Acceptable Error Research Articles

Examining the contact problem of a functionally graded layer supported by an elastic half‐plane with the analytical and numerical methods

This study offers a comparative study of the analytical and numerical methods for investigating a contact problem. The contact problem comprises a functionally graded layer supported by a half‐plane and loaded with a distributed load from the top surface. First, the analytical and numerical solutions to the problem are acquired by utilizing a theory of elasticity and finite element method, respectively. The problem is transformed into a system of integral equations in which the contact stress is an unknown function. The solution of the integral equation was achieved with Gauss–Jacobi integration formulation. The finite element model of the problem is created using ANSYS software, and the two‐dimensional analysis of the problem is performed. Results were obtained from the samples for different material properties and loading conditions. The distributed load width and non‐homogeneity parameters significantly impact on contact mechanics. The results indicate that the contact area and the contact stress obtained from finite element method (FEM) are close to the analytical results. As a result, acceptable error rates were obtained. Finally, this study provides evidence of a good agreement between the two methods.

Feed intake in housed dairy cows: validation of a three-dimensional camera−based feed intake measurement system

Measuring feed intake accurately is crucial to determine feed efficiency and for genetic selection. A system using three-dimensional (3D) cameras and deep learning algorithms can measure the volume of feed intake in dairy cows, but for now, the system has not been validated for feed intake expressed as weight of feed. The aim of this study was to validate the weight of feed intake predicted from the 3D cameras with the actual measured weight. It was hypothesised that diet−specific coefficients are necessary for predicting changes in weight, that the relationship between weight and volume is curvilinear throughout the day, and that manually pushing the feed affects this relationship. Twenty-four lactating Danish Holstein cows were used in a cross-over design with four dietary treatments, 2 × 2 factorial arranged with either grass-clover silage or maize silage as silage factor, and barley or dried beet pulp as concentrate factor. Cows were adapted to the diets for 11 d, and for 3 d to tie-stall housing before camera measurements. Six cameras were used for recording, each mounted over an individual feeding platform equipped with a weight scale. When building the predictive models, four cameras were used for training, and the remaining two for testing the prediction of the models. The most accurate predictions were found for the average feed intake over a period when using the starting density of the feed pile, which resulted in the lowest errors, 6% when expressed as RMSE and 5% expressed as mean absolute error. A model including curvilinear effects of feed volume and the impact of manual feed pushing was used on a dataset including daily time points. When cross-validating, the inclusion of a curvilinear effect and a feed push effect did not improve the accuracy of the model for neither the feed pile nor the feed removed by the cow between consecutive time points. In conclusion, measuring daily feed intake from this 3D camera system in the present experimental setup could be accomplished with an acceptable error (below 8%), but the system should be improved for individual meal intake measurements if these measures were to be implemented.

Characteristic structures of liquid crystal monomers in EI-MS analysis and the potential application in suspect screening

Liquid crystal monomers (LCMs) are of emerging concern due to their ubiquitous presence in indoor and outdoor environments and their potential negative impacts on human health and ecosystems. Suspect screening approaches have been developed to monitor thousands of LCMs that could enter the environment, but an updated suspect list of LCMs is difficult to maintain given the rapid development of material innovations. To facilitate suspect screening for LCMs, in-silico mass fragmentation model and quantitative structure-activity relationship (QSPR) models were applied to predict electron ionization (EI) mass spectra of LCMs. The in-silico model showed limited predictive power for EI mass spectra, while the QSPR models trained with 437 published mass spectra of LCMs achieved an acceptable absolute error of 12 percentage points in predicting the relative intensity of the molecular ion, but failed to predict the mass-to-charge ratio of the base peak. A total of 41 characteristic structures were identified from an updated suspect list of 1606 LCMs. Multi-phenyl groups form the rigid cores of 85% of LCMs and produce 154 characteristic peaks in EI mass spectra. Monitoring the characteristic structures and fragments of LCMs may help identify new LCMs with the same rigid cores as those in the suspect list.

