Search results for: binary outcomes

Authors: Ajit Kumar Sah, Rajesh Kumar Jha, Phoolgen Sah, Dev Kumar Shah

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Older individuals often suffer from multiple systemic diseases and are particularly more vulnerable to potentially inappropriate medicine prescribing. Inappropriate medication can cause serious medical problem for the elderly. The purpose of this study was to determine the prevalence of potentially inappropriate medicine (PIM) prescribing in older Nepalese patients in a medicine outpatient department. Beers’ criteria are the most widely used tools to assess PIM to elderly patients. Prospective observational analysis of drugs prescribed in medicine out-patient department (OPD) of a hospital of Bharatpur, Chitwan, Nepal during November 2011 to October 2012 to 869 older adults aged 65 years and above. The use of potentially inappropriate medications (PIM) in elderly patients was analyzed using Beers Criteria updated to 2013. In the 869 patients included the average number of drugs prescribed per prescription was 5.56. The most commonly used drugs were atenolol (24.3%), amlodipine (23.16%), paracetamol (17.6%), salbutamol (15.72%) and vitamin B complex (13.26%). The total number of medications prescribed was 4833. At least one instance of PIM was experienced by approximately 26.3% of patients when evaluated using the Beers criteria. Potentially inappropriate medications are highly prevalent among older patients attending medical OPD and are associated with a number of medications prescribed. Further research is warranted to study the impact of PIMs towards health-related outcomes in these elderly.

Keywords: Beers criteria, elderly, polypharmacy, potentially inappropriate medicines

Procedia PDF Downloads 566

Authors: Aliya Kamilyevna Salahova

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This study examines the effectiveness of the Kids Science Labs intervention program, based on STEM, in fostering independent thinking among preschool and elementary school children and its influence on their academic achievement. Through a comprehensive methodology involving interviews, surveys, observations, case studies, and statistical tests, data were collected from various sources to accurately analyze the program's effects. The findings indicate a significant positive impact on children's independent thinking abilities, leading to improved academic performance in mathematics and science, enhanced learning motivation, and a propensity to critically evaluate problem-solving approaches. This research contributes to the theoretical understanding of how STEM activities can foster independent thinking and academic success in young children, providing valuable insights for the development of educational programs. Introduction: The goal of this study is to investigate the influence of the Kids Science Labs intervention program, grounded in STEM, on the development of independent thinking skills among preschool and elementary school children. By addressing this objective, we aim to explore the program's potential to enhance academic performance in mathematics and science. The study's findings have theoretical significance as they shed light on the ways in which STEM activities can foster independent thinking in young children, thus enabling educators to design effective learning programs that promote academic success. Methodology: This study employs a robust methodology that includes interviews, surveys, observations, case studies, and statistical tests. These methods were carefully selected to collect comprehensive data from multiple sources, such as documents and records, ensuring a thorough analysis of the program's effects. The use of diverse data collection and analysis procedures facilitated an in-depth exploration of the research questions and yielded reliable results. Results: The results indicate that children participating in the Kids Science Labs program experienced a sustained positive impact on their independent thinking abilities. Moreover, these children demonstrated improved academic performance in mathematics and science, displaying higher learning motivation and the capacity to critically evaluate problem-solving methods and seek optimal solutions. Theoretical Importance: This study contributes significantly to the existing theoretical knowledge by elucidating how STEM activities can foster independent thinking and enhance academic success in preschool and elementary school children. The findings have practical implications for educators, empowering them to develop learning programs that stimulate independent thinking, leading to improved academic performance in young children. Discussion: The findings of this research affirm that the Kids Science Labs intervention program is highly effective in fostering independent thinking among preschool and elementary school children. The program's positive impact extends to improved academic performance in mathematics and science, highlighting its potential to enhance learning outcomes. Educators can leverage these findings to develop educational programs that promote independent thinking and elevate academic achievement in young children. Conclusion: In conclusion, the Kids Science Labs intervention program has been found to be highly effective in fostering independent thinking among preschool and elementary school children. Furthermore, participation in the program correlates with improved academic performance in mathematics and science. The study's outcomes underscore the importance of developing educational initiatives that stimulate independent thinking in young children, thereby enhancing their academic success.

