Search results for: regression coefficients

Authors: Pranav Gulati, Isha Sharma

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Hypertension and hypotension are known to have repercussions on the health of an individual, with hypertension contributing to an increased probability of risk to cardiovascular diseases and hypotension resulting in syncope. This prompts the development of a non-invasive, non-intrusive, continuous and cuff-less blood pressure monitoring system to detect blood pressure variations and to identify individuals with acute and chronic heart ailments, but due to the unavailability of such devices for practical daily use, it becomes difficult to screen and subsequently regulate blood pressure. The complexities which hamper the steady monitoring of blood pressure comprises of the variations in physical characteristics from individual to individual and the postural differences at the site of monitoring. We propose to develop a continuous, comprehensive cardio-analysis tool, based on reflective photoplethysmography (PPG). The proposed device, in the form of an eyewear captures the PPG signal and estimates the systolic and diastolic blood pressure using a sensor positioned near the temporal artery. This system relies on regression models which are based on extraction of key points from a pair of PPG wavelets. The proposed system provides an edge over the existing wearables considering that it allows for uniform contact and pressure with the temporal site, in addition to minimal disturbance by movement. Additionally, the feature extraction algorithms enhance the integrity and quality of the extracted features by reducing unreliable data sets. We tested the system with 12 subjects of which 6 served as the training dataset. For this, we measured the blood pressure using a cuff based BP monitor (Omron HEM-8712) and at the same time recorded the PPG signal from our cardio-analysis tool. The complete test was conducted by using the cuff based blood pressure monitor on the left arm while the PPG signal was acquired from the temporal site on the left side of the head. This acquisition served as the training input for the regression model on the selected features. The other 6 subjects were used to validate the model by conducting the same test on them. Results show that the developed prototype can robustly acquire the PPG signal and can therefore be used to reliably predict blood pressure levels.

Keywords: blood pressure, photoplethysmograph, eyewear, physiological monitoring

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Authors: Nagham Ismail, Djamel Ouahrani

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Thermal resilience in the built environment reflects the building's capacity to adapt to extreme climate changes. In hot climates, power outages in office buildings pose risks to the health and productivity of workers. Therefore, it is of interest to quantify the thermal resilience of office buildings by developing a user-friendly simplified model. This simplified model begins with creating an assessment metric of thermal resilience that measures the duration between the power outage and the point at which the thermal habitability condition is compromised, considering different power interruption times (morning, noon, and afternoon). In this context, energy simulations of an office building are conducted for Qatar's summer weather by changing different parameters that are related to the (i) wall characteristics, (ii) glazing characteristics, (iii) load, (iv) orientation and (v) air leakage. The simulation results are processed using SPSS to derive linear regression equations, aiding stakeholders in evaluating the performance of commercial buildings during different power interruption times. The findings reveal the significant influence of glazing characteristics on thermal resilience, with the morning power outage scenario posing the most detrimental impact in terms of the shortest duration before compromising thermal resilience.

Keywords: thermal resilience, thermal envelope, energy modeling, building simulation, thermal comfort, power disruption, extreme weather

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Authors: Dwi Wijiastutik, Tarjo, Yuni Rimawati

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The purpose of this empirical study is to analyse how to the market reaction and the conservative degree changes on the adoption of International Financial Reporting Standart (IFRS) through Jakarta Islamic Index. The study also has given others additional analysis on the profitability, capital structure and size company toward IFRS adoption. The data collection methods used in this study reveals as secondary data and deep analysis to the company’s annual report and daily price stock at yahoo finance. We analyse 40 companies listed on Jakarta Islamic Index from 2010 to 2014. The result of the study concluded that IFRS has given a different on the depth analysis to the two of variance analysis: Moderated Regression Analysis and Wilcoxon Signed Rank to test developed hypotheses. Our result on the regression analysis shows that market response and conservatism principle is not significantly after IFRS Adoption in Jakarta Islamic Index. Furthermore, in addition, analysis on profitability, capital structure, and company size show that significantly after IFRS adoption. The findings of our study help investor by showing the impact of IFRS for making decided investment.

