TM Model Research Articles

Effects of spherical fillers reinforcement on the efficacy of thermal conductivity in epoxy and polyester matrices: Experimental validation and prediction using finite element method

AbstractThe creation of a theoretical heat conduction model for polymers embedded with spherical inclusions is described in this study. It also contains the experimental confirmation of the suggested correlation for utilizing the model to estimate the effective thermal conductivity (K) of such composites. According to ASTM‐E‐1530, composites are made with varying amounts of aluminium oxide and pine wood dust reinforced in polyester resin. The effective thermal conductivities (Keff) of the composites are then determined using the Unitherm TM model 2022. Ansys 19.R2 software is used to evaluate the effective thermal conductivity of these composites with a uniform filler distribution, while Digimat‐FE software is used determine the thermal conductivity values of such particle filled polymer composites with a random filler distribution. After comparison and validation with experimental data, these values are shown to be fairly good agreement with the theoretical values from the suggested correlation. The investigation is further expanded to determine the thermal conductivities for epoxy composites using wood apple shell dust and coir dust particle. Also epoxy and polyester composites reinforced with SiO2 and TiO2 have been investigated in the similar manner. The main thrust of this report to validate the numerical results of composites by varying numerous polymers. The thermal conductivity all the composites grow monotonically with increase in filler content. The thermal conductivity of silicon dioxide, titanium oxide, and aluminium oxide filled epoxy composites is measured as 1.5, 7, and 35 W/m‐K respectively.Highlights In this study spherical fillers are successfully used as a potential filler material in polyester composites The thermal conductivity predicted by proposed mathematical model of polyester is validated with measured value and found better agreement. The Ansys 19.R2 and digimat software are used to predict the thermal conductivity values of these composites. The mathematical model is further used to predict thermal conductivity of epoxy composites to check the accuracy of the model.

Research on the refinement of atmospheric weighted average temperature model in Xi’an based on machine learning

One of the most important parameters for calculating the atmospheric precipitable water vapor (PWV) is the atmospheric weighted mean temperature (Tm). Meteorological parameters that are measured typically constrain the accuracy and regional applicability of commonly used empirical models. When precipitable water is inverted using the Global Navigation Satellite System (GNSS), it results in erroneous Tm being obtained. Thus, in this paper, a small-scale Tm model for Xi’an is proposed by least squares method based on data provided by the European Centre for Mesoscale Weather Forecasts (ECMWF) from 2019 to 2022, while improving the functional model’s capacity to fit nonlinear residuals through the Random Forest method and enabling it to predict peaks more accurately. It is based on a random forest to confirm the weighted average temperature model’s correctness (RF Tm). When comparing the RF Tm model’s projected Tm accuracy to the GPT3, UNB3, and regional Bevis models, using the 2023 ERA5 data as the experimental data, the improvements are 23.6 %, 18.6 %, and 11.4 %, respectively. With the sounding station data as the reference value, the BIAS of RF Tm is only 1.51 K. In determining the atmospheric weighted average temperatures, the two models’ accuracy was comparable to that of the Long Short-Term Memory neural network model (LSTM). However, the LSTM model requires short-term forward data for practical usage, the RF Tm model compensated for the shortfall. The RF Tm model is used to invert the atmospheric precipitable water vapor at the appropriate monitoring stations using data from GNSS monitoring stations in Xi’an. It was demonstrated that the RF Tm model’s PWV inversion accuracy outperformed the PWV inverted by the GPT3 model by at least 30 %. Moreover, Xi’an rainfall data integration shows that the PWV inverted by the RF Tm model reacts to brief rainfall episodes efficiently. To sum up, this study’s RF Tm model provides a dependable and effective approach to meteorological observation and climate research.

