Factorial Regression Models Research Articles

Genetic and environmental considerations for the utilization of Pinus pinaster Ait. provenances across a region lacking proper local genetic materials

Maritime pine (Pinus pinaster Ait.) covers vast areas and is of economic importance in southwestern Europe, particularly in Galicia (NW Spain). Galicia is a heterogeneous region with an Atlantic climate in the coast and a Mediterranean-like climate in the inland, where forest reproductive materials (FRM) with proper adaptation, productivity and timber quality are not available. Thus, there is a need for tailored FRM recommendations for reforestation in this region. P. pinaster is particularly sensitive to environmental variation and shows significant intraspecific genetic variability in this sensitivity, so understanding population responses to environmental variation becomes crucial for proper selection of FRM. Taking advantage of volume growth and stem straightness 13 years after planting, assessed on c. 7500 trees from 25 P. pinaster populations established in seven common gardens across inland Galicia, we analyzed intraspecific variation in sensitivity to climate, geographic, edaphic and site quality factors. We used Mantel correlations and factorial regression models to distinguish the environmental parameters explaining the observed population × site interaction. We also estimated population phenotypic plasticity across sites and the existing genetic relationship between growth and stem straightness to define the optimal selection strategy for productivity purposes. Results showed a quantitatively significant population × site interaction for growth whereas it was almost negligible for stem straightness. In the case of growth, no specific environmental factor was able to explain the population relative performance across test sites, being site quality the only significant factor but with low power to describe the patterns found. Population differences were maximized in higher site quality conditions, where the largest gains from planting selected populations would be expected. Tree growth and straightness were not genetically correlated. Based on these results, subdividing inland Galicia for deployment of P. pinaster FRM throughout specific selections for each subregion is not recommended. Selecting FRM based on average growth and its phenotypic stability should be also avoided, as both properties were negatively correlated. We recommend selecting FRM based on the results from high quality sites, where Atlantic origin populations with high levels of genetic improvement showed the fastest growth. The inclusion of selection criteria based on stem straightness did not alter recommendations given that fast growing FRM showed intermediate or slightly above-average straightness. The limited explanatory power of climate factors for the population × site interaction prevents adjusting recommendations in light of the projected climate change.

Digital transformation as a factor in reducing labor shortages in Russian agriculture

This article examines the dynamics of the number of employees of agricultural organizations, the dynamics of energy availability, electrical equipment, the level of mechanization and technical equipment as key conditions for the introduction of digital solutions and the reduction of labor shortages in the industry. In addition, an analysis of the dynamics of labor costs in the sectoral and product context was carried out, which allows us to identify the most labor-intensive and requiring the introduction of digital solutions in the agricultural sub-sector. The key digital technologies that contribute to reducing the shortage of labor resources have been studied. A factorial regression model is proposed that allows us to study the impact of digital transformation on reducing the labor costs of producing a unit of production. The results of the study allowed us to propose an approach to assessing the impact of the digital transformation of agriculture on labor costs and reducing the shortage of labor resources of an industry or an individual enterprise (group of enterprises), consisting in analyzing the dynamics of the need for workers, the conditions of technical equipment of production, labor costs and approximation based on a regression model of the impact of digital transformation technologies on labor cost reduction.

GGE analysis and stability of traits in tomato cultivars grown under organic farming conditions: A two-year study

The increasing conversion of agricultural land to organic farming requires the development of specifically adapted cultivars. So far, in tomato there is lack of research for selection of germplasm suitable for sustainable agroecosystems. In this study, we investigated the genotypic and environmental factors affecting the variation of plant, fruits, and root traits in 39 tomato genotypes grown under organic farming conditions. Four independent experiments were conducted in Italy and Spain across two consecutive seasons in 2019 and 2020. For all traits, the factorial linear regression model to estimate the main effects of genotype (G), location (L), year of cultivation (Y) and their interactions, revealed highly significant (P < 0.001) variations, with the G factor being largely predominant for most traits. The implementation of the “which-won-where”, “mean performance versus stability” and “discriminative vs representativeness” patterns in the GGE (Genotype plus Genotype by Environment interaction) analysis, allowed the identification of superior cultivars with high stability across the testing environments. Genomic characterization with 30 890 high quality SNPs from ddRADseq genotyping analysis, revealed that a specific cluster of cherry tomato accessions were low performing in terms of yield and fruit weight, on the contrary, showed a high content of soluble solids, which in agreement with GGE analysis. Results of this study provide a framework for the potential use of this locally adapted tomato germplasm to address the needs of more sustainable agriculture.

