Beyond the Scalpel: Assessing ChatGPT's potential as an auxiliary intelligent virtual assistant in oral surgery by Ana Suárez, Jaime Jiménez, María Llorente de Pedro, Cristina Andreu-Vázquez, Víctor Díaz-Flores García, Margarita Gómez Sánchez, Yolanda Freire

“…Noteworthy advances in AI algorithms and large language models (LLM) have led to the development of natural generative language (NGL) systems such as ChatGPT. …”
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Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity by Yoonjae Lee, Minjae Kim, Jaehyun Noh, Gyoujin Cho, Changwoo Lee

“…By leveraging tension data, optimal feature sets are identified, emphasizing the Mean, Fast Fourier Transform, and other key variables that capture crucial system dynamics, including wrap angles and sensor proximities. Classification algorithms were used to compare the performance of models employing these optimized FCM-based features against traditional models utilizing a full suite of 40 statistical features. …”
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Depression and Anxiety Screening for Pregnant Women via Free Conversational Speech in Naturalistic Condition by Rafael T. Sousa, Gustavo D. M. Silva, Paula L. L. Pinto, Juliano Backes, Amanda S. Mota, Thiago M. Paixao, Maria G. S. Teixeira, Wilian H. Hisatugu, Anilton S. Garcia, Kelly S. Prado, Rodrigo S. Dias, Marco A. Galletta, Hermano Tavares

“…The research involved collecting conversational speech samples from pregnant women attending a high-risk pregnancy outpatient service, employing smartphones to capture naturalistic speech in everyday contexts. Machine learning algorithms were utilized combined with different audio feature sets to analyze these recordings in conjunction with PHQ-4 questionnaire scores. …”
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A critical review on the applications of Sentinel satellite datasets for soil moisture assessment in crop production by Anela Mkhwenkwana, Trylee Nyasha Matongera, Ciara Blaauw, Onisimo Mutanga

“…Findings indicate that Sentinel-1′s synthetic aperture radar (SAR) data, particularly VV and VH polarizations, and Sentinel-2′s multispectral indices, such as NDVI and NDMI, are widely integrated with machine learning algorithms to enhance soil moisture estimation. However, dense vegetation and complex topography reduce retrieval accuracy, necessitating sensor fusion and calibration for improved reliability. …”
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Uterine hydatidosis: casuistry is possible by A. L. Tikhomirov, V. V. Kazenashev, A. A. Dubinin, R. R. Sadikova, M. V. Maminova, J. S. Globa, A. V. Bukharov

“…Compared with common gynecological disease such as uterine fibroids, ovarian cyst and malignancies uterine hydatidosis may be identified only in 0.16 % cases.Aim: to present a clinical case of uterine hydatid cyst in order to optimize algorithms for differential diagnosis of primary pelvic echinococcosis and gynecological pathology, which is necessary for successfully conducted timely surgical treatment.Clinical case. …”
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Identifying Lactylation-related biomarkers and therapeutic drugs in ulcerative colitis: insights from machine learning and molecular docking by Yao Yang, Xu Sun, Bin Liu, Yunshu Zhang, Tong Xie, Junchen Li, Jifeng Liu, Qingkai Zhang

“…Through machine learning algorithms, the diagnostic model was established. Further elucidating the mechanisms and regulatory network of the model gene in UC were GSVA, immunological correlation analysis, transcription factor prediction, immunofluorescence, and single-cell analysis. …”
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Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) by Jorge A. Fernandez-Jibaja, Nilton Atalaya-Marin, Yeltsin A. Álvarez-Robledo, Victor H. Taboada-Mitma, Juancarlos Cruz-Luis, Daniel Tineo, Malluri Goñas, Darwin Gómez-Fernández

“…Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. …”
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Artificial Intelligence in Education: Analysis and Assessment of Legal Knowledge Using AI Tools by Anvar J. Khurramov, Mavluda A. Axmedshaeva, Fiyuza A. Mukhitdinova, Gulnora A. Xudayberdiyeva, Shahnoza S. Almosova, Makhmud M. Makhamatov, Sherzod R. Khayitov

“…The study examined ethical concerns such as algorithmic bias, transparency, and student privacy, as well as legal dimensions related to consent, data protection, and institutional responsibility. …”
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Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis by Ana Maria Cristina Jura, Daniela Eugenia Popescu, Cosmin Cîtu, Marius Biriș, Corina Pienar, Corina Paul, Oana Maria Petrescu, Andreea Teodora Constantin, Alexandru Dinulescu, Ioana Roșca

“…This study assessed how maternal pathologies, medications, and neonatal factors affect the risk of PDA using traditional statistics and ML algorithms: Random Forest (RF) and XGBoost (XGB). <i>Materials and Methods</i>: A retrospective 3-year cohort study of 201 NICU neonates assessed maternal and neonatal factors. …”
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Towards representation learning of radar altimeter waveforms for sea ice surface classification by L. Happ, L. Happ, S. Patil, S. Hendricks, R. Fellegara, L. Kaleschke, A. Gerndt, A. Gerndt

