Search results for: binary logistic model

Authors: Dmitriy Kuptsov

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Tree data structures, such as binary or in general k-ary trees, are essential in computer science. The applications of these data structures can range from data search and retrieval to sorting and ranking algorithms. Naive implementations of these data structures can consume prohibitively large volumes of random access memory limiting their applicability in certain solutions. Thus, in these cases, more advanced representation of these data structures is essential. In this paper we present the design of the compact version of ternary tree data structure and demonstrate the results for the experimental evaluation using static dictionary problem. We compare these results with the results for binary and regular ternary trees. The conducted evaluation study shows that our design, in the best case, consumes up to 12 times less memory (for the dictionary used in our experimental evaluation) than a regular ternary tree and in certain configuration shows performance comparable to regular ternary trees. We have evaluated the performance of the algorithms using both 32 and 64 bit operating systems.

Keywords: algorithms, data structures, succinct ternary tree, per- formance evaluation

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Authors: Kim Lindsay F. Restua, Jeehan Kyra A. Rivero, Joneka Myles D. Taguba

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Lean construction is defined as an approach in construction with the purpose of reducing waste in the process without compromising the value of the project. There are numerous lean construction tools that are applied in the construction process, which maximizes the efficiency of work and satisfaction of customers while minimizing waste. However, the complexity and differences of construction projects cause a rise in challenges on achieving the lean benefits construction can give, such as improvement in safety performance. The objective of this study is to determine the relationship between lean construction tools and their effects on safety performance. The relationship between construction tools applied in construction and safety performance is identified through Logistic Regression Analysis, and Correlation Analysis was conducted thereafter. Based on the findings, it was concluded that almost 60% of the factors listed in the study, which are different tools and effects of lean construction, were determined to have a significant relationship with the level of safety in construction projects.

Keywords: correlation analysis, lean construction tools, lean construction, logistic regression analysis, risk management, safety

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Sadia Arshad, Yifa Tang, Dumitru Baleanu

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A fractional order mathematical model is proposed that incorporate CD8+ cells, natural killer cells, cytokines and tumor cells. The tumor cells growth in the absence of an immune response is modeled by logistic law as it was the simplest form for which predictions also agreed with the experimental data. Natural Killer Cells are our first line of defense. NK cells directly kill tumor cells through several mechanisms, including the release of cytoplasmic granules containing perforin and granzyme, expression of tumor necrosis factor (TNF) family members. The effect of the NK cells on the tumor cell population is expressed with the product term. Rational form is used to describe interaction between CD8+ cells and tumor cells. A number of cytokines are produced by NKs, including tumor necrosis factor TNF, IFN, and interleukin (IL-10). Source term for cytokines is modeled by Michaelis-Menten form to indicate the saturated effects of the immune response. Stability of the equilibrium points is discussed for biologically significant values of bifurcation parameters. We studied the treatment of fractional order system by investigating analytical conditions of tumor eradication. Numerical simulations are presented to illustrate the analytical results.

Keywords: cancer model, fractional calculus, numerical simulations, stability analysis

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Authors: Jiaqian Yin, Ruoling Chen, David Churchill, Huijuan Zou, Peipei Guo, Chunmei Liang, Xiaoqing Peng, Zhikang Zhang, Weiju Zhou, Yunxia Cao

