Search results for: logistic networks

Authors: Anita Chhatwal

Abstract:

Purpose: The purpose of this paper is to evaluate and investigate the scope of usage of Academic Social Networking Websites (ASNs) by the Science faculty members across universities of North India, viz. Panjab University, Punjabi University and University of Delhi, Delhi. Design/Methodology/Approach: The present study is based upon the primary data collected from 81 science faculty participants from three universities of North India. Questionnaire method was used as an instrument for survey. The study is descriptive and research-based to investigate the popular ASNs amongst the participants from three sample universities and the purpose for which they use them along with the problems they encounter while using ASNs. Findings: The findings of the study revealed that majority of the participants were using ASNs for their academic needs. It was observed that majority of the participants (78%) used ASNs to access scientific papers, while 73.8% of the participants used them to share their research publications. ResearchGate (60.5%) and Google Scholar (59.7%) were the top two most preferred and widely used ASNs by the participants. The critical analysis of the data shows that laptops (86.3%) emerged as major tools for accessing ASNs. Shortage of computers was found to be the chief obstacle in accessing ASNs by the participants. Results of the study demonstrate that 56.3% of participants suggested conduct of seminars and training as the most effective method to increase the awareness of ASNs. Research Limitations/Implications: The study in hand absorbed the 81 faculty (Assistant Professors) members from 15 Science teaching departments across three sample universities of North India. The findings of this study will help the Government of India to regulate and simultaneously make effort to develop and enhance ASNs usage among faculty, researchers, and students. The present study will add to the existing library and information science literature and will be advantageous for all the information professionals as well. Originality/Value: This study is original survey based on primary data investigate the usage of ASNs by the academia. This study will be useful for research scholars, academicians and students all over the world.

Keywords: academic social networks, awareness and usage, North India, scholarly communication, web 2.0

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Authors: Samira Rabiei, Faezeh Askari, Reza Rastmanesh

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Objective: To evaluate the effects of nutrition education and adherence to a healthy diet on glycemic and lipidemic control in patients with T2DM. Material and Methods: A randomized controlled trial was conducted on 494 patients with T2DM, aged 14-87 years from both sexes who were selected by convenience sampling from referees to Aliebneabitaleb hospital in Ghom. The participants were divided into two 247 person groups by stratified randomization. Both groups received a diet adjusted based on ideal body weight, and the intervention group was additionally educated about healthy food choices regarding diabetes. Information on medications, psychological factors, diet and physical activity was obtained from questionnaires. Blood samples were collected to measure FBS, 2 hPG, HbA1c, cholesterol, and triglyceride. After 2 months, weight and biochemical parameters were measured again. Independent T-test, Mann-Whitney, Chi-square, and Wilcoxon were used as appropriate. Logistic regression was used to determine the odds ratio of abnormal glycemic and lipidemic control according to the intervention. Results: The mean weight, FBS, 2 hPG, cholesterol and triglyceride after intervention were significantly lower than before that (p < 0.05). Discussion: Nutrition education plus a weigh reducer diet is more effective on glycemic and lipidemic control than a weight reducer diet, alone.

Keywords: type 2 diabetes mellitus, nutrition education, glycemic control, lipid profile

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Authors: Kaili Wang, Botao Zhang, Enming Zhang

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Background: Low back pain (LBP) is a common problem affecting athletes' training and competition. Although the association between a limited hip range of motion and prevalence of low back pain has been studied extensively, it has not been studied in table tennis. Aim: The main purposes of this study in table tennis players were (1) to investigate if there is a difference in hip internal rotation (HIR) and external rotation (HER) range of motion (ROM) between players with LBP and players without LBP and (2) to analyze the association between HIR and HER ROM and LBP. Methods: Forty-six table tennis players from the Chinese table tennis team were evaluated for passive maximum HIR and HER ROM. LBP was retrospectively recorded for the last 12 months before the date of ROM assessment by a physical therapist. The data were analyzed the difference in HIR and HER ROM between players with LBP and players without LBP by Mann-Whitney U test, and the association between the difference in HIR and HER ROM and LBP was analyzed via a binary logistic regression. Results: The 54% of players had developed LBP during the retrospective study period. Significant difference between LBP group and the asymptomatic group for HIR ROM (z=4.007, p<0.001) was observed. Difference between LBP group and asymptomatic group for HER ROM (z=1.117, p=0.264) was not significant. Players who had HIR ROM deficit had an increased risk of LBP compared with players without HIR ROM deficit (OR=5.344, 95%CI: 1.006-28.395, P=0.049). Conclusion: HIR ROM of a table tennis player with LBP was less than a table tennis player without LBP. Compared with player whose HIR ROM was normal, player who had HIR ROM deficit appeared to have a higher risk for LBP.

