Search results for: large language models

Authors: Asma Ahmed, Farah Khalid, Sahlah Sohail, Saira Banusokwalla, Sabiha Banu, Inaara Akbar, Safia Awan, Syed Iqbal Azam

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Background and Objectives: The level of knowledge regarding obesity as a disease condition and health-seeking behavior regarding its management is grossly lacking. We present data from our multidisciplinary obesity clinic at the large tertiary care facility in Karachi, Pakistan, to provide baseline profiles and outcomes of patients attending these clinics. Methods: 260 who attended the obesity clinic between June 2018 to March 2020 were enrolled in this study. The analysis included descriptive and ROC analysis to identify the best cut-offs of theanthropometric measurements to diagnose obesity-related comorbid conditions. Results: The majority of the studied population were women (72.3%) and employed(43.7%) with a mean age of 35.5 years. Mean BMIwas 37.4, waist circumference was 112.4 cm, visceral fat was 11.7%, and HbA1C was 6.9%. The most common comorbidities were HTN & D.M (33 &31%, respectively). The prevalence of MetS was 16.3% in patients and was slightly higher in males. Visceral fat was the main factor in predicting D.M (0.750; 95% CI: 0.665, 0.836) and MetS (0.709; 95% CI: 0.590, 0.838) compared to total body fat, waist circumference, and BMI. The risk of predicting DM &MetS for the visceral fat above 9.5% in women had the highest sensitivity (80% for D.M & 79% for MetS) and an NPV (92.75% for D.M & 95% for MetS). Conclusions: This study describes and establishes characteristics of these obese individuals, which can help inform clinical practices. These practices may involve using visceral fat for earlier identification and counseling-based interventions to prevent more severe surgical interventions down the line.

Keywords: obesity, metabolic syndrome, tertiary care facility, BMI, waist circumference, visceral fat

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Authors: Khadem Hichem

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With the new technology challenges, dynamics and challenges of the contemporary world, most teachers are struggling to maintain effective and successful teaching /learning environment for learners. Teachers as a key to the success of reforms in the educational setting, they must improve their competencies to teach effectively. Many researchers emphasis on the ongoing professional development of the teacher by enhancing their experiences and encouraging their responsibility for learning, and thus promoting self-reliance, collaboration, and reflection. In short, teachers are considered as learners and they need to learn together. The educational system must support, both conceptually and financially, the teachers’ development as lifelong learners Teachers need opportunities to grow in language proficiency and in knowledge. Changing nature of language and culture in the world, all teachers must have opportunities to update their knowledge and practices. Many researchers in the field of foreign or additional languages indicate that teachers keep side by side of effective instructional practices and they need special support with the challenging task of developing and administering proficiency tests to their students. For significant change to occur, each individual teacher’s needs must be addressed. The teacher must be involved experientially in the process of development, since, by itself, knowledge of how to change does not mean change will be initiated. For improvement to occur, new skills have to be guided, practiced, and reflected upon in collaboration with colleagues. Clearly, teachers are at different places developmentally; therefore, allowances for various entry levels and individual differences need to be built into the professional development structure. Objectives must be meaningful to the participant and teacher improvement must be stated terms of student knowledge, student performance, and motivation. The most successful professional development process acknowledges the student-centered nature of good teaching. This paper highlights the importance of teacher professional development process and institutional supports as way to enhance good teaching and learning environment.

Keywords: teacher professional development, teacher competencies, institutional support, teacher education

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Authors: Heba Salem

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This paper aims to share the researcher’s experience of using e-Portfolios as an assessment tool to follow up on students’ learning experiences and performance throughout the semester. It also aims at highlighting the importance of students’ self-reflection in the process of language learning. The paper begins by introducing the advanced media course, with its focus on listening and speaking skills, and introduces the students’ profiles. Then it explains the students’ role in the e-portfolio process as they are given the option to choose a listening text they studied throughout the semester and to choose a recorded oral production of their collection of artifacts throughout the semester. Students showcase and reflect on their progress in both listening comprehension and speaking. According to the research, re-listening to work given to them and to their production is a means of reflecting on both their progress and achievement. And choosing the work students want to showcase is a means to promote independent learning as well as self-expression. Students are encouraged to go back to the class learning outcomes in the process of choosing the work. In their reflections, students express how they met the specific learning outcome. While giving their presentations, students expressed how useful the experience of returning and going over what they covered to select one and going over their production as well. They also expressed how beneficial it was to listen to themselves and literally see their progress in both listening comprehension and speaking. Students also reported that they grasped more details from the texts than they did when first having it as an assignment, which coincided with one of the class learning outcomes. They also expressed the fact that they had more confidence speaking as well as they were able to use a variety of vocabulary and idiomatic expressions that students have accumulated. For illustration, this paper includes practical samples of students’ tasks and instructions as well as samples of their reflections. The results of students’ reflections coincide with what the research confirms about the effectiveness of the e-portfolios as a means of assessment. The employment of e-Portfolios has two-folded benefits; students are able to measure the achievement of the targeted learning outcomes, and teachers receive constructive feedback on their teaching methods.

