Search results for: curriculum models

Authors: Amit Gill, Lalith Wijerathne, Sebastian Poledna

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This research presents the details of the implementation of high performance computing (HPC) extension of agent-based economic models (ABEMs) to simulate hundreds of millions of heterogeneous agents. ABEMs offer an alternative approach to study the economy as a dynamic system of interacting heterogeneous agents, and are gaining popularity as an alternative to standard economic models. Over the last decade, ABEMs have been increasingly applied to study various problems related to monetary policy, bank regulations, etc. When it comes to predicting the effects of local economic disruptions, like major disasters, changes in policies, exogenous shocks, etc., on the economy of the country or the region, it is pertinent to study how the disruptions cascade through every single economic entity affecting its decisions and interactions, and eventually affect the economic macro parameters. However, such simulations with hundreds of millions of agents are hindered by the lack of HPC enhanced ABEMs. In order to address this, a scalable Distributed Memory Parallel (DMP) implementation of ABEMs has been developed using message passing interface (MPI). A balanced distribution of computational load among MPI-processes (i.e. CPU cores) of computer clusters while taking all the interactions among agents into account is a major challenge for scalable DMP implementations. Economic agents interact on several random graphs, some of which are centralized (e.g. credit networks, etc.) whereas others are dense with random links (e.g. consumption markets, etc.). The agents are partitioned into mutually-exclusive subsets based on a representative employer-employee interaction graph, while the remaining graphs are made available at a minimum communication cost. To minimize the number of communications among MPI processes, real-life solutions like the introduction of recruitment agencies, sales outlets, local banks, and local branches of government in each MPI-process, are adopted. Efficient communication among MPI-processes is achieved by combining MPI derived data types with the new features of the latest MPI functions. Most of the communications are overlapped with computations, thereby significantly reducing the communication overhead. The current implementation is capable of simulating a small open economy. As an example, a single time step of a 1:1 scale model of Austria (i.e. about 9 million inhabitants and 600,000 businesses) can be simulated in 15 seconds. The implementation is further being enhanced to simulate 1:1 model of Euro-zone (i.e. 322 million agents).

Keywords: agent-based economic model, high performance computing, MPI-communication, MPI-process

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Authors: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

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Authors: J. Fox, J. Randhawa, M. Chan, L. Campbell, A. Weakely, D. J. Harvey, S. Tomaszewski Farias

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Early identification of individuals at risk for conversion to dementia provides an opportunity for preventative treatment. Many older adults (30-60%) report specific subjective cognitive decline (SCD); however, previous research is inconsistent in terms of what types of complaints predict future cognitive decline. The purpose of this study is to identify which specific complaints from the Everyday Cognition Scales (ECog) scales, a measure of self-reported concerns for everyday abilities across six cognitive domains, are associated with: 1) conversion from a clinical diagnosis of normal to either MCI or dementia (categorical variable) and 2) progressive cognitive decline in memory and executive function (continuous variables). 415 cognitively normal older adults were monitored annually for an average of 5 years. Cox proportional hazards models were used to assess associations between self-reported ECog items and progression to impairment (MCI or dementia). A total of 114 individuals progressed to impairment; the mean time to progression was 4.9 years (SD=3.4 years, range=0.8-13.8). Follow-up models were run controlling for depression. A subset of individuals (n=352) underwent repeat cognitive assessments for an average of 5.3 years. For those individuals, mixed effects models with random intercepts and slopes were used to assess associations between ECog items and change in neuropsychological measures of episodic memory or executive function. Prior to controlling for depression, subjective concerns on five of the eight Everyday Memory items, three of the nine Everyday Language items, one of the seven Everyday Visuospatial items, two of the five Everyday Planning items, and one of the six Everyday Organization items were associated with subsequent diagnostic conversion (HR=1.25 to 1.59, p=0.003 to 0.03). However, after controlling for depression, only two specific complaints of remembering appointments, meetings, and engagements and understanding spoken directions and instructions were associated with subsequent diagnostic conversion. Episodic memory in individuals reporting no concern on ECog items did not significantly change over time (p>0.4). More complaints on seven of the eight Everyday Memory items, three of the nine Everyday Language items, and three of the seven Everyday Visuospatial items were associated with a decline in episodic memory (Interaction estimate=-0.055 to 0.001, p=0.003 to 0.04). Executive function in those reporting no concern on ECog items declined slightly (p <0.001 to 0.06). More complaints on three of the eight Everyday Memory items and three of the nine Everyday Language items were associated with a decline in executive function (Interaction estimate=-0.021 to -0.012, p=0.002 to 0.04). These findings suggest that specific complaints across several cognitive domains are associated with diagnostic conversion. Specific complaints in the domains of Everyday Memory and Language are associated with a decline in both episodic memory and executive function. Increased monitoring and treatment of individuals with these specific SCD may be warranted.

