Multivariate Regression Equation Research Articles

Multiobjective Optimization of the Performance and Emissions of a Large Low-Speed Dual-Fuel Marine Engine Based on MNLR-MOPSO

With increasingly strict emission regulations and growing environmental concerns, it is urgent to improve engine performance and reduce emissions. In this paper, multivariate nonlinear regression (MNLR) combined with multiobjective particle swarm optimization (MOPSO) was implemented to optimize the performance and emissions of a large low-speed two-stroke dual-fuel marine engine. First, a simulation model of a dual-fuel engine was established using AVL-BOOST software. Next, a single-factor scanning value method was applied to control a range of variables, including intake pressure, intake temperature, and natural gas mass fraction. Then, a nonlinear regression model was established using the statistical multivariate nonlinear regression equation. Finally, the multiobjective optimization algorithm implementing MOPSO was used to solve the trade-off between performance and emissions. It was found that when the intake pressure was 3.607 bar, the intake temperature was 297.15 K and the natural gas mass fraction was 0.962. The engine power increased by 0.34%, the brake specific fuel consumption (BSFC) reduced by 0.21%, and the NOx emissions reduced by 39.56%. The results show that the combination of multiple nonlinear regression and intelligent optimization algorithm is an effective method to optimize engine parameter settings.

Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease.

BackgroundRecent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST.MethodsThe GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction.ResultsA total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively.ConclusionsThis study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

Risk management related to the use of payment systems

Payment system is a complex system of managing funds transfers and settlements between economic entities, which ensures the rational implementation of the basic functions, uninterrupted financing of activities using the latest methods and payment instruments used to reduce cash flow and ensure the effectiveness of monetary, monetary policy countries. In the course of its functioning, the payment system, like any economic system, can be exposed to the risks of its professional activity. Characteristic risks of the payment system are credit risk, liquidity risk, currency risk, operational, legal and systemic. In the event of risks, an effective management system should be implemented, taking into account all possible scenarios of impact, both micro- and macroeconomic factors, which could cause significant losses at all levels of government. An important aspect of a payment system's risk management policy is that it is adequately assessed. The main stages of assessing the level of financial risks generated by the use of payment systems and their impact on the performance indicators are: 1. Systematization and formalization of financial risk factors generated by the use of payment systems. 2. Constructing a multiple multivariate regression equation, realizing the relation between factor (X) and performance (B). 3. The choice of factor traits to include them in the equation of the economic-mathematical model of standardized regression. 4. Economic interpretation of the results of correlation-regression analysis and their use in the decision-making process. Information base for assessing the level of risks generated by the use of payment systems is the NBU's official data on the activity of Ukrainian banks. It should be noted that the most sensitive to the risks generated by the use of payment systems are: liquidity risk; credit risk, currency risk. Thus, in a context of uncertainty and environmental volatility for banking institutions, the strategic aspect should be to focus on securing competitive positions, determining the acceptable level of risk through the use of the latest economic and mathematical modeling tools, extrapolation forecasting, fuzzy set theory, and choice of payment systems with them should be a strategic priority for each organization.

Exploring Medical Students’ Readiness for E-Learning and Knowledge Sharing Behaviors in Emergency Remote Learning Environments during Covid-19

The rapid spread of Covid-19 pandemic has profoundly affected the educational practices all over the world and educational institutions sought ways to adapt to this unprecedented situation. The problem was that most of these institutions, educators and students were not ready for online learning environments and the stakeholders were not yet sure about how to share the knowledge in a better way. This study aims to examine the medical students’ readiness for e-learning and knowledge sharing behaviors in online learning environments during Covid-19, and to analyze the relationship between these variables. The study has been designed according to correlational research methodology. The data were collected using “Knowledge Sharing Behavior in Online Learning Environments Scale” and “E-Learning Readiness Self-Assessment Instrument”. 725 medical students participated in the study. Students' readiness for e-learning and knowledge sharing behaviors were analyzed using descriptive statistics, and the relationship between the variables was modeled using multivariate regression equation. The results revealed that the students with high ability to use computer, mobile phone and tablet PC have higher readiness level for e-learning in terms of technology access, technical skills, online relationships, motivation, online skills, importance of success. Similarly, they have a higher level of knowledge sharing behaviors in online learning environments.

