Multiple Reference Points Research Articles

Multi-species cryoEM calibration and workflow verification standard.

Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. Here, a cryoEM calibration sample consisting of a mixture of compatible macromolecules is introduced that can not only be used for resolution optimization, but also provides multiple reference points for evaluating instrument performance, data quality and image-processing workflows in a single experiment. This combined test specimen provides researchers with a reference point for validating their cryoEM pipeline, benchmarking their methodologies and testing new algorithms.

A newsvendor model with multiple reference points: Target-setting for aspirational newsvendors

Prospect theory posits that the determination of an outcome as a gain or loss hinges upon the reference points, thereby exerting a substantial influence on the decision-making processes of individuals. These reference points can encompass both external targets and internal aspirations (self-goals), forming two potential candidates. Despite a growing body of evidence showcasing the concurrent impact of multiple reference points on decision-makers’ choices, scant attention has been accorded to this phenomenon within the realm of operations management literature. In light of this, the present study delves into the influence wielded by an external target, functioning as a reference point, on the decisions and profitability of newsvendors who also harbor a well-defined aspiration (self-goal) reference point. The study introduces two distinct archetypes of newsvendors: the balanced newsvendors, adept at harmonizing personal needs with target attainment and thus navigating decisions influenced by dual reference points; and the focused newsvendors, singularly fixated on target realization and thereby governed by a sole target reference point. Our findings reveal that the effects of target-setting in boosting expected profits on both categories of newsvendors are contingent upon the specific value attributed to their self-goal. The implications of target-setting can either yield advantages or drawbacks, contingent upon the self-goal. This asymmetry in outcomes holds true for both balanced and focused newsvendors, with the former exhibiting a higher likelihood of deriving benefits from target-setting. Moreover, an exploration of scenarios wherein a newsvendor’s self-goal may undergo updates underscores the potential for further enhancement in the performance of balanced newsvendors.

Multi-species cryoEM calibration and workflow verification standard.

Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. We introduce a cryoEM calibration sample that is a mixture of compatible macromolecules that can be used not only for resolution optimization but also provides multiple reference points for evaluating instrument performance, data quality, and image processing workflows in a single experiment. This combined test specimen provides researchers a reference point for validating their cryoEM pipeline, benchmarking their methodologies, and testing new algorithms.

An absolutely convergent fixed-point fast sweeping WENO method on triangular meshes for steady state of hyperbolic conservation laws

High order accuracy fast sweeping methods have been developed in the literature to efficiently solve steady state solutions of hyperbolic partial differential equations (PDEs) on rectangular meshes, while they were not available yet on unstructured meshes. In this paper, we extend high order accuracy fast sweeping methods to unstructured triangular meshes by applying fixed-point iterative sweeping techniques to a fifth-order finite volume unstructured WENO scheme, for solving steady state solutions of hyperbolic conservation laws. Similar as other fast sweeping methods, fixed-point fast sweeping methods use the Gauss-Seidel iterations and alternating sweeping strategy to cover characteristics of hyperbolic PDEs in each sweeping order to achieve fast convergence rate to steady state solutions. An advantage of fixed-point fast sweeping methods which distinguishes them from other fast sweeping methods is that they are explicit and do not require inverse operation of nonlinear local systems. This also provides certain convenience in designing high order fast sweeping methods on unstructured meshes. As in the first order fast sweeping methods on triangular meshes, we introduce multiple reference points to determine alternating sweeping directions on unstructured meshes. All the cells on the mesh are ordered according to their centroids' distances to those reference points, and the resulted orderings provide sweeping directions for iterations. To make the residue of the fast sweeping iterations converge to machine zero / round off errors, we follow the approach in our early work of developing the absolutely convergent fixed-point fast sweeping WENO methods on rectangular meshes, and adopt high order WENO scheme with unequal-sized sub-stencils, specifically here a fifth-order finite volume unstructured WENO scheme for spatial discretization. Extensive numerical experiments, including problems with complex domain geometries, are performed to show the accuracy, computational efficiency, and absolute convergence of the presented fifth-order fast sweeping scheme on triangular meshes. Furthermore, the proposed method is compared with the forward Euler time marching method and the popular third order total variation diminishing Rung-Kutta (TVD-RK3) time-marching method for steady state computations. Numerical examples show that the developed fixed-point fast sweeping WENO method is the most efficient scheme among them, and especially it can save up to 70% CPU time costs than TVD-RK3 in order to converge to steady state solutions.

