Search results for: probability weighted moment estimation

Authors: Prabha Rohatgi

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To obtain an efficient control over a huge amount of inventory of drugs in pharmacy department of any hospital, generally, the medicines are categorized on the basis of their cost ‘ABC’ (Always Better Control), first and then categorize on the basis of their criticality ‘VED’ (Vital, Essential, desirable) for prioritization. About one-third of the annual expenditure of a hospital is spent on medicines. To minimize the inventory investment, the hospital management may like to keep the medicines inventory low, as medicines are perishable items. The main aim of each and every hospital is to provide better services to the patients under certain limited resources. To achieve the satisfactory level of health care services to outdoor patients, a hospital has to keep eye on the wastage of medicines because expiry date of medicines causes a great loss of money though it was limited and allocated for a particular period of time. The objectives of this study are to identify the categories of medicines requiring incentive managerial control. In this paper, to minimize the total inventory cost and the cost associated with the wastage of money due to expiry of medicines, an inventory control model is used as an estimation tool and then nonlinear programming technique is used under limited budget and fixed number of orders to be placed in a limited time period. Numerical computations have been given and shown that by using scientific methods in hospital services, we can give more effective way of inventory management under limited resources and can provide better health care services. The secondary data has been collected from a hospital to give empirical evidence.

Keywords: ABC-VED inventory classification, multi item inventory problem, nonlinear programming technique, optimization of EOQ

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Catalina Sau Man Ng

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Workplace bullying is generally defined as a form of direct and indirect maltreatment at work including harassing, offending, socially isolating someone or negatively affecting someone’s work tasks. Workplace bullying is unfortunately commonplace around the world, which makes it a social phenomenon worth researching. However, the measurements and estimation methods of workplace bullying seem to be diverse in different studies, leading to dubious results. Hence, this paper attempts to examine the prevalence of workplace bullying in Hong Kong using the latent class analysis approach. It is often argued that the traditional classification of workplace bullying into the dichotomous 'victims' and 'non-victims' may not be able to fully represent the complex phenomenon of bullying. By treating workplace bullying as one latent variable and examining the potential categorical distribution within the latent variable, a more thorough understanding of workplace bullying in real-life situations may hence be provided. As a result, this study adopts a latent class analysis method, which was tested to demonstrate higher construct and higher predictive validity previously. In the present study, a representative sample of 2814 employees (Male: 54.7%, Female: 45.3%) in Hong Kong was recruited. The participants were asked to fill in a self-reported questionnaire which included measurements such as Chinese Workplace Bullying Scale (CWBS) and Chinese Version of Depression Anxiety Stress Scale (DASS). It is estimated that four latent classes will emerge: 'non-victims', 'seldom bullied', 'sometimes bullied', and 'victims'. The results of each latent class and implications of the study will also be discussed in this working paper.

Keywords: latent class analysis, prevalence, survey, workplace bullying

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Authors: Ratna Kuswardani

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This paper aims to study the impact of recent Global Financial Crisis (GFC) on 6 selected emerging Asian economies (Indonesia, Malaysia, Thailand, Philippines, Singapore, and South Korea). We first figure out the contagion of GFC from the US and Europe to the selected emerging Asian countries by studying the tail dependence of market stock returns between those countries. We apply the concept of Extreme Value Theory (EVT) to model the dependence between multiple returns series of variables under examination. We explore the factors causing the contagion between the regions. We find dependencies between markets that are influenced by their size, especially for large markets in emerging Asian countries that tend to have a higher dependency to the market in the more advanced country such as the U.S. and some countries in Europe. The results also suggest that the dependencies between market returns and bank stock returns in the same region tend to be higher than dependencies between these returns across two different regions. We extend our analysis by studying the impact of GFC on the systemic in the banking system. We also find that larger institution has more dependencies with the market stock, suggesting that larger size bank can cause disruption in the market. Further, the higher probability of extreme loss can be seen during the crisis period, which is shown by the non-linear dependency between the pre-crisis and the post-crisis period. Finally, our analysis suggests that systemic risk appears in the domestic banking systems in emerging Asia, as shown by the extreme dependencies within banks in the system. Overall, our results provide caution to policy makers and investors alike on the possible contagion of the impact of global financial crisis across different markets.

Keywords: contagion, extreme value theory, global financial crisis, systemic risk

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Authors: Rajesh Kumar G

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A challenging aspect of any clinical trial is to carefully plan the study design to meet the study objective in optimum way and to validate the assumptions made during protocol designing. And when it is a pediatric study, there is the added challenge of stringent guidelines and difficulty in recruiting the necessary subjects. Unlike adult trials, there is not much historical data available for pediatrics, which is required to validate assumptions for planning pediatric trials. Typically, pediatric studies are initiated as soon as approval is obtained for a drug to be marketed for adults, so with the adult study historical information and with the available pediatric pilot study data or simulated pediatric data, the pediatric study can be well planned. Generalizing the historical adult study for new pediatric study is a tedious task; however, it is possible by integrating various statistical techniques and utilizing the advantage of hybrid study design, which will help to achieve the study objective in a smoother way even with the presence of many constraints. This research paper will explain how well the hybrid study design can be planned along with integrated technique (SEV) to plan the pediatric study; In brief the SEV technique (Simulation, Estimation (using borrowed adult data and applying Bayesian methods)) incorporates the use of simulating the planned study data and getting the desired estimates to Validate the assumptions.This method of validation can be used to improve the accuracy of data analysis, ensuring that results are as valid and reliable as possible, which allow us to make informed decisions well ahead of study initiation. With professional precision, this technique based on the collected data allows to gain insight into best practices when using data from historical study and simulated data alike.

