Search results for: performance predicting formula

Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

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Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

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Authors: Yu-Chuan Huang

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Introduction: There is a higher mortality rate for unplanned transfer to intensive care units. It also needs a longer length of stay and makes the intensive care unit beds cannot be effectively used. It affects the immediate medical treatment of critically ill patients, resulting in a drop in the quality of medical care. Purpose: The purpose of this study was using SOFA and APACHEII score to analyze the mortality rate of the cases transferred from ED to ICU. According to the score that should be provide an appropriate care as early as possible. Methods: This study was a descriptive experimental design. The sample size was estimated at 220 to reach a power of 0.8 for detecting a medium effect size of 0.30, with a 0.05 significance level, using G-power. Considering an estimated follow-up loss, the required sample size was estimated as 242 participants. Data were calculated by medical system of SOFA and APACHEII score that cases transferred from ED to ICU in 2016. Results: There were 233 participants meet the study. The medical records showed 33 participants’ mortality. Age and sex with QSOFA , SOFA and sex with APACHEII showed p>0.05. Age with APCHHII in ED and ICU showed r=0.150, 0,268 (p < 0.001**). The score with mortality risk showed: ED QSOFA is r=0.235 (p < 0.001**), exp(B)=1.685(p = 0.007); ICU SOFA 0.78 (p < 0.001**), exp(B)=1.205(p < 0.001). APACHII in ED and ICU showed r= 0.253, 0.286 (p < 0.001**), exp(B) = 1.041,1.073(p = 0.017,0.001). For SOFA, a cutoff score of above 15 points was identified as a predictor of the 95% mortality risk. Conclusions: The SOFA and APACHE II were calculated based on initial laboratory data in the Emergency Department, and during the first 24 hours of ICU admission. In conclusion, the SOFA and APACHII score is significantly associated with mortality and strongly predicting mortality. Early predictors of morbidity and mortality, which we can according the predicting score, and provide patients with a detail assessment and proper care, thereby reducing mortality and length of stay.

Keywords: SOFA, APACHEII, mortality, ICU

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Authors: Mohammadreza Nezamirad, Azadeh Yazdi, Sepideh Amirahmadian, Nasim Sabetpour, Amirmasoud Hamedi

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In this study, the Reynolds-Stress-Navier-Stokes framework is utilized to investigate the flow inside the diesel injector nozzle. The flow is assumed to be multiphase as the formation of vapor by pressure drop is visualized. For pressure and velocity linkage, the coupled algorithm is used. Since the cavitation phenomenon inherently is unsteady, the quasi-steady approach is utilized for saving time and resources in the current study. Schnerr-Sauer cavitation model is used, which was capable of predicting flow behavior both at the initial and final steps of the cavitation process. Two different turbulent models were used in this study to clarify which one is more capable in predicting cavitation inception and super-cavitation. It was found that K-ε was more compatible with the Shnerr-Sauer cavitation model; therefore, the mentioned model is used for the rest of this study.

Keywords: CFD, RANS, cavitation, fuel, injector

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Authors: Sairi Satari

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Introduction: Six-sigma is a metric that quantifies the performance of processes as a rate of Defects-Per-Million Opportunities. Sigma methodology can be applied in chemical pathology laboratory for evaluating process performance with evidence for process improvement in quality assurance program. In the laboratory, these methods have been used to improve the timeliness of troubleshooting, reduce the cost and frequency of quality control and minimize pre and post-analytical errors. Aim: The aim of this study is to evaluate the sigma values of the Cobas 8000 analyzer based on the minimum requirement of the specification. Methodology: Twenty-one analytes were chosen in this study. The analytes were alanine aminotransferase (ALT), albumin, alkaline phosphatase (ALP), Amylase, aspartate transaminase (AST), total bilirubin, calcium, chloride, cholesterol, HDL-cholesterol, creatinine, creatinine kinase, glucose, lactate dehydrogenase (LDH), magnesium, potassium, protein, sodium, triglyceride, uric acid and urea. Total error was obtained from Clinical Laboratory Improvement Amendments (CLIA). The Bias was calculated from end cycle report of Royal College of Pathologists of Australasia (RCPA) cycle from July to December 2016 and coefficient variation (CV) from six-month internal quality control (IQC). The sigma was calculated based on the formula :Sigma = (Total Error - Bias) / CV. The analytical performance was evaluated based on the sigma, sigma > 6 is world class, sigma > 5 is excellent, sigma > 4 is good and sigma < 4 is satisfactory and sigma < 3 is poor performance. Results: Based on the calculation, we found that, 96% are world class (ALT, albumin, ALP, amylase, AST, total bilirubin, cholesterol, HDL-cholesterol, creatinine, creatinine kinase, glucose, LDH, magnesium, potassium, triglyceride and uric acid. 14% are excellent (calcium, protein and urea), and 10% ( chloride and sodium) require more frequent IQC performed per day. Conclusion: Based on this study, we found that IQC should be performed frequently for only Chloride and Sodium to ensure accurate and reliable analysis for patient management.