Keywords: alzheimer’s disease, dementia, memory complaints, mild cognitive impairment, risk factors, subjective cognitive decline

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Authors: Sabitha Rahim

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In the context of teaching vocabulary of English as Foreign Language (EFL), the confluence of memory and retention is one of the most significant factors in students' language acquisition. The progress of students engaged in foreign language acquisition is often stymied by vocabulary attrition, which leads to learners' lack of confidence and motivation. However, among other factors, little research has investigated the importance of neuroplasticity in Foreign Language acquisition and how underused neural pathways lead to the loss of plasticity, thereby affecting the learners’ vocabulary retention and motivation. This research explored the effect of enhancing vocabulary acquisition of EFL students in the Foundation Year at King Abdulaziz University through various methods and neuroplasticity exercises that reinforced their attention, motivation, and engagement. It analyzed the results to determine if stimulating the brain of EFL learners by various physical and mental activities led to the improvement in short and long term memory in vocabulary retention. The main data collection methods were student surveys, assessment records of teachers, student achievement test results, and students' follow-up interviews. A key implication of this research is for the institutions to consider having multiple varieties of student activities promoting brain plasticity within the classrooms as an effective tool for foreign language acquisition. Building awareness among the faculty and adapting the curriculum to include activities that promote brain plasticity ensures an enhanced learning environment and effective language acquisition in EFL classrooms.

Keywords: language acquisition, neural paths, neuroplasticity, vocabulary attrition

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Authors: Md. Shakhawat Ullah Chowdhury

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Mobile school is an innovative methodology in non-formal education to increase access to education for children during conflict through theatre for education for appropriate basic education to children during conflict. The continuous exposure to harsh environments and the nature of the lifestyles of children in conflict make them vulnerable. However, the mobile school initiative takes into consideration the mobile lifestyle of children in conflict. Schools are provided considering the pocket area of the street children with portable chalkboards, tin of books and materials as communities move. Teaching is multi-grade to ensure all children in the community benefit. The established mobile schools, while focused on basic literacy and numeracy skills according to traditions of the communities. The school teachers are selected by the community and trained by a theatre activist. These teachers continue to live and move with the community and provide continuous education for children in conflict. The model proposed a holistic team work to deliver education focused services to the street children’s pocket area where the team is mobile. The team consists of three members –an educator (theatre worker), a psychological counsellor and paramedics. The mobile team is responsible to educate street children and also play dramas which specially produce on the basis of national curriculum and awareness issues for street children. Children enjoy play and learn about life skills and basic literacy and numeracy skills which may be a pillar of humanitarian aid during conflict.

Keywords: vulnerable, children in conflict, mobile schooling, child-friendly

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Authors: Lan Cheng, Harry Qin, Yang Wang

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Background: COVID-19 has induced the largest digital learning experiment in history. There is also emerging research evidence that students have paid a high cost of learning loss from virtual learning. University-wide survey results demonstrate that digital learning remains difficult for students who struggle with learning challenges, isolation, or a lack of resources. Large-scale efforts are therefore increasingly utilized for digital education. To better prepare students in higher education for this grand scientific and technological transformation, STEM education has been prioritized and promoted as a strategic imperative in the ongoing curriculum reform essential for unfinished learning needs and whole-person development. Building upon five key elements identified in the STEM education literature: Problem-based Learning, Community and Belonging, Technology Skills, Personalization of Learning, Connection to the External Community, this case study explores the potential of pedagogical innovation that integrates computational and experimental methodologies to support, enrich, and navigate STEM education. Objectives: The goal of this case study is to create a high-fidelity prototype design for STEM education with knowledge transfer technology that contains a Cooperative Multi-Agent System (CMAS), which has the objectives of (1) conduct assessment to reveal a virtual learning mechanism and establish strategies to facilitate scientific learning engagement, accessibility, and connection within and beyond university setting, (2) explore and validate an interactional co-creation approach embedded in project-based learning activities under the STEM learning context, which is being transformed by both digital technology and student behavior change,(3) formulate and implement the STEM-oriented campaign to guide learning network mapping, mitigate the loss of learning, enhance the learning experience, scale-up inclusive participation. Methods: This study applied a case study strategy and a methodology informed by Social Network Analysis Theory within a cross-disciplinary communication paradigm (students, peers, educators). Knowledge transfer technology is introduced to address learning challenges and to increase the efficiency of Reinforcement Learning (RL) algorithms. A co-creation learning framework was identified and investigated in a context-specific way with a learning analytic tool designed in this study. Findings: The result shows that (1) CMAS-empowered learning support reduced students’ confusion, difficulties, and gaps during problem-solving scenarios while increasing learner capacity empowerment, (2) The co-creation learning phenomenon have examined through the lens of the campaign and reveals that an interactive virtual learning environment fosters students to navigate scientific challenge independently and collaboratively, (3) The deliverables brought from the STEM educational campaign provide a methodological framework both within the context of the curriculum design and external community engagement application. Conclusion: This study brings a holistic and coherent pedagogy to cultivates students’ interest in STEM and develop them a knowledge base to integrate and apply knowledge across different STEM disciplines. Through the co-designing and cross-disciplinary educational content and campaign promotion, findings suggest factors to empower evidence-based learning practice while also piloting and tracking the impact of the scholastic value of co-creation under the dynamic learning environment. The data nested under the knowledge transfer technology situates learners’ scientific journey and could pave the way for theoretical advancement and broader scientific enervators within larger datasets, projects, and communities.

