Tracing the evolution and charting the future of geothermal energy research and development by Rohit, Ramena Venkata Satya, Vipin Raj, R., Kiplangat, Dennis C., Raj, Veena, Rajan, Jose, Pradeepkumar, A. P., Kumar, Kishen Satheesh Anil

“…The development, limitations, and opportunities of each cluster are examined, and it becomes evident that the focus of research ranges from geothermal evaluation, long-term effects of borehole heat exchangers, shallow systems that employ urbanisation's ground heating, enhanced geothermal systems (EGS) for district heating, combined and hybridised geothermal power generating models, including multi-generation and poly-generation, geothermal fluids, reinjection and their dual nature, environmental effects in geothermal water and mineral scaling, enhanced geothermal systems aiming to increase permeability without causing seismicity, and finally to social acceptability. …”
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Integrating Sponge City Concept and Neural Network into Land Suitability Assessment: Evidence from a Satellite Town of Shenzhen Metropolitan Area by Keyu Luo, Zhenyu Wang, Wei Sha, Jiansheng Wu, Hongliang Wang, Qingliang Zhu

“…Taking Shenzhen–Shantou special cooperation zone (SSCZ), we constructed a storm flooding model based on the SCS flow generation model and GIS to explore the spatial distribution characteristics of the flooding risk in a rainstorm of 100-year lasting 1 h. …”
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Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans by Lambert T. Leong, Michael C. Wong, Yong E. Liu, Yannik Glaser, Brandon K. Quon, Nisa N. Kelly, Devon Cataldi, Peter Sadowski, Steven B. Heymsfield, John A. Shepherd

“…Conclusions This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.…”
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The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature by Linda My Huynh, Yeagyeong Hwang, Olivia Taylor, Michael J. Baine

“…Of 218 articles on MRI-derived prostate radiomics, 33 (15.1%) generated models for PCa risk stratification. Prediction of Gleason score (GS), adverse pathology, postsurgical recurrence, and postradiation failure were the primary endpoints in 15 (45.5%), 11 (33.3%), 4 (12.1%), and 3 (9.1%) studies. …”
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English grammar multiple-choice question generation using Text-to-Text Transfer Transformer by Peerawat Chomphooyod, Atiwong Suchato, Nuengwong Tuaycharoen, Proadpran Punyabukkana

“…In this system, the questions for MCQs are generated using a text generation model trained using the Text-to-Text Transfer Transformer (T5) architecture, a powerful deep learning model for performing text generation tasks, with a keyword and a part-of-speech (POS) template as the input for content and grammar topic control. …”
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Development and validation of focal adhesion-related genes signature in gastric cancer by Guanghui Zhao, Tianqi Luo, Zexian Liu, Jianjun Li

“…Furthermore, we found a correlation between high-risk gastric cancer and extracellular matrix (ECM) receptor interaction, high infiltration of macrophages, CD44, and HLA-DOA.Conclusion: The generated model based on the genetic characteristics of the focal adhesion prognostic gene can aid in the prognosis of gastric cancer patients in the future.…”
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Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis by Marc Boubnovski Martell, Kristofer Linton-Reid, Sumeet Hindocha, Mitchell Chen, Paula Moreno, Marina Álvarez‐Benito, Ángel Salvatierra, Richard Lee, Joram M. Posma, Marco A. Calzado, Eric O. Aboagye

“…In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. …”
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Deep Learning-Based Optimization of Visual–Auditory Sensory Substitution by Mooseop Kim, Yunkyung Park, Kyeongdeok Moon, Chi Yoon Jeong

“…The process of sensory substitution in humans was modeled to use generative models to assess the extent of visual perception from the substituted sensory signals. …”
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Effect of multi filters in glucoma detection using random forest classifier by Alice K, Deepa N, Devi T, BeenaRani B B, Bharatha Devi N, Nagaraju V

“…The proposed work generate models for recognizing the presence of glaucoma based on open access public dataset of retinal fundus images using machine learning algorithms with the help of image feature descriptors. …”
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The World of GPCR dimers – Mapping dopamine receptor D2 homodimers in different activation states and configuration arrangements by Beatriz Bueschbell, Pedro R. Magalhães, Carlos A.V. Barreto, Rita Melo, Anke C. Schiedel, Miguel Machuqueiro, Irina S. Moreira

“…In this study, we developed a computational framework to generate models of symmetric and asymmetric GPCR dimers using different monomer activation states and identified their most likely interfaces with molecular details. …”
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IDGAN: Information-Driven Generative Adversarial Network of Coverless Image Steganography by Chunying Zhang, Xinkai Gao, Xiaoxiao Liu, Wei Hou, Guanghui Yang, Tao Xue, Liya Wang, Lu Liu

