Two-factor Interaction Effects Research Articles

From efficiency to resilience: unraveling the dynamic coupling of land use economic efficiency and urban ecological resilience in Yellow River Basin

This study investigates the Dynamic Coupling between Land Use Economic Efficiency (LUEE) and Urban Ecological Resilience (UER) in the Yellow River Basin (YRB). This exploration is pivotal for elucidating the interaction mechanisms between economic growth and ecological governance. Furthermore, understanding this relationship is essential for fostering high-quality, sustainable urban development in the YRB. Utilizing panel data from 56 cities spanning 2003 to 2020, this study employed the coupling coordination degree (CCD) model, spatial correlation analysis, Kernel density estimation, convergence model, and Geodetector to systematically analyze the spatio-temporal distribution, dynamic trend, and determinants of the CCD between LUEE and UER in the YRB. The findings indicate that: (1) A general upward trend in both LUEE and UER, accompanied by a steady improvement in their CCD. (2) Significant spatial disparities in their CCD, with higher levels in the lower reaches. (3) Marked positive spatial autocorrelation, predominantly characterized by clusters where high (low) values are surrounded by high (low) values. (4) Regarding the impact of individual factors, government fiscal budget expenditure demonstrates the most robust explanatory power for the CCD within the YRB. Concerning the effects of two-factor interactions, the interplay between industrial structure upgrading and government fiscal budget expenditure emerges as the most significant determinant in influencing the CCD between LUEE and UER. This study enhances our comprehensive understanding of the interplay between economic and ecological systems. It offers scientific insights and strategic direction for harmonizing ecological governance with urban economic growth at both the regional and global scales.

Optimal choice designs for the main effects plus specified two-factor interaction effects model for binary attributes

Unlike optimal utility-neutral binary choice designs requiring a large number of choice sets to estimate the main effects and all two-factor interactions, we construct optimal binary choice designs with fewer choice sets for estimating the main effects plus specified two-factor interactions.

Convective heat transfer across silicon rods under uniform wall heat flux in a laboratory-scale Siemens reactor

The effects of operating and geometric parameters on convective heat transfer (CHT) in Siemens reactor with a laboratory-scale are investigated. A dimensionless analysis of the CHT is performed considering the flow characteristics, rod diameter (d), rod length (L), number of rods, and the distance between adjacent silicon rods. A dimensionless parameter (Hou) is proposed to represent the number of rods and the distance between adjacent rods. Additionally, three dimensionless parameters that affect the CHT, namely, the Rayleigh number, d/L, and Hou, are obtained. The two-factor interaction effects of the dimensionless parameters on the CHT are analyzed, and their relationships are determined for calculating the CHT flux. Based on these relationships, the Nusselt number is calculated for 7- and 8-rod laboratory-scale Siemens reactors, and the relative errors between the predicted and calculated results are 1.4 and 3.2 %, respectively.

An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects

An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects

The Spatio-Temporal Changes of Small Lakes of the Qilian Mountains from 1987 to 2020 and Their Driving Mechanisms

Small lakes (areas ranging from 0.01 km2 to 1 km2) are highly sensitive to climate change and human activities. However, few studies have investigated the long-term intra-annual trends in the number and area of small lakes and their driving mechanisms in the Qinghai–Tibet Plateau (QTP). As a significant water tower in northwest China, the Qilian Mountains region (QMR) in the QTP is essential for sustaining regional industrial and agricultural production, biodiversity, and human well-being. We conducted an analysis of the dynamics of small lakes in the QMR region. In this study, we employed Geodetector and examined nine factors to investigate the driving mechanisms behind the long-term variations in the small lake water bodies (SLWBs). We specifically focused on understanding the effects of single-factor and two-factor interactions. The results indicate that the number and area of small lakes had a fluctuating trend from 1987 to 2020. Initially, there was a decrease followed by an increase, which was generally consistent with trends in the large lakes on the QTP. All basins had far more expanding than shrinking lakes. The area of seasonal SLWBs in each basin was increasing more rapidly than permanent SLWBs. The distribution and trends in the area and number of small lakes varied widely across elevation zones. Runoff, snow depth, and temperature contributed the most to SLWB changes. Human activities and wind speed contributed the least. However, the main drivers varied across basins. The impact of two-factor interactions on SLWB changes in basins was greater than that of single factors. Our results provide useful information for planning and managing water resources and studies of small lakes.

