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This paper studies an adaptive coding scheme for B5G (beyond 5th generation) mobile system-enhanced transmission technology. Different from the existing works, the authors develop a class of rate-compatible, non-binary, low-density parity check (RC-NB-LDPC) codes, which expresses the strong connection between the algebra-based and graph-theoretic-based constructions. The constructed codes can not only express rate-compatible (RC) features, but also possess a quasi-cyclic (QC) structure that facilitates the encoding implementation. Further, in order to achieve the code rate-adaptive allocation scheme, the authors propose using the K-means++ clustering algorithm to cluster different channel environments, considering various factors that affect channel characteristics. Finally, in order to present the advantages of the adaptive coding scheme, the authors construct a coding scheme for image transmission. The numerical results demonstrate that the developed code can obtain better waterfall performance in a larger code rate range, which is more suitable for data transmission; the adaptive coding transmission scheme can obtain higher reconstructed image quality compared to the fixed code rate-coding scheme. Moreover, when considering unequal error protection (UEP), the proposed scheme can further improve the reconstructed image quality.

We propose a linear alternating variable step-size adaptive long-range prediction (AVSS-ALRP) scheme to predict fading signals which is especially suitable for a versatile two-state land mobile satellite (LMS) channel model at S-band. A three-step design procedure is presented to optimize the prediction performance. Firstly, we establish the Gilbert-Elliot channel model based on first-order Markov chain for satellite communication downlink and take advantage of smoothing average to obtain channel observed values. At a second stage, eigenvalue decomposition method is applied to predict future long-range channel state instead of weighted prediction. Finally, combining variable step-size least mean squares and adaptive long-range prediction, we introduce the VSS-ALRP algorithm to predict LMS channel fading signals in the case of “good” state, and the obtained prediction results would be revised based on the linear prediction of error when shadowing condition is in the “bad” state. Simulation results show that the proposed scheme can not only offer an accurate prediction for long-range channel state and fading signals over the two-state Gilbert-Elliot channel model and greatly enhance the fading signals’ autocorrelation, but also have considerably better performance than long-range prediction (LRP) algorithm from the results of mean square error (MSE) and correlation coefficient.

In order to provide the accurate real-time knowledge of future channel state information for the adaptive transmission of narrowband land mobile satellite (LMS) communication systems, an adaptive long-range prediction (ALRP) method, suitable for a three-state LMS channel model at S-band, is proposed. The very slow shadowing conditions’ variations of satellite propagation channel can be described as a three-state Gilbert–Elliot channel model based on Markov chain. The channel state of future long-range is predicted based on weighting prediction while the parameters of linear auto-regression model are updated using an iterative adaptive tracking algorithm. The future channel fading series are predicted by the latest observations within current channel state. Simulation results show that the proposed method can be used to predict the future long-range channel state and fading series of three-state Gilbert–Elliot channel model accurately; and it is observed from the mean square error results that the prediction performance of the ALRP method is much more accurate than that of long-range prediction method. Moreover, the method has advantage of real-time and low-complexity which will make the adaptive transmission techniques feasible for LMS channel models.

The weak signal, which is usually submerged in strong noise, is very difficult to detecte for its amplitude and frequency. The dynamic properties of VanderPol-Duffing are studied in this paper. Such system can go into the chaos under certain parameters. In chaotic state the disturbance of weak periodic signals can make the system dynamic behavior change dramatically. Our research results show that the system is from period doubling state to chaotic state when the amplitude of input signal is changed. And it has a remarkable impact influence on the system dynamic performance when the input frequency is varied. The unknown frequency can be detected through counting the numbers of turning point in phase diagram. The simulation results verified that the presented method is feasible and there are a lot of theory values in the research.

In order to improve the decoding performance of MIMO-OFDM system in the case of the channel state information is not accurate enough, a new algorithm is proposed, which combines SAGE algorithm, DFT-LS channel estimation and maximum likelihood detection algorithm. The algorithm utilizes joint iterative technology to achieve channel estimation and decoding effect, thereby enhances the reliability of the system. Theoretical study and simulation results show that the proposed algorithm can track the channel change correctly without increasing the system overhead, and the convergence speed is accelerated. Besides, the performance is superior to the commonly used joint detection algorithm. Moreover, comparing with the ideal channel estimation under the maximum likelihood detection algorithm, the new proposed algorithm only has a loss of 0.5dB with the same bit error rate.

