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Objective To investigate the clinical characteristics and prognosis of crown fractures in immature permanent incisors due to trauma, and identify factors affecting their prognosisto provide a reference for clinical treatment. Methods This study was approved by the Medical Ethics Committee of West China Stomatology Hospital, Sichuan University. The study subjects were patients admitted to the pediatric stomatology department from December 2011 to December 2021, and a retrospective analysis was conducted on young permanent teeth with anterior crown fracture caused by injury and followed up for at least 1 year, which were diagnosed as enamel fractures, enamel-dentin fractures, and complicated crown fracture and treated by observation, pulpotomy etc. in the first appointment. The age, sex and time elapsed from trauma to baseline visit of the patients and the location, mobility, stage of root development, diagnosis and treatments were collected. The occurrence of pulp infection, pulp necrosis, and other events in the affected tooth is defined as clinical failure. Record whether clinical failure occurred and the timing of their occurrence of the traumatized tooth. Analyze factors related to the prognosis of various types of crown fractures in young permanent incisors and performsurvival analysis on the affected teeth. Results Among 358 cases of young permanent incisors, 50 cases were diagnosed with enamel fracture, 176 cases with enamel-dentin fracture, and 132 cases with complicated crown fracture. The clinical success rate of crown fractures was 73.7% (264/358) in young permanent incisors. The incidence rates of clinical failure cases, including pulp infection and necrosis, were 4% (2/50) for enamel fractures, 33.3% (58/176) for enamel-dentin fractures, and 25.8% (34/132) for complicated crown fractures respectively. The clinical failure rate of enamel-dentin fracture treated with indirect pulp capping and restoration was higher than restoration only and pulpotomy (χ2 = 10.077, P = 0.004). The clinical failure rate of complicated crown fractures treated with direct pulp capping was higher than pulpotomy (χ2 = 5.501, P = 0.038). The clinical failure rates between observation, smoothing edges and restoration of enamel fractures exhibit no significant differences (χ2 = 0.588, P = 0.999). Risk factors for clinical failure in this study population included patient age over 9 years old (HR = 2.11, 95%CI: 1.1-3.9, P = 0.017)、time elapsed from trauma to baseline visit greater than 3 days (HR = 2.3, 95%CI: 1-4.8, P = 0.028), traumatized teeth with mobility (HR = 1.95, 95%CI: 1.2-3, P = 0.004), enamel-dentin fractures treated with restoration (HR = 6.89, 95%CI: 1.6-29.6, P = 0.010), enamel-dentin fractures treated with indirect pulp capping and restoration (HR = 13.8, 95%CI: 3.2-58.3, PHR = 46.07, 95%CI: 8-263.8, P

The assessment of the load-bearing capacity of piles holds significant importance in the design of pile foundations. This paper presents hybridized support vector regression (SVR) models by utilizing the Artificial Rabbit Optimization (ARO) and the Black Widow Optimization Algorithm (BWOA) to predict the bearing capacity of concrete piles. A repository comprising 472 reports on static load tests conducted on driven piles was employed for the study. The dataset was allocated into three parts: the training set (70%), the validation set ( 15% ), and the testing set ( 15% ). Multiple criteria for assessing quality were utilized to evaluate the effectiveness of the models. The first rank belonged to the SVR model integrated with the ARO algorithm, where it could gain the higher value of R^2 in all of training (R^2 = 0.9876), validating (R^2 = 0.9778), and testing sections (R^2 = 0.9874), and the lowest value of RMSE in all the training ( RMSE = 39.393 ), validating ( RMSE = 53.727 ) and testing sections ( RMSE = 38.082). The findings indicate that the suggested model is highly appropriate for predicting the capacity of concrete piles.

Introduction: Hypothyroidism has long been known to cause metabolic deceleration, including its effects on haematopoietic tissue, leading to anaemia. Anaemia and hypothyroidism can co-exist and present challenges for each other, resulting in a decline in quality of life. Aim: To address the gender-specific association of Thyroid Stimulating Hormone (TSH) with various haematological indices among a cohort of hypothyroid patients. Materials and Methods: A cross-sectional observational study was conducted at a tertiary care teaching hospital in Hyderabad, Telangana, India, from October 2022 to February 2023, after obtaining ethical clearance from the institutional ethics committee. A total of 347 newly diagnosed, biochemically proven hypothyroid patients attending the hospital were included in the study. The patients were divided into subclinical and overt hypothyroid groups based on their TSH values and thyroxine levels. All patients underwent blood tests for a complete blood count, including all haematological indices such as erythrocyte count, Haemoglobin (Hb), Packed Cell Volume (PCV), Mean Corpuscular Volume (MCV), Mean Corpuscular Haemoglobin (MCH), Mean Corpuscular Haemoglobin Concentration (MCHC), and Red cell Distribution Width (RDW). Gender-based stratification of the study population was performed. Data were analysed using the Student’s unpaired t-test, Chi-square test, and univariate linear regression with Statistical Package for the Social Sciences (SPSS) version 22.0. Results: The study highlighted a high prevalence of anaemia among overt hypothyroid patients 88 (54%) compared to subclinical hypothyroid patients 63 (34.24%). Moreover, the prevalence of anaemia was higher in females than in males. The most common type of anaemia encountered was microcytic anaemia, followed by normocytic anaemia. Linear regression analysis yielded a positive correlation between TSH and all haematological parameters, including RDW. This correlation was especially significant among females. Conclusion: TSH affected all the haematological parameters, including RDW, and the association was stronger in females as compared to males in both subclinical and overt hypothyroidism.

Objective To establish a prognostic risk scoring model for patients with pancreatic ductal adenocarcinoma (PDAC) based on cell ferroptosis-related genes, and to investigate its application value. Methods The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus database, and Genotype-Tissue Expression database were used to obtain the transcriptome sequencing data of PDAC tissue and normal pancreatic tissues and the clinical data of PDAC patients including overall survival (OS), and the GeneCards database was used to obtain the data on cell ferroptosis-related genes. R software was used to identify the differentially expressed genes (DEGs) between PDAC tissue and normal pancreatic tissue. The univariate Cox regression analysis and LASSO regression were used to establish a prognostic risk scoring model for PDAC patients, and based on this risk scoring model, the PDAC cases in TCGA database were divided into low- and high-risk groups. The Kaplan-Meier survival curves were plotted to compare OS between the two groups. CCK-8 assay and Western blotting were used to further validate the association between the key genes of cell ferroptosis in the risk scoring model and cell ferroptosis in PDAC. Results A prognostic risk scoring model for PDAC patients was successfully established based on seven cell ferroptosis-related genes, i.e., SLC6A14, DKK1, KRT19, AURKA, EMP1, ANXA2, and LGALS3, and the Kaplan-Meier survival curve showed that the low-risk group had a significantly longer OS than the high-risk group (P

Abstract Background The curriculum system is a central component in achieving the goals and specifications of talent training schemes. However, problems and difficulties exist in curriculum provision due to a lack of curriculum system design logic. This study aimed to investigate the correlation between the university curriculum system and graduate quality and to reveal the design logic of the curriculum system. Methods A total of 699 stomatology graduates from a university in Zhejiang were selected as research subjects from 2015 to 2022. The students’ curriculum system and graduate quality data were collected and classified. The graduate quality information contained (1) the National Board Dental Examination (NBDE) pass rate and score, (2) the postgraduate entrance rate and destination, and (3) the employment rate and work institution. Regression analysis was also conducted to assess the correlation between the curriculum system and graduate quality. Results Regression analysis revealed significant associations between general education, specialization, and stomatology X courses and NBDE score, postgraduate entrance rate and destination, and work institution. All courses except public elective courses had significant impacts on NBDE score. General basic, medical English, and employment guidance courses significantly influenced the postgraduate entrance rate and destination. Restricted elective and public elective courses had significant effects on employment rate and work institution. Conclusions Increasing the quality of specialized and stomatology X courses in the curriculum system is beneficial for deepening graduates’ expertise and enhancing their education. Moreover, English courses are suggested to be offered in the early stage to lay a better language foundation.