Keywords: e-portfolios, assessment, self assessment, listening and speaking progress, foreign language, reflection, learning out comes, sharing experience

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Authors: Mario Perez-Won, Roberto Lemus-Mondaca, Fernanda Marin, Constanza Olivares

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This study presents the simultaneous application of high hydrostatic pressure (HHP) and osmotic dehydration of jumbo squid (Dosidicus gigas) slice. Diffusion coefficients for both components water and solids were improved by the process pressure, being influenced by pressure level. The working conditions were different pressures such as 100, 250, 400 MPa and pressure atmospheric (0.1 MPa) for time intervals from 30 to 300 seconds and a 15% NaCl concentration. The mathematical expressions used for mass transfer simulations both water and salt were those corresponding to Newton, Henderson and Pabis, Page and Weibull models, where the Weibull and Henderson-Pabis models presented the best fitted to the water and salt experimental data, respectively. The values for water diffusivity coefficients varied from 1.62 to 8.10x10⁻⁹ m²/s whereas that for salt varied among 14.18 to 36.07x10⁻⁹ m²/s for selected conditions. Finally, as to quality parameters studied under the range of experimental conditions studied, the treatment at 250 MPa yielded on the samples a minimum hardness, whereas springiness, cohesiveness and chewiness at 100, 250 and 400 MPa treatments presented statistical differences regarding to unpressurized samples. The colour parameters L* (lightness) increased, however, but b* (yellowish) and a* (reddish) parameters decreased when increasing pressure level. This way, samples presented a brighter aspect and a mildly cooked appearance. The results presented in this study can support the enormous potential of hydrostatic pressure application as a technique important for compounds impregnation under high pressure.

Keywords: colour, diffusivity, high pressure, jumbo squid, modelling, texture

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Authors: Ram Mohan, Richard Haney, Ajit Kelkar

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Current developments in computing have shown the advantage of using one or more Graphic Processing Units (GPU) to boost the performance of many computationally intensive applications but there are still limits to these GPU-enhanced systems. The major factors that contribute to the limitations of GPU(s) for High Performance Computing (HPC) can be categorized as hardware and software oriented in nature. Understanding how these factors affect performance is essential to develop efficient and robust applications codes that employ one or more GPU devices as powerful co-processors for HPC computational modeling. This research and technical presentation will focus on the analysis and understanding of the intrinsic interrelationship of both hardware and software categories on computational performance for single and multiple GPU-enhanced systems using a computationally intensive application that is representative of a large portion of challenges confronting modern HPC. The representative application uses unstructured finite element computations for transient composite resin infusion process flow modeling as the computational core, characteristics and results of which reflect many other HPC applications via the sparse matrix system used for the solution of linear system of equations. This work describes these various software and hardware factors and how they interact to affect performance of computationally intensive applications enabling more efficient development and porting of High Performance Computing applications that includes current, legacy, and future large scale computational modeling applications in various engineering and scientific disciplines.

Keywords: graphical processing unit, software development and engineering, performance analysis, system architecture and software performance

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Authors: Parker Murray, Marci Johnson, Tyson S. Burnham, Alina K. Fong, Mark D. Allen, Bruce McIff

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Purpose: Pathological dysregulation of Neurovascular Coupling (NVC) caused by mild traumatic brain injury (mTBI) is the predominant source of chronic post-concussion syndrome (PCS) symptomology. fNCI has the ability to localize dysregulation in NVC by measuring blood-oxygen-level-dependent (BOLD) signaling during the performance of fMRI-adapted neuropsychological evaluations. With fNCI, 57 brain areas consistently affected by concussion were identified as PCS neural markers, which were validated on large samples of concussion patients and healthy controls. These neuromarkers provide the basis for a computation of PCS severity which is referred to as the Severity Index Score (SIS). The SIS has proven valuable in making pre-treatment decisions, monitoring treatment efficiency, and assessing long-term stability of outcomes. Methods and Materials: After being scanned while performing various cognitive tasks, 476 concussed patients received an SIS score based on the neural dysregulation of the 57 previously identified brain regions. These scans provide an objective measurement of attentional, subcortical, visual processing, language processing, and executive functioning abilities, which were used as biomarkers for post-concussive neural dysregulation. Initial SIS scores were used to develop individualized therapy incorporating cognitive, occupational, and neuromuscular modalities. These scores were also used to establish pre-treatment benchmarks and measure post-treatment improvement. Results: Changes in SIS were calculated in percent change from pre- to post-treatment. Patients showed a mean improvement of 76.5 percent (σ= 23.3), and 75.7 percent of patients showed at least 60 percent improvement. Longitudinal reassessment of 24 of the patients, measured an average of 7.6 months post-treatment, shows that SIS improvement is maintained and improved, with an average of 90.6 percent improvement from their original scan. Conclusions: fNCI provides a reliable measurement of NVC allowing for identification of concussion pathology. Additionally, fNCI derived SIS scores direct tailored therapy to restore NVC, subsequently resolving chronic PCS resulting from mTBI.

