Ideal Point Research Articles

Contextual Fear Conditioning Uncovers Spine and Learning Deficits in the Retrosplenial Cortex in a Familial Alzheimer’s Disease Mouse Model

While excitatory synapse loss is documented in the brains of Alzheimer’s Disease (AD) patients, its role in episodic learning decline, one of the first evident AD symptoms, is unclear. The retrosplenial cortex (RSC), a cortical structure required for episodic learning, provides an ideal entry point to study the role of synapse loss in cognitive decline in AD since it is a site of high amyloid load and dysfunctional early in AD progression. Further, excitatory synapse assembly, both turnover and clustered synapse formation, is highly correlated with episodic learning performance in the RSC. We hypothesize alterations in synapse assembly in the RSC of AD patients contribute to early cognitive decline in the disease. To address this hypothesis, we examined both excitatory synapse density in the RSC and contextual learning in a familial AD (fAD) mouse model, an early onset amyloid model of AD. Importantly, we find age-dependent excitatory synapse loss in the RSC of fAD mice, as measured by imaging and quantification of GFP-labeled dendritic spines. Further, prior to frank spine loss in 5-month-old fAD mice we found episodic learning deficits, as measured by contextual fear conditioning (CFC). Using this early time point, we examined spine dynamics in vivo with MP imaging in the RSC of fAD mice or controls while engaged in CFC learning. Interestingly, we find deficits both in spine turnover and clustered spine formation and a loss of correlations of these spine metrics with CFC performance, suggesting aberrant spine dynamics may be causal in episodic learning deficits in this AD model, consistent with our hypothesis. Future studies will pharmacologically target these aberrant spine dynamics in fAD mice in an effort to strengthen the causal link between alterations in synapse assembly and cognitive decline in AD as well as provide potential therapeutic approaches to reverse early stages of the disease.

Estimating Ideal Points of British MPs Through Their Social Media Followership

Abstract Ideal points of MPs in the UK House of Commons (HoC) are characteristically difficult to ascertain due to tight party discipline and strategic voting by opposition members. This research note generates left/right ideal point estimates for 591 British MPs sitting in the HoC as of 22/08/2022, ascertained through their social media followership. Specifically, estimates are derived by conducting correspondence analysis (CA) on MP Twitter (X) follower networks, which are subsequently validated against an expert survey, confirming that these estimates have a high degree of between-party (R2 = 0.93) and within-party (Con: r = 0.84; Lab: r = 0.81) accuracy. The informative value of these estimates is then demonstrated by predicting candidate endorsement in the September 2022 Conservative leadership contest, confirming that an MP's ideal point was a statistically significant predictor of candidate endorsement, with Liz Truss drawing support primarily from the further right of the party.

Optimal design of solar collector network in novel hybrid desalination plant

In this study, a new hybrid system for producing fresh water from seawater using solar energy is proposed. It suggests a modified and optimized approach to reduce the reliance on fossil fuels and decrease pollutants by also harnessing solar energy. The system includes flat-plate solar collectors, a multi-effect distillation through thermal vapor compression (MED-TVC) desalination unit, a pump, and a boiler. The flow rate of the produced fresh water and the specific cost of generating fresh water volume were considered objective functions and the system was optimized using as evolutionary algorithm. The algorithm was employed to determine 12 design variables. Series, parallel, and series-parallel configurations were examined as layouts for flat plate collectors. The results indicated that the series-parallel arrangement was the most effective layout for the collectors. The modeling was carried out for the city of Bandar Abbas in Hormozgan Province, Iran. The production of fresh water in series-parallel mode has increased by 12.15% and 27.83%, respectively, compared to the parallel and series configurations at the ideal point. An assessment of the results suggests that choosing the design point within the flow rate range of 4.19 L/s to 4.65 L/s is preferable.

