Mean Absolute Average Deviation Research Articles

Monitoring the Sleep Respiratory Rate with Low-Cost Microcontroller Wi-Fi in a Controlled Environment

Sleep apnea, characterized by breathing interruptions or slow breathing at night, can cause various health issues. Detecting respiratory rate (RR) using Wireless Fidelity (Wi-Fi) can identify sleep disorders without physical contact avoiding sleep disruption. However, traditional methods using Network Interface Cards (NICs) like the Intel Wi-Fi Link 5300 NIC are often costly and limited in channel state information (CSI) resolution. Our study introduces an effective strategy using the affordable ESP32 single-board computer for tracking RR through detailed analysis of Wi-Fi signal CSI. We developed a technique correlating Wi-Fi signal fluctuations with RR, employing signal processing methods—Hampel Filtering, Gaussian Filtering, Linear Interpolation, and Butterworth Low Pass Filtering—to accurately extract relevant signals. Additionally, noise from external movements is mitigated using a Z-Score for anomaly detection approach. We also implemented a local peak function to count peaks within an interval, scaling it to bpm for RR identification. RR measurements were conducted at different rates—Normal (12–16 bpm), Fast (>16 bpm), and Slow (<12 bpm)—to assess the effectiveness in both normal and sleep apnea conditions. Tested on data from 8 participants with distinct body types and genders, our approach demonstrated accuracy by comparing modeled sleep RR against actual RR measurements from the Vernier Respiration Monitor Belt. Optimal parameter settings yielded an overall average mean absolute deviation (MAD) of 2.60 bpm, providing the best result for normal breathing (MAD = 1.38). Different optimal settings were required for fast (MAD = 1.81) and slow breathing (MAD = 2.98). The results indicate that our method effectively detects RR using a low-cost approach under different parameter settings.

A Digital Approach to via Edge Roughness Characterization and Quantification

This paper introduces a new digital integration that combines edge diffractometry with convolutional neural networks (CNN) for via metrology and inspection. The beam propagation method (BMP) was used to simulate the interferogram generated by edge diffractometry to characterize via edge roughness (VER). A comprehensive database was established to link different fringe patterns to VER for CNN training. The well-trained CNN-based methodology provided a fast and accurate assessment of VER, with a root mean squared error (RMSE) of 0.073 and an average mean absolute deviation ratio (MADR) of 2.274%. In addition, the proposed digital approach was compared to the multilayer perceptron machine (MLP) in terms of computational efficiency and predictive accuracy. The proposed digital integration greatly improved the accuracy and speed of VER measurement, characterization, and quantification, potentially enhancing device yield and reliability. The successful application of this digital approach could open up possibilities for various types of via or pattern metrology.

Interobserver reliability of biplanar radiography is unaffected by clinical factors relevant to individuals at risk of pathological lower limb torsion

BackgroundAssessments of lower limb torsion are ubiquitous in clinical gait analysis practice as pathologic lower limb rotational deformity may contribute to gait abnormalities, anterior knee pain, as well as other debilitating conditions. Understandably, the overall utility of any torsional assessment is dependent on the measurement method’s intrinsic accuracy, precision, and robustness to clinical interference factors. Recently, biplanar radiography (BPR) measurements of torsion have been shown to be both accurate and precise, but the robustness of BPR to potential interference factors is unknown. Research questionHow robust are BPR lower limb torsional assessments to six potential interference factors: amount of torsion, skeletal maturity, radiograph quality, prior osteotomy, presence of implants, and observer training background and experience? MethodsIn this retrospective cohort study, four observers of diverse backgrounds and experience generated digital 3D reconstructions of 44 lower limbs using BPR images obtained during standard of care visits (age range 7–35 years). From each reconstruction, four lower limb torsional parameters were computed: femoral torsion, femorotibial rotation, tibial torsion, and transmalleolar axis equivalent. The mean absolute deviation (MAD) of each torsional parameter – calculated across the four observers – was used as the measure of reliability and tested against all interference factors. ResultsResults demonstrated that the average MAD was 2.1 degrees for femoral torsion, 3.0 degrees for transmalleolar axis equivalent, 3.8 degrees for femorotibial rotation, and 4.7 degrees for tibial torsion. None of the six potential interference factors were found to systematically influence BPR reliability across all four torsional parameters. Of the factors found to statistically influence one or more torsional parameter, none affected MAD values to a clinically meaningful extent. SignificanceIn addition to being accurate and precise, BPR appears to be robust to several clinical factors relevant to children and young adults with or at risk for pathological lower limb torsion.

