Analysis of Microbiome for AP and CRC Discrimination by Alessio Rotelli, Ali Salman, Leandro Di Gloria, Giulia Nannini, Elena Niccolai, Alessio Luschi, Amedeo Amedei, Ernesto Iadanza

“…However, limited data availability often hinders research progress, and synthetic data generation could offer a promising solution to this problem. …”
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Multidimensional Evaluation Framework and Classification Strategy for Low-Carbon Technologies in Office Buildings by Hongjiang Liu, Yuan Song, Yawei Du, Tao Feng, Zhihou Yang

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Experimental Study on Compressive Capacity Behavior of Helical Anchors in Aeolian Sand and Optimization of Design Methods by Qingsheng Chen, Wei Liu, Linhe Li, Yijin Wu, Yi Zhang, Songzhao Qu, Yue Zhang, Fei Liu, Yonghua Guo

“…The research findings demonstrate the following: (1) Compressive capacity exhibits significant enhancement with increasing helix diameter yet displays limited sensitivity to helix number. (2) Load–displacement curves progress through three distinct phases—initial quasi-linear, intermediate non-linear, and terminal quasi-linear stages—under escalating pressure. (3) At embedment depths of <i>H</i> < 5<i>D</i>, tensile capacity diminishes by approximately 80% relative to compressive capacity, manifesting as characteristic shallow anchor failure patterns. (4) When <i>H</i> ≥ 5<i>D</i>, stress redistribution transitions from bowl-shaped to elliptical contours, with ≤10% divergence between uplift/compressive capacities, establishing 5<i>D</i> as the critical threshold defining shallow versus deep anchor behavior. (5) The helix spacing ratio (<i>S</i>/<i>D</i>) governs the failure mode transition, where cylindrical shear (CS) dominates at <i>S</i>/<i>D</i> ≤ 4, while individual bearing (IB) prevails at <i>S</i>/<i>D</i> > 4. (6) XGBoost feature importance analysis confirms internal friction angle, helix diameter, and embedment depth as the three parameters exerting the most pronounced influence on capacity. (7) The proposed computational models for <i>N</i>_q and <i>K</i>_u demonstrate exceptional concordance with numerical simulations (mean deviation = 1.03, variance = 0.012). …”
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GRAVITY ANOMALIES OF THE CRUST AND UPPER MANTLE FOR CENTRAL AND SOUTH ASIA by V. N. Senachin, A. A. Baranov

“…Numerous subsequent geophysical projects have researched the crust to a level sufficient to develop regional models, that can give quite adequate information on the depths of external and internal boundaries of the crust and suggest the distribution patterns of seismic velocities and density values. …”
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Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting by Deuk-Jin Park, Hong-Tae Kim, Sang-A Park, Tae-Yeon Kim, Jeong-Bin Yim

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Examining the interconnections among income, food prices, food insecurity, and health expenditure: a multicausality approach by Ahmet Murat Günal, Sevde Cantürk, Salim Yılmaz, Canser Boz, Derya Karabay

“…Addressing economic disparities, stabilizing food prices, and enhancing welfare systems could reduce both food-related and healthcare challenges. Future research should explore regional patterns and broader socioeconomic indicators to support sustainable policy design.…”
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Scenario Modelling for Reproducing Investment Potential of Institutional Sectors in the Regions of the Siberian Federal District by I. V. Naumov, A. V. Trynov, A. O. Safonov

“…The authors analyze the trends and patterns for reproducing investment potential of institutional sectors. …”
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Possibilities of statistical methodology in the study of economic security of the region by Dmitry V. Dianov, Elena A. Radugina

“…The official materials of the state statistics and Internal Affairs Bodies of the Russian Federation, as well as the official statistics of the Ministry of Economy and Finance of Moscow Region Government were used to perform practical calculations.Results: A key aspect of this research work is related to the selection and formation of a set of statistical indicators, which in the interconnection and addition gave a complete comprehensive picture of the existing relationships and established patterns. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…The clinical community has a lot of diabetes diagnostic data. Machine learning algorithms may simplify finding hidden patterns, retrieving data from databases, and predicting outcomes. …”
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A Low-Cost Antipodal Vivaldi Antenna-Based Peanut Defect Rate Detection System by Yuanyuan Yin, Fangyan Ma, Xiaohong Liu, Shuhao Wang, Junjie Xia, Liqing Zhao

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Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data by Bo Lu, Bo Lu, Qingyun Liu, Hao Liu, Tianxiang Long, Tianxiang Long

“…The analysis of nonlinear effects revealed distinct thresholds and patterns of influence that differ from other urban environments, with some variables exhibiting fluctuated or U-shaped effects. …”
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Utilizing Sentinel-2 Remote Sensing for Water Quality Monitoring in Deran Lake, Bosnia and Herzegovina by A. Batina, A. Šiljeg, G. Pedić, M. Parać

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A hybrid ultra-short-term photovoltaic power prediction framework integrating ant colony optimization for clustering with Bi-GRU by Xinyi Liu, Zitao Wang, Yang Wang, Shanke Liu, Lijun Yu

“…This hybrid framework employs an improved Ant Colony Optimization algorithm fused with K-Means pre-clustering (K-MACO) to perform unsupervised learning on samples within the physical feature space of radiation patterns, humidity, and temperature dynamics, classifying weather scenarios into sunny, cloudy, and rainy types. …”
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Frequency Characteristics of Rhythm Non-Assimilation in Canine Ventricular Fibrillation by M. I. Guryanov

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Critical soil moisture detection and water–energy limit shift attribution using satellite-based water and carbon fluxes over China by Y. Liu, J. Xiao, X. Li, Y. Li

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An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín, Sarvenaz Sadat Khatami

“…To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. To enhance model performance, we introduce a novel non-dominated sorting artificial bee colony (NSABC) algorithm, which fine-tunes the hyper-parameters of the CNN-RNN structure, including weights, biases, the number of layers, and neuron configurations. …”
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Machine Learning and Multilayer Perceptron-Based Customized Predictive Models for Individual Processes in Food Factories by Byunghyun Lim, Dongju Kim, Woojin Cho, Jae-Hoi Gu

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Timing of singleton births by onset of labour and mode of birth in NHS maternity units in England, 2005-2014: A study of linked birth registration, birth notification, and hospital... by Peter Martin, Mario Cortina-Borja, Mary Newburn, Gill Harper, Rod Gibson, Miranda Dodwell, Nirupa Dattani, Alison Macfarlane

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Perceiving Fifth Facade Colors in China’s Coastal Cities from a Remote Sensing Perspective: A New Understanding of Urban Image by Yue Liu, Richen Ye, Wenlong Jing, Xiaoling Yin, Jia Sun, Qiquan Yang, Zhiwei Hou, Hongda Hu, Sijing Shu, Ji Yang

“…This study targeted 56 Chinese coastal cities, decoding the spatiotemporal patterns of their fifth facade color (FFC). Through developing an innovative natural color optimization algorithm, the oversaturation and color bias of Sentinel-2 imageries were addressed. …”
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Chest CT-based analysis of radiomic and volumetric differences in epicardial adipose tissue in HFrEF patients with and without AF by Xiu-ying Song, Yu-lan Ma, Jia-min Han, Qian Xie, Hong-xia Zhang, Yu-hua Ma, Jian-feng Hu, Ai-rong Yang

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