Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…Enhanced strategies for effective fruit picking using the Eye-in-Hand system involve the development of more dexterous robotic hands and improved algorithms for precisely predicting the optimal picking point of each fruit. …”
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Catatonia in anti-NMDA receptor encephalitis: a case series and approach to improve outcomes with electroconvulsive therapy by F. Gabriela Kraiter, Dakota T. May, Ryan D. Slauer, Nandini Abburi, Christopher Eckstein, Suma Shah, Jonathan R. Komisar, Jacob P. Feigal

“…Patients received an average of 29.9 ECT treatments in total.Conclusions Based on clinical prediction scores, this cohort had better than expected functional outcomes. …”
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Intelligent Methods of Operational Response to Accidents in Urban Water Supply Systems Based on LSTM Neural Network Models by Aliaksey A. Kapanski, Nadezeya V. Hruntovich, Roman V. Klyuev, Aleksandr E. Boltrushevich, Svetlana N. Sorokova, Egor A. Efremenkov, Anton Y. Demin, Nikita V. Martyushev

“…The objective of this study was to evaluate the effectiveness of LSTM models for pressure prediction tasks. To acquire real-time pressure data, an information system based on Internet of Things (IoT) technology using the MQTT protocol was proposed. …”
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A Comparative Analysis of Automated Machine Learning Libraries for Electricity Price Forecasting by O’Leary Christian, Lynch Conor, Toosi Farshad Ghassemi

“…Access to electricity price predictions earlier than this publication time allows stakeholders an expanded timeframe to facilitate energy cost-aware scheduling.…”
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AI and Custom Marketing for Cultural Initiatives in Regional Projects: Perspectives on the Territorial Integration of Communities with Tourism by Mariflora Caruso, Laura Leopardi, Saverio Giulio Malatesta

“…This approach aims to predict and meet individual preferences, redefining engagement and outreach strategies in cultural settings.…”
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Plasma methylated HIST1H3G as a non-invasive biomarker for diagnostic modeling of hepatocellular carcinoma by Weiwei Zhu, Weiwei Zhu, Huifen Wang, Huifen Wang, Yudie Cai, Yudie Cai, Jun Lei, Jun Lei, Jia Yu, Jia Yu, Ang Li, Zujiang Yu

“…Diagnostic and prognostic prediction models were formulated using the random forest algorithm, and the performance of these models was rigorously evaluated through receiver operating characteristics curve (ROC) analysis.ResultsThe methylation level of HIST1H3G was markedly elevated in both HCC tissues and plasma samples derived from HCC patients. …”
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Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods by Longlin Jiang, Kexun Li, Simiao Lu, Zhou Hong, Yifang Wang, Qin Xie, Qin He, Sirui Wei, Aoru Zhou, Hong Kang, Xuefeng Leng, Qing Yang, Yan Miao

“…Conclusions This study developed an optimized mapping algorithm to predict the utility index from the QLQ-C30 QLQ-LC13 to the SF-6D. …”
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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

“…Conclusion The model we developed was a powerful predictive tool for BLCA prognosis and revealed the impact of mitotic catastrophe heterogeneity on BLCA in multiple dimensions, which then guided clinical decision-making. …”
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Advanced health state intelligent diagnosis of lithium-ion batteries based on CNN-WNN-WBiLSTM model with attention mechanism by Walid Mchara, Mohamed Abdellatif Khalfa, Lazhar Manai

“…Our results underscore the efficacy of our algorithm in Li-ion battery health management, offering higher prediction accuracy and precise multi-step SOH prediction compared to other neural network methods.…”
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An analysis of the relationship of triglyceride glucose index with diffuse large B-cell lymphoma prognosis: a retrospective study by QingQing Luo, Zhixiang Lei, Haizhou Miao, Ting Huang, Li Yu

“…Calibration curves showed good agreement between the nomogram predictions and actual outcomes, and DCA highlighted the high clinical utility of the model.ConclusionThe TyG index is an independent prognostic factor in DLBCL patients, and the TyG-based nomogram model provides enhanced predictive accuracy compared to the IPI. …”
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A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. …”
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AI-driven multi-omics profiling of sepsis immunity in the digestive system by Yuan Gao, Hong Chen, Ruolan Wu, ZuJun Zhou

“…It focuses on the synthesis of clinical biomarkers of sepsis and parameters related to the gut microenvironment with the help of artificial intelligence, enabling marker identification, immunostratification and predictive modeling. This feasible clinical decision-making algorithm based on “combinatorial typing” is an important tool for realizing precision medicine for sepsis patients.…”
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Dynamic Modeling and Composite Attitude Cooperative Control for Dumbbell-Shaped Spacecraft by Qingmu Ai, Yingzi Guan, Yuzhi Wang, Shunli Li

“…Then, leveraging graph and consensus theories, an attitude coordination consistency algorithm is developed. A virtual leader is designed using model predictive control. …”
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Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing by Ming Li, Klaus Joehnk, Peter Toscas, Luis Riera Garcia, Huidong Jin, Tapas K. Biswas

“…The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. …”
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Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies by Nanzhe Wang, Haibin Chang, Dongxiao Zhang

“…Abstract Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial to characterize underground geologic properties and reduce prediction uncertainty. …”
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Optimization of Fused Deposition Modeling Parameters for Mechanical Properties of Polylactic Acid Parts Based on Kriging and Cuckoo Search by Yuan Yang, Yiyang Wang, Bowen Xue, Changxu Wang, Bo Yang

“…Secondly, a Kriging-based prediction model for mechanical properties was constructed by learning sample data, and the nonlinear mapping relationship between process parameters and tensile strength was obtained. …”
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Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer by Man Wu, Wanyi Feng, Xinya Li, Yunan Liu, Chuxin Cao

“…Therefore, in order to improve prediction accuracy, this study designs a short-term power load forecasting model integrating multi-scale GCN and the improved Transformer, as well as the prediction method based on this model. …”
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Centrality nearest-neighbor projected-distance regression (C-NPDR) feature selection for correlation-based predictors with application to resting-state fMRI study of major depressi... by Elizabeth Kresock, Bryan Dawkins, Henry Luttbeg, Yijie Jamie Li, Rayus Kuplicki, B A McKinney

“…<h4>Background</h4>Nearest-neighbor projected-distance regression (NPDR) is a metric-based machine learning feature selection algorithm that uses distances between samples and projected differences between variables to identify variables or features that may interact to affect the prediction of complex outcomes. …”
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ICD-10-AM codes for cirrhosis and related complications: key performance considerations for population and healthcare studies by Kelly L Hayward, Amy L Johnson, Benjamin J Mckillen, Vikas Bansal, Leigh U Horsfall, Gunter Hartel, Chris Moser, Elizabeth E Powell, Patricia C Valery

“…Accuracy of individual codes and grouped combinations was determined by calculating sensitivity, positive predictive value (PPV), negative predictive value and Cohen’s kappa coefficient (κ).Results The PPVs for ‘grouped cirrhosis’ codes (0.96), hepatocellular carcinoma (0.97) ascites (0.97) and ‘grouped varices’ (0.95) were good (κ all &gt;0.60). …”
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HEURISTIC OPTIMIZATION OF ANTENNA DEPLOYMENT FOR COMBINATIONAL TWO-DIMENSIONAL RFID-BASED LOCALIZATION METHOD by Yu. B. Gimpilevich, D. A. Savochkin

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