Search results for: Kolb Learning Styles Inventory

Authors: Shoba Rathilal

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High unemployment rates and a shortage of experienced and qualified employees appear to be a paradox that currently plagues most countries worldwide. In a developing country like South Africa, the rate of unemployment is reported to be approximately 35%, the highest recorded globally. At the same time, a countrywide deficit in experienced and qualified potential employees is reported in South Africa, which is causing fierce rivalry among firms. Employers have reported that graduates are very rarely able to meet the demands of the job as there are gaps in their knowledge and conceptual understanding and other 21st-century competencies, attributes, and dispositions required to successfully negotiate the multiple responsibilities of employees in organizations. In addition, the rates of unemployment and suitability of graduates appear to be skewed by race and social class, the continued effects of a legacy of inequitable educational access. Higher Education in the current technologically advanced and dynamic world needs to serve as an agent of transformation, aspiring to develop graduates to be creative, flexible, critical, and with entrepreneurial acumen. This requires that higher education curricula and pedagogy require a re-envisioning of our selection, sequencing, and pacing of the learning, teaching, and assessment. At a particular Higher education Institution in South Africa, Design Thinking and Project Based learning are being adopted as two approaches that aim to enhance the student experience through the provision of a “distinctive education” that brings together disciplinary knowledge, professional engagement, technology, innovation, and entrepreneurship. Using these methodologies forces the students to solve real-time applied problems using various forms of knowledge and finding innovative solutions that can result in new products and services. The intention is to promote the development of skills for self-directed learning, facilitate the development of self-awareness, and contribute to students being active partners in the application and production of knowledge. These approaches emphasize active and collaborative learning, teamwork, conflict resolution, and problem-solving through effective integration of theory and practice. In principle, both these approaches are extremely impactful. However, at the institution in this study, the implementation of the PBL and DT was not as “smooth” as anticipated. This presentation reports on the analysis of the implementation of these two approaches within higher education curricula at a particular university in South Africa. The study adopts a qualitative case study design. Data were generated through the use of surveys, evaluation feedback at workshops, and content analysis of project reports. Data were analyzed using document analysis, content, and thematic analysis. Initial analysis shows that the forces constraining the implementation of PBL and DT range from the capacity to engage with DT and PBL, both from staff and students, educational contextual realities of higher education institutions, administrative processes, and resources. At the same time, the implementation of DT and PBL was enabled through the allocation of strategic funding and capacity development workshops. These factors, however, could not achieve maximum impact. In addition, the presentation will include recommendations on how DT and PBL could be adapted for differing contexts will be explored.

Keywords: design thinking, project based learning, innovative higher education pedagogy, student and staff capacity development

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Authors: Khashane Stephen Malatji, Makwalete Johanna Malatji

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Student evaluation of teaching (SET) is the common way of assessing teaching quality at universities and tracing the professional growth of lecturers. The aim of this study was to investigate the role played by student evaluation in the teaching and learning agenda at one South African University. The researchers used a qualitative approach and a case study research design. With regards to data collection, document analysis was used. Evaluation reports were reviewed to monitor the growth of lecturers who were evaluated during the academic years 2020 and 2021 in one faculty. The results of the study reveal that student evaluation remains the most relevant tool to inform the teaching agenda at a university. Lecturers who were evaluated were found to grow academically. All lecturers evaluated during 2020 have shown great improvement when evaluated repeatedly during 2021. Therefore, it can be concluded that student evaluation helps to improve the pedagogical and professional proficiency of lecturers. The study therefore, recommends that lecturers conduct an evaluation for each module they teach every semester or annually in case of year modules. The study also recommends that lecturers attend to all areas that draw negative comments from students in order to improve.