Multi-criteria techniques based proposals for the End of Childhood Index: Reference levels and compensation issues

Child well-being is a subject of paramount importance, since a careful analysis of all the aspects related to childhood may have a major impact on the society as a whole. In this paper, based on the End of Childhood Index, which is a fully compensatory composite indicator published annually by the international organisation Save the children, we analyse the added information that can be provided through the use of multi-criteria analysis techniques. On the one hand, rather than analysing the overall performance of the countries by the use of the fully compensatory scenario, we make use of the international reference levels and performance bands provided by Save the Children itself, allowing to measure the distance of each country with respect to these levels. Besides, the use of a non-compensatory scenario offers interesting insights about the possible imbalances of each country, which is helpful in decision making processes. First, based on the data of the last available year, 2021, an overview of the worldwide analysis and a further detailed single region analysis is carried out. Second, a dynamic analysis over a period of five years (2017–2021) is undertaken. In this line, in order to guide childhood decision makers towards the identification of possible opportunities for improvement and the implementation of the corresponding action plans, the multiple reference point technique makes it possible to analyse the compliance in each one of the indicators, based on the use of distance reference levels and the joint consideration of a fully and non-compensatory composite indicators.

A novel mathematical model for the accurate measurement of face gears by considering the geometric deviations of multiple teeth

In contact coordinate measurement, measurement error sources would affect the establishment of the workpiece coordinate system (WCS) and the exact measurement for the points on the complicated surfaces. This issue would be more complicated for face gears with complex features, especially when having huge machining errors. To address the above problem, this paper proposes a novel precise mathematical model to measure face gears by considering the geometric deviations of multiple teeth, including the deviations of tooth surfaces, pitch, and tooth thickness. An iterative method is presented to establish the WCS on machines by considering multiple reference points on different teeth. Subsequently, the correction model of measured points is established, and the sensitivity analysis is conducted. Further, a multi-objective optimization is proposed to comprehensively compensate for measurement errors, and the algorithm is given. The experiment of a face gear with huge overcut tooth surfaces validates the proposed method is effective and robust.

Sustainability of European agri-food supply chain using MRP-PCI multicriteria analysis method

The need for a more sustainable agri-food system is a topic that has attracted growing interest in recent years. Several international and European policies such as Agenda 2030 and the European Green Deal have been defined with the aim of making agri-food systems more sustainable at all stages of the supply chain, from production to consumption. Particularly, the European Union concentrates several policies on it. Therefore, the assessment of the level of sustainability among the states of the European Union is a key aspect to properly address and evaluate the implementation of these policies. The objective of this paper is to measure the sustainability of the global agri-food supply chain (AFSC) of the European Union countries through the application of a multi-criteria analysis. In particular, the method used is the Multiple Reference Point Partially Compensatory Indicator, which allows the creation of composite indicators using different levels of compensation across them. A set of 50 indicators, referred to 2011 and 2019, were built and then divided into the three basic dimensions of sustainability (economic, social and environmental), aggregated into the four main AFSC sectors (agriculture, food industry, distribution, and consumption) in order to obtain an overall sustainability index. Through such an index we provided a sustainability ranking for the EU countries, while the analysis of dimensions of each sector contributed to increase the knowledge about the supply chain that can be used by decision-makers. According to the results, Italy achieved the best level of sustainability of the AFSC with a value of 48.53, followed by Sweden, Austria, Spain, France, Germany, the Czech Republic, Portugal and Slovakia. In relation to the different sectors, the biggest problems were observed in the consumption sector, where most countries did not perform well, especially for the social dimension (e.g. excess of overweight and obese persons). In contrast, the performance of the agricultural sector was good, with few exceptions, showing a fair state of sustainability.

