Least-squares Research Articles

A hybrid annual runoff prediction model using echo state network and gated recurrent unit based on sand cat swarm optimization with Markov chain error correction method

ABSTRACT This study proposes a hybrid model based on the combination of Sand Cat Swarm Optimization (SCSO), Echo State Network (ESN), Gated Recurrent Unit (GRU), Least Squares Method (LSM), and Markov Chain (MC) to improve the accuracy of annual runoff prediction. Firstly, conduct correlation analysis on multi-factor data related to runoff to determine the input of the model. Secondly, the SCSO algorithm is used to optimize the parameters of the ESN and GRU models, and the SCSO-ESN and SCSO-GRU models are established. Next, the prediction results of these two models are coupled using LSM to obtain the preliminary prediction results of the SCSO-ESN-GRU model. Finally, the initial prediction results are corrected for errors using MC to get the final prediction results. Choose Changshui Station and Lanxi Station for experiments, and evaluate the predictive performance of the model through five evaluation indicators. The results show that the combined prediction model corrected by the MC achieved the optimal prediction performance at both experimental stations. This study emphasizes that using a combination prediction model based on Markov chain correction can significantly improve the accuracy of annual runoff prediction, providing a reliable basis for predicting annual runoff in complex watersheds.

A Combined UWB/IMU Localization Method with Improved CKF.

Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg-Marquardt algorithm (L-M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively.

Statistical analysis of the inverse power Zeghdoudi model: Estimation, simulation and modeling to engineering and environmental data

Abstract In this research, we investigate a brand-new two-parameter distribution as a modification of the power Zeghdoudi distribution (PZD). Using the inverse transformation technique on the PZD, the produced distribution is called the inverted PZD (IPZD). Its usefulness in producing symmetric and asymmetric probability density functions makes it the perfect choice for lifetime phenomenon modeling. It is also appropriate for a range of real data since the relevant hazard rate function has one of the following shapes: increasing, decreasing, reverse j-shape or upside-down shape. Mode, quantiles, moments, geometric mean, inverse moments, incomplete moments, distribution of order statistics, Lorenz, Bonferroni, and Zenga curves are a few of the significant characteristics and aspects explored in our study along with some graphical representations. Twelve effective estimating techniques are used to determine the distribution parameters of the IPZD. These include the Kolmogorov, least squares (LS), a maximum product of spacing, Anderson-Darling (AD), maximum likelihood, minimum absolute spacing distance, right-tail AD, minimum absolute spacing-log distance, weighted LS, left-tailed AD, Cramér-von Mises, AD left-tail second-order. A Monte Carlo simulation is used to examine the effectiveness of the obtained estimates. The visual representation and numerical results show that the maximum likelihood estimation strategy regularly beats the other methods in terms of accuracy when estimating the relevant parameters. The usefulness of the recommended distribution for modelling data is illustrated and displayed visually using two real data sets through comparisons with other distributions.

Development and calibration of large deformation-compliant six-axis force sensor

Improving the measurement accuracy and minimising the coupling between directions are the keys to researching the compliant six-axis force sensors. The use of a six-axis force sensor to accurately monitor the ground reaction force and centre of pressure during human motion is of great significance in the fields of biomechanics and pathological gait diagnosis. Although complete force information can be obtained using a commercial six-axis force sensor, its high stiffness affects the natural gait and easily leads to human fatigue. A compliant six-axis force sensor based on a flexible optical waveguide is proposed, in which the force and torque of six dimensions are detected by reasonably arranging six modular sensing units, and the mechanical decoupling of some dimensions is realised in theory. For the interdimensional coupling and error caused by machining process factors, as well as the nonlinear relationship between the input and output of the proposed compliant six-axis force sensor, a DE-RBF decoupling algorithm is proposed to decouple the calibration data. Compared with the least squares method (LSM) and the radial basis function (RBF) neural network decoupling algorithm, the obtained type-I errors were reduced by 87.7629%, 43.6265%, respectively, and type-II errors by 35.3312%, 56.9162%, respectively. The decoupling result’s maximum type-I and type-II errors were reduced from 7.7125% and 2.7382% in LSM and 3.1029% and 2.8917% in RBF to 0.5916% and 0.9558%, respectively. The measurement accuracy of the compliant six-axis force sensor was significantly higher; however, the time effectiveness of the proposed DE-RBF decoupling algorithm was slightly lower than that of the RBF neural network by 2.47%. In conclusion, the decoupling accuracy and timeliness of the proposed DE-RBF decoupling algorithm can satisfy the requirements of compliant six-axis force sensors to monitor low-frequency biomechanical signals, such as human motion.