Estimating the impact of automated truck platoons on asphalt pavement’s fatigue life using artificial neural networks

ABSTRACT Automated truck platooning may cause up to 85% reduction in truck following distance compared to conventional vehicles and therefore reduces rest periods between loading cycles on pavement materials. Fatigue cracking is one of the major distresses in asphalt pavement structures, and pavement fatigue life is highly dependent on rest periods between loading cycles. In this paper, Artificial Neural Network (ANN) modeling was applied on the existing NCHRP 09-44A project’s database of extensive laboratory beam fatigue testing results on asphalt mixtures with various rest periods. The developed ANN model was used to predict number of cycles to failure as a function of rest periods and to estimate the impact of truck platooning on pavement fatigue life. In addition, a stand-alone linear multivariate regression equation of fatigue life was developed independently from the ANN model. Based on the results, an 85% reduction in the following distance of platooned trucks may lead to between 7% and 25% reduction in pavement fatigue life. The Platooning Fatigue Life Ratio (PFLR) was found to be dependent on temperature, applied strain level, and mixture parameters. Finally, the applied strain level was the most significant testing factor and binder grade was the most significant mixture parameter on PFLR.

Clinical course and risk factors of fatal adverse outcomes in COVID-19 patients in Korea: a nationwide retrospective cohort study

We investigated association between epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19) patients and clinical outcomes in Korea. This nationwide retrospective cohort study included 5621 discharged patients with COVID-19, extracted from the Korea Disease Control and Prevention Agency (KDCA) database. We compared clinical data between survivors (n = 5387) and non-survivors (n = 234). We used logistic regression analysis and Cox proportional hazards model to explore risk factors of death and fatal adverse outcomes. Increased odds ratio (OR) of mortality occurred with age (≥ 60 years) [OR 11.685, 95% confidence interval (CI) 4.655–34.150, p < 0.001], isolation period, dyspnoea, altered mentality, diabetes, malignancy, dementia, and intensive care unit (ICU) admission. The multivariable regression equation including all potential variables predicted mortality (AUC = 0.979, 95% CI 0.964–0.993). Cox proportional hazards model showed increasing hazard ratio (HR) of mortality with dementia (HR 6.376, 95% CI 3.736–10.802, p < 0.001), ICU admission (HR 4.233, 95% CI 2.661–6.734, p < 0.001), age ≥ 60 years (HR 3.530, 95% CI 1.664–7.485, p = 0.001), malignancy (HR 3.054, 95% CI 1.494–6.245, p = 0.002), and dyspnoea (HR 1.823, 95% CI 1.125–2.954, p = 0.015). Presence of dementia, ICU admission, age ≥ 60 years, malignancy, and dyspnoea could help clinicians identify COVID-19 patients with poor prognosis.

Evaluation of Prognostic Factors for Clinical Pregnancy Rate Following Artificial Insemination by Husband in the Chinese Population.

Background: To determine the independent prognostic factors and develop a multivariate logistic regression model for predicting successful pregnancy following artificial insemination by husband (AIH) in infertile Chinese couples.Methods: A total of 3,015 AIH cycles with superovulation from 1,853 infertile Chinese couples were retrospectively analyzed. The clinical characteristics and sperm parameters were compared between the pregnant and non-pregnant groups. Multivariate logistic regression analysis was performed to remove the confounding factors and create an equation to predict the successful pregnancy. Receiver operating characteristic (ROC) curves were constructed for evaluating the abilities for prognostic classification of the independent predictors and the equation.Results: The overall pregnancy rate was 13.0%. The pregnancy rate of double intrauterine insemination (IUI) (18.9%) was significantly higher than that of single IUI (11.4%). The pregnancy rate of the stimulated cycle (14.4%) was significantly higher than that of the natural cycle (9.0%). The pregnancy rates of the age groups <40 years are ~3 times higher than that of the ≥40 years age group. Among sperm parameters, the influencing factors included straight-line velocity (VSL), sperm deformity index (SDI), and normal form rate (all P < 0.05). A multivariate logistic regression equation was created based on the above influencing factors. ROC analysis showed that the prognostic power of the equation is better than those of individual predictors.Conclusion: Cycle treatment options, single/double IUI, female age, sperm VSL, SDI, and normal form rate could predict successful pregnancy following AIH in China. The multivariate logistic regression equation exhibited a greater value for prognostic classification than single predictors.

