Limitations of quantum approximate optimization in solving generic higher-order constraint-satisfaction problems by Thorge Müller, Ajainderpal Singh, Frank K. Wilhelm, Tim Bode

“…Here we analyze the QAOA's performance on random Max-kXOR as a function of k and the clause-to-variable ratio. …”
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Effects of Sheep Manure Combined with Chemical Fertilizers on Maize Yield and Quality and Spatial and Temporal Distribution of Soil Inorganic Nitrogen by Chun Yang, Wenbin Du, Lili Zhang, Zhaorong Dong

“…The nitrate nitrogen of 40–60 cm soil layer is increased by 30.4%, 60–80 cm soil layer is increased by 56.5%, and 80–100 cm soil layer is increased by 11.6%. …”
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Wireless key generation system for internet of vehicles based on deep learning by Han WANG, Liquan CHEN, Zhongmin WANG, Tianyu LU

“…In recent years, the widespread application of internet of vehicles technology has garnered attention due to its complex nature and point-to-point communication characteristics.Critical and sensitive vehicle information is transmitted between different devices in internet of vehicles, necessitating the establishment of secure and reliable lightweight keys for encryption and decryption purposes in order to ensure communication security.Traditional key generation schemes have limitations in terms of flexibility and expandability within the vehicle network.A popular alternative is the physical layer key generation technology based on wireless channels, which offers lightweight characteristics and a theoretical basis of security in information theory.However, in the context of internet of vehicles, the movement speed of devices impacts the autocorrelation of generated keys, requiring improvements to traditional channel modeling methods.Additionally, the randomness and consistency of generated wireless keys are of higher importance in applications in internet of vehicles.This research focused on a key generation system based on the wireless physical layer, conducting channel modeling based on line-of-sight and multipath fading effects to reflect the impact of vehicle speed on autocorrelation.To enhance the randomness of key generation, a differential quantization method based on cumulative distribution function was proposed.Furthermore, an information reconciliation scheme based on neural network auto-encoder was introduced to achieve a dynamic balance between reliability and confidentiality.Compared to the implementation of Slepian-Wolf low-density parity-check codes, the proposed method reduces the bit disagreement rate by approximately 30%.…”
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Explainable Prediction of Long-Term Glycated Hemoglobin Response Change in Finnish Patients with Type 2 Diabetes Following Drug Initiation Using Evidence-Based Machine Learning App... by Chandra G, Lavikainen P, Siirtola P, Tamminen S, Ihalapathirana A, Laatikainen T, Martikainen J, Röning J

“…Various ML models—including linear regression (LR), multi-layer perceptron (MLP), ridge regression (RR), random forest (RF), and XGBoost (XGB)—were evaluated using R² and RMSE metrics. …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. …”
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Laser-Induced Breakdown Spectroscopy Quantitative Analysis Using a Bayesian Optimization-Based Tunable Softplus Backpropagation Neural Network by Xuesen Xu, Shijia Luo, Xuchen Zhang, Weiming Xu, Rong Shu, Jianyu Wang, Xiangfeng Liu, Ping Li, Changheng Li, Luning Li

“…The BOTS-BPNN model also shows superior performance over other common machine learning models like random forest (RF). This work indicates the potential of BOTS-BPNN as an effective chemometric method for analyzing Mars in situ LIBS data and sheds light on the use of chemometrics for data analysis in future planetary explorations.…”
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A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips by Zipeng Zhang, Zhengwen Fang, Yongwang Du, Yilin He, Changsong Qian, Weijian Ye, Ning Zhang, Jianan Zhang, Xiangdong Ding

“…In this study, we presented a Multi-Layer Perceptron (MLP) model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs. …”
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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…This study introduces a novel D3Net model for half-hourly EP prediction, integrating Seasonal-Trend decomposition using LOESS (STL) and Variational Mode Decomposition (VMD) with Multi-Layer Perceptron (MLP), Random Forest Regression (RFR), and Tabular Neural Network (TabNet). …”
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A Comprehensive Framework for Parkinson's Disease Detection Using Spiral Drawings and Advanced Machine Learning Techniques by Mohamed J. Saadh, Waleed K. Abdulsahib, Hardik Doshi, Anupam Yadav, J. Gowrishankar, Mayank Kundlas, Nargiza Mansurova, Kamal Kant Joshi, Fadhil Feez Sead, Bagher Farhood

