Spatio-temporal mapping reveals changes in soil organic carbon stocks across the contiguous United States since 1955 by Chenconghai Yang, Feixue Shen, Xiang Li, Wenkai Cui, Lei Zhang, Lin Yang, Chenghu Zhou

“…The contiguous United States (CONUS) has abundant and well-documented soil samples with time labels, which lays the groundwork for us to estimate the long-term SOC dynamics in multiple soil layers over that region with high resolution. Specifically, we propose leveraging time-series soil data from World Soil Information Service (WoSIS) and International Soil Carbon Network (ISCN) to build ST-DSM models at different soil depths based on matching environmental covariates and machine learning techniques (random forest framework). …”
Get full text

Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging by Jessica Reyes-Rojas, Julien Guigue, Daniel Žížala, Vít Penížek, Tomáš Hrdlička, Petra Vokurková, Aleš Vaněk, Tereza Zádorová

“…The within-horizon/layer variability of CaCO3, on the other hand, is independent of the dynamics of deposition. …”
Get full text

Drunk Driver Detection Using Thermal Facial Images by Chin-Heng Chai, Siti Fatimah Abdul Razak, Sumendra Yogarayan, Ramesh Shanmugam

“…Convolutional Neural Networks (CNNs) and YOLO (You Only Look Once) algorithms were employed to extract facial features, while classifiers such as Support Vector Machines (SVMs), Multi-Layer Perceptron (MLP), and K-Nearest Neighbors (KNN), as well as Random Forest and linear regression, classify individuals as sober or intoxicated based on their thermal images. …”
Get full text

Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. …”
Get full text

PLASMON MODES IN A MULTILAYER STRUCTURE WITH 3-BILAYER GRAPHENE SHEETS by Nguyen Van Men, Dong Thi Kim Phuong, Vu Dong Duong

“…This paper presents results calculated within random phase approximation at zero temperature for collective excitations, an important characteristic of materials, in a three-layer structure consisting of three bilayer graphene sheets in an inhomogeneous background dielectric. …”
Get full text

Image Based Detection of Coating Wear on Cutting Tools with Machine Learning by Jan Wolf, Nithin Kumar Bandaru, Martin Dienwiebel, Hans-Christian Möhring

“…It has been shown that the wear rate increases dramatically once the coating is worn through. Detecting coating layer loss is therefore a good indicator of the remaining useful life of the cutting tool. …”
Get full text

Modeling the Influence of Non-Constant Poisson’s Ratio on Crack Formation Under Uniaxial Compression of Rocks and Concrete by Gennady Kolesnikov, Vitali Shekov, Timmo Gavrilov

“…A technique for analyzing random deviations of Poisson’s ratio from the variable mathematical expectation is proposed. …”
Get full text

Active Vibration Control of a Cantilever Beam Structure Using Pure Deep Learning and PID with Deep Learning-Based Tuning by Abdul-Wahid A. Saif, Ahmed Abdulrahman Mohammed, Fouad AlSunni, Sami El Ferik

“…The first approach involves the direct application of deep learning techniques, specifically multi-layer neural networks and RNNs, to control the beam’s dynamic behavior. …”
Get full text

Resource Allocation for Federated Learning with Heterogeneous Computing Capability in Cloud–Edge–Client IoT Architecture by Xubo Zhang, Yang Luo

“…To address the issue of high-computation-capability clients waiting due to varying computing capabilities under heterogeneous device conditions, this paper proposes an improved resource allocation scheme based on a three-layer FL framework. This scheme optimizes the communication parameter volume from clients to the edge by implementing a method based on random dropout and parameter completion before and after communication, ensuring that local models can be transmitted to the edge simultaneously, regardless of different computation times. …”
Get full text

A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats by Dheyaaldin Alsalman

“…In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. …”
Get full text

Sampling plan for using a motorized penetrometer in soil compaction evaluation by Lindolfo Storck, Sarha G. K. Kobata, Betania Brum, André B. Soares, Alcir J. Modolo, Tangriani S. Assmann

