Search results for: hybrid genetic algorithms

Authors: Alexandra Monteiro, K. Bousson, Fernando J. O. Moreira, Ricardo Reis

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Fixed-wing flying vehicles are usually controlled by means of control surfaces such as elevators, ailerons, and rudders. The failure of these systems may lead to severe or even fatal crashes. These failures resulted in increased popularity for research activities on propulsion control in the last decades. The present work deals with a hybrid control architecture in which the propulsion-controlled vehicle maintains its traditional control surfaces, addressing the issue of robust lateral-directional dynamics control. The challenges stem from the parameter uncertainties in the stability and control derivatives and some unknown terms in the flight dynamics model. Two approaches are implemented and tested: linear quadratic regulation with robustness characteristics and H∞ control. The problem is centered on roll-yaw controller design with full state-feedback, which is able to deal with a standalone propulsion control mode as well as a hybrid mode combining both propulsion control and conventional control surface concepts while maintaining the original flight maneuverability characteristics. The results for both controllers emphasized very good control performances; however, the H∞ controller showed higher stabilization rates and robustness albeit with a slightly higher control magnitude than using the linear quadratic regulator.

Keywords: robust propulsion control, h-infinity control, lateral-directional flight dynamics, parameter uncertainties

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Authors: Prikhil Agrawal, N. Priyanka

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With the increase of millions of users on Internet day by day, it is very essential to maintain highly reliable and secured data communication between various corporations. Although there are various traditional security imparting techniques such as antivirus software, password protection, data encryption, biometrics and firewall etc. But still network security has become the main issue in various leading companies. So IDSs have become an essential component in terms of security, as it can detect various network attacks and respond quickly to such occurrences. IDSs are used to detect unauthorized access to a computer system. This paper describes various intrusion detection techniques using GA approach. The intrusion detection problem has become a challenging task due to the conception of miscellaneous computer networks under various vulnerabilities. Thus the damage caused to various organizations by malicious intrusions can be mitigated and even be deterred by using this powerful tool.

Keywords: genetic algorithm (GA), intrusion detection system (IDS), dataset, network security

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Authors: Aftab Ahmed, Ghulam Mehboob

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The object of presentation is to highlight the importance of condition which is a very rare genetic disorder although Paget’s disease is common but its juvenile type is very rare and a late presentation due to very slow onset and lack of earlier standard management. We present a case of 25 years old male with a chronic history of bone pain and a slow onset of mild swelling, later on diagnosed as juvenile Paget disease of bone. Rarity of this condition with inaccessibility for standard health treatment can lead to a significant delay in presentation and its management. There have been 50 reported cases worldwide according to Genetic Home Reference. There is increased osteoclastic activity along with osteoblastic activity related to gene alteration and osteoprotegrin deficiency. Morbidity of disease is very significant which lead children to become immobilize.

Keywords: juvenile, Paget’s disease, bone, Northern Area of Pakistan

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Authors: Muhammad Rehan Usman, Muhammad Arslan Usman, Soo Young Shin

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The new era of digital communication has brought up many challenges that network operators need to overcome. The high demand of mobile data rates require improved networks, which is a challenge for the operators in terms of maintaining the quality of experience (QoE) for their consumers. In live video transmission, there is a sheer need for live surveillance of the videos in order to maintain the quality of the network. For this purpose objective algorithms are employed to monitor the quality of the videos that are transmitted over a network. In order to test these objective algorithms, subjective quality assessment of the streamed videos is required, as the human eye is the best source of perceptual assessment. In this paper we have conducted subjective evaluation of videos with varying spatial and temporal impairments. These videos were impaired with frame freezing distortions so that the impact of frame freezing on the quality of experience could be studied. We present subjective Mean Opinion Score (MOS) for these videos that can be used for fine tuning the objective algorithms for video quality assessment.

