Search results for: fault prediction

Authors: Mohammed Abubakar Mawoli

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Theory of Planned Behavior (TPB) is a useful tool for predicting entrepreneurial intentions among individuals or groups of people. In view of the Nigerian government’s renewed educational policies and programs to prepare Nigerian undergraduates towards self-reliance and employers of labor after graduation, it becomes pertinent to empirically examine and predict the undergraduate’s entrepreneurial intentions at graduation. Thus, this study primarily examines the undergraduates entrepreneurial intentions using TPB, which includes perceived desirability, perceived social norm, and perceived feasibility factors. In so doing, a questionnaire research method was adopted in which 219 copies of a questionnaire distributed to final year undergraduates were belonging to five departments with a total population of 487 students. A combination of relative frequency, mean standard deviation and multiple regression statistical tools were employed for data analysis. The study found that TPB components exert a significant composite effect on undergraduate’s entrepreneurial intentions. Based on individual contribution of the independent variables, Perceived Desirability is the strongest predictor of the undergraduate’s entrepreneurial intentions, while Perceived Social Norm is a strong predictor of the undergraduate’s entrepreneurial intentions. However, Perceived Feasibility is not a strong predictor of student’s entrepreneurial intentions. The study therefore, recommends that the Perceived desirability, which is formed and shaped by ones level of education and skills acquisition, be improved upon to create the expected positive impact on graduates entrepreneurial intentions and possible venture creation.

Keywords: entrepreneurship, entrepreneurship education, entrepreneurial intentions, planned behaviour, prediction, Nigeria

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Authors: P. Byrnes, F. A. DiazDelaO

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The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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Authors: Hee-Chang Lim

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The study aims to understand the surface pressure distribution around the bodies such as the suction pressure in the leading edge on the top and side-face when the aspect ratio of bodies and the wind direction are changed, respectively. We carried out the wind tunnel measurement and numerical simulation around a series of rectangular bodies (40^d×80^w×80^h, 80^d×80^w×80^h, 160^d×80^w×80^h, 80^d×40^w×80^h and 80^d×160^w×80^h in mm³) placed in a deep turbulent boundary layer. Based on a modern numerical platform, the Navier-Stokes equation with the typical 2-equation (k-ε model) and the DES (Detached Eddy Simulation) turbulence model has been calculated, and they are both compared with the measurement data. Regarding the turbulence model, the DES model makes a better prediction comparing with the k-ε model, especially when calculating the separated turbulent flow around a bluff body with sharp edged corner. In order to observe the effect of wind direction on the pressure variation around the cube (e.g., 80^d×80^w×80^h in mm), it rotates at 0º, 10º, 20º, 30º, and 45º, which stands for the salient wind directions in the tunnel. The result shows that the surface pressure variation is highly dependent upon the approaching wind direction, especially on the top and the side-face of the cube. In addition, the transverse width has a substantial effect on the variation of surface pressure around the bodies, while the longitudinal length has little or no influence.

Keywords: rectangular bodies, wind direction, aspect ratio, surface pressure distribution, wind-tunnel measurement, k-ε model, DES model, CFD

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Authors: Hyunsik Park

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As the rate of cerebrovascular disease increases in old populations, the prevalence rate of vascular dementia would be expected. Therefore, authors designed this study to find out the possibility of somatosensory evoked potentials(SSEP) as a parameter for early diagnosis and prognosis prediction of vascular dementia in cortical vascular dementia patients. 21 patients who met the criteria for vascular dementia according to DSM-IV,ICD-10and NINDS-AIREN with the history of recent cognitive impairment, fluctuation progression, and neurologic deficit. We subdivided these patients into two groups; a mild dementia and a severe dementia groups by MMSE and CDR score; and analysed comparison between normal control group and patient control group who have been cerebrovascular attack(CVA) history without dementia by using N20 latency and amplitude of median nerve. In this study, mild dementia group showed significant differences on latency and amplitude with normal control group(p-value<0.05) except patient control group(p-value>0.05). Severe dementia group showed significant differences both normal control group and patient control group.(p-value<0.05, <001). Since no significant difference has founded between mild dementia group and patient control group, SSEP has limitation to use for early diagnosis test. However, the comparison between severe dementia group and others showed significant results which indicate SSEP can predict the prognosis of vascular dementia in cortical vascular dementia patients.

