Mean Absolute Deviation Research Articles

Assessing the radiofrequency shielding effect of titanium mesh on diffusion-weighted imaging: a comparative study of the twice-refocused spin-echo and Stejskal-Tanner sequences.

This study compared twice-refocused spin-echo sequence (TRSE) and Stejskal-Tanner sequence (ST) to evaluate their respective effects on the image quality of magnetic resonance (MR) diffusion-weighted imaging in the presence of radiofrequency (RF) shielding effect of titanium mesh in cranioplasty. A 1.5-T MR scanner with a Head/Neck coil 20 channels and a phantom simulating the T2 and apparent diffusion coefficient (ADC) value of the human brain were used. Imaging was performed with and without titanium mesh placed on the phantom in TRSE and ST, and normalized absolute average deviation (NAAD), Dice similarity coefficient (DSC), and ADC values were calculated. The NAAD values were significantly lower for TRSE than for ST in the area below the titanium mesh, and the drop rates due to titanium mesh were 14.1% for TRSE and 9.8% for ST. The DSC values were significantly lower for TRSE than for ST. The ADC values were significantly higher for TRSE than for ST without titanium mesh. The ADC values showed no significant difference between TRSE and ST with titanium mesh. The ST had a lower RF shielding effect of titanium mesh than the TRSE.

A metric for comparison and visualization of age disparities in cancer survival.

Diagnostic age is an important determinant of cancer survival but the methods generally used to analyze age-group-specific survival are not developed for ready visualization of survival differences. We aim at developing a novel metric for comparing and visualizing age-group-specific survival data over different cancers, sexes, periods and countries. The metric describes the mean absolute deviation between age-groups. The metric can be used in two variations, one showing the mean variation and its 95% confidence intervals and the other highlighting individually each age-groups distinguishing positive or negative deviations. We demonstrate the applications with age-group- specific 5-year relative survival data from the NORDCAN database RESULTS: The mean absolute deviation between age-groups for Swedish colon cancer survival declined from about 5% in 1972-1981-1% in 1992-2001 and to 1.3% in 2012-2021. Patients diagnosed before age 50 years accounted for the largest positive deviation. For acute myeloid leukemia (AML) the mean deviation increased from 4% (female) to 17% and 23%. Patients diagnosed at age below 50 years showed the largest deviations. Comparing colon cancer mean deviations between the Nordic countries, a time-related decline was observed for all, those in Sweden ending at the lowest and in Finland the highest level. We demonstrated the usefulness of the devised metric for summarizing age-specific survival data between cancers, sexes, periods and countries. The two variations of the metric allow a simple visual presentation of the survival experience as to deviation of the survival data, its 95%CIs and its highlighted individual age-group components.

An Electromechanical Impedance-Based Imaging Algorithm for Damage Identification of Chemical Milling Stiffened Panel

The multiple intersecting stiffeners on the chemical milling stiffened panel (CMSP) limit the application of active health monitoring methods on it. An imaging algorithm based on electromechanical impedance (EMI) and probability-weighting is proposed to achieve quantitative evaluation and localization of the damage on CMSP. The proposed algorithm compensates for the difference in sensor performance with coefficients and there is no need to determine the key parameters of the algorithm through prior experiments. In the paper, the applicability of ultrasonic guided wave (GW) and EMI on CMSP was first studied through the finite element method. Based on EMI and the mean absolute percentage deviation (MAPD), the selected damage indicator (DI), a probability-weighted damage imaging algorithm are proposed and experimentally verified. The results indicate that due to the reflection and attenuation effects of stiffeners on GW, the signal characteristics of damage scattering waves are contaminated, making it difficult to quantitatively characterize the damage from GW signals through DIs. MAPD is positively correlated with the damage degree and has consistency in characterizing the signal of different PZTs under the same working condition. The feasibility and accuracy of the proposed algorithm are verified through experiments which show a strong engineering application capability.

