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Journal articles on the topic 'Gpt3'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Chen, Jian, Yushuang Jiang, Ya Fan, Xingwang Zhao, and Chao Liu. "Comprehensive Analysis of the Global Zenith Tropospheric Delay Real-Time Correction Model Based GPT3." Atmosphere 14, no. 6 (2023): 946. http://dx.doi.org/10.3390/atmos14060946.

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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 follow

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2

Li, Li, Ying Gao, Siyi Xu, Houxian Lu, Qimin He, and Hang Yu. "The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function." Atmosphere 13, no. 10 (2022): 1648. http://dx.doi.org/10.3390/atmos13101648.

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Compared to the zenith hydrostatic delay (ZHD) obtained from the Saastamonien model based on in-situ measured meteorological (IMM) data and radiosonde-derived weighted mean temperature (), the ZHD and deviations of the GPT3 model have shown obvious periodic trends. This article analyzed the seasonal variations of GPT3-ZHD and GPT3- during the 2016–2020 period in the Yangtze River Delta region, and the new improved ZHD and models were established by the multi-order Fourier function. The precision of the improved-ZHD model was verified using IMM-ZHD products from 7 GNSS stations during the 2016–

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3

Weerasinghe, Janith, Ovendra Seepersaud, Genesis Smothers, Julia Jose, and Rachel Greenstadt. "Be Sure to Use the Same Writing Style: Applying Authorship Verification on Large-Language-Model-Generated Texts." Applied Sciences 15, no. 5 (2025): 2467. https://doi.org/10.3390/app15052467.

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Recently, there have been significant advances and wide-scale use of generative AI in natural language generation. Models such as OpenAI’s GPT3 and Meta’s LLaMA are widely used in chatbots, to summarize documents, and to generate creative content. These advances raise concerns about abuses of these models, especially in social media settings, such as large-scale generation of disinformation, manipulation campaigns that use AI-generated content, and personalized scams. We used stylometry (the analysis of style in natural language text) to analyze the style of AI-generated text. Specifically, we

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4

Yang, Fei, Jiming Guo, Chaoyang Zhang, Yitao Li, and Jun Li. "A Regional Zenith Tropospheric Delay (ZTD) Model Based on GPT3 and ANN." Remote Sensing 13, no. 5 (2021): 838. http://dx.doi.org/10.3390/rs13050838.

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The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we proposed a regional ZTD model that makes full use of the ZTD calculated from regional GNSS data and the corresponding ZTD estimated by global pressure and temperature 3 (GPT3) model, adopting the ar

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Li, Sen, Keke Xu, Kai Wang, Haofei Ban, Chen Xue, and Yongxiao Guo. "Characterization of Surface Pressure Accuracy and Spatial and Temporal Variations of The GPT3 And HGPT2 Models in The Chinese Region." Academic Journal of Science and Technology 10, no. 1 (2024): 163–70. http://dx.doi.org/10.54097/sc0h7w14.

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The accuracy of surface pressure is crucial for obtaining high-precision zenith hydrostatic delay(ZHD). In order to verify the accuracy of the Global Pressure and Temperature model (HGPT2 ) and the Global Pressure and Temperature (GPT3) in obtaining ground air pressure in China, as well as the spatiotemporal variation characteristics of the errors of the two models, this paper uses the ERA5 reanalysis dataset and combines it with actual meteorological data from weather stations to evaluate their performance. The results show that the results show that the HGPT2 model has better accuracy in sim

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6

Nutas, Andrei. "The Artificial Philosophical Counselor." International Journal of Philosophical Practice 8, no. 1 (2022): 124–36. http://dx.doi.org/10.5840/ijpp2022819.

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Till date most people still believe that compute grows exponentially in accordance with Moore’s law, meaning that computational capacity doubles approximately every 18 months. This, however, does not hold for Machine Learning. Since 2012, the computational capacity of Machine Learning has doubled every 3.4 months.1 Given this incredible growth rate we need to start considering whether Artificial Intelligence through the practice of Machine Learning will be able to automate the philosophical counseling profession. I will begin by giving an overview of AI and of GPT3 the AI model used in the exp

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Li, Junyu, Feijuan Li, Lilong Liu, Liangke Huang, Lv Zhou, and Hongchang He. "A Calibrated GPT3 (CGPT3) Model for the Site-Specific Zenith Hydrostatic Delay Estimation in the Chinese Mainland and Its Surrounding Areas." Remote Sensing 14, no. 24 (2022): 6357. http://dx.doi.org/10.3390/rs14246357.