Abstract The outcome of this study was to identify 9-year survivors of intertrochanteric fracture at each stage of chronic kidney disease (CKD) and to investigate the risk factors associated with mortality following surgery with proximal femoral nail anti-rotation (PFNA). 443 elderly intertrochanteric fractures underwent PFNA fixation were recruited. Mortality rate was identified until 9 years. We compared the survival time of hip fracture in each stage of CKD. A regression analysis was used to determine the association between risk factors and one-year mortality. The overall median survival time was 7.1 years. The Kaplan–Meier curve was significantly different in each CKD stage especially in CKD5. In addition, the incidence rate of mortality was highest in CKD 5 (17.4%) and the median survival time in CKD 5 was 3.3 years. The multivariate analysis demonstrated that heart disease, operative time > 60 min, presence of pulmonary embolism, and poor to fair Harris hip score were significantly increased mortality. CKD stage 5 is associated with the highest mortality rate and the shortest median time of survival during the 9-year follow up. Patients who have high risk should focus on long-term care planning, including the counseling for their healthcare providers and families.

Abstract Background The severity of COVID-19 outbreaks is disproportionate across settings (e.g., long-term care facilities (LTCF), schools) across Canada. Few studies have examined factors associated with outbreak severity to inform prevention and response. Our study objective was to assess how outbreak severity, as measured using outbreak intensity and defined as number of outbreak-associated cases divided by outbreak duration, differed by setting and factors known to influence SARS-CoV-2 transmission. Methods We described outbreak intensity trends in 2021 using data from the Canadian COVID-19 Outbreak Surveillance System from seven provinces/territories, representing 93% of the Canadian population. A negative binomial fixed-effects model was used to assess for associations between the outcome, outbreak intensity, and characteristics of outbreaks: setting type, median age of cases, number at risk, and vaccination coverage of at least 1 dose. Also included were variables previously reported to influence SARS-CoV-2 transmission: stringency of non-pharmaceutical interventions (NPI) and the predominant SARS-CoV-2 variant detected by surveillance. Results The longest outbreaks occurred in LTCF (mean = 25.4 days) and correctional facilities (mean = 20.6 days) which also reported the largest outbreaks (mean = 29.6 cases per outbreak). Model results indicated that outbreak intensity was highest in correctional facilities. Relative to correctional facilities (referent), the second highest adjusted intensity ratio was in childcare centres (intensity ratio = 0.58 [95% CI: 0.51–0.66]), followed by LTCF (0.56 [95% CI: 0.51–0.66]). Schools had the lowest adjusted intensity ratio (0.46 [95% CI: 0.40–0.53]) despite having the highest proportion of outbreaks (37.5%). An increase in outbreak intensity was associated with increases in median age, the number at risk, and stringency of NPI. Greater vaccination coverage with at least 1 dose was associated with reduced outbreak intensity. Conclusion Descriptive and multivariable model results indicated that in Canada during 2021, outbreak intensity was greatest in closed congregate living facilities: correctional facilities and LTCF. Findings from this study support the importance of vaccination in reducing outbreak intensity when vaccines are effective against infection with circulating variants, which is especially important for closed congregate living facilities where NPIs are more challenging to implement.

To investigate the impact of variety, slice thickness, and drying temperature on the quality of dried kiwifruit under hot air drying conditions, three different kiwifruit varieties with slice thicknesses of 8, 10, and 12 mm were selected as research subjects. The dried kiwifruit was dried at hot air drying temperatures of 60, 65, and 70 ℃, and texture profile analysis (TPA) and sensory evaluation were conducted on the dried fruit. The results showed that there were significant differences in hardness, springiness and chewiness of all dried fruits when temperature was varied (P0.05). The adhesiveness, springiness, and resilience of the green heart samples could be used to replace the sensory evaluation, while the hardness, springiness, and chewiness of the yellow heart samples could be used to replace the sensory evaluation. Red heart samples still need further research. In conclusion, indices that play a decisive role in the evaluation of the quality of dried kiwifruit are hardness, chewiness, and gumminess, and the correlation analysis of the index of the parameters of dried kiwifruit of different species are significantly different.

This study investigated the geotechnical and geophysical properties of the soil layers at the Missan combined-cycle power plant in Iraq. The data from 69 boreholes, including physical and chemical soil properties, were analyzed. The soil is primarily classified as silty clay with moderate to high plasticity, with some sandy layers. Since the Missan governorate is located in a seismically active region represented by the Iraq–Iran border, a study on the seismic properties of the site is also performed. Seismic downhole tests were conducted to determine wave velocities and dynamic moduli. The site was classified as soft clay soil according to FEMA and Eurocode 8 standards. Correlations for the physical and dynamic soil properties were evaluated. The correlations were executed via regression statistical analysis via Microsoft Excel software (2013). The results of the correlation equations and the coefficient of correlation R2 show that the physical correlations were considered medium to good correlations, whereas the dynamic soil correlations were perfectly correlated such that the R2 values were close to 1. This paper provides comprehensive data and soil property correlations, which can be valuable for future construction projects in the Missan area and similar geological formations.

Abstract The understanding that students on varsity baseball teams exhibit lower academic performance amid the growth of baseball in Taiwan is well established. However, some studies have indicated a positive correlation between sports participation and academic achievement. Therefore, this study delved into the causal relationships between baseball-team participation and academic performance among ninth-grade students in Taiwan. It further explored the influence of various background factors based on the three facets of family capital on their academic performance. Data collected from an education longitudinal survey of secondary school students. Through bivariate analysis, factor analysis, and multiple regression analysis, the following findings emerged: (a) Baseball-team students exhibited 47.5 points lower academic performance than non-baseball-team ones. (b)The low academic performance was not exclusively a result of the poorer academic performance of indigenous students. (c)The hypothesis that three dimensions of family capital impact on academic performance was supported. (d) Baseball-team participation contributed to the low academic performance, which was not caused by the low eighth-grade results of the students. They exhibited low BCT scores after their 8th-grade results was controlled.

This research aims to shed light on the appropriate measures countries should take when battling a pandemic. It explores the effectiveness of various non-pharmaceutical interventions (also known as quarantine restrictions) on the reduction of the spread of COVID-19 using Kazakhstan and Kyrgyzstan in a natural experiment. All types of non-pharmaceutical interventions are effective at reducing the spread of the virus. Comparing the results for the two countries, it appears that in the summer 2020, the partial lockdown in Kyrgyzstan was just as effective as the full-lockdown in Kazakhstan at reducing the growth rate of new cases. Further, and interesting enough, the decrease was not immediate but gradual as it reduced the growth rate at roughly .3 percentage points per day for each country. Therefore, policymakers should not expect immediate results in the reduction of the number of new cases after imposing restrictions. It is only when the growth rate of new cases turns negative that new cases begin to fall. This might take a few weeks.

In order to reveal the response relationship between the neutrons and void height of steel-shell concrete composite structures，and establish a new quantitative detection method for the void height，based on the principle of fast neutron moderation and neutron scattering，an indoor equivalent concrete physical simulation method was used to construct a void model and conduct tests in this paper. Univariate regression analysis was carried out on the tests data，and it was found that there was a significant linear correlation between the thermal neutron count rate and the steel plate thickness，concrete moisture content and void height in the steel-shell concrete composite structure. The calibration relationship between the void height of steel-shell concrete and the thermal neutron count rate was further established，and the verification test was carried out. The results show that the error between the concrete void height value calculated by the calibration relationship and the actual void height value is small，the two have good consistency，the thermal neutrons generated by the action of fast neutrons penetrated through the steel plate and concrete can be used to quantitatively calculate the void height of the steel-shell concrete composite structure，the neutron method can solve the problem of inaccurate quantification in current detection.

