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To compare the transcutaneous oxygen pressure (tcpO2) measurement values and changes in compressed areas of surgical patients before and after surgery and to explore the related factors influencing the tcpO2 changes before and after surgery. Researchers selected 100 patients who underwent elective surgery in a tertiary comprehensive hospital from November 2021 to September 2022. A self-designed general information questionnaire was used to collect patient general information and disease-related data, including gender, age, smoking and drinking history, hypertension, diabetes, local skin temperature and humidity, related biochemical indicators, and activities of daily living score. Researchers used a transcutaneous oxygen pressure meter to measure and record the tcpO2 of the compressed areas (sacrococcygeal area, scapula area, and heel area) before and after surgery. Among the 100 patients, 37.00 % (37/100) developed type I/II pressure ulcers after surgery, and 30 patients (81.08 %) showed regression within 2 h after surgery. There was no statistically significant difference in the preoperative tcpO2 measurement values of the scapula and heel areas between the group with and without pressure ulcers, but the preoperative tcpO2 measurement value of the sacrococcygeal area in the group without pressure ulcers was higher than that in the group with pressure ulcers (P < 0.01). The factors affecting the preoperative tcpO2 measurement value of the sacrococcygeal area were smoking and surgical type. After surgery, the tcpO2 measurement values of the three areas in the group with pressure ulcers were significantly lower than those in the group without pressure ulcers (P < 0.01). Comparing the tcpO2 values of different areas, it was found that the tcpO2 value was lowest in the sacrococcygeal area, followed by the heel area, and the tcpO2 value in the scapula area was highest both before and after surgery (P < 0.01). The main factors affecting the postoperative tcpO2 measurement value were diabetes, Glassgow score, surgical time, and intraoperative red blood cell transfusion. The measurement of tcpO2 is related to the incidence of surgically acquired pressure ulcers, and this technology may become an important tool for quantitative assessment of the risk of pressure ulcers. [ABSTRACT FROM AUTHOR]

Hydrologists pursue the long-term goals of studying the relationship between hydrological processes and geomorphological processes, establishing quantitative relationships between these processes and finding ways to directly derive hydrological processes from topographic parameters to reduce the dependence on hydrological data. In this study, the river network of the Haihe River Basin was extracted based on DEM, and 18 representative small basins were selected as samples. Four river network parameters (the river network density, average branch ratio, average length ratio and fractal dimension) and four topographic parameters (the slope, topographic relief, surface roughness and roundness rate) were calculated for 18 small basins. The river network features and topographic features of the basin were analysed. Correlation analysis, one-dimensional linear regression, partial correlation analysis and multiple linear regression (stepwise regression) methods were used to analyse the quantitative relationship between topographic parameters and river network parameters. The analysis results were tested by a significance test, the final goodness of fit of the regression model was good, and the results were reliable. The correlations are spatially heterogeneous and may show variable results in different regions. This study guides river network planning and management, and managers should pay attention to the correlation between river networks and topography in the long term. [ABSTRACT FROM AUTHOR]

Early diagnosis of prevailing factors that either promote or impede learning is necessary in order to minimize problems in chemistry instruction and overall student performance. Extracted from reports on various factors affecting the teaching and learning of Chemistry, this study draws out determinants that affect first year university students' achievement in chemistry courses. Using descriptivecorrelational research, university students (n= 253) were surveyed using a checklist instrument (CKI = 0.70) to determine the factors that influenced their Chemistry learning. Multiple regression analysis revealed five determinants that significantly correlated with their achievement in this course namely, high school chemistry grade, parents' educational attainment, books, type of school enrolled, and fire extinguishers. A supportive learning environment along with other motivational factors can help underprepared students to be academically successful even in the most challenging course like Chemistry. [ABSTRACT FROM AUTHOR]

The purpose of this research is to explore the COVID-19 effect on the Pakistan Stock Market Capitalization (PSMC) using data of daily prices of Pakistan Stock Exchange and daily numbers of COVID-19 new cases, death cases and recovery cases. The study uses combination of time series and event study method to analyze the daily changes to the size of market capitalization before, during and after COVID-19 from Jan/2020 to Dec/2021. We applied data normality and stationery test before applying Regression model. Regression models were applied for both periods i.e., 2020 and 2020-2021 and investigated the relationship among Daily CASES, DCASES, RECOVERY and Stock Market Capitalization. The study concludes that CASES and DCASES have no significant effect on the Pakistan Stock Market Capitalization; however, RECOVERY has significant and positive effect on the Pakistan Stock Market Capitalization. [ABSTRACT FROM AUTHOR]

Copyright of Acta Universitaria is the property of Universidad de Guanajuato/Acta Universitaria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Physical Education (24482455) is the property of Universidade Estadual de Maringa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Plastic pollution, driven by micro- and nanoplastics (MNPs), poses a major environmental threat, exposing humans through various routes. Despite human colorectal adenocarcinoma Caco-2 cells being used as an in vitro model for studying the intestinal epithelium, uncertainties linger about MNPs harming these cells and the factors influencing adverse effects. Addressing this lacuna, our study aimed to elucidate the pivotal MNP parameters influencing cytotoxicity in Caco-2 cells, employing meta-analysis and machine learning techniques for quantitative assessment. Initial scrutiny of 95 publications yielded 17 that met the inclusion criteria, generating a dataset of 320 data points. This dataset underwent meticulous stratification based on polymer type, exposure time, polymer size, MNP concentration, and biological assays utilised. Subsequent dose-response curve analysis revealed moderate correlations for selected subgroups, such as the (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) MTT biological assay and exposure time exceeding 24 h, with coefficient of determination (R2) values of 0.50 (p -value: 0.0065) and 0.60 (p -value: 0.0018) respectively. For the aforementioned two subgroups, the MNP concentrations surpassing 10 μg/mL led to diminished viability of Caco-2 cells. Notably, we observed challenges in employing meta-analysis to navigate this multidimensional MNP dataset. Leveraging a random forest model, we achieved improved predictive performance, with R2 values of 0.79 and a root mean square error (RMSE) of 0.14 for the prediction of the Log Response Ratio on the test set. Model interpretation indicated that size and concentration are the principal drivers influencing Caco-2 cell cytotoxicity. Additionally, the partial dependence plot illustrating the relationship between the size of MNPs and predicted cytotoxicity reveals a complex pattern. Our study provides crucial insights into the health impacts of plastic pollution, informing policymakers for targeted interventions, thus contributing to a comprehensive understanding of its human health consequences. [Display omitted] • Meta-Analysis was unable to manage multidimensional data of micro- and nanoplastics (MNPs). • Machine learning (ML) predicted the cytotoxicity of MNPs in Caco-2 cells with a low error. • Good ML model predictive capability was evidenced by R2 values of 0.79. • ML model showed that size and concentrations are key drivers of cytotoxicity. [ABSTRACT FROM AUTHOR]