Keywords: assessment, injury prevention, low back pain, table tennis players

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Authors: Pelin Kinay, Andy P. Morse, Elmer V. Villanueva, Karyn Morrissey, Philip L Staddon, Shanzheng Zhang, Jingjing Liu

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The present study aims to evaluate the climate change and health related knowledge, attitudes and practices (KAP) of the elderly population (60 years plus) in Hefei and Suzhou cities of China (n=300). This cross-sectional study includes 150 participants in each city. Data regarding demographic characteristics, KAP, and climate change perceptions were collected using a semi-structured questionnaire. When asked about the potential impacts of climate change over 79% of participants stated that climate change affected their lifestyle. Participants were most concerned about storms (51.7%), food shortage (33.3%) and drought (26%). The main health risks cited included water contamination (32%), air pollution related diseases (38.3%) and lung disease (43%). Finally, a majority (68.3%) did not report receiving government assistance on climate change issues. Logistic regression models were used to analyse the data in order to understand the links between socio-demographical factors and KAP of the participants. These findings provide insights for potential adaptation strategies targeting the elderly. It is recommended that government should take responsibility in creating awareness strategies to improve the coping capacity of elderly in China to climate change and its health impacts and develop climate change adaptation strategies.

Keywords: China, climate change, elderly, KAP

Procedia PDF Downloads 266

Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: M. Hussain, Aqsa Farooq

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Let G = (V,E) be a connected graph and distance between any two vertices a and b in G is a−b geodesic and is denoted by d(a, b). A set of vertices W resolves a graph G if each vertex is uniquely determined by its vector of distances to the vertices in W. A metric dimension of G is the minimum cardinality of a resolving set of G. In this paper line graph of honeycomb network has been derived and then we calculated the metric dimension on line graph of honeycomb network.

Keywords: Resolving set, Metric dimension, Honeycomb network, Line graph

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Authors: Mansur Lubabah Kwanbo

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Existing literatures have demonstrated the importance of executive duality (ED) and audit committee (AC) in the financial growth of companies. To some extent this points to corporate governance mechanism aiming at addressing makers and implementers of company policies to be centered on promoting only company objectives. However, furthering organizational objectives needs an adequate structure of control to realize that. Recent development in the various industries in Nigeria have indicated the internal control system (ICS)has not been able to adequately address most of the activities that results in ills of sustaining growth for these industries. It is from this premise the study has as one of its objective to determine the extent to which ICS significantly relates to ED and AC in listed Nigerian corporation. Data were sourced from 308 financial statements and accounts of the corporations that made the sample of the study. Logistic regression aided the test of the hypothesis formulated for the study. Findings revealed a significant relationship between the study variables. The study concludes that the internal control system (ICS) is effective despite the bifurcation of executive duality (ED) and the presence of the Audit Committee (AC) to the extent of preventing ills that encourage lack of sustainability of company’s growth. Sustaining legitimate policies that translate into huge earnings, and create value to stake holders should be pursued.

