Search results for: predictive coding

Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

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Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Ahmad Shahin, Walid Moudani, Ali Bekraki

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This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources.

Keywords: healthcare management, acute appendicitis, data mining, classification, decision tree

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Authors: Ying Wang, Jianhong Xiang, Yu Zhong

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The offset Quadrature Amplitude Modulation-based Filter Bank Multi-Carrier (FBMC) system provides superior spectral properties over Orthogonal Frequency Division Multiplexing. However, seriously affected by imaginary interference, its performances are hampered in many areas. In this paper, we propose a Precoding-Assisted Frequency Division Multiple Access (PA-FDMA) modulation scheme. By spreading FBMC symbols into the frequency domain and transmitting them with a precoding matrix, the impact of imaginary interference can be eliminated. Specifically, we first generate the coding pre-solution matrix with a nonuniform Fast Fourier Transform and pick the best columns by introducing auxiliary factors. Secondly, according to the column indexes, we obtain the precoding matrix for one symbol and impose scaling factors to ensure that the power is approximately constant throughout the transmission time. Finally, we map the precoding matrix of one symbol to multiple symbols and transmit multiple data frames, thus achieving frequency-division multiple access. Additionally, observing the interference between adjacent frames, we mitigate them by adding frequency Cyclic Prefixes (CP) and evaluating them with a signal-to-interference ratio. Note that PA-FDMA can be considered a CP-available FBMC technique because the underlying strategy is FBMC. Simulation results show that the proposed scheme has better performance compared to Single Carrier Frequency Division Multiple Access (SC-FDMA), etc.

Keywords: PA-FDMA, SC-FDMA, FBMC, non-uniform fast fourier transform

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Authors: Tie-Qing Zhang, Seunghun Jung, Young-Bae Kim

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This research is devoted to developing a membrane reactor to flexibly meet the hydrogen demand of onboard fuel cells, which is an important part of green energy development. Among many renewable chemical products, dimethyl ether (DME) has the advantages of low reaction temperature (400 °C in this study), high hydrogen atom content, low toxicity, and easy preparation. Autothermal reforming, on the other hand, has a high hydrogen recovery rate and exhibits thermal neutrality during the reaction process, so the additional heat source in the hydrogen production process can be omitted. Therefore, the DME auto thermal reforming process was adopted in this study. To control the temperature of the reaction catalyst bed and hydrogen production rate, a Model Predictive Control (MPC) scheme was designed. Taking the above two variables as the control objectives, stable operation of the reformer can be achieved by controlling the flow rates of DME, steam, and high-purity air in real-time. To prevent catalyst poisoning in the fuel cell, the hydrogen needs to be purified to reduce the carbon monoxide content to below 50 ppm. Therefore, a Pd-Ag hydrogen semi-permeable membrane with a thickness of 3-5 μm was inserted into the auto thermal reactor, and the permeation efficiency of hydrogen was improved by steam purging on the permeation side. Finally, hydrogen with a purity of 99.99 was obtained.

Keywords: hydrogen production, auto thermal reforming, membrane, fuel cell

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Authors: Cromwell F. Gopo, Joy L. Picar

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This study aimed to predict the employability of senior high school graduates for S.Y. 2018- 2019 in the Davao del Norte Division through quantitative research design using the descriptive status and predictive approaches among the indicated parameters, namely gender, school type, academics, academic award recipient, skills, values, and strand. The respondents of the study were the 33 secondary schools offering senior high school programs identified through simple random sampling, which resulted in 1,530 cases of graduates’ secondary data, which were analyzed using frequency, percentage, mean, standard deviation, and binary logistic regression. Results showed that the majority of the senior high school graduates who come from large schools were females. Further, less than half of these graduates received any academic award in any semester. In general, the graduates’ performance in academics, skills, and values were proficient. Moreover, less than half of the graduates were not employed. Then, those who were employed were either contractual, casual, or part-time workers dominated by GAS graduates. Further, the predictors of employability were gender and the Information and Communications Technology (ICT) strand, while the remaining variables did not add significantly to the model. The null hypothesis had been rejected as the coefficients of the predictors in the binary logistic regression equation did not take the value of 0. After utilizing the model, it was concluded that Technical-Vocational-Livelihood (TVL) graduates except ICT had greater estimates of employability.