Keywords: frame freezing, mean opinion score, objective assessment, subjective evaluation

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Authors: Richard O’Riordan, Saritha Unnikrishnan

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Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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Authors: Edison E. Haro, Akindele G. Odeshi, Jerzy A. Szpunar

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Hybrid bio-composites are developed for use in protective armor through positive hybridization offered by reinforcement of high-density polyethylene (HDPE) with Kevlar short fibers and palm wood micro-fillers. The manufacturing process involved a combination of extrusion and compression molding techniques. The mechanical behavior of Kevlar fiber reinforced HDPE with and without palm wood filler additions are compared. The effect of the weight fraction of the added palm wood micro-fillers is also determined. The Young modulus was found to increase as the weight fraction of organic micro-particles increased. However, the flexural strength decreased with increasing weight fraction of added micro-fillers. The interfacial interactions between the components were investigated using scanning electron microscopy. The influence of the size, random alignment and distribution of the natural micro-particles was evaluated. Ballistic impact and dynamic shock loading tests were performed to determine the optimum proportion of Kevlar short fibers and organic micro-fillers needed to improve impact strength of the HDPE. These results indicate a positive hybridization by deposition of organic micro-fillers on the surface of short Kevlar fibers used in reinforcing the thermoplastic matrix leading to enhancement of the mechanical strength and dynamic impact behavior of these materials. Therefore, these hybrid bio-composites can be promising materials for different applications against high velocity impacts.

Keywords: hybrid bio-composites, organic nano-fillers, dynamic shocking loading, ballistic impacts, energy absorption

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Authors: Yakubu Bala Mohammed, Nadire Chavus, Mohammed Bulama

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Generative Pre-trained Transformer (ChatGPT) represents an artificial intelligence (AI) tool capable of swiftly generating comprehensive responses to prompts and follow-up inquiries. This emerging AI tool was introduced in November 2022 by OpenAI firm, an American AI research laboratory, utilizing substantial language models. This present study aims to delve into the potential future consequences of ChatGPT usage in education using AI-based algorithms. The paper will bring forth the likely potential risks of ChatGBT utilization, such as academic integrity concerns, unfair learning assessments, excessive reliance on AI, and dissemination of inaccurate information using four machine learning algorithms: eXtreme-Gradient Boosting (XGBoost), Support vector machine (SVM), Emotional artificial neural network (EANN), and Random forest (RF) would be used to analyze the study collected data due to their robustness. Finally, the findings of the study will assist education stakeholders in understanding the future implications of ChatGPT usage in education and propose solutions and directions for upcoming studies.

Keywords: machine learning, ChatGPT, education, learning, implications

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Authors: Safeer Hussain Khan

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In this paper, we introduce a two-step iterative algorithm to prove a strong convergence result for approximating common fixed points of three contractive-like operators. Our algorithm basically generalizes an existing algorithm..Our iterative algorithm also contains two famous iterative algorithms: Mann iterative algorithm and Ishikawa iterative algorithm. Thus our result generalizes the corresponding results proved for the above three iterative algorithms to a class of more general operators. At the end, we remark that nothing prevents us to extend our result to the case of the iterative algorithm with error terms.

Keywords: contractive-like operator, iterative algorithm, fixed point, strong convergence

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Authors: J. Thirunavukkarasu, M. Poulet, T. Turner, S. Pickering

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Composite waste from manufacturing can consist of different fiber materials, including blends of different fiber. Commercially, the recycling of composite waste is currently limited to carbon fiber waste and recycling glass fiber waste is currently not economically viable due to the low cost of virgin glass fiber and the reduced mechanical properties of the recovered fibers. For this reason, the recycling of hybrid fiber materials, where carbon fiber is combined with a proportion of glass fiber, cannot be processed economically. Therefore, a separation method is required to remove the glass fiber materials during the recycling process. An electrostatic separation method is chosen for this work because of the significant difference between carbon and glass fiber electrical properties. In this study, an experimental rig has been developed to measure the electrostatic charge achievable as the materials are passed through a tube. A range of particle lengths (80-100 µm, 6 mm and 12 mm), surface state conditions (0%SA, 2%SA and 6%SA), and several tube wall materials have been studied. A polytetrafluoroethylene (PTFE) tube and recycled without sizing agent was identified as the most suitable parameters for the electrical separation method. It was also found that shorter fiber lengths helped to encourage particle flow and attain higher charge values. These findings can be used to develop a separation process to enable the cost-effective recycling of hybrid fiber composite waste.