Keywords: SSEP, cortical vascular dementia, N20 latency, N20 amplitude

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Authors: Halefom Kidane

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This study aims to assess the wind resource potential and characterize the urban area wind patterns in Hawassa City, Ethiopia. The estimation and characterization of wind resources are crucial for sustainable urban planning, renewable energy development, and climate change mitigation strategies. A secondary data collection method was used to carry out the study. The collected data at 2 meters was analyzed statistically and extrapolated to the standard heights of 10-meter and 30-meter heights using the power law equation. The standard deviation method was used to calculate the value of scale and shape factors. From the analysis presented, the maximum and minimum mean daily wind speed at 2 meters in 2016 was 1.33 m/s and 0.05 m/s in 2017, 1.67 m/s and 0.14 m/s in 2018, 1.61m and 0.07 m/s, respectively. The maximum monthly average wind speed of Hawassa City in 2016 at 2 meters was noticed in the month of December, which is around 0.78 m/s, while in 2017, the maximum wind speed was recorded in the month of January with a wind speed magnitude of 0.80 m/s and in 2018 June was maximum speed which is 0.76 m/s. On the other hand, October was the month with the minimum mean wind speed in all years, with a value of 0.47 m/s in 2016,0.47 in 2017 and 0.34 in 2018. The annual mean wind speed was 0.61 m/s in 2016,0.64, m/s in 2017 and 0.57 m/s in 2018 at a height of 2 meters. From extrapolation, the annual mean wind speeds for the years 2016,2017 and 2018 at 10 heights were 1.17 m/s,1.22 m/s, and 1.11 m/s, and at the height of 30 meters, were 3.34m/s,3.78 m/s, and 3.01 m/s respectively/Thus, the site consists mainly primarily classes-I of wind speed even at the extrapolated heights.

Keywords: artificial neural networks, forecasting, min-max normalization, wind speed

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Authors: Sabyasachi Biswas, Indrajit Ghosh

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The random and heterogeneous behavior of vehicles in India puts up a greater challenge for researchers. Stop-and-go modeling at signalized intersections under heterogeneous traffic conditions has remained one of the most sought-after fields. Vehicles are often caught up in the dilemma zone and are unable to take quick decisions whether to stop or cross the intersection. This hampers the traffic movement and may lead to accidents. The purpose of this work is to develop a stop and go prediction model that depicts the drivers’ decision during the yellow time at signalised intersections. To accomplish this, certain traffic parameters were taken into account to develop surrogate model. This research investigated the Stop and Go behavior of the drivers by collecting data from 4-signalized intersections located in two major Indian cities. Model was developed to predict the drivers’ decision making during the yellow phase of the traffic signal. The parameters used for modeling included distance to stop line, time to stop line, speed, and length of the vehicle. A Kriging base surrogate model has been developed to investigate the drivers’ decision-making behavior in amber phase. It is observed that the proposed approach yields a highly accurate result (97.4 percent) by Gaussian function. It was observed that the accuracy for the crossing probability was 95.45, 90.9 and 86.36.11 percent respectively as predicted by the Kriging models with Gaussian, Exponential and Linear functions.

Keywords: decision-making decision, dilemma zone, surrogate model, Kriging

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Authors: Ghada Ben Brahim, Aicha El Rabia, Mohamed Hedi Inoubli

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Climate change has exacerbated disparities in the distribution of water resources in Tunisia, resulting in significant degradation in quantity and quality over the past five decades. The Mio-Plio-Quaternary aquifer, the primary water source in the Zarzis region, is subject to climatic, geographical, and geological challenges, as well as human stress. The region is experiencing uneven distribution and growing threats from groundwater salinity and saltwater intrusion. Addressing this challenge is critical for the arid region’s socioeconomic development, and effective water resource management is required to combat climate change and reduce water deficits. This study uses a multidisciplinary approach to determine the groundwater potential of this aquifer, involving geophysics and hydrogeology data analysis. We used advanced techniques such as 3D Euler deconvolution and power spectrum analysis to generate detailed anomaly maps and estimate the depths of density sources, identifying significant Bouguer anomalies trending E-W, NW-SE, and NE-SW. Various techniques, such as wavelength filtering, upward continuation, and horizontal and vertical derivatives, were used to improve the gravity data, resulting in consistent results for anomaly shapes and amplitudes. The Euler deconvolution method revealed two prominent surface faults, trending NE-SW and NW-SE, that have a significant impact on the distribution of sedimentary facies and water quality within the Mio-Plio-Quaternary aquifer. Additionally, depth maxima greater than 1400 m to the North indicate the presence of a Cretaceous paleo-fault. Geoelectrical models and resistivity pseudo-sections were used to interpret the distribution of electrical facies in the Mio-Plio-Quaternary aquifer, highlighting lateral variation and depositional environment type. AI optimises the analysis and interpretation of exploration data, which is important to long-term management and water security. Machine learning algorithms and deep learning models analyse large datasets to provide precise interpretations of subsurface conditions, such as aquifer salinisation. However, AI has limitations, such as the requirement for large datasets, the risk of overfitting, and integration issues with traditional geological methods.