Vaporization enthalpy of silanes fluids: A new correlation based on the corresponding states principle

A new and simple correlation based on the corresponding-states principle is proposed for the relation of the vaporization enthalpy with the temperature for saturated silanes. The primary accepted data for 7 silanes obtained from the most recent version of the DIPPR database are used as reference to define the model. Then predictions are made for other 29 silanes and compared with DIPPR data. The proposed correlation reproduces these data with average absolute deviations (AAD) lower than 1% for 8 silanes, AAD<5% for 30 fluids and AAD<10% for 35. The predictive power of the model is also checked by considering vaporization enthalpy data available for other 9 silanes not included in DIPPR. In this case, the mean AAD is 2%. Comparison with other previously published corresponding-states correlations is made, the here proposed being simpler, accurate, and predictive.

Surface Tension for Silanes, Refrigerants, and Carboxylic Acids: Simple Corresponding State Correlations versus DIPPR Data.

A simple corresponding state-based correlation, whose analytical expression contains just one term with an exponential function, is proposed to calculate the surface tension of 36 silanes, 29 carboxylic acids, and 81 refrigerants as a function of temperature. This correlation only requires critical temperature, maximum value of surface tension, and its corresponding temperature in the DIPPR database as inputs for each liquid considered. The correlation allows us to calculate the accepted DIPPR data for silanes with a mean absolute average deviation (AAD) of 2.75%, 2.42% for acids, and 3.32% for refrigerants. Moreover, it gives AADs below 3% for 99 fluids. The mean deviation for the 146 fluids is 3.0%, which is practically equal to the results obtained when two terms are used in the analytical expression.

Accuracy of different tooth surfaces on 3D printed dental models: orthodontic perspective

BackgroundFew studies have been reported regarding the accuracy of 3D-printed models for orthodontic applications. The aim of this study was to assess the accuracy of 3D-printed dental models of different tooth surfaces.MethodsThirty volunteers were recruited from the hospital, and then their dental models were produced by means of oral scanning and a stereolithography-based 3D printer. Each printed model was digitally scanned and compared with the oral-scanned STL file via superimposition analysis. A color map was used to assess the accuracy of different surfaces (occlusal, buccal, lingual) of anterior and posterior teeth. The Tukey test was used to evaluate the differences between the superimposition.ResultsStatistically significant differences were found in the average deviations of different tooth surfaces (P < 0.05). The mean average absolute deviations of the occlusal surfaces of posterior teeth were greater than those of other surfaces. Percentages of points beyond the upper and lower limits of different tooth surfaces displayed the same results (P < 0.05).ConclusionsOcclusal surfaces, especially pits and fissures of posterior teeth on 3D printed maxillary dental models, showed greater distortions than those of other teeth and regions.

Human age prediction using DNA methylation and regression methods

Determination of a person’s age can be an important factor in forensic investigation. DNA methylation (DNAm) is a well-known factor signifying change during the aging process but also necessary for the development of mammals. Several studies reported that DNAm can be used as an important marker in predicting the age of a human. This study is carried out to develop the age prediction model using three different regression methods. Multiple linear regression, Support vector regression, and Random forest regression methods are applied using a set of four highly age-correlated CpG sites. For 180 blood samples having age between 2 and 87 years, the mean absolute deviation (MAD) for multiple linear regression method is 8.43 years, for support vector regression is 7.86 years and for random forest regression method is 8.25 years. Further, these models are tested on five different age-groups. The average MAD for multiple linear regression, support vector regression and random forest regression are 3.46, 3.44 and 3.56, respectively. Support vector regression gave the highest accuracy for combined samples as well as for 5 different age groups. It has been concluded from the results that support vector regression is a reliable method for human age prediction.

