Statistical Linear Regression Model Research Articles

EFFECT OF PUBLIC PRIVATE PARTNERSHIP FINANCING STRUCTURE ON COMPLETION OF GEOTHERMAL ENERGY DEVELOPMENT PROJECTS IN KENYA

Objective: To determine the effect of Public Private Partnership (PPP) financing structure on the completion of geothermal energy development projects in Kenya. Theoretical Framework: The study used agency and public choice theories to analyze the dynamics between PPP financing structure and completion of geothermal energy development projects. Methodology: A cross-sectional census survey in Kenya surveyed 48 geothermal energy development projects, collecting quantitative data through structured questionnaires and qualitative insights from industry regulators, financiers, and practitioners. A combination of descriptive, correlation, and linear regression statistical models were applied. Multiple diagnostics tests and data triangulation were performed. Results and Discussion: The study results provide strong statistical evidence depicting a positive relationship between PPP financing structure and the completion of geothermal energy development projects in Kenya. The findings suggest that as the level of PPP involvement increases, so does the likelihood of faster project completion. The study also reveals that a balanced PPP financing structure has a substantial effect on the completion of geothermal energy development projects. The analysis demonstrates that a hybrid PPP financing structure comprising recommended proportions of private and government equity, commercial and concessional loans, hybrid finance, and grants significantly affects project completion outcomes. Research Implications: The study emphasizes the significance of a structured PPP financing model, comprising private and government equity, concessional and commercial loans, hybrid finance, and mixed grants, in mitigating inherent PPP risks for enhanced project completion outcomes. In addition, effective government support measures and robust delivery capability of developers jointly influence the two variables in a significant manner. Originality/Value: This study uniquely analyzes the influence of PPP financing structure on the completion of geothermal energy development projects in Kenya, integrating quantitative and qualitative data for a comprehensive perspective, thus offering essential insights for stakeholders.

Effect of a pragmatic lifestyle modification intervention on physical activity levels and body mass index among obese and overweight adolescents in Udupi, India: a cluster randomized trial.

Determine the effects of a multifactorial lifestyle intervention on physical activity (PA), BMI and health-related quality of life (QoL) in obese and overweight adolescents. Nine schools in India were clustered randomly in a 12-month study with students allocated to a multifactorial intervention (MFI), or exercise only (EX) or control (CON) group. Participants were adolescents aged 11-16 years (n=671). In the MFI group, adolescents and their parents received lifestyle education using a validated booklet combined with a PA intervention for school students. The EX group received school-based PA only; the CON group continued regular activities. Primary outcomes were PA levels measured with the PAQ-A, and BMI; the secondary outcome was health-related QoL. A linear regression statistical model was used to analyse time, group effects and interactions, with Bonferroni correction for within-group differences at baseline (T0) and at 12-weeks (T1) (post-intervention), 6-month (T2) and 12-month (T3) follow-ups. Significant time and group effects observed for all groups with PA scores (p<0.001), with MFI group having largest increase in PA; with BMI (p<0.001) and MFI showing the least gain in BMI; and HRQOL (p<0.001), with MFI group showing greatest improvement in scores. There were significant increases in PA at T1 and T3 time-points with the EX group, and at T3 time-point only for MFI and CON, with MFI group showing largest increase in HRQOL scores. BMI increased significantly for all groups at T2 (MFI p=0.001, EX p<0.001) and T3 (p<0.001), while HRQOL increased significantly for both MFI and EX at both follow-ups (p<0.001). School-based lifestyle MFI was more effective for improving PA, lifestyle behaviours and HRQOL than exercise alone for adolescents, although BMI was not reduced. MFI with PA could be an effective school-based approach for behaviour modification but BMI has limitations for measuring body composition changes. CTRI/2019/04/018834 (30/04/2019).

Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors

This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine learning (ML) models, incorporating Random Forest (RF), Decision Tree (DT), Extreme Gradient Boost (XGB) and K-Nearest Neighbor (KNN) algorithms, were developed and compared with the Linear Regression (LR) statistical model, using 75% of the data for training and 25% for validation. The predictions of ML algorithms were more accurate than the LR, while XGB and RF provided the best predictions. To explain the ML results, Explainable artificial intelligence (XAI) was employed by using Shapley Additive Explanations (SHAP) to predict the global behavior of variables. RF was the most reliable model, with a coefficient of correlation of 0.97 and a mean absolute percentage error of 1.8% after hyperparameter tuning. Results indicated that discharge, velocity, water depth, and vegetation all influenced microplastic transport. Discharge and vegetation enhanced and reduced microplastic transport, respectively, and showed a response to different vegetation patterns. A strong linear positive correlation (R2 = 0.8) was noted between microplastic density and retention. In the absence of dedicated microplastic transport analytical models and infeasibility of using classical sediment transport models in predicting microplastic transport, ML proved to be helpful. Moreover, the use of XAI will reduce the black-box nature of ML models with effective interpretation enhancing the trust of domain experts in ML predictions. The developed model offers a promising tool for real-world open channel predictions, informing effective management strategies to mitigate microplastic pollution.

Service quality: perspective of people with type 2 diabetes mellitus and hypertension in rural and urban public primary healthcare centers in Iran

ObjectiveThis study aimed to assess the service quality (SQ) for Type 2 diabetes mellitus (T2DM) and hypertension in primary healthcare settings from the perspective of service users in Iran.MethodsThe Cross-sectional study was conducted from January to March 2020 in urban and rural public health centers in the East Azerbaijan province of Iran. A total of 561 individuals aged 18 or above with either or both conditions of T2DM and hypertension were eligible to participate in the study. The study employed a two-step stratified sampling method in East Azerbaijan province, Iran. A validated questionnaire assessed SQ. Data were analyzed using One-way ANOVA and multiple linear regression statistical models in STATA-17.ResultsAmong the 561 individuals who participated in the study 176 (31.3%) were individuals with hypertension, 165 (29.4%) with T2DM, and 220 (39.2%) with both hypertension and T2DM mutually. The participants’ anthropometric indicators and biochemical characteristics showed that the mean Fasting Blood Glucose (FBG) in individuals with T2DM was 174.4 (Standard deviation (SD) = 73.57) in patients with T2DM without hypertension and 159.4 (SD = 65.46) in patients with both T2DM and hypertension. The total SQ scores were 82.37 (SD = 12.19), 82.48 (SD = 12.45), and 81.69 (SD = 11.75) for hypertension, T2DM, and both conditions, respectively. Among people with hypertension and without diabetes, those who had specific service providers had higher SQ scores (b = 7.03; p = 0.001) compared to their peers who did not have specific service providers. Those who resided in rural areas had lower SQ scores (b = -6.07; p = 0.020) compared to their counterparts in urban areas. In the group of patients with T2DM and without hypertension, those who were living in non-metropolitan cities reported greater SQ scores compared to patients in metropolitan areas (b = 5.09; p = 0.038). Additionally, a one-point increase in self-management total score was related with a 0.13-point decrease in SQ score (P = 0.018). In the group of people with both hypertension and T2DM, those who had specific service providers had higher SQ scores (b = 8.32; p < 0.001) compared to the group without specific service providers.ConclusionStudy reveals gaps in T2DM and hypertension care quality despite routine check-ups. Higher SQ correlates with better self-care. Improving service quality in primary healthcare settings necessitates a comprehensive approach that prioritizes patient empowerment, continuity of care, and equitable access to services, particularly for vulnerable populations in rural areas.

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

Statistical Analysis of Morphological Characteristics of Inconel 718 Formed by High Deposition Rate and High Laser Power Laser Cladding.