Optimisation of Exhaust Emissions, Vibration, and Noise of Unmodified Diesel Engine Fuelled with Canola Biodiesel-Diesel Blends with Natural Gas Addition by Using Response Surface Methodology

The paper presents methods to determine the optimum input parameters of CNG addition, biodiesel blend ratio, and engine speed to improve engine responses in terms of exhaust emissions, vibration, and noise of CNG-biodiesel-diesel fuelled engine. Box-Behnken based on response surface methodology was used to predict and optimise input parameters. Variance analysis was applied to determine the significant relationship between the input parameters and engine responses. At optimum input parameters (CNG addition=9.24 L/min, biodiesel blend ratio=40%, engine speed=1524.24 rpm), the optimum engine responses of NOx, CO, CO2, O2, engine vibration acceleration, and noise were 93.77 ppm, 438.05 ppm, 1.47%, 18.59%, 37.17 m/s2 and 91.34 dB[A], respectively. In terms of coefficient determination of R2, the values were 99.11%, 99.22%, 99.41%, 99.70%, 98.65%, and 98.60% respectively. The correlation between the optimised result and the engine test result showed an acceptable error limit for NOx, CO, CO2, O2, engine vibration acceleration, and noise as 4.2%, 3.8%, 4.9%, 0.25%, 4.12%, and 0.17%, respectively.

Optimizing 2-Nitrophenol and Chromium Adsorption from Water with Aluminium Oxide via Response Surface Methodology

Optimizing 2-Nitrophenol and Chromium Adsorption from Water with Aluminium Oxide via Response Surface Methodology

Comparative performance analysis of precise point positioning technique in the UAV − based mapping

Uncrewed Aerial Vehicle (UAV) photogrammetry is a very efficient, low-cost approach for mapping small or medium-sized local areas. This approach is adequate for achieving the needed accuracy in many applications, and onboard positioning sensors have made this technique more feasible by decreasing the need for ground control stations (GCPs). When real-time positioning strategies are used, photogrammetric mapping can also be performed instantaneously in the field. This is especially crucial in applications like disaster management, where current situation must be detected rapidly. It is critical to choose a georeferencing approach in applications that is appropriate for the physical parameters of the mapping area, the intended use of the photogrammetric mapping, and the needed accuracy. In order to accurately georeference the photogrammetric outputs, this study assesses the real-time and post-processed performances of absolute and relative GNSS positioning approaches. Finally, it compares these strategies in terms of the position accuracy of the created models. In the evaluations, the 3D models of the study area were created utilizing network-based Real Time Kinematic (n-RTK), Post-Processed Kinematic (PPK) Positioning, and Precise Point Positioning (PPP) approaches, with direct georeferencing of the UAV-based photogrammetric images. These models were then compared to models developed using traditional indirect georeferencing with GCPs and aided indirect georeferencing. According to the findings, direct georeferencing using the n-RTK and PPK techniques provided a horizontal position accuracy in terms of root mean square errors (rms2D = 1.6 cm and 1.9 cm) almost equal to indirect georeferencing using GCPs (rms2D = 1.9 cm). In terms of vertical position accuracies, the n-RTK and PPK approaches in direct georeferencing (rmsh = 3.7 cm and 3.9 cm, respectively) produced rather different results than the GCPs-based conventional strategy (rmsh = 1.6 cm), although the discrepancies were within acceptable error limits. Direct georeferencing with the PPP approach produced the lowest horizontal and vertical position accuracies (rms2D = 5.9 cm, rmsh= 5.7 cm). However, this level of accuracy was deemed adequate for use in large-scale mapping in Turkey's authorized regulation.

Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixtures

AbstractThe concrete compressive strength is essential for the design and durability of concrete infrastructure. Silica fume (SF), as a cementitious material, has been shown to improve the durability and mechanical properties of concrete. This study aims to predict the compressive strength of concrete containing SF by dual‐objective optimization to determine the best balance between accurate prediction and model simplicity. A comprehensive dataset of 2995 concrete samples containing SF was collected from 36 peer‐reviewed studies ranging from 5% to 30% by cement weight. Input variables included curing time, SF content, water‐to‐cement ratio, aggregates, superplasticizer levels, and slump characteristics in the modeling process. The gray wolf optimization (GWO) algorithm was applied to create a model that balances parsimony with an acceptable error threshold. A determination coefficient (R2) of 0.973 demonstrated that the CatBoost algorithm emerged as a superior predictive tool within the boosting ensemble context. A sensitivity analysis confirmed the robustness of the model, identifying curing time as the predominant influence on the compressive strength of SF‐containing concrete. To further enhance the applicability of this research, the authors proposed a web application that facilitates users to estimate the compressive strength using the optimized CatBoost algorithm by following the link: https://sf-concrete-cs-prediction-by-javid-toufigh.streamlit.app/.

Toward Personalized Orthopedic Care: Validation of a Smart Knee Brace

Introduction: Wearable technology offers a promising solution to advance current rehabilitation strategies for post-operative orthopedic care. The aim of this study was to determine the level of agreement and concurrent validity of a smart knee brace compared to the gold standard measurement system GAITRite® for assessing lower limb gait parameters. Methods: Thirty-four healthy participants were fitted with the smart knee brace (Digital Knee®) on their dominant limb. Gait parameters (stride length, stride time, and gait velocity) were measured simultaneously using the Digital Knee® and the GAITRite® electronic walkway. Two walks were performed at a comfortable speed and two at a fast-walking speed. Results: At a comfortable walking speed, stride time was moderately valid (ICC2,1 = 0.66 s), and stride length and gait velocity demonstrated poor validity (ICC2,1 = 0.29; ICC2,1 = 0.41). All gait parameters demonstrated poor validity at a fast-walking speed (ICC2,1 = −0.16 to −0.01). Bias ranged from −0.08 to 0.28, with more clinically acceptable percentage errors at a comfortable walking speed (14.1–30%) versus at a fast-walking speed (26.4–42.6%). Gait velocity and stride length had substantially higher biases in the fast-walking speed compared to the comfortable walking speed (0.28 ± 0.39 m s−1 vs. 0.02 ± 0.21 m s−1; 0.15 ± 0.23 m vs. −0.04 ± 0.17 m). Limits of agreement were considered narrower for stride time compared to stride length and gait velocity. Conclusion: The Digital Knee® is a promising approach to improving post-operative rehabilitation outcomes in patients with osteoarthritis. The Digital Knee® demonstrated good agreement and moderate concurrent validity for measuring gait metrics at a comfortable walking speed. These findings highlight the opportunity of the wearable sensor as an intervention for post-operative orthopedic care. This was a laboratory-based study; thus, further research is required to validate the wearable sensor in real-world contexts and in patients with knee pathologies. Further, refinement of the algorithm for measuring gait metrics at slow- and fast-walking speed with the Digital Knee® is warranted.

Raman spectral quantification of Li2SO4, Na2SO4, K2SO4 and Cs2SO4 at 25 to 250 °C and its potential implications for the determination of sulfate contents in natural fluid inclusions

The quantitative study of the sulfate content in natural fluid inclusions (FIs) will help our understanding of the processes of metal transport and related mineralization. The sulfate-rich FIs typically contain several types of sulfates with diverse cations such as Na+, K+, and so on. In order to get the sulfates contents in natural FIs, in situ Raman spectral quantitative calibration method is used to calibrate the Li2SO4, Na2SO4, K2SO4 and Cs2SO4 solutions at temperatures ranging from 25 °C to 250 °C in our study. The results show that the A(SO42−)/A(H2O) ratio increases linearly with increasing mSO42− in sulfate solutions containing single cation of Li, Na, K or Cs at the set temperatures, where A is Raman peak area and m is molality (mol/kg H2O), and a negative linear relationship exists between k (quantitative calibration coefficient) and temperature for sulfate solutions with specific cations up to 250 °C. Meanwhile, the slopes and the intercepts of the k-T correlation curves are obviously different among sulfate solutions with different cations, and the coefficients k of these sulfate solutions at certain temperature decrease with the increase of atomic number of the cation, in accordance with the following order: Li+ > Na+ > K+ > Cs+. As Na+ and K+ are the main cations in the natural sulfate-bearing FIs, the quantification of the multi-cation (Na ± K ± Li) sulfate solutions was evaluated by using the Raman quantitative calibration curves of Li2SO4, Na2SO4, K2SO4 and Cs2SO4 separately, the errors exist due to the use of calibration curves with different cations; the average concentration errors derived from the use of Na2SO4 or K2SO4 Raman quantitative calibration curves are within about 5 %, while those from the use of Li2SO4 or Cs2SO4 Raman quantitative calibration curves are within about 15 %. Therefore, the acceptable errors suggest that the calibration curves of individual sulfates can be applied to the determination of sulfate content of natural sulfate-bearing FIs.