Exploring the Moderating Effect of Generational Status on the Link Between External Environment and Entrepreneurial Motivation of Indian Women Entrepreneurs

This study examines how external environmental factors affect the motivation of first- and second-generation women entrepreneurs in India, and how their generational status moderates this relationship. We used a survey design method to collect data from 459 women business owners in India, who were classified as first- or second-generation based on whether they were the first or second generation in their family to start a business. We employed the partial least-squares structural equation modelling method to test our hypotheses and evaluate our model. We found that external environmental factors, such as education and training, financial support, government policies and non-financial support, had positive effects on entrepreneurial motivation for both groups of women entrepreneurs. However, we also found that generational status had a significant moderating effect on the link between education and training and entrepreneurial motivation, but not on the other three external environmental factors. Specifically, we found that education and training influenced second-generation women entrepreneurs more strongly than first-generation women entrepreneurs. Our findings have important implications for management practice and policymaking, as well as for the literature on women entrepreneurship. We suggest that concerned authorities should develop differential strategies for first- and second-generation women entrepreneurs, taking into account their different responses to external environmental factors. We also suggest that future research should explore other internal and external environmental factors that might interact with generational status or other variables to shape women entrepreneurs’ motivation.

LSTM network optimization and task network construction based on heuristic algorithm

This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neural networks to reconstruct and optimize task networks derived from time series data. Additionally, a trend-based noise smoothing scheme is introduced to mitigate data noise effectively. The approach entails a thorough analysis of historical data, followed by applying trend-based noise smoothing, rendering the processed data more scientifically robust. Subsequently, the network reconstruction problem for time series data originating from one-dimensional dynamic equations is addressed using an algorithm based on the principles of Stochastic Gradient Descent (SGD). This algorithm decomposes time series data into smaller samples and yields optimal learning outcomes in conjunction with an adaptive learning rate SGD approach. Experimental results corroborate the remarkable fidelity of the weight matrix reconstructed by this algorithm to the true weight matrix. Moreover, the algorithm exhibits efficient convergence with increasing data volume, manifesting shorter time requirements per iteration while ensuring the attainment of optimal solutions. When the sample size remains constant, the algorithm’s execution time is directly proportional to the square of the number of nodes. Conversely, as the sample size scales, the SGD algorithm capitalizes on the availability of more information, resulting in improved learning outcomes. Notably, when the noise standard deviation is 0.01, models predicated on SGD and the Least-Squares Method (LSM) demonstrate reduced errors compared to instances with a noise standard deviation of 0.1, highlighting the sensitivity of LSM to noise. The proposed methodology offers valuable insights for advancing research in complex network studies.

Analysis of the outage performance of energy-harvesting cooperative-NOMA system with relay selection methods

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance.

Lithium-ion point-of-care ultrasound battery joint state of charge estimation

With recent advances in ultrasound technology, including accessibility and portability, point-of-care ultrasound systems (POCUS) are becoming ubiquitous in urban and rural health services. The efficient use of POCUS battery power is necessary due to the limited access to electricity in rural locations. Accurate estimation of the state-of-charge (SOC) of the onboard lithium-ion battery is crucial in ensuring the safe, efficient, and reliable operation of the battery management system (BMS). This research suggests a novel approach for striking a reasonable balance between the POCUS battery management systems computation time and SOC estimate accuracy. Given that the lithium-ion battery is a non-linear and time-varying system, it is modelled by a second-order RC equivalent circuit model (2-RC ECM). Real-time parametric identification is carried out using the method of recursive least squares with respect to an adaptive forgetting factor (AFFRLS). The joint SOC evaluation technique, ordering the results of real-time identification and the Extended Kalman Filter (EKF) recommended for estimating battery SOC at temperatures between 0 and 45 °C. Simulations were carried out using experimental data from the DST (Dynamic Stress Test), incremental current and, the Federal Urban Driving Program (FUDS) of the CALCE research group to evaluate the proposed approach. Based on the monitoring of dynamic changes in model parameters and the computational complexity of existing algorithms, the results reveal that the joint AFFRLS-EKF estimation proposed for the SOC strategy achieves good accuracy, high adaptability and rapid convergence of SOC estimation.

