A direct regeneration of cathode materials from spent LiFePO4 batteries using a solid phase sintering method has been proposed in this article.

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We provide a shrinkage type methodology which allows for simultaneous model selection and estimation of vector error correction models (VECM) when the dimension is large and can increase with sample size. Model determination is treated as a joint selection problem of cointegrating rank and autoregressive lags under respective practically valid sparsity assumptions. We show consistency of the selection mechanism by the resulting Lasso-VECM estimator under very general assumptions on dimension, rank and error terms. Moreover, with computational complexity of a linear programming problem only, the procedure remains computationally tractable in high dimensions. We demonstrate the effectiveness of the proposed approach by a simulation study and an empirical application to recent CDS data after the financial crisis.

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Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree.

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Tris­(1,3-dichloro-2-propyl)­phosphate (TDCIPP) has commonly been used as an additive flame retardant and frequently detected in the aquatic environment and in biological samples worldwide. Recently, it was found that exposure to TDCIPP inhibited the growth of zebrafish, but the relevant molecular mechanisms remained unclear. In this study, 5 day-old crucian carp (Carassius auratus) larvae were treated with 0.5, 5, or 50 μg/L TDCIPP for 90 days; the effect on growth was evaluated; and related molecular mechanisms were explored. Results demonstrated that 5 or 50 μg/L TDCIPP treatment significantly inhibited the growth of crucian carp and downregulated the expression of growth hormones (ghs), growth hormone receptor (ghr), and insulin-like growth factor 1 (igf1). Molecular docking, dual-luciferase reporter gene assay, and in vitro experiments demonstrated that TDCIPP could bind to the growth hormone releasing hormone receptor protein of crucian carp and disturb the stimulation of growth hormone releasing hormone to the expression of ghs, resulting in the decrease of the mRNA level of gh1 and gh2 in pituitary cells. Our findings provide new perceptions into the molecular mechanisms of developmental toxicity of TDCIPP in fish.