Search results for: PREDICT score

Authors: Martín Pratto Burgos

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The Engineering School of the University of the Republic in Uruguay offers an Introductory Mathematical Course from the second semester of 2019. This course has been designed to assist students in preparing themselves for math courses that are essential for Engineering Degrees, namely Math1, Math2, and Math3 in this research. The research proposes to build a model that can accurately predict the student's activity and academic progress based on their performance in the three essential Mathematical courses. Additionally, there is a need for a model that can forecast the incidence of the Introductory Mathematical Course in the three essential courses approval during the first academic year. The techniques used are Principal Component Analysis and predictive modelling using the Generalised Linear Model. The dataset includes information from 5135 engineering students and 12 different characteristics based on activity and course performance. Two models are created for a type of data that follows a binomial distribution using the R programming language. Model 1 is based on a variable's p-value being less than 0.05, and Model 2 uses the stepAIC function to remove variables and get the lowest AIC score. After using Principal Component Analysis, the main components represented in the y-axis are the approval of the Introductory Mathematical Course, and the x-axis is the approval of Math1 and Math2 courses as well as student activity three years after taking the Introductory Mathematical Course. Model 2, which considered student’s activity, performed the best with an AUC of 0.81 and an accuracy of 84%. According to Model 2, the student's engagement in school activities will continue for three years after the approval of the Introductory Mathematical Course. This is because they have successfully completed the Math1 and Math2 courses. Passing the Math3 course does not have any effect on the student’s activity. Concerning academic progress, the best fit is Model 1. It has an AUC of 0.56 and an accuracy rate of 91%. The model says that if the student passes the three first-year courses, they will progress according to the timeline set by the curriculum. Both models show that the Introductory Mathematical Course does not directly affect the student’s activity and academic progress. The best model to explain the impact of the Introductory Mathematical Course on the three first-year courses was Model 1. It has an AUC of 0.76 and 98% accuracy. The model shows that if students pass the Introductory Mathematical Course, it will help them to pass Math1 and Math2 courses without affecting their performance on the Math3 course. Matching the three predictive models, if students pass Math1 and Math2 courses, they will stay active for three years after taking the Introductory Mathematical Course, and also, they will continue following the recommended engineering curriculum. Additionally, the Introductory Mathematical Course helps students to pass Math1 and Math2 when they start Engineering School. Models obtained in the research don't consider the time students took to pass the three Math courses, but they can successfully assess courses in the university curriculum.

Keywords: machine-learning, engineering, university, education, computational models

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Authors: Gezahegn Gizaw

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The FDRE Tourism Training Institute organizes forums for debates, best practices exchange and focus group discussions to forge a sustainable and growing tourism sector while minimizing negative impacts on the environment, communities, and cultures. This study aimed at applying empirical research method to identify and quantify relative importance of success factors and individual engagement indicators that were identified in these forums. Response to the 12-question survey was collected from a total of 437 respondents in academic training institutes (212), business executive and employee (204) and non-academic government offices (21). Overall, capacity building was perceived as the most important driver of success for stakeholder engagement. Business executive and employee category rated capacity building as the most important driver of success (53%), followed by decision-making process (27%) and community participation (20%). Among educators and students, both capacity building and decision-making process were perceived as the most important factors (40% of respondents), whereas community participation was perceived as the most important success factor only by 20% of respondents. Individual engagement score in capacity building, decision-making process and community participation showed highest variability by educational level of participants (variance of 3.4% - 5.2%, p<0.001). Individual engagement score in capacity building was highly correlated to perceived benefit of training on improved efficiency, job security, higher customer satisfaction and self-esteem. On the other hand, individual engagement score in decision making process was highly correlated to its perceived benefit on lowering business costs, improving ability to meet the needs of a target market, job security, self-esteem and more teamwork. The study provides a set of recommendations that help educators, business executives and policy makers to maximize the individual and synergetic effect of training, decision making process on sustainability and growth of the tourism sector in Ethiopia.

Keywords: engagement score, driver of success, capacity building, tourism

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Authors: Prozorkevitch D., Mishurov A., Sokolov K., Karsakov L., Pestrikova L.

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The current body of knowledge about the marine environment and the dynamics of marine ecosystems includes a huge amount of heterogeneous data collected over decades. It generally includes a wide range of hydrological, biological and fishery data. Marine researchers collect these data and analyze how and why the ecosystem changes from past to present. Based on these historical records and linkages between the processes it is possible to predict future changes. Multivariate analysis of trends and their interconnection in the marine ecosystem may be used as an instrument for predicting further ecosystem evolution. A wide range of information about the components of the marine ecosystem for more than 50 years needs to be used to investigate how these arrays can help to predict the future.

