Variable Error Research Articles

Data-driven tracking control for discrete-time nonlinear systems: a backward difference iterative learning approach

This article investigates the trajectory tracking problem of repeatable discrete-time nonlinear systems with unknown dynamics. The conventional paradigm in this field is to construct an iterative learning controller with the approximately identified model, considering only tracking error information at the current iteration. This leads to the neglect of the error variation trend along historical iterations. To circumvent unmodeled dynamics and capitalise on multi-step iteration error information, a backward difference iterative learning control (BD-ILC) approach is formulated. Initially, a vector data transfer mechanism employing the backward difference iteration error is introduced based on the dynamic data mapping. Within this mechanism, the proportional, differential and quadratic differential control terms are firstly incorporated simultaneously along the iteration domain. Furthermore, a convergence analysis approach is developed using the error deflation spanning tree technique. Simulations validate the enhanced efficacy of the BD-ILC approach, surpassing that of distinct control approaches.

Grip force release is impaired in parkinson's disease during a force tracking task.

The controlled release of grasping forces underlies skilled dexterous interactions with objects. While declines in force generation and maintenance are well documented in people with Parkinson's disease (PwPD), limited data exist related to how PD impacts the motor control of grasping force release. The aim of this project was to determine how PD impacts grip force release relative to the generation and maintenance of force. It was hypothesized that PwPD would exhibit global deficits in force control relative to controls but would perform disproportionately worse during the controlled release of grip force. Ten PwPD and 10 age-matched controls completed a force-tracking paradigm requiring grip force generation, maintenance, and release. Compared to controls, PwPD were less accurate (i.e. less time within target range), had greater error (i.e. greater relative root mean squared error), and had more trial-to-trial variability in error during grip force release. Ongoing studies are examining the potential neural mechanism(s) underlying of force release impairments in PD, and the relationships between PD severity, manual dexterity, and force release declines.

Enhancing Diagnostic Precision and Clinical Outcomes With FNAC in Parotid Gland Masses.

Parotid gland masses encompassing both neoplastic and non-neoplastic conditions present diagnostic challenges. Fine-needle aspiration cytology (FNAC) is commonly used to differentiate between benign and malignant lesions but has limitations such as variable sensitivity and potential sampling errors. This study assesses FNAC's accuracy for parotid gland lesions, evaluates the Milan Salivary Gland Cytopathology Reporting System (MSRSGC), and compares FNAC results with histopathological findings. This retrospective study at our medical center analyzed data from 2321 patients who underwent parotid gland FNAC followed by surgical resection over 20 years. Patients were included if they had both cytological analysis and surgery between January 2004 and July 2024. Surgical procedures varied based on mass characteristics and FNAC results, and all samples were assessed using MSRSGC. Statistical analysis compared FNAC results with histopathology findings, calculating sensitivity, specificity, and accuracy. This study included 352 patients who underwent both FNAC and surgical resection. FNAC had an overall accuracy of 93.9%, with a sensitivity of 78.3% and specificity of 91.5%. The risk of malignancy was 30% for Milan Category I (nondiagnostic), 6.3% for Category II (non-neoplastic), 0% for Category III (Atypia of Undetermined Significance), 2.3% for Category IVa (benign neoplasm), 5.8% for Category IVb (neoplasm of uncertain malignant potential), 58.8% for Category V (suspicious for malignancy), and 95% for Category VI (malignant). The false negative rate was 2.4%, while the false positive rate was 3.4%. The malignancy risk increased with age, and malignant masses were larger and more likely to cause facial paralysis. FNAC is a key diagnostic tool for parotid gland masses, offering high specificity and accuracy despite some limitations. Patients with young ages and inappropriate histories should be assessed more carefully. MSRSGC is a useful system to show appropriate risk of malignancy.

Study on quantitative interpretation of uranium spectral gamma-ray logging based on machine learning algorithm

