Real Performance Research Articles

Research on Performance Prediction Method Based on Gaussian Process Regression

Utilizing performance prediction models to predict students' graduation outcomes can offer crucial data support for academic early warning systems in higher education institutions, potentially becoming an essential tool for enhancing teaching quality. Therefore, in order to build a more accurate performance prediction model, a performance prediction method based on Gaussian process regression is proposed, and a series number of random experiments are carried out on the real performance data of seven majors such as broadcast and television directing major in a university to verify the effectiveness and feasibility of the method. The experimental results show that the proposed method has higher prediction accuracy and stability, and can provide effective reference for improving teaching quality.

‘Self-Sustainability’ and ‘Performance’ in Microfinance – The Contextual Relevance of the Terms

The microfinance sector’s Paradigm Shift (PS) of the 1990s caused a drastic shift in the way Microfinance Organizations (MFOs) ensured their long-term survival and sustainability. The PS required MFOs to adopt a commercial approach in all their operations and pursue profitability and self-sustainability rather than depending on the provision of subsidized funds. Resorting to Schatzki’s (2002) ‘site of the social’ theoretical construct and DiMaggio and Powell’s (1983) institutional theory, this empirical paper aims to evaluate the real performance of Pakistan’s microfinance sector, following the PS, in terms of its strict adherence to its founding objectives of eradicating poverty and serving the extreme poor. This qualitative empirical research paper resorts to the Case Study approach to investigate the pre- and the post-Paradigm Shift eras of Pakistan’s microfinance sector for evaluating the sector’s real ‘success’ in accomplishing its founding aim of eradicating poverty at the grass root level. Analysis of the data reveals that the microfinance sector, in its pursuit of profitability and self-sustainability, following the PS, is compromising its founding commitment to its target beneficiaries – the extreme poor. The study contributes a theory, literature and empirical evidence-informed conceptual framework to help guide organizational change initiatives in the peculiar context of a social service/not-for-profit sector and predict outcome quality of such a change for all stakeholders. The study outcomes apprise policy-makers and practitioners on the possible negative consequences of the microfinance sector’s gradual transition from a ‘social-service’ to a ‘commercial’ model for the poor.

Automated Crack Identification, to Ease Maintenance of Reinforced Concrete Bridges

One of the critical factors in the regional economic development and sustainability is the regular maintenance of road infrastructure. The preliminary step in the structural maintenance of bridges is in-situ inspection.Accurate and reliable infrastructure inspection plays a vital role in making the further steps’ decision, whether more assessment needs to put into action or the bridge is in good condition.The visual assessment, at the moment, is performed manually by the engineers, being therefore a too time-consuming and expensive operation, and in case of inspecting the critical points puts the engineers in danger, otherwise those parts will be missing of data. Overcoming these barriers, over the past few decades, considerable efforts have been made to construct the fundamental of implementing artificial intelligent techniques as a tool, to do autonomous inspection.This research engages in the data analysis section of the autonomous inspection with segmenting the cracks on the images acquired by them. This goal is achieved by implementing semantic segmentation deep learning development which is being trained on 8192 images and their binary masks as dataset, using the U-Net network. The model prediction shows 98.2% accuracy in crack segmentation. However, the novelty of our research extends beyond these results, we introduce a unique evaluation method named Pixel Average Error Distance (PAED) instead of the commonly used Intersection over Union (IoU) metric for segmentation assessment. Our methodology aims to forego the IoU metrics unreliable results in case of small deviation of crack patterns. It enhances the understanding of our model real performance with the PAED metrics lower than 0.5 which demonstrates a well-designed model, while the average IoU with 0.65 value shows a poor-designed model.Overall, this method will provide the engineer the possibility to observe all surfaces of a bridge and have a reliable and precise evaluation of the existing cracks.

Comparison of longitudinal emittance of various RFQs

In various projects a large variety of RFQs has been developed, for different application, with different average current, frequency, and energy range. In this article, a comparison, in a scaled way, will be done, using the build RFQs of IFMIF, ESS, SPES, ANTHEM. In particular, the characteristics of the beam dynamics will be analyzed, such as transmission, longitudinal output emittance, and real performance versus simulation.

