Search results for: regression models drone

Authors: Laraib Niaz

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This paper analyzes the ways in which education and economic deprivation are linked to terrorism in Pakistan using data for terrorist incidents from the Global Terrorism Database (GTD). It employs the technique of negative binomial regression for the years between 1990 and 2013, presenting evidence for a positive association between education and terrorism. Conversely, a negative correlation with economic deprivation is signified in the results. The study highlights the element of radicalization as witnessed in the curriculum and textbooks of public schools as a possible reason for extremism, which in turn may lead to terrorism.

Keywords: education, Pakistan, terrorism, poverty

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Noor Taleb Ismael, Areej Abd Al Kareem Al Titi, Ala'a Fayez Jaber

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Objectives: To investigate self-perceived functional performance among adults with IDD who are Jordanian residential care and rehabilitation centers residents. Also, to investigate their functional abilities (i.e., motor, and cognitive). In addition, to determine the motor and cognitive predictors of their functional performance. Methods: The study utilized a cross-sectional descriptive design; the sample included 180 individuals with IDD (90 males and 90 females) aged 18 to 75 years. The inclusion criteria encompassed: 1) Adults with a confirmed IDD by their physician’s professional and 2) residents in Jordanian Residential Care and Rehabilitation Centers affiliated with the Jordanian Ministry of Social Development. The exclusion criteria were: 1) bedridden or totally dependent on their care providers; 2) who had an accident or acquired neurological conditions. Researchers conducted semi-structured interviews to complete the outcome measures that include the Canadian Occupational Performance Measure (COPM), the Functional Independence Measure (FIM), the Montreal Cognitive Assessment (MoCA), the Mini-Mental Status Examination (MMSE), and the sociodemographic questionnaire. Data analyses consisted of descriptive statistics, analysis of frequencies, correlation, and regression analyses. Result: Individuals with IDD showed low functional performance in all daily life areas, including self-care, productivity, and leisure; there was severe cognitive impairment and poor independence and functional performance. (COPM Performance M= 1.433, SD±.57021, COPM Satisfaction M= 1.31, SD±.54, FIM M= 3.673, SD± 1.7918). Two predictive models were validated for the COPM performance and FIM total scores. First, significant predictors of high self-perceived functional performance on COPM were high scores on FIM Motor sub scores, FIM cognitive sub scores, young age, and having a high school educational level (R2=0.603, p=0.012). Second, significant predictors of high functional capacity on FIM were a high score on the COPM performance subscale, a high MMSE score, and having a cerebral palsy (CP) diagnosis (R2=0.671, p<0.001). Conclusions: Evaluating functional performance and associated factors is important in rehabilitation to provide better services and improve health and QoL for individuals with IDD. This study suggested conducting future studies targeting integrated individuals with IDD who live with their families in the communities.

Keywords: functional performance, intellectual and developmental disabilty, cognitive abilities, motor abilities

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Authors: Abd Al-Salam Al-Masgari, Haylay Tsegab, Ismailalwali Babikir, Monera A. Shoieb

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We present a study of Malaysia's Central Luconia region, which is an essential deposit of Miocene carbonate reservoirs. This study aims to identify and map areas of selected carbonate platforms, develop high-resolution statistical karst models, and generate comprehensive karst geobody models for selected carbonate fields. This study uses seismic characterization and advanced geophysical surveys to identify karst signatures in Miocene carbonate reservoirs. The results highlight the use of variance, RMS, RGB colour blending, and 3D visualization Prop seismic sequence stratigraphy seismic attributes to visualize the karstified areas across the F23 field of Central Luconia. The offshore karst model serves as a powerful visualization tool to reveal the karstization of carbonate sediments of interest. The results of this study contribute to a better understanding of the karst distribution of Miocene carbonate reservoirs in Central Luconia, which are essential for hydrocarbon exploration and production. This is because these features significantly impact the reservoir geometry, flow path and characteristics.

Keywords: karst, central Luconia, seismic attributes, Miocene carbonate build-ups

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Authors: Djeridi Rachid, Ould Ouali Mohand

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The search of a mechanism for the elastomer abrasive wear study is an open issue. The practice difficulties are complex due to the complexity of deformation mechanism, to the complex mechanism of the material tearing and to the marked interactions between the tribological parameters. In this work, we present an experimental technique to study the elastomers abrasive wear. The interaction 'elastomer/indenter' implicate dependant ant temporary of different tribological parameters. Consequently, the phenomenon that governs this interaction is not easy to explain. An optimal elastomers compounding and an adequate utilization conditions of these materials that define its resistance at the abrasion is discussed. The results are confronted to theoretical models: the weight loss variation in function of blade angle or in function of cycle number is in agreement with rupture models and with the mechanism of fissures propagation during the material tearing in abrasive wear of filler elastomers. The weight loss in function of the sliding velocity shows the existence of a critical velocity that corresponds to the maximal wear. The adding of silica or black carbon influences in a different manner on wear abrasive behavior of filler elastomers.