Keywords: IFRS, earnings response coefficient, conservatism principle

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Arega Shumetie Ademe, Belay Kassa, Degye Goshu, Majaliwa Mwanjalolo

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Currently the world economy is suffering from one critical problem, climate change. Some studies done before identified that impact of the problem is region specific means in some part of the world (temperate zone) there is improvement in agricultural performance but in some others like in the tropics there is drastic reduction in crop production and crop income. Climate variability is becoming dominant cause of short-term fluctuation in rain-fed agricultural production and income of developing countries. The purely rain-fed Ethiopian agriculture is the most vulnerable sector to the risks and impacts of climate variability. Thus, this study tried to identify impact of climate variability on crop income of smallholders in Ethiopia. The research used eight rounded unbalanced panel data from 1994- 2014 collected from six villages in the study area. After having all diagnostic tests the research used fixed effect method of regression. Based on the regression result rainfall and temperature deviation from their respective long term averages have negative and significant effect on crop income. Other extreme devastating shocks like flood, storm and frost, which are sourced from climate variability, have significant and negative effect on crop income of households’. Parameters that notify rainfall inconsistency like late start, variation in availability at growing season, and early cessation are critical problems for crop income of smallholder households as to the model result. Given this, impact of climate variability is not consistent in different agro-ecologies of the country. Rainfall variability has similar impact on crop income in different agro-ecology, but variation in temperature affects cold agro-ecology villages negatively and significantly, while it has positive effect in warm villages. Parameters that represent rainfall inconsistency have similar impact in both agro-ecologies and the aggregate model regression. This implies climate variability sourced from rainfall inconsistency is the main problem of Ethiopian agriculture especially the crop production sub-sector of smallholder households.

Keywords: climate variability, crop income, household, rainfall, temperature

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Authors: M. A. Grigoryev, A. N. Shishkov, D. A. Sychev

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The article defines the necessity of choosing the optimal power circuits scheme of the electric drive with field regulated reluctance machine. The specific weighting factors are calculation, the linear regression dependence of specific losses in semiconductor frequency converters are presented depending on the values of the rated current. It is revealed that with increase of the carrier frequency PWM improves the output current waveform, but increases the loss, so you will need depending on the task in a certain way to choose from the carrier frequency. For task of optimization by criterion of the minimum electrical losses regression dependence of the electrical losses in the frequency converter circuit at a frequency of a PWM signal of 0 Hz. The surface optimization criterion is presented depending on the rated output torque of the motor and number of phases. In electric drives with field regulated reluctance machine with at low output power optimization criterion appears to be the worst for multiphase circuits. With increasing output power this trend hold true, but becomes insignificantly different optimal solutions for three-phase and multiphase circuits. This is explained to the linearity of the dependence of the electrical losses from the current.

Keywords: field regulated reluctance machine, the electrical losses, multiphase power circuit, the surface optimization criterion

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Authors: Basil Nkosilathi Dube, Wilson R. Nyemba, Panashe Mandevu

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In highway driving, over 50 percent of the power produced by the engine is used to overcome aerodynamic drag, which is a force that opposes a body’s motion through the air. Aerodynamic drag and thus fuel consumption increase rapidly at speeds above 90kph. It is desirable to minimize fuel consumption. Aerodynamic drag reduction in highway driving is the best approach to minimize fuel consumption and to reduce the negative impacts of greenhouse gas emissions on the natural environment. Fuel economy is the ultimate concern of automotive development. This study aims to design and analyze drag-reducing devices for a Mazda T3500 truck, namely, the cab roof and rear (trailer tail) fairings. The aerodynamic effects of adding these append devices were subsequently investigated. To accomplish this, two 3D CAD models of the Mazda truck were designed using the Design Modeler. One, with these, append devices and the other without. The models were exported to ANSYS Fluent for computational fluid dynamics analysis, no wind tunnel tests were performed. A fine mesh with more than 10 million cells was applied in the discretization of the models. The realizable k-ε turbulence model with enhanced wall treatment was used to solve the Reynold’s Averaged Navier-Stokes (RANS) equation. In order to simulate the highway driving conditions, the tests were simulated with a speed of 100 km/h. The effects of these devices were also investigated for low-speed driving. The drag coefficients for both models were obtained from the numerical calculations. By adding the cab roof and rear (trailer tail) fairings, the simulations show a significant reduction in aerodynamic drag at a higher speed. The results show that the greatest drag reduction is obtained when both devices are used. Visuals from post-processing show that the rear fairing minimized the low-pressure region at the rear of the trailer when moving at highway speed. The rear fairing achieved this by streamlining the turbulent airflow, thereby delaying airflow separation. For lower speeds, there were no significant differences in drag coefficients for both models (original and modified). The results show that these devices can be adopted for improving the aerodynamic efficiency of the Mazda T3500 truck at highway speeds.

Keywords: aerodynamic drag, computation fluid dynamics, fluent, fuel consumption

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Authors: Muhamad N. Borhan, Nur S. Darus, Siti Z. Ishak, Rozmi Ismail, Siti F. M. Razali

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Children are at the greater risk to be involved in road traffic collisions due to the complex interaction of various elements in our transportation system. It encompasses interactions between the elements of children and driver behavior along with physical and social environment factors. The present study examined the effect between the collisions severity and physical environment factors on child pedestrian collisions. The severity of collisions is categorized into four injury outcomes: fatal, serious injury, slight injury, and damage. The sample size comprised of 2487 cases of child pedestrian-vehicle collisions in which children aged 7 to 12 years old was involved in Malaysia for the years 2006-2015. A multinomial logistic regression was applied to establish the effect between severity levels and physical environment factors. The results showed that eight contributing factors influence the probability of an injury road surface material, traffic system, road marking, control type, lighting condition, type of location, land use and road surface condition. Understanding the effect of physical environment factors may contribute to the improvement of physical environment design and decrease the collision involvement.

