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Catalysts play a pivotal role in the efficient conversion of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) through electrochemical oxidation. In this work, a cost-effective and highly efficient cobalt-based electrocatalyst for the synthesis of bio-based carboxylic acids was reported. By the employment of cellulose, which was dissolved in the alkaline-urea with cobalt ion, as the precursor, it derived a carbon-coated Co3O4 (Co3O4@NC) catalyst with a high specific surface area and rich porous structure. When utilized in the electrocatalytic conversion of HMF to FDCA, the catalyst exhibited exceptional yields and Faradaic efficiency which surpassed 95 %. In-situ Raman spectra unveiled that a dual-pathway process occurred on this catalyst, with part of Co3O4 serving as active sites for HMF adsorption, while other Co3O4 transformed into CoOOH during the reaction. This dual-pathway electrocatalysis facilitated the highly efficient conversion of HMF. Additionally, using bio-based alcohols/aldehydes as the feedstocks, eight carboxylic acids were successfully synthesized with yields ranging from 91.5 % to 99 %. This study presents a highly efficient electrocatalyst derived from biomass, enabling diverse bio-based carboxylic acid production with significant potential for sustainable chemical synthesis.

Abstract In this study, a sulfur–phosphorus co‐doped nanocarbon (SPC) catalyst was synthesized using a straightforward one‐step colloidal carbonization method and demonstrated high performance in the metal‐free direct oxidation of alcohols to aldehydes. This metal‐free SPC catalyst showed exceptional efficiency, achieving a conversion rate of 90% for benzyl alcohol and a selectivity of 94% toward benzaldehyde within only 1 h at 130°C. Moreover, it displays exceptional cycle stability and a high turnover frequency (17.1 × 10−3 mol g−1 h−1). Theoretical analysis suggested that the catalyst's superior performance is attributed to the presence of unsaturated edge defects and S−P− moieties, which increase the density of states at the Fermi level, lower the band gap energy, and promote electron localization. Additionally, the doping introduces cooperative co‐active S−P−C sites, facilitating a synergistic multisite catalytic effect that lowers the energy barriers. These findings represent a significant advancement in the field of metal‐free direct alcohol oxidation.

Effective underwater wireless communications (UWCs) are essential for a variety of military and civil applications, such as submarine communication and discovery of new natural resources in the underwater environment [...]

Abstract Background In China, data on the prevalence and characteristics of comorbid stroke and traumatic brain injury (TBI) in real-world populations are still lacking but of paramount importance for the evidence-based prevention and control of the comorbidity of the two diseases. This study aimed to investigate the prevalence and characteristics of comorbid stroke and TBI in a real-world population. Methods In 2013, a nationally representative, door-to-door survey on stroke and TBI using a complex, multistage, probability sampling design was conducted among approximately 600,000 participants from 155 urban and rural areas in China (Ethic ID: KY2013-006-01). The weighted prevalence of comorbid stroke and TBI was estimated using individuals’ final weight. A Poisson regression analysis was used to compare the rate ratio of the comorbidity prevalence among different subgroups of the population, including age, sex, place of residence, and geographic location subgroups. For analyses of associations between the comorbidities and predictors of interest, all other variables were adjusted for in a multinomial logistic regression model. Results Among the 596,536 people, 219 patients with comorbid stroke and TBI were identified. The point prevalence of comorbid stroke and TBI weighted to the China 2010 census population was 29.30 (95% CI: 22.69–37.84) per 100,000 population in China. The adjusted prevalence of post-TBI stroke in patients with previous TBI was significantly higher than that of post-stroke TBI in patients with previous stroke (6021.3 vs. 811.1 per 100,000 people; rate ratio: 11.001; 95% CI: 8.069–14.998). Patients with nonconcussion had significantly higher rates of both pre-stroke TBI (odds ratio: 4.694; 95% CI: 3.296–6.687) and post-stroke TBI (odds ratio: 6.735; 95% CI: 3.719–12.194) than patients with concussion. Compared to patients with ischaemic stroke, patients with subarachnoid haemorrhage (odds ratio: 2.044; 95% CI: 1.097–3.809) and intracerebral haemorrhage (odds ratio: 1.903; 95% CI: 1.296–2.795) had significantly higher rates of post-TBI stroke. Conclusions The high prevalence of stroke among TBI patients is becoming a new public health issue. TBI patients, especially those with nonconcussion TBI, are more likely to develop comorbid stroke and TBI than stroke patients, especially ischaemic stroke patients.

