Search results for: generalized autoregressive score model

Authors: Seema Dutt Bandhu, Yashi Bansal, Tania Moudgil, Barinder Kaur

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Introduction: Scleral perforation during the passage of suture needle is a known complication of strabismus surgery. The present study was conducted to evolve a safe and easy technique of passing the suture needle through the sclera. A scleral tunnel was created with a 26-guage needle through which the suture needle was passed. The rest of the steps of strabismus surgery were carried out as usual. Material and Methods: After taking clearance from the Institutional Ethics Committee, an interventional study was carried out on twenty patients. The scleral tunnel technique was performed on the patients of strabismus after taking written informed consent. Before passing the suture needle through the sclera during strabismus surgery, a tunnel through approximately half the thickness of the sclera was created with the help of a bent 26-gauge needle. The suture needle was then passed through this tunnel. Rest of the steps of the surgery were carried out in the conventional manner. In a control group of same number of patients, the surgery was performed in the conventional method. Both the groups were followed up for any complications. Ease of passing suture and surgeons’ satisfaction with the technique was noted on a 10-point Likert scale. Results: None of the patients in either group suffered from any complications. Four surgeons participated in the study. The average Likert scale score of the surgeons for satisfaction with the technique was 4.5 on a scale of 5. The score for ease of passage of suture needle was 5 on a score of 5. Discussion: Scleral perforation during passing the sutures through the sclera is a known complication of strabismus surgery. Incidence reported is 7.8% It occurs due to inappropriate engagement of the scleral tissue or passage of the suture needle along a wrong axis during the process of passing the suture needle. The needle track technique eases the passage of passing the suture needle through the sclera as the engagement of the scleral tissue can be done with greater control with a 26-guage needle. The surgeons have reported that they are highly satisfied with the technique and they have reported that the technique eased the passage of the suture needle through the sclera.

Keywords: suture, scleral tunnel, strabismus, scleral perforation

Procedia PDF Downloads 81

Authors: Jolanta Tamošaitienė

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The article focuses on a developed comprehensive model to be used in an agile environment for the risk assessment and selection based on multi-attribute methods. The model is based on a multi-attribute evaluation of risk in construction, and the determination of their optimality criterion values are calculated using complex Multiple Criteria Decision-Making methods. The model may be further applied to risk assessment in an agile construction environment. The attributes of risk in a construction project are selected by applying the risk assessment condition to the construction sector, and the construction process efficiency in the construction industry accounts for the agile environment. The paper presents the comprehensive risk assessment model in an agile construction environment. It provides a background and a description of the proposed model and the developed analysis of the comprehensive risk assessment model in an agile construction environment with the criteria.

Keywords: assessment, environment, agile, model, risk

Procedia PDF Downloads 258

Authors: Hui-Yen Lee, Hsiu-Yun Wei, Guey-Jen Lin, Pi-Yueh Lee Lee

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Background: Unplanned hospital readmissions increase healthcare costs and have been considered a marker of poor healthcare performance. The elderly face a higher risk of unplanned readmission due to elderly-specific characteristics such as deteriorating body functions and the relatively high incidence of complications after treatment of acute diseases. Purpose: The aim of this study was exploring the factors that relate to the unplanned readmission of elderly within 14 days of discharge at our hospital in southern Taiwan. Methods: We retrospectively reviewed the medical records of patients aged ≥65 years who had been re-admitted between January 2018 and December 2018.The Charlson Comorbidity score was calculated using previous used method. Related factors that affected the rate of unplanned readmission within 14 days of discharge were screened and analyzed using the chi-squared test and logistic regression analysis. Results: This study enrolled 829 subjects aged more than 65 years. The numbers of unplanned readmission patients within 14 days were 318 cases, while those did not belong to the unplanned readmission were 511 cases. In 2018, the rate of elderly patients in unplanned 14 days readmissions was 38.4%. The majority patients were females (166 cases, 52.2%), with an average age of 77.6 ± 7.90 years (65-98). The average value of Charlson Comorbidity score was 4.42±2.76. Using logistic regression analysis, we found that the gastric or peptic ulcer (OR=1.917 , P< 0.002), diabetes (OR= 0.722, P< 0.043), hemiplegia (OR= 2.292, P< 0.015), metastatic solid tumor (OR= 2.204, P< 0.025), hypertension (OR= 0.696, P< 0.044), and skin ulcer/cellulitis (OR= 2.747, P< 0.022) have significantly higher risk of 14-day readmissions. Conclusion: The results of the present study may assist the healthcare teams to understand the factors that may affect unplanned readmission in the elderly. We recommend that these teams give efficient approach in their medical practice, provide timely health education for elderly, and integrative healthcare for chronic diseases in order to reduce unplanned readmissions.