Keywords: adaptive design, simulation, borrowing data, bayesian model

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Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Haruna Maama

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Environmental, social and governance (ESG) reporting is gaining considerable attention despite being voluntary. Meanwhile, it consumes resources to provide ESG reporting, raising a question of its value relevance. The study examined the impact of ESG reporting on the market value of listed firms in SSA. The annual and integrated reports of 276 listed sub-Sahara Africa (SSA) firms. The integrated reporting scores of the firm were analysed using a content analysis method. A multiple regression estimation technique using a GMM approach was employed for the analysis. The results revealed that ESG has a positive relationship with firms’ market value, suggesting that investors are interested in the ESG information disclosure of firms in SSA. This suggests that extensive ESG disclosures are attempts by firms to obtain the approval of powerful social, political and environmental stakeholders, especially institutional investors. Furthermore, the market value analysis evidence is consistent with signalling theory, which postulates that firms provide integrated reports as a signal to influence the behaviour of stakeholders. This finding reflects the value placed on investors' social, environmental and governance disclosures, which affirms the views that conventional investors would care about the social, environmental and governance issues of their potential or existing investee firms. Overall, the evidence is consistent with the prediction of signalling theory. In the context of this theory, integrated reporting is seen as part of firms' overall competitive strategy to influence investors' behaviour. The findings of this study make unique contributions to knowledge and practice in corporate reporting.

Keywords: environmental accounting, ESG accounting, signalling theory, sustainability reporting, sub-saharan Africa

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Authors: Fally Titikpina, Abderafi Charki, Antoine Caucheteux, David Bigaud

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The building sector is one of the largest energy consumer with about 40% of the final energy consumption in the European Union. Ensuring building energy performance is of scientific, technological and sociological matter. To assess a building energy performance, the consumption being predicted or estimated during the design stage is compared with the measured consumption when the building is operational. When valuing this performance, many buildings show significant differences between the calculated and measured consumption. In order to assess the performance accurately and ensure the thermal efficiency of the building, it is necessary to evaluate the uncertainties involved not only in measurement but also those induced by the propagation of dynamic and static input data in the model being used. The evaluation of measurement uncertainty is based on both the knowledge about the measurement process and the input quantities which influence the result of measurement. Measurement uncertainty can be evaluated within the framework of conventional statistics presented in the \textit{Guide to the Expression of Measurement Uncertainty (GUM)} as well as by Bayesian Statistical Theory (BST). Another choice is the use of numerical methods like Monte Carlo Simulation (MCS). In this paper, we proposed to evaluate the uncertainty associated to the use of a simplified model for the estimation of the energy consumption of a given building. A detailed review and discussion of these three approaches (GUM, MCS and BST) is given. Therefore, an office building has been monitored and multiple sensors have been mounted on candidate locations to get required data. The monitored zone is composed of six offices and has an overall surface of 102 $m^2$. Temperature data, electrical and heating consumption, windows opening and occupancy rate are the features for our research work.

Keywords: building energy performance, uncertainty evaluation, GUM, bayesian approach, monte carlo method

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Authors: A. B. M. Rezaul Islam, Ernur Karadogan

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Shape memory alloys (SMAs) are known for their shape memory effect and pseudoelasticity behavior. Their thermomechanical behaviors are modeled by numerous researchers using microscopic thermodynamic and macroscopic phenomenological point of view. Tanaka, Liang-Rogers and Ivshin-Pence models are some of the most popular SMA macroscopic phenomenological constitutive models. They describe SMA behavior in terms of stress, strain and temperature. These models involve material parameters and they have associated uncertainty present in them. At different operating temperatures, the uncertainty propagates to the output when the material is subjected to loading followed by unloading. The propagation of uncertainty while utilizing these models in real-life application can result in performance discrepancies or failure at extreme conditions. To resolve this, we used probabilistic approach to perform the sensitivity and uncertainty analysis of Tanaka, Liang-Rogers, and Ivshin-Pence models. Sobol and extended Fourier Amplitude Sensitivity Testing (eFAST) methods have been used to perform the sensitivity analysis for simulated isothermal loading/unloading at various operating temperatures. As per the results, it is evident that the models vary due to the change in operating temperature and loading condition. The average and stress-dependent sensitivity indices present the most significant parameters at several temperatures. This work highlights the sensitivity and uncertainty analysis results and shows comparison of them at different temperatures and loading conditions for all these models. The analysis presented will aid in designing engineering applications by eliminating the probability of model failure due to the uncertainty in the input parameters. Thus, it is recommended to have a proper understanding of sensitive parameters and the uncertainty propagation at several operating temperatures and loading conditions as per Tanaka, Liang-Rogers, and Ivshin-Pence model.

