Search results for: variant selection

Authors: H. Sakamoto, S. Kurosawa, M. Suzuki, S. Oguri

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In Arabidopsis, several genes encoding proteins with ankyrin repeats and trans-membrane domains (AtANKTM) have been identified as mediators of biotic and abiotic stress responses. It has been known that the expression of an AtANKTM gene, At2g24600, is induced in response to abiotic stress and that there are four splicing variants derived from this locus. In this study, by RT-PCR and sequencing analysis, an unknown splicing variant of the At2g24600 transcript was identified. Based on differences in the predicted amino acid sequences, the five splicing variants are divided into three groups. The three predicted proteins are highly homologous, yet have different numbers of ankyrin repeats and trans-membrane domains. It is generally considered that ankyrin repeats mediate protein-protein interaction and that the number of trans-membrane domains affects membrane topology of proteins. The protein variants derived from the At2g24600 locus may have different molecular functions each other.

Keywords: alternative splicing, ankyrin repeats, trans-membrane domains, arabidopsis

Procedia PDF Downloads 375

Authors: Aliza Lee, Mark Wilt, John Bonk, Scott Floyd, Bradley Hoffman, Karen Uchmanowicz

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Bipartite medial cuneiforms are relatively rare but may play a significant role in biomechanical and gait abnormalities. It is believed that a bipartite medial cuneiform may alter the available range of motion due to its larger morphological variant, thus limiting the metatarsal plantarflexion needed to achieve adequate hallux dorsiflexion for normal gait. Radiographic and clinical assessments were performed on 2 patients who reported foot pain along the first ray. Both patients had visible bipartite medial cuneiforms on MRI. Using gait plate and Metascan™ analysis, both were noted to have four measurements far beyond the expected range. Medial and lateral heel peak pressure, hallux peak pressure, and 1st metatarsal peak pressure were all noted to be increased. These measurements are believed to be increased due to the hindrance placed on the available ROM of the 1st ray by the increased size of the medial cuneiform. A larger patient population would be needed to fully understand this developmental anomaly.

Keywords: bipartite medial cuneiforms, cuneiform, developmental anomaly, gait abnormality

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Authors: Josselyn Mata Calidonio, Arianna I. Maddox, Kimberly Hamad-Schifferli

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Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown viruses has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross-reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

Keywords: adaptive immunoassay, detecting unknown viruses, gold nanoparticles, paper immunoassay, repurposing antibodies

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Authors: S. Cobos-Guzman

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This paper presents the results of a mechatronic design based on a 4-wheel omnidirectional mobile robot that can be used in indoor logistic applications. The low-level control has been selected using two open-source hardware (Raspberry Pi 3 Model B+ and Arduino Mega 2560) that control four industrial motors, four ultrasound sensors, four optical encoders, a vision system of two cameras, and a Hokuyo URG-04LX-UG01 laser scanner. Moreover, the system is powered with a lithium battery that can supply 24 V DC and a maximum current-hour of 20Ah.The Robot Operating System (ROS) has been implemented in the Raspberry Pi and the performance is evaluated with the selection of the sensors and hardware selected. The mechatronic system is evaluated and proposed safe modes of power distribution for controlling all the electronic devices based on different tests. Therefore, based on different performance results, some recommendations are indicated for using the Raspberry Pi and Arduino in terms of power, communication, and distribution of control for different devices. According to these recommendations, the selection of sensors is distributed in both real-time controllers (Arduino and Raspberry Pi). On the other hand, the drivers of the cameras have been implemented in Linux and a python program has been implemented to access the cameras. These cameras will be used for implementing a deep learning algorithm to recognize people and objects. In this way, the level of intelligence can be increased in combination with the maps that can be obtained from the laser scanner.