Keywords: employability, senior high school graduates, Davao del Norte, Philippines

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Authors: Christian Kapuku, Seung-Young Kho

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An accurate representation of the transportation system serving the region is one of the important aspects of transportation modeling. Such representation often requires developing an abstract model of the system elements, which also requires important amount of data, surveys and time. However, in some cases such as in developing countries, data deficiencies, time and budget constraints do not always allow such accurate representation, leaving opportunities to assumptions that may negatively affect the quality of the analysis. With the emergence of Internet open source data especially in the mapping technologies as well as the advances in Geography Information System, opportunities to tackle these issues have raised. Therefore, the objective of this paper is to demonstrate such application through a practical case of the development of the transportation network for the city of Kinshasa. The GIS geo-referencing was used to construct the digitized map of Transportation Analysis Zones using available scanned images. Centroids were then dynamically placed at the center of activities using an activities density map. Next, the road network with its characteristics was built using OpenStreet data and other official road inventory data by intersecting their layers and cleaning up unnecessary links such as residential streets. The accuracy of the final network was then checked, comparing it with satellite images from Google and Bing. For the validation, the final network was exported into Emme3 to check for potential network coding issues. Results show a high accuracy between the built network and satellite images, which can mostly be attributed to the use of open source data.

Keywords: geographic information system (GIS), network construction, transportation database, open source data

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Authors: Dina Tareq Ismail, Alexandria A. Proff

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The United Arab Emirates (UAE) is a nation seeking to advance in all fields, particularly education. One area of focus for UAE 2021 agenda is to restructure UAE schools and universities by equipping them with highly developed technology. The agenda also advises educational institutions to prepare students with applicable and transferrable Information and Communication Technology (ICT) skills. Despite the emphasis on ICT and computer literacy skills, there exists limited empirical data on the use of M-Learning in the literature. This qualitative study explores the motivation of higher primary Arabic teachers in private schools toward implementing and integrating M-Learning apps in their classrooms. This research employs a phenomenological approach through the use of semistructured interviews with nine purposefully selected Arabic teachers. The data were analyzed using a content analysis via multiple stages of coding: open, axial, and thematic. Findings reveal three primary themes: (1) Arabic teachers with high levels of procedural knowledge in ICT are more motivated to implement M-Learning; (2) Arabic teachers' perceptions of self-efficacy influence their motivation toward implementation of M-Learning; (3) Arabic teachers implement M-Learning when they possess high utility and/or intrinsic value in these applications. These findings indicate a strong need for further training, equipping, and creating buy-in among Arabic teachers to enhance their ICT skills in implementing M-Learning. Further, given the limited availability of M-Learning apps designed for use in the Arabic language on the market, it is imperative that developers consider designing M-Learning tools that Arabic teachers, and Arabic-speaking students, can use and access more readily. This study contributes to closing the knowledge gap on teacher-motivation for implementing M-Learning in their classrooms in the UAE.

Keywords: ICT skills, m-learning, self-efficacy, teacher-motivation

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Authors: Wesam Darawsheh

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Purpose: There is an absence of studies directed to evaluate the effectiveness of Community Based Rehabilitation (CBR) programs in Jordan. This research study is aimed at investigating the effectiveness of the services of CBR programmes in Jordan. Method: A questionnaire anonymized survey was carried out with forty-seven participants (stakeholders and volunteers) from four CBR centres in Jordan. It comprised eighteen questions that collected both qualitative and quantitative data with both closed- and open-ended questions. The survey assessed participants’ knowledge of CBR and perception of the effectiveness of services provided. The quantitative data were analyzed using SPSS Version 22.0 (2016, IBM Corporation New York). Qualitative data were analyzed through thematic content and analysis and open coding to identify emergent themes. Results: The ROC curve revealed that the AUC for questions of the survey to be (AUC=0.846) which indicated a good specificity and sensitivity of the questions of the survey. The MANOVA revealed insignificant results in the effect of the CBR site (p= 0.157), and the level of education of participants (p=0.549), on the perception of the effectiveness of CBR services. There were insignificant differences between the scores of PWDs and volunteers (p=0.781). 40.4% evaluated the effectiveness of CBR services to be low. This mainly stemmed out from the lack of efforts of the CBR programmes to raise the knowledge of the local community about CBR, disability and the role toward PWDs. Conclusions: A speculation for priorities of CBR programmes in Jordan was offered where efforts need to be directed at promoting livelihood and the empowerment components, in order to actualize the main three principles of CBR mainly by promoting multispectral collaboration as a way of operation.

