Order Polynomial Regression Research Articles

Modeling and Simulation of Vehicle Velocity-Density on Buah Batu Road Using Second-Order Polynomial Regression

The problem of traffic density is a complex problem in the world of land transportation, especially in urban areas, including Bandung City. Buah Batu Road, one of the main roads in Bandung City which is 13 meters wide and 1.70 kilometers long, connecting Bandung City and Bandung Regency. And this study examines the relationship between vehicle speed and traffic density on Buah Batu Road, Bandung. Using the macroscopic Lighthill-Whitham Richards (LWR) model, Second Order Polynomial Regression, and Lax-Wendroff scheme simulation. This study aims to obtain the speed-density function for traffic. The introduction emphasizes the importance of understanding traffic flow dynamics to reduce congestion, especially in areas with significant vehicle growth. The methodology used is direct observation of the Buah Batu Road section with an observed length of 18 meters, with data collected through cellphone camera recordings at various times. These observation data provide insight into vehicle density and speed under various conditions.

Response surface methodology-based optimization of hairy roots cultures for in vitro AM production

Arbuscular mycorrhizal fungi can do wonders in promoting the crop growth as well maintaining soil health. In vitro root organ culture technology is an exciting avenue for AM biofertilizer production. This study aims to optimize the key influencing factors for enhancing the hairy root production used for in vitro AMF culturing using response surface methodology, a statistical and mathematical tool used for designing optimization studies. Study uses Rhizobium rhizogenes MTCC 2364 for transformation in carrot explant. Factors considered for optimization are concentration of gelling agent (phytagel), carbon source (sucrose) and pH of the Modified Strullu and Romand medium (MSR). Design Expert software uses second order polynomial regression equation for predicting the outcome of each experiment. Totally, 17 experiments were run following Box-Behnken design and average hairy root length and average side branch emergence were taken as response. ANOVA analysis reveals that the concentration of phytagel and sucrose had a strong influence on root length, while the phytagel and pH of the medium had a strong effect on side branch emergence. Overall, taking into account the both responses, concentration of phytagel had a significant impact on hairy root production. The maximum average hairy root length obtained was 1.12 cm and number of side branches emerged were 6.78 per day. Based on these results, the optimal parameters were MSR medium with 3gL-1 phytagel, 11gL-1 sucrose and a pH of 4 for boosting hairy root development. This study is a cost-effective approach and minimizes the time taken for establishing the hairy root technology.

Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems

Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower’s vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R2=0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R2=0.99647, but is still considered a competitive model. A reduced model was introduced trading off some accuracy (R2=0.9916) for significantly reduced data collection requirements and effort.

Efficacy of Okra Leaf [Abelmoschus esculentus L.] Extract for Removal of Egg Adhesiveness During Artificial Propagation of African Catfish [Clarias gariepinus, Burchel 1822

This study evaluated the most effective immersion duration and concentration of okra leaf extract for removing the adhesive properties of Clarias gariepinus eggs. For the breeding experiment, a male and a female C. gariepinus broodstock, weighing 1.3 kg and 1.4 kg respectively, were used. The okra leaf extract was tested at three different concentrations: 1%, 3%, and 5%. Tannic acid at a concentration of 0.75 g per liter of water served as the reference de-adhesion agent, while plain water (without tannic acid or plant extract) was used as the control. The fish eggs were rinsed with the solutions for one, three, and five minutes. Each concentration and rinsing time were recorded in triplicate. The generated data was subjected to an Analysis of Variance Test, followed by a third order polynomial regression analysis to determine the best concentration and immersion period for removing C. gariepinus egg adhesiveness. The results of the study showed that the use of okra leaf extract at 1% lowest concentration and 1 minute immersion period was not significantly different (p>0.05) from the use of tannic acid solution. Therefore, this study found that okra leaf extract at 1% concentration with a one-minute immersion period can effectively remove egg adhesiveness and increase hatching in C. gariepinus.