In recent years, the transformer architecture has demonstrated exceptional feature extraction capabilities in the field of computer vision (CV). Building on this, our paper aims to fully exploit the potential of the attention in transformers and apply it to the task of hyperspectral unmixing (HU). We propose the Spatial Spectral Collaborative Attention Network (SSCA-Net) model. We obtain spectral information with continuous spatial attributes from HSIs in advance, and input it into SSCA-Net together with HSIs. The improved self-cross attention can collaboratively extract spatial-spectral domain information of HSIs, thereby obtaining more accurate abundance scores. In addition, we conduct ablation experiments to investigate the influence of attention with various configurations on the performance of the unmixing process. The performance of the proposed model is evaluated on three real-world datasets: Samson, Jasper, and Houston, and compared with the performance of FCLSU, GLMM, DAEU, CNNAEU, CyCU, and DHTN algorithms.

In this paper, the Star-Transformer model is improved to obtain more accurate direction of arrivals (DOA) estimation of underwater sonar uniform linear array (ULA) under low signal-to-noise ratio (SNR) conditions. The ideal real covariance matrix is divided into three channels: real part channel, imaginary part channel, and phase channel to obtain more input features. In training, the real covariance matrix is used under different SNRs. In testing, the covariance matrix of samples in the real environment is used as input. The on-grid form is used to estimate the DOA of multiple signal sources, which is modelled as a multilabel classification problem. The results show that the model can be effective and can still have a good DOA estimation performance under the conditions of trained and untrained SNRs, different snapshots, signal power mismatch, different separation angles, signal correlation, and so on. It shows that the model has excellent robustness.

In recent years, with the continuous development and popularization of unmanned aerial vehicle (UAVs) technology, the surge in the number of UAVs has led to an increasingly serious problem of illegal flights. Traditional acoustic-based UAV localization techniques have limited ability to extract short-time and long-time signal features, and have poor localization performance in low signal-to-noise ratio environments. For this reason, in this paper, we propose a deep learning-based UAV localization technique in low signal-to-noise ratio environments. Specifically, on the one hand, we propose a multiple signal classification (MUSIC) pseudo-spectral normalized mean processing technique to improve the direction of arrival (DOA) performance of a traditional broadband MUSIC algorithm. On the other hand, we design a DOA estimation algorithm for UAV sound sources based on a time delay estimation neural network, which solves the problem of limited DOA resolution and the poor performance of traditional time delay estimation algorithms under low signal-to-noise ratio conditions. We verify the feasibility of the proposed method through simulation experiments and experiments in real scenarios. The experimental results show that our proposed method can locate the approximate flight path of a UAV within 20 m in a real scenario with a signal-to-noise ratio of −8 dB.

Currently, several studies have demonstrated that cold stress can cause the accumulation of reactive oxygen species (ROS) in fruit. However, little is known about the roles of gibberellic acid (GA3) on the antioxidant system in the mitochondria of fruit. To explore the molecular basis of GA3 interference with the chilling tolerance of fruit, ‘Jinqiuhongmi’ peach fruit was treated with 0.1 mmol L−1 GA3 after harvest. Exogenous GA3 treatment relieved the chilling injury of postharvest peach fruit with a lower cold injury index and higher antioxidant level. In addition, GA3 delayed the senescence of peaches by reducing the firmness, respiratory action, and ethylene production. The antioxidant enzyme activities were elevated, including superoxide dismutase (SOD) and catalase (CAT). Moreover, GA3-treated peaches exhibited lower hydrogen peroxide (H2O2) and malondialdehyde (MDA) in comparison with the control. These results showed that the application of 0.1 mmol L−1 of GA3 enhanced the chilling resistance of peach fruit by regulating the antioxidant system.