A new Egyptian Grid Weighted Mean Temperature (EGWMT) model using hourly ERA5 reanalysis data in GNSS PWV retrieval

Precise modeling of weighted mean temperature (Tm) is essential for Global Navigation Satellite System (GNSS) meteorology. In retrieving precipitable water vapor (PWV) from GNSS, Tm is a crucial parameter for the conversion of zenith wet delay (ZWD) into PWV. In this study, an improved Tm model, named EGWMT, was developed to accurately estimate Tm at any site in Egypt. This new model was established using hourly ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) covering the period from 2008 to 2019 with a spatial resolution of 0.25° × 0.25°. The performance of the proposed model was evaluated using two types of data sources, including hourly ERA5 reanalysis data from 2019 to 2022 and radiosonde profiles over a six-year period from 2017 to 2022. The accuracy of the EGWMT model was compared to that of four other models: Bevis, Elhaty, ANN and GGTm-Ts using two statistical quantities, including mean absolute bias (MAB) and root mean square error (RMSE). The results demonstrated that the EGWMT model outperformed the Bevis, Elhaty, ANN and GGTm-Ts models with RMSE improvements of 32.5%, 30.8%, 39% and 48.2%, respectively in the ERA5 data comparison. In comparison with radiosonde data, the EGWMT model achieved RMSE improvements of 22.5%, 34%, 38% and 19.5% against Bevis, Elhaty, ANN and GGTm-Ts models, respectively. In order to determine the significance of differences in means and variances, statistical tests, including t-test and F-test, were conducted. The results confirmed that there were significant differences between the EGWMT model and the four other models.

An improved global Tm stratification model for GNSS-PWV retrieval

The atmospheric weighted mean temperature (Tm) is a key parameter for converting tropospheric wet delay (ZWD) into precipitable water vapor (PWV) and plays a vital role in Global Navigation Satellite System (GNSS) meteorology. Existing Tm models suffer from limitations such as the lack of consideration of full periodicity, poor accuracy at high altitudes, and the adoption of single-resolution grid data for modeling. Hence, an improved global Tm stratification model is developed based on ERA5 hourly reanalysis data from 2015 to 2019, called IGTmS, which takes seasonal and intraday variations into consideration and adopts a stratification mode to improve the vertical accuracy of Tm estimation. The accuracy and applicability of the IGTmS model are evaluated using Tm data at 417 radiosonde stations in 2020. The results show that the mean bias and RMS of Tm for the IGTmS model are −0.34 K and 3.53 K, and the accuracy is improved by approximately 7.8 %, 4.6 % and 2.5 % compared with that of GPT3, GTm-III and GTrop models on a global scale, respectively. IGTmS model can achieve the mean σPWV and σPWV/PWV values of 0.38 mm and 1.3 %, respectively, which shows optimal performance among the four models, especially in the Antarctic and Tibetan Plateau regions. Furthermore, three resolutions of the IGTmS models have been developed and all show high accuracy compared to GPT3 models. Users can choose the suitable model according to the desired accuracy and resolution. The developed stratification model IGTmS can effectively weaken the influence of large height difference on Tm estimation and can obtain high-accuracy and stable Tm over the globe and provide data support for real-time GNSS PWV retrieval.

Higher accuracy estimation of the weighted mean temperature (Tm) using GPT3 model with new grid coefficients over China

The weighted mean temperature (Tm) is an essential parameter in the field of Global Navigation Satellite System (GNSS) meteorology, as it enables the conversion of zenith wet delay to atmospheric precipitable water vapor (PWV). The existing Tm models, such as the GPT3 model, utilize the empirical annual and semi-annual amplitudes of Tm at grid points along with corresponding trigonometric functions to estimate Tm, making it difficult to provide a more detailed description of the daily variation of Tm. Therefore, the measured surface temperature and water vapor pressure were introduced to calculate the new series of grid coefficients using the least-square method, which enables the GPT3 model to estimate Tm with higher accuracy. In this process, the ERA5 reanalysis data derived from ECMWF from 2001 to 2010 was utilized to compute Tm values, and the new grid coefficient obtained by introducing only surface temperature was considered as scheme #1 and that achieved by introducing both surface temperature and surface water vapor pressure was regarded as scheme #2. The Tm data of 2023 calculated both by the radiosonde and ERA5 reanalysis data were utilized to assess the new grid coefficients for Tm estimation, and the results show that the grid coefficients based on these two surface parameters outperformed those only using the temperature. In the compassion using the ERA5 data, the bias/MAE/RMSE with the two types of new grid coefficients are −0.09/1.89/2.41 K and − 0.07/1.82/2.31 K, respectively, and the improvements reach to 50%/32.5%/34.2% and 61.1%/35%/36.9% compared with GPT3 model. In the comparison with radiosonde data, the scheme #1 and #2 performed a 0.55 K(13.8%) and 0.68 K(17.1%) improvement in the mean RMSE of stations compared with the GPT3 model. Note that the proposed two schemes can effectively improve the accuracy of estimating Tm in the comparison of regions, epochs and seasons with the Bevis and GPT3 model. Furthermore, the impact of Tm on PWV can be improved by the proposed two schemes, which was demonstrated using the theoretical RMSE and relative error of PWV.