Modeli rasta prirodnih sastojina kalabrijskog bora u središnjem mediteranskom području Turske

Stand growth models are needed for a variety of forestry practices, primarily management plans and silvicultural studies. The goal of this study was to create stand-level models for natural, pure even-aged stands of Calabrian pine in the central Mediterranean region of Turkey. The study area consists of pure and natural Calabrian pine stands located within the boundaries of the Antalya and Mersin Regional Forestry Directorates in the central Mediterranean region of Turkey. Data was collected from 486 temporary plots scattered throughout the region. Two trees (the dominant tree and the tree representing the quadratic mean diameter of the stand) were measured in each plot, yielding 972 trees. The data showed that the age varied from 6 to 135 years, the site index (SI) from 8.5 to 33.5 m, and the density from 0.3 to 12.4. The density-dependent yield tables were generated using regression equations based on stand age, SI, and stand density with individual, two-factor, and three-factor interaction effects. For SI classes I, II, and III, the optimal rotation period that would result in the highest yields for pure Calabrian pine stands is 60, 65, and 75 years, respectively. The stand growth models developed (i.e., density-dependent yield tables) agreed with the fundamental growth principles and data provided in the literature.

Niger Seed Oil-Based Biodiesel Production Using Transesterification Process: Experimental Investigation and Optimization for Higher Biodiesel Yield Using Box–Behnken Design and Artificial Intelligence Tools

The present work aims at cost-effective approaches for biodiesel conversion from niger seed (NS) oil by employing the transesterification process, Box–Behnken design (BBD), and artificial intelligence (AI) tools. The performances of biodiesel yield are reliant on transesterification variables (methanol-to-oil molar ratio M:O, reaction time Rt, catalyst concentration CC, and reaction temperature RT). BBD matrices representing the transesterification parameters were utilized for experiment reductions, analyzing factor (individual and interaction) effects, deriving empirical equations, and evaluating prediction accuracy. M:O showed a dominant effect, followed by CC, Rt, and RT, respectively. All two-factor interaction effects are significant, excluding the two interactions (Rt with RT and M:O with RT). The model showed a good correlation or regression coefficient with a value equal to 0.9869. Furthermore, the model produced the best fit, corresponding to the experimental and predicted yield of biodiesel. Three AI algorithms were applied (the big-bang big-crunch algorithm (BB-BC), firefly algorithm (FA), and grey wolf optimization (GWO)) to search for the best transesterification conditions that could maximize biodiesel yield. GWO and FA produced better fitness (biodiesel yield) values compared to BB-BC. GWO and FA experimental conditions resulted in a maximum biodiesel yield equal to 95.3 ± 0.5%. The computation time incurred in optimizing the biodiesel yield was found to be equal to 0.8 s for BB-BC, 1.66 s for GWO, and 15.06 s for FA. GWO determined that the optimized condition is recommended for better solution accuracy with a slight compromise in computation time. The physicochemical properties of the biodiesel yield were tested according to ASTM D6751-15C; the results are in good agreement and the biodiesel yield would be appropriate to use in diesel engines.

Impact of Select Geometric and Operational Parameters on Hydrodynamics in Dissolution Apparatus 2 (Paddle Apparatus): A Design of Experiments Analysis Based on Computational Fluid Dynamics Simulations