Keywords: mio-plio-quaternary aquifer, Southeastern Tunisia, geophysical methods, hydrogeological analysis, artificial intelligence

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Authors: Zakiullah Kalwar, Shabeer Abbassi

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This research encompasses the study of structural styles and evaluation of the hydrocarbon potential of Kotrum and Drigri anticlines located in Rajanpur Area, Midddle Indus Basin of Pakistan with the approach of geophysical data integration. The study area is situated between the Sulaiman Foldbelt on the west and Indus River in the east. It is an anticlinal fold, located to the southeast of Sakhi Sarwar anticline and separated from a prominent syncline. The structure has a narrow elongated crest, with the axis running in SSW-NNE direction. In the east, the structure is bounded by a gentle syncline. Structural Styles are trending East-West and perpendicular to tectonic transport and stress direction and the base of the structures gradually dipping Eastward beneath the deformation frontal part in Eastern Sulaiman Fold Belt. Middle Indus Basin can be divided into Foreland, Sulaiman fold belt and a broad foredeep. Sulaiman represents a blind thrust front, which suggests that all frontal folds of the fold belt are cored by blind thrust. The deformation of frontal part of Sulaiman Lobe represents the passive roof duplex stacked beneath the frontal passive roof thrust. The passive roof thrust, which has a back thrust sense of motion and extends into the interior of Fold belt. Left lateral Kingri Fault separates Eastern and Central Sulaiman fold belt. In Central Sulaiman fold belt the deformation front moved further towards fore deep as compared to Eastern Sulaiman. Two wells (Kotrum-01, Drigri-01) have been drilled in the study area with the objective to determine the potential of oil and gas in Habib Rahi Limestone of Eocene age, Dunghan Limestone of Paleocene age and Pab Sandstone of cretaceous age and role of structural styles in hydrocarbon potential of study area. Kotrum-01 well was drilled to its T.D of 4798m. Besides fishing and side tracking, tight whole conditions, high pressure, and losses of circulation were also encountered. During production, testing Pab sandstone were tested but abandoned found. Drigri-01 well was drilled to its T.D 3250 m. RFT was carried out at different points, but all points showed no pressure / seal failure and the well was plugged and declared abandoned.

Keywords: hydrocarbon potential, structural style, reserve calculation, enhance production

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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: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

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Authors: Ali Akyar, Osman Serdar Gedik

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Depth estimation is a challenging computer vision task that involves estimating the distance between objects in a scene and the camera. It predicts how far each pixel in the 2D image is from the capturing point. There are some important Monocular Depth Estimation (MDE) studies that are based on Vision Transformers (ViT). We benchmark three major studies. The first work aims to build a simple and powerful foundation model that deals with any images under any condition. The second work proposes a method by mixing multiple datasets during training and a robust training objective. The third work combines generalization performance and state-of-the-art results on specific datasets. Although there are studies with thermal images too, we wanted to benchmark these three non-thermal, state-of-the-art studies with a hybrid image dataset which is taken by Multi-Spectral Dynamic Imaging (MSX) technology. MSX technology produces detailed thermal images by bringing together the thermal and visual spectrums. Using this technology, our dataset images are not blur and poorly detailed as the normal thermal images. On the other hand, they are not taken at the perfect light conditions as RGB images. We compared three methods under test with our thermal dataset which was not done before. Additionally, we propose an image enhancement deep learning model for thermal data. This model helps extract the features required for monocular depth estimation. The experimental results demonstrate that, after using our proposed model, the performance of these three methods under test increased significantly for thermal image depth prediction.

