Search results for: PREDICT score

Authors: Y. T. Gee, I. N. Umar

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Students’ achievement and motivation in learning English in Malaysia is a worrying trend as it is lagging behind several other countries in Asia. Thus, necessary actions have to be taken by the parties concerned to overcome this problem. The purpose of this research was to study the effects of drill and practice courseware on students’ achievement and motivation in learning English language. A multimedia courseware was developed for this purpose. The independent variable was the drill and practice courseware while the dependent variables were the students’ achievement and motivation. Their achievement was measured using pre-test and post-test scores, while motivation was measured using a questionnaire adapted from Keller’s (1979) Instructional Materials Motivation Scale. A total of 60 students from three vernacular primary schools in a northern state in Malaysia were randomly selected in this study. The findings indicate: (1) a significant difference between the students’ pre-test and post-test scores after using the courseware, (2) no significant difference in the achievement score between male and female students after using the courseware, (3) a significant difference in motivation score between the female and the male students, and (4) while the female students scored significantly higher than the male students in the aspects of relevance, confidence and satisfaction, no significant difference in terms of attention was observed between them. Overall, the findings clearly indicate that although the female students are significantly more motivated than their male students, they are equally good in terms of achievement after learning from the courseware. Through this study, the drill and practice courseware is proven to influence the students’ learning and motivation.

Keywords: courseware, drill and practice, English learning, motivation

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Satish Kumar Pappu

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The most common questions which are frequently asked in oil and gas industry are how much is the current production rate from a particular well and what is the approximate predicted life of that well. These questions can be answered through forecasting of important realistic data like flowing tubing hole pressures FTHP, Production decline curves which are used predict the future performance of a well in a reservoir. With the advent of directional drilling, cluster well drilling has gained much importance and in-fact has even revolutionized the whole world of oil and gas industry. An oil or gas reservoir can generally be described as a collection of several overlying, producing and potentially producing sands in to which a number of wells are drilled depending upon the in-place volume and several other important factors both technical and economical in nature, in some sands only one well is drilled and in some, more than one. The aim of this study is to derive important information from the data collected over a period of time at regular intervals on a depleted well in a reservoir sand and apply this information to predict the performance of other wells in that reservoir sand. The depleted wells are the most common observations when an oil or gas field is being visited, w the application of this study more realistic in nature.

Keywords: decline curve analysis, estimation of future gas reserves, reservoir sands, reservoir risk profile

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Authors: Amal Abo El-Maaty, Amira Mohamed, Nashwa Abu-Aita, Hisham Morgan

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For estimating markers of overweight or obesity of brood mares used for riding and training, 17 mares of different body conditions were subjected to blood sampling and ultrasound examination to measure rump fat thickness and monitor ovulation for six consecutive weeks. Also length (L), heart girth (G) and withers height (H) were measured to estimate body weight (BW), body fat %, body fat mass (BFM) and body mass index (BMI). Mares were classified into three groups according to both body condition score (BCS) and rump back fat (BF). Overweight mares (O) were having BCS > 7 and BF thickness >7mm, moderate body condition (M) mares were having BCS >3and ≤7and BF <3and <7mm, and emaciated mares (E) were having BCS ≤3 and BF ≤3mm. glucose, triglycerides, nitric oxide, ovarian, thyroid, insulin, insulin like growth factor-I (IGF-1), and leptin hormones were measured. Results revealed that BCS, G, L, L*G*H, BW, BF, fat %, BFM were significantly (P<0.0001) decreasing linearly from O to E. T4 concentrations of E were significantly high (P=0.04) compared to M and O but T3 concentrations tended to decrease in E (P>0.05). Insulin and IGF-1 concentrations tended to be high in O (P>0.05) and decrease with the decrease of body condition. M had (P=0.007) the highest leptin, but E mares had the lowest P4 concentrations (P=0.01). Concentrations of glucose and NO decreased with the decrease of BCS and BF but triglycerides of O were insignificantly high. In conclusion, exercise could prevent the development of metabolic syndrome in horses and back fat and morphometric measurements were the easiest and simple assessment of overweight and deviation to obesity.