Keywords: STEM in preschool, STEM in elementary school, kids science labs, independent thinking, STEM activities in early childhood education

Procedia PDF Downloads 87

Authors: Seyyed Mohammad Hasheminia, Heoung Jae Chun, Jong Chan Park, Hong Suk Chang

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Composite joints have been getting attention recently due to their high specific mechanical strength to weight ratio that is crucial for structures such as aircrafts and automobiles. In this study on hybrid joints, quasi-static experiments and finite element analysis were performed to investigate the failure mechanism of hybrid composite joint with respect to the joint properties such as the adhesive material, clamping force, and joint geometry. The outcomes demonstrated that the stiffness of the adhesive is the most imperative design parameter. In this investigation, two adhesives with various stiffness values were utilized. Regarding the joints utilizing the adhesive with the lower stiffness modulus, it was observed that the load was exchanged promptly through the adhesive since it was shared more proficiently between the bolt and adhesive. This phenomenon permitted the hybrid joints with low-modulus adhesive to support more prominent loads before failure when contrasted with the joints that utilize the stiffer adhesive. In the next step, the stress share between the bond and bolt as a function of various design parameters was studied using a finite element model in which it was understood that the geometrical parameters such as joint overlap and width have a significant influence on the load sharing between the bolt and the adhesive.

Keywords: composite joints, composite materials, hybrid joints, single-lap joint

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Authors: Amna Batool

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Agile methodology is the current most popular technique in software development projects. Agile methods in software development bring optimistic impact on software performances, quality and customer satisfaction. There are some organizations and small-medium enterprises adopting agile into their local software development projects as well as in distributed software development projects. Adopting agile methods in local software development projects is valuable. However, agile global software deployment needs an attention. There are different key challenges in agile global software development that need to resolve and enhance the global software development cycles. The proposed systematic literature review investigates all key challenges of agile in global software development. Moreover, a quantitative methodology (an actual survey) targeted to present a real case scenario of these particular key challenges faced by one of the software houses that is BestWeb Malaysia. The outcomes of systematic literature and the results of quantitative methodology are compared with each other to evaluate if the key challenges pointed out in systematic review still exist. The proposed research and its exploratory results can assist small medium enterprises to avoid these challenges by adopting the best practices in their global software development projects. Moreover, it is helpful for novice researchers to get valuable information altogether.

Keywords: agile software development, ASD challenges, agile global software development, challenges in agile global software development

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Authors: Sara Matsuzaka

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At the center of emerging policy debates on the rights of transgender individuals in public accommodations is the collision of gender binary views with transgender perspectives that challenge conventional gender norms. The results of such socio-political debates could have significant ramifications for the policies and infrastructures of public and private institutions nationwide, including within the addiction treatment field. Despite having disproportionately high rates of substance use disorder compared to the general population, transgender individuals experience significant barriers to engaging in addiction treatment programs. Inpatient addiction treatment centers were originally designed to treat heterosexual cisgender populations and, as such, feature gender segregated housing, bathrooms, and counseling sessions. Such heteronormative structural barriers, combined with exposures to stigmatic al attitudes, may dissuade transgender populations from benefiting from the addiction treatment they so direly need. A literature review is performed to explore the mechanisms by which gender segregation alienates transgender populations within inpatient addiction treatment. The constituent parts of the current debate on the rights of transgender individuals in public accommodations are situated the context of inpatient addiction treatment facilities. Minority Stress Theory is used as a theoretical framework for understanding substance abuse issues among transgender populations as a maladaptive behavioral response for coping with chronic stressors related to gender minority status and intersecting identities. The findings include that despite having disproportionately high rates of substance use disorder compared to the general population, transgender individuals experience significant barriers to engaging in and benefiting from addiction treatment. These barriers are present in the form of anticipated or real interpersonal stigma and discrimination by service providers and structural stigma in the form of policy and programmatic components in addiction treatment that marginalize transgender populations. Transphobic manifestations within addiction treatment may dissuade transgender individuals from seeking help, if not reinforce a lifetime of stigmatic experience, potentially exacerbating their substance use issues. Conclusive recommendations for social workers and addiction treatment professionals include: (1) dismantling institutional policies around gender segregation that alienate transgender individuals, (2) developing policies that provide full protections for transgender clients against discrimination based on their gender identity, and (3) implementing trans-affirmative cultural competency training requirements for all staff. Directions for future research are provided.