Spectral gamma-ray logging stands out as an efficacious methodology in the exploration of uranium(U). In order to address the challenges associated with low statistical efficacy in spectral analysis and mitigate the impact of spectral drift, machine learning algorithms such as Back Propagation (BP) neural network, Generalized Regression Neural Network (GRNN), and Support Vector Machine (SVM) have been employed for the quantitative interpretation of uranium. A method rooted in machine learning algorithms for energy spectral analysis has been proposed, catering to the analysis of high-speed spectra logging and gamma-ray spectra characterized by spectral drift. Examinations conducted on standard model wells containing uranium revealed that the BP neural network exhibited commendable accuracy in uranium interpretation, achieving quantification accuracy rates of 86.986 % and 93.478 % for low-grade and medium-high-grade uranium ores in the Testing Set, respectively. Notably, marginal variations in the model's quantification errors were observed under diverse logging speeds and spectral drift conditions, underscoring the capacity of gamma-ray spectral quantitative interpretation through machine learning algorithms to effectively surmount the impacts of logging speed and spectral drift. This machine learning-based approach to energy spectral analysis proved instrumental in enhancing traditional logging speeds to 6 m/min, presenting a novel perspective for the quantitative interpretation of uranium spectral gamma-ray logging at heightened logging speeds.

Experimental investigation of damping ratio of sand-laponite and sand-bentonite mixtures using one-dimensional bender elements

Damping ratio and dynamic shear modulus are fundamental parameters for assessing the seismic response of geotechnical structures such as retaining walls, dams, tunnels, foundations, landfill covers, and embankments. Numerous seismic wave sources can substantially impact the stability and integrity of geotechnical structures, influencing design choices and the implementation of alternative solutions. In this study, the damping ratio of sand mixed with small quantities of laponite was determined by an experimental set-up using bender elements meticulously constructed for this study. Comparative tests were conducted with pure sand (i.e., control test) and sand mixed with bentonite to evaluate the effects of two types of nanoparticles. The results revealed that the damping ratio (ξ) of pure sand was approximately 7.48 %, which is generally compatible with values reported in the literature, taking into account the variations of sand and errors associated with laboratory equipment and electronic devices. Over time, the damping ratio of pure sand gradually decreased, reaching equilibrium at 0.99 % after 3–4 days of continuous shaking. The highest observed damping ratios for sand-laponite mixtures were 59 %, 69.7 %, and 98.6 % for sand+1 % laponite, sand+2 % laponite, and sand+3 % laponite, respectively. After reaching the peak, the damping ratio gradually decreased to equilibrium at 11 %, 16 %, and 19.4 %, respectively. The higher damping values for sand-laponite specimens reflect a viscous damping contribution from the presence of laponite at sand grain contacts, with higher laponite content resulting in increased damping. In comparison, the peak damping ratios for sand-bentonite mixtures were 21.9 %, 42.7 %, and 67.9 % for sand+1 %, +2 %, and +3 % bentonite, respectively. The findings highlight the potential of laponite to enhance the damping capacity of sands, which could be valuable for seismic design applications requiring improved energy dissipation.

A Novel Checklist Approach to Reduce Time Under Anesthesia in Neurosurgery

BackgroundNeurosurgical procedures require meticulous preparation, including extra measures to ensure patient safety and the appropriate setup of the operating room, which must be fully established before the surgeon can initiate the first incision. Neurosurgical delay encompasses the time from anesthesia induction start to when the neurosurgeon makes the first incision. Methods30 neurosurgery procedures were observed randomly. Data was collected at four specific time intervals related to the process of 30 neurosurgery procedures in an operating room. Results30 random observations show the bottleneck being anesthesia induction complete to the surgeon's first cut which accounts for 50% of times greater than the average time (mean 33 minutes, n=30). Minimizing the duration of anesthesia from anesthesia induction complete to surgeon first cut reduces risks, improves patient care and patient satisfaction, ensures a seamless flow of activities, and minimizes the variability in neurosurgical operating time. ConclusionsOperational neurosurgical delays can be improved using sequential checklists by constraining the variability in each phase. The paper provides a conceptual novel checklist that provides a modular approach and completion of all steps in a phase reduces the variability of error to the next phase. This approach eventually reduces the patient's time under anesthesia.

Impact of varying solar angles on Arctic iceberg area retrieval from Sentinel-2 near-infrared data

Abstract Icebergs are part of the glacial mass balance and they interact with the ocean and with sea ice. Optical satellite remote sensing is often used to retrieve the above-waterline area of icebergs. However, varying solar angles introduce an error to the iceberg area retrieval that had not been quantified. Herein, we approximate the iceberg area error for top-of-atmosphere Sentinel-2 near-infrared data at a range of solar zenith angles. First, we calibrate an iceberg threshold at a $56^\circ$ solar zenith angle with reference to higher resolution airborne imagery at Storfjorden, Svalbard. A reflectance threshold of 0.12 yields the lowest relative error of 0.19% ± 15.74% and the lowest interquartile spread. Second, we apply the 0.12 reflectance threshold to Sentinel-2 data at 14 solar zenith angles between $45^\circ$ and $81^\circ$ in the Kangerlussuaq Fjord, south-east Greenland. Here we quantify the error variation with the solar zenith angle for a consistent set of large icebergs. The error variation is then standardized to the error obtained in Svalbard. Up to a solar zenith angle of $65^\circ$ , the mean standardized iceberg area error remains between 5.9% and −5.67%. Above $65^\circ$ , iceberg areas are underestimated and inconsistent, caused by a segregation into shadows and sun-facing slopes.