Factorial effects contributing to food security in Mexico during COVID-19 context

The COVID-19 pandemic has altered the social, economic and environmental order of the world, and it wreaked havoc in many aspects of the life of societies, including the food insecurity (FI) rates in the regions. Due to the multifaceted nature of this phenomenon, it is required a better understanding of the associated variables that contribute to food security (FS), such as the socioeconomic and demographic variables for designing effective and efficient policy interventions. In this sense, this study describes an innovative way of interpreting the causal influence of the interaction effects between the socioeconomic and demographic variables on the level of FS experienced by Mexican households during the pandemic crisis. The period from March to May of 2021, a digital survey was applied to 630 households to analyze the harmonized version of the Latin American and Caribbean Food Security Scale (ELCSA). For this, a factorial regression model was used to estimate the factorial effects contribution of the mentioned variables under study, obtaining important results on the level of FS in two Mexican states: Nayarit and the State of Mexico. The obtained model shows the contribution factors, involved on the level of FS, are the following: mother's job, household incomes, the portion of income destined for food, the interaction between the family members with the place of residence, the interaction of the gender of the household head with the place of residence, the interaction of the mother's job with the father's schooling, and the interaction of the father's schooling with the portion of income spent on food. These findings offer essential insights for identifying vulnerable groups susceptible to experiencing food insecurity, in order to facilitate effective intervention in the multiple areas that require improvement, thus enhancing food security in the regions and contributing to the achievement of the SDG.

The influence of the laser cutting process parameters on the quality of the cut edge

The article describes the influence of the parameters of laser cutting on the cut edge quality of 10 mm S355J2 structural steel. The cutting process was carried out with a fiber laser. The basic evaluation criteria were measurements of perpendicularity tolerance, average profile height of roughness Rz5, visual evaluation, and measurements of hardness HV10. Moreover, the factorial regression models were prepared to investigate the standardized effects of the process parameters on the cutting quality. The variable process parameters were laser beam power, cutting velocity and working gas pressure. For regression modeling, low and high levels of these factors were considered representing the effective range of each parameter. The research presented in this paper proves that there are statistical correlations between the examined factors that have a significant impact on the achieved cutting effect. By the analysis of the process control parameters, it is possible to achieve an optimal level of quality that facilitates further technological operations on a given cutting product.

Machinability study of silicon carbide and cenosphere reinforced Al7075 composite using three different tool materials

AbstractThis research conducted a machinability study on Al7075 composite reinforced with nano‐sized (100 nm) silicon carbide and cenosphere (industrial waste) particulates with 1.8 % weight. These composites were fabricated utilizing an ultrasonically assisted stir‐casting setup and scanning electron microscope investigation was conducted to evaluate the dispersion properties of the reinforcement in the matrix phase. During the study, the effect of variation of feed rate, cutting speed and depth of cut on cutting forces and tool tip temperature has been studied. A total of 253 experiments were conducted using three different tool inserts polycrystalline diamond, cermet, and coated carbide under dry cutting conditions. Among the two components of the cutting force, it was noted that the primary cutting force was the largest. A full factorial response surface regression model has been developed and it is found that the regression model can predict cutting force and temperature with fair accuracy.

