Search results for: endogenous switching regression

Authors: Muhammad Swilam

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Solutions for the wrong division problem of the Extended Multi-Modulus Divider (EMMD) that occurs during modulus extension (i.e. switching the modulus value between two different ranges of division ratios), in open loop fractional dividers and fractional multiplying delay locked loop, is proposed. A detailed study for the MMD with Sigma-Delta is also presented. Moreover, extensive simulations for the divider are presented to ensure and verify its functionality and compared with the conventional dividers.

Keywords: extended multi-modulus divider (EMMD), fractional multiplying delay locked loop, open loop fractional divider, sigma delta modulator

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Kenneth Okelo, Esther Waithaka, Maureen Mweru

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Development of oral language skills is a precursor to writing and reading acquisition. Oral skill is a means of communication through which people express their desires, ideas, excitements, amusements, disappointments and exchange information. In addition, oral skills have been found to be an important tool for thinking and concept development in children. Research carried out in industrialised countries have identified some appropriate teaching strategies used to enhance acquisition of oral language skills such as repetition, substitution, explanation, contrast, exemplification and code-switching. However, these studies’ geographical locations do not reflect the diversity of the Kenyan society. In addition, studies conducted in Kenya in the past have not established why pre-primary school teachers are not using appropriate teaching strategies. The purpose of this study was to find out whether teachers’ experience, academic qualification and type of training influences their choice of teaching strategies in the development of oral language skills inside and out of the classroom in selected preschools in Kibra Sub-County, Nairobi County. In addition, this study aimed at finding out the strategies used by teachers in Kibra Sub-County to promote oral skills development among pre-primary school children. The study was guided by Holdaway’s theory of language acquisition. Descriptive survey design was employed during this study. Questionnaires and observation schedules were used to collect data. Eighty-three (83) preschool teachers were sampled using multistage sampling methods for observation. Data was analysed using SPSS version 20. The researcher carried out content analysis on the qualitative data. The main descriptive methods used were tabulation of frequencies and percentages. Chi squire test was the inferential statistic used to test the relationship between variables. The main findings of the study indicate that teaching strategies that were mostly used by pre-primary school teachers were code-switching, examples, repetition, substitution and explanation. While questions, direction, expansion of children words and contrast were the least used teaching strategies when teaching oral language skills. The study revealed that the there is a slight correlation between the type of training of teachers and the teaching strategies as most of DICECE trained teachers used more teaching strategies when teaching oral skills compared to other teachers. The findings also revealed that there was a partial significant correlation between teacher’s academic qualifications and a few teaching strategies. A similar correlation was also observed between teaching experience and a few teaching strategies. Since the strategies used by pre-primary school teachers under the study were less than half of the recommended teaching strategies to promote oral skills, the study recommends that teachers should be encouraged to use more in structural strategies to improve children’s oral language skills.

Keywords: Kenya early childhood education, Kenya education, oral language skills acquisition, teaching methods

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Authors: Qasem Qananwah, Ahmad Dagamseh, Hiam AlQuran, Khalid Shaker Ibrahim

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Blood pressure helps the physicians greatly to have a deep insight into the cardiovascular system. The determination of individual blood pressure is a standard clinical procedure considered for cardiovascular system problems. The conventional techniques to measure blood pressure (e.g. cuff method) allows a limited number of readings for a certain period (e.g. every 5-10 minutes). Additionally, these systems cause turbulence to blood flow; impeding continuous blood pressure monitoring, especially in emergency cases or critically ill persons. In this paper, the most important statistical features in the photoplethysmogram (PPG) signals were extracted to estimate the blood pressure noninvasively. PPG signals from more than 40 subjects were measured and analyzed and 12 features were extracted. The features were fed to principal component analysis (PCA) to find the most important independent features that have the highest correlation with blood pressure. The results show that the stiffness index means and standard deviation for the beat-to-beat heart rate were the most important features. A model representing both features for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) was obtained using a statistical regression technique. Surface fitting is used to best fit the series of data and the results show that the error value in estimating the SBP is 4.95% and in estimating the DBP is 3.99%.

