Error Research Articles

Short Term Electrical Load Forecasting Based on Weather Parameters under Multiple FIS of Processing

The electrical load forecasting plays a vital role on the economy of a country in context of fuel saving, working hours of employee and depreciation cost of equipment of power generating station. In this paper, we use several machine learning techniques relevant to fuzzy system to forecast the demand of electrical load on short-term basis. Here, we consider temperature, humidity, wind speed, types of day such as working day or holiday, barometric pressure as the parameters, which govern the demand of electrical load. To cope with the variables and the power demand, the previous data of Bangladesh Power Development Board (BPDB) and Bangladesh Space Research and Remote Sensing Organization (SPARRSO) were taken for training purpose and then data of current day was used as the test data. For each of the weather parameter several membership functions (MFs) were used as the fuzzy input and then Takagi-Sugeno, Mamdani rule, FCM + Mamdani and ANFIS were applied to acquire the output as the demand of load. The average percentage of error as the difference between forecasted demand and actual demand of test data was found 1.675% for Takagi-Sugeno, 1.91% for Mamdani (centroid), 2.56% for FCM + Mamdani and 3.62% for ANFIS, which were found superior to some previous research works.

Tackling the Temporal Stiffness of Kinetic Monte Carlo Simulations of Well-Mixed Chemical Systems via On-the-Fly Scaling and Cost-Error Optimization.

Reaction kinetics in biological systems are often subject to stochastic effects due to the low populations of reacting molecules, necessitating the adoption of kinetic Monte Carlo methods for their study. Such methods, however, can be computationally expensive, especially in the case of stiff systems, where some reactions are executed at much higher frequencies than others. We present an algorithm that reduces the reaction rate constants of the fast processes on-the-fly, thereby saving computational time, while keeping the approximation error within desirable limits. The algorithm couples the Modified Next Reaction Method for simulating stochastic systems with the Common Random Number framework and calculates accurate metrics for both the computational cost and approximation error by generating multiple sets of trajectories that correspond to increasingly reduced (downscaled) reaction rate constants. The optimum downscale factor is chosen via optimization of two conflicting objectives: (a) maximizing the speedup and (b) minimizing the approximation error introduced, and it is straightforward to tune the performance of the method, favoring accuracy versus speed or vice versa. Our approach is demonstrated on a biology-inspired well-mixed stiff system and is shown to accelerate the stochastic simulation thereof from 66 h down to 90 min, achieving a speed-up factor of 44×, without distorting the dynamics of the system studied.

Development of item banks to assess financial hardship in cancer survivors using item response theory

BackgroundFinancial hardship is common after a cancer diagnosis. Current financial hardship measures have advanced the field, but assessing the dimensions of financial hardship remains challenging. We created item banks to assess four financial hardship dimensions using item response theory (IRT). IRT-based item banks can be tailored to each context and used in computerized adaptive testing (CAT) to reduce participant burden.MethodsCancer survivors (n = 459) were recruited from a survivorship program and online survey panel to complete an online or paper survey. Four item banks were developed based on previous studies, expert feedback, and patient interviews: financial coping: 41 items, financial consequences: 21 items, financial depression: 15 items, and financial worry: 21 items. We used the two-parameter logistic and graded response models for analysis.ResultsThe IRT model fit well for all four item banks: financial coping root mean square error of approximation (RMSEA) = 0.06, financial consequences RMSEA = 0.03; financial depression RMSEA = 0.05; and financial worry RMSEA = 0.03. The accuracy parameters ranged from 1.01 to 6.53, indicating good reliability for each item. The severity parameters showed each item bank assessed financial hardship across two to three standard deviations, supporting content validity. Short forms were developed for financial consequences, depression, and worry.ConclusionThe item banks can be used to create brief screening measures and, using CAT, efficiently screen for each dimension of financial hardship while minimizing burden. Future research is required to assess the clinical utility of using the item banks to screen for financial hardship.