The aim of the present work is to develop a novel method for predicting age in individuals over 50 years old, utilizing regression models. The conducted study is of an analytical cross-sectional nature, involving a sample of 44 young subjects and 107 elderly subjects. The necessary data for this research were extracted from "The New Mexico Decedent Image Database." Based on the phenomenon of height shrinkage with age, we created models for young subjects and applied them to elderly subjects, allowing us to extract the variables. We obtained highly encouraging results with an R2 of 0.73, a mean absolute error of 3.94, and stable cross-validation. We used a Student's t-test, which demonstrated no significant difference between predicted and actual values (p-value >0.05). We also conducted a learning curve analysis and examined residuals against predicted values. This suggests that the forecasts are accurate, with no significant bias in predictions. This work has allowed us to conclude that it is possible to reliably estimate the age of subjects over 50, taking into account age-related physiological and pathological changes. • This study introduces a novel age prediction regression method for individuals over 50. • We found promising accuracy (R2 = 0.73, MAE = 3.94) in improving post-mortem identification, especially for the elderly. • It significantly enhances forensic medicine, by addressing age-related challenges and advancing identification methods. [ABSTRACT FROM AUTHOR]

Tech start-ups and, especially, unicorns have become a hot topic of today business world. Significant amounts of money have been invested in this sector by venture capital funds in the hope of a good return. However, the criteria and determinants of these investments are fuzzy, and the author has identified a gap in the scientific literature. Therefore, the objective of the present paper is to discover the determinants of venture capital investments in tech start-ups. Based on a secondary dataset, a multifactorial regression model is proposed that explains the appetence for investment of venture capital funds into tech start-ups. The model shows that the amount of funding in start-ups will increase as the number of unicorns and exits strengthens. [ABSTRACT FROM AUTHOR]

Healthcare sector is one of the most important sectors of any country as a big part of the country's economy is associated with it. The research is about to contribute to the health sector by minimizing the expenses of a lung cancer diagnosis. The study tries to devise an efficient method for initial screening of the patients with symptoms through their demographic and clinical data. The study seeks appropriate feature transformation techniques from dimensionality reduction techniques in combination with an apposite regression model that can perform this task robustly using the lung cancer dataset for early carcinoma diagnosis. To equip the health sector with state-of-the-art technology and for the betterment of humankind, the most beneficial tool of today is machine learning. Lungs play a vivacious role in the human body, oxygen is circulated in the body, and the air is taken from the atmosphere and sends it into the bloodstream. Several people decease every year because of lung carcinoma as lung cancer is normally identified at the latter phase due to lack of awareness. It stays unidentified because people have pneumonia often, which converts into lung cancer later. The projected research seeks to enable health professionals to the rationalization of primary diagnosis and treatment of lung carcinoma in developing countries. The proposed methodology has selected the optimized combination of regression-based machine learning technique and feature transformation technique for the available patterns based on demographic and clinical features of lung cancer patients. Based on the gathered results during training (RMSE = 0.1324, R2-Score = 0.7428) and testing (RMSE = 0.1273, R2-Score = 0.7405) it can be concluded that the fast independent component analysis and elastic net regression technique provided optimized results and outperform the other aspirant techniques. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore the expressions of confidence by a group of South African mathematics teachers about teaching mathematics and statistics concepts from various perspectives. The participants were 75 mathematics teachers who were teaching Grades 4 to 12 in KwaZulu-Natal (KZN) schools. They were asked to express their opinion on their level of confidence in teaching using 17 confidence items on a 5-point Likert scale from very low to very high. The study drew upon factor analysis, Rasch analysis, as well as regression analysis. The findings suggest that teachers' confidence in teaching mathematics concepts is quite different from their confidence in teaching statistics concepts and concepts that require connections across topics. Furthermore, the study found differences in teachers' confidence level by gender during the middle teaching years as well as a significant interaction between phases of teaching and whether teachers completed additional professional qualifications. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is a form of artificial intelligence in which computer algorithms improve automatically with experience. Recently, ML has been utilized to predict operative characteristics and patient outcomes for orthopedic procedures, thereby allowing for better patient selection and preoperative planning. This study sought to develop ML models to aid in predicting operative time and 30-day postoperative complications for elective total shoulder arthroplasty and to compare them to regression models. This cross-sectional national database study identified patients who underwent elective total shoulder arthroplasty from 2012 to 2018 in the American College of Surgeons National Surgical Quality Improvement Program registry. Boosted decision tree and artificial neural network (ANN) ML models were developed to predict prolonged operative time and 30-day postoperative complication rates. Model performance was measured using the area under the receiver operating characteristic curve and overall accuracy. Multivariate binary logistic regression analyses were also used to identify variables that predicted prolonged operative time and 30-day postoperative complication rates. ML model performance was then compared to the regression models in predicting outcomes. In total, 21,544 elective total shoulder arthroplasty procedures met inclusion criteria. Variables associated with greater odds of prolonged operative time included male sex (odds ratio [OR] = 0.66; 95% confidence interval [CI] = 0.61-0.71; P <.001), obesity (OR = 1.19; 95% CI = 1.09-1.29; P <.001), age under 70 years (OR = 0.77; 95% CI = 0.71-0.85; P <.001), smoking history (OR = 1.16; 95% CI = 1.03-1.32; P =.022), and history of cancer (OR = 2.91; 95% CI = 1.52-5.54; P =.001). The boosted decision tree model yielded an area under the curve (AUC) of 0.642, with an overall accuracy of 85.6% for predicting prolonged operative time. The ANN model had an AUC of 0.906 and overall accuracy of 84.7%, while the regression model had an AUC of 0.590 with overall accuracy of 85.6%. Thirty-day complication rate (7.7% vs. 3.9%, respectively; P <.001) and reoperation rate (1.8% vs. 1.2%, respectively; P =.006) also differed significantly between the prolonged operative duration and normal operative duration cohorts. This is the first study to successfully develop ML models for predicting operative time in total shoulder arthroplasty and compare them to existing methods of data analysis. The ANN model was superior to the other models in predicting prolonged operative time. With regard to 30-day postoperative complications, both ML models displayed fair predictive capacity, compared to the regression model, which had poor predictive performance. Level III; Database Retrospective Comparative Cohort Study [ABSTRACT FROM AUTHOR]