Keywords: sigma matrics, analytical performance, total error, bias

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

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Authors: Ritu Vashistha, Jitender Kumar

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Clustering using the K-means algorithm is a very common way to understand and analyze the obtained output data. When a similar object is grouped, this is called the basis of Clustering. There is K number of objects and C number of cluster in to single cluster in which k is always supposed to be less than C having each cluster to be its own centroid but the major problem is how is identify the cluster is correct based on the data. Formulation of the cluster is not a regular task for every tuple of row record or entity but it is done by an iterative process. Each and every record, tuple, entity is checked and examined and similarity dissimilarity is examined. So this iterative process seems to be very lengthy and unable to give optimal output for the cluster and time taken to find the cluster. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses the Euclidian distance formula as well melanosis to find the minimum distance between slots as technically we called clusters and the same approach we have also applied to Ant Colony Optimization(ACO) algorithm, which results in the production of two and multi-dimensional matrix.

Keywords: ant colony optimization, data clustering, centroids, data mining, k-means

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Authors: Angkrit Phitchayangkoon, Ar-Aishah Dadeh

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Background: A key aim of triage is to identify the patients with high risk of cardiac arrest because they require intensive monitoring, resuscitation facilities, and early intervention. We aimed to identify the predicting factors such as initial vital signs, serum pH, serum lactate level, initial capillary blood glucose, and Modified Early Warning Score (MEWS) which affect the occurrence of cardiac arrest in an emergency department (ED). Methods: We conducted a retrospective data review of ED patients in an emergency department (ED) from 1 August 2014 to 31 July 2016. Significant variables in univariate analysis were used to create a multivariate analysis. Differentiation of predicting factors between cardiac arrest patient and non-cardiac arrest patients for occurrence of cardiac arrest in an emergency department (ED) was the primary outcome. Results: The data of 527 non-trauma patients with Emergency Severity Index (ESI) 1-3 were collected. The factors found to have a significant association (P < 0.05) in the non-cardiac arrest group versus the cardiac arrest group at the ED were systolic BP (mean [IQR] 135 [114,158] vs 120 [90,140] mmHg), oxygen saturation (mean [IQR] 97 [89,98] vs 82.5 [78,95]%), GCS (mean [IQR] 15 [15,15] vs 11.5 [8.815]), normal sinus rhythm (mean 59.8 vs 30%), sinus tachycardia (mean 46.7 vs 21.7%), pH (mean [IQR] 7.4 [7.3,7.4] vs 7.2 [7,7.3]), serum lactate (mean [IQR] 2 [1.1,4.2] vs 7 [5,10.8]), and MEWS score (mean [IQR] 3 [2,5] vs 5 [3,6]). A multivariate analysis was then performed. After adjusting for multiple factors, ESI level 2 patients were more likely to have cardiac arrest in the ER compared with ESI 1 (odds ratio [OR], 1.66; P < 0.001). Furthermore, ESI 2 patients were more likely than ESI 1 patients to have cardiovascular disease (OR, 1.89; P = 0.01), heart rate < 55 (OR, 6.83; P = 0.18), SBP < 90 (OR, 3.41; P = 0.006), SpO2 < 94 (OR, 4.76; P = 0.012), sinus tachycardia (OR, 4.32; P = 0.002), lactate > 4 (OR, 10.66; P = < 0.001), and MEWS > 4 (OR, 4.86; P = 0.028). These factors remained predictive of cardiac arrest at the ED. Conclusion: The factors related to cardiac arrest in the ED are ESI 1 patients, ESI 2 patients, patients diagnosed with cardiovascular disease, SpO2 < 94, lactate > 4, and a MEWS > 4. These factors can be used as markers in the event of simultaneous arrival of many patients and can help as a pre-state for patients who have a tendency to develop cardiac arrest. The hemodynamic status and vital signs of these patients should be closely monitored. Early detection of potentially critical conditions to prevent critical medical intervention is mandatory.