As an essential nutrient, lutein (LUT) has the ability to aid in the prevention of eye diseases, cardiovascular diseases, and cancer. However, the application of LUT is largely restricted by its poor solubility and susceptibility to oxidative degradation. Thus, in this study, LUT-loaded nanogel (OVM-COS-LUT) was prepared by a self-assembly of ovomucin (OVM) and chitosan oligosaccharide (COS) to enhance the effective protection and bioavailability of LUT. The nanogel had excellent dispersion (PDI = 0.25) and an 89.96% LUT encapsulation rate. XRD crystal structure analysis confirmed that the encapsulated LUT maintained an amorphous morphology. In addition, the nanogel showed satisfactory stability with pH levels ranging from 2 to 9 and high ionic strengths (>100 mM). Even under long-term storage, the nanogel maintained an optimistic stabilization and protection capacity; its effective retention rates could reach 96.54%. In vitro, digestion simulation showed that the bioaccessibility and sustained release of OVM-COS-LUT nanogel was superior to that of free LUT. The nanogel provided significant antioxidant activity, and no significant harmful effects were detected in cytotoxicity analyses at higher concentrations. In summary, OVM-COS-LUT can be utilized as a potential safe oral and functional carrier for encapsulating LUT.

The world population is rapidly increasing, which in turn increases food needs. On the other hand, food production is the main cause of water withdrawal. Precision agriculture, based on the Internet of Underground Things (IoUT), has recently been proposed to reduce water withdrawals. IoUT comprises sensors and communication devices that are partially or fully submerged beneath the ground surface. However, the current technology used in IoUT is challenged by inefficient spectral and energy efficiency. This paper proposes a deep learning (DL)-based index modulation technique to increase spectral efficiency without raising the system’s bit error rate. The article decreases the peak-to-average power ratio (PAPR) and enhances energy efficiency by employing the X-transform time-domain synchronous index modulation (TDS-IM) orthogonal frequency division multiplexing (OFDM) in underground communication. Unlike the radio frequency channel, the underground channel is environment-dependent. Therefore, in this paper, we propose a DL receiver detector to eliminate such environmental dependency and simplify the system’s complexity. This proposal can establish new identification parameters, achieving accurate estimation of the modulated symbols even in harsh communication channels. The results of the simulation indicate the superior performance of the proposed scheme in terms of both spectral and energy efficiency compared to the benchmarks, as well as its ability to improve the system’s bit error rate.

Abstract Based on Chebyshev polynomials, the two‐way slant range is first approximated by the fourth‐order polynomial approximation. With the series reversion method, the point target reference spectrum (PTRS) which is further used for the development of imaging algorithm in this letter is obtained. The presented method processes the echoed signal corresponding to each transmitter/receiver pair. And then a high‐resolution synthetic aperture sonar (SAS) image is obtained by coherently superposing all coarse‐resolution images. Comparing the peak sidelobe ratio (PSLR) and integration sidelobe ratio (ISLR), the performance of the presented method is superior to that of the traditional method. Simulations are further validated by the presented method.

Abstract In this Letter, the authors present a novel imaging algorithm for multireceiver synthetic aperture sonar (SAS). The Loffeld's bistatic formula (LBF) including quasi‐monostatic (QM) and multireceiver deformation (MD) phases is first reformulated into range‐variant and range‐invariant phases. After compensating the range‐invariant phase, the subsequent steps are the extended Stolt mapping for each bistatic SAS. Based on simulations, the presented method can also provide high‐resolution result which is similar to that of back projection (BP) algorithm.

An increasing number of scholars are researching underwater acoustic sensor networks (UASNs), including the physical layer, the protocols of the routing layer, the MAC layer, and the cross-layer. In UASNs, the ultimate goal is to transmit data from the seabed to the surface, and a well-performed routing protocol can effectively achieve this goal. However, the nodes in the network are prone to drift, and the topology is easily changed because of the movement caused by ocean currents, resulting in a routing void. The data cannot be effectively aggregated to the sink terminal on the surface. Thus, it is extremely important to determine how to find an alternative node as a relay node after node drift and how to rebuild a reliable transmission path. Although many relay routing protocols have been proposed to avoid routing voids, few of them consider the relay node selection between the outage probability and the ocean current model. Therefore, we propose an ocean current motion model based routing (OCMR) protocol to avoid the routing void in UASNs. We predicted underwater node movement based on the ocean current motion model and designed a protection radius to construct a limited search coverage based on the optimal outage probability; then, the node with the best fitness value within the protection radius was selected as the alternative relay node using an improved WOA. In OCMR, the problem of the routing void caused by ocean current motion is effectively suppressed. The simulation results show that, compared with VBF, HH-VBF, and QELAR, the proposed OCMR platform performs well in terms of the PDR (packet delivery ratio), average end-to-end delay, and average energy consumption.