Acute psychological impact of coronavirus disease 2019 outbreak among psychiatric professionals in China: a multicentre, cross-sectional, web-based study

ObjectivesTo assess the magnitude of mental health outcomes and associated factors among psychiatric professionals in mental health services during COVID-19 in China.Design, setting and participantsThis cross-sectional, survey-based, region-stratified study collected...

Risk factors of low cardiac output syndrome after cardiac valvular surgery in elderly patients with valvular disease complicated with giant left ventricle

Objective: To explore the risk factors of low cardiac output syndrome (LCOS) after cardiac valvular surgery in elderly patients with valvular disease complicated with giant left ventricle. Methods: This was a retrospective study. The clinical data of patients over 60 years old with giant left ventricle who underwent cardiac valvular surgery in Henan Provincial People's Hospital (Fuwai Central China Cardiovascular Hospital) from January 2016 to January 2020 were collected in this study. Patients were divided into LCOS group and non-LCOS group. The clinical data, preoperative echocardiographic results and surgical data of all patients were collected. Taking LCOS as dependent variable and statistically significant variables in univariate analysis as independent variable, multivariate logistic regression equation was constructed to identify the risk factors of LCOS after cardiac valvular surgery in elderly patients with valvular disease complicated with giant left ventricle. On the basis of logistic regression, the risk factors of continuous variables were put into the regression model for trend test. Results: A total of 112 patients were included, among whom 76 patients were male, the mean age was (65.3±3.8) years. There were 21 cases in LCOS group and 91 cases in non LCOS group. Univariate analysis showed that age≥70 years, preoperative NYHA cardiac function class Ⅳ, preoperative renal dysfunction, preoperative cerebrovascular disease, preoperative LVEF<40%, blood loss/total blood volume>20%, cardiopulmonary bypass (CPB) time>130 minutes and aortic cross-clamp time>90 minutes all had statistically significant differences between the two groups (all P<0.05). Multivariate logistic regression analysis showed that age≥70 years (OR=5.067, 95%CI 1.320-19.456, P=0.018), preoperative NYHA cardiac function class Ⅳ (OR=3.100, 95%CI 1.026-9.368, P=0.045), renal dysfunction (OR=3.627, 95%CI 1.018-12.926, P=0.047), CPB time>130 minutes (OR=4.539, 95%CI 1.483-13.887, P=0.008) were the independent risk factors of LCOS after cardiac valvular surgery in elderly patients with giant left ventricle. Risk of LCOS was significantly higher in patients aged from 65 to 70 years (OR=1.784, 95%CI 0.581-5.476) and aged 70 years and above (OR=4.400, 95%CI 1.171-16.531) than in patients aged from 60 to 65 years. The trend test results showed that the risk of LCOS increased significantly in proportion with the increase of age (P for trend=0.024). Risk of LCOS was significantly higher in patients with CPB time between 90 and 110 minutes (OR=1.917, 95%CI 0.356-10.322), 110 and 130 minutes (OR=1.437, 95%CI 0.114-18.076) and 130 minutes and above (OR=5.750, 95%CI 1.158-28.551) than in patients with CPB time ≤ 90 minutes (P for trend=0.009). Conclusions: The risk factors of LCOS after cardiac valvular surgery are age≥70 years, preoperative NYHA cardiac function class Ⅳ, renal dysfunction, CPB time>130 minutes in elderly patients with giant left ventricle.