Abstract Diamond-like carbon (DLC) coatings are gaining wider attraction due to their excellent mechanical and tribological properties. This research presents statistical models to predict the wear and friction coefficient of duplex CrN/DLC and nano-multilayer DLC-W coatings through analysis of variance (ANOVA). The coatings were deposited using a hybrid PVD/PECVD system onto carburized and quenched martensitic valve tappets. Tribological tests were conducted on the coatings in a ball-on-plane configuration using a reciprocating slide on an Optimol SRV® v4 device under dry conditions. ANOVA and regression analysis were employed to analyze the wear rate and coefficient of friction (COF), leading to the development of a prediction equation. The results indicated that both applied load and operating temperature had nearly equal influence on the COF and wear of the coatings. The reliability of the proposed statistical models was approximately 92–96% for duplex CrN/DLC and 90–97% for nano-multilayer DLC-W. These models are applicable only to the tested parameters within the deformation region. They can be used to predict the tribological operating conditions for the duplex CrN/DLC and nano-multilayer DLC-W coatings, helping to prevent premature failure.

Abstract The aim of this study is to investigate the influence of psychological capital on college students’ entrepreneurial intentions. Through a combination of relevant analysis and linear regression, the primary focus is on exploring the relationship between psychological capital and its four dimensions with entrepreneurial intentions. Firstly, the items in the psychological capital questionnaire were revised to align more closely with entrepreneurial contexts. Subsequently, the average deviations and standard deviations of each dimension of psychological capital were analyzed. Then, the correlation between psychological capital and entrepreneurial intentions was examined to explore the extent of their relationship. Finally, regression analysis was conducted on both psychological capital and entrepreneurial intentions, and utilizing a recurrent neural network model, the covariant relationship between entrepreneurial psychological capital and intentions was explored. The results indicated that the average scores for entrepreneurial self-efficacy, optimism, hope, and resilience were 3.91, 4.27, 4.19, and 4.15, respectively. The average value of psychological capital was 4.13, indicating a moderately high level. The correlation analysis between psychological capital and entrepreneurial intentions yielded a result of 0.562, indicating a moderate degree of correlation. The correlation coefficients of the four dimensions with entrepreneurial intentions were 0.390, 0.494, 0.531, and 0.467, respectively. The standardized coefficients for psychological capital and its four dimensions were 0.564, 0.382, 0.510, 0.536, and 0.468, all of which were statistically significant. Overall, psychological capital exhibited better predictive power for entrepreneurial intentions than its individual dimensions. The results from the deep learning model similarly demonstrated the positive role of psychological capital in entrepreneurial intentions, though the influence of ideological and political education (IPE) factors was relatively weaker. In conclusion, both psychological capital and IPE have a promotive effect on entrepreneurial intentions. This study provides a reference for the accurate evaluation of college students’ entrepreneurial intentions.

Abstract Solar energy production is an intermittent process that is affected by weather and climate conditions. This can lead to unstable and fluctuating electricity generation, which can cause financial losses and damage to the power grid. To better control power production, it is important to predict solar energy production. Big data and machine learning algorithms have yielded excellent results in this regard. This study compares the performance of two different machine learning approaches to solar energy production prediction: regression and classification. The regression approach predicts the actual power output, while the classification approach predicts whether the power output will be above or below a certain threshold. The study found that the random forest regressor algorithm performed the best in terms of accuracy, with mean absolute errors and root mean square errors of 0.046 and 0.11, respectively. However, it did not predict peak power values effectively, which can lead to higher errors. The long short-term memory algorithm performed better in classifying peak power values. The study concluded that classification models may be better at generalizing than regression models. This proposed approach is valuable for interpreting model performance and improving prediction accuracy.

Abstract Background Understanding the determinants of global quality of life in cancer patients is crucial for improving their overall well-being. While correlations between various factors and quality of life have been established, the causal relationships remain largely unexplored. This study aimed to identify the causal factors influencing global quality of life in cancer patients and compare them with known correlative factors. Methods We conducted a retrospective analysis of European Organization for Research and Treatment of Cancer Quality of Life Questionnaire data, alongside demographic and disease-related features, collected from new cancer patients during their initial visit to an oncology outpatient clinic. Correlations with global quality of life were identified using univariate and multivariate regression analyses. Causal inference analysis was performed using two approaches. First, we employed the Dowhy Python library for causal analysis, incorporating prior information and manual characterization of an acyclic graph. Second, we utilized the Linear Non-Gaussian Acyclic Model (LiNGAM) machine learning algorithm from the Lingam Python library, which automatically generated an acyclic graph without prior information. The significance level was set at p

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

In a dynamic business environment, the accuracy of sales forecasts plays a pivotal role in strategic decision making and resource allocation. This article offers a systematic review of the existing literature on techniques and methodologies used in forecasting, especially in sales forecasting across various domains, aiming to provide a nuanced understanding of the field. Our study examines the literature from 2013 to 2023, identifying key techniques and their evolution over time. The methodology involves a detailed analysis of 516 articles, categorized into classical qualitative approaches, traditional statistical methods, machine learning models, deep learning techniques, and hybrid approaches. The results highlight a significant shift towards advanced methods, with machine learning and deep learning techniques experiencing an explosive increase in adoption. The popularity of these models has surged, as evidenced by a rise from 10 articles in 2013 to over 110 by 2023. This growth underscores their growing prominence and effectiveness in handling complex time series data. Additionally, we explore the challenges and limitations that influence forecasting accuracy, focusing on complex market structures and the benefits of extensive data availability.

Currently, in pork auctions in Korea, only carcass weight and backfat thickness provide information on meat quantity, while the production volume of primal cuts and fat contents remains largely unknown. This study aims to predict the production of primal cuts in pigs and investigate how these carcass traits affect pricing. Using the VCS2000, the production of shoulder blade, loin, belly, shoulder picnic, and ham was measured for gilts (17,257 pigs) and barrows (16,365 pigs) of LYD (Landrace × Yorkshire × Duroc) pigs. Single and multiple regression analysis were conducted to analyze the relationship between the primal cuts and carcass weight. The study also examined the correlation between each primal cut, backfat thickness (1st thoracic vertebra backfat thickness, grading backfat thickness, and Multi-brached muscle middle backfat thickness), pork belly fat percentage, total fat yield, and auction price. A multiple regression analysis was conducted between the carcass traits that showed a high correlation and the auction price. After conducting a single regression analysis on the primal cuts of gilt and barrow, all coefficients of determination (R2) were 0.77 or higher. In the multiple regression analysis, the R2 value was 0.98 or higher. The correlation coefficient between the carcass weights and the auction price exceeded 0.70, while the correlation coefficients between the primal cuts and the auction prices were above 0.65. In terms of fat content, the backfat thickness of gilt exhibited a correlation coefficient of 0.70, and all other items had a correlation coefficient of 0.47 or higher. The correlation coefficients between the Forequarter, Middle, and Hindquarter and the auction price were 0.62 or higher. The R2 values of the multiple regression analysis between carcass traits and auction price were 0.5 or higher for gilts and 0.4 or higher for barrows. The regression equations between carcass weight and primal cuts derived in this study exhibited high determination coefficients, suggesting that they could serve as reliable means to predict primal cut production from pig carcasses. Elucidating the correlation between primal cuts, fat contents and auction prices can provide economic indicators for pork and assist in guiding the direction of pig farming.