Keywords: monocular depth estimation, thermal dataset, benchmarking, vision transformers

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Authors: Aya Effat, Ossama A. Hosny, Elkhayam M. Dorra

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Estimating costs is a crucial step in construction management and should be completed at the beginning of every project to establish the project's budget. The precision of the cost estimate plays a significant role in the success of construction projects as it allows project managers to effectively manage the project's costs. Site overhead costs constitute a significant portion of construction project budgets, necessitating accurate prediction and management. These costs are influenced by a multitude of factors, requiring a thorough examination and analysis to understand their relative importance and impact. Thus, the main aim of this research is to enhance the contractor’s ability to predict and manage site overheads by identifying and analyzing the main factors influencing the site overheads costs in the Egyptian construction industry. Through a comprehensive literature review, key factors were first identified and subsequently validated using a thorough comparative analysis of data from 55 real-life construction projects. Through this comparative analysis, the relationship between each factor and site overheads percentage as well as each site overheads subcategory and each project construction phase was identified and examined. Furthermore, correlation analysis was done to check for multicollinearity and identify factors with the highest impact. The findings of this research offer valuable insights into the key drivers of site overhead costs in the Egyptian construction industry. By understanding these factors, construction professionals can make informed decisions regarding the estimation and management of site overhead costs.

Keywords: comparative analysis, cost estimation, construction management, site overheads

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Authors: Mohd Khairezan Rahmat

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Since the impetus of becoming a develop country by the year 2020, the Malaysian government have been proactive in strengthening the integration of ICT into the national educational system. Teacher-education programs have the responsibility to prepare the nation future teachers by instilling in them the desire, confidence, and ability to fully utilized the potential of ICT into their instruction process. In an effort to fulfill this responsibility, teacher-education program are beginning to create alternatives means for preparing cutting-edge teachers. One of the alternatives is the student’s learning portal. In line with this mission, this study investigates the Faculty of Education, University Teknologi MARA (UiTM) pre-service teachers’ perception of usefulness, attitude, and ability toward the usage of the university learning portal, known as iLearn. The study also aimed to predict factors that might hinder the pre-service teachers’ decision to used iLearn as their platform in learning. The Structural Equation Modeling (SEM), was employed in analyzed the survey data. The suggested findings informed that pre-service teacher’s successful integration of the iLearn was highly influenced by their perception of usefulness of the system. The findings also suggested that the more familiar the pre-service teacher with the iLearn, the more possibility they will use the system. In light of similar study, the present findings hope to highlight the important to understand the user’s perception toward any proposed technology.

Keywords: e-learning, prediction factors, pre-service teacher, structural equation modeling (SEM)

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Authors: Amir Alani, Mojtaba Mahmoodian, Anna Romanova, Asaad Faramarzi

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Pipelines are extensively used engineering structures which convey fluid from one place to another. Most of the time, pipelines are placed underground and are encumbered by soil weight and traffic loads. Corrosion of pipe material is the most common form of pipeline deterioration and should be considered in both the strength and serviceability analysis of pipes. The study in this research focuses on concrete pipes in sewage systems (concrete sewers). This research firstly investigates how to involve the effect of corrosion as a time dependent process of deterioration in the structural and failure analysis of this type of pipe. Then three probabilistic time dependent reliability analysis methods including the first passage probability theory, the gamma distributed degradation model and the Monte Carlo simulation technique are discussed and developed. Sensitivity analysis indexes which can be used to identify the most important parameters that affect pipe failure are also discussed. The reliability analysis methods developed in this paper contribute as rational tools for decision makers with regard to the strengthening and rehabilitation of existing pipelines. The results can be used to obtain a cost-effective strategy for the management of the sewer system.

Keywords: reliability analysis, service life prediction, Monte Carlo simulation method, first passage probability theory, gamma distributed degradation model

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Authors: Majid Farsadrooh, Mehran Feizi-Dehnayebi

