Polynomial Regression Research Articles

Raindrop size distribution (DSD) retrieval from polarimetric radar observations using neural networks

As a key component of rainfall estimation, the understanding of raindrop size distribution (DSD) is a long-standing goal in meteorology and hydrology. Given that weather radar can observe the precipitation microphysics over large spatial and temporal scales, it has been broadly applied in DSD estimation. Traditional polynomial regression algorithms that correlate DSD parameters and radar signatures are still widely applied due to their simple structure and acceptable accuracy. This study proposes a new DSD retrieval model using dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes able to retrieve the parameters of a normalized gamma DSD (LSTM-D0, LSTM-Nw, and LSTM-μ) are proposed with different combinations of polarimetric radar measurement inputs. All LSTM estimators exhibit better performance than the polynomial regression method. The Nash-Sutcliffe efficiency coefficient for estimates of drop median diameter (D0) and intercept parameter (Nw) increases from 0.93 and 0.70 to 0.95 and 0.93 respectively at Chilbolton station. Poor estimates of the shape parameter (μ) using the polynomial regression estimator complicate real applications, whereas the remarkable improvement of LSTM model estimation facilitates practical applications. The temporal and spatial predictability is then estimated to investigate long-term estimator performance for various radars, or at least for all radar pixels of a single radar. The predictability, measured by the Nash coefficient, increases temporally by 0.08, 0.31, and 0.39 and spatially by 0.03, 0.19, and 0.23 for the parameters D0, log10Nw, and μ respectively. This study contributes to improving quantitative precipitation estimates from radar polarimetry, enabling a better understanding of precipitation microphysics.

Deep learning method for online parameter identification of lithium-ion batteries using electrochemical synthetic data

Accurately sensing the internal state of lithium-ion batteries and identifying parameters is crucial for developing effective battery safety and health management strategies. With the advancement of artificial intelligence, the integration of deep learning (DL) and electrochemical techniques has ushered in new avenues for high-level battery management. However, gaining proprietary battery datasets for neural network (NN) training is difficult at limited data scenarios. To address this problem, a DL method based on synthetic data and convolutional neural network (CNN) is proposed to achieve online parameter identification. Firstly, an electrochemical model (EM) considering the impact of solid electrolyte interface (SEI) film growth on battery performance is established. Then, nine features strongly correlated with electrochemical parameters are extracted from charge and discharge segments for the construction of CNN. Based on offline identified parameters, substantial electrochemical parameter-feature datasets are further generated through polynomial fitting and model calculations, followed by the application of principal component analysis and density-based spatial clustering of applications with noise (PCA-DBSCAN) to filter synthesized data for network training. Finally, the developed online parameter identification method is experimentally validated using full life cycle data of three cobalt-acid lithium (LiCoO2) batteries, confirming reasonable accuracy of the identified battery electrochemical parameters.

Impact of preoperative anxiety on postoperative outcomes in patients undergoing minimally invasive thoracoscopic surgery: A prospective cohort study

BackgroundPreoperative anxiety is a common preoperative psychological state in patients with cancer and associated with worsening perioperative outcomes. However, high-quality prospective studies on preoperative anxiety in patients undergoing lung surgery are scarce. MethodsWe conducted a prospective cohort study, enrolling a total of 540 patients. Preoperative anxiety in patients undergoing thoracic surgery was measured using the Hospitalization Anxiety Scale. Patients were grouped according to the Hospitalization Anxiety Scale scores as follows: no anxiety (score <8) and anxiety (score ≥8). The association of preoperative anxiety with postoperative complications and non-complicated adverse events was determined by univariate regression and polynomial regression analyses. ResultsA total of 121 patients (22.4 %) experienced preoperative anxiety. The anxiety group had a longer average hospital stay (4.33 vs. 3.85 days). Postoperative complications were similar between groups, but the anxiety group reported worse sleep quality (measured by the Athens Insomnia Scale). Regarding postoperative pain, both groups had comparable rates of mild and severe pain on postoperative day 1. However, the anxiety group experienced significantly higher rates of severe pain on postoperative day 2 and mild pain on postoperative day 3. Additionally, the incidence of postoperative nausea and vomiting was significantly higher in the anxiety group on postoperative day 1. ConclusionsPreoperative anxiety may not increase the rates of postoperative complications in patients undergoing lung surgery. However, it may be associated with postoperative sleep disturbances, pain, nausea, and vomiting, as well as prolong the length of postoperative hospitalization.