Keywords: child pedestrian, collisions, primary school, road injuries

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Authors: Nattabhorn Leamcharaskul

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This study aims to examine determining factors of the dynamics of poverty in Thailand by using panel data of 3,567 households in 2007-2017. Four techniques of estimation are employed to analyze the situation of poverty across households and time periods: the multinomial logit model, the sequential logit model, the quantile regression model, and the difference in difference model. Households are categorized based on their experiences into 5 groups, namely chronically poor, falling into poverty, re-entering into poverty, exiting from poverty and never poor households. Estimation results emphasize the effects of demographic and socioeconomic factors as well as unexpected events on the economic status of a household. It is found that remittances have positive impact on household’s economic status in that they are likely to lower the probability of falling into poverty or trapping in poverty while they tend to increase the probability of exiting from poverty. In addition, not only receiving a secondary source of household income can raise the probability of being a never poor household, but it also significantly increases household income per capita of the chronically poor and falling into poverty households. Public work programs are recommended as an important tool to relieve household financial burden and uncertainty and thus consequently increase a chance for households to escape from poverty.

Keywords: difference in difference, dynamic, multinomial logit model, panel data, poverty, quantile regression, remittance, sequential logit model, Thailand, transfer

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Authors: Girlly Marchlina Listyono, Abdurrokhim Abdurrokhim, Emi Sukiyah, Pulung Arya Pranantya

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Jakarta Area is composed by deposit which has various lithology characteristics. Based on its lithology types, colors, textures, mineral dan organic content from 22 wells scattered on Jakarta, lithofacies analysis and intra-wells data correlation can be done. From the analysis, it can be interpretated that Jakarta deposit deposited in marine, transition and terrestrial depositional environments. Terrestrial deposit characterized by domination of relatively coarse clastics and content of remaining roots, woods, plants, high content of quartz, lithic fragment, calcareous and oxidated appearace. The thickness of terrestrial deposit is thickening to south. Transitional deposit characterized by fine to medium clastics with dark color, high content of organic matter, various thickness in any ways. Marine deposit characterized by finer clastics, contain remain of shells, fosil, coral, limestone fragments, glauconites, calcareous. Marine deposit relatively thickening to north. Those lateral variety caused by tectonic, subsidence and stratigraphic condition. Deposition of Jakarta deposit from the data research was started on marine depositional environment which surrounded by the event of cycle of regression and transgression then ended with regression which ongoing until form shore line in north Jakarta nowadays.

Keywords: deposit, Indonesia, Jakarta, sediment, stratigraphy

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Authors: Abadou Yacine, Faid Hayette

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Significant investigations were performed on the use and impact on physical properties along with the mechanical strength of the recycled and reused E-glass waste powder. However, it has been modelled how recycled display e-waste glass may affect the characteristics and qualities of dune sand mortar. To be involved in this field, an investigation has been done with the substitution of dune sand for recycled E-glass waste and constant water-cement ratios. The linear relationship between the dune sand mortar and E-glass mortar mix % contributes to the model's reliability. The experimental data was exposed to regression analysis using JMP Statistics software. The regression model with one predictor presented the general form of the equation for the prediction of the five properties' characteristics of dune sand mortar from the substitution ratio of E-waste glass and curing age. The results illustrate that curing a long-term process produced an E-glass waste mortar specimen with the highest compressive strength of 68 MPa in the laboratory environment. Anova analysis indicated that the curing at long-term has the utmost importance on the sorptivity level and ultrasonic pulse velocity loss. Furthermore, the E-glass waste powder percentage has the utmost importance on the compressive strength and improvement in dynamic elasticity modulus. Besides, a significant enhancement of radiation-shielding applications.