Keywords: autonomous, indoor robot, mechatronic, omnidirectional robot

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Authors: Dana Madalina Pohrib, Elena Irina Ciobanu

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In compaction works, the most often used codes and standards are those for road embankments and refer to a maximum filling height of 3.00m. When filling a height greater than 3.00m, such codes are no longer valid and thus their application may lead to technical difficulties in the process of compaction and to the achievement of a sufficient degree of compaction. For this reason, in the case of controlled fillings with heights greater than 3.00m it is necessary to formulate and apply a number of special techniques, which can be determined by performing a full scale test. This paper presents the results of the studies and full scale tests conducted for the stabilization of a ravine with vertical banks and a depth of about 12.00m. The fillings will support a heavy traffic road connecting the two parts of a village in Vaslui County, Romania. After analyzing two comparative intervention solutions, the variant of a controlled filling bordered by a monolith concrete retaining wall was chosen. The results obtained by the authors highlighted the need to insert a geogrid reinforcement at every 2.00m for creating a 12.00m thick compacted fill.

Keywords: compaction, dynamic probing, stability, soil stratification

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Authors: Mahsa Namvari, Kari Ullakko

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It is well-known that the Co-doping of ferromagnetic shape memory alloys (FSMAs) is a crucial tool to control their multifunctional properties. The present work investigates the use of small quantities of Co to fine-tune the transformation, structure, microstructure, mechanical and magnetic properties of the polycrystalline Ni₄₉.₈Mn₂₈.₅Ga₂₁.₇ (at.%) alloy, At Co concentrations of 1-1.5 at.%, a microstructure with an average grain size of about 2.00 mm was formed with a twin structure, enabling the experimental observation of magnetic-field-induced twin variant rearrangement. At higher levels of Co-doping, the grain size was essentially reduced, and the crystal structure of the martensitic phase became 2M martensite. The decreasing grain size and changing crystal structure are attributed to the progress of γ-phase precipitates. Alongside the academic aspect, the results of the present work point to the commercial advantage of fabricating 10M Co-doped Ni-Mn-Ga actuating elements made from large grains of polycrystalline ingots obtained by a standard melting facility instead of grown single crystals.

Keywords: Ni-Mn-Ga, ferromagnetic shape memory, martensitic phase transformation, grain growth

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Ali Bayram, Burak Uz, Remzi Yiğiter

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The aim of the present study was to investigate the expression levels of homo sapiens nuclear factor of kappa light polypeptide gene enhancer in B-cells 1, transcript variant 1 (NFKB1*1) mRNA in the peripheral blood of patients with Parkinson to elucidate the role in the pathogenesis of Parkinson disease (PD). The study group comprised 50 patients with the diagnosis of PD who have applied to Gaziantep University Faculty of Medicine, and Department of Neurology. 50 healthy individuals without any neuro degenerative disease are included as controls. Ribonucleic acid (RNA) was obtained from blood samples of patient and control groups. Complementary deoxyribonucleic acid (cDNA) was obtained from RNA samples using reverse transcription polymerase chain reaction (RT-PCR) technique. The gene expression of NFKB1*1 in patient/control groups were observed to decrease significantly, and the differences between groups with the Mann-Whitney method within 95% confidence interval (p<0.05) were analyzed. This salient finding provide a clue for our hypothesis that reduced activity of NFKB1*1 gene might play a role, at least partly, in the pathophysiology of PD.

Keywords: Parkinson’s Disease, NFKB1, mRNA expression, RT-PCR

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Authors: Gia Sirbiladze

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Route planning problems are among the activities that have the highest impact on logistical planning, transportation, and distribution because of their effects on efficiency in resource management, service levels, and client satisfaction. In extreme conditions, the difficulty of vehicle movement between different customers causes the imprecision of time of movement and the uncertainty of the feasibility of movement. A feasibility level of vehicle movement on the closed route of the disaster-streaking zone is defined for the construction of an objective function. Experts’ evaluations of the uncertain parameters in q-rung ortho-pair fuzzy numbers (q-ROFNs) are presented. A fuzzy bi-objective combinatorial optimization problem of fuzzy vehicle routine problem (FVRP) is constructed based on the technique of possibility theory. The FVRP is reduced to the bi-criteria partitioning problem for the so-called “promising” routes which were selected from the all-admissible closed routes. The convenient selection of the “promising” routes allows us to solve the reduced problem in real-time computing. For the numerical solution of the bi-criteria partitioning problem, the -constraint approach is used. The main results' support software is designed. The constructed model is illustrated with a numerical example.