Набор методических тестовых программ для численного исследования параметров высокопроизводительных вычислительных систем

An integral part of the process of creating high-performance computing systems designed to solve problems of numerical modeling of various physical processes is to check their compliance with the characteristics stated during their design. At the same time, there is a problem of evaluating the performance of computing systems on synthetic tests, which are significantly primitive in mathematical complexity to real applied problems. The article considers a set of test programs developed by the authors, which allows to more accurately assess the real performance of computing systems.

Design of broadband metamaterial absorber utilized by flower-shaped unit loaded with lumped-resistor

A metamaterial broadband absorber is designed by means of metal pattern and lumped resistance. The optimal structural parameters and resistance are scanned in certain steps to determine. The maximum absorbing bandwidth can achieve up to 8.2 GHz with 3 mm thickness. Subsequently, the angle stability of the absorber can be improved by adding vertical metal through holes. After optimization, the maximum absorbing bandwidth can be further increased to 9.1 GHz (8.3–17.4 GHz), and the effective absorption bandwidth of 3.7 GHz (9.53–13.25 GHz) can still be achieved when the incident angle is 60°. Further analysis reveals that the dissipation of the electromagnetic wave is achieved by ohmic loss caused by the resistive element and magnetic resonance caused by the induced circular current, rather than by temperature or other factors. Finally, to verify the real performance of the designed metamaterial absorber, a 30 cm × 30 cm sample was fabricated, and the reflection coefficient was tested by the NRL arch test method. The results showed that the measured return loss of the absorber was consistent with the simulation results.

Αποτύπωση της Οικονομικής Κατάστασης των Κρατικών Νοσοκομείων: Χ ρηματοοικονομική Ανάλυση των Δημοσιευμένων Ισολογισμών (2019-2021)

Financial statements of public hospitals are prepared by accountants and are audited by Certified Public Accountants and they are the only reliable information that reflects the real financial performance. The accounting information provided is useful to users for transparency, accountability purposes, and decision-making regarding the public management of hospitals. The purpose of this study is to investigate the preparation of audited financial statements, the benchmarking of key operational and financial figures, the analysis of financial figures with cost indicators, and the qualitative analysis of certified public accountants’ audit reports per hospital and per health region for the period from 2019 until 2021. The audited Financial Statements as published on the website of the Ministry of Health and the yearly hospital activity open data from the Business Intelligence (BI-Health) Platform were collected and processed. The analysis demonstrates key financial figures of the Balance Sheet and the Profit and Loss Statement and useful cost indicators for the period from 2014 until 2021. Overall, this study strengthens the idea that accrual accounting and the preparation of financial statements in public hospitals should be a management tool and not only a legal obligation. The financial reporting enhances performance management, effective management of public assets, and policymaking for public health.

The Influence of Workload and Rewards on Employee Performance at the Regional Drinking Water Company in Depok City Through Job Satisfaction as an Intervening Variable

Purpose: The objectives of this research are: To determine the effect of workload on employee performance at the Depok City Regional Drinking Water Company. To determine the effect of Rewards on Employee Performance at the Depok City Regional Drinking Water Company. To determine the effect of workload on job satisfaction at the Regional Drinking Water Company in Depok City. Theoretical framework: Research study approach in the model of Rewards, workload model, and Job Satisfaction model on Employee Performance. Design/methodology/approach: The type of research carried out in this research is descriptive research using survey methods. Using a saturated sampling technique, a sample of 84 employees at the Depok City Regional Drinking Water Company was taken. The analysis method in this research was carried out using the Structural Equation Model (SEM). The model used is multiple regression analysis. Findings: The results of this research show that: 1) Workload influences employee performance. 2) Rewards influence employee performance. 3) Workload influences job satisfaction. 4) Rewards influence Job Satisfaction. 5) Job satisfaction has no effect on employee performance. 6) Workload has no effect on employee performance and its impact on job satisfaction. 7) Rewards have no effect on employee performance which has an impact on job satisfaction. Research, Practical & Social implications: The study recommended several recommendations, among which; Companies need to carry out a comprehensive evaluation regarding the workload given to employees. Ensure that the workload given is in accordance with employee capacity and abilities. Provide training and development to employees to improve skills and efficiency in completing their tasks. Management needs to assess the need for additional personnel if the existing workload is too heavy for existing employees. Originality/value: The value of the study is to provide a clear outlook regarding how to companies need to review their existing reward systems to ensure that they are fair and motivating to employees. Consider providing rewards based on real performance achievements and based on clear criteria. Ensure that the salaries and allowances given to employees comply with applicable standards, such as the Provincial Minimum Wage (UMP) that applies in the region.

