Variable Error Research Articles

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.

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.

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.

Study and Analysis of the Thunder Source Location Error Based on Acoustic Ray-Tracing

Error analysis and estimation of thunder source location results is a prerequisite for obtaining accurate location results of thunder sources, which is of great significance for a deeper understanding of the physical process of lightning channel discharges. Most of the thunder source location algorithms are based on the simplified model of the straight-line propagation of acoustic waves to determine the location of the thunder source; however, the acoustic wave is affected by the inhomogeneity of the atmosphere medium in the propagation process and its acoustic ray will be bent. Temperature and humidity are the main factors affecting the vertical distribution of the velocity of sound in the atmosphere, therefore, it is necessary to study the changes in location errors under the models of uniform vertical distribution of temperature only and uniform vertical distribution of humidity only. This paper focuses on the theory of acoustic ray-tracing in neglecting the presence of the wind and the acoustic attenuation and the theoretical derivation of the location error of thunder source inversion for the three models is carried out by using MATLAB R2019b programming. Then, simulation analysis and comparative study on the variation law of thunder source location error with the height of the source, ground temperature, ground humidity, and array position under the three models are carried out. The results of the study show that the maximum location error can be obtained from the straight-line propagation model, the location error obtained from the model of uniform vertical distribution of temperature only is the second, and the location error obtained from the model of uniform vertical distribution of humidity only is the least and can be negligible compared to the first two models. In the trend of error variation, the variation of location error with temperature and humidity is relatively flat in the first two models; however, the variation of location error with the height of the thunder source is more drastic, which can be more than 80%. The location error obtained from the array inversion closer to the thunder source increases linearly with the height of the thunder source, the location error obtained from the more distant array inversion shows a fast-decreasing trend at the height of the thunder source from 500 to 3500 m, and a flat trend above 3500 m. The location error varies relatively smoothly with the height of the thunder source, the ground temperature, and the ground humidity in the model of uniform vertical distribution of humidity only. In addition, the position of the array also has an important effect on lightning location. The further the horizontal distance from the source, the greater the location error will be obtained in the first two models, and when the thunder source is at a low height and detected at a long distance, the location error will be very large, so relevant data should be modified in actual observation.

Clinical benchmark dataset for AI accuracy analysis: quantifying radiographic annotation of pelvic tilt

Radiographic landmark annotation determines patients’ anatomical parameters and influences diagnoses. However, challenges arise from ambiguous region-based definitions, human error, and image quality variations, potentially compromising patient care. Additionally, AI landmark localization often presents its predictions in a probability-based heatmap format, which lacks a corresponding clinical standard for accuracy validation. This Data Descriptor presents a clinical benchmark dataset for pelvic tilt landmarks, gathered through a probabilistic approach to measure annotation accuracy within clinical environments. A retrospective analysis of 115 pelvic sagittal radiographs was conducted for annotating pelvic tilt parameters by five annotators, revealing landmark cloud sizes of 6.04 mm-17.90 mm at a 95% dataset threshold, corresponding to 9.51°–16.55° maximum angular disagreement in clinical settings. The outcome provides a quantified point cloud dataset for each landmark corresponding to different probabilities, which enables assessment of directional annotation distribution and parameter-wise impact, providing clinical benchmarks. The data is readily reusable for AI studies analyzing the same landmarks, and the method can be easily replicated for establishing clinical accuracy benchmarks of other landmarks.

The Evaluation of Global and Regional Applications of Model for Prediction Across Scales-Atmosphere (MPAS) Against Weather Research Forecast (WRF) Model over California for a Winter (2013 DISCOVER-AQ) and Summer (2016 CABOTS) Episode

The Model for Prediction Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus on California. The results of both global and regional applications of MPAS were compared against the surface and upper air rawinsonde observations while the variations of characteristic meteorological variables and modeling errors were evaluated in space, time, and statistical sense. The results of the Advanced Weather Research and Forecast (WRF-ARW, hereafter WRF) model simulations for the same episodes were also used to evaluate the results of both applications of MPAS. The temporal analyses performed at surface stations indicate that both global and regional applications of MPAS and WRF model predict the diurnal evolution of characteristic meteorological parameters reasonably well in both winter and summer episodes studied here. The average diurnal bias in predicting 2 m temperature by MPAS and WRF are about the same with a maximum of 2 °C in winter and 1 °C in summer while that of 2 m mixing ratio is within 1 g/kg for all three modeling applications. The rawinsonde profiles of temperature, dew point temperature, and wind direction agree reasonably well with observations while wind speed is underestimated by all three applications. The comparisons of the spatial distribution of anomaly correlation and mean bias errors calculated from each model results for 2 m temperature, 2 m water vapor mixing ratio, 10 m wind speed and wind direction indicate that all three models have similar magnitudes of agreement with observations as well as errors away from observations throughout California.