Sales Prediction Based on ARIMA Time Series and Multifactorial Linear Model

Contemporarily, sales forecasting based on historical data is one of the key topics of research. Researchers have found that different analytical forecasting models can predict the future sales volume to a certain extent based on different actual conditions. However, it is also very important to compare the prediction results of these models, and a reasonable choice of models can help managers to make more scientific decisions. Therefore, in this paper, the historical sales data of a store on the Kaggle is utilized to make predictions based on different models, and investigate the impact of linear regression model and time series model on sales prediction. In this paper, multiple factorial linear regression model and ARIMA model in time series analysis will be adopted based on R to build prediction models to forecast sales volume, respectively. According to the analysis, the root mean square error of the prediction results under the multiple factorial linear regression model and the ARIMA model were 21.135 and 23.221, respectively. The study surface that there were differences in the degree of fit of the prediction results by different pairs of models. Therefore, the choice of the researcher needs to select the appropriate model for prediction according to the specific object of analysis, combined with the data characteristics to achieve the expected results. These results shed on guiding further exploration of differences in predictions from different models.

Comparative and Combined Effects of Epigallocatechin-3-gallate and Caffeine in Reducing Lipid Accumulation in Caenorhabditis elegans.

Epigallocatechin-3-gallate (EGCG) and caffeine, two phytochemicals found in a wide range of natural dietary sources, have been reported to have protective effects against hyperlipidemia, a major risk factor for cardiovascular disease. However, their relative efficacy and synergy in lowering lipid level are unclear. This study intended to compare lipid-lowering activity of EGCG and caffeine and to elucidate their joint action using Caenorhabditis elegans (C. elegans) as a model organism. The worms were exposed to EGCG, caffeine or both agents, and lipid accumulation determined by levels of total lipids, triglycerides and cholesterol was monitored. A 3 × 3 factorial design combined with response surface methodology was used to characterize the nature of interactive effects. Total lipids, triglycerides and cholesterol in C. elegans were reduced by either EGCG or caffeine in a dose-dependent manner, with EGCG displaying a stronger lipid-lowering efficacy than caffeine. Overall, the EGCG/caffeine combination for lowering lipids was more effective than either substance alone. Factorial regression models revealed that the combination was antagonistic for total lipid reduction, perhaps due to a "ceiling" effect, and was synergistic for triglyceride-lowering and additive for cholesterol-lowering. Taken together, our work proposes the use of a combination of EGCG and caffeine as an alternative dietary intervention for the prevention of hyperlipidemia, and additionally highlights the suitability of C. elegans model for evaluating lipid-lowering capacity of natural products.

Genotype by Environment Interaction Analysis of Agronomic Spring Barley Traits in Iceland Using AMMI, Factorial Regression Model and Linear Mixed Model

Spring barley (Hordeum vulgare L.) is the most important cereal in Iceland and its national breeding program aims to select barley genotypes adapted to its environment. A critical step to understand the adaptation of Nordic barley material to a cool maritime climate is to assess the genotype by environment interaction (GxE). In this study, we evaluated the yield and thousand-kernel weight (TKW) of 32 spring barley genotypes in seven Icelandic environments. We applied three methods to analyze GxE: the additive main effects and multiplicative interaction model, a factorial model, and a linear mixed model. For yield, GxE was mainly caused by a better response of six-row genotypes compared to two-row genotypes in high fertility soils. For TKW, GxE showed a pattern along a gradient of daily mean temperatures. This pattern translated into a divergent TKW response between the 2-row and 6-row genotypes, with substantial crossovers along the temperature gradient. This GxE pattern was disentangled using all three methods, illustrating the value of cross-analysis. As yield is the main trait of interest for barley cultivation in Iceland, and few crossovers of genotype performance have been observed between environments, the definition of one mega-environment was recommended for Icelandic cultivation and breeding. We identified promising genetic material for both traits and highlighted the superiority of six-row genotypes for yield.

Neural Networks to Estimate Generalized Propensity Scores for Continuous Treatment Doses.