PurposeA Design of Experiments (DOE) analysis driven by Computational Fluid Dynamics (CFD) simulations was used to evaluate individual and two-factor interaction effects of varying select geometric and operational parameters on the hydrodynamics in dissolution apparatus 2 (paddle apparatus).MethodsSimulations were run with meshing controls and solution strategies retained from a mesh-independent validated baseline model. Distance between vessel and impeller bottom surfaces, impeller offset, vessel radius and impeller rotation speed were considered as input parameters. The velocity magnitudes at four locations near the vessel bottom surface were considered as output parameters. Response surfaces and Pareto charts were generated to understand individual and two-factor interaction effects of input parameters on the output parameters.ResultsImpeller offset has a dominating influence of a linear and quadratic nature on the output parameters and affects overall hydrodynamics. Changes to other input parameters have limited influence on velocity magnitudes at locations closest to the vessel axis and on overall hydrodynamics. However, these parameters have important influences of varying degrees on velocity magnitudes at locations away from the vessel axis.ConclusionsThe hydrodynamics in Apparatus 2 is influenced differently by different parameters and their combinations. Impeller offset has a stronger influence when compared to parameters that do not alter apparatus symmetry.

Assessment of CVD- and PVD-Coated Carbides and PVD-Coated Cermet Inserts in the Optimization of Surface Roughness in Turning of AISI 1045 Steel.

In this study, an experimental and statistic investigation approach based on analysis of variance (ANOVA) and response surface methodology (RSM) techniques was performed to find the significant main effects and two-factor interaction effects and to determine how the controllable factors such as cutting speed, feed rate, depth of cut (DOC), tool nose radius, substrate and coating method of cutting tools influence surface quality in turning of AISI 1045 steel. The first optimal or near-optimal conditions for the quality of the generated surface and the second ones, including maximum material removal rate, were established using the proposed regression equations. The group mean roughness of the turned workpieces was lower from using chemical vapor deposition (CVD)-coated carbide inserts than the group means of other types of inserts; however they could not achieve the specific lowest roughness. The physical vapor deposition (PVD)-coated carbide and cermet inserts achieved the best surface quality when the specific combinations within the range interval of controllable factors were used in the experiment, showing that they may be applied to finish turning processes or even to particular high material removal rate conditions associated with the lowest roughness.

Orthogonal blocking arrangements for 24-run and 28-run two-level designs

Much research has been done concerning 24-run orthogonal two-level designs involving 3–23 factors and 28-run orthogonal two-level designs involving 3–27 factors. The focus of this research was on completely randomized screening designs and led to lists of recommended designs for each number of factors. When using the recommended designs, there is either no aliasing between the main effects and the two-factor interaction effects or only a limited amount. Among all designs with this attractive property, the recommended designs minimize the aliasing among the two-factor interaction effects. It is, however, unclear which 24- and 28-run designs are best when complete randomization is infeasible and the designs have to be arranged in blocks. In this article, we address this issue and present the best arrangements of 24-run designs in 3, 4, and 6 blocks and the best arrangements of 28-run designs in 7 blocks.

Optimal Paired Choice Block Designs

Choice experiments mirror real world situations closely and helps manufacturers, policy-makers and other researchers in taking business decisions on their product characteristics based on its perceived utility. In a paired choice experiment, several pairs of options are shown to respondents. The respondents are asked to give their preference among the two options for each of the choice pairs shown to them. In order to conduct an experiment, a choice design is customarily used to efficiently estimate the parameters of interest which essentially consists of either the main effects only or the main plus two-factor interaction effects of the attributes. Traditionally, every respondent is shown the same collection of choice pairs under an untenable assumption that the respondents are alike in every respect. Also, as the attributes or the number of levels under each attribute increases, the number of choice pairs in an optimal paired choice design increases rapidly. To address these concerns, under the multinomial logit model or the linear paired comparison model, we first incorporate the respondent effects and then present optimal designs for the parameters of interest. We provide optimal paired choice designs for estimating the main effects for symmetric and asymmetric multi-level attributes with smaller number of choice pairs shown to each respondent. We also provide optimal paired choice designs for estimating the main effects only and the main plus two-factor interaction effects under the main plus two-factor interaction effects model.