Keywords: unplanning readmission, elderly, Charlson comorbidity score, logistic regression analysis

Procedia PDF Downloads 133

Authors: Janne Engblom, Elias Oikarinen

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A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models are dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Arellano-Bover/Blundell-Bond Generalized method of moments (GMM) estimator which is an extension of the Arellano-Bond model where past values and different transformations of past values of the potentially problematic independent variable are used as instruments together with other instrumental variables. The Arellano–Bover/Blundell–Bond estimator augments Arellano–Bond by making an additional assumption that first differences of instrument variables are uncorrelated with the fixed effects. This allows the introduction of more instruments and can dramatically improve efficiency. It builds a system of two equations—the original equation and the transformed one—and is also known as system GMM. In this study, Finnish housing price dynamics were examined empirically by using the Arellano–Bover/Blundell–Bond estimation technique together with ordinary OLS. The aim of the analysis was to provide a comparison between conventional fixed-effects panel data models and dynamic panel data models. The Arellano–Bover/Blundell–Bond estimator is suitable for this analysis for a number of reasons: It is a general estimator designed for situations with 1) a linear functional relationship; 2) one left-hand-side variable that is dynamic, depending on its own past realizations; 3) independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; 4) fixed individual effects; and 5) heteroskedasticity and autocorrelation within individuals but not across them. Based on data of 14 Finnish cities over 1988-2012 differences of short-run housing price dynamics estimates were considerable when different models and instrumenting were used. Especially, the use of different instrumental variables caused variation of model estimates together with their statistical significance. This was particularly clear when comparing estimates of OLS with different dynamic panel data models. Estimates provided by dynamic panel data models were more in line with theory of housing price dynamics.

Keywords: dynamic model, fixed effects, panel data, price dynamics

Procedia PDF Downloads 1516

Authors: Guowei Hou, Lixin Yu, Wei Zhuang, Hui Qin, Xue Yang

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Formal verification is proposed to ensure the correctness of the design and make functional verification more efficient. As cache plays a vital role in the design of System on Chip (SoC), and cache with Memory Management Unit (MMU) and cache memory unit makes the state space too large for simulation to verify, then a formal verification is presented for such system design. In the paper, a formal model checking verification flow is suggested and a new cache memory model which is called “exhaustive search model” is proposed. Instead of using large size ram to denote the whole cache memory, exhaustive search model employs just two cache blocks. For cache system contains data cache (Dcache) and instruction cache (Icache), Dcache memory model and Icache memory model are established separately using the same mechanism. At last, the novel model is employed to the verification of a cache which is module of a custom-built SoC system that has been applied in practical, and the result shows that the cache system is verified correctly using the exhaustive search model, and it makes the verification much more manageable and flexible.

Keywords: cache system, formal verification, novel model, system on chip (SoC)

Procedia PDF Downloads 501

Authors: S. P. Hewagama

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Different kinds of distressing events happen in the world causing a wide range of reactions and feelings. Psychological first aid (PFA) is humane supportive response for suffering. All health workers especially PHMs who play a major role as first responders in a disaster should be able to provide basic PFA effectively. Aim of this study was to assess the knowledge, attitudes and associated factors on the provision of PFA among PHMs during disasters. A descriptive cross-sectional study was carried out among 307 Public Health Midwives in Colombo RDHS area. In the study population, 86.6% (n=266) of the respondents were aware of the term “Psychological first aid” while 13.4% (n=41) were not aware. The total knowledge score was good in majority 85.4%(n=262) of the respondents while only 14.3%(n=45) had a poor knowledge on PFA. There was the statistically significant difference in relation to the level of education with the total knowledge score. Comprehensive desirable attitudes towards PFA was low (30.61%, n=94). According to the study, only a less than a quarter (21.82%, (n = 67)) of the study population had received training on PFA. More than half (56%, n=172) of the respondents had experience in responding to disasters. Conclusions and Recommendations: The overall knowledge and attitudes were found to be satisfactory. However, it is important to improve the knowledge level of the PHMs by providing training and workshops on PFA.