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The prior zenith hydrostatic delay (ZHD) is an essential parameter for the Global Navigation Satellite System (GNSS) and very long baseline interferometry (VLBI) high-precision data processing. Meanwhile, the precise ZHD facilitates the separation of the high-precision zenith wet delay (ZWD) to derive precipitable water vapor (PWV). This paper analyzes the temporal variations in the residuals between GPT3 ZHD and reference ZHD from radiosonde (RS) sites, and a calibrated GPT3 (CGPT3) model is proposed for the site-specific ZHD estimation in the Chinese mainland and its surrounding areas based

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8

Osah, S., A. A. Acheampong, C. Fosu, and I. Dadzie. "Comparative analysis of blind tropospheric correction models in Ghana." Journal of Geodetic Science 11, no. 1 (2021): 14–26. http://dx.doi.org/10.1515/jogs-2020-0104.

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Abstract The impact of the earth’s atmospheric layers, particularly the troposphere on Global Navigation satellite system (GNSS) signals has become a major concern in GNSS accurate positioning, navigation, surveillance and timing applications. For precise GNSS applications, tropospheric delay has to be mitigated as accurately as possible using tropospheric delay prediction models. However, the choice of a particular prediction model can signifi-cantly impair the positioning accuracy particularly when the model does not suit the user’s environment. A performance assessment of these prediction m

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9

Cai, Meng, Junyu Li, Lilong Liu, et al. "Weighted Mean Temperature Hybrid Models in China Based on Artificial Neural Network Methods." Remote Sensing 14, no. 15 (2022): 3762. http://dx.doi.org/10.3390/rs14153762.

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The weighted mean temperature (Tm) is crucial for converting zenith wet delay to precipitable water vapor in global navigation satellite system meteorology. Mainstream Tm models have the shortcomings of poor universality and severe local accuracy loss, and they cannot reflect the nonlinear relationship between Tm and meteorological/spatiotemporal factors. Artificial neural network methods can effectively solve these problems. This study combines the advantages of the models that need in situ meteorological parameters and the empirical models to propose Tm hybrid models based on artificial neur

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10

Nguyễn, Ngọc Lâu, та Anh Dũng Phạm. "Độ chính xác của mô hình GPT3 và ảnh hưởng của nó vào định vị điểm chính xác cao ở khu vực biển Đông". Tạp chí Khoa học Đo đạc và Bản đồ, № 48 (1 червня 2021): 1–7. http://dx.doi.org/10.54491/jgac.2021.48.242.

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Chúng tôi trích lọc độ trễ đối lưu từ 19 ngày dữ liệu GNSS (29/10 đến 16/11/2020) nằm trong cao điểm mùa mưa bão ở khu vực biển Đông của 2 trạm GNSS thường trực tại Philippines và Việt Nam. Khi so sánh với kết quả tính độ trễ đối lưu từ mô hình GPT3, độ lệch lớn nhất lên đến hơn 1 dm. Điều này dẫn đến định vị điểm chính xác cao khi hiệu chỉnh độ trễ đối lưu dùng mô hình GPT3 và VMF3 đã làm giảm tỷ lệ thành công của việc giải tham số đa trị và gây ra sai số hệ thống lớn ở thành phần độ cao. Sai số định vị theo hướng Bắc, Đông và độ cao đạt được (0.005, 0.004, 0.136) m khi xử lý tĩnh 24h và (0.0

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11

Ma, Yongjie, Qingzhi Zhao, Kan Wu, et al. "Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China." Remote Sensing 14, no. 14 (2022): 3435. http://dx.doi.org/10.3390/rs14143435.

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Atmospheric weighted mean temperature (Tm) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical Tm models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal Tm model for PWV retrieval in China, this paper analyzes and evaluates some typical Tm models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the Tm model for China (CTm), the Global Weighted Me

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12

Wang, Xin, Ge Zhu, Liangke Huang, et al. "Development of a ZTD Vertical Profile Model Considering the Spatiotemporal Variation of Height Scale Factor with Different Reanalysis Products in China." Atmosphere 13, no. 9 (2022): 1469. http://dx.doi.org/10.3390/atmos13091469.

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Tropospheric delay is one of the key factors that may affect high-precision satellite navigation and positioning and water vapor retrieval performance. Its variation in the vertical direction is much greater than that in the horizontal direction. Therefore, the vertical profile model of zenith total delay (ZTD) is important for the spatial interpolation of high-precision ZTD products and the development of ZTD models. However, in China, low spatial and temporal resolutions remain persistent in ZTD vertical profile models and limit their application. In this study, ZTD vertical profile grid mod

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13

Zhu, Ge, Liangke Huang, Lilong Liu, et al. "A New Approach for the Development of Grid Models Calculating Tropospheric Key Parameters over China." Remote Sensing 13, no. 17 (2021): 3546. http://dx.doi.org/10.3390/rs13173546.