Style Drift and Performance Evaluation of Green Funds in China

Based on data of China's open equity and partial equity hybrid green funds from 2018 to 2022, this paper conducts the sharp style analysis model to construct green fund style drift indicator, and apply the TM model to construct stock selection and timing indicators. Moreover, this paper constructs a panel fixed-effect model to empirically analyze the impact of green fund style drift, stock selection and timing on green fund performance. This research finds that there is a drift of investment style in green funds. Such style drift and timing have a negative impact on the performance of green funds. Stock selection has a significant positive impact on green funds. Furthermore, this research introduces style drift, stock selection and timing indicators into green fund performance evaluation, and use principal component analysis method to build a green fund performance-evaluation system. Then, this paper applies the rating system to the existing green fund market and effectively supplements the existing rating system.

Prediction Model for Annual Variation in Total Pollen by Allergenic Trees in Korean Cities.

Pollen forecasting systems can provide information for coping with respiratory allergies. They estimate daily pollen production, dispersal, deposition, and removal based on daily weather conditions to predict daily pollen concentrations and provide allergy warnings. As of 2023, the Korea Meteorological Administration (KMA) provides 2-day forecast of allergenic pollens. However, unlike these models, long-term analysis of annual observations of tree pollen reveal annual variations. Therefore, in this study, we aimed to develop annual prediction models for allergenic tree pollens based on long-term multi-site pollen and meteorological data. Daily pollen concentrations were observed using Hirst-type volumetric spore traps at nine sites in Korea from 1998 to 2021, and daily weather data from the closest KMA stations were utilized. Models were developed to predict the seasonal pollen integral of seven tree species based on monthly mean temperature, wind speed, and total precipitation using three variable selection methods: 1) the t-test based key variable screening followed by linear regression with stepwise procedure (TM), 2) direct linear regression with stepwise procedure from the full variable model (FM), and 3) LASSO regression from the full variable model (LM). Data obtained during 1998-2017 and 2018=2021 were utilized for model development and validation, respectively. The root mean squared error, mean absolute error, mean error, and coefficient of determination (R²) revealed that the TM models were best suited for actual forecasting, even though R² in the TM model was lower than those of the FM and LM models. The annual variation model in this study can be integrated with the daily pollen forecast model by controlling the annual pollen potential, and the accuracy of the daily forecast can be improved accordingly.

A new rate equation model of continuous wave Tm:YAP laser

A new rate equation model of continuous wave Tm:YAP laser

A Refined Atmospheric Weighted Average Temperature Model Considering Multiple Factors in the Qinghai–Tibet Plateau Region