Keywords: disaster, humane supportive assistance, psychological first aid, public health midwives

Procedia PDF Downloads 264

Authors: Habtamu Ayenew Asegie

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Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

Procedia PDF Downloads 51

Authors: Owamah Hilary, O. C. Izinyon

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Many existing biogas kinetic models are difficult to apply to substrates they were not developed for, as they are substrate specific. Biodegradability kinetic (BIK) model and maximum biogas production potential and stability assessment (MBPPSA) model were therefore developed in this study for the anaerobic co-digestion of food waste and maize husk. Biodegradability constant (k) was estimated as 0.11d-1 using the BIK model. The results of maximum biogas production potential (A) obtained using the MBPPSA model corresponded well with the results obtained using the popular but complex modified Gompertz model for digesters B-1, B-2, B-3, B-4, and B-5. The (If) value of MBPPSA model also showed that digesters B-3, B-4, and B-5 were stable, while B-1 and B-2 were unstable. Similar stability observation was also obtained using the modified Gompertz model. The MBPPSA model can therefore be used as alternative model for anaerobic digestion feasibility studies and plant design.

Keywords: biogas, inoculum, model development, stability assessment

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

Procedia PDF Downloads 99

Authors: Misganaw Chere Siferih

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This study assessed the impact of crossbreed dairy cows on household income and food security. The study area is found in Dejen Woreda, East Gojam Zone, and Amhara region of Ethiopia. Random sampling technique was used to obtain a sample of 80 crossbreed dairy cow owners and 176 indigenous dairy cow owners. The study employed food consumption score analytical framework to measure food security status of the household. No Statistical significant mean difference is found between crossbreed owners and indigenous owners. Logistic regression was employed to investigate crossbreed dairy cow adoption determinants , the result indicates that gender, education, labor number, land size cultivated, dairy cooperatives membership, net income and food security status of the household are statistically significant independent variables, which explained the binary dependent variable, crossbreed dairy cow adoption. Propensity score matching (PSM) was employed to analyze the impact of crossbreed dairy cow owners on farmers’ income and food security. The average net income of crossbreed dairy cow owners was found to be significantly higher than indigenous dairy cow owners. Estimates of average treatment effect of the treated (ATT) indicated that crossbreed dairy cow is able to impact households’ net income by 42%, 38.5%, 30.8% and 44.5% higher in kernel, radius, nearest neighborhood and stratification matching algorithms respectively as compared to indigenous dairy cow owners. However, estimates of average treatment of the treated (ATT) suggest that being an owner of crossbreed dairy cow is not able to affect food security significantly. Thus, crossbreed dairy cow enables farmers to increase income but not their food security in the study area. Finally, the study recommended establishing dairy cooperatives and advice farmers to become a member of them, attention to promoting the impact of crossbreed dairy cows and promotion of nutrition focus projects.

Keywords: crossbreed dairy cow, net income, food security, propensity score matching

Procedia PDF Downloads 69

Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

Procedia PDF Downloads 160

Authors: Chui Ka Shing

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This case study research attempts to examine the use of the Bar Model Method approach in learning and teaching mathematics in a primary school in Hong Kong. The objectives of the study are to find out to what extent (a) the Bar Model Method approach enhances the construction of students’ mathematics concepts, and (b) the school-based mathematics curriculum development with adopting the Bar Model Method approach. This case study illuminates the effectiveness of using the Bar Model Method to solve mathematics problems from Primary 1 to Primary 6. Some effective pedagogies and assessments were developed to strengthen the use of the Bar Model Method across year levels. Suggestions including school-based curriculum development for using Bar Model Method and further study were discussed.