Efficient use of the TM-2441 ambulatory blood pressure measurement device in patients with diabetes.

Ambulatory blood pressure monitoring (ABPM) requires automatic measurement and the use of a validated device, according to current guidelines and international standardization. The TM-2441 (A&D Co. Ltd, Tokyo, Japan) ABPM device is small, lightweight, and suitable for use in ambulatory settings. It was validated against the ISO 81060-2:2013 standard in the general population. This study investigated the reliability of the ABPM device for patients with type 2 diabetes. Individuals aged more than 12 years affected with type 2 diabetes were recruited by our outpatient clinic. The blood pressure assessment was performed using the opposite limb simultaneous measurement according to the updated ISO 81060-2:2018. Forty-five subjects were included in the clinical investigation. The mean difference between blood pressure values measured by the TM-2441 ABPM device and the reference sphygmomanometer was within limits allowed by the ISO standard. Bland-Altman plots of the measurements and differences between the values obtained from the study device and those from the reference device showed no systematic variations in error. It was not possible to perform a stress test in most patients due to refusal or poor physical condition. The TM-2441 ABPM device fulfilled all the requirements of the ISO standard for ambulatory testing not only in a general population but also in the subgroup of subjects with type 2 diabetes.

Fractional order PID controller optimised by hybrid algorithm for instant torque control of IM motor

ABSTRACT ‘Direct torque control (DTC) is an advanced motion control technique introduced to produce fast torque control in Induction motors’. However, IM has encountered faults in the performance of flux, speed and torque. For improving the performance of induction motors, several controlling techniques were developed in the literature. This paper intends to propose an ‘Optimized Fractional Order Proportional Integral Controller (FoPID)’ for induction motors to accomplish an efficient control of speed by tuning the torque. Moreover, it is planned to exploit a direct instant model, where the speed error (variation amongst estimated speed and reference speed of induction motor) is given as input to the FoPID controller. As a major contribution, the FoPID controller parameters are fine-tuned by means of an innovative hybrid algorithm known as ‘Moth Flame Integrated Dragonfly Algorithm (MFI-DA), which combines both the concepts of Moth Flame Optimization (MFO) and Dragonfly (DA) model’. At last, the efficiency of the accepted method is verified by means of various measures.

Estimating the True Effect of Lifestyle Risk Factors for Myopia: A Longitudinal Study of UK Children.

Lifestyle risk factors are implicated in driving the current surge in myopia prevalence yet, paradoxically, known risk factors explain little of the variation in refractive error in the population. Here, we applied "instrumental variable" (IV) methods designed to avoid reverse causation and decrease confounding bias, to gauge lifestyle risk factor effect sizes. Three myopia risk factors (time outdoors, time reading, and sleep duration) were assessed in participants of the Avon Longitudinal Study of Parents and Children: a cross-sectional sample of 2302 children aged 15years old and a longitudinal sample of 3086 children followed from age 7 to 15years. Seven IVs were considered jointly: dog ownership, cat ownership, bedtime variability, birth order, and polygenic scores quantifying genetic predisposition to spend additional time outdoors, years in fulltime education, and time asleep overnight. Risk factor effect sizes were 4-fold to 9-fold higher in the IV analyses compared with conventional regression analyses. In IV analyses, one extra hour spent outdoors every day during childhood was associated with a shift toward hyperopia of +0.53 to +0.94 diopters (D), whereas 1 extra hour spent reading every day was associated with a shift toward myopia of -0.44 to -0.88 D. There was inconsistent evidence that sleep duration influenced refractive error. Myopia risk factor effects were underestimated up to 9-fold in conventional analyses in this sample, compared with IV analyses. We speculate that the effects of lifestyle risk factors for myopia have been underestimated in past studies.