Keywords: blood pressure, noninvasive optical system, principal component analysis, PCA, continuous monitoring

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Authors: Arnab Roy, P. S. Anil Kumar

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Hall bar magnetic field sensors based on planar hall effect were fabricated from permalloy (Ni¬80Fe20) thin films grown by pulsed laser ablation. As large as 400% planar Hall voltage change was observed for a magnetic field sweep within ±4 Oe, a value comparable with present day TMR sensors at room temperature. A very large planar Hall sensitivity of 1200 Ω/T was measured close to switching fields, which was not obtained so far apart from 2DEG Hall sensors. In summary, a highly sensitive low magnetic field sensor has been constructed which has the added advantage of simple architecture, good signal to noise ratio and robustness.

Keywords: planar hall effect, permalloy, NiFe, pulsed laser ablation, low magnetic field sensor, high sensitivity magnetic field sensor

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Zoe Chang, Max Williams, Gautham Das

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The purpose of this study is to evaluate the incorporation of hemp fibers (HF) in concrete. Hemp fiber reinforced concrete (HFRC) is becoming more popular as an alternative for regular mix designs. This study was done to evaluate the compressive strength of HFRC regarding mix procedure. Hemp fibers were obtained from the manufacturer and hand-processed to ensure uniformity in width and length. The fibers were added to the concrete as both wet and dry mixes to investigate and optimize the mix design process. Results indicated that the dry mix had a compressive strength of 1157 psi compared to the wet mix of 985 psi. This dry mix compressive strength was within range of the standard mix compressive strength of 1533 psi. The statistical analysis revealed that the mix design process needs further optimization and uniformity concerning the addition of HF. Regression analysis revealed the standard mix design had a coefficient of 0.9 as compared to the dry mix of 0.375, indicating a variation in the mixing process. While completing the dry mix, the addition of plain hemp fibers caused them to intertwine, creating lumps and inconsistency. However, during the wet mixing process, combining water and hemp fibers before incorporation allows the fibers to uniformly disperse within the mix; hence the regression analysis indicated a better coefficient of 0.55. This study concludes that HRFC is a viable alternative to regular mixes; however, more research surrounding its characteristics needs to be conducted.

Keywords: hemp fibers, hemp reinforced concrete, wet & dry, freeze thaw testing, compressive strength

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Authors: Wenting Zhang, Shishi Liu, Peihong Fu

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As an indicator of air quality and directly related to concentration of ground PM2.5, the spatial-temporal variation and impact factor analysis of Aerosol Optical Depth (AOD) have been a hot spot in air pollution. This paper concerns the non-stationarity and the autocorrelation (with Moran’s I index of 0.75) of the AOD in Wuhan agglomeration (WHA), in central China, uses the geographically weighted regression (GRW) to identify the spatial relationship of AOD and its impact factors. The 3 km AOD product of Moderate Resolution Imaging Spectrometer (MODIS) is used in this study. Beyond the economic-social factor, land use density factors, vegetable cover, and elevation, the landscape metric is also considered as one factor. The results suggest that the GWR model is capable of dealing with spatial varying relationship, with R square, corrected Akaike Information Criterion (AICc) and standard residual better than that of ordinary least square (OLS) model. The results of GWR suggest that the urban developing, forest, landscape metric, and elevation are the major driving factors of AOD. Generally, the higher AOD trends to located in the place with higher urban developing, less forest, and flat area.

Keywords: aerosol optical depth, geographically weighted regression, land use change, Wuhan agglomeration

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Authors: Linda Hassaine, Abdelhamid Mraoui, Mohamed Rida Bengourina

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In grid-connected photovoltaic systems, significant improvements can be carried out in the design and implementation of inverters: reduction of harmonic distortion, elimination of the DC component injected into the grid and the proposed control. This paper proposes a control strategy based on PWM switching patterns for an inverter for the photovoltaic system connected to the grid in order to control the injected current. The current injected must be sinusoidal with reduced harmonic distortion. An additional filter is designed to reduce high-order harmonics on the output side. This strategy exhibits the advantages: Simplicity, reduction of harmonics, the size of the line filter, reduction of the memory requirements and power calculation for the control.