Keywords: bar model method, curriculum development, mathematics education, problem solving

Procedia PDF Downloads 225

Authors: Maria L. G. Lourenço, Keylla H. N. P. Pereira, Viviane Y. Hibaru, Fabiana F. Souza, Joao C. P. Ferreira, Simone B. Chiacchio, Luiz H. A. Machado

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In veterinary medicine, the standard procedure during a caesarian section is clamping the umbilical cord immediately after birth. In human neonates, when the umbilical cord is kept intact after birth, blood continues to flow from the cord to the newborn, but this procedure may prove to be difficult in dogs due to the shorter umbilical cord and the number of newborns in the litter. However, a possible detachment of the placenta while keeping the umbilical cord intact may make the residual blood to flow to the neonate. This study compared the effects on neonatal vitality between clamping and no clamping the umbilical cord of dogs born through cesarean section, assessing them through Apgar and reflex scores. Fifty puppies delivered from 16 bitches were randomly allocated to receive clamping of the umbilical cord immediately (n=25) or to not receive the clamping until breathing (n=25). The neonates were assessed during the first five min of life and once again 10 min after the first assessment. The differences observed between the two moments were significant (p < 0.01) for both the Apgar and reflex scores. The differences observed between the groups (clamped vs. not clamped) were not significant for the Apgar score in the 1st moment (p=0.1), but the 2nd moment was significantly (p < 0.01) in the group not clamped, as well as significant (p < 0.05) for the reflex score in the 1st moment and 2nd moment (p < 0.05), revealing higher neonatal vitality in the not clamped group. The differences observed between the moments (1st vs. 2nd) of each group as significant (p < 0.01), revealing higher neonatal vitality in the 2nd moments. In the no clamping group, after removing the neonates together with the umbilical cord and the placenta, we observed that the umbilical cords were full of blood at the time of birth and later became whitish and collapsed, demonstrating the blood transfer. The results suggest that keeping the umbilical cord intact for at least three minutes after the onset breathing is not detrimental and may contribute to increase neonate vitality in puppies delivered by cesarean section.

Keywords: puppy vitality, newborn dog, cesarean section, Apgar score

Procedia PDF Downloads 157

Authors: Hassan Younis

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Purpose: The objective of this study is to review influential papers in the sustainability and supply chain studies domain, leveraging insights from this review to develop a structured framework for academics and researchers. This framework aims to assist scholars in identifying the most impactful publications for their scholarly pursuits. Subsequently, the study will apply and trial the developed framework on selected scholarly articles within the sustainability and supply chain studies domain to evaluate its efficacy, practicality, and reliability. Design/Methodology/Approach: Utilizing the "Publish or Perish" tool, a search was conducted to locate papers incorporating "sustainability" and "supply chain" in their titles. After rigorous filtering steps, a panel of university professors identified five crucial criteria for evaluating research robustness: average yearly citation counts (25%), scholarly contribution (25%), alignment of findings with objectives (15%), methodological rigor (20%), and journal impact factor (15%). These five evaluation criteria are abbreviated as “ACMAJ" framework. Each paper then received a tiered score (1-3) for each criterion, normalized within its category, and summed using weighted averages to calculate a Final Normalized Score (FNS). This systematic approach allows for objective comparison and ranking of the research based on its impact, novelty, rigor, and publication venue. Findings: The study's findings highlight the lack of structured frameworks for assessing influential sustainability research in supply chain management, which often results in a dependence on citation counts. A complete model that incorporates five essential criteria has been suggested as a response. By conducting a methodical trial on specific academic articles in the field of sustainability and supply chain studies, the model demonstrated its effectiveness as a tool for identifying and selecting influential research papers that warrant additional attention. This work aims to fill a significant deficiency in existing techniques by providing a more comprehensive approach to identifying and ranking influential papers in the field. Practical Implications: The developed framework helps scholars identify the most influential sustainability and supply chain publications. Its validation serves the academic community by offering a credible tool and helping researchers, students, and practitioners find and choose influential papers. This approach aids field literature reviews and study suggestions. Analysis of major trends and topics deepens our grasp of this critical study area's changing terrain. Originality/Value: The framework stands as a unique contribution to academia, offering scholars an important and new tool to identify and validate influential publications. Its distinctive capacity to efficiently guide scholars, learners, and professionals in selecting noteworthy publications, coupled with the examination of key patterns and themes, adds depth to our understanding of the evolving landscape in this critical field of study.