Keywords: concussion, functional magnetic resonance imaging (fMRI), neurovascular coupling (NVC), post-concussion syndrome (PCS)

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Authors: Kedar Nath Nepal, Dyutiman Choudhary, Naba Raj Pandit, Yam Gahire

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Crop fertilizer recommendations are outdated as these are based on the response trails conducted over half a century ago. Further, these recommendations were based on the response trials conducted over large geographical area ignoring the large spatial variability in indigenous nutrient supplying capacity of soils typical of most smallholder systems. Application of fertilizer following such blanket recommendation in fields with varying native nutrient supply capacity leads to under application in some places and over application in others leading to reduced nutrient-use-efficiency (NUE), loss of profitability, and increased environmental risks associated with loss of unutilized nutrient through emissions or leaching. Opportunities exist to further increase yield and profitability through a significant gain in fertilizer use efficiency with commercialization of affordable and precise application technologies. We conducted participatory trails in Maize (Zea Mays), Cauliflower (Brassica oleracea var. botrytis) and Tomato (Solanum lycopersicum) in Mid Hills of Nepal to evaluate the efficacy of Urea Deep Placement (UDP and Polymer Coated Urea (PCU);. UDP contains 46% of N having individual briquette size 2.7 gm each and PCU contains 44% of N . Both PCU and urea briquette applied at reduced amount (100 kg N/ha) during planting produced similar yields (p>0.05) compared with regular urea (200 Kg N/ha). . These fertilizers also reduced N fertilizer by 35 - 50% over government blanket recommendations. Further, PCU and urea briquette increased farmer’s net income by USD 60 to 80.

Keywords: high efficiency fertilizers, urea deep placement, briquette polymer coated urea, zea mays, brassica, lycopersicum, Nepal

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Authors: Hyeonseok Yoo, Kiseok Oh, Jinsub Choi

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Titanium oxide nanotubes have attracted great attention because of its photocatalytic activity and large surface area. For enhancing electrochemical properties, catalysts should be doped into the structure because titanium oxide nanotubes themselves have low electroconductivity and catalytic activity. It has been reported that Ru and Ir doped titanium oxide electrodes exhibit high efficiency and low overpotential in the oxygen evolution reaction (OER) for water splitting. In general, titanium oxide nanotubes with high aspect ratio cannot be easily doped by conventional complex methods. Herein, two types of facile routes, namely single step anodization and potential shock, for Ru doping into high aspect ratio titanium oxide nanotubes are introduced in detail. When single step anodization was carried out, stability of electrodes were increased. However, onset potential was shifted to anodic direction. On the other hand, when high potential shock voltage was applied, a large amount of ruthenium/ruthenium oxides were doped into titanium oxide nanotubes and thick barrier oxide layers were formed simultaneously. Regardless of doping routes, ruthenium/ ruthenium oxides were homogeneously doped into titanium oxide nanotubes. In spite of doping routes, doping in aqueous solution generally led to incorporate high amount of Ru in titanium oxide nanotubes, compared to that in non-aqueous solution. The amounts of doped catalyst were analyzed by X-ray photoelectron spectroscopy (XPS). The optimum condition for water splitting was investigated in terms of the amount of doped Ru and thickness of barrier oxide layer.

Keywords: doping, potential shock, single step anodization, titanium oxide nanotubes

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Authors: Avi Shrivastava

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Title 42, an emergency health decree, has forced the federal authorities to turn away asylum seekers and all other border crossers since last year. When Title 42 was first deployed in immigration detention centers, where many migrants are held when they arrive at the U.S.-Mexico border, the Trump administration embraced it as a strategy. Expulsions Policy and New Border Challenges will be examined in regard to Title 42 concerns. Humanitarian measures for refugees arriving at the US-Mexico border are the focus of this article. To a large extent, this article addresses the implications of the United States' use of Title 42 in expelling refugees and the possible ramifications of doing away with it. A secondary data collecting strategy was used to gather the information for this study, allowing researchers to examine a large number of previously collected data sets. Information about Title 42 may be found in a variety of places, such as scholarly publications, newspapers, books, and the internet. The inquiry employed qualitative and explanatory research approaches. The claim that 1.7 million individuals were forced to leave the country as a result of it was withdrawn. Since CBP and ICE were limited in their ability to process deportees, it employed a very random patchwork technique in selecting the expelled individuals. As a consequence, repeat offenders, particularly those who were single, got a reduced punishment. The government will be compelled to focus on long-overdue but vital border enhancements if expulsions are halted. Title 42 provisions may help expedite the processing of asylum and other types of humanitarian relief. The government is prepared for an increase in arrivals, but ending the program would lead to a return to arrival levels seen during the Title 42 period.