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Pressure, water vapor pressure, temperature, and weighted mean temperature (Tm) are tropospheric parameters that play an important role in high-precision global navigation satellite system navigation (GNSS). As accurate tropospheric parameters are obligatory in GNSS navigation and GNSS water vapor detection, high-precision modeling of tropospheric parameters has gained widespread attention in recent years. A new approach is introduced to develop an empirical tropospheric delay model named the China Tropospheric (CTrop) model, providing meteorological parameters based on the sliding window algo

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14

Ding, Junsheng, and Junping Chen. "Assessment of Empirical Troposphere Model GPT3 Based on NGL’s Global Troposphere Products." Sensors 20, no. 13 (2020): 3631. http://dx.doi.org/10.3390/s20133631.

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Tropospheric delay is one of the major error sources in GNSS (Global Navigation Satellite Systems) positioning. Over the years, many approaches have been devised which aim at accurately modeling tropospheric delays, so-called troposphere models. Using the troposphere data of over 16,000 global stations in the last 10 years, as calculated by the Nevada Geodetic Laboratory (NGL), this paper evaluates the performance of the empirical troposphere model GPT3, which is the latest version of the GPT (Global Pressure and Temperature) series model. Owing to the large station number, long time-span and

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15

Xie, Jun, Hongjin Shi, Huake Wang, Chong Li, and Haifeng Wang. "The Function of GPT2 in Tumor Progression." Cancer Plus 4, no. 3 (2023): 393. http://dx.doi.org/10.18063/cp.393.

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Glutamate pyruvate transaminase 2 (GPT2) is one of the GPTs and is widely used as a biomarker of hepatocellular injury, along with GPT1. GPT2, a glutamine-metabolizing transaminase found in mitochondria, catalyzes the reversible process between glutamate, pyruvate, &alpha;-ketoglutarate, and alanine. Compared to GPT1, the intracellular abundance of GPT2 is higher, suggesting that its enzymatic activity has a considerable role in glucose metabolism, amino acid metabolism, and lipid metabolism. In recent years, it has been discovered that deletion or mutation of&nbsp;GPT2&nbsp;causes

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16

Wei, Min, Xuexiang Yu, Fuyang Ke, Xiangxiang He, and Keli Xu. "A Refined Zenith Tropospheric Delay Model Based on a Generalized Regression Neural Network and the GPT3 Model in Europe." Atmosphere 14, no. 12 (2023): 1727. http://dx.doi.org/10.3390/atmos14121727.

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An accurate model of the Zenith Tropospheric Delay (ZTD) plays a crucial role in Global Navigation Satellite System (GNSS) precise positioning, water vapor retrieval, and meteorological research. Current empirical models (such as the GPT3 model) can only reflect the approximate change trend of ZTD but cannot accurately reflect nonlinear changes such as rapid fluctuations in ZTD. In recent years, the application of machine learning methods in the modeling and prediction of ZTD has gained prominence, yielding commendable results. Utilizing the ZTD products from 53 International GNSS Service (IGS

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17

Bogdan, Daniela Aura. "Literatura și Inteligența Artificială (AI)." Comunicare interculturală și literatură 30, no. 1 (2024): 48–53. https://doi.org/10.35219/cil.2023.1.07.

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L’intelligence Artificielle est devenue une astuce de pouvoir dominant la force créatrice des mots. Des applications telles que Paperpal, Living Writer, Sudowrite, Chat GPT3 ont prouvé qu'elles pouvaient écrire un poème, voire un roman, à partir d'une simple commande transposée par l'homme à l'ordinateur. La littérature de l'avenir sera-t-elle annulée par l’Intelligence Artificielle ? La créativité humaine conservera-t-elle encore sa position de souveraineté ?

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Zhou, Yaozong, Yidong Lou, Weixing Zhang, Peida Wu, Jingna Bai, and Zhenyi Zhang. "WTM: The Site-Wise Empirical Wuhan University Tropospheric Model." Remote Sensing 14, no. 20 (2022): 5182. http://dx.doi.org/10.3390/rs14205182.