Keywords: supply chain management, sustainability, framework, model

Procedia PDF Downloads 55

Authors: Anitha Narasimhan, Aarthy Anandan, Madhan Karky, C. N. Subalalitha

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Games can be the excellent tools for teaching a language. There are few e-learning games in Indian languages like word scrabble, cross word, quiz games etc., which were developed mainly for educational purposes. This paper proposes a Tamil word game called, “Porul”, which focuses on education as well as on players’ thinking and decision-making skills. Porul is a multiple choice based quiz game, in which the players attempt to answer questions correctly from the given multiple options that are generated using a unique algorithm called the Option Selection algorithm which explores the semantics of the question in various dimensions namely, synonym, rhyme and Universal Networking Language semantic category. This kind of semantic exploration of the question not only increases the complexity of the game but also makes it more interesting. The paper also proposes a Scoring Algorithm which allots a score based on the popularity score of the question word. The proposed game has been tested using 20,000 Tamil words.

Keywords: Porul game, Tamil word game, option selection, flash card, scoring, algorithm

Procedia PDF Downloads 407

Authors: Ozlem Karabulutlu, Kiymet Yesilcicek Calik, Nazli Akar

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Objectives: To examine fear of childbirth according to parity, gestational age, prenatal education, and obstetric history. Methods: The study was performed as a questionnaire design in a State Hospital in Kars, Turkey with 403 unselected pregnant who were recruited from the delivery unit. The data were collected via 3 questionnaires; the first with sociodemographic and obstetric features, the second with Wijma Delivery Expectance/Experience Questionnaire (W-DEQ) scale, and the third with the scale of Beck Anxiety Inventory (BAI). Results: The W-DEQ and BAI scores were higher in nulliparous than multiparous woman (W-DEQ 67.08±28.33, 59.87±26.91, P=0.039<0.05, BAI 18.97±9.5, 16.65±11.83, P=0.0009<0.05 respectively). Moreover, W-DEQ and BAI scores of pregnant whose gestational week was ≤37 / ≥41 and who didn’t receive training and had vaginal delivery was higher than those whose gestational week was 38-40 weeks and who received prenatal training and had cesarean delivery (W-DEQ 67.54±29.20, 56.44±22.59, 69.72±25.53 p<0.05, BAI 21.41±9.07; 15.77±11.20, 18.36±10.57 p<0.05 respectively). Both in nulliparous and multiparous, as W-DEQ score increases BAI score increases too (r=0.256; p=0.000<0.05). Conclusions: Severe fear of childbirth and anxiety was more common in nulliparous women, preterm and post-term pregnancy and who did not receive prenatal training and had vaginal delivery.

Keywords: Beck Anxiety Inventory (BAI), fear of birth, parity, pregnant women, Wijma Delivery Expectance/Experience Questionnaire (W-DEQ)

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Authors: Yao Jie, Yeo Khoon Seng

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In recent decades, flapping wing aerodynamics has attracted great interest. Understanding the physics of biological flyers such as birds and insects can help improve the performance of micro air vehicles. The present research focuses on the aerodynamics of insect-like flapping wing flight with the approach of numerical computation. Insect model of hawkmoth is adopted in the numerical study with rigid wing assumption currently. The numerical model integrates the computational fluid dynamics of the flow and active control of wing kinematics to achieve stable flight. The computation grid is a hybrid consisting of background Cartesian nodes and clouds of mesh-free grids around immersed boundaries. The generalized finite difference method is used in conjunction with single value decomposition (SVD-GFD) in computational fluid dynamics solver to study the dynamics of a free hovering hummingbird hawkmoth. The longitudinal dynamics of the hovering flight is governed by three control parameters, i.e., wing plane angle, mean positional angle and wing beating frequency. In present work, a PID controller works out the appropriate control parameters with the insect motion as input. The controller is adjusted to acquire desired maneuvering of the insect flight. The numerical scheme in present study is proven to be accurate and stable to simulate the flight of the hummingbird hawkmoth, which has relatively high Reynolds number. The PID controller is responsive to provide feedback to the wing kinematics during the hovering flight. The simulated hovering flight agrees well with the real insect flight. The present numerical study offers a promising route to investigate the free flight aerodynamics of insects, which could overcome some of the limitations of experiments.