Keywords: abrasion wear, filler elastomer, tribology, hyperelastic

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Authors: Karam Ignaim, Joao M. Fernandes, Andre L. Ferreira

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Mapping the artifacts coming from a set of similar products family developed in an ad-hoc manner to make up the resulting software product line (SPL) plays a key role to maintain the consistency between requirements and code. This paper presents a feature mapping approach that focuses on tracing the artifact coming from the migration process, the current feature model (FM), to the other artifacts of the resulting SPL, the reference architecture, and code. Thus, our approach relates each feature of the current FM to its locations in the implementation code, using the reference architecture as an intermediate artifact (as a centric point) to preserve consistency among them during an SPL evolution. The approach uses a particular artifact (i.e., traceability tree) as a solution for managing the mapping process. Tool support is provided using friendlyMapper. We have evaluated the feature mapping approach and tool support by putting the approach into practice (i.e., conducting a case study) of the automotive domain for Classical Sensor Variants Family at Bosch Car Multimedia S.A. The evaluation reveals that the mapping approach presented by this paper fits the automotive domain.

Keywords: feature location, feature models, mapping, software product lines, traceability

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Authors: Bilgen Ozluk, Ayfer Karaaslan

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Introduction: Adaptation to university life is a difficult process for students. In peer support, students are expected to help other students or sometimes adults using their helping skills. Therefore, it is expected that peer support will have significant effect on students’ adaptation to university life. Aim: This study was conducted with the aim of determining the effect of peer support on adaptation to university life in the first year students of the faculty of health sciences. Methods: The population consists of 340 first year university students receiving education in the departments of nursing, health management, social services, nutrition and dietetics, physiotherapy and rehabilitation at an university located in the province of Konya. The sample of the study consisted of 274 students who voluntarily participated in the study. The data were collected between the dates 23 May 2016 and 3 June 2016. The data were collected using the socio-demographic information, the peer support scale and the university life adaptation scale. Ethical approvals for the study and permission from the university were taken. Numbers, percentages, averages, one-Way ANOVA, pearson correlation analysis and regression analysis have been used in assessing the data. Findings: When the problems most frequently encountered by students just starting the university were ordered, problems regarding their classes took the first place by 41.6%, socio-cultural problems took the second place by 38.7%, and economic problems took the third place by 37.6%. The mean total score of the Adaptation to University Life Scale was found to be 216.78±32.15. Considering that the lowest and highest scores that can be gained from the scale are 132 and 289 respectively, it was found that the adaptation to university life levels of the students were higher than the average. The mean adaptation to university life score of the nursing students was higher than those of the students of other departments. The mean score of ‘the Peer Support Scale’ was found to be 47.24±10.27. Considering that the lowest and highest scores that can be gained from the scale are 17 and 68 respectively, it was found that the peer support levels of the students were higher than the average. As a result of the regression analysis, it was found that 20% of the total variance regarding adaptation to university life was explained by peer support. Conclution: Receiving the support peer groups becomes highly important in the university adaptation process of first-year students. Peer support will create the means for easier completion of this difficult transition process.

Keywords: adaptation to university life, first years, peer support, university student

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Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy

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Authors: Fouzia Khanam, Belal Hossain, Kaosar Afsana, Mahfuzar Rahman

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Aim: Although cardiovascular diseases (CVD) has already been recognized as a major cause of death in developed countries, its prevalence is rising in developing countries as well, and engendering a challenge for the health sector. Bangladesh has experienced an epidemiological transition from communicable to non-communicable diseases over the last few decades. So, the rising prevalence of CVD and its risk factors are imposing a major problem for the country. We aimed to examine the prevalence of CVDs and socioeconomic and lifestyle factors related to it from a population-based survey. Methods: The data used for this study were collected as a part of a large-scale cross-sectional study conducted to explore the overall health status of children, mothers and senior citizens of Bangladesh. Multistage cluster random sampling procedure was applied by considering unions as clusters and households as the primary sampling unit to select a total of 11,428 households for the base survey. Present analysis encompassed 12338 respondents of ≥ 35 years, selected from both rural areas and urban slums of the country. Socio-economic, demographic and lifestyle information were obtained through individual by a face-to-face interview which was noted in ODK platform. And height, weight, blood pressure and glycosuria were measured using standardized methods. Chi-square test, Univariate modified Poisson regression model, and multivariate modified Poisson regression model were done using STATA software (version 13.0) for analysis. Results: Overall, the prevalence of CVD was 4.51%, of which 1.78% had stroke and 3.17% suffered from heart diseases. Male had higher prevalence of stroke (2.20%) than their counterparts (1.37%). Notably, thirty percent of respondents had high blood pressure and 5% population had diabetes and more than half of the population was pre-hypertensive. Additionally, 20% were overweight, 77% were smoker or consumed smokeless tobacco and 28% of respondents were physically inactive. Eighty-two percent of respondents took extra salt while eating and 29% of respondents had deprived sleep. Furthermore, the prevalence of risk factor of CVD varied according to gender. Women had a higher prevalence of overweight, obesity and diabetes. Women were also less physically active compared to men and took more extra salt. Smoking was lower in women compared to men. Moreover, women slept less compared to their counterpart. After adjusting confounders in modified Poisson regression model, age, gender, occupation, wealth quintile, BMI, extra salt intake, daily sleep, tiredness, diabetes, and hypertension remained as risk factors for CVD. Conclusion: The prevalence of CVD is significant in Bangladesh, and there is an evidence of rising trend for its risk factors such as hypertension, diabetes especially in older population, women and high-income groups. Therefore, in this current epidemiological transition, immediate public health intervention is warranted to address the overwhelming CVD risk.