Keywords: q-rung ortho-pair fuzzy sets, facility location selection problem, multi-objective combinatorial optimization problem, partitioning problem

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Authors: Abdelaziz Fellah, Allaoua Maamir

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We propose a domain-independent merging-cluster filter approach complemented with a set of algorithms for identifying approximate duplicate entities efficiently and accurately within a single and across multiple data sources. The near-optimal merging-cluster filter (MCF) approach is based on the Monge-Elkan well-tuned algorithm and extended with an affine variant of the Smith-Waterman similarity measure. Then we present constant, variable, and function threshold algorithms that work conceptually in a divide-merge filtering fashion for detecting near duplicates as hierarchical clusters along with their corresponding representatives. The algorithms take recursive refinement approaches in the spirit of filtering, merging, and updating, cluster representatives to detect approximate duplicates at each level of the cluster tree. Experiments show a high effectiveness and accuracy of the MCF approach in detecting approximate duplicates by outperforming the seminal Monge-Elkan’s algorithm on several real-world benchmarks and generated datasets.

Keywords: data mining, data cleaning, approximate duplicates, near-duplicates detection, data mining applications and discovery

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Kamal Kumar Gupta

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The present research work was carried out on Dysdercus koenigii to understand various aspects of reproductive behavior such as mate finding and recognition, mate selection and mating preference, mating receptivity, and prolonged copulation. The studies carried out on mate searching and courtship behaviour of Dysdercus reflected the courtship behaviour in Dysdercus was brief. The opposite sexes are brought together by the pheromone. The males responded to female sex pheromones by showing directional movements toward the sex partners. Change in mating receptivity pattern of female Dysdercus was ascertained using three parameters of mating behaviour i.e. numbers of male’s encounter, the time taken to mate successfully and per cent females responding to mating. It was seen that a receptive female responded positively to the courting males and a high percentage of females mate usually in a very short time span. The females of Dysdercus showed continued mating receptivity throughout their life. The studies pertaining to mate selection by females showed that females generally do not discriminate among males and usually mate with any male they encountered first. The adults of Dysdercus remain in continuous copula up to 72hr. and mate 5-7 time in their life span. Studies pertaining to significance of prolonged mating in the life time reproductive success of the female Dysdercus indicated that fecundity and fertility and oviposition behavior of the female Dysdercus was related to duration of mating. In order to understand sperm precedence, the sterilized males were produced by exposing them to Gamma radiation. Our studies indicated that a dose of 50 Gy of Gamma radiations induced 95% sterility but does not impair the mating behaviour drastically. To understand role of sperms which were transfer during second mating in fertilizing the subsequent egg batches the sperm utilization pattern of doubly mated female was assessed. The females were mated with normal male or sterilized male in a combination. The sperm utilization pattern was determined by P2 value, our studies indicated a very high P2 value of 0.966, and indicated that sperms of last mating were utilized by the female for fertilization. In light of some of the unique reproductive behaviour of Dysdercus koenigii, such as brief courtship behavior, generalized mate selection by the female, continued mating receptivity and a prolonged pre oviposition period, the present studies on sperm precedence provides an explanation to an unusually prolonged copulation in Dysdercus.

