Search Results (25)

Background: Large language models (LLMs) are increasingly used in clinical medicine, yet their ability to interpret advanced intraoperative hemodynamic monitoring—particularly in the context of liver transplantation—remains largely unexplored. In this proof-of-concept study, we evaluated ChatGPT’s capacity to interpret multimodal hemodynamic data derived from both standard anesthesia monitoring and the PiCCO system. The study also employed a structured assessment instrument (ARQuAT), adapted through a Delphi-based process to evaluate LLM-generated clinical interpretations. Methods: Ten key surgical–hemodynamic phases of liver transplantation were identified using a modified Delphi approach to capture the major physiological transitions of the procedure. Sequential screenshots representing these phases were obtained from five liver transplant recipients, yielding a total of 50 images. Each screenshot, along with standardized clinical background information, was submitted to ChatGPT. Five expert anesthesiologists independently assessed the model’s responses using the modified ARQuAT tool, which includes six content-quality domains (Accuracy, Up-to-dateness, Contextual Consistency, Clinical Usability, Trustworthiness, Clarity) and a separate catastrophic Risk item. Descriptive statistics were calculated for domain-level performance. Inter-rater reliability (Kendall’s W) and internal consistency (Cronbach’s alpha, McDonald’s omega) were also analyzed. All statistical analyses and visualizations were performed using NumIQO. Results: ChatGPT demonstrated consistently high performance across all content-quality domains, with median scores ranging from 4.6 to 4.8 and more than 90% of all ratings classified as satisfactory. Lower scores appeared only in a small subset of frames associated with abrupt hemodynamic changes and did not indicate a recurring weakness in any specific domain. Catastrophic Risk exhibited a pronounced floor effect, with 86% of ratings scored as 0 and only three isolated high-risk assessments across the dataset. Internal consistency of the six ARQuAT content domains was excellent, while inter-rater agreement was modest, reflecting ceiling effects and tied ratings among evaluators. Conclusions: ChatGPT generated clinically acceptable, contextually aligned interpretations of complex intraoperative hemodynamic data in liver transplant recipients, with minimal evidence of unsafe recommendations. These findings suggest preliminary promise for LLM-assisted interpretation of advanced monitoring, while underscoring the need for future studies involving larger datasets, dynamic physiological inputs, and expanded evaluator groups. The reliability characteristics observed also provide initial support for further refinement and broader validation of the Delphi-derived ARQuAT framework. Full article

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

This study investigated the effects of fermented Chinese herb (FCH) on the growth indices, leukocyte activity, and biochemical indices of carp (Cyprinus carpio). Astragalus membranaceus (AM), Pericarpium Citri Reticulatae (PCR), and Glycyrrhizae Radix et Rhizoma (GRR) as feed additives enhance immune function, promote growth, and exert anti-inflammatory effects, respectively. Therefore, this study investigated the effects of co-fermented blends of these three herbs on growth performance and related parameters in common carp. By adding 2%, 5%, and 10% of the FCH to co-incubate with carp leukocytes, the results show that all three experimental treatments could enhance the respiratory burst activity and phagocytic activity of carp leukocytes. After 28 days of feeding with basal feed supplemented with 2%, 5%, and 10% (w/v) of the FCH, the weight gain rate and specific growth rate of carp were significantly higher than those of the control treatment without additives (ANOVA, p < 0.05), with the 5% treatment showing the highest. The activities of intestinal digestive enzymes were significantly increased (ANOVA, p < 0.05). On the 21st day, the activities of amylase (AMS), lipase (LPS), and chymotrypsin were increased compared to the control treatment. The 5% and 10% treatments showed significantly higher intestinal digestive enzyme activities compared to the 2% treatment. The serum superoxide dismutase (SOD) levels in both the control and experimental treatments initially increased and then decreased, with all three experimental treatments having higher levels than the control treatment. The activities of liver glutamic-oxaloacetic transaminase (GOT) and glutamic-pyruvic transaminase (GPT) in the experimental treatments showed no significant changes compared to the control treatment (ANOVA, p > 0.05). However, the serum GPT activity in the 5% treatment was significantly lower than that of the control treatment (ANOVA, p < 0.05), while no significant differences were observed in the other treatments. The results indicate that adding 2~10% of FCH to carp feed can improve intestinal digestion, enhance phagocytic activity and the body’s antioxidant defense capabilities, and effectively promote the growth of carp. It can significantly improve farming efficiency and economic benefits, reduce dependence on chemical drugs, and lower environmental pollution, showing good application prospects in production. 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