Keywords: ANOVA analysis, E-glass waste, durability and sustainability, radiation-shielding

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Authors: Navid Kaboudi, Ali Shayanfar

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Introduction: Feeding infants with safe milk from the beginning of their life is an important issue. Drugs which are used by mothers can affect the composition of milk in a way that is not only unsuitable, but also toxic for infants. Consuming permeable drugs during that sensitive period by mother could lead to serious side effects to the infant. Due to the ethical restrictions of drug testing on humans, especially women, during their lactation period, computational approaches based on structural parameters could be useful. The aim of this study is to develop mechanistic models to predict the M/P ratio of drugs during breastfeeding period based on their structural descriptors. Methods: Two hundred and nine different chemicals with their M/P ratio were used in this study. All drugs were categorized into two groups based on their M/P value as Malone classification: 1: Drugs with M/P>1, which are considered as high risk 2: Drugs with M/P>1, which are considered as low risk Thirty eight chemical descriptors were calculated by ACD/labs 6.00 and Data warrior software in order to assess the penetration during breastfeeding period. Later on, four specific models based on the number of hydrogen bond acceptors, polar surface area, total surface area, and number of acidic oxygen were established for the prediction. The mentioned descriptors can predict the penetration with an acceptable accuracy. For the remaining compounds (N= 147, 158, 160, and 174 for models 1 to 4, respectively) of each model binary regression with SPSS 21 was done in order to give us a model to predict the penetration ratio of compounds. Only structural descriptors with p-value<0.1 remained in the final model. Results and discussion: Four different models based on the number of hydrogen bond acceptors, polar surface area, and total surface area were obtained in order to predict the penetration of drugs into human milk during breastfeeding period About 3-4% of milk consists of lipids, and the amount of lipid after parturition increases. Lipid soluble drugs diffuse alongside with fats from plasma to mammary glands. lipophilicity plays a vital role in predicting the penetration class of drugs during lactation period. It was shown in the logistic regression models that compounds with number of hydrogen bond acceptors, PSA and TSA above 5, 90 and 25 respectively, are less permeable to milk because they are less soluble in the amount of fats in milk. The pH of milk is acidic and due to that, basic compounds tend to be concentrated in milk than plasma while acidic compounds may consist lower concentrations in milk than plasma. Conclusion: In this study, we developed four regression-based models to predict the penetration class of drugs during the lactation period. The obtained models can lead to a higher speed in drug development process, saving energy, and costs. Milk/plasma ratio assessment of drugs requires multiple steps of animal testing, which has its own ethical issues. QSAR modeling could help scientist to reduce the amount of animal testing, and our models are also eligible to do that.

Keywords: logistic regression, breastfeeding, descriptors, penetration

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Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Nicolas R. Finkler, Wesley A. Saltarelli, Taison A. Bortolin, Vania E. Schneider, Davi G. F. Cunha

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Estimating the relative contribution of nonpoint or point sources of pollution in low-orders streams is an important tool for the water resources management. The location of headwaters in areas with anthropogenic impacts from urbanization and agriculture is a common scenario in developing countries. This condition can lead to conflicts among different water users and compromise ecosystem services. Water pollution also contributes to exporting organic loads to downstream areas, including higher order rivers. The purpose of this research is to preliminarily assess nutrients and solids loads exported by water bodies located in watersheds with different types of land uses in São Carlos - SP (Latitude. -22.0087; Longitude. -47.8909) and Caxias do Sul - RS (Latitude. -29.1634, Longitude. -51.1796), Brazil, using regression analysis. The variables analyzed in this study were Total Kjeldahl Nitrogen (TKN), Nitrate (NO3-), Total Phosphorus (TP) and Total Suspended Solids (TSS). Data were obtained in October and December 2015 for São Carlos (SC) and in November 2012 and March 2013 for Caxias do Sul (CXS). Such periods had similar weather patterns regarding precipitation and temperature. Altogether, 11 sites were divided into two groups, some classified as more pristine (SC1, SC4, SC5, SC6 and CXS2), with predominance of native forest; and others considered as impacted (SC2, SC3, CXS1, CXS3, CXS4 and CXS5), presenting larger urban and/or agricultural areas. Previous linear regression was applied for data on flow and drainage area of each site (R² = 0.9741), suggesting that the loads to be assessed had a significant relationship with the drainage areas. Thereafter, regression analysis was conducted between the drainage areas and the total loads for the two land use groups. The R² values were 0.070, 0.830, 0.752 e 0.455 respectively for SST, TKN, NO3- and TP loads in the more preserved areas, suggesting that the loads generated by runoff are significant in these locations. However, the respective R² values for sites located in impacted areas were respectively 0.488, 0.054, 0.519 e 0.059 for SST, TKN, NO3- and P loads, indicating a less important relationship between total loads and runoff as compared to the previous scenario. This study suggests three possible conclusions that will be further explored in the full-text article, with more sampling sites and periods: a) In preserved areas, nonpoint sources of pollution are more significant in determining water quality in relation to the studied variables; b) The nutrient (TKN and P) loads in impacted areas may be associated with point sources such as domestic wastewater discharges with inadequate treatment levels; and c) The presence of NO3- in impacted areas can be associated to the runoff, particularly in agricultural areas, where the application of fertilizers is common at certain times of the year.