Keywords: constitutive models, FAST sensitivity analysis, sensitivity analysis, sobol, shape memory alloy, uncertainty analysis

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Authors: Xinqi Guo, Karen R. Dobkins

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Background and Objectives: Humans are probably the only animals whose minds are constantly filled with thoughts, feelings and emotions. Previous studies have investigated human minds from different dimensions, including its proportion of time for not being present, its representative format, its personal relevance, its temporal locus, and affect valence. The current study aims at characterizing human mind by employing Experience Sampling Methods (ESM), a self-report research procedure for studying daily experience. This study emphasis on answering the following questions: 1) How does the contents of the inner experience vary across demographics, 2) Are certain types of inner experiences correlated with level of mindfulness and mental well-being (e.g., are people who spend more time being present happier, and are more mindful people more at-present?), 3) Will being prompted to report one’s inner experience increase mindfulness and mental well-being? Methods: Participants were recruited from the subject pool of UC San Diego or from the social media. They began by filling out two questionnaires: 1) Five Facet Mindfulness Questionnaire-Short Form, and 2) Warwick-Edinburgh Mental Well-being Scale, and demographic information. Then they participated in the ESM part by responding to the prompts which contained questions about their real-time inner experience: if they were 'at-present', 'mind-wandering', or 'zoned-out'. The temporal locus, the clarity, and the affect valence, and the personal importance of the thought they had the moment before the prompt were also assessed. A mobile app 'RealLife Exp' randomly delivered these prompts 3 times/day for 6 days during wake-time. After the 6 days, participants completed questionnaire (1) and (2) again. Their changes of score were compared to a control group who did not participate in the ESM procedure (yet completed (1) and (2) one week apart). Results: Results are currently preliminary as we continue to collect data. So far, there is a trend that participants are present, mind-wandering and zoned-out, about 53%, 23% and 24% during wake-time, respectively. The thoughts of participants are ranked to be clearer and more neutral if they are present vs. mind-wandering. Mind-wandering thoughts are 66% about the past, consisting 80% of inner speech. Discussion and Conclusion: This study investigated the subjective account of human mind by a tool with high ecological validity. And it broadens the understanding of the relationship between contents of mind and well-being.

Keywords: experience sampling method, meta-memory, mindfulness, mind-wandering

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Authors: Christian H. Girsang, M. Razi B. Mansoor, Noorizal N. Huang

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An offshore platform was installed in 1977 at about 260km offshore West Malaysia at the water depth of 73.6m. Twelve (12) piles were installed with four (4) are skirt piles. The piles have 1.219m outside diameter and wall thickness of 31mm and were driven to 109m below seabed. Deterministic analyses of the pile capacity under axial loading were conducted using the current API (American Petroleum Institute) method and the four (4) CPT-based methods: the ICP (Imperial College Pile)-method, the NGI (Norwegian Geotechnical Institute)-Method, the UWA (University of Western Australia)-method and the Fugro-method. A statistical analysis of the model uncertainty associated with each pile capacity method was performed. There were two (2) piles analysed: Pile 1 and piles other than Pile 1, where Pile 1 is the pile that was most affected by shallow gas problems. Using the mean estimate of soil properties, the five (5) methods used for deterministic estimation of axial pile capacity in compression predict an axial capacity from 28 to 42MN for Pile 1 and 32 to 49MN for piles other than Pile 1. These values refer to the static capacity shortly after pile installation. They do not include the effects of cyclic loading during the design storm or time after installation on the axial pile capacity. On average, the axial pile capacity is expected to have increased by about 40% because of ageing since the installation of the platform in 1977. On the other hand, the cyclic loading effects during the design storm may reduce the axial capacity of the piles by around 25%. The study concluded that all piles have sufficient safety factor when the pile aging and cyclic loading effect are considered, as all safety factors are above 2.0 for maximum operating and storm loads.

Keywords: axial capacity, cyclic loading, pile ageing, shallow gas

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Authors: A. van Staden, M. M. Coetzee

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Despite numerous efforts to increase the literacy level of South African learners, for example, through the implementation of educational policies such as the Revised National Curriculum statement, advocating mother-tongue instruction (during a child's formative years), in reality, the majority of South African children are still being educated in a second language (in most cases English). Moreover, despite the fact that a significant percentage of our country's budget is spent on the education sector and that both policy makers and educationalists have emphasized the importance of learning English in this globalized world, the poor overall academic performance and English literacy level of a large number of school leavers are still a major concern. As we move forward in an attempt to comprehend the nuances of English language and literacy development in our country, it is imperative to explore both extrinsic and intrinsic factors that contribute or impede the effective development of English as a second language. In the present study, the researchers set out to investigate how intrinsic factors such as motivation and self-concept contribute to or affect English language learning amongst high school learners in South Africa. Emanating from the above the main research question that guided this research is the following: Is there a significant relationship between high school learners' self-concept, motivation, and English second language performances? In order to investigate this hypothesis, this study utilized quantitative research methodology to investigate the interplay of self-concept and motivation in English language learning. For this purpose, we sampled 201 high school learners from various schools in South Africa. Methods of data gathering inter alia included the following: A biographical questionnaire; the Academic Motivational Scale and the Piers-Harris Self-Concept Scale. Pearson Product Moment Correlation Analyses yielded significant correlations between L2 learners' motivation and their English language proficiency, including demonstrating positive correlations between L2 learners' self-concept and their achievements in English. Accordingly, researchers have argued that the learning context, in which students learn English as a second language, has a crucial influence on students' motivational levels. This emphasizes the important role the teacher has to play in creating learning environments that will enhance L2 learners' motivation and improve their self-concepts.