OBESITY AMONG TEENAGERS AND IT'S RELATION WITH HYPERTENSION IN BAHRAIN

Abstract Objective: To determine the prevalence of overweight and obesity among teenagers in Bahrain as part of raising awareness on this matter. The correlation with gender, age, waist and Hypertension was tested. Central obesity as compared to BMI estimation was assessed and their prevalence within both genders were evaluated. Design and method: This is a cross sectional, prospective study to screen for obesity prevalence among teenagers in Bahrain. Students attending middle and high schools where the target sample. A specific date was scheduled for each school. A group of physicians and nurses measured and registered the height, weight, waist circumference and blood pressure for all the students between 6th till the 12th grades. The study was started in May 2015 and these data are inclusive until Dec. 2018. Independent factors correlated with high BMI were assessed in univariate as well as multivariate correlations. Difference between central obesity and BMI was analyzed as well as different prevalence among genders. Results: The total number of the students included were 2742 from 14 schools. After testing the correlation of the independent variables with BMI, we found that gender, waist circumference, Systolic pressure, Diastolic pressure, Pulse pressure, mean arterial pressure and Pulse rate were significantly correlated with BMI. Calculating a multivariate linear regression equation, including the significant independent variables, mentioned above, in relation to BMI revealed that gender, waist and pulse rate were the only significant variables. However, systolic, diastolic, pulse pressure, and MAP were not significantly correlated. The coefficient of determination, R square, was 0.26 for this model. Conclusions: A significant difference was noticed between genders in relation to central obesity, which was more prevalent among females, while a BMI classified obesity were more common among males. Gender, waist and pulse rate were significantly related to BMI. The BP was not related to BMI.

Soil conservation assessment via climate change and vegetation growth scenarios in the Nile River basin

Soil conservation by vegetation can mitigate soil erosion hazard and prevent reductions in food productivity. However, previous research applies little consideration to the interaction between vegetation and climate change in the estimation of future soil conservation change. Therefore, based on the Revised Universal Soil Loss Equation (RUSLE), Representative Concentration Pathways (RCPs, specifically RCP4.5 and RCP8.5), and the vegetation index and precipitation datasets, we built a multivariate regression equation that considers changes in vegetation growth under climate change scenarios in the context of soil conservation. Using the Nile River basin as a case study, via our established methods, we modelled and projected the impact of vegetation and climate change on future soil conservation between 2020 and 2100, where three main results were obtained: (1) under the scenarios of RCP4.5 and RCP8.5 from 2020 to 2100, soil conservation in the Nile Basin will first increase and then decrease, with its highest value in the years 2060, at 117.72 (t ha−1 y−1), and 2070, at 134.39 (t ha−1 y−1). (2) Soil conservation under RCP4.5 is lower than that under the RCP8.5 scenario, with a maximum difference of 27 (t ha−1 y−1) in 2040 and a minimum difference of 0.2 (t ha−1 y−1) in 2100. (3) The vegetation and climate change models in 2100 had soil conservation values of 110.77 (t ha−1 y−1) under RCP4.5 and 38.70 (t ha−1 y−1) under RCP8.5. In conclusion, although vegetation growth can increase soil conservation in the Nile River basin, the change in precipitation can offset the soil conservation enhanced by vegetation growth.

Stillbirth: are we making more progress than we think? A retrospective cohort study.

To quantify how the changing stillbirth risk profile of women is affecting the interpretation of the stillbirth rate. A retrospective, population-based cohort study from 1983 to 2018. Victoria, Australia. A total of 2419923 births at ≥28weeks of gestation. Changes in maternal characteristics over time were assessed. A multivariable logistic regression model was developed for stillbirth, based on maternal characteristics in 1983-1987, and used to calculate individual predictive probabilities of stillbirth from the regression equation. The number of expected stillbirths per year as a result of the change in maternal demographics was then calculated, assuming no changes in care and in the associations between maternal characteristics and stillbirth over time. Stillbirth. Compared with 1983-1987, there were more women in older age groups giving birth, more nulliparous women, more indigenous women and women born in Oceania, Asia and Africa, more multiple pregnancies and more women with pre-existing diabetes in 2014-2018. Despite this, the rate of stillbirth fell from 5.42 per 1000 births in 1983 to 1.72 per 1000 births in 2018 (P<0.001). Applying the multivariable logistic regression equation, derived from the 1983-87 data, to each year, had there been no changes in care or in the associations between maternal characteristics and stillbirth, the rate of stillbirth would have increased by 12%, from 4.94 per 1000 in 1983 to 5.54 per 1000 in 2018, as a result of the change in maternal characteristics. Population rates of stillbirth are falling faster than is generally appreciated. Population reductions in stillbirth have been underestimated as a result of changing maternal characteristics.