Influence of cusp-shaped magnetic fields on plasma density and thrust in an RF plasma thruster with a magnetic nozzle

In a radiofrequency (RF) plasma thruster device utilizing a cusp-shaped magnetic field, we investigate the dependence of plasma parameters on operational conditions. Among the conditions, this study focused on the cusp-field condition and found that the axial profiles of the plasma parameters vary depending on the field conditions. The plasma density profile is affected by the distance between the cusp point and the position of the RF antenna. When the cusp strength increases, the cusp condition enhances plasma density and the total thrust, which is the sum of the thrust components of the electron static pressure and diamagnetic current. We propose an ideal cusp point concerning the antenna position for optimal performance in the thruster device. This paper highlights how cusp-shaped magnetic fields influence electron dynamics as an operational index of the RF plasma thruster with a magnetic nozzle.

Point spread function engineering enable resolution enhanced imaging for Interferenceless coded aperture correlation holography

The point spread function (PSF) of an optical system could characterize the resolving ability of the whole optical system for point light sources. Therefore, the imaging performance of the system could be significantly improved by regulating and optimizing the PSF. In this paper, we innovatively propose a single-exposure hologram resolution enhanced cross-correlation (RECC) method for Interferenceless coded aperture holography(I-COACH) system, circumventing the necessity to obtain the point spread hologram (PSH) of an ideal point object. The RECC method firstly acquires an approximate image of a large-size point object by Lucy-Richardson (LR) algorithm in lens imaging mode, and takes it as a PSF to acquire a PSH with ideal size of the I-COACH system by LR algorithm again, and finally acquires a reconstructed image by the single-exposure hologram RECC method. In the RECC method, the approximate ideal PSHs at different axial positions of the system are acquired by offline operation, therefore, it has a high imaging temporal resolution, and the imaging transverse resolution is not affected by the size of the point objects at the time of recording the PSH, which provides a high imaging signal-to-noise ratio and stable resolution. The proposed method provides powerful technical support for further extending the application field of the I-COACH system, and provides technical reference for other incoherent imaging.

Intelligent parking lot power management: Augmented epsilon‐constraint concept with correlation analysis

AbstractThis paper addresses essential aspects of decision‐making and management in energy resources. To achieve this, a tri‐objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants’ output in the proposed multi‐objective optimization problem as a scenario‐based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load‐shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug‐in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83‐bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi‐objective problem defined for each scenario is solved by the augmented epsilon‐constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision‐making method.

Measuring the impact of laser relative intensity noise on heterodyne interferometers using differential wavefront sensing

Laser-intensity fluctuations cause undesired phase noise even in balanced heterodyne interferometers. However, in space missions in particular, such as LISA Pathfinder or LISA, direct measurements of these fluctuations at the relevant frequencies are often not available. Hence, it can be challenging to estimate their impact on the interference phase. To address this, we propose a new method for characterizing laser relative intensity noise (RIN) using differential wavefront sensing (DWS), with the latter being a well-established technique typically used for angular sensing and control. Unlike other methods, this approach does not require an additional reference interferometer and instead takes advantage of the inherent phase subtraction of DWS. This allows us to estimate the RIN value at the heterodyne frequency and its harmonic, relative to the sensor noise floor of the total measurement system. Moreover, it provides a strategy to identify the ideal set point for minimizing RIN couplings in DWS. Published by the American Physical Society 2024

Evaluation system of vocational education construction in China based on linked TOPSIS analysis

In response to the task of vocational education reform in the new era, in order to cultivate innovative skilled talents, it is necessary to improve traditional evaluation methods to enhance their efficiency. This study proposes an evaluation model for vocational education construction that combines grey relational analysis and approximate ideal point method (TOPSIS). By selecting indicators and combining subjective analytic hierarchy process and objective entropy weight method for weight allocation, the weights of the indicators are determined using subjective analytic hierarchy process and objective entropy weight method. Through experimental analysis, the results showed that among the 11 analysis indicators, the weight of policy environment indicators was the highest at 0.124, while the weight of employment rate and contract signing rate indicators was the lowest at 0.050. The above results indicate that the TOPSIS comprehensive evaluation system based on grey correlation analysis has better application effects in practical scenarios and is more suitable for the development requirements of vocational education.