New methods for calculating bare soil land surface temperature over mountainous terrain

Land surface temperature (LST) causes the phase change of water, links to the partitioning of surface water and energy budget, and becomes an important parameter to hydrology, meteorology, ecohydrology, and other researches in the high mountain cold regions. Unlike air temperature, which has common altitudinal lapse rates in the mountainous regions, the influence of terrain leads to complicated estimation for soil LST. This study presents two methods that use air temperature and solar position, to estimate bare LST with high temporal resolution over horizontal sites and mountainous terrain with a random slope azimuth. The data from three horizontal meteorological stations and fourteen LST observation fields with different aspects and slopes were used to test the proposed LST methods. The calculated and measured LST were compared in a range of statistical analysis, and the analysis showed that the average RMSE (root mean square error), MAD (mean absolute deviation), and R2 (correlation coefficient) for three horizontal sites were 5.09°C, 3.66°C, 0.92, and 5.03°C, 3.52°C, 0.85 for the fourteen complex terrain sites. The proposed methods showed acceptable accuracy, provide a simple way to estimate LST, and will be helpful for simulating the water and energy cycles in alpine mountainous terrain.

An interval mean–average absolute deviation model for multiperiod portfolio selection with risk control and cardinality constraints

Interval number is a kind of special fuzzy number and the interval approach is a good method to deal with some uncertainty. An interval mean---average absolute deviation model for multiperiod portfolio selection is presented by taking risk control, transaction costs, borrowing constraints, threshold constraints and cardinality constraints into account, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. In the proposed model, the return and risk are characterized by the interval mean and interval average absolute deviation of return, respectively. Cardinality constraints limit the number of assets to be held in an efficient portfolio. Threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Based on interval theories, the model is converted to a dynamic optimization problem. Because of the transaction costs, the model is a dynamic optimization problem with path dependence. A forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, the comparison analysis of the different desired number is provided by a numerical example to illustrate the efficiency of the proposed approach and the designed algorithm.

An analysis of a widely used version of the CUSUM tracking signal

Tracking signals are used to determine whether a sequence of forecasts is unbiased. We analyse a version of the CUSUM tracking signal that is based on the unsmoothed average mean absolute deviation of forecasting errors. We make the common assumption that demand values in different periods are independently, identically, and normally distributed. We show that, when forecast errors in different periods are also normal iid, this version of the tracking signal can indicate that the sequence is not unbiased even when it is unbiased. We suggest a simple modification to the tracking signal that serves to eliminate this flaw.

