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Realizing improved strength–ductility synergy in eutectic alloys acting as in situ composite materials remains a challenge in conventional eutectic systems, which is why eutectic high-entropy alloys (EHEAs), a newly-emerging multi-principal-element eutectic category, may offer wider in situ composite possibilities. Here, we use an AlCoCrFeNi 2.1 EHEA to engineer an ultrafine-grained duplex microstructure that deliberately inherits its composite lamellar nature by tailored thermo-mechanical processing to achieve property combinations which are not accessible to previously-reported reinforcement methodologies. The as-prepared samples exhibit hierarchically-structural heterogeneity due to phase decomposition, and the improved mechanical response during deformation is attributed to both a two-hierarchical constraint effect and a self-generated microcrack-arresting mechanism. This work provides a pathway for strengthening eutectic alloys and widens the design toolbox for high-performance materials based upon EHEAs. Producing in situ composite materials with superior strength and ductility has long been a challenge. Here, the authors use lamellar microstructure inherited from casting, rolling, and annealing to produce an ultrafine duplex eutectic high entropy alloy with outstanding properties.

MEMS suspended inductors are susceptible to deformation under external forces, which can lead to the degradation of their electrical properties. The mechanical response of the inductor to a shock load is usually solved by a numerical method, such as the finite element method (FEM). In this paper, the transfer matrix method of linear multibody system (MSTMM) is used to solve the problem. The natural frequencies and mode shapes of the system are obtained first, then the dynamic response by modal superposition. The time and position of the maximum displacement response and the maximum Von Mises stress are determined theoretically and independently of the shock. Furthermore, the effects of shock amplitude and frequency on the response are discussed. These MSTMM results agree well with those determined using the FEM. We achieved an accurate analysis of the mechanical behaviors of the MEMS inductor under shock load.

The infectious pneumonia caused by the 2019 novel coronavirus (COVID-19) has spread rapidly worldwide, crippling the global tourism industry’s development and operations. In Hong Kong, where tourism is a pillar industry, the hotel industry is essential to maintaining a stable economy. Facing multiple forms of pressure, the industry’s status deserves close attention. More than 200 hotels in Hong Kong were taken as the research set in this study. A Python-based web crawler was used to collect daily hotel prices from various online travel agencies. Repeated-measures analysis of variance (ANOVA), correlation analysis, and descriptive analysis were employed to study hotels’ room rate fluctuations over time. Results indicated that room rates across hotels in Hong Kong were primarily influenced by holidays and festivities prior to COVID-19, whereas rates tended to decline after the outbreak. Data analysis based on hotels’ star ratings revealed that 5-star hotels were relatively less affected by COVID-19 while 4- and 4.5-star hotels were most seriously affected. District-level analysis also showed that hotel room rates were differentially influenced by the virus: Hong Kong’s Islands district was hit hardest, followed by Kowloon. These findings offer valuable implications for hotel managers and relevant government departments in making rational decisions based on the current market state.

China is experiencing unprecedented urbanization and aging. Previous studies mostly ignored the internal mechanism of the effect of urban green space on the mental health of older adults. Consequently, the relationship between social participation in urban green spaces and mental health remains uncertain. Therefore, this study explored the impact of urban green spaces, social participation, and other factors on the mental health of older adults and investigated the mechanisms of these effects. This study used linear regression models and conducted a moderating effect analysis using data from the 2018 China Labor Dynamics Survey, comprising 3501 older adults in 146 cities in China. Furthermore, we analyzed differences between solitary and non-solitary older adults. The results indicated that urban green space, road density, physical health, history of hospitalization, subjective well-being, and economic satisfaction significantly affected mental health. Social participation played a significant positive moderating role in the connection between green spaces and mental health among older adults. For solitary older adults, social participation weakened the positive impact of green spaces on mental health; for non-solitary older adults, social participation enhanced the positive impact of green spaces on mental health. These findings could contribute to the future construction of aging-friendly cities in China and help optimize urban construction and strategies for building healthy environments.

As face forgery techniques, particularly the DeepFake method, progress, the imperative for effective detection of manipulations that enable hyper-realistic facial representations to mitigate security threats is emphasized. Current spatial domain approaches commonly encounter difficulties in generalizing across various forgery methods and compression artifacts, whereas frequency-based analyses exhibit promise in identifying nuanced local cues; however, the absence of global contexts impedes the capacity of detection methods to improve generalization. This study introduces a hybrid architecture that integrates Efficient-ViT and multi-level wavelet transform to dynamically merge spatial and frequency features through a dynamic adaptive multi-branch attention (DAMA) mechanism, thereby improving the deep interaction between the two modalities. We innovatively devise a joint loss function and a training strategy to address the imbalanced data issue and improve the training process. Experimental results on the FaceForensics++ and Celeb-DF (V2) have validated the effectiveness of our approach, attaining 97.07% accuracy in intra-dataset evaluations and a 74.7% AUC score in cross-dataset assessments, surpassing our baseline Efficient-ViT by 14.1% and 7.7%, respectively. The findings indicate that our approach excels in generalization across various datasets and methodologies, while also effectively minimizing feature redundancy through an innovative orthogonal loss that regularizes the feature space, as evidenced by the ablation study and parameter analysis.