“…Six different classifiers (Support Vector Machine [SVM], Random Forest [RF], Multi‐Layer Perceptron [MLP], XGBoost, CatBoost, and voting classifiers) were tested. …”
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Individual and Combined Effects of Diabetes and Glaucoma on Total Macular Thickness and Ganglion Cell Complex Thickness: A Cross-sectional Analysis by Dhruven Shah, Rita Dhamankar, Vijay Shetty, Suhas S Haldipurkar, Prakash Chipade, Shabnam Tanwar, Prachi Sankhe, Devendra Venkatramani, Paresh Mhatre, Maninder Singh Setia

“…We used random effects multivariate analysis to adjust for potential confounders. …”
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CTAB modified SnO₂ PEDOT PSS heterojunction humidity sensor with enhanced sensitivity stability and machine learning evaluation by Poundoss Chellamuthu, Kirubaveni Savarimuthu, M Gulam Nabi Alsath, R. Krishnamoorthy, Yuvaraj T, Feras Alnaimat, Mohammad Shabaz

“…Among the tested models, Random Forest (RF) Regression achieved the highest predictive accuracy (R² = 0.99), confirming the sensor’s robustness and reproducibility in dynamic environments. …”
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Machine learning algorithms to predict stroke in China based on causal inference of time series analysis by Qizhi Zheng, Ayang Zhao, Xinzhu Wang, Yanhong Bai, Zikun Wang, Xiuying Wang, Xianzhang Zeng, Guanghui Dong

“…Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi-Layer Perceptron (MLP). …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…To optimize the models’ performance, the impact of dropout with a 50% probability and the number of neurons in the hidden layers were examined. Our proposed CNN model consists of an architecture with four convolutional layers, with 224 × 224 RGB images as input to the first layer, which has 16 filters, followed by additional convolutional layers with 32, 64, and 128 filters respectively. …”
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A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters by Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu

“…Our approach enables hyperparameter tuning for deep learning models with two hidden layers and multiple types of hyperparameters, enhancing the model’s capacity to work with complex optimization problems. …”
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Molecular Simulation of Hydrogen Physisorption and Chemisorption in Nanoporous Carbon Structures by K. Vasanth Kumar, Alaaeldin Salih, Linghong Lu, Erich A. Müller, Francisco Rodríguez-Reinoso

“…Under physisorption conditions, the simulation results show that carbons with a slit-shaped pore geometry are more efficient than other geometries at storing hydrogen, particularly at pore distances which allow the formation of fluid layers commensurate with the pore geometry. Slit pores appear to store a maximum of 1.77 wt% hydrogen by physisorption as opposed to carbon foams (1.48 wt%), carbons with a random structure (1.04 wt%) and carbon nanotubes (0.31 wt%). …”
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CoPaD-Mark: A Coded Parallelizable Deep Learning-Based Scheme for Robust Image Watermarking by Andy M. Ramos, Cecilio Pimentel, Daniel P. B. Chaves

“…The embedding layer employs a parallel structure with convolutional neural networks inspired by the Inception Net, while the extraction layer uses deformable convolutions. …”
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Extreme Learning-Based Robust Adaptive Path Tracking Control of Underactuated Unmanned Surface Vessel by Xinyu HE, Ning WANG, Haojun WU

“…Secondly, the unknown dynamics including system uncertainties and external disturbances were encapsulated into a lumped unknown term, and hidden layer nodes were randomly generated by the single-hidden layer feedforward network(SLFN) of the extreme learning machine to identify the unknown term and avoid relying on USV prior knowledge and dimension explosion problem. …”
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Effect of Adding Lecithin to Diets on Productive Performance of Laying Hens by Mustafa A. Mohsen, B.H. Mousa

“…The layer hens were distributed randomly into five treatments with four replicates per treatment (3 hens/repeat). …”
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Comparison of the effect of skin closure materials on skin closure during cesarean delivery. by Ye Huang, Xinbo Yin, Junni Wei, Suhong Li

“…<h4>Methods</h4>We searched EMBASE、PubMed、Scopus、Cochrane CENTRAL for randomized controlled trials (RCTs) on the use of closure materials for skin closing effect during cesarean delivery. …”
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Applying a Parameterized Quantum Circuit to Anomaly Detection by Jehn-Ruey Jiang, Jyun-Sian Li

“…As anomaly detection datasets are often imbalanced, resampling techniques, such as random oversampling, the synthetic minority oversampling technique (SMOTE), random undersampling, and Tomek-Link undersampling, are applied to reduce the imbalance. …”
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