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The tropospheric model is the key model in space geodetic techniques such as Global Navigation Satellite Systems (GNSS) and Very Long Baseline Interferometry (VLBI). In this paper, we established the site-wise empirical Wuhan University Tropospheric Model (WTM) by using 10-year (2011–2020) monthly mean and 5-year (2016–2020) hourly ERA5 reanalysis data, where the Zenith Path Delay (ZPD), mapping function, and horizontal gradient as well as meteorological parameters are provided at 1583 specific space geodetic stations with additionally considering the diurnal and semi-diurnal variations. The m

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19

Wang, Shukai, Qiuying Guo, Guihong Hua, Yingjun Sun, Wengang Sang, and Zhengyu Wang. "Optimization and Construction of Jinan Regional Tm Model Based on LSTM and Analysis of Its Influence on the Accuracy of GNSS Inversion PWV." Atmosphere 16, no. 3 (2025): 278. https://doi.org/10.3390/atmos16030278.

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Water vapor constitutes a vital component of atmospheric precipitation, serving as the fundamental material basis for weather phenomena such as rainfall, and is a significant factor contributing to extreme weather events. The Weighted Mean Temperature (Tm) is a crucial factor in the calculation of Precipitable Water Vapor (PWV) in the atmosphere, directly impacting the quality of GNSS-PWV inversion. The TmN, TmL1, and TmL2 models were constructed through regression analysis and LSTM based on data from the Zhangqiu Radiosonde Station in the Jinan region from 2020 to 2022, as well as ERA5 data.

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20

Yoon, Ilpyung, Jihwan Mun, and Kyeong-Sik Min. "Comparative Study on Energy Consumption of Neural Networks by Scaling of Weight-Memory Energy Versus Computing Energy for Implementing Low-Power Edge Intelligence." Electronics 14, no. 13 (2025): 2718. https://doi.org/10.3390/electronics14132718.

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Energy consumption has emerged as a critical design constraint in deploying high-performance neural networks, especially on edge devices with limited power resources. In this paper, a comparative study is conducted for two prevalent deep learning paradigms—convolutional neural networks (CNNs), exemplified by ResNet18, and transformer-based large language models (LLMs), represented by GPT3-small, Llama-7B, and GPT3-175B. By analyzing how the scaling of memory energy versus computing energy affects the energy consumption of neural networks with different batch sizes (1, 4, 8, 16), it is shown th

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Gao, Mingze. "The Advance of GPTs and Language Model in Cyber Security." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 195–202. http://dx.doi.org/10.54097/hset.v57i.10001.

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Nature language processing (NLP), one of the most remarkable machine learning techniques currently available, is gaining traction with the public and has achieved great success in many applications. Many companies have developed language models, such as BERT, BART models from Google, and GPT (generative pre-trained transformer) series models from OpenAI. GPT is an unsupervised learning model that generates responses and uses unsupervised pre-training and supervised fine-tuning. GPT-2 is a multitask unsupervised learner that completes tasks using an unsupervised pre-trained model, including a z

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Landskron, Daniel, and Johannes Böhm. "VMF3/GPT3: refined discrete and empirical troposphere mapping functions." Journal of Geodesy 92, no. 4 (2017): 349–60. http://dx.doi.org/10.1007/s00190-017-1066-2.

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23

Jiang, Chunhua, Xiang Gao, Huizhong Zhu, et al. "An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors." Geoscientific Model Development 17, no. 15 (2024): 5939–59. http://dx.doi.org/10.5194/gmd-17-5939-2024.

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Abstract. Atmospheric pressure and zenith wet delay (ZWD) are essential for global navigation satellite system (GNSS) tropospheric correction and precipitable water vapor (PWV) retrieval. As the development progresses of real-time GNSS kinematic technology, moving platforms, such as airborne and shipborne, require high-quality tropospheric delay information to pre-correct errors. Most existing tropospheric models are only applicable to the Earth's surface and exhibit poor accuracies in high-altitude areas due to simple vertical fitting functions and limited temporal resolution of the underlyin

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24

Li, Feijuan, Junyu Li, Lilong Liu, Liangke Huang, Lv Zhou, and Hongchang He. "Machine Learning-Based Calibrated Model for Forecast Vienna Mapping Function 3 Zenith Wet Delay." Remote Sensing 15, no. 19 (2023): 4824. http://dx.doi.org/10.3390/rs15194824.