Development of a fast and simple electrochemical sensor for trace determination of sulfite using copper(II) triazole complexes

A glassy carbon electrode (GCE) was modified with copper triazole complexes (Cu(L)2) containing three different substituents (OMe, Br, and NO2), and the fabricated electrochemical sensors were used to detect sulfite. The materials were characterized by cyclic voltammetry and scanning electron microscopy (SEM/EDX). The copper(II) 5-methyl-1-(4-nitrophenyl)-1H-1,2,3-triazole-4-carboxylate (Cu(LNO2)2) complex exhibited the best electrochemical performance toward the sulfite electron transfer process regarding onset potential and peak current. Best results were obtained at pH 4, and the following analytical parameters for sulfite detection were determined by amperometry at 0.90 V, as follows: reproducibility (1.9 %), repeatability (1.6 %), limit of detection (0.75 µmol/L), limit of quantification (2.27 µmol/L), dynamic concentration range (10 µmol/L to 5.00 mmol/L, R2 = 0.995), and sensitivity (7.43x10-3 µA µmol−1 L). Precision was evaluated in terms of repeatability, and a relative standard deviation < 15 % was found at three concentration levels. The method́s accuracy was assessed by determining sulfite in commercial wine samples, and acceptable errors were obtained compared to values reported by an external analytical laboratory. The selectivity towards interfering species (Ca2+, Zn2+, Mg2+, NO3−, glucose, sucrose, NaCl, and KCl) and the stability was also evaluated. The practical application of the Cu(LNO2)2/GCE sensor was examined by analyzing the sulfite content in white and red commercial wines.

Exploring the Influence of Dynamic Indicators in Urban Spaces on Residents’ Environmental Behavior: A Case Study in Shanghai Utilizing Mixed-Methods Approach and Artificial Neural Network (ANN) Modeling

The main aim of this article is to evaluate the impact of dynamic indicators associated with urban spaces on the environmental behavior of residents in Shanghai, China. With the city experiencing rapid urbanization and increasing environmental concerns, it is crucial to understand how the design and management of urban spaces can encourage pro-environmental attitudes and actions among the population. The study specifically focuses on dynamic indicators, namely vitality, accessibility, quality, and walkability, to develop a comprehensive understanding of the utilization and experience of urban spaces. The research outcomes will make valuable contributions towards Shanghai’s objectives of achieving environmental sustainability, while also providing insights that have broader relevance to sustainable urban development globally. As the built environment significantly influences energy consumption, resource utilization, pollution generation, and overall human wellbeing, attaining urban sustainability is of paramount importance. The findings of this study will aid in informing strategies and policies that promote sustainable practices, not only benefiting Shanghai but also serving as a valuable resource for urban development initiatives worldwide. Sustainable urban design principles, including compactness, density, mixed land use, greening, and walkability, have been associated with pro-environmental behaviors, including reduced reliance on automobiles, increased walking and cycling, and heightened environmental consciousness. Nevertheless, the relationship between the built environment and sustainability behaviors is intricate and is influenced by multiple factors. Consequently, further research is necessary to comprehend how specific spatial and temporal dynamics impact environmental behaviors within urban settings. In this study, an artificial neural network (ANN) was developed to estimate the quality and walkability of an area and environmental behaviors by considering the augmented vitality and accessibility factors. The ANN’s predictions demonstrate that higher levels of vitality and accessibility positively contribute to improved walkability and environmental behaviors. The accuracy of the ANN’s predictions was assessed using linear regression, which yielded acceptable error rates when compared with experimental results.