Laser cladding is one of the emerging additive manufacturing technologies and has been adopted in various industrial fields. In this study, the morphological characteristics of a single clad of Inconel 718 manufactured by coaxial laser cladding with high laser power from 4200 W to 5400 W, powder feeding rate from 25 g/min to 50 g/min, and cladding speed from 20 mm/s to 50 mm/s are studied. The cross-section of the melt pool is analyzed and classified by type into three types: shallow dilution, flat dilution, and fluctuating dilution. Nine parameters are designed to describe the morphological characteristics of the clad, and the corresponding linear regression models are developed to establish a quantitative relationship between the combined process parameters and morphological characteristics. The results indicate that the total area of the cross-section A, the clad area above the substrate Ac, the area of the molten substrate Am, the total height of the cross-section H, the height of the clad above the substrate hc, the penetration depth hm, the clad width W, the dilution ratio D, and the wetting angle θ are determined by complex coupling of energy input and mass accumulation, and they are proportional to PF0.4/V, P0.5F/V, P/F0.2/V0.4, P2F0.6/V, PF0.7/V, P2/F/V0.3, P/V0.8, P/FV0.2, and PF7/V0.8, respectively. The large linear regression coefficients and the analysis residuals indicate the high reliability of the statistical linear regression models. This work aims to provide a comprehensive understanding of the influence of the main processing parameters on the morphological characteristics of the clad, which is of great value in providing a reference and laying a basis for the practical application of laser cladding technology at a high deposition rate.

Impact of topographic factors on animal field pathings: Analysis and prediction of deer movement patterns

Understanding and tracking the complexities of animal movement patterns is of paramount importance in wildlife management, conservation efforts, and the sustainable use of natural resources. An infinite number of factors influence the movement path of animals within their respective habitats, including: the structure of the habitat, the availability of resources, the presence of natural predators, social memory, the topographic attributes of the environment, etc.Numerous studies have attempted to delineate the spatial boundaries of animal habitats by elucidating the complexities of their movement dynamics. These investigations have highlighted the profound impact of factors such as environmental topography and the presence of natural impediments and other anthropogenic structures on animal mobility, but very few have analyzed topographic factors at a fine three-dimensional spatial scale.This research focuses on a novel methodology for identifying animal trajectories at a fine scale and evaluating the influence of topographic factors on these trajectories, specifically of deer herds in southern Spain. To understand movement patterns, transects recorded in the field due to continued use by deer are analyzed. Topographical information was obtained in two steps: first with a graphical analysis of orthophotos for the incorporation of the sufficient data set. Secondly, the veracity of this data was verified using Global Positioning System (GPS) tracking technology. The integration of data from multiple sources with Geographic Information Systems (GIS) allowed the analysis to be automated. Next a statistical linear regression model, based on both the ascent and descent lengths and the total length of the path traveled, was designed to infer the trajectories between two designated points within the study area. Using topographical variables obtained in the study environment, such as the slope, the elevation difference (cumulative vertical distance), and the 3D length of the transect paths, the influence of these variables on the movement decisions of animals within their habitat is established in order to facilitate their subsequent prediction.Analytical tests of the trajectories have shown that the movement behavior of cervids is predictable. The results demonstrate the usefulness of the methodology presented which, by providing and collect valuable topographic information on movement and transit areas, can guide sustainable management practices for deer populations and their habitats.

Multi-Sensor Fusion Target Tracking Based on Maximum Mixture Correntropy in Non-Gaussian Noise Environments with Doppler Measurements

This paper addresses the multi-sensor fusion target tracking problem based on maximum mixture correntropy in non-Gaussian noise environments exclusively using Doppler measurements. As Doppler measurements are non-linear, a statistical linear regression model is constructed using the unscented transformation. Then, a centralized measurement model is developed, and the mixture correntropy is determined, which contains the high-order statistics of state prediction and the measurement error caused by noise. Then, a robust fusion filter is proposed by maximizing the mixture-correntropy-based cost. To improve numerical stability, the information filter and corresponding square root version are also derived. Furthermore, the performance of the proposed algorithm is analyzed, and the selection of the kernel width is discussed. Experiments are performed using simulated data and automatic driving software. The results show that the estimation performance of the proposed algorithm is better with respect to outliers and mixture Gaussian noise than that of traditional methods.

