Search results for: steering vector

Authors: A. Oukaci, R. Toufouti, D. Dib, l. Atarsia

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This article presents a multitude of alternative techniques to control the vector control, namely the nonlinear control and sliding mode control. Moreover, the implementation of their control law applied to the high-performance to the induction motor with the objective to improve the tracking control, ensure stability robustness to parameter variations and disturbance rejection. Tests are performed numerical simulations in the Matlab/Simulink interface, the results demonstrate the efficiency and dynamic performance of the proposed strategy.

Keywords: Induction Motor (IM), Non-linear Control (NLC), Sliding Mode Control (SMC), nonlinear sliding surface

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Authors: Tomola Obamuyi

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The banking system in Nigeria adopted innovative banking, with the aim of enhancing financial inclusion, and making financial services readily and cheaply available to majority of the people, and to contribute to the efficiency of the financial system. Some of the innovative services include: Automatic Teller Machines (ATMs), National Electronic Fund Transfer (NEFT), Point of Sale (PoS), internet (Web) banking, Mobile Money payment (MMO), Real-Time Gross Settlement (RTGS), agent banking, among others. The introduction of these payment systems is expected to increase bank efficiency and customers' satisfaction, culminating in better performance for the commercial banks. However, opinions differ on the possible effects of the various innovative payment systems on the performance of commercial banks in the country. Thus, this study empirically determines how commercial banks use innovation to gain competitive advantage in the specific context of Nigeria's finance and business. The study also analyses the effects of financial innovation on the performance of commercial banks, when different periods of analysis are considered. The study employed secondary data from 2009 to 2018, the period that witnessed aggressive innovation in the financial sector of the country. The Vector Autoregression (VAR) estimation technique forecasts the relative variance of each random innovation to the variables in the VAR, examine the effect of standard deviation shock to one of the innovations on current and future values of the impulse response and determine the causal relationship between the variables (VAR granger causality test). The study also employed the Multi-Criteria Decision Making (MCDM) to rank the innovations and the performance criteria of Return on Assets (ROA) and Return on Equity (ROE). The entropy method of MCDM was used to determine which of the performance criteria better reflect the contributions of the various innovations in the banking sector. On the other hand, the Range of Values (ROV) method was used to rank the contributions of the seven innovations to performance. The analysis was done based on medium term (five years) and long run (ten years) of innovations in the sector. The impulse response function derived from the VAR system indicated that the response of ROA to the values of cheques transaction, values of NEFT transactions, values of POS transactions was positive and significant in the periods of analysis. The paper also confirmed with entropy and range of value that, in the long run, both the CHEQUE and MMO performed best while NEFT was next in performance. The paper concluded that commercial banks would enhance their performance by continuously improving on the services provided through Cheques, National Electronic Fund Transfer and Point of Sale since these instruments have long run effects on their performance. This will increase the confidence of the populace and encourage more usage/patronage of these services. The banking sector will in turn experience better performance which will improve the economy of the country. Keywords: Bank performance, financial innovation, multi-criteria decision making, vector autoregression,

Keywords: Bank performance, financial innovation, multi-criteria decision making, vector autoregression

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Authors: Semachew M. Kassa, Africa M Geremew, Tezera F. Azmatch, Nandyala Darga Kumar

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Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events.

Keywords: artificial neural network, logistic regression, landslide susceptibility, naïve Bayes, random forest, support vector machine

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Authors: M. Hussain, Aqsa Farooq

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Let G = (V,E) be a connected graph and distance between any two vertices a and b in G is a−b geodesic and is denoted by d(a, b). A set of vertices W resolves a graph G if each vertex is uniquely determined by its vector of distances to the vertices in W. A metric dimension of G is the minimum cardinality of a resolving set of G. In this paper line graph of honeycomb network has been derived and then we calculated the metric dimension on line graph of honeycomb network.

