Prelithiation/presodiation techniques are regarded as indispensable procedures in electrochemical energy storage (EES) systems, which can effectively compensate irreversible capacity loss, raise working voltage, and increase Li+/Na+ concentration in the electrolyte. Various prelithiation/presodiation methods have been successfully exploited and a revolutionary impact has been achieved through the utilization of prelithiation/presodiation techniques. It is well acknowledged that different prelithiation/presodiation strategies possess their own specific mechanisms, which play vital roles in the advancement of EES systems. However, there has rarely been systematical reviews about the concept and progress of prelithiation/presodiation techniques. Hence, in this review various prelithiation/presodiation approaches are comprehensively analyzed and summarized, and in‐depth prelithiation/presodiation behaviors and other innovative applications (including optimization of separators, amelioration of binders, regeneration of spent batteries) are discussed in detail. Finally, suggested future directions of prelithiation/presodiation techniques are proposed and it is expected that these prelithiation/presodiation techniques could provide guidance for construction of advanced EES systems and propel the commercialization process with a focus on safety considerations.

No abstract

No abstract

No abstract

No abstract

This paper presents a learning-based steganalysis/detection method to attack spatial domain least significant bit (LSB) matching steganography in grayscale images, which is the antetype of many sophisticated steganographic methods. We model the message embedded by LSB matching as the independent noise to the image, and theoretically prove that LSB matching smoothes the histogram of multi-order differences. Because of the dependency among neighboring pixels, histogram of low order differences can be approximated by Laplace distribution. The smoothness caused by LSB matching is especially apparent at the peak of the histogram. Consequently, the low order differences of image pixels are calculated. The co-occurrence matrix is utilized to model the differences with the small absolute value in order to extract features. Finally, support vector machine classifiers are trained with the features so as to identify a test image either an original or a stego image. The proposed method is evaluated by LSB matching and its improved version "Hugo". In addition, the proposed method is compared with state-of-the-art steganalytic methods. The experimental results demonstrate the reliability of the new detector.

Let ˇ> 1 be a real number. Let T ˇdenote the ˇ-transformation on OE0; 1. A cylinder of order n is a set of real numbers in OE0; 1 having the same first n digits in their ˇ-expansion. A cylinder is called full if it has maximal length, i.e., if its length is equal to ˇ n . In this paper, we show that full cylinders are well distributed in OE0; 1 in a suitable sense. As an application to the metrical theory of ˇ-expansions, we determine the Hausdorff dimension of the set fx 2 OE0; 1 W jT n ˇx z n j < e Snf .x/ for infinitely many n 2 Ng;where fz n g n 1 is a sequence of real numbers in OE0; 1, the function f W OE0; 1 ! R C is continuous, and S n f .x/ denotes the ergodic sum f .x/ C C f .T n 1 ˇx/.

No abstract