Quantum Synchronizable Codes From Quadratic Residue Codes and Their Supercodes

Quantum synchronizable codes are quantum error-correcting codes designed to correct the effects of both quantum noise and block synchronization errors. While it is known that quantum synchronizable codes can be constructed from cyclic codes that satisfy special properties, only a few classes of cyclic codes have been proved to give promising quantum synchronizable codes. In this paper, using quadratic residue codes and their supercodes, we give a simple construction for quantum synchronizable codes whose synchronization capabilities attain the upper bound. The method is applicable to cyclic codes of prime length

Characterization of Cell Glycocalyx with Mass Spectrometry Methods.

The cell membrane plays an important role in protecting the cell from its extracellular environment. As such, extensive work has been devoted to studying its structure and function. Crucial intercellular processes, such as signal transduction and immune protection, are mediated by cell surface glycosylation, which is comprised of large biomolecules, including glycoproteins and glycosphingolipids. Because perturbations in glycosylation could result in dysfunction of cells and are related to diseases, the analysis of surface glycosylation is critical for understanding pathogenic mechanisms and can further lead to biomarker discovery. Different mass spectrometry-based techniques have been developed for glycan analysis, ranging from highly specific, targeted approaches to more comprehensive profiling studies. In this review, we summarized the work conducted for extensive analysis of cell membrane glycosylation, particularly those employing liquid chromatography with mass spectrometry (LC-MS) in combination with various sample preparation techniques

Information-Coupled Turbo Codes for LTE Systems

We propose a new class of information-coupled (IC) Turbo codes to improve the transport block (TB) error rate performance for long-term evolution (LTE) systems, while keeping the hybrid automatic repeat request protocol and the Turbo decoder for each code block (CB) unchanged. In the proposed codes, every two consecutive CBs in a TB are coupled together by sharing a few common information bits. We propose a feed-forward and feed-back decoding scheme and a windowed (WD) decoding scheme for decoding the whole TB by exploiting the coupled information between CBs. Both decoding schemes achieve a considerable signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and our proposed IC Turbo codes from the EXIT functions of underlying convolutional codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes is derived and calculated by the constructed EXIT charts. Numerical results show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for various code parameters at a TB error rate level of $10^{-2}$, which complies with the derived SNR gain upper bound.Comment: 13 pages, 12 figure

Data Asset Management and Visualization Based on Intelligent Algorithm: Taking Power Equipment Data as An Example

Data asset management is adequate in solving the problem of data silence and data idleness for enterprises. Through intelligent algorithms such as neural network, in-depth learning and block chain, and guided by business needs, it extracts, analyzes and visualizes the existing business precipitation data, and forms scattered and disordered data into valuable information to support the development of the company, so as to activate data assets. Taking the management data of electric power equipment as an example, this paper proposes a method of fusion of multiple intelligent control algorithms. The specific modules include the fusion of heterogeneous data; feature extraction of equipment asset management data based on machine learning; intelligent control of multi-objective optimization environment based on energy consumption data; BIM data visualization based on data classification-energy extraction-neural network (SVM-CART-SAE-DNN) algorithm fusion. The algorithm can effectively improve the efficiency of equipment management and enhance the security and economy of power infrastructure through intelligent control of equipment management

Diagnosing the controls on desert dust emissions through the Phanerozoic

Desert dust is a key component of the climate system, as it influences Earth's radiative balance and biogeochemical cycles. It is also influenced by multiple aspects of the climate system, such as surface winds, vegetation cover, and surface moisture. As such, geological records of dust deposition or dust sources are important palaeoclimate indicators; for example, dust records can be used to decipher aridity changes over time. However, there are no comprehensive records of global dust variations on tectonic timescales (tens of millions of years). Furthermore, although some modelling studies have focused on particular time periods of Earth's history, there has also been very little modelling work on these long timescales. In this study, we establish for the first time a continuous model-derived time series of global dust emissions over the whole Phanerozoic (the last 540 million years). We develop and tune a new offline dust emission model, DUSTY1.0, driven by the climate model HadCM3L. Our results quantitatively reveal substantial fluctuations in dust emissions over the Phanerozoic, with high emissions in the late Permian to Early Jurassic (× 4 pre-industrial levels) and low emissions in the Devonian–Carboniferous (× 0.1 pre-industrial levels). We diagnose the relative contributions from the various factors driving dust emissions and identify that the non-vegetated area plays a dominant role in dust emissions. The mechanisms of palaeohydrological variations, specifically the variations in low-precipitation-induced aridity, which primarily control the non-vegetated area, are then diagnosed. Our results show that palaeogeography is the ultimate dominating forcing, with dust emission variations explained by indices reflecting the land-to-sea distance of tropical and subtropical latitudes, whereas CO2 plays a marginal role. We evaluate our simulations by comparing them with sediment records and find reasonable agreement. This study contributes a quantified and continuous dust emission reconstruction and an understanding of the mechanisms driving palaeohydroclimate and dust changes over Earth's Phanerozoic history.</p