Keywords: body condition score, insulin, leptin, mares, rump fat

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Authors: Vladyslav Povoroznyuk, Nataliia Dzerovych, Larysa Martynyuk, Tetiana Kovtun

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Obesity and osteoporosis are the two diseases whose increasing prevalence and high impact on the global morbidity and mortality, during the two recent decades, have gained a status of major health threats worldwide. Obesity purports to affect the bone metabolism through complex mechanisms. Debated data on the connection between the bone mineral density and fracture prevalence in the obese patients are widely presented in literature. There is evidence that the correlation of weight and fracture risk is site-specific. The aim of this study was to evaluate the Bone Mineral Density (BMD) and Trabecular Bone Score (TBS) in the obese Ukrainian women. We examined 1025 40-89-year-old women, divided them into the groups according to their body mass index: Group a included 360 women with obesity whose BMI was ≥30 kg/m2, and Group B – 665 women with no obesity and BMI of < 30 kg/m2. The BMD of total body, lumbar spine at the site L1-L4, femur and forearm were measured by DXA (Prodigy, GEHC Lunar, Madison, WI, USA). The TBS of L1-L4 was assessed by means of TBS iNsight® software installed on our DXA machine (product of Med-Imaps, Pessac, France). In general, obese women had a significantly higher BMD of lumbar spine, femoral neck, proximal femur, total body, and ultradistal forearm (p<0.001) in comparison with women without obesity. The TBS of L1-L4 was significantly lower in obese women compared to non-obese women (p<0.001). The BMD of lumbar spine, femoral neck and total body differed to a significant extent in women of 40-49, 50-59, 60-69, and 70-79 years (p<0.05). At same time, in women aged 80-89 years the BMD of lumbar spine (p=0.09), femoral neck (p=0.22) and total body (p=0.06) barely differed. The BMD of ultradistal forearm was significantly higher in women of all age groups (p<0.05). The TBS of L1-L4 in all the age groups tended to reveal the lower parameters in obese women compared with the non-obese; however, those data were not statistically significant. By contrast, a significant positive correlation was observed between the fat mass and the BMD at different sites. The correlation between the fat mass and TBS of L1-L4 was also significant, although negative. Women with vertebral fractures had a significantly lower body weight, body mass index and total body fat mass in comparison with women without vertebral fractures in their anamnesis. In obese women the frequency of vertebral fractures was 27%, while in women without obesity – 57%.

Keywords: obesity, trabecular bone score, bone mineral density, women

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

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The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

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Authors: Mostafa Jafarian Abyaneh, Khashayar Jafari, Vahab Toufigh

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The cost of experiments on different types of concrete has raised the demand for prediction of their behavior with numerical analysis. In this research, an advanced numerical model has been presented to predict the complete elastic-plastic behavior of polymer concrete (PC), high-strength concrete (HSC), high performance concrete (HPC) along with different steel fiber contents under uniaxial compression. The accuracy of the numerical response was satisfactory as compared to other conventional simple models such as Mohr-Coulomb and Drucker-Prager. In order to predict the complete elastic-plastic behavior of specimens including softening behavior, disturbed state concept (DSC) was implemented by nonlinear finite element analysis (NFEA) and hierarchical single surface (HISS) failure criterion, which is a failure surface without any singularity.

Keywords: disturbed state concept (DSC), hierarchical single surface (HISS) failure criterion, high performance concrete (HPC), high-strength concrete (HSC), nonlinear finite element analysis (NFEA), polymer concrete (PC), steel fibers, uniaxial compression test

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Authors: Mohammed Asiri, Saleh Alsuwayt, Mohammed Bin Mugren, Abdulmalik Almufarrih, Tariq Alotaibi, Saad Almodameg

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Background: End-stage renal disease is a major public health concern with high incidence and mortality rate. Most of ESRD patients are on hemodialysis therapy which is a long-term treatment that disturbs patients’ lifestyle. As a result, he will be susceptible to develop psychiatric disorders like anxiety that may direct him to non-compliance on physician instructions and hemodialysis therapy. Although there are studies conducted on psychiatric issues in hemodialysis patients, but few studies focused on the effect of anxiety disorder and the patient’s compliance. Hence, we are interested in determining the prevalence of anxiety disorder among hemodialysis patients in Saudi Arabia, as well as in defining the correlation between anxiety disorder and compliance on physician instructions and hemodialysis therapy. We hypothesize that our study will show a higher prevalence of anxiety in hemodialysis patients than in general population. Also, we expect the anxiety to have a negative impact on their compliance. Methodology: We used a cross-sectional study design carried out at dialysis unit of four major hospitals in Riyadh, KSA. We interviewed 235 End Stage Renal Disease male and female patients who are on hemodialysis. We divided the patients into two categories according to their compliance. we used modified general questionnaire to get their demographic data, then we used a psychometric response scale called visual analog scale (VAS) to assess patient’s compliance to hemodialysis and physician’s instructions. Also, we used the Arabic validated version of the hospital anxiety and depression scale (HAD scale) used mainly for anxiety assessment. Results: The overall response rate was 54%. Respondents included 147 (62.6%) males and 88 (37.4%) females. The prevalence of anxiety among hemodialysis patients is 13.3%. According to visual analog scale, we found that 189 compliant patients and 45 non-compliant patients. For HAD scale, the mean ± standard deviation of the total score for females was (4.44 ± 4.7) and it’s higher than males which was 2.65 ± 3.08 (P-value= 0.002). The mean ± standard deviation of HAD score in the non-compliant group was (5.88  4.88) and it was higher than the compliant group (2.7  3.32) (P-value= 0.004). Among non-complaint group, 33.3% of anxious patients were males and 66.6% were females. There was a negative correlation between HAD score of anxiety and visual analog scale (R= - 0.285). Conclusion: We conclude that there is a high prevalence of anxiety among patients with End Stage Renal Disease that was higher in females with association of non-compliance to physician’s instructions and hemodialysis therapy.