Keywords: addiction treatment, gender segregation, stigma, transgender

Procedia PDF Downloads 211

Authors: Kari M. Solomon

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Companies with a culture accepting of change management and performance excellence are better suited to determine their sustainability performance and impacts. A mature corporate culture supportive of performance excellence is better positioned to integrate sustainability management tools into their standard business strategy. Companies use various sustainability management tools and reporting standards to communicate levels of sustainability performance to their stakeholders, more often focusing on shareholders and investors. A research gap remains in understanding how companies adapt business excellence models to define corporate sustainability performance. A content analysis of medium-sized enterprises using corporate sustainability reports and business excellence models reveals the challenges and opportunities of reporting sustainability performance in the context of organizational excellence. The outcomes of this content analysis contribute knowledge on the resources needed for companies to build sustainability performance management systems integral to existing management systems. The findings of this research inform academic research areas of corporate sustainability performance, the business community contributing to sustainable development initiatives, and integrating sustainable development issues into business excellence models. There are potential research links between sustainability performance management and the alignment of the United Nations Sustainable Development Goals (UN SDGs) when organizations promote a culture of performance or business excellence.

Keywords: business excellence, corporate sustainability, performance excellence, sustainability performance

Procedia PDF Downloads 182

Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

Procedia PDF Downloads 58

Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: V. Howard, R. Maguire, S. Corrigan

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Background: Type 1 Diabetes (T1D) is a chronic autoimmune disease, typically diagnosed in childhood. T1D puts an enormous strain on families; controlling blood-glucose in children is difficult and the consequences of poor control for patient health are significant. Successful illness management and better health outcomes can be dependent on quality of caregiving. On diagnosis, parent-caregivers face a steep learning curve as T1D care requires a significant level of knowledge to inform complex decision making throughout the day. The majority of illness management is carried out in the home setting, independent of clinical health providers. Parent-caregivers vary in their level of knowledge and their level of engagement in applying this knowledge in the practice of illness management. Enabling researchers to quantify these aspects of the caregiver experience is key to identifying targets for psychosocial support interventions, which are desirable for reducing stress and anxiety in this highly burdened cohort, and supporting better health outcomes in children. Currently, there are limited tools available that are designed to capture this information. Where tools do exist, they are not comprehensive and do not adequately capture the lived experience. Objectives: Development of quantitative tools, informed by lived experience, to enable researchers gather data on parent-caregiver knowledge and engagement, which accurately represents the experience/cohort and enables exploration of questions that are of real-world value to the cohort themselves. Methods: This research employed an engaged approach to address the problem of quantifying two key aspects of caregiver diabetes management: Knowledge and engagement. The research process was multi-staged and iterative. Stage 1: Working from a constructivist standpoint, literature was reviewed to identify relevant questionnaires, scales and single-item measures of T1D caregiver knowledge and engagement, and harvest candidate questionnaire items. Stage 2: Aggregated findings from the review were circulated among a PPI (patient and public involvement) expert panel of caregivers (n=6), for discussion and feedback. Stage 3: In collaboration with the expert panel, data were interpreted through the lens of lived experience to create a long-list of candidate items for novel questionnaires. Items were categorized as either ‘knowledge’ or ‘engagement’. Stage 4: A Delphi-method process (iterative surveys) was used to prioritize question items and generate novel questions that further captured the lived experience. Stage 5: Both questionnaires were piloted to refine wording of text to increase accessibility and limit socially desirable responding. Stage 6: Tools were piloted using an online survey that was deployed using an online peer-support group for caregivers for Juveniles with T1D. Ongoing Research: 123 parent-caregivers completed the survey. Data analysis is ongoing to establish face and content validity qualitatively and through exploratory factor analysis. Reliability will be established using an alternative-form method and Cronbach’s alpha will assess internal consistency. Work will be completed by early 2024. Conclusion: These tools will enable researchers to gain deeper insights into caregiving practices among parents of juveniles with T1D. Development was driven by lived experience, illustrating the value of engaged research at all levels of the research process.

Keywords: caregiving, engaged research, juvenile type 1 diabetes, quantified engagement and knowledge

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Authors: Muna Mohamed Omer Mirghani

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This paper responds to one of the key national development strategies and a typical challenge in the Gash Basin as well as in different parts of Sudan, namely managing water scarcity in view of climate change impacts in minor water systems sustaining over 50% of the Sudan population. While now focusing on the Gash river basin, the ultimate aim is to replicate the same approach in similar water systems in central and west Sudan. The key objective of the paper is the identification of outcome-based water governance interventions in Gash Basin, guided by the global Sustainable Development Goal six (SDG 6 on water and sanitation) and the Sudan water resource policy framework. The paper concluded that improved water resources management of the Gash Basin is a prerequisite for ensuring desired policy outcomes of groundwater use and flood risk management purposes. Analysis of various water governance dimensions in the Gash indicated that the operationalization of a Basin-level institutional reform is critically focused on informed actors and adapted practices through knowledge and technologies along with the technical data and capacity needed to make that. Adapting the devolved Institutional structure at state level is recommended to strengthen the Gash basin regulatory function and improve compliance of groundwater users.