Analysis on natural influencing factors of groundwater depth in Dengkou County and forecast of its variation trend

To reveal the main natural factors that influences groundwater depth of Dengkou county and predict the variation trend of groundwater depth, this thesis, based on the principal component analysis (PCA), conducts analysis on natural factors that are likely to affect the groundwater depth, and establishes a multiple linear regression model to show the quantitative relationship between the main natural factors and the groundwater depth. Then grey prediction method is adopted to predict possible change of main natural influence factors in the coming five years with employment of multivariate regression equation for further fitting. Results: Among the selected natural influencing factors, the most influential one is evaporation, followed by air temperature and sunshine hours, which are negatively correlated with groundwater depth, and the reason for this is related to the flood season of Yellow River. Relative humidity and precipitation have little influence on groundwater depth. According to the grey prediction results, the temperature, evaporation and sunshine duration will all be on the rise in the coming five years, and the average groundwater level will also increase slowly. The average depth of groundwater in summer is about 2.5m. Due to local dry climate and vigorous evaporation, secondary salinization of the land is likely to occur. Especially, the water level in the irrigation area of the Yellow River is generally high, deserving enhanced vigilance. This paper has reference significance for the ecological construction & planning of Dengkou County in the next five years.

Stock Price Synchronicity with Analyst Coverage and Disclosure of Information in Iran Capital Market

In this study, Stock Price Synchronicity with Analyst Coverage and Disclosure of Information during the years 1390 to 1396 have been studied and compared. The statistical population of the study consists of companies listed on the Tehran Stock Exchange and Iran Fara Bourse(IFB). Research hypotheses have been tested through a multivariate regression equation. The main research hypothesis regarding the relationship between stock price synchronization and analyst coverage and disclosure of significant positive relationship has been confirmed. Other findings suggest a significant positive relationship between stock price synchronization and disclosure and irrelevency between analyst coverage and stock price synchronization. An increase in the number of analysts on the one hand and a slight increase in the number of announcements published by the companies surveyed in the Codal system on the other hand has shown a positive relationship between these two variables and the price variable. In other words, as the synchronicity increases, the greater the number of announcements and the greater the number of analysts, the more synchronicity the stock price will be. Key w ords : Price Synchronicity, Analyst Coverage, Disclosure of Information. DOI: 10.7176/RJFA/12-3-01 Publication date: February 28 th 2021

Performance of Regression-Based Norms for Cognitive Functioning of Persons With Multiple Sclerosis in an Independent Sample.