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Purpose: This study aimed to explore the interaction of male and female age on implantation failure from in vitro fertilisation (IVF)/ intracytoplasmic sperm injection (ICSI) treatments in couples following their first cycles using the Anhui Maternal-Child Health Study (AMCHS). Methods: The AMCHS recruited 2042 infertile couples who were physically fit for in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI) treatment at the Reproductive Centre of the First Affiliated Hospital of Anhui Medical University between May 2017 to April 2021. This prospective cohort study analysed the data from 1910 cohort couples for the current paper data analysis. The multivariate logistic regression model was used to identify the effect of male and female age on implantation failure after controlling for confounding factors. Male age and female age were examined as continuous and categorical (male age: 20-<25, 25-<30, 30-<35, 35-<40, ≥40; female age: 20-<25, 25-<30, 30-<35, 35-<40, ≥40) predictors. Results: Logistic regression indicated that advanced maternal age was associated with increased implantation failure (P<0.001). There was evidence of an interaction between maternal age (30-<35 and ≥ 35) and paternal age (≥35) on implantation failure. (p<0.05). Only when the male was ≥35 years of increased maternal age was associated with the risk of implantation failure. Conclusion: In conclusion, there was an additive effect on implantation failure with advanced parental age. The impact of advanced maternal age was only seen in the older paternal age group. The delay of childbearing in both men and women will be a serious public issue that may contribute to a higher risk of implantation failure in patients needing assisted reproductive technology (ART).

Keywords: parental age, infertility, cohort study, IVF

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Authors: Shu-Hsi Ho

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The problems of aging and number of widowed peers gradually rise in Taiwan. It is worth to concern the related issues for elderly after the death of a spouse. Hence, this study is to examine the impact of spousal death on the surviving spouse’s self-perceived health and mental health for the elderly in Taiwan. A cross section data design and ordered logistic regression models are applied to investigate whether marriage is associated significantly to self-perceived health and mental health for the widowed older Taiwanese. The results indicate that widowed marriage shows significant negative effects on self-perceived health and mental health regardless of widows or widowers. Among them, widows might be more likely to show worse mental health than widowers. The belief confirms that marriage provides effective sources to promote self-perceived health and mental health, particularly for females. In addition, since the social welfare system is not perfect in Taiwan, the findings also suggest that family and social support reveal strongly association with the self-perceived health and mental health for the widows and widowers elderly.

Keywords: logistic regression models, self-perceived health, widow, widower

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Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

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Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

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Authors: Aditi Viswanathan, Shree Ranjani, Aruna Govada

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With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Keywords: distributed algorithm, MapReduce, multi-class, support vector machine

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Authors: Tajinder Kaur, Anu Bala

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An image retrieval system is a computer system for browsing, searching, and retrieving images from a large database of digital images a new algorithm meant for content-based image retrieval (CBIR) is presented in this paper. The proposed method combines the color and texture features which are extracted the global and local information of the image. The local texture feature is extracted by using local binary patterns (LBP), which are evaluated by taking into consideration of local difference between the center pixel and its neighbors. For the global color feature, the color histogram (CH) is used which is calculated by RGB (red, green, and blue) spaces separately. In this paper, the combination of color and texture features are proposed for content-based image retrieval. The performance of the proposed method is tested on Corel 1000 database which is the natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and CH.

Keywords: color, texture, feature extraction, local binary patterns, image retrieval

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Authors: Jarrod Sio Jyh Lih

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This paper discusses the factors contributing to student achievement in literacy and numeracy in primary schools within Sibu division. The study involved 694 level 1 primary schoolteachers. Using descriptive statistics, the study observed high levels of practice for teacher efficacy, instructional leadership and professional learning communities (PLCs). The differences between gender, teaching experience and academic qualification were analyzed using the t-test and one-way analysis of variance (ANOVA). The study reported significant differences in respondent perceptions based on teaching experience vis-à-vis teacher efficacy. Here, the post hoc Tukey test revealed that efficaciousness grows with experience. A correlation test observed positive and significant correlations between all independent variables. Binary logistic regression was applied to predict the independent variables’ influence on student achievement. The findings revealed that a dimension of instructional leadership – ‘monitoring student progress’ - emerged as the best predictor of student achievement for literacy and numeracy. The result indicated the students were more than 4 times more likely to achieve the national key performance index for both literacy and numeracy when student progress was monitored. In conclusion, ‘monitoring student progress’ had a positive influence on students’ achievement for literacy and numeracy, hence making it a possible course of action for school heads. However, more comprehensive studies are needed to ascertain its consistency within the context of Malaysia.