Pengaruh Penggunaan Abu Cangkang Terhadap Kuat Tekan Beton

The background of this research is that there is still a lot of oyster shell material that has not been utilized, meanwhile the shell has good mechanical properties as well as concrete. Up to this moment, the shell is not used properly so it needs to studied the use as concrete mixture. By the utilization of this waste is expected to reduce the environmental impact. This study was aims to obtain a regression equation of the compressive strength of concrete using shells, the shells used in this study were oyster shells (ostreidae), tested at 28 days of age with 3 variations in the percentage of the use of shell ash material and with 1 variation of normal concrete. The percentage variations used are 0% variation, 5% variation, 10% variation, and 15% variation. In addition, based on the regression equation, the strong relationship between variables will be obtained which is indicated by the value of r square. The implementation of this research was carried out by carrying out a compressive test of concrete cylinder samples with 3 variations of the water cement rasio (W/C), is W/C 0.4, W/C 0.5, and W/C 0.6. The activity was continued by recording and analyzing data by conducting a regression analysis between the relationship between the percentage of shell use and the compressive strength of concrete. The regression equation used is the exponential regression equation, linear regression, polynomial regression order 2. From the test data, it is found that the regression equation is the best for W/C variation 0.4, obtained polynomial regression equation order 2, y = -0.0351x2 + 0, 3279x + 25.075 with r² = 0.9999, for the W/C variation of 0.5, the second order polynomial regression equation is obtained, y = -0.0702x2 + 0.8062x + 23.406 with r² = 0.8983, and for the W/C variation of 0.6 , the second order polynomial regression equation is obtained, y = -0.0404x2 + 0.4008x + 19.074 with a value of r² = 0.9167..

Optimal level of dietary arginine enhances growth performance, haemato-biochemical status, metabolic responses and growth-related gene expression of GIFT juveniles reared in inland saline water

The effects of arginine levels in diets on growth performance, survival, and haemato-biochemical responses and gene expression of genetically improved farmed tilapia (GIFT) juveniles raised in inland saline water (ISW) of 10 ppt were evaluated through a 60-day growth trial. Seven isoproteinaceous (⁓320 g protein/kg), isolipidic (80 g/kg) and isoenergetic (16.8 MJ DE/kg) diets with graded levels of arginine viz. A10 (10 g/kg), A12.5 (12.5 g/kg), A15 (15.0 g/kg), A17.5 (17.5 g/kg), A20 (20.0 g/kg), A22.5 (22.5 g/kg) and A25 (25.0 g/kg) were prepared. Acclimated GIFT juveniles (3.75 ± 0.017 g) were randomly assigned into seven treatments in triplicate with 15 fish/replicate and fed with respective diets thrice a day on a satiation basis. The percent weight gain, specific growth rate, protein efficiency ratio, whole carcass protein, tissue alanine and aspartic acid aminotransferase activities, serum total protein, albumin and globulin concentration and expression of IGF-1 & IGF-1R genes of fish significantly increased, while feed conversion ratio significantly decreased with increasing arginine in the diet up to 17.5 g/kg and decreased thereafter. Additionally, white blood cell count increased with rising arginine levels in the diet. However, body indices, survival, whole carcass lipid, total ash and essential amino acids contents, activities of digestive and antioxidant enzymes, serum glucose level and blood haemoglobin level and red blood cell count of fish remained unchanged for different treatments. Based on broken-line regression and second order polynomial regression models in relation to hepatic IGF-1 gene expression and percent weight gain, the dietary optimal arginine level was found to be 15.97 and 16.76 g/kg and 17.06 and 17.41 g/kg, respectively with the optimum range of 15.97 to 17.41 g/kg for successful culture of GIFT in ISW of 10 ppt salinity.