Exploring Mianzi consciousness congruence and its impact on unethical pro-organizational behavior

BackgroundIn Chinese culture, the concept of Mianzi holds significant importance in interpersonal interactions. Mianzi represents one’s social standing, dignity, and reputation, influencing behaviors and decisions within various contexts. Mianzi consciousness manifests in two primary forms: proactive and defensive. Proactive Mianzi consciousness involves efforts to enhance one’s social image, while defensive Mianzi consciousness focuses on protecting one’s existing reputation. Analyzing the impact of the two Mianzi consciousness dimensions on individuals’ attitudes and behaviors is effective for understanding interpersonal dynamics in China. This study specifically examined the relationship between high Mianzi consciousness congruence and unethical pro-organizational behavior (UPB). UPB refers to actions taken by employees that are intended to benefit their organization but are unethical or morally questionable. By investigating how congruence in proactive and defensive Mianzi consciousness influences the likelihood of engaging in UPB, this research aimed to uncover the underlying social and psychological mechanisms driving such behavior.MethodsEmploying polynomial regression and response surface analysis method, this study developed a model that combines the proactive Mianzi consciousness and the defensive Mianzi consciousness into different Mianzi management strategies and tested the relationship between high Mianzi consciousness congruence and UPB.ResultsSample data collected at two time points one month apart supported all hypotheses. Specifically, the findings revealed that high levels of Mianzi consciousness congruence (i.e., all-around type in Mianzi management strategies) positively relate to UPB, and verified the mediation effect of external work locus of control and the moderation effect of relational psychological contract.ConclusionThis research advanced a novel, synergistic perspective on the role of social Mianzi and contributed to the localized UPB research, thus helping to find a path to prevent UPB from occurring in the Chinese sociocultural context.

A Comparative Study of Detrending Methods on Crop Yield Time Series for Drought Studies

Detrending is a statistical technique that removes systematic variations or trends from time series data, allowing analysts to focus on the underlying patterns or fluctuations. While multiple detrending approaches have been applied but rarely discussed their consistency of outcomes and effectiveness in accurately capturing better yield trends. The validation of drought occurrences has proven to be a challenging task due to the non-stationary characteristics of time series data related to crop yield. This research utilizes time series of cotton yield data from the Marathwada region covering the period from 1998 to 2021. Three traditional trend models, including simple linear regression, second-order polynomial regression and central moving average were applied. Additionally, two machine learning models (random forest and support vector regression) were tested with a novel approach. Moreover, two decomposition models (additive and multiplicative) were used to remove non-linear trends in crop yield time series data. The performance of the chosen models was evaluated based on metrics such as root mean square error, mean absolute error, Nash-Sutcliffe efficiency, and index of agreement. The results suggest that the most effective detrending approach involves combining a random forest machine learning model with an additive decomposition model.

Moisture Sorption Isotherms of Polydextrose and Its Gelling Efficiency in Inhibiting the Retrogradation of Rice Starch.

As an anti-staling agent in bread, the desorption isotherm of polydextrose has not been studied due to a very long equilibrium time. The adsorption and desorption isotherms of five Chinese polydextrose products were measured in the range of 0.1-0.9 aw and 20-35 °C by a dynamic moisture sorption analyzer. The results show that the shape of adsorption and desorption isotherms was similar to that of amorphous lactose. In the range of 0.1-0.8 aw, the hysteresis between desorption and adsorption of polydextrose was significant. The sorption isotherms of polydextrose can be fitted by seven commonly used models, and our developed seven-parameter polynomial, the adsorption equations of generalized D'Arcy and Watt (GDW) and Ferro-Fontan, and desorption equations of polynomial and Peleg, performed well in the range of 0.1-0.9 aw. The hysteresis curves of polydextrose at four temperatures quickly decreased with aw increase at aw ˂ 0.5, andthereafter slowly decreased when aw ≥ 0.5. The polynomial fitting hysteresis curves of polydextrose were divided into three regions: ˂0.2, 0.2-0.7, and 0.71-0.9 aw. The addition of 0-10% polydextrose to rice starch decreased the surface adsorption and bulk absorption during the adsorption and desorption of rice starch, while it increased the water adsorption value at aw ≥ 0.7 due to polydextrose dissolution. DSC analysis showed that polydextrose as a gelling agent inhibited the retrogradation of rice starch, which could be used to maintain the quality of cooked rice.