Keywords: efficacy, instructional, literacy, numeracy

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Authors: Muhammad Kamran Hanif Khan, Fahad Mushtaq

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Globally, the death rate from cardiovascular disease has risen over the past 20 years, but especially in low and middle-income countries (LMICS), reports the World Health Organization (WHO). Around 17.5 million deaths, or 31% of all deaths worldwide in 2012, were attributed to CVD, 80% of which occurred in low- and middle-income nations, and eighty five percent of all worldwide disability is attributable to cardiovascular disease. This study assessed the dietary habit and Cardiovascular Risk factors among the patients of coronary artery disease against matched controls. The research was a case-control study. Sample size for this case-control study was 410 CAD cases and 410 healthy controls. The case-control ratio was 1:1. Patients diagnosed with coronary artery disease were recruited from the outpatient departments and emergency rooms of four hospitals in Pakistan. The ages of people who were diagnosed with coronary artery disease were not significantly different from (mean 57.97 7.39 years) the healthy controls (mean 57.12 6.73 years). In order to determine the relationship between food consumption and the two binary outcomes, logistic regression analysis was carried out. Chicken (0.340 (0.245-0.47), p-value 0.0001), beef (0.38 (0.254-0.56), p-value 0.0001), eggs (0.297 (0.208-0.426), p-value 0.0001), and junk food (0.249 (0.167-0.372), p-value 0.0001)) were protective, while yogurt consumption more than twice weekly was risk. Conclusion: In conclusion, poor dietary habits are closely linked to the risk of CAD. Investigations based on dietary trends offer vital and practical knowledge about societal patterns.

Keywords: dietary habbits, cardiovasculardisease, CVD risk factors, hypercholesterolemia

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Man Kumar Tamang, Uday Narayan Yadav

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Background and objectives: Every country in the world is facing a demographic challenge due to drastic growth of population over 60 years. Adequate diet and nutritional status are important determinants of health in elderly populations. This study aimed to assess the nutritional status among the elderly population and factors associated with malnutrition at the community setting in rural Nepal. Methods: This is a community-based cross-sectional study among elderly of age 60 years or above in the three randomly selected VDCs of Morang district in eastern Nepal, between August and November, 2016. A multi stage cluster sampling was adopted with sample size of 345 of which 339 participated in the study. Nutritional status was assessed by MNA tool and associated socio-economic, demographic, psychological and nutritional factors were checked by binary logistic regression analysis. Results: Among 339 participants, 24.8% were found to be within normal nutritional status, 49.6% were at risk of malnutrition and 24.8% were malnourished. Independent factors associated with malnutrition status among the elderly people after controlling the cofounders in the bivariate analysis were: elderly who were malnourished were those who belonged to backward caste according to traditional Hindu caste system [OR=2.69, 95% CI: 1.17-6.21), being unemployed (OR=3.23, 95% CI: 1.63-6.41),who experienced any mistreatment from caregivers (OR=4.05, 95% CI: 1.90-8.60), being not involved in physical activity (OR=4.67, 95% CI: 1.87-11.66) and those taking medication for any co-morbidities. Conclusion: Many socio-economic, psychological and physiological factors affect nutritional status in our sample population and these issues need to be addressed for bringing improvement in elderly nutrition and health status.

Keywords: elderly, eastern Nepal, malnutrition, nutritional status

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Authors: S. A. Salau, I. F. Ayanda, I. Afe, M. O. Adesina, N. B. Nofiu