The coronavirus disease was announced in 2019 as a pandemic. Despite the wised measures taken by a Governments and herd immunity, many waves of the virus and its mutants continued to brutally confront the world. There has been no misgiving that vaccination is the only or most influential method of combating it, as happened before in many epidemics such as measles, smallpox, and cholera. So governments and the World Health Organization have sought to make vaccines to confront the virus and approved in less than a year. This rapid vaccine development endeavor raised several concerns regarding the vaccines’ effectiveness and safety. Since there is not sufficient, time to conduct sufficient clinical studies about vaccines. There are important questions the vaccinations not affect or will this damn virus ever end? We developed regression with a 7th-degree polynomial and offered case studies from 10 nations to back up our conclusions. The countries are the USA, Spain, Qatar, France, Brazil, Colombia, India, Russia, Kuwait, and Egypt. We fitted a model for every country by comparing the number of cases before vaccination and after vaccination for 8 weeks approximately. Our prediction in this article is based on the data sets given by the World Health Organization. Using machine learning is proved that vaccination cuts down on the number of diseased people and deaths despite the mutations by applying the models to many countries. We are concluding with further development for the domain of the proposed model. [ABSTRACT FROM AUTHOR]

• The paper involved a large number of experimental subjects. • The dimensionality reduction of muscle synergy was based on the physiological knowledge of the human body. • The isometric processing was able to accommodate different walking speeds. • The paper analyzed each subject and provided reference suggestions based on the processing results. Surface Electromyography (sEMG) is an important source for obtaining human motion information, the joint angle of the human body can be predicted. Therefore, this paper proposed a method for predicting the knee joint angle of the lower limb based on sEMG signals. Firstly, the muscles with the strongest correlation during the walking state were selected by using muscle synergy. Then the root mean square (RMS) of the selected muscles and their gradients were used as features for predicting the knee joint angle. Finally, regression models were used to predict the knee joint angle. We chose to use publicly available dataset from the Exoskeleton and Prosthetic Intelligent Control (EPIC) laboratory to validate the proposed method in this paper, and the predicted angle can provide real-time reference for the control process of the lower limb exoskeleton and other devices. [ABSTRACT FROM AUTHOR]

Malaysian government recently implemented a compulsory waste separation program in Kuala Lumpur as an effort to reach the national target of 40% waste diversion from landfill and 22% recycling rate by 2020. This study aims to investigate the effectiveness of the program in the aspect of disposal and recycling trends. The results showed that the annual average daily generation rate was reduced by 105 tonnes/day or 5.5% after the implementation of the program. The collection amounts of recyclable materials were only about 1.3 tonnes/day. This low amount might be due to illegal collection by recycling vendors, poor enforcement by the authority and selling of the recyclable materials by residents directly to vendors. [ABSTRACT FROM AUTHOR]

The addiction to social networks by young people is something that has been increasing in recent years. It has led to the application of methods to analyze the behavior of this variable and generate strategies that help mitigate its negative impacts. This research contributes to this, using a Markovian model and a regression model in a novel way. In the first instance, the variable time of permanence in social networks is analyzed by considering socio-affective, socio-economic, academic and demographic factors. Then, the Markovian model is built. Finally, the incidence of associated factors in addiction to social networks is analyzed. The results indicate that the probability of using social networks in less than one hour is 33%, and between two and three hours is 62%. Most students then spend many hours on social networks, affecting academic aspects, job performance, interpersonal relationships, and others. [ABSTRACT FROM AUTHOR]

In hot climates, achieving a good indoor environmental quality (IEQ) in existing buildings is important especially with climate change challenges as future heat waves will increase in frequency, duration, and intensity. In educational buildings, there is much more focus on the IEQ parameters and the interactions among them that need to be in line with the continuously changing learning environment. This study assesses the IEQ parameters (represented by noise, temperature and humidity) at three selected campus areas (lecture rooms of an administrative department building (LR), main hall of a management department building (MH) and a central library building (CL)) at the Al-Najaf Technical Institute (NTI), Al-Najaf City, Iraq, for the period from May to December 2019. A statistical analysis using a multi-linear regression model was performed to determine the relationship between the selected IEQ parameters and explain the noise level behavior as a function of the temperature and relative humidity. The research indicated that the noise levels and temperature values exceeded the maximum standard limits in all buildings reflecting the displeasing sound and heating quality within the studied areas, while the readings for relative humidity within each building environment complied with standards. Moreover, for both LR and MH buildings (R2 ≥ 0.8, significance F ≤ 0.01), the noise values were satisfactorily modeled by temperature and relative humidity highlighting the interactions between temperature, humidity and noise under consistent conditions. However, the results for the CL building (R2 = 0.6, significance F = 0.1) showed no relationship between the IEQ parameters, highlighting the fact that this building is exposed to unsteady conditions (an irregular number of people using this building during the daytime) resulting in a high variation of data measurements. The current results demonstrate that detailed modeling can be helpful to predict IEQ parameters depending on other known parameters in buildings. The results of the predictive model aligned with the directly measured data. Therefore, its performance is equally effective, but with a significant reduction in cost and time consumed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Physical Education (24482455) is the property of Universidade Estadual de Maringa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The effect of the impact load exerted by the baggage impacting light baggage carousels may be manifested as mechanical damage to the carousel as a result of the stress-strain processes. In order to describe the phenomena related to the baggage impact, it is important to monitor the tensile strength of rubber carousels of light conveyor belts intended for the conveyance of baggage at airports. The output of the article is monitoring the mechanical load of the carousel, the comparison of the results thereof with the outputs of the CAE analysis, as well as the determination of the optimal material model and the approximation thereof to the experimental model. [ABSTRACT FROM AUTHOR]