Assessment of abiotic reduction rates of organic compounds by interpretable structural factors and experimental conditions in anoxic water environments

For organic contaminants in lake sediments, aquifers, and anaerobic bioreactors, their reduction is one of the primary transformation paths in these anoxic water environments. A simple model is introduced to predict pseudo-first order rate constants (kobs) for the abiotic reduction of organic compounds featuring diverse reducible functional groups. It utilizes the largest experimental dataset of –log kobs, encompassing 59 organic compounds (278 data points). Unlike available complex quantitative structure–activity relationship (QSAR) methods, the novel approach requires both experimental conditions and structural parameters. In comparison to one of the available general QSAR methods, the new model demonstrates favorable performance. The average absolute deviation (AAD), absolute maximum deviation (ADmax), average absolute relative deviation (AARD%), and R-squared (R2) values of the estimated outputs for 54/5 training/test data sets of the new model are 0.641/1.761, 1.761/1.417, 20.52/83.87, and 0.797/0.949, respectively. On the other hand, the available general comparative QSAR method shows the AAD: 1.311/2.301, ADmax: 3.795/3.732, AARD%: 641.0/821.2, and R2: 0.003/0.447. For the test set, AAD, AARD%, ADmax, and R2 values for the new/comparative models are 0.649/2.403, 62.20/190.5, 1.215/3.732 and 0.974/0.789, respectively. In summary, the new model offers a straightforward approach for the manual calculation of –log kobs, demonstrating excellent goodness-of-fit, reliability, precision, and accuracy.

Distributionally Robust Observable Strategic Queues

This paper presents an extension of Naor’s analysis on the join-or-balk problem in observable M/M/1 queues. Although all other Markovian assumptions still hold, we explore this problem assuming uncertain arrival rates under the distributionally robust settings. We first study the problem with the classical moment ambiguity set, where the support, mean, and mean-absolute deviation of the underlying distribution are known. Next, we extend the model to the data-driven setting, where decision makers only have access to a finite set of samples. We develop three optimal joining threshold strategies from the perspectives of an individual customer, a social optimizer, and a revenue maximizer such that their respective worst-case expected benefit rates are maximized. Finally, we compare our findings with Naor’s original results and the traditional sample average approximation scheme. Funding: This research was supported by the National Science Foundation [Grants 2342505 and 2343869].

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This is quite an important industrial and academic trend as the efficiency of energy systems depends on the cooling systems. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids’ superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modeling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN’s superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithm models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable of considering many effective parameters into account with much higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

UWB-enabled Sensing for Fast and Effortless Blood Pressure Monitoring

Blood Pressure (BP) is a critical vital sign to assess cardiovascular health. However, existing cuff-based and wearable-based BP measurement methods require direct contact between the user's skin and the device, resulting in poor user experience and limited engagement for regular daily monitoring of BP. In this paper, we propose a contactless approach using Ultra-WideBand (UWB) signals for regular daily BP monitoring. To remove components of the received signals that are not related to the pulse waves, we propose two methods that utilize peak detection and principal component analysis to identify aliased and deformed parts. Furthermore, to extract BP-related features and improve the accuracy of BP prediction, particularly for hypertensive users, we construct a deep learning model that extracts features of pulse waves at different scales and identifies the different effects of features on BP. We build the corresponding BP monitoring system named RF-BP and conduct extensive experiments on both a public dataset and a self-built dataset. The experimental results show that RF-BP can accurately predict the BP of users and provide alerts for users with hypertension. Over the self-built dataset, the mean absolute error (MAE) and standard deviation (SD) for SBP are 6.5 mmHg and 6.1 mmHg, and the MAE and SD for DBP are 4.7 mmHg and 4.9 mmHg.

New insight and experimental study of ineffective void in hydrodynamic performance of countercurrent‐flow packing column