Keywords: Resolving set, Metric dimension, Honeycomb network, Line graph

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Authors: Khelifi Mustapha, A. Moulay lakhdar, I. Elawady

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We will study the unequal error protection for VQ image. We have used the Reed Solomon (RS) Codes as Channel coding because they offer better performance in terms of channel error correction over a binary output channel. One such channel (binary input and output) should be considered if it is the case of the application layer, because it includes all the features of the layers located below and on the what it is usually not feasible to make changes.

Keywords: vector quantization, channel error correction, Reed-Solomon channel coding, application

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Authors: Forouzan Salehi Fergeni

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Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

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Authors: Abdullah E. Al-Mazrooei

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In this paper, we present a new kind of the bilinear systems in the form of state space model. The evolution of this system depends on the product of state vector by its self. The well known Lotak Volterra and Lorenz models are special cases of this new model. We also present here a generalization of Kalman filter which is suitable to work with the new bilinear model. An application to real measurements is introduced to illustrate the efficiency of the proposed algorithm.

Keywords: bilinear systems, state space model, Kalman filter, application, models

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Authors: Renzo Munoz-Najar, Cristina Wong, Daniel De La Torre Ugarte

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The literature on price transmission usually deals with asymmetries related to different commodities and/or the short and long term. The role of domestic regional differences and the relationship with asymmetries within a country are usually left out. This paper looks at the asymmetry in the transmission of rice prices from the international price to the farm gate prices in four northern regions of Peru for the last period 2001-2016. These regions are San Martín, Piura, Lambayeque and La Libertad. The relevance of the study lies in its ability to assess the need for policies aimed at improving the competitiveness of the market and ensuring the benefit of producers. There are differences in planting and harvesting dates, as well as in geographic location that justify the hypothesis of the existence of differences in the price transition asymmetries between these regions. Those differences are due to at least three factors geography, infrastructure development, and distribution systems. For this, the Threshold Vector Error Correction Model and the Autoregressive Vector Model with Threshold are used. Both models, collect asymmetric effects in the price adjustments. In this way, it is sought to verify that farm prices react more to falls than increases in international prices due to the high bargaining power of intermediaries. The results of the investigation suggest that the transmission of prices is significant only for Lambayeque and La Libertad. Likewise, the asymmetry in the transmission of prices for these regions is checked. However, these results are not met for San Martin and Piura, the main rice producers nationwide. A significant price transmission is verified only in the Lambayeque and La Libertad regions. San Martin and Piura, in spite of being the main rice producing regions of Peru, do not present a significant transmission of international prices; a high degree of self-sufficient supply might be at the center of the logic for this result. An additional finding is the short-term adjustment with respect to international prices, it is higher in La Libertad compared to Lambayeque, which could be explained by the greater bargaining power of intermediaries in the last-mentioned region due to the greater technological development in the mills.

Keywords: asymmetric price transmission, rice prices, price transmission, regional economics

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

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Authors: Igor A. Krichtafovitch