Optimization of WEDM parameters for AZ91D magnesium biomaterial: A GRA-GA hybrid approach

Machining magnesium alloys using traditional methods pose several challenges which includes high flammability, accelerating wear, high material degradation, oxidation, and poor surface finish under machining forces. These challenges are overcome with non contact substrate type of wire electrical discharge machining (WEDM). This study carried out WEDM of AZ91D magnesium alloy by conducting various experiments based on Taguchi L18 orthogonal array. The key attributes which influence WEDM such as wire feed rate (WF), fluid pressure (FP), gap voltage (GV), pulse on period (PTON), and pulse off period (PTOFF) were utilized to assess the quality characteristics like surface irregularity (Ra), material removal rate (MRR), and microhardness (MH). Additionally, a hybrid method combining gray relational analysis (GRA) with genetic algorithm (GA) were developed and applied to achieve multi objective optimization of quality characteristics. The effects of key attributes on the quality characteristics have been studied. The percentage improvement between the hybrid GRA-GA and GRA approach contributed 33.552% for MRR, 20.8619% for Ra, and 8.34% for MH. The percentage error difference between the expected and experimental values varies within the range of ±5%, which implies strong correlation. The optimal setting were fluid pressure:- 1 bar; pulse-on-period:- 125 µs; pulse-off period:- 60 µs; gap voltage:- 10 V; wire feed rate:- 4 mm/min for minimum material removal rate and surface irregularity and for maximum microhardness. The optimized parameters can enhance the accuracy and quality of machining for medical implants, leading to improved biocompatibility and reduced risk of complications.

Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells.

Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. However, translation of metabolomics MS methods to multiyear studies conducted across multiple batches is limited by variability in electrospray ionization response, making batch-to-batch comparisons challenging. This limitation creates an artificial divide between nontargeted discovery work that is broad in scope but limited in terms of absolute quantitation ability and targeted work that is highly accurate but limited in scope due to the need for matched isotopically labeled standards. These issues are often observed in stem cell studies using metabolomic and lipidomic MS approaches, where patient recruitment can be a years-long process and samples become available in discrete batches every few months. To bridge this gap, we developed a machine learning model that predicts electrospray ionization sensitivity for lipid classes that have shown correlation with stem cell potency. Molecular descriptors derived from these lipids' chemical structures are used as model input to predict electrospray response, enabling quantitation by MS with moderate accuracy (semiquantitation). Model performance was evaluated via internal and external validation using cultured cells from various stem cell donors, achieving global percent errors of 40% and 20% for positive and negative electrospray ion modes, respectively. Although this accuracy is typically insufficient for traditional targeted lipidomics experiments, it is sufficient for semiquantitative estimation of lipid marker concentrations across batches without the need for specific chemical standards that many times are unavailable. Furthermore, the precision for model-predicted concentrations was 16.9% for the positive mode and 7.5% for the negative mode, indicating promise for data harmonization across batches. The set of molecular descriptors used by the models described here was able to yield higher accuracy than those previously published in the literature, showing high promise toward semiquantitative lipidomics.

Disturbance Observer–Based Adaptive Dynamic Event‐Triggered Control for a Class of Nonlinear Systems With Input Saturation

ABSTRACTFor the adaptive tracking control of a class of nonlinear systems with asymmetric input saturation and external disturbances, a novel dynamic event‐triggered neural network control scheme on the foundation of full‐state constraints is proposed. Firstly, by adding a compensation term into the traditional dynamic surface control framework, the filtering error ignored generally is eliminated. Secondly, a nonlinear disturbance observer is constructed to observe the compound interference united by external disturbances and approximation errors based on the approximation ability of neural network. Thirdly, an auxiliary variable is combined with a smooth function used to deal with the saturated input function to offset the influence of input saturation constraints. In addition, for ensuring that all states of the system are constrained by preset time‐varying ranges, the adaptive control method and dynamic event‐triggered condition are posed with the help of barrier Lyapunov function. Then, the tracking performance and the boundness of all signals in the closed‐loop system can be guaranteed by employing the presented adaptive control approach with dynamic event‐triggered mechanism, as well as the Zeno behavior does not appear. Finally, the validity of the designed control method can be demonstrated in simulation examples.

Non-iterative generation of optimized meshes for finite element simulations with deep learning

The finite element method is one of the most widely used computational methods in engineering and science. It provides approximate solutions to boundary value problems. The quality of these solutions critically depends on the underlying discretization, the so-called mesh. To optimize the mesh, adaptive refinement methods have been proposed over the last years that can improve mesh quality over a series of iteration steps. Herein, we propose a novel deep learning architecture that can cut short the process of mesh optimization. This architecture exploits fundamental invariance and equivariance properties to keep the amount of training data modest. It can generate high-quality meshes for a given boundary value problem and a desired target approximation error in a direct, non-iterative way. We demonstrate the performance of our method by the application to standard two-dimensional linear-elastic elasticity problems. There, our method generates meshes that reduce the solution error by 22.6% (median) compared to uniform meshes with the same computational demand.