Keywords: water governance, Gash Basin, integrated groundwater management, Sudan

Procedia PDF Downloads 177

Authors: Kwame Amoah

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Artificial intelligence (AI) has the prospect of transforming project management, significantly improving efficiency and accuracy. By automating specific tasks with defined guidelines, AI can assist project managers in making better decisions and allocating resources efficiently, with possible risk mitigation. This study explores how AI is already impacting project management and likely future AI's impact on the field. The AI's reaction has been a divided opinion; while others picture it as a destroyer of jobs, some welcome it as an innovation advocate. Both sides agree that AI will be disruptive and revolutionize PM's functions. If current research is to go by, AI or some form will replace one-third of all learning graduate PM jobs by as early as 2030. A recent survey indicates AI spending will reach $97.9 billion by the end of 2023. Considering such a profound impact, the project management profession will also see a paradigm shift driven by AI. The study examines what the project management profession will look like in the next 5-10 years after this technological disruption. The research methods incorporate existing literature, develop trend analysis, and conduct structured interviews with project management stakeholders from North America to gauge the trend. PM professionals can harness the power of AI, ensuring a smooth transition and positive outcomes. AI adoption will maximize benefits, minimize adverse consequences, and uphold ethical standards, leading to improved project performance.

Keywords: project management, disruptive teacnologies, project management function, AL applications, artificial intelligence

Procedia PDF Downloads 83

Authors: Muntanavadee Maytapattana

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The purposes of the study were to evaluate the effectiveness of the Parent Training Program for Obesity Reduction (PTPOR) on health behaviors of school-age children. An Ecological Systems Theory (EST) was approached the study and a randomized control trial was used in this study. Participants were school-age overweight or obese children and their parents. One hundred and one parent-child dyads were recruited and random assigned into the PTPOR (N=30), Educational Intervention or EI (N=32), and control group (N=39). The parents in the PTPOR group participated in five sessions including an educational session, a cooking session, aerobic exercise training, 2-time group discussion sessions, and 4-time telephoned counseling sessions. Repeated Measure ANCOVA was used to analyze data. The results presented that the outcomes of the PTPOR group were better than the EI and the control groups at 1st, 8th, and 32nd weeks after finishing the program such as child exercise behavior (F(2,97) = 3.98, p = .02) and child dietary behavior (F(2,97) = 9.42, p = .00). The results suggest that nurses and health care providers should utilize the PTPOR for child weight reduction and for the health promotion of a lifestyle among overweight and obese children.

Keywords: parent training program, obesity reduction, child health behaviors, school-age children

Procedia PDF Downloads 443

Authors: Anukul K. Thakur, Ram Bilash Choudhary, Mandira Majumder

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In this technologically driven world, it is requisite to develop better, faster and smaller electronic devices for various applications to keep pace with fast developing modern life. In addition, it is also required to develop sustainable and clean sources of energy in this era where the environment is being threatened by pollution and its severe consequences. Supercapacitor has gained tremendous attention in the recent years because of its various attractive properties such as it is essentially maintenance-free, high specific power, high power density, excellent pulse charge/discharge characteristics, exhibiting a long cycle-life, require a very simple charging circuit and safe operation. Binary and ternary composites of conducting polymers with carbon and other layered transition metal dichalcogenides have shown tremendous progress in the last few decades. Compared with bulk conducting polymer, these days conducting polymers have gained more attention because of their high electrical conductivity, large surface area, short length for the ion transport and superior electrochemical activity. These properties make them very suitable for several energy storage applications. On the other hand, carbon materials have also been studied intensively, owing to its rich specific surface area, very light weight, excellent chemical-mechanical property and a wide range of the operating temperature. These have been extensively employed in the fabrication of carbon-based energy storage devices and also as an electrode material in supercapacitors. Incorporation of carbon materials into the polymers increases the electrical conductivity of the polymeric composite so formed due to high electrical conductivity, high surface area and interconnectivity of the carbon. Further, polymeric composites based on layered transition metal dichalcogenides such as molybdenum disulfide (MoS2) are also considered important because they are thin indirect band gap semiconductors with a band gap around 1.2 to 1.9eV. Amongst the various 2D materials, MoS2 has received much attention because of its unique structure consisting of a graphene-like hexagonal arrangement of Mo and S atoms stacked layer by layer to give S-Mo-S sandwiches with weak Van-der-Waal forces between them. It shows higher intrinsic fast ionic conductivity than oxides and higher theoretical capacitance than the graphite.

