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Consumers relate to brands in ways analogous to how they relate with people. And, just as consumers have norms that guide their interpersonal relationships, they also have norms that guide their relationships with brands. While such relational norms are essential in determining brand value, extant literature offers no measure to effectively operationalize the framework that governs consumer–brand relationships. Preliminary work offers important insight but suffers from several methodological shortcomings, rendering subsequent empirical applications problematic and hindering theoretical and practical advancements. In seven studies (ntotal = 3121), we revisit seminal work on the consumer–brand relational norms construct to refine and validate its measurement. Our findings reveal a unidimensional set of items that display strong psychometric properties, offering a parsimonious measure that promotes consistent operationalizations of the construct across studies and enables comparison and integration of empirical results. [ABSTRACT FROM AUTHOR]

Species distribution modeling (SDM) is a fundamental tool in theoretical and applied ecology. However, relatively little is known about the performance of different approaches for scale optimization, model selection, and algorithmic prediction in the context of nonlinear, multiscale and interactive relationships between environmental variables and species occurrence. Modelers often struggle to optimize a tradeoff between ecological relevance, model robustness, complexity, and overfitting. In this paper, we investigated several methods designed to optimize spatial scale and variable selection in SDMs, in each case evaluating model fitness, parsimony and predictive performance. We used a simulation approach to produce a large pool of alternative underlying habitat relationships that reflect a broad range of realistic habitat associations. We also compared several different modeling algorithms, including logistic regression with a generalized linear model (GLM), Lasso and Elastic-Net Regularized GLMs (GLMNet), and random forest (RF), as well as alternative variable and scale selection methods. We found that GLM methods employing all-subsets dredge routines for variable selection were consistently the best predictors based on all criteria of our model performance assessment and across all attributes of the simulated underlying relationship, including nonlinearity and interaction. We had expected machine learning approaches, such as random forest, to perform better in these more complex forms of species-environment relationships. GLM using dredge variable selection was also the method that included the fewest spurious covariates and included the most correct predictors as a proportion of all predictors. We found that univariate scaling was the most robust method of variable and scale selection, along with Minimal Redundancy Maximal Relevancy (MRMR) which performed equivalently. The simulation experiment presented here provides a robust assessment of simulated multi-species distribution model performance, complexity and fidelity. By simulating a large range of potential habitat relationships with varying spatial scale, effect sizes, linearity, and interactions, we comprehensively evaluated model performance across gradients of complexity of the underlying relationships and violations of classical statistical assumptions. This study provides a valuable assessment and a broader example of the power and utility of controlled simulation experiments in habitat relationships and other ecological spatial predictive modeling.

Aiming at the problems of small good workspace, many singular configurations, and limited carrying capacity of non-redundant parallel mechanisms, a full-redundant drive parallel mechanism is designed and developed, and its performance evaluation, good workspace identification, and scale optimization design are studied. First, the kinematics analysis of the planar 6R parallel mechanism is completed. Then, the motion/force transmission performance evaluation index of the mechanism is established, and the singularity analysis of the mechanism is completed. Based on this, the fully redundant driving mode of the mechanism is determined, and the good transmission workspace of the mechanism in this mode is identified. Then, the mapping relationship between the performance and scale of the mechanism is established by using the space model theory, and the scale optimization of the mechanism is completed. Finally, the robot prototype is made according to the optimal scale, and the performance verification is carried out based on the research of dynamics and control strategy. The results show that the fully redundant actuation parallel mechanism obtained by design optimization has high precision and large bearing capacity. The position repeatability and position accuracy are 0.053 mm and 0.635 mm, respectively, and the load weight ratio can reach 15.83%. The research results of this paper complement and improve the performance evaluation and scale optimization system of redundantly actuated parallel mechanisms. [ABSTRACT FROM AUTHOR]