Validation and assessment of the arabic psychological first aid scale among physicians, nurses, and counselors in Jordan

BackgroundPsychological first aid (PFA) involves strategies to tackle problems that occur as a result of disasters. This study aimed to validate the Arabic version of PFA scale through validity (face, content, convergent, discriminant validity, and confirmatory factor analysis) and reliability (Cronbach’s alpha). Furthermore, it aimed to assess the perceived levels of knowledge, skills, and attitudes of PFA among healthcare providers (HCPs). Moreover, it determined the differences between HCPs based on their knowledge, skills, and attitudes of PFA.MethodsA sample size of 389 HCPs (physicians = 68, nurses = 173, and counselors = 148) was conveniently selected to participate during the period of 10 December 2023 to 10 February 2024 after completing the translated PFA scale into the Arabic language. Descriptive, translation, validation process, and reliability were conducted.ResultsThe translation process was satisfied. The content validity index was 0.91, yielding proper clarity of items. Three constructs were loaded with a total variation of 63.43%, indicating a proper model fit. The goodness-of-fit indices for the PFA model revealed that relative chi-square was 1.47, comparative fit index was 0.91, Tucker-Lewis index was 0.89, and the root mean square of error approximation was 0.046. Cronbach alpha values for knowledge, skills, and attitudes of PFA were 0.90, 0.89, 0.87, respectively. We found that counselors have more knowledge (64.4%), skills (73.4%), and attitudes (73.4%) compared to physicians and nurses. There are significant differences (p < 0.001) between the selected HCP groups, particularly for the counselors’ group in terms of knowledge, skills, and attitudes of PFA.ConclusionsThe Arabic PFA scale is valid and reliable among HCPs. The results highlight the emergent need to provide knowledge, skills, and attitudes of PFA interventions among medical professionals, particularly for physicians and nurses. The Arabic version of PFA helps all HCPs in Arab countries to assess, apply, and implement PFA interventions.

Approximating Fixed‐Horizon Forecasts Using Fixed‐Event Forecasts

ABSTRACTMany forecast surveys ask their participants for fixed‐event forecasts. As fixed‐event forecasts have seasonal properties, users often employ an ad hoc approach to approximate fixed‐horizon forecasts based on these fixed‐event forecasts. We derive an optimal approximation for fixed‐horizon forecasts by minimizing the mean‐squared approximation error. Like the ad hoc approach, our approximation employs a weighted average of the fixed‐event forecasts. The optimal weights tend to differ substantially from those of the ad hoc approach. In empirical applications, the gains from using optimal instead of ad hoc weights turn out to be sizeable. The approximation approach proposed can also be useful in other applications.

Theoretical and Experimental Investigation of Critical Buckling Load of Combined Columns

The work done in this research is dedicated to compute the critical buckling load of combined columns. This is performed using theoretical and experimental analyses. The combined column is made of two materials, namely copper and aluminum alloys. The theoretical approach involves numerical analysis using ANSYS and analytical analysis using the equivalent stiffness method. The study determines the critical buckling load by considering many parameters. These parameters include lengths of copper and aluminum in the combined columns, cross-sectional geometries (square, circle, and hexagon), and boundary conditions. The experimental work is carried out using a Strut Bucking Apparatus on a combined column with circular cross-sections and different end supports. The findings show that the critical buckling loads achieved from numerical and analytical analysis are in good agreement with those obtained from experimental analysis. It is found that the critical buckling load increases with increasing the length of copper in the combined column and with more restrictive end supports. There is a good match of critical buckling load between those achieved from the equivalent stiffness method and ANSYS model for any considered cross-sectional geometries (square, circle, and hexagon) and two types of support (fixed-fixed and fixed-pinned). The results reveal that the combined columns with square cross-sections have higher critical buckling loads than those with circular or hexagonal cross-sections. The highest percentage errors of critical buckling load between the experimental method and theoretical methods (equivalent stiffness method and ANSYS models) are (-13.120, -12.768, and -12.453) % and (-13.083, -18.039, and -19.052) % for pinned-pinned, fixed-pinned, and fixed-fixed columns respectively.