Keywords: electrostatic charging, hybrid fiber composites, recycling, short fiber composites

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Authors: Talaat Ahmed, Amna Babssail

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Mangroves are evergreen trees and grow along the coastal areas of Qatar. The largest and oldest area of mangroves can be found around Al-Thakhira and Al-Khor. Other mangrove areas originate from fairly recent plantings by the government, although unfortunately the picturesque mangrove lake in Al-Wakra has now been uprooted. Avicinnia marina is the predominant mangrove species found in the region. Mangroves protect and stabilize low lying coastal land, and provide protection and food sources for estuarine and coastal fishery food chains. They also serve as feeding, breeding and nursery grounds for a variety of fish, crustaceans, reptiles, birds and other wildlife. A total of 21 individuals of A. marina, representing seven diverse Natural and artificial populations, were sampled throughout its range in Qatar. Leaves from 2-3 randomly selected trees at each location were collected. The locations are as follows: Al-Rawis, Ras-Madpak, Fuwairt, Summaseima, Al-khour, AL-Mafjar and Zekreet. Total genomic DNA was extracted using commercial DNeasy Plant System (Qiagen, Inc., Valencia, CA) kit to be used for genetic diversity analysis. Total of 12 (Inter-Simple Sequence Repeat) ISSR primers were used to amplify DNA fragments using genomic DNA. The 12 ISSR primers amplified polymorphic bands among mangrove samples in different areas as well as within each area indicating the existing of variation within each area and among the different areas of mangrove in Qatar. The results could characterize Avicinnia marina populations exist in different areas of Qatar and establish DNA fingerprint documentations for mangrove population to be used in further studies. Moreover, existing of genetic variation within and among Avicinnia marina populations is a strong indication for the ability of such populations to adapt different environmental conditions in Qatar. This study could be a warning to save mangrove in Qatar and save the environment as well.

Keywords: DNA fingerprint, Avicinnia marina, genetic analysis, Qatar

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Authors: Abubakar Muhammad Umar

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Glyphosate [N-(phosphonomethyl) glycine] is among the most broadly and generally recognized broad-spectrum herbicides used in agriculture due to its low cost and effectiveness in weed management. The pollution of glyphosate in the aquatic environment can be via water run-off from agricultural lands or by spray drift, aerial spraying or due to industrial discharge, which may be seen as a threat to aquatic biota. Fish is one of the best organisms to study the toxicological aspects of glyphosate. A 49-day experiment was conducted under laboratory conditions to ascertain the effects of technical grade glyphosate on behavior and histopathological conditions in the gills of red hybrid tilapia using a light inverted microscope. Air gasping, erratic swimming, fin movement, mucus secretion, hemorrhages and loss of scales were observed as behavioural changes in the exposed fish. There was no histopathological complication observed in the gill of the control fish, but various levels of alterations were seen in the gills of the fish exposed to glyphosate herbicide. These include lifting of primary lamella, congestion of secondary lamella, as well as hyperplasia in both primary and secondary gill lamella and hypertrophy of secondary gill lamella. Based on the findings of this study, glyphosate herbicide exerts behavioural and histopathological changes in the gill of red hybrid tilapia, and therefore, the fish is considered a good bioindicator in aquatic environment monitoring. Excessive usage of glyphosate herbicide near aquatic habitats should be discouraged.

Keywords: behavioural, histopathological, oreochromis niloticus, glyphosate

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Maher Aburrous, Adel Khelifi, Manar Abu Talib

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Transactions performed by financial institutions on daily basis require XML encryption on large scale. Encrypting large volume of message fully will result both performance and resource issues. In this paper a novel approach is presented for securing financial XML transactions using classification data mining (DM) algorithms. Our strategy defines the complete process of classifying XML transactions by using set of classification algorithms, classified XML documents processed at later stage using element-wise encryption. Classification algorithms were used to identify the XML transaction rules and factors in order to classify the message content fetching important elements within. We have implemented four classification algorithms to fetch the importance level value within each XML document. Classified content is processed using element-wise encryption for selected parts with "High", "Medium" or “Low” importance level values. Element-wise encryption is performed using AES symmetric encryption algorithm and proposed modified algorithm for AES to overcome the problem of computational overhead, in which substitute byte, shift row will remain as in the original AES while mix column operation is replaced by 128 permutation operation followed by add round key operation. An implementation has been conducted using data set fetched from e-banking service to present system functionality and efficiency. Results from our implementation showed a clear improvement in processing time encrypting XML documents.