Keywords: cardiovascular diseases, diabetes, hypertension, stroke

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Authors: Armin Bodaghkhani, Bruce Colbourne, Yuri S. Muzychka

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The process of spray-cloud formation and flow kinematics produced from breaking wave impact on vertical and slant lab-scale bow-shaped models were experimentally investigated. Bubble Image Velocimetry (BIV) and Image Processing (IP) techniques were applied to study the various types of wave-model impacts. Different wave characteristics were generated in a tow tank to investigate the effects of wave characteristics, such as wave phase velocity, wave steepness on droplet velocities, and behavior of the process of spray cloud formation. The phase ensemble-averaged vertical velocity and turbulent intensity were computed. A high-speed camera and diffused LED backlights were utilized to capture images for further post processing. Various pressure sensors and capacitive wave probes were used to measure the wave impact pressure and the free surface profile at different locations of the model and wave-tank, respectively. Droplet sizes and velocities were measured using BIV and IP techniques to trace bubbles and droplets in order to measure their velocities and sizes by correlating the texture in these images. The impact pressure and droplet size distributions were compared to several previously experimental models, and satisfactory agreements were achieved. The distribution of droplets in front of both models are demonstrated. Due to the highly transient process of spray formation, the drag coefficient for several stages of this transient displacement for various droplet size ranges and different Reynolds number were calculated based on the ensemble average method. From the experimental results, the slant model produces less spray in comparison with the vertical model, and the droplet velocities generated from the wave impact with the slant model have a lower velocity as compared with the vertical model.

Keywords: spray charachteristics, droplet size and velocity, wave-body interactions, bubble image velocimetry, image processing

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Authors: Irene Buj-Corral, Ali Bagheri, Alejandro Dominguez-Fernandez

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In recent years, use of ceramic prostheses as an implant in some parts of body has become common. In the present study, research has focused on replacement of the acetabulum bone, which is a part of the pelvis bone. Metallic prostheses have shown some problems such as release of metal ions into patient's blood. In addition, fracture of liners and squeezing between surface of femoral head and inner surface of acetabulum have been reported. Ceramic prostheses have the advantage of low debris and high strength, although they are more difficult to be manufactured than metallic ones. Specifically, new designs try to attempt an acetabulum in which the outer surface will be porous for proliferation of cells and fixation of the prostheses by means of osteointegration, while inner surface must be smooth enough to assure that the movement between femoral head and inner surface will be carried out with on feasibility. In the present study, 3D printing technologies are used for manufacturing ceramic prostheses. In Fused Deposition Modelling (FDM) process, 3D printed plastic prostheses are obtained by means of melting of a plastic filament and subsequent deposition on a glass surface. A similar process is applied to ceramics in which ceramic powders need to be mixed with a liquid polymer before depositing them. After 3D printing, parts are subjected to a sintering process in an oven so that they can achieve final strength. In the present paper, influence of printing parameters on surface roughness 3D printed ceramic parts are presented. Three parameter full factorial design of experiments was used. Selected variables were layer height, infill and nozzle diameter. Responses were average roughness Ra and mean roughness depth Rz. Regression analysis was applied to responses in order to obtain mathematical models for responses. Results showed that surface roughness depends mainly on layer height and nozzle diameter employed, while infill was found not to be significant. In order to get low surface roughness, low layer height and low infill should be selected. As a conclusion, layer height and infill are important parameters for obtaining good surface finish in ceramic 3D printed prostheses. However, use of too low infill could lead to prostheses with low mechanical strength. Such prostheses could not be able to bear the static and dynamic charges to which they are subjected once they are implanted in the body. This issue will be addressed in further research.

Keywords: ceramic, hip prostheses, surface roughness, 3D printing

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Authors: Csaba Kalman

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In spite of the existence of numerous second language (L2) motivation theories, the teacher’s role in motivating learners has remained an under-researched niche to this day. If we narrow down our focus on the teacher’s role on motivating adult learners of English in an English as a Foreign Language (EFL) context in corporate environments, empirical research is practically non-existent. This study fills the above research niche by exploring the most motivating aspects of the teacher’s personality, behaviour, and teaching practices that affect adult learners’ L2 motivation in corporate contexts in Hungary. The study was conducted in a wide range of industries in 18 organisations that employ over 250 people in Hungary. In order to triangulate the research, 21 human resources managers, 18 language teachers, and 466 adult learners of English were involved in the investigation by participating in interview studies, and quantitative questionnaire studies that measured ten scales related to the teacher’s role, as well as two criterion measure scales of intrinsic and extrinsic motivation. The qualitative data were analysed using a template organising style, while descriptive, inferential statistics, as well as multivariate statistical techniques, such as correlation and regression analyses, were used for analysing the quantitative data. The results showed that certain aspects of the teacher’s personality (thoroughness, enthusiasm, credibility, and flexibility), as well as preparedness, incorporating English for Specific Purposes (ESP) in the syllabus, and focusing on the present, proved to be the most salient aspects of the teacher’s motivating influence. The regression analyses conducted with the criterion measure scales revealed that 22% of the variance in learners’ intrinsic motivation could be explained by the teacher’s preparedness and appearance, and 23% of the variance in learners’ extrinsic motivation could be attributed to the teacher’s personal branding and incorporating ESP in the syllabus. The findings confirm the pivotal role teachers play in motivating L2 learners independent of the context they teach in; and, at the same time, call for further research so that we can better conceptualise the motivating influence of L2 teachers.