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An accurate estimation of zenith wet delay (ZWD) is crucial for global navigation satellite system (GNSS) positioning and GNSS-based precipitable water vapor (PWV) inversion. The forecast Vienna Mapping Function 3 (VMF3-FC) is a forecast product provided by the Vienna Mapping Functions (VMF) data server based on the European Centre for Medium-Range Weather Forecasts (ECMWF)-based numerical weather prediction (NWP) model. The VMF3-FC can provide ZWD at any time and for any location worldwide; however, it has an uneven accuracy distribution and fails to match the application requirements in cert

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Zhang, Jihong, Xiaoqing Zuo, Shipeng Guo, et al. "A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China." Remote Sensing 16, no. 11 (2024): 2023. http://dx.doi.org/10.3390/rs16112023.

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Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations

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Li, Longjiang, Suqin Wu, Kefei Zhang, et al. "A new zenith hydrostatic delay model for real-time retrievals of GNSS-PWV." Atmospheric Measurement Techniques 14, no. 10 (2021): 6379–94. http://dx.doi.org/10.5194/amt-14-6379-2021.

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Abstract. The quality of the zenith hydrostatic delay (ZHD) could significantly affect the accuracy of the zenith wet delay (ZWD) of the Global Navigation Satellite System (GNSS) signal, and from the ZWD precipitable water vapor (PWV) can be obtained. The ZHD is usually obtained from a standard model – a function of surface pressure at the GNSS station. When PWV is retrieved from the GNSS stations that are not equipped with dedicated meteorological sensors for surface pressure measurements, blind models, e.g., the global pressure and temperature (GPT) models, are commonly used to determine the

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Gupta, Aayush Kumar, and Sheenu Rizvi. "Study of Language Models: Evolution & Limitations." Journal of Management and Service Science (JMSS) 2, no. 1 (2022): 1–7. http://dx.doi.org/10.54060/jmss/002.01.006.

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We have come far from the days when rule-based language models used to be the predominant thing in the market. Machine Learning came into play and changed the Language Model industry. In this paper, we will look at how RNN did a much better task for generating output based on its previous results and then how LSTM fulfilled the memory requirement for RNN. Also, we will take a look at how Transformer is much better than RNN combined with LSTM, which is the state-of-the-art language model on which the two best natural processing models like BERT and GPT3.

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Fan, Yongzhao, Fengyu Xia, Zhimin Sha, and Nana Jiang. "A Refined Spatiotemporal ZTD Model of the Chinese Region Based on ERA and GNSS Data." Remote Sensing 16, no. 23 (2024): 4515. https://doi.org/10.3390/rs16234515.

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Empirical tropospheric models can improve the performance of GNSS precise point positioning (PPP) by providing a priori zenith tropospheric delay (ZTD) information. However, existing models experience insufficient ZTD profile refinement, inadequate correction for systematic bias between the ZTD used in empirical modelling and the GNSS ZTD, and low time efficiency in model updating as more data become available. Therefore, a refined spatiotemporal empirical ZTD model was developed in this study on the basis of the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis

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Yang, Ling, Jinfang Wang, Haojun Li, and Timo Balz. "Global Assessment of the GNSS Single Point Positioning Biases Produced by the Residual Tropospheric Delay." Remote Sensing 13, no. 6 (2021): 1202. http://dx.doi.org/10.3390/rs13061202.

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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

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Chen, Junping, Jungang Wang, Ahao Wang, Junsheng Ding, and Yize Zhang. "SHAtropE—A Regional Gridded ZTD Model for China and the Surrounding Areas." Remote Sensing 12, no. 1 (2020): 165. http://dx.doi.org/10.3390/rs12010165.

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A regional zenith tropospheric delay (ZTD) empirical model, referred to as SHAtropE (SHanghai Astronomical observatory tropospheric delay model—Extended), is developed and provides tropospheric propagation delay corrections for users in China and the surrounding areas with improved accuracy. The SHAtropE model was developed based on the ZTD time series of the continuous GNSS sites from the Crustal Movement Observation Network of China (CMONOC) and GNSS sites of surrounding areas. It combines the exponential and periodical functions and is provided as regional grids with a resolution of 2.5° ×

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Xian, Tian, Ke Su, Jushuo Zhang, Huaquan Hu, and Haipeng Wang. "Precipitable Water Vapor Retrieval Based on GNSS Data and Its Application in Extreme Rainfall." Remote Sensing 17, no. 13 (2025): 2301. https://doi.org/10.3390/rs17132301.

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Water vapor plays a crucial role in maintaining global energy balance and water cycle, and it is closely linked to various meteorological disasters. Precipitable water vapor (PWV), as an indicator of variations in atmospheric water vapor content, has become a key parameter for meteorological and climate monitoring. However, due to limitations in observation costs and technology, traditional atmospheric monitoring techniques often struggle to accurately capture the distribution and variations in space–time water vapor. With the continuous advancement of Global Navigation Satellite System (GNSS)

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Tunalı, Engin. "Water vapor monitoring with IGS RTS and GPT3/VMF3 functions over Turkey." Advances in Space Research 69, no. 6 (2022): 2376–90. http://dx.doi.org/10.1016/j.asr.2021.12.036.