Keywords: audit committee (AC), executive duality (ED), internal control system (ICS), Nigeria

Procedia PDF Downloads 295

Authors: Kaycee Bills

Abstract:

Studies have demonstrated that middle and high school students with disabilities are more likely to experience bullying than other student groups. The high rates of bullying victimization observed among youth with disabilities can result in severe socio-emotional consequences. These socio-emotional consequences often manifest in detrimental impacts on the students’ personal relationships. Past studies have indicated that participating in extracurricular athletic activities can have several socio-emotional benefits for students with disabilities. Given the findings of past studies demonstrating the positive relationship between mental health and participation in sports among students with disabilities, it is possible that participating in athletics could have a moderating relationship on the severity of the impact that bullying has on a student’s relationships with family and friends. Using the National Crime Victimization Survey/School Crime Supplement (NCVS/SCS), this study employs an ordinal logistic regression to determine if participation in extracurricular athletic activities mitigates the damaging impact bullying has on the personal relationships with friends and family among students who have disabilities. This study identified statistically significant results suggesting that students with disabilities who participate in athletics reported reduced levels of negative personal relationships resulting from bullying compared to their peers who did not participate in athletics.

Keywords: disability, inclusion, bullying, relationships

Procedia PDF Downloads 169

Authors: Carole Salis, Marie F. Wilson, Fabrizio Murgia, Cristian Lai, Franco Atzori, Giulia M. Orrù

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ANDASA is a knowledge management platform for the capitalization of knowledge and cultural assets for the artistic and cultural sectors. It was built based on the priorities expressed by the participating artists. Through mapping artistic activities and specificities, it enables to highlight various aspects of the artistic research and production. Such instrument will contribute to create networks and partnerships, as it enables to evidentiate who does what, in what field, using which methodology. The platform is accessible to network participants and to the general public.

Keywords: cultural promotion, knowledge representation, cultural maping, ICT

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Authors: Rasikha Ramanand, Priyanka Dixit

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Background: Continuum of maternal care which includes antenatal care, delivery care and postnatal care aids in averting maternal deaths. The objective of this paper is to identify the association between previous experiences of child death on Continuum of Care (CoC) of recent child. Further, the study aimed at understanding where the drop-out rate was high in the continuum. Methods: The study was based on the Nation-wide District Level Household and Facility Survey (DLHS-4) conducted during 2012-13, which provides information on antenatal care, delivery care, percentage of women who received JSY benefits, percentage of women who had any pregnancy, delivery, the place of delivery etc. The sample included women who were selected from the non-EAG states who delivered at least two children. The data were analyzed using SPSS 20.Binary Logistic regression was applied to the data in which the Continuum of Care (CoC) was the dependent variable while the independent variables were entered as the covariates. Results: A major finding of the study was the antenatal to delivery care period where the drop-out rates were high. Also, it was found that a large proportion of women did not receive any of the services along the continuum. Conclusions: This study has clearly established the relationship between previous history of child loss and continuum of maternal care.

Keywords: antenatal care, continuum of care, child loss, delivery care, India, maternal health care, postnatal care

Procedia PDF Downloads 402

Authors: Amr Mansour, Boaz Granot, Amani Tatar, Assil Mahamid, Mohammad Haj Yahia, Fairoz Jayyusi, Eyal Behrbalk