The Qinghai–Tibet Plateau region has significant altitude fluctuations and complex climate changes. However, the current global weighted average temperature (Tm) model does not fully consider the impact of meteorological and elevation factors on it, resulting in existing models being unable to accurately predict the Tm in the region. Therefore, this study constructed a weighted average temperature refinement model (XTm) related to surface temperature, water vapor pressure, geopotential height, annual variation, and semi-annual variation based on measured data from 13 radiosonde stations in the Qinghai–Tibet Plateau region from 2008 to 2017. Using the Tm calculated via the numerical integration method of radiosonde observations in the Qinghai–Tibet Plateau region from 2018 to 2019 as a reference value, the quality of the XTm model was tested and compared with the Bevis model and GPT2w (global pressure and temperature 2 wet) model. The results show that for 13 modeling stations, the bias and root-mean-square (RMS) values of the XTm model were −0.02 K and 2.83 K, respectively; compared with the Bevis, GPT2-1, and GPT2w-5 models, the quality of XTm was increased by 47%, 38%, and 47%, respectively. For the four non-modeling stations, the average bias and RMS values of the XTm model were 0.58 K and 2.78 K, respectively; compared with the other three Tm models, the RMS values and the mean bias were both minimal. In addition, the XTm model was also used to calculate the global navigation satellite system (GNSS) precipitable water vapor (PWV), and its average values for the theoretical RMSPWV and RMSPWV/PWV generated by water vapor calculation were 0.11 mm and 1.03%, respectively. Therefore, in the Qinghai–Tibet Plateau region, the XTm model could predict more accurate Tm values, which, in turn, is important for water vapor monitoring.

Understanding Netosis Priming and Induction through Nucleosome Changes and Real-Time Monitoring in Isolated Neutrophils and in an Ex-Vivo Model

Sepsis is a leading cause of death, affecting 1.7 million adults annually in the US, where it is implicated in ~350,000 deaths (1). Rapid recognition and intervention are required to avoid progression to septic shock and death. Sepsis results from a dysregulated host response to infection (2). We aimed to study part of this dysregulated response by analyzing NETosis in neutrophils. Neutrophils are integral to the innate immune response. They can undergo a unique type of programmed cell death, NETosis, where nuclear chromatin is extruded out of the cell to capture and deactivate pathogens and prevent dissemination. These neutrophil extracellular traps (NETs) contain nucleosomes, DNA and histone proteins. NETs help to trap invading pathogens but in excess, lead to endothelial damage, excessive activation of platelets and promote microthrombosis in distal organs leading to organ failure. Our understanding of NETosis largely stems from mouse models, immortalized cell lines, and isolated neutrophils. However, these don't entirely mirror the complexity of human immune response. Here we present a model of ex vivo NETosis induction in whole blood, which can be used to provide greater clarity on the complex signaling and chromatin changes associated with NETosis. We developed an ex vivo Synthetic Sepsis TM model for observing induced NETosis in whole blood. Whole blood from healthy volunteers was induced with Phorbol 12-myristate 13-acetate (PMA), Calcium Ionophore (CI) or Lipopolysaccharides (LPS) and NETosis was monitored using H3.1 Nu.Q® levels. In parallel we adapted a SpectraMax plate reader for whole-blood fluorescent readings via Sytox Green interacalation to measure DNA release associated with NETosis. Neutrophils were isolated using the MACSxpress® Whole Blood Neutrophil Isolation Kit and NETosis of isolated neutrophils was visualized using the Incucyte® system from Sartorius. A myeloperoxidase (MPO) inhibitor, ABAH, was used to assess NETosis inhibition in PMA-induced isolated neutrophils, which allowed us to detect a reduction in both nucleosome levels using our H3.1 Nu.Q® assay and the measured DNA release via real-time fluorescence techniques. We next compared the NETosis profiles in our ex vivo Synthetic Sepsis TM model with isolated neutrophils to assess the impact of other blood cells and circulating proteins. Further, Methylation analysis post-ONT sequencing facilitated cell of origin analysis. H3.1 Nu.Q® levels increased with NETosis induction, consistent with DNA release as measured by Sytox green. NETosis was detected 1.5 hours after PMA induction using H3.1 Nu.Q® and Sytox assays. DNA Methylation analysis confirmed neutrophil cell of origin post induction. MPO inhibition resulted delayed onset of NETosis, supporting NET release rather than other forms of cell death. Netosis visualization using microscopy confirmed NETosis. Importantly, we found that although PMA successfully induced NETosis in both contexts, CI failed to elicit a response in the ex vivo whole bloodsystem, highlighting the importance of a full blood model for unbiased NETosis profiling. Our findings demonstrate that H3.1 nucleosome levels are elevated in response to NETosis-inducing treatments, with a time course that is consistent with NETosis and correlates with other established NETosis markers, validating its utility as a reliable indicator of NETosis. The successful induction of NETosis in an ex vivo whole blood model provides a valuable tool for studying disease-associated NETosis within a biologically relevant context, which will likely shed additional insight which is not possible using isolated neutrophils. Utilizing these approaches provides a platform to scrutinize a wide array of both synthetic and naturally occurring NETosis regulators. It facilitates insight into the signalling mechanisms at the core of NETosis and the influence of chromatin-associated changes within an in vivo like microenvironment of whole blood. By unravelling the intricate dynamics of neutrophil biology and its interplay with the microenvironment, we can develop tailored interventions to modulate NETosis in sepsis and other inflammatory conditions. 1. https://www.cdc.gov/sepsis/index.html. 2. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016 Feb 23;315(8):801.