Background: Cognitive impairment is common in multiple sclerosis (MS). Interpretation of neuropsychological tests requires the use of normative data. Traditionally, normative data have been reported for discrete categories such as age. More recently continuous norms have been developed using multivariable regression equations that account for multiple demographic factors. Regression-based norms have been developed for use in the Canadian population for tests included in the MACFIMS and BICAMS test batteries. Establishing the generalizability of these norms is essential for application in clinical and research settings.Objectives: We aimed to (i) test the performance of previously published Canadian regression-based norms in an independently collected sample of Canadian healthy controls; (ii) compare the ability of Canadian and non-Canadian regression-based norms to discriminate between healthy controls and persons with MS; and (iii) develop regression-based norms for several cognitive tests drawn from batteries commonly used in MS that incorporated race/ethnicity in addition to age, education, and sex.Methods: We included 93 adults with MS and 96 healthy adults in this study, with a replication sample of 104 (MS) and 39 (healthy adults). Participants reported their sociodemographic characteristics, and each was administered the oral Symbol Digit Modalities Test (SDMT), the California Verbal Learning Test (CVLT-II), and the Brief Visuospatial Memory Test-Revised (BVMT-R). From the healthy control data, we developed regression-based norms incorporating race, age, education and sex. We then applied existing discrete norms and regression-based norms for the cognitive tests to the healthy controls, and generated z-scores which were compared using Spearman rank and concordance coefficients. We also used receiver operating characteristic (ROC) curves to compare the ability of each set of norms to discriminate between participants with and without MS. Within the MS samples we compared the ability of each set of norms to discriminate between differing levels of disability and employment status using relative efficiency.Results: When we applied the published regression norms to our healthy sample, impairment classification rates often differed substantially from expectations (7%), even when the norms were derived from a Canadian (Ontario) population. Most, but not all of the Spearman correlations between z-scores based on different existing published norms for the same cognitive test exceeded 0.90. However, concordance coefficients were often lower. All of the norms for the SDMT reliably discriminated between the MS and healthy control groups. In contrast, none of the norms for the CVLT-II or BVMT-R discriminated between the MS and healthy control groups. Within the MS population, the norms varied in their ability to discriminate between disability levels or employment status; locally developed norms for the SDMT and CVLT-II had the highest relative efficiency.Conclusion: Our findings emphasize the value of local norms when interpreting the results of cognitive tests and demonstrate the need to consider and assess the performance of regression-based norms developed in other populations when applying them to local populations, even when they are from the same country. Our findings also strongly suggest that the development of regression-based norms should involve larger, more diverse samples to ensure broad generalizability.

Big Data Interprets US Opioid Crisis

Since 2010, there has been a new round of drug crises in the United States. The abuse of opioids has led to a sharp increase in the number of people involved in drug crimes in the United States. There is an urgent need to explore solutions to the drug crisis in the United States. In this paper, the model of in-depth analysis is established under the condition of obtaining the opioid data and the influence factor data of the large sample of five state[1]. In the first part, we use the Highway Safety Research Institute model based on the differential equation model to predict the initial value, find the initial position of the drug transfer, and obtain the curve of the number of different groups over time by fitting the data, so that the curves can be predicted the changing trends of the groups in the future. It was found that in Kentucky State, the county’s most likely to start using opioids were Pike and Bale. In Ohio, the county’s most likely to start using opioids are Jackson and Scioto. In Pennsylvania State, Mercer and Lackawanna are the counties most likely to start using opioids. Martinsville and Galax are the counties where Virginia State is most likely to start using opioids. Logan and Mingo are the counties where West Virginia State is most likely to start using opioids. In the second part, the gray prediction model is used to further analyze the time series of each factor, the maximum likelihood estimation method is used to obtain the weight of each factor, and the weight coefficient matrix is used to simulate the multivariate regression equation, and the factors that have the greatest influence on opioid abuse are educational background and family composition. In the third part, the hypothesis test model of two groups (the data type is proportional) is used to verify the difference between the influence factors (including the predicted values) in the first two parts of the states, thus verifying the feasibility between them. At the same time, we put forward a few suggestions to combine the current situation in the United States with the CDC data. We believe that in order to address the opium crisis, the U.S. government needs to strengthen not only oversight of doctors' prescriptions, but also make joint efforts of all sectors of society to fundamentally reduce the barriers to the use of opioids.

Effects of Primary Decompressive Craniectomy on the Outcomes of Serious Traumatic Brain Injury with Mass Lesions, and Independent Predictors of Operation Decision