Optimized Conditions for the Production of Yogurt from Soya Bean (Glycine max) and Cinnamon (Cinnamomum verum)

This study aimed at producing functional soy bean yoghurt by optimizing the production conditions using response surface methodology. The quantity of ferment (LYOFAST Y439A) (50–100 g) and quantity of cinnamon (10-40 g) were optimized using central composite design. Responses (global acceptability and scavenging activity) obtained from experimental runs were fitted into second order polynomial regression model. Also, the multiple optimization technic was used to obtain the compromised optimum condition. The optimized yoghurt was evaluated for its pH and proximate composition using standard methods. The optimum conditions for the production of this yoghurt was as thus: 69.64 g for quantity of ferment and 10 g for quantity of cinnamon, 100 g soya beans, 100 g of sugar and 1 L of water soybeans. Soy bean yoghurt made from optimized conditions had a global acceptability of 6.8, scavenging activity of 34 %. Also, protein, fat, carbohydrate, calcium, sodium magnesium and energy value was respectively 17.1%, 2.91%, 17.93%, 144 mg/100 g, 159.91 mg/100g, 159.91 mg/100g, 63.18 mg/100g and 166.31 kcal/100g. Thus, acceptable yoghurt with functional properties can be obtained from soya beans and cinnamon.

Hot Deformation Behavior of Inconel 718 Alloy Using Regression-based Dynamic Material Model and Applications in Ring Rolling Manufacturing

Inconel 718 nickel-based alloy is extensively used in the aerospace industry (e.g., gas turbine engine components) because of its excellent corrosion resistance and high mechanical properties at elevated temperatures. However, there is a certain limit to manufacturing the alloy through plastic deformation due to its high deformation resistance and complicated deformation behaviors. In this study, the hot deformation behavior of Inconel 718 alloy was investigated to establish how processing conditions of flow stress-strain, at strain rates from 0.001 to 10 s<sup>-1</sup>, and temperatures from 850 to 1200<sup>o</sup>C, affected dynamic recrystallization. The regression-based material model was utilized to calculate the strain-rate sensitivity, and subsequently depict the efficiency of the power dissipation and instability criterion of hot deformation. The processing map and instability criterion predicted by the developed 3<sup>rd</sup>-order polynomial regression model corresponded with the experimental results and in particular, showed a better prediction for instability regime compared to the existing discrete derivative approach. Predicting the strain-rate sensitivity values on a continuous scale with regression analysis covered the additional instability region of the high strain rate near 10 s<sup>-1</sup>. The dynamic recrystallization deformation was also characterized by microstructural analysis along with the processing map. Consequently, ring-rolled aviation parts were manufactured with the optimum processing parameters, which conform to the AMS 5663 standard (Aerospace material specifications for Inconel 718).

Workload Comparison of Contemporary Interval Throwing Programs and a Novel Optimized Program for Baseball Pitchers.

In the rehabilitation of injured baseball pitchers, there is lack of consensus on how to guide a player back to pitching. It is unknown how different contemporary interval throwing programs (ITPs) progress in the amount of throwing workload. To 1) evaluate three prominent ITPs commonly employed in baseball pitcher rehabilitation and assess whether these ITPs produce training loads that increase in a controlled, graduated manner and 2) devise an ITP that produced training loads which increased steadily over time. Cross-sectional study. Three publicly available ITPs from prominent sports medicine institutions were analyzed. Elbow varus torque per throw was calculated from a 2nd order polynomial regression based upon a relationship between recorded torque measurements and throwing distance measured from a database of 111,196 throws. The relative rate of workload increase was measured as an acute:chronic workload ratio (ACWR). For each ITP, throw counts, daily/acute/chronic workloads, and ACWR were calculated and plotted over time. Finally, an original ITP was devised based upon a computational model that gradually increases ACWR over time and finished with an optimal chronic workload. Each ITP exhibited a unique progression of throwing distances, quantities, and days to create different workload profiles. The three ITPs had throwing schedules ranging from 136 days to 187 days, ACWR spiked above or fell below a literature-defined "safe" range (i.e.0.7 - 1.3) 19, 21, and 23 times. A novel ITP, predicated on a 146-day schedule and with a final chronic workload of 14.2, was designed to have no spikes outside of the safe range. Existing ITPs widely utilized for rehabilitation of baseball pitchers exhibit significantly inconsistent variation in the rate of throwing load progression. Computational modeling may facilitate more incremental workload progression in ITPs, thereby reducing injury during rehabilitation and more efficiently condition a pitcher for return to competition. 3b.