Keywords: co-creation, cross-disciplinary, knowledge transfer, STEM education, social network analysis

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Authors: Mary Holstein

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Objective: The aim of this study was to evaluate nurse leader confidence in emergency management and disaster preparedness in the state of Texas. My project was a replication study of a survey conducted in 2022 by Reedy et al, for members of the Northwest Organization for Nurse Leaders (NONL). Background: In 2022, the American Association of Colleges of Nursing (AACN) approved new essentials for academic nursing education programs to demonstrate competencies in disaster management, yet no integration of such information into nursing curriculum had been reported in the literature. Research replicated by members of the Texas Organization for Nursing Leadership suggested significant gaps in nurse leader confidence across roles and in structured education that prepares nurse leaders across the spectrum of experience to lead in a crisis. Methods: An exploratory, cross-sectional survey used a sample of 86 RNs who were members of TONL. Results: Results replicated comparable results with significant variance in nurse leader confidence across roles, experience, and previous disaster-related education. Positive associations regarding nurse leaders' confidence in managing disasters were obvious with more advanced positions, further education, and mandatory training. Conclusions: Nursing leaders in Texas lack mandatory and structured education to prepare for emergency and disaster management. The call for mandatory emergency management training and disaster preparedness for nurse leaders remains unmet.

Keywords: confidence, disaster, education, emergency

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Authors: Shielilo R. Amihan, Ederliza De Jesus

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The study determined the predictors of college performance of 2nd Yr students of UPHSD-Calamba. This quantitative study conducted a survey using the Scholastic Abilities Test for Adults (SATA), and the retrieval of entrance examinations results and current General Weighted Average (GWA) of the 242 randomly selected respondents. The mean, Pearson r and multiple regression analyses through SPSS revealed that students are capable of verbal, non-verbal and quantitative reasoning, reading vocabulary, comprehension, math calculation, and writing mechanics but have difficulty in math application and writing composition. The study found out the Scholastic Ability and Achievement, except in mathematics, are significantly related to college performance. It concludes that students with high ability and achievement may perform better in college. However, only English subset results in the entrance exam predicts the academic success of students in college while SATA and Math entrance exam results do not. The study recommends providing pre-college Math and Writing courses as requisites in college. It also suggests implementing formative curriculum-based enhancement programs on specific priority areas, profiling programs towards informed individual academic decision-making, revising the Entrance Examinations, monitoring the development of the students, and exploring other predictors of college academic performance such as non-cognitive factors.

Keywords: scholastic ability, scholastic achievement, entrance exam, college performance

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Authors: Alireza Nikbakht Shahbazi

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There are various factors that affect climate changes; drought is one of those factors. Investigation of efficient methods for estimating climate change impacts on drought should be assumed. The aim of this paper is to investigate climate change impacts on drought in Karoon3 watershed located south-western Iran in the future periods. The atmospheric general circulation models (GCM) data under Intergovernmental Panel on Climate Change (IPCC) scenarios should be used for this purpose. In this study, watershed drought under climate change impacts will be simulated in future periods (2011 to 2099). Standard precipitation index (SPI) as a drought index was selected and calculated using mean monthly precipitation data in Karoon3 watershed. SPI was calculated in 6, 12 and 24 months periods. Statistical analysis on daily precipitation and minimum and maximum daily temperature was performed. LRAS-WG5 was used to determine the feasibility of future period's meteorological data production. Model calibration and verification was performed for the base year (1980-2007). Meteorological data simulation for future periods under General Circulation Models and climate change IPCC scenarios was performed and then the drought status using SPI under climate change effects analyzed. Results showed that differences between monthly maximum and minimum temperature will decrease under climate change and spring precipitation shall increase while summer and autumn rainfall shall decrease. The precipitation occurs mainly between January and May in future periods and summer or autumn precipitation decline and lead up to short term drought in the study region. Normal and wet SPI category is more frequent in B1 and A2 emissions scenarios than A1B.

Keywords: climate change impact, drought severity, drought frequency, Karoon3 watershed

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Authors: Junior George Martin

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The purpose of this study was to explore college instructors’ integration of technology in their content area curriculum. The instructors indicated that they were in need of additional training to successfully integrate technology in their subject areas. The findings point to the implementation of a proposed the Technological, Pedagogical, and Content Knowledge (TPACK) model professional development workshop to satisfactorily address the weaknesses of the instructors in technology integration. The professional development workshop is proposed as a rational solution to adequately address the instructors’ inability to the successful integration of technology in their subject area in an effort to improve their pedagogy. The intense workshop would last for 5 days and will be designed to provide instructors with training in areas such as a use of technology applications and tools, and using modern methodologies to improve technology integration. Exposing the instructors to the specific areas identified will address the weaknesses they demonstrated during the study. Professional development is deemed the most appropriate intervention based on the opportunities it provides the instructors to access hands-on training to overcome their weaknesses. The purpose of the TPACK professional development workshop will be to improve the competence of the instructors so that they are adequately prepared to integrate technology successfully in their curricula. At the end of the period training, the instructors are expected to adopt strategies that will have a positive impact on the learning experiences of the students.