Although operative indications for traumatic brain injury (TBI) are known, neurosurgeons are unsure whether to remove the bone flap after mass lesion extraction, and an efficient scoring system for predicting which patients should undergo decompressive craniectomy (DC) does not exist. Nine parameters were assessed. In total, 245 patients with severe TBI were retrospectively assessed from June 2015 to May 2019, who underwent DC or craniotomy to remove mass lesions. The 6-month mortality and Extended Glasgow Outcome Scale scores were compared between the DC and craniotomy groups. Using univariable and multivariable logistic regression equations, receiver operating characteristic curves were obtained for predicting the decision for DC. The overall 6-month mortality in the entire cohort was 11.43% (28/245). Patients undergoing DC had lower mean preoperative Glasgow Coma Scale scores (P= 0.01), and higher amounts of individuals with a Glasgow Coma Scale score of 6 (P= 0.007), unresponsive pupillary light reflex (P < 0.001), closed basal cisterns (P < 0.001), and diffuse injury (P= 0.025), compared with the craniotomy group. Because of high disease severity, individuals administered primary DC showed increased 6-month mortality compared with the craniotomy group. However, in surviving patients, favorable Extended Glasgow Outcome Scale rates were similar in both groups. Pupillary light reflex and basal cisterns were independent predictors of the DC decision. Based on receiver operating characteristic curves, the model had sensitivity and specificity of 81.6% and 84.9%, respectively, in predicting the probability of DC. These preliminary data showed that primary DC may benefit some patients with severe TBI with mass lesions. In addition, unresponsive preoperative pupil reaction and closed basal cistern could predict the DC decision.

Draft force prediction for a high-speed disc implement using discrete element modelling

Draft force is an important dynamic parameter for agricultural soil-engaging tools and implements. Soil-disc interaction dynamics has changed enormously in modern tillage practice because of the use of larger machines, faster speeds, and different tool arrangements. Most existing studies on draft force prediction are for conventional tillage. In this study, a discrete element model (DEM) was developed to predict draft forces for high-speed (12 km h−1 or higher) tillage. Draft forces were measured for an individually mounted disc using soil bin tests at low speeds in a sandy loam soil. Results were compared for the accuracy with the predicted values from the ASABE standard equation. Results showed that relative error ranged from 8% to 14%. Due to the limitations of the testing facility, the DEM model was calibrated using 6 km h−1 tillage speed and verified with other test speed measurement. The calibrated model was able to predict draft force with a minimum relative error of 1%. The calibrated particle stiffness, was found to be 20 kN m−1. The calibrated model was then used to examine the effects of operating parameters including gang angle, tilt angle, operating speed, disc diameter and tillage depth on draft forces at 16 km h−1. From these simulations, it was shown that draft increased with an increase in gang angle, tillage depth, and speed. The draft force was reduced with an increase in tilt angle and remained relatively constant with the change of disc diameter. Finally, the simulated data were used to develop a multivariate regression equation to predict the draft for a high-speed tillage operation. This equation is suitable for the soil conditions studied and further validation is required to verify this equation.

Evaluation of the impact of transformational leadership style on knowledge management considering the mediating role of organisational structure and organisational culture

The main objective of the current research is to evaluate the impact of transformational leadership style on knowledge management considering the mediating role of organisational culture and organisational structure in a selected public organisation in Iran. The research population included 220 employees of this public organisation. The research sample was selected using Cochran's formula in a limited population and randomised sampling method. The required data were collected based on the research model through a questionnaire. Multivariate regression and structural equation modelling (path analysis) were used to evaluate the relationship between variables and testing the hypotheses. The results revealed a significant and positive relationship between the transformational leadership style and knowledge management at the 95% confidence level. Other research results also confirmed the effect of mediating variables of organisational culture and organisation structure on the relationship between transformational leadership style and knowledge management.

Evaluation of the impact of transformation leadership style on knowledge management considering the mediating role of organizational structure and organizational culture

The main objective of the current research is to evaluate the impact of transformational leadership style on knowledge management considering the mediating role of organisational culture and organisational structure in a selected public organisation in Iran. The research population included 220 employees of this public organisation. The research sample was selected using Cochran's formula in a limited population and randomised sampling method. The required data were collected based on the research model through a questionnaire. Multivariate regression and structural equation modelling (path analysis) were used to evaluate the relationship between variables and testing the hypotheses. The results revealed a significant and positive relationship between the transformational leadership style and knowledge management at the 95% confidence level. Other research results also confirmed the effect of mediating variables of organisational culture and organisation structure on the relationship between transformational leadership style and knowledge management.