Keywords: motivation, self-concept, language learning, English second language learners (L2)

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Authors: Maria I. Nuñez-Hernández, Maria L. Riesco

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Background: Exclusive breastfeeding (EBF) up to 6 months old infant have been considered one of the most important factors in the overall development of children. Nevertheless, as resources are scarce, it is essential to identify the most vulnerable groups that have major risk of EBF abandonment, in order to deliver the best strategies. Children of adolescent mothers are within these groups. Aims: To determine the EBF abandonment rate among adolescent mothers and to analyze the associated factors. Methods: Prospective cohort study of adolescent mothers in the southern area of Santiago, Chile, conducted in primary care services of public health system. The cohort was established from 2014 to 2015, with a sample of 105 adolescent mothers and their children at 2 months of life. The inclusion criteria were: adolescent mother from 14 to 19 years old; not twin babies; mother and baby leaving the hospital together after birthchild; correct attachment of the baby to the breast; no difficulty understanding the Spanish language or communicating. Follow-up was performed at 4 and 6 months old infant. Data were collected by interviews, considering EBF as breastfeeding only, without adding other milk, tea, juice, water or other product that not breast milk, except drugs. Data were analyzed by descriptive and inferential statistics, by Kaplan-Meier estimator and Log-Rank test, admitting the probability of occurrence of type I error of 5% (p-value = 0.05). Results: The cumulative EBF abandonment rate at 2, 4 and 6 months was 33.3%, 52.2% and 63.8%, respectively. Factors associated with EBF abandonment were maternal perception of the quality of milk as poor (p < 0.001), maternal perception that the child was not satisfied after breastfeeding (p < 0.001), use of pacifier (p < 0.001), maternal consumption of illicit drugs after delivery (p < 0.001), mother return to school (p = 0.040) and presence of nipple trauma (p = 0.045). Conclusion: EBF abandonment rate was higher in the first 4 months of life and is superior to the population of women who breastfeed. Among the EBF abandonment factors, one of them is related to the adolescent condition, and two are related to the maternal subjective perception.

Keywords: adolescent, breastfeeding, midwifery, nursing

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Ismail Bamidele Afolabi, Abdulmujeeb Babatunde Aremu, Lawal Abdurraheem Maidoki, Nnodimele Onuigbo Atulomah

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Background: Hepatitis B virus infection remains a significant global public health challenge with infectivity as well as the potential for transmission more than 50 to 100 times that of HIV. Annually, global HBV-related mortality is linked primarily to cirrhosis and liver carcinoma. The ever-increasing endemicity of HBV among children under-5-years, owing to vertical transmission and its lingering chronicity in developing countries, will hamper the global efforts concertedly endorsed towards eliminating viral hepatitis as a global public health threat by 2030. Objective: This study assessed information motivation behavioral skills model constructs as predictors of HBV infection prevention practices among consenting expectant mothers attending antenatal care in Central Uganda as a focal point of intervention towards breaking materno-foetal transmission of HBV. Methods: A cross-sectional study with a quantitative data collection approach based on the constructs of the IMB model was used to capture data on the study variables among 385 randomly selected pregnant women between September and October 2020. Data derived from the quantitative instrument were transformed into weighted aggregate scores using SPSS version 26. ANOVA and regression analysis were done to ascertain the study hypotheses with a significance level set as (p ≤ 0.05). Results: Relatively 60% of the respondents were aged between 18 and 28. Expectant mothers with secondary education (42.3%) were predominant. Furthermore, an average but inadequate knowledge (X ̅=5.97±6.61; B=0.57; p<.001), incorrect perception (X ̅=17.10±18.31; B=0.97; p=.014), and good behavioral skills (X ̅=12.39±13.37; B=0.56; p<.001) for adopting prevention practices all statistically predicted the unsatisfactory level of prevention practices (X ̅=15.03±16.20) among the study respondents as measured on rating scales of 12, 33, 21 and 30 respectively. Conclusion: Evidence from this study corroborates the imperativeness of IMB constructs in reducing the burden of HBV infection in developing countries. Therefore, the inadequate HBV knowledge and misperception among obstetric populations necessitate personalized health education during antenatal visits and subsequent health campaigns in order to inform better prevention practices and, in turn, reduce the lingering chronicity of HBV infection in developing countries.

Keywords: behavioral skills, HBV infection, knowledge, perception, pregnant women, prevention practices

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Authors: Muhammad Umair, Yasir Nawab, Khubab Shaker, Muhammad Maqsood, Adeel Zulfiqar, Danish Mahmood Baitab

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Fabric is a flexible woven material consisting of a network of natural or artificial fibers often referred to as thread or yarn. Today fabrics are produced by weaving, braiding, knitting, tufting and non-woven. Weaving is a method of fabric production in which warp and weft yarns are interlaced perpendicular to each other. There is infinite number of ways for the interlacing of warp and weft yarn. Each way produces a different fabric structure. The yarns parallel to the machine direction are called warp yarns and the yarns perpendicular to the machine direction are called weft or filling yarns. Air jet weaving is the modern method of weft insertion and considered as high speed loom. The twist loss in air jet during weft insertion affects the strength. The aim of this study was to investigate the effect of twist change in weft yarn during air-jet weft insertion. A total number of 8 samples were produced using 1/1 plain and 3/1 twill weave design with two fabric widths having same loom settings. Two different types of yarns like cotton and PC blend were used. The effect of material type, weave design and fabric width on twist change of weft yarn was measured and discussed. Twist change in the different types of weft yarn and weave design was measured and compared the twist change in the weft yarn with the yarn before weft yarn insertion and twist loss is measured. Wider fabric leads to higher twist loss in the yarn.