Keywords: control, inverters, LCL filter, grid-connected photovoltaic system

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Authors: Rafia Rafique, Mutmina Zainab

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The present study investigated the relationship between fear of negative evaluation (FNE), social support and well-being in people with Vitiligo. It was hypothesized that low level of FNE and greater social support is likely to predict well-being. It was also hypothesized that social support is likely to moderate the relationship between FNE and well-being. Correlational research design was used for the present study. Non-probability purposive sampling technique was used to collect a sample (N=122) of people with Vitiligo. Hierarchical Moderated Regression analysis was used to test prediction and moderation. Brief Fear of Negative Evaluation Scale, Multidimensional Scale of Perceived Social Support (MSPSS) and Mental Health Continuum-Short form (MHC-SF) were used to evaluate the study variables. Fear of negative evaluation negatively predicted well-being (emotional and psychological). Social support from significant others and friends predicted social well-being. Social Support from family predicted emotional and psychological well-being. It was found that social support from significant others moderated the relationship between FNE and emotional well-being and social support from family moderated the relationship between FNE and social well-being. Dermatologists treating people with Vitiligo need to educate them and their families about the buffering role of social support (family and significant others). Future studies need to focus on other important mediating factors that can possibly explain the relationship between fear of negative evaluation and wellbeing.

Keywords: fear of negative evaluation, hierarchical moderated regression, vitiligo, well-being

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Authors: Libena Cernohorska, Barbora Sutorova, Petr Teply

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There are pending discussions over an impact of global regulatory efforts on banks. In this paper we present a literature review on the profitability-risk-capital relationship in banking. Research papers dealing with this topic can be divided into two groups: the first group focusing on a capital-risk relationship and the second group analyzing a capital-profitability relationship. The first group investigates whether the imposition of stricter capital requirements reduces risk-taking incentives of banks based on a simultaneous equations model. Their model pioneered the idea that the changes in both capital and risk have endogenous and exogenous components. The results obtained by the authors indicate that changes in the capital level are positively related to the changes in asset risk. The second group of the literature concentrating solely on the relationship between the level of held capital and bank profitability is limited. Nevertheless, there are a lot of studies dealing with the banks’ profitability as such, where bank capital is very often included as an explanatory variable. Based on the literature review of dozens of relevant papers in this study, an empirical research on banks’ profitability and risk adjustment decisions under new banking rules Basel III rules can be easily undertaken.

Keywords: bank, Basel III, capital, decision making, profitability, risk, simultaneous equations model

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Authors: Hassan Barau Singhry

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Purpose: Leadership or the absence of it is an important behaviour affecting employees’ job satisfaction. Although, there are many models of leadership, one that stands out in a period of change is the transformational behaviour. The aim of this study is to investigate the role of an organizational justice on the relationship between transformational leadership and employee job satisfaction. The study is based on the assumption that change begins with leaders and leaders should be fair and just. Methodology: A cross-sectional survey through structured questionnaire was employed to collect the data of this study. The population is selected the three tiers of government such as the local, state, and federal governments in Nigeria. The sampling method used in this research is stratified random sampling. 418 middle managers of public organizations respondents to the questionnaire. Multiple regression aided by structural equation modeling was employed to test 4 hypothesized relationships. Finding: The regression results support for the mediating role of organizational justice such as distributive, procedural, interpersonal and informational justice in the link between transformational leadership and job satisfaction. Originality/value: This study adds to the literature of human resource management by empirically validating and integrating transformational leadership behaviour with the four dimensions of organizational justice theory. The study is expected to be beneficial to the top and middle-level administrators as well as theory building and testing.