Comparative and optimal study of energy, exergy, and exergoeconomic performance in supercritical CO2 recompression combined cycles with organic rankine, trans-critical CO2, and kalina cycle

The bottom cycle in supercritical carbon dioxide (sCO2) recompression Brayton combined cycle (RCBC) effectively recovers substantial waste heat for electricity generation, enhancing energy utilization. The recovery efficiency is significantly influenced by the bottom cycle configurations and specific working conditions. Besides, implementing these bottom cycles introduces challenges related to increased system dimensions and design complexity. The present study aims to understand and evaluate the characteristics and optimal trade-offs of various bottom cycles, including trans-critical, subcritical, and non-azeotropic mixed working fluids, by establishing 3E (energy, exergy and Exergoeconomic) models for sCO2/tCO2, sCO2/ORC, and sCO2/KC combined cycle systems. To analysis and enhance interpretability, statistical Global Sensitivity Analysis (GSA) alongside unsupervised learning-based Self-Organizing Map (SOM) techniques and parametric analysis are employed. These methods identify key parameters and discern system patterns. Subsequently, the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) and entropy weight-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are applied to establish a Pareto-optimal decision space and identify compromised ideal design points for each combined cycle. The results quantitatively rank sensitivity for objective variables within the design space in different combined cycles and visually represent system nonlinearity and coupling relationships in 2D weight planes using SOM. The TOPSIS trade-off points based on the Pareto frontier for the three combined cycles are ηth = 45.08 %, cp,tot = 8.5199 $/GJ, ηth = 45.13 %, cp,tot = 8.3739 $/GJ, and ηth = 48.97 %, cp,tot = 8.4783 $/GJ, respectively. Integrating machine learning techniques provides a comprehensive understanding of system patterns, offering valuable insights for decision-makers in the design and optimization of combined cogeneration cycles.

Analysis of the ‘Good’ performance indicators of Non-Governmental Development Organizations

The current economic and political crisis has brought about a change in the environment in which Non-Governmental Development Organizations (NGDOs) have traditionally operated. This change can be summed up as a reduction in the funds received accompanied by an increase in the population they must serve. It is thus important to possess mechanisms that allow an analysis of the excellent work performed by NGDOs. A knowledge of the NGDOs’ efficiency in managing previous projects can contribute to improving their future achievements. This research aims to establish some objective indicators that permit an evaluation of the efficiency of these organizations. Firstly, a detailed analysis of the Spanish Agency for International Development Cooperation (AECID) regulation is conducted. This allows us to synthesise the indicators of a good performance by an NGDO based on a study of the eligibility criteria of public donors. The study concludes that the internal good practices criteria of the NGDOs of the AECID can be grouped into four criteria: experience, transparency and internal operations, human resources, and financial resources. Subsequently, a global performance measure of the NGDOs is built using a multi-criteria decision-making approach that minimises the distance from an ideal point whilst maximising the distance from an anti-ideal point. Our approach has been applied in the evaluation of 20 Spanish NGDOs.

Experiences of patients with hard-to-heal wounds: insights from a pilot survey.

To learn about the experiences of people who seek treatment for hard-to-heal wounds, we distributed a nationwide pilot survey, asking questions about the nature of their wound, how it shaped their daily lives, pathways to receiving care and experiences with treatment. The long-term objective is to quantify the journey of patients with hard-to-heal wounds to identify ideal intervention points that will lead to the best outcomes. This article summarises the findings, implications, limitations and suggestions for future research. Qualitative data were self-reported from patients with hard-to-heal wounds (open for ≥4 weeks) in a pilot chatbot survey, (Wound Expert Survey (WES)) provided online in the US on Meta platforms (Facebook and Instagram) between 2021 and 2022. The US national pilot survey attracted responses from 780 patients, 27 of whom provided a video testimonial. Some 57% of patients delayed treatment because they believed their wound would heal on its own, and only 4% saw a wound care specialist. Respondents reported the cost of care as the most frequent reason for not following all of a doctor's treatment recommendations. Queries regarding quality of life (QoL) revealed that more than half (65%) said they have negative thoughts associated with their wound at least every few days. Some 19% of respondents said their wound had an odour and, of them, 34% said odour had a major or severe negative impact on their self-confidence. Economically, nearly one-quarter of respondents said having a wound led to a drop in their total household income and 17% said their wound led to a change in their employment status. A national pilot survey of patients with hard-to-heal wounds revealed that many delay seeking professional assistance and only a small minority see a wound care specialist. Experiencing an ulcer, even for a few months, can have significant negative effects on a patient's QoL. Patients frequently had negative thoughts associated with their wound, and odour compounded these negative effects, leading to major or severe negative impacts on self-confidence. Households experienced a decline in income, due to both the direct reduction or loss of patient employment and the additional time spent by family members assisting in patient recovery. Thus, a variety of factors contribute to poor outcomes for patients with hard-to-heal wounds. To validate and extend these preliminary results, future surveys of patients with hard-to-heal wounds should focus on additional reasons patients do not seek professional help sooner. To improve health outcomes and QoL, assessment of patient socioeconomic variables should occur whenever wound closure stalls.