Keywords: anxiety, end-stage renal disease, renal failure, anxiety disorder

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Authors: Supriadi Hardianto, Aditya Daniswara

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Today's workplace, flooded by millennial Generation or known also as Generation Y. A common problem that faced by the company towards Gen Y is a high turnover rate, attitudes problem, communication style, and different work style than the older generation. This is common in private sector. The objective of this study is to get a better understanding of the Gen Y Career Attitude at the workplace. The subject of this study is focusing on 430 respondent of Gen Y which age between 20 – 35 years old who works for a private company. The Questionnaire as primary data source captured 9 aspects of career attitude based on Career Attitudes Strategy Inventory (CASI). This Survey distributes randomly among Gen Y in the IT Industry (125 Respondent) and Manufacture Company (305 Respondent). A Random deep interview was conducted to get the better understanding of the etiology of their primary obstacles. The study showed that most of Indonesia Gen Y have a moderate score on Job satisfaction but in the other aspects, Gen Y has the lowest score on Skill Development, Career Worries, Risk-Taking Style, Dominant Style, Work Involvement, Geographical Barrier, Interpersonal Abuse, and Family Commitment. The top 5 obstacles outside that 9 aspects that faced by Gen Y are 1. Lower communication & networking support; 2. Self-confidence issues; 3. Financial Problem; 4. Emotional issues; 5. Age. We also found that parent perspective toward the way they are nurturing their child are not aligned with their child’s real life. This research fundamentally helps the organization and other Gen Y’s Stakeholders to have a better understanding of Gen Y Career Attitude at the workplace.

Keywords: career attitudes, CASI, Gen Y, career attitude at workplace

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Authors: Jae-Hyun Choi, Sung-Soo Bae, Sangyoung Yoon, Hong-Ku Yun, Jiyoung Kwahk

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This study was conducted to investigate the effect of ocular dominance on recognition performance using a single-lens smart display designed for cycling. A total of 36 bicycle riders who have been cycling consistently were recruited and participated in the experiment. The participants were asked to perform tasks riding a bicycle on a stationary stand for safety reasons. Independent variables of interest include ocular dominance, bike usage, age group, and information layout. Recognition time (i.e., the time required to identify specific information measured with an eye-tracker), error rate (i.e. false answer or failure to identify the information in 5 seconds), and user preference scores were measured and statistical tests were conducted to identify significant results. Recognition time and error ratio showed significant difference by ocular dominance factor, while the preference score did not. Recognition time was faster when the single-lens see-through display on the dominant eye (average 1.12sec) than on the non-dominant eye (average 1.38sec). Error ratio of the information recognition task was significantly lower when the see-through display was worn on the dominant eye (average 4.86%) than on the non-dominant eye (average 14.04%). The interaction effect of ocular dominance and age group was significant with respect to recognition time and error ratio. The recognition time of the users in their 40s was significantly longer than the other age groups when the display was placed on the non-dominant eye, while no difference was observed on the dominant eye. Error ratio also showed the same pattern. Although no difference was observed for the main effect of ocular dominance and bike usage, the interaction effect between the two variables was significant with respect to preference score. Preference score of daily bike users was higher when the display was placed on the dominant eye, whereas participants who use bikes for leisure purposes showed the opposite preference patterns. It was found more effective and efficient to wear a see-through display on the dominant eye than on the non-dominant eye, although user preference was not affected by ocular dominance. It is recommended to wear a see-through display on the dominant eye since it is safer by helping the user recognize the presented information faster and more accurately, even if the user may not notice the difference.