Keywords: adult learners, corporate contexts, motivation, teacher’s role

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Authors: Velimir Monty Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: W. Meron Mebrahtu, R. Absi

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Velocity distribution in turbulent open-channel flows is organized in a complex manner. This is due to the large spatial and temporal variability of fluid motion resulting from the free-surface turbulent flow condition. This phenomenon is complicated further due to the complex geometry of channels and the presence of solids transported. Thus, several efforts were made to understand the phenomenon and obtain accurate mathematical models that are suitable for engineering applications. However, predictions are inaccurate because oversimplified assumptions are involved in modeling this complex phenomenon. Therefore, the aim of this work is to study velocity distribution profiles and obtain simple, more accurate, and predictive mathematical models. Particular focus will be made on the acceptable simplification of the general transport equations and an accurate representation of eddy viscosity. Wide rectangular open-channel seems suitable to begin the study; other assumptions are smooth-wall, and sediment-free flow under steady and uniform flow conditions. These assumptions will allow examining the effect of the bottom wall and the free surface only, which is a necessary step before dealing with more complex flow scenarios. For this flow condition, two ordinary differential equations are obtained for velocity profiles; from the Reynolds-averaged Navier-Stokes (RANS) equation and equilibrium consideration between turbulent kinetic energy (TKE) production and dissipation. Then different analytic models for eddy viscosity, TKE, and mixing length were assessed. Computation results for velocity profiles were compared to experimental data for different flow conditions and the well-known linear, log, and log-wake laws. Results show that the model based on the RANS equation provides more accurate velocity profiles. In the viscous sublayer and buffer layer, the method based on Prandtl’s eddy viscosity model and Van Driest mixing length give a more precise result. For the log layer and outer region, a mixing length equation derived from Von Karman’s similarity hypothesis provides the best agreement with measured data except near the free surface where an additional correction based on a damping function for eddy viscosity is used. This method allows more accurate velocity profiles with the same value of the damping coefficient that is valid under different flow conditions. This work continues with investigating narrow channels, complex geometries, and the effect of solids transported in sewers.

Keywords: accuracy, eddy viscosity, sewers, velocity profile

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Authors: Abolfazl Hosseinkhani, Sepehr Sanaye

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Different approaches have been used to predict the performance of the vertical axis wind turbines (VAWT), such as experimental, computational fluid dynamics (CFD), and analytical methods. Analytical methods, such as momentum models that use streamtubes, have low computational cost and sufficient accuracy. The double multiple streamtube (DMST) is one of the most commonly used of momentum models, which divide the rotor plane of VAWT into upwind and downwind. In fact, results from the DMST method have shown some discrepancy compared with experiment results; that is because the Darrieus turbine is a complex and aerodynamically unsteady configuration. In this study, analytical-experimental-based corrections, including dynamic stall, streamtube expansion, and finite blade length correction are used to improve the DMST method. Results indicated that using these corrections for a SANDIA 17-m VAWT will lead to improving the results of DMST.

Keywords: vertical axis wind turbine, analytical, double multiple streamtube, streamtube expansion model, dynamic stall model, finite blade length correction

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Authors: Xiyue Wang, Shaoning Yan

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Cities in China today is faced with an increasingly serious river ecological crisis accompanying with the development of urbanization: waterlogging on account of the fragmented urban natural hydrological system; the limited ecological function of the hydrological system caused by a destruction of water system and waterfront ecological environment. Additionally, the eco-hydrological processes of rivers are affected by various environmental factors, which are more complex in the context of urban environment. Therefore, efficient hydrological monitoring and analysis tools, accurate and visual hydrological and hydraulic models are becoming more important basis for decision-makers and an important way for landscape architects to solve urban hydrological problems, formulating sustainable and forward-looking schemes. The study mainly introduces the river and flood analysis model based on the platform of Autodesk Civil 3D. Taking the Luanhe River in Qian'an City of Hebei Province as an example, the 3D models of the landform, river, embankment, shoal, pond, underground stream and other land features were initially built, with which the water transfer simulation analysis, river floodplain analysis, and river ecology analysis were carried out, ultimately the real-time visualized simulation and analysis of rivers in various hypothetical scenarios were realized. Through the establishment of digital hydrological and hydraulic model, the hydraulic data can be accurately and intuitively simulated, which provides basis for rational water system and benign urban ecological system design. Though, the hydrological and hydraulic model based on Autodesk Civil3D own its boundedness: the interaction between the model and other data and software is unfavorable; the huge amount of 3D data and the lack of basic data restrict the accuracy and application range. The hydrological and hydraulic model based on Autodesk Civil3D platform provides more possibility to access convenient and intelligent tool for urban planning and monitoring, a solid basis for further urban research and design.