Reliability Analysis of Transmission Tower Based on Unscented Transformation Under Ice and Wind Loads

Due to the complexity of the transmission tower structure and the correlation between wind and ice loads in the actual project, it is difficult to analyze the reliability of transmission towers with traditional methods. To solve this problem, the unscented transformation (UT) principle is presented concisely and used in the reliability analysis of transmission towers in this paper. Moreover, the finite element model of the target transmission tower is created. The reliability indices of the transmission tower under various loading cases are evaluated using UT and analyzed relative to the outcomes of the Monte Carlo method (MCS). Lastly, by analyzing and validating a wine-cup shape tangent tower, the simulation results show that the UT yields reliability indices with less than 6% relative error compared with MCS results for the transmission towers with lower reliability, which are more important in engineering. Variations in error caused by the change in correlation coefficients among variables are small. Consequently, the efficiency of calculations is improved by the UT-based reliability calculations for transmission towers in the case of correlated variables, which better meet engineering application requirements. It is proved that the method of reliability analysis for transmission towers based on the UT is applicable.

The Impact of Training Distances (20 m, 50 m, and 70 m) on Concentration and Anaerobic Endurance in Archery Sub-Elite Athletes

This study aims to investigate the effects of a new training method for archery athletes with different distances, namely 20 meters, 50 meters, and 70 meters, in improving concentration and anaerobic endurance. The pre- and post-control group design was used in this study with the sample selection method using the purposive sampling technique. The criteria for participants in this study include: actively practicing archery, aged 14-20 years, having at least 5 years of archery competition experience, domiciled in East Java Province, not injured, and willing to participate in the study until the end. 40 sub-elite archery athletes (20 males, 20 females) became participants in this study and were divided into 4 groups: conventional training (CON), training with a shooting distance of 20 m (NEAR), training with a shooting distance of 50 m (MID), and training with a shooting distance of 70 m (LONG) training groups. Each group underwent a 12-week training program specific to their assigned distance. The instrument used to measure concentration is the standardised archery mirror drawing test and to measure anaerobic endurance using the RAST test instrument. The results of the paired sample t-test analysis showed that in the anaerobic endurance variable, the NEAR, MID, and LONG groups had a p-value <0.05, while the CON group had a p-value > 0.05. In the concentration variable (time), all groups had a p-value <0.05, while in the concentration variable (error), there were only two groups (NEAR and CON) that had a p-value <0.05. The Anova Test results showed a significant difference between the CON and MID groups and CON and LONG (p <0.05) in the anaerobic endurance, while in the concentration (time) there was a significant difference between the CON and LONG groups and NEAR and LONG (p<0.05). In conclusion, the MID and LONG groups significantly improve anaerobic endurance and concentration, two important factors for archery success. Longer training distances appear to have a more pronounced effect on concentration.

Reliability of sEMG data of back muscles during static submaximal loading situations − Values and pitfalls

Alterations of trunk muscle activity can be objectified using surface electromyography (sEMG). But for diagnostic purposes reliable reference values are needed. Therefore, the present study evaluated sEMG measurements of representative back muscles concerning their reliability.For this purpose, 49 healthy individuals (24 women) were subjected to static loads with defined portions of their upper body weight by applying whole-body tilts up to 90° in upright posture. Reliability analyses were carried out for amplitude values and normalized values according 90° tilt angle. Reliability measures included intraclass correlation coefficient (ICC), standard error of measurement (SEm), standard error of the mean (SEM), and coefficient of variation of method errors (CVME).ICC values for the amplitude values were > 0.79 for M. multifidus (MF) and M. longissimus (LO). For the normalized values, distinctively lower ICC levels were generally detected (0.33–0.81). The values of SEm and SEM also increased with increasing load, while CVME levels of the amplitude values maintained similar levels.For defined static extension loads, good to excellent reliability can be demonstrated for back muscles. The established normative values for SEm, SEM and CVME parameters may be used for diagnostic purposes during defined submaximal load situations. This does not apply to normalized data.

Investigations on mesh stiffness functions in three-dimensional spur and helical gear models. Definitions and numerical simulations

A definition of mesh stiffness as a scalar function connecting spur and helical three-dimensional pinions and gears is presented along with its conceptual limitations. Since it relies on global parameters namely, variations in torque and transmission errors, it can therefore be applied to the vast majority of the models found in the literature. In parallel, a multi-foundation (MF) model of mesh interface is introduced with the objective of being as accurate as three-dimensional finite element (FE) simulations for highly reduced computational costs. Numerous results on mesh stiffness and tooth contact conditions are presented for unmodified spur and helical gears. It is shown that the proposed mesh stiffness functions based on global parameters are sound and that the MF and FE results compare well, particularly when gear body deflections are limited. The contributions of off-line of action contacts, elastic couplings when several tooth pairs are loaded and axial deflections in helical gears are analysed. Finally, the notion of mesh stiffness function representative of the elasticity of a pinion-gear pair for static and dynamic conditions is examined.