Keywords: air jet loom, twist per inch, twist loss, weft yarn

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Authors: Weiliang Gong, Lissa Gomes, Lucile Raymond, Hui Xu, Werner Lutze, Ian L. Pegg

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Alkali-activated materials, particularly geopolymers, have attracted much interest in academia. Commercial applications are on the rise, as well. Geopolymers are produced typically by a reaction of one or two aluminosilicates with an alkaline solution at room temperature. Fly ash is an important aluminosilicate source. However, using low-Ca fly ash, the byproduct of burning hard or black coal reacts and sets slowly at room temperature. The development of mechanical durability, e.g., compressive strength, is slow as well. The use of fly ashes with relatively high contents ( > 6%) of unburned carbon, i.e., high loss on ignition (LOI), is particularly disadvantageous as well. This paper will show to what extent these impediments can be mitigated by mixing the fly ash with one or two more aluminosilicate sources. The fly ash used here is generated at the Orlando power plant (Florida, USA). It is low in Ca ( < 1.5% CaO) and has a high LOI of > 6%. The additional aluminosilicate sources are metakaolin and blast furnace slag. Binary fly ash-metakaolin and ternary fly ash-metakaolin-slag geopolymers were prepared. Properties of geopolymer pastes before and after setting have been measured. Fresh mixtures of aluminosilicates with an alkaline solution were studied by Vicat needle penetration, rheology, and isothermal calorimetry up to initial setting and beyond. The hardened geopolymers were investigated by SEM/EDS and the compressive strength was measured. Initial setting (fluid to solid transition) was indicated by a rapid increase in yield stress and plastic viscosity. The rheological times of setting were always smaller than the Vicat times of setting. Both times of setting decreased with increasing replacement of fly ash with blast furnace slag in a ternary fly ash-metakaolin-slag geopolymer system. As expected, setting with only Orlando fly ash was the slowest. Replacing 20% fly ash with metakaolin shortened the set time. Replacing increasing fractions of fly ash in the binary system by blast furnace slag (up to 30%) shortened the time of setting even further. The 28-day compressive strength increased drastically from < 20 MPa to 90 MPa. The most interesting finding relates to the calorimetric measurements. The use of two or three aluminosilicates generated significantly more heat (20 to 65%) than the calculated from the weighted sum of the individual aluminosilicates. This synergetic heat contributes or may be responsible for most of the increase of compressive strength of our binary and ternary geopolymers. The synergetic heat effect may be also related to increased incorporation of calcium in sodium aluminosilicate hydrate to form a hybrid (N,C)A-S-H) gel. The time of setting will be correlated with heat release and maximum heat flow.

Keywords: alkali-activated materials, binary and ternary geopolymers, blends of fly ash, metakaolin and blast furnace slag, rheology, synergetic heats

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Authors: Reza Talebi

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Wheat (Triticum aestivum) is one of the most important food staples in Iran. Understanding genetic variability among the landrace wheat germplasm is important for breeding. Landraces endemic to Iran are a genetic resource that is distinct from other wheat germplasm. In this study, 60 Iranian landrace wheat accessions were characterized AFLP and ISSR markers. Twelve AFLP primer pairs detected 128 polymorphic bands among the sixty genotypes. The mean polymorphism rate based on AFLP data was 31%; however, a wide polymorphism range among primer pairs was observed (22–40%). Polymorphic information content (PIC value) calculated to assess the informativeness of each marker ranged from 0.28 to 0.4, with a mean of 0.37. According to AFLP molecular data, cluster analysis grouped the genotypes in five distinct clusters. .ISSR markers generated 68 bands (average of 6 bands per primer), which 31 were polymorphic (45%) across the 60 wheat genotypes. Polymorphism information content (PIC) value for ISSR markers was calculated in the range of 0.14 to 0.48 with an average of 0.33. Based on data achieved by ISSR-PCR, cluster analysis grouped the genotypes in three distinct clusters. Both AFLP and ISSR markers able to showed that high level of genetic diversity in Iranian landrace wheat accessions has maintained a relatively constant level of genetic diversity during last years.

Keywords: wheat, genetic diversity, AFLP, ISSR

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Authors: Hamid Abdulroof Saleh