Keywords: cardiac arrest, predicting factor, emergency department, emergency patient

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Naresh Reddy Kolanu

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The stability and collapse behavior of thin-walled composite structures, particularly carbon fiber-reinforced plastic (CFRP) panels, are paramount concerns for structural designers. Accurate prediction of collapse loads necessitates precise modeling of damage evolution in the post-buckling regime. This study conducts a comparative assessment of various finite element (FE) methodologies employed in predicting post-buckling collapse in stiffened CFRP panels. A systematic approach is adopted, wherein FE models with various damage capabilities are constructed and analyzed. The study investigates the influence of interacting intra- and interlaminar damage modes on the post-buckling response and failure behavior of the stiffened CFRP structure. Additionally, the capabilities of shell and brick FE-based models are evaluated and compared to determine their effectiveness in capturing the complex collapse behavior. Conclusions are drawn through quantitative comparison with experimental results, focusing on post-buckling response and collapse load. This comprehensive evaluation provides insights into the most effective FE methodologies for accurately predicting the collapse behavior of stiffened CFRP panels, thereby aiding structural designers in enhancing the stability and safety of composite structures.

Keywords: CFRP stiffened panels, delamination, Hashin’s failure, post-buckling, progressive damage model

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Authors: Bita Mashayekhi, Amin Jahangard, Milad Samavat, Saeid Homayoun

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In the modern competitive business environment, one cannot overstate the importance of corporate social responsibility. The controversial link between the social and financial performance of firms has become a topic of interest for scholars. Hence, this study examines the social and financial performance link by taking into account the mediating role of innovation performance. We conducted the Covariance-based Structural Equation Modeling (CB-SEM) method on an international sample of firms provided by the ASSET4 database. In this research, to explore the black box of the social and financial performance relationship, we first examined the effect of social performance separately on financial performance and innovation; then, we measured the mediation role of innovation in the social and financial performance link. While our results indicate the positive effect of social performance on financial performance and innovation, we cannot document the positive mediating role of innovation. This possibly relates to the long-term nature of benefits from investments in innovation.

Keywords: ESG, financial performance, innovation, social performance, structural equation modeling

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Authors: Michael Luc Andre, Célia Maintenant

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Theory of mind is a milestone in child development which allows children to understand that others could have different mental states than theirs. Understanding the developmental stages of theory of mind in children leaded researchers on two Connected research problems. In one hand, the link between executive function and theory of mind, and on the other hand, the relationship of theory of mind and syntax processing. These two lines of research involved a great literature, full of important results, despite certain level of disagreement between researchers. For a long time, these two research perspectives continue to grow up separately despite research conclusion suggesting that the three variables should implicate same developmental period. Indeed, our goal was to study the relation between theory of mind, executive function, and language via a unique research question. It supposed that between executive function and language, one of the two variables could play a critical role in the relationship between theory of mind and the other variable. Thus, 112 children aged between three and six years old were recruited for completing a receptive and an expressive vocabulary task, a syntax understanding task, a theory of mind task, and three executive function tasks (inhibition, cognitive flexibility and working memory). The results showed significant correlations between performance on theory of mind task and performance on executive function domain tasks, except for cognitive flexibility task. We also found significant correlations between success on theory of mind task and performance in all language tasks. Multiple regression analysis justified only syntax and general abilities of language as possible predictors of theory of mind performance in our preschool age children sample. The results were discussed in the perspective of a great role of language abilities in theory of mind development. We also discussed possible reasons that could explain the non-significance of executive domains in predicting theory of mind performance, and the meaning of our results for the literature.