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INTRODUCTION: Distal radius fractures are common orthopedic injuries often treated in the fast-paced, high-stress environment of emergency departments (EDs). In such settings, patient satisfaction can be significantly influenced by the clarity of communication and the accessibility of information This study explores the impact of providing an informational pamphlet that outlines ED processes, treatment expectations, and follow-up instructions on patient satisfaction across key domains, including trust, communication, organization, responsiveness, and overall experience. We hypothesize that a structured informational pamphlet will enhance patient satisfaction by fostering better understanding and aligning patient expectations with the realities of the ED visit. METHODS: A total of 100 adult patients treated for distal radius fractures between January and August 2024 participated in this survey-based study. Patients were randomized into two equal groups: one group received an informational pamphlet detailing their condition and treatment, while the other did not. Satisfaction levels were assessed using a structured questionnaire addressing five domains. Fisher's exact test was used to compare satisfaction measures between the two groups, and multivariate logistic regression analysis was conducted to evaluate the association between receiving an information sheet and high satisfaction. The study was approved by the Institutional Review Board. RESULTS SECTION: Patients who received an informational pamphlet reported significantly higher satisfaction across all five domains (p < .001). In Trust and Understanding, 82% of info-sheet recipients felt “in good hands,” compared to 10% of non-recipients. For Communication, 86% rated doctor explanations as “very clear,” versus 16% among non-recipients. Logistic regression showed that receiving an informational pamphlet was a significant predictor of high satisfaction with Discharge Explanation—clarity on condition, treatment, and follow-up (OR = 17.65, 95% CI: 4.74 - 65.77, p < .001) and Reasonable Solution—feeling their primary concern was resolved (OR = 37.82, 95% CI: 8.75 - 163.42, p < .001). Other predictors, including fracture reduction, gender, and age, were not significant. DISCUSSION: This study highlights the substantial role that simple, cost-effective interventions like informational pamphlets can play in enhancing patient satisfaction in emergency care. By improving communication, fostering trust, and promoting a patient-centered approach, informational pamphlets offer a valuable tool for healthcare providers seeking to enhance the quality of care and patient experience in high-pressure emergency environments. However, the study's limitations, including its single-center design and reliance on self-reported satisfaction scores, may affect the generalizability of the results. Future research should consider a multi-center approach and explore long-term outcomes to further validate the efficacy of informational pamphlets in diverse ED settings. Ultimately, sustained improvement in patient satisfaction is a complex and dynamic issue necessitating a multifactorial approach, and other methods should also be explored to complement this strategy. SIGNIFICANCE/CLINICAL RELEVANCE: This study demonstrates that providing an informational pamphlet in the ED setting can significantly improve patient satisfaction across multiple domains, emphasizing its potential as a simple, cost-effective tool to enhance communication, trust, and overall patient experience during emergency care for distal radius fractures. Integrating such interventions into standard ED protocols may foster a more patient-centered approach, improving both patient outcomes and healthcare efficiency.

Keywords: distal radius fracture, quality care, patient satisfaction, emergency medicine, patient-centered care, communication

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Authors: Abdullah A. Al Orainy

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Small cell backhaul solutions need to be cost-effective, scalable, and easy to install. This paper presents an overview of small cell backhaul technologies. Wireless solutions including TV white space, satellite, sub-6 GHz radio wave, microwave and mmWave with their backhaul characteristics are discussed. Recent research on issues like beamforming, backhaul architecture, precoding and large antenna arrays, and energy efficiency for dense small cell backhaul with mmWave communications is reviewed. Recent trials of 5G technologies are summarized.

Keywords: backhaul, small cells, wireless, 5G

Procedia PDF Downloads 510

Authors: Joy Cao, Min Zhou

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Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

Procedia PDF Downloads 88

Authors: Asma Ameur, Dhafer Malouche

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Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

Procedia PDF Downloads 136

Authors: J. L. Oliver, T. Agryzkov, L. Tortosa, J. Vicent, J. Santacruz

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This paper aims to demonstrate how the use of Network Theory can be applied to a very interesting and complex urban situation: The parts of a city which may have some patrimonial value, but because of their lack of relevant architectural elements, they are not considered to be historic in a conventional sense. In this paper, we use the suburb of La Villaflora in the city of Quito, Ecuador as our case study. We first propose a system of indicators as a tool to characterize and quantify the historic value of a geographic area. Then, we apply these indicators to the suburb of La Villaflora and use Network Theory to understand and propose actions.

Keywords: graphs, mathematics, networks, urban studies

Procedia PDF Downloads 369

Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Carly Madge, Melanie Zurba, Ryan Bullock