Machine learning-based radiomics to differentiate immune-mediated necrotizing myopathy from limb-girdle muscular dystrophy R2 using MRI.

This study aimed to assess the feasibility of a machine learning-based radiomics tools to discriminate between Limb-girdle muscular dystrophy R2 (LGMDR2) and immune-mediated necrotizing myopathy (IMNM) using lower-limb muscle magnetic resonance imaging (MRI) examination. After institutional review board approval, 30 patients with genetically proven LGMDR2 (12 females; age, 34.0 ± 11.3) and 45 patients with IMNM (28 females; age, 49.2 ± 16.6) who underwent lower-limb MRI examination including T1-weighted and interactive decomposition water and fat with echos asymmetric and least-squares estimation (IDEAL) sequences between July 2014 and August 2022 were included. Radiomics features of muscles were obtained, and four machine learning algorithms were conducted to select the optimal radiomics classifier for differential diagnosis. This selected algorithm was performed to construct the T1-weighted (TM), water-only (WM), or the combined model (CM) for calf-only, thigh-only, or the calf and thigh MR images, respectively. And their diagnostic performance was studied using area under the curve (AUC) and compared to the semi-quantitative model constructed by the modified Mercuri scale of calf and thigh muscles scored by two radiologists specialized in musculoskeletal imaging. The logistic regression (LR) model was the optimal radiomics model. The performance of the WM and CM for thigh-only images (AUC 0.893, 0.913) was better than those for calf-only images (AUC 0.846, 0.880) except the TM. For "calf + thigh" images, the TM, WM, and CM models always performed best (AUC 0.953, 0.907, 0.953) with excellent accuracy (92.0, 84.0, 88.0%). The AUCs of the Mercuri model of the calf, thigh, and "calf + thigh" images were 0.847, 0.900, and 0.953 with accuracy (84.0, 84.0, 88.0%). Machine learning-based radiomics models can differentiate LGMDR2 from IMNM, performing better than visual assessment. The model built by combining calf and thigh images presents excellent diagnostic efficiency.

Atomic connectivity group contribution method for predicting the boiling and melting points of organic compounds

Boiling point (Tb) and melting point (Tm) are the fundamental physical properties in the petroleum-related and chemical industries. In this work, the Tb and Tm models are established by the atomic connectivity group contribution (ACGC) method. In the proposed methodology, shape factors (SFs) and atomic connectivity factors (ACFs) are employed to distinguish among isomers and improve the prediction accuracy. In addition, external and internal validations and Y-randomization tests are performed to analyze and evaluate the predictability and robustness of the ACGC models. The results indicate that the ACGC method could predict the Tb with high accuracy, with a squared correlation coefficient (R2) of 0.9767 and an absolute average relative error (AARE) of 1.70%. Moreover, the ACGC model for Tm achieves an R2 of 0.8423 and an AARE of 8.73%, demonstrating that the ACGC method can effectively predict the Tm.