Keywords: land use, linear regression, point and non-point pollution sources, streams, water resources management

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Authors: Azita Behbahaninia

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This research has been carried out to determine the water quality and physico-chemical properties Polour River, one of the most branch of Haraz River. Polour River was studied for a period of one year Samples were taken from different stations along the main branch of River polour. In water samples determined pH, DO, SO4, Cl, PO4, NO3, EC, BOD, COD, Temprature, color and number of Caliform per liter. ArcGIS was used for the zoning of phosphate concentration in the polour River basin. The results indicated that the river is polluted in polour village station, because of discharge domestic wastewater and also river is polluted in Ziar village station, because of agricultural wastewater and water is contaminated in aquaculture station, because of fish ponds wastewater. Statistical analysis shows that between independent traits and coliform regression relationship is significant at the 1% level. Coefficient explanation index indicated independent traits control 80% coliform and 20 % is for unknown parameters. The causality analysis showed Temperature (0.6) has the most positive and direct effect on coliform and sulfate has direct and negative effect on coliform. The results of causality analysis and the results of the regression analysis are matched and other forms direct and indirect effects were negligible and ignorable. Kruskal-Wallis test showed, there is different between sampling stations and studied characters. Between stations for temperature, DO, COD, EC, sulfate and coliform is at 1 % and for phosphate 5 % level of significance.

Keywords: coliform, GIS, pollution, phosphate, river

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Authors: Jamila Garba Audu, Shehu Usman Hassan

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The widespread failure in the financial information quality has created the need to improve the financial information quality and to strengthen the control of managers by setting up good firms structures. Published accounting information in financial statements is required to provide various users - shareholders, employees, suppliers, creditors, financial analysts, stockbrokers and government agencies – with timely and reliable information useful for making prudent, effective and efficient decisions. The relationship between corporate governance and performance to financial reporting quality is imperative; this is because despite rapid researches in this area the findings obtained from these studies are constantly inconclusive. Data for the study were extracted from the firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences; the data was empirically tested. A multiple regression was employed to test the model as a technique for data analysis. The results from the analysis revealed a negative association between all the regressors and financial reporting quality except the performance of listed manufacturing firms in Nigeria. This indicates that corporate governance plays a significant role in mitigating earnings management and improving financial reporting quality while performance does not. The study recommended among others that the composition of audit committee should be made in accordance with the provision for code of corporate governance which is not more than six (6) members with at least one (1) financial expert.

Keywords: corporate governance, financial reporting quality, manufacturing firms, Nigeria, performance

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Authors: Golayeh Yousefi, Mehdi Homaee, Ali Akbar Norouzi

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Ongoing monitoring of soil contamination by heavy metals is critical for ecosystem stability and environmental protection, and food security. The conventional methods of determining these soil contaminants are time-consuming and costly. Spectroscopy in the visible near-infrared (VNIR) - short wave infrared (SWIR) region is a rapid, non-destructive, noninvasive, and cost-effective method for assessment of soil heavy metals concentration by studying the spectral properties of soil constituents. The aim of this study is to derive spectral bands and important ranges that are sensitive to heavy metals and can be used to estimate the concentration of these soil contaminants. In other words, the change in the spectral properties of spectrally active constituents of soil can lead to the accurate identification and estimation of the concentration of these compounds in soil. For this purpose, 325 soil samples were collected, and their spectral reflectance curves were evaluated at a range of 350-2500 nm. After spectral preprocessing operations, the partial least-squares regression (PLSR) model was fitted on spectral data to predict the concentration of Cu and Ni. Based on the results, the spectral range of Cu- sensitive spectra were 480, 580-610, 1370, 1425, 1850, 1920, 2145, and 2200 nm, and Ni-sensitive ranges were 543, 655, 761, 1003, 1271, 1415, 1903, 2199 nm. Finally, the results of this study indicated that the spectral data contains a lot of information that can be applied to identify the soil properties, such as the concentration of heavy metals, with more detail.

Keywords: heavy metals, spectroscopy, spectral bands, PLS regression

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Predicting the risk of diabetic retinopathy (DR) in Indian type 2 diabetes patients is immensely necessary. India, being the second largest country after China in terms of a number of diabetic patients, to the best of our knowledge not a single risk score for complications has ever been investigated. Diabetic retinopathy is a serious complication and is the topmost reason for visual impairment across countries. Any type or form of DR has been taken as the event of interest, be it mild, back, grade I, II, III, and IV DR. A sample was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of DR. Cox proportional hazard regression is used to design risk scores for the prediction of retinopathy. Model calibration and discrimination are assessed from Hosmer Lemeshow and area under receiver operating characteristic curve (ROC). Overfitting and underfitting of the model are checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Optimal cut off point is chosen by Youden’s index. Five-year probability of DR is predicted by both survival function, and Markov chain two state model and the better technique is concluded. The risk scores developed can be applied by doctors and patients themselves for self evaluation. Furthermore, the five-year probabilities can be applied as well to forecast and maintain the condition of patients. This provides immense benefit in real application of DR prediction in T2DM.

