Error Research Articles

Pathway analysis of the impact of dysphagia on the prognosis of patients with stroke: based on structural equation modeling.

Dysphagia is significantly correlated with prognostic outcomes in patients with stroke; however, the intrinsic mechanism of action between the two remains unclear. This study aimed to model the intrinsic mechanism of action between dysphagia and prognostic outcomes in patients with stroke based on structural equation modeling. A retrospective analysis of 900 inpatients with stroke from three large hospitals was performed. AMOS software (version 23.0) was used to construct the structural equation modeling. The overall model showed a good fit (chi-square = 27.3, root mean square error of approximation = 0.01, standardized root mean square residual = 0.032, comparative fit index = 0.98, and adjusted goodness of fit = 0.94). Structural equation modeling showed that the total effect of dysphagia on the prognosis of patients with stroke was 0.694, with a direct effect of 0.599, accounting for 86.31% of the total effect. The total indirect effect was 0.095, with the mediating effects of serum albumin level and pneumonia accounting for 6.48% and 7.35% of the total effect, respectively. The moderating effects of sex on dysphagia and the relationship between activities of daily living, modified Rankin scale score, and length of hospital stay were insignificant (ΔR2 = 0.063, P = 0.145; ΔR2 = 0.002, P = 0.620; ΔR2 = 0.001, P = 0.307). Dysphagia can directly affect the prognostic outcomes of patients with stroke and indirectly affect prognosis by triggering pneumonia and lowering albumin levels. Sex was not found to play a moderating role in the relationship between dysphagia and prognosis.

METHOD OF INTEGRAL TRANSFORMATIONS IN SOLVING THE PROBLEM OF DETECTING SMALL VEHICLES IN IMAGES

The article proposes a method for forming a set of features on images based on three integral transformations: the Radon transform, the Steklov function and the Fourier transform. Using a discrete analog of these transformations, the values that form a set of features are calculated. Depending on the transformation parameters, the number of features can be changed. We selected the values of these parameters such that the number of features is 903. The use of this approach in solving the problem of detecting small-sized, and therefore poorly visible, vehicles in video images is shown. In addition, we have developed an improved version of the least squares method based on processing the obtained features using some transformations of the labels of the training set of images. The main essence of this method is to perform an affine transformation of the label value into a small neighborhood of its original value. A priori estimates show a decrease in the approximation error using the least squares method. The paper also shows a comparison of the developed approach with convolutional neural networks. This comparison allows us to say that it is not much inferior to them in such an indicator as the percentage of correct predictions. At the same time, in terms of prediction execution time, the method presented in the article works 3-4 times faster depending on the model used. In the practical part of the work, software tools from the OpenCV, Keras and Scikit-learn libraries were used.

Italian version of the Headache Disability Inventory: Cross-cultural adaptation, validity, and reliability.

To translate and cross-culturally adapt the Headache Disability Inventory (HDI) into Italian and study its reliability and validity. A total of 132 participants with primary and secondary headaches were included. The translation was performed following international guidelines with forward and back translation procedures. Structural validity, internal consistency, test-retest reliability, measurement error, and construct validity were studied. Test-retest reliability and measurement error were tested on a subsample of 32 participants. The cross-cultural adaptation into Italian was performed without issues. Confirmatory factor analysis supports the structural validity partially (comparative fit index = 0.963; Tucker-Lewis index = 0.959; root mean square error of approximation = 0.051; standardized root mean square residual = 0.084), showing a two-factor structure (i.e., emotional and functional). Each subscale presented high internal consistency (α = 0.87 and 0.87 for the emotional and functional subscales, respectively), excellent and good test-retest reliability (intraclass correlation coefficient = 0.93 and 0.88 for the emotional and functional subscales, respectively), and acceptable measurement error (standard error of the measurement [SEM] = 3.6 points, minimal detectable change [MDC] = 10.0 points for the emotional subscale; SEM = 3.8 points, MDC = 10.7 points for the functional subscale). Construct validity was satisfactory for the emotional subscale and moderate for the functional subscale, as 85.7% (6/7) and 57.1% (4/7) of a priori hypotheses were met, respectively. The HDI was successfully translated into Italian and has acceptable psychometric properties. The Italian version of the HDI can be used in daily clinical practice and research to assess the functional and emotional impact of primary and secondary headaches. Further research should study other psychometric properties (i.e., content validity, responsiveness, and reliability in a larger sample).