Prediction of photovoltaic waste generation in Canada using regression-based model.

The global surge in photovoltaic (PV) installations and the resulting increase in PV waste are a growing concern. The aims of this study include predicting the volume of photovoltaic waste in Canada. The forecasting of solar waste volume employed linear regression, 2nd order polynomial regression, and power regression models. The study's results indicate that Canada is on the verge of facing challenges related to the end-of-life treatment of photovoltaic modules in the coming years due to the significant growth in PV capacity over recent decades. According to the analysis, for early loss, the PV waste volume in 2045 could range from 180,000 MT to 270,000 MT, and for regular loss, it could range from 160,000 MT to 180,000 MT. This research is anticipated to assist relevant government agencies in assessing the prospective volume of PV waste to establish a sustainable and resilient PV waste management plan for Canada. These findings may shed light on the feasibility of a circular economy and advocate for the involvement of all stakeholders in a carefully coordinated strategy to mitigate potential environmental impacts and optimize resource utilization efficiency.

СОВМЕСТНОЕ ИСПОЛЬЗОВАНИЕ ПОЛИАМИДНОЙ СМОЛЫ И КАНИФОЛЬНОЙ ДИСПЕРСИИ ПРИ ИЗГОТОВЛЕНИИ ЦЕЛЛЮЛОЗОСОДЕРЖАЩИХ КОМПОЗИЦИОННЫХ МАТЕРИАЛОВ

The article is devoted to studying the influence of the “polyamide resin - rosin dispersion” system on the hydrophobic and physical-mechanical properties of cellulose-containing composite materials. The relevance of the article is due to the need to describe the features of the combined use of polyamide resin “ProChem DUO” and rosin dispersion “Hi-pHase 40T АР” in the production of paper and cardboard in the context of an increasing variety of chemical auxiliaries. In order to establish the dependence of the hydrophobic and physical-mechanical properties of paper on the consumption of polyamide resin and rosin dispersion, methods of planned experiment were used. The input parameters were the consumption of chemical auxiliaries, the output parameters were absorbency during unilateral wetting, breaking length and energy of internal bonds according to Scott of paper samples made from a cellulose suspension with a degree of 40 °SR, with a mass capacity of 80 g/m2. In the implementation of the experimental plan (Kono plan), 2nd order polynomial regression equations were obtained that describe the influence of the consumption of polyamide resin rosin dispersion on the hydrophobic and physical-mechanical properties of paper made from bleached softwood sulphate cellulose. Microstructural analysis of the surface of paper samples made it possible to establish that the above-mentioned chemical auxiliaries in the manufacture of paper contributed to an increase in its hydrophobicity and Scott internal bond energy by 11.25-61.25 and 5.75-30.00 % respectively.

Efficacy of Tannin Extract from Cape Gooseberry Root Physalis peruviana L. as Egg De-Adhesion Agent during Artificial Propagation of African Catfish Clarias gariepinus Burchell