Research on the Influence of Housing Prices on the Economic Performance of Chinese Real Enterprises

According to the Report of the 20th National Congress of the Communist Party of China, with importance of developing China’s real economy has become increasingly prominent, the economic development should focus on the real economy. Moreover, the various impacts brought by housing price changes in recent years are also intertwined with the sustained and superior economic development in China. With this context in mind, this paper uses panel data of housing prices and real enterprise performance in 30 major Chinese provinces and cities from 2005 to 2020—aside from Hong Kong, Macao, Taiwan, and Xizang—to empirically test the relationship between housing prices and the economic performance of real enterprises in China. The findings indicate that there is a nonlinear link between housing prices and real firms' economic performance and that excessively high housing prices are detrimental to real companies' ability to function economically. Further investigation employing heterogeneity analysis reveals that the limitation of exorbitant housing prices has a greater impact on entity companies in China's west and center.

The Impact of Inflation on Real Estate Investment Perfomance And Effective Investment Decisions

Purpose: This study aims to examine the impact of inflation on key variables of real estate investment performance and investment decisions. Theoretical Reference: This paper considers several models of real estate investment performance indicators because the effect of inflation on key variables of real estate investment performance is an important factor in investment decisions. Since effective investment decision is key to avert investment loss especially during inflation period, this paper follows the suggestions by Otegbulu (2022) and Georgiv, et al (2002) that property incomes and values are not static during periods of inflation bur responds to economic dynamics, and could infact trigger favourable investment opportunities in real estate. This paper therefore follows these models/suggestions of Otegbulu (2022) and Geoargiv, et al, (2002) to examine several real estate investment to offer appropriate reflection of how inflation impacts of incomes from real estate investment. Method: Data collected were analysed using Pearson Correlation Analysis to determine the relationship between inflation rate and real estate investment performance (measured by annual returns). Regression analysis was also employed on the data to determine the level of contribution or degree of impact of inflation on real estate performance. Also data collected on the exchange rate and real estate performance over the study period were analysed using Pearson Correlation Analysis to determine the relationship between exchange rate and real estate investment performance (measured by annual returns on investment). Regression analysis was also employed on the data to determine the degree of impact of exchange rate on real estate investment performance. Data collected for the analysis and hypotheses testing were secondary data over a period from 2005 – 2022. Result: The study reveals that real estate investment market generates average total annual returns of 21.39% under average inflation rate of 12.5%. The test statistics (Pearson Correlation) results shows that the higher the inflation rate, the lower the performance of the real estate investment. Also, the study reveals that as inflation persists causing exchange rate fluctuations, the test statistics (Pearson Correlation) results shows that higher exchange rate causes lower performance of real estate investment. This could be because more than 90% of the real estate construction materials in the study are imported. Conclusion: It is clear and obvious that inflation impacts highly on real estate and capable of distorting projections in property investment. Rational investors must therefore factor in inflation risk in investment decisions. The study has shown that inflation has significant impact on annual returns of real estate and consequently affects investment performance. The study concluded that the higher the inflation, the lower the performance of the real estate investment and that the continuous rise in inflation rate reduces the purchasing power of the local currency leading to local currency devaluation. Also, it was concluded that higher exchange rate causes lower performance of the real estate in terms of annual returns. Research Implications: One of the major implications of the findings from this study is that as inflation raises the volatility of local currency which adversely affect the cost of construction, real estate investment performance is hindered. The risk element increases which could lead to project cost over-run, abandonment or increased vacancy rate. Investors become skeptical in making investment decisions because of uncertainties. Policy makers should put in place policies and guidelines that will attract cross-border investors who can take advantage of the local currency devaluation to improve their real estate investment portfolios. Originality/Value: The relationship between inflation rate, exchange rate and real estate performance (measured by rate of returns on the investment) is of great concern for real estate practitioners and investors. The study is original well thought efforts of the authors as contribution to real estate practice and education in developing economies currently being threatened by rising inflation. It is believed that this study will be very useful to global investors who will like to invest in the study area in particular and other emerging markets in general.