Optimizing energy threshold selection for low-concentration contrast agent quantification in small animal photon-counting CT

Objective.Gold nanoparticles (GNPs) are widely used for biological research and applications. The in-vivo concentration of GNPs is usually low due to biological safety concerns, thus posing a challenge for imaging. This work investigates on optimal energy threshold selection in photon-counting detector(PCD)-based CT (PCCT) for the quantification of low-concentration GNPs.Approach.We derived the mathematical expression of the upper bound of the material decomposition error in the gold image. Comprehensive simulations were implemented for cylindrical phantom with inserts of different GNP concentrations. CT scans of this phantom were simulated with a 140 kVp x-ray beam under a realistic pre-clinical CT dose range. The PCD energy thresholds from 30 to 110 keV were enumerated for 2,3-channel PCCT and the optimal energy thresholds were determined by searching for the lowest decomposition error.Main results.The optimal energy threshold(s) to minimize the decomposition error in gold image was 44 keV for the 2-channel PCCT and{34,40}keV for the 3-channel case. Numerical results also validated the derived upper bounds of the decomposition error.Significance.This work addressed the need for selecting appropriate energy thresholds for accurate quantification of contrast agent distributions in pre-clinical PCCT. Both the analytical expression of the upper bound of material decomposition error and simulation results showed that the balanced consideration on photon counting noise levels and the numerical properties of the decomposition matrix is required in selecting the appropriate energy thresholds to achieve the most accurate material decomposition.

Development and psychometric evaluation of a theory of planned behavior model-based instrument for evaluation of determinants of oral health behaviors in mothers of children younger than six years in Iraq

BackgroundThe theory of planned behavior (TPB) is one of the most commonly used theories to explain oral health behaviors. To gain a deeper understanding of the determinants of oral health behaviors, we aimed to design and evaluate the psychometrics of a theory of planned behavior model-based instrument for assessing the determinants of oral health behaviors in mothers of children younger than six years in Iraq.MethodsQuestionnaires and guidelines related to the topic were searched in 2023, and the appropriate items based on the domains of the TPB model were extracted. The items were revised and completed to develop the initial version of the instruments. Face validity and content validity of the instrument were assessed. The content validity index (CVI) and content validity ratio (CVR) were calculated subsequently. The construct validity was examined by confirmatory factor analysis (CFA). Cronbach’s alpha, intra-class correlation coefficient (ICC), and percentage of agreement were employed to check the tool’s internal consistency and reproducibility reliability. The data were analyzed using SPSS version 25 and Amos software.ResultsThe questionnaire had adequate content validity (CVR ≥ 0.75, CVI ≥ 0.75). The percentage of agreement, Kappa, and ICC coefficients were 57.1–95.3%, 0.52–0.89, and 0.43–0.90, respectively. The p-value for ICC was significant in all cases (p < 0.001).For the internal stability of the tool to keep Cronbach’s alpha values > 0.7 for all domains, we had to omit three questions from attitude, one from subjective norms, and one from perceived behavioral control. In terms of construct validity, the results were as follows: the Goodness of Fit Index (GFI) was 0.942, the Comparative Fit Index (CFI) was 0.900, the Adjusted Goodness of Fit Index (AGFI) was 0.921, and the Root Mean Square Error of Approximation (RMSEA) was 0.046. These values indicate that the instrument demonstrates appropriate construct validity based on the TPB.ConclusionThe psychometric evaluation of the designed instrument showed that it was a valid and reliable tool in Arabic-speaking countries for determining child-related oral health behaviors in mothers of children under six years old.

Dust Scattering Albedo at Millimeter Wavelengths in the TW Hya Disk

Abstract Planetary bodies are formed by coagulation of solid dust grains in protoplanetary disks. Therefore, it is crucial to constrain the physical and chemical properties of the dust grains. In this study, we measure the dust albedo at millimeter wavelength, which depends on dust properties at the disk midplane. Since the albedo and dust temperature are generally degenerate in observed thermal dust emission, it is challenging to determine them simultaneously. We propose to break this degeneracy by using multiple optically thin molecular lines as a dust–albedo-independent thermometer. In practice, we employ pressure-broadened CO line wings that provide an exceptionally high signal-to-noise ratio as an optically thin line. We model the CO J = 2–1 and 3–2 spectra observed by the Atacama Large Millimeter/submillimeter Array at the inner region (r &lt; 6 au) of the TW Hya disk and successfully derive the midplane temperature. Combining multiband continuum observations, we constrain the albedo spectrum at 0.9–3 mm for the first time without assuming a dust opacity model. The albedo at these wavelengths is high, ~0.5–0.8, and broadly consistent with the L. Ricci et al., DIANA, and DSHARP dust models. Even without assuming dust composition, we estimate the maximum grain size to be ~340 μm, the power-law index of the grain size distribution to be &gt;−4.1, and the porosity to be &lt;0.96. The derived dust size may suggest efficient fragmentation with a threshold velocity of ~0.08 m s−1. We also note that the absolute flux uncertainty of ~10% (1σ) is measured and used in the analysis, which is approximately twice the usually assumed value.