Comparative study of LSTM and ANN models for power consumption prediction of variable refrigerant flow (VRF) systems in buildings

The optimized control of variable refrigerant flow (VRF) system requires an accurate time series forecast model for power consumption. Currently, physics-based and black-box models widely used for forecasting power consumption may not be able to capture the dynamic and non-linear behavior of such complex systems. This study presents a long-short-term memory (LSTM), deep learning-based model to accurately predict the power consumption of a VRF system with heat recovery units. The model training used one year of VRF system field test data. The feature selection through the Pearson correlation coefficient was implemented to improve the model's accuracy and computational efficiency. The sensitivity analysis of feature selection was performed by preparing three feature sets, including different levels of relationship with the predicted target. Additionally, the hyperparameters of the models were optimized by Bayesian optimization with the Tree-structured Parzen Estimator algorithm. The deep learning model, LSTM model, was compared to the baseline machine learning model, Artificial Neural Network (ANN) and decision tree. The results show that LSTM-30feat with input time step 4 has the best testing performance of Coefficient of the Variation of the Root Mean Square Error (CvRMSE) 23.3%. The best ANN model is ANN-10feat with input time step 8, which has a CvRMSE of 27.8% in testing and 13,569 trainable parameters. However, LSTM-10feat with input time step 4 has the CvRMSE of 24.8% in testing, and the trainable parameters are 1,809. A higher number of trainable parameters in models might result in increased memory usage on the computer and be computationally expensive.

Exploring the optimal combination of Ru/Ta bilayer mask stacks and illumination source shapes to mitigate mask 3D effects at high-NA extreme ultraviolet lithography

The imaging performance of extreme ultraviolet (EUV) lithography is determined by the optical properties and thickness of the photomask absorber material and the illumination source shape. Optimizing the trade-offs between imaging metrics, such as normalized image log slope, telecentricity error, and best focus variation through pitch (collectively known as mask-3-dimensional (M3D) effects), is crucial to improve the throughput of the EUV lithography process. This study aims to optimize Ru/Ta bilayer photomask absorber stacks and illumination source shapes to mitigate M3D effects using mask diffraction analysis. It intends to raise questions about the conventional absorber reflectivity or induced phase shift-based approach.

Influence on micro-geometry and surface characteristics of laser powder bed fusion built 17-4 PH miniature spur gears in laser shock peening

Micro-geometrical errors, surface roughness, and surface integrity (microstructure, residual stresses, microhardness) play an important role in defining the quality of the gears as they directly affect their noise, vibration characteristics and service life during their use. In the present work, underwater laser shock peening (LSP) is employed to improve the quality of the laser powder bed fusion built 17-4 PH small-size spur gears (12 mm outside diameter). LSP was employed near the spur gear root, and effects were measured in terms of residual stresses, variation in microgeometry errors, surface roughness, porosity, microstructure, and microhardness. It was observed that LSP could impart compressive residual stresses up to 0.4 mm of measured depth, while the surface roughness has improved by 32%. Microgeometry and microhardness of gears showed minor variations. Additionally, LSP has shown an impact on the microstructure as the grain orientation was altered and grain size reduced by 15.6% due to shock wave transmission. The study paves the way to use LSP as a post-processing technique to modify the surface characteristics of LPBF-built miniature spur gears with minimal impact on the gear microgeometry.

Tracking Small Animals in Complex Landscapes: AComparison of Localisation Workflows for Automated Radio Telemetry Systems.

Automated radio telemetry systems (ARTS) have the potential to revolutionise our understanding of animal movement by providing a near-continuous record of individual locations in the wild. However, localisation errors in ARTS data can be very high, especially in natural landscapes with complex vegetation structure and topography. This curtails the research questions that may be addressed with this technology. We set up an ARTS grid in a valley with heterogeneous vegetation cover in the Colombian high Andes and applied an analytical pipeline to test the effectiveness of localisation methods. We performed calibration trials to simulate animal movement in high- or low-flight, or walking on the ground, and compared workflows with varying decisions related to signal cleaning, selection, smoothing, and interpretation, along with four multilateration approaches. We also quantified the influence of spatial features on the system's accuracy. Results showed large variation in localisation error, ranging between 0.4-43.4 m and 474-1929 m, depending on the localisation method used. We found that the selection of higher radio signal strengths and data smoothing based on the temporal autocorrelation are useful tools to improve accuracy. Moreover, terrain ruggedness, height of movement, vegetation type, and the location of animals inside or outside the grid area influence localisation error. In the case of our study system, thousands of location points were successfully estimated for two high-altitude hummingbird species that previously lacked movement data. Our case study on hummingbirds suggests ARTS grids can be used to estimate small animals' home ranges, associations with vegetation types, and seasonality in occurrence. We present a comparative localisation pipeline, highlighting the variety of possible decisions while processing radio signal data. Overall, this study provides guidance to improve the resolution of location estimates, broadening the application of this tracking technology in the study of the spatial ecology of wild populations.