Segmentation is crucial in geographic object-based image analysis for accurate land use and land cover mapping. However, obtaining outstanding segmentation results in all scenarios proves challenging with a single algorithm. This study investigates seven segmentation algorithms: mean shift (MF), O Sistema de Processamento de informações georreferenciadas (the geographic information and image processing system) (SPRING), Estimation of scale parameter 2 (ESP2) (three global-scale algorithms), image object detection approach (IODA), SA, edge-guided image object detection approach (EIODA) (three local-scale optimization algorithms), and segment anything model (SAM) (deep learning). In the custom dataset and semantic segmentation datasets, we apply visual interpretation, unsupervised, and supervised evaluation methods with 15 test images, using a total of 17 evaluation indices to assess the segmentation results. Based on the evaluation results, the effectiveness and adaptability of the algorithms in scene segmentation are comprehensively analyzed. The results report that global-scale segmentation approaches encounter difficulties in distinguishing meaningful objects in complicated scenarios. Both MF and SPRING methods are prone to over-segmentation. In many cases, ESP2 tends to generate homogeneous segments (low weighted variance), whereas EIODA tends to produce heterogeneous adjacent segments (low Moran's I). ED3 and segmentation evaluation index demonstrate that scale parameter (SA) and IODA can to some extent identify geo-objects, with SA being more effective and performing exceptionally well in building extraction. The EIODA performs well in areas with clear boundaries, like aquaculture ponds and water-land transitions. SAM accurately detects objects of various sizes, displaying rich semantic content and high consistency with reference polygons. The average intersection over union reaches 71.10% and F measure attains 0.77 under normal conditions.

To address the challenges of extension/retraction distance, stiffness, and accuracy in space telescope sleeve-type stretching arms, a wheel-type variable-diameter internal drive device has been employed as a replacement for the existing driving mechanism. However, during the research process, it was discovered that when the elastic body comes into contact with a non-continuous rigid surface, there is a transitional instability phenomenon caused by elastic friction contact and elastic collision. To tackle this issue, the analysis begins by utilizing fractal theory and Hertz contact theory to optimize the scale of micro-convex bodies. This analysis considers the deformation of the substrate and the micro-convex bodies after their interaction with the elastic body surface. Additionally, the model incorporates the effects of elastic rolling friction and collision time, establishing a normal contact stiffness model for the contact surface during the inter-stage transition. Subsequently, finite element analysis and experimental prototypes are employed to verify and analyze the findings. The results indicate that the scale variation and deformation of elastic micro-convex bodies after interaction have a significant influence on the normal contact stiffness of the contact surface. Therefore, optimizing the scale and spatial arrangement of elastic micro-convex bodies can effectively enhance mechanical properties and ensure stability during the transition on non-continuous rigid surfaces. [ABSTRACT FROM AUTHOR]

Species distribution modeling (SDM) is a fundamental tool in theoretical and applied ecology. However, relatively little is known about the performance of different approaches for scale optimization, model selection, and algorithmic prediction in the context of nonlinear, multiscale and interactive relationships between environmental variables and species occurrence. Modelers often struggle to optimize a tradeoff between ecological relevance, model robustness, complexity, and overfitting. In this paper, we investigated several methods designed to optimize spatial scale and variable selection in SDMs, in each case evaluating model fitness, parsimony and predictive performance. We used a simulation approach to produce a large pool of alternative underlying habitat relationships that reflect a broad range of realistic habitat associations. We also compared several different modeling algorithms, including logistic regression with a generalized linear model (GLM), Lasso and Elastic-Net Regularized GLMs (GLMNet), and random forest (RF), as well as alternative variable and scale selection methods. We found that GLM methods employing all-subsets dredge routines for variable selection were consistently the best predictors based on all criteria of our model performance assessment and across all attributes of the simulated underlying relationship, including nonlinearity and interaction. We had expected machine learning approaches, such as random forest, to perform better in these more complex forms of species-environment relationships. GLM using dredge variable selection was also the method that included the fewest spurious covariates and included the most correct predictors as a proportion of all predictors. We found that univariate scaling was the most robust method of variable and scale selection, along with Minimal Redundancy Maximal Relevancy (MRMR) which performed equivalently. The simulation experiment presented here provides a robust assessment of simulated multi-species distribution model performance, complexity and fidelity. By simulating a large range of potential habitat relationships with varying spatial scale, effect sizes, linearity, and interactions, we comprehensively evaluated model performance across gradients of complexity of the underlying relationships and violations of classical statistical assumptions. This study provides a valuable assessment and a broader example of the power and utility of controlled simulation experiments in habitat relationships and other ecological spatial predictive modeling. • Scale optimization, model selection, and algorithmic prediction in the context of nonlinear, multiscale and interactive relationships between environmental variables and species occurrence. • We used a simulation experiment to investigate spatial scale and variable selection in SDMs. • We found that GLM methods employing all-subsets dredge routines for variable selection were consistently the best. • GLM methods using dredge variable selection were also the methods that included the fewest spurious covariates and included the most correct predictors as a proportion of all predictors. • We found that univariate scaling was the most robust method of variable and scale selection, along with Minimal Redundancy Maximal Relevancy (MRMR). [ABSTRACT FROM AUTHOR]

