Search Results (20)

Background: Large Language Models (LLMs) such as ChatGPT, Claude, and Gemini are being increasingly adopted in medicine; however, their reliability in cardiology remains underexplored. Purpose of the study: To compare the performance of three general-purpose LLMs in response to cardiology-related clinical queries. Study design: Seventy clinical prompts stratified by diagnostic phase (pre or post) and user profile (patient or physician) were submitted to ChatGPT, Claude, and Gemini. Three expert cardiologists, who were blinded to the model’s identity, rated each response on scientific accuracy, completeness, clarity, and coherence using a 5-point Likert scale. Statistical analysis included Kruskal–Wallis tests, Dunn’s post hoc comparisons, Kendall’s W, weighted kappa, and sensitivity analyses. Results: ChatGPT outperformed both Claude and Gemini across all criteria (mean scores: 3.7–4.2 vs. 3.4–4.0 and 2.9–3.7, respectively; p < 0.001). The inter-rater agreement was substantial (Kendall’s W: 0.61–0.71). Pre-diagnostic and patient-framed prompts received higher scores than post-diagnostic and physician-framed ones. Results remained robust across sensitivity analyses. Conclusions: Among the evaluated LLMs, ChatGPT demonstrated superior performance in generating clinically relevant cardiology responses. However, none of the models achieved maximal ratings, and the performance varied by context. These findings highlight the need for domain-specific fine-tuning and human oversight to ensure a safe clinical deployment. Full article

A capacitive wireless power transfer (CPT) system based on parity–time (PT) symmetry achieves constant output characteristics under distance variation without additionally increasing the system complexity of the control strategy, where the concept of PT symmetry is derived from quantum mechanics, and the systems satisfying PT symmetry are invariant under space and time inversion. However, the exact PT-symmetric region (i.e., strong coupling region) of the general system is limited by the symmetry of the structure and parameters. To overcome this limitation, a novel generalized parity–time (GPT)-symmetric CPT system is proposed in this article. According to the equivalent circuit method, the circuit model of the proposed system is built, and the transfer characteristics are analyzed. Furthermore, a prototype is implemented to verify the feasibility of the proposed CPT system. The results show that the PT-symmetric region is extended by 169.23% compared with the traditional PT-based CPT system, and a constant output power of 21.5 W is transferred with a constant transfer efficiency of 90%. Full article

Background/objectives:To date, no studies have evaluated Chat Generative Pre-Trained Transformer (ChatGPT) as a large language model chatbot in optical applications for digestive endoscopy images. This study aimed to weigh the performance of ChatGPT-4 in assessing bowel preparation (BP) quality for colonoscopy. Methods: ChatGPT-4 analysed 663 anonymised endoscopic images, scoring each according to the Boston BP scale (BBPS). Expert physicians scored the same images subsequently. Results: ChatGPT-4 deemed 369 frames (62.9%) to be adequately prepared (i.e., BBPS > 1) compared to 524 frames (89.3%) assessed by human assessors. The agreement was slight (κ: 0.099, p = 0.0001). The raw human BBPS score was higher at 3 (2–3) than that of ChatGPT-4 at 2 (1–3), demonstrating moderate concordance (W: 0.554, p = 0.036). Conclusions: ChatGPT-4 demonstrates some potential in assessing BP on colonoscopy images, but further refinement is still needed. Full article

Although hardware trojans impose a relevant threat to the hardware security of RISC-V and Web3 applications, existing datasets have a limited set of examples, as the most famous hardware trojan dataset TrustHub has 106 different trojans. RISC-V specifically has study cases of three and four different hardware trojans, and no research was found regarding Web3 hardware trojans in modules such as a hardware wallet. This research presents a dataset of 290 Verilog examples generated with ChatGPT-4 Large Language Model (LLM) based on 29 golden models and the TrustHub taxonomy. It is expected that this dataset supports future research endeavors regarding defense mechanisms against hardware trojans in RISC-V, hardware wallet, and hardware Proof of Work (PoW) miner. Full article

The delay of GNSS signals in the neutral atmosphere allow the determination of atmospheric water vapor. The conversion factor of the delay in the water vapor uses the water vapor-weighted mean temperature, T_m, which is a crucial parameter to improve the quality of conversion. This study analyzed two different types of models: linear models such as Bevis, Mendes and Ortiz de Galisteo, and empirical models such as GPT2w, GPT3 and GWMT_D. The performance of the models was analyzed using the models as the source of T_m to obtain the precipitable water vapor (PWV), which was compared to a reference set of PWV obtained from a matched radiosonde site. The results show a better performance of the linear models, with the Bevis model achieving the best performance. Full article