Keywords: visualization, hydrological and hydraulic model, Autodesk Civil 3D, urban river

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Authors: Tondani Mudau, Sherine B. Van Wyk, Zuhayr Kafaar, Janan Dietrich

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Female adolescents residing in a patriarchal society such as South Africa are more inclined to embrace feminine ideologies. Internalizing these ideologies may expose female adolescents to mental health challenges such as depression and anxiety. This study explored whether the internalisation of adolescent femininity ideologies namely, objectified relationship with own body (ORB) and inauthentic self in relationships (ISR) predicted anxiety and depression in late female adolescents at Stellenbosch University. The sample of the study consisted of 1451 (18-24) female undergraduate and postgraduate students enrolled at Stellenbosch University. The mean age of the participants was 20 (SD=1.46), and most participants (39.7%) were first-year students. The study employed a cross-sectional quantitative research design. Data was collected through an online self-completion survey, the survey consisted of three sections, the first section asked biographical questions regarding age, gender, race and family background. The second section measured the internalisation of feminine ideologies by using the adolescent femininity ideology scale which has two subscales namely inauthentic self in relationship with others (ISR) and objectified relationship with one’s own body (ORB). The ISR scale had the Cronbach Alpha of 0.76, and the ORB scale had the Cronbach Alpha of 0.83. The third section measured mental health (depression and anxiety) by using the Hopkins Symptoms 25-checklist which had the Cronbach Alpha of 0.93. Data were analysed through multiple linear regression from IBM SPSS (Statistical Package for the Social Sciences Version 24). The overall results of the multiple linear regression showed that The AFIS combination accounted for 14% for anxiety as measured by the Hopkins Symptoms Checklist R² = .142, F (2, 682) = 56.431, p < .001. The combination also accounted for 24% for depression as measured by the Hopkins Symptoms Checklist R² = .239, F (2, 682) = 106.971, p < .0. The findings in this study affirm the objectification and feminist theory contentions that internalising femininity ideologies (ISR and ORB) predict negative mental health in female adolescents.

Keywords: adolescents, anxiety, depression, feminine ideologies, inauthentic self, mental health, self-objectification, South Africa

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Authors: Yichao Ma, Chengsiong Chin, Wailok Woo

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Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and three-dimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who is the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: hdd noise, jury test, neural network model, psychoacoustic annoyance

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Authors: Fatemeh Abdollahi, Munn-Sann Lye, Jamshid Yazdani Charati, Mehran Zarghami

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Fathers are at risk of depression during the postpartum period. Some studies have been reported maternal depression is the key predictor of paternal postpartum depression (PPD). This study aimed to estimate the prevalence and predictors of parental PPD and its association with maternal PPD. In a cross-sectional study, via a stratified random and convenience sampling method, participants referring to health centers during 2-8 weeks postpartum were recruited from March to October 2017. Paternal PPD and its relation to maternal PPD and other related factors were assessed using multiple logistic regression. Participants were 591 literate couples who referred to Mazandaran province primary health centers during to study period. Couples were screened for depression using Edinburgh Postnatal Depression Scale (EPDS). Fathers provided information on socio-demographic characteristics, life events, neonatal stressor, perceived stress (Perceived Stress Scale), social support (Multidimensional Scale of Perceived Social Support), and general health status using General Health Questionnaire (GHQ) as well. Data on mothers ‘demographic characteristics and obstetrics factors was also gathered. Overall, 93 fathers (15.7%) and 188 mothers (31.8%) reported depressive symptoms above the cut-off EPDS score of 12. In the multiple logistic regression model, older age [OR=1.20, (95%CI: 1.05- 1.36)], maternal depressive symptoms [OR=1.15, (95%CI: 1.04-1.27)], higher GHQ scores [OR=1.21, (95%CI: 1.11-1.33)] and increased recent life events [OR=1.42, (95%CI: 1.01-1.2.00)] were related to paternal PPD. A significant inverse association was found between number of children and paternal PPD [OR=0.20, (95%CI: 0.07-0.53)]. Depressive symptoms, especially in first-time fathers following the birth of a child, are not uncommon. Maternal depressive symptoms and paternal well-being were strong predictors of parental PPD. Creating opportunities for men to access special health care services, parental education to help adapting to parenthood, screening programs, and psychiatric/psychosocial interventions to decrease the suffering of depression for both depressed parents are recommended.

Keywords: depression, men, postpartum, risk factors, women

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Authors: Semaw A., Awet H., Yohannes M.

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Background: Hepatitis B Virus (HBV) is a major global public health problem. Individuals living in Sub-Sahara Africa have 60% lifetime risk of acquiring HBV infection. Evidences showed that 80-90% of those born from infected mothers developed chronic HBV. Perinatal HBV transmission is a major determinant of HBV carrier status, its chronic squeal and maintains HBV transmission across generations. Method: Institution based cross-sectional study was conducted among 406 pregnant mothers attending Antenatal clinics at Mekelle and Ayder referral hospital from January 30 to April 1/2014. Epidata version 3.1 was used for data entry and SPSS version 21 statistical software was used for data cleaning, management and finally determine associated factors of hepatitis B surface antigen adjusting important confounders using multivariable logistic regression analysis at 5% level of significance. Result: The overall prevalence of hepatitis B surface antigen among pregnant women was 33 (8.1%). The socio-demographic characteristic of the study population showed that there is high positivity among secondary school 189 (46.6%). In the multivariable logistic regression analysis, history of a contact with individuals who had history of hepatitis B infection or jaundice and lifetime number of multiple sexual partners were found to be significantly associated with HBsAg positivity at AOR = 3.73 95%C.I (1.373-10.182) and AOR = 2.57 95%C.I (1.173-5.654), respectively. Moreover, Human Immunodeficiency Virus (HIV) and HBV confection rate was found 3.6%. Conclusion: This study has shown that HBV prevalence in pregnant women is highly prevalent (8.1%) in the study area. Contact with individuals who had a history of hepatitis or have jaundice and report of multiple lifetime sexual partnership were associated with hepatitis B infection. Education about HBV transmission and prevention as well as screening all pregnant mothers shall be sought to reduce the serious public health crisis of HBV.