Keywords: eye tracking, information recognition, ocular dominance, smart headware, wearable device

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Scholz Heiko, Kuhne Petra, Heckenberger Gerd

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Problems during the calving period (December to May) often result in a high body condition score (BCS) at this time. At the end of the grazing period (frequently after early weaning), however, an increase of BCS can often be observed under German conditions. In the last eight weeks before calving, the body condition should be reduced or at least not increased. Rations with a higher amount of crude fiber can be used (rations with straw or late mowed grass silage). Fermentative digestion of fiber is slow and incomplete; that’s why the fermentative process in the rumen can be reduced over a long feeding time. Viewed in this context, feed intake of suckler cows (8 weeks before calving) in different rations and fermentation in the rumen should be checked by taking rumen fluid. Eight suckler cows (Charolais) were feeding a Total Mixed Ration (TMR) in the last eight weeks before calving and grass silage after calving. By the addition of straw (30 % [TMR1] vs. 60 % [TMR2] of dry matter) was varied the amount of crude fiber in the TMR (grass silage, straw, mineral) before calving. After calving of the cow's grass, silage [GS] was fed ad libitum, and the last measurement of rumen fluid took place on the pasture [PS]. Rumen fluid, plasma, body weight, and backfat thickness were collected. Rumen fluid pH was assessed using an electronic pH meter. Volatile fatty acids (VFA), sedimentation, methylene-blue and amount of infusorians were measured. From these 4 parameters, an “index of rumen fermentation” [IRF] in the rumen was formed. Fixed effects of treatment (TMR1, TMR2, GS and PS) and a number of lactations (3-7 lactations) were analyzed by ANOVA using SPSS Version 25.0 (significant by p ≤ 5 %). Rumen fluid pH was significant influenced by variants (TMR 1 by 6.6; TMR 2 by 6.9; GS by 6.6 and PS by 6.9) but was not affected by other effects. The IRF showed disturbed fermentation in the rumen by feeding the TMR 1+2 with a high amount of crude fiber (Score: > 10.0 points) and a very good environment for fermentation during grazing the pasture (Score: 6.9 points). Furthermore, significant differences were found for VFA, methylene blue and the number of infusorians. The use of rations with the high amount of crude fiber from weaning to calving may cause deviations from undisturbed fermentation in the rumen and adversely affect the utilization of the feed in the rumen.

Keywords: suckler cow, feeding systems, crude fiber, digestibilty of organic matter

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Authors: Mania Nosratinia, Zahra Deris

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The purpose of the present study was to investigate the relationship between EFL learners' self-regulation (SR) and willingness to communicate (WTC). To this end, 520 male and female EFL learners, ranging between 19 and 34 years old (Mage = 26), majoring in English Translation, English Language Teaching and English Literature at Islamic Azad University, Fars Province, were randomly selected. They were given two questionnaires: Self-Regulation Questionnaire devised by Brown, Miller, and Lawendowski (1999) and Willingness to Communicate Scale devised by McCroskey and Baer (1985). Preliminarily, pertinent analyses were performed on the data to check the assumptions of normality, linearity, and homoscedasticity. Since the assumption of normality was violated, Spearman's rank-order correlation was employed to probe the relationships between SR and WTC. The results indicated a significant and positive correlation between the two variables, ρ = .56, n = 520, p < .05, which signified a large effect size supplemented by a very small confidence interval (0.503 – 0.619). The results of the Kruskal-Wallis tests indicated that there is a statistically significant difference in WTC score between the different levels of SR, χ2(2) = 157.843, p = 0.000 with a mean rank SR score of 128.13 for low-SR level, 286.64 for mid-SR level, and 341.12 for high-SR level. Also, a post-hoc comparison through running a Dwass-Steel-Critchlow-Fligner indicated significant differences among the SR level groups on WTC scores. Given the findings of the study, the obtained results may help EFL teachers, teacher trainers, and material developers to possess a broader perspective towards the TEFL practice and to take practical steps towards the attainments of the desired objectives and effective instruction.

Keywords: EFL learner, self-regulation, willingness to communicate, relationship

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Authors: Cherrie Mae S. Sia, Noel J. Belonguel, Jarungchai Anton S. Vatanagul