The study assessed the best immersion period and concentrations of the Cape gooseberry root Physalis peruviana that can efficiently remove egg adhesiveness of Clarias gariepinus. One male and one female C. gariepinus brood stock weighing 1.0 kg and 1.3 kg respectively, were used for the experiment. Three different concentrations (0.5%, 1%, and 2%) of tannin extract from Cape gooseberry root were used. Tannic acid (0.75g) diluted in 1 litre of water was used as reference de-adhesion agent, while water was used as control. The fish eggs were rinsed with the solutions at different durations of 0.5, 1 and 2 minutes. Each concentration and rinsing time was recorded in triplicates. Data generated were subjected to Univariate Analysis of Variance Test, and third order Polynomial regression analysis was then used to determine the best concentration and immersion period that neutralizes adhesiveness in eggs of C. gariepinus. Results of the study showed that there were no significant differences (p≤0.05) in the non-adhesive eggs and hatching of eggs immersed in tannin extract from Cape gooseberry root and Tannic acid solution but significantly different from the control. Therefore, this study showed that tannin extract from Cape gooseberry root at 1% concentration and 1 minute immersion period is more efficient for egg adhesion and increase hatching in C. gariepinus.

Optimal dietary lysine improves the growth performance, molting frequency, lipid metabolism, immunity and antioxidant of postlarval mud crab Scylla paramamosain

A 56-day feeding trial was designed to investigate the effect of different dietary lysine (Lys) levels on the growth, molting, lipid metabolism, antioxidant, and immunity of postlarva Scylla paramosain. Six diets with 1.20%, 1.46%, 1.84%, 2.06%, 2.35%, and 2.67% Lys levels (dry matter) were formulated and experimented on the postlarva Scylla paramamosain. The results showed that Scylla paramamosain fed with the 2.06% lysine obtained the highest growth performance and the highest molting frequency. Appropriate dietary Lys levels (1.73–2.03%) can improve the antioxidant capacity of Scylla paramamosain. Although unaffected by dietary Lys, the whole-body lipid content of the 1.20% Lys group was numerically higher than those of the 1.46–2.67% Lys groups. The variation in whole-body crude protein content showed a similar trend with those in the content of EAA and NEAAs, with the highest whole-body crude protein content appearing in the 2.06% Lys group. The crabs obtained the highest expression of igf, fas, EcR and fabp genes in the 2.06% Lys group. When dietary Lys levels were > 1.20%, the transcription level of immune related-genes increased. As suggested by the results of the second order polynomial regression analysis of weight gain, the Lys requirement of postlarva Scylla paramamosain is 2.03% (4.51% of dietary protein).

Differences in lower extremity joint stiffness during drop jump between healthy males and females

The primary purpose of this study was to examine sex differences in lower extremity joint stiffness during vertical drop jump performance. A secondary purpose was to examine the potential influence of sex on the relationship between joint stiffness and jump performance. Thirty healthy and active individuals performed 15-drop jumps from 30 and 60 cm boxes. Hip, knee, and ankle joint stiffnesses were calculated for subphases of landing using a 2nd order polynomial regression model. Males had greater hip stiffness during the loading phase in drop jumps from both box heights than females’ drop jump from 60 cm box. Also, males had a greater ground reaction force at the end of eccentric phase, net jump impulse, and jump height regardless of box height. The 60 cm box height increased knee stiffness during the loading phase, but reduced hip stiffness during the loading phase and knee and ankle stiffness during the absorption phase regardless of sex. Joint stiffnesses significantly predicted drop jump height for females (p < .001, r2 = 0.579), but not for males (p = .609, r2 = −0.053). These results suggest that females may have different strategies to maximize drop jump height as compared to males.

Viscosity of Fe2O3-water nanofluids by molecular dynamics simulations: Effects of nanoparticle content, temperature and size