The Optimal Infrastructure Design for Grid-to-Vehicle (G2V) Service: A Case Study Based on the Monash Microgrid

The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency. Intelligent charging techniques using Information and Communication Technology, known as smart charging, enable the transformation of the EV fleets from passive consumers to active participants within the grid ecosystem. This concept facilitates the EV fleet’s contribution to various grid services, enhancing grid functionality and resilience. This paper investigates the optimal infrastructure design for a smart charging system within the Monash microgrid (Clayton campus). We introduce a centralized Grid-to-Vehicle (G2V) algorithm and formulate three optimization problems utilizing linear and least-squares programming methods. These problems address tariff structures between the main grid and microgrid, aiming to maximize aggregator profits or minimize load fluctuations while meeting EV users’ charging needs. Additionally, our framework incorporates network-aware coordination via the Newton–Raphson method, leveraging EVs’ charging flexibility to mitigate congestion and node voltage issues. We evaluate the G2V algorithm’s performance under increasing EV user demand through simulation and analyze the net present value (NPV) over 15 years. The results highlight the effectiveness of our proposed framework in optimizing grid operation management. Moreover, our case study offers valuable insights into an efficient investment strategy for deploying the G2V system on campus.

Equivalent continuous method based novel design of a nonlinear TMD with piecewise stiffness

Tuned mass Dampers (TMDs) are extensively used passive control devices for high-rise buildings to mitigate undesirable responses. In practice, limit devices are frequently applied due to the large displacement of TMD, resulting in the emergence of piecewise nonlinearity. In this study, a TMD with piecewise stiffness (PSTMD) is developed to investigate the piecewise nonlinearity, including piecewise linear stiffness (PLS) and piecewise nonlinear stiffness (PNS). Initially, a novel equivalent continuous method (ECM) is proposed based on the principles of the least squares method (LSM) to approximate the PSTMD using a nonlinear TMD with Duffing stiffness. The equivalent nonlinear TMD is proven to be accurate enough to approximate the PSTMD. Subsequently, frequency response analysis is conducted on the PSTMD designed by linear method, results indicate that the linear method fails to design PSTMD. Thirdly, the optimal parameters of PSTMD are determined based on the ECM and the improved design method for nonlinear TMD proposed by the author. Results indicate that this novel design method effectively enhances the performance of PSTMD and mitigates the effect of piecewise nonlinearity. Finally, the influence of parameters of PSTMD on the optimal tuning frequency is investigated. The influence of damping ratio of PSTMD is explored by numerical simulation, with results indicating that employing the damping ratio suggested by traditional linear design method as the optimal parameter is reasonable. In summary, the novel method proposed in this study holds significance for the engineering design of TMDs with piecewise characteristics.

Acceleration model considering multi‐stress coupling effect and reliability modeling method based on nonlinear Wiener process

AbstractEstablishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling.

The application of gated recurrent unit algorithm with fused attention mechanism in UWB indoor localization

Amidst the increasing reliance on indoor positioning in areas such as large shopping malls, airports, and train stations, the precision of ultra-wideband (UWB) technology stands out for its centimeter-level accuracy. However, non-line-of-sight (NLOS) errors caused by people’s movements significantly reduce indoor positioning accuracy. In response, this paper presents an innovative blend of gated recurrent unit algorithm with fused attention mechanism (GRU_Attention). This algorithm adeptly captures the dynamism in UWB signals and intelligently focuses on pivotal features to actualize accurate three-dimensional positioning while effectively reducing NLOS error. It has been demonstrated that GRU_Attention algorithm can reach 5.6 cm accuracy level through experiments, which improved by 74.47 %, 87.44 %, 14.46 %, 43.82 %, and 29.07 % respectively compared with the backpropagation (BP), least square (LS), GRU, convolutional neural network (CNN), and fused attention mechanism convolutional neural network (CNN_Attention) algorithms. This algorithm charts a new pathway to overcome NLOS errors in indoor personnel movement scenes.