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Food insecurity is still a critical challenge among rural and urban households in Nigeria. The country’s food insecurity situation became more pronounced due to frequent conflict between pastoralist and crop farmers. Thus, this study assesses pastoralist-crop farmers’ conflict and food security of farming households in Kwara state, Nigeria. The specific objectives are to measure the food security status of the respondents, quantify pastoralist- crop farmers’ conflict, determine the effect of pastoralist- crop farmers conflict on food security and describe the effective coping strategies adopted by the respondents to reduce the effect of food insecurity. A combination of purposive and simple random sampling techniques will be used to select 250 farming households for the study. The analytical tools include descriptive statistics, Likert-scale, logistic regression, and food security index. Using the food security index approach, the percentage of households that were food secure and insecure will be known. Pastoralist- crop farmers’ conflict will be measured empirically by quantifying loses due to the conflict. The logistic regression will indicate if pastoralist- crop farmers’ conflict is a critical determinant of food security among farming households in the study area. The coping strategies employed by the respondents in cushioning the effects of food insecurity will also be revealed. Empirical studies on the effect of pastoralist- crop farmers’ conflict on food security are rare in the literature. This study will quantify conflict and reveal the direction as well as the extent of the relationship between conflict and food security. It could contribute to the identification and formulation of strategies for the minimization of conflict among pastoralist and crop farmers in an attempt to reduce food insecurity. Moreover, this study could serve as valuable reference material for future researches and open up new areas for further researches.

Keywords: agriculture, conflict, coping strategies, food security, logistic regression

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Yeonjeong Lee, Kyoung-jae Kim, Youngtae Kim

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This study proposes a novel recommender system that uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user’s preference. The proposed model consists of two steps. In the first step, this study uses logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. Then, this study combines the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. In the second step, this study uses the market basket analysis to extract association rules for co-purchased products. Finally, the system selects customers who have high likelihood to purchase products in each product group and recommends proper products from same or different product groups to them through above two steps. We test the usability of the proposed system by using prototype and real-world transaction and profile data. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The results also show that the proposed system may be useful in real-world online shopping store.

Keywords: product recommender system, ensemble technique, association rules, decision tree, artificial neural networks

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Authors: Şule Turhan, Özlem Turan

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Permanence in the international markets for many global companies is about being known as having effective logistics which targets customer satisfaction management and lower costs. Under competitive conditions, the necessity of providing the products to customers quickly and on time for the companies which constantly aim to improve their profitability increased the strategic importance of the logistics concept. Food logistic is one of the most difficult areas in logistics. In the process from manufacturer to final consumer, quality and hygiene standards must be provided constantly. In food logistics, reliable and extensive service network has great importance and on time delivery is the target. Developing logistics industry provide the supply of foods in the country and the development of export markets more quickly and has an important role in providing added value to the country's economy. Turkey that creates a bridge between the east and the west is an attractive market for logistics companies. In this study, by examining both the place and the importance of logistics in Turkish food sector, recommendations will be made for the food industry.

Keywords: logistics, Turkish food industry, competition, food industry

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Authors: Kaycee L. Bills

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Adolescents and youth who are members of a minority group are more likely to experience higher rates of bullying in comparison to other student demographics. Specifically, adolescents with intellectual disabilities are a minority population that is more susceptible to experience unfair treatment in social settings. This study employs the 2015 Wave of the National Crime Victimization Survey – School Crime Supplement (NCVS/SCS) longitudinal dataset to explore bullying rates experienced among adolescents with intellectual disabilities. This study uses chi-square testing and a logistic regression to analyze if having a disability influences the likelihood of being bullied in comparison to other student demographics. Results of the chi-square testing and the logistic regression indicate that adolescent students who were identified as having a disability were approximately four times more likely to experience higher bullying rates in comparison to all other majority and minority student populations. Thus, it means having a disability resulted in higher bullying rates in comparison to all student groups.

Keywords: disability, bullying, social work, school bullying

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Authors: Rachna Agarwal, R. Naveen, Darpan Thakre, Rohit Shahi, Maryam Abbasi, Upendra Rathore, Latika Gupta