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Dudaš, Adam, and Jarmila Skrinarova. "Natural Language Processing in Translation of Relational Languages." IPSI Transactions on Internet Research 19, no. 01 (2023): 17–23. http://dx.doi.org/10.58245/ipsi.tir.2301.04.

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Methods of data manipulation in combination with sturcture of the data and integrity constraints define data model used in the relational databases. This article focuses on the methods and processes of operation sets which are used for selection of data from relational database and translation between various formats of this manipulation. The article presents the design, implementation and experimental evaluation of tool for translating between relational algebra, tuple relational calculus, Structured Query Language and unrestricted natural language in all directions. Presented software tool t

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Mahmoud, Suhad Mohammed, Athraa Zaidan Hassan, Salah Mahdi Hassan, and Safaa Abdulkareem Alwaysi. "Determination of Alpha-fetoprotein, Golgi protein-73 and Glypican-3 Levels in Chronic Viral Hepatitis B Patients with Fibrosis in Baghdad Gastroenterology and Hepatology Hospital." Al-Rafidain Journal of Medical Sciences ( ISSN 2789-3219 ) 8, no. 2(Special) (2025): 23–28. https://doi.org/10.54133/ajms.v8i2(special).1401.

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Background: Chronic hepatitis B (CHB) infects the liver and is considered the leading cause of fibrosis and hepatocellular carcinoma (HCC). As liver biopsy is invasive, biomarkers that predict CHB noninvasively could affect the overall complication of this dreaded disease. Objective: The aim of this study was to evaluate the use of alpha-fetoprotein (AFP), Golgi protein-73 (GP73), and glypican-3 levels as new predictive biomarkers for assessing hepatitis B infection patients with fibrosis. Method: This study included 60 participants and classified 30 chronic hepatitis B patients with fibrosis

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Wu, Yin, Lu Huang, Wei Feng, and Su Tian. "A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered." Atmosphere 15, no. 1 (2024): 121. http://dx.doi.org/10.3390/atmos15010121.

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The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotem

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Li, Song, Tianhe Xu, Yan Xu, Nan Jiang, and Luísa Bastos. "Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica." Atmosphere 13, no. 1 (2022): 78. http://dx.doi.org/10.3390/atmos13010078.

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Antarctica has a significant impact on global climate change. However, to draw climate change scenarios, there is a need for meteorological data, such as water vapor content, which is scarce in Antarctica. Global navigation satellite system (GNSS) networks can play a major role in overcoming this problem as the tropospheric delay that can be derived from GNSS measurements is an important data source for monitoring the variation of water vapor content. This work intends to be a contribution for improving the estimation of the zenith tropospheric delay (ZTD) obtained with the latest global press

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Hazarika, Devamanyu, Mahdi Namazifar, and Dilek Hakkani-Tür. "Attention Biasing and Context Augmentation for Zero-Shot Control of Encoder-Decoder Transformers for Natural Language Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 10738–48. http://dx.doi.org/10.1609/aaai.v36i10.21319.

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Controlling neural network-based models for natural language generation (NLG) to realize desirable attributes in the generated outputs has broad applications in numerous areas such as machine translation, document summarization, and dialog systems. Approaches that enable such control in a zero-shot manner would be of great importance as, among other reasons, they remove the need for additional annotated data and training. In this work, we propose novel approaches for controlling encoder-decoder transformer-based NLG models in zero shot. While zero-shot control has previously been observed in m

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Nie, Xiaonan, Yi Liu, Fangcheng Fu, et al. "Angel-PTM: A Scalable and Economical Large-Scale Pre-Training System in Tencent." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3781–94. http://dx.doi.org/10.14778/3611540.3611564.

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Recent years have witnessed the unprecedented achievements of large-scale pre-trained models, especially Transformer models. Many products and services in Tencent Inc., such as WeChat, QQ, and Tencent Advertisement, have been opted in to gain the power of pre-trained models. In this work, we present Angel-PTM, a productive deep learning system designed for pre-training and fine-tuning Transformer models. Angel-PTM can train extremely large-scale models with hierarchical memory efficiently. The key designs of Angel-PTM are a fine-grained memory management via the Page abstraction and a unified

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Suhad, Mohammed Mahmoud, Zaidan Hassan Athraa, M. Hassan Salah, and A. A. Alwaysi Safaa. "Assessment of serum AFP, GP73, GPC-3 for diagnosis cirrhosis and hepatocellular carcinoma in Iraqi patients with chronic hepatitis B." Appl. Biochem. Microbiol 59, Special Issue (2023): 170–75. https://doi.org/10.5281/zenodo.7559171.