The generalized propensity score (GPS) addresses selection bias due to observed confounding variables and provides a means to demonstrate causality of continuous treatment doses with propensity score analyses. Estimating the GPS with parametric models obliges researchers to meet improbable conditions such as correct model specification, normal distribution of variables, and large sample sizes. The purpose of this Monte Carlo simulation study is to examine the performance of neural networks as compared to full factorial regression models to estimate GPS in the presence of Gaussian and skewed treatment doses and small to moderate sample sizes. A detailed conceptual introduction of neural networks is provided, as well as an illustration of selection of hyperparameters to estimate GPS. An example from public health and nutrition literature uses residential distance as a treatment variable to illustrate how neural networks can be used in a propensity score analysis to estimate a dose-response function of grocery spending behaviors. We found substantially higher correlations and lower mean squared error values after comparing true GPS with the scores estimated by neural networks. The implication is that more selection bias was removed using GPS estimated with neural networks than using GPS estimated with classical regression. This study proposes a new methodological procedure, neural networks, to estimate GPS. Neural networks are not sensitive to the assumptions of linear regression and other parametric models and have been shown to be a contender against parametric approaches to estimate propensity scores for continuous treatments.

Modelling of genotype by environment interaction to improve the recommendation of sugarcane cultivars for the state of Goiás, Brazil

Abstract The objective of this work was to identify environmental factors with significant effects on the genotype by environment interaction (GEI) of sugarcane, and to generate thematic maps yield adaptability of genotypes for the state of Goiás, Brazil, through the integrated use of factorial regression models and the geographic information system (GIS). The study was based on the yield of recoverable sugar (YRS) from cultivar field trials carried out in nine locations. Fourteen environmental factors were used, out of which 11 were divided into 10 crop growth phases, totaling 113 environmental covariates (ECs). The selection of ECs was done by successive simple linear regressions, and the respective genotypic sensitivity coefficients were used to generate adaptability maps. Approximately 57% of the GEI effects were related to the covariates longitude, average temperature at crop germination phase, and maximum temperature at the beginning of the phase of greatest growth. For YRS, the RB034128 and RB034021 clones show specific yield adaptations, and the RB034045 cultivar can share the growing area with the RB867515 check cultivar.

Factorial approach for multiple optimization of mechanical properties of alternative composite material for manufacture/repair flight surfaces (WING TIP - FAIRING ASSY)/CIDFAE

Abstract Currently there is a trend in the use of alternative composite materials to be implemented in the manufacture and repair of aircraft flight surfaces of the CIDFAE (Center Research and Development of Ecuadorian Air Force); In order to obtain a material with similar or superior properties to conventional ones, studies are generated where new materials are investigated. This research proposes a method of optimization of the mechanical properties to traction, bending and impact, with a factorial approach, based on the analysis of the desirability function, this statistical methodology allows to determine the optimal combination of manufacturing parameters, in which we can measure the overall quality of the statistical model used and control the statistical significance. With the present investigation it is defined that the optimal combination that takes advantage of the mechanical properties analyzed is the one that is composed of: 6 layers of carbon fibers at 0°, 1 layer of carbon fiber at 135° and 1 layer of copper fiber at 0°. The general factorial regression model used in this research, to globally describe the analyzed properties of the alternative compound, explains the variability of the data obtained with the tests by 95.01%. This model efficiently reduces the uncontrolled variability.

Genetic basis of phenotypic plasticity and genotype × environment interactions in a multi-parental tomato population.

Deciphering the genetic basis of phenotypic plasticity and genotype × environment interactions (G×E) is of primary importance for plant breeding in the context of global climate change. Tomato (Solanum lycopersicum) is a widely cultivated crop that can grow in different geographical habitats and that displays a great capacity for expressing phenotypic plasticity. We used a multi-parental advanced generation intercross (MAGIC) tomato population to explore G×E and plasticity for multiple traits measured in a multi-environment trial (MET) comprising optimal cultural conditions together with water deficit, salinity, and heat stress over 12 environments. Substantial G×E was observed for all the traits measured. Different plasticity parameters were estimated by employing Finlay-Wilkinson and factorial regression models and these were used together with genotypic means for quantitative trait loci (QTL) mapping analyses. In addition, mixed linear models were also used to investigate the presence of QTL × environment interactions. The results highlighted a complex genetic architecture of tomato plasticity and G×E. Candidate genes that might be involved in the occurrence of G×E are proposed, paving the way for functional characterization of stress response genes in tomato and for breeding climate-adapted cultivars.