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Aim: Retinal changes are the window to systemic vasculature. Therefore, we explored retinal changes in patients with idiopathic inflammatory myopathies (IIM) as a surrogate for vascular health. Methods: Adult and juvenile IIM patients visiting a tertiary care centre in 2021 satisfying the International Myositis Classification Criteria were enrolled for detailed ophthalmic examination in comparison with healthy controls (HC). Patients with conditions that precluded thorough posterior chamber examination were excluded. Scale variables are expressed as median (IQR). Multivariate analysis (binary logistic regression-BLR) was conducted, adjusting for age, gender, and comorbidities besides factors significant in univariate analysis. Results: 43 patients with IIM [31 females; age 36 (23-45) years; disease duration 5.5 (2-12) months] were enrolled for participation. DM (44%) was the most common diagnosis. IIM patients exhibited frequent attenuation of retinal vessels (32.6% vs. 4.3%, p <0.001), AV nicking (14% vs. 2.2%, p=0.053), and vascular tortuosity (18.6% vs. 2.2%, p=0.012), besides decreased visual acuity (53.5% vs. 10.9%, p<0.001) and immature cataracts (34.9% vs. 2.2%, p<0.001). Attenuation of vessels [OR 10.9 (1.7-71), p=0.004] emerged as significantly different from HC after adjusting for covariates in BLR. Notably, adults with IIM were more predisposed to retinal abnormalities [21 (57%) vs. 1 (16%), p=0.068], especially attenuation of vessels [14(38%) vs. 0(0), p=0.067] than jIIM. However, no difference was found in retinal features amongst the subtypes of adult IIM, nor did they correlate with MDAAT, MDI, or HAQ-DI. Conclusion: Retinal microvasculopathy and diminution of vision occur in nearly one-third to half of the patients with IIM. Microvasculopathy occurs across subtypes of IIM, and more so in adults, calling for further investigation as a surrogate for damage assessment and potentially even systemic vascular health.

Keywords: idiopathic inflammatory myopathies, vascular health, retinal microvasculopathy, arterial attenuation

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Authors: Ali Dalirbod, Peyman Ahmadian

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Inclusions are unavoidable part of all steels. Non-metallic inclusions have significant effects on mechanical properties of steel. The effects of inclusion on stress concentration around the matrix/inclusion have been extensively studied. The results relating to single inclusion behavior, describe properly the behavior of stress but not the elongation drop. The raised stress in inclusion/matrix results in crack initiation. The influence of binary inclusions on stress concentration around matrix is a major aim of this work which is representative of the simple pattern distribution of non-metallic inclusions. Stress concentration around inclusions in this case depends on parameters like distance between two inclusions (d), angle between centrally linking line of two inclusions, load axis (φ), and rotational angle of inclusion (θ). FEM analysis was applied to investigate the highest and lowest ductility versus varying parameters above. The simulation results show that there is a critical distance between two cubic inclusions in which bigger than the threshold, the stress, and strain field in matrix/inclusions interface converts into individual fields around each inclusion.

Keywords: nitride inclusion, simulation, tensile properties, inclusion-matrix interface

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Authors: Usama Bin Zubair, Muhammad Azeem

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Introduction: Depression has been one of the most commonly diagnosed mental health disorders in Pakistan. A Census conducted by the government of Pakistan in 2017 showed that more than 10000 trans-genders live in Pakistan. HIV, illicit substance use and mental health issues, including depression, have been the main health problems faced by them. Trans-gender population has been suffering from depressive illness more than normal population all over the world. Aim: To assess the prevalence of depression among the transgender population and analyze the relationship of socio-demographic factors with depression. Subjects and Methods: The sample population comprised of one hundred and forty-two transgender people of Rawalpindi and Islamabad. Beck depressive inventory II (BDI-II) was used to record the presence and severity of the depressive symptoms. Depressive symptoms were categorized as mild, moderate and severe. Relationship of the age, smoking, family income, illicit substance use and education were studied with the presence of depressive symptoms among these transgender people of twin cities of Pakistan. Results: A total of 142 transgender people were included in the final analysis. The mean age of the study participants was 39.55 ± 6.18. Out of these, 45.1% had no depressive symptoms while 31.7% had mild, 12.7% had moderate and 10.6% had severe depressive symptomatology. After applying the binary logistic regression, we found that the presence of depressive symptoms had a significant association with illicit substance use among the target population. Conclusion: This study showed a high prevalence of depressive symptoms among the transgender population in the twin cities of Pakistan. Use of illicit substances like tobacco, cannabis, opiates, and alcohol should be discouraged to prevent mental health problems.