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Serum albumin is the most abundant protein that is present in the circulatory system of a wide variety of organisms. Although it is a significant macromolecule, it can contribute to osmotic blood pressure and also, plays a superior role in drug disposition and efficiency. Molecular docking simulation can improve in silico drug design and discovery procedures to propound a lead compound and develop it from the discovery step to the clinic. In this study, the molecular docking simulation was applied to select a lead molecule through an investigation of the interaction of the two anticancer drugs (Alitretinoin and Abemaciclib) with Human Serum Albumin (HSA). Then, a series of new compounds (a-e) were suggested using lead molecule modification. Density functional theory (DFT) including MEP map and HOMO-LUMO analysis were used for the newly proposed compounds to predict the reactivity zones on the molecules, stability, and chemical reactivity. DFT calculation illustrated that these new compounds were stable. The estimated binding free energy (ΔG) values for a-e compounds were obtained as -5.78, -5.81, -5.95, -5,98, and -6.11 kcal/mol, respectively. Finally, the pharmaceutical properties and toxicity of these new compounds were estimated through OSIRIS DataWarrior software. The results indicated no risk of tumorigenic, irritant, or reproductive effects and mutagenicity for compounds d and e. As a result, compounds d and e, could be selected for further study as potential therapeutic candidates. Moreover, employing molecular docking simulation with the prediction of pharmaceutical properties helps to discover new potential drug compounds.

Keywords: drug design, anticancer, computational studies, DFT analysis

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Authors: Naresh Reddy Kolanu

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The stability and collapse behavior of thin-walled composite structures, particularly carbon fiber-reinforced plastic (CFRP) panels, are paramount concerns for structural designers. Accurate prediction of collapse loads necessitates precise modeling of damage evolution in the post-buckling regime. This study conducts a comparative assessment of various finite element (FE) methodologies employed in predicting post-buckling collapse in stiffened CFRP panels. A systematic approach is adopted, wherein FE models with various damage capabilities are constructed and analyzed. The study investigates the influence of interacting intra- and interlaminar damage modes on the post-buckling response and failure behavior of the stiffened CFRP structure. Additionally, the capabilities of shell and brick FE-based models are evaluated and compared to determine their effectiveness in capturing the complex collapse behavior. Conclusions are drawn through quantitative comparison with experimental results, focusing on post-buckling response and collapse load. This comprehensive evaluation provides insights into the most effective FE methodologies for accurately predicting the collapse behavior of stiffened CFRP panels, thereby aiding structural designers in enhancing the stability and safety of composite structures.

Keywords: CFRP stiffened panels, delamination, Hashin’s failure, post-buckling, progressive damage model

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Authors: Raju Ranjha, Surbhi Aggarwal

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Background: Inflammation plays an important role in infectious and non-infectious diseases. MiRNA is also reported to play role in inflammation and associated cancers. Chemokine CXCL12 is also known to play role in inflammation and various cancers. CXCL12/CXCR4 chemokine axis was involved in pathogenesis of IBD specially UC. Supplementation of CXCL12 induces homing of dendritic cells to spleen and enhances control of plasmodium parasite in BALB/c mice. We looked at the regulation of CXCL12β by miRNA in UC colitis. Prolonged inflammation of colon in UC patient increases the risk of developing colorectal cancer. We looked at the expression differences of CXCl12β and its targeting miRNA in cancer susceptible area of colon of UC patients. Aim: Aim of this study was to find out the expression regulation of CXCL12β by miRNA in inflammation. Materials and Methods: Biopsy samples and blood samples were collected from UC patients and non-IBD controls. mRNA expression was analyzed using microarray and real-time PCR. CXCL12β targeting miRNA were looked by using online target prediction tools. Expression of CXCL12β in blood samples and cell line supernatant was analyzed using ELISA. miRNA target was validated using dual luciferase assay. Results and conclusion: We found miR-200a regulate the expression of CXCL12β in UC. Expression of CXCL12β was increased in cancer susceptible part of colon and expression of its targeting miRNA was decreased in the same part of colon. miR-200a regulate CXCL12β expression in inflammation and may be an important therapeutic target in inflammation associated cancer.

Keywords: inflammation, miRNA, regulation, CXCL12

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Authors: Leonard D. Agana, Wendell Ace Dela Cruz, Arjan C. Lingaya, Bonifacio T. Doma Jr.