Keywords: dysdercus koenigii, mating behaviour, reproductive performance, entomology

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Kevin Molina, Daniel Ortega, Manuel Martinez, Andres Gonzalez-Estrada, William Pinto

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Due to the increasing environmental impact, the wind energy is getting strong. This research studied the relationship between the power produced by a horizontal axis wind turbine (HAWT) and the aerodynamic profiles used for its construction. The analysis is studied using the Computational Fluid Dynamic (CFD), presenting the parallel between the energy generated by a turbine designed with selected profiles and another one optimized. For the study, a selection process was carried out from profile NACA 6 digits recommended by the National Renewable Energy Laboratory (NREL) for the construction of this type of turbines. The selection was taken into account different characteristics of the wind (speed and density) and the profiles (aerodynamic coefficients Cl and Cd to different Reynolds and incidence angles). From the selected profiles, was carried out a sensitivity analysis optimization process between its geometry and the aerodynamic forces that are induced on it. The 3D model of the turbines was realized using the Blade Element Momentum method (BEM) and both profiles. The flow fields on the turbines were simulated, obtaining the forces induced on the blade, the torques produced and an increase of 3% in power due to the optimized profiles. Therefore, the results show that the sensitivity analysis optimization process can assist to increment the wind turbine power.

Keywords: blade element momentum, blade, fluid structure interaction, horizontal axis wind turbine, profile design

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Authors: Ahmed Mahmoud Ahmed Abouelmagd

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The diversity is the usual remedy of the transmitted signal level variations (Fading phenomena) in radio frequency channels. Diversity techniques utilize two or more copies of a signal and combine those signals to combat fading. The basic concept of diversity is to transmit the signal via several independent diversity branches to get independent signal replicas via time – frequency - space - and polarization diversity domains. Coding and decoding processes can be an alternative remedy for fading phenomena, it cannot increase the channel capacity, but it can improve the error performance. In this paper we propose the use of replication decoding with BCH code class, and Viterbi decoding algorithm with convolution coding; as examples of coding and decoding processes. The results are compared to those obtained from two optimized selection space diversity techniques. The performance of Rayleigh fading channel, as the model considered for radio frequency channels, is evaluated for each case. The evaluation results show that the coding and decoding approaches, especially the BCH coding approach with replication decoding scheme, give better performance compared to that of selection space diversity optimization approaches. Also, an approach for combining the coding and decoding diversity as well as the space diversity is considered, the main disadvantage of this approach is its complexity but it yields good performance results.

Keywords: Rayleigh fading, diversity, BCH codes, Replication decoding, ‎convolution coding, viterbi decoding, space diversity

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Authors: E. L. Albuquerque, U. L. Fulco, G. S. Ourique

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The focus of this work is on the numerical investigation of the charge transport properties of the de novo-designed alpha3 polypeptide, as well as in its variants, all of them probed by gene engineering. The theoretical framework makes use of a tight-binding model Hamiltonian, together with ab-initio calculations within quantum chemistry simulation. The alpha3 polypeptide is a 21-residue with three repeats of the seven-residue amino acid sequence Leu-Glu-Thr-Leu-Ala-Lys-Ala, forming an alpha–helical bundle structure. Its variants are obtained by Ala→Gln substitution at the e (5th) and g (7th) position, respectively, of the alpha3 polypeptide amino acid sequence. Using transmission electron microscopy and atomic force microscopy, it was observed that the alpha3 polypeptide and one of its variant do have the ability to form fibrous assemblies, while the other does not. Our main aim is to investigate whether or not the biased alpha3 polypeptide and its variants can be also identified by quantum charge transport measurements through current-voltage (IxV) curves as a pattern to characterize their fibrous assemblies. It was observed that each peptide has a characteristic current pattern, which may be distinguished by charge transport measurements, suggesting that it might be a useful tool for the development of biosensors.

Keywords: charge transport properties, electronic transmittance, current-voltage characteristics, biological sensor

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Authors: Panagiotis Karadimos, Leonidas Anthopoulos

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Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: actual cost and duration, attribute selection, bridge construction, neural networks, predicting models, FANN TOOL, WEKA

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Authors: C. V. Aravinda, H. N. Prakash

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In this paper, we tried to convey fusion and state of art pertaining to SIL character recognition systems. In the first step, the text is preprocessed and normalized to perform the text identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. The three stages which involved are Data acquisition and preprocessing, Feature extraction, and Classification. Here we concentrated on two techniques to obtain features, Feature Extraction & Feature Selection. Edge-hinge distribution is a feature that characterizes the changes in direction of a script stroke in handwritten text. The edge-hinge distribution is extracted by means of a windowpane that is slid over an edge-detected binary handwriting image. Whenever the mid pixel of the window is on, the two edge fragments (i.e. connected sequences of pixels) emerging from this mid pixel are measured. Their directions are measured and stored as pairs. A joint probability distribution is obtained from a large sample of such pairs. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting recognition based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.