Application of GC-PPC-SAFT EoS to ammonia and its mixtures

The GC-PPC-SAFT equation of state (EoS) is a combination of the PC-SAFT EoS proposed by Gross and Sadowski [J. Gross, G. Sadowski, Ind. Eng. Chem. Res. 40 (2001) 1244–1260] and a group contribution method proposed by Tamouza [S. Tamouza et al., Fluid Phase Equilib. 222–223 (2004) 67–76; S. Tamouza et al., Fluid Phase Equilib. 228–229 (2005) 409–419] which was extended to polar molecules by Nguyen-Huynh [D. Nguyen-Huynh et al., Fluid Phase Equilib. 264 (2008) 62–75]. It is here applied to ammonia for which parameters have been regressed on pure component data (vapour pressures and liquid molar volumes), and some mixture vapour–liquid equilibrium data. Ammonia is described with an association scheme containing four associating sites (one donor site on the nitrogen atom and an acceptor site on each hydrogen atom). It also contains a dipole moment. These resulting parameters have then been tested on vapour–liquid equilibrium (VLE) and liquid–liquid equilibrium (LLE) of mixtures with n-alkanes, cycloalkanes, aromatics and small molecules such as methane, hydrogen or nitrogen whose parameters were previously published. The vapour–liquid equilibrium and the mixing enthalpy have also been evaluated for the ammonia+water binary system. For these systems a binary interaction parameter kij and corrective parameters for the cross-association energy wαβ and volume uαβ have been used in some cases.The agreement with experimental data is encouraging as correlations and predictions are both qualitatively and quantitatively satisfactory. In terms of pure component properties, the mean absolute average deviation (AAD) for the vapour pressures is about 2.5% whereas it is about 2.7% for the saturated liquid volumes. Furthermore, predictions on mixtures with n-alkanes, cycloalkanes or aromatics show that LLE lines predicted by GC-PPC-SAFT are perfectly well-located. Finally, results for VLE with n-alkanes, aromatics, small molecules or water are as accurate as it was for previously investigated systems. These results also show that the model is able to correctly represent the sign and the order of magnitude of the mixing enthalpy for the ammonia+water system.For systems containing ammonia with alkylbenzenes, this study have highlighted that a re-adjustment of alkybenzenes groups parameters was necessary. This paper explains the fitting procedure used for the new alkylbenzenes groups parameters and show satisfactory results for VLE and LLE of systems containing ammonia and alkylbenzenes.

A Generalized Formula for Estimating Pure Alkenes Viscosity

The importance of liquid viscosity in chemical process design makes it one of the most measured transport properties. Nevertheless, in the pure-component database, no experimental data on liquid viscosity for nearly 50% of the compounds are available. Therefore, prediction methods for liquid viscosity of alkenes over a wide range of absolute temperature for each components are necessary. Moreover, experimental data measured at lower temperatures are often extrapolated to higher temperatures with erroneous results. To improve liquid viscosity prediction of experimental data to temperatures and carbon numbers, we propose an empirical rule for estimating the viscosity of alkenes compounds. A predictive method, based physical properties (absolute temperature and carbon numbers) as its inputs, to correlate liquid viscosity by the statistical analysis is proposed. For a group of 19 compounds, the mean average absolute deviation was 4.6% for 118 data points. These values are better than other predictive methods and show that the statistical analysis model is stable and can be used to obtain good predictions for compounds that were not used in the model calibration.

Application of equation of state and artificial neural network to prediction of volumetric properties of polymer melts

The modified ISM EOS and artificial neural network (ANN) methods were used to predict the PVT behavior of polymer melts containing polystyrene (PS), polycarbonate bisphenol-A (PC), polyvinylidene fluoride (PVDF), polypropyleneglycol (PPG), polyethylene glycol (PEG), polypropylene (PP), poly vinylchloride's properties (PVC), poly(1-butene) (PB1), polycaprolactone (PCL), polyethylene (PE) and polyvinyl methyl ether (PVME) over the entire range of available temperature and pressure. The obtained results show that the modified ISM EOS and ANN models have good agreement with the experimental data with mean average absolute deviation of 0.58% and 0.20%, respectively.

Quantitative Assessment of Substituent Effects on Cation−π Interactions Using Molecular Electrostatic Potential Topography