In this study, Molecular Dynamics (MD) Simulations are used to calculate the viscosity of Fe2O3-water nanofluid within the Green-Kubo framework considering the influence of nanoparticle volume fraction (2.05, 4.08, 6.08 vol%), nanofluid temperature (T = 5, 25, 45 and 65 °C), and size (d = 1, 1.24, and 1.44 nm). First, by screening some existing water models, SPC/E model is found to be the winning candidate for viscosity calculation under the temperature range. Second, the simulation method and parameters for Fe2O3-Water nanofluid interactions are developed and verified against experimental studies. Fe-O (RDF) of Fe2O3 nanoparticle first peak occurs at r = 1.879 Å with the maximum error of 7.4% in comparison to experimental and density functional theory (DFT) reported values. Simulation results show that the viscosity of Fe2O3-water nanofluid is significantly larger compared to that of the water base fluid (>57%). It is also demonstrated that the viscosity of the nanofluid increases with the particle volume fraction and decreases with temperature and size of nanoparticles with local sensitivities of about 0.69, −0.65, and −0.37, respectively. A second order polynomial regression analysis is performed on various volume fractions to express the relationship between the volume fraction and the viscosity. Our results are in agreement with existing experimental results. It was found that the presence of nanoparticles created a structured water layer near the nanoparticle surface, and that the interactions between water molecules and iron oxide nanoparticles were prominent in the Fe_Ow (iron-oxygen of water) pair interactions. Parameters such as SASA (Solvent Accessible Surface Area), surface to volume ratio, moment of inertia around principal axes, radius of gyration, and RDF are used to provide insights into the behavior of Fe2O3-water nanofluids and the underlying mechanisms. To the authors’ best knowledge, this is the first MD simulation of Fe2O3 nanofluids conducted to date.

Buildability Analysis of 3D Concrete Printing Process: A Parametric Study Using Design of Experiment Approach

Plastic collapse and buckling are the key structural failure criteria in 3D concrete printing (3DCP). This study aims to analyze the effect of different geometrical designs and printing factors on the buildability performance of 3DCP structures. Due to the high number of variables involved, the Design of Experiment (DOE) has been used to reduce the number of simulations. In geometrical design parameters, the structure’s design is more sensitive, followed by the width and length of the printed design. The buildability increases when we move from sharp corners to more stable structures like fillets and circular geometry. For geometrical design parameters, a maximum buildability of 74% of the designed height is achieved for circular design with the highest width and lowest diameter. For printing parameters, the highest buildability of 486 mm (81%) is achieved for the lower values of printing speed and layer height. The study analyzed failure phenomena of buckling and yield strength for the tested combination of parameters. The study analyzed the sensitivity analysis of individual parameters and their combination for maximum buildability and developed the low order polynomial regression equation for each printing parameter and geometrical factors. Based on the analysis of the results, the study also proposed different new printing strategies to increase the overall performance of the printing process.

Analyzing and Modeling the Spatial-Temporal Changes and the Impact of GLOTI Index on Precipitation in the Marmara Region of Türkiye

Precipitation is a particularly important part of the Earth’s hydrological cycle and, therefore, is a necessary variable for maintaining natural balance. This study investigated past, present, and future changes in precipitation in the Marmara region, and examined the effects of global warming on this variable. The study period was from 1960 to 2020, and the climate data of 15 synoptic stations in the Marmara region were used for this purpose. To achieve the objectives of the study, linear and 6th order polynomial regression, ombrothermic and hythergraph diagrams, geostatistical models, Mann-Kendall test, Pearson correlation, standard Z-scores, and multi-layer perceptron artificial neural network models (MLP-ANN) were used to model and predict precipitation. The results of the linear regression analysis showed that of the 15 stations, 6 stations had an increasing trend, 6 stations had a trendless pattern, and 3 stations had a decreasing trend. In terms of periodic analysis, the main downward trend started in 1964 and continued until 1992, while the main periodic upward trend started in 1992 and continued until 2016. The synoptic stations in the Marmara region showed a lack of precipitation over six to seven months of the year, and the precipitation changes in the region were stronger than the temperature changes. In addition, the highest precipitation was observed on the southeast coast of the Black Sea, and the lowest precipitation was observed in the eastern parts of the region. Moreover, except for the Bilecik and Kocaeli stations, the changes in the long-term trend of precipitation at the other stations were significant. Among the 15 stations, only the Kocaeli and Sarıyer stations showed a positive correlation with global temperature during the annual period. In addition, the developed ANN model was accurate in simulating and predicting precipitation and showed an upward trend over the next seven years.