Keywords: aerodynamics, flight control, computational fluid dynamics (CFD), flapping-wing flight

Procedia PDF Downloads 353

Authors: Rahul Saini, Roshan Lal

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The present paper deals with the free axisymmetric vibrations of bi-directional functionally graded circular plates using Mindlin’s plate theory and physical neutral surface. The temperature-dependent, as well as temperature-independent mechanical properties of the plate material, varies in radial and transverse directions. Also, temperature profile for one- and two-dimensional temperature variations has been obtained from the heat conduction equation. A simple computational formulation for the governing differential equation of motion for such a plate model has been derived using Hamilton's principle for the clamped and simply supported plates at the periphery. Employing the generalized differential quadrature method, the corresponding frequency equations have been obtained and solved numerically to retain their lowest three roots as the natural frequencies for the first three modes. The effect of various other parameters such as temperature profile, functionally graded indices, and boundary conditions on the vibration characteristics has been presented. In order to validate the accuracy and efficiency of the method, the results have been compared with those available in the literature.

Keywords: bi-directionally FG, GDQM, Mindlin’s circular plate, neutral axis, vibrations

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Authors: Irtaza M. Syed, Kaamran Raahemifar

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This paper presents modeling of a Alternating Current (AC) Photovoltaic (PV) module using Matlab/Simulink. The proposed AC-PV module model is simple, realistic, and application oriented. The model is derived on module level as compared to cell level directly from the information provided by the manufacturer data sheet. DC-PV module, MPPT control, BC, VSI and LC filter, all were treated as a single unit. The model accounts for changes in variations of both irradiance and temperature. The AC-PV module proposed model is simulated and the results are compared with the datasheet projected numbers to validate model’s accuracy and effectiveness. Implementation and results demonstrate simplicity and accuracy, as well as reliability of the model.

Keywords: PV modeling, AC PV Module, datasheet, VI curves irradiance, temperature, MPPT, Matlab/Simulink

Procedia PDF Downloads 577

Authors: Yamin Sayyari

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In this paper, we generalized the Jensen's inequality for m-convex functions and also we present a correction of Jensen's inequality which is a better than the generalization of this inequality for m-convex functions. Finally, we have found new lower and new upper bounds for Jensen's discrete inequality.

Keywords: Jensen's inequality, m-convex function, Convex function, Inequality

Procedia PDF Downloads 149

Authors: Kirill Filianin, Satu-Pia Reinikainen, Tuomo Sainio

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Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.

Keywords: abnormal process behavior, failure detection, principal component analysis, solvent extraction

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Authors: Keng H. Kong, Louis C. Tan, Wing L. Aw, Kay Y. Tay

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Background: Fatigue is a common problem in patients with Parkinson's disease, with reported prevalence of up to 70%. Fatigue can be disabling and has adverse effects on patients' quality of life. There is currently no satisfactory treatment of fatigue. Acupuncture is effective in the treatment of fatigue, especially that related to cancer. Its role in Parkinson's disease-related fatigue is uncertain. Aims: To evaluate the clinical efficacy of acupuncture treatment in Parkinson's disease-related fatigue. Hypothesis: We hypothesize that acupuncture is effective in alleviating Parkinson's disease-related fatigue. Design: A single center, randomized, controlled study with two parallel arms. Participants: Forty participants with idiopathic Parkinson's disease will be enrolled. Interventions: Participants will be randomized to receive verum (real) acupuncture or placebo acupuncture. The retractable non-invasive sham needle will be used in the placebo group. The intervention will be administered twice a week for five weeks. Main outcome measures: The primary outcome will be the change in general fatigue score of the multidimensional fatigue inventory at week 5. Secondary outcome measures include other subscales of the multidimensional fatigue inventory, movement disorders society-unified Parkinson's disease rating scale, Parkinson's disease questionnaire-39 and geriatric depression scale. All outcome measures will be assessed at baseline (week 0), completion of intervention (week 5) and 4 weeks after completion of intervention (week 9). Results: To date, 23 participants have been recruited and nine have completed the study. The mean age is 63.5±14.2 years, mean duration of Parkinson’s disease is 6.4±1.8 years and mean MDS-UPDRS score is 8.3±2.8. The mean general fatigue score of the multidimensional fatigue inventory is 13.5±4.6. No significant adverse event related to acupuncture is noted. Potential significance: If the results are as expected, this study will provide preliminary scientific evidence for the efficacy of acupuncture in Parkinson's Disease-related fatigue, and opens the door for a larger multicentre trial to be performed. In the longer term, it may lead to the integration of acupuncture in the care of patients with Parkinson's disease.