A molecular electrostatic potential (MESP) topography based approach has been proposed to quantify the substituent effects on cation-π interactions in complexes of mono-, di-, tri-, and hexasubstituted benzenes with Li(+), Na(+), K(+), and NH(4)(+). The MESP minimum (V(min)) on the π-region of C(6)H(5)X showed strong linear dependency to the cation-π interaction energy, E(M(+)). Further, cation-π distance correlated well with V(min)-π distance. The difference between V(min) of C(6)H(5)X and C(6)H(6) (ΔV(min)) is proposed as a good parameter to quantify the substituent effect on cation-π interaction. Compared to benzene, electron-donating groups stabilize the di-, tri-, and hexasubstituted cation-π complexes while electron-withdrawing groups destabilize them. In multiple substituted complexes, E(M(+)) is almost equal (∼95%) to the sum of the individual substituent contributions (E(M(+)) ≈ Σ(ΔE(M(+)))), suggesting that substituent effect on cation-π interactions is largely additive. The ΔV(min) of C(6)H(5)X systems and additivity feature have been used to make predictions on the interaction energies of 80 multiple substituted cation-π complexes with above 97% accuracy. The average mean absolute deviation of the V(min)-predicted interaction energy, E(M(+))(V) from the calculated E(M(+)) is -0.18 kcal/mol for Li(+), -0.09 kcal/mol for Na(+), -0.43 kcal/mol for K(+), and -0.67 kcal/mol for NH(4)(+), which emphasize the predictive power of V(min) as well as the additive feature of the substituent effect.

Application of GC-PPC-SAFT EoS to amine mixtures with a predictive approach

The GC-PPC-SAFT equation of state (EoS) is a combination of a group contribution method [S. Tamouza et al., Fluid Phase Equilib. 222–223 (2004) 67–76; S. Tamouza et al., Fluid Phase Equilib. 228–229 (2005) 409–419] and the PC-SAFT EoS [J. Gross, G. Sadowski, Ind. Eng. Chem. Res. 40 (2001) 1244–1260] which was adapted to the polar molecules [D. Nguyen-Huynh et al., Fluid Phase Equilib. 264 (2008) 62–75]. It is here applied to the vapour pressure and liquid molar volume of primary, secondary and tertiary amines and their mixtures with n-alkanes, primary and secondary alcohols, using previously published group parameters. The mixing enthalpy is also evaluated for the binary systems. Binary interaction parameters k ij are computed using a group-contribution pseudo-ionization energy, as proposed by Nguyen-Huynh [D. Nguyen-Huynh et al., Ind. Eng. Chem. Res. 47 (2008) 8847–8858]. A unique corrective parameter for the cross-association energy between amines and alcohols is used. The agreement with experimental data in correlation and prediction were found rather encouraging. The mean absolute average deviation (AAD) on bubble pressure is about 3.5% for pure amines. The mean AAD on the vapour–liquid equilibria (VLE) are respectively 2.2% and 5.5% for the amine mixtures with n-alkanes and alcohols. The AADs on saturated liquid volume are about 0.7% for the pure compounds and 0.9% for the mixtures. Prediction results are qualitatively and quantitatively accurate and they are comparable to those obtained with GC-PPC-SAFT on previously investigated systems.

The optimisation on the multi-period mean-average absolute deviation portfolio selection in friction market

This paper is devoted to solving the multi-period portfolio selection problem under transaction costs and trade volumes for which the objective is to maximise a utility function in expected return and average absolute deviation. An efficient method, named discrete approximate iteration algorithm, is proposed for solving the optimal portfolio policy. A further analysis shows that the rate of convergence of the iterates is linear.

Surface tension of non-ideal binary and ternary liquid mixtures at various temperatures and p = 81.5 kPa

Experimental surface tensions for binary mixtures (1,2-ethandiol + water), (1,2-ethandiol + acetonitrile), and (acetonitrile + water) at temperatures of 283.15 K, 298.15 K, and 308.15 K and the ternary mixture (1,2-ethandiol/water/acetonitrile) at 298.15 K have been measured with the Du Noüy ring tensiometer. The surface tension of the above mentioned binary and ternary systems were correlated with empirical and thermodynamic based models. The methods of Pando et al. and Ku et al. were used to correlate the ternary surface tension data. The Fu et al., Kalies et al. and Wang et al. models were also applied to predict surface tension in the ternary system. The mean average absolute deviations obtained from the comparison of experimental and calculated surface tension values for ternary system with three models are less than 2.4%, which leads to concluding that these models show a good accuracy in different situations in comparison with other predictive equations.