Any carrier frequency offset will cause a loss of subcarrier orthogonality which results in ICI and hence performance degrades severely in MIMO-OFDM systems. In this paper, a time and frequency synchronization solution for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed. The synchronization is achieved using one Constant Amplitude Zero Auto Correlation (CAZAC) sequence-based preamble which is simultaneously transmitted from all transmit antennas in the same OFDM time instant. The synchronization is accomplished sequentially by coarse time synchronization, fractional frequency offset estimation, integral frequency offset estimation and fine time synchronization. The simulation shows that the proposed algorithm can estimate the timing and frequency offsets efficiently in MIMO-OFDM systems, especially in low signal-to-noise ratio condition.

In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.

Progressive-edge-growth (PEG) algorithm is one of the best known methods for constructing LDPC codes at short and intermediate block lengths, however, the codes directly designed by such algorithm has high encoding complexity, especially for q-ary LDPC codes, encoding complexity increases with the increase of q value rapidly, which hinder the development of q-ary LDPC code’s implementation seriously. To such problem, the paper presents a improved method based on PEG algorithm which can be encoded by the iterative encoding algorithm with the liner operation complexity. The simulation results indicate: Though the error correcting capability of irregular q-ary LDPC codes constructed by the proposed methods in the paper is basically identical with the codes constructed by the PEG algorithm, but the powerful advantage makes it be easily implemented by the hardware .

Acute liver failure （ALF） is a life-threatening illness. The extracorporeal cell-based bioartiflcial liver （BAL） system could bridge liver transplantation and facilitate liver regeneration for ALF patients by providing met- abolic detoxification and synthetic functions. Previous BAL systems, based on hepatoma cells and non-human hepatocytes, achieved limited clinical advances, largely due to poor hepatic functions, cumbersome preparation or safety concerns of these cells. We previously generated human functional hepatocytes by lineage conversion （hiHeps）. Here, by improving functional maturity of hiHeps and producing hiHeps at clinical scales （3 billion cells）, we developed a hiHep-based BAL system （hiHep-BAL）. In a porcine ALF model, hiHep-BAL treatment restored liver functions, corrected blood levels of ammonia and bilirubin, and prolonged survival. Importantly, human albumin and a-l-antitrypsin were detectable in hiHep-BAL-treated ALF pigs. Moreover, hiHep-BAL treatment led to attenuated liver damage, resolved inflammation and enhanced liver regeneration. Our findings indicate a promising clinical application of the hiHep-BAL system.

Background Investigating the underlying molecular mechanisms responsible for endometrial dysfunction in women with PCOS is essential, particularly focusing on the role of hyperinsulinemia. Methods We explored the role of insulin in the decidualization process using a synthetic decidualization assay. To dissect the effects of PI3K/AKT-NR4A signaling, we employed small interfering RNAs (siRNAs) targeting the NR4A genes and inhibitors of the PI3K/AKT pathway. We also investigated the disruption of AKT-NR4A1 signaling in the endometrium of PCOS female rats induced with dehydroepiandrosterone (DHEA). Quantitative real-time PCR (qRT-PCR) and Western blot (WB) analyses were utilized to evaluate gene expression regulation. Results Insulin was found to suppress the expression of decidualization markers in human endometrial stromal cells (hESC) in a dose-dependent manner, concurrently triggering an inappropriate activation of the PI3K/AKT pathway. Members of the NR4A family, as downstream effectors in the PI3K/AKT pathway, were implicated in the insulin-induced disruptions during the decidualization process. Moreover, the endometrium of PCOS models showed significantly elevated levels of phosphorylated (Ser473) AKT, with a corresponding reduction in Nr4a1 protein. Conclusions Our research demonstrates that insulin negatively regulates decidualization in hESC via the PI3K/AKT-NR4A pathway. In vivo analysis revealed a significant dysregulation of the AKT-NR4A1 pathway in the endometrium of PCOS rats. These findings offer novel insights into the pathogenesis of infertility and endometrial disorders associated with hyperinsulinemia in PCOS.