Keywords: students’ voices, teaching agenda, evaluation, feedback, responses

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Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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

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

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Authors: Cansu Bozer, Saadet İrem Turgut

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Artificial intelligence (AI) has become one of the most important technologies of the digital age and is transforming many sectors, including education. The advantages offered by AI, such as automation, personalised learning, and data analytics, create new opportunities for both teachers and students in education systems. Preschool education plays a fundamental role in the cognitive, social, and emotional development of children. In this period, the foundations of children's creative thinking, problem-solving, and critical thinking skills are laid. Educational technologies, especially artificial intelligence-based applications, are thought to contribute to the development of these skills. For example, artificial intelligence-supported digital learning tools can support learning processes by offering activities that can be customised according to the individual needs of each child. However, the successful use of artificial intelligence-based applications in preschool education can be realised under the guidance of teachers who have the right knowledge about this technology. Therefore, it is of great importance to measure preschool teachers' awareness levels of artificial intelligence and to understand which variables affect this awareness. The aim of this study is to measure preschool teachers' awareness levels of artificial intelligence and to determine which factors are related to this awareness. In line with this purpose, teachers' level of knowledge about artificial intelligence, their thoughts about the role of artificial intelligence in education, and their attitudes towards artificial intelligence will be evaluated. The study will be conducted with 100 teachers working in Turkey using a descriptive survey model. In this context, ‘Artificial Intelligence Awareness Level Scale for Teachers’ developed by Ferikoğlu and Akgün (2022) will be used. The collected data will be analysed using SPSS (Statistical Package for the Social Sciences) software. Descriptive statistics (frequency, percentage, mean, standard deviation) and relationship analyses (correlation and regression analyses) will be used in data analysis. As a result of the study, the level of artificial intelligence awareness of preschool teachers will be determined, and the factors affecting this awareness will be identified. The findings obtained will contribute to the determination of studies that can be done to increase artificial intelligence awareness in preschool education.

Keywords: education, child development, artificial intelligence, preschool teachers

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Authors: Khalil Aryanfar, Shahrzad Sanjari, Pariya Gholipor, Elmira Hafez

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The present study aimed to investigate and compare the characteristics of entrepreneurship among students in senior secondary schools in the academic year 2008-2009 in Tabriz city. Research employed survey method respectively. The study population consisted of all students in the senior branch of theoretical, technical, professional and vocational (1033 patients) were included. Sample size of 493 was calculated according to Morgan table. Sampling method was random cluster and stratified sampling. Data collected by researcher made questionnaire based on the theory of MC clleland (1963) and Brvkhavs (1980). These tools would indicators be for achievement, Independence, disposition, creativity, risk-taking, self-control, tolerance for ambiguity, team work approach in the future. To determine the psychometric properties of the questionnaire, content validity of the survey was approved by relevant experts. In addition, to estimate the internal consistency of Cronbach's alpha coefficient was calculated for 84% of total inventory. Collected data Using mean, standard deviation and were analyzed by ANOVA. The results showed that there is a significant difference between students' entrepreneurial potential fields of theoretical, technical, professional and vocational level (0/01).

Keywords: entrepreneurship, achievement motivation, risk taking, creativity, self-control, independence, tolerance of ambiguity, foresight

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Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

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Authors: Martha Hough RN

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Background: New nursing graduates lack the critical thinking skills required to provide safe nursing care. Critical thinking is essential in providing safe, competent, and skillful nursing interventions. Educational institutions must provide a curriculum that improves nursing students' critical thinking abilities. In addition, the recent pandemic resulted in nursing students who previously received in-person clinical but now most clinical has been converted to remote learning, increasing the use of simulations. Unfolding medium and high-fidelity simulations and Socratic questioning are used in many simulations debriefing sessions. Methodology: Google Scholar was researched with the keywords: critical thinking of nursing students with unfolding simulation, which resulted in 22,000 articles; three were used. A second search was implemented with critical thinking of nursing students Socratic questioning, which resulted in two articles being used. Conclusion: Unfolding simulations increase nursing students' critical thinking, especially during the briefing (pre-briefing and debriefing) phases, where most learning occurs. In addition, the use of Socratic questions during the briefing phases motivates other questions, helps the student analyze and critique their thinking, and assists educators in probing students' thinking, which further increases critical thinking.

Keywords: briefing, critical thinking, Socratic thinking, unfolding simulations

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Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

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Authors: Musa Auwal Mamman, S. G. Isa

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This study was carried out in order to investigate the effectiveness of GeoGebra software in teaching and learning of linear and quadratic equations amongst senior secondary school students in Fagge Local Government Area, Kano State–Nigeria. Five research items were raised in objectives, research questions and hypotheses respectively. A random sampling method was used in selecting 398 students from a population of 2098 of SS2 students. The experimental group was taught using the GeoGebra software while the control group was taught using the conventional teaching method. The instrument used for the study was the mathematics performance test (MPT) which was administered at the beginning and at the end of the study. The results of the study revealed that students taught with GeoGebra software (experimental group) performed better than students taught with traditional teaching method. The t- test was used to analyze the data obtained from the study.