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The purpose of this paper is to study the predict the fuel performance parameters, which include drivability index (DI), vapor lock index (VLI), and vapor lock potential using distillation curve and Reid vapor pressure (RVP) of dual alcohol-gasoline fuel blends. Distillation curve and Reid vapor pressure were predicted using artificial neural networks (ANN) with macroscopic properties such as boiling points, RVP, and molecular weights as the input layers. The ANN consists of 5 hidden layers and was trained using Bayesian regularization. The training mean square error (MSE) and R-value for the ANN of RVP are 91.4113 and 0.9151, respectively, while the training MSE and R-value for the distillation curve are 33.4867 and 0.9927. Fuel performance analysis of the dual alcohol–gasoline blends indicated that highly volatile gasoline blended with dual alcohols results in non-compliant fuel blends with D4814 standard. Mixtures of low-volatile gasoline and 10% methanol or 10% ethanol can still be blended with up to 10% C3 and C4 alcohols. Intermediate volatile gasoline containing 10% methanol or 10% ethanol can still be blended with C3 and C4 alcohols that have low RVPs, such as 1-propanol, 1-butanol, 2-butanol, and i-butanol. Biography: Graduate School of Chemical, Biological, and Materials Engineering and Sciences, Mapua University, Muralla St., Intramuros, Manila, 1002, Philippines

Keywords: dual alcohol-gasoline blends, distillation curve, machine learning, reid vapor pressure

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Authors: Jia Zhang, Fengmei Yao, Yanjing Tan

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The accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C4 crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS-P-YEC (Remote-Sensing-Photosynthesis-Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS-P-YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002-2011. The statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (P < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002-2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale.

Keywords: process-based model, C4 crop, maize yield, remote sensing, Northeast China Plain

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Authors: Allen Yushark Fosu, Ndue Kanari, James Vaughan, Alexandre Changes

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Spodumene is a lithium-bearing mineral of great interest due to increasing demand of lithium in emerging electric and hybrid vehicles. The conventional method of processing the mineral for the metal requires inevitable thermal transformation of α-phase to the β-phase followed by roasting with suitable reagents to produce lithium salts for downstream processes. The selection of appropriate reagent for roasting is key for the success of the process and overall lithium recovery. Several researches have been conducted to identify good reagents for the process efficiency, leading to sulfation, alkaline, chlorination, fluorination, and carbonizing as the methods of lithium recovery from the mineral.HSC Chemistry is a thermochemical software that can be used to model metallurgical process feasibility and predict possible reaction products prior to experimental investigation. The software was employed to investigate and explain the various reagent characteristics as employed in literature during spodumene roasting up to 1200°C. The simulation indicated that all used reagents for sulfation and alkaline were feasible in the direction of lithium salt production. Chlorination was only feasible when Cl2 and CaCl2 were used as chlorination agents but not NaCl nor KCl. Depending on the kind of lithium salt formed during carbonizing and fluorination, the process was either spontaneous or nonspontaneous throughout the temperature range investigated. The HSC software was further used to simulate and predict some promising reagents which may be equally good for roasting the mineral for efficient lithium extraction but have not yet been considered by researchers.

Keywords: thermochemical modelling, HSC chemistry software, lithium, spodumene, roasting

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Authors: Rezvan Ravanfar Haghighi, S. Chatterjee, Pratik Kumar, V. C. Vani, Priya Jagia, Sanjiv Sharma, Susama Rani Mandal, R. Lakshmy

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The dual energy computed tomography (DECT) aims at noting the HU(V) values for the sample at two different voltages V=V1, V2 and thus obtain the electron densities (ρe) and effective atomic number (Zeff) of the substance. In the present paper, we aim to obtain a numerical algorithm by which (ρe, Zeff) can be obtained from the HU(100) and HU(140) data, where V=100, 140 kVp. The idea is to use this inversion method to characterize and distinguish between the lipid and fibrous coronary artery plaques.With the idea to develop the inversion algorithm for low Zeff materials, as is the case with non calcified coronary artery plaque, we prepare aqueous samples whose calculated values of (ρe, Zeff) lie in the range (2.65×1023≤ ρe≤ 3.64×1023 per cc ) and (6.80≤ Zeff ≤ 8.90). We fill the phantom with these known samples and experimentally determine HU(100) and HU(140) for the same pixels. Knowing that the HU(V) values are related to the attenuation coefficient of the system, we present an algorithm by which the (ρe, Zeff) is calibrated with respect to (HU(100), HU(140)). The calibration is done with a known set of 20 samples; its accuracy is checked with a different set of 23 known samples. We find that the calibration gives the ρe with an accuracy of ± 4% while Zeff is found within ±1% of the actual value, the confidence being 95%.In this inversion method (ρe, Zeff) of the scanned sample can be found by eliminating the effects of the CT machine and also by ensuring that the determination of the two unknowns (ρe, Zeff) does not interfere with each other. It is found that this algorithm can be used for prediction of chemical characteristic (ρe, Zeff) of unknown scanned materials with 95% confidence level, by inversion of the DECT data.