Quantification of the out-of-plane loading fatigue response of bistable CFRP laminates using a machine learning approach

This study proposes data-driven machine learning models to predict the nonlinear load-displacement response in constant amplitude high-cycle fatigue loading of unsymmetric, cross-ply bistable carbon fiber reinforced polymer composites. Four selected ML models are trained on experimental fatigue data with eleven unique frequency, temperature, and boundary conditions combinations. Stiffness and damage index values, which serve as additional evaluation metrics, are calculated using the predicted load data. The models capture the nonlinear load response with acceptable error for in-domain experimental conditions. Model expandability demonstrates the sensitivity of machine learning models to training features but suggests an economical alternative to extensive fatigue experiments.

Augmenting input-output system under the curse of dimensionality: AHP-RAS integration and algorithm simulation

ABSTRACT In the estimation of input-output tables, one frequently encountered challenge is the aggregation of sectors in initial tables, which complicates the estimation of more detailed tables due to missing data—a phenomenon referred to as the “curse of dimensionality” when augmenting from lower to higher dimensions. We propose an AHP-RAS method to address the substantial data deficiencies encountered in the expansion of input-output tables. The method applies the Analytical Hierarchy Process (AHP) to transform the subjective relative importance of sectors into input coefficients using Pairwise Comparison Matrices (PCM) and eigenvector methods. Subsequently, it incorporates the RAS approach to supplement objective data, thereby integrating subjective judgement and objective information to tackle the curse of dimensionality. Unlike traditional AHP methods, this study employs binary integer programming to decompose large-scale matrices into subsets, making the AHP technique applicable to input-output tables with a broader range of sectors. We employ Monte Carlo simulation techniques to evaluate the robustness of the proposed methodology, specifically assessing the impacts of subjective judgement, objective data utilization, and the variation in sector numbers. Overall, the method overcomes the curse of dimensionality, yielding coefficients within an acceptable error margin, thereby demonstrating a significant advancement in the field of input-output analysis.

Validity and Reliability of the Lower Extremity Motor Coordination Test (LEMOCOT) in Patients with Multiple Sclerosis

The validity and reliability of the Lower Extremity Motor Coordination Test (LEMOCOT) were assessed in patients with multiple sclerosis (MS). The study involved 60 participants diagnosed with MS (mean age: 48.13 years, range: 24–80 years). Intra-rater reliability was assessed within-day (n = 12), and inter-rater reliability was assessed within-day (n = 45) and between-days (n = 22). For known-groups validity, test scores were compared between participants of different sexes; for convergent validity, test scores were correlated with age, the Five-Repetition Sit-to-Stand test (FSTS), the Berg Balance Scale (BBS), the Lower Extremity Functional Scale (LEFS), and the Expanded Disability Status Scale (EDSS). To test the discriminant validity, the LEMOCOT test scores were correlated with the Mini-Mental State Evaluation (MMSE) and compared according to social history (living alone or not). The LEMOCOT test was tested under three different conditions and found to be very reliable (intraclass correlation coefficient, ICC2,1 &gt; 0.94) with an acceptable error level (standard error of the measurement, SEM, between 1.39 and 3.47 targets and 95% minimum detectable change; MDC95%, between 3.84 and 9.58 targets). Convergent validity was verified, as the LEMOCOT registered very strong correlations with the FSTS test (r = −0.851) and the BBS scale (r = 0.815), a strong correlation with the LEFS scale (r = 0.78), a moderate correlation with the EDSS scale (r = −0.634), all highly significant (p &lt; 0.001), and a weak correlation with age (r = −0.332, p &lt; 0.01). Discriminant validity was also confirmed, as the LEMOCOT demonstrated a weak correlation with the MMSE scale (r = 0.365, p = 0.017) and no differences relative to living environment or sex. The LEMOCOT demonstrated excellent reliability and validity for patients with MS.