Keywords: community based rehabilitation (CBR), people with disabilities (PWDS), CBR centres, rehabilitation services, Jordan, mixed-methods, evaluative study

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Authors: Carlotta D’Alessandro, Giuseppe Ioppolo, Katarzyna Szopik-Depczyńska

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— The growing scarcity of resources and the mounting pressures of climate change, water pollution, and chemical contamination have prompted societies, governments, and businesses to seek ways to minimize their environmental impact. To combat climate change, and foster sustainability, Industrial Symbiosis (IS) offers a powerful approach, facilitating the shift toward a circular economic model. IS has gained prominence in the European Union's policy framework as crucial enabler of resource efficiency and circular economy practices. The essence of IS lies in the collaborative sharing of resources such as energy, material by-products, waste, and water, thanks to geographic proximity. It can be exemplified by eco-industrial parks (EIPs), which are natural environments for boosting cooperation and resource sharing between businesses. EIPs are characterized by group of businesses situated in proximity, connected by a network of both cooperative and competitive interactions. They represent a sustainable industrial model aimed at reducing resource use, waste, and environmental impact while fostering economic and social wellbeing. IS, combined with Artificial Intelligence (AI)-driven technologies, can further optimize resource sharing and efficiency within EIPs. This research, supported by the “CE_IPs” project, aims to analyze the potential for IS and AI, in advancing circularity and sustainability at Fosso Imperatore. The Fosso Imperatore Industrial Park in Nocera Inferiore, Italy, specializes in agriculture and the industrial transformation of agricultural products, particularly tomatoes, tobacco, and textile fibers. This unique industrial cluster, centered around tomato cultivation and processing, also includes mechanical engineering enterprises and agricultural packaging firms. To stimulate the shift from a traditional to a circular economic model, an AI-powered Local Development Plan (LDP) is developed for Fosso Imperatore. It can leverage data analytics, predictive modeling, and stakeholder engagement to optimize resource utilization, reduce waste, and promote sustainable industrial practices. A comprehensive SWOT analysis of the AI-powered LDP revealed several key factors influencing its potential success and challenges. Among the notable strengths and opportunities arising from AI implementation are reduced processing times, fewer human errors, and increased revenue generation. Furthermore, predictive analytics minimize downtime, bolster productivity, and elevate quality while mitigating workplace hazards. However, the integration of AI also presents potential weaknesses and threats, including significant financial investment, since implementing and maintaining AI systems can be costly. The widespread adoption of AI could lead to job losses in certain sectors. Lastly, AI systems are susceptible to cyberattacks, posing risks to data security and operational continuity. Moreover, an Analytic Hierarchy Process (AHP) analysis was employed to yield a prioritized ranking of the outlined AI-driven LDP practices based on the stakeholder input, ensuring a more comprehensive and representative understanding of their relative significance for achieving sustainability in Fosso Imperatore Industrial Park. While this study provides valuable insights into the potential of AIpowered LDP at the Fosso Imperatore, it is important to note that the findings may not be directly applicable to all industrial parks, particularly those with different sizes, geographic locations, or industry compositions. Additional study is necessary to scrutinize the generalizability of these results and to identify best practices for implementing AI-driven LDP in diverse contexts.

Keywords: artificial intelligence, climate change, Fosso Imperatore, industrial park, industrial symbiosis

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Authors: Hanan Yousef, Nisreen Naji Al-Khawaldeh

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The present study investigates the use of the expression of refusal by native speakers of Jordanian Arabic (NSsJA) in different social situations (i.e. invitations, suggestions, and offers). It also investigates the influence of gender on the refusal realization patterns within the Jordanian culture to provide a better insight into the relation between situations, strategies and gender in the Jordanian culture. To that end, a group of 70 participants, including 35 male and 35 female students from different departments at the Hashemite University (HU) participated in this study using mixed methods (i.e. Discourse Completion Test (DCT), interviews and naturally occurring data). Data were analyzed in light of a developed coding scheme. The results showed that NSsJA preferred indirect strategies which mitigate the interaction such as "excuse, reason and, explanation" strategy more than other strategies which aggravate the interaction such as "face-threatening" strategy. Moreover, the analysis of this study has revealed a considerable impact of gender on the use of linguistic forms expressing refusal among NSsJA. Significant differences in the results of the Chi-square test relating the effect of participants' gender indicate that both males and females were conscious of the gender of their interlocutors. The findings provide worthwhile insights into the relation amongst types of communicative acts and the rapport between people in social interaction. They assert that refusal should not be labeled as face threatening act since it does not always pose a threat in some cases especially where refusal is expressed among friends, relatives and family members. They highlight some distinctive culture-specific features of the communicative acts of refusal.