A novel GIS-based tool to reveal spatial trends in reaction norm: upland rice case study

The upland rice crop system located within Brazilian savannas and Amazon Rainforest is the largest rainfed rice growing area in Latin America. To develop and release higher yield and adapted cultivars for this large region, the upland rice breeders need to conduct multiple-location trials aiming to model the genotype × location (G × L) and evaluate the germplasm yield adaptability. Here we hypothesize that regional patterns of G × L across this extensive region can be modeled by integrating factorial regression models with a geographic information system (GIS). Two sets of advanced yield trials from different germplasm pool were used in this study. From GIS tools, we collect and process geographic covariates and produce thematic maps of yield adaptability. One advantage of the methodology is that adaptability can be dissected into genotypic-sensibility coefficients related to the reaction norm for the geographic gradient. Then, breeders can discriminate different types of adaptability over a region, such as responsiveness for elevation, longitudinal or latitudinal adaptation, identifying possible ideotypes to solve current adaptation gaps for target regions. We observed that about of 53–59% of the G × L effects are due to predictable geographic-related factors. However, the upland rice germplasm is better adapted to higher elevations (> 700 m), which may indicate limitations in cultivar development because these regions do not represent the current upland rice growing region. We suggest to exploit geographic-related factors by increasing breeding efforts for northern and western Brazil environments located at lower elevations (< 300 m) and Equador’s near latitudes (2° S–2° N).

An Experimental and Statistical Analysis of Double Slope Single Basin Solar Still in Active and Passive Mode with Different Water Depth.

In the present work, experimental work on double slope single-basin still is investigated. The statistical analysis shows the effect of various factors related to the efficiency of still. The water level in the basin investigated at 10 cm, 6 cm and 2 cm depth. The process temperature TW, TG and TB were recorded one each day. The still is used in the Agra city climate. The mode of operation affects its water yield as an active mode of operation produces more water yield than a passive mode of operation. In the statistical analysis R2=99.29 (passive mode) and R2=99.11 (active mode) recommend the satisfactory agreement of the factorial design regression model.

Method to assess the drying performance of water vapour-permeable membrane pouches for fruit juice preservation

The aim of this work was to develop a method for assessing the drying performance of a hygienic, small-scale fruit juice preservation process called Solar Assisted Pervaporation (SAP). SAP is a batch process that allows for fruit juices or purées to be solar dried in water vapour-permeable membrane pouches in rural and remote areas. Traditional methods for measuring water vapour flux through membrane materials are not directly applicable for assessing the performance of SAP pouches, and so a new method was developed, considering a constant boundary condition on the inside of the pouch (i.e. water activity of 1.0). This paper presents a statistical validation of the method and then illustrates how it can be used to characterise membrane performance, identify a two-level factorial regression model for drying flux as a function of temperature, relative humidity and air velocity and their interaction effects, and estimate an operational window for drying juices in SAP pouches in an indirect solar dryer. The repeatability of the method is high with a precision of 95% or more. The regression model and operational window can be used to optimise the design of an indirect solar dryer specifically suited for SAP pouches filled with fruit juices.

Gender Differences in Threat Biases: Trauma Type Matters in Posttraumatic Stress Disorder.

Women are diagnosed with posttraumatic stress disorder (PTSD) at twice the rate of men. This gender difference may be related to differences in PTSD experiences (e.g., more hypervigilance in women) or types of trauma experienced (e.g., interpersonal trauma). We examined whether attentional threat biases were associated with gender, PTSD diagnosis, and/or trauma type. Participants were 70 civilians and veterans (38 women, 32 men; 41 with PTSD, 29 without PTSD) assessed with the Clinician Administered PTSD Scale for DSM-IV who completed a facial dot-probe attention bias task and self-report measures of psychiatric symptoms and trauma history. Factorial ANOVA and regression models examined associations between gender, PTSD diagnosis, index trauma type, lifetime traumatic experiences, and attentional threat biases. Results revealed that compared to women without PTSD and men both with and without PTSD, women with PTSD demonstrated attentional biases toward threatening facial expressions, d = 1.19, particularly fearful expressions, d = 0.74. Psychiatric symptoms or early/lifetime trauma did not account for these attentional biases. Biases were related to interpersonal assault index traumas, ηp 2 = .13, especially sexual assault, d = 1.19. Trauma type may be an important factor in the development of attentional threat biases, which theoretically interfere with trauma recovery. Women may be more likely to demonstrate attentional threat biases due to higher likelihood of interpersonal trauma victimization rather than due to gender-specific psychobiological pathways. Future research is necessary to clarify if sexual assault alone or in combination with gender puts individuals at higher risk of developing PTSD.