Automatic Fingerprint Data Labeling Using WiFi Signal and Smartphone Camera for Indoor Positioning

WiFi fingerprinting has been one of the most practical approaches for implementing an indoor positioning system. However, the need to measure location labels for fingerprint data has hindered the deployment of WiFi fingerprint‐based positioning systems. To reduce the cost of location labeling, several methods for automatically labeling locations or bypassing location labeling have been proposed. However, these methods still need to measure location labels for partial fingerprint data, or the estimated location labels are inaccurate. In this paper, we propose Heimdall, an automatic fingerprint data labeling method using a smartphone camera and WiFi signal for an indoor positioning system. Since Heimdall utilizes camera geometry for accurate automatic labeling, it aims to find the location labels of unlabeled fingerprint data composed of WiFi received signal strengths (RSSs) and photos collected using smartphones in multiple reference points (RPs). Using structure from motion (SfM) with all photos in the unlabeled fingerprint dataset, the location and orientation of the cameras can be recovered on an arbitrary world coordinate system. However, the world coordinate system derived by SfM is warped. In other words, it is different from the real‐world coordinate system where we express the location labels since SfM has similarity ambiguity, including scale, rotation, and translation ambiguity. Heimdall finds the location labels by estimating and applying an unwarping transformation that removes those ambiguities. We use two types of geometric constraints for estimating the unwarping transformation. First, several geometric entities in the warped‐world coordinates and real‐world coordinates are extracted from the photos and prior information about the indoor layout. The unwarping transformation transforms the geometric entities in warped coordinates to those in real‐world coordinates. Second, we utilize the log‐distance pathloss model with RSSs to derive the constraint about distances from the location label to the access points. We construct the optimization problem subject to those constraints to estimate the unwarping transformation. To evaluate the performance of Heimdall, we performed several experiments. Heimdall yields a mean location labeling error of 0.12 m and a standard deviation of location labeling error of 0.06 m for the best case in the experiment. These results demonstrate that a fingerprint‐based indoor positioning system can use the location labels estimated by Heimdall without performance degradation.

QUICK PROTOCOL FOR INTEGRATING THE ATTRIBUTE INFORMATION OF UNSTRUCTURED POINT CLOUD DATA INTO A SOLAR ENVELOPE SIMULATION

ABSTRACT This study proposes a novel method of solar geometry by considering the potential application of point cloud data combined with the simulation of solar radiation. With the support of geometric and radiometric information stored in the point cloud such as position information (XYZ) color information (RGB), and reflection intensity (I), architects may compensate for missing information on the existing context during the simulation, especially due to the limited capacity of current 3D modelling sites. However, the dataset often comes in the format of unstructured point cloud data retrieved from merged data scans and as a result, the radiometric information is difficult to occupy due to multiple reference points. Through a 3D subtractive procedure, this study not only examines volumetric samples of the three-dimensional matrix that fulfills the criteria of solar envelopes but also finds the optimal values of the merged data scan for input of solar radiation. In this regard, simulation of solar radiation contributes to identifying the most and the least exposed areas to the sun in existing contexts. This provides information related to visible sun hours that can be used to perform ray tracing analysis between the proposed 3D plot and surrounding contexts. Our proposed method ultimately helps architects not only generate solar geometry based on real contextual settings but also to understand comprehensively the microclimate conditions of the design context.