Keywords: HBsAg, hepatitis B, pregnant women, prevalence

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Authors: Josephine Aikpitanyi, Friday Okonofua, Lorrettantoimo, Sandy Tubeuf

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Every day, 800 women die from conditions related to pregnancy and childbirth, resulting in an estimated 300,000 maternal deaths worldwide per year. Over 99 percent of all maternal deaths occur in developing countries, with more than half of them occurring in sub-Saharan Africa. Nigeria being the most populous nation in sub-Saharan Africa bears a significant burden of worsening maternal and child health outcomes with a maternal mortality rate of 917 per 100,000 live births and child mortality rate of 117 per 1,000 live births. While several studies have documented that financial barriers disproportionately discourage poor women from seeking needed maternal and child healthcare, other studies have indicated otherwise. Evidence shows that there are instances where health facilities with skilled healthcare providers exist, and yet maternal, and child health outcomes remain abysmally low, indicating the presence of non-cognitive and behavioural factors that may affect the utilization of healthcare services. This study investigated the influence of locus of control and self-esteem on utilization of maternal and child healthcare services in Nigeria. Specifically, it explored the differences in utilization of antenatal care, skilled birth care, postnatal care, and child vaccination by women having an internal and external locus of control and women having high and low self-esteem. We collected information on non-cognitive traits of 1411 randomly selected women, along with information on utilization of the various indicators of maternal and child healthcare. Estimating logistic regression models for various components of healthcare services utilization, we found that women’s internal locus of control was a significant predictor of utilization of antenatal care, skilled birth care, and completion of child vaccination. We also found that having high self-esteem was a significant predictor of utilization of antenatal care, postnatal care, and completion of child vaccination after adjusting for other control variables. By improving our understanding of non-cognitive traits as possible barriers to maternal and child healthcare utilization, our findings offer important insights for enhancing participant engagement in intervention programs that are initiated to improve maternal and child health outcomes in low-and-middle-income countries.

Keywords: behavioural economics, health-seeking behaviour, locus of control and self-esteem, maternal and child healthcare, non-cognitive traits, and healthcare utilization

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Authors: Paolo Guzzini, Emad Samuel M. Ebeid

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Bridges and Railways are critical infrastructures. Ensuring safety for transports using such assets is a primary goal as it directly impacts the lives of people. By the way, improving safety could require increased investments in O&M, and therefore optimizing resource usage for asset maintenance becomes crucial. Drones4Safety (D4S), a European project funded under the H2020 Research and Innovation Action (RIA) program, aims to increase the safety of the European civil transport by building a system that relies on 3 main pillars: • Drones operating autonomously in swarm mode; • Drones able to recharge themselves using inductive phenomena produced by transmission lines in the nearby of bridges and railways assets to be inspected; • Data acquired that are analyzed with AI-empowered algorithms for defect detection This paper describes the business model behind this disruptive project. The Business Model is structured in 2 parts: • The first part is focused on the design of the business model Canvas, to explain the value provided by the Drone4safety project; • The second part aims at defining a detailed financial analysis, with the target of calculating the IRR (Internal Return rate) and the NPV (Net Present Value) of the investment in a 7 years plan (2 years to run the project + 5 years post-implementation). As to the financial analysis 2 different points of view are assumed: • Point of view of the Drones4safety company in charge of designing, producing, and selling the new system; • Point of view of the Utility company that will adopt the new system in its O&M practices; Assuming the point of view of the Drones4safety company 3 scenarios were considered: • Selling the drones > revenues will be produced by the drones’ sales; • Renting the drones > revenues will be produced by the rental of the drones (with a time-based model); • Selling the data acquisition service > revenues will be produced by the sales of pictures acquired by drones; Assuming the point of view of a utility adopting the D4S system, a 4th scenario was analyzed taking into account the decremental costs related to the change of operation and maintenance practices. The paper will show, for both companies, what are the key parameters affecting most of the business model and which are the sustainable scenarios.

Keywords: a swarm of drones, AI, bridges, railways, drones4safety company, utility companies

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Authors: Swati Srivastava, Ashish Kumar Upadhyay