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<strong>ABSTRACT</strong> <strong>   Background: </strong>Hepatitis B Virus (HBV) is a major global health problem, it can cause chronic infection and puts people at high risk of death from cirrhosis and liver cancer. Therefore, in the present study, it was aimed to evaluate the application value of AFP, GP73 and GPC3 in the diagnosis of  liver cirrhosis and HCC for provides a basis for better clinical diagnosis and treatment of Chronic hepatitis B (CHB). <strong>   Material and methods:-</strong> This study included 60 cases and classified as 30 patients  with ch

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Sun, Zhangyu, Bao Zhang, and Yibin Yao. "Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods." Remote Sensing 13, no. 5 (2021): 1016. http://dx.doi.org/10.3390/rs13051016.

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As a crucial parameter in estimating precipitable water vapor from tropospheric delay, the weighted mean temperature (Tm) plays an important role in Global Navigation Satellite System (GNSS)-based water vapor monitoring techniques. However, the rigorous calculation of Tm requires vertical profiles of temperature and water vapor pressure that are difficult to acquire in practice. As a result, empirical models are widely used but have limited accuracy. In this study, we use three machine learning methods, i.e., random forest (RF), backpropagation neural network (BPNN), and generalized regression

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Li, Haojie, Junyu Li, Lilong Liu, Liangke Huang, Qingzhi Zhao, and Lv Zhou. "Random Forest-Based Model for Estimating Weighted Mean Temperature in Mainland China." Atmosphere 13, no. 9 (2022): 1368. http://dx.doi.org/10.3390/atmos13091368.

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The weighted mean temperature (Tm) is a vital parameter for converting zenith wet delay (ZWD) into precipitation water vapor (PWV) and plays an essential part in the Global Navigation Satellite System (GNSS) inversion of PWV. To address the inability of current mainstream models to fit the nonlinear relationship between Tm and meteorological and spatiotemporal factors, whose accuracy is limited, a weighted mean temperature model using the random forest (named RFTm) was proposed to enhance the accuracy of the Tm predictions in mainland China. The validation with the Tm from 84 radiosonde statio

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Xiong, Si, Jiamu Mei, Xinchuang Xu, Ziyu Shen, and Liangke Huang. "Methods and Evaluation of AI-Based Meteorological Models for Zenith Tropospheric Delay Prediction." Remote Sensing 16, no. 22 (2024): 4231. http://dx.doi.org/10.3390/rs16224231.

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Zenith Tropospheric Delay (ZTD) is a significant error source affecting the accuracy of certain space geodetic measurements. This study evaluates the performance of Artificial Intelligence (AI) based meteorological models, such as Fengwu and Pangu, in estimating real-time ZTD. The results from these AI models were compared with those obtained from the Global Navigation Satellite System (GNSS), the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5), and the third generation of the Global Pressure–Temperature data model (GPT3) to assess thei

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Zhang, Jingkui, Liu Yang, Jian Wang, Yifan Wang, and Xitian Liu. "A New Empirical Model of Weighted Mean Temperature Combining ERA5 Reanalysis Data, Radiosonde Data, and TanDEM-X 90m Products over China." Remote Sensing 16, no. 5 (2024): 855. http://dx.doi.org/10.3390/rs16050855.

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Weighted mean temperature (Tm) is an important parameter in the water vapor inversion of global navigation satellite systems (GNSS). High-precision Tm values can effectively improve the accuracy of GNSS precipitable water vapor. In this study, a new regional grid Tm empirical model called the RGTm model over China and the surrounding areas was proposed by combining ERA5 reanalysis data, radiosonde data, and TanDEM-X 90m products. In the process of model establishment, we considered the setting of the reference height in the height correction formula and the bias correction for the Tm lapse rat

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Akar, Ali Utku, and Cevat İnal. "VMF veri sunucusundan türetilen grid bazlı VMF3 ve GPT3 troposfer modellerinin karşılaştırılması: Avrupa bölgesi için bir çalışma." Journal of Geodesy and Geoinformation 12, no. 1 (2024): 32–41. https://doi.org/10.9733/jgg.2025r0003.t.

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Huang, Liangke, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu. "A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes." Geoscientific Model Development 16, no. 24 (2023): 7223–35. http://dx.doi.org/10.5194/gmd-16-7223-2023.