Keywords: migrants, refugees, title 42, medical, trump administration

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Authors: Ahmed Mantawy, James C. Anderson

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Numerous cases of earthquakes (main-shocks) that were followed by aftershocks have been recorded in California. In 1992 a pair of strong earthquakes occurred within three hours of each other in Southern California. The first shock occurred near the community of Landers and was assigned a magnitude of 7.3 then the second shock occurred near the city of Big Bear about 20 miles west of the initial shock and was assigned a magnitude of 6.2. In the same year, a series of three earthquakes occurred over two days in the Cape-Mendocino area of Northern California. The main-shock was assigned a magnitude of 7.0 while the second and the third shocks were both assigned a value of 6.6. This paper investigates the effect of a main-shock accompanied with aftershocks of significant intensity on reinforced concrete (RC) frame buildings to indicate nonlinear behavior using PERFORM-3D software. A 6-story building in San Bruno and a 20-story building in North Hollywood were selected for the study as both of them have RC moment resisting frame systems. The buildings are also instrumented at multiple floor levels as a part of the California Strong Motion Instrumentation Program (CSMIP). Both buildings have recorded responses during past events such as Loma-Prieta and Northridge earthquakes which were used in verifying the response parameters of the numerical models in PERFORM-3D. The verification of the numerical models shows good agreement between the calculated and the recorded response values. Then, different scenarios of a main-shock followed by a series of aftershocks from real cases in California were applied to the building models in order to investigate the structural behavior of the moment-resisting frame system. The behavior was evaluated in terms of the lateral floor displacements, the ductility demands, and the inelastic behavior at critical locations. The analysis results showed that permanent displacements may have happened due to the plastic deformation during the main-shock that can lead to higher displacements during after-shocks. Also, the inelastic response at plastic hinges during the main-shock can change the hysteretic behavior during the aftershocks. Higher ductility demands can also occur when buildings are subjected to trains of ground motions compared to the case of individual ground motions. A general conclusion is that the occurrence of aftershocks following an earthquake can lead to increased damage within the elements of an RC frame buildings. Current code provisions for seismic design do not consider the probability of significant aftershocks when designing a new building in zones of high seismic activity.

Keywords: reinforced concrete, existing buildings, aftershocks, damage accumulation

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Authors: Lovesh Goyal, Pushkar Dave, Prajyot Jadhav, GonnaYaswanth, Sakshi Giri, Sahil Dharme, Rushika Joshi, Rishabh Verma, Shital Chiddarwar

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With the rising need for human-robot interaction, researchers have proposed and tested multiple models with varying degrees of success. A few of these models performed on aerial platforms are commonly known as Tethered Aerial Systems. These aerial vehicles may be powered continuously by a tether cable, which addresses the predicament of the short battery life of quadcopters. This system finds applications to minimize humanitarian efforts for industrial, medical, agricultural, and service uses. However, a significant challenge in employing such systems is that it necessities attaining smooth and secure robot-human interaction while ensuring that the forces from the tether remain within the standard comfortable range for the humans. To tackle this problem, a hybrid control method that could switch between two control techniques: constant control input and the steady-state solution, is implemented. The constant control approach is implemented when a person is far from the target location, and error is thought to be eventually constant. The controller switches to the steady-state approach when the person reaches within a specific range of the goal position. Both strategies take into account human velocity feedback. This hybrid technique enhances the outcomes by assisting the person to reach the desired location while decreasing the human's unwanted disturbance throughout the process, thereby keeping the interaction between the robot and the subject smooth.

Keywords: unmanned aerial vehicle, tethered system, physical human-robot interaction, hybrid control

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Authors: Jiří F. Urbánek, Alena Oulehlová, Hana Malachová, Jiří J. Urbánek Jr.

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Crisis situations investigation and modelling are introduced and made within the complex system of energetic critical infrastructure, operating on peril´s environments. Every crisis situations and perils has an origin in the emergency/ crisis event occurrence and they need critical/ crisis interfaces assessment. Here, the emergency events can be expected - then crisis scenarios can be pre-prepared by pertinent organizational crisis management authorities towards their coping; or it may be unexpected - without pre-prepared scenario of event. But the both need operational coping by means of crisis management as well. The operation, forms, characteristics, behaviour and utilization of crisis management have various qualities, depending on real critical infrastructure organization perils, and prevention training processes. An aim is always - better security and continuity of the organization, which successful obtainment needs to find and investigate critical/ crisis zones and functions in critical infrastructure organization models, operating in pertinent perils environment. Our DYVELOP (Dynamic Vector Logistics of Processes) method is disposables for it. Here, it is necessary to derive and create identification algorithm of critical/ crisis interfaces. The locations of critical/ crisis interfaces are the flags of crisis situation in organization of critical infrastructure models. Then, the model of crisis situation will be displayed at real organization of Czech energetic crisis infrastructure subject in real peril environment. These efficient measures are necessary for the infrastructure protection. They will be derived for peril mitigation, crisis situation coping and for environmentally friendly organization survival, continuity and its sustainable development advanced possibilities.