The design of an energy-efficient tracking framework is a well-investigated issue and a prominent sensor network application. The current research state shows a clear scope for developing algorithms that can work, accompanying both energy efficiency and accuracy. The prediction-based algorithms can save network energy by carefully selecting suitable nodes for continuous target tracking. However, the conventional prediction algorithms are confined to fixed motion models and generally fail in accelerated target movements. The neural networks can learn any non-linearity between input and output as they are model-free estimators. To design a lightweight neural network-based prediction algorithm for resource-constrained tiny sensor nodes is a challenging task. This research aims to develop a simpler, energy-efficient, and accurate network-based tracking scheme for linear and non-linear target movements. The proposed technique uses an autoregressive model to learn the temporal correlation between successive samples of a target trajectory. The simulation results are compared with the traditional Kalman filter (KF), Interacting Multiple models (IMM), Current Statistical model (CSM), Long Short Term Memory (LSTM), Decision Tree (DT), and Random Forest (RF) based tracking approach. It shows that the proposed algorithm can save up to 70% of network energy with improved prediction accuracy. • Energy efficient target tracking using resource constrained Wireless Sensor Network. • A lightweight and less complex Neural Network. • Auto Regressive model for time series prediction. • A non-conventional neural network with memory elements for tracking framework. • Sensor Nodes Selection using Auto Regressive Neural network. [ABSTRACT FROM AUTHOR]

Multimodal tactile sensors that can detect multiple external stimuli in a single device hold great promise within the domains of wearable technology and robotics. However, accurate decoupling of complex intermixed stimuli remains a significant challenge for real-time detective sensory system, hampering their versatile utilization. Here, we present a multimodal sensor platform that integrates a dual-stream deep learning process and microporous ionotronic multimodal tactile sensors. Importantly, the synergetic combination of carbon black and poly(vinylidene fluoride-co-hexafluoropropylene)/ion-gel (CBIG) facilitated a dual-mode sensing capability for both pressure and temperature (in capacitive and resistive modes), with high sensitivity of 0.350 kPa−1 and − 0.745% ℃−1, respectively. Micro-computed tomography revealed that the large capacitive change with pressure is attributed to the decrease of micro-pore volume and enlarged contact area between the electrode and the CBIG foam where an electric-double-layer is formed. By adopting a deep learning process based on a regression model, highly accurate identification of arbitrary intermixed pressure and temperature stimuli was possible, showing mean-absolute-percentage-error values of 1.58% and 2.37%, respectively. By utilizing the CBIG sensor integrated with the deep learning framework, simultaneous detection of surface temperature and pressure is demonstrated using a robotic arm, showcasing the versatile utilization of CBIG sensors in energy-efficient intelligent sensory systems. [Display omitted] • A multimodal sensor platform integrating deep learning and ionotronic sensor is demonstrated. • The combination of CB/ion-gel facilitated dual-mode sensing for pressure and temperature. • Dual-stream deep learning enabled highly accurate identification of arbitrary intermixed stimuli. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the indoor and outdoor particulate matters in residential areas, and to evaluate the effects of building characteristics and climatic conditions on indoor particle concentrations. The concentration of particles was measured simultaneously indoor and outdoor air during four seasons. Information on climatic conditions and building characteristics was collected through questionnaires during the sampling period. Linear regression models were adopted for determining the relationship between the dependent variable of I/O ratio and environmental factors. The I/O ratios of PM1, PM2.5, PM4, PM7, and PM10 were 0.67, 0.64, 0.61, 0.55, and 0.52, respectively. Moreover, the concentration of PM in the indoor air of the buildings were considerably lower than those of the outdoors (p<0.05). The results also suggest the ventilation mode and outside temperature had the most important role in the entrance of particles into the indoor environment. [ABSTRACT FROM AUTHOR]

Pelvic floor muscle (PFM) weakness is a common presentation among older adults, assessment of which is complicated by privacy issues, availability of perineometer, and lack of trained therapists. Several factors seem to influence PFM strength. Hence, this study developed an alternate method using a regression model to determine PFM strength among institutionalized older women using 27 independent variables. A total of 102 participants were assessed for their PFM strength using peritron perineometer. Age, years of menopause, pelvic surgical procedures, strength of hip muscles, and degenerative disc disorders were found to have a significant odds ratio and were included in the regression model (r2 = 0.484). It may be used as a simple and easy-toadminister method for the evaluation of PFM weakness. [ABSTRACT FROM AUTHOR]

Faced with limited land resources, large and medium-sized cities are compelled to make use of land in an efficient and intensive way. At the same time, the available traffic resources in many cities cannot meet the soaring trip demands. From the perspective of bicycle travel, this paper explores the characteristics of land use and bicycle travel in different types of urban blocks in Xi'an, based on the data collected from field surveys, cellular signaling data, Mobike and other sources. Through correlation analysis, the indices of land use were screened, forming an evaluation index system. On this basis, a model of the relationship between the proportion of bicycle travel and land use indices for each type of blocks was established. Finally, the interval under each land use index was optimized to promote bicycle travel in some blocks. According to the research results, there is a positive correlation between the land intensity of Xi'an city block and the bicycle travel ratio within a certain range. However, when the land intensity exceeds a certain value, bicycle travel will be inhibited. The interval values of the three types of land use indicators are as follows. It is recommended that the range of floor area ratio for the first ring is between 2.18 and 2.25, and the range of mixing degree is between 0.43 and 1.16. It is recommended that the range for the floor area ratio of the second ring is between 2.63 and 3.50, and the mixing degree is between 0.98 and 1.43. It is recommended that the range for the floor area ratio and mixing degree of the third ring should be between 1.52 and 1.80 and 0.72–1.38, respectively. The purpose of this study is to provide reference methods, ideas and operational indicators for the optimization and reconstruction of land use. [ABSTRACT FROM AUTHOR]