AbstractExtensive experimental research and hydrodynamic models have been proposed to guide the design of superior packings. However, most research has concentrated on the effective void (ε − H) of packing while ignoring the ineffective void (ε − H − HL), which causes discrepancies in hydrodynamic performance compared to actual observations. This study evaluated the hydrodynamic performance under diverse conditions considering the liquid holdup (H), pressure drop (ΔP), and gas flooding velocity (uf). A novel approach to hydrodynamic model construction is introduced by incorporating an ineffective void. The results indicate that at a constant hold‐up area, liquid flow (L) and viscosity (μ) significantly influence liquid hold up, moderated by the gas velocity in the flooding area. The pressure drop rises as the viscosity, gas flow rate, and liquid flow rate increase. Notably, a considerable pressure drop initiates flooding at the bottom of the absorber. Elevated liquid flow rates and viscosities correlate with higher ineffective void values (HL) in the packing column. At low gas flow rates, the gas flow rate marginally affects HL values. However, after the flooding point was achieved, the values of HL rapidly increased as the gas flow rate increased. Moreover, a linear relationship emerges between the liquid holdup and HL, as evidenced by the consistent variation in the liquid holdup and the F‐factor. Utilizing the ineffective void yields a more accurate fit for the experimental data, reducing the average absolute relative deviation to 10.2%, 7.4%, and 10.8%, respectively.

Heat capacity of liquid octamethyltrisiloxane (MDM) and hexamethyldisiloxane (MM): Measurement and molecular dynamics study

Accurate heat capacity data and models of fluids are necessary to reduce the design margins of engineering equipment. Also, heat capacities are indispensable in the development of high-accuracy equation of state. In this work, liquid heat capacities of Octamethyltrisiloxane (MDM, C8H24O2Si3) and Hexamethyldisiloxane (MM, C6H18OSi2) were obtained with a modified steady-flow calorimeter. Experiments were performed over the temperature range of 313 to 453 K and pressures up to 10 MPa. The expanded relative uncertainty in measurement of heat capacity is less than 0.015. Then, the present experimental heat capacity data were correlated with an empirical model, yielding an average absolute deviation (AAD) of 0.26 % for MDM and 0.38 % for MM. In addition, non-equilibrium molecular dynamics was used to investigate the heat capacity of MDM and MM. The simulation results show not only a reasonable heat capacity’s dependence on temperature but also a high accuracy.

Modeling self‐diffusion coefficients of ionic liquids using ePC‐SAFT and FVT combined with Einstein relation

AbstractThe electrolyte perturbed‐chain statistical associating fluids theory (ePC‐SAFT) coupled with free volume theory (FVT) was combined with Einstein relation, that is, ePC‐SAFT‐FVT‐E, to describe self‐diffusion coefficients (SDCs) of ionic liquids (ILs). In modeling, ePC‐SAFT was used to calculate density, while FVT parameters, determined from viscosity data, were utilized to calculate the summation of ionic SDCs through the Einstein relation with one parameter. Two strategies were employed to determine this parameter: fitting experimental data for each IL or estimating a universal parameter from van der Waals volume. Comparative analysis reveals good agreement with experimental data, with average absolute relative deviations (ARDs) of 8.14% (strategy 1) and 10.29% (strategy 2). Subsequently, cationic and anionic SDCs were reliably determined from the summation of ionic SDCs, with average ARDs of 10.80% and 10.21%, respectively. This study indicates the ePC‐SAFT‐FVT‐E model, employing viscosity‐derived parameters and three universal parameters, reliably predicts SDCs of ILs in wide temperature and pressure ranges.

Electronic structure and optical spectral analysis of the MnO42- anion with consideration of site and Jahn-Teller distortion.

The ground state of 3d1 MnO42- was studied by density functional theory (DFT) and complete active space self-consistent field (CASSCF) methods in terms of a variety of molecular point group structures to ascertain the site and Jahn-Teller (JT) distortion effect. Modeling results from UB3LYP/6-31+G(d) calculations with natural bond orbital analysis show the four Mn-O bonds are coordinate covalent. The one-electron matrix elements from CASSCF(AILFT) (abinitio ligand field theory) with a second order perturbation treatment were used to calculate the parameters of the angular overlap model. These allowed the calculation of JT stabilization energies and first and second order JT coupling constants for MnO42- with C2v and D2d symmetry. Absorption spectra and excited state transition energies were calculated assuming the lattice distorted geometry was C2v with time-dependent DFT (TDDFT) using the unrestricted coulomb-attenuating UCAM-B3LYP density functional and with the spin-adapted spin-flip DFT using the UBH&HLYP density functional, both with the AUG-CC-PVTZ basis set. The mean absolute deviation of the calculations from experimental excited states for the ligand field and ligand-to-metal charge transfer (LMCT) bands was better than 0.1eV. Several new assignments for LMCT excited states were made on the basis of the TDDFT excitation results.

