Effectiveness of using a slider-crank mechanism for waffle background milling by I. N. Drozdov, A. Yu. Popov

“…The paper presents the design of an experimental cutting tool feed drive based on a slider-crank mechanism. A feature of the technology of milling a regular waffle background pattern is the need for regular repetition of cells, usually rectangular shape. …”
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An intelligent intrusion detection system for cyber-physical systems using GAN-LSTM networks by Md Shakil Siddique, Md. Ashikur Rahman Khan, Ishtiaq Ahammad, Nishu Nath, Joysri Rani Das, Fardowsi Rahman

“…The primary objectives are: (i) developing an adversarial learning framework where the generator synthesizes realistic attack patterns while the discriminator improves detection robustness, (ii) introducing a hybrid anomaly scoring mechanism combining reconstruction and discrimination loss, and (iii) validating performance on real-world CPS datasets (SWaT and WADI). …”
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Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques by Chinna Gopi Simhadri, Hari Kishan Kondaveeti, Valli Kumari Vatsavayi, Alakananda Mitra, Preethi Ananthachari

“…Image processing techniques are used to extract features from diseased leaf images, such as the color, texture, vein patterns, and shape of lesions. …”
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Aquaporins are critical for provision of water during lactation and intrauterine progeny hydration to maintain tsetse fly reproductive success. by Joshua B Benoit, Immo A Hansen, Geoffrey M Attardo, Veronika Michalková, Paul O Mireji, Joel L Bargul, Lisa L Drake, Daniel K Masiga, Serap Aksoy

“…Tsetse flies undergo drastic fluctuations in their water content throughout their adult life history due to events such as blood feeding, dehydration and lactation, an essential feature of the viviparous reproductive biology of tsetse. …”
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A two phase ensembled deep learning approach of prominent gene extraction and disease risk prediction by Prajna Paramita DEBATA, Alakananda TRIPATHY, Pournamasi PARHI, Smruti Rekha DAS

“… Unlocking novel insights from gene expression of individual patient profiles, clinicians and researchers can discern patterns, biomarkers, and therapies. Moreover, accurate classification enables the development of predictive models for prognosis and treatment response, facilitating personalized medicine approaches. …”
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Understanding EMS response times: a machine learning-based analysis by Peter Hill, Jakob Lederman, Daniel Jonsson, Peter Bolin, Veronica Vicente

“…Advanced ML techniques, including Gradient Boosting models, were applied to evaluate the influence of diverse variables such as call handling times, travel times, weather patterns, and resource availability. Feature engineering was employed to extract meaningful insights, and statistical models were used to validate the relationships between key predictors and response times. …”
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Explainable Artificial Intelligence for predicting the compressive strength of soil and ground granulated blast furnace slag mixtures by Ahmed Mohammed Awad Mohammed, Omayma Husain, Muyideen Abdulkareem, Nor Zurairahetty Mohd Yunus, Nadiah Jamaludin, Elamin Mutaz, Hashim Elshafie, Mosab Hamdan

“…A database of 200 samples was compiled from the literature, and six ML models—linear regression, decision trees, random forest, artificial neural networks, gradient boosting, and extreme gradient boosting were developed and evaluated. The study highlights the performance of these models and employs SHAP and LIME analysis to evaluate feature importance. …”
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A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting by Iman Ahmadianfar, Aitazaz Ahsan Farooque, Mumtaz Ali, Mehdi Jamei, Mozhdeh Jamei, Zaher Mundher Yaseen

“…Achieving this objective necessitates the development of highly accurate river water level forecasts. …”
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Modeling Visual Fatigue in Remote Tower Air Traffic Controllers: A Multimodal Physiological Data-Based Approach by Ruihan Liang, Weijun Pan, Qinghai Zuo, Chen Zhang, Shenhao Chen, Sheng Chen, Leilei Deng