Gene expression data for IC/BPS patients and controls were obtained from publicly available GEO datasets (GSE11783, GSE11839, GSE28242, and GSE57560). Weighted gene co-expression network analysis (WGCNA) identified clinically significant gene modules with KRT6A as a risk factor. [...]the upregulated gene KRT6A potentially serves as an IC/BPS risk factor [Supplementary Figure 1, http://links.lww.com/CM9/C530]. Proteomic analysis using data-independent acquisition identified 230 differentially expressed proteins (DEPs, P <0.05, |log FC| >2) between the si-KRT6A and control groups.

Although many studies have considered the effects of online reviews on tourists’ decisions, none have directly investigated how to leverage open data analyses to create early choice sets and facilitate destination planning. This paper illustrates how salient characteristics can be mined from the shared experiences embedded in review data and incorporated into a predictive model to build a travel counseling approach. The model is designed by first defining a prediction-based mechanism from online reviews and then generating a multinomial classification problem on all candidate destinations of interest. The model is implemented by applying Natural Language Processing (NLP) and Deep Learning (DL) technologies to review textual features. The model is validated using 75,315 reviews from TripAdvisor along with destinations from 257 U.S. national parks. Empirical results indicate a best classification accuracy of 67%, outperforming two previous approaches. Findings shed light on how to exploit past tourists’ experiences to generate early destination recommendations to identify items for choice sets and reduce tourists’ travel-planning effort. Theoretical and managerial implications regarding social media analytics are provided based on online review meta-data in touristic management. Plain Language Summary A destination recommendation system is developed in the brand awareness stage. A predictive framework is proposed using online reviews and deep learning. A many-to-one mapping is reverse built between reviews and their destinations. A generator is established to create items for an early choice set. Tourists’ knowledge is broadened and trip-planning effort is reduced.

The enduring enigma surrounding the near-infrared (NIR) emission of Mn 2+ continues to ignite intense academic discussions. Numerous hypotheses have emerged from extensive research endeavors to explain this phenomenon, such as the formation of Mn 2+ –Mn 2+ ion pairs, Mn 2+ occupying cubically coordinated sites, as well as conjectures positing the involvement of Mn 3+ oxidized from Mn 2+ or defects. Despite these diverse and valuable insights, none of the hypotheses have yet achieved broad consensus. In this study, we have observed prolonged fluorescence lifetimes (~10 ms) for the NIR emissions of Mn 2+ ions, hinting at these ions occupying the high-symmetry octahedral sites inherent to the garnet lattice. This inference is supported by the corroborating results from X-ray absorption fine structure analysis and first-principles calculations. The intense crystal field of octahedral sites, similar to that of AlO 6 , facilitates the splitting of d – d energy levels, thereby inducing a red-shift in the emission spectrum to the NIR region due to the transition 4 T 1 ( 4 G) →  6 A 1 ( 6 S) of isolated Mn 2+ . Our findings not only offer a plausible rationale for the NIR emission exhibited by other Mn 2+ -activated garnet phosphors but also pave a definitive route towards understanding the fundamental mechanisms responsible for the NIR emission of Mn 2+ ions. The enduring enigma surrounding the near-infrared (NIR) emission of Mn 2+ persists, sparking intense academic discussions without reaching a consensus viewpoint.

The classification of biological signals is important in detecting abnormal conditions in observed biological subjects. The classifiers are trained on feature vectors, which often constitute the parameters of the observed time series data models. Since the feature extraction is usually the most time-consuming step in training a classifier, in this paper, signal folding and the associated folding operator are introduced to reduce the variability in near-cyclostationary biological signals so that these signals can be represented by models that have a lower order. This leads to a substantial reduction in computational complexity, so the classifier can be learned an order of magnitude faster and still maintain its decision accuracy. The performance of different classifiers involving signal folding as a pre-processing step is studied for sleep apnea detection in one-lead ECG signals assuming ARIMA modeling of the time series data. It is shown that the R-peak-based folding of ECG segments has superior performance to other more general, similarity based signal folding methods. The folding order can be optimized for the best classification accuracy. However, signal folding requires precise scaling and alignment of the created signal fragments.

The effect of axial static magnetic field (ASMF) on inclusion removal during the magnetically controlled electroslag remelting M2 high-speed-steel was investigated. The results showed that the application of ASMF can significantly increase the inclusion removal efficiency, especially for the inclusions larger than 20 μm. The reason for the accelerated removal of inclusions was attributed to the alternating Lorentz force and the magnetically controlled spin-vibration induced in the liquid melt film after the application of ASMF.