Product quality and profitability at masisa

This research work was born to address the problem of the negative impact generated by defective products of Masisa Peru during 2022 on its profitability, given that according to the background researched, it is certain that these defective products generate higher operating costs and customer dissatisfaction, thus resulting in lower profits; The main objective of the study is to determine the influence that the quality of these products has on the company's profits, for which a qua entitative methodology with causal descriptive scope was used, and it was executed through the documentary content analysis technique of the historical records corresponding to the number of defective units per month, the number of good products, total sales, costs and profitability obtained during the period 2022 in order to establish the possible relationship between these variables. The results showed through a linear regression statistical model that defective products have a significant negative correlation that explains almost 80% of the variability in profitability; however, a strong linear connection between melamine in good condition and sales or operating costs was not found, but this does not eliminate the possibility that in the long term there could be a strong relationship between defective products and costs, if the company continues with its current economic activity and does not implement corrective measures. Therefore, it was concluded that the poor quality represented by defective products causes economic losses by reducing profits, as well as this negative relationship generates an adverse effect on profits, causing economic losses, customer dissatisfaction, bad reputation, and even legal problems.

Robust UKF Relative Positioning Approach for Tightly Coupled Vehicle Ad Hoc Networks Based on Adaptive M-Estimation

Huber M-estimation, as an estimation method based on mixed norm as cost function, provides an effective method for robust filtering to deal with measurement outliers. Based on statistical linear regression model approximating nonlinear measurement model, M-estimation algorithm is used to realize measurement update of states. However, when the measurement noise pollution rate is high, the filtering performance will be seriously degraded. In order to further improve the actual performance of data fusion under abnormal measurement noise, a vehicle cooperative positioning scheme based on adaptive M-estimation robust unscented Kalman filter (AMRUKF) was proposed. This algorithm combines Huber’s linear regression problem with covariance matching method, and calculates the adaptive matrix through the innovation covariance estimator based on fading memory index weighting to adaptively adjust the measurement noise covariance used in Huber M estimation method. The analytical and experimental results of tightly-coupled vehicle relative positioning show that the proposed AMRUKF method can effectively improve the robustness and accuracy of relative positioning and provide a practical control scheme for vehicle cooperative positioning.

Prediction of harbour vessel fuel consumption based on machine learning approach

Fuel consumption influences both the economic and environmental perspectives of shipping. With the help of machine learning, meaningful knowledge and complex relationships can be extracted from high-dimensional historical data. In this study, machine learning models were developed to predict the fuel consumption of harbour vessels with ship-related and meteorological factors. The superiority of machine learning models over statistical linear regression model (Ridge regression) has been proved. This study further investigated whether the use of meteorological factors enhances the prediction of fuel consumption. A case study on the prediction of tugboat fuel consumption was conducted. The Random Forest model outperformed the other models. Comparative experiments showed that meteorological factors collectively add value to the fuel consumption prediction, which enhances the accuracy from 0.7 to 38.9%. The potential uses of the prediction results are highlighted in terms of both operational management and environmental evaluation aspects.

Measuring reader fatigue in the interpretation of screening digital breast tomosynthesis (DBT).

The interpretation of digital breast tomosynthesis (DBT) screening examinations is a complex task for an already overstretched workforce which has the potential to increase pressure on readers leading to fatigue and patient safety issues. Studies in non-medical and medical settings have suggested that changes in blink characteristics can reflect fatigue. The purpose of this study is to investigate the use of blink characteristics as an objective marker of fatigue in readers interpreting DBT breast screening examinations. Twenty-six DBT readers involved in the UK PROSPECTS trial interpreted a test set of 40 DBT cases while being observed by an eye tracking device from November 2019 to February 2021. Raw data from the eye tracker were collected and automated processing software was used to produce eye blinking characteristics data which were analysed using multiple linear regression statistical models. Of the 26 DBT readers recruited, eye tracking data from 23 participants were analysed due to missing data rendering 3 participants' data uninterpretable. The mean reading time per DBT case was 2.81 min. There was a statistically significant increase in blinking duration of 0.38 ms/case as the reading session progressed (p < 0.0001). This was the result of a significant decrease in the number of ultra-short blinks lasting ≤50 ms (p = 0.0005) and a significant increase in longer blinks lasting 51-100 ms (p = 0.008). Changes in blinking characteristics could serve as objective measures of reader fatigue and may prove useful in the development of DBT reading protocols. Blink characteristics can be used as an objective measure of fatigue; however there is limited evidence of their use in radiological settings. Our study suggests that changes in blink duration and frequency could be used to monitor fatigue in DBT reading sessions.