Keywords: XML transaction, encryption, Advanced Encryption Standard (AES), XML classification, e-banking security, fuzzy classification, cryptography, intelligent encryption

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, production process optimization, evolutionary algorithms, policy optimization, actor critic approach

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Md. Minhazul Islam, Shah Ashisul Abed Nipun, Majharul Islam, Md. Abdur Rakib Rahat, Jonayet Miah, Salsavil Kayyum, Anwar Shadaab, Faiz Al Faisal

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The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques.

Keywords: Machine Learning, K-means, Hierarchical Clustering, Myocardial Infarction, Heart Disease

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Authors: Kreangkri Ratchagit

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This paper is concerned with exponential stability of switched linear systems with interval time-varying delays. The time delay is any continuous function belonging to a given interval, in which the lower bound of delay is not restricted to zero. By constructing a suitable augmented Lyapunov-Krasovskii functional combined with Leibniz-Newton’s formula, a switching rule for the exponential stability of switched linear systems with interval time-varying delays and new delay-dependent sufficient conditions for the exponential stability of the systems are first established in terms of LMIs. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: exponential stability, hybrid systems, timevarying delays, Lyapunov-Krasovskii functional, Leibniz-Newton’s formula

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Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

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Authors: Masoud Mohammadpour, Nima Yazdian, Radovan Kovacevic

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The Oil and Gas industries are vigorously looking for new ways to increase the efficiency of their pipeline constructions. Besides the other approaches, implementing of new welding methods for joining pipes can be the best candidate on this regard. Hybrid Laser Arc Welding (HLAW) with the capabilities of high welding speed, deep penetration, and excellent gap bridging ability can be a possible alternative method in pipeline girth welding. This paper investigates the feasibility of applying the HLAW to join ASTM A106-B as the mostly used piping material for transporting high-temperature and high-pressure fluids and gases. The experiments were carried out on six-inch diameter pipes with the wall thickness of 10mm. AWS ER 70 S6 filler wire with diameter of 1.2mm was employed. Relating to this welding procedure, characterization of welded samples such as hardness, tensile testing and Charpy V-notch testing were performed and the results will be reported in this paper. In order to have better understanding about the thermal history and the microstructural alterations caused by the welding heat cycle, a comprehensive Finite Element (FE) model was also conducted. The obtained results have shown that the Gas Metal Arc Welding (GMAW) procedure with the minimum number of 5 passes to complete the wall thickness, was reduced to only single pass by using the HLAW process with the welding time less than 15s.

Keywords: finite element modeling, high-temperature service, hybrid laser/arc welding, welding pipes

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Authors: Halim Djerroud, Arab Ali Cherif

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We assume that conscious functions are implemented automatically. In other words that consciousness as well as the non-consciousness aspect of human thought, planning, and perception, are produced by biologically adaptive algorithms. We propose that the mechanisms of consciousness can be produced using similar adaptive algorithms to those executed by the mechanism. In this paper, we propose a computational model of consciousness, the ”Global Abstraction Workspace” which is an internal environmental modelling perceived as a multi-agent system. This system is able to evolve and generate new data and processes as well as actions in the environment.

Keywords: artificial consciousness, cognitive architecture, global abstraction workspace, multi-agent system

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Authors: Girma Yohannis Bade, Olga Kolesnikova, Grigori Sidorov

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Named entity recognition (NER) is a building block for information extraction. Though the information extraction process has been automated using a variety of techniques to find and extract a piece of relevant information from unstructured documents, the discovery of targeted knowledge still poses a number of research difficulties because of the variability and lack of structure in Web data. NER, a subtask of information extraction (IE), came to exist to smooth such difficulty. It deals with finding the proper names (named entities), such as the name of the person, country, location, organization, dates, and event in a document, and categorizing them as predetermined labels, which is an initial step in IE tasks. This survey paper presents the roles and importance of NER to IE from the perspective of different algorithms and application area domains. Thus, this paper well summarizes how researchers implemented NER in particular application areas like finance, medicine, defense, business, food science, archeology, and so on. It also outlines the three types of sequence labeling algorithms for NER such as feature-based, neural network-based, and rule-based. Finally, the state-of-the-art and evaluation metrics of NER were presented.