Keywords: word segmentation and recognition, character recognition, optical character recognition, hand written character recognition, South Indian languages

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Zarema Urustamova, Chunsheng Shi, Ghulam Mujtaba Kayani 

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This research paper helps to understand the comparative analysis of recruitment problems which majorly faced by HRD of Public/Semi-Govt. and private sectors of Russia. The natures of different recruitment problems faced by HRD are different in both sector of Russia. Recruitment is one of very critical and important decision taken by HR department and some recruitment problems are highly faced by HR department of public/semi Govt. sector but are not major problems for private sector. Moreover, some problems are majorly influence in private sector but are not major problems in public/semi-govt. sector of Russia in recruitment. It is also identified that some recruitment problems are majorly affect in recruitment in both sectors. This paper helps to understand the recruitment problems faced by HR department while recruiting the new employee in both sectors. This paper also identified that “environment” and “prejudice” in public sector have higher affect and considered as a major problems in employee recruitment and “reference”, “selection standards” are considered as a least affecting problems of recruitment in public sector. Further, in private sector, “prejudice” and “culture” are major issues and “selection standards” and “reference” is considered as least affecting recruitment problems in private sector of Russia. So, HR department will be able to hire right person on right time, and it is possible when different HR departments focus to overcome these recruitment problems more efficiently and effectively.

Keywords: Govt. /Semi-Govt. vs. private sector, HR department, recruitment problems, Russia

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Authors: Ahmad Afif, Teuku Yuri M. Zagloel

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Sustainable supply chain management is a new pattern that has emerged recently in industry and companies. The procurement process is one of the key factors for efficiency in supply chain management practices. Partnership is one of the procurement strategies for strategic items. The success factors of the partnership must be determined to avoid things that endanger the financial and operational status of the company. The current supplier partnership research focuses on the selection of general criteria and sustainable supplier selection. Currently, there is still limited research on the success factors of supplier partnerships that focus on strategic items in the coal mining industry. Meanwhile, the procurement of coal mining has its own characteristics, and there are regulations related to the procurement of goods. Therefore, this research was conducted to determine the categories of goods that are included in the strategic items and to design the success factors of supplier partnerships. The main factors studied are general, financial, production, reputation, synergies, and sustainable. The research was conducted using the Kraljic method to determine the categories of goods that are included in the strategic items. To design a supplier partnership success factor using the Hybrid Multi Criteria Decision Making method. Integrated Fuzzy AHP-Fuzzy TOPSIS is used to determine the weight of the success factors of supplier partnerships and to rank suppliers on the factors used.

Keywords: supplier, partnership, strategic item, success factors, and coal mining industry

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Authors: Rowland R. Ogunrinde, Yusmadi Y. Jusoh, Noraini Che Pa, Wan Nurhayati W. Rahman, Azizol B. Abdullah

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Enterprise Applications (EAs) aid the organizations achieve operational excellence and competitive advantage. Over time, most Small and Medium Enterprises (SMEs), which are known to be the major drivers of most thriving global economies, use the costly on-premise versions of these applications thereby making business difficult to competitively thrive in the same market environment with their large enterprise counterparts. The advent of cloud computing presents the SMEs an affordable offer and great opportunities as such EAs can be cloud-hosted and rented on a pay-per-use basis which does not require huge initial capital. However, as there are numerous Cloud Service Providers (CSPs) offering EAs as Software-as-a-Service (SaaS), there is a challenge of choosing a suitable provider with Quality of Service (QoS) that meet the organizations’ customized requirements. The proposed model takes care of that and goes a step further to select the most affordable among a selected few of the CSPs. In the earlier stage, before developing the instrument and conducting the pilot test, the researchers conducted a structured interview with three experts to validate the proposed model. In conclusion, the validity and reliability of the instrument were tested through experts, typical respondents, and analyzed with SPSS 22. Results confirmed the validity of the proposed model and the validity and reliability of the instrument.