This paper proposes a double-layer structure RRT* algorithm based on objective bias called DOB-RRT*. The algorithm adopts an initial path with an online optimization structure for motion planning. The first layer of RRT* introduces a feedback-based objective bias strategy with segment forward pruning processing to quickly obtain a smooth initial path. The second layer of RRT* uses the heuristics of the initial tree structure to optimize the path by using reverse maintenance strategies. Compared with conventional RRT and RRT* algorithms, the proposed algorithm can obtain the initial path with high quality, and it can quickly converge to the progressive optimal path during the optimization process. The performance of the proposed algorithm is effectively evaluated and tested in real experiments on an actual wheeled robotic vehicle running ROS Kinetic in a real environment.

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.

Deep learning techniques have made certain breakthroughs in direction-of-arrival (DOA) estimation in recent years. However, most of the current deep-learning-based DOA estimation methods view the direction finding problem as a grid-based multi-label classification task and require multiple samplings with a uniform linear array (ULA), which leads to grid mismatch issues and difficulty in ensuring accurate DOA estimation with insufficient sampling and in underdetermined scenarios. In order to solve these challenges, we propose a new DOA estimation method based on a deep convolutional generative adversarial network (DCGAN) with a coprime array. By employing virtual interpolation, the difference co-array derived from the coprime array is extended to a virtual ULA with more degrees of freedom (DOFs). Then, combining with the Hermitian and Toeplitz prior knowledge, the covariance matrix is retrieved by the DCGAN. A backtracking method is employed to ensure that the reconstructed covariance matrix has a low-rank characteristic. We performed DOA estimation using the MUSIC algorithm. Simulation results demonstrate that the proposed method can not only distinguish more sources than the number of physical sensors but can also quickly and accurately solve DOA, especially with limited snapshots, which is suitable for fast estimation in mobile agent localization.

Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio.

Abstract With the Loffeld's bistatic formula, the point target reference spectrum (PTRS) of multireceiver synthetic aperture sonar (SAS) can be decomposed into the quasi monostatic term and bistatic deformation (BD) term. The former term is analogous to the monostatic SAS PTRS while the latter one describes the bistatic phase introduced by the bistatic displacement between the transmitter and receiver. The multireceiver SAS echo signal can be transformed into monostatic SAS equivalent data after correcting the BD term. Based on the range sub‐block processing method, this data transformation is conducted by using the preprocessing step. Then, fast imaging processors based on monostatic SAS system can be directly exploited to process the monostatic SAS equivalent data. In this paper, the authors mainly focus on the application of non‐linear chirp scaling algorithm. At last, the simulations and real data are used to validate the presented method. The processing results show that the imaging performance of presented method is nearly close to that of traditional method. Besides, the imaging efficiency is highly improved compared to the phase centre approximation (PCA) method.

