leisure activities with social-meaningful reasons. The two cultural groups also significantly differ on their perception of failure. The results showed that French participants used more meaningful social factors to explain why they failed to practice their leisure activities than did Koreans participants. Finally, Koreans and French significantly differed regarding their obsession on their leisure activities. In general, French tend to have more obsession than Koreans about their leisure activities. Those results validated the four-dimensional model of leisure, as well as, the cultural differences in leisure practices. However, further studies are needed to validate this model at an individual and cultural level.

Keywords: culture, leisure, meaning, pleasure

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Authors: Fahd Sabry Esmail, M. Badr Senousy, Mohamed Ragaie

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In recent years, there has been an explosion in the rate of using technology that help discovering the diseases. For example, DNA microarrays allow us for the first time to obtain a "global" view of the cell. It has great potential to provide accurate medical diagnosis, to help in finding the right treatment and cure for many diseases. Various classification algorithms can be applied on such micro-array datasets to devise methods that can predict the occurrence of Leukemia disease. In this study, we compared the classification accuracy and response time among eleven decision tree methods and six rule classifier methods using five performance criteria. The experiment results show that the performance of Random Tree is producing better result. Also it takes lowest time to build model in tree classifier. The classification rules algorithms such as nearest- neighbor-like algorithm (NNge) is the best algorithm due to the high accuracy and it takes lowest time to build model in classification.

Keywords: data mining, classification techniques, decision tree, classification rule, leukemia diseases, microarray data

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Authors: Fazli Rahim Shinwari, Ulrich Dittmer

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Like many megacities around the world, Kabul is facing severe problems due to the rising frequency of urban flooding. Since 2001, Kabul is experiencing rapid population growth because of the repatriation of refugees and internal migration. Due to unplanned development, green areas inside city and hilly areas within and around the city are converted into new housing towns that had increased runoff. Trenches along the roadside comprise the unplanned drainage network of the city that drains the combined sewer flow. In rainy season overflow occurs, and after streets become dry, the dust particles contaminate the air which is a major cause of air pollution in Kabul city. In this study, a stormwater management model is introduced as a basis for a systematic approach to urban drainage planning in Kabul. For this purpose, Kabul city is delineated into 8 watersheds with the help of one-meter resolution LIDAR DEM. Storm, water management model, is developed for Wazirabad catchment by using available data and literature values. Due to lack of long term metrological data, the model is only run for hourly rainfall data of a rain event that occurred in April 2016. The rain event from 1st to 3rd April with maximum intensity of 3mm/hr caused huge flooding in Wazirabad Catchment of Kabul City. Model-estimated flooding at some points of the catchment as an actual measurement of flooding was not possible; results were compared with information obtained from local people, Kabul Municipality and Capital Region Independent Development Authority. The model helped to identify areas where flooding occurred because of less capacity of drainage system and areas where the main reason for flooding is due to blockage in the drainage canals. The model was used for further analysis to find a sustainable solution to the problem. The option to construct new canals was analyzed, and two new canals were proposed that will reduce the flooding frequency in Wazirabad catchment of Kabul city. By developing the methodology to develop a stormwater management model from digital data and information, the study had fulfilled the primary objective, and similar methodology can be used for other catchments of Kabul city to prepare an emergency and long-term plan for drainage system of Kabul city.

Keywords: urban hydrology, storm water management, modeling, SWMM, GEO-SWMM, GIS, identification of flood vulnerable areas, urban flooding analysis, sustainable urban drainage

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Authors: Rafael Sousa Santos, Clara Pimena Do Vale, Barbara Bogoni, Poul Henning Kirkegaard

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The representative component, a determining aspect of the architect's training, has been marked by an exponential and unprecedented development. However, the multiplication of possibilities has also multiplied uncertainties about architectural design teaching, and by extension, about the very principles of architectural education. In this paper, it is intended to present the results of a research developed on the following problem: the relation between the forms of representation and the architectural design teaching-learning processes. The research had as its object the educational model of two schools – the Politecnico di Milano (POLIMI) and the Faculty of Architecture of the University of Porto (FAUP) – and was led by three main objectives: to characterize the educational model followed in both schools focused on the representative component and its role; to interpret the relation between forms of representation and the architectural design teaching-learning processes; to consider their possibilities of valorisation. Methodologically, the research was conducted according to a qualitative embedded multiple-case study design. The object – i.e., the educational model – was approached in both POLIMI and FAUP cases considering its Context and three embedded unities of analysis: the educational Purposes, Principles, and Practices. In order to guide the procedures of data collection and analysis, a Matrix for the Characterization (MCC) was developed. As a methodological tool, the MCC allowed to relate the three embedded unities of analysis with the three main sources of evidence where the object manifests itself: the professors, expressing how the model is assumed; the architectural design classes, expressing how the model is achieved; and the students, expressing how the model is acquired. The main research methods used were the naturalistic and participatory observation, in-person-interview and documentary and bibliographic review. The results reveal the importance of the representative component in the educational model of both cases, despite the differences in its role. In POLIMI's model, representation is particularly relevant in the teaching of architectural design, while in FAUP’s model, it plays a transversal role – according to an idea of 'general training through hand drawing'. In fact, the difference between models relative to representation can be partially understood by the level of importance that each gives to hand drawing. Regarding the teaching of architectural design, the two cases are distinguished in the relation with the representative component: while in POLIMI the forms of representation serve essentially an instrumental purpose, in FAUP they tend to be considered also for their methodological dimension. It seems that the possibilities for valuing these models reside precisely in the relation between forms of representation and architectural design teaching. It is expected that the knowledge base developed in this research may have three main contributions: to contribute to the maintenance of the educational model of POLIMI and FAUP; through the precise description of the methodological procedures, to contribute by transferability to similar studies; through the critical and objective framework of the problem underlying the forms of representation and its relation with architectural design teaching, to contribute to the broader discussion concerning the contemporary challenges on architectural education.