ASSESSMENT OF GAS PARAMETERS UNCERTAINTY IN A DAMAGED GAS PIPELINE BASED ON A STEADY-STATE GAS FLOW MODEL

Detecting gas leaks due to damage to gas pipelines and determining the volume of natural gas losses is important for reducing the imbalance in gas pipeline systems and increasing their reliability. The volume of lost gas can be determined based on the pressure and temperature of the gas at the location of the damage and the geometric characteristics of the damage. Since the gas parameters at the place of damage to the gas pipeline may differ significantly from the measured values ​​of the gas parameters at the nodal points, they should be determined based on mathematical models of gas movement. While determining the volume of lost gas, it is also essential to estimate the uncertainty of this volume, which requires also estimating the uncertainty of the pressure and temperature of the gas at the point of damage. This article is devoted to developing analytical equations for assessing the uncertainty of gas pressure and temperature at the damage point, obtained using a mathematical model of steady-state gas flow. The methodology for evaluating the uncertainty of gas pressure and temperature at the pipeline damage point is considered, taking into account various factors such as pressure, temperature, flow rate, gas composition, and the geometric characteristics of the pipeline. The research relies on standardized procedures and methods for uncertainty analysis presented in JCGM 100:2008 and ISO 5168:2013. As a result of the study, equations for the relative standard uncertainty of pressure and temperature of natural gas at the damage point were derived, and the influence coefficients of the uncertainty components were found, enabling the assessment of these parameters' uncertainty while considering the uncertainties of the pipeline's geometric characteristics and the flow gas-dynamic parameters. Examples of calculating the relative standard uncertainty of pressure and temperature of the gas at the damage point are provided for the case of a complete pipeline rupture. The obtained equations for the uncertainty of pressure and temperature of gas at the damage point will serve as the basis for the methodology for estimating the uncertainty of the volume of gas lost due to damage.

Localized structural thinning monitoring with high-order Lamb wave mode enabled by direct-write ultrasonic transducers

This article presents a method for monitoring depth of a localized thinning in a structure by analyzing the arrival time and peak absolute amplitude of selected high-order ultrasonic Lamb waves. The high-order Lamb waves are generated and detected by piezoelectric ultrasonic transducers that are in-situ fabricated on a structure, known as direct-write ultrasonic transducers (DWTs). Unlike conventional discrete ultrasonic transducers, a DWT is lightweight, low-profile, and has high conformality, allowing implementation in confined space due to its reduced design footprint. Additionally, the DWT exhibits enhanced excitability and detectability for the high-order Lamb wave due to its enhanced Lamb wave mode selectivity attributed to its comb-shaped electrode design. This design simplifies signal analysis by directly selecting the high-order Lamb wave mode that possesses the fastest group velocity as shown in the dispersion curves. Our experimental test demonstrates great agreement with the theoretical analyses with the highest percentage error of only 13.46%. Comparing to fundamental Lamb wave mode (S0), selecting high-order Lamb wave mode (S4) for ultrasonic structural health monitoring enables smaller transducer footprint particularly suitable for the applications in small and complex structures.

Estimation of the Uniform Approximation Error by Interpolating Multilinear Spline with lp Distances

Estimation of the Uniform Approximation Error by Interpolating Multilinear Spline with lp Distances

Measurement of viscosity during vibroextrusion of fiber concrete

Currently, the rheological properties of cement mortars containing dispersed reinforcement have not been studied enough, so it is important to develop a method for measuring the viscosity of fiber concrete mixtures that would simulate the technological process. To consider the flow process and calculate the rheological characteristics of fiber concrete mixtures during vibroextrusion, hydrodynamic theories were applied due to the fact that these mixtures behave like liquids in a vibration field. When solving flow problems, it is taken into account that vibrating fiber concrete mixtures are Newtonian systems. To obtain objective rheological characteristics of fiber concrete mixtures, it is proposed to measure viscosity directly during vibro-extrusion molding of flat products. The peculiarities of the flow of Newtonian fluids between flat fixed walls, which converge in a flat symmetrical rectangular narrowing channel, are considered. It is proposed to determine the reduction of the flow rate of the mixture in the rectangular channel compared to the flow between the plates by the braking coefficient. A graph of the dependence of the braking coefficient on the ratio of the sides of the cross section of the rectangular channel is plotted. 2 formulas are proposed for calculating the braking coefficient. The comparative analysis of the proposed formulas gives a good convergence of the calculation results, the average approximation error is equal to 0.0126%, which indicates a high level of coincidence of the regression equation with a more complex analytical formula. Taking into account the inhibition of the liquid flow in the end zones of the rectangular channel, a formula was proposed for calculating the viscosity of the fiber concrete mixture during the formation of flat products in the narrowing vibroextruder channel. The calculation formula contains the geometric dimensions of the channel, the density and the flow time of the fiber concrete mixture, and to determine the viscosity of the mixture, it is necessary to measure only the time of its complete flow during vibroextrusion. The proposed formula and methodology for calculating viscosity can be used to determine the rheological characteristics of any Newtonian fluids using a flat symmetrical rectangular narrowing channel. In the future, it is planned to continue researching the properties of fiber concrete mixtures under the influence of vibration.

Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection

In order to improve the efficiency of electricity theft detection, the power theft detection area and users should be better integrated, we proposed a Blending ensemble learning electricity theft detection model based on the Base Learner Selection Strategy (BLSS). Firstly, the adaptive synthetic (ADASYN) sampling method is used to process the unbalanced power consumption data, and the sample distribution of training data is balanced. Secondly, the BLSS selection method is used to screen the optimal base learner combination and construct the Blending ensemble learning model. Then, based on the historical data, the model makes a short-term prediction of the power consumption of the station area the next day, and focuses on the verification of the suspected energy-stealing station area where the Root Mean Square Percentage Error (RSPE) exceeds the threshold, so as to lock in the potential energy stealing users. Finally, through the comparison and verification of real examples, the search scope for electricity theft inspections was reduced by 79.17%, greatly improving the detection efficiency of the power supply company. At the same time, the model’s electricity theft detection and recognition accuracy rate can be as high as 97.50%. The Blending ensemble learning electricity stealing detection model based on the BLSS base learner selection method has strong electricity stealing detection and recognition ability.

Unraveling the Multifactorial Determinants of Miscarriage: Insights from SEM Analysis of Maternal Characteristics, Lifestyle Factors, and Antenatal Care

Background: This paper establishes that miscarriage is still a major determinant of maternal health, with the implication that factors such as maternal age, lifestyle factors, socioeconomic status and antenatal care visits affect the condition. This knowledge of factors involved can help in designing ways of ensuring they do not lead to miscarriages. Objective: The purpose of this work was to examine the relationship between a range of maternal lifestyle behaviors, education, visits to the antenatal care facility, and miscarriage rates by using structural equation modelling (SEM). Methods: Assuming a female population of 2000, data was generated on Maternal Age, education, Smoking, alcohol, exercise, Antenatal Care visits and an outcome variable representing miscarriage. SEM was employed to test the hypothesized relationships of these variables with Latent Lifestyle Risks captured under the observed behavior (smoking, alcohol use, exercise). To check the model fit, the following fit measure was used; Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR). Results: Consequently, the assumption of good model fit in the current SEM was obtained with CFI = 0.984, TLI = 0.971, RMSEA = 0.033, and SRMR = 0.021. Hence in this study maternal age was significantly related to miscarriage risk (Estimate = 0.028, p < 0.001), while higher level of education was inversely inclined to miscarriage (Estimate = -0.042, p < 0.001). Latent Lifestyle Risks were identified to have a positive relationship with miscarriages (Estimate = 0.200, p < 0.001 while antenatal care visits have a negative association with miscarriage risks (Estimate = -0.022, p < 0.001). Conclusions: Descriptive findings also reveal that maternal age, specific lifestyle patterns, educational attainment and utilization of antenatal care services are potential determinants of miscarriage. We found that offering changes to lifestyle behaviors and increasing the accessibility of antenatal care could lower miscarriage risk, and hence there can be great potential in public health approaches which address such factors to enhance the quality of maternal health.

OPTIMIZATION OF NEW CONSTRUCTIVE HEURISTIC ALGORITHMS FOR PERMUTATION FLOW SHOP SCHEDULING PROBLEM

This paper presents a new heuristic designed specifically for minimizing makespan in scheduling problems. The proposed approach incorporates a dual bottleneck phase combined with a pre-initial arrangement to enhance optimization of new heuristic. By introducing both major and minor bottleneck identification phases, the heuristic effectively identifies critical processing machines with significant completion times. To evaluate the performance, this study employed the Taillard benchmark and the upper bound (UB) makespan as comparative tools, assessing the new heuristic against the well-known NEH heuristic. Computational results clearly demonstrate that the new heuristic significantly outperforms the NEH heuristic in reducing the total completion time. The consistent lower RPD values and negative percentage errors indicate that ICHA is more effective in approaching the optimal makespan as indicated by the Taillard UB.