Consensus based phase connectivity identification for distribution network with limited observability

The mitigation of distribution network (DN) unbalance and the use of single-phase flexibility for congestion mitigation requires accurate phase connection information, which is often not available. For a large DN, the naïve phase identification proposed in the majority of the prior works using a single voltage reference, this does not scale well for a multi-feeder DN. We present a consensus algorithm-based phase identification mechanism which uses multiple three-phase reference points to improve the prediction of phases. Due to the absence of real measurements for a real-suburban German DN, the algorithms are developed and evaluated over synthetic data using a digital twin. To utilize strongly correlated measurements, the DN is clustered into zones. We observe those reference measurements located in the same zone as the single-phase consumer, with unknown phase connectivity, leads to accurate prediction of DN phases. Four consensus algorithms are developed and compared. Using numerical results, we recommend the most robust phase identification mechanism. In our evaluation, measurement error, and the impact of the neutral conductor are also assessed. We assume limited DN observability and apply our findings to a German DN without smart meters, but only less than 8% of nodes have measurement boxes along with single-phase consumers with a home energy management system. Voltage time series for 1 month (hourly sampled) is utilized. The numerical results indicate that for 1% accuracy class measurement, the phase connectivity of 308 out of 313 single-phase consumers in a German DN can be identified. Further, we also propose metrics quantifying the goodness of the phase identification. The phase identification framework based on consensus algorithms for DN zones is scalable for large DN and robust towards measurement errors as the estimation is not dependent on a single measurement point.

Unsupervised methods for size and shape

The aim of this article is to propose unsupervised classification methods for size-and-shape considering two-dimensional images (planar shapes). We present new methods based on hypothesis testing and the K-means algorithm. We also propose combinations of algorithms using ensemble methods: bagging and boosting. We consider simulated data in three scenarios in order to evaluate the performance of the proposed methods. The numerical results have indicated that for the data sets, when the centroid sizes change, the performance of the algorithms improves. In addition, bagging-based algorithms outperform their basic versions. Moreover, two real-world datasets have been considered: great ape skull and mice vertebrae references. These datasets have different configurations, such as multiple reference points and variability. Bagged K-means and boosted K-means methods achieved the best performance on the datasets. Lastly, considering the synthetic and real data, the bagged K-means proved to be the best method.

A unified theory of discrete and continuous responding.

Understanding the cognitive processes underlying choice requires theories that can disentangle the representation of stimuli from the processes that map these representations onto observed responses. We develop a dynamic theory of how stimuli are mapped onto discrete (choice) and onto continuous response scales. It proposes that the mapping from a stimulus to an internal representation and then to an evidence accumulation process is accomplished using multiple reference points or "anchors." Evidence is accumulated until a threshold amount for a particular response is obtained, with the relative balance of support for each anchor at that time determining the response. We tested this multiple anchored accumulation theory (MAAT) using the results of two experiments requiring discrete or continuous responses to line length and color stimuli. We manipulated the number of options for discrete responses, the number of different stimuli, and the similarity among them, and compared the outcomes to continuous response conditions. We show that MAAT accounts for several key phenomena: more accurate, faster, and more skewed distributions of responses near the ends of a response scale; lower accuracy and slower responses as the number of discrete choice options increases; and longer response times and lower accuracy when alternative responses are more similar to the target response. Our empirical and modeling results suggest that discrete and continuous response tasks can share a common evidence representation, and that the decision process is sensitive to the perceived similarity among the response options. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An Efficient Method for Three-Dimensional Precise Measurement Using Laser Spot Traversal and Target Point Interpolation

Discrete point interpolation has emerged as a prime candidate technology for dual-station accurate intersection in 3-D precise measurement. However, the operation is low-efficiency for capturing multiple reference points to measure a single target point. The quantitative model of linear small angle in the interpolation is proposed by analyzing the movement trajectories of laser beam and laser spot. An efficient method for 3-D precise measurement using laser spot traversal and target point interpolation is proposed to achieve accurate intersection without multiple approximations. The laser spot traverses the field of view of the camera to establish an interpolation grid. The target point inserts the traversal grid and can be aimed by laser beam in arbitrary position. The traversal step size is determined by the determining equation of linear small angle. The approach is time-efficient for capturing all measured points in the field of view. The experimental results show that the proposed method is suitable for multiple points and surface measurement.