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Background: Dioxins are one of the most widely distributed environmental pollutants. Dioxins consist of feedstock during the preparation of some industries, such as the paper industry as they can be produced in the atmosphere during the process of burning garbage and waste, especially medical waste. Dioxins can be found in the adipose tissues of animals in the food chain as well as in human breast milk. 2,3,7,8-Tetrachlorodibenzo-pdioxin (TCDD) is the most toxic component of a large group of dioxins. Humans are exposed to TCDD through contaminated food items like meat, fish, milk products, eggs etc. Recently, natural formulations relating to reducing or eliminating TCDD toxicity have been in focus. Ginger rhizome (Zingiber officinale R., family: Zingiberaceae), is used worldwide as a spice. Both antioxidative and androgenic activity of Z. officinale was reported in animal models. Researchers showed that ginger oil has dominative protective effect on DNA damage and might act as a scavenger of oxygen radical and might be used as an antioxidant. Aim of the work: The present study was undertaken to evaluate the toxic effect of TCDD on the structure and histoarchitecture of the testis and the protective role of co-administration of ginger root extract to prevent this toxicity. Materials & Methods: Male adult rats of Sprague-Dawley strain were assigned to four groups, eight rats in each; control group, dioxin treated group (given TCDD at the dose of 100 ng/kg Bwt/day by gavage), ginger treated group (given 50 mg/kg Bwt/day of ginger root extract by gavage), dioxin and ginger treated group (given TCDD at the dose of 100 ng/kg Bwt/day and 50 mg/kg Bwt/day of ginger root extract by gavages). After three weeks, rats were weighed and sacrificed where testis were removed and weighted. The testes were processed for routine paraffin embedding and staining. Tissue sections were examined for different morphometric and histopathological changes. Results: Dioxin administration showed a harmful effects in the body, testis weight and other morphometric parameters of the testis. In addition, it produced varying degrees of damage to the seminiferous tubules, which were shrunken and devoid of mature spermatids. The basement membrane was disorganized with vacuolization and loss of germinal cells. The co-administration of ginger root extract showed obvious improvement in the above changes and showed reversible morphometric and histopathological changes of the seminiferous tubules. Conclusion: Ginger root extract treatment in this study was successful in reversing all morphometric and histological changes of dioxin testicular damage. Therefore, it showed a protective effect on testis against dioxin toxicity.

Keywords: dioxin, ginger, rat, testis

Procedia PDF Downloads 407

Authors: Mohamed Mohamed, Philip Bonello, Peter Russhard

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Blade Tip Timing (BTT) is a technology concerned with the estimation of both frequency and amplitude of rotating blades. A BTT system comprises two main parts: (a) the arrival time measurement system, and (b) the analysis algorithms. Simulators play an important role in the development of the analysis algorithms since they generate blade tip displacement data from the simulated blade vibration under controlled conditions. This enables an assessment of the performance of the different algorithms with respect to their ability to accurately reproduce the original simulated vibration. Such an assessment is usually not possible with real engine data since there is no practical alternative to BTT for blade vibration measurement. Most simulators used in the literature are based on a simple spring-mass-damper model to determine the vibration. In this work, a more realistic experimentally validated simulator based on the Finite Element (FE) model of a bladed disc (blisk) is first presented. It is then used to generate the necessary data for the assessment of different BTT algorithms. The FE modelling is validated using both a hammer test and two firewire cameras for the mode shapes. A number of autoregressive methods, fitting methods and state-of-the-art inverse methods (i.e. Russhard) are compared. All methods are compared with respect to both synchronous and asynchronous excitations with both single and simultaneous frequencies. The study assesses the applicability of each method for different conditions of vibration, amount of sampling data, and testing facilities, according to its performance and efficiency under these conditions.

Keywords: blade tip timing, blisk, finite element, vibration measurement

Procedia PDF Downloads 296

Authors: Ali Reza Ghanbarnezhad Ghazvini, Seyyed Hamid Reza Mosayyebi

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In recent years, an earthquake has occurred approximately every five years in certain regions of Iran. To mitigate the impact of these seismic events, it is crucial to identify and thoroughly assess the vulnerability of buildings and infrastructure, ensuring their safety through principled reinforcement. By adopting new methods of risk assessment, we can effectively reduce the potential risks associated with future earthquakes. In our research, we have observed that the coefficient of behavior in the fourth chapter is 1.65 for the initial structure and 1.72 for the Superframe structure. This indicates that the Superframe structure can enhance the strength of the main structural members by approximately 10% through the utilization of super beams. Furthermore, based on the comparative analysis between the two structures conducted in this study, we have successfully designed a stronger structure with minimal changes in the coefficient of behavior. Additionally, this design has allowed for greater energy dissipation during seismic events, further enhancing the structure's resilience to earthquakes. By comprehensively examining and reinforcing the vulnerability of buildings and infrastructure, along with implementing advanced risk assessment techniques, we can significantly reduce casualties and damages caused by earthquakes in Iran. The findings of this study offer valuable insights for civil engineering professionals in the field of structural engineering, aiding them in designing safer and more resilient structures.

Keywords: modal pushover analysis, response modification factor, high-strength concrete, concrete shear walls, high-rise building