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The evolutionary processes are not linear. Long periods of quiet and slow development turn to rather rapid emergences of new species and even phyla. During Cambrian explosion, 22 new phyla were added to the previously existed 3 phyla. Contrary to the common credence the natural selection or a survival of the fittest cannot be accounted for the dominant evolution vector which is steady and accelerated advent of more complex and more intelligent living organisms. Neither Darwinism nor alternative concepts including panspermia and intelligent design propose a satisfactory solution for these phenomena. The proposed hypothesis offers a logical and plausible explanation of the evolutionary processes in general. It is based on two postulates: a) the Biosphere is a single living organism, all parts of which are interconnected, and b) the Biosphere acts as a giant biological supercomputer, storing and processing the information in digital and analog forms. Such supercomputer surpasses all human-made computers by many orders of magnitude. Living organisms are the product of intelligent creative action of the biosphere supercomputer. The biological evolution is driven by growing amount of information stored in the living organisms and increasing complexity of the biosphere as a single organism. Main evolutionary vector is not a survival of the fittest but an accelerated growth of the computational complexity of the living organisms. The following postulates may summarize the proposed hypothesis: biological evolution as a natural life origin and development is a reality. Evolution is a coordinated and controlled process. One of evolution’s main development vectors is a growing computational complexity of the living organisms and the biosphere’s intelligence. The intelligent matter which conducts and controls global evolution is a gigantic bio-computer combining all living organisms on Earth. The information is acting like a software stored in and controlled by the biosphere. Random mutations trigger this software, as is stipulated by Darwinian Evolution Theories, and it is further stimulated by the growing demand for the Biosphere’s global memory storage and computational complexity. Greater memory volume requires a greater number and more intellectually advanced organisms for storing and handling it. More intricate organisms require the greater computational complexity of biosphere in order to keep control over the living world. This is an endless recursive endeavor with accelerated evolutionary dynamic. New species emerge when two conditions are met: a) crucial environmental changes occur and/or global memory storage volume comes to its limit and b) biosphere computational complexity reaches critical mass capable of producing more advanced creatures. The hypothesis presented here is a naturalistic concept of life creation and evolution. The hypothesis logically resolves many puzzling problems with the current state evolution theory such as speciation, as a result of GM purposeful design, evolution development vector, as a need for growing global intelligence, punctuated equilibrium, happening when two above conditions a) and b) are met, the Cambrian explosion, mass extinctions, happening when more intelligent species should replace outdated creatures.

Keywords: supercomputer, biological evolution, Darwinism, speciation

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Authors: Kunal Garg, Louise Theusen Hermansan, Kanoktip Puttaraska, Oliver Hendricks, Heidi Pirttinen, Leona Gilbert

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The Germ Theory (one infectious determinant is equal to one disease) has unarguably evolved our capability to diagnose and treat infectious diseases over the years. Nevertheless, the advent of technology, climate change, and volatile human behavior has brought about drastic changes in our environment, leading us to question the relevance of the Germ Theory in our day, i.e. will vector-borne disease (VBD) sufferers produce multiple immune responses when tested for multiple microbes? Vector diseased patients producing multiple immune responses to different microbes would evidently suggest human polymicrobial infections (HPI). Ongoing diagnostic tools are exceedingly unequipped with the current research findings that would aid in diagnosing patients for polymicrobial infections. This shortcoming has caused misdiagnosis at very high rates, consequently diminishing the patient’s quality of life due to inadequate treatment. Equipped with the state-of-art scientific knowledge, PolyScan intends to address the pitfalls in current VBD diagnostics. PolyScan is a multiplex and multifunctional enzyme linked Immunosorbent assay (ELISA) platform that can test for numerous VBD microbes and allow simultaneous screening for multiple types of antibodies. To validate PolyScan, Lyme Borreliosis (LB) and spondyloarthritis (SpA) patient groups (n = 54 each) were tested for Borrelia burgdorferi, Borrelia burgdorferi Round Body (RB), Borrelia afzelii, Borrelia garinii, and Ehrlichia chaffeensis against IgM and IgG antibodies. LB serum samples were obtained from Germany and SpA serum samples were obtained from Denmark under relevant ethical approvals. The SpA group represented chronic LB stage because reactive arthritis (SpA subtype) in the form of Lyme arthritis links to LB. It was hypothesized that patients from both the groups will produce multiple immune responses that as a consequence would evidently suggest HPI. It was also hypothesized that the multiple immune response proportion in SpA patient group would be significantly larger when compared to the LB patient group across both antibodies. It was observed that 26% LB patients and 57% SpA patients produced multiple immune responses in contrast to 33% LB patients and 30% SpA patients that produced solitary immune responses when tested against IgM. Similarly, 52% LB patients and an astounding 73% SpA patients produced multiple immune responses in contrast to 30% LB patients and 8% SpA patients that produced solitary immune responses when tested against IgG. Interestingly, IgM immune dysfunction in both the patient groups was also recorded. Atypically, 6% of the unresponsive 18% LB with IgG antibody was recorded producing multiple immune responses with the IgM antibody. Similarly, 12% of the unresponsive 19% SpA with IgG antibody was recorded producing multiple immune responses with the IgM antibody. Thus, results not only supported hypothesis but also suggested that IgM may atypically prevail longer than IgG. The PolyScan concept will aid clinicians to detect patients for early, persistent, late, polymicrobial, & immune dysfunction conditions linked to different VBD. PolyScan provides a paradigm shift for the VBD diagnostic industry to follow that will drastically shorten patient’s time to receive adequate treatment.