The work is devoted to solving the current problem of forecasting real estate prices by building a predictive model based on the generated database of real estate in Moscow, posted on the Move Real Estate website. Existing machine learning methods for solving the forecasting problem are considered and one of them is applied - multiple linear regression. A regression analysis of the obtained results of solving the forecasting problem was carried out. Eleven independent variables are considered as control parameters. The influence of the variables taken into account when constructing the model on the results of solving the problem of forecasting real estate prices was studied. It was determined which of the independent variables have the greatest impact on the results of the model. To improve the quality of the model, preprocessing and standardization of features were carried out. Identification of outliers and omissions of values was carried out during the formation of the database. The coefficients of the multiple linear regression model were determined using the least squares method. To assess the quality of the model, the following model parameters are analyzed: R-squared, adjusted R-squared, p-value. The result of constructing a predictive model is the resulting regression equation. The application of the resulting equation can be used to subsequently take into account specific characteristics when solving the problem of forecasting real estate prices. The work shows the advantages of using this method and the prospects for applying the obtained result.

Abstract The adoption of English-Medium Instruction (EMI) has gained global traction, spurred on by higher education internationalization trends. Despite the widespread usage of EMI, its influence on students’ language development remains relatively unexplored. This study investigated the impact of EMI instruction on the self-efficacy and English performance of Taiwanese English as a Foreign Language (EFL) undergraduates. The research aimed to discern whether EMI courses improve EFL undergraduates’ self-efficacy and how this self-efficacy can predict English performance. 272 Taiwanese EFL undergraduates were recruited for the study, and all of them joined the EMI courses for at least one semester. Data collection instruments included a revised questionnaire and TOEIC test, and a mixed approach was applied for data analyses. Quantitative analyses of repeated measure of ANOVA revealed a significant enhancement in the students’ self-efficacy across all four English skills (listening, speaking, reading, and writing) following EMI instruction. Moreover, the regression analysis established a moderate to strong positive relationship between English self-efficacy and English performance. The qualitative analysis of structured interview specifically elaborated on what participants’ attitudes were toward the implementation of EMI. These findings underscore the positive influence of EMI instruction on EFL undergraduates’ English self-efficacy and performance, substantiating the role of self-efficacy as a significant predictor of performance. These insights offer valuable implications for curriculum design, learner motivation, and academic success in EFL contexts.

We propose an adaptive method for building a multivariate regression given by a weighted linear convolution of known scalar functions of deterministic input variables with unknown coefficients. As, for example, when multivariate regression is given by a multivariate polynomial. In contrast to the general procedure of the least squares method that minimizes only a single scalar quantitative measure, the adaptive method uses six different quantitative measures and represents a systemically connected set of different algorithms which allow each applied problem to be solved on their basis by an individual adaptive algorithm that, in the case of an active experiment, even for a relatively small volume of experimental data, implements a strategy of a statistically justified solving. The small amount of data of the active experiment we use in the sense that, for such an amount, the variances of estimates of unknown coefficients obtained by the general procedure of the least squares method do not allow to guarantee the accuracy acceptable for practice. We also proposed to significantly increase the efficiency of the proposed by O. A. Pavlov. and M. M. Holovchenko modified group method of data handling for building a multivariate regression which is linear with respect to unknown coefficients and given by a redundant representation. We improve it by including some criteria and algorithms of the adaptive method for building a multivariate regression. For the multivariate polynomial regression problem, the inclusion of a partial case of the new version of the modified group method of data handling in the synthetic method proposed by O. A. Pavlov, M. M. Golovchenko, and V. V. Drozd, for building a multivariate polynomial regression given by a redundant representation, also significantly increases its efficiency.

Abstract This research is important for both the academic and business environments due to the extraordinary results obtained. Additionally, the significance of the study is also attributed to the addressed topic, which is intensively studied in the world of corporate finance. The primary aim of this research is to scrutinize a cohort of 66 information and technology (IT) companies, all of which are constituents of the American Standard and Poor’s 500 Index (S&P 500). The study period spans two decades, covering the years 2003–2022. To summarize the outcomes, the analytical framework incorporated linear models with both fixed (fe) and random effects (re), as well as quantile regression models. This study's key outcomes highlight that firm size, sales growth, current ratio, long-term debt to capital, free cash flow, asset turnover and receivable turnover, board meeting frequency, female board representation, chief executive officer age, audit committee independence, and the presence of compensation and nomination committees, alongside a pandemic indicator, positively impact firm performance. Conversely, firm age, dividend payout ratio, effective tax rate, board size, chief executive officer duality, and corporate social responsibility committee presence have negative effects on performance. Also, regarding quantile regressions, CEO duality significantly influences companies with high profitability rates, and companies with low to medium profitability rates are more strongly and negatively influenced by board size. The implications of the core policy in this research focusing on corporate governance will consider certain rules and guidelines regarding financial transparency and protecting shareholders' interests. Additionally, it will take into account the independence of the board of directors and the presence of its committees, as well as ethical leadership practices.

With the financialization of the real estate market, the application of the income approach through the Discounted Cash Flow Analysis (DCFA) for estimating the market value has significantly increased. In this income approach method, the Discount Rate (DR) is one of the factors that decisively affects the result. Despite the several procedures proposed for the estimation of the DR and the numerous parameters considered, the valuer is generally «forced» to the use of the risk premium as a corrective that takes into account the “communis aestimatio” borrowed for the assessment of the DR. Starting from these premises, the present research attempts to replicate the ordinary behavior that leads the valuer to the determination of DR and to outline a methodology that allows estimating a reliable and objective value of DR on the bases of the factors generally taken into consideration.

Abstract Solar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently.

Most publications justify that digitalization is a source of economic growth, to a greater extent in the countries with higher levels of economic development. However, some papers fail to find empirical evidence supporting this claim, attributing it to digitalization being in its initial stages or to the presence of the digitalization paradox. Economic literature exhibits a degree of ambiguity in its conclusions regarding the role of the digital economy in growth. Researchers employ different measures of digitalization, country and year samples, control variables in regression equation, as well as different regression methods, which can provoke ambiguous effects on the parameters of digital variables. The purpose of the article is to identify the role of digitalization in economic growth by justifying an economic growth model with the digitalization index and providing detailed discussions on our sample of countries over recent years, along with options for regression estimators. The article utilizes statistical data from the World Bank, European Commission, and Heritage Foundation. Methods such as system analysis, statistical analysis, and the regression estimator of panel data with fixed effects are implemented to identify the role of digitalization in economic growth. We have designed an economic growth model incorporating the Digital Economy and Society Index (DESI) of EU member states for the years 2017-2022, utilizing available data. The results reveal a positive and significant causal effect of digitization on gross output growth. Specifically, a 1% increase in the digitalization index results in GDP growth of almost 0.2%. Moreover, we observe the important roles of capital, labor, trade, human capital, and intellectual property rights in driving growth, consistent with the theory of endogenous growth. Proving the importance of digitalization for economic growth can be an argument for policies aimed at promoting digitalization. We suggest further research on the role of digitalization at a more disaggregated level of industries. However, our study does not demonstrate a statistically significant effect of research activity and institutional quality on gross output. We have discussed possible reasons for this and propose further research in these areas.

