Spectral Components of Honey Bee Sound Signals Recorded Inside and Outside the Beehive: An Explainable Machine Learning Approach to Diurnal Pattern Recognition by Piotr Książek, Urszula Libal, Aleksandra Król-Nowak

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Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study by Zheng Zhang, Jian Wu, Yi Duan, Linwei Liu, Yaru Liu, Jinghan Wang, Li Xiao, Zhifeng Gao

“…This study aimed to develop explainable supervised machine learning (ML) models to classify patients with HIBPV and to identify structural perioperative patterns associated with HIBPV through model interpretation.Materials and Methods This retrospective cohort study analyzed 47,520 noncardiac surgery cases from Beijing Tsinghua Changgung Hospital. …”
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Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder by Lei Tang, Liling Wu, Mengqin Dai, Nian Liu, Lu liu

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Machine Learning for Movement Pattern Changes during Kinect-Based Mixed Reality Exercise Programs in Women with Possible Sarcopenia: Pilot Study by Yunho Sung, Ji-won Seo, Byunggul Lim, Shu Jiang, Xinxing Li, Parivash Jamrasi, So Young Ahn, Seohyun Ahn, Yuseon Kang, Hyejung Shin, Donghyun Kim, Dong Hyun Yoon, Wook Song

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Leveraging multiple cell-death patterns based on machine learning to decipher the prognosis, immune, and immune therapeutic response of soft tissue sarcoma by Binfeng Liu, Shasha He, Chenbei Li, Zijian Xiong, Zhaoqi Li, Chengyao Feng, Hua Wang, Chao Tu, Zhihong Li

“…Nonetheless, the precise role of multiple cell death patterns in STS is yet to be clarified. We employed 96 machine-learning algorithm combination frameworks to identify novel cell death-related signatures (CDSigs) with the highest mean c-index, indicating their excellence. …”
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Snow depth inversion and mapping at 500 m resolution from 1980 to 2020 in Northeast China using radiative transfer model and machine learning by Yanlin Wei, Xiaofeng Li, Lingjia Gu, Zhaojun Zheng, Xingming Zheng, Tao Jiang

“…To overcome these limitations, a novel method considering multiple influencing factors was proposed by integration a radiation transfer model with a machine learning model for SD retrieval, and a 500 m resolution SD dataset (NCSD) was generated for 1980 − 2020 in Northeast China by combining downscaling model. …”
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Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach by Huiting Yan, Hao Chen, Fei Wang, Linjing Qiu

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Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning by Truong Phan-Xuan Nguyen, Minh-Khang Le, Sittiruk Roytrakul, Shanop Shuangshoti, Nakarin Kitkumthorn, Somboon Keelawat

Subjects: “…follicular pattern thyroid tumors…”
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Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon by Johansson Moa, Atterfors Johan, Lamm Johan

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Fourier Transformation-Based Analysis of X-Ray Diffraction Pattern of Keratin for Cancer Detection by Alexander Alekseev, Oleksii Avdieiev, Sasha Murokh, Delvin Yuk, Alexander Lazarev, Daizie Labelle, Lev Mourokh, Pavel Lazarev

“…To analyze the obtained X-ray diffraction patterns of keratin, we propose a method based on the two-dimensional Fourier transformation of the images. …”
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Assessing data and sample complexity in unmanned aerial vehicle imagery for agricultural pattern classification by Linara Arslanova, Sören Hese, Marcel Fölsch, Friedemann Scheibler, Christiane Schmullius

“…This article assesses the use of high-resolution Unmanned Aerial Vehicle (UAV) data from commercial field sensors for classifying small-scale agricultural patterns in four crop types (Winter Wheat, Spring Barley, Rapeseed, and Corn) acquired at ground sample distances (GSDs) of 0.027 m, 0.053 m and 0.064 m. …”
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Time series analysis of energy usage patterns by Tanzania large power users by Diana Rwegasira, Edward Ngailo, Elimboto Yohana, Noel Masasi

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Analyzing Student Graduation and Dropout Patterns Using Artificial Intelligence and Survival Strategies by Behrouz Alefy, Vahid Babazadeh

“…The study applies state-of-the-art machine learning techniques to establish dominant patterns and offer forecasts using a wide range of student records. …”
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The classification of EMG signals using machine learning for the construction of a silent speech interface. by Chandrashekhar, V.

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IMPLEMENTATION AND COMPARISON IN USING STATE PATTERN ON MAIN CHARACTER MOVEMENT (CASE STUDY : POCONG JUMP VIDEO GAME VERSION 1.0) by Sanriomi Sintaro, Deiby Tineke Salaky, Luther Alexander Latumakulita, Mahardika Inra Takaendengan, Bernard Bernard, Ade Surahman, Noorul Islam

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Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model. by Shozen Dan, Yu Chen, Yining Chen, Melodie Monod, Veronika K Jaeger, Samir Bhatt, André Karch, Oliver Ratmann, Machine Learning & Global Health network

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Spatial-temporal distribution patterns change of grassland formation in Inner Mongolia since the 1980s by Anan Zhang, Jiakui Tang, Na Zhang, Xuefeng Xu, Wuhua Wang, Xiaofan Li, Maojin Li, Kaihui Li, Mengquan Wu, Shuohao Cai

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Tertiary lymphoid structures-driven immune infiltration patterns and their association with survival in neuroblastoma by Xuelian Liu, Jian Deng, Bingqing Yu, Jiaxiong Tan, Xiaoliang Lu, Minmin Zhang

“…A prognostic signature (CMLS) was created using machine learning and validated with Kaplan-Meier and receiver operating characteristic (ROC) curves. …”
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Quantitative modeling of mortality patterns in dogs exposed to alpha particle emitting radionuclides: Insights from competing risks and causal inference machine learning. by Eric Wang, Igor Shuryak, David J Brenner

“…This study improves current knowledge of cancer and non-cancer mortality patterns from densely-ionizing radiation in mammals by using machine learning to analyze combined historical data on dogs exposed to different radionuclides, modeling multiple variables, nonlinear dependencies, and causal relationships.…”
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Dynamic Lactate Clearance Patterns During Normothermic Machine Perfusion Predict Posttransplant Biliary Complications in Donation After Circulatory Death Liver Transplantation by Ayato Obana, MD, Miho Akabane, MD, Hannah Chi, MD, Nolan Ladd, MD, Andrew Nguyen, BA, Lin Abigail Tan, BS, Rithin Punjala, MBBS, Kejal Shah, MD, Matthew Hamilton, DO, Ashley Limkemann, MD, MPH, Navdeep Singh, MBBS, Musab Alebrahim, MD, Khalid Mumtaz, MBBS, Austin Schenk, MD, PhD, Sylvester Black, MD, PhD, Kenneth Washburn, MD

“…Background. Normothermic machine perfusion (NMP) enables real-time assessment of liver graft viability through various biomarkers. …”
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