Exploring the association between gender equality in the family and fertility intentions: an explanation of the findings in low-fertility countries by Victor Leocádio, Ana Paula Verona, Simone Wajnman

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A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods by Asok Mathew, Pradeep K. Yadalam, Ahmed Radeideh, Shrouk Hady, Rona Swed, Reyyan Cheema, Majd Mousa AL-Mohammad, Mohammed Alsaegh, SR Shetty

“…Advanced predictive models like logistic Regression and XGBoost analyze critical variables, identify patterns, and perform predictive analysis. These models can identify potential impactions, assess impaction type, and develop treatment plans. …”
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A non-electrical pneumatic hybrid oscillator for high-frequency multimodal robotic locomotion by Genliang Chen, Yongzhou Long, Siyue Yao, Shujie Tang, Junjie Luo, Hao Wang, Zhuang Zhang, Hanqing Jiang

“…Leveraging the characteristic capabilities of the oscillator, three distinct robots are developed, including a bionic jumping robot with high motion speed, a crawling robot with a pre-programmed logic gait, and a swimming robot with adjustable motion patterns. …”
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Trajectory Prediction and Intention Recognition Based on CNN-GRU by Jinghao Du, Dongdong Lu, Fei Li, Ke Liu, Xiaolan Qiu

“…Separate models were developed for trajectory prediction and intention recognition, with the trajectory prediction outcomes subsequently applied to enhance the accuracy of intention recognition. …”
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Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning by Swetha Valavarasu, Yasaswini Sangu, Tanmaya Mahapatra

“…Abstract Antimicrobial resistance is a growing global health threat, and artificial intelligence offers a promising avenue for developing advanced tools to address this challenge. …”
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lexible sensors for food monitoring. Part II: Applications by D. Luo, M. A. Nikitina, X. Xiao

“…This article attempts to critically review the present state-of-arts developments in relation to the materials, manufacturing techniques and sensing mechanisms of the devices, as well as the applications of the electrically-transduced flexible sensors. …”
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Addressing Sensor Data Heterogeneity and Sample Imbalance: A Transformer-Based Approach for Battery Degradation Prediction in Electric Vehicles by Bi Wu, Shi Qiu, Wenhe Liu

“…To tackle data heterogeneity, we developed a multimodal feature fusion strategy that effectively integrates battery sensor data from different sources and formats, including time-series charge-discharge sensor data and spectral impedance sensor measurements. …”
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Automated Deadlift Techniques Assessment and Classification Using Deep Learning by Wegar Lien Grymyr, Isah A. Lawal

“…Furthermore, we designed an application that integrates keypoint visualizations with motion pattern classifications. This tool provides users with valuable feedback on their performance and includes a replay feature for self-assessment, helping lifters refine their technique and reduce the risk of injury.…”
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A novel deep learning approach to field-road semantic segmentation by Bei Wang, Wenze Wang

“…This study presents a deep learning framework that implements a novel semantic segmentation model designed to segment field-road trajectories, addressing the marked differences in spatial characteristics. The developed AI approach integrates transformer and semantic technologies to create an advanced semantic encoder that generates high-quality semantic prior maps and associated mask features. …”
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Empowering Dysarthric Communication: Hybrid Transformer-CTC-Based Speech Recognition System by R. Vinotha, D. Hepsiba, L. D. Vijay Anand, P. Malin Bruntha, Linh Dinh, Hien Dang

“…The Transformer architecture employs a self-attention mechanism that models complex dependencies between speech features, enabling it to identify and emphasize important patterns even when training data is limited. …”
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Sign language recognition method based on deep learning by He Mu

“…Sign language recognition, as an interdisciplinary field involving computer vision, pattern recognition, and natural language processing, holds profound research significance and extensive application value. …”
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Early detection of emerging SARS-CoV-2 Variants from wastewater through genome sequencing and machine learning by Xiaowei Zhuang, Van Vo, Michael A. Moshi, Ketan Dhede, Nabih Ghani, Shahraiz Akbar, Ching-Lan Chang, Angelia K. Young, Erin Buttery, William Bendik, Hong Zhang, Salman Afzal, Duane Moser, Dietmar Cordes, Cassius Lockett, Daniel Gerrity, Horng-Yuan Kan, Edwin C. Oh

“…We then develop a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources. …”
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A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone by Liangjun Li, Zhihao Ren, Cheng Tang, Shengning Lu, Yong Liang

“…Moreover, the rhythm-based indicators developed herein show strong potential for applications in biodiversity assessment and sustainable ecosystem management within dynamic coastal environments.…”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…This framework enables probabilistic estimation of treatment resistance likelihood while providing transparent feature contribution analyses essential for developing patient-specific management protocols.…”
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Early detection of risky spatio-temporal congestion in urban traffic by Shaobo Sui, Dan Xu, Mingyang Bai, Xiaoke Zhang, Zhaojun Mao, Daqing Li

“…However, large-scale congestion in city exhibits an unique spatio-temporal growth pattern. In this article, we develop a detection method for risky congestion based on its spatio-temporal evolution feature, which can detect risky spatio-temporal congestion clusters (SCCs) when they are small. …”
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Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption. by Abrar Ahmed, Safdar Ali, Ali Raza, Ibrar Hussain, Ahmad Bilal, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. …”
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HISTORICAL, FAUNISTIC AND ZOOGEOGRAPHICAL CHARACTERISTIC OF MAMMALS OF THE CAUCASUS by G. M. Abdurakhmanov, A. M. Batkhiev

“…The aim of the work was to conduct a historical analysis of the development of a modern faunal zoogeographical structure of the Caucasus theriofauna.Methods. …”
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Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach by Saadia Iftikhar, Andrew R. Bond, Asim I. Wagan, Peter D. Weinberg, Anil A. Bharath

“…To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the spatial neighbourhood of each pixel and employs a Support Vector Machine (SVM) as a classifier. …”
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Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems by Vijayakumar Kondepogu, Budhaditya Bhattacharyya

“…AE handles feature extraction, while Bi-LSTM performs estimation. …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…In this work, we present QProteoML as an integration of quantum techniques purposefully developed to deal with high-dimensional, imbalanced and redundant data. …”
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