Keywords: barents sea ecosystem, abiotic, biotic, data sets, trends, prediction

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Authors: R. Hassan, J. Duncombe, E. Darke, A. Dias, K. Anderson, R. G. Middleton

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Background: NHS England has set itself the task of delivering a “Net Zero” National Health service by 2040. It is incumbent upon all health care practioners to work towards this goal. Orthopaedic surgeons are no exception. Distal radial fractures are the most common fractures sustained by the adult population. However, studiesare shortcoming on individual patient experience. The aim of this study was to assess the patient’ssatisfaction and outcomes with woodcast used in the conservative management of distal radius fractures. Methods: For all patients managed with woodcast in our unit, we undertook a structured questionnairethat included the Patient Rated Wrist Evaluation (PRWE) score, The EQ-5D-5L score, and the pain numerical score at the time of injury and six weeks after. Results: 30 patients were initially managed with woodcast.80% of patients tolerated woodcast for the full duration of their treatment. Of these, 20% didn’t tolerate woodcast and had their casts removed within 48 hours. Of the remaining, 79.1% were satisfied about woodcast comfort, 66% were very satisfied about woodcast weight, 70% were satisfied with temperature and sweatiness, 62.5% were very satisfied about the smell/odour, and 75% were satisfied about the level of support woodcast provided. During their treatment, 83.3% of patients rated their pain as five or less. Conclusion: For those who completed their treatment in woodcast, none required any further intervention or utilised the open appointment because of ongoing wrist problems. In conclusion, when woodcast is tolerated, patients’ satisfaction and outcome levels were good. However, we acknowledged 20% of patients in our series were not able to tolerate woodacst, Therefore, we suggest a comparison between the widely used synthetic plaster of Pariscasting and woodcast to come in order.

Keywords: distal radius fractures, ecological cast, sustainability, woodcast

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Vladyslav Povoroznyuk, Anna Musiienko, Nataliia Dzerovych, Roksolana Povoroznyuk

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Osteoporosis and obesity are widespread diseases in people over 50 years associated with changes in structure and body composition. Нigher body mass index (BMI) values are associated with greater bone mineral density (BMD). However, trabecular bone score (TBS) indirectly explores bone quality, independently of BMD. The aim of our study was to evaluate the relationship between the BMD and TBS parameters in Ukrainian men suffering from obesity. We examined 396 men aged 40-89 years. Depending on their BMI all the subjects were divided into two groups: Group I – patients with obesity whose BMI was ≥ 30 kg/m² (n=129) and Group II – patients without obesity and BMI of < 30 kg/m² (n=267). The BMD of total body, lumbar spine L₁-L₄, femoral neck 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 DXA machine (product of Med-Imaps, Pessac, France). In general, obese men had a significantly higher BMD of lumbar spine L₁-L₄, femoral neck, total body and ultradistal forearm (p < 0.001) in comparison with men without obesity. The TBS of L₁-L₄ was significantly lower in obese men compared to non-obese ones (p < 0.001). BMD of lumbar spine L₁-L₄, femoral neck and total body significantly differ in men aged 40-49, 50-59, 60-69, and 80-89 years (p < 0.05). At the same time, in men aged 70-79 years, BMD of lumbar spine L₁-L₄ (p=0.46), femoral neck (p=0.18), total body (p=0.21), ultra-distal forearm (p=0.13), and TBS (p=0.07) did not significantly differ. A significant positive correlation between the fat mass and the BMD at different sites was observed. However, the correlation between the fat mass and TBS of L₁-L₄ was also significant, though negative.

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

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Authors: Gali Moritz, Jacqueline H. Becker, Jyoti V. Ankam, Kimberly Arcoleo, Matthew Wysocki, Roee Holtzer, Juan Wisnivesky, Paula J. Busse, Alex D. Federman, Sunit P. Jariwala, Jonathan M. Feldman