Abstract The shallow water noise shows obvious impulsive property, which greatly degrades the direction of arrival (DOA) performance due to the conventional design concept based on the Gaussian assumption. In this paper, DOA estimator in presence of impulsive noise utilising variational Bayesian inference is proposed. The Middleton's Class A noise model is considered as a typical underwater noise model to analyse the performance of DOA estimation. The DOA estimation problem is modelled as the sparse signal recovery problem, and the hierarchical Bayesian learning framework is formulated by considering the common sparsity of signal and the element‐wise sparsity of the impulsive noise. The variational Bayesian inference realises the posterior estimation of signal and impulsive noise components. To mitigate the basis mismatches, the root sparse Bayesian learning method is applied to refine the steering vectors. Simulations verify the advantages of the proposed DOA method in terms of spatial resolution, root mean square error, accuracy, and robustness compared with the state‐of‐the‐art benchmarks in the presence of Middleton's Class A noise.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) has great potential for the implementation of high spectral-efficiency underwater acoustic (UWA) communications. However, general receivers consisting of the optimal maximum likelihood detection suffer from high computational load, which prohibits real-time data transmissions in underwater scenarios. In this paper, we propose a detection based on a vector approximate message passing (VAMP) algorithm for UWA OFDM-IM communications. Firstly, a VAMP framework with a non-loopy factor graph for index detection is formulated. Secondly, by utilizing the sparsity inherently existing in OFDM-IM symbols, a novel shrinkage function is derived based on the minimum mean square error criterion, which guarantees better posterior estimation. To reduce the errors from estimated non-existing indices, one trick is utilized to search the elements from the look-up table with the minimal Euclidean distance for the replacement of erroneously estimated indices. Experiments verify the advantages of the proposed detector in terms of low complexity, robustness and effectiveness compared with the state-of-art benchmarks.

Fermentation is an important means of fish preservation and processing. Fermentation gives fish unique flavor and texture, and microorganisms play an important role in the process of fermentation. This paper introduces the composition of microorganisms in fermented fish and its role in inhibiting miscellaneous bacteria, substance degradation, flavor formation and biogenic amine accumulation, in order to help understand the dominant microorganisms and their fermentation characteristics, prepare industrial starter and improve the yield and quality of fermented fish. In addition, the further study of fermented fish is prospected.

Abstract Traditional chirp scaling algorithm is often developed based on the quadratic approximation of point target reference spectrum, which neglects the higher‐order coupling between range and azimuth dimensions. Besides, the scaling operation is conducted based on quadratic term of reference range, and the residual quadratic error increases with target range far away from reference range. The analysis of phase error shows that both errors are noticeable under the case of wide‐bandwidth signal, and the imaging performance would be seriously distorted. To solve this problem, the higher‐order coupling and quadratic error are compensated by exploiting sub‐block processing method in the range dimension when the chirp scaling algorithm is exploited to reconstruct targets. Compared to traditional methods, the presented method significantly improves the focussing performance based on the simulation results. The processing of real data further shows that the performance of presented method is superior to that of traditional method.

Objectives To report the prevalence and distribution of lacunar stroke in different regions of China, as well as the demographical characteristics of symptomatic and asymptomatic lacunar stroke.Design Cross-sectional study.Setting Data were derived from NESS-China Study that was conducted in 157 sites covering all 31 provinces, including 64 urban and 93 rural areas in mainland China between 1 September 2013 and 31 December 2013. Lacunar stroke was defined as being previously diagnosed according to the participants’ medical history. Patients were further divided into symptomatic or asymptomatic groups, depending on whether they were initially diagnosed with neurological symptoms.Participants 458 833 participants aged ≥20 years were enrolled in this study.Results A total of 7520 participants (1.63%) were diagnosed with lacunar stroke. The peak rate of diagnosis was between the ages of 70 and 79 years in both men and women. Geographically, the age-standardised and sex-standardised prevalence was highest in Northeast China (2495.3/100 000 persons) and lowest in Southeast China (599.7/100 000 persons), showing a geographical disparity. Over 90% of patients with lacunar stroke were diagnosed in secondary or tertiary hospitals. Patients with symptomatic lacunar stroke had significantly different demographic characteristics in age, sex and geographical regions compared with those who were asymptomatic.Conclusions In this study, the prevalence and distribution of lacunar stroke were reported at population level across China. Special attention and prevention should be given to the age, sex and geographical groups that are vulnerable to lacunar stroke.

The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver.

The ocean, covering 71% of the Earth’s surface, is integral to human life [...]

In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FBMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.