Procedia PDF Downloads 127

Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

Procedia PDF Downloads 199

Authors: Tanida Julvanichpong

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Exercise has been regarded as a necessary and important aspect to enhance physical performance and psychology health. Body weight statistics of students in junior high school students in Chonburi Province beyond a standard risk of obesity. Promoting exercise among Junior high school students in Chonburi Province, essential knowledge concerning factors influencing exercise is needed. Therefore, this study aims to (1) determine the levels of perceived exercise behavior, exercise behavior in the past, perceived barriers to exercise, perceived benefits of exercise, perceived self-efficacy to exercise, feelings associated with exercise behavior, influence of the family to exercise, influence of friends to exercise, and the perceived influence of the environment on exercise. (2) examine the predicting ability of each of the above factors while including personal factors (sex, educational level) for exercise behavior. Pender’s Health Promotion Model was used as a guide for the study. Sample included 652 students in junior high schools, Chonburi Provience. The samples were selected by Multi-Stage Random Sampling. Data Collection has been done by using self-administered questionnaires. Data were analyzed using descriptive statistics, Pearson’s product moment correlation coefficient, Eta, and stepwise multiple regression analysis. The research results showed that: 1. Perceived benefits of exercise, influence of teacher, influence of environmental, feelings associated with exercise behavior were at a high level. Influence of the family to exercise, exercise behavior, exercise behavior in the past, perceived self-efficacy to exercise and influence of friends were at a moderate level. Perceived barriers to exercise were at a low level. 2. Exercise behavior was positively significant related to perceived benefits of exercise, influence of the family to exercise, exercise behavior in the past, perceived self-efficacy to exercise, influence of friends, influence of teacher, influence of environmental and feelings associated with exercise behavior (p < .01, respectively) and was negatively significant related to educational level and perceived barriers to exercise (p < .01, respectively). Exercise behavior was significant related to sex (Eta = 0.243, p=.000). 3. Exercise behavior in the past, influence of the family to exercise significantly contributed 60.10 percent of the variance to the prediction of exercise behavior in male students (p < .01). Exercise behavior in the past, perceived self-efficacy to exercise, perceived barriers to exercise, and educational level significantly contributed 52.60 percent of the variance to the prediction of exercise behavior in female students (p < .01).

Keywords: predictive factors, exercise behaviors, Junior high school, Chonburi Province

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Authors: Anita Singh, Richa Naula, Manoj Raghav

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Nutrient rich and high-yielding varieties of fenugreek can be developed by using genotypes which are naturally high in nutrients. Gene banks harbour scanty germplasm collection of Trigonella spp. and a very little background information about its genetic diversity. The extent of genetic diversity in a specific breeding population depends upon the genotype included in it. The present investigation aims at the estimation of macronutrient (phosphorus by spectrophotometer and potassium by flame photometer), micronutrients, namely, iron, zinc, manganese, and copper from seeds of fenugreek genotypes using atomic absorption spectrophotometer, protein by Rapid N Cube Analyser and Steroidal Saponins. Twenty-eight genotypes of fenugreek along with two standard checks, namely, Pant Ragini and Pusa Early Bunching were collected from different parts of India, and nutrient contents of each genotype were determined at G. B. P. U. A. & T. Laboratory, Pantnagar. Highest potassium content was observed in PFG-35 (1207 mg/100g). PFG-37 and PFG-20 were richest in phosphorus, iron and manganese content among all the genotypes. The lowest zinc content was found in PFG-26 (1.19 mg/100g), while the maximum zinc content was found in PFG- 28 (4.43 mg/100g). The highest content of copper was found in PFG-26 (1.97 mg/100g). PFG-39 has the highest protein content (29.60 %). Significant differences were observed in the steroidal saponin among the genotypes. Saponin content ranged from 0.38 g/100g to 1.31 g/100g. Steroidal Saponins content was found the maximum in PFG-36 (1.31 g/100g) followed by PFG-17 (1.28 g/100g). Therefore, the genotypes which are rich in nutrient and oil content can be used for plant biofortification, dietary supplements, and herbal products.

Keywords: genotypes, macronutrients, micronutrient, protein, seeds

Procedia PDF Downloads 241

Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

Procedia PDF Downloads 331

Authors: Sanjukta Badhai, Durga Barik, Bairagi C. Mallick

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In the present study, Beta-Sitosterol in Lawsonia methanolic leaf extract embedded in controlled release nanocomposite was prepared and evaluated for in vivo anticancer efficacy in dimethyl hydrazine (DMH) induced colon cancer. In the present study, colon cancer was induced by s.c injection of DMH (20 mg/kg b.wt) for 15 weeks. The animals were divided into five groups as follows control, DMH alone, DMH and Beta Sitosterol nanocomposite (50mg/kg), DMH and Beta Sitosterol nanocomposite (100 mg/kg) and DMH and Standard Silymarin (100mg/kg) and the treatment was carried out for 15 weeks. At the end of the study period, the blood was withdrawn, and serum was separated for haematological, biochemical analysis and tumor markers. Further, the colonic tissue was removed for the estimation of antioxidants and histopathological analysis. The results of the study displays that DMH intoxication elicits altered haematological parameters (RBC,WBC, and Hb), elevated lipid peroxidation and decreased antioxidants level (SOD, CAT, GPX, GST and GSH), elevated lipid profiles (cholesterol and triglycerides), tumor markers (CEA and AFP) and altered colonic tissue histology. Meanwhile, treatment with Beta Sitosterol nanocomposites significantly restored the altered biochemicals parameters in DMH induced colon cancer mediated by its anticancer efficacy. Further, Beta Sitosterol nanocomposite (100 mg/kg) showed marked efficacy.

Keywords: nanocomposites, herbal formulation, henna, beta sitosterol, colon cancer, dimethyl hydrazine, antioxidant, lipid peroxidation

Procedia PDF Downloads 146

Authors: Rakesh Kumar, Fatima Electricwala

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One of the major thrusts of the Bus Rapid Transit System is to reduce the commuter’s dependency on private vehicles and increase the shares of public transport to make urban transportation system environmentally sustainable. In this study, commuter mode choice analysis is performed that examines behavioral responses to the proposed Bus Rapid Transit System (BRTS) in Surat, with estimation of the probable shift from private mode to public mode. Further, evaluation of the BRTS scenarios, using Surat’s transportation ecological footprint was done. A multi-modal simulation model was developed in Biogeme environment to explicitly consider private users behaviors and non-linear environmental impact. The data of the different factors (variables) and its impact that might cause modal shift of private mode users to proposed BRTS were collected through home-interview survey using revealed and stated preference approach. A multi modal logit model of mode-choice was then calibrated using the collected data and validated using proposed sample. From this study, a set of perception factors, with reliable and predictable data base, to explain the variation in modal shift behaviour and their impact on Surat’s ecological environment has been identified. A case study of the proposed BRTS connecting the Surat Industrial Hub to the coastal area is provided to illustrate the approach.