Hiroko Kiyoshi-Teo,1 Bryanna De Lima,2 Nathan F Dieckmann,1 Jennifer L Vincenzo,3 Elizabeth Eckstrom2 1School of Nursing, Oregon Health & Science University, Portland, OR, USA; 2Division of General Internal Medicine & Geriatrics, Department of Medicine, Oregon Health & Science University, Portland, OR, USA; 3Department of Physical Therapy, Center for Implementation Research, University of Arkansas for Medical Sciences, Fayetteville, AR, USACorrespondence: Hiroko Kiyoshi-Teo, School of Nursing, Oregon Health & Science University, 3455 S.W. U.S. Veterans Hospital Road, Portland, OR, 97239, USA, Email kiyoshi@ohsu.eduPurpose: Individuals identified as high fall risk are expected to have high concern about falling. However, perception and individual factors that influence concern about falling have yet to be thoroughly studied. We aimed to understand factors that influence concern about falling among older adults with increased risk for falling.Patients and Methods: This was a secondary analysis of a clinical trial among community-dwelling older adults (age ≥ 65 years old) at high risk for falls (n = 178). Descriptive and regression analyses were used. We analyzed the relationship between participants’ baseline concern about falling – categorized into three groups: low (7– 8), moderate (9– 13), and high (≥ 14) – and factors that may impact their concern. Exploratory factors included age, sex, self-reported health status and confidence to address fall risks, fall risk scores, and physical performance measures.Results: Among these individuals, 15.2% reported low concern about falling. On average, individuals in higher concern about falling groups had higher fall risk scores (low [5.7], moderate [6.4], and high [8.0]; p < 0.001). Our regression model showed that the odds of being in a higher concern group increased by 21% for every one unit increase in fall risk score and increased by 67% for every one unit increase toward poorer health rating. Conversely, for every one unit increase in self-reported confidence, the odds of being in a higher concern group decreased by 27.5%.Conclusion: Knowledge of older adults’ fall risk, health status, and concerns about falling can be used to assist in the personalization of fall prevention interventions for a more holistic approach.Keywords: accidental falls, risk factors, perception, regression analysis

Abstract Reflection attacks are one of the most intimidating threats organizations face. A reflection attack is a special type of distributed denial-of-service attack that amplifies the amount of malicious traffic by using reflectors and hides the identity of the attacker. Reflection attacks are known to be one of the most common causes of service disruption in large networks. Large networks perform extensive logging of NetFlow data, and parsing this data is an advocated basis for identifying network attacks. We conduct a comprehensive analysis of NetFlow data containing 1.7 billion NetFlow records and identified reflection attacks on the network time protocol (NTP) and NetBIOS servers. We set up three regression models including the Ridge, Elastic Net and LASSO. To the best of our knowledge, there is no work that studied different regression models to understand patterns of reflection attacks in a large network. In this paper, we (a) propose an approach for identifying correlations of reflection attacks, and (b) evaluate the three regression models on real NetFlow data. Our results show that (a) reflection attacks on the NTP servers are not correlated, (b) reflection attacks on the NetBIOS servers are not correlated, (c) the traffic generated by those reflection attacks did not overwhelm the NTP and NetBIOS servers, and (d) the dwell times of reflection attacks on the NTP and NetBIOS servers are too small for predicting reflection attacks on these servers. Our work on reflection attacks identification highlights recommendations that could facilitate better handling of reflection attacks in large networks.

Background and Objectives: The nation's urban sector has experienced significant changes since industrialization, both in terms of growth and improvement in terms of creating jobs and the depletion of natural resources. The development and destruction are commendable and need mention and attention. These days, issues such as environmental degradation, the wealth gap, and unequal access to opportunities and resources are increasing. These concerns have increased the need for urban management through sustainable and planned development. The development cannot be sustained unless the depletion is controlled and taken care of. The current study focused on how urban regions have changed and how sustainable development helps cope with the changes. Furthermore, the study focused on enhancing the well-being of urban communities and promoting inclusivity to develop sustainable businesses that are economically sustainable in the long run. The novelty of the study is that explores how sustainable development can mitigate the impacts of urban change and enhance community well-being and inclusivity.Methods: Quantitative methodologies, such as surveys and statistical analyses, may have shed light on the broader impact of sustainable business practices on urban development. To complement these findings, qualitative case studies and interviews could have fostered deeper insights into specific initiatives and the perspectives of entrepreneurs navigating this landscape. A mixed-methods approach, weaving together quantitative trends with rich qualitative narratives, would have further enriched the understanding of motivations and challenges encountered. Ultimately, regardless of the specific methodologies employed, this research likely aimed to achieve a holistic understanding of the intricate connections between urban management, sustainable business practices, and the innovative spirit of entrepreneurs driving change in India. In this study, the researcher used regression analysis and factor analysis to achieve the research objectives.Findings:The entrepreneurs examined in the study exhibited a proactive stance towards critical aspects of sustainable business, notably waste management, energy efficiency, and resource optimization. The study identifies significant associations between key variables and sustainable urban development. Urban management demonstrates a positive impact (B = 1.286, SE = 0.621, Beta = 0.116, T = 2.071, P = 0.0039), highlighting its crucial role in shaping sustainable practices. Sustainable practices, in turn, exhibit a strong positive correlation (B = 1.088, SE = 0.257, Beta = 0.238, T = 4.242, P = 0.0001), indicating their pivotal role in driving urban development towards sustainability. These findings underscore the importance of integrating effective urban management strategies and sustainable practices to foster inclusive and resilient urban communities.Conclusion: The findings provide the government with all the criteria essential for a valid approach to the sustainable development of the urban regions of the country and provide the appropriate balance between growth and development. The researcher recommended that creating supportive policies and incentives, investing in green infrastructure, supporting local entrepreneurship ecosystems, collecting and sharing data, and promoting community engagement are requirements for urban management. In addition, recommended that focusing on triple bottom line impact, collaborating with other stakeholders, Innovating and adapting, communicating transparently, and advocating for change are required for Sustainable Businesses by Entrepreneurs.

Forests contribute significantly in mitigating the effects of climate change by sequestering atmospheric carbon in biomass and soil. To comprehend carbon stock and sequestration, forest degradation, and climate change mitigation, precise calculation biomass is needed. The present study in Arunachal Pradesh, Northeast India, used Landsat OLI spectral variables, land surface temperature, and field inventory data to estimate aboveground biomass (AGB) and carbon stock in selected forest types. The stepwise multilinear regression model was developed using Landsat-derived spectral data, land surface temperature, and soil moisture The stand density varies from 122 individual ha−1 to 833 individual ha−1 . The estimated AGB density varies from 2.64 t ha−1 to 534.21 t ha−1 among the sample plots. The mean land surface temperature was 14.41°C. The predictive integrated model showed that the mean AGB ranged from 9.45 t ha−1 to 330 t ha−1 and dense forest recorded the maximum mean AGB (239.34 t ha−1). Akaike information criteria (AIC) and Bayesian information criteria (BIC) were utilised to evaluate the predictive model, and the AIC (176.13) and BIC (184.58) were omparatively lower than other regression models.To evaluate the accuracy of the integrated model (IM), the linear regression was performed between the predicted and observed AGB. The findings of the study will be useful in formulating site-specific suitable management plan.

The focus of this study is to propose statistical models for predicting wear and coefficient of friction of the 16MnCr5 steel valve tappet using the analysis of variance (ANOVA). The 16MnCr5 steel valve tappet was fabricated from a steel strip and thermochemically treated by carburizing, quenching, and tempering. Under dry conditions, tribological tests were performed for 16MnCr5 steel valve tappet with a ball-on-plane configuration in a reciprocating slide using an Optimol SRV® v4 device. Subsequently, the results of the coefficient of friction (COF) and the wear rate were then analyzed using the ANOVA. Regression analysis was used to derive the predictive equations for both friction coefficient and wear rate. The applied load was found to be the most significant parameter affecting the COF and wear rate. The proposed statistical models has 88 - 92% percent reliability. These models can be beneficial for predicting the tribological operating conditions for the 16MnCr5 steel valve tappet to avoid premature failure within the tested load and temperature conditions.