Forecasting River Water Temperature Using Explainable Artificial Intelligence and Hybrid Machine Learning: Case Studies in Menindee Region in Australia

Water temperature (WT) is a crucial factor indicating the quality of water in the river system. Given the significant variability in water quality, it is vital to devise more precise methods to forecast temperature in river systems and assess the water quality. This study designs and evaluates a new explainable artificial intelligence and hybrid machine-learning framework tailored for hourly and daily surface WT predictions for case studies in the Menindee region, focusing on the Weir 32 site. The proposed hybrid framework was designed by coupling a nonstationary signal processing method of Multivariate Variational Mode Decomposition (MVMD) with a bidirectional long short-term memory network (BiLSTM). The study has also employed a combination of in situ measurements with gridded and simulation datasets in the testing phase to rigorously assess the predictive performance of the newly designed MVMD-BiLSTM alongside other benchmarked models. In accordance with the outcomes of the statistical score metrics and visual infographics of the predicted and observed WT, the objective model displayed superior predictive performance against other benchmarked models. For instance, the MVMD-BiLSTM model captured the lowest Root Mean Square Percentage Error (RMSPE) values of 9.70% and 6.34% for the hourly and daily forecasts, respectively, at Weir 32. Further application of this proposed model reproduced the overall dynamics of the daily WT in Burtundy (RMSPE = 7.88% and Mean Absolute Percentage Error (MAPE) = 5.78%) and Pooncarie (RMSPE = 8.39% and MAPE = 5.89%), confirming that the gridded data effectively capture the overall WT dynamics at these locations. The overall explainable artificial intelligence (xAI) results, based on Local Interpretable Model-Agnostic Explanations (LIME), indicate that air temperature (AT) was the most significant contributor towards predicting WT. The superior capabilities of the proposed MVMD-BiLSTM model through this case study consolidate its potential in forecasting WT.

Target shape matching of a 1D lithium-ion battery actuator array

Abstract Large volume change in Si anodes can be harnessed to produce lithium-ion (Li-ion) pouch cells that change shape when charged and discharged. In this paper, complex, tailorable three-dimensional shapes are modeled with multiple 1D Li-ion battery (LIB) actuators connected in parallel by a compliant membrane-like material. A shape matching, design optimization is conducted to match these multimorph actuators against complex three-dimensional target shapes. Three-dimensional shapes in LIBs might readily be used for mobile soft robot applications such as minimally invasive surgical tooltips, shape-morphing structural batteries, and active custom rehabilitative aids. This paper models the compliant, membrane-like material as springs that align and transmit force between the actuators. Three case studies are presented that optimize membrane interactions in multi-member actuators. Important results include the successful shape matching of a multi-member, bimorph actuator optimized to shape match a complex 3D shape with less than three percent error. In bimorph multi-actuators, shape error is reduced by enforcing design variable symmetry and implementing different state of charge (SOC) to further reduce the number of design variables. Thus, for design optimization of multi-actuator batteries, enforcement of symmetry is recommended with design variables to include both differential SOC (or equivalent actuation strain parameter) and active layer coating thickness to achieve complex, tailorable shapes with a LIB actuator array. Differential SOC is further discovered to allow for the decoupling of bending and SOC allowing for more tailorable battery actuator applications.

Development of three biofuel CRMs for the quality parameters in biodiesel and wood pellet via a joint research project.