DNS (Domain Name System) tunnels almost obscure the true network activities of users, which makes it challenging for the gateway or censorship equipment to identify malicious or unpermitted network behaviors. An efficient way to address this problem is to conduct a temporal‐spatial analysis on the tunnel traffic. Nevertheless, current studies on this topic limit the DNS tunnel to those with a single protocol, whereas more than one protocol may be used simultaneously. In this paper, we concentrate on the refined identification of two protocols mixed in a DNS tunnel. A feature set is first derived from DNS query and response flows, which is incorporated with deep neural networks to construct a regression model. We benchmark the proposed method with captured DNS tunnel traffic, the experimental results show that the proposed scheme can achieve identification accuracy of more than 90%. To the best of our knowledge, the proposed scheme is the first to estimate the ratios of two mixed protocols in DNS tunnels. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The research aimed to study the status of electronic wastes in Mosul city as it is considered a new threat to the environment. The study involves composition, generation rate of electronic waste and its relationships with some of the creation source features. A questionnaire was prepared to collect the data, which was conducted through a 57 visits. The results showed that monitors and televisions represent the higher percentage among other types with a percentage of 51.4% according to weight. The electronic waste generation rate in Mo-sul city was 1.78 kg/capita/year for the period 2009-2014. It increased in families with high education level head and those with high income, while it decreased as family members increased. Additionally, families in the metropolitan quarters have higher generation rate than those in the mixed quarters. The environmental sense of the risk of electronic wastes ranged between 85.5-100% according to waste type. A regression model results showed that the education level of family head had the highest effect on the variation in electronic waste generation rate followed by quarter type and then number of family members. The research recommended to stop importing the electronic waste with-in the bales from China. [ABSTRACT FROM AUTHOR]

Violation of constant error variance assumption in any linear regression model leads to the problem of heteroscedasticity. In practices, variance of error terms are unequal and unknown in nature, but there is need to determine the presence or absence of this problem that do exist in unknown error term as a preliminary diagnosis on the set of data we are to analyze or perform hypothesis testing on. Although, there are several forms of heteroscedasticity and several detection methods of heteroscedasticity, but for any researcher to arrive at a reasonable and correct decision, best and consistent performed methods of heteroscedasticity detection under any forms or structured of heteroscedasticity must be determined. This paper consider seven heteroscedasticity structures that were originally proposed by different authors for developing statistical tools for heteroscedasticity detection in linear regression model. Nine heteroscedasticity detection methods were considered in other to determine some heteroscedasticity methods that are best to be used for the determination of unequal variance presence among the error terms in a linear regression model when there exist no forms of correlation between the exogenous variables of the model via the power of the test. In this work, Monte Carlo experiment was conducted one thousands (1000) times on a linear regression model with three predictor variable that exhibit no degree of multicollinearity (p = 0) and seven sample sizes (n = 15,20,30,50,100 and 250). The parameters of the model were specified to be β0 = 4, β1 = 0.4, β2 = 1.5, β3 = 3.6 and the various tests were examined at 0.1, 0.05 and 0.01 levels of significance. The Confident interval criterion (C.I) was used to determine the performances of the methods. The study concluded that when power of a test is considered to determine the preferred methods of heteroscedasticity detection in a model where there exist no multicollinearity between the exogenous variables at significance level of a = 0.1, BG test or GFQ test are the preferred methods, when testing under significance level of a = 0.05, the preferred method to used is GFQ method, while at a = 0.01, the preferred method to use in testing for the presence of heteroscedasticity is either BG method or NVST method. [ABSTRACT FROM AUTHOR]

A quadratic model was developed in this study to predict equipment life span for a preventive maintenance planning of KHS DMG-VF84 bottling line system. This life span forecasting model for KHS DMGVF84 bottling line system predict equipment life span at an instant of time. The six key predictors that were significant in the developed model are: Availability (A), Reliability (R), Mean time to Repair (T), Failure rate (F), Operational time (O) and Mean time before failure (B). The model utilized the polynomial method to predict the end of life of the bottling line system. The test data showed that the mean absolute percentage error for this model is 7.5% and has the ability to predict life span of the bottling line system with a good degree of accuracy of 79.65% with ± 0.20% error and the coefficients of determination R² for the developed model is 0.7965. This indicates that the relationship between the dependent variable and the independent variables of the developed model is good and the predicted values from a forecast model fit with the real-life data as well. Therefore, maintenance professionals should adopt this model for accurate estimates, to enable good detection of possible failures in production machineries. [ABSTRACT FROM AUTHOR]

• We have developed a new method to track tumor position using fluoroscopic images. • Tracking accuracy averaged over seven liver cases was 1.30 ± 0.54 mm. • Computation time was < 33 ms for a pair of fluoroscopic images. We have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data. Our method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time. The mean positional error in diaphragm tracking was 0.57 ± 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 ± 0.30 mm by modeling error, and 0.81–2.37 mm with 1–2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 ± 0.54 mm and the mean computation time was 69.0 ± 4.6 ms and 23.2 ± 1.3 ms per frame for the training and tracking stages, respectively. Our markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases. [ABSTRACT FROM AUTHOR]

Time series analysis and prediction have been widely accepted in various domains from last two decades. Business analytics, Medical drugs & pharmaceutical, Dynamic Marketing, Weather forecasting, Pollution measures, financial portfolio analysis and Stock market prediction are the favorite domains among research communities under time series analysis. Since air quality is one of the paramount factors which make life possible on earth and monitoring air quality data as time series analysis is a one of prime area. The most affected air quality parameters on health are carbon monoxide (CO),carbon dioxide (CO2), Ammonia(NH3) and Acetone ((CH3)2CO). In this paper we have taken the sensor's data of three specific locations of Delhi and National Capital Region (NCR) and predict air quality of next day using linear regression as machine learning algorithm. Model is evaluated through four performance measures Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The study further assesses with benchmark model and obtains significant results. [ABSTRACT FROM AUTHOR]