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The Boreal Forest has experienced some of the most drastic climate change-induced temperature rises in Canada, with average winter temperatures increasing by 3°C since 1948. One of the main concerns of the province of Saskatchewan, and particularly wildfire managers, is the increased risk of wildfires due to climate change. With these concerns in mind Sakaw Askiy Management Inc., a forestry corporation located in Prince Albert, Saskatchewan with operations in the Boreal Forest biome, is developing wildfire risk reduction strategies that are supported by the shareholders of the corporation as well as the stakeholders of the Prince Albert Forest Management Area (which includes citizens, hunters, trappers, cottage owners, and outfitters). In the past, wildfire management strategies implemented through harvesting have been received with skepticism by some community members of Prince Albert. Engagement of the stakeholders of the Prince Albert Management Area through the development of the wildfire risk reduction strategies aims to reduce this skepticism and rebuild some of the trust that has been lost between industry and community. This research project works with the framework of social learning, which is defined as the learning that occurs when individuals come together to form a group with the purpose of understanding environmental challenges and determining appropriate responses to them. The project evaluates the social learning processes that occur through the development of the risk reduction strategies and how the learning has allowed Sakaw to work towards implementing the strategies into their forest harvesting plans. The incorporation of wildfire risk reduction strategies works to increase the adaptive capacity of Sakaw, which in this case refers to the ability to adjust to climate change, moderate potential damages, take advantage of opportunities, and cope with consequences. Using semi-structured interviews and wildfire workshop meetings shareholders and stakeholders shared their knowledge of wildfire, their main wildfire concerns, and changes they would like to see made in the Prince Albert Forest Management Area. Interviews and topics discussed in the workshops were inductively coded for themes related to learning, adaptive capacity, areas of concern, and preferred methods of wildfire risk reduction strategies. Analysis determined that some of the learning that has occurred has resulted through social interactions and the development of networks oriented towards wildfire and wildfire risk reduction strategies. Participants have learned new knowledge and skills regarding wildfire risk reduction. The formation of wildfire networks increases access to information on wildfire and the social capital (trust and strengthened relations) of wildfire personnel. Both factors can be attributed to increases in adaptive capacity. Interview results were shared with the General Manager of Sakaw, where the areas of concern and preferred strategies of wildfire risk reduction will be considered and accounted for in the implementation of new harvesting plans. This research also augments the growing conceptual and empirical evidence of the important role of learning and networks in regional wildfire risk management efforts.

Keywords: adaptive capacity, community-engagement, social learning, wildfire risk reduction

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Authors: Maomao Cao

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Background and aims While the association between higher consumption of fish and lower liver cancer risk has been confirmed, however, the association between specific fish intake and liver cancer risk remains unknown. We aimed to identify the association between specific fish consumption and the risk of liver cancer. Methods: Based on a community-based seropositive hepatitis B cohort involving 18404 individuals, face to face interview was conducted by a standardized questionnaire to acquire baseline information. Three common fish types in this study were analyzed, including freshwater fish, sea fish, and small fish (shrimp, crab, conch, and shell). All participants received liver cancer screening, and possible cases were identified by CT or MRI. Multivariable logistic models were applied to estimate the odds ratio (OR) and 95% confidence intervals (CI). Multivariate multiple imputations were utilized to impute observations with missing values. Results: 179 liver cancer cases were identified. Consumption of freshwater fish and sea fish at least once a week had a strong inverse association with liver cancer risk compared with the lowest intake level, with an adjusted OR of 0.53 (95% CI, 0.38-0.75) and 0.38 (95% CI, 0.19-0.73), respectively. This inverse association was also observed after the imputation. There was no statistically significant association between intake of small fish and liver cancer risk (OR=0.58, 95%, CI 0.32-1.08). Conclusions: Our findings suggest that consumption of freshwater fish and sea fish at least once a week could reduce liver cancer risk.

Keywords: cross-sectional study, fish intake, liver cancer, risk factor

Procedia PDF Downloads 271

Authors: Khader A. Almhdawi, Hassan Alrabbaie

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Background: Musculoskeletal disorders (MSDs) represent a significant challenge for registered nurses. To our best knowledge, there is no published study that investigated the prevalence of MSDs among nurses and their associated factors comprehensively in Jordan. This study aimed to find the prevalence of MSDs, their possible predictors among registered nurses in Jordanian hospitals. Methods: A cross-sectional design was used. Outcome measures included Nordic Musculoskeletal Questioner (NMQ), Depression Anxiety Stress Scale (DASS), Pittsburgh Sleep Quality Index (PSQI), IPAQ, and sociodemographic data. Prevalence of musculoskeletal complaints was reported using descriptive analysis. Logistic regression analyses were conducted to identify predictors of MSDs. Results: 597 nurses from different hospitals in Jordan participated in this study. Reported MSDs prevalence was the highest at neck (61.1%), followed by upper back (47.2%), shoulder (46.7%), wrist and hands (27.3%), and elbow (13.9%). Significant predictors of MSDs among Jordanian nurses included: being a female, poor sleep quality, high physical activity levels, poor ergonomics, increased workload, and mental stress. Conclusion: This study showed a high prevalence of MSDs among Jordanian nurses and identified their significant predictors. Future studies are needed to investigate the progressive nature of MSDs and their effective treatment strategies.