Biomass is a key element in biofuels which can be defined as a fuel produced through contemporary biological processes, and its increased use can support the EU's aims of reducing greenhouse gas emissions. Information on the nature and the quality of the biomass or biofuel is important in order to support the optimization of their combustion with respect to realizing higher efficiencies and lower emissions during energy production. Three reference materials were produced by a collaborative approach among national metrology institutes and designated institutes within the scope of the EMPIR project: BIOFMET.The project was aimed to establish advanced traceable measurement standards for the determination of the calorific value, impurities, and other parameters such as density, kinematic viscosity, moisture, and ash. This paper presents the sampling and processing methodology, homogeneity, stability, characterization campaign, the assignment of property values, and their associated uncertainties in compliance with ISO 17034 for biofuel reference materials: biodiesel, wood pellet powder, and wood pellet. Parameters of interest in biodiesel reference material-UME BIOFMET CRM 01 are gross calorific value (GCV), density, viscosity, and mass fractions of Ca, K, Mg, Na, P, and S elements. Parameters to be certified in wood pellet powder reference material-UME BIOFMET CRM 02 are GCV, moisture, ash, and mass fractions of Al, Cr, K, Mg, Mn, Ni, S, and Zn elements. Parameters to be certified in the wood pellet reference material-UME BIOFMET CRM 03 are GCV and moisture. The homogeneity and stability of the materials were assessed in accordance with ISO 33405. The materials were characterized by interlaboratory comparison studies among competent metrology institute and designated institute laboratories. Assigned values and uncertainties of the certified values were calculated in accordance with ISO 33405, and uncertainties include characterization, homogeneity, and stability components. The developed CRMs are intended to be used for the development and validation of measurement procedures for the determination and quality control/assurance purposes of the quality parameters for biofuels. It should be emphasized that the UME BIOFMET CRM 01-Biodiesel CRM is the first biodiesel reference material certified for calorific value. Among the developed wood CRMs, thepellet form, UME BIOFMET CRM 03, was found to be more stable than the powder one, UME BIOFMET CRM 02, for the moisture parameter. Sixfold lower relative uncertainty value for short-term stability at 45°C and twofold lower relative uncertainty value for long-term stability at 22°C were obtained for the moisture parameter of the CRM in pellet form compared to the CRM in powder form.

Exploring the Association Between Adolescents' Health Literacy and Health Behavior by Using the Short Health Literacy (HLS19-Q12) Questionnaire.

Health literacy (HL) is an important element of public health that is linked to health behavior in children and adolescents. This study aimed to investigate the structural validity and reliability of the HLS19-Q12 in the adolescent population, to measure the general HL of adolescents, and to assess the relationship between adolescents' HL and health behavior. This cross-sectional study involved 825 students aged 15-19 years attending Lithuanian gymnasiums. The participants completed an online survey that collected information on key sociodemographic, HL, and health behavior indicators. Factor analysis, Cronbach's alpha, and McDonald's omega were used to validate the Lithuanian version of the HLS19-Q12. Health behavior indicators, including physical activity, smoking, alcohol consumption, self-rated health, and body mass index (BMI), were measured. We used regression analyses to assess the associations. The HLS19-Q12 showed an acceptable reliability index (Cronbach's α = 0.885, McDonald's omega ω = 0.877) and adequate structural validity (comparative fit index = 0.89, Tucker and Lewis's index of fit = 0.88, root mean square error of approximation = 0.06). We found that 67.1% of the adolescents had excellent or sufficient HL, 27.7% had problematic HL, and 5.2% had inadequate HL. Compared with girls, boys had greater HL. HL was positively related to physical activity and self-rated health and negatively related to alcohol use and smoking. HLS19-Q12 is a reliable and valid measure of HL among adolescents in upper grades; higher levels of HL have been found to result in better health behaviors.

Analyzing and Modeling the Dynamic Electrical Characteristics of Nanocomposite Large-Range Strain Gauges.

Flexible high-deflection strain gauges have been demonstrated to be cost-effective and accessible sensors for capturing human biomechanical deformations. However, the interpretation of these sensors is notably more complex compared to conventional strain gauges, particularly during dynamic motion. In addition to the non-linear viscoelastic behavior of the strain gauge material itself, the dynamic response of the sensors is even more difficult to capture due to spikes in the resistance during strain path changes. Hence, models for extracting strain from resistance measurements of the gauges most often only work well under quasi-static conditions. The present work develops a novel model that captures the complete dynamic strain-resistance relationship of the sensors, including resistance spikes, during cyclical movements. The forward model, which converts strain to resistance, comprises the following four parts to accurately capture the different aspects of the sensor response: a quasi-static linear model, a spike magnitude model, a long-term creep decay model, and a short-term decay model. The resulting sensor-specific model accurately predicted the resistance output, with an R-squared value of 0.90. Additionally, an inverse model which predicts the strain vs. time data that would result in the observed resistance data was created. The inverse model was calibrated for a particular sensor from a small amount of cyclic data during a single test. The inverse model accurately predicted key strain characteristics with a percent error as low as 0.5%. Together, the models provide new functionality for interpreting high-deflection strain sensors during dynamic strain measurement applications, including wearables sensors used for biomechanical modeling and analysis.