Keywords: cloud service provider, enterprise application, quality of service, selection criteria, small and medium enterprise

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Thanduxolo Nomngcoyiya, Simon M. Kang’ethe

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The paper aimed to critically analyse gaps pertaining to the cultural policy environments governing traditional male circumcision in the Eastern Cape as exemplified by an empirical case study. The original study which this paper is derived from utilized qualitative paradigm; and encompassed 28 participants. It used in-depth one-on-one interviews complemented by focus group discussions and key informants as a method of data collection. It also adopted interview guide as a data collection instrument. The original study was cross-sectional in nature, and the data was audio recorded and transcribed later during the data analysis and coding process. The study data analysis was content thematic analysis and identified the following key major findings on the culture of male circumcision policy: Lack of clarity on culture of male circumcision policy operations; Myths surrounding procedures on culture of male circumcision; Divergent views on cultural policies between government and male circumcision custodians; Unclear cultural policies on selection criteria of practitioners; and Lack of policy enforcement and implementation on transgressors of culture of male circumcision. It recommended: a stringent selection criteria of practitioners; a need to carry out death-free male circumcision; a need for male circumcision stakeholders to work with other culture and tradition-friendly stakeholders.

Keywords: human rights, policy enforcement, traditional male circumcision, traditional surgeons and nurses

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Authors: Walailak Atthirawong

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By the end of 2015, the Association of Southeast Asian Nations (ASEAN) countries proclaim to transform into the next stage of an economic era by having a single market and production base called ASEAN Economic Community (AEC). One objective of the AEC is to establish ASEAN as a single market and one production base making ASEAN highly competitive economic region and competitive with new mechanisms. As a result, it will open more opportunities to enterprises in both trade and investment, which offering a competitive market of US$ 2.6 trillion and over 622 million people. Location decision plays a key role in achieving corporate competitiveness. Hence, it may be necessary for enterprises to redesign their supply chains via enlarging a new production base which has low labor cost, high labor skill and numerous of labor available. This strategy will help companies especially for apparel industry in order to maintain a competitive position in the global market. Therefore, in this paper a generic model for location selection decision for Thai apparel industry using Fuzzy Analytical Network Process (FANP) is proposed. Myanmar, Vietnam and Cambodia are referred for alternative location decision from interviewing expert persons in this industry who have planned to enlarge their businesses in AEC countries. The contribution of this paper lies in proposing an approach model that is more practical and trustworthy to top management in making a decision on location selection.

Keywords: apparel industry, ASEAN Economic Community (AEC), Fuzzy Analytical Network Process (FANP), location decision

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Authors: Artem Kim, Clara Savary, Christele Dubourg, Wilfrid Carre, Houda Hamdi-Roze, Valerie Dupé, Sylvie Odent, Marie De Tayrac, Veronique David