Keywords: child development, executive function, general language, syntax, theory of mind

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Farhad Shafiepour Motlagh, Narges Salehi

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The aim of the present study was to investigate the relationship between inspirational leadership style and perceived social capital through the mediation of organizational knowledge resource development. The research method was descriptive-correlational. The statistical population consisted of all 3537 secondary school teachers in Isfahan. Sample selection was based on Cochran's formula volume formula for 338 people and multi-stage random sampling. The research instruments included a researcher-made inspirational leadership style questionnaire, a perceived social capital questionnaire (Putnam, 1999), and a researcher-made questionnaire of perceived organizational knowledge resources. Kolmogorov statistical tests, Pearson correlation, stepwise multiple regression, and structural equation modeling were used to analyze the data. In general, the results showed that there is a significant relationship between inspirational leadership style and the use of perceived social capital at the level of P <0.05. Also, the development of organizational knowledge resources mediates the relationship between inspirational leadership style and the use of perceived social capital at the level of P <0.05.

Keywords: inspirational leadership style, perceived social capital, perceived organizational knowledge

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Authors: Pranav Gunhal., Krish Jhurani

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This study tackles the significant computational challenge of predicting excited state dynamics in organic photovoltaic (OPV) materials—a pivotal factor in the performance of solar energy solutions. Time-dependent density functional theory (TDDFT), though effective, is computationally prohibitive for larger and more complex molecules. As a solution, the research explores the application of transformer neural networks, a type of artificial intelligence (AI) model known for its superior performance in natural language processing, to predict excited state dynamics in OPV materials. The methodology involves a two-fold process. First, the transformer model is trained on an extensive dataset comprising over 10,000 TDDFT calculations of excited state dynamics from a diverse set of OPV materials. Each training example includes a molecular structure and the corresponding TDDFT-calculated excited state lifetimes and key electronic transitions. Second, the trained model is tested on a separate set of molecules, and its predictions are rigorously compared to independent TDDFT calculations. The results indicate a remarkable degree of predictive accuracy. Specifically, for a test set of 1,000 OPV materials, the transformer model predicted excited state lifetimes with a mean absolute error of 0.15 picoseconds, a negligible deviation from TDDFT-calculated values. The model also correctly identified key electronic transitions contributing to the excited state dynamics in 92% of the test cases, signifying a substantial concordance with the results obtained via conventional quantum chemistry calculations. The practical integration of the transformer model with existing quantum chemistry software was also realized, demonstrating its potential as a powerful tool in the arsenal of materials scientists and chemists. The implementation of this AI model is estimated to reduce the computational cost of predicting excited state dynamics by two orders of magnitude compared to conventional TDDFT calculations. The successful utilization of transformer neural networks to accurately predict excited state dynamics provides an efficient computational pathway for the accelerated discovery and design of new OPV materials, potentially catalyzing advancements in the realm of sustainable energy solutions.

Keywords: transformer neural networks, organic photovoltaic materials, excited state dynamics, time-dependent density functional theory, predictive modeling

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Authors: Aida Nurul Safitri, Rosita Sari

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Physics is science, which in its learning there are some product such as theory, fact, concept, law and formula. So that to understand physics lesson students not only need a theory or concept but also mathematical calculation to solve physics problem through formula or equation. This is can be taken from mathematics lesson which obtained by students. This research is to know the influence of mathematics learning outcomes towards physics ability in Senior High School through authentic assessment system. Based on the researches have been discussed, is obtained that mathematic lesson have an important role in physics learning but it according to one aspect only, namely cognitive aspect. In Indonesia, curriculum of 2013 reinforces displacement in the assessment, from assessment through test (measuring the competence of knowledge based on the result) toward authentic assessment (measuring the competence of attitudes, skills, and knowledge based on the process and results). In other researches are mentioned that authentic assessment system give positive responses for students to improve their motivation and increase the physics learning in the school.

Keywords: authentic assessment, curriculum of 2013, mathematic, physics

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Authors: Thachamon Sinsoongsud, Noppawan Piaseu

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This predictive study was aimed to 1) describe self efficacy for risk reduction and preventive behavior for osteoporosis, and 2) examine factors predicting preventive behavior for osteoporosis in nursing students. Through purposive sampling, the sample included 746 nursing students in a public university in Bangkok, Thailand. Data were collected by a self-reported questionnaire on self efficacy and preventive behavior for osteoporosis. Data were analyzed using descriptive statistics and multiple regression analysis with stepwise method. Results revealed that majority of the students were female (98.3%) with mean age of 19.86 + 1.26 years. The students had self efficacy and preventive behavior for osteoporosis at moderate level. Self efficacy and level of education could together predicted 35.2% variance of preventive behavior for osteoporosis (p< .001). Results suggest approaches for promoting preventive behavior for osteoporosis through enhancing self efficacy among nursing students in a public university in Bangkok, Thailand.