Keywords: supercapacitor, layered transition-metal dichalcogenide, conducting polymer, ternary, carbon

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Authors: S. Pradhan, D. Pradhan, G. Tripathy, T. Dasmohapatra

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Suppressors of cytokine signalling (SOCS3), a newly indentified anti-apoptotic molecule is a downstream effecter of the receptor tyrosine kinase-Ras signalling pathway. Current study has uncovered that SOCS3 may have wide and imperative capacities, particularly because of its close correlation with malignant tumors. To investigate the impact of SOCS3 on MDR, we analyzed the expression of P-gp and SOCS3 by immune-histochemistry and found there was positive correlation between them. At that point we effectively interfered with RNA translation by the contamination of siRNA of SOCS3 into MCF7/ADM breast cancer cell lines through a lentivirus, and the expression of the target gene was significantly inhibited. After RNAi the drug resistance was reduced altogether and the expression of MDR1 mRNA and P-gp in MCF7/ADM cell lines demonstrated a significant decrease. Likewise the expression of P53 protein increased in a statistically significant manner (p ≤ 0.01) after RNAi exposure. Moreover, flowcytometry analysis uncovers that cell cycle and anti-apoptotic enhancing capacity of cells changed after RNAi treatment. These outcomes proposed SOCS3 may take part in breast cancer MDR by managing MDR1 and P53 expression, changing cell cycle and enhancing the anti-apoptotic ability.

Keywords: SOCS3gene, breast cancer, multidrug resistance, MDR1 gene, RNA interference

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Authors: Giselle Maggie Fer Castañeda, Diego Iván González

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The objective of this work was to study the autoshaped choice behavior in the Donahoe, Burgos and Palmer (DBP) neural network model and analyze it under the matching law. Autoshaped choice can be viewed as a form of economic behavior defined as the preference between alternatives according to their relative outcomes. The Donahoe, Burgos and Palmer (DBP) model is a connectionist proposal that unifies operant and Pavlovian conditioning. This model has been used for more than three decades as a neurobehavioral explanation of conditioning phenomena, as well as a generator of predictions suitable for experimental testing with non-human animals and humans. The study consisted of different simulations in which, in each one, a ratio of reinforcement was established for two alternatives, and the responses (i.e., activations) in each of them were measured. Choice studies with animals have demonstrated that the data generally conform closely to the generalized matching law equation, which states that the response ratio equals proportionally to the reinforcement ratio; therefore, it was expected to find similar results with the neural networks of the Donahoe, Burgos and Palmer (DBP) model since these networks have simulated and predicted various conditioning phenomena. The results were analyzed by the generalized matching law equation, and it was observed that under some contingencies, the data from the networks adjusted approximately to what was established by the equation. Implications and limitations are discussed.

Keywords: matching law, neural networks, computational models, behavioral sciences

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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: Phannarithisen Ong

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Children's independent mobility for school trips promotes physical and mental well-being, reduces parental chauffeuring and traffic congestion, and boosts children's public confidence. However, in Thailand, despite a decade of rail mass transit development in Bangkok City, cars still queue to drop students at schools near transit stations. This worsens congestion, urging better independent mobility among children in mass transit regions. The high reliance on the private vehicle will influence the private mode in the children's adulthood. This research emphasizes mass transit use among high school students near transit systems. Through a questionnaire survey, quantitative and qualitative methods reveal key factors impacting school trip mode choice. Preliminary findings highlight children's independence as crucial. The socioeconomic, demographic, trip, and transportation traits explain private car use, even schools near mass transit stations. The outcomes of this study will shed light on urban strategic policies for improvement, advocacy, and encouragement of students using mass transit for school trips, which will help normalize the use of mass transit for such trips.