Parametric investigation of heat transfer enhancement and pressure loss in louvered fins with longitudinal vortex generators

The present work aims to perform a parametric study of heat transfer enhancement and the associated pressure loss applied to louvered fins with rectangular winglet vortex generators (RWL). The contributions of a set of design variables on heat transfer and pressure drop are evaluated for two geometry types, L1 and L2. The procedure to investigate the relative importance of the geometrical design variables on thermal-hydraulic performance is enabled by a Design of Simulations (DOS) method called Latin Hypercube Sampling (LHS) in association with a modern non-parametric statistical method (Smoothing Spline ANOVA method) and Computational Fluid Dynamics. Outcomes from the screening analyses turned out that the louver angle is the unique contributor to friction factor for both geometry types and this behaviour is independent of Reynolds number. With respect to heat transfer, the contributions of the input variables are different for both geometry types and Reynolds numbers. For ReDh=120, no important two-factor interaction effects were observed for both L1 and L2, in the same way as occurred in terms of friction factor. Conversely, for ReDh=240 one relevant interaction effect was observed.

On three-level [formula omitted]-optimal paired choice designs

Considering three-level D-optimal paired choice designs for estimating all the main effects and two-factor interaction effects under the utility-neutral multinomial logit model, we provide general characteristics (and examples) required in generators allowing significant reduction in the number of choice pairs.

A novel highly efficient adsorbent {[Co4(L)2(μ3-OH)2(H2O)3(4,4′-bipy)2]·(H2O)2}n: Synthesis, crystal structure, magnetic and arsenic (V) absorption capacity

A novel highly efficient adsorbent-microporous tetranuclear Co(II)-based polymer, {[Co4(L)2(μ3-OH)2(H2O)3(4,4′-bipy)2]·(H2O)2}n (1, H3L = 4-(N,N′-bis(4-carboxybenzyl)amino) benzenesulfonic acid, 4,4′-bipy = 4,4′-bipyridine), was hydrothermally synthesized. The complex 1 is a metal–organic framework (MOF) material which was characterized by single-crystal X-ray diffraction, BET and platon software. Co-MOF (complex 1) reveals excellent adsorption property. The capacity of Co-MOF to remove arsenic As(V) from sodium arsenate aqueous solutions was investigated (The form of As(V) is AsO43-). The experimental results showed that Co-MOF had a higher stable and relatively high As(V) removal rate (> 98%) at pH 4–10. The adsorption kinetics followed a pseudo-second-order kinetic model, and the adsorption isotherm followed the Langmuir equation. Co-MOF exhibits a very high adsorption capacity of As(V) in aqueous solution (Qmax of 96.08 mg/g). Finally, the optimal adsorption conditions for the model were obtained through a Box–Behnken response surface experiment which was designed with adsorption time, dose, temperature and rotational speed of the shaker as the influencing factors to determine two-factor interaction effects. Co-MOF was further characterized using FTIR, PXRD, X-ray photoelectron spectroscopy before and after adsorption As (V). The magnetism of Co-MOF was also discussed.

Optimal two-level choice designs for estimating main and specified two-factor interaction effects

ABSTRACTUnder the multinomial logit model, designs for choice experiments are usually based on an a priori assumption that either only the main effects of the factors or the main effects and all two-factor interaction effects are to be estimated. However, in practice, there are situations where interest lies in the estimation of main plus some two-factor interaction effects. For example, interest on such specified two-factor interaction effects arise in situations when one or two factor(s) like price and/or brand of a product interact individually with the other factors of the product. For two-level choice experiments with n factors, we consider a model involving the main plus all two-factor interaction effects, with our interest lying in the estimation of the main effects and a specified set of two-factor interaction effects. The two-factor interaction effects of interest are either (i) one factor interacting with each of the remaining factors or (ii) each of the two factors interacting with each of the ...

Analysis on $$s^{n-m}$$ s n - m designs with general minimum lower-order confounding

An optimal design should minimize the confounding among factor effects, especially the lower-order effects, such as main effects and two-factor interaction effects. Based on the aliased component-number pattern, general minimum lower-order confounding (GMC) criterion can provide the confounding information among factors of designs in a more elaborate and explicit manner. In this paper, we extend GMC theory to s-level regular designs, where s is a prime or prime power. For an \(s^{n-m}\) design D with \(N=s^{n-m}\) runs, the confounding of design D is given by complementary set. Further, according to the factor number n, we discuss two cases: (i) \(N/s<n\le (N-1)/(s-1)\), and (ii) \((N/s+1)/2<n\le N/s\). We not only provide the lower-order confounding information among component effects of D, but also obtain three necessary conditions for design D to have GMC.