Improving the prediction of liquid saturation densities from models based on the corresponding states principle

The liquid saturation density of pure fluids is commonly predicted using models based on the corresponding states principle. Nevertheless, it is well-known that these models are not always accurate near the triple point. In this work we use simple modifications of several of those models in order obtain good accuracy at any temperature. In particular, for each model we change a fixed coefficient to a variable coefficient, which permits the model to reproduce exactly the value of the density at the triple point. The accuracy of a model based on a scale-variable-reduced-coordinate framework, and of a new simple predictive model recently proposed by us is also checked. This latter model does not contain adjustable coefficients but only two variable coefficients that depend on the temperature and density at both the critical and the triple point. We find that a simple modification of the well-known and straightforward Rackett model gives excellent accuracy (mean average absolute deviation 0.9%) in the whole temperature range for 107 fluids of different kinds.

Modeling bacterial clearance from the bloodstream using computational fluid dynamics and Monte Carlo simulation

(HOB) elevation (between 00 and 300) and mild hypoand hyperventilation (achieved by changing the respiratory rate, RR). Each experimental session consisted of between 1 and 6 changes in HOB and/or RR over periods varying from 15 to 130 minutes. Multiple sessions were recorded for 6 patients. ICP and other signals were recorded continuously, along with clinical annotations to indicate the precise timing of the physiologic challenges. Data from early in single long session or from prior sessions were used to estimate patient-specific parameter values for a computer model of ICP dynamics that is similar to other models reported in the literature. The parameter estimates were calculated using a curvefitting optimization algorithm with the objective of minimizing the squared error between the ICP calculated by the model and the actual ICP data. The resulting patient-specific models were then used to predict the patient's ICP response to interventions at later time periods, either later in the same session or during subsequent sessions. Results: Mean absolute error (MAE) between model-calculated ICP and the actual data averaged 1.9 mm Hg for full sessions and 1.7 mm Hg for partial sessions. The average mean absolute deviation (MAD) for these segments was 3.1 and 2.4 mm Hg, respectively. The MAE for the predictions was 4.0 mm Hg within the same session and 6.7 mm Hg across sessions. Conclusions: Despite the achievement of low model errors in the training segments, the error in predicted segments was too large for the model to be useful clinically, that is to say, prediction is much more difficult than explanation. One implication for researchers is that a degree of skepticism is warranted: the fact that a model can be made to fit historical data does not mean that the model will be able to predict anything. These results could be seen as tending to support the general lack of interest from clinicians regarding computer models of ICP dynamics.

Computational Methods in Organic Thermochemistry. 1. Hydrocarbon Enthalpies and Free Energies of Formation

Standard state enthalpies and free energies of formation can be computed with reasonable accuracy (usually within 4 and often 2 kJ/mol) using high level model chemistries. A comparison set of nearly 300 organic compounds ranging from 1 to 10 carbon atoms having a variety of functional groups for which enthalpy and free energy literature values are available has been examined using G2, G2MP2, G3, G3MP2, G3B3, G3MP2B3, CBS-QB3, and density functional (B3LYP/6-311+G(3df,2p)) model chemistries. G3 gives an average mean absolute deviation of 3.0 and 13.4 kJ/mol for the enthalpies and free energies, respectively, using the atomization method and 3.1 and 3.7 kJ/mol when bond separation reactions are employed. G3 and G3B3 are the most accurate overall; the related G3MP2 and G3MP2B3 are nearly as accurate and can compute larger molecules. CBS-QB3 was also found to be accurate but is more limited in the size of molecules that can be computed. The density functional energies were found to have large deviations from the literature values using either the atomization or the bond separation method. Regardless of the model employed, the free energies are increasingly underestimated by computation as the size of the molecule increases. A series of corrections applied to the aliphatic hydrocarbons is presented, which usually reduces the deviations to less than 4 kJ/mol regardless of the size of the molecule.