Causal completion of globally hyperbolic conformally flat spacetimes

In their paper Geroch, Kronheimer, and Penrose, “Ideal points in space-time,” [Proc. R. Soc. London, Ser. A 327, 545–567 (1972)] introduced a way to attach ideal points to a spacetime M, defining the causal completion of M. They established that this is a topological space which is Hausdorff when M is globally hyperbolic. In this paper, we prove that if, in addition, M is simply-connected and conformally flat, its causal completion is a topological manifold with boundary homeomorphic to S × [0, 1] where S is a Cauchy hypersurface of M. We also introduce three remarkable families of globally hyperbolic conformally flat spacetimes and provide a description of their causal completions.

Improvement of brown yogurt quality by Lactiplantibacillus plantarum S58 and oat β-glucan: Physicochemical properties, sensory quality, and metabolic changes

The flavor quality of brown yogurt is affected by the different fermentation methods. In this study, we investigated the effects of probiotics (Lactiplantibacillus plantarum S58), prebiotics (oat β-glucan), and synbiotics (Lactiplantibacillus plantarum S58 combined with oat β-glucan) on physicochemical properties, consumer acceptance, flavor substances, and metabolites of brown yogurt. The results showed that the addition of probiotics and synbiotics significantly shortened the curdling time of brown yogurt (30 min and 40 min, respectively), and synbiotics improved the physicochemical properties and flavor components of the yogurt. Compared with the control group, the volatile flavor substances in the synbiotic yogurt, such as acetophenone, 2-decanone, and octanoic acid, were significantly increased. Sensory analysis of 111 consumers showed that the sample of synbiotic brown yogurt was highly accepted and close to the ideal point. Synbiotic fermentation alters the metabolic profile of yogurt, increasing the levels of beneficial metabolites and reducing harmful metabolites. In conclusion, synbiotic brown yogurt achieved the highest quality. This study provides a theoretical basis and data support for the development of brown yogurt with high probiotic activity and a unique flavor.

Measuring Household Thermal Discomfort Time: A Japanese Case Study

This study proposes a metric to measure households’ discomfort related to thermal consumption time (hereafter referred to as t-discomfort). This metric relies on an ideal thermal consumption and calculates the gap between the usage times of thermal devices in vulnerable households compared to the ideal household. The t-discomfort is quantified using thermal data collected from 1298 households in the Tokyo and Oita prefectures in Japan. To create the ideal usage times of thermal devices, households are categorized into three clusters—Vulnerable (Vu), Semi-vulnerable (SVu), and Invulnerable (IVu)—based on their energy poverty ratio, and t-discomfort is subsequently calculated for each group. The IVu households are used as the ideal reference point for measuring thermal device usage in the other two categories. The findings of the study indicate that energy poverty does not necessarily lead to t-discomfort. Interestingly, the consumption time of heating devices among Vu households in both prefectures is longer than that of IVu households, despite the high energy prices. Conversely, SVu households, which do not experience severe energy poverty, tend to sacrifice their comfort by reducing their thermal consumption time. Additionally, the consumption time of cooling devices among Vu households in Oita is longer than that of IVu households, whereas in Tokyo, it is shorter. Two treatment strategies are evaluated to mitigate thermal discomfort in households without compromising resource availability. The first strategy integrates the thermal device consumption time with Japan’s current regulated time-of-use rates plan (daytime and nighttime). The results propose a three-tiered tariff plan (off-peak, mid-peak, and peak) to reduce the energy cost burden for Vu households. The second strategy recommends the installation of 12 rooftop solar panels for households in Tokyo and 11 panels for households in Oita. This strategy aims to maintain thermal comfort via a sustainable natural energy resource while minimizing energy costs.