Keywords: musculoskeletal disorders, nursing, ergonomic, occupational stress

Procedia PDF Downloads 97

Authors: Kaycee Bills

Abstract:

Studies have demonstrated that middle and high school students with disabilities are more likely to experience bullying than other student groups. The high rates of bullying victimization observed among youth with disabilities can result in severe socio-emotional consequences. These socio-emotional consequences often manifest in detrimental impacts on the students’ personal relationships. Past studies have indicated that participating in extracurricular athletic activities can have several socio-emotional benefits for students with disabilities. Given the findings of past studies demonstrating the positive relationship between mental health and participation in sports among students with disabilities, it is possible that participating in athletics could have a moderating relationship on the severity of the impact that bullying has on a student’s relationships with family and friends. Using the National Crime Victimization Survey/School Crime Supplement (NCVS/SCS), this study employs an ordinal logistic regression to determine if participation in extracurricular athletic activities mitigates the damaging impact bullying has on the personal relationships with friends and family among students who have disabilities. This study identified statistically significant results suggesting that students with disabilities who participate in athletics reported reduced levels of negative personal relationships resulting from bullying compared to their peers who did not participate in athletics.

Keywords: disability, inclusion, bullying, relationships

Procedia PDF Downloads 181

Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

Procedia PDF Downloads 317

Authors: Sabah Khan

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The idea of universal Islam tends to exaggerate the extent of homogeneity in Islamic beliefs and practices across Muslim communities. In the age of migration, various Muslim communities are in diaspora. The immediate implication of this is what happens to Islam in diaspora? How Islam gets represented in new forms? Such pertinent questions need to be dealt with. This paper shall draw on the idea of religious transnationalism, primarily transnational Islam. There are multiple ways to conceptualize transnational phenomenon with reference to Islam in terms of flow of people, transnational organizations and networks; Ummah oriented solidarity and the new Muslim public sphere. This paper specifically deals with the new Muslim public sphere. It primarily refers to the space and networks enabled by new media and communication technologies, whereby Muslim identity and Islamic normativity are rehearsed, debated by people in different locales. A new sense of public is emerging across Muslim communities, which needs to be contextualized. This paper uses both primary and secondary data. Primary data elicited through content analysis of audio-visuals on social media and secondary sources of information ranging from books, articles, journals, etc. The basic aim of the paper is to focus on the emerging Muslim public sphere and the role of media in expanding public spheres of Islam. It also explores how Muslims in diaspora negotiate Islam and Islamic practices through media and the new Muslim public sphere. This paper cogently weaves in discussions firstly, of re-intellectualization of Islamic discourse in the public sphere. In other words, how Muslims have come to reimagine their collective identity and critically look at fundamental principles and authoritative tradition. Secondly, the emerging alternative forms of Islam by young Muslims in diaspora. In other words, how young Muslims search for unorthodox ways and media for religious articulation, including music, clothing and TV. This includes transmission and distribution of Islam in diaspora in terms of emerging ‘media Islam’ or ‘soundbite Islam’. The new Muslim public sphere has offered an arena to a large number of participants to critically engage with Islam, which leads not only to a critical engagement with traditional forms of Islamic authority but also emerging alternative forms of Islam and Islamic practices.