As a major contributor to the energy consumption and carbon emissions of the whole society, carbon reduction from residential building operation holds significant importance for achieving the carbon peak and neutrality goals. Although research on carbon emissions of the building sector on a global and regional scale has been conducted, it is challenging to identify overall patterns representing the operational carbon emissions of residential buildings in cities. This study utilized operational energy use to analyze and characterize the relevant carbon emissions of residential buildings in 34 representative cities across China over a decade from 2012 to 2021. The average emissions were assessed as 1.0 tCO 2e per capita which were relatively higher in cold regions. Based on a machine learning algorithm, regression analysis was conducted to predict the operational carbon emissions considering the features including population, economy, geography, urbanization renewable energy, and building electrification. Five models based on polynomial regression, support vector regression, random forest, gradient boosting decision tree, and extreme gradient boosting were trained. It was found that the extreme gradient boosting model exhibited the best performance for predicting operational carbon emissions per capita with R2 value of 0.948 for the testing dataset. Further analysis identified building climate zone, population, urbanization rate, and building electrification rate as the most influential features on the emissions. The predictive approach for estimating operational carbon emissions in cities can provide valuable insight into carbon reduction of the building sector. • Operational carbon emissions of residential buildings assessed for 34 cities. • Five machine learning regression models developed for the emission prediction. • XGB determined as the best-fitted model for predicting operational emissions. • Identifying the climate as the most influential factor of operational emissions. [ABSTRACT FROM AUTHOR]

To study the characteristics of postmortem ethanol production and its relation with alcohol congeners in postmortem rat liver and muscle tissues. Postmortem liver and muscle tissues in Sprague–Dawley rats, from postmortem time interval (PMI) day 0–20, were analyzed via headspace gas chromatograph flame ionization detection to observe production of postmortem ethanol and 5 selected alcohol congeners. 1. Putrid ethanol production increased gradually to a peak and then decreased with the prolongation of PMI; 2. Acetaldehyde, 1-propanol, and 3-methyl-butyraldehyde were produced along with postmortem ethanol; 1-butanol was only detected from day 11–20; 3. The concentrations of acetaldehyde, 1-propanol and 3-methyl-butyraldehyde was related with ethanol production. Fifteen mathematical models were constructed for putrid ethanol production based on acetaldehyde, 1-propanol, and 3-methyl-butyraldehyde. A peak in postmortem ethanol production was identified. The production trends of acetaldehyde, 1-propanol, and 3-methyl-butyraldehyde in the liver, and of 1-propanol in muscle, were consistent with those of ethanol, and could potentially to be used as biomarkers of postmortem ethanol production. Further human samples and data analysis are needed to verify this. • Putrid ethanol production increased gradually to a peak and then decreased with the prolongation of postmortem time interval. • The production trends of alcohol congeners could potentially to be used as biomarkers of postmortem ethanol production. • The parenchymatous organs could be an alternative for postmortem ethanol analysis when blood cannot be obtained. [ABSTRACT FROM AUTHOR]

This paper focuses on the effect of some water quality parameters of river water which helps in the calculation of water quality index (WQI) that culminates in the development of a regression model for prediction of WQI of the river system in India. The index was calculated by arithmetic index method using twelve various experimentally estimated water quality parameters such as potential hydrogen, chlorides, dissolved oxygen saturation, nitrates, sulphates, phosphates, total dissolved solids, biochemical oxygen demand, electrical conductivity, total hardness, turbidity and total coliform of the water at eight locations, for a 55 km stretch of Chalakkudy river November 2013 to December 2018. It was identified that total coliform is the major parameter contributing to the bad quality of water. Water quality regression model has been developed as a function of total coliform content. The performance of the model in predicting the WQI has been tested by comparing with the calculated WQI for the following year 2018. The regression model has been found to be good with an absolute average relative error and root mean square error values of 0.693 and 0.5 respectively. The results indicate that the basin is slowly getting into a serious drinking water crisis. [ABSTRACT FROM AUTHOR]

Nowadays, deep learning is utilized in a wide range of fields and has achieved great results. However, in order to use deep learning, it is necessary to prepare a large amount of training data, and this becomes a big obstacle in some fields. In this study, we show the case to estimate quantities from pictures by using the regression model of deep learning, which is trained by CG images generated automatically. And, to improve the estimation accuracy of this case, we propose the following methods: mixing pictures into the CG images which is training data; converting the color of pictures for estimation according to the CG images. And, we evaluate these methods and show their effectiveness. [ABSTRACT FROM AUTHOR]

Abstract The precise and convenient calculation of bottom hole pressure can provide a guarantee for safe drilling. Most of the existing models for determining the downhole pressure or annular pressure gradient (PG) are applied only to the circular wellbore. Due to the nonuniform in situ stress and the anisotropism of formation rock, the borehole formed by rotary drilling is the closest to the ellipse, that is, the irregular circular borehole. Therefore, it is of importance to research YPL fluid flow through the eccentric elliptical annulus (EEA) for the accurate prediction of bottom hole pressure. In this paper, a numerical model for determining the PG of YPL fluid flow in the EEA is developed on the basis of fluid mechanics. Then, through parameter studies, a new regression model (RM) of the pressure ratio in the EEA is established. The commercial software ANSYS was used to research the laminar flow of yield-power-law fluids through the EEA, and the simulation results are used to verify the numerical model and new regression model. The main conclusions are as follows. (1) The PG increases quasi-linearly or linearly with the mean velocity, fluid consistency index and yield stress and hardly changes with the azimuth of the inner pipe center. The PG decreases with a / b ( η) and eccentricity ( ε) when the mean velocity is constant. (2) The new regression model is valid for the ranges 0.2≤ K d ≤ 0.8, 0≤ ε ≤ 0.95, 0.2 ≤ n ≤ 1.0, and 1.0≤ η ≤ 1.2 and shows excellent agreement with existing model and computational fluid dynamics (CFD) simulations, within ±5% error bars. (3) The maximum velocity first increases and then decreases with ε . The results of axial velocity and PG using CFD software are found to be in excellent agreement with the numerical model solutions. (4) Predicting the PG in the EEA using the new regression model has high precision and without complicated numerical computation. Highlights • A numerical model was established to predict YPL fluid flow in an eccentric elliptical annulus. • A new regression model was developed to predict the PR for YPL fluid flow in the eccentric elliptical annulus. • CFD simulations were used to validate the numerical model and new regression model. • The regression model was shown to be in excellent agreement with CFD simulations, within ±5% error bars. [ABSTRACT FROM AUTHOR]