Keywords: Cox proportional hazard regression, diabetic retinopathy, ROC curve, type 2 diabetes mellitus

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Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Priscila A. Bernardes, Marcos E. Buzanskas, Luciana C. A. Regitano, Ricardo V. Ventura, Danisio P. Munari

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The best linear unbiased predictor (BLUP) is a method commonly used in genetic evaluations of breeding programs. However, this approach can lead to higher inbreeding coefficients in the population due to the intensive use of few bulls with higher genetic potential, usually presenting some degree of relatedness. High levels of inbreeding are associated to low genetic viability, fertility, and performance for some economically important traits and therefore, should be constantly monitored. Unreliable pedigree data can also lead to misleading results. Genomic information (i.e., single nucleotide polymorphism – SNP) is a useful tool to estimate the inbreeding coefficient. Runs of homozygosity have been used to evaluate homozygous segments inherited due to direct or collateral inbreeding and allows inferring population selection history. This study aimed to evaluate runs of homozygosity (ROH) and inbreeding in a population of Nelore beef cattle. A total of 814 animals were genotyped with the Illumina BovineHD BeadChip and the quality control was carried out excluding SNPs located in non-autosomal regions, with unknown position, with a p-value in the Hardy-Weinberg equilibrium lower than 10⁻⁵, call rate lower than 0.98 and samples with the call rate lower than 0.90. After the quality control, 809 animals and 509,107 SNPs remained for analyses. For the ROH analysis, PLINK software was used considering segments with at least 50 SNPs with a minimum length of 1Mb in each animal. The inbreeding coefficient was calculated using the ratio between the sum of all ROH sizes and the size of the whole genome (2,548,724kb). A total of 25.711 ROH were observed, presenting mean, median, minimum, and maximum length of 3.34Mb, 2Mb, 1Mb, and 80.8Mb, respectively. The number of SNPs present in ROH segments varied from 50 to 14.954. The longest ROH length was observed in one animal, which presented a length of 634Mb (24.88% of the genome). Four bulls were among the 10 animals with the longest extension of ROH, presenting 11% of ROH with length higher than 10Mb. Segments longer than 10Mb indicate recent inbreeding. Therefore, the results indicate an intensive use of few sires in the studied data. The distribution of ROH along the chromosomes showed that chromosomes 5 and 6 presented a large number of segments when compared to other chromosomes. The mean, median, minimum, and maximum inbreeding coefficients were 5.84%, 5.40%, 0.00%, and 24.88%, respectively. Although the mean inbreeding was considered low, the ROH indicates a recent and intensive use of few sires, which should be avoided for the genetic progress of breed.

Keywords: autozygosity, Bos taurus indicus, genomic information, single nucleotide polymorphism

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Bo Wang

Abstract:

This paper proposes a method that can estimate PM2.5 by the images of GF-1 Satellite that called WFOV images (Wide Field of View). AOD (Aerosol Optical Depth) over land surfaces was retrieved in Shanghai area based on DDV (Dark Dense Vegetation) method. PM2.5 information, gathered from ground monitoring stations hourly, was fitted with AOD using different polynomial coefficients, and then the correlation coefficient between them was calculated. The results showed that, the GF-1 WFOV images can meet the requirement of retrieving AOD, and the correlation coefficient between the retrieved AOD and PM2.5 was high. If more detailed and comprehensive data is provided, the accuracy could be improved and the parameters can be more precise in the future.

Keywords: remote sensing retrieve, PM 2.5, GF-1, aerosol optical depth

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Authors: Thoetchai Peeraphatdit, Peter A. Brady, Suraj Kapa, Samuel J. Asirvatham, Niyada Naksuk