Extraction and analysis of short-term variation characteristics of code multipath error

Multipath errors (MP) can seriously affect positioning accuracy. Extracting and analyzing the variation characteristics of MP can provide a basis for mitigating it, but the current studies primarily focus on the characteristics of long-term variation of multipath errors while ignoring its short-term variation, which leads to incomplete understanding of the MP. Code and carrier phase dual-frequency observation combination and moving average method are combined to achieve accurate extraction of short-term code multipath error variation (MPvar), and different moving average strategies are adopted to satisfy the needs of real-time and after-the-fact extraction. The variation characteristics of MPvar between sea and land, among different GNSS systems, among different orbits of the BDS system are compared and analyzed. Study indicates that the carrier-to-noise ratio (C/N0) at sea is low and fluctuates greatly compared with the C/N0 at land, but the MPvar at sea is much smoother. There are differences in the magnitude of MPvar for each GNSS system, but they are all correlated with the elevation angle. For BDS GEO satellites, although the elevation angle variations are minimal, the MPvar has significant variations. Therefore, this study suggests that the MP variations of the BDS GEO satellites cannot be regarded as a smooth process when MP sources exist in the vicinity of the static observation stations. The extraction method and analysis results in this study helps to provide ideas for mitigating MP from a perspective of short-term variation.

Reconsidering assumptions in the analysis of muscle fibre cross-sectional area.

Cross-sectional area (CSA) is a fundamental variable in characterizing muscle mechanical properties. Typically, the CSA of a single muscle fibre is assessed by measuring either one or two diameters, and assuming the cross-section is either circular or elliptical in shape. However, fibre cross-sections have irregular shapes. The accuracy and precision of CSAs determined using circular and elliptical shape assumptions are unclear for mammalian skinned muscle fibres. Second harmonic generation imaging of skinned rabbit soleus fibres revealed that the circular assumption overstated real CSA by 5.3±25.9% whereas the elliptical assumption overstated real CSA by 2.8±6.9%. A preferred rotational alignment can bias the circular assumption, as real CSA was overstated by 22.1±24.8% when using the larger fibre diameter and understated by 11.4±13% when using the smaller fibre diameter. With 73% lower variable error and reduced bias, the elliptical assumption is superior to the circular assumption when assessing the CSA of skinned mammalian fibres.

Do Subsampling Strategies Reduce the Confounding Effect of Errors in Bispectral Retrievals on Estimates of Aerosol Cloud Interactions?

AbstractBi‐spectral retrievals of droplet effective radius and cloud optical depth are widely utilized to estimate aerosol cloud interactions (ACI) in warm clouds in the marine boundary layer. Here, we assess the effect of retrieval errors due to the neglect of 3D radiative transfer during the retrieval process on this analysis of ACI. We use an ensemble of stochastically‐modeled cloud fields and 3D radiative transfer simulations to study the retrieval errors at a solar zenith angle of 30°. Simulated retrieval biases in droplet number concentration (Nd) for all three MODIS channels vary systematically from +35% to −80% as cloud heterogeneity increases. Pixel‐level errors can be much larger. Commonly utilized subsampling strategies do not reduce the systematic variation in retrieval error. Negative error correlations between optical depth and droplet effective radius produce spuriously negative slopes between the logarithm of liquid water path and Nd (−1.0 to −0.3). Pixels at the center of (8 km)2 patches that are not overcast have a relative bias in Nd of −50%. The relative frequency of these biased pixels varies linearly with the clear fraction in MODIS data and form the basis for a simple parameterization of Nd bias that varies with cloud fraction (CF). Using this parameterization, synthetic data experiments indicate that estimates of the first indirect effect in the tropical ocean can be overestimated by up to 30%, and the effect of aerosol on CF is overestimated by 50%, due to neglecting the correlation between retrieval errors and cloud microphysics.

Using dynamic ultrasound to assess Achilles tendon mechanics during running: The effect on running pattern and muscle–tendon junction tracking