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Holoprosencephaly (HPE) is a rare congenital brain malformation resulting from the incomplete separation of the two cerebral hemispheres. It is characterized by a wide phenotypic spectrum and a high degree of locus heterogeneity. Genetic defects in 16 genes have already been implicated in HPE, but account for only 30% of cases, suggesting that a large part of genetic factors remains to be discovered. HPE has been recently redefined as a complex multigenic disorder, requiring the joint effect of multiple mutational events in genes belonging to one or several developmental pathways. The onset of HPE may result from accumulation of the effects of multiple rare variants in functionally-related genes, each conferring a moderate increase in the risk of HPE onset. In order to decipher the genetic basis of HPE, unconventional patterns of inheritance involving multiple genetic factors need to be considered. The primary objective of this study was to uncover possible disease causing combinations of multiple rare variants underlying HPE by performing trio-based Whole Exome Sequencing (WES) of familial cases where no molecular diagnosis could be established. 39 families were selected with no fully-penetrant causal mutation in known HPE gene, no chromosomic aberrations/copy number variants and without any implication of environmental factors. As the main challenge was to identify disease-related variants among a large number of nonpathogenic polymorphisms detected by WES classical scheme, a novel variant prioritization approach was established. It combined WES filtering with complementary gene-level approaches: transcriptome-driven (RNA-Seq data) and clinically-driven (public clinical data) strategies. Briefly, a filtering approach was performed to select variants compatible with disease segregation, population frequency and pathogenicity prediction to identify an exhaustive list of rare deleterious variants. The exome search space was then reduced by restricting the analysis to candidate genes identified by either transcriptome-driven strategy (genes sharing highly similar expression patterns with known HPE genes during cerebral development) or clinically-driven strategy (genes associated to phenotypes of interest overlapping with HPE). Deeper analyses of candidate variants were then performed on a family-by-family basis. These included the exploration of clinical information, expression studies, variant characteristics, recurrence of mutated genes and available biological knowledge. A novel bioinformatics pipeline was designed. Applied to the 39 families, this final integrated workflow identified an average of 11 candidate variants per family. Most of candidate variants were inherited from asymptomatic parents suggesting a multigenic inheritance pattern requiring the association of multiple mutational events. The manual analysis highlighted 5 new strong HPE candidate genes showing recurrences in distinct families. Functional validations of these genes are foreseen.

Keywords: complex genetic disorder, holoprosencephaly, multiple rare variants, whole exome sequencing

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Authors: Yoftahe Nigussie

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This report presents a final year project pertaining to the design of Fire tube boiler for the purpose of producing saturated steam. The objective of the project is to produce saturated steam for different purpose with a capacity of 2000kg/h at 12bar design pressure by performing a design of a higher performance fire tube boiler that considered the requirements of cost minimization and parameters improvement. This is mostly done in selection of appropriate material for component parts, construction materials and production methods in different steps of analysis. In the analysis process, most of the design parameters are obtained by iterating with related formulas like selection of diameter of tubes with overall heat transfer coefficient optimization, and the other selections are also as like considered. The number of passes is two because of the size and area of the tubes and shell. As the analysis express by using heavy oil fuel no6 with a higher heating value of 44000kJ/kg and lower heating value of 41300kJ/kg and the amount of fuel consumed 140.37kg/hr. and produce 1610kw of heat with efficiency of 85.25%. The flow of the fluid is a cross flow because of its own advantage and the arrangement of the tube in-side the shell is welded with the tube sheet, and the tube sheet is attached with the shell and the end by using a gasket and weld. The design of the shell, using European Standard code section, is as like pressure vessel by considering the weight, including content and the supplementary accessories such as lifting lugs, openings, ends, man hole and supports with detail and assembly drawing.

Keywords: steam generation, external treatment, internal treatment, steam velocity

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Authors: Ridwan Kiay Demak, Firmansyahrullah, Muchammad Sibro Mulis, Eko Tri Wasisto, Nixon Poltak Frederic, Agung Putu Andika, Lapo Ajis Kamamu, Muhammad Sobirin, Kornelius Eppang

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PC Prove LSM successfully improved the efficiency of Own Used Fuel Gas with the "Lotion" method in the PT Pertamina EP Cepu Donggi Matindok Generating System. The innovation of using the "LOTION" (LOAD PRIORITY SELECTION) method in the generating system is modeling that can provide a priority qualification of main and non-main equipment to keep gas processing running even though it leaves 1 GTG operating. GTG operating system has been integrated, controlled, and monitored properly through PC programs and web-based access to answer Industry 4.0 problems. The results of these improvements have succeeded in making Donggi Matindok Field Production reach 98.77 MMSCFD and become a proper EMAS candidate in 2022-2023. Additional revenue from increasing the efficiency of the use of own used gas amounting to USD USD 5.06 Million per year and reducing operational costs from maintenance efficiency (ABO) due to saving running hours GTG amounted to USD 3.26 Million per year. Continuity of fuel gas availability for the GTG generation system can maintain the operational reliability of the plant, which is 3.833333 MMSCFD. And reduced gas emissions wasted to the environment by 33,810 tons of C02 eq per year.