Keywords: depression, transgender, prevalence, sociodemographic factors

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Authors: A. Abdullah, N. Faghih Mirzaei, S. Hany Haron

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Neighbourhood environment walkability on reported physical activity (PA) levels of students of Universiti Sains Malaysia (USM) in Malaysia. Compared with previous generations, today’s young people spend less time playing outdoors and have lower participation rates in PA. Research suggests that negative perceptions of neighbourhood walkability may be a potential barrier to adolescents’ PA. The sample consisted of 200 USM students (to 24 years old) who live outside of the main campus and engage in PA in sport halls and sport fields of USM. The data were analysed using the t-test, binary logistic regression, and discriminant analysis techniques. The present study found that youth PA was affected by neighbourhood environment walkability factors, including neighbourhood infrastructures, neighbourhood safety (crime), and recreation facilities, as well as street characteristics and neighbourhood design variables such as facades of sidewalks, roadside trees, green spaces, and aesthetics. The finding also illustrated that active students were influenced by street connectivity, neighbourhood infrastructures, recreation facilities, facades of sidewalks, and aesthetics, whereas students in the less active group were affected by access to destinations, neighbourhood safety (crime), and roadside trees and green spaces for their PAs. These results report which factors of built environments have more effect on youth PA and they message to the public to create more awareness about the benefits of PA on youth health.

Keywords: fear of crime, neighbourhood built environment, physical activities, street characteristics design

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Authors: Birtukan Atinkut Asmare, Bernhard Freyer, Jim Bingen

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Though it is possible to transform the farming system towards a healthy, sustainable, and toxic-free food system by reducing pesticide use both in the field and postharvest, pesticides, including those that have been banned or severely restricted from use in developed countries, are indiscriminately used in African agriculture. Drawing on social practice theory, this study is about pesticide use practices in smallholder farms and its adverse impacts on women’s health and the environment, with reference to Africa, with an empirical focus on Ethiopia. Data have been collected via integrating diverse quantitative and qualitative approaches such as household surveys (n= 318), focus group discussions (n=6), field observations (n=30), and key informant interviews (n=18), with people along the pesticide value chain, including sellers and extension workers up to women farmers. A binary logistic regression model was used to investigate the factors that influence the adoption of personal protective equipment among female headed households. The findings show that Female-headed households carried out risky and unsafe practices from pesticide purchasing up to disposal, largely motivated by material elements (such as labor, income, time, and the provisioning system) but were notably shaped by competences (skills and knowledge), and meanings (norms, values, rules, and shared ideas). The main meaning or material aspect for pesticide purchasing were the perceptions of efficacy on pests, diseases, and weeds (65%), cost and availability in smaller quantities (60.7%), and a woman’s available time and mobility (58.9%). Pesticide hazards to human health or the environment seem not to be relevant for most female headed households. Unsafe practices of pesticide use among women led to the loss of biodiversity and ecosystem degradation, let alone their and family’s health. As the regression results show, the significant factors that influenced PPE adoption among female headed households were age and retailer information (p < 0.05). In line with the empirical finding, in addition to changing individual competences through advisory services and training, a foundational shift is needed in the sociocultural environment (e.g., policy, advisory), or a change in the meanings (social norms), where women are living and working.

Keywords: biodiversity, competences, ecosystems, ethiopia, female headed households, materials, meanings, pesticide purchasing, pesticide using, social practice theory

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Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

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Authors: Saumya Srivastava, Rina Maiti

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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

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Authors: Hamidreza Bagheri, Alireza Shariati

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There are many difficulties in the purification of raw components and products. However, researchers are seeking better ways for purification. One of the recent methods is extraction using supercritical fluids. In this study, the phase equilibria of benzoic acid-supercritical carbon dioxide system were investigated. Regarding the phase equilibria of this system, the modeling of solid-supercritical fluid behavior was performed using the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) and Peng-Robinson equations of state (PR EoS). For this purpose, five PC-SAFT EoS parameters for pure benzoic acid were obtained using its experimental vapor pressure. Benzoic acid has association sites and the behavior of the benzoic acid-supercritical fluid system was well-predicted using both equations of state, while the binary interaction parameter values for PR EoS were negative. Genetic algorithm, which is one of the most accurate global optimization algorithms, was also used to optimize the pure benzoic acid parameters and the binary interaction parameters. The AAD% value for the PC-SAFT EoS, were 0.22 for the carbon dioxide-benzoic acid system.