Keywords: children's independence mobility, mode choice, school trips, TOD, extraneous variable, children's independency

Procedia PDF Downloads 141

Authors: Yongsheng Guo, Mengyu Lu

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This study investigates how Chinese SMEs manage financial risks in financing from customers from the perspectives of ethics and national culture. A grounded theory approach is adopted to identify the causal conditions, actions/interactions, and consequences. 32 interviews were conducted, and systematic coding methods were used to identify themes and categories. This study found that Chinese ethical principles, including integrity, friendship, and reciprocity, and cultural traits, including collectivism, acquaintance society, and long-term orientation, provide conditions for financing from customers. The SMEs establish trust-based relationships with customers through personal communications and social networks and reduce financial risk through diversification, frequent operations, and enterprise reputations. Both customers and SMEs can get benefits like financial resources and customer experiences. This study creates a theoretical framework that connects the causal conditions, processes, and outcomes, providing a deeper understanding of financing from customers. A resource and process capability theory of SMEs and a customer capital and customer value model are proposed to connect accounting and finance concepts. Suggestions are proposed for the authorities as more guidance and regulations are needed for this informal finance.

Keywords: CRM, culture, ethics, SME, risk management

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Authors: Saeed Sayyad Hagh Shomar

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Modeling users’ decisions on departure time choice is the main motivation for this research. In particular, it examines the impact of social-demographic features, household, job characteristics and trip qualities on individuals’ departure time choice. Departure time alternatives are presented as adjacent discrete time periods. The choice between these alternatives is done using a discrete choice model. Since a great deal of early morning trips and traffic congestion at that time of the day comprise work trips, the focus of this study is on the work trip over the entire day. Therefore, this study by using the users’ stated preference in questionnaire models users’ departure time choice affected by congestion pricing schemes in high traffic suburban entrance roads of Tehran. The results demonstrate efficient social-demographic impact on work trips’ departure time. These findings have substantial outcomes for the analysis of transportation planning. Particularly, the analysis shows that ignoring the effects of these variables could result in erroneous information and consequently decisions in the field of transportation planning and air quality would fail and cause financial resources loss.

Keywords: congestion pricing, departure time, modeling, travel timing, time of the day, transportation planning

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Authors: Lai Tzu-Hsuan, Lai Jung-Nien, Lin Jaung-Geng, Kao Shung-Te, Hsuan-Kuang Jung

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Background: Whether acupuncture therapy contributes to preserving residual renal function (RRF) remains largely unknown. This case series evidenced the potential beneficial effects of acupuncture for preserving RRF in five patients with the end-stage renal disease under hemodialysis (HD) treatment. Participants: Five patients on HD receiving eight sessions of weekly 30-min interdialytic acupuncture (Inter-A) with residual urine volume (rUV) and residual glomerular filtration rate (rGFR) recorded once every two weeks were included for analysis. Outcomes: Changes in rUV and rGFR calculated using 24-hour urine collection data were analyzed to assess RRF. Variations in hemoglobin, urea Kt/V and serum albumin levels measured monthly were analyzed to evaluate HD adequacy. Results: After eight Inter-A sessions, mean (standard deviation (SD)) rUV and rGFR increased from 612 (184) ml/day and 1.48 (.94) ml/min/1.73 m2 at baseline to 803(289) ml/day and 2.04(1.17) ml/min/1.73m2 at 2- and 4-week follow-up, respectively. The mean percentage difference increased by 31% in rUV and 38% in rGFR. Routine measurements on HD adequacy also showed improvement. Conclusions: Acupuncture might be an optional add-on treatment for HD population with poor control of water; however, further well-designed controlled trials are warranted.

Keywords: end-stage renal disease, hemodialysis, acupuncture, residual renal function, residual urine volume

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Authors: Lea Lachaud, Aida Raoult, Marion Trousselard, Francois B. Vialatte

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This paper addresses mindfulness from a psychological and neuroscientific perspective, by studying how it modulates attention. Being mindful defines a state characterized by 1-an attention directed to the subjective experience of present moment, 2-an unconditional acceptance of this experience, and 3-the rejection of systematic rationalization in favor of plain awareness. The aim of this study is to investigate whether perceptual salience filters are lowered in a ‘mindful’ condition by exploring the role of being mindful in focused visual attention. Over the past decade, mindfulness therapies have seen a surge in popularity. While the outcomes of these therapies have been widely discussed, the mechanisms whereby meditation affects the brain remain mostly unknown. To explore the role of mindfulness in focused visual attention, we conducted a change blindness experiment on 24 subjects, 12 of them being mindful according to the Freiburg Mindfulness Inventory (FMI) scale. Our results suggest that mindful subjects are less affected by change blindness than non-mindful subjects. Furthermore, EEG measurements performed during the experiments may expose neural correlates specific to the mindful state on P300 evoked potentials. Finally, the analysis of both amplitude and latency caused by the perception of a change over 864 recordings may reveal biomarkers that are typical of this state. The paper concludes by discussing the implications of these results for further research.