“…The sample size for the layer of 0-10 cm is larger than in the deeper layers (0-20, 0-30 and 0-40 cm) and smaller for cones with larger diameter.…”
Get full text

RWP-NSGA II: Reinforcement Weighted Probabilistic NSGA II for Workload Allocation in Fog and Internet of Things Environment by Hafsa Raissouli, Samir Brahim Belhaouari, Ahmad Alauddin Bin Ariffin

“…IoT devices can process a portion of the workload locally and offload the rest to the fog layer. This workload is then allocated to the fog nodes. …”
Get full text

Neutrosophic Set and Machine Learning Models for Detection of DoS Attack Resilience by Ahmad M. Nagm, Mamdouh Gomaa, Rabih Sbera, Darin Shafek, Ahmed A El-Douh, Ahmed Abdelhafeez, Ahmed E Fakhry

“…To mitigate DDoS assaults, the suggested IDS makes use of an application layer dataset that is openly accessible. Then we use the neutrosophic set model to select the best ML model under different evaluation matrices. …”
Get full text

Relative Intensity Noise of Gain-Switched Dual-State Lasing for an Insein(113)B Quantum Dot Laser by Nuran Dogru, Erkan Cengiz, Hilal S. Duranoglu Tunc

“…Excited- and ground-state carrier noises strongly affected the RIN spectrum, whereas the wetting-layer carrier noise had a negligible effect. In addition, the capture and escape times significantly affected the RIN spectrum. …”
Get full text

Data-driven analysis of hysteresis and stability in perovskite solar cells using machine learning by Sharun Parayil Shaji, Wolfgang Tress

“…We find that the cell stack as a whole plays a crucial role in hysteresis and not a single layer. We statistically confirm that p-i-n and higher-efficient solar cells generally show reduced hysteresis. …”
Get full text

Study on the Time-dependent Reliability of Corroded Reinforced Concrete Bridge Structures due to Ship Impact by Tao Fu, Zhixin Zhu, Yan Li, Yue Sun, Lingxiao Meng

“…Based on the durability decay model of reinforced concrete structures in a chloride ion erosion environment, the time-dependent resistance analysis of bridge structures is carried out considering the reduction of yield strength of longitudinal reinforcement and hoop reinforcement in pile sections and the decay of compressive strength of protective layer concrete. Based on the study of the probabilistic model and parameters of random variables affecting the time-dependent reliability of ship-bridge collision, the typical damage modes of bridge structures under ship impact are analyzed, the time-dependent reliability analysis model of bridge structures under ship impact is established based on the structural damage criterion, the ship-bridge crash limit state functional function is given, and the time-dependent reliability analysis of ship-bridge collision is carried out based on the response surface method.…”
Get full text

Dual-unitary shadow tomography by Ahmed A. Akhtar, Namit Anand, Jeffrey Marshall, Yi-Zhuang You

“…To quantify the performance of DUST, we study operator spreading and Pauli weight dynamics in one-dimensional qubit systems, evolved by random two-local dual-unitary gates arranged in a brick-wall structure, ending with a measurement layer. …”
Get full text

Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting by Michalis Patsiarikas, George Papageorgiou, Christos Tjortjis

“…Followed by preprocessing, feature engineering and selection techniques, three corresponding datasets are generated and their impact on future prices is examined, by employing ML models, such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). …”
Get full text

Virtual Reality Video Image Classification Based on Texture Features by Guofang Qin, Guoliang Qin

“…Finally, based on DenseNet, an improved shallow layer dense convolutional neural network (L-DenseNet) is proposed, which can compress network parameters and improve the feature extraction ability of the network. …”
Get full text

Is a thermal pulsation system (LipiFlow) effective as a standalone treatment for meibomian gland dysfunction and dry eye? A systematic review and meta-analysis by Kai-Yang Chen, Hoi-Chun Chan, Chi-Ming Chan

“…The primary outcomes assessed were OSDI and TBUT, with secondary outcomes including meibomian gland expression scores, corneal fluorescein staining (CFS), MGYSS, and lipid layer thickness (LLT). Meta-analyses were conducted using a random-effects model, and heterogeneity was assessed using I ² statistics. …”
Get full text