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Objective: Cognitive impairment (CI), whose incidence is greater among ethnic/racial minorities, is a significant barrier to asthma self-management (SM) behaviors and outcomes in older adults. The aim of this study was to examine the relationships between CI, assessed using the Montreal Cognitive Assessment (MoCA), and asthma SM behaviors and outcomes in a sample of predominantly Black and Hispanic participants. Additionally, we evaluated whether using two different MoCA cutoff scores influenced the association between CI and study outcomes. Methods: Baseline cross-sectional data were extracted from a longitudinal study of older adults with asthma (N=165) age≥ 60 years and used for analysis. Cognition was assessed using the MoCA. Asthma control, asthma-related quality of life (QOL), inhaled corticosteroid (ICS) dosing, and ICS adherence were assessed using self-report. The inhaler technique was observed and rated. Results: Using established MoCA cutoff scores of 23 and 26 yielded 45% and 74% CI rates, respectively. CI, defined using the 23 cutoff score, was significantly associated with worse asthma control (p=.04) and worse ICS adherence (p=.01). With a cutoff score of 26, only asthma-related QOL was significantly associated with CI (p=.03). Race/ethnicity and education did not moderate the relationships between CI and asthma SM behaviors and outcomes. Conclusions: CI in older adults with asthma is associated with important clinical outcomes, but this relationship is influenced by the cutoff score used to define CI.

Keywords: cognition, respiratory, elderly, testing, adherence, validity

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Adriano Pinargote, Josue Mosquera, Eduardo Montero, Dalton Noboa, Jenny Venegas, Genesis Vasquez Escuela

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In the framework of Pre University learning of the Polytechnic School of the Litoral, Guayaquil, Ecuador, the methodology of Active Learning (Flipped Classroom) has been implemented for applicants who wish to obtain a quota within the university. To complement the Active Learning cycle, it has been proposed that the respective students influence the qualification of their work groups, for which a web platform has been created that allows them to evaluate the performance of their peers through a digital coevaluation that measures through statistical methods, the group and individual performance score that can reflect in numbers a weighting score corresponding to the grade of each student. Their feedback provided by the group help to improve the performance of the activities carried out in classes because the note reflects the commitment with their classmates shown in the class, within this analysis we will determine if this implementation directly influences the performance of the grades obtained by the student.

Keywords: active learning, coevaluation, flipped classroom, pre university

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Authors: Santi Gopal Maji, R. A. J. Syngkon

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The changing attitude of the corporate enterprises from maximizing economic benefit to corporate sustainability after the publication of Brundtland Report has attracted the interest of researchers to investigate the sustainability practices of firms and its impact on financial performance. To enrich the empirical literature in Asian context, this study examines the disclosure pattern of corporate sustainability and the influence of sustainability reporting on financial performance of firms from four Asian countries (Japan, South Korea, India and Indonesia) that are publishing sustainability report continuously from 2009 to 2016. The study has used content analysis technique based on Global Reporting Framework (3 and 3.1) reporting framework to compute the disclosure score of corporate sustainability and its components. While dichotomous coding system has been employed to compute overall quantitative disclosure score, a four-point scale has been used to access the quality of the disclosure. For analysing the disclosure pattern of corporate sustainability, box plot has been used. Further, Pearson chi-square test has been used to examine whether there is any difference in the proportion of disclosure between the countries. Finally, quantile regression model has been employed to examine the influence of corporate sustainability reporting on the difference locations of the conditional distribution of firm performance. The findings of the study indicate that Japan has occupied first position in terms of disclosure of sustainability information followed by South Korea and India. In case of Indonesia, the quality of disclosure score is considerably less as compared to other three countries. Further, the gap between the quality and quantity of disclosure score is comparatively less in Japan and South Korea as compared to India and Indonesia. The same is evident in respect of the components of sustainability. The results of quantile regression indicate that a positive impact of corporate sustainability becomes stronger at upper quantiles in case of Japan and South Korea. But the study fails to extricate any definite pattern on the impact of corporate sustainability disclosure on the financial performance of firms from Indonesia and India.

Keywords: corporate sustainability, quality and quantity of disclosure, content analysis, quantile regression, Asian countries

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Authors: Soroor K. H. Al-Khafaji, Manal Mohammad Abed

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The research aim of this paper is to evaluate the reliability of a complex engineering system and to design a fuzzy model for the reliability estimation. The designed model has been applied on Vegetable Oil Purification System (neutralization system) to help the specialist user based on the concept of FMEA (Failure Mode and Effect Analysis) to estimate the reliability of the repairable system at the vegetable oil industry. The fuzzy model has been used to predict the system reliability for a future time period, depending on a historical database for the two past years. The model can help to specify the system malfunctions and to predict its reliability during a future period in more accurate and reasonable results compared with the results obtained by the traditional method of reliability estimation.