Achilles tendon strain can be quantified using dynamic ultrasound, but its use in running is limited. Minimal effects on running pattern and acceptable test–retest reliability of muscle–tendon junction (MTJ) tracking are prerequisites for ultrasound use during running. We aimed to assess (i) the effect of wearing an ultrasound transducer on running pattern and (ii) the test–retest reliability of MTJ tracking during running. Sixteen long-distance runners (nine injury-free, seven with Achilles tendinopathy) ran at different speeds on an instrumented treadmill with a 10-camera system tracking skin-mounted retroreflective markers, first without and then with an ultrasound transducer attached to the lower leg to track the MTJ of the gastrocnemius medialis. Spatiotemporal parameters, joint kinematics and kinetics were compared between conditions using mixed ANOVAs and paired t-tests. MTJ tracking was performed manually twice by three raters in ten participants. Variability and standard error of measurement (SEM) quantified the inter- and intra-tester test–retest reliability. The running pattern was not affected by wearing the ultrasound transducer, except for significantly less knee flexion during midstance (1.6°) and midswing (2.9°) found when wearing the transducer. Inter-rater and intra-rater SEMs for MTJ tracking to assess the tendon strain (0.43%, and 0.56%, respectively) were about four times as low as between-group differences presented in literature. The minimal effects found on the running pattern and acceptable test–retest reliability indicates that dynamic ultrasound during running can be appropriately used to study Achilles tendon mechanics and thereby help improve our understanding of Achilles tendon behavior during running, injury development and recovery.

The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Effectiveness of Transcranial Direct Current Stimulation (tDCS) during a Virtual Reality Task in Women with Fibromyalgia-A Randomized Clinical Study.

Fibromyalgia (FM) is a chronic condition characterized by widespread musculoskeletal pain, fatigue, and impaired motor performance. This study aimed to investigate the effects of transcranial direct current stimulation (tDCS) during virtual reality (VR) tasks on the motor performance of women with FM. Participants were divided into two groups: Group A received active tDCS for 10 days followed by sham tDCS for 10 days, while Group B received the opposite sequence. Both groups performed VR tasks using MoveHero software (v. 2.4) during the tDCS sessions. Motor performance was assessed by the number of hits (movement with correct timing to reach the targets) and absolute (accuracy measure) and variable (precision measure) errors during VR tasks. Participants were 21 women, aged 30-50 years, and diagnosed with FM. Group A, which received active tDCS first, presented significant improvements in motor performance (number of hits and absolute and variable errors). The benefits of active tDCS persisted into the sham phase, suggesting a lasting neuroplastic effect. tDCS during VR tasks significantly improved motor performance in women with FM, particularly in complex, extensive movements. These findings indicate that tDCS enhances neuroplasticity, leading to sustained motor improvements, making it a promising therapeutic tool in FM rehabilitation.

Temporal Statistical Analysis and Retrospective Assessment of Mortality Rate in the United States from 1999 to 2018

Objectives: To retrospectively examine the mortality trend in the USA from 1999 to 2018 using a basic statistical model while examining multiple variables. The inferential and descriptive statistical tools will be prioritized to elucidate the mortality of both years in simple terms. Methods: We conducted a retrospective observational descriptive analysis of data on the 10 leading causes of death in the United States by age, race, and sex over 2 decades (1999 to 2018). The data in this report are based on information from all death certificates filed in the 50 states and the District of Columbia. The causes of death are classified by the International Classification of Diseases, 10th Revision (ICD–10), which is ranked according to the number of deaths (Cause-of-death statistics are based on the underlying cause of death). This report supplements “Deaths: Final Data for 2020,” the National Center for Health Statistics’ annual report of final mortality statistics. SPSS (statistical package for social sciences) version 29 Software in Excel sheet was used to analyze the generated datasets. Descriptive and inferential (parametric and non-parametric) analyses were used. The statistically significant relationships and assumptions were made using the chi-square/t-test, Kruskal-Wallis, and Fischer's exact tests with the significance level set at p ≤ 0.05. The analysis and determination of the level of agreement between the initial diagnosis and findings from the new analysis done. Results: The proportion of age-adjusted death rates of the heart (such as coronary artery disease) account for the highest proportion of 24%, while Malignant neoplasms (cancers) represent 23%, Chronic lower respiratory diseases contribute 6%, Unintentional injuries (accidents) also play a significant role, accounting for 5% of deaths. The regression analysis reveals a strong and statistically significant relationship between the number of causes in females and males. There was a high death rate of around 60 years in 1999 and a consistent decline over the years, reaching below 40 by 2018, with a 15/100,000 death fluctuation mark. Asian or Pacific Islander has the lowest age adjustment value of 8762.4, American Indian or Alaska Native, Hispanic, or Latino(11673.2), Black or African American, Hispanic, or Latino (12992.1), White, not Hispanic or Latino(15457.1), Black or African American (15708.2), Hispanic or Latino(19624.1), American Indian or Alaska Native, not Hispanic or Latino (16287.9).Asian or Pacific Islander, not Hispanic or Latino( 8791.3), and 99.2% of the variability in the dependent variable and standard error of 6.7240. Conclusions: Cardiovascular death remains the most common cause of death, with a significant decrease in the death rate from 1999 to 2018 due to improved health care and the provision of measures to improve health care. The regression analysis reveals a strong and statistically significant relationship between the number of causes in females and males. Therefore, based on the results, the number of causes in females strongly predicts the number of causes in males.