Keywords: distributive justice, job satisfaction, organizational justice, procedural justice, transformational leadership

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Authors: Liang Cheng Chang, Wei Ju Huang, You Cheng Chen

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Investigation and research are keys for conjunctive use of surface and groundwater resources. The hydrogeological structure is an important base for groundwater analysis and simulation. Traditionally, the hydrogeological structure is artificially determined based on geological drill logs, the structure of wells, groundwater levels, and so on. In Taiwan, groundwater observation network has been built and a large amount of groundwater-level observation data are available. The groundwater level is the state variable of the groundwater system, which reflects the system response combining hydrogeological structure, groundwater injection, and extraction. This study applies analytical tools to the observation database to develop a methodology for the identification of confined and unconfined aquifers. These tools include frequency analysis, cross-correlation analysis between rainfall and groundwater level, groundwater regression curve analysis, and decision tree. The developed methodology is then applied to groundwater layer identification of two groundwater systems: Zhuoshui River alluvial fan and Pingtung Plain. The abovementioned frequency analysis uses Fourier Transform processing time-series groundwater level observation data and analyzing daily frequency amplitude of groundwater level caused by artificial groundwater extraction. The cross-correlation analysis between rainfall and groundwater level is used to obtain the groundwater replenishment time between infiltration and the peak groundwater level during wet seasons. The groundwater regression curve, the average rate of groundwater regression, is used to analyze the internal flux in the groundwater system and the flux caused by artificial behaviors. The decision tree uses the information obtained from the above mentioned analytical tools and optimizes the best estimation of the hydrogeological structure. The developed method reaches training accuracy of 92.31% and verification accuracy 93.75% on Zhuoshui River alluvial fan and training accuracy 95.55%, and verification accuracy 100% on Pingtung Plain. This extraordinary accuracy indicates that the developed methodology is a great tool for identifying hydrogeological structures.

Keywords: aquifer identification, decision tree, groundwater, Fourier transform

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Authors: Tayyab Hassan, Talha Rehman, Qasim Abdul Aziz, Ahmad Salman

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The constant monitoring of blood glucose level is necessary for maintaining health of patients and to alert medical specialists to take preemptive measures before the onset of any complication as a result of diabetes. The current clinical monitoring of blood glucose uses invasive methods repeatedly which are uncomfortable and may result in infections in diabetic patients. Several attempts have been made to develop non-invasive techniques for blood glucose measurement. In this regard, the existing methods are not reliable and are less accurate. Other approaches claiming high accuracy have not been tested on extended dataset, and thus, results are not statistically significant. It is a well-known fact that acetone concentration in breath has a direct relation with blood glucose level. In this paper, we have developed the first of its kind, reliable and high accuracy breath analyzer for non-invasive blood glucose measurement. The acetone concentration in breath was measured using MQ 138 sensor in the samples collected from local hospitals in Pakistan involving one hundred patients. The blood glucose levels of these patients are determined using conventional invasive clinical method. We propose a linear regression classifier that is trained to map breath acetone level to the collected blood glucose level achieving high accuracy.

Keywords: blood glucose level, breath acetone concentration, diabetes, linear regression

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

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In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r2) and the cross-validated coefficient (q2) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions.

Keywords: 3D QSAR, CoMSIA, triazoles, novel heterocycles

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Authors: Maryam Khazaee-Pool, Tahereh Pashaei, Koen Ponnet

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Background: The global prevalence and morbidity of Coronavirus disease 2019 (COVID-19) are high. Preventive behaviors are proven to reduce the damage caused by the disease. There is a paucity of information on determinants of preventive behaviors in response to COVID-19 in Mazandaran province, north of Iran. So, we aimed to evaluate the protection motivation theory (PMT) in promoting preventive behaviors of COVID-19 in Mazandaran province. Materials and Methods: In this descriptive cross-sectional study, 1220 individuals participated. They were selected via social networks using convenience sampling in 2020. Data were collected online using a demographic questionnaire and a valid and reliable scale based on PMT. Data analysis was done using the Pearson correlation coefficient and linear regression in SPSS V24. Result: The mean age of the participants was 39.34±8.74 years. The regression model showed perceived threat (ß =0.033, P =0.007), perceived costs (ß=0.039, P=0.045), perceived self-efficacy (ß =0.116, P>0.001), and perceived fear (ß=0.131, P>0.001) as the significant predictors of COVID-19 preventive behaviors. This model accounted for 78% of the variance in these behaviors. Conclusion: According to constructs of the PMT associated with protection against COVID-19, educational programs and health promotion based on the theory and benefiting from social networks could be helpful in increasing the motivation of people towards protective behaviors against COVID-19.