Keywords: fuzzy logic, reliability, repairable systems, FMEA

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Authors: Issam Aouari, Abdelmalek Abdelhamid

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For seismologists, the characterization of seismic demand should include the amplitude and duration of strong shaking in the system. The duration of ground shaking is one of the key parameters in earthquake resistant design of structures. This paper proposes a nonlinear statistical model to estimate earthquake ground motion duration in soft soils using multiple seismicity indicators. Three definitions of ground motion duration proposed by literature have been applied. With a comparative study, we select the most significant definition to use for predict the duration. A stochastic model is presented for the McCann and Shah Method using nonlinear regression analysis based on a data set for moment magnitude, source to site distance and site conditions. The data set applied is taken from PEER strong motion databank and contains shallow earthquakes from different regions in the world; America, Turkey, London, China, Italy, Chili, Mexico...etc. Main emphasis is placed on soft site condition. The predictive relationship has been developed based on 600 records and three input indicators. Results have been compared with others published models. It has been found that the proposed model can predict earthquake ground motion duration in soft soils for different regions and sites conditions.

Keywords: duration, earthquake, prediction, regression, soft soil

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Authors: Sohaib Raza, Parvez Mantry

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Frailty is a multi-system impairment associated with stressors such as age, disease, and invasive surgical procedures. This multi-system impairment can lead to increased post-transplant mortality and functional decline. Additionally, the prevalence and/or severity of frailty increases when patient pre-habilitation is unsatisfactory or lacking. We conducted a retrospective study to examine whether the COVID-19 Pandemic, and subsequent lack of patient access to pre-habilitation and physical therapy resources, led to an increase in the prevalence and severity of frailty, sarcopenia, and other comorbidities including diabetes, hypertension, and COPD. Secondarily, we examined the correlation between patient survival rate and liver frailty index as well as muscle wasting/sarcopenia. Data were analyzed in order to correlate variables associated with these parameters. Three hundred sixty-nine liver transplant candidates at Methodist Dallas Medical Center were administered pre-transplant frailty assessments, which consisted of chair stands, grip strength, and position balance time. A frailty score less than 3.2 indicated a robust condition, a score from 3.3 to 4.4 indicated a pre-frail condition, and a score greater than 4.5 indicated a frail condition. Greater than 50 percent of patients were found to have muscle wasting in the COVID-19 period (March 13, 2020 to February 28, 2022), an increase of 16.5 percent from the pre-COVID period (April 1st, 2018 to March 12, 2020). Additionally, sarcopenia was associated with a two-fold increase in patient mortality rate. Furthermore, high liver frailty index scores were associated with increased patient mortality. However, there was no significant difference in liver frailty index or number of comorbidities between patients in the two cohorts. Conclusion: The COVID-19 Pandemic exacerbated sarcopenia-related muscle wasting in liver transplant candidates, and patient survival rate was directly correlated with liver frailty index score and the presence of sarcopenia.

Keywords: frailty, sarcopenia, covid-19, patient mortality, pre-habilitation, liver transplant candidates

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Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

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The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

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Authors: Andre Kadandji, Mouhamadou Fall, Francois Koum Ekalle

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This paper aims to analyze the sound banking through the effects of the damage of the loan portfolio in the Cameroonian banking sector through the Z-score. The approach is to test the effect of other CAMEL indicators and macroeconomics indicators on the relationship between the non-performing loan and the soundness of Cameroonian banks. We use a dynamic panel data, made by 13 banks for the period 2010-2013. The analysis provides a model equations embedded in panel data. For the estimation, we use the generalized method of moments to understand the effects of macroeconomic and CAMEL type variables on the ability of Cameroonian banks to face a shock. We find that the management quality and macroeconomic variables neutralize the effects of the non-performing loan on the banks soundness.

Keywords: loan portfolio, sound banking, Z-score, dynamic panel

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Authors: Adian Fatchur Rochim, Abdul Muis, Riri Fitri Sari

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H-index has been widely used as a performance indicator of researchers around the world especially in Indonesia. The Government uses Scopus and Google scholar as indexing references in providing recognition and appreciation. However, those two indexing services yield to different H-index values. For that purpose, this paper evaluates the difference of the H-index from those services. Researchers indexed by Webometrics, are used as reference’s data in this paper. Currently, Webometrics only uses H-index from Google Scholar. This paper observed and compared corresponding researchers’ data from Scopus to get their H-index score. Subsequently, some researchers with huge differences in score are observed in more detail on their paper’s publisher. This paper shows that the H-index of researchers in Google Scholar is approximately 2.45 times of their Scopus H-Index. Most difference exists due to the existence of uncertified publishers, which is considered in Google Scholar but not in Scopus.