Keywords: Islam, media, Muslims, public sphere

Procedia PDF Downloads 269

Authors: Somayeh Pashaee

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The ever-increasing growth of the Internet and the development of information and communication technology have prompted the politicians of different countries to use virtual networks as an efficient tool for their foreign policy. The communication of governments and countries, even in the farthest places from each other, through electronic networks, has caused vast changes in the way of statecraft and governance. Importantly, in the meantime, diplomacy, which is always based on information and communication, has been affected by the new prevailing conditions and new technologies more than other areas and has faced greater changes. The emergence of virtual space and the formation of new communication tools in the field of public diplomacy has led to the redefinition of the framework of diplomacy and politics in the international arena and the appearance of a new aspect of diplomacy called digital diplomacy. Digital diplomacy is in the concept of changing relations from a face-to-face and traditional way to a non-face-to-face and new way, and its purpose is to solve foreign policy issues using virtual space. Digital diplomacy, by affecting diplomatic procedures and its change, explains the role of technology in the visualization and implementation of diplomacy in different ways. The purpose of this paper is to investigate the position of digital diplomacy in the public diplomacy of the Islamic Republic of Iran. The paper tries to answer these two questions in a descriptive-analytical way, considering the progress of communication and the role of virtual space in the service of diplomacy, what is the approach of the Islamic Republic of Iran towards digital diplomacy and the use of a new way of establishing foreign relations in public diplomacy? What capacities and damages are facing the country after the use of this type of new diplomacy? In this paper, various theoretical concepts in the field of public diplomacy and modern diplomacy, including Geoff Berridge, Charles Kegley, Hans Tuch and Ronald Peter Barston, as well as the theoretical framework of Marcus Holmes on digital diplomacy, will be used as a conceptual basis to support the analysis. As a result, in order to better achieve the political goals of the country, especially in foreign policy, the approach of the Islamic Republic of Iran to public diplomacy with a focus on digital diplomacy should be strengthened and revised. Today, only emphasizing on advancing diplomacy through traditional methods may weaken Iran's position in the public opinion level from other countries.

Keywords: digital diplomacy, public diplomacy, islamic republic of Iran, foreign policy, opportunities and challenges

Procedia PDF Downloads 113

Authors: Bhawna Verma

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Looking at the contraceptive use and unmet need specific to the different age groups would help to understand various determinants and characteristics of women from different age groups, which are often being neglected. The study explores contraceptive behavior, unmet need for family planning and its correlates among young nulliparous women aged 15-29, using data from NFHS-4 (2015-16), India. Method: The study utilized information from 26,924 currently married women, who has no child or who have had first terminated pregnancy and was aged 15-29 at the time of the survey. Chi-Square and logistic regression analysis have been used to assess the effects of socio-economic characteristics. Results: Of all the considered explanatory variables religion, caste, education, current age, age at marriage, media exposure and regional differences were found to be significantly affecting the behavior of contraceptive use. Women of the 25-29 age group are 0.6 percent less likely to have an unmet need than women of 12-19 age group. Unmet need is increasing with the increased level of education. Muslim women are 0.3 percent less likely to have an unmet need than women of Hindu category. Conclusion: Separate considerations must be given to the needs for family planning formation among nulliparous women along with the factors associated with the use and non-use of contraceptives among them. Separate considerations must be given for effective promotion of FP knowledge through print, electronic media, towards the unequal access to the contraceptives among nulliparous women. Marriages after legal minimum age and encouraging women for higher education may address existing socio-economic barriers.

Keywords: contraceptive use, unmet need, family planning, contraceptive behavior

Procedia PDF Downloads 111

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

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Both obesity and intimate partner violence (IPV) are growing health threats. This study aimed to assess the prevalence and risk factors of both IPV and obesity and their association. In this cross-sectional study, 530 women aged 16-65 years attending Mazandaran primary health centers were recruited through the stratified random sampling method (2019-2020). Data were collected using the modified World Health Organization Domestic Violence questionnaire, Perceived Stress Scale, and socio-demographic, obstetric, and anthropometric questionnaires. The data were analyzed using descriptive statistics, the chi-square test, and multiple logistic regression. The prevalence of overweight, obesity and psychological, physical, and sexual IPV were 47.6%, 26.7%, 70.4%, 17.9%, and 6.4%, respectively. Increasing women’s educational level and exposure to violence during their lifespan increased the odds of any type of IPV while living in a nuclear family reduced it. In groups of women who were subjected to any type of IPV and only psychological IPV, experiencing violence during the lifespan was significant in predicting obesity. The alarming prevalence of IPV and obesity-overweight in this study points to the need for collaborative socio-political and health intervention. The link between experiencing violence during lifespan and obesity in some subgroups of women highlights the detrimental consequences of chronic violence and the urgent need for effective preventive programs.