Abstract Background and purpose Epidemiological data on primary brain tumours (PBTs) are lacking due to the difficulty in case ascertainment among the population. Thus, we aimed to estimate mortality due to PBTs in China nationwide and the detection rate in people with suspected symptoms. Methods A multistage, complex sampling survey regarding mortality due to PBTs in Chinese individuals was carried out by reviewing all causes of death within a year. The detection rates in people with suspected symptoms were estimated based on PBT symptom screening and neurologist reviews and compared between groups by logistic regression analysis. Results Weighted mortality due to PBT was 1.6 (0.8–3.3) per 100,000 population in Chinese individuals, 1.8 (0.7–4.6) per 100,000 population in men, and 1.5 (0.5–4.5) per 100,000 population in women. Among 14,990 people with suspected symptoms, the PBT detection rate was 306.9 (95% CI 224.7–409.3) per 100,000 population in the total population, 233.0 (95% CI 135.7–373.1) per 100,000 population in men, and 376.9 (95% CI 252.4–546.3) per 100,000 population in women. People with an unsteady gait (OR 2.46; 95% CI 1.09–5.51; P=0.029), visual anomalies (3.84; 1.88–7.85; P

To use monostatic based imaging algorithms for multireceiver synthetic aperture sonar, the monostatic conversion is often carried out based on phase centre approximation, which is widely exploited by multireceiver SAS systems. This article presents a novel aspect for dealing with the multireceiver SAS imagery, which still depends on the idea of monostatic conversion. The approach in this article is based on Loffeld's bistatic formula that consists of two important terms, i.e., quasi monostatic and bistatic deformation terms. Our basic idea is to preprocess the bistatic deformation term and then incorporate the quasi monostatic term into an analogous monostatic spectrum. With this new spectrum, traditional imaging algorithms designed for monostatic synthetic aperture sonar can be easily exploited. In this article, we show that Loffeld's bistatic formula can be reduced to the same formula as spectrum based on phase centre approximation when certain conditions are met. Based on our error analysis, the maximum error magnitude of PCA method is about 1 rad, which would noticeably affect the SAS imagery. Fortunately, the error magnitude of presented method can be always kept within ${{\rm{\pi }} \mathord{/ {\vphantom {{\rm{\pi }} 4}} \kern-\nulldelimiterspace} 4}$. It means that Loffeld's bistatic formula provides a more accurate approximation of the spectrum compared to that based on phase centre approximation. After that, this article develops a new imaging scheme and presents imaging results. Based on quantitative comparisons, the presented method well focuses multireceiver SAS data, and it provides better image compared to phase centre approximation method.

Abstract An in‐depth understanding of the electronic structures of catalytically active centers and their surrounding vicinity is key to clarifying the structure–activity relationship, and thus enabling the design and development of novel metal‐free carbon‐based materials with desired catalytic performance. In this study, boron atoms are introduced into phosphorus‐doped nanoporous carbon via an efficient strategy, so that the resulting material delivers better catalytic performance. The doped B atoms alter the electronic structures of active sites and cause the adjacent C atoms to act as additional active sites that catalyze the reaction. The B/P co‐doped nanoporous carbon shows remarkable catalytic performance for benzyl alcohol oxidation, achieving high yield (over 91% within 2 h) and selectivity (95%), as well as low activation energy (32.2 kJ mol−1). Moreover, both the conversion and selectivity remain above 90% after five reaction cycles. Density functional theory calculations indicate that the introduction of B to P‐doped nanoporous carbon significantly increases the electron density at the Fermi level and that the oxidation of benzyl alcohol occurs via a different reaction pathway with a very low energy barrier. These findings provide important insights into the relationship between catalytic performance and electronic structure for the design of dual‐doped metal‐free carbon catalysts.

In wireless communication systems, Rice factor ratio (RFR) defined as K/(1 + K) is a key parameter not only to evaluate the quality of communication channel since it can reveal the severity of the small-scale fading, but also to be employed as a priori information for estimation of other parameters such as frequency. Consequently, its estimation is important for a variety of wireless application scenarios. In this paper, we propose an estimation algorithm on the RFR for the received signals that are disturbed by the Rician doubly selective fading channels and additive noise. During the estimation periods, we initially utilize the known signals to multiply the received signals. Second-order and fourth-order statistics are then employed to further deal with the processed signals mentioned above, which disposes of influence of some unnecessary parameters, e.g., indistinguishable multipaths, maximum Doppler shift, Doppler shift, and noise variance. Finally, a useful expression on the RFR estimation is derived for the Rician frequency selective fast fading channels by flexibly mathematical calculation. Furthermore, the presented method only uses the maximum estimation values of the second-order and fourth-order statistics defined in this paper, which can reduce the computational complexity. Importantly, the investigated scheme is robust to the signal-to-noise ratio over 0 dB and frequency offset (maximum Doppler shift and Doppler shift), and shows a slight improvement on the estimation performance with an increase of the aided data length. The performance and benefits of the proposed approach are verified and evaluated through computer simulations.