Construction of Search Designs From Orthogonal Arrays

Search designs form an important class of experimental designs that allow the identifying of the true model, consisting of a set of factorial effects, among many. Most of the work in this field has been made in the cases where there are at most one or two two-factor interaction effects considered nonnegligible. This article focuses on model identification through the use of search linear models containing, apart from the general mean and the main effects, up to five nonnegligible two-factor interaction effects. The new search designs are based exclusively on orthogonal arrays.

Parameter optimization of Nd:Yag laser scribing process on core loss of grain-oriented magnetic silicon steels

The core loss of the grain-oriented silicon steel influences the energy loss of transformer. Pulse energy, point spacing, and scribing spacing are the three key parameters of laser scribing process, which determine the core loss of silicon steel. In this study, the relationship between the three key parameters and core loss reduction was investigated and analyzed. Experiments of laser scribing on 30Q130 silicon steel with different parameter combinations were implemented. The results showed that there was no obvious silicon coating burns in the surface of the steel after laser scribing and that the core loss reduction rate (reduction rate) significantly increased with the increasing laser energy density. However, when the energy density exceeded a certain range, the reduction rate decreased and gradually tended to a stable level. Hence, the parameters had significant influence on the reduction rate of core loss, and the two-factor interaction effects of reduction rate of core loss were prominent when parameters of high pulse energy, small point spacing, and small scribing spacing were selected. The optimization combination values of the three key parameters of laser scribing process on 30Q130 silicon steel was acquired by genetic algorithm–enhanced artificial neural network algorithm. For achieving high reduction of core loss, the optimized combination of pulse energy of 2.52 mJ, small point spacing of 0.22 mm, and small scribing spacing of 3.03 mm were selected, and the reduction rate of 13.12 % was verified through experiment.

Optimization of replenishment policies for decentralized and centralized capacitated supply chains under various demands

In this paper, the optimal replenishment policies of capacitated supply chains (SC) operating under two different control strategies (decentralized vs. centralized) and various demands are determined and insights useful to management are discussed. The details of the system used, including the underlying supply chain inventory model and the simulation-based optimization framework, are presented. The effectiveness and efficiency of the proposed optimization framework is illustrated by comparing it with brute force estimation of the true optimum on three selected scenarios. The entire study was carried out by following a design of experiment, covering ten demand patterns, four levels of capacity constraints, and two control strategies. The main and two-factor interaction effects were analyzed via ANOVA. Detailed analyses of the ordering patterns, cost distribution over the SC and internal service level were carried out to provide useful insights. For demand with high variation, capacity constraint may lead to significant changes in ordering patterns. Failing to detect those changes may lead to unnecessary cost increase. Overall it is beneficial to adopt centralized control and cost savings realized are dependent upon the demand patterns. An incentive mechanism is proposed to coordinate the decentralized system so that each player in the SC will be better off. Additional experiments were also carried out to investigate the effect of using different allocation rules by the distributor.

An investigation of design parameters that affect commercial high-rise office building energy consumption and demand

There are many factors that drive the energy consumption and demand in high-rise commercial office buildings. Understanding the effects of individual building parameters and two-factor interactions can be very useful for directing building audits and developing energy simulation models. A fractional factorial analysis was conducted to evaluate a large number of building parameters in an effort to quantify their effect on building energy consumption and demand. The analysis utilized building data collected from 22 building audits of high-rise commercial office buildings located in the downtown Chicago Loop area. Simulation results for three of the buildings show the effects of each factor and two-factor interactions on energy consumption and demand over a set of climate zones. The factors of primary importance include lighting and equipment power density, chiller efficiency, window U-value, the mass of interior furnishings and supply fan static pressure.