Highlights • Green synthesis of Fe 3 O 4 nanoparticles using a combination of urea (U) and gram bean extract (GBE, Cicer arietinum L.). • Uniform particle size distribution and highly stable (more than 6 months under ambient conditions). • Particle size ∼13 nm (Sample C) shows optimum SAR value, which is near to the therapeutic temperature (43 °C) with least variance. • A multiple linear regression model (MLRM) is used to correlate experimental parameters on the SAR values. • A positive correlation of SAR with R m and AMF values and negative correlation with M s and W t values. Abstract A green synthesis of biocompatible magnetite (Fe 3 O 4) nanoparticles (MNPs) using a combination of urea (U) and gram-bean extract (GBE, Cicer arietinum L.) is reported. The particle size of ∼13 nm and highly stable magnetite phase is observed for GBE-U mediated MNPs. On the other hand, the MNPs synthesized using either U or GBE shows larger particle size and uneven size distribution. Interestingly, the sample with particle size ∼13 nm shows optimum heat generation capacity (measured in specific absorption rate, i.e., SAR) near to the therapeutic temperature (43 °C) with least-variance. To investigate the influence of various factors such as variation in MNPs weight concentration (W t), applied alternating magnetic field (AMF), saturation magnetization (M s), magnetization rate (R m), etc. on SAR, a multiple linear regression model (MLRM) is used. The study reveals a positive correlation of SAR with R m and AMF values while the negative correlation with M s and W t. Ultimately, the present green synthesis is the affordable approach for preparing stable and tiny MNPs. Moreover, MLRM is found to be a useful theoretical tool for understanding the influence of MNPs on hyperthermia performance. Graphical abstract Image, graphical abstract Size distribution of synthesized magnetite particles (left Y -axis) and box plot (right Y -axis) to display variation in temperature gain. Accordingly, the variation in temperature gain with the size distribution of MNPs is shown, which depicts superiority of sample C regarding hyperthermia treatment as it contains relatively smaller sized MNPs (∼13 nm) and provides temperature with least variance. [ABSTRACT FROM AUTHOR]

Abstract Remote heart rate measurement has many powerful applications, such as measuring stress in the workplace, and the analysis of the impact of cognitive tasks on breathing and heart rate variability (HRV). Although many methods are available to measure heart rate remotely from face videos, most of them only work well on stationary subjects under well-controlled conditions, and their performance significantly degrades under subject's motions and illumination variation. We propose a novel algorithm to estimate heart rate. Also, it can differentiate between a photo of a human face and an actual human face meaning that it can detect false signals and skip them. The method obtains ROIs using facial landmarks, then it rectifies illumination based on Normalized Least Mean Square (NLMS) adaptive filter and eliminates non-rigid motions based on standard deviation of fixed length of the signal's segments. The method employs the RADICAL technique to extract independent subcomponents. The heart rate measures for each subcomponent, are estimated by analysis of frequency signal to find the one with the highest magnitude. A two-steps data fusion method is also introduced to combine current and previous measured heart rates to calculate a more accurate result. In this paper, we explore the potential of our algorithm on two self-collected, and DEAP databases. The results of three experiments demonstrate that our algorithm substantially outperforms all previous methods. Moreover, we investigate the behavior of our algorithm under challenging conditions including the subject's motions and illumination variation, which shows that our algorithm can reduce the influences of illumination interference and rigid motions significantly. Also, it indicates that our algorithm can be used for the online environment. Finally, the application of our algorithm in search and rescue scenarios using drones is considered and an experiment is conducted to investigate the algorithm's potential to be embedded in drones. [ABSTRACT FROM AUTHOR]

Inconel-718 is a nickel based super alloy and is extensively in use for working at very high temperature (upto 2000 °C) such as aero engine gas path equipment, nuclear equipment etc. Drilling micro size hole in such material with laser beam has been a proven choice and laser drilling process produces geometrically and dimensionally improved hole. Hole geometrical features can be improved further if laser drilling system operated at optimal input parameter setting. This paper experimentally investigates the behavior of hole geometrical features hole circularity and hole taper in laser trepan drilling of Inconel -718 sheet. Optimal value of laser input parameters for improved hole circularity and reduced hole taper have been suggested with the help of computational intelligence technique particle swarm optimisation. The effect of each laser input parameter on hole quality characteristics are also discussed and demonstrated graphically. Finally the experimental validation of the predicted results has been carried out. [ABSTRACT FROM AUTHOR]

• Analyzing impact of social reaction to disaster situations on electricity consumption. • Electricity consumption prediction using a multiple non-linear regression model. • Robust-stochastic optimization utilization for system resilience enhancement. • Introducing a novel resilience criterion in line with resilience assessment of MGs. In this paper, a new multi-objective two-stage robust-stochastic (MOTSRS) optimization approach for assessing microgrids and distribution system resilience is proposed. A novel approach based on deep learning is presented to model social behavior, specifically Social Reactions to Disasters (SRDs), in assessing microgrid and distribution system resilience. It is initially demonstrated that the proposed robust-stochastic approach is efficient for assessing the resilience of microgrids and distribution systems. A new resilience criterion is also proposed to be used for sensitivity analyses at the conservative level of the model. Subsequently, the effect of SRDs on system resilience is investigated through a supervised deep regression method. First, a large dataset is generated through a proposed nested Monte Carlo simulation. A numerical feature and five binary social behavior features are included in the dataset. Next, a feedforward neural network is utilized to predict the load during both normal and disaster circumstances based on the six features. The proposed supervised deep regression model with mean squared errors between 2e-5 and 2e-6 is capable of predicting electrical load during normal and disaster events. It is concluded that the resilience metrics of MGs, as well as expected operation costs and regret values, are influenced by the SRD effect. [ABSTRACT FROM AUTHOR]