Solubility prediction of refrigerants in PEC lubricants based on back-propagation neural network combined with genetic algorithm

In the present study, a backpropagation neural network combined with genetic algorithm (GA-BP) model was constructed for prediction the solubility of refrigerants in linear chained precursors of POE lubricants (PECs). A total of 2248 experimental solubility data of refrigerants in PECs reported in literature were collected with temperatures from 243.15 K to 363.15 K and pressures up to 10 MPa. The input variables of the model were optimized using non-dominated sorting genetic algorithm with elite strategy (NSGA-II). The optimized inputs include temperature, pressure, molecular weight, critical temperature, and acentric factor. Results indicate that the GA-BP model using the optimized inputs can correlate the solubility data with good accuracy, the average absolute relative deviation between calculated results from the model and the literature is 0.98 %. Moreover, in order to validate the predictive ability of the established GA-BP model, the solubility of R1243zf in PEC4 and PEC5 was measured at the temperature range from 278.15 K to 343.15 K. The calculated values from the GA-BP model were compared with the experimental data, and the average absolute relative deviation is 8.85 %. Finally, sensitivity analysis was performed by Partial Derivatives (PaD) method to assess the contribution of input variables. Leverage approach was used for outlier detection to ensure the robustness of the model.

Calculations of solubilities of pure noble gases and He–CO2/CH4/N2 gas mixtures in aqueous solutions with accurate thermodynamic models

Determining solubilities of pure noble gases and He–CO2/CH4/N2 gas mixtures in aqueous solutions is essential for various applications of noble gases in geological processes and for understanding the migration and accumulation of helium. This study developed two thermodynamic models for calculations of solubilities of five pure noble gases (i.e. He, Ne, Ar, Kr, and Xe) and He–CO2/CH4/N2 gas mixtures in water and in aqueous NaCl solution. The first model is constructed by extending the improved SAFT-LJ equation of state (EOS) to five binary H2O-noble gas systems and four ternary systems (i.e. H2O–He–NaCl, H2O–He–CO2, H2O–He–CH4, and H2O–He–N2). Both the non-ideality of aqueous phase and that of vapor phase of these systems were described using the improved SAFT-LJ EOS (φ-φ model). The second model is developed by modifying the gas solubility model proposed by Duan and his coworkers (one type of γ-φ model). The Pitzer theory was used to describe the non-ideality of aqueous phase and the improved SAFT-LJ EOS was used to describe the non-ideality of vapor phase. Both binary adjustable parameters of the improved SAFT-LJ EOS and parameters of the modified solubility model for pure gas were evaluated from experimental solubility data for pure gas. Both models are predictive for solubilities of gas mixtures since there is no need to evaluate their parameters from solubility data of gas mixtures.Comparisons of calculations of two models with experimental data show that both models can well represent solubilities and Henry's coefficients of five noble gases in pure water, as well as the solubility of He in aqueous NaCl solutions over a wide P-T range (273–600 K, 1–2000 bar for the solubility of He in water). The absolute average deviation of the improved SAFT-LJ EOS from experimental solubility data for five noble gases in pure water is 3.14% and that of the modified solubility model is 2.21%. Predictions of the improved SAFT-LJ EOS for Henry's coefficients for He in the He–CO2–H2O system are consistent with available experimental data. According to predictions of the improved SAFT-LJ EOS, Henry's coefficient for He in the He–CO2–H2O system is always lower than that in the He–H2O system at the same depth. Within 0–5 km depth, He solubility of He–CO2 gas mixture is much greater than that of pure He at the same depth and He partial pressure, He solubility of He–N2 mixture is a little greater than that of pure He, and He solubility of He–CH4 mixture is between that of He–CO2 mixture and that of He–N2 mixture. A computer program for calculations of solubilities of pure noble gases and He–CO2/CH4/N2 gas mixtures in pure water and in aqueous NaCl solutions can be obtained from the corresponding author via email.