To directly measure the strength and permeability of concrete, a core should be separated from the concrete and then tested in the laboratory with destructive methods. For example, to measure the permeability of concrete by existing standards, concrete must be broken and divided into half. Also, acute conditions such as temperature cycles that concrete faces in summer can have negative effects on concrete characteristics. Therefore, in this research, by using innovative tests, in addition to investigating the effect of temperature cycles (40, 80, 120, and 160 cycles) on the permeability and surface resistance of concrete at different ages, measuring the depth of water penetration and resistance Concrete is compressed without breaking it. According to the results, by using the calibration charts and the equations obtained from the methods of the cylindrical chamber and friction transfer, it is possible to obtain the depth of water penetration and the compressive strength of concrete without the need to break the concrete and with a correlation coefficient of over 96%. It was also observed that the cycles of temperature changes have negative effects on the durability and surface resistance of concrete in such a way that the number of 40, 80, 120, and 160 cycles of temperature changes increases the permeability of concrete by 4.1, 8.7, and 7 12.5 and 16.5 have been equaled. Also, the acute conditions of temperature changes have negative effects on the surface resistance of concrete in such a way that the surface layer resistance of concrete has decreased by more than 44% under 160 cycles of temperature changes. Considering that the surface layer of concrete has a direct relationship with the penetration of harmful substances into the concrete, it was observed that with the increase in the strength of the surface layer of concrete, the volume of water penetration into concrete has decreased. By increasing the resistance of the surface layer by 15%, the volume of water infiltration into the concrete has decreased by about 140%.

Aim – to analyze the effect of carotid stenosis of varying severity in combination with other factors on the risks of primary and recurrent ischemic stroke, using methods of mathematical regression analysis. Material and methods. The study included 606 patients, examined by a neurologist, contrast-enhanced CT scan of the brain, ultrasound of the vessels of the head and neck, other tests and instrumental studies. The degree of carotid stenosis was assessed using the NASCET method (stenosis 0-49%, 50-69%, 70% or more). For the purpose of mathematical analysis, patients were divided into 3 comparison groups: those without a history of stroke, patients with a single stroke, and a group of patients with two or more ischemic strokes. In patients with a history of stroke, the size of the ischemic lesion was assessed according to CT data, the severity of neurological deficit according to the NIHSS scale and the recovery degree, and the functional outcome of ischemic stroke (Rankin scale, Rivermead index). The study used logistic regression analysis to assess the relationship between the dependent variable (presence of primary or recurrent stroke) and a set of predictors, which were a number of clinical and instrumental indicators. Results. The presence of carotid stenosis of 50-69%, when combined with pathology of the cardiovascular system, acts as a factor that significantly increases the risk of developing primary ischemic stroke. Stenosis exceeding 70% has a high level of significance in the risk of recurrent ischemic stroke, this fact should be taken into account for secondary prevention of stroke. Conclusion. A preliminary assessment and analysis of carotid stenosis degree and other comorbid factors co-influencing the risks of primary and recurrent ischemic stroke has an undoubted potential. This approach can reduce the risk of recurrent vascular accidents and help organizing a personalized approach to surgical treatment of patients.

Introduction. The technological operation of soil tillage is an energy-intensive preparatory operation. The traction resistance of tillage units has a direct impact on this indicator. Aim of the Study. The study is aimed at searching optimal operating parameters for the field cultivator tool through determining the traction resistance created by this tool. Materials and Methods. The study was carried out in the field on an experimental facility using a full factorial experiment technique with a working model based on a three-level design. Results. Through the regression analysis of experimental data, there was obtained a mathematical model that allows determining the optimal operating parameters for the field cultivator tool when performing a technological operation of soil tillage. Discussion and Conclusion. While calculating, it was found that the lowest traction resistance PТ = 0,72 kN created by the tool is for the following values: tillage depth h = 4 cm, crumbling angle of the left-hand and right-hand flat-cutting wings f = 0° and the speed of the tillage unit v = 2,61 m/s. As the optimal operating parameters for a field cultivator tool, determined using the obtained mathematical model, it is recommended to use the crumbling angle of the left-hand and right-hand flat-cutting wings of the working body f = 0°, and the speed of the tillage unit v = 2,91 m/s. Taking into account the recommended optimal operating parameters of the tool, it is possible to calculate the value of the traction resistance index at different tillage depths. The traction resistance will change within the range of 0.79–1.81 kN, when the soil tillage depth changes from 4 to 12 cm. The resulting mathematical model of the traction resistance created by the tool will allow a more accurate approach to the issue of designing agricultural machines.

Research Question: Which are the trends and the impact in the research area covering corporate governance in sensitive industries? Motivation: The research enhances prior literature on corporate governance under specific risk, uncertainty and socio-economic conditions. The referenced topics and timeframe highlight the focus on sensitive industries as a challenge, considering the mixed effects of crises conditions. Idea: This paper creates a comprehensive overview on the corporate governance in sensitive industries research landscape, considered under universalist, social constructivist, and presentation perspectives. Data: Information on publication trend, papers’ main characteristics, academic collaboration, and factors with significant influence on times cited are collected for a sample of 926 scientific publications from the Web of Science database. Tools: The network visualization for co-authorship and co-occurrence of keywords is based on using VOSviewer software, while statistical and econometric analysis of the database is performed using SPSS Statistics V.23. Findings: The results emphasize that most of the papers are published in leading journals, while 2022 is the year that stands out with the highest number of publications. From the perspective of the business field, the research focus is on Banking, Energy, Logistic, Pharmaceutical, and Tourism industries. Muchmore, statistical analysis demonstrates that there is a significant difference between the types of publications. Regression analysis shows that times cited are positively and statistically significant correlated with cited reference count and article age. Contribution: This study may be of great interest to academic and business environments. It summarizes information for a specific topic, whose evolution managers and practitioners are interested in. Furthermore, it fills the gap in examining the research impact based on several characteristics that exert an influence on times cited, contributing to the body of knowledge regarding the classification of research features under the universalist, social constructivist, and presentation perspectives.

Abstract The study aims to unravel the impact of Environmental Social Governance (ESG) scores on the firm’s market performance of polluting companies. Moreover, the study also finds out moderating effect of green initiatives. The study’s population consisted of 67 companies that were chosen from the list of polluting companies given by the Central Pollution Control Board of India for the post-COVID-19 timeframe of 2020–2023. Regression analysis was conducted to analyze the relationship between the variables. The results indicate that the performance of ESG will improve the financial performance of the company. In most cases, the results suggest that firms with heightened ESG performances have better market performance. Furthermore, the results show that there is no moderating effect of green initiatives. This study’s findings have important implications for stakeholders. The examination’s findings should help managers understand the state of ESG and financial market performance after the coronavirus crisis, as well as the relationship between FP and ESG responsibility fulfilment. The study has significant practical implications that may help managers create plans and guidelines for implementing and improving ESG scores to maximize performance. Population was the major limitation of the study.

The soil response under the inherent cyclic loading conditions when dealing with offshore foundations can be considered by using contour plots. These plots are derived from several cyclic laboratory tests and characterize the general cyclic soil behaviour. In the design process with explicit numerical methods, such plots are needed in order to assess the soil behaviour under arbitrary loading conditions and hence estimate the cyclic foundation response. In the paper, excess pore pressure contour plots for a poorly graded medium sand are derived from numerous constant volume (CV) cyclic direct simple shear (DSS) tests and a new approach for parametrization of the plots is presented. Subsequently, the data are assessed regarding scaling for other sand soils, i.e., construction of contour plots with only a small number of test results by using the general trends observed.

Abstract Here, identification of processes and systems in the sense of the least sum of absolute values is taken into consideration. The respective absolute value estimators are recognised as exceptionally insensitive to large measurement faults or other defects in the processed data, whereas the classical least squares procedure appears to be completely impractical for processing the data contaminated with such parasitic distortions. Since the absolute value quality index cannot be minimised analytically, an iterative solution is used to find optimal estimates of the parameters of the underlying regression model. In addition, an approximate recursive estimator is proposed and implemented for on‐line evaluation of system parameters. The convergence (basic property) of the iterative estimator is show to be proven and some aspects related to the absolute value criterion are explained. This allows for the formulation of practical conclusions and indication of directions for further research. In addition, the effectiveness of the described iterative‐recursive estimation procedures is practically verified by appropriate numerical experiments.