An audio signal detection system based on laser speckle and multi-channel data fusion is presented. A linear CMOS array is used as the detector, which owns a fast line rate and suitable sensing size. The signals from the pixels are selected and fused to enhance the reconstructed signal. The reconstructed audio signals are evaluated with a segmental SNR (SegSNR) algorithm. The experimental results of three categories of audio sources (single voice audio, conversation and music) show that data fusion can improve the SegSNR scores. Especially, direct phase-error based filtering (pbf) fusion gives a nearly 3.0 dB increase and obtains another 1.0 dB increase with the combination of single channel process. The experimental results show that the fusion algorithms are not sensitive to audio types and the performance of multi-channel data fusion is not weakened with the increase of measuring distance. This feature has potential applications in remote sensing. The intelligibility of the fused audio signals is evaluated with normalized subband envelope correlation (NSEC) algorithm and the evaluation results shows that fusion can also enhance the intelligibility of the recovered signal.

Background and PurposeThe epidemiological characteristics of traumatic spinal cord injury (TSCI) in China are unclear. Thus, we aimed to study prevalence, incidence, and external causes of TSCI in China nationwide.MethodsIn 2013, we conducted a nationally representative, door-to-door epidemiological survey on TSCI in China using a complex, multistage, probability sampling design.ResultsIn China, the point prevalence of TSCI standardized to the China census population 2010 was 569.7 (95% CI: 514.2–630.4) per 1,000,000 in the population, 753.6 (95% CI: 663.3–854.3) per 1,000,000 among men, and 387.7 (95% CI: 324.8–461.1) per 1,000,000 among women. The incidence of TSCI standardized to the China census population 2010 was 49.8 (95% CI: 34.4–70.7) per 1,000,000 per year in the population, 63.2 (95% CI: 38.9–98.5) per 1,000,000 among men, and 36.9 (95% CI: 19.5–65.9) per 1,000,000 among women. Among the 415 TSCI events in 394 prevalent cases, the top three injury causes were falls (55.2%), motor vehicle collisions (MVCs) (26.5%), and strike injuries (10.1%), while other injury causes including gunshot and explosion accounted for 8.2%. Among the 394 prevalent cases, the mean age of patients at the time of injury was 43.7 ± 17.1 years; the male-to-female ratio was 1.86:1.ConclusionIt is estimated that there are 759,302 prevalent patients with TSCI in total and 66,374 new TSCI cases annually in China. Falls and MVCs are still 2 major external causes for TSCI in China.

At present, marine autonomous surface ships (MASSs) play a huge role in marine shipping, surveying and mapping, safeguarding the rights and interests of marine space, and other maritime tasks. The cooperative operation of a swarm of MASSs can extend the scope of operation of the MASSs, and thus allow them to carry out more complex tasks. Path tracking is an important problem for the control of a swarm of MASSs. In this paper, the control of underactuated MASSs is decoupled, to control the heading and speed, respectively. First of all, in the path tracking, the improved arc LOS guidance law is introduced, and the heading torque controller is designed, so that the MASS can track the reference path efficiently and accurately. Then the single-path guided path tracking without formation of the swarm of MASSs is studied, the reference path of the swarm center tracking is defined, and the heave thrust controller of the swarm of MASSs is designed based on consensus theory, so that the surge velocity of the MASS can tend towards consistentcy, and finally converge to the desired speed. Finally, the effectiveness of the proposed control strategy is verified by two groups of simulation experiments.