Geographic disaggregation of household surveys. Modified estimators for correcting misclassification bias

ABSTRACT We present a two-stage method to estimate spatial conditional means at a higher spatial resolution than the data actually have. In the first stage, we increase the spatial resolution of the data using classification tools and ancillary data. In the second stage, we estimate spatial conditional means (conditioning on the new spatial resolution). The estimation procedure in the second stage is not straightforward because the new finer spatial areas are subject to misclassification (measurement error). We prove that the least square (LS) estimators are biased under this framework and propose a consistent and asymptotically normal estimator under non-differential measurement errors. Given that the proposed estimator depends on unobservable terms, we also present its feasible version. Unlike most of the spatial downscaling methods, our proposal is non-model-based, and does not require area homogeneity assumptions. We assess analytical results by some Monte Carlo simulations, showing that our proposals work properly and outperform the spatial microsimulation approach. Finally, we conduct an empirical application where we analyse poverty and unemployment in one of the main urban agglomerates of Argentina known as Gran Rosario, we spatially disaggregate the original database to make inferences at a finer geographic scale.

Smoothed gradient least squares estimator for linear threshold models

In the presence of fixed threshold effects, the least squares (LS) estimator of the threshold parameter poses challenges for statistical inference due to its nonstandard limiting distribution, which also presents challenges for bootstrap methods. To address this issue, we propose a novel estimator: a two-step smoothed gradient least squares (SGLS) estimator. Our proposed method achieves a normal limiting distribution for the threshold parameter with minimal efficiency loss compared to the LS estimator. Furthermore, our modified bootstrap method significantly enhances computational efficiency, leading to improved bootstrap confidence intervals (CIs) for the threshold parameter compared to asymptotic CIs. Our method is validated through a small Monte Carlo study and demonstrated with an empirical application.

Tensor-Based Least-Squares Solutions for Multirelational Signals and Applications.

The approach of least squares (LSs) has been quite popular and widely adopted for the common linear regression analysis, which can give rise to the solution to an arbitrary critically-, over-, or under-determined system. Such a linear regression analysis can be easily applied for linear estimation and equalization in signal processing for cybernetics. Nonetheless, the current LS approach for linear regression is unfortunately limited to the dimensionality of data, that is, the exact LS solution can involve only a data matrix. As the dimension of data increases and such data need to be represented by a tensor, the corresponding exact tensor-based LS (TLS) solution does not exist due to the lack of a pertinent mathematical framework. Lately, some alternatives such as tensor decomposition and tensor unfolding were proposed to approximate the TLS solutions to the linear regression problems involving tensor data, but these techniques cannot provide the exact or true TLS solution. In this work, we would like to make the first-ever attempt to present a new mathematical framework for facilitating the exact TLS solutions involving tensor data. To demonstrate the applicability of our proposed new scheme, numerical experiments regarding machine learning and robust speech recognition are illustrated and the associated memory and computational complexities are also studied.

ORGANIZATIONAL AND INNOVATION MANAGEMENT OF FINANCIAL AND PERSONNEL SUPPORT FOR THE INTELLECTUAL POTENTIAL OF HIGHER EDUCATION INSTITUTIONS

At this historical stage of development, Ukraine is going through the most difficult times since the period of gaining its independence. The logic of state administration, financial and credit relations, social guidelines and the foreign policy of our country in general has changed. The sectors of education and science, in which not always positive changes are taking place, were also affected by such changes. Rather, on the contrary, there is a trend of stagnation in education caused by weak institutional support, and imperfect personnel and financial policies. The aggressive policy of the Russian Federation in relation to our country caused a powerful surge of internal and external migration of the population, including highly professional and narrowly qualified workers who were involved in educational and scientific processes and proved their effectiveness. In such conditions, it becomes necessary to determine the orientations of the organizational and innovative management of financial and personnel support of academies, institutes, universities and other educational institutions in the conditions of the restoration of intellectual potential. It is especially important to develop a methodology for its assessment and forecasting. In this context, the article proposes ways of evaluating, forecasting and improving the organizational and innovative management of financial and personnel support of higher education institutions. In the process of writing the article, economic and mathematical methods, tools and models were used, in particular two-factor regression models, Farrar-Glober tools, the method of least squares, and Fisher's F-criterion analysis. Taking into account the trends of forecasting financial and personnel support in order to improve the management of higher education institutions, managers should pay attention to the need to attract investments to improve the quality of scientific research.