Keywords: acupuncture, fatigue, Parkinson's disease, trial

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Janet Joy, Shri Shailesh Amarkhed, Pradeep M. Kulkarni

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Introduction: Ureteral stent-related symptoms significantly impact patients' quality of life. This study compared the efficacy of Mirabegron, Tolterodine, and Fesoterodine in managing these symptoms. Methodology: In this prospective, randomized, placebo-controlled trial, two hundred patients undergoing ureteral stenting were randomly assigned to receive Mirabegron, Tolterodine, Fesoterodine, or placebo for two weeks. Symptoms were assessed using the Ureteral Stent Symptom Questionnaire (USSQ) at stent removal. Results: 200 patients completed the study. Mirabegron demonstrated the lowest mean USSQ score (31.6 ± 6.4), followed by Fesoterodine (34.0 ± 6.9) and Tolterodine (35.0 ± 7.2), all significantly lower than placebo (48.6 ± 8.7, p<0.001). Mirabegron showed superior efficacy in reducing urinary symptoms (score: 16.5 ± 3.9) compared to Fesoterodine (17.8 ± 4.1) and Tolterodine (18.2 ± 4.3). Side effects, such as parched mouth, were less frequent with Mirabegron (6%) than with Tolterodine (28%) and Fesoterodine (24%). Conclusion: All three medications significantly improved stent-related symptoms compared to placebo. Mirabegron demonstrated a trend toward superior efficacy and fewer side effects, suggesting its potential as a first-line treatment for stent-related discomfort.

Keywords: ureteral stent, mirabegron, tolterodine, fesoterodine, USSQ, stent-related symptoms

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Authors: Seema Dhariwal, Kiran Maan, Ruchi Baghel, Apoorva Sharma, Poonam Rana

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Introduction: Traumatic brain injury (TBI) is defined as the temporary or permanent alteration in brain function and pathology caused by an external mechanical force. It represents the leading cause of mortality and morbidity among children and youth individuals. Various models of TBI in rodents have been developed in the laboratory to mimic the scenario of injury. Blast overpressure injury is common among civilians and military personnel, followed by accidents or explosive devices. In addition to this, the lateral Controlled cortical impact (CCI) model mimics the blunt, penetrating injury. Method: In the present study, we have developed two different mild TBI models using blast and CCI injury. In the blast model, helium gas was used to create an overpressure of 130 kPa (±5) via a shock tube, and CCI injury was induced with an impact depth of 1.5mm to create diffusive and focal injury, respectively. C57BL/6J male mice (10-12 weeks) were divided into three groups: (1) control, (2) Blast treated, (3) CCI treated, and were exposed to different injury models. Serum was collected on Day1 and day7, followed by biphasic extraction using MTBE/Methanol/Water. Prepared samples were separated on Charged Surface Hybrid (CSH) C18 column and acquired on UHPLC-Q-TOF-MS using ESI probe with inhouse optimized parameters and method. MS peak list was generated using Markerview TM. Data were normalized, Pareto-scaled, and log-transformed, followed by multivariate and univariate analysis in metaboanalyst. Result and discussion: Untargeted profiling of lipids generated extensive data features, which were annotated through LIPID MAPS® based on their m/z and were further confirmed based on their fragment pattern by LipidBlast. There is the final annotation of 269 features in the positive and 182 features in the negative mode of ionization. PCA and PLS-DA score plots showed clear segregation of injury groups to controls. Among various lipids in mild blast and CCI, five lipids (Glycerophospholipids {PC 30:2, PE O-33:3, PG 28:3;O3 and PS 36:1 } and fatty acyl { FA 21:3;O2}) were significantly altered in both injury groups at Day 1 and Day 7, and also had VIP score >1. Pathway analysis by Biopan has also shown hampered synthesis of Glycerolipids and Glycerophospholipiods, which coincides with earlier reports. It could be a direct result of alteration in the Acetylcholine signaling pathway in response to TBI. Understanding the role of a specific class of lipid metabolism, regulation and transport could be beneficial to TBI research since it could provide new targets and determine the best therapeutic intervention. This study demonstrates the potential lipid biomarkers which can be used for injury severity diagnosis and identification irrespective of injury type (diffusive or focal).