Establishing a high‐precision real‐time Precipitable Water Vapor model in China with Global Navigation Satellite System and Fifth‐Generation Reanalysis Model data

AbstractThe real‐time high precision of Precipitable Water Vapor (PWV) is widely recognized as crucial for advancing numerical weather prediction and enhancing our understanding of climate change. PWV is usually measured with radiosondes, microwave radiometers, and meteorological satellites. Nevertheless, these instruments have limitations including low temporal or spatial resolutions, high cost and weather dependence. To address these issues, we developed a real‐time PWV grid model for China by integrating the ground‐based Global Navigation Satellite System (GNSS) network with ERA5 reanalysis products. In this study, we explored an alternative method using ERA5 products to construct an accurate Elevation Normalization Factor Model (ENFM) and Tm models. The culmination of our efforts resulted in the creation of a real‐time 1° × 1° PWV grid model for China. To validate the models of Tm and PWV, we compared them against the data obtained from radiosondes in China throughout 2021. The results indicate that the root‐mean‐square error (RMSE) value of the deviation between the Tm derived from our new Tm model, excluding meteorological parameters, and the radiosonde‐derived Tm, is better than 4.22 K. The Tm values of the new Tm model are improved by 15.31% compared to those estimated based on the Bevis model plus HGPT2 temperature model. The RMSE values of the deviations between the new grid PWV and the radiosonde‐derived PWV are less than 3.45 mm. The precision of our new PWV grid model is improved by 37.2% compared to that of the traditional Askne and Nordius model.

Short-Term Rainfall Forecasting by Combining BP-NN Algorithm and GNSS Technique for Landslide-Prone Areas

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting rainfall on this scale. To solve the above problem, this study proposes a short-term rainfall forecasting method for landslide-prone areas by combining the back-propagation neural network (BP-NN) algorithm and global navigation satellite system (GNSS) observations to achieve accurate short-term rainfall forecasting in landslide-prone areas. Firstly, a high-precision atmospheric weighted-average temperature (Tm) model is established using radiosonde data to obtain high-precision precipitable water vapor (PWV) estimates. Secondly, the BP-NN algorithm is introduced, and the GNSS-derived PWV, temperature and pressure from a meteorological station, and rainfall for the previous and next hour are used as input parameters to establish a BP-NN-based rainfall forecast model. As an illustrative case, experiments are conducted in a landslide-prone area in Yunnan Province using data from 15 GNSS stations and the corresponding meteorological station. Statistical results show that the established regional Tm model has high accuracy, with an average root mean square (RMS) and bias of 3 K and 0.15 K, respectively. In addition, the short-term rainfall forecast model based on the BP algorithm achieves a true detection rate of up to 93.70% and a false forecast rate of as low as 38.30%, which is significant for short-term rainfall forecasting in landslide-prone areas.

Research on Modeling Weighted Average Temperature Based on the Machine Learning Algorithms

In response to the nonlinear fitting difficulty of the traditional weighted average temperature (Tm) modeling, this paper proposed four machine learning (ML)-based Tm models. Based on the seven radiosondes in the Yangtze River Delta region from 2014 to 2019, four forecasting ML-based Tm models were constructed using Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Tree (CART) algorithms. The surface temperature (Ts), water vapor pressure (Es), and atmospheric pressure (Ps) were identified as crucial influencing factors after analyzing their correlations to the Tm. The ML-based Tm models were trained using seven radiosondes from 2014 to 2018. Then, the mean bias and root mean square error (RMSE) of the 2019 dataset were used to evaluate the accuracy of the ML-based Tm models. Experimental results show that the overall accuracy of the LightGBM-based Tm model is superior to the SVM, CART, and RF-based Tm models under different temporal variations. The mean RMSE of the daily LightGBM-based Tm model is reduced by 0.07 K, 0.04 K, and 0.13 K compared to the other three ML-based models, respectively. The mean RMSE of the monthly LightGBM-based Tm model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean RMSE of the quarterly LightGBM-based Tm model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean bias of the LightGBM-based Tm model is also smaller than that of the other ML-based Tm models. Therefore, the LightGBM-based Tm model can provide more accurate Tm and is more suitable for obtaining GNSS precipitable water vapor in the Yangtze River Delta region.