Reliability and validity of the new portable Nordic hamstring test device (IVMES H-Bord)

The aim of this study is to determine the reliability and validity of the IVMES H-Bord eccentric hamstring strength measurement device. Thirty-eight healthy male athletes (age: 20.9 ± 2.11 years, height: 1.79 ± 0.03 m, body weight: 76.6 kg, body mass index: 22.6 ± 0.69 kg/m2) participated in this study. Each test protocol was applied in eccentric mode and isometric mode respectively. Between-session test-retest reliability of the IVMES H-Bord was analyzed for the intraclass correlation coefficient (ICC) with 95% coefficient intervals (CI). Validity of the IVMES H-Bord was examined for eccentric mode (NHTecc), and isometric mode (NHTiso) using Pearson’s correlation coefficient with 95% CI and limits of agreement. Bland-Altman plots were also created to provide a representation of the agreement between two test devices. Strong correlations, good ICC values and acceptable typical errors of the estimate were observed between the NHT and isokinetic strength testing (IST) during both limbs with no proportional bias for validity analysis. In conclusion, the “IVMES H-Bord” is a reliable and valid measurement device for eccentric hamstring muscle strength and it provides important advantages for medical staff, fitness coaches and practitioners because it is portable, low cost, and field-based assessment.

Nutritional reference values using the DRIS method and sample size for peach palm production

ABSTRACT One of the challenges in the peach palm production system is the interpretation of leaf analyses and the adaptation of fertilization recommendations. Tools that enhance fertilizer use efficiency are therefore needed. This study aimed to establish norms for evaluating the nutrient status of peach palms using the Diagnosis and Recommendation Integrated System (DRIS) and to determine the adequate number of palm heart samples necessary for a more accurate assessment of productivity. Production, leaf nutrient content, and soil fertility data were collected from 102 commercial stands of peach palm in the Ribeira Valley, state of São Paulo, Brazil, between 2015 and 2020. Adequate number of individual samples (palm hearts) to be collected per stand for productivity assessment was estimated. DRIS norms were established by dividing the database into high-yield (reference population) and low-yield subpopulations, using average productivity as a criterion. By assuming an acceptable error of 5 to 10 % for the assessment of peach palm productivity, taking into account total palm heart weight and/or the weight of cylinders, respectively, 16 plants per stand should be sampled. DRIS was not sensitive enough to diagnose differences in the probability of positive response to fertilization; however, the P, K, Ca, Mg, S, B, Fe, Cu, Mn and Zn contents were positively correlated with the respective nutrient indices.

Empirical Turbulence Interaction Noise Model for Permeable Flat Plate Leading Edges

One of the major aerodynamic noise generation mechanisms is the interaction of a turbulent flow with a downstream blunt object, such as the blade of a rotating fan or a fixed stator. A possible method to reduce this noise is the use of flow-permeable leading edges, which has been investigated experimentally on a set of flat plates with different leading-edge perforations. The results show that these modifications can lead to notable noise reductions at low and medium frequencies, but also to a noise increase at high frequencies due to the contribution of surface roughness noise. The measured data were then used as an input to a symbolic regression modeling approach with the aim of obtaining a simple yet accurate model to predict the noise reduction capacity of the permeable leading edges. Three different prediction models were finally selected to demonstrate the capability of the current approach. One of the more complex models already predicts correct trends and shows an acceptable mean absolute error, making it a reliable candidate for engineering purposes.

Novel approach for solving higher-order differential equations with applications to the Van der Pol and Van der Pol–Duffing equations