Thermal performance estimation of a v-corrugated solar air heater using ann techniques

A solar air heater (SAH) is a unique form of solar thermal collector that utilizes solar energy emitted from the sun to produce heated air. Various experimental and theoretical investigations have been undertaken to improve the poor thermal performance of SAHs. The difficulties related to these studies drew attention toward a reliable soft computing technique exemplified by the Artificial Neural Network (ANN) technique. The current work applied actual meteorological data from Miskolc City, Hungary, to an ANN model with the structure of a Multi-layer Perceptron (MLP) to forecast the energy performance of a V-corrugated solar-powered air heater. Seven input parameters and one output parameter make up the ANN structure, with a single hidden layer. For the purpose of selecting the most effective network for predicting output parameters, ten neurons have been assessed. The suggested ANN model was trained with 336 data sets using the Levenberg-Marquardt (LM) learning technique. The comparison of anticipated and real thermal performance values shows a very good agreement. The statistical error analysis showed that the optimal ANN model structure of 7-8-1 can reliably and accurately predict SAH’s thermal performance and thus it can save both time and cost.

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS.

Synergizing perovskite solar cell and thermoelectric generator for broad-spectrum utilization: Model updating, performance assessment and optimization

Hybridization of perovskite solar cell with thermoelectric generator is a promising broad-spectrum harvesting strategy. However, the existing modeling studies omit the thermal effects within the hybrid system, leading to inaccuracy of available results. Herein, an updated photovoltaic/thermoelectric system model incorporating a perovskite solar cell, a solar selective absorber, and a thermoelectric generator is theoretically developed to more accurately predict its real performance, in which the external heat transfer irreversibility between subsystems as well as the Thomson effects within thermoelectric generator are considered, and the fundamentally physical processes within the perovskite solar cell are included. It is found that P-type BiTe and n-type BiTe are the best compatible thermoelectric materials for the thermoelectric generator. Extensive parametric studies identify that the operating temperature, operating voltage and two interface defect densities of perovskite solar cell, and two structure parameters of thermoelectric generator are optimizable. A series of optimizations predict that the hybrid system has a maximum energy efficiency of 22.36 %, which is 13.68 % and 12.59 % higher than that of the unoptimized single perovskite solar cell and that of the unoptimized system, respectively. The obtained results are helpful to evaluate and optimize the performance of such an actual system.

Development of the Phaseless Calibration Algorithm for a Digital Antenna Array

In this paper, we will discuss a calibration algorithm for a digital antenna array that diagnoses its real performance. It can be applied at such stages of the antenna system life cycle as design, tuning, and especially maintenance. A calibration implementation using a scalar method for a multi-beam digital antenna array is proposed and investigated. On-the-fly calibration ensures a continuous improvement in beam pointing accuracy by reducing internal errors in the receiving (transmitting) channels. The purpose of the study is to experimentally examine the capabilities of digital beamforming to increase the angle-of-arrival estimation accuracy. A simulation model of the receiving antenna was created in an anechoic chamber with a planar antenna positioner. The possibility of precise estimation of the direction of arrival using the digital beamforming with electronic scanning was demonstrated. The proposed simulation model made it possible to observe the convergence of the antenna array calibration process using the proposed method for various errors in the signal paths, as well as different signal-to-noise ratios. It has been proven that even under adverse conditions early in the calibration algorithm, the phase error detection converges with high accuracy, and its value decreases uniformly even to the fractions of an angular degree.

Green, green, it’s green they say: a conceptual framework for measuring greenwashing on firm level

AbstractThis paper proposes a conceptual framework to determine a greenwashing indicator on firm level, based on five pillars of information that capture the key drivers of greenwashing: soft ESG data, textual self-representation, green marketing expenses, green virtue for the apparent green performance, and hard ESG data as a measure of the real green performance. The proposed framework is built on a literature review of greenwashing typology and drivers and can be applied to a broad set of firms at the same time, while most existing approaches are built on a detailed investigation of individual cases. The greenwashing indicator in our framework is based on the difference between real and apparent green performance. This approach allows the implicit calculation of the real green performance using the concept of a firm misconduct factor that helps to identify the greenwashing indicator without knowledge of the real (and often hard to measure) green performance of a firm.