Keywords: Google Scholar, H-index, Scopus, performance indicator

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Authors: Pedro M. Abreu, Bruno R. Mendes

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The management of pharmacotherapy and the process of dispensing medicines is becoming critical in clinical pharmacy due to the increase of incidence and prevalence of chronic diseases, the complexity and customization of therapeutic regimens, the introduction of innovative and more expensive medicines, the unbalanced relation between expenditure and revenue as well as due to the lack of rationalization associated with medication use. For these reasons, co-payments emerged in Europe in the 70s and have been applied over the past few years in healthcare. Co-payments lead to a rationing and rationalization of user’s access under healthcare services and products, and simultaneously, to a qualification and improvement of the services and products for the end-user. This analysis, under hospital practices particularly and co-payment strategies in general, was carried out on all the European regions and identified four reference countries, that apply repeatedly this tool and with different approaches. The structure, content and adaptation of European co-payments were analyzed through 7 qualitative attributes and 19 performance indicators, and the results expressed in a scorecard, allowing to conclude that the German models (total score of 68,2% and 63,6% in both elected co-payments) can collect more compliance and effectiveness, the English models (total score of 50%) can be more accessible, and the French models (total score of 50%) can be more adequate to the socio-economic and legal framework. Other European models did not show the same quality and/or performance, so were not taken as a standard in the future design of co-payments strategies. In this sense, we can see in the co-payments a strategy not only to moderate the consumption of healthcare products and services, but especially to improve them, as well as a strategy to increment the value that the end-user assigns to these services and products, such as medicines.

Keywords: clinical pharmacy, co-payments, healthcare, medicines

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Authors: Adeep Hande, Shubham Agarwal

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This paper presents a study on identifying sexism in online texts using various state-of-the-art deep learning models based on BERT. We experimented with different feature sets and model architectures and evaluated their performance using precision, recall, F1 score, and accuracy metrics. We also explored the use of pseudolabeling technique to improve model performance. Our experiments show that the best-performing models were based on BERT, and their multilingual model achieved an F1 score of 0.83. Furthermore, the use of pseudolabeling significantly improved the performance of the BERT-based models, with the best results achieved using the pseudolabeling technique. Our findings suggest that BERT-based models with pseudolabeling hold great promise for identifying sexism in online texts with high accuracy.

Keywords: large language models, semi-supervised learning, sexism detection, data sparsity

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

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Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

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Authors: Nuralam Sam, Irawan Yusuf, Irfan Idris, Endi Adnan

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The most case in the shoulder in the the adult is the frozen shoulder. It make an uncomfortable sensation which disturbance daily activity. The studies of frozen shoulder are still limited. This study used a true experimental pre and post test design with a group design. The participant underwent dextrose prolotherapy injection in the rotator cuff, intraarticular glenohumeral joint, long head tendon biceps, and acromioclavicular joint injections with 15% dextrose, respectively, at week 2, week 4, and week 6. Participants were followed for 12 weeks. The specific biomarker MMP and TIMP, ROM, DASH score were measured at baseline, at week 6, and week 12. The data were analyzed by multivariate analysis (repeated measurement ANOVA, Paired T-Test, and Wilcoxon) to determine the effect of the intervention. The result showed a significant decrease in The Disability of the Arm, Shoulder, and Hand (DASH) score in prolo injection patients in each measurement week (p < 0.05). While the measurement of Range of Motion (ROM), each direction of shoulder motion showed a significant difference in average each week, from week 0 to week 6 (p <0.05).Dextrose prolotherapy injection results give a significant improvement in functional outcome of the shoulder joint, and ROMand did not show significant results in assessing the specific biomarker, MMP-1, and TIMP-1 in tissue repair. This study suggestion an alternative to the use of injection prolotherapy in Frozen shoulder patients, which has fewer side effects and better effectiveness than the use of corticosteroid injections.

Keywords: frozen shoulder, ROM, DASH score, prolotherapy, MMP-1, TIMP-1

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Authors: Mohammad Shokoohi-Yekta, Hamid Mirebrahim

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A body of research has been devoted to investigating the preferences of computer programmers. These researches used various questionnaires to find out what programming language is most popular among programmers. The problem with such research is that the programmers are usually familiar with only a few languages; therefore, disregarding a number of other languages which might have characteristics that match their preferences more closely. To overcome such a problem, we decided to investigate the preferences of programmers in regards to the characteristics of languages, which help us to discover the languages that include the most characteristics preferred by the users. We conducted a user study to measure the preferences of programmers on different characteristics of programming languages and then tried to compare existing languages in the areas of application, Web and system programming. Overall, the results of our study indicated that the Ruby programming language has the highest preference score in the two areas of application and Web, and C++ has the highest score in the system area. The results of our study can also help programming language designers know the characteristics they should consider when developing new programming languages in order to attract more programmers.