Keywords: algorithms, energetic infrastructure complex system, modelling, peril´s environment

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Magy Atef Awad Attia

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The objective of this study was to study the effect of social stories on social interaction of students with autism. The sample was at level 5 student with autism, Another University Demonstration School student, who was diagnosed by the Physician as High Functioning Autism since he was able to read, write, calculate and was studying in inclusive classroom. However, he still had disability in social interaction to participate in social activity group and communication. He could not learn how to develop friendship or create relationship. He had inappropriate behavior in social context. He did not understand complex social situations. In addition, he did seemed to not know time and place. He was not able to understand feeling of oneself as well as the others. Consequently, he could not express his emotion appropriately. He did not understand or express his non-verbal language for communicating with friends. He lacked of common interest or emotion with nearby persons. He greeted inappropriately or was not interested in greeting. In addition, he did not have eye contact. He used inadequate language etc. He was elected by Purposive Sampling. His parents were willing to allow them to participate in this study. The research instruments were the lesson plan of social stories, and the picture book of social stories. The instruments used for data collection, were the social interaction evaluation of autistic students. This research was Experimental Research as One Group Pre-test, Post-test Design. For the Pre-test, the experiment was conducted by social stories. Then, the Post-test was implemented. The statistic used for data analysis. The research results were shown by scale. The results revealed that the autistic students taught by social stories indicated better social reaction after being taught by social stories.

Keywords: autism, autistic behavior, stability, harsh environments, techniques, thermal, properties, materials, applications, brittleness, fragility, disadvantages, bank, branches, profitability, setting prediction, effective target, measurement, evaluation, performance, commercial, business, sustainability, financial, system.

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Authors: Seema Paul, Jesper Oppelstrup, Roger Thunvik, Vladimir Cvetkovic

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Lake Victoria is the second largest fresh water body in the world, located in East Africa with a catchment area of 250,000 km², of which 68,800 km² is the actual lake surface. The hydrodynamic processes of the shallow (40–80 m deep) water system are unique due to its location at the equator, which makes Coriolis effects weak. The paper describes a St.Venant shallow water model of Lake Victoria developed in COMSOL Multiphysics software, a general purpose finite element tool for solving partial differential equations. Depth soundings taken in smaller parts of the lake were combined with recent more extensive data to resolve the discrepancies of the lake shore coordinates. The topography model must have continuous gradients, and Delaunay triangulation with Gaussian smoothing was used to produce the lake depth model. The model shows large-scale flow patterns, passive tracer concentration and water level variations in response to river and tracer inflow, rain and evaporation, and wind stress. Actual data of precipitation, evaporation, in- and outflows were applied in a fifty-year simulation model. It should be noted that the water balance is dominated by rain and evaporation and model simulations are validated by Matlab and COMSOL. The model conserves water volume, the celerity gradients are very small, and the volume flow is very slow and irrotational except at river mouths. Numerical experiments show that the single outflow can be modelled by a simple linear control law responding only to mean water level, except for a few instances. Experiments with tracer input in rivers show very slow dispersion of the tracer, a result of the slow mean velocities, in turn, caused by the near-balance of rain with evaporation. The numerical and hydrodynamical model can evaluate the effects of wind stress which is exerted by the wind on the lake surface that will impact on lake water level. Also, model can evaluate the effects of the expected climate change, as manifest in changes to rainfall over the catchment area of Lake Victoria in the future.

Keywords: bathymetry, lake flow and steady state analysis, water level validation and concentration, wind stress

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Authors: Mohammad Ghiasvand, Babak Khorsandi, Morteza Kolahdoozan

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Under the influence of waves, oil in the sea is subject to vertical scattering in the water column. Scientists' knowledge of how oil is dispersed in the water column is one of the lowest levels of knowledge among other processes affecting oil in the marine environment, which highlights the need for research and study in this field. Therefore, this study investigates the distribution of oil in the water column in a turbulent environment with zero velocity characteristics. Lack of laboratory results to analyze the distribution of petroleum pollutants in deep water for information Phenomenon physics on the one hand and using them to calibrate numerical models on the other hand led to the development of laboratory models in research. According to the aim of the present study, which is to investigate the distribution of oil in homogeneous and isotropic turbulence caused by the oscillating Grid, after reaching the ideal conditions, the crude oil flow was poured onto the water surface and oil was distributed in deep water due to turbulence was investigated. In this study, all experimental processes have been implemented and used for the first time in Iran, and the study of oil diffusion in the water column was considered one of the key aspects of pollutant diffusion in the oscillating Grid environment. Finally, the required oscillation velocities were taken at depths of 10, 15, 20, and 25 cm from the water surface and used in the analysis of oil diffusion due to turbulence parameters. The results showed that with the characteristics of the present system in two static modes and network motion with a frequency of 0.8 Hz, the results of oil diffusion in the four mentioned depths at a frequency of 0.8 Hz compared to the static mode from top to bottom at 26.18, 57 31.5, 37.5 and 50% more. Also, after 2.5 minutes of the oil spill at a frequency of 0.8 Hz, oil distribution at the mentioned depths increased by 49, 61.5, 85, and 146.1%, respectively, compared to the base (static) state.