Accuracy of the Huawei GT2 Smartwatch for Measuring Physical Activity and Sleep Among Adults During Daily Life: Instrument Validation Study.

Smartwatches are increasingly popular for physical activity and health promotion. However, ongoing validation studies on commercial smartwatches are still needed to ensure their accuracy in assessing daily activity levels, which is important for both promoting activity-related health behaviors and serving research purposes. This study aimed to evaluate the accuracy of a popular smartwatch, the Huawei Watch GT2, in measuring step count (SC), total daily activity energy expenditure (TDAEE), and total sleep time (TST) during daily activities among Chinese adults, and test whether there are population differences. A total of 102 individuals were recruited and divided into 2 age groups: young adults (YAs) and middle-aged and older (MAAO) adults. Participants' daily activity data were collected for 1 week by wearing the Huawei Watch GT2 on their nondominant wrist and the Actigraph GT3X+ (ActiGraph) on their right hip as the reference measure. The accuracy of the GT2 was examined using the intraclass correlation coefficient (ICC), Pearson product-moment correlation coefficient (PPMCC), Bland-Altman analysis, mean percentage error, and mean absolute percentage error (MAPE). The GT2 demonstrated reasonable agreement with the Actigraph, as evidenced by a consistency test ICC of 0.88 (P<.001) and an MAPE of 25.77% for step measurement, an ICC of 0.75 (P<.001) and an MAPE of 33.79% for activity energy expenditure estimation, and an ICC of 0.25 (P<.001) and an MAPE of 23.29% for sleep time assessment. Bland-Altman analysis revealed that the GT2 overestimated SC and underestimated TDAEE and TST. The GT2 was better at measuring SC and TDAEE among YAs than among MAAO adults, and there was no significant difference between these 2 groups in measuring TST (P=.12). The Huawei Watch GT2 demonstrates good accuracy in step counting. However, its accuracy in assessing activity energy expenditure and sleep time measurement needs further examination. The GT2 demonstrated higher accuracy in measuring SC and TDAEE in the YA group than in the MAAO group. However, the measurement errors for TST did not differ significantly between the 2 age groups. Therefore, the watch may be suitable for monitoring several key parameters (eg, SC) of daily activity, yet caution is advised for its use in research studies that require high accuracy.

Calibration and volunteer testing of a prototype contactless respiratory motion detection system based on laser tracking.

The goal of this study was to assess the feasibility of a cost-effective prototype of a laser-based respiratory motion detection system utilizing a Leuze LDS for breath monitoring through calibration and volunteer tests. This study was performed using the Anzai AZ-773V and computerized imaging reference systems (CIRS) motion phantoms for calibration tests. The calibration of the laser-based respiratory motion detection system involved spatial accuracy testing, amplitude calibration, and temporal accuracy. Volunteer testing was conducted on eight volunteers at the inferior end of the sternum and the abdomen area. The accuracy of the data recorded by the laser-based respiratory motion detection system was validated against established clinical reference tracking systems namely real-time position management (RPM) and Anzai AZ-733V system. Calibration with an Anzai AZ-773V and CIRS phantoms demonstrated an average error of 1.17%±0.64% and an average amplitude calibration correlation coefficient of 0.975±0.004. Volunteer tests, compared to the Anzai AZ-733V clinical system and RPM system, revealed average correlation coefficients for deep inspiration breath-hold are 0.931±0.02 and 0.936±0.03, respectively, and for free breathing are 0.85±0.07 and 0.77±0.1, respectively. Overall, the data suggest that the in-house laser-based respiratory motion detection system performed well, with an error percentage below 10%. A reasonably good correlation coefficient was obtained, indicating that the readings obtained from the laser system are consistent with those set on the phantom and clinical respiratory motion detection systems. Although promising through the calibration process and volunteer tests, further studies are required to generate trigger data linked directly to computerized tomography and linear accelerator facilities, thereby advancing the clinical viability of this innovative laser-based respiratory motion detection system.

Predicting bitcoin cryptocurrency price behavior based on ARIMA and NNAR modelling

The development of models to predict the behavior of the Bitcoin cryptocurrency, using a public database (Yahoo! Finance) to predict price trends. The models used were ARIMA and NNAR with the validation of the models being carried out based on the daily closing values of the asset. Both models did not differ significantly, however the adjusted model NNAR (2.2) had a slightly better fit to the original data series, presenting an MPE (Mean Percentage Error) of -0.102.