Application of ANFIS for the Selection of Optimal Wire-EDM Parameters While Machining Ti-6Al-4V Alloy and Multi-Parametric Optimization Using GRA Method

The applications of artificial intelligence (AI) are becoming more popular and relevant research have been conducted in every field of engineering and science by using these AI techniques. Therefore, this research aims to examine the influence of wire electric-discharge machining (WEDM) parameters on performance parameters to improve the productivity with a higher surface finish of titanium alloy (Ti-6Al-4V) by using the artificial intelligent technique. In this experimental analysis, the Adaptive Network Based fuzzy Inference System (ANFIS) model has been highly-developed and the multi-parametric optimization has been done to find the optimal solution for the machining of the titanium superalloy. The peak current (Ip), taper angle, pulse on time (Ton), pulse of time (Toff) and the dielectric fluid flow rate were selected as operation constraints to conduct experimental trials. The surface roughness (SR) and MRR were considered as output responses. The influence on machining performance has been analyzed by an ANFIS model and the developed model was validated with the full factorial regression models. The developed models showed the minimum mean percentage error and the optimized parameters by the GRA method showed the considerable improvement in the process.

Dissection of Year Related Climatic Variables and Their Effect on Winter Rapeseed (Brassica Napus L.) Development and Yield

Winter oilseed rape (WOSR) production is dependent on weather conditions, but is also characterized by low nitrogen (N) use efficiency. The objectives of this study were to: (i) evaluate sources of variability for the seed yield and oil content of four rapeseed cultivars under the influence of three sowing dates (SD trial) and five nitrogen dosages (N trial) during four growing seasons; (ii) understand year-related interactions and the effect of climatic variables in different growth stages; and (iii) assess the presence of interactions cultivar by year (C × Y) and treatment by year (T × Y). Six climatic factors were observed, during germination, overwintering, budding, flowering and ripening. The mixed effect split-plot analysis of variance was used, as well as factorial regression models. The C × Y interaction was the most important for the oil content in both trials. The precipitation at budding stage (75.8%), relative air humidity at overwintering (63.3%) and flowering stage (53.0%) accounted for the highest proportion of T × Y interaction for the seed yield, as well as precipitation at flowering (92.0%) and ripening (85.0%) for the oil content. Water availability was the main determinant of the seed yield and/or oil content accompanied with cooler temperatures during the seed development. The study successfully dissected the effect of year-related climatic variables on the agronomical traits in winter rapeseed. Based on this, appropriate agronomic practices can be applied at specific growing stages to ensure a high seed and oil yield.

Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy

The applications of artificial intelligence (AI) mainly, the hybrid approaches are becoming more popular and the relevant researches have been conducted in every field of engineering and science by using these AI techniques. Therefore, this research aims to examine the influence of wire electric-discharge machining parameters on performance parameters to improve the productivity with a higher surface finish of Titanium alloy (Ti–6Al–4V) by using the artificial intelligent technique. In this experimental analysis, the adaptive network based fuzzy inference system (ANFIS) model has been highly-developed and the multi-parametric optimization has been done to find the optimal solution for the machining of titanium superalloy. The peak current (Ip), taper angle, pulse on time (Ton), pulse of time (Toff) and the dielectric fluid flow rate had selected as operation constraints to conduct experimental trials. The surface roughness and MRR were considered as output responses. The influence on machining performance has been analyzed by the ANFIS model and the developed model was validated with the full factorial regression models. The developed models showed the minimum mean percentage error and the optimized parameters by the GRA method showed the considerable improvement in the process.