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Background: Parkinson’s disease (PD) is a chronic progressive, neurodegenerative disorder known for its motor symptoms such as bradykinesia, resting tremor, muscle rigidity, and postural instability. Patients with PD also experience non-motor symptoms (NMS) such as depression, fatigue, and sleep disturbances that are most of the time unrecognized by clinicians. This may be due to the lack of spontaneous reports from the patients or partly because of the lack of systematic questioning from the healthcare professional. There is limited data with regards to these NMS especially that of Filipino patients with PD. Objectives: This study aims to determine the non-motor symptoms of Filipino patients with Parkinson’s disease. Materials and Methods: This is a prospective, cohort study involving thirty-four patients of Filipino-descent diagnosed with PD in three out-patient clinics in Cebu City from April to September 2014. Each patient was interviewed using the Non-Motor Symptom Scale (NMSS). A Cebuano version of the NMSS was also provided for the non-English speaking patients. Interview time was approximately ten to fifteen minutes for each respondent. Results: Of the thirty-four patients with Parkinson’s disease, majority was noted to be males (N=19) and the disease was noted to be more prevalent in patients with a mean age of 62 (SD±9) years old. Hypertension (59%) and diabetes mellitus (29%) were the common co-morbidities in the study population. All patients presented more than one NMS, with insomnia (41.2%), poor memory (23.5%) and depression (14.7%) being the first non-motor symptoms to occur. Symptoms involving mood/cognition (mean=2.21), and attention/memory (mean=2.05) were noted to be the most frequent and of moderate severity. Based on the NMSS, the symptoms that were noted to be mild and often to occur were those that involved the mood/cognition (score=3.84), attention/memory (score=3.50), and sleep/fatigue (score=3.00) domains. Levodopa-Carbidopa, Ropinirole, and Pramipexole were the most frequently used medications in the study population. Conclusion: Non-motor symptoms (NMS) are common in patients with Parkinson’s disease (PD). They appear at the time of diagnosis of PD or even before the motor symptoms manifest. The earliest non-motor symptoms to occur are insomnia, poor memory, and depression. Those pertaining to mood/cognition and attention/memory are the most frequent NMS and they are of moderate severity. Identifying these NMS by doing a questionnaire-guided interview such as the Non-Motor Symptom Scale (NMSS) before they can become more severe and affect the patient’s quality of life is a must for every clinician caring for a PD patient. Early treatment and control of these NMS can then be given, hence, improving the patient’s outcome and prognosis.

Keywords: non motor symptoms, Parkinson's Disease, insomnia, depression

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Authors: Siddharth Jain, Abhenil Mittal, Surendra Kumar Sharma

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Background: With its recent emergence and re-emergence, dengue has become a major international public health concern, imposing significant financial burden especially in developing countries. Despite aggressive control measures in place, India experienced one of its largest outbreaks in 2015 with Delhi being most severely affected. There is a lack of reliable predictors of disease severity and mortality in dengue. The present study was carried out to identify these predictors during the 2015 outbreak. Methods: This prospective observational study conducted at an apex tertiary care center in Delhi, India included confirmed adult dengue patients admitted between August-November 2015. Patient demographics, clinical details, and laboratory findings were recorded in a predesigned proforma. Appropriate statistical tests were used to summarize and compare the clinical and laboratory characteristics and derive predictors of mortality and severe disease, while developing a clinical risk score for mortality. Serotype analysis was also done for 75 representative samples to identify the dominant serotypes. Results: Data of 369 patients were analyzed (mean age 30.9 years; 67% males). Of these, 198 (54%) patients had dengue fever, 125 (34%) had dengue hemorrhagic fever (DHF Grade 1,2)and 46 (12%) developed dengue shock syndrome (DSS). Twenty two (6%) patients died. Late presentation to the hospital (≥5 days after onset) and dyspnoea at rest were identified as independent predictors of severe disease. Age ≥ 24 years, dyspnoea at rest and altered sensorium were identified as independent predictors of mortality. A clinical risk score was developed (12*age + 14*sensorium + 10*dyspnoea) which, if ≥ 22, predicted mortality with a high sensitivity (81.8%) and specificity (79.2%). The predominant serotypes in Delhi (2015) were DENV-2 and DENV-4. Conclusion: Age ≥ 24 years, dyspnoea at rest and altered sensorium were identified as independent predictors of mortality. Platelet counts did not determine the outcome in dengue patients. Timely referral/access to health care is important. Development and use of validated predictors of disease severity and simple clinical risk scores, which can be applied in all healthcare settings, can help minimize mortality and morbidity, especially in resource limited settings.