Keywords: GeoGebra Software, mathematics performance, random sampling, mathematics teaching

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Authors: Agni Stylianou-Georgiou, Eliza Pitri

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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: creative problem-based play, educational games, flexible thinking, tertiary education

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Authors: Riyadh Jamegh, AllaEldin Kassam, Sawsan Sabih

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In modern trends of market, organizations face high-pressure environment which is characterized by globalization, high competition, and customer orientation, so it is very crucial to control and know the weak and strong points of the supply chain in order to improve their performance. So the performance measurements presented as an important tool of supply chain management because it's enabled the organizations to control, understand, and improve their efficiency. This paper aims to identify supply chain performance measurement (SCPM) by using Fuzzy Analytical Hierarchical Process (FAHP). In our real application, the performance of organizations estimated based on four parameters these are cost parameter indicator of cost (CPI), inventory turnover parameter indicator of (INPI), raw material parameter (RMPI), and safety stock level parameter indicator (SSPI), these indicators vary in impact on performance depending upon policies and strategies of organization. In this research (FAHP) technique has been used to identify the importance of such parameters, and then first fuzzy inference (FIR1) is applied to identify performance indicator of each factor depending on the importance of the factor and its value. Then, the second fuzzy inference (FIR2) also applied to integrate the effect of these indicators and identify (SCPM) which represent the required output. The developed approach provides an effective tool for evaluation of supply chain performance measurement.

Keywords: fuzzy performance measurements, supply chain, fuzzy logic, key performance indicator

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Authors: Shu Tao

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Based on a global fuel consumption data product (PKU-FUEL-2007) compiled recently and a series of databases for emission factors of various sources, global emission inventories of a number of greenhouse gases and air pollutants, including CO2, CO, SO2, NOx, primary particulate matter (total, PM 10, and PM 2.5), black carbon, organic carbon, mercury, volatile organic carbons, and polycyclic aromatic hydrocarbons, from combustion sources have been developed. The inventories feather high spatial and sectorial resolutions. The spatial resolution of the inventories are 0.1 by 0.1 degree, based on a sub-national disaggregation approach to reduce spatial bias due to uneven distribution of per person fuel consumption within countries. The finely resolved inventories provide critical information for chemical transport modeling and exposure modeling. Emissions from more than 60 sources in energy, industry, agriculture, residential, transportation, and wildfire sectors were quantified in this study. With the detailed sectorial information, the inventories become an important tool for policy makers. For residential sector, a set of models were developed to simulate temporal variation of fuel consumption, consequently pollutant emissions. The models can be used to characterize seasonal as well as inter-annual variations in the emissions in history and to predict future changes. The models can even be used to quantify net change of fuel consumption and pollutant emissions due to climate change. The inventories has been used for model ambient air quality, population exposure, and even health effects. A few examples of the applications are discussed.

Keywords: air pollutants, combustion, emission inventory, sectorial information

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Authors: Sina Keyhanian, Abbas Ahmadi, Behrooz Karimi

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We present a multi-objective binary programming model which considers surgical cases are scheduling among operating rooms and the configuration of surgical instruments in limited capacity hospital trays, simultaneously. Many mathematical models have been developed previously in the literature addressing different challenges in health-care logistics such as assigning operating rooms, leveling beds, etc. But what happens inside the operating rooms along with the inventory management of required instruments for various operations, and also their integration with surgical scheduling have been poorly discussed. Our model considers the minimization of movements between trays during a surgery which recalls the famous cell formation problem in group technology. This assumption can also provide a major potential contribution to robotic surgeries. The tray configuration problem which consumes surgical instruments requirement plan (SIRP) and sequence of surgical procedures based on required instruments (SIRO) is nested inside the bin packing problem. This modeling approach helps us understand that most of the same-output solutions will not be necessarily identical when it comes to the rearrangement of surgeries among rooms. A numerical example has been dealt with via a proposed nested simulated annealing (SA) optimization approach which provides insights about how various configurations inside a solution can alter the optimal condition.

