Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…Abstract In this study, a small dataset of 370 datapoints of Mg alloys are selected for machine learning (ML), in which each datapoint includes five rare‐earth‐free alloying elements (Ca, Zn, Al, Mn and Sn), three extrusion parameters (extrusion speed, temperature and ratio), and three mechanical properties (yield strength [YS], ultimate tensile strength [UTS] and elongation [EL]). The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. …”
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A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets by Shengteng Jiang, Xiaoyuan Ren, Canyu Wang, Libing Jiang, Zhuang Wang

“…The experiments demonstrate that SFPD has better performance in feature point detection and feature point matching than usual algorithms.…”
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Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) by Haijing Qin, Yunchen Tian, Jianing Quan, Xueqi Cong, Qingfei Li, Jinzhu Sui

“…In addition, training on this benchmark dataset proposes an improved feeding intensity evaluation network, which achieves a good balance in prediction accuracy and parameter memory and offers the possibility of subsequent deployment of the model on mobile. …”
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Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization by Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal

“…One part of this AI approach is the use of population search algorithms to fine-tune hyperparameters for better prediction performing. …”
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Data-driven discovery of ultrahigh specific hardness alloys by Taeyeop Kim, Wook Ha Ryu, Geun Hee Yoo, Donghyun Park, Ji Young Kim, Eun Soo Park, Dongwoo Lee

“…This study employed an iterative process of ML prediction paired with combinatorial experimental verification to discover new ternary alloys with ultrahigh-specific hardness. …”
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An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. By integrating AI techniques, such as back propagation neural networks, the framework predicts variables with high computational complexity, improving accuracy and simulation efficiency. …”
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Adaptive FPGA-Based Accelerators for Human–Robot Interaction in Indoor Environments by Mangali Sravanthi, Sravan Kumar Gunturi, Mangali Chinna Chinnaiah, Siew-Kei Lam, G. Divya Vani, Mudasar Basha, Narambhatla Janardhan, Dodde Hari Krishna, Sanjay Dubey

“…This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. …”
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Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus by Ji Yang, Yikun Zhou, Jiarui Zhang, Yongqin Zheng, Jundong He

“…Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. …”
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Advancing agriculture with machine learning: a new frontier in weed management by Mohammad MEHDIZADEH, Duraid K. A. AL-TAEY, Anahita OMIDI, Aljanabi Hadi Yasir ABBOOD, Shavan ASKAR, Soxibjon TOPILDIYEV, Harikumar PALLATHADKA, Renas Rajab ASAAD

“…Validation and refinement of these algorithms are needed for practical application.…”
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Does the hepatologist still need to rely on aminotransferases in clinical practice? A reappraisal of the role of a classic biomarker in the diagnosis and clinical management of chr... by Patrizia Burra, Calogero Cammà, Pietro Invernizzi, Fabio Marra, Maurizio Pompili

“…ALT, a sensitive and cost-effective marker of liver injury, remains pivotal in predicting clinical outcomes and guiding interventions in several chronic liver diseases including metabolic dysfunction-associated steatotic liver disease, and chronic viral hepatitis. …”
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Deep learning in next-generation vaccine development for infectious diseases by Manojit Bhattacharya, Yi-Hao Lo, Srijan Chatterjee, Arpita Das, Zhi-Hong Wen, Chiranjib Chakraborty

“…However, integrated frameworks connecting the bioinformatics and DL approaches are rapidly progressing, which are necessary for DL-assisted epitope prediction and the subsequent steps for vaccine development. …”
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Application of Fourier transform infrared spectroscopy to exhaled breath analysis for detecting helicobacter pylori infection by Meiqi Qiu, Fei Liao, Yulin Tan, Junlong Zhang, Changjun Zheng, Hanyu Wang, Huangming Zhuang, Wanli Xiong, Qingfang Xie, Weiguo Dong

