statistical regression
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2022 ◽  
Vol 12 (2) ◽  
pp. 637
Author(s):  
Entesar Almogait ◽  
Aljawhara H. Almuqrin ◽  
Nourah Alhammad ◽  
M. I. Sayyed

A sensitization procedure is used to enhance the thermoluminescence (TL) sensitivity of phyllite to emit radiation. Phyllite is a type of foliated metamorphic rock made from slate. This study examines naturally grown phyllite rock, which had not been previously studied. Using a Thermo 3500 manual reader, the TL sensitivity of phyllite as a function of dosage was measured. The doses required to perform this study were administered using a 60Co source. The statistical regression test of the data had a significance level of p < 0.05. The study also included thermal and pre-dose effects. Using the sensitization procedure, the nonlinearity in TL dose–response was removed, and the sensitivity was increased 44 times that of its original value. The fading study showed a dependence on the test dose. According to the obtained results, the combination of linear dose–response and high sensitivity to gamma radiation makes phyllite an important rock for dating and retrospective dosimetry.


2022 ◽  
Vol 2022 ◽  
pp. 1-21
Author(s):  
Jing Ji ◽  
Chenyu Yu ◽  
Liangqin Jiang ◽  
Jiedong Zhan ◽  
Hongguo Ren ◽  
...  

In order to investigate the bearing capacity of H-shaped honeycombed steel web composite columns with rectangular concrete-filled steel tube flanges (STHCCs) subjected to eccentrical compression load, 33 full-scale STHCCs were designed with the eccentricity(e), the slenderness ratio (λ), the cubic compressive strength of concrete(fcuk), the thickness of the steel tube flange (t1), the thickness of honeycombed steel web (t2), diameter-depth ratio (d/hw), space-depth (s/hw), and the yield strength of the steel tube (fy) as the main parameters. Considering the nonlinear constitutive model of concrete and simplified constitutive model of steel, the finite element (FE) model of STHCCs was established by ABAQUS software. By comparison with the existing test results, the rationality of the constitutive model of materials and FE modeling was verified. The numerical simulation of 33 full-scale STHCCs was conducted, and the influence of different parameters on the ultimate eccentrical compression bearing capacity was discussed. The results show that the cross-sectional stress distribution basically conforms to the plane-section assumption. With the increase in e, λ, and d/hw, the ultimate eccentrical compression bearing capacity of the full-scale STHCCs decreases, whereas it gradually increases with the increase in fcuk, t1, t2, s/hw, and fy. By introducing bias-stress stability coefficient (φ), the calculation formula of full-scale STHCCs under eccentrical compression is proposed by statistical regression, which can lay a foundation for the popularization and application of these types of composite columns in practical engineering.


2022 ◽  
Vol 12 ◽  
Author(s):  
Paula Carolina Ciampaglia Nardi ◽  
Evandro Marcos Saidel Ribeiro ◽  
José Lino Oliveira Bueno ◽  
Ishani Aggarwal

The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression (MRL), k-dependence Bayesian (k-DB), and Random Forest (RF). The Bayesian inference and classification methods allow an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts (ADRs), performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing.


MAUSAM ◽  
2022 ◽  
Vol 46 (3) ◽  
pp. 275-278
Author(s):  
R. C. DUBEY ◽  
A. CHOWDHURY ◽  
J. D. KALE

ABSTRACT. The cotton yield of 12 years (1975-1987), for five districts in Vidarbha region of Maharashtra, was taken for statistical-regression study. It is found that the higher temperature during first fortnight of September, which is period of budding and flowering is favourable for better yield. The cooler nights during second fortnight of October, when the crop is generally in fruiting stage, also help in good increases in final cotton yield. Higher rainfall, dufing last week of June to first week of July, when the crop is in the germination period, causing logging, reduces the seedling and more number of rainy days in second fortnight of December hamper the bolll bursting and thus al1ecting the cotton yield adversely.  


Cells ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 103
Author(s):  
Lluis Rodas ◽  
Sonia Martínez ◽  
Aina Riera-Sampol ◽  
Hannah J. Moir ◽  
Pedro Tauler

Immune system functionality has been commonly assessed by a whole-blood or isolated-cell stimulation assay. The aim of this study was to determine whether cytokine production in whole-blood-stimulated samples is influenced by age, sex, and smoking. A descriptive cross-sectional study in 253 healthy participants aged 18–55 years was conducted. Whole blood samples were stimulated for 24 h with LPS and concentrations of IL-6, IL-10, and TNF-α were determined in the culture media. Among parameters considered, statistical regression analysis indicated that smoking (change in R2 = 0.064, p < 0.001) and sex (change in R2 = 0.070, p < 0.001) were the main predictors for IL-10 production, with higher values for women and non-smokers. Age was also found to be a significant predictor (change in R2 = 0.021, p < 0.001), with higher values for younger ages. Age (change in R2 = 0.089, p = 0.013) and smoking (change in R2 = 0.037, p = 0.002) were found to be negative predictors for IL-6 production. Regarding TNF-α-stimulated production, age (change in R2 = 0.029, p = 0.009) and smoking (change in R2 = 0.022, p = 0.022) were found to be negative predictors. Furthermore, sex (change in R2 = 0.016, p = 0.045) was found to be a significant predictor, with lower values for women. In conclusion, sex, age, and smoking were found to be independent determinants of stimulated cytokine production. While female sex is associated with higher IL-10 and lower TNF-α production, aging and smoking are associated with lower IL-6, IL-10, and TNF-α production.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 52
Author(s):  
Philip Shine ◽  
Michael D. Murphy

Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.