Keywords: chemical composition, dual-energy computed tomography, inversion algorithm

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Authors: Pattra Pukdeekiat, Siriluk Ruangrungrote

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This work was originated to precisely determine direct solar radiation by using atmospheric physics models since the accurate prediction of solar radiation is necessary and useful for solar energy applications including atmospheric research. The possible models and techniques for a calculation of regional direct solar radiation were challenging and compulsory for the case of unavailable instrumental measurement. The investigation was mathematically governed by six astronomical parameters i.e. declination (δ), hour angle (ω), solar time, solar zenith angle (θz), extraterrestrial radiation (Iso) and eccentricity (E0) along with two atmospheric parameters i.e. air mass (mr) and dew point temperature at Bangna meteorological station (13.67° N, 100.61° E) in Bangkok, Thailand. Analyses of five models of solar radiation determination with the assumption of clear sky were applied accompanied by three statistical tests: Mean Bias Difference (MBD), Root Mean Square Difference (RMSD) and Coefficient of determination (R2) in order to validate the accuracy of obtainable results. The calculated direct solar radiation was in a range of 491-505 Watt/m2 with relative percentage error 8.41% for winter and 532-540 Watt/m2 with relative percentage error 4.89% for summer 2014. Additionally, dataset of seven continuous days, representing both seasons were considered with the MBD, RMSD and R2 of -0.08, 0.25, 0.86 and -0.14, 0.35, 3.29, respectively, which belong to Kumar model for winter and CSR model for summer. In summary, the determination of direct solar radiation based on atmospheric models and empirical equations could advantageously provide immediate and reliable values of the solar components for any site in the region without a constraint of actual measurement.

Keywords: atmospheric physics models, astronomical parameters, atmospheric parameters, clear sky condition

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Authors: João Filipe Papel, Tatsuji Munaka

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With society's aging and the number of elderly with dementia rising, researchers have been actively studying how to support the elderly in the early stages of dementia with the objective of allowing them to have a better life quality and as much as possible independence. To make this possible, most researchers in this field are using the Internet Of Things to monitor the elderly activities and assist them in performing them. The most common sensor used to monitor the elderly activities is the Camera sensor due to its easy installation and configuration. The other commonly used sensor is the sound sensor. However, we need to consider privacy when using these sensors. This research aims to develop a system capable of predicting the early stages of dementia based on monitoring and controlling the elderly activities of daily living. To make this system possible, some issues need to be addressed. First, the issue related to elderly privacy when trying to detect their Activities of Daily Living. Privacy when performing detection and monitoring Activities of Daily Living it's a serious concern. One of the purposes of this research is to achieve this detection and monitoring without putting the privacy of the elderly at risk. To make this possible, the study focuses on using an approach based on using Magnet Sensors to collect binary data. The second is to use the data collected by monitoring Activities of Daily Living to predict the early stages of Dementia. To make this possible, the research team suggests developing a proprietary ontology combined with both data-driven and knowledge-driven.

Keywords: dementia, activity recognition, magnet sensors, ontology, data driven and knowledge driven, IoT, activities of daily living

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Benkouiten Imene, Chabli Ouerdia, Boutoutaou Hamid, Kadri Nesrine, Bouledroua Omar

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Algeria is actively enhancing gas productivity by augmenting the supply flow. However, this effort has led to increased internal pressure, posing a potential risk to the pipeline's integrity, particularly in the presence of corrosion defects. Sonatrach relies on a vast network of pipelines spanning 24,000 kilometers for the transportation of gas and oil. The aging of these pipelines raises the likelihood of corrosion both internally and externally, heightening the risk of ruptures. To address this issue, a comprehensive inspection is imperative, utilizing specialized scraping tools. These advanced tools furnish a detailed assessment of all pipeline defects. It is essential to recalculate the pressure parameters to safeguard the corroded pipeline's integrity while ensuring the continuity of production. In this context, Sonatrach employs symbolic pressure limit calculations, such as ASME B31G (2009) and the modified ASME B31G (2012). The aim of this study is to perform a comparative analysis of various limit pressure calculation methods documented in the literature, namely DNV RP F-101, SHELL, P-CORRC, NETTO, and CSA Z662. This comparative assessment will be based on a dataset comprising 329 burst tests published in the literature. Ultimately, we intend to introduce a novel approach grounded in the finite element method, employing ANSYS software.