This study delves into the impact of downstream obstruction angles on the discharge coefficient (Cd) over ogee weirs within open channel flows, a critical factor for accurate flow rate predictions in hydraulic engineering. Employing a series of detailed laboratory experiments, the influence of various obstruction angles on Cd was scrutinized applying a suite of regression analysis to develop predictive models. The analysis was enriched by considering hydraulic parameters such as flow rate, water level, and weir geometry. Despite the established importance of Cd in hydraulic designs the nuanced effects of downstream obstructions have received limited attention, highlighting a critical research gap. The findings highlight a strong correlation between obstruction angles and Cd, with developed regression models demonstrating notable predictive strength. Remarkably the models exhibited varying levels of accuracy, with the Random Forest regressor achieving an exceptionally low root mean square error (RMSE) of 0.005, indicating superior predictive performance. Conversely, traditional models like Decision tree and XG BOOST reflected higher RMSE values of 0.60, suggesting less predictive accuracy in this context. LASSO, Bayesian Ridge, and OMP regressors stood out with an RMSE of zero, denoting perfect predictions under the study's specific conditions. HIGHLIGHTS The study employed multilinear and polynomial regression techniques to predict variations in the coefficient of discharge for an ogee weir due to downstream obstructions at different angles. This approach provides a quantifiable understanding of how such obstructions impact hydraulic efficiency.; Results highlight the significant impact of obstructions on the discharge efficiency of ogee weirs, emphasizing the need for careful consideration of potential downstream barriers in the design and management of hydraulic structures to optimize performance and reduce flood risks.; The findings offer valuable insights for engineers and practitioners in the field, suggesting that regression models can serve as effective predictive tools for estimating the discharge coefficient under various scenarios, aiding in the design and operation of more efficient hydraulic systems.;

The combined cycle power plant must operate 24 hours without stopping so that in operation at any time an increase in the temperature of the gas generator RB211 can occur. The RB211 gas generator works well when the ambient temperature is low. The X combine cycle power plant uses a chiller to lower the inlet air temperature so that the air entering the compressor will be lower. The purpose of this study was to analyze and conclude the effect of the compressor inlet temperature on the thermal efficiency of the Rolls Royce RB211 gas generator in the combined cycle power plant. Data was collected by requesting historical data on September 5-11, 2022 in the central control room. The method used in this study is simple linear regression analysis because it only involves two variables. The independent variable is the inlet temperature of the compressor and the dependent variable is the thermal efficiency of the gas generator. It is known that the highest efficiency is at a temperature of 292,94 kelvin with a thermal efficiency value of 36,87% while the lowest efficiency is at a temperature of 301,89 kelvin with a thermal efficiency value of 35,61%. The regression analysis equation is Ŷ = 0,60665231-0,00082223X so the hypothesis results obtained through the significant F test with F table is that there is a significant effect between the compressor inlet temperature on the thermal efficiency of the gas generator.

Abstract Background Qingyang is located in the northwest of China. By analyzing the current situation and risk factors of bullying in junior high schools in Qingyang City, and identify relevant data for formulating prevention and control measures of bullying in western backward areas. Methods Qingyang City is divided into four regions based on economic level and population quality. One junior high school is randomly selected from each region, a total of 1200 students from 4 junior high schools of different levels in Qingyang City were randomly selected, and the “Questionnaire on Middle School Students’ School bullying” was administered between December 2021 and February 2022. Results The reporting rate of bullying in junior high schools in Qingyang was 47.35%. The incidence of campus bullying among urban-rural integration junior high schools, senior students, and male students is higher than that of municipal -level junior high schools, junior students, and female students (P

In today's modern world, marketing is a fruitful source to grow a business by reaching a larger audience. This paper considers a popular social media source called LinkedIn.com as a way of marketing or reaching a larger audience. We consider a regression approach to explore the statistically significant role of the LinkedIn medium as a tool for reaching a larger audience. In addition to statistically exploring the role of the LinkedIn medium in the financial sector, we also introduce a new Weibull-based probability distribution, namely, the exponential TX inverse Weibull (for short'ETXI-Weibull') distribution. The ETXI-Weibull distribution is specially designed for analyzing LinkedIn marketing data. The beauty of the ETXI-Weibull distribution is that it satisfies the heavy-tailed characteristics, which are explored visually and mathematically. For the ETXI-Weibull distribution, the estimators are derived whose performances are evaluated visually and numerically through a simulation study. The ETXI-Weibull distribution is applied practically by considering the LinkedIn marketing data set as an example. Using certain established statistical tests, we observe that the ETXI-Weibull distribution provides an optimal fit to the LinkedIn marketing data set. Furthermore, a bivariate extension of the exponential TX inverse Weibull model is also introduced. Finally, the bivariate extension of the exponential TX inverse Weibull model is illustrated by modeling the LinkedIn marketing and sales data.

The risk of typhoon disasters is high in the Zhejiang coastal areas; however, it has been difficult to characterize waves generated by these typhoons, particularly in shallow waters. In this study, the characteristics of nearshore typhoon waves were analyzed using data from 14 typhoon processes from a wave station on the central coast of Zhejiang Province for three consecutive years from 2016 to 2018. According to their paths, these typhoons were divided into landing and non-landing steering types. Wave parameters were evaluated using statistical analyses and linear regression. Variations in wave parameters and spectra are discussed. We obtained several key results: (1) The dominant wave direction was E-SE and the strong wave direction was SE. (2) There were strong correlations between characteristic wave heights and between characteristic periods. (3) Single-peak spectra were dominant, with a frequency of 92.33%. The minimum peak frequency was 0.06–0.1 Hz and the maximum peak period was 17.7 s. The typhoon waves were mainly composed of swells with a frequency of 94.54%. (4) Affected by Typhoon Maria (No. 201808), a maximum wave height of 8.20 m was observed at 04:00 on July 11, 2018. This study provides an important reference for the design of offshore structures and disaster prevention and mitigation.

This research explores the effectiveness of a Ground Source Heat Pump (GSHP) system for cooling purposes in Indian conditions. Key parameters considered include the water’s flow rate, grout material, run time, return air temperature, and borehole configuration. A series of trials were conducted to review the system’s cooling effectiveness by employing two types of grout materials, construction, and demolition (C&D) waste, and the surrounding soil in four distinct borehole layouts. A linear regression model was used to assess how critical factors affected the system’s efficiency for each type of grout. An analysis of variance was also performed to determine which factors had the most significant impact. The study found that factors such as borehole length, system efficiency, and thermal characteristics significantly influenced the GSHP system's thermal performance. The findings revealed that the Coefficient of Performance (COP) of the heat pump and the overall system increased with the number of boreholes and volumetric flow rate. At the same time, it decreased with the rise in initial air temperature and extended operation run time. The mean COP for the GSHP arrangement utilized in thermal management with C&D waste as grout material was determined to be 8.1% lower than the value achieved when using the surrounding soil as the grout material. The results signify that utilizing a GSHP arrangement has the potential to be a favorable choice for cooling purposes. The system is effective, offering considerable energy savings compared to traditional air-conditioning systems. Nevertheless, the effectiveness of the system is determined by various elements, such as the length of the borehole, overall system efficiency, and thermal properties. Therefore, it is vital to carefully consider these factors when designing and installing a GSHP system.