Validation of 3D thermal simulations of the Double C Block, a novel composite masonry unit, using in-situ U-value measurements

Energy efficient building envelopes, particularly innovative prefabricated building components, can significantly contribute to lower the building industry energy-related emissions. In this research the composite masonry unit Double C Block (DCB) is quantitatively tested by means of three-dimensional thermal simulation validated by in-situ U-value measurements carried out in both summer and winter conditions.The simulations were enhanced by the experimental measurement of the dry thermo-physical material properties (specific heat capacity, thermal conductivity, and density) in laboratory conditions for both concrete and polyurethane. The samples were extracted from real-scale DCB prototypes. Furthermore, site-specific boundary conditions of Malta — representative a case study of a Mediterranean climate − were utilised in the simulations. Both laboratory and in-situ assessments of thermal transmittance, or U-value, had their uncertainty specified as part of the calculations.The validation exercise was performed by means of hourly recorded surface temperature and the selected indices were the root mean square error and mean absolute error. Results showed that these indices were within the recommended accuracy thresholds during both winter and summer assessments. For the hourly-based heat flux measurements, the mean bias error and coefficient of variation of the root mean square error were found lower than values reported in literature, thus supporting the robustness of the overall simulation outputs. Hence the 3D CVM simulation was experimentally validated by in-situ assessments.The convergence value of the experimental U-value provided respectively 1.51 ± 0.20 W/(m2K) (winter) and 1.44 ± 0.19 W/(m2K) (summer). Then, the validated 3D CVM model was adapted to calculate the theoretical U-value of DCB, using monthly values of recorded surface temperatures obtained by the test cells (in both seasons). The predicted thermal transmittance was of 1.47 W/(m2K), in both conditions, and this almost matches the above-mentioned experimental assessments and thresholds established in previous works.

Prediction of building cooling load: an innovative design for GA-SVM and IG-SVM system for the estimation of hourly precision and computational fluctuation range

ABSTRACT Anticipating the loads that buildings will bear is a crucial strategy in the pursuit of energy efficiency and the reduction of emissions. In this paper, we introduce an interactive and comprehensive joint prediction model, specifically crafted to elevate the precision of forecasts related to building loads. The resultant Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model is put into action to provide hourly predictions of cooling loads for buildings. In scenarios where the prediction of building cooling load (BCL) fluctuations becomes imperative, especially in the face of extreme weather conditions, the information granulation (IG) method is employed. The findings of this validation process unveil significant improvements. The coefficient of variation of root mean square error (CV-RMSE) and mean absolute percentage error (MAPE) for the GA-SVM model are reduced by 58.85% and 68.04%, respectively, when compared to the conventional SVM model. Furthermore, in a comparative analysis against three widely utilized prediction models, the SVM model demonstrates a reduction in CV-RMSE and MAPE ranging from 2.04% to 68.04%. The application of the joint prediction model showcases impressive results, with R 2 values ranging from 97.27% to 99.68%, MAPE ranging from 2.59% to 2.84%, and CV-RMSE ranging from only 0.0249 to 0.0319.

Influence of multibody kinematic optimisation pipeline on marker residual errors

Residual errors are used as a goodness-of-fit metric of the musculoskeletal model to the experimental data in multibody kinematic optimisation (MKO) analyses. Despite many studies reporting residual errors as a criterion for evaluating their proposed algorithm or model, the validity of residual errors as a performance metric has been questioned, with studies indicating a non-causal relationship between residual errors and computed joint angles. Additionally, the impact of different parameters of an MKO pipeline on residual errors has not been analysed. In our study, we have investigated the effect of each step of the MKO pipeline on residual errors, and the existence of a causal relationship between residual errors and joint angles. Increases in residual errors from the baseline model (13.84 [12.72, 15.15]mm) were obtained for: models with marker registration errors of 1.25 cm (16.36 [15.37, 17.57]mm); models with segment scaling errors of 1.25 cm (14.84 [13.77, 16.24]mm); variation in marker weighting scheme (15.28[14.00, 16.85]mm); and models with differing joint constraints (18.21[17.37, 19.11]mm). We also observed that significant variation in residual errors results in significant variation in computed joint angles, with increases in residual error positively correlated with increases in joint angle errors when the same MKO pipeline is employed. Our findings support the existence of a causal relationship and present the significant effect the MKO pipeline has on residual errors. We believe our results can further the discussion of residual errors as a goodness-of-fit metric, specifically in the absence of artefact-free bone movement.