Keywords: higher education, modern technology tools, professional development, technology integration

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Authors: James Moir

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Recent evidence suggests that academic staff face difficulties in applying new technologies as a means of assessing higher order assessment outcomes such as critical thinking, problem solving and creativity. Although higher education institutional mission statements and course unit outlines purport the value of these higher order skills there is still some question about how well academics are equipped to design curricula and, in particular, assessment strategies accordingly. Despite a rhetoric avowing the benefits of these higher order skills, it has been suggested that academics set assessment tasks up in such a way as to inadvertently lead students on the path towards lower order outcomes. This is a controversial claim, and one that this papers seeks to explore and critique in terms of challenging the conceptual basis of assessing higher order skills through new technologies. It is argued that the use of digital media in higher education is leading to a focus on students’ ability to use and manipulate of these products as an index of their flexibility and adaptability to the demands of the knowledge economy. This focus mirrors market flexibility and encourages programmes and courses of study to be rhetorically packaged as such. Curricular content has become a means to procure more or less elaborate aggregates of attributes. Higher education is now charged with producing graduates who are entrepreneurial and creative in order to drive forward economic sustainability. It is argued that critical independent learning can take place through the democratisation afforded by cultural and knowledge digitization and that assessment needs to acknowledge the changing relations between audience and author, expert and amateur, creator and consumer.

Keywords: higher education, curriculum, new technologies, assessment, higher order skills

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Authors: M. Eskandarighadi, C. R. McGann

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It is observed from past experiences of earthquakes that local site conditions can significantly affect the strong ground motion characteristics experience at the site. One-dimensional seismic site response analysis is the most common approach for investigating site response. This approach assumes that soil is homogeneous and infinitely extended in the horizontal direction. Therefore, tying side boundaries together is one way to model this behavior, as the wave passage is assumed to be only vertical. However, 1D analysis cannot capture the 2D nature of wave propagation, soil heterogeneity, and 2D soil profile with features such as inclined layer boundaries. In contrast, 2D seismic site response modeling can consider all of the mentioned factors to better understand local site effects on strong ground motions. 2D wave propagation and considering that the soil profile on the two sides of the model may not be identical clarifies the importance of a boundary condition on each side that can minimize the unwanted reflections from the edges of the model and input appropriate loading conditions. Ideally, the model size should be sufficiently large to minimize the wave reflection, however, due to computational limitations, increasing the model size is impractical in some cases. Another approach is to employ free-field boundary conditions that take into account the free-field motion that would exist far from the model domain and apply this to the sides of the model. This research focuses on implementing free-field boundary conditions in OpenSees for 2D site response analysisComparisons are made between 1D models and 2D models with various boundary conditions, and details and limitations of the developed free-field boundary modeling approach are discussed.

Keywords: boundary condition, free-field, opensees, site response analysis, wave propagation

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Authors: Man-Yu Wong, Siyu Zhou, Zhiqiang Cao

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In quality of life, health service utilization is an important determinant of medical resource expenditures on Colorectal cancer (CRC) care, a better understanding of the increased utilization of health services is essential for optimizing the allocation of healthcare resources to services and thus for enhancing the service quality, especially for high expenditure on CRC care like Hong Kong region. In assessing the association between the health-related quality of life (HRQOL) and health service utilization in patients with colorectal neoplasm, count data models can be used, which account for over dispersion or extra zero counts. In our data, the HRQOL evaluation is a self-reported measure obtained from a questionnaire completed by the patients, misreports and variations in the data are inevitable. Besides, there are more zero counts from the observed number of clinical consultations (observed frequency of zero counts = 206) than those from a Poisson distribution with mean equal to 1.33 (expected frequency of zero counts = 156). This suggests that excess of zero counts may exist. Therefore, we study tests for detecting zero-inflation in models with measurement error in covariates. Method: Under classical measurement error model, the approximate likelihood function for zero-inflation Poisson regression model can be obtained, then Approximate Maximum Likelihood Estimation(AMLE) can be derived accordingly, which is consistent and asymptotically normally distributed. By calculating score function and Fisher information based on AMLE, a score test is proposed to detect zero-inflation effect in ZIP model with measurement error. The proposed test follows asymptotically standard normal distribution under H0, and it is consistent with the test proposed for zero-inflation effect when there is no measurement error. Results: Simulation results show that empirical power of our proposed test is the highest among existing tests for zero-inflation in ZIP model with measurement error. In real data analysis, with or without considering measurement error in covariates, existing tests, and our proposed test all imply H0 should be rejected with P-value less than 0.001, i.e., zero-inflation effect is very significant, ZIP model is superior to Poisson model for analyzing this data. However, if measurement error in covariates is not considered, only one covariate is significant; if measurement error in covariates is considered, only another covariate is significant. Moreover, the direction of coefficient estimations for these two covariates is different in ZIP regression model with or without considering measurement error. Conclusion: In our study, compared to Poisson model, ZIP model should be chosen when assessing the association between condition-specific HRQOL and health service utilization in patients with colorectal neoplasm. and models taking measurement error into account will result in statistically more reliable and precise information.