Keywords: EEG, change blindness, mindfulness, p300, perception, visual attention

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Authors: Leonardo A. Ambrosio, Carlos. H. Silva Santos, Ivan E. L. Rodrigues, Ayumi K. de Campos, Leandro A. Machado

Abstract:

In this work, we propose an algorithm developed under Python language for the modeling of ordinary scalar Bessel beams and their discrete superpositions and subsequent calculation of optical forces exerted over dielectric spherical particles. The mathematical formalism, based on the generalized Lorenz-Mie theory, is implemented in Python for its large number of free mathematical (as SciPy and NumPy), data visualization (Matplotlib and PyJamas) and multiprocessing libraries. We also propose an approach, provided by a synchronized Software as Service (SaaS) in cloud computing, to develop a user interface embedded on a mobile application, thus providing users with the necessary means to easily introduce desired unknowns and parameters and see the graphical outcomes of the simulations right at their mobile devices. Initially proposed as a free Android-based application, such an App enables data post-processing in cloud-based architectures and visualization of results, figures and numerical tables.

Keywords: Bessel Beams and Frozen Waves, Generalized Lorenz-Mie Theory, Numerical Methods, optical forces

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

Abstract:

Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Sobhy Fathy A. Hashesh

Abstract:

This study aimed at investigating the effect of Andalus Knowledge Phases and Times (ANPT) model of learning and the effect of 'Intel Education Contribution in ANPT' on the development of students’ academic performance and emotional quotient. The society of the study composed of Andalus Private Schools, elementary school students (N=700), while the sample of the study composed of four randomly assigned groups (N=80) with one experimental group and one control group to study "ANPT" effect and the "Intel Contribution in ANPT" effect respectively. The study followed the quantitative and qualitative approaches in collecting and analyzing data to answer the study questions. Results of the study revealed that there were significant statistical differences between students’ academic performances and emotional quotients for the favor of the experimental groups. The study recommended applying this model on different educational variables and on other age groups to generate more data leading to more educational results for the favor of students’ learning outcomes.

Keywords: Al Andalus, emotional quotient, students, academic performance development

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Authors: Savaş Karpuz, Sami Küçükşen

Abstract:

The aim of this study is to investigate the effectiveness of neurological rehabilitation in patients with traumatic brain injury. Participants were 45 consecutive adults with traumatic brain injury who were received the neurologic rehabilitation. Sociodemographic characteristics of the patients, the cause of the injury, the duration of the coma and posttraumatic amnesia, the length of stay in the other inpatient clinics before rehabilitation, the time between injury and admission to the rehabilitation clinic, and the length of stay in the rehabilitation clinic were recorded. The differences in functional status between admission and discharge were determined with Disability Rating Scale (DRS), Functional Independence Measure (FIM), and Functional Ambulation Scale (FAS) and levels of cognitive functioning determined with Ranchos Los Amigos Scale (RLAS). According to admission time, there was a significant improvement identified in functional status of patients who had been given the intensive in-hospital cognitive rehabilitation program. At discharge time, the statistically significant differences were obtained in DRS, FIM, FAS and RLAS scores according to admission time. Better improvement in functional status was detected in patients with lower scores in DRS, and higher scores FIM and RLAS scores at the entry time. The neurologic rehabilitation significantly affects the recovery of functional status after traumatic brain injury.

Keywords: traumatic brain injury, rehabilitation, functional status, neurological

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Authors: Hamid Yazdani, Fatemeh Abbasi

Abstract:

Biomimicry, where flora, fauna or entire ecosystems are emulated as a basis for design, is a growing area of research in the fields of architecture and engineering. This is due to both the fact that it is an inspirational source of possible new innovation and because of the potential it offers as a way to create a more sustainable and even regenerative built environment. The widespread and practical application of biomimicry as a design method remains however largely unrealised. A growing body of international research identifies various obstacles to the employment of biomimicry as an architectural design method. One barrier of particular note is the lack of a clear definition of the various approaches to biomimicry that designers can initially employ. Through a comparative literature review, and an examination of existing biomimetic technologies, this paper elaborates on distinct approaches to biomimetic design that have evolved. A framework for understanding the various forms of biomimicry has been developed, and is used to discuss the distinct advantages and disadvantages inherent in each as a design methodology. It is shown that these varied approaches may lead to different outcomes in terms of overall sustainability or regenerative potential. It is posited that a biomimetic approach to architectural design that incorporates an understanding of ecosystems could become a vehicle for creating a built environment that goes beyond simply sustaining current conditions to a restorative practice where the built environment becomes a vital component in the integration with and regeneration of natural ecosystems.