Keywords: object orientation, programming language design, programmers' preferences, characteristic

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Authors: Krishna Dahiya, Esha Charaya

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Induction of labour is a common obstetrical intervention. Around 1 in every 4 patient undergo induction of labour for different indications Purpose: To study the efficacy of the combination of Foley bulb and vaginal misoprostol in comparison to vaginal misoprostol alone for cervical ripening and induction of labour. Methods: A prospective randomised study was conducted on 150 patients with term singleton pregnancy admitted for induction of labour. Seventy-five patients were induced with both Foley bulb, and vaginal misoprostol and another 75 were given vaginal misoprostol alone for induction of labour. Both groups were then compared with respect to change in Bishop score, induction to the active phase of labour interval, induction delivery interval, duration of labour, maternal complications and neonatal outcomes. Data was analysed using statistical software SPSS version 11.5. Tests with P,.05 were considered significant. Results: The two groups were comparable with respect to maternal age, parity, gestational age, indication for induction, and initial Bishop scores. Both groups had a significant change in Bishop score (2.99 ± 1.72 and 2.17 ± 1.48 respectively with statistically significant difference (p=0.001 S, 95% C.I. -0.1978 to 0.8378). Mean induction to delivery interval was significantly lower in the combination group (11.76 ± 5.89 hours) than misoprostol group (14.54 ± 7.32 hours). Difference was of 2.78 hours (p=0.018,S, 95% CI -5.1042 to -0.4558). Induction to delivery interval was significantly lower in nulliparous women of combination group (13.64 ± 5.75 hours) than misoprostol group (18.4±7.09 hours), and the difference was of 4.76 hours (p=0.002, S, 95% CI 1.0465 to 14.7335). There was no difference between the groups in the mode of delivery, infant weight, Apgar score and intrapartum complications. Conclusion: From the present study it was concluded that addition of Foley catheter to vaginal misoprostol have the synergistic effect and results in early cervical ripening and delivery. These results suggest that the combination may be used to achieve timely and safe delivery in the presence of an unfavorable cervix. A combination of the Foley bulb and vaginal misoprostol resulted in a shorter induction-to-delivery time when compared with vaginal misoprostol alone without increasing labor complications.

Keywords: Bishop score, Foley catheter, induction of labor, misoprostol

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Authors: Gaye Demi̇rtaş Adli

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Research: It was conducted to determine the factors affecting the fear of self-injection and self-pricking of fingers of diabetic individuals.The study was conducted as a cross-sectional, relation-seeking, and descriptive study. The study was conducted on 122 patients who had just started insulin therapy. Data were obtained through The Descriptive Patient Form, The Diabetic Self-Injection, and the Fear of Testing Questionnaire Form (D-FISQ). Descriptive statistical methods used in the evaluation of data are the Mann-Whitney U test, Kruskal-Wallis H test, and the Spearman correlation. The factors affecting the scale scores were evaluated with multiple linear regression analysis. The value of P<0.05 was considered statistically significant. Study group: 56.6% of the patients are male patients. Fear of self-injection (injection), fear of self-testing (test), and total fear (total) scores of women were found to be statistically higher than men (p<0.001). Age, gender, and pain experience were important variables that affected patients' fear of injections. With a one-unit increase in age, the injection fear score decreased by 0.13 points, and the mean injection fear score of women was 2.11 points higher than that of men. It was determined that the patient's age, gender, living with whom, and blood donation history were important variables affecting the fear of self-testing. It is seen that the fear test score decreases by 0.18 points with an increase in age by one unit, and the fear test scores of women compared to men are on average 3,358 points, the fear test scores of those living alone are 4,711 points compared to those living with family members, and the fear test scores of those who do not donate blood are 2,572 compared to those who donate blood score, it was determined that those with more pain experience were 3,156 points higher on average than those with less fear of injection. As a result, it was seen that the most important factors affecting the fear of insulin injection and finger punching in individuals with diabetes were age, gender, pain experience, living with whom, and blood donation history.