Keywords: pipeline burst pressure, burst test, corrosion defect, corroded pipeline, finite element method

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Authors: Salem Alsanusi, Loubna Bentaher

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Response Surface Methods (RSM) provide statistically validated predictive models that can then be manipulated for finding optimal process configurations. Variation transmitted to responses from poorly controlled process factors can be accounted for by the mathematical technique of propagation of error (POE), which facilitates ‘finding the flats’ on the surfaces generated by RSM. The dual response approach to RSM captures the standard deviation of the output as well as the average. It accounts for unknown sources of variation. Dual response plus propagation of error (POE) provides a more useful model of overall response variation. In our case, we implemented this technique in predicting compressive strength of concrete of 28 days in age. Since 28 days is quite time consuming, while it is important to ensure the quality control process. This paper investigates the potential of using design of experiments (DOE-RSM) to predict the compressive strength of concrete at 28th day. Data used for this study was carried out from experiment schemes at university of Benghazi, civil engineering department. A total of 114 sets of data were implemented. ACI mix design method was utilized for the mix design. No admixtures were used, only the main concrete mix constituents such as cement, coarse-aggregate, fine aggregate and water were utilized in all mixes. Different mix proportions of the ingredients and different water cement ratio were used. The proposed mathematical models are capable of predicting the required concrete compressive strength of concrete from early ages.

Keywords: mix proportioning, response surface methodology, compressive strength, optimal design

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Authors: Alireza Monzavi Chaleshtori, Mahnaz Aliakbari Dehkordi, Maryam Aliakbari, Solmaz Seyed Mostafaii

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Objective: Considering the growing obesity among children and the role of mother's psychological factors as well as the need to prevent childhood obesity, this study aimed to investigate the mediating role of mother's self-efficacy in the relationship between mother's stress and depression and child obesity. Method: For this purpose, in a descriptive-correlation study, 222 mothers and children aged 1 to 5 years in Tehran, who had the opportunity to answer an online questionnaire, were selected by random sampling and to the depression scales of the Kroenke and Spitzer Patient Health Questionnaire, Cohen's stress and Self-efficacy of Berkeley mothers answered. Pearson correlation test and path analysis were used for data analysis. Findings: The findings showed that maternal depression had an indirect and significant effect on child obesity, and the effect of stress and depression on child obesity was indirect and non-significant. Therefore, the model has a good fit with the research data, and stress and depression indirectly predicted child obesity with the mediating role of self-efficacy. Conclusion: The hypothesized model tested based on mother's stress and depression with the mediating role of mother's self-efficacy was a good model in explaining the prediction of child obesity. Based on the findings of this research, a practical framework can be provided to explain the psychological factors of the mother in relation to child obesity and its treatment.

Keywords: stress, self-efficacy, child obesity, depression

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Authors: Hatim Laalej, Salvador Sumohano-Verdeja, Thomas McLeay

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Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.

Keywords: machining, milling operation, tool condition monitoring, tool wear prediction

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Authors: Kolade Olawande Owoeye, George Weir

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Terrorism and extremism activities on the internet are becoming the most significant threats to national security because of their potential dangers. In response to this challenge, law enforcement and security authorities are actively implementing comprehensive measures by countering the use of the internet for terrorism. To achieve the measures, there is need for intelligence gathering via the internet. This includes real-time monitoring of potential websites that are used for recruitment and information dissemination among other operations by extremist groups. However, with billions of active webpages, real-time monitoring of all webpages become almost impossible. To narrow down the search domain, there is a need for efficient webpage classification techniques. This research proposed a new approach tagged: SentiPosit-based method. SentiPosit-based method combines features of the Posit-based method and the Sentistrenght-based method for classification of terrorism and extremism webpages. The experiment was carried out on 7500 webpages obtained through TENE-webcrawler by International Cyber Crime Research Centre (ICCRC). The webpages were manually grouped into three classes which include the ‘pro-extremist’, ‘anti-extremist’ and ‘neutral’ with 2500 webpages in each category. A supervised learning algorithm is then applied on the classified dataset in order to build the model. Results obtained was compared with existing classification method using the prediction accuracy and runtime. It was observed that our proposed hybrid approach produced a better classification accuracy compared to existing approaches within a reasonable runtime.

Keywords: sentiposit, classification, extremism, terrorism

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Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

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Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

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