Keywords: biomimicry, bio-inspired design, ecology, ecomimicry, industrial ecology

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Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

Abstract:

The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: adaptive control, digital Fly-By-Wire, oscillations suppression, PIO

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Authors: Chander Shekhar Kohli

Abstract:

In the field of international relations, countries have been experiencing distinct nature of conflicts. But, in the case of Association of Southeast Asian Nations (ASEAN) for a long time, the members have witnessed conflicts, small and large. These conflicts, as a result, have given catastrophic outcomes, such as killings and destroying properties. For the resolution of such conflicts, nonetheless, efforts likewise have been made, simultaneously, in terms of establishing peace and security. In this background, the ASEAN presents a significant example as before it had faced several wars, like Vietnam War, Cambodia conflicts, and so on. This research paper, therefore, strives to examine the ASEAN as a case with the help of both primary and secondary sources. It likewise will be dealt with how changing norms and identity building facilitate the ASEAN countries to deal with their conflicts both internal and external. This paper also will discuss how internal developments within countries affect conflict resolution process as each member of ASEAN is guided by its national interest. It is then argued that conflict resolution in the ASEAN is moving from its existing power-based solution to norms and identity-based solution as member countries have become more dependent on other countries. The research, therefore, is concluded by saying that the conflicts could only be resolved through building norms and common identities, which of course are recognized crucial mechanisms among the ASEAN countries with some exceptions.

Keywords: ASEAN, conflict resolution, norms and identities, peace and security

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Authors: N. S. Yen, T. H. Yang, W. H. Huang, Y. F. Fang, H. W. Cho

Abstract:

Ultimatum game is an experimental paradigm to study human decision making. There are two players, a proposer and a responder, to split a fixed amount of money. According to the traditional economic theory on ultimatum game, proposer should propose the selfish offers to responder as much as possible to maximize proposer’s own outcomes. However, most evidences had showed that people chose more fair offers, hence two hypotheses – fairness favoring and strategic concern were proposed. In current study, we induced the motivation in participants to be either selfish or altruistic, and manipulated the task variables, the stake sizes (NT$100, 1000, 10000) and the share sizes (the 40%, 30%, 20%, 10% of the sum as selfish offers, and the 60%, 70%, 80%, 90% of the sum as altruistic offers), to examine the two hypotheses. The results showed that most proposers chose more fair offers with longer reaction times (RTs) no matter in choosing between the fair and selfish offers, or between the fair and altruistic offers. However, the proposers received explicit self-interest instruction chose more selfish offers accompanied with longer RTs in choosing between the fair and selfish offers. Therefore, the results supported the strategic concern hypothesis that previous proposers choosing the fair offers might be resulted from the fear of rejection by responders. Proposers would become more self-interest if the fear of being rejected is eliminated.

Keywords: ultimatum game, proposer, self-interest, fear of rejection

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Authors: Wu Di, Yeo Khoon Seng, Lim Tee Tai

Abstract:

The typical insects employ a flapping-wing mode of flight. The numerical simulations on free flight of a model fruit fly (Re=143) including hovering and are presented in this paper. Unsteady aerodynamics around a flapping insect is studied by solving the three-dimensional Newtonian dynamics of the flyer coupled with Navier-Stokes equations. A hybrid-grid scheme (Generalized Finite Difference Method) that combines great geometry flexibility and accuracy of moving boundary definition is employed for obtaining flow dynamics. The results show good points of agreement and consistency with the outcomes and analyses of other researchers, which validate the computational model and demonstrate the feasibility of this computational approach on analyzing fluid phenomena in insect flight. The present modeling approach also offers a promising route of investigation that could complement as well as overcome some of the limitations of physical experiments in the study of free flight aerodynamics of insects. The results are potentially useful for the design of biomimetic flapping-wing flyers.

Keywords: free hovering flight, flapping wings, fruit fly, insect aerodynamics, leading edge vortex (LEV), computational fluid dynamics (CFD), Navier-Stokes equations (N-S), fluid structure interaction (FSI), generalized finite-difference method (GFD)

Procedia PDF Downloads 410