In this paper a regression model is applied for validating the customers of a company. Using a Delphi method beside the expert panel the main variables which construct the regression model are extracted. A credit scoring system for validation of the customers is developed based on applied regression model. Then a Newton-Raphson method is used for determining the coefficients of regression model. Furthermore the MacFadden statistical value is calculated for approving the regression model. A case study is presented for application of proposed model. Three factors of sponsor's job, applicant's jobs and income are extracted as the main factors which are affecting the customer validation in the case. The result of the proposed model based on the case study showed that using a regression model which is empowered by Delphi system can provide a robust model for validation of the customers for deciding on granting to the customers. [ABSTRACT FROM AUTHOR]

Abstract In the paper, we shall establish some limit theorems for the nonparametric estimator of the regression model, which include L p -convergence, complete convergence, and strong convergence of the estimator. These results supplement and improve some known works. [ABSTRACT FROM AUTHOR]

The article discusses the problem of heteroskedasticity, which can arise in the process of calculating econometric models of large dimension and ways to overcome it. Heteroskedasticity distorts the value of the true standard deviation of the prediction errors. This can be accompanied by both an increase and a decrease in the confidence interval. We gave the principles of implementing the most common tests that are used to detect heteroskedasticity in constructing linear regression models, and compared their sensitivity. One of the achievements of this paper is that real empirical data are used to test for heteroskedasticity. The aim of the article is to propose a MATLAB implementation of many tests used for checking the heteroskedasticity in multifactor regression models. To this purpose we modified few open algorithms of the implementation of known tests on heteroskedasticity. Experimental studies for validation the proposed programs were carried out for various linear regression models. The models used for comparison are models of the Department of Higher Mathematics and Mathematical Methods in Economy of Simon Kuznets Kharkiv National University of Economics and econometric models which were published recently by leading journals. [ABSTRACT FROM AUTHOR]

Present study aimed to generate multiple regression models to estimate the formation of trihalomethanes (THMs), haloacetonitriles (HANs) and haloacetic acids (HAAs) during chloramination of source water obtained from Yangtze River Delta Region, China. The results showed that the regression models for trichloromethane (TCM), dichloroacetonitrile (DCAN), dichloroacetic acid (DCAA), dihaloacetic acids (DHAAs), 5 HAAs species regulated by U.S. EPA (HAA 5 ) and total haloacetic acids (HAA 9 ) have good evaluation ability (prediction accuracy reached 81–94%), while the models for total haloacetonitriles (HAN 4 ), trichloroacetic acid (TCAA), trihaloacetic acids (THAAs) and total trihalomethanes (THM 4 ), they appeared relative low prediction accuracy (58–72%). For THMs, dissolved organic nitrogen (DON) was their key organic precursor, yet for HAN, DHAAs and THAAs, UVA 254 played the dominant role. The other key factors influencing DBP formation included the bromide (THM 4 , DHAAs and HAA 9 ), reaction time (DCAN, HAN 4 ), chloramine dose (TCM, DCAA, TCAA, HAA 5 and THAAs). These results provided important information for water works to optimize the water treatment process to control DBPs, and give an evaluating method for DBPs levels when estimating the health risks related with DBP exposure during chloramination. [ABSTRACT FROM AUTHOR]

The German Aerospace Center has developed and applied a “classical” four-step model of forecasting passenger and flight volume at German airports for many years. However, it has become increasingly difficult to update and verify the model because of a lack of specific data. We have therefore developed a more versatile model based upon co-integration theory, which directly forecasts passenger and flight volume at German airports. The paper describes the model approaches and discusses the advantages and disadvantages of both the classical and new model approaches. The model includes demand shocks and estimated GDP-elasticity is 1.31. The model has been employed to estimate the effects of Brexit on traffic volume at German airports for the years 2016–2018. [ABSTRACT FROM AUTHOR]

The objective of this study was designed to evaluate the effects of nutrients (inorganic NPK fertilizer and organic fertilizer) and adsorptive amendment agents (activated carbon) as well as their interactions on bioremediation of soil contaminated with Weathered Crude Oil (WECO). The organic fertilizer was made up of cattle dungs, pig dungs and poultry droppings mixtures. Box Behnken Design (BBD) of Response Surface Methodology (RSM) with three levels and three factors was used having inorganic NPK fertilizer (3 - 5 g), activated carbon (20 - 40g) and organic fertilizer (30 - 50 g) as independent variables and percentage WECO degradation as dependent variable (response) in a four weeks remediation period. Results of the study showed that WECO removal rate in soil is invariably dependent on the amount of nutrient and adsorptive agents. The optimum amount of nutrient and adsorptive amendment agents obtained to achieve a predicted maximum WECO removal of 66.24% are: inorganic NPK fertilizer, 4.19 g; activated carbon, 35.42 g and organic fertilizer, 48.94 g. At this predicted optimum amount, validation experiment revealed that 65.55% of WECO removal was achieved. Statistical analyses revealed the closeness of the experimental results to model predictions which thus show the reliability of the quadratic regression model. Therefore, organic and inorganic nutrients as well as adsorptive amendment alone and/or in combination enhances soil indigenous microbial density and activity which thereby accentuates petroleum hydrocarbons biodegradation and subsequently reduces the period of bioremediation and remediation cost. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to investigate the relationship among income, diets and health, trying to answer the questions how income influences the food choices and to what extend diet plays a role in chronic diseases. In pursuing this, data regarding the average food consumption of the main agro-food products and the average income of people in Romania are analysed. The regression model show that an increase in income drives to a growth in consumption of food which have negative effects on human health, such as margarine, meat and meat products, and soft drinks. For each additional monetary unit gained, people will consume more with 0.00004 kg of margarine, 0.0039 litters of non-alcoholic beverages and 0.0004 kg of meat and meat products, which leads to higher quantities of unhealthy food products as revenues increase. The relevance of this study consists in preventing diseases through a healthy eating. By better understanding the relationships among diet, income and health, people can make informed and conscious choices on foods they consume on a regular base. [ABSTRACT FROM AUTHOR]