Keywords: health-care logistics, hospital tray configuration, off-line bin packing, simulated annealing optimization, surgical case scheduling

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Authors: Zachary Blanks, Solomon Sonya

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Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

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Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

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Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Rameesha Javaid

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Balochistan, the largest landmass named after and dominated by the 55% Baloch population, which has had a difficult anti-center history like their brothers the Kurds of Middle East, reluctantly acceded to Pakistan in 1947. The region, which attained the status of a province after two decades of accession, has lagged behind in social development and economic growth as compared to the other three federating units. The province has seen the least financial autonomy and administrative decentralization both in autocratic and democratic dispensations under geostrategic and security considerations. Significant corrections have been recently made in the policy framework through changing the formula for intra-provincial National Finance Award, curtailing the number of subjects under federal control, and reactivating the Council of Common Interests. Yet policymaking remains overwhelmingly bureaucratic under a weak parliamentary oversight. The provincial coalition governments are unwieldy and directionless. The government machinery has much less than the optimal capability, character, integrity, will, and opportunity to perform. Decentralization further loses its semblance in the absence of local governments for long intervals and with the hold of hereditary tribal chiefs. Increased allocations failed to make an impact in the highest per capita cost environment due to long distances and scattered settlements. Decentralization, the basic ingredient of federalism has remained mortgaged to geo-strategic factors, internal security perceptions, autocratic and individualistic styles of governments, bureaucratic policymaking structures, bad governance, non-existent local governments, and feudalistic tribal lords. This suboptimal federalism speaks for the present underdevelopment in Balochistan and will earmark the milestones in the future.

Keywords: Balochistan, economic development, federalism, political economy

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Anna Wendolowska, Dorota Czyzowska

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An intimate relationship is a significant factor that influences romantic partners’ well-being. In the face of stress, avoidant partners often employ a defense-against-intimacy strategy, leading to reduced relationship satisfaction, intimacy, interdependence, and longevity. Dyadic coping can buffer the negative effects of stress on relational satisfaction. Emotional competence mediates the relationship between insecure attachment and intimacy. In the current study, the link between attachment, different forms of dyadic coping, and various aspects of relationship satisfaction was examined. Both partners completed the attachment style questionnaire, the well matching couple questionnaire, and the dyadic coping inventory. The data was analyzed using the actor–partner interdependence model. The results highlighted a negative association between insecure-avoidant attachment style and intimacy. The actor effects of avoidant attachment on relational intimacy for women and for men were significant, whilst the partner effects for both spouses were not significant. The emotion-focused common dyadic coping moderated the relationship between avoidance of attachment and the partner's sense of intimacy. After controlling for the emotion-focused common dyadic coping, the actor effect of attachment on intimacy for men was slightly weaker, and the actor effect for women turned out to be insignificant. The emotion-focused common dyadic coping weakened the negative association between insecure attachment and relational intimacy. The impact of adult attachment and dyadic coping significantly contributes to subjective relational well-being.

Keywords: adult attachment, dyadic coping, relational intimacy, relationship satisfaction

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Authors: Talal Alsulaiman, Khaldoun Khashanah

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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

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Authors: Hernán G. Cortés-Mora, José I. Péna-Reyes, Alfonso Herrera-Jiménez

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This article describes the implementation experience of Project-Based Learning (PBL) in an engineering course of the Universidad Nacional de Colombia, with the aim of strengthening student skills necessary for the exercise of their profession under a sustainability framework. Firstly, we present a literature review on the education for sustainability field, emphasizing the skills and knowledge areas required for its development, as well as the commitment of the Faculty of Engineering of the Universidad Nacional de Colombia, and other engineering faculties of the country, regarding education for sustainability. This article covers the general aspects of the course, describes how students team were formed, and how their experience was during the first semester of 2017. During this period two groups of students decided to develop their course project aiming to solve a problem regarding a Non-Governmental Organization (NGO) that works with head-of-household mothers in a low-income neighborhood in Bogota (Colombia). Subsequently, we show how sustainability is involved in the course, how tools are provided to students, and how activities are developed as to strengthen their abilities, which allows them to incorporate sustainability in their projects while also working on the methodology used to develop said projects. Finally, we introduce the results obtained by the students who sent the prototypes of their projects to the community they were working on and the conclusions reached by them regarding the course experience.