Keywords: architectural design teaching, architectural education, educational models, forms of representation

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Authors: Amar Partap Singh Pharwaha, Shweta Rani

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To develop a reliable and cost effective communication platform for the telemedicine applications, novel antenna design has been presented using bacterial foraging optimization (BFO) technique. The proposed antenna geometry is achieved by etching a modified Koch curve fractal shape at the edges and a square shape slot at the center of the radiating element of a patch antenna. It has been found that the new antenna has achieved 43.79% size reduction and better resonating characteristic than the original patch. Representative results for both simulations and numerical validations are reported in order to assess the effectiveness of the developed methodology.

Keywords: BFO, electrical permittivity, fractals, Koch curve

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Authors: M. I. Oshimagye, V. O. Ayuba, P. A. Annune

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The effect of Sublethal Concentrations of Parkia biglobosa pod extract on the growth and survival of Clarias gariepinus juveniles (mean weight 32.73g ± 0.0) were investigated under laboratory conditions for 8 weeks using the static renewal and continuous aeration system. Statistical analysis showed that fish exposed to various concentrations had significantly lower (P<0.05) growth rate than the control groups. The reduction in growth was observed to be directly proportional to increase in concentration. However, at 50 mg/L no significant depression in weight was observed.

Keywords: Clarias gariepinus, Parkia biglobosa, pod, weight

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Authors: Abdelsalam A. Ahmed

Abstract:

Fast speed drives for Permanent Magnet Synchronous Motor (PMSM) is a crucial performance for the electric traction systems. In this paper, PMSM is drived with a Model-based Predictive Control (MPC) technique. Fast speed tracking is achieved through optimization of the DC source utilization using MPC. The technique is based on predicting the optimum voltage vector applied to the driver. Control technique is investigated by comparing to the cascaded PI control based on Space Vector Pulse Width Modulation (SVPWM). MPC and SVPWM-based FOC are implemented with the TMS320F2812 DSP and its power driver circuits. The designed MPC for a PMSM drive is experimentally validated on a laboratory test bench. The performances are compared with those obtained by a conventional PI-based system in order to highlight the improvements, especially regarding speed tracking response.

Keywords: permanent magnet synchronous motor, model-based predictive control, DC source utilization, cascaded PI control, space vector pulse width modulation, TMS320F2812 DSP

Procedia PDF Downloads 644

Authors: Rashid S. Mohammad, Shicheng Zhang, Xinzhe Zhao

Abstract:

In tight oil reservoirs, wettability alteration has generally been considered as an effective way to remove fracturing fluid retention on the surface of the fracture and consequently improved oil production. However, there is a lack of a reliable productivity prediction model to show the relationship between the wettability and oil production in tight oil well. In this paper, a new oil productivity prediction model of immiscible oil-water flow and miscible CO₂-oil flow accounting for wettability is developed. This mathematical model is established by considering two different length scales: nonporous network and propped fractures. CO₂ flow diffuses in the nonporous network and high velocity non-Darcy flow in propped fractures are considered by taking into account the effect of wettability alteration on capillary pressure and relative permeability. A laboratory experiment is also conducted here to validate this model. Laboratory experiments have been designed to compare the water saturation profiles for different contact angle, revealing the fluid retention in rock pores that affects capillary force and relative permeability. Four kinds of brines with different concentrations are selected here to create different contact angles. In water-wet porous media, as the system becomes more oil-wet, water saturation decreases. As a result, oil relative permeability increases. On the other hand, capillary pressure which is the resistance for the oil flow increases as well. The oil production change due to wettability alteration is the result of the comprehensive changes of oil relative permeability and capillary pressure. The results indicate that wettability is a key factor for fracturing fluid retention removal and oil enhancement in tight reservoirs. By incorporating laboratory test into a mathematical model, this work shows the relationship between wettability and oil production is not a simple linear pattern but a parabolic one. Additionally, it can be used for a better understanding of optimization design of fracturing fluids.