This study was performed to investigate the protective effects of Allium ochotense on fatty liver and hepatitis in chronic alcohol-induced hepatotoxicity. The physiological compounds of a mixture of aqueous and 60% ethanol (2:8, w/w) extracts of A. ochotense (EA) were identified as kestose, raffinose, kaempferol and quercetin glucoside, and kaempferol di-glucoside by UPLC Q-TOF MS^E. The EA regulated the levels of lipid metabolism-related biomarkers such as total cholesterol, triglyceride, low-density lipoprotein (LDL), and high-density lipoprotein (HDL)-cholesterol in serum. Also, EA ameliorated the levels of liver toxicity-related biomarkers such as glutamic oxaloacetic transaminase (GOT), glutamic pyruvic transaminase (GPT), and total bilirubin in serum. EA improved the antioxidant system by reducing malondialdehyde contents and increasing superoxide dismutase (SOD) levels and reduced glutathione content. EA improved the alcohol metabolizing enzymes such as alcohol dehydrogenase, acetaldehyde dehydrogenase, and cytochrome P450 2E1 (CYP2E1). Treatment with EA alleviated lipid accumulation-related protein expression by improving phosphorylation of AMP-activated protein kinase (p-AMPK) expression levels. Especially, EA reduced inflammatory response by regulating the toll-like receptor–4/nuclear factor kappa-light-chain-enhancer of activated B cells (TLR-4/NF-κB) signaling pathway. EA showed an anti-apoptotic effect by regulating the expression levels of B-cell lymphoma 2 (BCl-2), BCl-2-associated X protein (BAX), and caspase 3. Treatment with EA also ameliorated liver fibrosis via inhibition of transforming growth factor-beta 1/suppressor of mothers against decapentaplegic (TGF-β1/Smad) pathway and alpha-smooth muscle actin (α-SMA). Therefore, these results suggest that EA might be a potential prophylactic agent for the treatment of alcoholic liver disease. 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

The subarctic northeast Pacific (SNEP) is a high-nitrate, low-chlorophyll (HNLC) region in the ocean, where phytoplankton growth and productivity are limited by iron. Moreover, there is a limited application of high spatial (300 m) and temporal resolution (daily) ocean color (OC) satellite imagery in studying the phytoplankton dynamics in this region. To address this issue, we aim to validate the remote sensing reflectance (Rrs; sr⁻¹(λ)) and chlorophyll-a (Chla) concentration derived from the Polymer atmospheric correction algorithm against in situ data for the SNEP obtained during 2019 and 2020. Additionally, we performed qualitative analysis using weekly binned surface Chla maps to determine whether the product reflects the general pattern over a latitudinal and longitudinal domain. We processed the daily Level-1 image using Polymer and binned them weekly using Graphic Processing Tool (GPT). The validation results indicate that Polymer exhibits higher radiometric performance in the blue and green bands and fails to represent in situ Rrs in the red band. Furthermore, the Polymer slightly over- and underestimates reflectance between 0.0012 and 0.0018 sr⁻¹ in the green band. On the other hand, excellent agreement was found between satellite-derived versus in situ Chla, followed by a slight overestimation of in situ Chla in the range from 0.17 to 0.28 mg/m³. The weekly binned Chla spatial map revealed a spatially homogeneous distribution of surface Chla in Central Alaska, but a substantial increase in Chla (≥0.7 mg/m³) was recorded toward Southeast Alaska (SEA) and the British Columbia (BC) shelf. Furthermore, Chla derived from latitudinal and longitudinal transects indicates high Chla toward 57°N and −135°W, respectively. Overall, the results of this study emphasize the need to obtain high-quality matchups from under-sampled oligotrophic waters, which are crucial for satellite validation, in addition to highlighting the importance of using high spatial and temporal resolution satellite imagery to study phytoplankton dynamics in the SNEP. 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