Keywords: count data, measurement error, score test, zero inflation

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Authors: Meghamala Nugehally

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This paper hypothesises and draws inferences on the impact of minimalism in dance education on the development of artistic and aesthetic sensibilities in individuals in the age group of 5-18 yrs of age. This research and conclusions are within the context of Indian Classical Dance, which is based on Indian theories of aesthetics drawn from the Natyashastra, an ancient treatise on Indian dance and drama. The research employs training methods handed down through a strict one-on-one teacher-student tradition known as the Guru-Shishya Parampara. Aesthetic principles used are defined, and basic theories from the Natyashastra are explained to provide background for the research design. The paper also discusses dance curriculum design and training methodology design within the context of these aesthetic theories. The scope of the research is limited to two genres of Indian classical forms: Bharatanatyam and Odissi. A brief description of these dance forms is given as background and dance aesthetics specific to these forms are described. The research design includes individual case studies of subjects studied, independent predetermined attributes for observations and a qualitative scoring methodology devised for the purpose of the study. The study describes the training techniques used and contrasts minimal solo training techniques with the more elaborate group training techniques. Study groups were divided and the basis for the division are discussed. Study observations are recorded and presented as evidences. The results inform the conclusion and set the stage for further research in this area.

Keywords: dance aesthetics, dance education, Indian classical dance, minimalism

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Authors: P. Hooshangitabrizi, I. Contreras, N. Bhuiyan

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This work presents an online scheduling problem which accommodates multiple requests of patients for chemotherapy treatments in a cancer center of a major metropolitan hospital in Canada. To solve the problem, an adaptive flexible approach is proposed which systematically combines two optimization models. The first model is intended to dynamically schedule arriving requests in the form of waiting lists whereas the second model is used to reschedule the already booked patients with the goal of finding better resource allocations when new information becomes available. Both models are created as mixed integer programming formulations. Various controllable and flexible parameters such as deviating the prescribed target dates by a pre-determined threshold, changing the start time of already booked appointments and the maximum number of appointments to move in the schedule are included in the proposed approach to have sufficient degrees of flexibility in handling arrival requests and unexpected changes. Several computational experiments are conducted to evaluate the performance of the proposed approach using historical data provided by the oncology clinic. Our approach achieves outstandingly better results as compared to those of the scheduling system being used in practice. Moreover, several analyses are conducted to evaluate the effect of considering different levels of flexibility on the obtained results and to assess the performance of the proposed approach in dealing with last-minute changes. We strongly believe that the proposed flexible adaptive approach is very well-suited for implementation at the clinic to provide better patient-care services and to utilize available resource more efficiently.

Keywords: chemotherapy scheduling, multi-appointment modeling, optimization of resources, satisfaction of patients, mixed integer programming

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Authors: Junyu Chen, Peng Xu

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In the context of global efforts to enhance building energy efficiency, accurate thermal load forecasting is crucial for both device sizing and predictive control. Variable Refrigerant Flow (VRF) systems are widely used in buildings around the world, yet VRF zonal load prediction has received limited attention. Due to differences between VRF zones in building-level prediction methods, zone-level load forecasting could significantly enhance accuracy. Given that modern VRF systems generate high-quality data, this paper introduces transfer learning to leverage this data and further improve prediction performance. This framework also addresses the challenge of predicting load for building zones with no historical data, offering greater accuracy and usability compared to pure white-box models. The study first establishes an initial variable set of VRF zonal building loads and generates a foundational white-box database using EnergyPlus. Key variables for VRF zonal loads are identified using methods including SRRC, PRCC, and Random Forest. XGBoost and LSTM are employed to generate pre-trained black-box models based on the white-box database. Finally, real-world data is incorporated into the pre-trained model using transfer learning to enhance its performance in operational buildings. In this paper, zone-level load prediction was integrated with transfer learning, and a framework was proposed to improve the accuracy and applicability of VRF zonal load prediction.

Keywords: zonal load prediction, variable refrigerant flow (VRF) system, transfer learning, energyplus

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Authors: Deyadeen Ali Alshibani

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In the literature, the multi-armed bandit problem as a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time. There are several different algorithms models and their applications on this problem. In this paper, we evaluate the Regret-regression through comparing with Q-learning method. A simulation on determination of optimal treatment regime is presented in detail.

Keywords: optimal, bandit problem, optimization, dynamic programming

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Authors: Abubakar Sadiq Mensah, Hassan Usman

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The study compared the effectiveness of Audition, Multiplication, subtraction and Division (AMSD) and Addition, subtraction, Multiplication and Division (ASMD), sequence of teaching these four basic operations in mathematics to primary one pupil’s in Katsina Local Government, Katsina State. The study determined the sequence that was more effective and mostly adopted by teachers of the operations. One hundred (100) teachers and sixty pupils (60) from primary one were used for the study. The pupils were divided into two equal groups. The researcher taught these operations to each group separately for four weeks (4 weeks). Group one was taught using the ASMD sequence, while group two was taught using ASMD sequence. In order to generate the needed data for the study, questionnaires and tests were administered on the samples. Data collected were analyzed and major findings were arrived at: (i) Two primary mathematics text books were used in all the primary schools in the area; (ii) Each of the textbooks contained the ASMD sequence; (iii) 73% of the teachers sampled adopted the ASMD sequence of teaching these operations; and (iv) Group one of the pupils (taught using AMSD sequence) performed significantly better than their counter parts in group two (taught using AMSD sequence). On the basis of this, the researcher concluded that the AMSD sequence was more effective in teaching the operations than the ASMD sequence. Consequently, the researcher concluded that primary schools teachers, authors of primary mathematics textbooks, and curriculum planner should adopt the AMSD sequence of teaching these operations.