Patient Satisfaction and Perception of Physician Empathy in Outpatient Community General Neurology Telemedicine

Abstract
 Introduction: We assessed patients’ satisfaction and perception of physician empathy after implementation of video telemedicine service for general neurology follow-up visits at two community spoke clinics that serve patients in rural areas located approximately 45 miles from a medical center hub.
 Methods: Consecutive patients who completed a telemedicine neurology follow-up visit from February 12, 2020 to January 13, 2021 at the spoke clinic in Red Wing, MN and from July 21, 2021 to January 21, 2022 at the spoke clinic in Austin, MN were asked to complete a paper-based survey at the conclusion of the telemedicine visit. The neurologist conducted the telemedicine visit from the medical center hub site in Rochester, MN or from their own home using the InTouch (Teladoc Health™) operating system mobile telehealth platform. All patients had previously completed an initial traditional face-to-face consultation at the spoke clinic with the same neurologist performing the follow-up telemedicine visit. Primary outcomes were Telemedicine Patient Satisfaction Measure and Consultation and Relational Empathy scores and mean total favorable survey responses.
 Results: 31 patients at our clinic in Red Wing, MN and 38 patients at our clinic in Austin, MN participated in telemedicine neurology follow-up visit, completed the survey, and were included in the final analysis.
 The mean Telemedicine Patient Satisfaction Measure scores (possible score of 12-60) were 55 (range 42-60), and for all items ‘agree’ or ‘strongly agree’ was rated on average 94% of the time. The mean Consultation and Relational Empathy scores (possible score of 10–50) were 44 (range 28–50), and for all items ‘very good’ or ‘excellent’ was rated on average 90% of the time. 
 Both neurologists conducting telemedicine visits reported that this model of care improved work-life balance with reduced travel time.
 Discussion: We successfully implemented a telemedicine service for general neurology follow-up visits at two community spoke clinics serving patients in rural areas without compromising on perceived care. We were able to bridge the gap between patients’ needs for local care and physicians’ need for work-life balance. Patients’ perception of physician empathy and satisfaction with telemedicine neurology follow-up visits was high. This model of telemedicine avoids the barriers of limited internet access in rural areas and minimizes technology related anxiety that is often present in telemedicine visits to patients’ homes. This model allowed for high quality neurological examination with high resolution pan-tilt-zoom camera on a mobile platform, incorporation of vital signs, nursing support, and lab services that may have contributed to the patients’ and neurologists’ satisfaction. Our study supports our continuation and expansion of this TM model in our community clinic spoke sites and may help to improve access to neurological care for patients in rural areas.
 Keywords: Telemedicine, patient satisfaction, empathy, general neurology, teleneurology

Supporting VIP aircraft modeling using node symmetry and non-VIP assets

When developing acoustic and thermal solutions for private and commercial aircraft, extensive time and effort goes into the creation of models to support an optimized package that both delivers results and minimizes weight. Statistical Energy Analysis (SEA) is often used to model aircraft interior sound fields as it becomes applicable in the frequency ranges of interest to most passengers and engineers. This analysis is particularly sensitive to room dimensions and therefore ample research is done to ensure the model is as accurate as possible and historical data from similar aircraft can be used to calibrate the model before use. This takes a considerable amount of time and is often done early in the development stage of the aircraft. So how can one implement this same approach for VIP aircraft where the time to installation is measured in weeks and the aircraft interior is tailor-made making calibration with historical data difficult? This paper looks at ways to optimize the modeling process using symmetry in smaller subsections of the aircraft based on previously calibrated system and material measurements to help greatly reduce model creation time and increase model precision within the VAOne(tm) modeling suite.