A Novel Pareto-Optimal Algorithm for Flow Shop Scheduling Problem

Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems.

Ideal quadratic nodal point in half-Heusler semimetal LaPtBi

The research focus on topological states in electronic systems has recently expanded from traditional linear dispersions to encompass higher-order dispersions. These higher-order dispersions exhibit unique physical properties, including high topological charge, exotic magnetoresponse, and novel transport behaviors. In this study, we employ first-principles calculations to propose the half-Heusler material LaPtBi as an ideal quadratic nodal point semimetal. Our comprehensive analysis of the band structure reveals an ideal nodal point located precisely at the Fermi level, exhibiting quadratic dispersion in all directions. This finding is supported by symmetry analysis and effective model arguments. Moreover, when the spin-orbit coupling effect is considered, the nodal point transitions from triple to quadruple degeneracy, while maintaining its quadratic dispersion. The material also features multiple prominent arc surface states originating from the nodal point, which are distinctly separated from the bulk bands. This separation facilitates experimental verification. Overall, LaPtBi, with its quadratic nodal point and advantageous properties, serves as an ideal platform for further research. The study of this material is expected to enhance our understanding of higher-order topological states in electronic systems, potentially driving future advancements in the field.

Association of mean platelet volume (MPV), MPV/PLATELET (PLT) ratio, and lymphocyte/monocyte ratio (LMR) as poor prognostic factor in EGFR-mutant lung adenocarcinoma treated with EGFR tyrosine kinase inhibitor.

Platelets (PLT) and host systemic inflammatory response (SIR) are known to be effective in the aggregation of cancer cells and the formation of metastasis. There are studies pointing out to the prognostic efficacy of lymphocyte-monocyte ratio (LMR) showing SIR activation and mean platelet volume (MPV) values indicating platelet activation in various cancer types. We predict that easy-to-access hemogram parameters such as MPV, MPV/PLT, and LMR can be guiding in the clinical follow-up period of patients with epidermal growth factor receptor (EGFR) positive mutation and who received EGFR, tyrosine kinase inhibitor (TKI) in the first-line treatment in predicting the progression of the disease, predicting the survival time of the patients, and evaluating the response to treatment. The study is retrospective and included patients with stage III and stage IV pulmonary adenocarcinoma with positive EGFR mutations and for whom TKI was used in the first-line treatment between January 2011 and January 2021. MPV, MPV/PLT, and LMR values of the patients were calculated before treatment. Age, sex, comorbidity, smoking history, TNM stage, metastasis localizations, EGFR mutation types, TKI treatments used in first-line treatment, and MPV, MPV/PLT, and LMR values at the 1st month of treatment were recorded. With Kaplan-Meier, six-month, one-year, three-year, and five-year survival rates, average life expectancy, and 95% confidence intervals for these periods were calculated. Variables that may affect progression and overall survival (OS) were determined by performing univariate and multivariate Cox regression analysis. One hundred and two patients were included in the study. The mean age of the patients was 64.30 ± 12.6 years. Eighty-four patients were in stage IV at the time of diagnosis. The expected mean progression-free survival (PFS) period of the cases was found to be 13.3 months. The mean life expectancy of the cases was found to be 35.1 months. Web-based Cutoff Finder algorithm written in the R program (http://molpath.charite.de/cutoff) was used to determine the ideal cut points for MPV, MPV/PLT, and LMR. The cut-off values were found to be 7.55 fL for MPV, 0.251 for MPV/PLT, and 2.615 for LMR, respectively. In univariate Cox regression analysis, LMR level lower than 2.615 increased the rate of progression 1.747 times (95% confidence interval: 1.129-2.705) and the death rate 2.056 times (95% confidence interval: 1.217-3.475) (p= 0.012, p= 0.007). The mean PFS LMR cut-off value was 10.3 months, and 15.3 months, and mean OS durations were 25.1 months and 40.8 months for the groups with low and high cut-off values respectively (p= 0.011, p= 0.006 log-rank test). According to the results of multivariate Cox regression analysis, MPV/PLT < 0.251, smoking, presence of pleural and adrenal metastases, and gefitinib treatment were independent factors in determining PFS. The independent factors determining OS in multivariate Cox regression analysis were being male, platelet increase, MPV > 7.55, gefitinib treatment, and smoking. MPV, MPV/PLT, and LMR are potential biomarkers that can be used for the clinical follow-up of lung ADC patients receiving EGFR-TKI treatment.

Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.

Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.

Validation of Optimization Methods for Sensory Characteristics Using Rate-All-That-Apply and Intensity Scales: A Case Study of Apple Juice.

Preference mapping (PM), which integrates consumer and descriptive analysis (DA) data to identify attributes that drive consumer liking, is widely employed for product optimization. However, a limited group of trained panelists cannot fully represent the diverse consumer population or reliably predict market acceptance. Consequently, numerous studies have explored consumer-based methodologies as potential replacements for DA; however, their efficacy for product optimization remains limited. Therefore, this study was conducted to explore the potential of optimizing products using two consumer-based profiling techniques as alternatives to DA in external preference mapping (EPM). Overall, 8 trained panelists profiled 12 sensory attributes of 7 commercial apple juices, whereas 160 consumers assessed the same attributes using a 5-point rate-all-that-apply (RATA) scale and a 10 cm intensity scale (IS). Danzart's response surface ideal modeling was employed to identify optimal products using DA, RATA, and IS through barycenter calculations, focusing on three products from the original consumer test located around the group ideal point. Overall, the ideal products of the group and their sensory characteristics were successfully identified using DA, RATA, and IS. Regarding sensory intensities, high concordance was observed between DA and RATA (Rv = 0.92) and between DA and IS (Rv = 0.91). Overall liking and preference scores for products mixed at the optimal ratio for each method showed no significant differences in preference among the ideal products identified using DA, RATA, and IS. This study suggests that both RATA and IS are viable alternatives to DA in EPM for identifying ideal sensory profiles.

Reconciling Global Terrestrial Evapotranspiration Estimates From Multi‐Product Intercomparison and Evaluation

AbstractTerrestrial evapotranspiration (ET) is a vital process regulating the terrestrial water balance. However, significant uncertainties persist in global ET estimates. Focusing on the area between 60°, we performed an intercomparison of 90 state‐of‐the‐art ET products from 1980 to 2014. These products were obtained from various sources or methods and were grouped into six categories: remote sensing, reanalysis, land surface models, climate models, machine learning methods, and ensemble estimates. It is shown that global ET magnitudes of categories differ considerably, with averages ranging from 518.4 to 706.3 mm yr−1. Spatial patterns are generally consistent but with significant divergence in tropical rainforests. Global trends are mildly positive or negative (−0.10 to 0.37 mm yr−2) depending on categories but with distinct spatial variability. Evaluation against site measurements reveals various performances across land cover types; the ideal point error values range from 0.45 to 0.83, with wetlands performing the worst and open shrublands the best. Using the three‐cornered hat method, there are spatial differences in ET uncertainty, with lower uncertainty for ensemble estimates, showing less than 15% relative uncertainty in most areas. The best global ET data set varies depending on the intended use and study region. Distinct spatial patterns of controlling factors across categories have been identified, with precipitation driving arid and semi‐arid regions and leaf area index dominating tropical regions. It is suggested to include advancing precipitation inputs, incorporate vegetation dynamics, and employ hybrid modeling in future ET estimates. Constraining estimates using complementary data and robust theoretical frameworks can enhance credibility in ET estimation.