“…Individual exhalation spectral data after deducting baseline spectral data were used as the basis for the training and test sets through K-center clustering algorithm. Results: A total of 278 samples were collected (63 H. pylori infection cases, 215 healthy controls). …”
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Echocardiographic Left Ventricular Function in the Third Year After COVID-19 Hospitalization: A Follow-Up Pilot Study in South-East of Romania by Constantin-Marinel Vlase, Cristian Gutu, Roxana Elena Bogdan Goroftei, Andreea Boghean, Traian Florin Daniel Iordachi, Anca-Adriana Arbune, Manuela Arbune

“…<i>Conclusions</i>: COVID-19 is an independent predictive factor for GLS impairment, which can indicate myocardial contractile dysfunction, even in patients with asymptomatic heart disease. …”
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Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects by Jingyi Liu, Jun Sun, Yasong Wang, Xin Liu, Yingjie Zhang, Haijun Fu

“…In the future, efforts should be made to enhance the implementation of non-destructive testing technology in the fruit industry through technology integration, optimization algorithm, cost reduction, and expansion of industrial chain application, so as to help the premium growth of the fruit industry.…”
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A prospective, multicenter analysis of the integrated 31-gene expression profile test for sentinel lymph node biopsy (i31-GEP for SLNB) test demonstrates reduced number of unnecess... by J. Michael Guenther, Andrew Ward, Brian J. Martin, Mark Cripe, Rohit Sharma, Stanley P. Leong, Joseph I. Clark, John Hamner, Timothy Beard

“…Results No patients with < 5% i31-GEP predicted risk had a positive SLNB (0/35). Propensity matching demonstrated an 18.5% reduction in SLNBs performed (43.7% vs. 62.2%. p < 0.001). …”
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Topological Optimization of Dynamic Stiffness in Continuum Structure Based on Bidirectional Evolutionary Structural Optimization by JIANG Xudong, LIU Zheng, TENG Xiaoyan

“…By minimizing structural dynamic compliance, the programming scheme of optimal stiffness for continuum structure under dynamic load is proposed, by combination of Equivalent Static Loads with Bidirectional Evolutionary Structural Optimization To effectively decrease calculation scale for dynamic optimization in structural topology, the Equivalent Static Loads Method is applied to transforming dynamic optimization to static one that is solved by Bidirectional Evolutionary Structural Optimization in structural topology To enhance the optimal efficiency of the original BESO method to optimization of dynamic stiffness in continuum structure, a design domain reduction method is developed to establish a new volume control and a stop criterion in this study Numerical results show that the dynamic compliance is asymptotically convergent to optimal solution with volume constraint precisely 〖JP3〗satisfied, and optimal configuration can effectively inhibit the vibration induced by dynamic load Whereby the presented optimization algorithm is verified to be robust and adapt Consequently it is provided with theoretical significance to extend the original Evolutionary Structural Optimization Method to dynamic optimization in structural design…”
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Intelligent Energy Management in an Isolated DC Microgrid using Fuzzy Logic by Youssfi Abdelaziz, El Kadi Youssef Ait

“…The PV system is controlled by a MPPT method implementing the INC algorithm, while the storage system is managed by a PI controller to control battery charging and discharging. …”
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A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine by Zhongliang Lv, Baoping Tang, Yi Zhou, Chuande Zhou

“…A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. …”
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Multi-Objective Robustness of Integrated Energy System Considering Source-Load Uncertainty by LI Jianlin, ZHANG Zedong, LIANG Ce, ZENG Fei

“…A grid-connected electric-thermal-hydrogen operation model including fuel cells and electrolytic cells is developed, and the source-load uncertainty is added by using the hierarchical Latin hypercube sampling and Euclidean distance scenario reduction methods, and solved by a two-stage robust optimization algorithm. …”
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Optimal Operation Strategy of Cascade Hydro-Wind-Solar-Pumped Storage Complementary System Considering Flexible Regulation Ability by XIA Jinlei, TANG Yijie, WANG Lingling, JIANG Chuanwen, GU Jiu

“…To overcome the limitations of traditional models such as low predictive accuracy and the subjective selection of long short-term memory (LSTM) hyperparameters, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of LSTM and the optimized LSTM model is then used to forecast the output of wind and solar power. …”
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