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Abstract. The accuracy of tropospheric delay correction heavily depends on the quality of the tropospheric model, and the zenith tropospheric delay (ZTD) is an important factor affecting the tropospheric delay. Therefore, it is essential to establish a precise ZTD empirical model. The existing ZTD models are constrained by a single fitting function, lack consideration for daily cycle variations, and rely solely on data with one resolution for modeling. To address these limitations, we proposed a global piecewise ZTD empirical grid (GGZTD-P) model. This model considers the daily cycle variation

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Guo, Wei, Hor-Yue Tan, Sha Li, Ning Wang, and Yibin Feng. "Glutamic-Pyruvic Transaminase 1 Facilitates Alternative Fuels for Hepatocellular Carcinoma Growth—A Small Molecule Inhibitor, Berberine." Cancers 12, no. 7 (2020): 1854. http://dx.doi.org/10.3390/cancers12071854.

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Metabolic reprogramming is an essential hallmark of cancer. Besides the “Warburg effect”, cancer cells also actively reprogram amino acid metabolism to satisfy high nutritional demands in a nutrient-poor environment. In the glucose–alanine cycle, exogenous alanine taken up by hepatocytes is converted to pyruvate via glutamic-pyruvic transaminases (GPTs). However, the precise role of the glucose–alanine cycle in hepatocellular carcinoma (HCC) remains elusive. The current study revealed that alanine, as an alternative energy source, induced the metabolic reprogramming of HCC cells via activation

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Gao, Wenliang, Jingxiang Gao, Liu Yang, Mingjun Wang, and Wenhao Yao. "A Novel Modeling Strategy of Weighted Mean Temperature in China Using RNN and LSTM." Remote Sensing 13, no. 15 (2021): 3004. http://dx.doi.org/10.3390/rs13153004.

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In the meteorology of Global Navigation Satellite System, the weighted mean temperature (Tm) is a key parameter in the process of converting the zenith wetness delay into precipitable water vapor, and it plays an important role in water vapor monitoring. In this research, two deep learning algorithms, namely, recurrent neural network (RNN) and long short-term memory neural network (LSTM), were used to build a high-precision weighted mean temperature model for China using their excellent time series memory capability. The model needs site location information and measured surface temperature to

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Scerri, Daren. "Skilling for the Future: Enhancing Vocational Learning and Workplace Productivity with Creative AI Tools." MCAST Journal of Applied Research & Practice 8, no. 1 (2024): 150–78. http://dx.doi.org/10.5604/01.3001.0054.5099.

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Generative AI (GAI) tools have recently triggered an unprecedented disruption in the industry and education sectors, with both positive and negative effects requiring investigation. Several studies have already observed how the advanced language and dialogue capabilities (GPT3/4, LLaMA, Bard etc.), visual creativity (MidJourney), and GAI’s ability to adapt to diﬀerent scenarios is already impacting work processes within ﬁelds like customer care, marketing, and software development. This constructivist grounded theory study uses IT vocational education and industry as an example to understand t

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Yang, Liu, Jingxiang Gao, Dantong Zhu, Nanshan Zheng, and Zengke Li. "Improved Zenith Tropospheric Delay Modeling Using the Piecewise Model of Atmospheric Refractivity." Remote Sensing 12, no. 23 (2020): 3876. http://dx.doi.org/10.3390/rs12233876.

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As one of the atmosphere propagation delays, the tropospheric delay is a significant error source that should be properly handled in high-precision global navigation satellite system (GNSS) applications. We propose an improved zenith tropospheric delay (ZTD) modeling method whereby the piecewise model of the atmospheric refractivity is introduced. Compared with using the exponential model to fit ZTD in vertical direction, the ZTD piecewise model has a better performance. Based on ERA5 2.5° × 2.5° reanalysis data produced by the European Centre for Medium-Range Weather Forecasting (ECMWF) from

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Yang, Zhengyuan, Zhe Gan, Jianfeng Wang, et al. "An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3081–89. http://dx.doi.org/10.1609/aaai.v36i3.20215.

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Knowledge-based visual question answering (VQA) involves answering questions that require external knowledge not present in the image. Existing methods first retrieve knowledge from external resources, then reason over the selected knowledge, the input image, and question for answer prediction. However, this two-step approach could lead to mismatches that potentially limit the VQA performance. For example, the retrieved knowledge might be noisy and irrelevant to the question, and the re-embedded knowledge features during reasoning might deviate from their original meanings in the knowledge bas

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