Optimization of dietary protein based on ideal protein concept for genetically improved farmed tilapia (GIFT) juveniles reared in inland saline water

A 60-day feeding trial was performed to optimize dietary protein based on the ideal protein concept for genetically improved farmed tilapia (GIFT) juveniles reared in inland saline water (ISW) of 10 ppt salinity. Seven heteronitrogenous (373.6–256.5 g crude protein/kg), isocaloric (∼ 16 MJ digestible energy/kg) and isolipidic (∼ 80 g lipid/kg) semi-purified feeds were formulated and prepared with decreasing levels of crude protein (CP), namely 373.6 (CP1), 352.2 (CP2), 334.5 (CP3), 314.9 (CP4), 293.7 (CP5), 275.2 (CP6) and 256.5 g/kg (CP7). The proportion of total essential and semi-essential amino acids was kept constant in all diets as ideal amino acid profiles and total non-essential amino acids were reduced linearly to reduce the dietary CP levels. Three hundred fifteen juveniles (average weight 4.76 ± 0.01 g) were randomly assigned into seven groups in triplicate. Fish of various groups were fed thrice daily at an apparent satiation level. The growth of juveniles increased linearly and quadratically (p<0.05) when the CP level of diets was decreased from 373.6 (CP1) to 334.5 g/kg (CP3), then declined linearly. The CP2 and CP3 groups had significantly higher feed intake and lower feed conversion ratio. In contrast, low CP-fed groups had superior protein utilization, viscerosomatic index and hepatosomatic index than other groups. The CP1 and CP2 groups had significantly higher whole-body protein and lower lipid content. The whole-body non-essential amino acid content diminished linearly (p<0.05) as dietary CP levels were decreased; the whole-body essential amino acid content remained unchanged (p>0.05). The high CP-fed juveniles (CP1 and CP2) showed a significantly (p<0.05) elevated protease activity, whereas the opposite trend was noticed for amylase activity. The aminotransferase activities of the liver and muscle were reduced linearly (p<0.05) with reduced dietary CP levels. Serum glucose and cholesterol contents elevated linearly (p<0.05) with decreased dietary CP levels. Whereas, the hemoglobin content and RBC count decreased (p<0.05) in the high CP-fed juveniles. The second-order polynomial regression analysis revealed that the optimal dietary CP levels could range from 311.1 to 318.0 g/kg for GIFT juveniles reared in ISW of 10 ppt salinity. Furthermore, by adopting the ideal protein concept, the CP levels in the diet of GIFT juveniles could be reduced from 373.6 to 311.1 g/kg without affecting growth performance. The outcome of this study will assist in developing a low-protein feed with balanced essential amino acids for GIFT culture in ISW.

Variation of temperature increase rate in the Northern Hemisphere according to latitude, longitude and altitude: the Turkey example

Global climate change notably influences meteorological variables such as temperature, affecting regions and countries worldwide. In this study, monthly average temperature data spanning 73 years (1950–2022) were analyzed for 28 stations in the city centers across seven regions of Turkey. The station warming rates (SWR) were calculated for selected stations and the overall country using Singular Spectrum Analysis (SSA) and Least Square Polynomial Fit (LSPF) methods. The temperature trend in Turkey exhibited a decline until the late 1970s, followed by a continuous rise due to global warming. Between 1980 and 2022, the average SWR in Turkey was found to be 0.52 °C/decade. The SWR was determined to be the lowest in Antakya (0.28 °C/decade) and the highest in Erzincan (0.69 °C/decade). The relationship between SWR and latitude, longitude, altitude, and distance to Null Island (D2NI) was explored through linear regression analysis. Altitude and D2NI were found to be the most significant variables, influencing the SWR. For altitude, the correlation coefficient (R) was 0.39 with a statistically significant value (p) of 0.039. For D2NI, R, and p values were 0.39 and 0.038, respectively. Furthermore, in the multiple regression analysis involving altitude and D2NI, R and p values were determined to be 0.50 and 0.029, respectively. Furthermore, the collinearity analysis indicates no collinearity between altitude and D2NI, suggesting that their effects are separated in the multiple regression.