Degree of Concordance Between Cone Beam Computerized Tomography (Cbct) And 2d Digital Radiography System Used for Caries Diagnosis: An Observational Study

Background: Caries is a disease with a high prevalence, which may be due to the lack of adequate methods for its early diagnosis, becomes difficult to detect initial caries lesions in posterior teeth due to the occlusal anatomy of these and because on many occasions they start in the interproximal zone below the contact areas. Different techniques have been used for the diagnosis of caries, such as visual-tactile inspection, radiography, tomography, and transillumination, but none of them seems to have complete sensitivity or specificity. Aim: This study aims to establish the degree of agreement between radiographic diagnosis with 2D intraoral digital radiography in relation to cone beam computed tomography in extracted carious and healthy posterior teeth, practicing the CariesCare International ™ practical guide as a reference. Materials and Methods: A total number of 117 teeth with or without carious lesions were selected. Digital images of all teeth were obtained from 2D digital radiographs (instrumentarium ™ films) obtained with Kavo focus equipment and CBCT scanner I-CAT ™ model 1719, ISI (imaging science international), Pennsylvania (blue sky plan 4 software). Observers evaluated the images for the detection of carious lesions. The sensitivity and specificity ratio between the systems was determined using the area under the curve statistical tool ROC (Receiver Operating Characteristic). For the statistical analysis, the statistical program STATA IC/16 was used, in which the calculation of absolute and relative frequencies for each category of CariesCare international in CBCT and radiography was performed, in addition to the Friedman and post-hoc Wilcoxon test by pairs; the distribution according to the classification of CariesCare international was contrasted between the CBCT slices. The calculation of weighted Kappa and dichotomous Kappa and the calculation of validity (sensitivity and specificity) and prediction (predictive values and predictive ratios) were performed. The distribution according to CariesCare international classification was contrasted between the CBCT slices. Results: The results of the present study confirm that diagnosis of caries with the 2D digital radiographic method, initial caries lesions tend to go undetected relative to CBCT. Conclusion: 2D digital radiographs showed low sensitivity for caries detection and high specificity relative to CBCT for caries lesions in vitro. Diagnosis with 2D digital Rx is considered non-discriminative in identifying initial caries lesions.

Accuracy Analysis of Real-Time Precise Point Positioning—Estimated Precipitable Water Vapor under Different Meteorological Conditions: A Case Study in Hong Kong

Precipitable water vapor (PWV) monitoring with real-time precise point positioning (PPP) is required for the improved early detection of increasingly common extreme weather occurrences. This study takes Hong Kong as the research object. The aim is to explore the accuracy of real-time global navigation satellite system (GNSS) PPP in estimating PWV at low latitudes and under different weather conditions. In this paper, real-time PPP is realized by using observation data from continuously operating reference stations (CORS) in Hong Kong and real-time products from the Centre National d’Etudes Spatiales (CNES). The Tm model calculated using numerical weather prediction (NWP) data converts the zenith tropospheric delay (ZTD) of real-time PPP inversion into PWV and evaluates its accuracy using postprocessing products. The experimental results show that compared with GPS, multi-GNSS can reduce the convergence time of PPP by 29.20% during rainfall periods and by 12.06% during nonrainfall periods. The improvement in positioning accuracy is not obvious, and the positioning accuracy of the two is equivalent. Real-time PPP ZTD experiments show that there are lower average values for bias, standard deviation (STDEV), and root mean square (RMS) during nonrainfall periods than during rainfall periods. Real-time PPP PWV experiments show that there are also lower bias, STDEV, and RMS values during nonrainfall periods than during rainfall periods. The comparative study between rainfall and nonrainfall periods is of great significance for the real-time monitoring and forecasting of water vapor changes.

Future Contingencies and the Arrow and Flow of Time in a Non-Deterministic World According to the Temporal-Modal System TM

It is shown how the temporal-modal system of events TM (axiomatized in Appendix) allows for the avoidance of the logical determinism without the rejection of the principle of bivalence. The point is that the temporal and the modal parts of TM are so inter-related that modalities are in-the-real-world-inherent modalities independently of whether they concern actual or only possible events. Though formulated in a tenseless language, whose interpretation does not require the assumption of tense facts at the basic level of reality, TM implies an objective, observer-independent difference between tenses based only on the way in which modalities are distributed along the time continuum. The conclusion is that the arrow of time is an intra-model characteristic of any model of TM that describes the non-deterministic real world up to a certain point of its history, while the flow of time is an inter-model characteristic of the continuous transition between these models.