Monte Carlo energy spectrum matching method for SiPM-based EJ254 plastic scintillator detector calibration

Accurate energy measurement is the precondition for the application of plastic scintillator-based spectrometers in space exploration missions, and appropriate energy calibration methods are very important for plastic scintillators. At the same time, the energy resolution is a key factor in evaluating the performance of spectrometers. In this paper, an energy calibration method that can be used for plastic scintillators is proposed, and the energy resolution calibration results were also obtained in the process. The key of the method is to focus on the accurate energy prediction of the reference points in the Compton Continuum by using the Monte Carlo energy spectrum matching method. In the calibration process, the experimental measurements and Monte Carlo simulations of 22Na and 137Cs were carried out, and the shapes of the obtained spectra were matched by a certain energy resolution model. By shape matching, the Monte Carlo simulations provided energies of reference points, and the experiments provided their corresponding channel numbers. Then the parameters of the energy calibration model were obtained by fitting with multiple reference points’ paired data. Moreover, the backscattering experiments with 131I, 137Cs, and 232Th sources were executed to equivalently provide monoenergetic electrons to verify the energy calibration and energy resolution model parameters obtained for the EJ254 spectrometer. The accuracy was high in the energy range of 341∼2381.6 keV, the maximum bias of the energy was −4.7 keV (@2381.6 keV), and the maximum bias of the energy resolution model was 4.7% (@341 keV).

Tiered discounts as multiple reference points for spending

AbstractTiered discounts offer larger discounts as consumers meet higher spending thresholds (e.g., spend $100+, receive 10% off; spend $200+, receive 20% off). This research investigates how consumers treat these multiple dollar thresholds as reference points for spending. We find that tiered discounts with smaller increments between thresholds encourage higher spending compared to those with larger increments. This effect occurs because consumers treat thresholds as motivational spending goals when the distance to higher thresholds is smaller (vs. larger). Consistent with this reasoning, signaling goal progress (i.e., displaying cart amount while shopping) attenuates the spending difference smaller versus larger increment sizes yield. Additionally, the effect of tier increment size on spending is more prominent for maximizers. From a theoretical perspective, this work contributes to our understanding of how individuals process multiple reference points within a single promotion and identifies that spending thresholds in price promotions may be treated as spending goals. From a managerial perspective, this work investigates the relationship between tiered discount design and consumer spending.

Third harmonic potential distribution based stator ground fault accurate location method for large generators

The stator single-phase grounding fault location of large generator can provide guidance for rapid maintenance of faults. A third harmonic potential distribution based stator ground fault accurate location method is proposed. The current location methods mainly take coil as location unit and may have location error. However, accurate locate to the slot number is more meaningful for troubleshooting. In addition, the third harmonic potential induced by adjacent slots has larger phase difference, and has more distinct fault characteristics than the fundamental potential. In this paper, the fault location optimization model is constructed based on the analysis of the distribution characteristics of the third harmonic potential. The exhaustive solution of the location model is realized by artificially setting multiple reference points, and finally the reference point closest to the real fault point is obtained. The proposed method can accurately locate the fault slot number. PSCAD simulation verifies that the proposed method is robust against the transition resistance error and has higher accuracy than the existing methods.

An extended best–worst multiple reference point method: application in the assessment of non-life insurance companies

In this paper a multi-criteria decision-making (MCDM) method is developed to rank a set of insurance companies. The proposed method is based on combining two MCDM methods: Extended Best–Worst (EBW) and Multiple Reference Point (MRP) methods. We formulate the problem of finding a priority vector from a set of interval pairwise comparisons applying an EBW method which allows the decision maker (DM) to use interval values in order to describe the relative importance of one criterion over another. The EBW method, using fuzzy set theory, can successfully handle the vagueness and ambiguity present in the judgments. Lastly, the MRP method is employed to obtain an overall score for each company using the weights established at the first stage. A case study is presented to rank Spanish non-life insurance companies based on the constructed model. Since the evaluation of insurance companies involves a great number of indicators, it is a complex MCDM issue. The results show the effectiveness of the proposed method and offer an insightful reference for an evaluation of the insurance industry.