Keywords: diagnostics, immune dysfunction, polymicrobial, TICK-TAG

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Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

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Authors: Seulki Lee, Seoung Bum Kim

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Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

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Authors: Danila Arbuzov

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In this article the author examines the communicative strategies used in the Colombian political discourse, following the example of the speeches of the Vice President of Colombia Francia Marquez, who took office in 2022 and marked a new development vector for the Colombian nation. The lexical and syntactic means are analyzed to achieve the communicative objectives. The material presented may be useful for those who are interested in investigating various aspects of discursive linguistics, particularly political discourse, as well as the implementation of communicative strategies in certain types of discourse.

Keywords: political discourse, communication strategies, Colombian political discourse, Colombia, manipulation

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

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Authors: Layal Mansour

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The aim of this paper is to identify whether oil price volatility or geopolitical risks can predict future financial stress periods or economic recessions in Kuwait. We construct the first Financial Stress Index for Kuwait (FSIK) that includes informative vulnerable indicators of the main financial sectors: the banking sector, the equities market, and the foreign exchange market. The study covers the period from 2000 to 2020, so it includes the two recent most devastating world economic crises with oil price fluctuation: the Covid-19 pandemic crisis and Ukraine-Russia War. All data are taken by the central bank of Kuwait, the World Bank, IMF, DataStream, and from Federal Reserve System St Louis. The variables are computed as the percentage growth rate, then standardized and aggregated into one index using the variance equal weights method, the most frequently used in the literature. The graphical FSIK analysis provides detailed information (by dates) to policymakers on how internal financial stability depends on internal policy and events such as government elections or resignation. It also shows how monetary authorities or internal policymakers’ decisions to relieve personal loans or increase/decrease the public budget trigger internal financial instability. The empirical analysis under vector autoregression (VAR) models shows the dynamic causal relationship between the oil price fluctuation and the Kuwaiti economy, which relies heavily on the oil price. Similarly, using vector autoregression (VAR) models to assess the impact of the global geopolitical risks on Kuwaiti financial stability, results reveal whether Kuwait is confronted with or sheltered from geopolitical risks. The Financial Stress Index serves as a guide for macroprudential regulators in order to understand the weakness of the overall Kuwaiti financial market and economy regardless of the Kuwaiti dinar strength and exchange rate stability. It helps policymakers predict future stress periods and, thus, address alternative cushions to confront future possible financial threats.

Keywords: Kuwait, financial stress index, causality test, VAR, oil price, geopolitical risks

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Authors: Frank Hespeler, Giuseppe Loiacono

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We propose measures for systemic risk generated through intra-sectorial interdependencies in the hedge fund sector. These measures are based on variations in the average cross-effects of funds showing significant interdependency between their individual returns and the moments of the sector’s return distribution. The proposed measures display a high ability to identify periods of financial distress, are robust to modifications in the underlying econometric model and are consistent with intuitive interpretation of the results.