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Background: According to United Nation Children’s Fund, it has been estimated that worldwide about 165 million children were stunted in 2012 and India alone accounts for 38% of global burden of stunting. In terms of incidence, India is home of more than 60 million stunted children worldwide. Our study aims to examine the effect of pregnancy intention and maternal postnatal depressive symptoms on early childhood stunting in India. We hypothesized that effect of pregnancy intention and postnatal maternal depressive symptoms were mediated by social support. Methods: We used data from first wave of Young Lives Study India. Out of 2011 children recruited in original cohort, 1833 children had complete information on pregnancy intention, maternal depression and other variables. A series of multivariate logistic regression model were used to examine the effect of pregnancy intention and postnatal depressive symptoms on early childhood stunting. Results: Bivariate result indicates that a higher percent of children born after unintended pregnancy (40%) were stunted than children of intended pregnancy (26%). Likewise, proportion of stunted children was also higher among women of high postnatal depressive symptoms (35%) than low level of depression (24%). Results of multivariate logistic regression model indicate that children born after unintended pregnancy were significantly more likely to be stunted than children born after intended pregnancy (Coefficient: 1.70, CI: 1.17, 2.48). Likewise, early childhood stunting was also associated with maternal postnatal depressive symptoms among women (Coefficient: 1.48, CI: 1.16, 1.88). The effect of pregnancy intention and postnatal depressive symptoms on early childhood stunting remains unchanged after controlling for social support and other variables. Conclusions: The findings of this study provide conclusive evidence regarding consequences of pregnancy intention and postnatal depressive symptoms on early childhood stunting in India. Therefore, there is need to identify the women with unintended pregnancy and incorporate the promotion of mental health into their national reproductive and child health programme.

Keywords: pregnancy intention, postnatal depressive symptoms, social support, childhood stunting, young lives study, India

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Authors: Prateek Singh, Dilshad Ahmad

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Growing demand for high-quality steel products has inspired researchers to investigate the unit operations involved in the manufacturing of these products (slabs, rods, sheets, etc.). One such operation is tundish operation, in which a vessel (tundish) acts as a buffer of molten steel for the solidification operation in mold. It is observed that tundish also plays a crucial role in the quality and cleanliness of the steel produced, besides merely acting as a reservoir for the mold. It facilitates removal of dissolved oxygen (inclusions) from the molten steel thus improving its cleanliness. Inclusion removal can be enhanced by increasing the residence time of molten steel in the tundish by incorporation of flow modifiers like dams, weirs, turbo-pad, etc. These flow modifiers also help in reducing the dead or short circuit zones within the tundish which is significant for maintaining thermal and chemical homogeneity of molten steel. Thus, it becomes important to analyze the flow of molten steel in the tundish for different configuration of flow modifiers. In the present work, effect of varying positions and heights/depths of dam and weir on the dead volume in tundish is studied. Steady state thermal and flow profiles of molten steel within the tundish are obtained using OpenFOAM. Subsequently, Residence Time Distribution analysis is performed to obtain the percentage of dead volume in the tundish. Design of Experiment method is then used to configure different tundish geometries for varying positions and heights/depths of dam and weir, and dead volume for each tundish design is obtained. A second-degree polynomial with two-term interactions of independent variables to predict the dead volume in the tundish with positions and heights/depths of dam and weir as variables are computed using Multiple Linear Regression model. This polynomial is then used in an optimization framework to obtain the optimal tundish geometry for minimizing dead volume using Sequential Quadratic Programming optimization.

Keywords: design of experiments, multiple linear regression, OpenFOAM, residence time distribution, sequential quadratic programming optimization, steel, tundish

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Authors: Pooria Norouzi, Nicholas Tsang, Adam van der Goes, Joseph Yu, Douglas Zheng, Sirine Maleej

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In this study, virtual meters will be designed and used for energy balance measurements of an air handling unit (AHU). The method aims to replace traditional physical sensors in heating, ventilation, and air conditioning (HVAC) systems with simulated virtual meters. Due to the inability to manage and monitor these systems, many HVAC systems have a high level of inefficiency and energy wastage. Virtual meters are implemented and applied in an actual HVAC system, and the result confirms the practicality of mathematical sensors for alternative energy measurement. While most residential buildings and offices are commonly not equipped with advanced sensors, adding, exploiting, and monitoring sensors and measurement devices in the existing systems can cost thousands of dollars. The first purpose of this study is to provide an energy consumption rate based on available sensors and without any physical energy meters. It proves the performance of virtual meters in HVAC systems as reliable measurement devices. To demonstrate this concept, mathematical models are created for AHU-07, located in building NE01 of the British Columbia Institute of Technology (BCIT) Burnaby campus. The models will be created and integrated with the system’s historical data and physical spot measurements. The actual measurements will be investigated to prove the models' accuracy. Based on preliminary analysis, the resulting mathematical models are successful in plotting energy consumption patterns, and it is concluded confidently that the results of the virtual meter will be close to the results that physical meters could achieve. In the second part of this study, the use of virtual meters is further assisted by artificial intelligence (AI) in the HVAC systems of building to improve energy management and efficiency. By the data mining approach, virtual meters’ data is recorded as historical data, and HVAC system energy consumption prediction is also implemented in order to harness great energy savings and manage the demand and supply chain effectively. Energy prediction can lead to energy-saving strategies and considerations that can open a window in predictive control in order to reach lower energy consumption. To solve these challenges, the energy prediction could optimize the HVAC system and automates energy consumption to capture savings. This study also investigates AI solutions possibility for autonomous HVAC efficiency that will allow quick and efficient response to energy consumption and cost spikes in the energy market.