Unveiling the impact of the congruence between artificial intelligence and explorative learning on supply chain resilience

PurposeDrawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia.Design/methodology/approachUsing survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses.FindingsWe find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience.Originality/valueOur study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature.

Implementation of Polynomial Regression on Coconut Charcoal Making System Integrated with IoT and Cloud in Real Time

Polynomial regression is an analytical method often used to model non-linear relationships between independent and dependent variables. This method is effective in various fields of application, such as prediction, estimation, and analysis. In this study, polynomial regression was applied to facilitate the coconut charcoal manufacturing process to predict the duration of drying time based on the measured temperature. Polynomial regression is implemented with Internet of Things (IoT) technology, where temperature data obtained from sensors is sent in real-time to a mobile application. This application provides convenience for users in monitoring and managing the coconut charcoal drying process, thereby enhancing the efficiency and quality of the final product. This integration shows excellent potential in optimizing the production process using data-driven innovative technology.

Computer-assisted regional-residual gravity anomaly separation with regularized first- and second-order derivatives

The regional-residual separation of gravity anomalies in crustal and mineral exploration was a graphical-based procedure before the advent of fast digital computers and the need for more efficient algorithms to process large data sets. However, since requiring the supervision of an experienced interpreter, the results, once obtained with graphical procedures, are often accepted as second to none in producing anomalies with geologic significance. Numerical methods based on spectral filtering and robust polynomial fitting have worked in many scenarios but seem not fully effective in replicating in algorithms the kind of results once obtained with interpreter-assisted graphical methods. We develop a computer-assisted regional residual separation (CARRS) procedure implemented by a set of short MATLAB scripts; in many aspects, CARRS simulates the operations of former graphical methods but requires few decisions and minor handwork from the interpreter. CARRS applies robust polynomial fitting to points with a low horizontal gradient and a vertical second-order derivative; thus, selecting fitting points as a real interpreter would do with graphical approaches in outlining the regional field. A regularized procedure is used to calculate the stable first- and second-order derivatives. Companion MATLAB codes allow for the replication of our results and further exploration with different threshold levels to identify flat domain regions. CARRS is illustrated with airborne gravity data CPRM-1123, freely available to download. A data window of the residual field is used to analyze the distribution of cassiterite deposits in greisen zones of the Paleoproterozoic Velho Guilherme granites in the Amazon Craton, as their distribution appears in the observed gravity anomaly and its corresponding residual field.

Effects of Dietary Chitosan on Growth Performance, Serum Biochemical Indices, Antioxidant Capacity, and Immune Response of Juvenile Tilapia (Oreochromis niloticus) under Cadmium Stress.