Keywords: hedge funds, systemic risk, vector autoregressive model, risk monitoring

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Authors: J. Adeppa, S. Samanta, O. K. Raina

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Fasciolosis is a widespread economically important parasitic infection throughout the world caused by Fasciola hepatica and F. gigantica. In order to identify novel immunogen conferring significant protection against fasciolosis, currently, research has been focused on the defined antigens viz. glutathione S-transferase, fatty acid binding protein, cathepsin-L, fluke hemoglobin, paramyosin, myosin and F. hepatica- Kunitz Type Molecule. Among various antigens, GST which plays a crucial role in detoxification processes, i.e. phase II defense mechanism of this parasite, has a unique position as a novel vaccine candidate and a drug target in the control of this disease. For producing the antigens in large quantities and their purification to complete homogeneity, the recombinant DNA technology has become an important tool to achieve this milestone. RT- PCR was carried out using F. gigantica total RNA as template, and an amplicon of 657 bp GST gene was obtained. TA cloning vector was used for cloning of this gene, and the presence of insert was confirmed by blue-white selection for recombinant colonies. Sequence analysis of the present isolate showed 99.1% sequence homology with the published sequence of the F. gigantica GST gene of cattle origin (accession no. AF112657), with six nucleotide changes at 72, 74, 423, 513, 549 and 627th bp found in the present isolate, causing an overall change of 4 amino acids. The 657 bp GST gene was cloned at BamH1 and HindIII restriction sites of the prokaryotic expression vector pPROEXHTb in frame with six histidine residues and expressed in E. coli DH5α. Recombinant protein was purified from the bacterial lysate under non-denaturing conditions by the process of sonication after lysozyme treatment and subjecting the soluble fraction of the bacterial lysate to Ni-NTA affinity chromatography. Western blotting with rabbit hyper-immune serum showed immuno-reactivity with 25 kDa recombinant GST. Recombinant protein detected F. gigantica experimental as well as field infection in buffaloes by dot-ELISA. However, cross-reactivity studies on Fasciola gigantica GST antigen are needed to evaluate the utility of this protein in the serodiagnosis of fasciolosis.

Keywords: fasciola gigantic, fasciola hepatica, GST, RT- PCR

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Authors: Raquel Vianna Duarte Cardoso, Eduarda Lobato Faria, José Jorge Boueri

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Rapid urbanization has transformed the global landscape, presenting significant challenges to public health. This article delves into the impact of urbanization on the spread of infectious diseases in cities and identifies crucial strategies to enhance urban community resilience. Massive urbanization over recent decades has created conducive environments for the rapid spread of diseases due to population density, mobility, and unequal living conditions. Urbanization has been observed to increase exposure to pathogens and foster conditions conducive to disease outbreaks, including seasonal flu, vector-borne diseases, and respiratory infections. In order to tackle these issues, a range of cross-disciplinary approaches are suggested. These encompass the enhancement of urban healthcare infrastructure, emphasizing the need for robust investments in hospitals, clinics, and healthcare systems to keep pace with the burgeoning healthcare requirements in urban environments. Moreover, the establishment of disease monitoring and surveillance mechanisms is indispensable, as it allows for the timely detection of outbreaks, enabling swift responses. Additionally, community engagement and education play a pivotal role in advocating for personal hygiene, vaccination, and preventive measures, thus playing a pivotal role in diminishing disease transmission. Lastly, the promotion of sustainable urban planning, which includes the creation of cities with green spaces, access to clean water, and proper sanitation, can significantly mitigate the risks associated with waterborne and vector-borne diseases. The article is based on a review of scientific literature, and it offers a comprehensive insight into the complexities of the relationship between urbanization and health. It places a strong emphasis on the urgent need for integrated approaches to improve urban resilience in the face of health challenges.

Keywords: infectious diseases dissemination, public health, urbanization impacts, urban resilience

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: M.A.Bakry1, *, Aryn T. Shafeek1, +

Abstract:

In this article, absolute Parallelism geometry is used to study the torsional gravitational field. And discovered the tetrad fields, torsion vector, and torsion scalar of Schwarzschild space. The new solution of the torsional gravitational field is a generalization of Schwarzschild in the context of general relativity. The results are applied to the planetary orbits.