Abstract Habitat selection is strongly scale‐dependent, and inferring the characteristic scale at which an organism responds to environmental variation is necessary to obtain reliable predictions. The occupancy framework is frequently used to model species distribution with the advantage of accounting for imperfect observation, but occupancy studies typically do not define the characteristic scale of the modelled variables. We used camera trap data from winter wildlife surveys in the Swiss part of the Jura Mountains to model occupancy of wild boar (Sus scrofa) and roe deer (Capreolus capreolus). We used a three‐step approach: (1) first, we identified factors influencing detectability; (2) second, we optimized the characteristic scale of each candidate explanatory variable; and (3) third, we fit multivariable, multiscale occupancy models in relation to land cover, human presence and topography. Wild boar occupancy was mainly influenced by the interaction between elevation within 2500 m and the proportion of forested areas within a 2500 m, with a nonsignificant additional effect of the interaction between ruggedness within 1900 m and the proportion of forested areas within 2500 m as well as the distance to urban areas. Roe deer occupancy was mainly associated with the interaction between ruggedness within 900 m and the proportion of open landscape within 900 m, with an additional nonsignificant effect of the interaction between elevation within 1500 m and the proportion of open landscape within 900 m as well as the distance to urban areas. Incorporating scale optimization in occupancy modelling of camera trap data can greatly improve the understanding of species‐environment relationships by combining the possibility of occupancy models to correct for detection bias and simultaneously allowing to infer the characteristic scale at which certain factors influence the distribution of the organisms studied.

Habitat selection is strongly scale‐dependent, and inferring the characteristic scale at which an organism responds to environmental variation is necessary to obtain reliable predictions. The occupancy framework is frequently used to model species distribution with the advantage of accounting for imperfect observation, but occupancy studies typically do not define the characteristic scale of the modelled variables. We used camera trap data from winter wildlife surveys in the Swiss part of the Jura Mountains to model occupancy of wild boar (Sus scrofa) and roe deer (Capreolus capreolus). We used a three‐step approach: (1) first, we identified factors influencing detectability; (2) second, we optimized the characteristic scale of each candidate explanatory variable; and (3) third, we fit multivariable, multiscale occupancy models in relation to land cover, human presence and topography. Wild boar occupancy was mainly influenced by the interaction between elevation within 2500 m and the proportion of forested areas within a 2500 m, with a nonsignificant additional effect of the interaction between ruggedness within 1900 m and the proportion of forested areas within 2500 m as well as the distance to urban areas. Roe deer occupancy was mainly associated with the interaction between ruggedness within 900 m and the proportion of open landscape within 900 m, with an additional nonsignificant effect of the interaction between elevation within 1500 m and the proportion of open landscape within 900 m as well as the distance to urban areas. Incorporating scale optimization in occupancy modelling of camera trap data can greatly improve the understanding of species‐environment relationships by combining the possibility of occupancy models to correct for detection bias and simultaneously allowing to infer the characteristic scale at which certain factors influence the distribution of the organisms studied. [ABSTRACT FROM AUTHOR]

Context: Species are influenced by factors operating at multiple scales, but multi-scale species distribution and abundance models are rarely used. Though multi-scale species distribution models outperform single-scale models, when compared through model selection, multi- and single-scale models built with computer learning algorithms have not been compared. Objectives: We compared the performance of models using a simple and accessible, multi-scale, machine learning, species distribution and abundance modeling framework to pseudo-optimized and unoptimized single-scale models. Methods: We characterized environmental variables at four spatial scales and used boosted regression trees to build multi-scale and single-scale distribution and abundance models for 28 bird species. For each species and across species, we compared the performance of multi-scale models to pseudo-optimized and lowest-performing unoptimized single-scale models. Results: Multi-scale distribution models consistently performed as well or better than pseudo-optimized single-scale models and significantly better than unoptimized single-scale models. Abundance model performance showed a similar, but less pronounced pattern. Mixed-effects models, that controlled for species, provided strong evidence that multi-scale models performed better than unoptimized single-scale models. Although mean improvement in model performance across species appeared minor, for individual species, arbitrary selection of scale could result in discrepancies of up to fourteen percent for area of suitable habitat and population estimates. Conclusions: Scale selection should be explicitly addressed in distribution and abundance modeling. The multi-scale species distribution and abundance modeling framework presented here provides a concise and accessible alternative to standard pseudo-scale optimization while addressing the scale-dependent response of species to their environment. [ABSTRACT FROM AUTHOR]