Keywords: sustainability, project-based learning, engineering education, higher education for sustainability

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Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

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Authors: Daphne Alroy-Thiberge

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Trauma-impacted individuals present unique treatment challenges that include high reactivity, hyper-and hypo-arousal, poor adherence to therapy, as well as powerful transference and counter-transference experiences in therapy. This work provides an overview of the clinical tenets most often encountered in trauma-impacted individuals. Further, it provides readily applicable clinical techniques to optimize therapeutic rapport and facilitate accelerated positive mental health outcomes. Finally, integrated neuroscience and clinical evidence-based data are discussed to shed new light on crisis states in trauma-impacted individuals. This knowledge is utilized to provide effective and concrete interventions towards rapid and successful de-escalation of the impacted individual. A highly interactive, adult-learning-principles-based modality is utilized to provide an organic learning experience for participants. The information and techniques learned aim to increase clinical effectiveness, reduce staff injuries and burnout, and significantly enhance positive mental health outcomes and self-determination for the trauma-impacted individuals treated.

Keywords: clinical competencies, crisis interventions, psychotherapy techniques, trauma informed care

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Authors: Ademola Ibukunolu Atanda, Gbenga Nathaniel Adeola

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The study examined awareness of child rights as a determinant of effective student personnel services in public secondary schools in Southwestern Nigeria. It was survey research. The sample comprised 433 teachers, 137 school administrators, and 968 students who were drawn by simple random sampling techniques. The respondents were given copies of questionnaires tagged “school administrator/teacher’s awareness of child’s rights and student personnel services elements inventory.” Key Informant Interview (KII) was also employed. The data were analysed using frequency count, percentages, weighted average, grand mean, standard deviation, and Pearson Product Moment Correlation, while KII was qualitatively analysed. The findings of the study revealed that public secondary school administrator awareness of child rights was at a moderate level, but the awareness of child rights was low among the teachers. The study equally revealed that student personnel services are moderately provided in public secondary schools in Southwestern Nigeria, but security remains a major challenge. It was also found that there was a significant relationship between awareness of child rights and effective student personnel services. It was therefore recommended, based on the findings, that attention should be given to heightening awareness of child rights among public secondary school administrators and teachers for effective student personnel services. Copies of the Child Right Act 2003 should also be made available in all public secondary schools in Southwestern Nigeria, as the study revealed that the documents were not available.

Keywords: student personnel, child right, administrator awareness, practice of child right

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Authors: Renato Roda

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With the emergence of the so-called 'Knowledge Society', the implementation of policies to attract, grow and retain talents, in an academic context as well, has become critical –both in the perspective of didactics and research and as far as administration and institutional management are concerned. At the same time, the contemporary philanthropic entities/organizations, which are evolving from traditional types of social support towards new styles of aid, envisaged to go beyond mere monetary donations, face the challenge of brand-new forms of complexity in supporting such specific dynamics of the global human capital market. In this sense, it becomes unavoidable for the philanthropic foundation, while carrying out their daily charitable tasks, to resort to innovative ways to facilitate the acquisition and the promotion of talents by academic and research institutions. In order to deepen such a specific perspective, this paper features the case of Turin, former 'factory city' of Italy’s North West, headquarters -and main reference territory- of Italy’s largest and richest private formerly bank-based philanthropic foundation, the Fondazione Compagnia di San Paolo. While it was assessed and classified as 'medium' in the city Global Talent Competitiveness Index (GTCI) of 2020, Turin has nevertheless acquired over the past months status of impact laboratory for a whole series of innovation strategies in the competition for the acquisition of excellence human capital. Leading actors of this new city vision are the foundations with their specifically adjusted financial engagement and a consistent role of stimulus towards innovation for research and education institutions.