Keywords: absolute parallelism geometry, tetrad fields, torsion vectors, torsion scalar

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Authors: Ahmad Fakhriy Hassan, Rohani Ahmad, Zurainee Mohamed Nor, Wan Najdah Wan Mohamad Ali

Abstract:

In Malaysia, it was realized that while the incidence of human malaria is decreasing, the incidence of Plasmodium knowlesi malaria appears to be on the rise, especially in rural areas of Sabah, East Malaysia. The primary vector for P. knowlesi malaria in Sabah is An. balabacensis a species found abundant in rural areas, shown to rest and feed outdoor throughout the night, which makes its control very challenging. This study aims to examine the distribution of malaria-infected Anopheles mosquitoes in three areas in Sabah, namely Kudat, Ranau, and Tenom, known as areas in Sabah that presented high number of malaria cases. Briefly, mosquitoes were caught every 6 weeks for the period of 18 months using Human Landing Catching (HLC) technique from May 2016 to November 2017. Identification of species was done using microscopy and molecular methods. Molecular method is also used to detect malaria parasite in all mosquito collected. An. balabacensis was present in all the study areas. In Kudat, six other Anopheles species were also detected, namely, An. barumbrosus, An. latens, An. letifer, An. maculatus, An. sundaicus and An. tesselatus. In Ranau five other Anopheles species were detected, namely, An. barumbrosus, An. donaldi., An. hodgkini, An. maculatus, and An. tesselatus while in Tenom seven more species An. donaldi, An. umbrosus, An. barumbrosus, An.latens, An. hodgkini, An. maculatus, and An. tesselatus were detected. This study showed 24% out of 259, 39% out of 127, and 26% out of 265 Anopheles mosquito collected in Kudat, Ranau, and Tenom were detected positive for malaria parasite respectively. In Kudat An. balabacensis, An. barumbrosus, An. latens, An. maculatus, An. sundaicus and An. tesselatus were the six out of eight Anopheles species that were found infected with malaria parasite. All Anopheles species collected in Ranau were positive for malaria while In Tenom, only five out of eight species; An. balabacensus, An. donaldi, An. hodgkini, An. maculatus, and An. latens were detected positive for malaria parasite. Interestingly, for all study areas An. balabacensis was shown to be the only species infected with four malaria species; P. falciparum, P. knowlesi, P. vivax, and Plasmodium sp. This finding clearly indicates that An. balabacensis is the dominant malaria vector in Kudat, Ranau, and Tenom.

Keywords: Anopheles balabacensis, human landing catching technique, nested PCR, Plasmodium knowlesi, Simian malaria

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Authors: Narayan Moger, Divya B., Preethi Jambagi, Krishnaveni C. K., Apsana M. R., B. R. Patil, Basvaraj Bagewadi

Abstract:

Salinity is one of the major threats to world food security. Considering the requirement for salt tolerant crop plants in the present study was undertaken to clone and transferred the salt tolerant ectA gene from marine ecosystem into agriculture crop system to impart salinity tolerance. Ectoine is the compatible solute which accumulates in the cell membrane, is known to be involved in salt tolerance activity in most of the Halophiles. The present situation is insisting to development of salt tolerant transgenic lines to combat abiotic stress. In this background, the investigation was conducted to develop transgenic tomato lines by cloning and transferring of ectA gene is an ectoine derivative capable of enzymatic action for the production of acetyl-diaminobutyric acid. The gene ectA is involved in maintaining the osmotic balance of plants. The PCR amplified ectA gene (579bp) was cloned into T/A cloning vector (pTZ57R/T). The construct pDBJ26 containing ectA gene was sequenced by using gene specific forward and reverse primers. Sequence was analyzed using BLAST algorithm to check similarity of ectA gene with other isolates. Highest homology of 99.66 per cent was found with ectA gene sequences of isolates Halomonas elongata with the available sequence information in NCBI database. The ectA gene was further sub cloned into pRI101-AN plant expression vector and transferred into E. coli DH5α for its maintenance. Further pDNM27 was mobilized into A. tumefaciens LBA4404 through tri-parental mating system. The recombinant Agrobacterium containing pDNM27 was transferred into tomato plants through In planta plant transformation method. Out of 300 seedlings, co-cultivated only twenty-seven plants were able to well establish under the greenhouse condition. Among twenty-seven transformants only twelve plants showed amplification with gene specific primers. Further work must be extended to evaluate the transformants at T1 and T2 generations for ectoine accumulation, salinity tolerance, plant growth and development and yield.