Neonatal Mortality at Felege Hiwot Comprehensive Specialized Hospital in Ethiopia Over 5 years: Trends and Associated Factors.

Globally, neonatal mortality remains a serious catastrophic problem for newborns, particularly in a low-resource setting. There were no neonatal mortality trend studies in the study area. This study aimed to determine the trends and risk factors of neonatal mortality at the neonatal intensive care unit of Felege Hiwot Comprehensive Specialized Hospital, Northwest Ethiopia. An institution-based retrospective cross-sectional study was conducted among 870 admitted neonates from January 1, 2016 to December 31, 2020 in the neonatal intensive care unit by a stratified simple random sampling technique. Data were entered into EpiData and then exported to STATA 14.0 for analysis. A linear regression statistical model was used for trend analysis and binary logistic regression was carried out to identify explanatory variables of neonatal mortality. Overall, neonatal mortality averagely increased by 2.1% per year throughout the 5 consecutive years. In this study, rural residency [adjusted odds ratio (AOR): 1.96, 95% confidence interval (CI): (1.26, 3.06)], birth asphyxia (AOR: 7.73, 95% CI: 4.31, 13.84), congenital deformity (AOR: 3.61, 95% CI: 1.17, 11.18), low birth weight (AOR: 2.13, 95% CI: 1.23, 3.67), respiratory distress syndrome (AOR: 3.32, 95% CI: 1.97, 5.59), Ambu-bag resuscitation (AOR: 0.16, 95% CI: 0.07, 0.38), taking antibiotics (AOR: 0.50, 95% CI: 0.27, 0.90), glucose (AOR: 0.47, 95% CI: 0.30, 0.72), and oxygen (AOR: 0.26, 95% CI: 0.16, 0.41) were associated with neonatal mortality. This 5-year trend analysis revealed an increased trend of NMR, indicating more work is still needed to make progress toward meeting the SDG goal by 2030. Rural residency, birth asphyxia, congenital deformity, low birth weight, respiratory distress syndrome, Ambu-bag resuscitation, taking antibiotics, glucose, and oxygen were associated with neonatal mortality. Therefore, all stakeholders shall give due attention to reducing this timely-increasing trend of neonatal mortality.

Simple, accurate, adjustable-parameter-free prediction of NMR shifts for molecules in solution.

Accurate prediction of NMR shifts is invaluable for interpreting and assigning NMR spectra, especially for complex applications such as determining the identity of unknown substances or resolving stereochemical assignments. Statistical linear regression models have proven effective for accurately correlating density functional theory predictions of chemical shieldings with experimentally-measured shifts, but lack transferability - they must be reparameterised using a reasonably extensive training set at each level of theory and for each choice of NMR solvent. We have previously introduced a novel two-point "shift-and-scale" correction procedure for gas phase shieldings that overcomes these limitations without significant loss of accuracy. In this work, we demonstrate that this approach is equally applicable for predicting solution-phase shifts from computed gas phase shieldings, using acetaldehyde as an experimentally and computationally convenient reference system. We also present all of the required experimental reference data to enable this approach to be used for any target analyte in a range of commonly used NMR solvents (chloroform, dichloromethane, acetonitrile, methanol, acetone, DMSO, D2O, benzene, pyridine).

Extended kernel Risk-Sensitive loss unscented Kalman filter based robust dynamic state estimation

The traditional unscented Kalman filter (UKF) with mean square error (MSE) criterion for dynamic state estimation (DSE) is sensitive for unknown non-Gaussian noise and outliers. Leading to biased state estimates. This paper proposes a novel robust UKF with extended kernel risk-sensitive loss (EKRSL) for DSE considering unknown non-Gaussian process and measurement noises. Instead of MSE criterion, a novel robust EKRSL via the generalized Gaussian density is defined in KRSL framework, and we further develop a new robust UKF using the EnKRSL(called EKRSL-UKF). To obtain the recursive form of EKRSL-UKF, the statistical linear regression model is used and the fixed-point iteration is further utilized to iteratively get the optimal state estimate. An error constrained method is also introduced to restrict the error to address the numerical instability problem caused by large outliers. Furthermore, an enhanced EKRSL-UKF is established by using an exponential function of innovation to improve the estimation accuracy in the presence of noise uncertainties. Numerical results carried out on the IEEE 39-bus test system demonstrate that the proposed method can achieve desired robustness without loss of estimation accuracy under various conditions.