Keywords: matematic, high school, four basic operations, effectiveness of teaching

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Authors: Hu Zhenxing, Gao Jianxin

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Stereo-based digital image correlation (also referred to as three-dimensional (3D) digital image correlation (DIC)) is a technique for both 3D shape and surface deformation measurement of a component, which has found increasing applications in academia and industries. The accuracy of the reconstructed coordinate depends on many factors such as configuration of the setup, stereo-matching, distortion, etc. Most of these factors have been investigated in literature. For instance, the configuration of a binocular vision system determines the systematic errors. The stereo-matching errors depend on the speckle quality and the matching algorithm, which can only be controlled in a limited range. And the distortion is non-linear particularly in a complex imaging acquisition system. Thus, the distortion correction should be carefully considered. Moreover, the distortion function is difficult to formulate in a complex imaging acquisition system using conventional models in such cases where microscopes and other complex lenses are involved. The errors of the distortion correction will propagate to the reconstructed 3D coordinates. To address the problem, an accurate mapping method based on 2D B-spline functions is proposed in this study. The mapping functions are used to convert the distorted coordinates into an ideal plane without distortions. This approach is suitable for any image acquisition distortion models. It is used as a prior process to convert the distorted coordinate to an ideal position, which enables the camera to conform to the pin-hole model. A procedure of this approach is presented for stereo-based DIC. Using 3D speckle image generation, numerical simulations were carried out to compare the accuracy of both the conventional method and the proposed approach.

Keywords: distortion, stereo-based digital image correlation, b-spline, 3D, 2D

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Authors: Kristen Erekson, Sarah Spendlove Caswell

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Nurse educators have the challenge of training future nurses that will provide compassionate care to an increasingly diverse population of patients in a culturally sensitive way. One approach to this challenge is an immersive public and global health experience as part of the nursing program curriculum. Undergraduate nursing students at our institution are required to participate in a Public and Global Health course. They participate in a didactic preparatory course followed by a 3-to-4-week program in one of the following locations: The Czech Republic, Ecuador, Finland/Poland, Ghana, India, Spain, Taiwan, Tonga, an Honor Flight to Washington D.C. with Veterans, or in local (Utah) communities working with marginalized populations (including incarcerated individuals, refugees, etc.). The students are required to complete 84 clinical hours and 84 culture hours (which involve exposure to local history, art, architecture, customs, etc.). As Faculty, we feel strongly that these public and global health experiences help cultivate cultural awareness in our students and prepare nurses who are better prepared to serve a diverse population of patients throughout their careers. This presentation will highlight our experiences and provide ideas for other nurse educators who have an interest in developing similar programs in their schools but do not know where to start. Suggestions about how to start building relationships that can lead to these opportunities, along with logistics for continuing the programs, will be highlighted.

Keywords: global health nursing, nursing education, clinical education, public health nursing

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Authors: Mohammad Zamani, Ramin Mansouri

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Spillways are one of the most important hydraulic structures of dams that provide the stability of the dam and downstream areas at the time of flood. A circular vertical spillway with various inlet forms is very effective when there is not enough space for the other spillway. Hydraulic flow in a vertical circular spillway is divided into three groups: free, orifice, and under pressure (submerged). In this research, the hydraulic flow characteristics of a Circular Vertical Spillway are investigated with the CFD model. Two-dimensional unsteady RANS equations were solved numerically using Finite Volume Method. The PISO scheme was applied for the velocity-pressure coupling. The mostly used two-equation turbulence models, k-ε and k-ω, were chosen to model Reynolds shear stress term. The power law scheme was used for the discretization of momentum, k, ε, and ω equations. The VOF method (geometrically reconstruction algorithm) was adopted for interface simulation. In this study, three types of computational grids (coarse, intermediate, and fine) were used to discriminate the simulation environment. In order to simulate the flow, the k-ε (Standard, RNG, Realizable) and k-ω (standard and SST) models were used. Also, in order to find the best wall function, two types, standard wall, and non-equilibrium wall function, were investigated. The laminar model did not produce satisfactory flow depth and velocity along the Morning-Glory spillway. The results of the most commonly used two-equation turbulence models (k-ε and k-ω) were identical. Furthermore, the standard wall function produced better results compared to the non-equilibrium wall function. Thus, for other simulations, the standard k-ε with the standard wall function was preferred. The comparison criterion in this study is also the trajectory profile of jet water. The results show that the fine computational grid, the input speed condition for the flow input boundary, and the output pressure for the boundaries that are in contact with the air provide the best possible results. Also, the standard wall function is chosen for the effect of the wall function, and the turbulent model k-ε (Standard) has the most consistent results with experimental results. When the jet gets closer to the end of the basin, the computational results increase with the numerical results of their differences. The mesh with 10602 nodes, turbulent model k-ε standard and the standard wall function, provide the best results for modeling the flow in a vertical circular Spillway. There was a good agreement between numerical and experimental results in the upper and lower nappe profiles. In the study of water level over crest and discharge, in low water levels, the results of numerical modeling are good agreement with the experimental, but with the increasing water level, the difference between the numerical and experimental discharge is more. In the study of the flow coefficient, by decreasing in P/R ratio, the difference between the numerical and experimental result increases.