Distribution rule for allocation problems with priority agents using the least square method

We use the least-squares method for inconsistent systems to find a new allocation rule for rationing and surplus problems. In particular, we study allocation problems considering different priorities to satisfy the agents’ demands, which influence how the distribution is carried out. The new distribution rule is proposed by choosing different inner products defined in linear algebra and providing explicit formulas for assigning resources to the agents. Moreover, we illustrate how it can recover different allocation rules by adequately defining the priorities. As an application of the rationing problem with priority agents, we consider real data for allocating police officers in the states of Mexico. In this example, the agents represent the states of Mexico, and their priorities were established based on the criminal incidence. Also, we compute and compare the resource distribution given by classic allocation rules and the percentage of loss obtained with each one.

Life Function Analysis of a Two-Parameter Exponential Distribution Using Robust Rank Regression

In this research, a proposed method is presented to estimate the life function of the two-parameter exponential distribution by using the robust rank regression on the dependent variable, which depends on the robust variance-covariance matrix (the FMCD and OGK method) and the robust mean vector through which the rank regression coefficients are estimated using the method of ordinary least squares and employing it in estimating the scale and location parameters of the exponential distribution and thus analyzing the life function on its basis. A comparison was also made between the proposed method and the classical method of rank regression on the dependent variable of life data at different sample sizes (from 10 to 25) to show the efficiency of the proposed method based on the mean square error of the estimated parameters, through a program prepared by the researcher in MATLAB language dedicated to this purpose. The practical application results concluded that the proposed robust method of rank regression on the dependent variable in estimating the parameters of the exponential distribution was more efficient than the classical method. Paper type: Research paper.

Assessment of the Success of Digital Signature and Stamp Implementation in the National Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Utilization technology in sector industry non-bank finance in Indonesia is increasingly widespread, including in the guarantee credit of state-owned companies. The company uses System Application Guarantee for transaction guarantee credit with produce Certificate Guarantee as proof of approval. The request can be active in the context of the COVID-19 pandemic and the Indonesian government's efforts to implement the company's National Economic Recovery (PEN) program guarantee. Implementation feature sign hand and digital stamp on the System Application PEN underwriting allows provision of document Guarantee Certificate in the form of legal and supportive softcopy restrictions contact physique during a pandemic. Study This aim is to measure the successful implementation features of the success models by Delone and McLean and analyze the connection between variables in research. The benefits cover assessment, evaluation, and strategy-based results evaluation using feature sign hand and digital stamp. Delone and McLean's original model was modified to eliminate variable intention to use. The research was conducted by analyzing the results of questionnaires from 108 officials' user systems throughout Indonesia. Process of data analysis using the method of Partial Least Square with device SMARTPLS software. Research results prove that quality systems will influence the satisfaction of future users who will be influential to benefit clean from the feature sign hand and digital stamp on the System Application PEN Guarante.

Global evapotranspiration from high-elevation mountains has decreased significantly at a rate of 3.923 %/a over the last 22 years

Clarifying the responses of human activities and climate change to the water cycle under variable environments is crucial for accurately assessing regional water balance. An analysis of the changes in actual evapotranspiration and its driving factors was conducted in the global high-elevation mountains during the period from 2001 to 2022. Utilizing 18 formulas for calculating evapotranspiration, which are based on comprehensive, temperature, radiation, and mass transfer, and then simulated the variations in reference evapotranspiration. Furthermore, we optimized the ET simulation model based on the most effective simulation results and projected future changes using scenario simulation data. Our findings reveal that: 1) ET at high-elevation mountains has significantly decreased at an average rate of 3.923 %/a, with monthly values ranging from 31.179 to 33.652 mm and an average of 32.646 mm; 2) The radiation-based model of Irmark-Allen is particularly well-suited for simulating ET at high-elevation mountains, with precision analysis and the Taylor diagram confirming its superior simulation performance. After optimizing the model using the method of least squares, the value of R2 before and after the optimization were 0.633 and 0.853, respectively. 3) An upward trend in ET under both SSP245 and SSP585 scenario in future simulation projections. Attribution analysis has identified Vapor Pressure Deficit as the key positive driver influencing the change of ET in global high-elevation mountains. Structural equation modeling further reveals that variations in net radiation and precipitation play a significant role in altering evapotranspiration rates. Meanwhile，The water balance analysis reveals that ET has been declining from 2001 to 2022. This phenomenon can be largely attributed to the substantial decline in vapor pressure deficit, the rise in the Normalized Difference Vegetation Index signifying increased vegetation cover, and the reduction in shallow soil moisture during the same period. These factors collectively explain the notable decrease in ET observed in high-elevation mountains.