Keywords: osteoporosis, self-efficacy, preventive behavior, nursing students

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Authors: Thushel Jayaweera

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In performance evaluation literature, there has been no investigation indicating the impact of job characteristics, working conditions and motivation on the job performance among the hotel workers in Britain. This study tested the relationship between working conditions (physical and psychosocial working conditions) and job performance (task and contextual performance) with motivators (e.g. recognition, achievement, the work itself, the possibility for growth and work significance) as the mediating variable. A total of 254 hotel workers in 25 hotels in Bristol, United Kingdom participated in this study. Working conditions influenced job performance and motivation moderated the relationship between working conditions and job performance. Poor workplace conditions resulted in decreasing employee performance. The results point to the importance of motivators among hotel workers and highlighted that work be designed to provide recognition and sense of autonomy on the job to enhance job performance of the hotel workers. These findings have implications for organizational interventions aimed at increasing employee job performance.

Keywords: hotel workers, working conditions, motivation, job characteristics, job performance

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Authors: Hend K. Moosa, Eman A. Rashwan, Ezzat M. Hassan, Amany A. Ghazy, Amel G. Sheredy

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The stability of microRNA (miR) in the circulation can show a great progress toward the discovery of non-invasive diagnostic and prognostic biomarkers in many diseases. In the present study, circulatory miR-122 and miR-130a were analysed in chronic hepatitis C Egyptian patients in predicting the clinical outcome of interferon treatment. In addition, their expression levels were correlated to viral RNA levels, necro-inflammatory markers (AST, ALT) and to each other. This study was conducted on 51 subjects where 36 were chronic HCV patients in which they were divided into naive and interferon treated HCV patients (responders and non-responders) and 15 matched healthy controls. Serum quantification of miR-122 and miR-130a were performed by quantitative Real-time Polymerase Chain Reaction (qRT-PCR). The results showed a significant upregulation of miR-122 in non-responder patients (P=0.049). By receiver operating characteristic analysis curve, miR-122 revealed 65% sensitivity and 92.3% specificity in predicting non-responsiveness of patients to IFN treatment, while miR-130a showed a sensitivity of 100% and specificity of 53.85%. Remarkably, there was a significant positive correlation between miR-122 and miR-130a in naive HCV patients (r=0.714, p=0.003). However, there was no significant correlation between serum miR-122, miR-130a expression levels and necro-inflammatory markers (AST, ALT). To conclude, miR-122 and miR-130a have a significant association with viral RNA levels and accordingly, they may have a synergistic power in promoting viral replication. Interestingly, miR-122 and miR-130a have a predictive power in predicting clinical outcome of IFN treatment which can be further studied in currently used drugs in order to reduce the socio-economic burden of potentially non-responders.

Keywords: hepatitis C, microRNA, miR-122, miR-130a

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Authors: Mohammad Mehdi Bagheri, Tianying Ma, Meng Gong

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Nail laminated timber (NLT) is widely used for constructing timber bridge decks in North America. Butt joints usually exist due to the length limits of lumber, leading to concerns about the decrease of structural performance of NLT. This study aimed at investigating the provisions incorporated in Canadian highway bridge design code on the use of but joints in wooden bridge decks. Three and five layers NLT specimens with various configurations were tested under 3-point bending test. It was found that the standard equation is capable of predicting the bending stiffness reduction due to butt joints and 1-m band limit in which, one but joint in every three adjacent lamination is allowed, sounds reasonable. The strength reduction also followed a pattern similar to stiffness reduction. Also reinforcement of the butt joint through nails and steel side plates was attempted. It was found that nail reinforcement recovers the stiffness slightly. In contrast, reinforcing the butt joint through steel side plate improved the flexural performance significantly when compared to the nail reinforcement.