Inconsistent Performance Feedback and Firm Internationalization: Can CEO Remove the Haze?

Research on performance aspiration argues that firms use multiple reference points, historical and social aspirations, to compare their performance. Consequently, these performance aspirational points sometimes time to inconsistent feedback – negative correlation between performance relative to historical and social aspiration – pose challenges for the decision-makers. We argue that the inconsistent performance feedback will lead to dissimilar behavior for firms with overlapping ownership and management configuration. We examine the how moderating role of different bases of chief executive officers' (CEO) power in the relationship between inconsistent performance feedback and internationalization. We tested our hypothesis in the context of internationalization within Indian family firms from 2008-2015. We find that CEOs' structural, ownership and prestige power increases reluctance for internationalization due to inconsistency in performance feedback whereas CEO expert power based on international experience has a positive effect on family firm internationalization.

SERS and Fluorescence-Active Multimodal Tessellated Scaffolds for Three-Dimensional Bioimaging.

With the ever-increasing use of 3D cell models toward studying bio-nano interactions and offering alternatives to traditional 2D in vitro and in vivo experiments, methods to image biological tissue in real time and with high spatial resolution have become a must. A suitable technique therefore is surface-enhanced Raman scattering (SERS)-based microscopy, which additionally features reduced photocytotoxicity and improved light penetration. However, optimization of imaging and postprocessing parameters is still required. Herein we present a method to monitor cell proliferation over time in 3D, using multifunctional 3D-printed scaffolds composed of biologically inert poly(lactic-co-glycolic acid) (PLGA) as the base material, in which fluorescent labels and SERS-active gold nanoparticles (AuNPs) can be embedded. The combination of imaging techniques allows optimization of SERS imaging parameters for cell monitoring. The scaffolds provide anchoring points for cell adhesion, so that cell growth can be observed in a suspended 3D matrix, with multiple reference points for confocal fluorescence and SERS imaging. By prelabeling cells with SERS-encoded AuNPs and fluorophores, cell proliferation and migration can be simultaneously monitored through confocal Raman and fluorescence microscopy. These scaffolds provide a simple method to follow cell dynamics in 4D, with minimal disturbance to the tissue model.

Using accounting dataset for agricultural sustainability assessment through a multi‐criteria approach: an Italian case study

AbstractSustainability has become a key issue for agricultural systems. In this context, sustainability assessment implies considering different aspects of agricultural activities, including environmental, economic and social issues, and it is usually based on practice‐based indicators. However, proposed methods and applications are still rare due to the difficulty to build a strong general framework and to find appropriate data. The present work develops a performance indicator framework using farm accounting data from the Italian Agricultural Accounting Information Network and proposes a multi‐criteria approach based on the multiple reference point weak‐strong composite indicators (MRP‐WSCI) method to assess the degree of sustainability at the farm level. This method, widely applied to sustainability assessment, calculates the relative rankings including the weak and strong approaches to sustainability and facilitates an understanding of the different indices used, but it is still unused in the agricultural sector. The proposed evaluation framework shows that MRP‐WSCI can handle a holistic set of indicators and rank the farms according to their level of sustainability, making it suitable for the agricultural farm sustainability assessment. Our results show that none of the farms in the case study sample can be classified as “good,” according to some of the indices produced, that is, the strong–strong and strong–weak index, while just two of them are good for the weak–weak index. Although the size of the sample used is small, the methodological approach proposed can be straightforwardly extended to larger samples. This methodology can provide information regarding the sustainability of farms to regional and/or national agricultural offices, agricultural extension workers, and farmers themselves, helping in understanding issues in specific dimensions.