Keywords: dengue, mortality, predictors, severity

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Authors: Meseret Mulugeta, Alemmebrat Kiflu, Belaynehchikle

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The purpose of the present study was to examine the preferred leadership style and motivation of premier league volleyball players. The sample encompassed 46 female premier league volleyball players whose ages ranged between 15 and 35 years. The data were collected using standardized questionnaires. The questionnaires were distributed to 46 female players from five volleyball clubs in the Premier League. To evaluate the motivational level of the players, the Sports Motivation Scale (SMS-6) was used. The leadership scale for sport was used to evaluate leadership. Descriptive statistics and the person correlation coefficient (P <0.05) were used to validate the relationship between leadership style and motivation. The result showed that there is a meaningful and significant relationship between leadership style and motivation. Concerning preferred coaching styles, the most preferred style was training and instruction, with a mean score of 4.10, and the least preferred style was autocratic, with a mean score of 3.37. The result of the Pearson correlation coefficient showed that the correlation between motivation types and leadership styles showed that motivation was significantly and positively correlated with all independent variables except autocratic leadership style, which is negatively correlated with motivation. This study’s nobility is to provide evidence for the most effective coaching to practice the training and instruction behaviour and social support behaviour leadership styles and refrain from using the autocratic leadership style.

Keywords: autocratic, training and instruction, motivation, leadership style

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Authors: Sajjad Baghernezhad

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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

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Authors: A. Zurraini, Syed Alwi Sar, H. Helmy, H. Nazeefah

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Early Dementia Questionnaire (EDQ) was developed as a screening tool to detect patients with early dementia in primary care. It was developed based on 20 symptoms of dementia. From a preliminary study, EDQ had been shown to be a promising alternative for screening of early dementia. This study was done to further test on EDQ’s reliability and validity. Using a systematic random sampling, 200 elderly patients attending primary health care centers in Kuching, Sarawak had consented to participate in the study and were administered the EDQ. Geriatric Depression Scale (GDS) was used to exclude patients with depression. Those who scored >21 MMSE, were retested using the EDQ. Reliability was determined by Cronbach’s alpha for internal consistency and construct validity was assessed using confirmatory factor analysis (principle component with varimax rotation). The result showed that the overall Cronbach’s alpha coefficient was good which was 0.874. Confirmatory factor analysis on 4 factors indicated that the Cronbach’s alpha for each domain were acceptable with memory (0.741), concentration (0.764), emotional and physical symptoms (0.754) and lastly sleep and environment (0.720). Pearson correlation coefficient between the first EDQ score and the retest EDQ score among those with MMSE of >21 showed a very strong, positive correlation between the two variables, r = 0.992, N=160, P <0.001. The results of the validation study showed that Early Dementia Questionnaire (EDQ) is a valid and reliable tool to be used as a screening tool to detect early dementia in primary care.

Keywords: Early Dementia Questionnaire (EDQ), screening, primary care, construct validity

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Authors: Zahra Emami, Mohammad Ebrahim Khamseh, Nahid Hashemi Madani, Iman Kermani

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The aim of the study was to map scientific research on adrenal gland diseases in the Middle East countries through the Web of Science database using scientometric analysis. Data were analyzed with Excel software; and HistCite was used for mapping of the scientific texts. In this study, from a total of 268 retrieved records, 1125 authors from 328 institutions published their texts in 138 journals. Among 17 Middle East countries, Turkey ranked first with 164 documents (61.19%), Israel ranked second with 47 documents (15.53%) and Iran came in the third place with 26 documents. Most of the publications (185 documents, 69.2%) were articles. Among the universities of the Middle East, Istanbul University had the highest science production rate (9.7%). The Journal of Clinical Endocrinology & Metabolism had the highest TGCS (243 citations). In the scientific mapping, 7 clusters were formed based on TLCS (Total Local Citation Score) & TGCS (Total Global Citation Score). considering the study results, establishment of scientific connections and collaboration with other countries and use of publications on adrenal gland diseases from high ranking universities can help in the development of this field and promote the medical practice in this regard. Moreover, investigation of the formed clusters in relation to Congenital Hyperplasia and puberty related disorders can be research priorities for investigators.

Keywords: mapping, scientific research, adrenal gland diseases, scientometric

Procedia PDF Downloads 246

Authors: Sara Patricia Ibarra-Zavaleta, Rabindranarth Romero-Lopez, Rosario Langrave, Annie Poulin, Gerald Corzo, Mathias Glaus, Ricardo Vega-Azamar, Norma Angelica Oropeza

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The progressive change in climatic conditions worldwide has increased frequency and severity of extreme hydrometeorological events (EHE). Mexico is an example; this has been affected by the presence of EHE leaving economic, social and environmental losses. The objective of this research was to apply a Canadian distributed hydrological model (DHM) to tropical conditions and to evaluate its capacity to predict flows in a basin in the central Gulf of Mexico. In addition, the DHM (once calibrated and validated) was used to calculate the theoretical hydraulic power and the performance to predict streamflow before the presence of an EHE. The results of the DHM show that the goodness of fit indicators between the observed and simulated flows in the calibration process (NSE=0.83, RSR=0.021 and BIAS=-4.3) and validation: temporal was assessed at two points: point one (NSE=0.78, RSR=0.113 and BIAS=0.054) and point two (NSE=0.825, RSR=0.103 and BIAS=0.063) are satisfactory. The DHM showed its applicability in tropical environments and its ability to characterize the rainfall-runoff relationship in the study area. This work can serve as a tool for identifying vulnerabilities before floods and for the rational and sustainable management of water resources.