Keywords: LipidBlast, lipidomic biomarker, LIPID MAPS®, TBI

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

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Authors: Lukman Oyelami

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Studies have identified trade as a veritable tool for inclusive economic growth and poverty reduction in developing countries. However, contrary to the overwhelming pieces of evidence of the Asian tiger as a success story of beneficial trade, many African countries still experience poverty unabatedly despite active engagement in trade. Consequently, this study seeks to investigate the contributory effect of technological innovation on total export performance and specifically manufacturing exports of African countries. This is with a view to exploring manufacturing exports as a viable option for diversification. To achieve the empirical investigation this study, require Systems Generalized Method of Moments (sys-GMM) estimation technique was adopted based on the econometric realities inherent in the data utilized. However, the static technique of panel estimation of the Fixed Effects (FE) model was utilized for baseline analysis and robustness check. The conclusion from this study is that innovation generally impacts export performance of African countries positively, however, manufacturing export shows more sensitivity to innovation than total export. And, this provides a clear pathway for export diversification for many African countries that run a resource-based economy.

Keywords: innovation, export, GMM, Africa

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Authors: Elfatih M. Abdel-Rahman, Tobias Landmann, Richard Kyalo, George Ong’amo, Bruno Le Ru

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Maize (Zea mays L.) is a major staple food crop in Africa, particularly in the eastern region of the continent. The maize growing area in Africa spans over 25 million ha and 84% of rural households in Africa cultivate maize mainly as a means to generate food and income. Average maize yields in Sub Saharan Africa are 1.4 t/ha as compared to global average of 2.5–3.9 t/ha due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In East Africa, yield losses due to stem borers are currently estimated between 12% to 40% of the total production. The objective of the present study was therefore to predict stem borer larvae density in maize fields using RapidEye reflectance data and generalized linear models (GLMs). RapidEye images were captured for a test site in Kenya (Machakos) in January and in February 2015. Stem borer larva numbers were modeled using GLMs assuming Poisson (Po) and negative binomial (NB) distributions with error with log arithmetic link. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were employed to assess the models performance using a leave one-out cross-validation approach. Results showed that NB models outperformed Po ones in all study sites. RMSE and RPD ranged between 0.95 and 2.70, and between 2.39 and 6.81, respectively. Overall, all models performed similar when used the January and the February image data. We conclude that reflectance data from RapidEye data can be used to estimate stem borer larvae density. The developed models could to improve decision making regarding controlling maize stem borers using various integrated pest management (IPM) protocols.

Keywords: maize, stem borers, density, RapidEye, GLM

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Authors: Gianna Zou

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Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

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Authors: Catherine Maware, Olufemi Adetunji

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The paper is aimed at developing a model to measure the impact of Lean manufacturing deployment on organizational performance. The model will help industry practitioners to assess the impact of implementing Lean constructs on organizational performance. It will also harmonize the measurement models of Lean performance with the house of Lean that seems to have become the industry standard. The sheer number of measurement models for impact assessment of Lean implementation makes it difficult for new adopters to select an appropriate assessment model or deployment methodology. A literature review is conducted to classify the Lean performance model. Pareto analysis is used to select the Lean constructs for the development of the model. The model is further formalized through the use of Structural Equation Modeling (SEM) in defining the underlying latent structure of a Lean system. An impact assessment measurement model developed can be used to measure Lean performance and can be adopted by different industries.

Keywords: impact measurement model, lean bundles, lean manufacturing, organizational performance

Procedia PDF Downloads 489