A novel squaramide-containing metal-organic framework (MOF) has been designed and synthesized, which represents the first example of the luminescence selective detection of lactose over other monosaccharides and disaccharides. It was also used for the quantitative determination of lactose in milk.

A novel cobalt(II)-based metal−organic framework, Co−MB, was prepared by hydrothermal reaction of Co(NO 3 ) 2 •6H 2 O, 3,3′-methylenediphthalic acid (H 4 mda), and 1,4-bis(imidazol-1-ylmethyl)-benzene in sodium hydroxide aqueous solution and structurally characterized. It shows a high stability in water within the pH range from 2.2 to 11.6, which could be used as a highly selective and sensitive luminescent sensor for Ag(I) detection in a luminescent enhancement manner, with LOD about 23 nM. Importantly, such stable Co−MB could also work as proton reduction catalyst for photodriven hydrogen production coupled with visible-light organic dyes as photosensitizer. The influence factors of hydrogen production including pH, TEA (triethylamine) contents, and kinds of organic dyes are studied in detail. Under optimal condition, the TON value was up to 5133 per cycle, and this Co−MB could also be reused at least 3 times.

Retail e-commerce, a vital significant industry, developed greatly due to the advance in data science, particularly in covid-19 pandemic. Data analytics is becoming a wide application in retail e-commerce. In this paper, we firstly do an industry overview, which includes the history, current business dynamics and the challenges of retail e-commerce. Then, we integrate the value chain for the retail e-commerce industry and further demonstrate current applications of data analytics across the value chain. Lastly, the future applications of data analytics in retail e-commerce are contemplated.

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In fiber Bragg grating (FBG) sensor networks, the highly overlapped spectral signals can lead to considerable errors in center wavelength demodulation. To tackle this problem, we utilize the fully convolutional time-domain audio separation network (Conv-TasNet) model to produce a distinct spectral signal, which is then demodulated using the dual-weight centroid approach to determine the spectral signal's center wavelength. Specifically, we first demonstrate the theoretical feasibility of the Conv-TasNet model on simulated data. Experimental results show that the Conv-TasNet model can separate the signals of three FBG sensors. After that, we collect the spectral data and further train and validate the model based on the pretrained model of the simulated data to see how it performs on the real data. The experiments consistently illustrate superior performance of our Conv-TasNet model that can also separate actual spectrum signals. The same performance can be achieved by applying the pretrained model but with less training data. The model obtains a competitive performance compared to currently available methods. Moreover, the method provides a solution for improving the multiplexing performance of the FBG sensor network.