Effective acoustic parameters for automatic classification of performed and synthesized Guzheng music

This study focuses on exploring the acoustic differences between synthesized Guzheng pieces and real Guzheng performances, with the aim of improving the quality of synthesized Guzheng music. A dataset with consideration of generalizability with multiple sources and genres is constructed as the basis of analysis. Classification accuracy up to 93.30% with a single feature put forward the fact that although the synthesized Guzheng pieces in subjective perception evaluation are recognized by human listeners, there is a very significant difference to the performed Guzheng music. With features compensating to each other, a combination of only three features can achieve a nearly perfect classification accuracy of 99.73%, with the essential two features related to spectral flux and an auxiliary feature related to MFCC. The conclusion of this work points out a potential future improvement direction in Guzheng synthesized algorithms with spectral flux properties.

Fuel Cell Hybrid Electric Vehicles: Fuel Cell experimental characterization and modeling towards the development of a Hardware-in-the-Loop platform for advanced powertrain design

Greenhouse gas emissions in transport sector is a critical issue, with automobiles and trucks responsible for emitting nearly 5.9Gt of CO2. Addressing climate change is then of paramount importance to avoid irreversible consequences. Advanced technologies are being developed to achieve this goal. Among them, Fuel Cell Hybrid Electric Vehicles (FCHEVs) offer several benefits, including wide applicability and range, short refueling times, and no tailpipe emissions, that can only be exploited if optimizing their design. This requires time and cost-consuming iterative processes, and Digital Twins (DTs) and Hardware-in-the-Loop (HIL) test benches are then gaining importance with respect to prototype manufacturing. In this study, the basis for a HIL application is posed, including a 2kW Fuel Cell (FC) and a bidirectional DC power supply used to emulate the electric motor. After characterizing the FC, DTs have been calibrated and validated through experimental tests; DTs have then been included into a FCHEV microcar model and its performances have been analyzed. Results have shown high accuracy in representing the real FC performance, with a 5% maximum error from the energy standpoint. Moreover, HIL approach has been proved to be fundamental for catching peculiar transient phenomena, significantly impacting on the powertrain design and performance.

An Enhanced Filtering Generalized Integrator-Based Control for Improved Performance of a Grid-Tied PV System at Adverse Grid Voltages

This paper deals with a single stage photovoltaic (PV) system with improved resilience against adverse grid conditions. This is majorly achieved in two ways. First, it presents an enhanced filtering generalized integrator (EFGI) based filtering stage, to process the prevalent grid voltages and derive their positive sequence components free of harmonics components, unbalance, and DC offset. The presented EFGI delivers a considerable improvement over the conventional filters and helps to improve the power quality (PQ) in case of unequal or distorted voltage conditions. Second, to address large voltage dip or rise scenarios, a dual mode mechanism is applied for the selection of the DC link voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> ) reference and the corresponding generated PV array power. At regular voltage scenario and at availability of PV power, the reference <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> is governed by the necessity to keep the PV array operating at its peak generation levels. However, at loss of PV power or divergent grid voltages, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> control is quickly transferred to an alternate approach, varying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> as per the existing grid voltages. Using this, the injected currents are limited, and system de-synchronization is prevented at voltage dips. The increased <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> at grid voltage rise avoids deterioration in the grid PQ. The grid PQ is also assured despite the presence of local non-ideal loads. The EFGI benefits and the real time performance of the presented system, are assessed using test results.

Is Mandatory Carbon Disclosure Associated With Real Carbon Performance: A Study of South African Companies Listed on JSE

With the increasing importance of carbon disclosure as a tool to reduce carbon emissions, the enduring question remains whether carbon disclosure reduces carbon emissions. This paper analyzes the rapport between "carbon disclosure and carbon performance" of 82 Johannesburg Stock Exchange (JSE) listed mining and manufacturing firms grounded on data released from 2010 to 2021. The environmental disclosure index is used in this study to quantify carbon disclosure, and the carbon intensity of the selected firms is used to gauge carbon performance. Using IBM SPSS Statistics 26, the study discovers that carbon disclosure (p&lt; -0.023) is negatively associated with carbon performance. It means that an increase in carbon disclosure minimizes carbon performance. Additionally, the study finds that carbon disclosure improves financial performance proxied by return on assets. The findings strongly support King Code III and IV's position that companies can improve environmental and financial performance by extensively disclosing environmental impacts. In this case, carbon disclosure can act as an instrument to improve corporate sustainability and counteract climate change. In line with the research results, it is recommended that policymakers in South Africa promote and adopt policies that compel and regulate companies to disclose their environmental impacts.

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.