Keywords: supercritical fluids, solubility, solid, PC-SAFT EoS, genetic algorithm

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Authors: Ahmed A. A. Fadol, Guangji Tong, Wlaa Mohamed

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This paper provides a comprehensive analysis of food insecurity in Sudan, focusing on consumption patterns and their implications, employing the Integrated Food Security Phase Classification (IPC) assessment framework. Years of conflict and economic instability have driven large segments of the population in Sudan into crisis levels of acute food insecurity according to the (IPC). A substantial number of people are estimated to currently face emergency conditions, with an additional sizeable portion categorized under less severe but still extreme hunger levels. In this study, we explore the multifaceted nature of food insecurity in Sudan, considering its historical, political, economic, and social dimensions. An analysis of consumption patterns and trends was conducted, taking into account cultural influences, dietary shifts, and demographic changes. Furthermore, we employ logistic regression and random forest analysis to identify significant independent variables influencing food security status in Sudan. Random forest clearly outperforms logistic regression in terms of area under curve (AUC), accuracy, precision and recall. Forward projections of the IPC for Sudan estimate that 15 million individuals are anticipated to face Crisis level (IPC Phase 3) or worse acute food insecurity conditions between October 2023 and February 2024. Of this, 60% are concentrated in Greater Darfur, Greater Kordofan, and Khartoum State, with Greater Darfur alone representing 29% of this total. These findings emphasize the urgent need for both short-term humanitarian aid and long-term strategies to address Sudan's deepening food insecurity crisis.

Keywords: food insecurity, consumption patterns, logistic regression, random forest analysis

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Authors: Gizem İntepe, Eti Mizrahi

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Investment and financing decisions are influenced by various economic features. Detailed analysis should be conducted in order to make decisions not only by companies but also by governments. Such analysis can be conducted either at the company level or on a sectoral basis to reduce risks and to maximize profits. Sectoral disaggregation caused by seasonality effects, subventions, data advantages or disadvantages may appear in sectors behaving parallel to BIST (Borsa Istanbul stock exchange) Index. Proposed logistic indices could serve market needs as a decision parameter in sectoral basis and also helps forecasting activities in import export volume changes. Also it is an indicator of logistic activity, which is also a sign of economic mobility at the national level. Publicly available data from “Ministry of Transport, Maritime Affairs and Communications” and “Turkish Statistical Institute” is utilized to obtain five logistics indices namely as; exLogistic, imLogistic, fLogistic, dLogistic and cLogistic index. Then, efficiency and reliability of these indices are tested.

Keywords: economic activity, export trade data, import trade data, logistics indices

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Authors: Nikola Vlahovic, Blazenka Knezevic, Petra Batalic

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In this paper, we will present a research about feasibility of implementing unmanned aerial vehicles, also known as 'drones', in logistics. Research is based on available information about current incentives and experiments in application of delivery drones in commercial use. Overview of current pilot projects and literature, as well as an overview of detected challenges, will be compiled and presented. Based on these findings, we will present a conceptual model of business process that implements delivery drones in business to business logistic operations. Business scenario is based on a pharmaceutical supply chain. Simulation modeling will be used to create models for running experiments and collecting performance data. Comparative study of the presented conceptual model will be given. The work will outline the main advantages and disadvantages of implementing unmanned aerial vehicles in delivery services as a supplementary distribution channel along the supply chain.

Keywords: business process, delivery drones, logistics, simulation modelling, unmanned aerial vehicles

Procedia PDF Downloads 381