ABSTRACT: Fine‐scale knowledge of how anthropogenic effects may alter habitat selection by wolves (Canis lupus) is important to inform conservation management, especially where wolf populations are expanding into more populated areas or where human activity and development are increasingly encroaching on formerly pristine environments. From 1999 to 2003, we documented rendezvous sites (n = 31) used by wolves (n = 6 packs) in a previously unstudied wolf population in the southern Apennines, Italy (Pollino National Park [PNP]), where conditions are ideal to unveil behavioral adaptations of wolves living in human‐dominated landscapes. We adopted a hierarchical, multi‐scale habitat selection approach by using a set of environmental, topographic, and anthropogenic factors within multi‐grain resource selection functions (MRSFs) at the landscape and the territory extent. Habitat selection by wolves was scale‐dependent and different habitat components affected wolves at different grains. When establishing a territory at the landscape scale, wolves avoided areas featuring high densities of humans, paved roads, and trails, and they preferentially located territories where higher forest cover and rough terrain enhanced concealment and ensured reduced accessibility by humans. Concurrently, wolves also selected open areas and, at coarse grain, areas of high density of dirt roads and trails to enhance hunting and traveling efficiency. When selecting rendezvous sites within territories, wolves still selected for forest cover and lessened avoidance of anthropogenic features apparent at the lower order of selection. When choosing rendezvous sites, wolves also avoided low‐use anthropogenic linear features and rough terrain, unveiling trade‐offs in selection decisions across different spatial and temporal scales. Our results reinforce the notion that occurrence of wolves in human‐modified landscapes is shaped by avoidance of anthropogenic pressure, but they also indicate that rendezvous site selection by wolves conform to a hierarchical decision‐making process that is extent‐ and grain‐dependent. Spatially explicit models of rendezvous site selection such as the one we developed for PNP enhance management effectiveness for strategies such as regulating human access and activity during the pup rearing seasons, preventing human persecution, mitigating livestock depredations, and designing cost‐effective monitoring programs. © 2018 The Wildlife Society. [ABSTRACT FROM AUTHOR]

There is lacking of floating wind turbine supporting large wind turbine, i.e. 10 MW, aiming at shallow water applications. In this paper, a 10 MW semi-submersible floating wind turbine named OUCwind is proposed for shallow water, and a scale optimization procedure is proposed during the preliminary design stage. It offers an engineering-practical method to determine the main parameters of a new-proposed floater, and critical floater characteristics, such as floater stability, natural period, platform mass etc. are chosen as important design criteria. The hydrodynamic performance with different scale parameters are investigated and it is found that enlarging distance between columns is a cost-effective way to get better stability and hydrodynamic performance. The structural performance of the new-designed OUCwind floater are compared with other two well-known braceless 10 MW platforms, 10 MW-CSC, and OO-star. From the frequency-domain analysis results, the surge linear transfer function, mean drift force and quadratic transfer function near the surge natural frequency of OUCwind is relatively small compared with other two platforms. In the time domain, the new-designed OUCwind also performs better in surge motion and mooring line tension response compared with 10 MW-CSC and OO-star, especially under extreme condition. Nevertheless, for the heave and pitch motions, the wave-frequency response of the OUCwind platform is relatively large under extreme load conditions. • A 10 MW semi-submersible floating wind turbine named OUCwind is proposed at relatively shallow water depth. • A scale optimization procedure is proposed, which offers a practical method to determine main parameters of a new floater. • The influence of different scale parameters on the hydrodynamic performance of the floater are investigated. • The structural performance of the new-designed floater is compared with other two well-known braceless 10 MW floaters. • The new-designed floater shows good performance in mooring line tension and surge motion, especially under extreme condition. [ABSTRACT FROM AUTHOR]