Keywords: HYDROTEL, hydraulic power, extreme hydrometeorological events, streamflow

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Authors: Guler Duru Asiret, Cemile Kutmec Yilmaz, Gulcan Bagcivan, Tugce Turten Kaymaz

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This descriptive study was conducted to determine the relationship between caregiver burden and life satisfaction who give home care to elderly individuals. The sample was recruited from the internal medicine unit and palliative unit of a state hospital located in Turkey on June 2016-2017. The study sample consisted of 231 primary caregiver family member, who met the eligibility criteria and agreed to participate in the study. The inclusion criteria were as follows: inpatient’s caregiver, primary caregiver for at least 3 months, at least 18 years of age, no communication problem or mental disorder. Data were gathered using an Information Form prepared by the researchers based on previous literature, the Zarit Burden Interview (ZBI), and the Satisfaction with Life Scale (SWLS). The data were analyzed using IBM SPSS Statistics software version 20.0 (SPSS, Chicago, IL). The descriptive characteristics of the participant were analyzed using number, percentage, mean and standard deviation. The suitability of normal distribution of scale scores was analyzed using Kolmogorov-Smirnov and Shapiro-Wilk test. Relationships between scales were analyzed using Spearman’s rank-correlation coefficient. P values less than 0.05 were considered to be significant. The average age of the caregivers was 50.11±13.46 (mean±SD) years. Of the caregivers, 76.2% were women, 45% were primary school graduates, 89.2% were married, 38.1% were the daughters of their patients. Among these, 52.4% evaluated their income level to be good. Of them, 53.6% had been giving care less than 2 years. The patients’ average age was 77.1±8.0 years. Of the patients, 55.8% were women, 56.3% were illeterate, 70.6% were married, and 97.4% had at least one chronic disease. The mean Zarit Burden Interview score was 35.4±1.5 and the Satisfaction with Life Scale score was 20.6±6.8. A negative relationship was found between the patients’ score average on the ZBI, and on the SWLS (r= -0.438, p=0.000). The present study determined that the caregivers have a moderate caregiver burden and the life satisfaction. And the life satisfaction of caregivers decreased as their caregiver burden increase. In line with the results obtained from the research, it is recommended that to increase the effectiveness of discharge training, to arrange training and counseling programs for caregivers to cope with the problems they experienced, to monitor the caregivers at regular intervals and to provide necessary institutional support.

Keywords: caregiver burden, family caregivers, nurses, satisfaction

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: Abdul Hakim Chikho

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Calculating the ultimate load of a column in a sway framed structure involves, in the currently used design method, the calculation of the column effective length and utilizing the interaction formulas or tables. Therefore, no allowance is usually made for the effects of the presence of semi rigid connections or the presence of infill panels. In this paper, a new and simple design procedure is recommend to calculate the ultimate load of a framed Column allowing for the presence of rotational end restraints, semi rigid connections, the column end moments resulted from the applied vertical and horizontal loading and infill panels in real steel structure. In order to verify the accuracy of the recommended method to predict good and safe estimations of framed column ultimate loads, several examples have been solved utilizing the recommended procedure, and the results were compared to those obtained using a second order computer program, and good correlation had been obtained. Therefore, the accuracy of the proposed method to predict the Behaviour of practical steel columns in framed structures has been verified.

Keywords: column ultimate load, semi rigid connections, steel column, infill panel, steel structure

Procedia PDF Downloads 159

Authors: Emily Toon

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Nurses play a critical role in the prevention of ventilator-associated pneumonia through the maintenance of endotracheal tubes and use of subglottic secretion drainage via subglottic suctioning endotracheal tubes. The purpose of this evidence based practice project is to develop a staff education program on subglottic suctioning endotracheal tubes for critical care nurses at Middlesex Health with the aim of determining and documenting increased knowledge and/or practice change. The setting included registered nurses within Middlesex Health’s critical care unit who were recruited to complete a pre-test (n=14), view a presentation, and complete a post-test (n=10). Average pre-test scores were compared to average post-test scores to determine an increase in knowledge and/or practice change. The overall mean pre-test score was 59.7 percent, compared with the mean post-test score of 88.1 percent. Pre- and post-test scores were unmatched, so statistical significance could not be determined. The hypothesis that a staff education program on subglottic suctioning endotracheal tubes would demonstrate an increase in knowledge was supported, but not statistically. By integrating a pre-test/post-test design into educational presentations to evaluate increased knowledge, data generated may be used to improve methods and practices of delivering education and enhance staff learning.