Keywords: questionnaire development, validation, intention, prevention, covid-19

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Authors: Syed Afrozuddin Ahmed, Junaid Saghir Siddiqi, Sabah Quaiser

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Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan. In this study it has been investigated that spatial pattern of environment causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. The climatic or environmental structure data and the Dengue Fever (DF) data was processed by coding, editing, tabulating, recoding, restructuring in terms of re-tabulating was carried out, and finally applying different statistical methods, techniques, and procedures for the evaluation. Five climatic variables which we have studied are precipitation (P), Maximum temperature (Mx), Minimum temperature (Mn), Humidity (H) and Wind speed (W) collected from 1980-2012. The dengue cases in Karachi from 2010 to 2012 are reported on weekly basis. Principal component analysis is applied to explore the climatic variables and/or the climatic (structure) which may influence in the increase or decrease in the number of dengue fever cases in Karachi. PC1 for all the period is General atmospheric condition. PC2 for dengue period is contrast between precipitation and wind speed. PC3 is the weighted difference between maximum temperature and wind speed. PC4 for dengue period contrast between maximum and wind speed. Negative binomial and Poisson regression model are used to correlate the dengue fever incidence to climatic variable and principal component score. Relative humidity is estimated to positively influence on the chances of dengue occurrence by 1.71% times. Maximum temperature positively influence on the chances dengue occurrence by 19.48% times. Minimum temperature affects positively on the chances of dengue occurrence by 11.51% times. Wind speed is effecting negatively on the weekly occurrence of dengue fever by 7.41% times.

Keywords: principal component analysis, dengue fever, negative binomial regression model, poisson regression model

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Authors: Chul Park, Youngseok Kim, Sangsik Choi

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This study developed an electric jig for underwater structure inspection in order to solve the problem of the application of side scan sonar to underwater inspection, and analyzed correlations of empirical data in order to enhance sonar data resolution. For the application of tow-typed sonar to underwater structure inspection, an electric jig was developed. In fact, it was difficult to inspect a cross-section at the time of inspection with tow-typed equipment. With the development of the electric jig for underwater structure inspection, it was possible to shorten an inspection time over 20%, compared to conventional tow-typed side scan sonar, and to inspect a proper cross-section through accurate angle control. The indoor test conducted to enhance sonar data resolution proved that a water depth, the distance from an underwater structure, and a filming angle influenced a resolution and data quality. Based on the data accumulated through field experience, multiple regression analysis was conducted on correlations between three variables. As a result, the relational equation of sonar operation according to a water depth was drawn.

Keywords: underwater structure, SONAR, safety inspection, resolution

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Authors: Sayeeda Laeque Bangi

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Objectives: Treating white spot lesions (WSL) to create a sound and esthetically pleasing enamel surface is a question yet to be fully answered. The objective of this randomized controlled trial was to measure and compare the degree of regression of WSL during orthodontic treatment achieved by using three commercially available materials. Methods: A single-blinded randomized prospective clinical trial, comprising 80 patients categorized into four groups (one control group and three experimental groups, with 20 subjects per group) using block randomization, was conducted. Group A (control group): Colgate strong toothpaste; and experiments groups were Group B: GC tooth mousse, Group C: Phos-Flur mouthwash and Group D: SHY-NM. Subjects were instructed to use the designated dentifrice/mouthwash and photographs were taken at baseline, third and sixth months, and white spot lesions were reassessed in the maxillomandibular anterior teeth. Results: All the three groups had shown an improvement in WSL. But Group B has shown the greatest difference in mean values of decalcification index (DI) scores. Conclusion: All three commercially available products showed a regression of WSL over a 6-month duration. GC tooth mousse proved to be the most effective means of treating WSL over other regimens.