“…We introduced an attention mechanism on top of the original GAN model to improve image accuracy. In the generation model, we replaced some transposed convolution operations with image interpolation for better quality of dense images. …”
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A sanity check for earthquake recurrence models used in PSHA of slowly deforming regions: the case of SW Iberia by M. Ramalho, L. Matias, M. Neres, M. Neres, M. M. C. Carafa, A. Carvalho, P. Teves-Costa

“…We propose two consistency tests to address the variability of earthquake generation models found in PSHA studies: (i) one rule-of-thumb test where the seismic moment release from the model is converted to an average slip on a typical fault and compared with known plate kinematics or GNSS deformation field and (ii) a test where using a neotectonic model the computed deformation is converted into seismic moment release and to a synthetic earthquake catalogue. …”
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An automated machine learning approach for detecting anomalous peak patterns in time series data from a research watershed in the northeastern United States critical zone by Ijaz Ul Haq, Byung Suk Lee, Donna M. Rizzo, Julia N. Perdrial

“…The framework employs Time-series Generative Adversarial Networks (TimeGAN) as the synthetic dataset generator. The generated model instances are evaluated using a combination of accuracy and computational cost metrics, including training time and memory, during the anomaly detection process. …”
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Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks by Zongjian Yu, Anxiang Zhang, Huali Feng, Huaming Du, Shaopeng Wei, Yu Zhao

“…<span style="font-variant: small-caps;">CopyFet</span> can integrate two operations: (<b>i</b>) the regular way of making type inference from the whole type set in the generation model; (<b>ii</b>) the new copy mechanism which can identify the semantic type of a mention with reference to the type-copying vocabulary from a knowledge graph in the copy model. …”
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Experimental and Mathematical Modelling of Factors Influencing Carbon Dioxide Absorption into the Aqueous Solution of Monoethanolamine and 1-Butyl-3-methylimidazolium Dibutylphosph... by Fatin Nor Arissa Azhar, Mohd Faisal Taha, Siti Musliha Mat Ghani, Muhammad Syafiq Hazwan Ruslan, Noor Mona Md Yunus

“…The accuracy of the developed model was confirmed through additional experiments where the experimental values were found to be within the 95% confidence interval. From the RSM-generated model, the optimum conditions for CO₂ absorption in aqueous 30 wt% MEA-(BMIM)(DBP) were 20 wt% of (BMIM)(DBP), a temperature of 41.1 °C and a pressure of 30 bar.…”
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Quality Marker Discovery and Quality Evaluation of <i>Eucommia ulmoides</i> Pollen Using UPLC-QTOF-MS Combined with a DPPH-HPLC Antioxidant Activity Screening Method by Fengqian Guo, Yichun Yang, Yu Duan, Chun Li, Huimin Gao, Hongyu Liu, Qiping Cui, Zhongyuan Guo, Xiaoqian Liu, Zhimin Wang

“…Fourth, the intracellular ROS generation model of RAW264.7 induced by H₂O₂ was used to verify and evaluate the activity of candidate active ingredients to determine their feasibility as Q-markers. …”
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Analysis of entropy generation in the flow of MHD water–ethylene glycol nanofluid over a spinning down pointing vertical cone by Zahoor Iqbal, A.K Abdul Hakeem, S. Yashodha, Qasem M. Al-Mdallal, Sharifa E. Alhazmi, Bader Alqahtani, Dowlath Fathima, Elsyed Tag Eldin

“…Additionally, the entropy generation model is verified using the thermodynamics second law.…”
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UV Absorption Spectrum for Dissolved Oxygen Monitoring: A Low-Cost Proposal for Water Quality Monitoring by Aika Miura, Lorena Parra, Jaime Lloret, Mónica Catalá-Icardo

“…The differences beyond 260 nm for the studied dissolved oxygen saturations were null. The generated models had correlation coefficients from 0.99 to 0.97 for ultrapure water, 0.98 to 0.95 for distilled water, and 0.90 to 0.83 for river water. …”
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Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art by Tanujit Chakraborty, Ujjwal Reddy K S, Shraddha M Naik, Madhurima Panja, Bayapureddy Manvitha

“…Consisting of a discriminative network and a generative network engaged in a minimax game, GANs have revolutionized the field of generative modeling. In February 2018, GAN secured the leading spot on the ‘Top Ten Global Breakthrough Technologies List’ issued by the Massachusetts Science and Technology Review. …”
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A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint by Ubaid Ullah, Jeong-Sik Lee, Chang-Hyeon An, Hyeonjin Lee, Su-Yeong Park, Rock-Hyun Baek, Hyun-Chul Choi

“…This study successively follows previous surveys on T2I, adding value by analogously evaluating the diverse range of existing methods, including different generative models, several types of visual output, critical examination of various approaches, and highlighting the shortcomings, suggesting the future direction of research.…”
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