VARIABILITY OF SPELLING SYSTEMS IN ROMANI DICTIONARIES PUBLISHED IN HUNGARY BETWEEN 1797 AND 1935

The study argues that based on the analysis of 48 Hungarian Romani dictionaries, the works can be chronologically dated, and the first phase (1797–1935) is also important from an orthographic point of view since in this period we find spontaneous writing systems that were no longer used for scientific or educational purposes. The analysis has revealed that the choice of letters was not only motivated by individual intuition and mutual copying but also by an intended adaptation to Hungarian orthography and the written representation of special sounds in the Romani language. This resulted in a high degree of variability. In addition, sounds were marked inconsistently, with special regard to vowel length, aspirated sounds, and the velar fricative. This general inconsistency means that the sources do not come from a single informant even if the author said so and that a thorough examination of the dictionaries requires knowledge based on fieldwork so that a distinction can be made between error and dialectal variation.

Machine Learning-Driven Analysis of Heat Transfer in Chemically Reactive Fluid Flow Considering Soret-Dufour Effects

ObjectiveThe study investigates tangent hyperbolic nanofluid flow across an extended media, emphasizing magnetohydrodynamic influences, mixed convection, and nanoparticle transport with the help of an innovative and advanced technique. MethodologyIt employs a sophisticated Gaussian Neural Network and Hybrid Cuckoo Search to mimic complex fluid dynamics accurately, ensuring efficient computation and solution precision. By transforming the governing system of nonlinear partial differential equations into ordinary differential equations via similarity transformation, the methodology attains computational efficiency and solution precision. Core FindingsStatistical measures confirm the model's resilience, indicating absolute error variations from E−05 to E−12 and mean squared errors continuously between E−02 − E−07, highlighting the reliability of the model, while the Error in Nash-Sutcliffe Efficiency values fall within the interval E−05 − E−15. The Theil's Inequality Coefficient values lie in the range of E−01 − 10−07. Future Direction and ApplicationsThe findings underscore the model's capacity to improve thermal management, energy efficiency, and process dependability in sectors like chemical processing, environmental engineering, and sustainable energy production. This study positions the Gaussian Neural Networks framework as a robust computational instrument, providing a foundation for subsequent investigations into intricate geometries, multi-physics phenomena, and magnetohydrodynamic systems for novel technology applications.

A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation

Hydrological studies depend heavily on environmental variables, such as precipitation and resulting runoff, which exhibit highly seasonal and intermittent behaviour in semiarid basins. In these basins, the use of traditional methods to adjust biases in time series projections can lead to inaccurate results regarding the impacts of climate change. This study introduces a non-stationary bias adjustment methodology (NS) specifically designed for environmental variables characterized by sporadic events and substantial intensity variability, such as precipitation. The methodology involves establishing a probability threshold to adapt the occurrence of precipitation events and utilizes a quantile mapping method based on a non-stationary theoretical and parametric distribution to adjust biases associated with precipitation intensity. The NS method is applied to daily precipitation projections from seven regional climatic models under the RCP 8.5 scenario spanning 2006–2100, alongside historical concurrent data from 1970 to 2005. The present method is compared to the widely used quantile delta mapping approach (QDM), revealing significant differences in performance related to the distribution of precipitation events throughout the year and the behaviour of mean and extreme intensity values. Both approaches show reliable performance, with root mean square errors between the empirical distribution functions of the corrected hindcast time series and the observations being lower than or close to 1 mm for the percentiles smaller than 50th. The error increases with the percentiles, particularly at stations located at higher altitudes. In these locations, QDM doubles or triples the error obtained with the non-stationary approach for percentiles higher than 75th, reaching up to 34 mm. The proposed methodology demonstrates promising potential in reducing uncertainties associated with systematic errors in inter-annual precipitation variability. It is a first step to jointly apply a similar approach to all involved driving variables. Such methods are key to assessing hydrological responses and associated impacts in semi-arid mountainous basins all around the Globe.