The objective of this study was to examine the effects of varying levels of dietary chitosan supplementation on mitigating cadmium stress and its influence on growth performance, serum biochemical indices, antioxidant capacity, immune response, inflammatory response, and the expression of related genes in juvenile Genetically Improved Farmed Tilapia (GIFT, Oreochromis niloticus). Five groups of juvenile tilapias (initial body weight 21.21 ± 0.24 g) were fed five diets with different levels (0%, 0.5%, 1.0%, 1.5%, and 2.0%) of chitosan supplementation for 60 days under cadmium stress (0.2 mg/L Cd2+). The findings indicated that, compared with the 0% chitosan group, dietary chitosan could significantly increase (p < 0.05) the final weight (Wf), weight gain rate (WGR), specific growth rate (SGR), daily growth index (DGI), and condition factor (CF), while the feed conversion ratio (FCR) expressed the opposite trend in juvenile GIFT. Dietary chitosan could significantly increase (p < 0.05) the activities (contents) of cholinesterase (CHE), albumin (ALB), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), acid phosphatase (ACP), and lysozyme (LZM), while glutamic pyruvic transaminase (GPT), glutamic oxaloacetic transaminase (GOT), and complement 3 (C3) in the serum of juvenile GIFT expressed the opposite trend. Dietary chitosan could significantly increase (p < 0.05) the activities of superoxide dismutase (SOD) and catalase (CAT) and significantly decrease (p < 0.05) the activities (contents) of glutathione S-transferase (GST), glutathione peroxidase (GSH-Px), and malondialdehyde (MDA) in the serum of juvenile GIFT. Dietary chitosan could significantly increase (p < 0.05) the activities (contents) of CAT, GST, GSH-Px, and total antioxidant capacity (T-AOC) and significantly decrease (p < 0.05) the contents of MDA in the liver of juvenile GIFT. Dietary chitosan could significantly increase (p < 0.05) the activities (contents) of SOD, GSH-Px, T-AOC, Na+-K+-ATPase, and Ca2+-ATPase and significantly decrease (p < 0.05) the activities (contents) of CAT, GST, and MDA in the gills of juvenile GIFT. Dietary chitosan could significantly up-regulate (p < 0.05) the gene expression of cat, sod, gst, and gsh-px in the liver of juvenile GIFT. Dietary chitosan could significantly up-regulate (p < 0.05) the gene expression of interferon-γ (inf-γ) in the gills and spleen and significantly down-regulate (p < 0.05) the gene expression of inf-γ in the liver and head kidney of juvenile GIFT. Dietary chitosan could significantly down-regulate (p < 0.05) the gene expression of interleukin-6 (il-6), il-8, and tumor necrosis factor-α (tnf-α) in the liver, gills, head kidney, and spleen of juvenile GIFT. Dietary chitosan could significantly up-regulate (p < 0.05) the gene expression of il-10 in the liver, gills, head kidney, and spleen of juvenile GIFT. Dietary chitosan could significantly up-regulate (p < 0.05) the gene expression of transforming growth factor-β (tgf-β) in the liver and significantly down-regulate (p < 0.05) the gene expression of tgf-β in the head kidney and spleen of juvenile GIFT. In conclusion, dietary chitosan could mitigate the impact of cadmium stress on growth performance, serum biochemical indices, antioxidant capacity, immune response, inflammatory response, and related gene expression in juvenile GIFT. According to the analysis of second-order polynomial regression, it was found that the optimal dietary chitosan levels in juvenile GIFT was approximately 1.42% to 1.45%, based on its impact on Wf, WGR, SGR, and DGI.

Evaluation of SENTINEL-2 products-based algorithms in estimating water pollutants of the River Niger in Bamako

Monitoring surface water quality on a spatio-temporal scale is very important to restrict the entry of polluting components into water bodies, particularly rivers. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. Our study aims to test the use of Sentienl-2 multispectral imaging sensors in estimating three important water quality parameters: chlorophyll-a, Colored Dissolved Organic Matter and Total Suspended Matter in Niger River. Sentinel-2 satellite data were acquired in 2020. Atmospheric correction was performed using Sen2cor from the Sentinel toolbox to obtain a geometrically corrected Sentinel-2 multispectral image. We selected multiple water-body indices from the literature based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points in order to obtain an optimal water body index for estimating water quality parameters (WQP) in Niger River. Five regression functions were used in this study: linear regression, exponential, logarithmic, power and polynomial regression. The performance and accuracy of these regression models were evaluated by correlating spectral reflectance band ratio against the in situ water quality parameters (WQP) concentrations. Polynomial regression gave a higher performance and accuracy based on their R values. The best spectral index was selected to assess the spatio-temporal distribution of water pollutants in the Niger River in Bamako and its surroundings. The results showed that the polynomial regression of 6th degree provided the best fit had the best spectral band ratio and in situ Chl-a, CDOM and TSM concentrations which were respectively achieved with band index of B2/B3 (R2 = 0.78), B3/B6 (R 2 = 0.79) and B3/B4 (R 2 = 0.63). Therefore, the best band ratio was selected to evaluate Chl-a spatio-temporal in Niger River water in Bamako. The results of this study showed seasonal variability of the water pollutants in the Niger River. This highlights the potential of the Sentinel-2 products for water quality analysis. As a conclusion, the Sentinel-2 images could be helpful for precise water quality control of Niger River in Bamako.

Evaluating machine learning models for precipitation prediction in Casablanca City

Accurate precipitation forecasting is a vital task for many domains, such as agriculture, water management, flood prevention, and crop yield estimation. The use of machine learning (ML) approaches has improved precipitation forecasting accuracy, exhibiting promising results in capturing the intricate connections between various meteorological variables and precipitation patterns. However, given the vast array of available ML models, a comparative analysis is imperative for identifying the most effective models for precipitation prediction. This study aims to examine the capacities of ML algorithms to forecast precipitation based on weather data for the city of Casablanca, Morocco, which faces challenges in water management and climate change adaptation. Eight different ML models’ performances are compared: linear regression, polynomial regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), XGBoost, and an ensemble learning model. These models are evaluated based on their mean absolute error (MAE), mean squared error (MSE), and R-squared (R2 ) value to determine their effectiveness. The study showcases the potential of ML models in predicting precipitation by utilizing meteorological parameters such as temperature, humidity, wind speed, and pressure.