Energy-preserving methods for gyrocenter system in strong magnetic field

Abstract We formulate second-order and fourth-order energy-preserving methods with two explicit corrections for gyrocenter system in a strong magnetic field 1 ϵ B ( X ) where the ∥ B ( X ) ∥ 2 = O ( 1 ) and ϵ &lt; &lt; 1. We theoretically show that the error bound of the two methods are independent of ϵ. The two explicit corrections are both manifold corrections. The first correction is confined to the manifold that the Hamiltonian at current time step should equal to the Hamiltonian at former time step and the second correction is confined to the manifold that the Hamiltonian at current time step should equal to the original Hamiltonian. Numerical experiments illustrate the impact of the ϵ on the phase orbit and the global error. Additionally, the numerical results show that the energy-preserving methods with the first correction show its advantage in long-term energy conservation than the energy-preserving methods without correction. The energy-preserving methods with the second correction behaves better in energy conservation than those with the first correction as the energy error of the former ones can be bounded by the machine precision over long time.

A constructible conductivity cloak via homogenisation*

Abstract We show that approximate cloaks can be constructed by a finite number of layers of isotropic materials. We detail a construction method, and we provide an upper bound on the number of layers required. Our approach is to view anisotropic matrix-valued materials obtained by the cloaking-by-mapping method as the effective limit of a multiple materials rank-one laminate, and to quantify precisely the approximation error. We illustrate our result and a variant in dimension two with an example.

Analysis of the Frictional Properties of Carbon nanotubes-coated Aramid Fiber-Reinforced Epoxy Composites Using Machine Learning Techniques

Abstract This study examines the effects of mechanical behavior, thermal characteristics, and tribological variables (sliding frequency, normal load, and temperature) on the tribological performance of CNTs-coated aramid fabric-reinforced epoxy composites using a computational and data-driven machine learning (ML) approach. Predictive models for the coefficient of friction (COF) were developed based on previous tribological, mechanical, and thermal data, employing three ML algorithms: Artificial neural network (ANN), Gradient Boosting Machines (GBM), and Random Forest (RF). The models showed that ANN achieved an (R2 = 0.9088), GBM (R2 = 0.92807), and RF (R2 = 0.85294) with the GBM model providing the best predictions. The dataset with the best performance had an error percentage of 0.003658%, while the poorest performance showed 13.56625%. Feature score analysis highlighted load, sliding frequency, and CNTs content as key factors influencing COF. This data-driven ML analysis offers significant insights into the tribological behavior of fiber-reinforced polymer composites, aiding in material design and performance optimization.

Optimization of FSW input parameters for dissimilar butt-welded joints of Al6061-AZ31 alloys by GRA

In this paper, the optimization of Friction Stir Welding (FSW) parameters used to produce dissimilar non-ferrous butt joints of Al6061 and AZ31 alloys is investigated carefully. FSW parameters including tool rotation speeds (TRS) ranging between 700–1100 rpm, traverse speed (TS) 40–80 mm/min, and axial load (LA) 7–11 KN are considered for finding suitable FSW parameters. The experiments are carried out at different combinations of FSW parameters and the results are measured using respective equipment. The L18 orthogonal array of Taguchi design of experiments is adapted for Grey relation analysis (GRA) multi-objective optimization aiming for high tensile strength and less wear rate with measured responses. Based on grey relational grade, TRS, TS, and axial load of 1100 rpm, 60 mm/min, 9KN are found to best suitable FSW parameters with reasonable tensile strength of 132.43 MPa, microhardness of 70.4 Hv, and lower wear rate of 5.156 mm3/m, respectively. The confirmatory test has been performed to verify the finding of the optimization study with a maximum error percentage of 2%. Furthermore, the corrosion analysis has also been performed for the produced FSW butt joint at a suitable parametric combination. Corrosion behavior is improved due to equiaxed grain distribution and formed hard Mg2Si precipitates that reduce the differential dissolution effect when embedded in Al matrix. It signifies the improvement in chemical inertness in application to extreme temperature environments.

Factor Structure of the Brief Pain Inventory-Short Form in African American Older Adults With Osteoarthritis.