Concepts empower cognitive intelligence. Extracting flat, nested, and discontinuous name entities and concept mentions from natural language texts is significant for downstream tasks such as concept knowledge graphs. Among the algorithms that uniformly detect these types of name entities and concepts, Li et al. proposed a novel architecture by modeling the unified mention recognition as the classification of word–word relations, named W²NER, achieved state-of-the-art (SOTA) results in 2022. However, there is still room for improvement. This paper presents three improvements based on W²NER. We enhanced the grid-tagging network by demonstration learning and tag attention feature extraction, so our modified model is named DTaE. Firstly, addressing the issue of insufficient semantic information in short texts and the lack of annotated data, and inspired by the demonstration learning from GPT-3, a demonstration is searched during the training phase according to a certain strategy to enhance the input features and improve the model’s ability for few-shot learning. Secondly, to tackle the problem of W²NER’s subpar recognition accuracy problem for discontinuous entities and concepts, a multi-head attention mechanism is employed to capture attention scores for different positions based on grid tagging. Then, the tagging attention features are embedded into the model. Finally, to retain information about the sequence position, rotary position embedding is introduced to ensure robustness. We selected an authoritative Chinese dictionary and adopted a five-person annotation method to annotate multiple types of entities and concepts in the definitions. To validate the effectiveness of our enhanced model, experiments were conducted on the public dataset CADEC and our annotated Chinese dictionary dataset: on the CADEC dataset, with a slight decrease in recall rate, precision is improved by 2.78%, and the comprehensive metric F1 is increased by 0.89%; on the Chinese dictionary dataset, the precision is improved by 2.97%, the recall rate is increased by 2.35%, and the comprehensive metric F1 is improved by 2.66%. Full article

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 RMS_PWV and RMS_PWV/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. Full article

To obtain a higher accuracy for the real-time Zenith Tropospheric Delay (ZTD), a refined tropospheric delay correction model was constructed by combining the tropospheric delay correction model based on meteorological parameters and the GPT3 model. The meteorological parameters provided by the Global Geodetic Observing System (GGOS) Atmosphere and the zenith tropospheric delay data provided by Centre for Orbit Determination in Europe (CODE) were used as references, and the accuracy and spatial–temporal characteristics of the proposed model were compared and studied. The results show the following: (1) Compared with the UNB3m, GPT and GPT2w models, the accuracy and stability of the GPT3 model were significantly improved, especially the estimation accuracy of temperature, the deviation (Bias) of the estimated temperature was reduced by 90.60%, 32.44% and 0.30%, and the root mean square error (RMS) was reduced by 42.40%, 11.02% and 0.11%, respectively. (2) At different latitudes, the GPT3 + Saastamoinen, GPT3 + Hopfield and UNB3m models had great differences in accuracy and applicability. In the middle and high latitudes, the Biases of the GPT3 + Saastamoinen model and the GPT3 + Hopfield model were within 0.60 cm, and the RMS values were within 4 cm; the Bias of the UNB3m model was within 2 cm, and the RMS was within 5 cm; in low latitudes, the accuracy and stability of the GPT3 + Saastamoinen model were better than those of the GPT3 + Hopfield and UNB3m models; compared with the GPT3 + Hopfield model, the Bias was reduced by 22.56%, and the RMS was reduced by 5.67%. At different heights, the RMS values of the GPT3 + Saastamoinen model and GPT3 + Hopfield model were better than that of the UNB3m model. When the height was less than 500 m, the Biases of the GPT3 + Saastamoinen, GPT3 + Hopfield and UNB3m models were 3.46 cm, 3.59 cm and 4.54 cm, respectively. At more than 500 m, the Biases of the three models were within 4 cm. In different seasons, the Bias of the ZTD estimated by the UNB3m model had obvious global seasonal variation. The GPT3 + Saastamoinen model and the GPT3 + Hopfield model were more stable, and the values were within 5 cm. The research results can provide a useful reference for the ZTD correction accuracy and applicability of GNSS navigation and positioning at different latitudes, at different heights and in different seasons. Full article

Tropospheric delay models based on GNSS observations are essential for studying tropospheric changes. However, the uneven distribution of GNSS stations reduces the accuracy of GNSS tropospheric delay models in remote areas. Moreover, the accuracy of the tropospheric delay calculated by traditional models, which rely on meteorological parameters, is lower compared to the accuracy achieved by GNSS tropospheric models. At present, there are sufficient surface meteorological observation facilities around the world that can obtain surface meteorological parameters in real time. It is of great importance to make full use of the measured meteorological parameters to establish tropospheric correction models. Moreover, the empirical tropospheric models use free and open data, and one can obtain tropospheric parameters through the model without requiring any auxiliary information. We established a provincial real-time regional tropospheric fusion model using ground-based GNSS observations, a meteorological model, and empirical model data. Results showed that the tropospheric delay observations of the three models can be fused to establish a real-time tropospheric delay model with better accuracy and higher spatiotemporal resolution. The accuracy of Zenith Total Delay (ZTD) estimated by the fusion model reached 0.96/1.04/3.11 cm during the tropospheric quiet/active/typhoon period. Full article