Keywords: diabetes, needle phobia, fear of injection, insulin injection

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Authors: Mohammad Tahir Yousafzai, Amna Rehana Siddiqui, Naveed Zafar Janjua

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We assessed the frequency and predictors of sharp injuries (SIs) among health care workers (HCWs) at first level care facilities (FLCF) in rural Pakistan. HCWs working at public clinic (PC), privately owned licensed practitioners’ clinic (LPC) and non-licensed practitioners’ clinic (NLC) were interviewed on universal precautions (UPs) and constructs of health belief model (HBM) to assess their association with SIs through negative-binomial regression. From 365 clinics, 485 HCWs were interviewed. Overall annual rate of Sis was 192/100 HCWs/year; 78/100 HCWs among licensed prescribers, 191/100 HCWs among non-licensed prescribers, 248/100 HCWs among qualified assistants, and 321/100 HCWs among non-qualified assistants. Increasing knowledge score about bloodborne pathogens (BBPs) transmission (rate-ratio (RR): 0.93; 95%CI: 0.89–0.96), fewer years of work experience, being a non-licensed prescriber (RR: 2.02; 95%CI: 1.36–2.98) licensed (RR: 2.86; 9%CI: 1.81–4.51) or non-licensed assistant (RR: 2.78; 95%CI: 1.72–4.47) compared to a licensed prescriber, perceived barriers (RR: 1.06;95%CI: 1.03–1.08), and compliance with UPs scores (RR: 0.93; 95%CI: 0.87–0.97) were significant predictors of SIs. Improved knowledge about BBPs, compliance with UPs and reduced barriers to follow UPs could reduce SIs to HCWs.

Keywords: health belief model, sharp injuries, needle stick injuries, healthcare workers

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Authors: Joe Collins, Matthias Rother

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Background: Oral non-steroidal anti-inflammatory drugs (NSAIDs) are widely used for the relief of joint pain during and post-exercise. However, oral NSAIDs increase the risk of systemic side effects, even in healthy individuals, and retard recovery from muscle soreness. TDT 064 (Flexiseq®), a topical formulation containing ultra-deformable drug-free Sequessome™ vesicles, has demonstrated equivalent efficacy to oral celecoxib in reducing osteoarthritis-associated joint pain and stiffness. TDT 064 does not cause NSAID-related adverse effects. We describe clinical study data and a case report on the effectiveness of TDT 064 in reducing joint pain after exercise. Methods: Participants with a pain score ≥3 (10-point scale) 12–16 hours post-exercise were randomized to receive TDT 064 plus oral placebo, TDT 064 plus oral ketoprofen, or ketoprofen in ultra-deformable phospholipid vesicles plus oral placebo. Results: In the 168 study participants, pain scores were significantly higher with oral ketoprofen plus TDT 064 than with TDT 064 plus placebo in the 7 days post-exercise (P = 0.0240) and recovery from muscle soreness was significantly longer (P = 0.0262). There was a low incidence of adverse events. These data are supported by clinical experience. A 24-year-old male professional rugby player suffered a traumatic lisfranc fracture in March 2014 and underwent operative reconstruction. He had no relevant medical history and was not receiving concomitant medications. He had undergone anterior cruciate ligament reconstruction in 2008. The patient reported restricted training due to pain (score 7/10), stiffness (score 9/10) and poor function, as well as pain when changing direction and running on consecutive days. In July 2014 he started using TDT 064 twice daily at the recommended dose. In November 2014 he noted reduced pain on running (score 2-3/10), decreased morning stiffness (score 4/10) and improved joint mobility and was able to return to competitive rugby without restrictions. No side effects of TDT 064 were reported. Conclusions: TDT 064 shows efficacy against exercise- and injury-induced joint pain, as well as that associated with osteoarthritis. It does not retard muscle soreness recovery after exercise compared with an oral NSAID, making it an alternative approach for the treatment of joint pain during and post-exercise.

Keywords: exercise, joint pain, TDT 064, phospholipid vesicles

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Authors: Spada Alessia, Fiore Mariantonietta, Lamonaca Emilia, Contò Francesco

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Bioeconomy can give the chance to face new global challenges and can move ahead the transition from a waste economy to an economy based on renewable resources and sustainable consumption. Air pollution is a grave issue in green challenges, mainly caused by anthropogenic factors. The agriculture sector is a great contributor to global greenhouse gases (GHGs) emissions due to lacking efficient management of the resources involved and research policies. In particular, livestock sector contributes to emissions of GHGs, deforestation, and nutrient imbalances. More effective agricultural research systems and technologies are crucial in order to improve farm productivity but also to reduce the GHGs emissions. Using data from FAOSTAT statistics and concern the EU countries; the aim of this research is to evaluate the impact of ASTI R&D (Agricultural Science and Technology Indicators) on GHGs emissions for countries EU in 2015 by generalized propensity score procedures, estimating a dose-response function, also considering a set of covariates. Expected results show the existence of the influence of ASTI R&D on GHGs across EU countries. Implications are crucial: reducing GHGs emissions by means of R&D based policies and correlatively reaching eco-friendly management of required resources by means of green available practices could have a crucial role for fair intra-generational implications.