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Background: Serum electrolyte abnormalities are a common cause of an acquired prolonged QT syndrome, especially, in the coronary care unit (CCU) setting. Optimal electrolyte ranges among the CCU patients have not been sufficiently investigated. Methods: We identified 8,498 consecutive CCU patients who were admitted to the CCU at Mayo Clinic, Rochester, the USA, from 2004 through 2013. Association between first serum electrolytes and baseline corrected QT intervals (QTc), as well as in-hospital mortality, was tested using multivariate linear regression and logistic regression, respectively. Serum potassium 4.0- < 4.5 mEq/L, ionized calcium (iCa) 4.6-4.8 mg/dL, and magnesium 2.0- < 2.2 mg/dL were used as the reference levels. Results: There was a modest level-dependent relationship between hypokalemia ( < 4.0 mEq/L), hypocalcemia ( < 4.4 mg/dL), and a prolonged QTc interval; serum magnesium did not affect the QTc interval. Association between the serum electrolytes and in-hospital mortality included a U-shaped relationship for serum potassium (adjusted odds ratio (OR) 1.53 and OR 1.91for serum potassium 4.5- < 5.0 and ≥ 5.0 mEq/L, respectively) and an inverted J-shaped relationship for iCa (adjusted OR 2.79 and OR 2.03 for calcium < 4.4 and 4.4- < 4.6 mg/dL, respectively). For serum magnesium, the mortality was greater only among patients with levels ≥ 2.4 mg/dL (adjusted OR 1.40), compared to the reference level. Findings were similar in sensitivity analyses examining the association between mean serum electrolytes and mean QTc intervals, as well as in-hospital mortality. Conclusions: Serum potassium 4.0- < 4.5 mEq/L, iCa ≥ 4.6 mg/dL, and magnesium < 2.4 mg/dL had a neutral effect on QTc intervals and were associated with the lowest in-hospital mortality among the CCU patients.

Keywords: calcium, electrocardiography, long-QT syndrome, magnesium, mortality, potassium

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Authors: Siqi Fang, Man Cheung Chung

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This study examined the inter-relationship between childhood maltreatment, cognitive distortion, alexithymia, eudemonic well-being, and psychological distress. One hundred and eighty-two university students participated in the study and completed an online survey comprising the Childhood Trauma Questionnaire, Cognitive Distortion Scale, Toronto Alexithymia Scale, Psychological Well-Being Scale, and General Health Questionnaire-28. Hierarchical multiple regression analysis showed that child maltreatment, perceptions of hopelessness and helplessness, preoccupation with danger, personal growth, and purpose in life predicted psychological distress. However, alexithymia was not a significant predictor. Further analysis using the regression models with bootstrapping procedure showed that feeling hopeless, helpless and preoccupation with danger mediated the path between child maltreatment and psychological distress. Meanwhile, coping with beliefs in personal growth and life purpose moderated the mediation effects of distorted cognition on psychological distress. To conclude, childhood maltreatment is associated with psychological distress. This relationship is influenced by people’s perceptions of life being hopeless, helpless or dangerous. At the same time, the effect of hopelessness, helplessness, and feelings of danger also depends on the degree of using coping strategies of positive psychological functioning.

Keywords: alexithymia, childhood maltreatment, cognitive distortion, eudemonic well-being, psychological distress

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Authors: Keigo Ide, Toru Maruyama, Michihiro Ito, Hiroyuki Fujimura, Yoshikatu Nakano, Shoichiro Suda, Sachiyo Aburatani, Haruko Takeyama

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Understanding environmental system is one of the important tasks. In recent years, conservation of coral environments has been focused for biodiversity issues. The damage of coral reef under environmental impacts has been observed worldwide. However, the casual relationship between damage of coral and environmental impacts has not been clearly understood. On the other hand, structure/diversity of marine bacterial community may be relatively robust under the certain strength of environmental impact. To evaluate the coral environment conditions, it is necessary to investigate relationship between marine bacterial composition in coral reef and environmental factors. In this study, the Time Scale Network Analysis was developed and applied to analyze the marine environmental data for investigating the relationship among coral, bacterial community compositions and environmental factors. Seawater samples were collected fifteen times from November 2014 to May 2016 at two locations, Ishikawabaru and South of Sesoko in Sesoko Island, Okinawa. The physicochemical factors such as temperature, photosynthetic active radiation, dissolved oxygen, turbidity, pH, salinity, chlorophyll, dissolved organic matter and depth were measured at the coral reef area. Metagenome and metatranscriptome in seawater of coral reef were analyzed as the biological factors. Metagenome data was used to clarify marine bacterial community composition. In addition, functional gene composition was estimated from metatranscriptome. For speculating the relationships between physicochemical and biological factors, cross-correlation analysis was applied to time scale data. Even though cross-correlation coefficients usually include the time precedence information, it also included indirect interactions between the variables. To elucidate the direct regulations between both factors, partial correlation coefficients were combined with cross correlation. This analysis was performed against all parameters such as the bacterial composition, the functional gene composition and the physicochemical factors. As the results, time scale network analysis revealed the direct regulation of seawater temperature by photosynthetic active radiation. In addition, concentration of dissolved oxygen regulated the value of chlorophyll. Some reasonable regulatory relationships between environmental factors indicate some part of mechanisms in coral reef area.

