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Enabling computers to understand the expressions of signers has been a challenging task that requires considering not only the temporal and spatial information of sign language videos,but also the complexity of sign language grammar.In the continuous sign language recognition task,sign language words and sign language actions share a consistent order.In contrast,in the continuous sign language translation task,the generated natural language sentences have to conform to the spoken description,and the word order may not coincide with the action order.To enable more accurate learning of signers' expressions,this paper proposes a novel deep neural network for simultaneous sign language recognition and translation.In this scheme,we explore the effectiveness of different classical pre-trained convolutional neural networks,and different multilayer temporal attention score functions on continuous sign language recognition,combined with Transformer language model,to obtain continuous sign language translation conforming to the spoken description based on continuous sign language recognition.First,this method is assessed on the first large-scale complex background Chinese continuous sign language recognition and translation dataset Tslrt.The complex contextual environment and rich action expressions of signers in Tslrt dataset are used to train our neural network model through different comparison experiments,resulting in a series of benchmark results.The best WER are 4.8% and 5.1% on the tasks of continuous sign language recognition and translation,respectively.To further demonstrate the effectiveness of our method,experiments are conducted on another Chinese continuous sign language recognition dataset Chinese-CSL and compared with other 13 methods.The results show that the WER of our method reaches 1.8%,which proves the effectiveness of the proposed method.

Impact electrochemistry allows for the investigation of the properties of single entities, ranging from nanoparticles (NPs) to soft bio-particles. It has introduced a novel dimension in the field of biological analysis, enhancing researchers' ability to comprehend biological heterogeneity and offering a new avenue for developing novel diagnostic devices for quantifying biological analytes. This review aims to summarize the recent advancements in impact electrochemistry-based biosensing over the past two to three years and provide insights into the future directions of this field. [ABSTRACT FROM AUTHOR]

Silicon has been a pivotal material for the next generation lithium-ion batteries, owing to its superior theoretical capacity (4200 mAh·g-1). However, the huge volume change of silicon during the process of lithiation/delithiation will lead to the instability of silicon-based anode. As a main composition of electrode, the binder plays a critical role in connecting the electrode components together and maintaining the stability of electrode, the use of an appropriate binder is essential to improve the cycling stability of silicon-based anode. Water-soluble binders which have abundant functional groups have received extensive attention due to their excellent performance in improving the electrochemical performance of silicon-based anodes. In this paper, the research progress of water-soluble binders used in silicon-based anode was reviewed. First, the properties of one-dimensional linear binders were summarized. On this basis, the research progress of composite binders with three-dimensional (3D) network structure was mainly introduced. The structure and properties of different 3D network binders were systematically analyzed. Finally, the preferred structure and properties of the water-soluble binders for silicon-based anode were proposed, and a train of thought for water-soluble binders’ choice and design was provided.

A new Zn(II) metal–organic framework (MOF) with the formula [Zn(BBIP)(2,6-NDC)]n (JXUST-44, BBIP = 3,5-bis(1H-benzo[d]imidazol-1-yl)pyridine and 2,6-H2NDC = 2,6-naphthalic acid) was synthesized under solvothermal conditions. The adjacent Zn(II) ions are connected by BBIP ligands to form one-dimensional (1D) chains, which are further linked by carboxylate groups of 2,6-NDC2− ligands to obtain a two-dimensional layer structure, resulting in a 2D → 3D polycatenated MOF. JXUST-44 can be simplified as a 4-connected sql topology and shows good chemical stability in a wide pH range from 2 to 12 at room temperature. The fluorescence experiments indicate that JXUST-44 presents sensitive and selective detection of salicylaldehyde (SA), acetylacetone (acac) and H2PO4− with the detection limits of 0.074, 10.311 and 0.011 ppm, respectively. It is worth noting that JXUST-44 shows turn-on fluorescence toward acac and H2PO4−, as well as good recyclability and stability for acac, SA and H2PO4−. Therefore, JXUST-44 can be considered as a good fluorescent sensor for acac, SA and H2PO4−. The sensing mechanism has also been studied in detail. [ABSTRACT FROM AUTHOR]

A new Zn(II) metal–organic framework (MOF) with the formula [Zn(BBIP)(2,6-NDC)]n (JXUST-44, BBIP = 3,5-bis(1H-benzo[d]imidazol-1-yl)pyridine and 2,6-H2NDC = 2,6-naphthalic acid) was synthesized under solvothermal conditions. The adjacent Zn(II) ions are connected by BBIP ligands to form one-dimensional (1D) chains, which are further linked by carboxylate groups of 2,6-NDC2− ligands to obtain a two-dimensional layer structure, resulting in a 2D → 3D polycatenated MOF. JXUST-44 can be simplified as a 4-connected sql topology and shows good chemical stability in a wide pH range from 2 to 12 at room temperature. The fluorescence experiments indicate that JXUST-44 presents sensitive and selective detection of salicylaldehyde (SA), acetylacetone (acac) and H2PO4− with the detection limits of 0.074, 10.311 and 0.011 ppm, respectively. It is worth noting that JXUST-44 shows turn-on fluorescence toward acac and H2PO4−, as well as good recyclability and stability for acac, SA and H2PO4−. Therefore, JXUST-44 can be considered as a good fluorescent sensor for acac, SA and H2PO4−. The sensing mechanism has also been studied in detail. [ABSTRACT FROM AUTHOR]

Objective Fourier transform infrared spectroscopy (FTIR) was used to analyze myocardial infarction tissues at different stages of pathological change to achieve the forensic pathology diagnosis of acute and old myocardial infarction. MethodsFTIR spectra data of early ischemic myocardium, necrotic myocardium, and myocardial fibrous tissue in the left ventricular anterior wall of the sudden death group of atherosclerotic heart disease and the myocardium of the normal control group were collected using hematoxylin-eosin (HE) and immunohistochemistry (IHC) staining as a reference, and the data were analyzed using multivariate statistical analysis. Results The mean normalized spectra of control myocardium, early ischemic myocardium and necrotic myocardium were relatively similar, but the mean second derivative spectra were significantly different. The peak intensity of secondary structure of proteins in early ischemic myocardium was significantly higher than in other types of myocardium, and the peak intensity of the α-helix in necrotic myocardium was the lowest. The peaks of amide and amide in the mean normalized spectra of myocardial fibrous tissue significantly shifted towards higher wave numbers, the peak intensities of amide and amide were higher than those of other types of myocardium, and the peak intensities at 1 338, 1 284, 1 238 and 1 204 cm-1 in the mean second derivative spectra were significantly enhanced. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) showed that FTIR could distinguish different types of myocardium. ConclusionFTIR technique has the potential to diagnose acute and old myocardial infarction, and provides a new basis for the analysis of the causes of sudden cardiac death. [ABSTRACT FROM AUTHOR]