Keywords: gender, Jordanian Arabic, politeness, refusals, speech act

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Authors: Maria Anjelica P. Ancheta, Roberto S. Soriano, Erickson L. Llaguno

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The main purpose of this study is to examine whether there is a significant relationship between socio-economic class and household water supply demand, through determining or verifying the factors governing water use consumption patterns of households from a sampling from different socio-economic classes in Metro Manila, the national capital region of the Philippines. This study is also an opportunity to augment the lack of local academic literature due to the very few publications on urban household water demand after 1999. In over 600 Metro Manila households, a rapid survey was conducted on their average monthly water consumption and habits on household water usage. The questions in the rapid survey were based on an extensive review of literature on urban household water demand. Sample households were divided into socio-economic classes A-B and C-D. Cluster analysis, dummy coding and outlier tests were done to prepare the data for regression analysis. Subsequently, backward stepwise regression analysis was used in order to determine different statistical models to describe the determinants of water consumption. The key finding of this study is that the socio-economic class of a household in Metro Manila is a significant factor in water consumption. A-B households consume more water in contrast to C-D families based on the mean average water consumption for A-B and C-D households are 36.75 m3 and 18.92 m3, respectively. The most significant proxy factors of socio-economic class that were related to household water consumption were examined in order to suggest improvements in policy formulation and household water demand management.

Keywords: household water uses, socio-economic classes, urban planning, urban water demand management

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Authors: Takalani Tshitangano

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Background: In Limpopo province the rate of new tuberculosis (TB) cases increase daily. The Infection Control (IC) plan is one of the essential actions for TB IC. This study aimed to establish the availability of these plans at health care facilities. Objectives: The objectives were to explore and describe the awareness and knowledge of health care workers (HCWs) of the availability and content of TB IC plan; and to identity the role of infection control committees from the perspective of HCWs. Method: A qualitative approach using a cross-sectional descriptive design was adopted. The target population was all HCWs from the seven hospitals of Vhembe district. A purposive sampling approach was used to select 57 participants. The approval to conduct this study was obtained from the relevant authorities and participants. Data were collected through seven focus group discussions comprising five to 10 members. An unstructured discussion guide was used to collect data, and an open-coding method was used to analyse the data. Lincoln and Guba’s criteria ensured trustworthiness of the study findings. Results: Findings revealed that HCWs were not aware of the availability and the information contained in the TB IC plans. No person was designated as TB IC officer at hospital level. There was lack of a TB IC Committee and teams as well as ineffective utilisation of those that did exist. Conclusions: It was concluded that if the TB IC plans are not available at health care facilities, then the TB IC practices implemented by HCWs vary, resulting in TB nosocomial infection transmission. It was recommended that the World Health Organisation’s TB IC plans be adopted and implemented in Vhembe district.

Keywords: health care workers' awareness, health care workers' knowledge, availability of TB infection control plans, rural hospitals

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Authors: Sadegh Lotfieblisofla, Arash Khodabakhshi

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Tissue plasminogen activator (tPA) as a serine protease plays an important role in the fibrinolytic system and the dissolution of fibrin clots in human body. The production of this drug in plants such as tobacco could reduce its production costs. In this study, expression of tPA gene and protein targeting to different plant cell compartments, using various signal peptides has been investigated. For high level of expression, Kozak sequence was used after CaMV35S in the beginning of the gene. In order to design the final construction, Extensin, KDEL (amino acid sequence including Lys-Asp-Glu-Leu) and SP (γ-zein signal peptide coding sequence) were used as leader signals to conduct this protein into apoplast, endoplasmic reticulum and cytosol spaces, respectively. Cloned human tPA gene under the CaMV (Cauliflower mosaic virus) 35S promoter and NOS (Nopaline Synthase) terminator into pBI121 plasmid was transferred into tobacco explants by Agrobacterium tumefaciens strain LBA₄₄₀₄. The presence and copy number of genes in transgenic tobacco was proved by Southern blotting. Enzymatic activity of the rt-PA protein in transgenic plants compared to non-transgenic plants was confirmed by Zymography assay. The presence and amount of rt-PA recombinant protein in plants was estimated by ELISA analysis on crude protein extract of transgenic tobacco using a specific antibody. The yield of recombinant tPA in transgenic tobacco for SP, KDEL, Extensin signals were counted 0.50, 0.68, 0.69 microgram per milligram of total soluble proteins.