Keywords: wettability, relative permeability, fluid retention, oil production, unconventional and tight reservoirs

Procedia PDF Downloads 236

Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

Abstract:

Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

Procedia PDF Downloads 172

Authors: Jorge Anibal Restrepo Morales, Sonia Martín Gómez

Abstract:

A model based on a logical framework was developed to quantify SMEs' internationalization capacity. To do so, linguistic variables, such as human talent, infrastructure, innovation strategies, FTAs, marketing strategies, finance, etc. were integrated. It is argued that a company’s management of international markets depends on internal factors, especially capabilities and resources available. This study considers internal factors as the biggest business challenge because they force companies to develop an adequate set of capabilities. At this stage, importance and strategic relevance have to be defined in order to build competitive advantages. A fuzzy inference system is proposed to model the resources, skills, and capabilities that determine the success of internationalization. Data: 157 linguistic variables were used. These variables were defined by international trade entrepreneurs, experts, consultants, and researchers. Using expert judgment, the variables were condensed into18 factors that explain SMEs’ export capacity. The proposed model is applied by means of a case study of the textile and clothing cluster in Medellin, Colombia. In the model implementation, a general index of 28.2 was obtained for internationalization capabilities. The result confirms that the sector’s current capabilities and resources are not sufficient for a successful integration into the international market. The model specifies the factors and variables, which need to be worked on in order to improve export capability. In the case of textile companies, the lack of a continuous recording of information stands out. Likewise, there are very few studies directed towards developing long-term plans, and., there is little consistency in exports criteria. This method emerges as an innovative management tool linked to internal organizational spheres and their different abilities.

Keywords: business strategy, exports, internationalization, fuzzy set methods

Procedia PDF Downloads 294

Authors: Yuko Ishiwaka, Tomohiro Yoshida, Tadateru Itoh

Abstract:

Human makes preparation called readiness potential unconsciously (RP) before awareness of their own decision. For example, when recognizing a button and pressing the button, the RP peaks are observed 200 ms before the initiation of the movement. It has been known that the preparatory movements are acquired before actual movements, but it has not been still well understood how humans can obtain the RP during their growth. On the proposition of why the brain must respond earlier, we assume that humans have to adopt the dangerous environment to survive and then obtain the behavior to cover the various time lags distributed in the body. Without RP, humans cannot take action quickly to avoid dangerous situations. In taking action, the brain makes decisions, and signals are transmitted through the Spinal Cord to the muscles to the body moves according to the laws of physics. Our research focuses on the time lag of the neuron signal transmitting from a brain to muscle via a spinal cord. This time lag is one of the essential factors for readiness potential. We propose a firing pattern controller model of a motor nerve system considered innervation and jitter, which produces time lag. In our simulation, we adopt innervation and jitter in our proposed muscle-skeleton model, because these two factors can create infinitesimal time lag. Q10 Hodgkin Huxley model to calculate action potentials is also adopted because the refractory period produces a more significant time lag for continuous firing. Keeping constant power of muscle requires cooperation firing of motor neurons because a refractory period stifles the continuous firing of a neuron. One more factor in producing time lag is slow or fast-twitch. The Expanded Hill Type model is adopted to calculate power and time lag. We will simulate our model of muscle skeleton model by controlling the firing pattern and discuss the relationship between the time lag of physics and neurons. For our discussion, we analyze the time lag with our simulation for knee bending. The law of inertia caused the most influential time lag. The next most crucial time lag was the time to generate the action potential induced by innervation and jitter. In our simulation, the time lag at the beginning of the knee movement is 202ms to 203.5ms. It means that readiness potential should be prepared more than 200ms before decision making.

Keywords: firing patterns, innervation, jitter, motor nerve system, readiness potential

Procedia PDF Downloads 829

Authors: Satya Pradhan, Venky Nanniyur

Abstract:

As the software industry transitions from a license-based model to a subscription-based Software-as-a-Service (SaaS) model, many software development groups are using a hybrid development model that incorporates Agile and Waterfall methodologies in different parts of the organization. The traditional metrics used for measuring software quality in Waterfall or Agile paradigms do not apply to this new hybrid methodology. In addition, to respond to higher quality demands from customers and to gain a competitive advantage in the market, many companies are starting to prioritize quality as a strategic differentiator. As a result, quality metrics are included in the decision-making activities all the way up to the executive level, including board of director reviews. This paper presents key challenges associated with measuring software quality in organizations using the hybrid development model. We introduce a framework called Prevention-Inspection-Evaluation-Removal (PIER) to provide a comprehensive metric definition for hybrid organizations. The framework includes quality measurements, quality enforcement, and quality decision points at different organizational levels and project milestones. The metrics framework defined in this paper is being used for all Cisco systems products used in customer premises. We present several field metrics for one product portfolio (enterprise networking) to show the effectiveness of the proposed measurement system. As the results show, this metrics framework has significantly improved in-process defect management as well as field quality.

Keywords: quality management system, quality metrics framework, quality metrics, agile, waterfall, hybrid development system

Procedia PDF Downloads 174