Keywords: nail laminated timber, butt joint, bending stiffness, reinforcement

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Authors: Zunaira Zaman, David Bohannon

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This study explores the application of machine learning (ML) for detecting security vulnerabilities in source code. The research aims to assist organizations with large application portfolios and limited security testing capabilities in prioritizing security activities. ML-based approaches offer benefits such as increased confidence scores, false positives and negatives tuning, and automated feedback. The initial approach using natural language processing techniques to extract features achieved 86% accuracy during the training phase but suffered from overfitting and performed poorly on unseen datasets during testing. To address these issues, the study proposes using the abstract syntax tree (AST) for Java and C++ codebases to capture code semantics and structure and generate path-context representations for each function. The Code2Vec model architecture is used to learn distributed representations of source code snippets for training a machine-learning classifier for vulnerability prediction. The study evaluates the performance of the proposed methodology using two datasets and compares the results with existing approaches. The Devign dataset yielded 60% accuracy in predicting vulnerable code snippets and helped resist overfitting, while the Juliet Test Suite predicted specific vulnerabilities such as OS-Command Injection, Cryptographic, and Cross-Site Scripting vulnerabilities. The Code2Vec model achieved 75% accuracy and a 98% recall rate in predicting OS-Command Injection vulnerabilities. The study concludes that even partial AST representations of source code can be useful for vulnerability prediction. The approach has the potential for automated intelligent analysis of source code, including vulnerability prediction on unseen source code. State-of-the-art models using natural language processing techniques and CNN models with ensemble modelling techniques did not generalize well on unseen data and faced overfitting issues. However, predicting vulnerabilities in source code using machine learning poses challenges such as high dimensionality and complexity of source code, imbalanced datasets, and identifying specific types of vulnerabilities. Future work will address these challenges and expand the scope of the research.

Keywords: code embeddings, neural networks, natural language processing, OS command injection, software security, code properties

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Authors: Saptiadi Nugroho, A. Nur Muhamad Afif

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Understanding factors that influence employee performance is very important. By finding the significant factors, organization could intervene to improve the employee performance that simultaneously will affect organization itself. In this research, four aspects consist of PCCD training, education level, corrective action, and work location were tested to identify their influence on employee performance. By using correlation analysis and T-Test, it was found that employee performance significantly influenced by PCCD training, work location, and corrective action. Meanwhile the education level did not influence employee performance.

Keywords: employee development, employee performance, performance management system, organization

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Authors: Hamid Ahmadi, Neda Azari Dodaran

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This paper aims to study the structural behavior of tubular KT-joints subjected to axial loading at fire induced elevated temperatures. At first, a finite element (FE) model was developed and validated against the data available from experimental tests. Then, a set of 810 FE analyses were performed to study the influence of temperature and dimensionless geometrical parameters (β, γ, θ, and τ) on the ultimate strength and initial stiffness. The joints were analyzed under two types of axial loading and five different temperatures (20 ºC, 200 ºC, 400 ºC, 550 ºC, and 700 ºC). Results show that the ultimate strength and initial stiffness of KT-joints decrease considerably by increasing the temperature. In the joints having bigger values of the β, the temperature elevation leads to less reduction in ultimate strength; while in the joints with bigger values of the γ, the temperature elevation results in more reduction in ultimate strength. The influence of the θ on the ultimate strength is independent from the temperature. To our knowledge, there is no design formula available for determining the ultimate strength of KT-joints at elevated temperatures. Hence, after parametric study, two equations were developed through nonlinear regression, for calculating the ultimate strength of KT-joints at elevated temperatures.

Keywords: axial loads, fire condition, parametric formula, static strength, tubular KT-joint

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Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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Authors: Sara Ataei, Maryam Taghizadeh-Ghehi, Amir Sarayani, Asieh Ashouri, Amirhossein Moslehi, Molouk Hadjibabaie, Kheirollah Gholami

Abstract:

Background: Acute kidney injury (AKI) is common in hematopoietic stem cell transplantation (HSCT) patients with an incidence of 21–73%. Prevention and early diagnosis reduces the frequency and severity of this complication. Predictive biomarkers are of major importance to timely diagnosis. Neutrophil gelatinase associated lipocalin (NGAL) is a widely investigated novel biomarker for early diagnosis of AKI. However, no study assessed NGAL for AKI diagnosis in HSCT patients. Methods: We performed further analyses on gathered data from our recent trial to evaluate the performance of urine NGAL (uNGAL) as an indicator of AKI in 72 allogeneic HSCT patients. AKI diagnosis and severity were assessed using Risk–Injury–Failure–Loss–End-stage renal disease and AKI Network criteria. We assessed uNGAL on days -6, -3, +3, +9 and +15. Results: Time-dependent Cox regression analysis revealed a statistically significant relationship between uNGAL and AKI occurrence. (HR=1.04 (1.008-1.07), P=0.01). There was a relation between uNGAL day +9 to baseline ratio and incidence of AKI (unadjusted HR=.1.047(1.012-1.083), P<0.01). The area under the receiver-operating characteristic curve for day +9 to baseline ratio was 0.86 (0.74-0.99, P<0.01) and a cut-off value of 2.62 was 85% sensitive and 83% specific in predicting AKI. Conclusions: Our results indicated that increase in uNGAL augmented the risk of AKI and the changes of day +9 uNGAL concentrations from baseline could be of value for predicting AKI in HSCT patients. Additionally uNGAL changes preceded serum creatinine rises by nearly 2 days.

Keywords: acute kidney injury, hemtopoietic stem cell transplantation, neutrophil gelatinase-associated lipocalin, Receiver-operating characteristic curve

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Authors: Suriya

Abstract:

Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Ashraf Mohammed M. Eid, Hossam El Badry

Abstract:

The bored pile friction resistance may be affected by many factors such as the method of construction, pile length and diameter, the soil properties, as well as the depth below ground level. These factors can be represented analytically to study the influence of diameter on the unit skin friction. In this research, the Egyptian Code of soil mechanics is used to assess the skin friction capacity for either the ordinary pile diameter as well as for the large pile diameter. The later is presented in the code and through the work of some researchers based on the results of investigations adopted for a sufficient number of field tests. The comparative results of these researchers with respect to the Egyptian Code are used to check the adequacy of both methods. Based on the results of this study, the traditional static formula adopted for piles of diameter less than 60 cm may be continually used for larger piles by correlating the analyzed formulae. Accordingly, the corresponding modified angle of internal friction is concluded demonstrating a reduction of shear strength due to soil disturbance along the pile shaft. Based on this research the difference between driven piles and bored piles constructed in same soil can be assessed and a better understanding can be evaluated for the effect of different factors on pile skin friction capacity.

Keywords: large piles, static formula, friction piles, sandy soils

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

Abstract:

A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Shengjun Zhang, Xu Cheng, Feng Shen

Abstract:

The passive containment cooling system (PCCS) is widely used in the advanced nuclear reactor in case of the loss of coolant accident (LOCA) and the main steam line break accident (MSLB). The internal heat exchanger is one of the most important equipment in the PCCS and its heat transfer characteristic determines the performance of the system. In this investigation, a theoretical model is presented for predicting the heat and mass transfer which accompanies condensation. The conduction through the liquid condensate is considered and the interface temperature is defined by iteration. The parameter in the correlation to describe the suction effect should be further determined through experimental data.

Keywords: non-condensable gas, condensation, heat transfer coefficient, heat and mass transfer analogy

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Authors: Hamed Hossein Afshari, Shahrzad Jalali, Amir Hossein Ghods, Bijan Raahemi

Abstract:

This paper introduces an automated clustering solution that applies to WiFi/Bluetooth sensing data and is later used for traffic management applications. The paper initially summarizes a number of clustering approaches and thereafter shows their performance for noise removal. In this context, clustering is used to recognize WiFi and Bluetooth MAC addresses that belong to passengers traveling by a public urban transit bus. The main objective is to build an intelligent system that automatically filters out MAC addresses that belong to persons located outside the bus for different routes in the city of Ottawa. The proposed intelligent system alleviates the need for defining restrictive thresholds that however reduces the accuracy as well as the range of applicability of the solution for different routes. This paper moreover discusses the performance benefits of the presented clustering approaches in terms of the accuracy, time and space complexity, and the ease of use. Note that results of clustering can further be used for the purpose of the origin-destination estimation of individual passengers, predicting the traffic load, and intelligent management of urban bus schedules.

Keywords: WiFi-Bluetooth sensing, cluster analysis, artificial intelligence, traffic management

Procedia PDF Downloads 221