Abstract 12528: Variants in Solute Cation Transporters 3 Associate with High Lp(a) Levels Independent of Apo(a) Isoform Size

Introduction: Lipoprotein (a) [Lp(a)] consists of a low-density lipoprotein (LDL) molecule attached to apolipoprotein(a) [apo(a)]. Apo(a) comprises two kringle domains (KIV and KV) derived from the plasminogen gene and an inactive protease domain. The KIV domain is subdivided into 10 subtypes (KIV1-10), with KIV2 occurring as a copy number variation (CNV) that varies among individuals. Elevated Lp(a) levels are a major risk factor for CVDs and are inversely correlated to the KIV2 CNV determining apo(a) isoform size. Objectives: This study aimed to identify variants associated with a high Lp(a) phenotype that were not linked to apo(a) isoform size. Methods: Illumina sequencing of 13 candidate genes was performed on a discovery set comprising 54 high Lp(a) European samples (&gt;50mg/dL). The sequenced genes included LPA, adjacent genes, and those coding for receptors involved in Lp(a) uptake. Variants commonly found in high samples were genotyped in the Otago LPA European cohort (n=589) and their association with Lp(a) levels was checked via Kruskal-Wallis and linear regression statistical models. The associations were then replicated in the much bigger German Chronic Kidney Disease (GCKD) cohort (n=4930). Results: Nine variants were significantly associated with high Lp(a) levels. Two variants were in the LPA gene (rs3124784 and rs3798220) and seven were in neighbouring genes (rs2282143 in SLC22A1, rs3127592, rs3127593, rs3103353 and rs3127594 in SLC22A2 and rs1810126 and rs3088442 in SLC22A3). Interestingly, the minor allele of all nine variants was significantly associated with increased LPA expression in liver tissues in the GTEx database. Seven of the variants were significantly associated with small apo(a) isoform size (P=1x10-4 to 2.9x10-18), while rs1810126 and rs3088442 in SLC22A3 were not associated with the small isoform size (P=0.43 and 0.42). Conclusion: Nine variants in the LPA and nearby genes were significantly associated with elevated Lp(a) levels and increased LPA expression, with the majority being linked to a short apo(a) isoform. The association of two SLC22A3 gene variants with Lp(a) levels independent of apo(a) isoform size warrants further investigation to establish any functional connection between LPA and this gene.

The Influence of Density on the Mechanical Response of Reinforced High-Density Polyurethane Foams: A Statistical Approach

In this work, rigid polyurethane foams (RPUF) reinforced by micro fibrillated cellulose (MFC) were manufactured using the free rising method and also under confinement inside a closed mould, aiming to increase apparent density and improve mechanical response. Neat RPUF were also manufactured for comparison. The mechanical response, evaluated by compression (following ASTM D1621 standard) tests were correlated with the final composite apparent density (evaluated following ASTM D1622 standard). Simple linear regression statistical models, based on F-test, were developed using stat graphics software, aiming to understand and correlate the increment in density and its influence on the improvement in mechanical response. Different models were developed to describe the foam behavior. The main results show a more significant influence of the density on strength than stiffness for the neat RPUF, unlike the MFC-reinforced RPUF, which presented an opposite response. These effects could be caused by the lower content of voids when the foams were produced under confinement, and by the greater crosslink density, when MFC was added.