2021 ◽  
Vol 5 (4) ◽  
pp. 94-102
Author(s):  
Maximilian Brait ◽  
Eduard Koppensteiner ◽  
Gerhard Schindelbacher ◽  
Jiehua Li ◽  
Peter Schumacher

The complex metallurgical interrelationships in the production of ductile cast iron can lead to enormous differences in graphite formation and local microstructure by small variations during production. Artificial intelligence algorithms were used to describe graphite formation, which is influenced by a variety of metallurgical parameters. Moreover, complex physical relationships in the formation of graphite morphology are also controlled by boundary conditions of processing, the effect of which can hardly be assessed in everyday foundry operations. The influence of relevant input parameters can be predetermined using artificial intelligence based on conditions and patterns that occur simultaneously. By predicting the local graphite formation, measures to stabilise production were defined and thereby the accuracy of structure simulations improved. In course of this work, the most important dominating variables, from initial charging to final casting, were compiled and analysed with the help of statistical regression methods to predict the nodularity of graphite spheres. We compared the accuracy of the prediction by using Linear Regression, Gaussian Process Regression, Regression Trees, Boosted Trees, Support Vector Machines, Shallow Neural Networks and Deep Neural Networks. As input parameters we used 45 characteristics of the production process consisting of the basic information including the composition of the charge, the overheating time, the type of melting vessel, the type of the inoculant, the fading, and the solidification time. Additionally, the data of several thermal analysis, oxygen activity measurements and the final chemical analysis were included.Initial programme designs using machine learning algorithms based on neural networks achieved encouraging results. To improve the degree of accuracy, this algorithm was subsequently adapted and refined for the nodularity of graphite.


2021 ◽  
Author(s):  
William Christopher Carleton ◽  
Dave Campbell

Data about the past contain chronological uncertainty that needs to be accounted for in statistical models. Recently a method called Radiocarbon-dated Event Count (REC) modelling has been explored as a way to improve the handling of chronological uncertainty in the context of statistical regression. REC modelling has so far employed a Bayesian hierarchical framework for parameter estimation to account for chronological uncertainty in count series of radiocarbon-dates. This approach, however, suffers from a couple of limitations. It is computationally inefficient, which limits the amount of chronological uncertainty that can be accounted for, and the hierarchical framework can produce biased, but highly precise parameter estimates. Here we report the results of an investigation in which we compared hierarchical REC models to an alternative with simulated data and a new R package called "chronup". Our results indicate that the hierarchical framework can produce correct high-precision estimates given enough data, but it is susceptible to sampling bias and has an inflated Type I error rate. In contrast, the alternative better handles small samples and fully propagates uncertainty into parameter estimates. In light of these results, we think the alternative method is more generally suitable for Palaeo Science applications.


2021 ◽  
Vol 13 (24) ◽  
pp. 13745
Author(s):  
Carolina Rojas Quezada ◽  
Felipe Jorquera

In an urbanized world, the sustainability of cities will depend on their form and urban structure. In this sense, fabrics that are compact, dense, green, and suitable for non-motorized transport methods are more environmentally efficient. For the purpose of contributing new tools to the design, urban planning, and sustainability of nature in residential areas, this study characterizes the urban fabrics of six urban wetlands in the Latin American city of Concepción (Chile), which is known for its blue–green spaces. In a wetland city, we model urban patterns through spatial relationship using a statistical regression model (OLS—ordinary least squares) with the urban variables of density, distance, population, housing, highways, green areas, and building permits. Concepción shows predominantly low- to medium-density fabrics, and it is not integrated with the urban wetlands. In fact, it was observed that the residential areas do not take advantage of the blue–green spaces and that the urban fabrics do not favor proximity, with a transportation network that promotes the use of cars, leading to the wetlands being inaccessible and fragmented. However, as they are still surrounded by open spaces with abundant vegetation, there are highly feasible opportunities for the future development of blue–green infrastructure.


Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2381
Author(s):  
Jing Ji ◽  
Wen Zeng ◽  
Liangqin Jiang ◽  
Wen Bai ◽  
Hongguo Ren ◽  
...  

In order to acquire the hysteretic behavior of the asymmetrical composite joints with concrete-filled steel tube (CFST) columns and unequal high steel beams, 36 full-scale composite joints were designed, and the CFST hoop coefficient (ξ), axial compression ratio (n0), concrete cube compressive strength (fcuk), steel tube strength (fyk), beam, and column section size were taken as the main control parameters. Based on nonlinear constitutive models of concrete and the double broken-line stress-hardening constitutive model of steel, and by introducing the symmetric contact element and multi-point constraint (MPC), reduced-scale composite joints were simulated by ABAQUS software. By comparing with the test curves, the rationality of the modeling method was verified. The influence of various parameters on the seismic performance of the full-scale asymmetrical composite joints was investigated. The results show that with the increasing of fcuk, the peak load (Pmax) and ductility of the specimens gradually increased. With the increasing of n0, the Pmax of the specimens gradually increases firstly and then gradually decreases after reaching a peak point. The composite joints have good energy dissipation capacity and the characteristic of stiffness degradation. The oblique struts force mechanism in the full-scale asymmetrical composite joint domain is proposed. By introducing influence coefficients (ξ1 and ξ2), the expression of shear bearing capacity of composite joints is obtained by statistical regression, which can provide theoretical support for the seismic design of asymmetrical composite joints.


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