Keywords: intimate partner violence, body mass index, obesity, risk factor, women

Procedia PDF Downloads 101

Authors: Xiping Wang，Shuran Yao, Liqin Dai

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Many studies report the winter is getting warmer and the minimum air temperature is obviously rising as the important climate warming evidences. The exacerbated air temperature fluctuation tending to bring more severe weather variation is another important consequence of recent climate change which induced more disasters to crop growth in quite a certain regions. Hebei Province is an important winter wheat growing province in North of China that recently endures more winter freezing injury influencing the local winter wheat crop management. A winter wheat freezing injury assessment Bayesian Network framework was established for the objectives of estimating, assessing and predicting winter wheat freezing disasters in Hebei Province. In this framework, the freezing disasters was classified as three severity degrees (SI) among all the three types of freezing, i.e., freezing caused by severe cold in anytime in the winter, long extremely cold duration in the winter and freeze-after-thaw in early season after winter. The factors influencing winter wheat freezing SI include time of freezing occurrence, growth status of seedlings, soil moisture, winter wheat variety, the longitude of target region and, the most variable climate factors. The climate factors included in this framework are daily mean and range of air temperature, extreme minimum temperature and number of days during a severe cold weather process, the number of days with the temperature lower than the critical temperature values, accumulated negative temperature in a potential freezing event. The Bayesian Network model was evaluated using actual weather data and crop records at selected sites in Hebei Province using real data. With the multi-stage influences from the various factors, the forecast and assessment of the event-based target variables, freezing injury occurrence and its damage to winter wheat production, were shown better scoped by Bayesian Network model.

Keywords: bayesian networks, climatic change, freezing Injury, winter wheat

Procedia PDF Downloads 407

Authors: C. Slim

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This paper presents an integrated model, which hybridized data envelopment analysis (DEA) and support vector machine (SVM) together, to class countries according to their efficiency and performance. This model takes into account aspects of multi-dimensional indicators, decision-making hierarchy and relativity of measurement. Starting from a set of indicators of performance as exhaustive as possible, a process of successive aggregations has been developed to attain an overall evaluation of a country’s competitiveness.

Keywords: Artificial Neural Networks (ANN), Support vector machine (SVM), Data Envelopment Analysis (DEA), Aggregations, indicators of performance

Procedia PDF Downloads 337

Authors: Izani Uzair Zubair, Mohd Ismail Ibrahim, Mohd Nazri Shafei, Hassan Merican Omar Naina Merican, Mohamad Sabri Othman, Mohd Izmi Ahmad Ibrahim, Rasilah Ramli, Rajpal Singh Karam Singh

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Nurses experience a higher prevalence of low back pain (LBP) and musculoskeletal complaints as compared to other hospital workers. Due to no proper policy related to LBP, the job has exposed them to the problem. Thus, the current study aims to look at the intensity of the problem and factors associated with development of LBP. Method and Tools: A cross sectional study was carried out among 1292 nurses from six public hospitals in Penang. They were randomly selected and those who were pregnant and have been diagnosed to have LBP were excluded. A Malay validated BACK Questionnaire was used. The associated factors were determined by using multiple logistic regression from SPSS version 20.0. Result: Most of the respondents were at mean age 30 years old and had mean working experience 86 months. The prevalence of LBP was identified as 76% (95% CI 74, 82). Factors that were associated with LBP among nurses include lifting a heavy object (OR2.626 (95% CI 1.978, 3.486) p =0.001 and the estimation weight of the lifted object (OR1.443 (95% CI 1.056, 1.970) p =0.021. Conclusion: Nurses who practice lifting heavy object and weight of the object lifted give a significant contribution to the development of LBP. The prevalence of the problem is significantly high. Thus, a proper no weight lifting policy should be considered.

Keywords: low back pain, nurses, Penang public hospital, Penang

Procedia PDF Downloads 486

Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

Procedia PDF Downloads 63