To evaluate the factor structure of the Brief Pain Inventory-Short Form (BPI-SF) and measurement invariance across two age groups for African American (AA) older adults with osteoarthritis (OA). Participants were AA older adults aged 50 to 94 years with self-reported OA and chronic pain (N = 110). Cross-sectional data from the BPI-SF were obtained from all participants, and confirmatory factor analysis (CFA) was used to evaluate the factor structure. Measurement invariance across young-old (aged 50 to 69 years) and old-old (aged 70 to 94 years) participants was examined at configural, full metric, and full scalar levels. CFA revealed that a three-factor model (i.e., pain intensity, activity interference, and affective interference) demonstrated the best fit (χ2/df = 1.595, comparative fit index [CFI] = 0.949, root-mean-square error of approximation = 0.074). The change of CFI between configural and metric invariance was below the cutoff point of 0.01, supporting full metric (i.e., factor loadings) invariance across the two age groups. However, full scalar (i.e., item intercepts) invariance was not demonstrated. Results support a three-factor structure of the BPI-SF, which is consistent across two age groups for AA older adults with OA. This study provides evidence that the BPI-SF can reliably measure pain intensity and two distinct dimensions of pain interference in this population. [Journal of Gerontological Nursing, 51(2), 13-22.].

First psychometric evaluation of the Mexican Spanish version of the 11-item BPFSC-11 and convergence with maladaptive self and interpersonal functioning.

The Borderline Personality Features Scale for Children (BPFSC-11) is a well-used, short, and easy-to-administer measure of borderline traits in young people. The aim of the current study was to evaluate the psychometric properties of the Mexican Spanish version of the BPFSC-11 in a large community-based sample of Mexican adolescents. In addition, we evaluated the convergence between the borderline construct as measured by the BPFSC-11 and general personality functioning consistent with more contemporary personality disorder formulations to assess the nomological net of the BPFSC-11 total score. A sample of 1212 adolescents (52.6% female, mean age = 15.11, SD = 1.74) were recruited from the community through schools in Mexico City and Michoacán area. Reliability of the BPFSC-11 was good (α = 0.805; ω = 0.806). A confirmatory factor analysis (CFA) model testing the unidimensional factor structure of the 11 items of the BPFSC-11 provided good fit (X2[44] = 137.96, p < 0.001; root mean square error of approximation (RMSEA) = 0.04 [90% CI: 0.03, 0.05]; comparative fit index (CFI) = 0.96; standardized root mean square residual (SRMR) = 0.03). Both configural and metric invariance for gender and age were demonstrated; however, only partial scalar invariance could be demonstrated with some items showing "bias" for gender and age. The Mexican Spanish BPFSC-11 also showed strong convergence with measures assessing contemporary conceptualization of general personality functioning. The Mexican Spanish BPFSC-11 appears to show similarly strong psychometric properties of other versions of the BPFSC-11; and the borderline construct as assessed with the BPFSC-11 shows good conversion with general personality functioning in adolescents.

Psychometric properties of a physiotherapy care satisfaction scale using telerehabilitation in caregivers of pediatric patients during the COVID-19 pandemic.

Evidence on the psychometric properties of satisfaction scales in telerehabilitation is limited, especially in specific populations such as caregivers of children. To determine the psychometric properties of a physiotherapy care satisfaction scale using telerehabilitation in caregivers of pediatric patients during the COVID-19 pandemic. A total of 155 caregivers were evaluated between June and December 2020. Approximately 91% (141) were women. Evidence of content validity for the scale was obtained through evaluation by three expert judges, who confirmed the adaptation of the University of Washington Telemedicine Patient Satisfaction Survey, in which the word "telemedicine" was replaced with "telerehabilitation" and "physician" with "physical therapist." For the confirmatory factor analysis, two models were tested. The first one-factor model with nine items did not fit satisfactorily based on the goodness-of-fit indices (χ 2/df = 13.96, comparative fit index [CFI] = 0.963, non-normed fit index [NNFI] = 0.951, root mean square error of approximation [RMSEA] = 0.290 [0.265, 0.316], and standardized root mean square residual [SRMR] = 0.178). In contrast, the second one-factor model, which involved respecification of Items 6 and 7, was considered acceptable (χ 2/df = 1.60, CFI = 0.998, NNFI = 0.998, RMSEA = 0.062 [0.021, 0.096], and SRMR = 0.057). Reliability was acceptable, with a value of 0.888. Additionally, network analysis confirmed the direct relationship between the items, with Item 7 showing the greatest strength centrality. The instrument demonstrated sufficient evidence of validity and reliability in the Peruvian context, supporting its use with pediatric patients.