Keywords: tPA, recombinant, transgenic, tobacco

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Authors: Kgaugelo Lekota, Ayesha Hassim, Henriette Van Heerden

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Bacillus anthracis is the causative agent of anthrax that is composed of three genetic groups, namely A, B, and C. Clade-A is distributed world-wide, while sub-clades B has been identified in Kruger National Park (KNP), South Africa. KNP is one of the endemic anthrax regions in South Africa with distinctive genetic diversity. Genomic surveillance of KNP B. anthracis strains was employed on the historical culture collection isolates (n=67) dated from the 1990’s to 2015 using a whole genome sequencing approach. Whole genome single nucleotide polymorphism (SNPs) and pan-genomics analysis were used to define the B. anthracis genetic population structure. This study showed that KNP has heterologous B. anthracis strains grouping in the A-clade with more prominent ABr.005/006 (Ancient A) SNP lineage. The 2012 and 2015 anthrax isolates are dispersed amongst minor sub-clades that prevail in non-stabilized genetic evolution strains. This was augmented with non-parsimony informative SNPs of the B. anthracis strains across minor sub-clades of the Ancient A clade. Pan-genomics of B. anthracis showed a clear distinction between A and B-clade genomes with 11 374 predicted clusters of protein coding genes. Unique accessory genes of B-clade genomes that included biosynthetic cell wall genes and multidrug resistant of Fosfomycin. South Africa consists of diverse B. anthracis strains with unique defined SNPs. The sequenced B. anthracis strains in this study will serve as a means to further trace the dissemination of B. anthracis outbreaks globally and especially in South Africa.

Keywords: bacillus anthracis, whole genome single nucleotide polymorphisms, pangenomics, kruger national park

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Authors: A. E. Kobryn

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We present both descriptive and predictive modeling of structural properties of blends of PCBM or organic-inorganic hybrid perovskites of the type CH3NH3PbX3 (X=Cl, Br, I) with P3HT, P3BT or squaraine SQ2 dye sensitizer, including adsorption on TiO2 clusters having rutile (110) surface. In our study, we use a methodology that allows computing the microscopic structure of blends on the nanometer scale and getting insight on miscibility of its components at various thermodynamic conditions. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site representation/model (RISM) and uses the universal force field. Input parameters for RISM, such as optimized molecular geometries and charge distribution of interaction sites, are derived with the use of the density functional theory methods. To compare the diffusivity of the PCBM in binary blends with P3HT and P3BT, respectively, the study is complemented with MD simulation. A very good agreement with experiment and the reports of alternative modeling or simulation is observed for PCBM in P3HT system. The performance of P3BT with perovskites, however, seems as expected. The calculated nanoscale morphologies of blends of P3HT, P3BT or SQ2 with perovskites, including adsorption on TiO2, are all new and serve as an instrument in rational design of organic/hybrid photovoltaics. They are used in collaboration with experts who actually make prototypes or devices for practical applications.

Keywords: multiscale theory and modeling, nanoscale morphology, organic-inorganic halide perovskites, three dimensional distribution

