Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design by Paul Adunola, Luis Felipe V. Ferrão, Juliana Benevenuto, Camila F. Azevedo, Patricio R. Munoz

“…To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe‐based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. …”
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The development of a C5.0 machine learning model in a limited data set to predict early mortality in patients with ARDS undergoing an initial session of prone positioning by David M. Hannon, Jaffar David Abbas Syed, Bairbre McNicholas, Michael Madden, John G. Laffey

“…A C5.0 classifier algorithm with adaptive boosting was trained on data gathered before, during, and after initial proning. …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. It also had the lowest Brier scores at all time points, and decision curve analysis (DCA) demonstrated the best clinical decision benefits compared to other models.…”
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Autonomous Waste Classification Using Multi-Agent Systems and Blockchain: A Low-Cost Intelligent Approach by Sergio García González, David Cruz García, Rubén Herrero Pérez, Arturo Álvarez Sanchez, Gabriel Villarrubia González

“…For the computer vision algorithm, three versions of YOLO (YOLOv8, YOLOv11, and YOLOv12) were used and evaluated with respect to their performance in automatic detection and classification of waste. …”
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Stacked ensemble model for NBA game outcome prediction analysis by Guangsen He, Hyun Soo Choi

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Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction by Rui Yang, Tao Huang, Renqi Yao, Di Wang, Yang Hu, Longbing Ren, Shaojie Li, Yali Zhao, Zhijun Dai

“…The tool can help clinical decision-making.…”
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Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 by Jenberu Mekurianew Kelkay, Deje Sendek Anteneh, Henok Dessie Wubneh, Abraham Dessie Gessesse, Gebeyehu Fassil Gebeyehu, Kalkidan Kassahun Aweke, Mikiyas Birhanu Ejigu, Mathias Amare Sendeku, Kirubel Adrissie Barkneh, Hasset Girma Demissie, Wubshet D. Negash, Birku Getie Mihret

“…"The most significant features that contribute to the accuracy of the top-performing models, notably the Random Forest should be highlighted because they outperformed the other model in the analysis.In general, ensemble learning algorithms can accurately predict short birth interval status, making them potentially useful as decision-support tools for the pertinent stakeholders.…”
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Reinforcement learning energy management control strategy of electric tractor based on condition identification by Liqiao Li, Jiangchun Chen, Jing Nie, Zongyu Gao

“…The historical driving data are used to construct the driving conditions of ET and obtain the Markov power state transfer probability matrix(MPSTPM) under different CI; Second, to minimize the energy consumption of lithium-titanate battery and supercapacitor hybrid power system(HPS), the power allocation strategy for ET under different CI is obtained by a Q-network RL algorithm; Finally, an learning vector quantization neural network(LVQNN) is used to identify the current ET driving CI through online and real-time, and the control system makes real-time power output decision through the current driving CI. …”
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Intellectual or developmental disabilities and curative female breast cancer treatment: A population-based retrospective cohort studyLay summary by Rebecca L. Hansford, Brooke Wilson, Rebecca Griffiths, Alyson L. Mahar

“…We included adult females diagnosed with stage I-III breast cancer in Ontario (2007–2018). IDD status was identified using an established algorithm. …”
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Generative Local Interpretable Model-Agnostic Explanations by Mohammad Nagahisarchoghaei, Mirhossein Mousavi Karimi, Shahram Rahimi, Logan Cummins, Ghodsieh Ghanbari

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Dynamic continuous simulation study of robotic arms for human-machine shared control by YAO Zhenqiu, ZHOU Xingqi, GUO Hao, FENG Chuanpu