Keywords: virtual meters, HVAC, artificial intelligence, energy consumption prediction

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Authors: Misagh Faezipour

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System dynamics is a systems engineering approach that can help address the complex challenges in different systems. Little is known about how the brain represents people to predict behavior. This work is based on how the brain simulates different personal behavior and responds to them in the case of young children ages one to five. As we know, children’s minds/brains are just as clean as a crystal, and throughout time, in their surroundings, families, and education center, they grow to develop and have different kinds of behavior towards the world and the society they live in. Hence, this work aims to identify how young children respond to various personality behavior and observes their reactions towards them from a system dynamics perspective. We will be exploring the Big Five personality traits in young children. A causal model is developed in support of the system dynamics approach. These models graphically present the factors and factor relationships that contribute to the big five personality traits and provide a better understanding of the entire behavior model. A simulator will be developed that includes a set of causal model factors and factor relationships. The simulator models the behavior of different factors related to personality traits and their impacts and can help make more informed decisions in a risk-free environment.

Keywords: personality traits, systems engineering, system dynamics, causal model, behavior model

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Authors: Safa’ Abdelkarim Hammad

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The electrical power system is one of the main parts of the nation's infrastructure, and the availability and cost of electricity are critical factors in industrial competitiveness and strategy. Restructuring of the electricity supply industries is a very complex exercise based on national energy strategies and policies, macroeconomic developments, and national conditions, and its application varies from country to country. Electricity regulation of natural monopolies is a challenging task. Regulators face the problem of providing appropriate incentives for improvement of efficiency. Incentive regulation is often considered as an efficient regulatory tool to handle the problem, and it is widely applied in several countries. However, the exact regulation methodologies differ from one country to another. Network quantitative reliability evaluation is an essential factor with regard to the quality of supply. The main factors used to judge the reliability of supply is measured by the number and duration of interruptions experienced by customers. Several indicators are used to evaluate reliability in distribution networks. This paper addresses the impact of incentive regulation and performance benchmarking in the field of electricity distribution in Jordan. The theory of efficiency measurement and the most common models; NCSQS and DEA models are presented.

Keywords: incentive regulations, reliability, restructuring, Tarrif

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Authors: Roohallah Yousefi

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Introduction: Voxelotor, also known as GBT 440, binds to the alpha cleft in HbS tetramers and promotes the stability of the relaxed or oxygenated state of HbS. This process hinders the conformational change of the HbS tetramers into the deoxygenated state. Voxelotor prevents interactions between HbS tetramers in the deoxygenated state, ultimately inhibiting the polymerization of HbS tetramers and resulting in significant clinical improvements, particularly in raising hemoglobin levels in patients. In this study, we have explored the use of herbal compound models, such as rosmarinic acid and compounds with similar structures that exhibit high binding affinity to Voxelotor's hemoglobin binding site. Materials and methods: The molecular model of hemoglobin (PDB: 5E83) was initially obtained from the RCSB PDB database. In addition, we collected 453 ligand models with structural similarity to rosmarinic acid from the PubChem database. To prepare these models for molecular docking, we utilized the Molegro Virtual Docker tool. Subsequently, we used the SwissADME web tool to predict the physicochemical properties and pharmacokinetics of these compounds. Results: We investigated the affinity and binding site of 453 compounds similar to rosmarinic acid on the hemoglobin model (PDB: 5E83). Our focus was on the alpha cleft between two alpha chains of the hemoglobin model (PDB: 5E83). The results showed that most compounds had molecular weights above 500 daltons, and some exhibited acceptable hydrophobicity. Furthermore, their solubility in aqueous solutions was good. None of the compounds were able to cross the blood-brain barrier or have gastrointestinal absorption. However, they did have varying inhibitory effects on CYP2C9 cytochromes. The skin penetration rate was generally low. Conclusion: Through our study, we identified three compounds (CID: 162739375, CID: 141386569, and CID: 24015539) with promising potential for further research. These compounds demonstrated high binding affinity to the hemoglobin model, favorable dissolution and digestive absorption rates, as well as suitable hydrophobicity, making them ideal candidates for continued laboratory investigation.

Keywords: voxelotor, binding site, hemoglobin, rosmarinic acid

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Authors: Siddhant Rao

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Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming. It usually requires a trained pathologist to manually examine histopathological images and identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F- measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.

Keywords: breast cancer, mitotic count, machine learning, convolutional neural networks

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Authors: Maruf Ariyo Raheem, Patrick Oseloka Ezepue

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This research aimed at determining most appropriate heteroskedastic model to predicting volatility of 10 major Nigerian banks: Access, United Bank for Africa (UBA), Guaranty Trust, Skye, Diamond, Fidelity, Sterling, Union, ETI and Zenith banks using daily closing stock prices of each of the banks from 2004 to 2014. The models employed include ARCH (1), GARCH (1, 1), EGARCH (1, 1) and TARCH (1, 1). The results show that all the banks returns are highly leptokurtic, significantly skewed and thus non-normal across the four periods except for Fidelity bank during financial crises; findings similar to those of other global markets. There is also strong evidence for the presence of heteroscedasticity, and that volatility persistence during crisis is higher than before the crisis across the 10 banks, with that of UBA taking the lead, about 11 times higher during the crisis. Findings further revealed that Asymmetric GARCH models became dominant especially during financial crises and post crises when the second reforms were introduced into the banking industry by the Central Bank of Nigeria (CBN). Generally, one could say that Nigerian banks returns are volatility persistent during and after the crises, and characterised by leverage effects of negative and positive shocks during these periods

Keywords: global financial crisis, leverage effect, persistence, volatility clustering

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