Keywords: the role of NER, named entity recognition, information extraction, sequence labeling algorithms, named entity application area

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Authors: Abdellahi Cheikh

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With the emergence of quantum computers with very powerful capabilities, the security of the exchange of shared keys between two interlocutors poses a big problem in terms of the rapid development of technologies such as computing power and computing speed. Therefore, the Diffie-Hellmann (DH) algorithm is more vulnerable than ever. No mechanism guarantees the security of the key exchange, so if an intermediary manages to intercept it, it is easy to intercept. In this regard, several studies have been conducted to improve the security of key exchange between two interlocutors, which has led to interesting results. The modification made on our model Diffie-Hellman-RSA-AES (DRA), which encrypts the information exchanged between two users using the three-encryption algorithms DH, RSA and AES, by using stenographic photos to hide the contents of the p, g and ClesAES values that are sent in an unencrypted state at the level of DRA model to calculate each user's public key. This work includes a comparative study between the DRA model and all existing solutions, as well as the modification made to this model, with an emphasis on the aspect of reliability in terms of security. This study presents a simulation to demonstrate the effectiveness of the modification made to the DRA model. The obtained results show that our model has a security advantage over the existing solution, so we made these changes to reinforce the security of the DRA model.

Keywords: Diffie-Hellmann, DRA, RSA, advanced encryption standard

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Authors: L. Bokopane, K. Kusakana, H. J. Vermaark

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The success of renewable powered electric vehicle charging station in isolated areas depends highly on the availability and sustainability of renewable resources all year round at a selected location. The main focus of this paper is to discuss the possible charging strategies that could be implemented to find the best possible configuration of an electric Tuk-Tuk charging station at a given location within South Africa. The charging station is designed, modeled and simulated to evaluate its performances. The techno-economic analysis of different feasible supply configurations of the charging station using renewable energies is simulated using HOMER software and the results compared in order to select the best possible charging strategies in terms of cost of energy consumed.

Keywords: electric tuk-tuk, renewable energy, energy Storage, hybrid systems, HOMER

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Authors: I. Boroňová, J. Bernasovská, J. Kľoc, Z. Tomková, E. Petrejčíková, S. Mačeková, J. Poráčová, M. M. Blaščáková

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Osteoporosis is a common multifactorial disease with a strong genetic component characterized by reduced bone mass and increased risk of fractures. Genetic factors play an important role in the pathogenesis of osteoporosis. The aim of our study was to identify the genotype and allele distribution of T245G polymorphism in OPG gene in Slovak postmenopausal women. A total of 200 unrelated Slovak postmenopausal women with diagnosed osteoporosis and 200 normal controls were genotyped for T245G (rs3134069) polymorphism of OPG gene. Genotyping was performed using the Custom Taqman®SNP Genotyping assays. Genotypes and alleles frequencies showed no significant differences (p=0.5551; p=0.6022). The results of the present study confirm the importance of T245G polymorphism in OPG gene in the pathogenesis of osteoporosis.

Keywords: OPG gene, T245G polymorphism, osteoporosis, T245G polymorphism, real-time PCR

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Authors: Adriana Haulica

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Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Sabitha Rani A., Nagamani V.

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Embelia ribes Burm.f (Family-Myrsinaceae) commonly known as Vidanga or Baibirang, is one of the important medicinal plants of India. The seed extract is reported to be antidiabetic, antitumour, analgesic, anti-inflammatory, antispermatogenic, free radical scavenging activities and widely used in more than 75 Ayurvedic commercial formulations. Among the 100 different species of Embelia, E. ribes is considered as a major source of Embelin, a bioactive compound. Because of high demand and low availability, the seeds of E. ribes are substituted with many cheaper alternatives. Therefore, the present study of RAPD-PCR analysis was undertaken to develop molecular markers for identification of E. ribes. A total of 13 different seed samples of Embelia were collected from different agro-climatic regions of India. The seeds of E.ribes were collected from Kalpetta, Kerala and three different seed samples were collected from traders of Odisha, Madhya Pradesh, Maharastra. The other nine seed samples were collected from local traders which they have collected from different regions of India. Genomic DNA was isolated from different seed samples E. ribes and RAPD-PCR was performed on 13 different seed samples using 47 random primers. Out of all the primers, only 22 primers produced clear and highly-reproducible banding patterns. The 22 selected RAPD primers generated a total of 280 alleles with an average of 12 alleles per primer pair. In the present study, we have identified three RAPD-PCR markers i.e. OPF5_480 bp, OPH11_520 bp and OPH4_530 bp which can be used for genetic fingerprinting of E. ribes. This methodology can be employed for identification of original E. ribes and also distinguishing it from other substitutes and adulterants.

