Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing by Amruta Awasthi, Lenka Krpalkova, Joseph Walsh

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Advancements and future outlook of Artificial Intelligence in energy and climate change modeling by Mobolaji Shobanke, Mehul Bhatt, Ekundayo Shittu

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Neutrophil-to-Lymphocyte Ratio, Monocyte-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, and Mean Platelet Volume-to-Platelet Count Ratio as Biomarkers in Critically Ill and Injured Patients: Which Ratio to Choose to Predict Outcome and Nature of Bacteremia? by Dragan Djordjevic, Goran Rondovic, Maja Surbatovic, Ivan Stanojevic, Ivo Udovicic, Tamara Andjelic, Snjezana Zeba, Snezana Milosavljevic, Nikola Stankovic, Dzihan Abazovic, Jasna Jevdjic, Danilo Vojvodic

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Comment on: FLT3 ligand kinetic profile predicts response to treatment in patients with high-risk myelodysplastic syndrome / chronic myelomonocytic leukemia receiving CPX-351: a study from the Groupe Francophone des Myélodysplasies by Qian Yan, Jinlin Liu

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Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective Observational Study by Using the Common Data Model by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

“…The study aimed to develop a simple machine learning (ML) model for predicting irHAEs associated with PD-1/PD-L1 inhibitors. …”
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Adjustment of the Framingham index by abdominal aortic calcification scores enables a more accurate prediction of long-term cardiac events in general population aged 40 years and above: evidence from NHANES 2013–2014 by Xiao-Jun Liu, Li Wang, Li-sha Zhang, Li-Fei Cao, Zhao-Fei Wan, Jia-Hong Xue, Yan-Ru He, Xin-Hong Wang, Xiao-Fang Bai

“…Our study aimed to investigate whether the predicted risk for cardiac death with the Framingham risk score (FRS) could be further improved with the addition of AAC score in general population aged ≥ 40 years. …”
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Prediction model for type 2 diabetes mellitus and its association with mortality using machine learning in three independent cohorts from South Korea, Japan, and the UK: a model development and validation studyResearch in context by Hayeon Lee, Seung Ha Hwang, Seoyoung Park, Yunjeong Choi, Sooji Lee, Jaeyu Park, Yejun Son, Hyeon Jin Kim, Soeun Kim, Jiyeon Oh, Lee Smith, Damiano Pizzol, Sang Youl Rhee, Hyunji Sang, Jinseok Lee, Dong Keon Yon

“…Thus, this study aimed to predict the incidence of T2DM within 5 years and the risk of mortality following the onset of T2DM. …”
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Comparison of objectively measured and estimated cardiorespiratory fitness to predict all-cause and cardiovascular disease mortality in adults: A systematic review and meta-analysis of 42 studies representing 35 cohorts and 3.8 million observations by Ben Singh, Cristina Cadenas-Sanchez, Bruno G.G. da Costa, José Castro-Piñero, Jean-Philippe Chaput, Magdalena Cuenca-García, Carol Maher, Nuria Marín-Jiménez, Ryan McGrath, Pablo Molina-García, Jonathan Myers, Bethany Gower, Francisco B. Ortega, Justin J. Lang, Grant R. Tomkinson

“…Background: Cardiorespiratory fitness (CRF) is a powerful health marker recommended by the American Heart Association as a clinical vital sign. Comparing the predictive validity of objectively measured CRF (the “gold standard”) and estimated CRF is clinically relevant because estimated CRF is more feasible. …”
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Predicting home delivery and identifying its determinants among women aged 15–49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016–2023: a machine learning algorithm by Adem Tsegaw Zegeye, Binyam Chaklu Tilahun, Makida Fekadie, Eliyas Addisu, Birhan Wassie, Berihun Alelign, Mequannet Sharew, Nebebe Demis Baykemagn, Abdulaziz Kebede, Tirualem Zeleke Yehuala

“…The objective of this study is to predict home delivery and identify the determinants using machine learning algorithm in sub-Saharan African. …”
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Predicting the depth of insertion of central venous catheters using a bronchoscopy-based carinal measurement technique or the Peres’ formula-based method in adults undergoing cardiac surgery: A randomised comparative double-blind study by Satyajeet Misra, Prasanta K. Das, Bikram K. Behera, Devishree Das, Tarun K. Patra

“…Background and Aims: Various methods are used to predict the depth of insertion of central venous catheters (CVCs). …”
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To Dissect or Not to Dissect? The Surgeon’s Perspective on the Prediction of Greater Than or Equal to 4 Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Comparative Analysis of the Per-Protocol Population of the SINODAR-ONE Clinical Trial by Corrado Tinterri, MD, Giuseppe Canavese, MD, Damiano Gentile, MD

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Saúde mental de universitários: Papel preditor de variáveis demográficas, relacionadas à experiência acadêmica e resiliência // Mental health of undergraduate students: Predictive role of demographic variables, related to academic experience and resilience by Renan Pereira Monteiro, Ana Caroline de Moraes Brandão, Lidiane Silva de Araújo

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Evaluating Item Content and Scale Characteristics Using a Pretrained Neural Network Model by Jeffrey Stanton, Angela Ramnarine-Rieks, Yisi Sang

Subjects: “…Answer behavior, Emotion prediction, Microphone, Natural Language Processing, Open-ended questions, Smartphone, Voice recordings…”
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Time-series forecasting of microbial fuel cell energy generation using deep learning by Adam Hess-Dunlop, Harshitha Kakani, Stephen Taylor, Dylan Louie, Jason Eshraghian, Colleen Josephson

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Mathematical modelling and simulation analysis of a modified Butterworth van Dyke circuit model for non‐invasive diabetes detection by Srinivasan Lekha, Suchetha Manikandan

Subjects: “…reliable noninvasive diabetes prediction system…”
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Computer simulation of the spatial structure of MUC 1 peptides capable of inhibiting apoptosis by N. V. Ivanisenko, I. N. Lavrik, V. A. Ivanisenko

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Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty: a simplified and validated model based on 154,519 total hip arthroplasties from the Swedish Arthroplasty Register by M. A. Alagha, Justin Cobb, Alexander D. Liddle, Henrik Malchau, Ola Rolfson, Maziar Mohaddes

“…Our study aimed to develop and validate predictive models of periprosthetic femoral fracture (PPFF) necessitating revision and reoperation after elective total hip arthroplasty (THA). …”
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Low-exhaustion peripheral circulating γδ T cells serve as a biomarker for predicting the clinical benefit rate of non-small cell lung cancer (NSCLC) patients to chemotherapy or targeted therapy: a single-center retrospective study by Dongdong Zhang, Guichao Liu, Jinhui Ye, Ke Li, Guojun Zhang, Qiang Quan, Xinhai Zhu, Peng Li

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Response to Comment on: FLT3 ligand kinetic profile predicts response to treatment in patients with high-risk myelodysplastic syndrome / chronic myelomonocytic leukemia receiving CPX-351: a study from the Groupe Francophone des Myélodysplasies by Pierre Peterlin, Maxime Jullien

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Neural Network-Based Parameter Estimation and Compensation Control for Time-Delay Servo System of Aeroengine by Hongyi Chen, Qiuhong Li, Zhifeng Ye, Shuwei Pang

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