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Pouya Khaleghi

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Dental implants are a successful treatment modality for restoring both function and aesthetics. Dental implant treatment has predictive results in the replacement of the lost teeth and has a high success rate even in the long term. The most important factor which is responsible for the positive course of implant treatment is the process of osseointegration between the implant structure and the host’s bone tissue. During recent years, many studies have focused on surgical and prosthetic factors, as well as the implant-related factors. However, implant failure still occurs despite the improvements that have led to the increased survival rate of dental implants, which suggests the possible role of some host-related risk factors. Vitamin D is a fat-soluble vitamin regulating calcium and phosphorus metabolism in tissues. The role of vitamin D in bone healing has been under investigation for several years. Vitamin D deficiency has also been associated with impaired and delayed callus formation and fractures healing; however, the role of vitamin D has not been clarified. Therefore, it is extremely important to study the phenomenon of a connection formed between bone tissue and the surface of a titanium implant and find correlations between the 25- hydroxycholecalciferol concentration in blood serum and the course of osseointegration. Because the processes of bone remodeling are very dynamic in the period of actual osseointegration, it is necessary to obtain the correct concentration of vitamin D3 metabolites in blood serum. In conclusion, the correct level of 25-hydroxycholecalciferol on the day of surgery and vitamin D deficiency treatment have a significant influence on the increase in the bone level at the implant site during the process of osseointegration assessed radiologically.

Keywords: implant, osseointegration, vitamin d, dental

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Authors: Giselle Maggie-Fer Castañeda Lozano

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The main objective of this research was to explore the role of neural network architectures in simulating behavioral phenomena as a potential explanation for selective associations, specifically related to biological constraints on learning. Biological constraints on learning refer to the limitations observed in conditioning procedures, where learning is expected to occur. The study involved simulations of five different experiments exploring various phenomena and sources of biological constraints in learning. These simulations included the interaction between response and reinforcer, stimulus and reinforcer, specificity of stimulus-reinforcer associations, species differences, neuroanatomical constraints, and learning in uncontrolled conditions. The overall results demonstrated that by manipulating neural network architectures, conditions can be created to model and explain diverse biological constraints frequently reported in comparative psychology literature as learning typicities. Additionally, the simulations offer predictive content worthy of experimental testing in the pursuit of new discoveries regarding the specificity of learning. The implications and limitations of these findings are discussed. Finally, it is suggested that this research could inaugurate a line of inquiry involving the use of neural networks to study biological factors in behavior, fostering the development of more ethical and precise research practices.

Keywords: comparative psychology, connectionism, conditioning, experimental analysis of behavior, neural networks

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Authors: Ahmed Khalil

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Increased energy demands around the world have led to the raise in power production which has resulted with more greenhouse gas emissions through fossil sources. These fossil sources and emissions cause deterioration in echo-system. Therefore, renewable energy sources come to the scene as echo-friendly and clean energy sourcing, whereas the electrical devices and energy needs decrease in the timeline. Each of these renewable energy sources contribute to the reduction of greenhouse gases and mitigate environmental deterioration. However, there are also some general and source-specific challenges, which influence the choice of the investors. The most prominent general challenge that effects end-users’ comfort and reliability is usually determined as the intermittence which derives from the diversions of source conditions, due to nature dynamics and uncontrolled periodic changes. Research and development professionals strive to mitigate intermittence challenge through material improvement for each renewable source whereas hybrid source mix stand as a solution. This solution prevails well, when single renewable technologies are upgraded further. On the other hand, integration of energy efficient devices and systems, raise the affirmative effect of such solution in means of less energy requirement in sustainability composition or scenario. This paper provides a glimpse on the advantages of composing renewable source mix versus single usage, with contribution of sampled e-efficient systems and devices. Accordingly it demonstrates the extended benefits, through planning and predictive estimation stages of Ahmadi Town Projects in Kuwait.

Keywords: e-efficient systems, hybrid source, intermittence challenge, renewable energy

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Authors: Mennatallah M. Hussein, Olivier de Weck

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The advancement of space exploration demands a robust space launch services program capable of reliably propelling payloads into orbit. Despite rigorous testing and quality assurance, launch failures still occur, leading to significant financial losses and jeopardizing mission objectives. Traditional failure prediction methods often lack the sophistication to account for multi-mode failure scenarios, as well as the predictive capability in complex dynamic systems. Traditional approaches also rely on expert judgment, leading to variability in risk prioritization and mitigation strategies. Hence, there is a pressing need for robust approaches that enhance launch vehicle reliability from lift-off until it reaches its parking orbit through comprehensive simulation techniques. In this study, the developed model proposes a multi-mode launch vehicle simulation framework for predicting failure scenarios when incorporating new technologies, such as new propulsion systems or advanced staging separation mechanisms in the launch system. To this end, the model combined a 6-DOF system dynamics with comprehensive data analysis to simulate multiple failure modes impacting launch performance. The simulator utilizes high-fidelity physics-based simulations to capture the complex interactions between different subsystems and environmental conditions.