Keywords: endotracheal tubes, staff education, subglottic secretion drainage, ventilator-associated pneumonia

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Authors: Yadgar Tayfur, Antony Darby, Tim Ibell, Mark Evernden, John Orr

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Fabric form-work is a technique to cast concrete structures with a great advantage of saving concrete material of up to 40%. This technique is particularly associated with the optimized concrete structures that usually have smaller cross-section dimensions than equivalent prismatic members. However, this can make the structural system produced from these members prone to smaller serviceability safety margins. Therefore, it is very important to understand the serviceability issue of non-prismatic concrete structures. In this paper, an analytical computer-based model to optimize concrete beams and to predict load-deflection behaviour of both prismatic and non-prismatic concrete beams is presented. The model was developed based on the method of sectional analysis and integration of curvatures. Results from the analytical model were compared to load-deflection behaviour of a number of beams with different geometric and material properties from other researchers. The results of the comparison show that the analytical program can accurately predict the load-deflection response of concrete beams with medium reinforcement ratios. However, it over-estimates deflection values for lightly reinforced specimens. Finally, the analytical program acceptably predicted load-deflection behaviour of on-prismatic concrete beams.

Keywords: fabric-formed concrete, continuous beams, optimisation, serviceability

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Authors: Aliyu B. Mustapha, Poul A. Gida

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Pot experiment was conducted at the landscape unit of Modibbo Adama University of Technology, Yola in 2015 and 2016 to determine the effect of some chemicals namely glyphosate, salt and detergent on Golden dodder (Cuscuta campestris Yunk). The experiment was laid in a completely randomized design (CRD) with three replications. The treatments include the following: glyphosate-T0= (control),(Og a.i/ha-1) T1=35g a.i/ha-1, T2=70g a.i/ha-1, T3=105g a.i/ha-1, T4=140 a.i/ha-1 and T5=175g a.i/ha-1: Salt (T0=control O mole/ha-1 T1=1mole/ha-1 T2=2mole/ha-1, T3=3mole/ha-1 , T4=4mole/ha-1 and T5=5mole/ha-1:washing detergent T0=Og/ha-1(control), T1=30ml detergent +70ml distilled water T2=45ml detergent+65ml distilled water T3=60ml detergent+40ml distilled water, T4=75ml detergent+25ml distilled water and T5=90ml detergent +10mldistilled water, the treatments were replicated three times. Data were collected include: plant height, number of leaves, leaf area, leaf area index and Cuscuta cover score at 3,6,9and 12 weeks after sprouting(WAS). Biomas of Vitex was also collected at the end of the experiment. Data collected were analyzed using software Genstat version 8.0. Results showed that glyphosate gave the least Cuscuta cover score and the tallest Vitex plant. However, detergent mildly controlled Cuscuta, while salt has no effect on Cuscuta campestris indicating that glyphosate could be used in the control of parasitic dodder (Cuscuta campestris) on Vitex plant.

Keywords: chemical, control, dudder, Vitex

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

Abstract:

Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Zhehao Zhang

Abstract:

Walking has become an indispensable and sustainable way of travel for college students in their daily lives; campus street is an important carrier for students to walk and take part in a variety of activities, improving the walkability of campus streets plays an important role in optimizing the quality of campus space environment, promoting the campus walking system and inducing multiple walking behaviors. The purpose of this paper is to explore the effect of campus layout, facility distribution, and location site selection on the walkability of campus streets, and assess the street design qualities from the elements of imageability, enclosure, complexity, transparency, and human scale, and further examines the relationship between street-level urban design perceptual qualities and walkability and its effect on walking behavior in the campus. Taking Tianjin University as the research object, this paper uses the optimized walk score method based on walking frequency, variety, and distance to evaluate the walkability of streets from a macro perspective and measures the urban design qualities in terms of the calculation of street physical environment characteristics, as well as uses behavior annotation and street image data to establish temporal and spatial behavior database to analyze walking activity from the microscopic view. In addition, based on the conclusions, the improvement and design strategy will be presented from the aspects of the built walking environment, street vitality, and walking behavior.

Keywords: walkability, streetscapes, pedestrian activity, walk score

Procedia PDF Downloads 130