Keywords: circular vertical, spillway, numerical model, boundary conditions

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Anuruddha Jayasuriya, Thanakorn Pheeraphan

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In this paper, a review of different mathematical models which can be used as prediction tools to assess the time to crack reinforced concrete (RC) due to corrosion is investigated. This investigation leads to an experimental study to validate a selected prediction model. Most of these mathematical models depend upon the mechanical behaviors, chemical behaviors, electrochemical behaviors or geometric aspects of the RC members during a corrosion process. The experimental program is designed to verify the accuracy of a well-selected mathematical model from a rigorous literature study. Fundamentally, the experimental program exemplifies both one-dimensional chloride diffusion using RC squared slab elements of 500 mm by 500 mm and two-dimensional chloride diffusion using RC squared column elements of 225 mm by 225 mm by 500 mm. Each set consists of three water-to-cement ratios (w/c); 0.4, 0.5, 0.6 and two cover depths; 25 mm and 50 mm. 12 mm bars are used for column elements and 16 mm bars are used for slab elements. All the samples are subjected to accelerated chloride corrosion in a chloride bath of 5% (w/w) sodium chloride (NaCl) solution. Based on a pre-screening of different models, it is clear that the well-selected mathematical model had included mechanical properties, chemical and electrochemical properties, nature of corrosion whether it is accelerated or natural, and the amount of porous area that rust products can accommodate before exerting expansive pressure on the surrounding concrete. The experimental results have shown that the selected model for both one-dimensional and two-dimensional chloride diffusion had ±20% and ±10% respective accuracies compared to the experimental output. The half-cell potential readings are also used to see the corrosion probability, and experimental results have shown that the mass loss is proportional to the negative half-cell potential readings that are obtained. Additionally, a statistical analysis is carried out in order to determine the most influential factor that affects the time to corrode the reinforcement in the concrete due to chloride diffusion. The factors considered for this analysis are w/c, bar diameter, and cover depth. The analysis is accomplished by using Minitab statistical software, and it showed that cover depth is the significant effect on the time to crack the concrete from chloride induced corrosion than other factors considered. Thus, the time predictions can be illustrated through the selected mathematical model as it covers a wide range of factors affecting the corrosion process, and it can be used to predetermine the durability concern of RC structures that are vulnerable to chloride exposure. And eventually, it is further concluded that cover thickness plays a vital role in durability in terms of chloride diffusion.

Keywords: accelerated corrosion, chloride diffusion, corrosion cracks, passivation layer, reinforcement corrosion

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Authors: Segen Asayehegn

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Introduction: In Tigray, Ethiopia, next to cervical cancer, breast cancer is one of the most common cancer health problems for women. Objectives: This article is proposed to identify the prospective and potential risk factors affecting the time-to-first-recurrence of breast cancer patients in Tigray, Ethiopia. Methods: The data were taken from the patient’s medical record that registered from January 2010 to January 2020. The study considered a sample size of 1842 breast cancer patients. Powerful non-parametric and parametric shared frailty survival regression models (FSRM) were applied, and model comparisons were performed. Results: Out of 1842 breast cancer patients, about 1290 (70.02%) recovered/cured the disease. The median cure time from breast cancer is found at 12.8 months. The model comparison suggested that the lognormal parametric shared a frailty survival regression model predicted that treatment, stage of breast cancer, smoking habit, and marital status significantly affects the first recurrence of breast cancer. Conclusion: Factors like treatment, stages of cancer, and marital status were improved while smoking habits worsened the time to cure breast cancer. Recommendation: Thus, the authors recommend reducing breast cancer health problems, the regional health sector facilities need to be improved. More importantly, concerned bodies and medical doctors should emphasize the identified factors during treatment. Furthermore, general awareness programs should be given to the community on the identified factors.

Keywords: acceleration factor, breast cancer, Ethiopia, shared frailty survival models, Tigray

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Tholokuhle T. Ntshakala, Seraphin D. Eyono Obono

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Nations are still finding it quite difficult to win mega sport competitions despite the major contribution of sport to society in terms of social and economic development, personal health, and in education. Even though the world of sports has been transformed into a huge global economy, it is important to note that the first step of sport is usually its introduction to children at school through physical education or PE. In other words, nations who do not win mega sport competitions also suffer from a weak and neglected PE system. This problem of the neglect of PE systems is the main motivation of this research aimed at examining the factors affecting the perceived awareness of physical education teachers on the ICT's that are adoptable for the teaching and learning of physical education. Two types of research objectives will materialize this aim: relevant theories will be identified in relation to the analysis of the perceived ICT awareness of PE teachers and subsequent models will be compiled and designed from existing literature; the empirical testing of such theories and models will also be achieved through the survey of PE teachers from the Camperdown magisterial district of the KwaZulu-Natal province of South Africa. The main hypothesis at the heart of this study is the relationship between the demographics of PE teachers, their behavior both as individuals and as social entities, and their perceived awareness of the ICTs that are adoptable for PE, as postulated by existing literature; except that this study categorizes human behavior under performance expectancy, computer attitude, and social influence. This hypothesis was partially confirmed by the survey conducted by this research in the sense that performance expectancy and teachers’ age, gender, computer usage, and class size were found to be the only factors affecting their awareness of ICT's for physical education.

