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The topological simulation of acoustic waves has induced unconventional propagation characteristics, thereby offering extensive application potential in the field of acoustics. In this paper, we propose a machine learning-assisted method for the inverse design of acoustic wave topological edge states and demonstrate its practical applicability. Leveraging the predictions from a trained artificial neural network algorithm, the design of wide-bandwidth topological insulators is achieved, with simulation results indicating an approximately 2.8-fold enlargement of the single-cell topological bandgap. Further investigation into their wide-bandwidth topological transport properties is conducted. Additionally, two distinct functional acoustic routing devices are devised. Superior performance of the wide-bandwidth acoustic topological devices has been verified through simulation experiments. This approach provides an efficient and viable avenue for the design and optimization of acoustic devices, with the potential to enhance the management and control efficiency of acoustic signal propagation. [ABSTRACT FROM AUTHOR]

The study of optical topological insulators (PTIs) has revealed intriguing optical properties that diversify the ways in which light can be manipulated, offering significant potential for a wide range of applications. This paper presents a machine learning (ML)-based approach for the reverse design of optical PTIs. Using finite element methods, the paper addresses the challenge of computing the band structure of a dual-band model, enabling the construction of a dataset suitable for ML training. With the goal of maximizing dual-band bandgaps, the study employs the random forest algorithm to predict target parameters and further designs topological edge states. Leveraging these boundary state patterns, two different optical PTI beam splitters are devised, and their transmission coefficients and losses are computed. The results demonstrate that optical devices designed using topological boundary states exhibit enhanced stability and robustness. This approach offers a reliable solution for applications in fields such as optical communication and optical sensing. [ABSTRACT FROM AUTHOR]

The plasmon coupling properties of assembled metal nanogap structures are highly sensitive to the distance, shape, orientation, and configuration of faceted nanoparticles. However, the orientation control of nanoparticles is usually limited to in-plane configurations. In this paper, Au@Ag nanocubes (NCs) with sharp edges and corners were assembled on a polystyrene (PS) microsphere array as a hierarchical surface-enhanced Raman spectroscopy (SERS) substrate via a facile self-assembly technique. The Au@Ag NC monolayer undergoes an out-of-plane reconstruction and forms V-shaped nanogaps under the modulation of the PS microsphere array curved surface. This unique V-shaped nanogap can generate electromagnetic fields stronger than those of traditional parallel gaps and make it easier for target molecules to diffuse into the nanogaps. The three-dimensional (3D) SERS substrates have a wider spectral response and 20 times higher SERS activity than those of the Au@Ag NC monolayer films on flat substrates. The high sensitivity was demonstrated by the detection limit down to 10–9 M for 4-MBA with a substrate enhancement factor up to 3.1 × 107. Furthermore, the optimized 3D SERS substrate was applied to determine As5+ in water with detection limits as low as 10–8 M assisted by machine learning, indicating that the 3D SERS substrates have promising applications in food safety, environmental monitoring, and biological diagnosis. [ABSTRACT FROM AUTHOR]

The reverse design method of acoustic structure based on a deep learning algorithm has been developed as an important means of metamaterial design. In this paper, a multi-band acoustic topological insulator is designed, and the improved competitive search algorithm Long Short-Term Memory (LSTM) algorithm model is used to predict its potential optimal parameter combination to assist the on-demand design of the working frequency band of the multi-band acoustic topology insulator. Finally, the numerical simulation model is established using the optimized structural parameters, and the topologically protected boundary state is studied, which verifies the effectiveness of the method. The research results provide a reference for the on-demand design of multi-band antennas, sound absorption, sound insulation, and other acoustic communication functional devices. [ABSTRACT FROM AUTHOR]

To form a unified configuration and information management platform, FCCMS (financial center configuration management system) will integrate and sort information based on various configuration data and relationships as well as integrate processes and permissions. However, the most serious issue that data centers are currently facing is how to effectively manage these infrastructures. For various infrastructures, the data center currently uses a decentralized operation and maintenance management model. When an infrastructure fails due to inexperienced configuration management, this mode is not conducive to quickly locating and resolving the problem. A detection method of RFCO (random forest algorithm based on clustering optimization) is proposed, and an appropriate tree is selected from RF to integrate, so as to achieve the best effect. In this paper, the target matching algorithm based on FSL (few-shot learning) is deeply studied, and the target detection model is applied to the target matching and positioning task by using the ML method. The performance of the algorithm is tested by experiments on relevant datasets to verify the effectiveness of the algorithm in various scenarios. [ABSTRACT FROM AUTHOR]

• In this paper, we found that the weighted random forest (WRF) model can better predict the incidence of hepatic encephalopathy (HE) in cirrhotic patients. • For unbalanced data, we use the random forest (RF) and support vector machine (SVM) algorithms to construct a risk prediction model for liver cirrhosis complicated by HE to improve the efficiency of its prediction. • This work is the first study to predict cirrhosis in patients with hepatic encephalopathy based on unbalanced data. Hepatic encephalopathy (HE) is among the most common complications of cirrhosis. Data for cirrhosis with HE is typically unbalanced. Traditional statistical methods and machine learning algorithms thus cannot identify a few classes. In this paper, we use machine learning algorithms to construct a risk prediction model for liver cirrhosis complicated by HE to improve the efficiency of its prediction. We collected medical data from 1,256 patients with cirrhosis and performed preprocessing to extract 81 features from these irregular data. To predict HE in cirrhotic patients, we compared several classification methods: logistic regression, weighted random forest (WRF), SVM, and weighted SVM (WSVM). We also used an additional 722 patients with cirrhosis for external validation of the model. The WRF, WSVM, and logistic regression models exhibited better recognition ability for patients with HE than traditional machine learning models (sensitivity> 0.70), but their ability to identify patients with uncomplicated HE was slightly lower (specificity approximately 85%). The comprehensive evaluation index of the traditional model was higher than those of other models (G-means> 0.80 and F-measure> 0.40). For the WRF, the G-means (0.82), F-measure (0.46), and AUC (0.82) were superior to those of the logistic regression and WSVM models, which means that it can better predict the incidence of HE in patients. The WRF model is more suitable for the classification of unbalanced medical data and can be used to construct a risk prediction and evaluation system for liver cirrhosis complicated with HE. The probabilistic prediction models of WRF can help clinicians identify high-risk patients with HE. [ABSTRACT FROM AUTHOR]