BackgroundNumerical methods are used to solve differential equations, but few are effective for nonlinear ordinary differential equations (ODEs) of order higher than one. This paper proposes a new method for such ODEs, based on Taylor series expansion. The new method is a second-order method for second-order ODEs, and it is equivalent to the central difference method, a well-known method for solving differential equations. The new method is also simple to implement for higher-order differential equations. The proposed technique was applied to solve the Van der Pol and Van der Pol–Duffing equations. It is stable over a wide range of nonlinearity and produces accurate and reliable results. For the self-excitation Van der Pol equation, the proposed technique was applied with different values of nonlinear damping.ResultsThe results were compared with those obtained using the ODE15s solver in MATLAB. The two sets of results showed excellent agreement. For the forced Van der Pol–Duffing equation, the proposed technique was applied with different values of exciting force amplitude and frequency. It was found that for certain conditions, the solution obtained using the proposed technique differed from that obtained using ODE15s.ConclusionsThe solution obtained using the proposed technique showed good agreement with the solutions obtained using ODE45 and Runge–Kutta fourth order. The results show that the proposed approach is very simple to apply and produces acceptable error. It is a powerful and versatile tool for solving of high-order nonlinear differential equations accurately.

Assessing the Accuracy of Systolic Aortic Pressure Estimation From a Brachial Cuff Alone: A Comparison With Radial Tonometry.

A novel method for estimating central systolic aortic pressure (cSAP) has emerged, relying solely on the peripheral mean (MBP) and diastolic (DBP) blood pressures. We aimed to assess the accuracy of this Direct Central Blood Pressure estimation using cuff alone (DCBPcuff = MBP2/DBP) in comparison to the use of a generalized transfer function to derive cSAP from radial tonometry (cSAPtono). This retrospective analysis involved the International Database of Central Arterial Properties for Risk Stratification (IDCARS) data (Aparicio et al., Am J Hypertens 2022). The dataset encompassed 10,930 subjects from 13 longitudinal cohort studies worldwide (54.8% women; median age 46.0 years; office hypertension: 40.1%; treated: 61.0%), documenting cSAPtono via SphygmoCor calibrated against brachial systolic BP (SBP) and DBP. Our analysis focused on aggregate group data from 12/13 studies (89% patients) where a full BP dataset was available. A 35% form factor was used to estimate MBP = (DBP + (0.35 × (SBP-DBP)), from which DCBPcuff was derived. The predefined acceptable error for cSAPtono estimation was set at ≤ 5 mm Hg. The cSAPtono values ranged from 103.8-127.0 mm Hg (n = 12). The error between DCBPcuff and cSAPtono was 0.2 ± 1.4 mm Hg, with no influence of the mean. Errors ranged from -1.8 to 2.9 mm Hg across studies. No significant difference in errors was observed between BP measurements obtained via oscillometry (n = 9) vs. auscultation (n = 3) (P = 0.50). Using published aggregate group data and a 35% form factor, DCBPcuff demonstrated remarkable accuracy in estimating cSAPtono, regardless of the BP measurement technique. However, given that individual BP values were unavailable, further documentation is required to establish DCBPcuff's precision.

Improving the reliability of underwater gait analysis using wearable pressure and inertial sensors.

This work addresses the lack of reliable wearable methods to assess walking gaits in underwater environments by evaluating the lateral hydrodynamic pressure exerted on lower limbs. Sixteen healthy adults were outfitted with waterproof wearable inertial and pressure sensors. Gait analysis was conducted on land in a motion analysis laboratory using an optoelectronic system as reference, and subsequently underwater in a rehabilitation swimming pool. Differences between the normalized land and underwater gaits were evaluated using temporal gait parameters, knee joint angles and the total water pressure on the lower limbs. The proposed method was validated against the optoelectronic system on land; gait events were identified with low bias (0.01s) using Bland-Altman plots for the stride time, and an acceptable error was observed when estimating the knee angle (10.96° RMSE, Bland-Altman bias -2.94°). The kinematic differences between the land and underwater environments were quantified, where it was observed that the temporal parameters increased by more than a factor of two underwater (p<0.001). The subdivision of swing and stance phases remained consistent between land and water trials. A higher variability of the knee angle was observed in water (CV = 60.75%) as compared to land (CV = 31.02%). The intra-subject variability of the hydrodynamic pressure on the foot ([Formula: see text] = 39.65%) was found to be substantially lower than that of the knee angle (CVz = 67.69%). The major finding of this work is that the hydrodynamic pressure on the lower limbs may offer a new and more reliable parameter for underwater motion analysis as it provided a reduced intra-subject variability as compared to conventional gait parameters applied in land-based studies.