Keywords: human behavior, ICT Awareness, physical education, teachers

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Authors: Jimoh Olumide Yusuf

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Various factors have continued to bedevil the effective teaching and learning of English Language in Nigeria and prominent among these factors are learners’ characteristics. Unfortunately, these particular factors seem to have recorded paucity of research efforts by scholars and the problem of lack of proficiency in the target language continues to linger. This study therefore investigates the relationship between specific learners’ characteristics and effective teaching of English as a Second Language (ESL) in senior secondary schools in Nigeria. To this end, Self-Determination, and Integrative Motivation Theories were applied to investigate motivation, language learning, learners’ characteristics and its relationship to language proficiency. A survey of 500 students and 100 English Language teachers across 20 schools was conducted. Descriptive statistics was used to analyze the data and findings revealed that; specific learners’ characteristics such as learners’ age, learning style and motivation significantly determine the performance of students in English Language. Specifically, students with appropriate school age, visual learning style and intrinsic motivation, demonstrated English Language proficiency; as they performed better than students with extrinsic motivation, audio and kinaesthetic learning styles. Moreover, teachers related factors such as teaching experience; teaching strategies and teachers’ extrinsic motivation also emerged as essential correlates of effective language teaching. The findings conclude that learning characteristics are significant factors that should be considered by the teachers and education planners for adequate, sequential and effective implementation of the ESL curriculum in Nigeria.

Keywords: senior secondary school, English as a second language, intrinsic motivation, Kinaesthetic learning style

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Authors: Sara Przetocka, Krzysztof Żak, Grzegorz Dubin, Tadeusz Holak

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Toll-like receptors (TLRs) play central role in the innate immune response and inflammation by recognizing pathogen-associated molecular patterns (PAMPs). A fundamental basis of TLR signalling is dependent upon the recruitment and association of adaptor molecules that contain the structurally conserved Toll/interleukin-1 receptor (TIR) domain. MyD88 (myeloid differentiation primary response gene 88) is the universal adaptor for TLRs and cooperates with Mal (MyD88 adapter-like protein, also known as TIRAP) in TLR4 response which is predominantly used in inflammation, host defence and carcinogenesis. Up to date two possible models of MyD88, Mal and TLR4 interactions have been proposed. The aim of our studies is to confirm or abolish presented models and accomplish the full structural characterisation of TIR domains interaction. Using molecular cloning methods we obtained several construct of MyD88 and Mal TIR domain with GST or 6xHis tag. Gel filtration method as well as pull-down analysis confirmed that recombinant TIR domains from MyD88 and Mal are binding in complexes. To examine whether obtained complexes are homo- or heterodimers we carried out cross-linking reaction of TIR domains with BS3 compound combined with mass spectrometry. To investigate which amino acid residues are involved in this interaction the NMR titration experiments were performed. 15N MyD88-TIR solution was complemented with non-labelled Mal-TIR. The results undoubtedly indicate that MyD88-TIR interact with Mal-TIR. Moreover 2D spectra demonstrated that simultaneously Mal-TIR self-dimerization occurs which is necessary to create proper scaffold for Mal-TIR and MyD88-TIR interaction. Final step of this study will be crystallization of MyD88 and Mal TIR domains complex. This crystal structure and characterisation of its interface will have an impact in understanding the TLR signalling pathway and possibly will be used in development of new anti-cancer treatment.

Keywords: cancer, MyD88, TIR domains, Toll-like receptors

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

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Authors: Chaliya Wamaloon, Malee Chaisaena, Nusara Prasertsri

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Informal caregivers providing home-based palliative and end-of-life (EOL) care to people with advanced cancer is needed, however, there has not been develop caregiver skills for the EOL in cancer patients. The aim of this research was to study the model development of caregiver skills for the EOL in cancer patients. Mixed methods research was conducted in 3 phases. All subjects were in Ubon Rathchathani Cancer Hospital including 30 EOL cancer patient caregivers, 30 EOL cancer patients, and 111 health care professionals who provided care for the EOL cancer patients and 30 EOL target participants who had been trained to be cancer patient caregivers. The research tools were questionnaires, semi structured interviews, and caregiver skills questionnaires. Data were analyzed by using percentage, mean, standard deviation, pair t-test, and content analysis. The result from this study showed the model development of caregiver skills for cancer patients consisted of 9 domains skills: 1. monitoring, 2. interpreting, 3. making decisions, 4. taking action, 5. making adjustments, 6. providing hands-on care, 7. accessing resources, 8. working together with the ill patients, and 9. navigating the healthcare system. The model composed of skills development curriculum for cancer patient caregivers, Manual of palliative care for caregivers, diary of health care records for cancer patients, and the evaluation model of development of caregiver skills for EOL cancer patients. The results of the evaluation in the development model of caregiver skills for EOL cancer patients showed that the caregivers were satisfied with the model of development for caregiver skills at a high level. The comparison of the caregiver skills before and after obtaining the development of caregivers skills revealed that it improved at a statistically significant level (p < 0.05).

Keywords: caregiver, caregiver skills, cancer patients, end of life

Procedia PDF Downloads 168