Keywords: coral environment, marine microbiology, network analysis, omics data analysis

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Authors: Mahshid Rezaei, Zahra Tajari, Zahra Esmaeily, Atefeh Eyvazkhani, Shahrzad Daei, Marjan Mansouri Dara, Mohaddesh Rezaei, Abolghassem Djazayeri, Ahmadreza Dorosti Motlagh

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Background: Musculoskeletal pain is one of the most prevalent symptoms in elderly age. Nutrition and diet are considered important underlying factors that could affect chronic musculoskeletal pain. The purpose of this study was to identify the relationship between empirical dietary inflammatory patterns (EDII) and musculoskeletal pain. Method: In this cross-sectional study, 213 elderly individuals were selected from several health centers. The usual dietary intake was evaluated by a valid and reliable 147-items food frequency questionnaire (FFQ). To measure the intensity of pain, Visual Analogue Scale (VAS) was used. Multiple Linear Regression was applied to assess the association between EDII and musculoskeletal pain. Results: The results of multiple linear regression analysis indicate that a higher EDII score was associated with higher musculoskeletal pain (β= 0.21: 95% CI: 0.24-1.87: P= 0.003). These results stayed significant even after adjusting for covariates such as sex, marital status, height, family number, sleep, BMI, physical activity duration, waist circumference, protector, and medication use (β= 0.16: 95% CI: 0.11-1.04: P= 0.02). Conclusion: Study findings indicated that higher inflammation of diet might have a direct association with musculoskeletal pains in elderlies. However, further investigations are required to confirm these findings.

Keywords: musculoskeletal pain, empirical dietary inflammatory pattern, elderlies, dietary pattern

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Authors: Nguyen Ngoc Que Tran

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Purpose: The purpose of this paper is to investigate the relationship between internal market orientation, external market orientation, employee commitment and job satisfaction. Design/methodology/approach: This study collected data through a survey and utilized simple linear regression and multiple regression analysis to determine if there was any support for the research hypotheses as presented in the previous chapter. Findings: Using data from 256 employees of four leading joint stock banks in Vietnam, the empirical results indicates that employee commitment is positively related with external market orientation, job satisfaction is positively related to employee commitment, and employee commitment and job satisfaction are positively related to external market orientation. However, job satisfaction has no significant positive effect on external market orientation. Theoretical contribution: The primary contribution to marketing theory arising from this study is the integration of job satisfaction, employee commitment, and external market orientation in a single research model. Practical implications: The major contribution to practice is an external market oriented bank has to respond rapidly to the future needs and preferences of its customers. This could result in high levels of commitment to the service process and in doing so provide Vietnamese joint-stock commercial banks with a competitive advantage. The finding is important for the banking service sector in general and the Vietnamese banking industry in particular.

Keywords: employee commitment, job satisfaction and external market orientation, vietnam, bank

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Authors: Boualem Chetti

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The dynamic characteristics of a three-lobe journal bearing lubricated with micropolar fluids are determined by the linear stability theory. Lubricating oil containing additives and contaminants is modeled as micropolar fluid. The modified Reynolds equation is obtained using the micropolar lubrication theory and the finite difference technique has been used to solve it. The dynamic characteristics in terms of stiffness, damping coefficients, the critical mass and whirl ratio are determined for various values of size of material characteristic length and the coupling number. The computed results show compared with Newtonian fluids, that micropolar fluid exhibits better stability.

Keywords: three-lobe bearings, micropolar fluid, dynamic characteristics, stability analysis

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Authors: Petr Gurný

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One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

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Authors: Liaqat Ali, Khalid Farooq, Shafieullah Khan, Nasir Orakzai, Qudratullah

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Objectives: To determine the risk factors for high resistance of ciprofloxacin in complicated urinary tract infections. Materials and Methods: It is an analytical study that was conducted in the department of Urology (Team ‘C’) at Institute of Kidney Diseases Hayatabad Peshawar from 1st June 2012 till 31st December 2012. Total numbers of 100 patients with complicated UTI was selected in the study. Multivariate analysis and linear regression were performed for the detection of risk factors. All the data was recorded on structured Proforma and was analyzed on SPSS version 17. Results: The mean age of the patient was 55.6 years (Range 3-82 years). 62 patients were male while 38 patients were female. 66 isolates of E-Coli were found sensitive to ciprofloxacin while 34 isolates were found Resistant for ciprofloxacin. Using multivariate analysis and linear regression, an increasing age above 50 (p=0.002) History of urinary catheterization especially for bladder outflow obstruction (p=0.001) and previous multiple use of ciprofloxacin (p=0.001) and poor brand of ciprofloxacin were found to be independent risk factors for high resistance of ciprofloxacin. Conclusion: UTI is common illness across the globe with increasing trend of antimicrobial resistance for ciprofloxacin against E Coli in complicated UTI. The risk factors for emerging resistance are increasing age, urinary catheterization and multiple use and poor brand of ciprofloxacin.

Keywords: urinary tract infection, ciprofloxacin, urethral catheterization, antimicrobial resistance

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