Keywords: launch vehicle, failure prediction, propulsion anomalies, rocket launch simulation, rocket dynamics

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Authors: Jonathan Galvan-Colin, Ariel Valladares, Renela Valladares, Alexander Valladares

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Both porous materials and bulk metallic glasses have been studied due to their potential applications and their exceptional physical and chemical properties. However, each material presents certain drawbacks which have been thought to be overcome by generating bulk metallic glass foams (BMGF). Although some experimental reports have been performed on multicomponent BMGF, still no ab initio works have been published, as far as we know. We present an approach based on the expanding lattice (EL) method to generate binary amorphous nanoporous Cu64Zr36. Starting from two different configurations: a 108-atom crystalline cubic supercell (cCu64Zr36) and a 108-atom amorphous supercell (aCu64Zr36), both with an initial density of 8.06 g/cm3, we applied EL method to halve the density and to get 50% of porosity. After the lattice expansion the supercells were subject to ab initio molecular dynamics for 500 steps at constant room temperature. Then, the samples were geometry-optimized and characterized with the pair and radial distribution functions, bond-angle distributions and a coordination number analysis. We found that pores appeared along specific spatial directions different from one to another and that they differed in size and form as well, which we think is related to the initial structure. Due to the lack of experimental counterparts our results should be considered predictive and further studies are needed in order to handle a larger number of atoms and its implication on pore topology.

Keywords: ab initio molecular dynamics, bulk mettalic glass, porous alloy

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Authors: Adel A. Ghobbar, Ahmad Bakkar

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Although special care is given to maintenance, aircraft systems fail, and these failures cause delays and cancellations. The occurrence of Delays and Cancellations affects operators and manufacturers negatively. To reduce technical delays and cancellations, one should be able to determine the important systems causing them. The goal of this research is to find a method to define the most expensive delays and cancellations systems for Airline operators. A predictive model was introduced to forecast the failure and their impact after carrying out research that identifies relevant information to tackle the problems faced while answering the questions of this paper. Data were obtained from the manufacturers’ services reliability team database. Subsequently, delays and cancellations evaluation methods were identified. No cost estimation methods were used due to their complexity. The model was developed, and it takes into account the frequency of delays and cancellations and uses weighting factors to give an indication of the severity of their duration. The weighting factors are based on customer experience. The data Analysis approach has shown that delays and cancellations events are not seasonal and do not follow any specific trends. The use of weighting factor does have an influence on the shortlist over short periods (Monthly) but not the analyzed period of three years. Landing gear and the navigation system are among the top 3 factors causing delays and cancellations for all three aircraft types. The results did confirm that the cooperation between certain operators and manufacture reduce the impact of delays and cancellations.

Keywords: reliability, availability, delays & cancellations, aircraft maintenance

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Authors: Betim Bahtiri, B. Arash, R. Rolfes

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Epoxy-based materials are frequently exposed to high-humidity environments in many engineering applications. As a result, their material properties would be degraded by water absorption. A full characterization of the material properties under hygrothermal conditions requires time- and cost-consuming experimental tests. To gain insights into the physics of diffusion mechanisms, atomistic simulations have been shown to be effective tools. Concerning the diffusion of water in polymers, spatial trajectories of water molecules are obtained from molecular dynamics (MD) simulations allowing the interpretation of diffusion pathways at the nanoscale in a polymer network. Conventional MD simulations of water diffusion in amorphous polymers lead to discrepancies at low temperatures due to the short timescales of the simulations. In the proposed model, this issue is solved by using a combined scheme of autonomous basin climbing (ABC) with kinetic Monte Carlo and reactive MD simulations to investigate the diffusivity of water molecules in epoxy resins across a wide range of temperatures. It is shown that the proposed simulation framework estimates kinetic properties of water diffusion in epoxy resins that are consistent with experimental observations and provide a predictive tool for investigating the diffusion of small molecules in other amorphous polymers.