Keywords: homogeneous and isotropic turbulence, oil distribution, oscillating grid, oil spill

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Authors: Yue Zhang, Bo Yang, Gang Xiong, Mohamed Elchalakanic, Shidong Nie

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This study aims to investigate the lateral torsional buckling of I-shaped cross-section beams fabricated from Q460GJ structural steel plates. Both experimental and numerical simulation results are presented in this paper. A total of eight specimens were tested under a three-point bending, and the corresponding numerical models were established to conduct parametric studies. The effects of some key parameters such as the non-dimensional member slenderness and the height-to-width ratio, were investigated based on the verified numerical models. Also, the results obtained from the parametric studies were compared with the predictions calculated by different design codes including the Chinese design code (GB50017-2003, 2003), the new draft version of Chinese design code (GB50017-201X, 2012), Eurocode 3 (EC3, 2005) and the North America design code (ANSI/AISC360-10, 2010). These comparisons indicated that the sectional height-to-width ratio does not play an important role to influence the overall stability load-carrying capacity of Q460GJ structural steel beams with welded I-shaped cross-sections. It was also found that the design methods in GB50017-2003 and ANSI/AISC360-10 overestimate the overall stability and load-carrying capacity of Q460GJ welded I-shaped cross-section beams.

Keywords: experimental study, finite element analysis, global stability, lateral torsional buckling, Q460GJ structural steel

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Authors: Gupta Renu, T. S. Roy

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Introduction: For the prospect of successful replacement therapies in treatment of Diabetes mallitus it is necessary to know events occurring during normal human pancreas development. Literature of human pancreas development are few in number as well as mainly related to first trimester because of ethical and technical difficulties. So the study was conducted on 12 fetuses from 12 gestational weeks (GW) to 5 months of infant to know normal development of exocrine and endocrine part of human pancreas. Material and Methods: Human fetalpancreases were screened by haematoxyline and eosin staining and done electron microscopy for suitable specimens to know ultrastructural detail of fetal pancreas. Results:It was observed arborized tubules, the cells budding out from these tubules differentiated into primitive acini and islets in 12thGW. At 14 weeks scanty granules were observed in the endocrine cells which coincided with the capillary invasion of the islets. The ducts and acini were surrounded by well-organized connective tissue. The acinihad elongated cells, small amount of cytoplasm and large open face euchromatic nuclei with single nucleolus. The mature form of islets of Langerhans was observed close to the acini and duct in 20 GW fetus. Connective tissue around the duct was well organized.No significant developmental change was observed early postnatal, infant. Conclusion: The development of both component exocrine as well as endocrine part of human fetal pancreas was studied by light and electron microscopy. Observations suggested that the fetal pancreas contained mainly ducts, few acini, many centroacinar cells, and large undifferentiated tissue.

Keywords: gestational weeks (GW), acini, islets of Langerhans, ducts

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Authors: Alana E. Jansen

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With such a large proportion of organisations relying on the use of team-based structures, it is surprising that so few teams would be classified as high-performance teams. While the impact of team composition on performance has been researched frequently, there have been conflicting findings as to the effects, particularly when examined alongside other team factors. To broaden the theoretical perspectives on this topic and potentially explain some of the inconsistencies in research findings left open by other various models of team effectiveness and high-performing teams, the present study aims to use the Job-Demands-Resources model, typically applied to burnout and engagement, as a framework to examine how team composition factors (particularly diversity in team member characteristics) can facilitate or hamper team effectiveness. This study used a virtual interview design where participants were asked to both rate and describe their experiences, in one high-performing and one low-performing team, over several factors relating to demands, resources, team composition, and team effectiveness. A semi-structured interview protocol was developed, which combined the use of the Likert style and exploratory questions. A semi-targeted sampling approach was used to invite participants ranging in age, gender, and ethnic appearance (common surface-level diversity characteristics) and those from different specialties, roles, educational and industry backgrounds (deep-level diversity characteristics). While the final stages of data analyses are still underway, thematic analysis using a grounded theory approach was conducted concurrently with data collection to identify the point of thematic saturation, resulting in 35 interviews being completed. Analyses examine differences in perceptions of demands and resources as they relate to perceived team diversity. Preliminary results suggest that high-performing and low-performing teams differ in perceptions of the type and range of both demands and resources. The current research is likely to offer contributions to both theory and practice. The preliminary findings suggest there is a range of demands and resources which vary between high and low-performing teams, factors which may play an important role in team effectiveness research going forward. Findings may assist in explaining some of the more complex interactions between factors experienced in the team environment, making further progress towards understanding the intricacies of why only some teams achieve high-performance status.

