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Bi4Br4 is a material rich in intriguing topological properties. Monolayer Bi4Br4 film exhibits helical edge states characteristic of a quantum spin Hall insulator, while bulk Bi4Br4 represents a higher-order topological insulator with hinge states. However, direct exfoliation from single crystal can only obtain thin nanowires due to the weak van der Waals forces between Bi4Br4 chains, which limits its optical analysis and application, while the growth of Bi4Br4 thin films is also full of challenges due to the extremely narrow growth temperature range and the accurate control of the BiBr3 flux. Here, we reported the controlled growth of α-Bi4Br4 thin films on intrinsic silicon substrates using molecular beam epitaxy. The growth temperature, BiBr3 flux, and the flux ratio of Bi and BiBr3 were accurately controlled. Then, the morphology, composition, and bonding of the prepared films were investigated using atomic force microscopy, X-ray photoelectron spectroscopy and Raman spectroscopy. The growth of large, uniform thin films provides an ideal material platform for studying the physical properties of Bi4Br4. Additionally, we utilized Fourier-transform infrared spectroscopy to explore the film's infrared characteristics, revealing strong absorption in the low frequency range due to the high proportion of one-dimensional topological edge states and laying the groundwork for further exploration of its potential applications in the optoelectronic field. [ABSTRACT FROM AUTHOR]

For a continuous‐time switched linear system, the spectral abscissa, that is, the maximum Lyapunov exponent, essentially characterizes the worst‐case asymptotic divergence or convergence rate under arbitrary switching. In this study, based on the n×(n+2)$$ n\times \left(n+2\right) $$ generalized coordinate transformation method, a computational algorithm is proposed to get the performance estimation of the switched linear system under arbitrary switching by estimating the spectral abscissa of the system. Simulation examples are presented to illustrate the effectiveness of the proposed scheme, which gives a better estimation of the spectral abscissa than the existing results. [ABSTRACT FROM AUTHOR]

Understanding the transport mechanism is crucial for developing inhibitors that block allergen absorption and transport and prevent allergic reactions. However, the process of how beta-conglycinin, the primary allergen in soybeans, crosses the intestinal mucosal barrier remains unclear. The present study indicated that the transport of beta-conglycinin hydrolysates by IPEC-J2 monolayers occurred in a time- and quantity-dependent manner. The beta-conglycinin hydrolysates were absorbed into the cytoplasm of IPEC-J2 monolayers, while none were detected in the intercellular spaces. Furthermore, inhibitors such as methyl-beta-cyclodextrin (MβCD) and chlorpromazine (CPZ) significantly suppressed the absorption and transport of beta-conglycinin hydrolysates. Of particular interest, sodium cromoglycate (SCG) exhibited a quantity-dependent nonlinear suppression model on the absorption and transport of beta-conglycinin hydrolysates. In conclusion, beta-conglycinin crossed the IPEC-J2 monolayers through a transcellular pathway, involving both clathrin-mediated and caveolae-dependent endocytosis mechanisms. SCG suppressed the absorption and transport of beta-conglycinin hydrolysates by the IPEC-J2 monolayers by a quantity-dependent nonlinear model via clathrin-mediated and caveolae-dependent endocytosis. These findings provide promising targets for both the prevention and treatment of soybean allergies. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to determine the role of body fat percentage (BFP) changes in diabetes remission (DR) and the association between baseline body composition and its changes after bariatric surgery. Methods: We analyzed 203 patients with type 2 diabetes who underwent Roux‐en‐Y gastric bypass. Body composition was measured using a gold‐standard‐derived predictive equation and magnetic resonance imaging. Body composition changes were calculated as 100 × (baseline value – follow‐up value)/baseline value. We verified the results in a laparoscopic sleeve gastrectomy cohort with 311 patients. Results: Compared with non‐remission patients in the Roux‐en‐Y gastric bypass cohort, those who achieved DR showed a higher baseline fat‐free mass index (FFMI) and experienced the most significant changes in BFP (p < 0.001). In comparative analyses, BFP changes were significantly better than BMI changes in identifying short‐ and long‐term DR. Linear regression analysis identified FFMI as the most significant baseline variable correlated with BFP changes (p < 0.001). Baseline BMI was positively correlated with changes in BFP but negatively correlated with changes in FFMI. These findings were replicated in the laparoscopic sleeve gastrectomy cohort. Conclusions: BFP changes determine DR after bariatric surgery, and baseline FFMI is crucial for BFP changes. A low initial BMI is associated with a smaller BFP reduction and greater FFMI loss after bariatric surgery. [ABSTRACT FROM AUTHOR]

Brain–computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification accuracy, they usually overlook the interrelationships between individual features, indirectly impacting the accuracy of feature classification. To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals, serving as the model's input. By integrating the enhanced adaptive L1 penalty and weighted fusion penalty into the sparse learning model, we select the most informative features from the matrix. Specifically, we measure the importance of features using mutual information and introduce an adaptive weight construction strategy to penalize regression coefficients corresponding to each variable adaptively. Moreover, the weighted fusion penalty balances weight differences among correlated variables, reducing the model's overreliance on specific variables and enhancing accuracy. The performance of the proposed method was validated on BCI Competition IV datasets IIa and IIb using the support vector machine. Experimental results demonstrate the effectiveness and superiority of the proposed model compared to the existing models. [ABSTRACT FROM AUTHOR]

The notion of topological insulators (TIs), characterized by an insulating bulk and conducting topological surface states, can be extended to higher-order topological insulators (HOTIs) hosting gapless modes localized at the boundaries of two or more dimensions lower than the insulating bulk. In this work, by performing high-resolution angle-resolved photoemission spectroscopy (ARPES) measurements with submicron spatial and spin resolution, we systematically investigate the electronic structure and spin texture of quasi-one-dimensional (1D) HOTI candidate Bi4Br4. In contrast to the bulk-state-dominant spectra on the (001) surface, we observe gapped surface states on the (100) surface, whose dispersion and spin-polarization agree well with our ab-initio calculations. Moreover, we reveal in-gap states connecting the surface valence and conduction bands, which is a signature of the hinge states inside the (100) surface gap. Our findings provide compelling evidence for the HOTI phase of Bi4Br4. The identification of the higher-order topological phase promises applications based on 1D spin-momentum locked current in electronic and spintronic devices. W. X. Zhao et al. study the higher-order topological insulator candidate Bi4Br4 by angle-resolved photoemission spectroscopy (ARPES) and ab-initio calculation. They provide strong evidence for the higher-order topological insulator phase, including a signature of the hinge states inside the (100) surface gap. [ABSTRACT FROM AUTHOR]

The establishment of the correspondence between the important features of the topside weld pool and the penetration state is a key link in penetration control. This article presents a scheme of variable gap welding penetration control based on rough-fuzzy modeling. By utilizing the basic concepts of variable precision rough sets and conditional information entropy, relevant algorithms for attribute reduction and rule extraction in decision information systems are addressed. Raw data for modeling are obtained through current–gap combination experiments, and then rough-set-based knowledge acquisition algorithms are used to generate classification rules for the penetration state. Based on the correspondence between the important features of the topside pool and the penetration state, a fuzzy control model is established. Welding experiments under variable gaps are conducted to examine the proposed control scheme, and the results indicate that the weld has good penetration and meets the requirements of welding specifications. [ABSTRACT FROM AUTHOR]