Optimal Portfolio Analysis of Listed Companies in IDX 30

Investors face the dual considerations of return and risk when making investment decisions. Therefore, proper analysis is crucial, especially during the COVID-19 crisis, to achieve maximum returns while minimizing risk. This research used three portfolio optimization models, the Mean-Variance Model, the Mean-Absolute Deviation Model and the Value-at-Risk Model, to construct a stock portfolio. The findings indicated that the Mean-Variance Model can yield an expected return of 16.55% and a portfolio risk of 258.66%. The result from the Mean-Absolute Deviation Model was that the target return is 16%, along with a portfolio risk of 282.43%. Keywords: Portfolio Optimization, Mean-Variance, Mean Absolute Deviation, Value at Risk, R Language, IDX30

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

The Perturbed Chain Polar Statistical Associating Fluid Theory (PCP-SAFT) equation of state (EoS) is widely used to predict fluid-phase thermodynamics, but parameterization of PCP-SAFT for individual molecules is often challenging. We propose a machine learning framework called ML-SAFT that can turn experimental data in predictive models of PCP-SAFT parameters. We demonstrate methods for automated large scale regression of PCP-SAFT parameters and thus create a large PCP-SAFT parameter dataset in the literature. We then evaluate several machine learning architectures for predicting PCP-SAFT parameters. We find that our best model provides accurate predictions for a wider range of molecules than existing predictive methods with 40 % average absolute deviation (% AAD) in vapor pressure predictions and 8 % AAD in density predictions.

Impervious cover change as an indicator of environmental equity

Pattern-focused environmental equity research has been underpinned by high-resolution remotely sensed data to uncover spatial relationships between environmental amenities (e.g., urban tree cover) and socio-economic status (SES). A constraint imposed by reliance on high-resolution data is the inability to examine temporal patterns, primarily because of the cost of data production and the nascent state of high-resolution land cover mapping. The lack of temporal monitoring is a clear gap in pattern-focused environmental equity research. We examined temporal (2001 – 2019) relationships between a disamenity, impervious cover (IC), and demographic attributes for the entirety of the conterminous United States. Our main finding was 2001 – 2019 increases in IC were more pervasive in minority communities but these communities were not necessarily poor, and only rarely poorly educated or non-English speaking. We supported our use of IC from moderate resolution data by comparing it to high-resolution data for 24 cities within the conterminous United States. Mean Absolute Deviation (MAD) was 4.8% overall, ranging from 2.2% to 11.3% across the 24 locations. Differences in classification objectives contributed to differences in %IC estimates between the two sources.

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis.

Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.

REAL-TIME HIP RANGE OF MOVEMENT MEASUREMENT IN TELEREHABILITATION; COMPARISON OF HUMAN POSE ESTIMATION TOOL, PHYSIOROM, VS 3D MOTION CAPTURE SYSTEM

INTRODUCTION AND AIMS Telerehabilitation is widely acknowledged, but its adoption in musculoskeletal cohorts has been restricted due to challenges in accurately measuring objective parameters such as range of movement (ROM). This research assesses the accuracy of a human pose estimation tool utilising two-dimensional (2D) video (PhysioROM, Coviu) for evaluating active hip flexion and abduction ROM during common rehabilitation exercises, compared to three-dimensional (3D) motion capture. METHODS Fifty participants (18 to 80 years) were recruited, each performing three common hip movements; standing hip flexion, standing hip abduction and a sit-to-stand. The motions were captured using the 12-camera, 3D Vicon motion capture system in conjunction with simultaneous 2D video, simulating a home telehealth consultation scenario. Joint kinematics were computed using either the Vicon Plug-in Gait model (3D Vicon) or the human pose estimation tool integrated into the PhysioROM algorithm (2D PhysioROM). RESULTS No significant differences were observed in peak hip ROM measurements during the tasks undertaken. Mean peak hip flexion was 2.4° (p=1.0) less for 3D Vicon versus 2D PhysioROM in standing hip flexion, though 2.1° (p=0.46) greater during sit-to-stand movement. Hip abduction was 5.3° (p< 0.001) less for 3D Vicon versus 2D PhysioROM in standing hip abduction. Statistical parametric mapping revealed significant differences (p<0.001) in hip flexion between Vicon 3D and 2D PhysioROM for 32% of timepoints for both standing hip flexion and sit-to-stand movements, clustered around full hip extension. During standing hip abduction, significant differences (p<0.001) were found at 58% of timepoints clustered around neutral postures. Overall mean absolute deviation was 10.1°, 9.7° and 9.0° for hip flexion/extension, sit-to-stand, and hip abduction respectively. CONCLUSION Validity of PhysioROM to accurately measure peak hip flexion and peak hip abduction was high. PhysioROM may provide an effective method of accurately and objectively quantifying hip movement during a telerehabilitation session using 2D video.