Influence of dosimetry accuracy on the correlation with treatment outcome in a preliminary PSMA radiopharmaceutical therapy study.

Despite the potential of dosimetry in optimizing personalized radiopharmaceutical therapy (RPT), its limited clinical implementation impedes the development of simplified protocols for routine adoption. However, simplifications may introduce errors in dosimetry, prompting questions about their impact on clinical practice. In this retrospective study, we analyzed data from 21 patients diagnosed with metastatic castration-resistant prostate cancer (mCRPC) who underwent multiple cycles of 177Lu-PSMA-617 RPT treatment. Cumulative dosimetry of all the treatment cycles was calculated using both the standard multi-time point dosimetry (MTPD) method and the single time-point dosimetry (STPD,Hänscheidapproximation) method for the same cohort. Their correlations with treatment outcome (PSA decline rate and overall survival, OS) and complication risk (anaemia grade) were investigated. The Fisher's Z-Transformed test was performed to statistically evaluate the difference between the correlations. STPD showed a non-significant difference in correlation with PSA decline rate, despite a mean percentage error (MPE) of up to 36.44% in tumor dosimetry compared to MTPD (MTPD: rho = -0.39, p < 0.001; STPD: rho = -0.46, p < 0.001; Z = 0.58, p = 0.56). Both STPDtotal and MTPDtotal demonstrated a significant impact on OS (STPDtotal: Hazard Ratio = 1.05, p < 0.05, log-transformed MTPDtotal: Hazard Ratio = 3.41, p < 0.05, log-transformed STPDtotal: Hazard Ratio = 8.06, p < 0.05). Additionally, despite a MPE of up to -40.26% in bone marrow dosimetry, STPD showed a non-significant difference in correlation with anemia grade (MTPD: rho = 0.35, p < 0.001; STPD: rho = 0.40, p < 0.001; Z = -0.39, p = 0.70). The preliminary findings from a small cohort indicate that the reduced accuracy of a clinically simplified protocol may not diminish the clinical therapy outcome predictive value of dosimetry. Future thorough systematic investigations may be needed to determine the clinically acceptable level of accuracy for dosimetry.

Structural validity of the Brazilian version of the quality care questionnaire-palliative care for use in individuals with diabetes mellitus eligible for palliative care

BackgroundThe Brazilian version of the Quality Care Questionnaire-Palliative Care (QCQ-PC) is an instrument designed to assess the quality of care provided in palliative care from the user’s perspective, featuring easy comprehension and applicability. It has demonstrated validity for use in individuals with cancer, but there is a need for validation in other populations due to the scarcity of instruments with this purpose.ObjectiveTo structurally validate the Brazilian version of the QCQ-PC for use in individuals with diabetes mellitus (DM) eligible for palliative care.MethodsThis is a structural validation study of a questionnaire according to the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN). The study was conducted with 100 individuals with DM. Data collection occurred in differents care services in São Luís (northeast Brazil) by means of the application of the QCQ-PC and a form with sociodemographic, clinical, and daily habit data. Descriptive data analysis was performed using absolute values, relative frequencies, and measures of central tendency and dispersion. Structural validity was assessed by means of exploratory factor analysis (EFA).ResultsOf the 100 participants included in the study, 66% were female, 54% were single, with a median age of 64 years, 44% were overweight (44%), 77% were on polypharmacy, and 70% were physically inactive. We found that one domain is the most appropriate for use of the QCQ-PC in individuals with DM eligible for palliative care, according to the parallel analysis implemented in the EFA. This domain was named “quality of care”. The fit indices for this one-dimensional internal structure were adequate: Kaiser–Meyer–Olkin test = 0.71, p value < 0.01 in Bartlett’s test, chi-square/degree of freedom = 1.07, comparative fit index = 0.993, Tucker-Lewis index. = 0.991, root mean square error of approximation = 0.028. The QCQ-PC presented factor loadings ranging from 0.480 to 0.883, maintaining a total of 12 items, which demonstrates the adequate relationship between the quality of care domain and its items.ConclusionTherefore, the internal structure with one domain (quality of care) is the most suitable for use in individuals with DM eligible for palliative care by means of the QCQ-PC.