In oil and gas exploration, elucidating the complex interdependencies among geological variables is paramount. Our study introduces the application of sophisticated regression analysis method at the forefront, aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging. Through a rigorous assessment, we explore the efficacy of eight regression models, bifurcated into linear and nonlinear groups, to accommodate the multifaceted nature of geological datasets. Our linear model suite encompasses the Standard Equation, Ridge Regression, Least Absolute Shrinkage and Selection Operator, and Elastic Net, each presenting distinct advantages. The Standard Equation serves as a foundational benchmark, whereas Ridge Regression implements penalty terms to counteract overfitting, thus bolstering model robustness in the presence of multicollinearity. The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models, enhancing their interpretability, while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator, offering a harmonized solution to model complexity and comprehensibility. On the nonlinear front, Gradient Descent, Kernel Ridge Regression, Support Vector Regression, and Piecewise Function-Fitting methods introduce innovative approaches. Gradient Descent assures computational efficiency in optimizing solutions, Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns, and Support Vector Regression is proficient in forecasting extremities, pivotal for exploration risk assessment. The Piecewise Function-Fitting approach, tailored for geological data, facilitates adaptable modeling of variable interrelations, accommodating abrupt data trend shifts. Our analysis identifies Ridge Regression, particularly when augmented by Piecewise Function-Fitting, as superior in recouping hydrocarbon losses, and underscoring its utility in resource quantification refinement. Meanwhile, Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A, evidencing its aptness for intricate geological structures. This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector. The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction, evaluation, and recovery.

Abstract Aim Patients with schizophrenia often exhibit poor life skills, posing significant clinical challenges. Life skills comprise cognitive functions crucial for planning daily activities, including divergent thinking. However, the cognitive deficits contributing to these diminished skills among patients with schizophrenia are underexplored. This study introduces a modified Tinkertoy Test (m‐TTT) to investigate the correlation between life skills, divergent thinking, and psychological assessment tools in patients with schizophrenia. Methods Fifty‐two patients with schizophrenia, alongside a control group, matched for sex, age, and education, were evaluated using psychological assessment tools. For the patient group, the Life Skills Profile (LSP) and Positive and Negative Syndrome Scale were administered to measure functional abilities and psychiatric symptoms, respectively. Additionally, duration of disease and antipsychotic daily dosage levels were assessed exclusively in the patient group. Both groups were evaluated with the m‐TTT, Idea Fluency Test (IFT), Design Fluency Test (DFT), and Brief Assessment of Cognition in Schizophrenia (BACS) to comprehensively assess cognitive functions. A stepwise multiple regression model was conducted to identify significant correlates of LSP total score among the patient group. Results The schizophrenia group scored notably lower than the neurotypical controls on the m‐TTT, IFT, DFT, and BACS. Our stepwise multiple regression analysis highlighted that the LSP total score was significantly correlated with the total m‐TTT score and presence of negative symptoms. Conclusion Divergent thinking could be a crucial factor in the life skills of individuals with schizophrenia. Rehabilitation programs based on this cognitive function might enhance their daily living capabilities.

High-entropy alloys (HEAs) are a category of material with exceptional mechanical, thermal, and chemical properties, making them suitable for a wide range of applications in industries such as aerospace, automotive, and energy storage. The wear rate is a crucial property in determining the reliability and performance of HEAs. It is crucial to accurately predict wear rates when optimizing material for selection and design. This study uses Machine Learning (ML) techniques to create predictive models for wear rate prediction in HEAs. Five regression models, including Linear Regression (LR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), Multilayer Perceptron (MLP), and Huber Regression (HR) are built and evaluated. These models are built using a dataset featuring elemental compositions, applied load, sliding distance, sliding velocity, and wear rates of HEAs. The results show that the MLP regressor outperforms other algorithms in terms of accuracy and model fit when predicting wear rates. For phase prediction of non-existing HEAs, Recurrent Neural Networks (RNNs) and Extreme Gradient Boosting (XGBoost) models are employed. Using the output from these phase predictions as input variables, the wear rate of these non-existing HEAs is predicted at different conditions. The prediction provides a large dataset with phase and wear prediction of non-existing HEAs, which can be experimentally designed later for specific applications. The present study highlights the potential of ML techniques in developing materials science and engineering by allowing the design of HEAs with tailored properties for specific applications.

Based on the Shenzhen-Zhongshan Link, the transient analytical solution of the steel shell immersed tunnel under the protection of fireproof board was deduced and verified through fire test. Then, the variation of the maximum temperature of the bottom steel shell with the thickness and thermal conductivity of the fireproof board was analyzed and suggestions on the selection of fireproof boards were given. Finally, a fitting formula for the maximum temperature of bottom steel shell was proposed. The results show that: (1) When the thermal contact resistance of the steel-concrete interface and the thermal resistance of cavity are 0.01 m2 °C/W and 0.02 m2 °C/W respectively under the case of 25 mm thick fireproof board, the variation of the analytical solution is consistent with test results, and the peak value is similar. (2) Based on the three-dimensional diagram of d -λ-T, with 300 °C as the fire resistance limit of structure, the range of the fireproof board parameters is obtained: when λ reaches 0.14 and 0.4 respectively, d shall not be less than 10 mm and 28.38 mm (3) The scatter points obtained from analysis were fitted using quadratic polynomials and linear functions respectively. Considering the accuracy and simplicity, the linear fitting formula is given as T = 275.66–7.41*d+644.6*λ.

The rise in demand of concrete products has led to overexploitation of river sand the main fine aggregate in concrete resulting in major environmental degradation. As a result, researchers have focused their efforts on developing eco-friendly concrete using alternative renewable materials like volcanic tuff and other natural pozzolana types. This study therefore, aims at investigating the use of Kenyan, Kitengela volcanic tuff as a partial replacement of river sand in self-compacting concrete, and determining the effects it will have on the rheological and mechanical properties of the self-compacting concrete. The study involved partially replacing river sand with volcanic tuff in percentages of 0%, 2.5%, 5%, 7.5% and 10% and carrying out rheological tests (V-funnel test, L-box test, T-500 test and J-ring test) on fresh concrete and mechanical tests (compressive strength and tensile strength tests) on hardened self-compacting concrete on days 7, 14, and 28 to determine the effects of volcanic tuff on properties of both fresh and hardened self-compacting concrete. There was a general decrease in rheological properties (flow and passing abilities) of self-compacting concrete with increase in volcanic tuff percentage replacement from 0 % to 10%, with least flow and passing abilities recorded at 10% replacement. Similarly, increase in volcanic tuff percentage replacement led to decrease in both compressive and tensile strength of self-compacting concrete with lowest values recorded at 10% volcanic tuff replacement.

Building envelope renovation solutions are critical to building performance and post-renovation economics. Building renovation is easier to carry out if the impacts of building envelope renovation solutions on building performance and post-renovation economic efficiency can be foreseen and a more sustainable and efficient renovation design can be achieved. This paper explores the optimal design of building envelope renovation using genetic optimization algorithms and statistical analysis methods, taking the performance of the renovated building and the economic efficiency of the renovation solution as the starting points. In Qingdao, a cold region of China, a survey was carried out on typical rural areas in seven regions. The selected design variables include the external wall and roof insulation material type(TWall, TRoof), external wall and roof insulation thickness(δWall, δRoof), type of window construction(TWc), and area radio of the window to the wall(WWR). Minimization of total building energy consumption(E) and renovation cost(COSTRE) and after renovation maximization of the combined incremental cost-benefit ratio over a 20-year life cycle(CICBR20-year life-cycle), effective natural daylighting illuminance(UDI), and percentage of time spent in indoor comfort(PTC) were considered in the optimization of the envelope. The results show that in cold regions, different envelope renovation solutions affect the optimization objectives to different degrees; changing the south-facing window-to-wall ratio(S_WWR) has a bigger effect on E, PTC, and UDI; and choosing different TRoof has a bigger effect on COSTRE and CICBR20-year life-cycle. Renovation of the selected typical rural residence with the optimal solution results in an energy-saving rate(Re) of 53.08 %, an increase of 940.86 h/year in annual indoor comfort hours(HIC) and 377.00 h/year in annual effective natural lighting hours(HUDI).