Keywords: human capital, post-Fordism, private foundation, war on talents

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Authors: M. Z. Abdul Rofiq Badril Rizal

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This research is aimed to know the improvement of students’ English writing skill by using brainstorming technique. The technique used in writing is able to help the students’ difficulties in generating ideas and to lead the students to arrange the ideas well as well as to focus on the topic developed in writing. The research method used is classroom action research. The data sources of the research are an English teacher who acts as an observer and the students of class X.MIA5 consist of 35 students. The test result and observation are collected as the data in this research. Based on the research result in cycle one, the percentage of students who reach minimum accomplishment criteria (MAC) is 76.31%. It shows that the cycle must be continued to cycle two because the aim of the research has not accomplished, all of the students’ scores have not reached MAC yet. After continuing the research to cycle two and the weaknesses are improved, the process of teaching and learning runs better. At the test which is conducted in the end of learning process in cycle two, all of the students reach the minimum score and above 76 based on the minimum accomplishment criteria. It means the research has been successful and the percentage of students who reach minimum accomplishment criteria is 100%. Therefore, the writer concludes that brainstorming technique is able to improve the students’ English writing skill at the tenth grade of SMAN 2 Jember.

Keywords: brainstorming technique, improving, writing skill, knowledge and innovation engineering

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Treder Natalia, Siemiński Mariusz

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Takotsubo cardiomyopathy (TC) is a recently defined clinical entity. First described by Japanese researchers, today is diagnosed worldwide in 1-2% of patients admitted with the preliminary diagnosis of Acute Coronary Syndrome. The etiology of takotsubo cardiomyopathy remains still largely unknown. Currently, the most likely cause of takotsubo is direct cytotoxicity caused by catecholamine surge triggered by emotional stress. There is a strong relation between recent severe emotional stress and TC. The aim of this study was to analysis the role of stress and personality as a risk factor of TT. The presented research involves 35 people who are diagnosed TC. All patients were women, mean age 60 years. The methods used in the research are popular psychological tests: Perceived Stress Scale, DS14 scale to measure type D personality, The Neo-Five Factor Inventory of Personality and psychological interview. The obtained results prove that stress events may directly precede or even release TC. The stressful events occurred directly before the symptoms in 75% examined. 65% assessed their family life as very stressful. Examiners have also a high level of experienced stress. Only 25% of the TC were classified as having type D personality but they have a high level of negative affectivity. The subjects had a high level of extraversion, openness to experiences and an average level of neuroticism. The results suggested that such a type of personality profile may predispose to the development of takotsubo cardiomyopathy. Patients with TT are the individuals who reveal joint tendency to the experience of negative emotions and very stressful family life.

Keywords: stress, personality trails, familiar problems, Takotsubo cardiomyopathy

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Authors: Maria L. Ulloa, Ruth M. Crawford, Stephanie Kelly, Joey Domdom

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In recent decades health and social service delivery have become more collaborative and interdisciplinary. Emerging trends suggest the need for an integrative and interprofessional approach to meet the challenges faced by professionals navigating the complexities of health and social service practice environments. Terms such as multidisciplinary practice, interprofessional collaboration, interprofessional education and transprofessional practice have become the common language used across a range of social services and health providers in western democratic systems. In Aotearoa New Zealand, one example of an interprofessional collaborative approach to curriculum design and delivery in health and social service is the development of an innovative Masters of Professional Practice programme. This qualification is the result of a strategic partnership between two tertiary institutions – Whitireia New Zealand (NZ) and the Wellington Institute of Technology (Weltec) in Wellington. The Master of Professional Practice programme was designed and delivered from the perspective of a collaborative, interprofessional and relational approach. Teachers and students in the programme come from a diverse range of cultural, professional and personal backgrounds and are engaged in courses using a blended learning approach that incorporates the values and pedagogies of interprofessional education. Students are actively engaged in professional practice while undertaking the programme. This presentation describes the themes of exploratory qualitative formative observations of engagement in class and online, student assessments, student research projects, as well as qualitative interviews with the programme teaching staff. These formative findings reveal the development of critical practice skills around the common themes of the programme: research and evidence based practice, education, leadership, working with diversity and advancing critical reflection of professional identities and interprofessional practice. This presentation will provide evidence of enhanced learning experiences in higher education and learning in multi-disciplinary contexts.

Keywords: diversity, exploratory research, interprofessional education, professional identity

Procedia PDF Downloads 301