Keywords: Embelia ribes, RAPD-PCR, primers, genetic analysis

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Authors: Adel Bouchakhchoukha, Hakim Akeb

Abstract:

In this work, we propose a hybrid heuristic in order to solve the Team Orienteering Problem (TOP). Given a set of points (or customers), each with associated score (profit or benefit), and a team that has a fixed number of members, the problem to solve is to visit a subset of points in order to maximize the total collected score. Each member performs a tour starting at the start point, visiting distinct customers and the tour terminates at the arrival point. In addition, each point is visited at most once, and the total time in each tour cannot be greater than a given value. The proposed heuristic combines beam search and a local optimization strategy. The algorithm was tested on several sets of instances and encouraging results were obtained.

Keywords: team orienteering problem, vehicle routing, beam search, local search

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Authors: Begna Tulu, Gobena Ameni

Abstract:

Understanding the types of Mycobacterium bovis (M. bovis) strains circulating in a country and exploring its zoonotic potential has significant contribution in the effort to design control strategies. The main aim of this study was to review and compile the results of studies conducted on M. bovis genotyping and its zoonotic potential of M. bovis in Ethiopia. A systematic search and review of articles published on M. bovis strains in Ethiopia were made. PubMed and Google Scholar databases were considered for the search while the keywords used were 'Mycobacteria,' 'Mycobacterium bovis,' 'Bovine Tuberculosis' and 'Ethiopia.' Fourteen studies were considered in this review and a total of 31 distinct strains of M. bovis (N=211) were obtained; the most dominant strains were SB0133 (N=62, 29.4%), SB1176 (N=61, 28.9%), and followed by SB0134 and SB1476 each (N=18, 8.5%). The clustering rate of M. bovis strains was found to be 42.0%. On the other hand, 6 strains of M. bovis were reported from human namely; SB0665 (N=4), SB0303 (N=2), SB0982 (N=2), SB0133 (N=1), SB1176 (N=1), and 1 new strain. Similarly, a total of 8 strains (N=13) of M. tuberculosis bacteria were also identified from animal subjects; namely SIT149 (N=3), SIT1 (N=2), SIT1688 (n=2), SIT262 (N=2), SIT53 (N=1), SIT59 (N=1), and one new-Ethiopian strain. The result showed that the genetic diversity of M. bovis strains reported from Ethiopia are less diversified and highly clustered. And also the result underlines that there is an ongoing active transmission of M. bovis and M. tuberculosis between human and animals in Ethiopia because a significant number strains of both type of bacteria were reported from human and animals.

Keywords: mycobacterium bovis, Mycobacterium tuberculosis, zoonotic potential, genetic diversity, Ethiopia

Procedia PDF Downloads 121

Authors: Tayab D. Memon, Shahji Farooque, Marvi Deshi, Imtiaz Hussain Kalwar, B. S. Chowdhry

Abstract:

Recently single-bit ternary FIR-like filter (SBTFF) hardware synthesize in FPGA is reported and compared with multi-bit FIR filter on similar spectral characteristics. Results shows that SBTFF dominates upon multi-bit filter overall. In this paper, an optimized adder module for ternary quantized sigma-delta modulated signal is presented. The adder is simulated using ModelSim for functional verification the area-performance of the proposed adder were obtained through synthesis in Xilinx and compared to conventional adder trees. The synthesis results show that the proposed adder tree achieves higher clock rates and lower chip area at higher inputs to the adder block; whereas conventional adder tree achieves better performance and lower chip area at lower number of inputs to the same adder block. These results enhance the usefulness of existing short word length DSP algorithms for fast and efficient mobile communication.

Keywords: short word length (SWL), DSP algorithms, FPGA, SBTFF, VHDL

Procedia PDF Downloads 327