Keywords: agricultural research systems, dose-response function, generalized propensity score, GHG emissions

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Authors: Patrick Mkhanyiseli Zimu

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A sedentary lifestyle and insufficient physical activity (PA) increases the risk of developing chronic non-communicable diseases (NCDs). Knowing the PA levels and patterns of adolescents from different socio-economic backgrounds is important to direct programs at schools and in communities to prevent NCDs risk factors, which can have long-term effects on the health of the adolescents. The study aimed to investigate adolescent PA levels, patterns, and influencing factors (age, gender, socio-economic status). The 353 participants (203 females and 150 males) from eight low socio-economic (LSES) and middle socio-economic (MSES) public secondary schools completed a Physical Activity Questionnaire for Adolescents (PAQ-A). The PAQ-A is a seven day recall instrument that assesses general estimates of PA levels and patterns for high school learners in Grades 9-12 and provides a summary of physical activity scores derived from seven items, each scored on a 5-point Likert scale. The seven items were PA during spare time and five domains (during physical education, lunch break, after school, in the evenings, on the weekend) and selecting one statement that described participant’s physical activity behaviour. The PA Levels (x̄=2.61, SD=.74) were below the international PA cut-off points of x̄=2.75. Physical education (PE) showed the highest PA score (x̄=3.05, SD=1.21) and lunch break showed the lowest PA score (x̄=2.09, SD=1.14). Positive correlations occurred between PA levels and SES (r=.122, p=0.022), and PA and gender (r=.223, p= 0.0001). LSES participant’s PA score was significantly lower (x̄=2.52; SD=.73) than those from MSES (x̄=2.70; SD=.74, p=0.022). Adolescents from low and middle socio-economic status communities are not sufficiently active. Their average PA score of 2.61 is below the PAQ-A global criterion referenced cut-off points of 2.75, which is considered sufficiently physically active for adolescents to ensure both short- and long-term health benefits. As adolescents are not sufficiently active, collaborative school and community PA programs need to be implemented to supplement physical education in order to prevent short- and long-term health problems.

Keywords: adolescents, health promotion, physical activity, physical education

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Authors: Yuan-lin Liu, Ye Li, Tian Xia

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There are some shortcomings in Chinese existing taxi management modes. This paper suggests to establish the third-party comprehensive information management platform and put forward an evaluation model based on credit. Four indicators are used to evaluate the drivers’ credit, they are passengers’ evaluation score, driving behavior evaluation, drivers’ average bad record number, and personal credit score. A weighted clustering method is used to achieve credit level evaluation for taxi drivers. The management of taxi industry is based on the credit level, while the grade of the drivers is accorded to their credit rating. Credit rating determines the cost, income levels, the market access, useful period of license and the level of wage and bonus, as well as violation fine. These methods can make the credit evaluation effective. In conclusion, more credit data will help to set up a more accurate and detailed classification standard library.

Keywords: credit, mobile internet, e-hailing taxi, management mode, weighted cluster

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Authors: Akram Sadat Jafari Roodbandi, Parisa Kahani, Fatollah Rahimi Bafrani, Ali Dehghan, Nava Seyedi, Vafa Feyzi, Zohreh Forozanfar

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Introduction: Movement function is considered as the ability to produce and maintain balance, stability, and movement throughout the movement chain. Having a score of 14 and above on 7 sub-tests in the functional movement screening (FMS) test shows agility and optimal movement performance. On the other hand, the person's body is an important factor in physical fitness and optimal movement performance. The aim of this study was to identify effective anthropometric dimensions in increasing motor function. Methods: This study was a descriptive-analytical and cross-sectional study using simple random sampling. FMS test and 25 anthropometric dimensions and subcutaneous in five body regions measured in 139 healthy students of Bam University of Medical Sciences. Data analysis was performed using SPSS software and univariate tests and linear regressions at a significance level of 0.05. Results: 139 students were enrolled in the study, 51.1% (71 subjects) and the rest were female. The mean and standard deviation of age, weight, height, and arm subcutaneous fat were 21.5 ± 1.45, 12.6 ± 64.3, 168.7 ± 9.8, 15.3 ± 7, respectively. 17 subjects (12.2%) of the participants in the study have a score of less than 14, and the rest were above 14. Using regression analysis, it was found that exercise and arm subcutaneous fat are predictive variables associated with obtaining a high score in the FMS test. Conclusion: Exercise and weight loss are effective factors for increasing the movement performance of individuals, and this factor is independent of the size of other physical dimensions.

Keywords: functional movement, screening test, anthropometry, ergonomics

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