Assessment of normal myelination in infants and young children using the T1w/T2w mapping technique.

White matter myelination is a crucial process of CNS maturation. The purpose of this study was to validate the T1w/T2w mapping technique for brain myelination assessment in infants and young children. Ninety-four patients (0-23 months of age) without structural abnormalities on brain MRI were evaluated by using the T1w/T2w mapping method. The T1w/T2w signal intensity ratio, which reflects white matter integrity and the degree of myelination, was calculated in various brain regions. We performed a Pearson correlation analysis, a LOESS regression analysis, and a 2nd order polynomial regression analysis to describe the relationships between the regional metrics and the age of the patients (in months). T1w/T2w ratio values rapidly increased in the first 6-9 months of life and then slowed thereafter. The T1w/T2w mapping technique emphasized the contrast between myelinated and less myelinated structures in all age groups, which resulted in better visualization. There were strong positive correlations between the T1w/T2w ratio values from the majority of white matter ROIs and the subjects' age (R = 0.7-0.9, p < 0.001). Within all of the analyzed regions, there were non-linear relationships between age and T1/T2 ratio values that varied by anatomical and functional location. Regions such as the splenium and the genu of the corpus callosum showed the highest R2 values, thus indicating less scattering of data and a better fit to the model. The T1w/T2w mapping technique may enhance our diagnostic ability to assess myelination patterns in the brains of infants and young children.

Non-steady-state closed dynamic chamber to measure soil CO2 respiration: A protocol to reduce uncertainty

Non-steady-state closed dynamic accumulation chambers are widely used to measure the respiration of terrestrial ecosystems, thanks to their low cost, low energy consumption and simple transportability, that allow measurements even in hostile and remote environments. However, the assessment of the accuracy and precision associated with the measurement system (independently of possible disturbances due to chamber-soil interactions) is rarely reported. This information is instead necessary for basic quality control, to compare data obtained by different devices and regression models and to provide Confidence Intervals (CIs) on the carbon flux values. This study quantifies the uncertainty associated with emission flux measurements, with a focus on very low fluxes. Calibration tests using different accumulation chambers and CO2 sensors were performed, and fluxes were calculated by means of different models (parametric, non-parametric and flux models). The results of this work show that the linear regression model has the best reproducibility when compared to the other tested models, regardless of the sensor used and the chamber volumes, while the second order polynomial regression has the best accuracy. We remark the importance of building a calibration curve in the range of the expected flux values, with an interval between the lowest and highest imposed flux that should not exceed two orders of magnitude. To evaluate the reproducibility of the measurement, performing replicates for each imposed flux value is essential. We also show that it is necessary to carefully identify the best time interval for interpolating the CO2 concentration curve in order to guarantee reproducibility and accuracy in flux estimates.

Predicting tensile strength of cemented paste backfill with aid of second order polynomial regression

The materials left behind after the process of separating an ore's valuable fraction from the unprofitable fraction are known as tailings in the mining industry. Mixing tailing, cement and water can create a new material called Cemented paste backfill (CPB). Research and solve the problem of predicting the tensile strength of cement paste backfill based on a polynomial model combined with the Monte Carlo Simulation method. Three models were built to evaluate performance. The optimal performance model is then used to predict the tensile strength of cement paste backfill. The results indicate that using the polynomial regression model has a satisfactory result for predicting the tensile strength of cement paste backfill. The best performance of second order polynomial regression model is evaluated by three metrics such as R2=0.958 RMSE=33.211 kPa MAE=29.097 kPa for testing part in predicting the tensile strength of cemented paste backfill. Finally, the influence of Cement/Tailings ratio and Solid content on the tensile strength on tensile strength and importance is also evaluated with aid of the best performance of second order polynomial regression model.