Keywords: diversity, high-performing teams, job demands and resources, team effectiveness

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Authors: Yi-Hsiu Lai

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The present study aimed to elucidate the nature of semantic processing in Chinese. Language and cognition related to the issue of aging are examined from the perspective of picture naming and category fluency tasks. Twenty Chinese-speaking adults (ranging from 25 to 45 years old) and twenty Chinese-speaking seniors (ranging from 65 to 75 years old) in Taiwan participated in this study. Each of them individually completed two tasks: a picture naming task and a category fluency task. Instruments for the naming task were sixty black-and-white pictures: thirty-five object and twenty-five action pictures. Category fluency task also consisted of two semantic categories – objects (or nouns) and actions (or verbs). Participants were asked to report as many items within a category as possible in one minute. Scores of action fluency and of object fluency were a summation of correct responses in these two categories. Category effects (actions vs. objects) and age effects were examined in these tasks. Objects were further divided into two major types: living objects and non-living objects. Actions were also categorized into two major types: action verbs and process verbs. Reaction time to each picture/question was additionally calculated and analyzed. Results of the category fluency task indicated that the content of information in Chinese seniors was comparatively deteriorated, thus producing smaller number of semantic-lexical items. Significant group difference was also found in the results of reaction time. Category Effect was significant for both Chinese adults and seniors in the semantic fluency task. Findings in the present study helped characterize the nature of semantic processing in Chinese-speaking adults and seniors and contributed to the issue of language and aging.

Keywords: semantic processing, aging, Chinese, category effects

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Authors: Piotr Dukalski, Bartlomiej Bedkowski, Tomasz Jarek, Tomasz Wolnik

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The article is concerned with the design of an electric in wheel hub motor installed in an electric car with two-wheel drive. It presents the construction of the motor on the 3D cross-section model. Work simulation of the motor (applicated to Fiat Panda car) and selected driving parameters such as driving on the road with a slope of 20%, driving at maximum speed, maximum acceleration of the car from 0 to 100 km/h are considered by the authors in the article. The demand for the drive power taking into account the resistance to movement was determined for selected driving conditions. The parameters of the motor operation and the power losses in its individual elements, calculated using the FEM 2D method, are presented for the selected car driving parameters. The calculated power losses are used in 3D models for thermal calculations using the CFD method. Detailed construction of thermal models with materials data, boundary conditions and losses calculated using the FEM 2D method are presented in the article. The article presents and describes calculated temperature distributions in individual motor components such as winding, permanent magnets, magnetic core, body, cooling system components. Generated losses in individual motor components and their impact on the limitation of its operating parameters are described by authors. Attention is paid to the losses generated in permanent magnets, which are a source of heat as the removal of which from inside the motor is difficult. Presented results of calculations show how individual motor power losses, generated in different load conditions while driving, affect its thermal state.

Keywords: electric car, electric drive, electric motor, thermal calculations, wheel hub motor

Procedia PDF Downloads 159

Authors: Marek Dosbaba

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Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

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Authors: Tanit Daniel Jodar Vecina, Adriane Prisco Petry

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The shape of the wind velocity profile changes according to local features of terrain shape and roughness, which are parameters responsible for defining the Atmospheric Boundary Layer (ABL) profile. Air flow characteristics over and around landforms, such as hills, are of considerable importance for applications related to Wind Farm and Turbine Engineering. The air flow is accelerated on top of hills, which can represent a decisive factor for Wind Turbine placement choices. The present work focuses on the study of ABL behavior as a function of slope and surface roughness of hill-shaped landforms, using the Computational Fluid Dynamics (CFD) to build wind velocity and turbulent intensity profiles. Reynolds-Averaged Navier-Stokes (RANS) equations are closed using the SST k-ω turbulence model; numerical results are compared to experimental data measured in wind tunnel over scale models of the hills under consideration. Eight hill models with slopes varying from 25° to 68° were tested for two types of terrain categories in 2D and 3D, and two analytical codes are used to represent the inlet velocity profiles. Numerical results for the velocity profiles show differences under 4% when compared to their respective experimental data. Turbulent intensity profiles show maximum differences around 7% when compared to experimental data; this can be explained by not being possible to insert inlet turbulent intensity profiles in the simulations. Alternatively, constant values based on the averages of the turbulent intensity at the wind tunnel inlet were used.

Keywords: Atmospheric Boundary Layer, Computational Fluid Dynamic (CFD), Numerical Modeling, Wind Tunnel

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Authors: Essam Mansour

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This study purposes to explore the adoption and use of social media as a source of information by Egyptian government journalists. It applied a survey with a total of 386 journalists representing the three official newspapers of Egypt. Findings showed that 27.2% of journalists were found to not use social media, mainly males (69.7%), older than 40 years (77.7%) and mostly with a BA degree (80.4%). On the other hand, 72.8% of them were found to use these platforms who were also males (59.1%), younger than 40 years (65.9%) and mostly with a BA degree (93.2%). More than two-thirds (69.9%) were somewhat old users whose experience ranged from seven to ten years, and more than two-thirds (73.5%) have been heavily using these platforms (four to more than six hours a day. Such results confirm that a large number (95.7%) of users were found to be at least advanced users. Social media users’ home and work were the most significant places to access these platforms, which were found to be easy and useful to use. Most types of social media used were social news, media sharing and micro blogging, blogs comments and forums, social networking sites and bookmarking sites to perform tasks, such as finding information, making communication, keeping up to date, checking materials, sharing information and making discussions. A large number of users tend to accept these media platforms to be a source of information since they are accessible, linked references updated sources, accurate, promote current work, convenient, secured, credible, reliable, stabled, easily identified, copyrighted, build confident and contain filtered information. However, lack of know-how to cite sources, followed by lack of credibility of the source of news, lack of quality of information sources and lack of time were at least significant to journalists when using social media platforms.