Keywords: white spot lesions, dentifrices, orthodontic therapy, remineralization

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Authors: Hongmeng Su, Luyu Zhao, Yanyan Qian, Hong Fan

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Aim: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors that endanger human health seriously. RNA methylation, especially N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), a crucial epigenetic transcriptional regulatory mechanism, plays an important role in tumorigenesis, progression and prognosis. This research aims to systematically evaluate the prognostic value of m⁶A and m⁵C modulators in HCC patients. Methods: Twenty-four modulators of m⁶A and m⁵C were candidates to analyze their expression level and their contribution to predict the prognosis of HCC. Consensus clustering analysis was applied to classify HCC patients. Cox and LASSO regression were used to construct the risk model. According to the risk score, HCC patients were divided into high-risk and low/medium-risk groups. The clinical pathology factors of HCC patients were analyzed by univariate and multivariate Cox regression analysis. Results: The HCC patients were classified into 2 clusters with significant differences in overall survival and clinical characteristics. Nine-gene risk model was constructed including METTL3, VIRMA, YTHDF1, YTHDF2, NOP2, NSUN4, NSUN5, DNMT3A and ALYREF. It was indicated that the risk score could serve as an independent prognostic factor for patients with HCC. Conclusion: This study constructed a Nine-gene risk model by modulators of m⁶A and m⁵C and investigated its effect on the clinical prognosis of HCC. This model may provide important consideration for the therapeutic strategy and prognosis evaluation analysis of patients with HCC.

Keywords: hepatocellular carcinoma, m⁶A, m⁵C, prognosis, RNA methylation

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Authors: Nermin Gozukirmizi, Buket Cakmak, Sevgi Marakli

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Sireviruses are genera of copia LTR retrotransposons with a unique genome structure among retrotransposons. Barley (Hordeum vulgare L.) is an economically important plant and has been studied as a model plant regarding its short annual life cycle and seven chromosome pairs. In this study, we used mature barley embryos, 10-day-old roots and 10-day-old leaves derived from the same barley plant to investigate SIRE1 retrotransposon movements by Inter-Retrotransposon Amplified Polymorphism (IRAP) technique. We found polymorphism rates between 0-64% among embryos, roots and leaves. Polymorphism rates were detected to be 0-27% among embryos, 8-60% among roots, and 11-50% among leaves. Polymorphisms were observed not only among the parts of different individuals, but also on the parts of the same plant (23-64%). The internal domains of SIRE1 (gag, env and rt) were also analyzed in the embryos, roots and leaves. Analysis of band profiles showed no polymorphism for gag, however, different band patterns were observed among samples for rt and env. The sequencing of SIRE1 gag, env and rt domains revealed 79% similarity for gag, 95% for env and 84% for rt to Ty1-copia retrotransposons. SIRE1 retrotransposon was identified in the soybean genome and has been studied on other plants (maize, rice, tomatoe etc.). This study is the first detailed investigation of SIRE1 in barley genome. The obtained findings are expected to contribute to the comprehension of SIRE1 retrotransposon and its role in barley genome.

Keywords: barley, polymorphism, retrotransposon, SIRE1 virus

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Authors: Irtaza M. Syed, Kaamran Raahemifar

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This paper presents a model predictive control (MPC) of a utility interactive (UI) single phase inverter (SPI) for a photovoltaic (PV) system at residential/distribution level. The proposed model uses single-phase phase locked loop (PLL) to synchronize SPI with the grid and performs MPC control in a dq reference frame. SPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a full bridge (FB) voltage source inverter (VSI). No PI regulators to tune and carrier and modulating waves are required to produce switching sequence. Instead, the operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a three kW PV system at the input of UI-SPI in Matlab/Simulink. Implementation and results demonstrate simplicity and accuracy, as well as reliability of the model.

Keywords: phase locked loop, voltage source inverter, single phase inverter, model predictive control, Matlab/Simulink

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Authors: Abderrahmane Soufi

Abstract:

The building sector is the largest consumer of energy in France, accounting for 44% of French consumption. To reduce energy consumption and improve energy efficiency, France implemented an energy transition law targeting 40% energy savings by 2030 in the tertiary building sector. Building simulation tools are used to predict the energy performance of buildings but the reliability of these tools is hampered by discrepancies between the real and simulated energy performance of a building. This performance gap lies in the simplified assumptions of certain factors, such as the behavior of occupants on air conditioning and heating, which is considered deterministic when setting a fixed operating schedule and a fixed interior comfort temperature. However, the behavior of occupants on air conditioning and heating is stochastic, diverse, and complex because it can be affected by many factors. Probabilistic models are an alternative to deterministic models. These models are usually derived from statistical data and express occupant behavior by assuming a probabilistic relationship to one or more variables. In the literature, logistic regression has been used to model the behavior of occupants with regard to heating and air conditioning systems by considering univariate logistic models in residential buildings; however, few studies have developed multivariate models for higher education and vocational training buildings in a Mediterranean climate. Therefore, in this study, occupant behavior on heating and air conditioning systems was modeled using logistic regression. Occupant behavior related to the turn-on heating and air conditioning systems was studied through experimental measurements collected over a period of one year (June 2023–June 2024) in three classrooms occupied by several groups of students in engineering schools and professional training. Instrumentation was provided to collect indoor temperature and indoor relative humidity in 10-min intervals. Furthermore, the state of the heating/air conditioning system (off or on) and the set point were determined. The outdoor air temperature, relative humidity, and wind speed were collected as weather data. The number of occupants, age, and sex were also considered. Logistic regression was used for modeling an occupant turning on the heating and air conditioning systems. The results yielded a proposed model that can be used in building simulation tools to predict the energy performance of teaching buildings. Based on the first months (summer and early autumn) of the investigations, the results illustrate that the occupant behavior of the air conditioning systems is affected by the indoor relative humidity and temperature in June, July, and August and by the indoor relative humidity, temperature, and number of occupants in September and October. Occupant behavior was analyzed monthly, and univariate and multivariate models were developed.

Keywords: occupant behavior, logistic regression, behavior model, mediterranean climate, air conditioning, heating

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Authors: Banu Aytül Ekmekçi

Abstract:

Salinity stress has negative effects on agricultural yield throughout the world, affecting production whether it is for subsistence or economic gain. This study investigates the inductive role of vitamin C and its application mode in mitigating the detrimental effects of irrigation with diluted (10, 20 and 30 %) NaCl + water on carthamus tinctorius plants. The results show that 10% of salt water exhibited insignificant changes, while the higher levels impaired growth by reducing seed germination, dry weights of shoot and root, water status and chlorophyll contents. However, irrigation with salt water enhanced carotenoids and antioxidant enzyme activities. The detrimental effects of salt water were ameliorated by application of 100 ppm ascorbic acid (vitamin C). The inductive role of vitamin was associated with the improvement of seed germination, growth, plant water status, carotenoids, endogenous ascorbic acid and antioxidant enzyme activities. Moreover, vitamin C alone or in combination with 30% NaCl water increased the intensity of protein bands as well as synthesized additional new proteins with molecular weights of 205, 87, 84, 65 and 45 kDa. This could increase tolerance mechanisms of treated plants towards water salinity.

Keywords: salinity, stress, vitamin c, antioxidant, NaCl, enzyme

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Authors: Haolin Li, Eric Bair, Jane Monaco, Quefeng Li

Abstract:

The temporomandibular disorder (TMD) is a series of musculoskeletal disorders ranging from jaw pain to chronic debilitating pain, and the risk factors for the onset and maintenance of TMD are still unclear. Prior researches have shown that the potential risk factors for chronic TMD are related to psychosocial factors, autonomic functions, and pain sensitivity. Using data from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study’s baseline case-control study, we examine whether the risk factors identified by prior researches are still statistically significant after taking all of the risk measures into account in one single model, and we also compare the relative influences of the risk factors in three different perspectives (psychosocial factors, autonomic functions, and pain sensitivity) on the chronic TMD. The statistical analysis is conducted by using ridge logistic regression and bootstrapping, in which the performance of the algorithms has been assessed using extensive simulation studies. The results support most of the findings of prior researches that there are many psychosocial and pain sensitivity measures that have significant associations with chronic TMD. However, it is surprising that most of the risk factors of autonomic functions have not presented significant associations with chronic TMD, as described by a prior research.