“…When the control weight of the robotic arm is switched, the joint angle changes abruptly due to the composition of the robotic arm, signal delay and other reasons, which affects the dynamic continuity of the control system.MethodsTo solve this problem, a feasible shared control strategy was proposed to determine the control weight, a local fitting algorithm based on the interpolation of 3 times Bezier curves was used to realize the optimization of the trajectory of the robotic arm, and the continuity of the control system of the robotic arm was simulated and verified with respect to the optimized trajectory of this algorithm.ResultsThe simulation results show that by comparing different trajectory planning curves, the adopted method can effectively inhibit the sudden change of the angle caused by the switching of control weights, realize the continuous smoothing of the joint angle and angular velocity curves at the switching moment, and effectively inhibit the acceleration oscillations, which has good robustness.…”
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ANALISIS CREDIT SCORING TERHADAP STATUS PEMBAYARAN BARANG ELEKTRONIK DAN FURNITURE MENGGUNAKAN BOOTSTRAP AGGREGATING K-NEAREST NEIGHBOR by Putri Sri Astuti, Memi Nor Hayati, Rito Goejantoro

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Algocracy in the Judiciary: Challenging Trust in the System by María Lorena Flórez Rojas

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The doctor and patient of tomorrow: exploring the intersection of artificial intelligence, preventive medicine, and ethical challenges in future healthcare by Paulo Santos, Paulo Santos, Isabel Nazaré, Isabel Nazaré

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OPTIMIZATION OF AUTOMOBILE TRANSPORTATION COSTS IN INDUSTRIAL ENTERPRISE by V. M. Kurganov, V. N. Mukayev, M. V. Gryaznov

“…The economic-mathematical model and the algorithm of decisions development on optimization of the automobile transportations costs in the industrial enterprise, which provide balance of interests of the customer to lower costs of motor transportation services and of the carrier to increase business profitability are offered. …”
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Selecting patients with sickle cell disease for gene addition or gene editing‐based therapeutic approaches: Report on behalf of a joint EHA Specialized Working Group and EBMT Hemog... by Lucia de Franceschi, Franco Locatelli, David Rees, Christian Chabannon, Jean‐Hugues Dalle, Stefano Rivella, Achille Iolascon, Stephan Lobitz, Miguel R. Abboud, Josu de la Fuente, Pagona Flevari, Emanuele Angelucci, Mariane de Montalembert

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Development and validation of a prediction nomogram for adverse pregnancy outcomes among urban Chinese women with hypothyroxinemia during early pregnancy by Yulai Zhou, Zixuan Huang, Jiabin Ren, Chunxiao Liu, Tiantian Yu, Weibin Wu, Jianxia Fan

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Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis by Ida Mohammadi, Setayesh Farahani, Asal Karimi, Saina Jahanian, Shahryar Rajai Firouzabadi, Mohammadreza Alinejadfard, Alireza Fatemi, Bardia Hajikarimloo, Mohammadhosein Akhlaghpasand

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Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study by Shi Yu Li, Yue Ming Li, Yong Qi Fang, Zhi Ying Jin, Jun Kang Li, Xiao Meng Zou, Si Si Huang, Rui Lan Niu, Nai Qing Fu, Yu Hong Shao, Xuan Tong Gong, Mao Ran Li, Wei Wang, Zhi Li Wang

“…Combining radiomics scores with clinical immunoinflammatory markers (platelet count and neutrophil-to-lymphocyte ratio) yielded a clinical-radiomics model predicting disease-free survival (DFS), with C-indices of 0.80, 0.73, and 0.79 across the three cohorts. In the external validation cohort, the clinical-radiomics model achieved higher AUCs for predicting 2-, 3-, and 5-year DFS compared to the clinical model alone (2-year: 0.79 vs. 0.66; 3-year: 0.83 vs. 0.70; 5-year: 0.78 vs. 0.64; all P < 0.05). …”
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Multiparametric MRI-based radiomics for preoperative prediction of parametrial invasion in early-stage cervical cancer by Chongshuang Yang, Man Li, Xin Yi, Lin Wang, Guangxian Kuang, Chunfang Zhang, Benyong Yao, Zhihong Qin, Tianliang Shi, Qiang Jiang

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