Physics-Based Artificial Neural Network Assisting in Extracting Transient Properties of Extrinsically Triggering Photoconductive Semiconductor Switches.

In this paper, a physics-based ANN assisting method for extracting transient properties of extrinsically triggering photoconductive semiconductor switches (ET-PCSSs) is proposed. It exploits the nonlinear mapping of ANN between transient current (input) and doping concentration (output). According to the basic laws of photoelectric device operating, two types of ANN models are constructed by gaussian and polynomial fitting. The mean absolute error (MAE) of forecasting transient photocurrent can be less than 10 A under low triggering optical powers, which verifies the feasibility of ANN assisting TCAD applied to PCSSs. The results are comparable to computation by Mixed-Mode simulation, yet even thousands of seconds of CPU runtime cost are saved in every period. To improve the robustness of the Poly-ANN predictor, Bayesian optimization (BO) is implemented for minimizing the curl deviation of photocurrent-time curves.

Novel Explicit Models for Assessing the Frictional Resistance of Pipe Piles Subjected to Seismic Effects

This paper introduces novel explicit models to predict the frictional resistance of open and closed-ended pipe piles subjected to seismic loading. This research employs genetic programming (GP) and multiobjective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA) to develop closed-form expressions for estimating pile frictional resistance, utilizing widely used input parameters for enhanced practicality and applicability in engineering practice. The proposed models are developed using only three input variables: the corrected standard penetration test (SPT) blow count (N1)60, the pile slenderness ratio (L/D), and the peak ground acceleration (PGA). This deliberate reduction in input complexity significantly enhances the models' applicability across a wide range of geotechnical scenarios and industries. The accuracy of the developed models was assessed via the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). In the case of the GP model, the evaluation metrics for the testing set for open-ended piles (R2, RMSE, and MAE values) are 0.89, 0.43, and 0.35, respectively, whereas the corresponding values for closed-ended piles are 0.93, 0.38, and 0.3, respectively. On the other hand, the EPR-MOGA approach achieves similarly encouraging results, with performance metrics of 0.92, 0.37, and 0.29 for open-ended piles and 0.91, 0.39, and 0.30 for closed-ended piles.

Interstellar Nitrogen Isotope Ratios: Measurements on Tracers of C14N and C15N

The nitrogen isotope ratio 14N/15N is a powerful tool to trace Galactic stellar nucleosynthesis and constrain Galactic chemical evolution. Previous observations have found lower 14N/15N ratios in the Galactic center and higher values in the Galactic disk. This is consistent with the inside-out formation scenario of our Milky Way. However, previous studies mostly utilized double isotope ratios also including 12C/13C, which introduces additional uncertainties. Here we present observations of C14N and its rare isotopologue, C15N, toward a sample of star-forming regions measured by the IRAM 30 m and/or the ARO 12 m telescope at λ ∼ 3 mm wavelength. For those 35 sources detected in both isotopologues, physical parameters are determined. Furthermore, we have obtained nitrogen isotope ratios using the strongest hyperfine components of CN and C15N. For those sources showing small deviations from local thermodynamical equilibrium and/or self-absorption, the weakest hyperfine component, likely free of the latter effect, was used to obtain reliable 14N/15N values. Our measured 14N/15N isotope ratios from C14N and C15N measurements are compatible with those from our earlier measurements of NH3 and 15NH3 (Paper I), i.e., increasing ratios to a Galactocentric distance of ∼9 kpc. The unweighted second-order polynomial fit yields C14NC15N=(−4.85±1.89)kpc−2×RGC2+(82.11±31.93)kpc−1×RGC−(28.12±126.62) . Toward the outer galaxy, the isotope ratio tends to decrease, supporting an earlier finding by H13CN/HC15N. Galactic chemical evolution models are consistent with our measurements of the 14N/15N isotope ratio, i.e., a rising trend from the Galactic center region to approximately 9 kpc, followed by a decreasing trend with increasing R GC toward the outer Galaxy.