Keywords: LOTION method, load priority selection, fuel gas efficiency, gas turbine generator, reduce emissions

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Authors: Byeongcheol Kang, Junyi Kim, Hyungsik Jung, Hyungjun Yang, Jaewoo An, Jonggeun Choe

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It is important to figure out reservoir properties for better production management. Due to the limited information, there are geological uncertainties on very heterogeneous or channel reservoir. One of the solutions is to generate multiple equi-probable realizations using geostatistical methods. However, some models have wrong properties, which need to be excluded for simulation efficiency and reliability. We propose a novel method of model selection scheme, based on distance-based clustering for reliable application of production optimization algorithm. Distance is defined as a degree of dissimilarity between the data. We calculate Hausdorff distance to classify the models based on their similarity. Hausdorff distance is useful for shape matching of the reservoir models. We use multi-dimensional scaling (MDS) to describe the models on two dimensional space and group them by K-means clustering. Rather than simulating all models, we choose one representative model from each cluster and find out the best model, which has the similar production rates with the true values. From the process, we can select good reservoir models near the best model with high confidence. We make 100 channel reservoir models using single normal equation simulation (SNESIM). Since oil and gas prefer to flow through the sand facies, it is critical to characterize pattern and connectivity of the channels in the reservoir. After calculating Hausdorff distances and projecting the models by MDS, we can see that the models assemble depending on their channel patterns. These channel distributions affect operation controls of each production well so that the model selection scheme improves management optimization process. We use one of useful global search algorithms, particle swarm optimization (PSO), for our production optimization. PSO is good to find global optimum of objective function, but it takes too much time due to its usage of many particles and iterations. In addition, if we use multiple reservoir models, the simulation time for PSO will be soared. By using the proposed method, we can select good and reliable models that already matches production data. Considering geological uncertainty of the reservoir, we can get well-optimized production controls for maximum net present value. The proposed method shows one of novel solutions to select good cases among the various probabilities. The model selection schemes can be applied to not only production optimization but also history matching or other ensemble-based methods for efficient simulations.

Keywords: distance-based clustering, geological uncertainty, particle swarm optimization (PSO), production optimization

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Authors: K. Ivandic, F. Dodigovic, D. Stuhec, S. Strelec

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A characteristic value of certain geotechnical parameter results from an engineering assessment. Its selection has to be based on technical principles and standards of engineering practice. It has been shown that the results of engineering assessment of different authors for the same problem and input data are significantly dispersed. A survey was conducted in which participants had to estimate the force that causes a 10 cm displacement at the top of a axially in-situ compressed pile. Fifty experts from all over the world took part in it. The lowest estimated force value was 42% and the highest was 133% of measured force resulting from a mentioned static pile load test. These extreme values result in significantly different technical solutions to the same engineering task. In case of selecting a characteristic value of a geotechnical parameter the importance of the influence of an engineering assessment can be reduced by using statistical methods. An informative annex of Eurocode 1 prescribes the method of selecting the characteristic values of material properties. This is followed by Eurocode 7 with certain specificities linked to selecting characteristic values of geotechnical parameters. The paper shows the procedure of selecting characteristic values of a geotechnical parameter by using a statistical method with different initial conditions. The aim of the paper is to quantify an engineering assessment in the example of determining a characteristic value of a specific geotechnical parameter. It is assumed that this assessment is a random variable and that its statistical features will be determined. For this purpose, a survey research was conducted among relevant experts from the field of geotechnical engineering. Conclusively, the results of the survey and the application of statistical method were compared.

Keywords: characteristic values, engineering judgement, Eurocode 7, statistical methods

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