Keywords: autonomic function, OPPERA study, pain sensitivity, psychosocial measures, temporomandibular disorder

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Authors: Vanja Misic, Mohamed El-Mogy, Yousef Haj-Ahmad

Abstract:

Endonuclease G (EndoG) is a well conserved mitochondrio-nuclear nuclease with dual lethal and vital roles in the cell. The aim of our study was to examine whether EndoG exerts its nuclease activity on exogenous DNA substrates such as plasmid DNA (pDNA), considering their importance in gene therapy applications. The effects of EndoG knockdown on pDNA stability and levels of encoded reporter gene expression were evaluated in the cervical carcinoma HeLa cells. Transfection of pDNA vectors encoding short-hairpin RNAs (shRNAs) reduced levels of EndoG mRNA and nuclease activity in HeLa cells. In physiological circumstances, EndoG knockdown did not have an effect on the stability of pDNA or the levels of encoded transgene expression as measured over a four day time-course. However, when endogenous expression of EndoG was induced by an extrinsic stimulus, targeting of EndoG by shRNA improved the perceived stability and transgene expression of pDNA vectors. Therefore, EndoG is not a mediator of exogenous DNA clearance, but in non-physiological circumstances it may non-specifically cleave intracellular DNA regardless of its origin. These findings make it unlikely that targeting of EndoG is a viable strategy for improving the duration and level of transgene expression from non-viral DNA vectors in gene therapy efforts.

Keywords: EndoG, silencing, exogenous DNA stability, HeLa cells

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Authors: Morteza Raei Dehaghi, Seyed Mohammad Mirhashemi

Abstract:

The current study explored the effect of economic development, financial development and institutional quality on environmental destruction in upper-middle income countries during the time period of 1999-2011. The dependent variable is logarithm of carbon dioxide emissions that can be considered as an index for destruction or quality of the environment given to its effects on the environment. Financial development and institutional development variables as well as some control variables were considered. In order to study cross-sectional correlation among the countries under study, Pesaran and Friz test was used. Since the results of both tests show cross-sectional correlation in the countries under study, seemingly unrelated regression method was utilized for model estimation. The results disclosed that Kuznets’ environmental curve hypothesis is confirmed in upper-middle income countries and also, financial development and institutional quality have a significant effect on environmental quality. The results of this study can be considered by policy makers in countries with different income groups to have access to a growth accompanied by improved environmental quality.

Keywords: economic development, environmental destruction, financial development, institutional development, seemingly unrelated regression

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Authors: Enemeri George Uweiyohowo

Abstract:

Improved achievement with respect to output control of photovoltaic (PV) systems is one of the major focus of PV in recent times. This is evident to its low carbon emission and efficiency. Power failure or outage from commercial providers, in general, does not promote development to public and private sector, these basically limit the development of industries. The need for a well-structured PV system is of importance for an efficient and cost-effective monitoring system. The purpose of this paper is to validate the maximum power point of an off-grid PV system taking into consideration the most effective tilt and orientation angles for PV's in the southern hemisphere. This paper is based on analyzing the system using a solar charger with MPPT from a pulse width modulation (PWM) perspective. The power conditioning device chosen is a solar charger with MPPT. The practical setup consists of a PV panel that is set to an orientation angle of 0∘N, with a corresponding tilt angle of 36∘, 26∘ and 16∘. Preliminary results include regression analysis (normal probability plot) showing the maximum power point in the system as well the best tilt angle for maximum power point tracking.

Keywords: poly-crystalline PV panels, information technology (IT), maximum power point tracking (MPPT), pulse width modulation (PWM)

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Authors: Bachir Belmadani, Rachid Taleb, M’hamed Helaimi

Abstract:

Multilevel inverters are well used in high power electronic applications because of their ability to generate a very good quality of waveforms, reducing switching frequency, and their low voltage stress across the power devices. This paper presents the hybrid pulse-width modulation (HPWM) strategy of a uniform step asymmetrical cascaded H-bridge nine-level Inverter (USACHB9LI). The HPWM approach is compared to the well-known sinusoidal pulse-width modulation (SPWM) strategy. Simulation results demonstrate the better performances and technical advantages of the HPWM controller in feeding a high power induction motor.

Keywords: uniform step asymmetrical cascaded h-bridge high-level inverter, hybrid pwm, sinusoidal pwm, high power induction motor

Procedia PDF Downloads 555