Exogenous regulation of the potatoes’ adaptive potential when using bio stimulants

Potato from the Solanaceae family is one of the most important crops in the world and its cultivation is common in many places. The average yield of this crop is 20 Mg·ha –1 and it is compatible with climatic conditions in many parts of the world. The experiment studied the possibility of exogenous regulation of the adaptive potential available for four potato cultivars through the use of growth stimulants with different action mechanisms: 24-epibrassinolide (EBL) and chitosan biopolymer (CHT). The results allowed us to establish significant differences in growth parameters, plant height, leaf index, vegetation index, chlorophyll content, and yield structure. Monitoring growth and predicting yields well before harvest are essential to effectively managing potato productivity. Studies have confirmed the empirical relationship between the normalised difference vegetation index ( NDVI) and N-tester vegetation index data at various stages of potato growth with yield data. Statistical linear regression models were used to develop an empirical relationship between the NDVI and N-tester data and yield at different stages of crop growth. The equations have a maximum determination coefficient (R 2) of 0.63 for the N-tester and 0.74 for the NDVI during the flowering phase (BBCH 1 65). NDVI and N-tester vegetation index positively correlated with yield data at all growth stages.

Pengaruh Sumber Daya Manusia Terhadap Kinerja Karyawan Di KAP Suparman Bambang Ghanis

This study aims to determine how much influence human resource planning on employee performance can help companies to improve employee performance. The data used is secondary data from the results of questionnaires to sample employees. The analysis used in this study is a quantitative method test using SPSS Version 20.00 with a significant (α) of 5% and using a simple linear regression statistical model, correlation, and the coefficient of determination (R2), with the influence of human resource planning as the dependent variable. Hypothesis testing with T test shows that the influence variable of human resource planning influences employee performance with t count &gt; t table. This means that employee performance is influenced by the influence variables of human resource planning, the rest is influenced by other variables not mentioned in the hypothesis testing results model, and the two independent variables have a significant effect on employee performance at a significant level of 5%.Based on the analysis, the relationship between the influence of human resource planning and employee performance on KAP Suparman Bambang Ghanis has a strong positive relationship. This can be seen and proved the relationship between the influence of human resource planning and employee performance in KAP Suparman Bambang Ghanis there is a strong positive relationship. This can be seen and proven from the value of Correlation = r = 0.878 (close to 1 relation x to strong Y positive). The results showed that (R square = 77.1%). So, the influence of human resource planning has an influence on employee performance by 77.1% while the remaining 28.9% is explained by other factors outside the variable sentences used in the study. Then the results of the study stated that the count of 8.343 is greater than t table (2.00856), the significance of 0.000 is smaller than 0.05, so Ho is rejected thus the influence of human resource planning (x) has a significant effect on employee performance (Y).Regression coefficient Y = a + bX, the effect of human resource planning (X) is 0, meaning that the employee's performance (Y) is positive 16,853. Regression coefficients influence variables of human resource planning (X) of 0.597, meaning that if the influence of human resource planning increases, employee performance increases, t count of 8.343 is greater than t table (2.00856), the significance of 0.000 is less than 0.05, so Ho is rejected thus the influence of human resource planning (x) has a significant effect on employee performance (Y).

Coconut oil effects on in vitro ruminal fermentation and methanogenesis

Methane emission from ruminants can be mitigated with supplementation of medium chain fatty acids (MCFA) such as lauric acid that present in coconut oil. However, the effects of coconut oil supplementation on in vitro rumen fermentation and methane emission have been varied. This study was conducted to examine the effect of dietary coconut oil supplementation on rumen fermentation and methanogenesis by integrating data from various in vitro studies. A linear regression statistical model approach was applied to a total of 14 studies and 38 data points. The response variables integrated were methane emission, fermentation products, nutrient digestibility, and microbial population. The data obtained were analyzed by using IBM SPSS software version 20. The results showed that methane production was decreased significantly (P<0.01) by supplementation of coconut oil. Coconut oil tended to decrease methanogenic archaea, protozoa, total volatile fatty acids and isovalerate proportion (P<0.1). However, the oil supplementation did not significantly affect dry matter digestibility, total gas production, N-ammonia, acetate, isobutyrate, valerate, acetate to propionate ratio, population of rumen bacteria, organic matter digestibility, pH, propionate and butyrate. It can be concluded that coconut oil supplementation is able to mitigate methane emission by modifying microbial population structure and fermentation in the rumen.