Keywords: BRTS, private modes, mode choice models, ecological footprint

Procedia PDF Downloads 505

Authors: Ranjan Banerjee, W. D. Gunawardana

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The free vibration and flutter analysis plays a major part in aircraft design which is indeed, a mandatory requirement. In particular, high aspect ratio transport airliner wings are prone to free vibration and flutter problems that must be addressed during the design process as demanded by the airworthiness authorities. The purpose of this paper is to carry out a detailed free vibration and flutter analysis for a wide range of high aspect ratio aircraft wings and generate design curves to provide useful visions and understandings of aircraft design from an aeroelastic perspective. In the initial stage of the investigation, the bending and torsional stiffnesses of a number of transport aircraft wings are looked at and critically examined to see whether it is possible to express the stiffness distributions in polynomial form, but in a sufficiently accurate manner. A similar attempt is made for mass and mass moment of inertia distributions of the wing. Once the choice of stiffness and mass distributions in polynomial form is made, the high aspect ratio wing is idealised by a series of bending-torsion coupled beams from a structural standpoint. Then the dynamic stiffness method is applied to compute the natural frequencies and mode shape of the wing. Next the wing is idealised aerodynamically and to this end, unsteady aerodynamic of Theodorsen type is employed to represent the harmonically oscillating wing. Following this step, a normal mode method through the use of generalised coordinates is applied to formulate the flutter problem. In essence, the generalised mass, stiffness and aerodynamic matrices are combined to obtain the flutter matrix which is subsequently solved in the complex domain to determine the flutter speed and flutter frequency. In the final stage of the investigation, an exhaustive parametric study is carried out by varying significant wing parameters to generate design curves which help to predict the free vibration and flutter behaviour of high aspect ratio transport aircraft wings in a generic manner. It is in the aeroelastic context of aircraft design where the results are expected to be most useful.

Keywords: high-aspect ratio wing, flutter, dynamic stiffness method, free vibration, aeroelasticity

Procedia PDF Downloads 274

Authors: Zama Mahmood

Abstract:

The increasing demand of food management, monitoring of the crop growth and forecasting its yield well before harvest is very important. These days, yield assessment together with monitoring of crop development and its growth are being identified with the help of satellite and remote sensing images. Studies using remote sensing data along with field survey validation reported high correlation between vegetation indices and yield. With the development of remote sensing technique, the detection of crop and its mechanism using remote sensing data on regional or global scales have become popular topics in remote sensing applications. Punjab, specially the southern Punjab region is extremely favourable for wheat production. But measuring the exact amount of wheat production is a tedious job for the farmers and workers using traditional ground based measurements. However, remote sensing can provide the most real time information. In this study, using the Normalized Differentiate Vegetation Index (NDVI) indicator developed from Landsat satellite images, the yield of wheat has been estimated during the season of 2013-2014 for the agricultural area around Bahawalpur. The average yield of the wheat was found 35 kg/acre by analysing field survey data. The field survey data is in fair agreement with the NDVI values extracted from Landsat images. A correlation between wheat production (ton) and number of wheat pixels has also been calculated which is in proportional pattern with each other. Also a strong correlation between the NDVI and wheat area was found (R2=0.71) which represents the effectiveness of the remote sensing tools for crop monitoring and production estimation.

Keywords: landsat, NDVI, remote sensing, satellite images, yield

Procedia PDF Downloads 318

Authors: Alok Pal Jain, Santram Lodhi

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Martynia annua Linn. (Martyniaccae) is traditionally used in inflammation and applied locally to tuberculosis glands of camel’s neck. The leaves used topically to bites of venomous insects and wounds of domestic animals. Chemical examination of Martynia annua leaves revealed the presence of glycosides, tannins, proteins, phenols and flavonoids. The present study was aimed to evaluate the anti-arthritic activity of methanolic extract of Martynia annua leaves. Methanolic extract of Martynia annua leaves was tested by using in vivo collagen-induced arthritis mouse model to investigate the anti-rheumatoid arthritis activity. In addition, antioxidant effect of methanolic extract was determined by the estimation of antioxidants level in joint tissues. The severity of arthritis was assessed by arthritis score and edema. Levels of cytokines TNF-α and IL-6, in the joint tissue homogenate were measured using ELISA. A high dose (250 mg/kg) of methanolic extract was significantly reduced the degree of inflammation in mice as compared with reference drug. Antioxidants level and malondialdehyde (MDA) in joint tissue homogenate found significantly (p < 0.05) higher. Methanolic extract at dose of 250 mg/kg modulated the cytokines production and suppressed the oxidative stress in the mice with collagen-induced arthritis. This study suggested that Martynia annua might be alternative herbal medicine for the management of rheumatoid arthritis.

Keywords: Martynia annua, collagen, rheumatoid arthritis, antioxidants

Procedia PDF Downloads 281