Keywords: social media, social networking sites, newspapers, journalists, Egypt

Procedia PDF Downloads 243

Authors: Vangmayee V. Subban, Suzan Deelan. Pinto, Somashekara Haralakatta Shivananjappa, Shwetha Prabhu, Jayashree S. Bhat

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Introduction and Need: During early years of schooling, the instruction in the schools mainly focus on children’s word decoding abilities. However, the skilled readers should master all the components of reading such as word decoding, reading fluency and comprehension. Nevertheless, the relationship between each component during the process of learning to read is less clear. The studies conducted in alphabetical languages have mixed opinion on relative contribution of word decoding and reading fluency on reading comprehension. However, the scenarios in alphasyllabary languages are unexplored. Aim and Objectives: The aim of the study was to explore the role of word decoding, reading fluency on reading comprehension abilities in children learning to read Kannada between the age ranges of 5.6 to 8.6 years. Method: In this cross sectional study, a total of 60 typically developing children, 20 each from Grade I, Grade II, Grade III maintaining equal gender ratio between the age range of 5.6 to 6.6 years, 6.7 to 7.6 years and 7.7 to 8.6 years respectively were selected from Kannada medium schools. The reading fluency and reading comprehension abilities of the children were assessed using Grade level passages selected from the Kannada text book of children core curriculum. All the passages consist of five questions to assess reading comprehension. The pseudoword decoding skills were assessed using 40 pseudowords with varying syllable length and their Akshara composition. Pseudowords are formed by interchanging the syllables within the meaningful word while maintaining the phonotactic constraints of Kannada language. The assessment material was subjected to content validation and reliability measures before collecting the data on the study samples. The data were collected individually, and reading fluency was assessed for words correctly read per minute. Pseudoword decoding was scored for the accuracy of reading. Results: The descriptive statistics indicated that the mean pseudoword reading, reading comprehension, words accurately read per minute increased with the Grades. The performance of Grade III children found to be higher, Grade I lower and Grade II remained intermediate of Grade III and Grade I. The trend indicated that reading skills gradually improve with the Grades. Pearson’s correlation co-efficient showed moderate and highly significant (p=0.00) positive co-relation between the variables, indicating the interdependency of all the three components required for reading. The hierarchical regression analysis revealed 37% variance in reading comprehension was explained by pseudoword decoding and was highly significant. Subsequent entry of reading fluency measure, there was no significant change in R-square and was only change 3%. Therefore, pseudoword-decoding evolved as a single most significant predictor of reading comprehension during early Grades of reading acquisition. Conclusion: The present study concludes that the pseudoword decoding skills contribute significantly to reading comprehension than reading fluency during initial years of schooling in children learning to read Kannada language.

Keywords: alphasyllabary, pseudo-word decoding, reading comprehension, reading fluency

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

Procedia PDF Downloads 391

Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

Procedia PDF Downloads 107

Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Mayowa A. Sofowora, Seraphin D. Eyono Obono

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The use of information and communication technologies such as computers, mobile phones and the internet is becoming prevalent in today’s world; and it is facilitating access to a vast amount of data, services, and applications for the improvement of people’s lives. However, this prevalence of ICTs is hampered by the problem of low income levels in developing countries to the point where people cannot timeously replace or repair their ICT devices when damaged or lost; and this problem serves as a motivation for this study whose aim is to examine the perceptions of teachers on the reliability of cellphones when used for teaching and learning purposes. The research objectives unfolding this aim are of two types: objectives on the selection and design of theories and models, and objectives on the empirical testing of these theories and models. The first type of objectives is achieved using content analysis in an extensive literature survey, and the second type of objectives is achieved through a survey of high school teachers from the ILembe and Umgungudlovu districts in the KwaZuluNatal province of South Africa. Data collected from this questionnaire based survey is analysed in SPSS using descriptive statistics and Pearson correlations after checking the reliability and validity of the questionnaire. The main hypothesis driving this study is that there is a relationship between the demographics and the attribution identity of teachers on one hand, and their perceptions on the reliability of cellphones on the other hand, as suggested by existing literature; except that attribution identities are considered in this study under three angles: intention, knowledge and ability, and action. The results of this study confirm that the perceptions of teachers on the reliability of cellphones for teaching and learning are affected by the school location of these teachers, and by their perceptions on learners’ cellphones usage intentions and actual use.

Keywords: attribution, cellphones, e-learning, reliability

Procedia PDF Downloads 384