TELEREHABILITATION ASSESSMENT OF KNEE RANGE OF MOVEMENT; COMPARISON OF HUMAN POSE ESTIMATION TOOL, PHYSIOROM, VS 3D MOTION CAPTURE SYSTEM

INTRODUCTION AND AIMS Telerehabilitation is increasingly acknowledged, though uptake remains limited by an inability to accurately undertake objective measurement. This study sought to determine the validity of a human pose estimation tool (PhysioROM, Coviu) assessed via two-dimensional (2D) video to measure active knee flexion and extension movement during common rehabilitation exercises, versus gold-standard three-dimensional (3D) motion capture. METHODS Fifty participants were recruited (aged 18 to 30), each performing three common knee rehabilitation exercises; sit-to-stand, seated and reclined active knee flexion. Movements were recorded using the 12-camera, 3D Vicon motion capture system simultaneously with 2D video simulating a home telehealth consultation. Joint kinematics were calculated using the Vicon Plug-in Gait model (3D Vicon) or the human pose estimation tool within the PhysioROM algorithm (2D PhysioROM). RESULTS Mean peak knee flexion was 3.1°, 9.9° and 24.8° (p<0.001) greater for 3D Vicon versus 2D PhysioROM (sit-to-stand, seated flexion and reclined flexion, respectively). For peak knee extension, Vicon 3D was 2.4° and 1.93° greater than 2D PhysioROM in sit-to-stand (p=0.020) and seated flexion (p< 0.001), whilst 3.6° less than PhysioROM for reclined knee flexion (p=0.030). Statistical parametric mapping revealed significant differences between Vicon 3D and PhysioROM for 71% and 42% of timepoints (p<0.001) for seated and reclined movements, clustered around full knee flexion. Significant differences were also found at an additional 37% (p<0.001) of timepoints clustered around knee extension for reclined postures. Overall mean absolute deviation was 8.7°, 7.6° and 16.5° for sit-to-stand, seated flexion and reclined flexion, respectively. CONCLUSION Validity of PhysioROM to accurately measure knee flexion was high, less so in extension, during sit-to stand movements in a seated posture. Measurement in a reclined position were less accurate. PhysioROM may provide an effective method of accurately and objectively quantifying knee movement during a telerehabilitation session using 2D video.

Open access, thermodynamically consistent, electrolyte NRTL model for piperazine, AMP, water, CO2 systems on Aspen Plus

This study developed a comprehensive, open-access, thermodynamically consistent, validated vapour-liquid equilibrium model for CO2 absorption into aqueous solutions of piperazine (PZ) activated 2-amino-2-methyl-1-propanol (AMP) on Aspen Plus. This solves a two-decade-old problem of inconsistent specification of PZ, and lacking AMP/PZ, thermodynamic models on Aspen Plus, supporting robust process modelling of AMP/PZ-based CO2 capture systems, considered the contemporary benchmark. The model coverage is wide: total amine concentrations from 30 wt% to 50 wt%, AMP/PZ mole ratios from 0.46 to 23.1, CO2 loadings from 0.04 to 1.07 mol CO2/mol amine, and temperatures from 20 °C to 160 °C. The significance lies in the model’s innovative treatment of the zwitterion PZH+CO2–, leading to accurate VLE predictions and closed charge balances. Absolute average relative deviations (AARD) are 16.6 % to 22.3 % for CO2 partial pressure predictions and 6.3 % to 7.7 % for absorption heats. Gibbs-Helmholz and flash calculated absorption heats consistently compare within 1.5 % to 7.0 %, signalling the model’s thermodynamic consistency.