Constructing accurate models that provide information about water vapor content in the troposphere improves the reliability of numerical weather forecasts and the position accuracy of low-cost Global Navigation Satellite System (GNSS) receivers. However, developing models with high spatial-temporal resolution demands compact observational datasets in the regions of interest. Empirical models, such as the Global Pressure and Temperature 3 (GPT3w), have been constructed based on the monthly averaged outputs of numerical weather models. These models are based on the assimilation of existing measurements to provide estimations of atmospheric parameters. Therefore, their accuracy may be reduced over regions with a low resolution of radiosonde or continuous GNSS stations. By emerging and increasing the Low-Earth-Orbiting (LEO) satellites that measure atmospheric parameter profiles using the Radio Occultation (RO) technique, new opportunities have appeared to acquire high-resolution atmospheric observations at different altitudes. This study aims to apply these RO observations to improve the accuracy of the GPT3w model over Iran, which is sparse in terms of long-term GNSS and radiosonde measurements. The temperature, pressure, and water vapor pressure parameters from the GPT3w model have been used as the input layers of the Extremely Learning Machine (ELM) technique. The wet refractivity indices from the RO technique are considered target parameters in the output layer to train the ELM. The RO observations of 2007–2020 are applied for training, and those of 2020–2022 for evaluating the performance of the developed ELM. Our numerical results indicate that the developed ELM decreases the Root-Mean-Square Error (RMSE) values of the wet refractivity indices by about 17 percent, compared to the original GPT3w RMSE values. Additionally, the wet refractivity indices from ELM have revealed correlation coefficients of about 0.64, which is about 1.9 times those related to the original GPT3w model. The performance of ELM has also been examined by comparison with the data of six located radiosonde stations covering the year 2020. This comparison shows an improvement of about 14 percent in the average RMSE values of the estimated wet refractivity indices. Full article

Weighted average temperature (Tm) plays a crucial role in global navigation satellite system (GNSS) precipitable water vapor (PWV) retrieval. Aiming at the poor applicability of the existing Tm models in Europe, in the article, we used observations from 48 radiosonde stations over Europe from 2014 to 2020 to establish a weighted average temperature model in Europe (ETm) by the nonlinear least squares estimation method. The ETm model takes into account factors such as ground temperature, water vapor pressure, latitude, and their annual variation, semiannual variation and diurnal variation. Taking the Tm obtained from the radiosonde data by the integration method in 2021 as the reference value, the accuracy of the ETm model was evaluated and compared with the commonly used Bevis model, ETmPoly model, and GPT2w model. The results of the 48 modeled stations showed that the mean bias and root mean square (RMS) values of the ETm model were 0.06 and 2.85 K, respectively, which were 21.7%, 11.5%, and 31.8% higher than the Bevis, ETmPoly, and GPT2w-1 (1° × 1° resolution) models, respectively. In addition, the radiosonde data of 12 non-modeling stations over Europe in 2021 were selected to participate in the model accuracy validation. The mean bias and RMS values of the ETm model were –0.07 and 2.87 K, respectively. Compared with the Bevis, ETmPoly, and GPT2w-1 models, the accuracy (in terms of RMS values) increased by 20.5%, 10.6%, and 35.2%, respectively. Finally, to further verify the superiority of the ETm model, the ETm model, and other Tm models were applied to the GNSS PWV calculation. The ETm model had mean RMS_PWV and RMS_PWV/PWV values of 0.17 mm and 1.03%, respectively, which were less than other Tm models. Therefore, the ETm model has essential applications in GNSS PWV over Europe. Full article

The weighted mean temperature (Tm) is a crucial parameter for determining the tropospheric delay in transforming precipitable water vapor. We used the reanalysis data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the distribution characteristics of Tm in the vertical direction in China. To address the problem that the precision of the traditional linear function model is limited in fitting the Tm profile, a scheme using the linear and Fourier functions to fit the Tm profile was constructed. Based on the least squares principle (LSQ) to fit the change in its coefficients over time, a Tm model for China with nonlinear elevation correction (CTm-h) was constructed. The experimental results show that, using ECMWF and radiosonde data to evaluate the precision of the CTm-h model, the RMS is 3.43 K and 4.64 K, respectively. Compared to GPT2w, the precision of the CTm-h model in China is increased by about 26.8%. The CTm-h model provides a significant improvement in the correction effect of Tm in the vertical direction, and the Tm profile calculated by the model is closer to the reference value. Full article