“…Satellite radar altimeters provide crucial insights into polar oceans and their sea ice cover, enabling the estimation of sea level, sea ice freeboard, and thickness. These retrieval algorithms depend on accurate discrimination between radar altimeter waveforms from sea ice and ocean surfaces in heterogeneous and dynamic surface conditions. …”
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Research on Dynamic and Steady-State Characteristics of Grid-Following&#x002F;Grid-Forming Hybrid Control Based on Model Predictive Control by Changzhou Yu, Qin Wang, Wei Fang, Yifei Wang, Haiyang Diao, Haizhen Xu, Leilei Guo

“…Second, model predictive control algorithms are thoroughly investigated for both modes, and an optimized control strategy is selected through comparative analysis. …”
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Analysis of Minerals Using Handheld Laser-Induced Breakdown Spectroscopy Technology by Naila Mezoued, Cécile Fabre, Jean Cauzid, YongHwi Kim, Marjolène Jatteau

“…Mineral spectra can serve as reference data to (i) identify relevant emission lines and spectral ranges for specific minerals, (ii) be compared to unknown LIBS spectra for mineral identification, or (iii) constitute input data for machine learning algorithms.…”
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Ancient and contemporary shellfish cultivation practices bolster bivalve communities and diversity‐biomass relationships by Kieran D. Cox, Hailey L. Davies, Ben Millard‐Martin, Morgan Black, Margot Hessing‐Lewis, Nicole F. Smith, Francis Juanes, Sarah E. Dudas

“…We examined these data using a combination of diversity indices, structural complexity metrics, non‐parametric multivariate statistics and supervised machine learning algorithms. We show that both cultivation methods create distinct biological communities, with community composition proportional to how each cultivation practice alters the habitats' substrate, sediment and physical complexity. …”
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Information Metasurface Technology-enabled Integrated Passive Wireless Communication System Based on Synthetic Aperture Radar by Hua LI, Kaiyu LIU, Yunkai DENG, Zhenning LI, Chao LUO, Jichen LI, Taoyu REN, Yao CHEN, Kaijiang XU

“…However, due to significant differences between the waveform characteristics, transceiver design, and signal processing algorithms of the technologies, integrating communication and remote sensing in spaceborne systems presents numerous challenges. …”
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Ferroptosis-disulfidptosis-related CHMP6 is a clinico-immune target in colorectal cancer by Yifei Zhu, Huixia Huang, Jiayu Chen, Keji Chen, Yanxi Yao, Yaxian Wang, Yuxue Li, Zhibing Qiu, Dawei Li, Ping Wei

“…Methods We developed a ferroptosis-disulfidptosis-related gene (FDRG) score using machine-learning algorithms to analyze gene modifications associated with these pathways in CRC, utilizing data from the TCGA and GEO databases. …”
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Geotechnical evaluation of embankment stability in seismic zones using Monte Carlo and subset simulations within an LRFD framework aided by machine learning by Furquan Ahmad, Pramod Kumar, Divesh Ranjan Kumar, Warit Wipulanusat, Pijush Samui

“…This study uniquely integrates probabilistic techniques with hybrid machine learning optimization algorithms (RF-YYPO, RF-BWOA, and RF-SMA) under an LRFD framework, which has not been previously implemented for seismic slope stability of railway embankments.…”
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LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars by Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao

“…Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. …”
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Preferential Lineage-Specific Differentiation of Osteoblast-Derived Induced Pluripotent Stem Cells into Osteoprogenitors by Casey L. Roberts, Silvia S. Chen, Angela C. Murchison, Rebecca A. Ogle, Michael P. Francis, Roy C. Ogle, Patrick C. Sachs

“…Osteoblasts were successfully reprogrammed and demonstrated by endogenous upregulation of Oct4, Sox2, Nanog, TRA-1-81, TRA-16-1, SSEA3, and confirmatory hPSC Scorecard Algorithmic Assessment. The hOB-iPSCs formed embryoid bodies with cells of ectoderm and mesoderm but have low capacity to form endodermal cells. …”
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Structural insights into SOD1: from in silico and molecular dynamics to experimental analyses of ALS-associated E49K and R115G mutants by Seyed Mahdi Hosseini Faradonbeh, Bagher Seyedalipour, Nasrin Keivan Behjou, Kimiya Rezaei, Payam Baziyar, Saman Hosseinkhani

“…Our analysis was performed through multiple algorithms on the structural characterization of the hSOD1 protein using computational predictions, molecular dynamics (MD) simulations, and experimental studies to understand the effects of amino acid substitutions. …”
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Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

“…This study constructs an ML model for accurate ductility prediction from sparse compositional data, accelerating the design of ductile RHEAs within infinite compositional space. Two ML algorithms, decision tree (DT) and CatBoost, are trained using physical parameters, with CatBoost demonstrating superior performance in RHEA ductility classification. …”
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