Research and analysis of the ecological environment index of broad-leaved Korean Pine Forest in Changbai mountain

In order to better study and analyze the ecological environment index of the broad-leaved Korean pine forest in Changbai Mountain, 30 sets of data related to the ecological environment index of the broad-leaved Korean pine forest and six influencing factors were collected. The influencing factors, including slope, temperature, humidity, soil erosion, elevation, and pressure, were analyzed for their correlation with the ecological environment index of the broad-leaved Korean pine forest through polynomial fitting. The results indicate that the ecological environment index increases with the increase of slope, temperature, humidity, and pressure, while it decreases with the increase of soil erosion and elevation. Soil erosion has the greatest impact on the variation of the ecological environment index of the broad-leaved Korean pine forest. Additionally, other factors such as slope, temperature, humidity, elevation, and pressure also affect the ecological environment index, thus requiring comprehensive consideration for the formulation of comprehensive ecological protection strategies.

Low-, moderate-, and high-risk obesity in association with cost drivers, costs over the lifecycle, and life expectancy

BackgroundThe prevalence of low-, moderate-, and high-risk obesity has been increasing globally. Our aim was to estimate the societal burden of these three obesity classes in the Austrian population by taking a societal–annual perspective and an individual–life-cycle perspective. Secondly, we sought to identify the respective cost drivers and the effects on life expectancy.MethodsWe used population-weighted survey data on the distribution of body mass index (BMI) and data on relative risks regarding 83 diseases. Using fractional polynomial regressions, we estimated relative risks per BMI unit for about 30 cost-intensive diseases up to BMI values of 50. The approach for the cost analysis was based on the use of population-attributable fractions applied to direct medical and indirect cost data. Macro-disease-specific data regarding cost factors came from cost-of-illness statistics and administrative sources.ResultsAbout 8.2% of deaths and 4.6% of health expenditure are attributable to obesity in Austria in 2019, causing 0.61% of GDP loss. A third of annual direct and indirect costs came from class 2 and class 3 obesity. From an individual perspective, life-cycle costs of class 2 and class 3 obesity were 1.9 and 3.6 times the costs of class 1 obesity, respectively. At the age of 45, people with high-risk obesity are about to lose approximately 4.9 life years and 9.7 quality-adjusted life years.ConclusionsWe have extended the method of population-attributable fractions, allowing us to estimate differences in the life-cycle direct medical and indirect costs between low-, moderate-, and high-risk obesity. We found that the lifetime costs of obesity are strikingly different between obesity classes, which has been veiled in the societal—annual perspective. Our findings provide a foundation for evaluating public health interventions aimed at combating obesity in order to mitigate the escalating individual and societal burden resulting from obesity prevalence and complications in the future.

Modeling and Prediction of The Mechanical Properties of Feedstock by Cooling-Slope Casting Process using MOJaya Algorithm

Cooling-slope (CS) casting parameters optimization is very challenging due to the uncertainties of parameter values that are influenced by many factors. Therefore, a combination of estimation and optimization models that can solve multi-objective problems using limited data is proposed to reduce the real casting operational cost and time. In casting optimization, modeling and optimization of CS parameters have been considered to identify optimal CS parameters that would lead to better feedstock performance. Computational techniques have been suggested by previous researchers to solve the optimization in the manufacturing process. The study assessed the CS process to determine the feedstock's quality by evaluating its mechanical qualities, namely Tensile strength and Impact strength. Hence, computational methods namely the MOJaya algorithm are utilized to model and optimize the parameters of CS to address the CS problem and forecast the performance of the feedstock. This work conducted a thorough analysis of the impact of CS process parameters on the tensile strength and impact strength using an experimental design. The experiment was conducted using a three-level full factorial design (FFD) of the experiment (DOE), for the parameters of pouring temperature, slanting angle, and pouring distance. The modeling technique involved the development of multiple polynomial regression (MPR) as an objective function in the MOJaya Algorithm. The outcome of the optimization procedure revealed that the optimal solutions obtained from MOJaya were projected to surpass both the initial experimental data and the MOJaya algorithm in terms of the highest values of tensile strength and impact strength. The difference between the two sets of values is 10.92% and 15.76%, respectively. Subsequently, this technique may be employed by a caster to forecast the performance of the feedstock without using repeated experiments that are costly and time-consuming.