This study investigates methods of deriving meteorological parameters needed in space geodetic applications, from the surface data of the numerical weather model (NWM). It is more efficient than pressure level data in terms of storage and transmission. Based on more realistic assumptions for the structure of the troposphere, formulas for accurate vertical reduction of pressure (P) and precipitable water vapor (PWV) are deduced, and they are applied with the gridded lapse rate data provided by the GPT2w model. The new method achieves better accuracy especially when a large height difference between the grid point and station exists. Validation with global radiosonde observations shows that the RMS errors of P, temperature (T), and water vapor pressure (e) derived from 2.5° × 2.5° ERA surface data are 1.16 hPa, 1.95 K, and 1.76 hPa respectively; zenith tropospheric delays (ZTDs) calculated from derived P, T, and e values have a mean RMS error of 3.26 cm, comparable to that obtained from in situ measurements; adding PWV will increase ZTD estimation accuracy to 1.52 cm, comparable to that obtained from NWM pressure level data. Validations with Global Navigation Satellite System estimated ZTDs from global and regional station networks display similar results on the globe, as well as features for localized regions. Using higher spatial resolution NWM seems to have little effect on the accuracy of ZTDs calculated from P, T, and e, while it apparently improves the accuracy of ZTDs calculated from P, T, e, and PWV. Full article

Accurate tropospheric delay (TD) and weighted mean temperature (Tm) are important for Global Navigation Satellite System (GNSS) positioning and GNSS meteorology. For this purpose, plenty of empirical models have been built to provide estimates of TD and Tm. However, these models cannot resolve TD and Tm variations at synoptic timescales since they only model the average annual, semi-annual, and/or daily variations. As a result, the existed empirical models cannot perform well under extreme weather conditions. To address this limitation, we propose to estimate Zenith Hydrostatic Delay (ZHD), Zenith Wet Delay (ZWD), and Tm directly from the stratified numerical weather forecasting products of the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_MESO) of China. The GRAPES_MESO forecasting data has a temporal resolution of 3 h, which provides the opportunity to resolve the synoptic variation. However, it is found that the estimated ZWD and Tm exhibit apparent systematic deviation from in situ observation-based estimates, which is due to the inherent biases in the GRAPES_MESO data. To solve this problem, we propose to correct these biases using a linear model and a spherical cap harmonic model. The estimates after correction are termed as the “CTropGrid” products. When validated by the radiosonde data, the CTropGrid product has biases of 1.5 mm, −0.7 mm, and −0.1 K, and Root Mean Square (RMS) error of 8.9 mm, 20.2 mm, and 1.5 K for ZHD, ZWD, and Tm. Compared to the widely used GPT2w model, the CTropGrid products have improved the accuracies of ZHD, ZWD, and Tm by 11.9%, 55.6%, and 60.5% in terms of RMS. When validating the Zenith Tropospheric Delay (ZTD) products (the sum of ZHD and ZWD) using the IGS ZTD data, the CTropGrid ZTD has a bias of −0.7 mm and an RMS of 35.8 mm, which is 22.7% better than the GPT2w model in terms of RMS. Besides the accuracy improvements, the CTropGrid products well model the synoptic-scale variations of ZHD, ZWD, and Tm. Compared to the existing empirical models that only capture the tidal (seasonal and/or diurnal) variations, the CTropGrid products capture well the non-tidal variations of ZHD, ZWD, and Tm, which enhances the tropospheric delay corrections and GNSS water vapor monitoring at synoptic timescales. Therefore, the CTropGrid product is an important progress in GNSS positioning and GNSS meteorology. Full article

The tropospheric delay is one of the main error sources that degrades the accuracy of Global Navigation Satellite Systems (GNSS) Single Point Positioning (SPP). Although an empirical model is usually applied for correction and thereby to improve the positioning accuracy, the residual tropospheric delay is still drowned in measurement noise, and cannot be further compensated by parameter estimation. How much this type of residual error would sway the SPP positioning solutions on a global scale are still unclear. In this paper, the biases on SPP solutions introduced by the residual tropospheric delay when using nine conventionally Zenith Tropospheric Delay (ZTD) models are analyzed and discussed, including Saastamoinen+norm/Global Pressure and Temperature (GPT)/GPT2/GPT2w/GPT3, University of New Brunswick (UNB)3/UNB3m, European Geostationary Navigation Overlay System (EGNOS) and Vienna Mapping Functions (VMF)3 models. The accuracies of the nine ZTD models, as well as the SPP biases caused by the residual ZTD (dZTD) after model correction are evaluated using International GNSS Service (IGS)-ZTD products from around 400 globally distributed monitoring stations. The seasonal, latitudinal, and altitudinal discrepancies are analyzed respectively. The results show that the SPP solution biases caused by the dZTD mainly occur on the vertical direction, nearly to decimeter level, and significant discrepancies are observed among different models at different geographical locations. This study provides references for the refinement and applications of the nine ZTD models for SPP users. Full article