Keywords: epoxy resins, water diffusion, autonomous basin climbing, kinetic Monte Carlo, reactive molecular dynamics

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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: Yasaman Sanayei, Alireza Bahiraie

Abstract:

This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: Tanveer Hussain, Misbah Hussain, Masroor Ellahi Babar, Muhammad Traiq Pervez, Fiaz Hussain, Sana Zahoor, Rashid Saif

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Interleukin 2 (IL-2) is a cytokine which is produced by activated T cells, play important role in immune response against antigen. It act in both autocrine and paracrine manner. It can stimulate B cells and various other phagocytic cells like monocytes, lymphokine-activated killer cells and natural killer cells. Acting in autocrine fashion, IL-2 protein plays a crucial role in proliferation of T cells. IL-2 triggers the release of pro and anti- inflammatory cytokines by activating several pathways. In present study, exon 1 of IL-2 gene of four local Pakistani breeds (Dera Din Panah, Beetal, Nachi and Kamori) from two provinces was amplified by using reported Ovine IL-2 primers, yielding PCR product of 501 bp. The sequencing of all samples was done to identify the polymorphisms in amplified region of IL-2 gene. Analysis of sequencing data resulted in identification of one novel nucleotide substitution (T→A) in amplified non-coding region of IL-2 gene. Comparison of IL-2 gene sequence of all four breeds with other goat breeds showed high similarity in sequence. While phylogenetic analysis of our local breeds with other mammals showed that IL-2 is a variable gene which has undergone many substitutions. This high substitution rate can be due to the decreased or increased changed selective pressure. These rapid changes can also lead to the change in function of immune system. This pioneering study of Pakistani goat breeds urge for further studies on immune system of each targeted breed for fully understanding the functional role of IL-2 in goat immunity.

Keywords: interleukin 2, mutational analysis, phylogeny, goat breeds, Pakistan

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Authors: R. Chinyakata, N. R. Raselekoane

Abstract:

Migration has always been part of human history. Migration is spurred by globalisation which connects nations by encouraging the flow of goods, services, ideas and people across borders. Migration does not only involve movement of adults from one country to another. It also affects and involves the youth as they are the most mobile group. Musina area, like many other border areas, experiences a variety of challenges as a result of the influx of people from the neighbouring Zimbabwe and other African countries. Of great concern about this migration is the fact that the host country or area may become unsafe and unstable as a result of huge influx of migrants. There may also be tensions between local people and migrants over the resources. The study sought to investigate the effects of the Zimbabwean youth migration on Musina area. The study was undertaken in Musina area which is situated 18km from the Beit-Bridge border post. A qualitative research approach was used. Semi-structured interviews were used to collect data. Non-probability quota sampling technique was used to select the respondents. The study sample consisted of sixteen female and male respondents. Thematic coding was used to analyse the data. Ethical considerations such as informed consent, confidentiality, anonymity and voluntary participation were taken into account to protect the participants. The study found that the effects of the Zimbabwean youth migration on the Musina area include, among others, tensions between locals and the Zimbabwean youth migrants over resources, job and business opportunities, overcrowding and crime. Multi-pronged strategies which involve different stakeholders should be applied to address tensions over job and business opportunities, overcrowding and crime in the Musina area.

Keywords: host country, effects, migrant, migration, Musina, youth, Zimbabwe

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Authors: Pegah Farshadmanesh, Jamshid Mohammadi, Mehdi Modares

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In nearly all earthquakes of the past century that resulted in moderate to significant damage, the occurrence of postearthquake fire ignition (PEFI) has imposed a serious hazard and caused severe damage, especially in urban areas. In order to reduce the loss of life and property caused by post-earthquake fires, there is a crucial need for predictive models to estimate the PEFI risk. The parameters affecting PEFI risk can be categorized as: 1) factors influencing fire ignition in normal (non-earthquake) condition, including floor area, building category, ignitability, type of appliance, and prevention devices, and 2) earthquake related factors contributing to the PEFI risk, including building vulnerability and earthquake characteristics such as intensity, peak ground acceleration, and peak ground velocity. State-of-the-art statistical PEFI risk models are solely based on limited available earthquake data, and therefore they cannot predict the PEFI risk for areas with insufficient earthquake records since such records are needed in estimating the PEFI model parameters. In this paper, the correlation between normal condition ignition risk, peak ground acceleration, and PEFI risk is examined in an effort to offer a means for predicting post-earthquake ignition events. An illustrative example is presented to demonstrate how such correlation can be employed in a seismic area to predict PEFI hazard.

Keywords: fire risk, post-earthquake fire ignition (PEFI), risk management, seismicity

Procedia PDF Downloads 382