Keywords: salinity, computable solutes, ectA, transgenic, in planta transformation

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

Abstract:

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: Ben Soltane Cheima, Ittansa Yonas Kelbesa

Abstract:

Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

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Authors: Mohammed Arif, Abdessalam Kifouche

Abstract:

Optical character recognition (OCR) has a main role in the present time. It's capable to solve many serious problems and simplify human activities. The OCR yields to 70's, since many solutions has been proposed, but unfortunately, it was supportive to nothing but Latin languages. This work proposes a system of recognition of an off-line Arabic handwriting. This system is based on a structural segmentation method and uses support vector machines (SVM) in the classification phase. We have presented a state of art of the characters segmentation methods, after that a view of the OCR area, also we will address the normalization problems we went through. After a comparison between the Arabic handwritten characters & the segmentation methods, we had introduced a contribution through a segmentation algorithm.

Keywords: OCR, segmentation, Arabic characters, PAW, post-processing, SVM

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Authors: Nicolay Zarutskiy, Roman Bulkin

Abstract:

The paper deals with the existing methods of force-torque sensor calibration with a number of components from one to six, analyzed their advantages and disadvantages, the necessity of introduction of a calibration method. Calibration method and its constructive realization are also described here. A calibration method allows performing automated force-torque sensor calibration both with selected components of the main vector of forces and moments and with complex loading. Thus, two main advantages of the proposed calibration method are achieved: the automation of the calibration process and universality.

Keywords: automation, calibration, calibration device, calibration method, force-torque sensors

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Authors: Venkatesh Krishna Murthy

Abstract:

The era before government-regulated automotive designs gave us some astonishing vehicles that are well worth to keep on the road. The fact that restoring an automobile in 2015 does not mean it will perform like one designed in 2021. This is one of the reasons that manufacturers continue to turn to vintage hardware for future enhancements in their vehicles. Now we need to understand that a modern chassis could possibly allow manufacturers to give vintage performance cars a level of braking capability, compatibility with tires, chassis rigidity, suspension sophistication, and steering response, an experience only racers got until now. However, half a century of advancements in engineering can have a great impact on design in any field, and the automotive realm which holds no exception. In the current situation, a growing number of companies offer chassis and braking components to onboard manufacturers to retrofit contemporary technology for their vintage vehicles to modernize them at the foundation level. The recent question arises on performance on lithium batteries, as opposed to simply bolting upgraded components, for ex. lithium batteries with graphene as superconductive material to enhance performance, an area deeply investigated. Serving as the “bones” of the vehicle, the chassis and frame play a central role in dictating how that automobile will perform. While the desire to maintain originality is alluring for many, the benefits of a modern chassis are vast. In some situations, it also allows builders to put cars back on the road that might otherwise be too far gone. “There’s a couple of different factors at play here – one of them being that these older cars from the ’40s, ’50s, and ’60s have seen a lot of weather and a lot of road miles over the years, more often than not,” says Craig Morrison of Art Morrison Enterprises.

Keywords: hybrid electric vehicles, internet of things, lithium graphene batteries, classic car chassis

Procedia PDF Downloads 155