The mechanical properties of the geo-materials are greatly affected by the internal micro structure system. Multiscale modelling and analysis are considered as an effective tool for revealing the mechanical process. Aiming at the problems of the light intensity decrease and the uneven illumination, a Retinex scale optimized image enhancement algorithm is proposed, based on the light reflection model and Retinex theory. Based on the Retinex theory, a Monte-Carlo random geometric numerical model of the microstructure is established, with some soil specimens in South China as the study object. After that, three-axis consolidation process of soft soil materials are simulated, which are compared with the experimental data. The results showthat: the Retinex scale optimized image enhancement algorithm can accurately estimate the illumination component, eliminate the influence of the uneven illumination, improve the contrast of image and retain the details of the microstructure simultaneously. The random field model with this algorithm is simple and effective, the image becomes clearer, and the contrast ratio is improved, after using Retinex algorithm to enhance the CT image of rock and soil. The threshold segmentation of the enhanced image, and the fidelity between the enhanced image and the original image, are higher than that of the homomorphic filtering method and histogram equalization method, which explained that the algorithm could preserve the microstructure information of the micro image well. The result of numerical simulation is similar with the one obtained from conventional three axis consolidation test, which proves that the simulation result is reliable. [ABSTRACT FROM AUTHOR]

An entirely numerical thermoelectric generator model is formulated by taking the heat transfer and flow resistance characteristics of exhaust fluid with different exchanger scales into consideration. Applied to automotive exhaust heat recovery, this study mainly focused on the effect of exhaust temperature on exhaust exchanger structure optimization, the objective being maximal net power output. Results show that the optimal height was unchanged at different exhaust temperatures, unlike the optimal length and width, which increased and decreased at higher temperatures, respectively. A small TEG module is necessary at high exhaust temperatures. [ABSTRACT FROM AUTHOR]

Context: Livestock predation by tiger and leopard in Bhutan is a major threat to the conservation of these felids. Conflict mitigation planning would benefit from an improved understanding of the spatial pattern of livestock kills by the two predators. Objectives: We aimed to identify the landscape features that predict livestock kills by tiger and leopard throughout Bhutan. Our goals were to: (1) identify the predictors that have the largest influence in determining livestock kills, (2) assess the influence of scale across the different predictors evaluated and identify the scale at which each was most important. Methods: We used livestock kills obtained from compensation records of tiger ( n = 326) and leopard ( n = 377) across Bhutan between 2003 and 2012 to run predation risk models with MaxEnt algorithm, using a multi-scale modeling approach (1, 2, 4, 8 and 16 km). Results: Human-presence (density of settlements and roads) and land-cover (percentage of tree cover and meadow patches) were the main variables contributing to livestock kills by both species. Livestock kills were likely driven by a trade-off between livestock density and predator ecology, and the balance of this trade-off varied with scale. Risk maps revealed different hotspots for tiger and leopard kills, and analysis showed both species preferentially killed equids over other livestock types. Conclusions: Our results highlight the importance of evaluating scale when investigating the spatial attributes of livestock kills by tiger and leopard. Our findings provide guidance for reducing conflict between humans and large felids throughout the country. [ABSTRACT FROM AUTHOR]

Cable-driven parallel robots (CDPRs) are gaining increasing attention due to their low weight, cost, and power consumption characteristics. Adaptive CDPRs are special cable-driven systems in which the locations of the pulley blocks can be modified. In this paper, we propose a new type of adaptive CDPR that can passively modify the attachment points on the end-effector (moving platform) without augmented kinematic redundancy, but this depends on the cable wrapping over these adaptive attachment points. We demonstrate the proposed approach on some practical cases of planar adaptive CDPRs to expand the workspace and dexterity of the robot especially for the large rotation capability. The proposed robot can be passively reconfigured to several configurations. The kinematics modeling and static equilibrium of the adaptive CDPRs are established. Scale optimization of the adaptive attachment point is performed. The results show that this design can effectively increase the workspace, especially the rotation workspace, without increasing the actuation redundancy and kinematic redundancy. • An adaptive cable-driven parallel robot (CDPR) with modified attachment points on the end-effector was proposed. • The adaptive CDPR modifies attachment points on the end-effector without increasing actuation or kinematic redundancy. • Improvement in dexterity and workspace compared a normal three-degree-of-freedom planar CDPR. [ABSTRACT FROM AUTHOR]

Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails (Rallus obsoletus yumanensis) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems. [ABSTRACT FROM AUTHOR]