“…The model achieved an average balanced accuracy of 0.92 and an F1 score of 0.90 under 12-fold cross-validation, demonstrating excellent predictive performance. The high-ranking features spanned four modalities, revealing typical physiological patterns of visual fatigue across ocular behavior, cortical activity, autonomic regulation, and arousal level. …”
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BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…We first construct a dual-feature interaction encoder that employs SAM to extract image features, which are then refined through trainable multi-scale adapters for learning architectural structures and semantic patterns. …”
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Lightweight Deep Learning Model for Fire Classification in Tunnels by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…This model integrates MobileNetV3 for spatial feature extraction, Temporal Convolutional Networks (TCNs) for temporal sequence analysis, and advanced attention mechanisms, including Convolutional Block Attention Modules (CBAMs) and Squeeze-and-Excitation (SE) blocks, to prioritize critical features such as flames and smoke patterns while suppressing irrelevant noise. …”
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Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area by Yongchuan Zhang, Yuhong Xu, Jie Gao, Zunya Zhao, Jing Sun, Fengyun Mu

“…TriNet comprises three specialized branches: ImgNet for spatial features extraction from images, POINet for functional density distribution features extraction from POI data, and TrajNet for spatiotemporal pattern features extraction from OD data. …”
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Monitoring and deformation of deep excavation engineering based on DFOS technology and hybrid deep learning by Yanli Peng, Jie Zhao, Yijiang Fan, Cheng Fan

“…Concurrently, as neural network models find application and development in deep excavation displacement prediction, traditional models face challenges such as insufficient accuracy and weak generalization capabilities, failing to meet the high-precision warning demands of practical engineering. …”
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SOPHROLOGY, PSYCHOSOMATICS, DEONTOLOGY AND CREATIVITY by Miroslav Prstačić

“…In this context, culture can be considered as a way of life that encompasses beliefs and religiosity, rituals and various arts (music, theatre, poetry, painting, dance…) mentality and patterns of historically transmitted symbols. At the same time, speech is marked by all physical, psychosomatic, psycho-emotional and spiritual manifestations and is the origin of hermeneutics itself. …”
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Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method by Tao Chen, Sizuo Chen, Luying Chen, Huayue Chen, Bochuan Zheng, Wu Deng

“…With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. …”
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MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model by Mohammed S. Alshuhri, Haitham F. Al-Mubarak, Abdulrahman Qaisi, Ahmad A. Alhulail, Abdullah G. M. AlMansour, Yahia Madkhali, Sahal Alotaibi, Manal Aljuhani, Othman I. Alomair, A. Almudayni, F. Alablani

“…A machine learning model was developed to classify irradiated tumors based on delta radiomic features, and statistical analyses were conducted to evaluate feature feasibility, stability, and predictive performance. …”
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MATHEMATICAL-CARTOGRAPHIC MODELING AND CARTOGRAPHY OP DEMOGRAPHIC SITUATION IN THE REGIONS OF EUROPE AND RUSSIA by A. I. Igonin, V. S. Tikunov

“…The multivariate typology gave heterogeneous results, which makes it possible to assess a wide range of territorial features of the demographic characteristics of the population and identify patterns of their distribution. …”
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A Multi-Agent and Attention-Aware Enhanced CNN-BiLSTM Model for Human Activity Recognition for Enhanced Disability Assistance by Mst Alema Khatun, Mohammad Abu Yousuf, Taskin Noor Turna, AKM Azad, Salem A. Alyami, Mohammad Ali Moni

“…Thus, we developed an ensemble activity recognition model, namely “Attention-CNN-BiLSTM with selective ML”. …”
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Multivariate quantitative analysis of glycan impact on IgG1 effector functions by Tamara Cvijić, Matej Horvat, Jakob Plahutnik, Ana Golob, Jaka Marušič

“…The intercorrelated nature of glycan patterns, combined with the low variability and lack of well-defined glycan patterns in process development and manufacture samples, makes studying the effects of individual glycan structures challenging. …”
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Identification of Earthquake Precursors Origin and AI Framework for Automatic Classification for One of These Precursors by Ghada Ali, Lotfy Samy, Omar M. Saad, Ali G. Hafez, El-Sayed Hasaneen, Kamal AbdElrahman, Ibrahim Salah, Mohammed S. Fnais, Hamed Nofel, Ahmed M. Mohamed

“…The current study also introduces this automatic classification by developing various machine learning (ML) and Convolutional Neural Network (CNN) models to highlight the features characterizing each pattern. …”
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