It is essential to promote the intelligence and autonomy of Maritime Autonomous Surface Ships (MASSs). This study proposed an automatic collision-avoidance method based on an improved Artificial Potential Field (APF) with the formation of MASSs (F-MASSs). Firstly, the navigation environment model was constructed by the S-57 Electronic Navigation Chart (ENC) data in Tianjin Port. The Formation Ship State Parameter (FSSP) definition was proposed for the port environment under multiple constraints that considered the navigation conditions of the MASSs. The formation pattern transformation was settled by changing the formation ship state parameter. Considering the constraints of an ‘unmanned–manned’ encounter situation, the static obstacles, and the design of the channel area improved artificial potential method for the formation. Finally, the simulation experiment was carried out in the sea near Tianjin Port to verify the effectiveness of the algorithm under multiple constraints. The results indicate that the method can satisfy the integrated operation of collision avoidance and path following in a constrained environment, and it can support the application of merchant F-MASS autonomous navigation in the future.

As a communication system for maritime monitoring and navigation, automatic identification system (AIS) has been employed for many years, but its service range is limited in 40 nautical miles. Fortunately, an updated service navigation system named as space-based AIS has been presented in recent years, which can provide global coverage service. However, it encounters the frequency offset issue caused by the relative satellites/ships motion. To cope with it, in this paper, a novel Doppler shift estimation algorithm is proposed for space-based AIS signals over satellite-to-ship wireless fading channels and non-Gaussian noise. In the estimation stage, the signals including the Doppler shift information are first separated from the received ones, but they are also disturbed by the noise. Then, Doppler shift is extracted from the above-detached signals via the higher moments which cancel some excrescent parameters by using several flexible algebra operations, and its estimated expression is presented. Finally, the numerical simulations are carried out to verify the performance of the proposed method, and simulation results demonstrate the benefits of the suggested scheme.

The recent growth in the maritime industry stemming to increase in global commerce as well as searching for hydrocarbons at offshore locations, the high data rate and cost affordable maritime communication is acquired. A mesh network is envisaged for maritime communications because of its expanded coverage, self-healing, and high-capacity. With the development of multi-radio technology, the frequency interference can be decreased sharply with proper channel assignment. The static channel assignment optimization is often applied to decrease the wireless frequency interference which is known as an NP-hard problem. In this paper, we focus on the static channel assignment issue and propose a heuristic algorithm to solve the optimization problem. The problem is addressed by assigning channels to communication links to minimize the interference from overall network. A modified particle swarm optimization (PSO) algorithm is proposed to optimize the problem, and a new merging solution is adopted to reassign channels for nodes, which violate the radio constraints. Multi-radio simulation is performed in NS-3 to validate the effectiveness of the proposed channel assignment algorithm. The results show that the algorithm is able to find an optimized assignment with fewer iterations than the previous work and improve network performance.

Whales communicate using whistle vocalizations that are essentially underwater acoustic frequency-modulated tones. Inevitable environmental noise decreases recognition accuracy of these sounds during wide range detection. In this paper, we propose a robust time - frequency analysis method that combines resonance sparse signal decomposition (RSSD) and spectrogram ridge extraction. We apply RSSD to extract whistle components from the raw signal, and then we segment the ridge regions of the whistle spectrograms. By applying a partial derivative method, we extract the whistle spectrogram ridge representing an accurate trace of the whistle vocalization. From these results, we extract ridge features and use an SVM or a random forest to identify the whale species. We evaluated our method using experiments with samples for four whale species. Compared with direct ridge extraction directly without RSSD, our proposed method achieved better extraction of frequency characteristics of the vocalizations. Our proposed method achieved an accuracy rate of over 98% for sounds from four species when using five training samples.

Non-orthogonal multiple access (NOMA) is considered a promising multiple access technique for fifth generation (5G) mobile networks and tactical internet due to its high spectral efficiency. Thanks to the high spectral efficiency of NOMA, it can be a strong candidate suitable for the limited channel bandwidth of underwater acoustic communication. The NOMA transmitter is employing superposition coding (SC). The NOMA receiver is based on the successive interference cancellation (SIC) technique. The multicarrier NOMA adopts orthogonal frequency division multiplexing (OFDM) as a multicarrier modulation (MCM) technique; however, conventional cyclic prefix OFDM (CP-OFDM) and zero padding (ZP-OFDM) have inefficient spectral efficiency. Thanks to efficient synchronization and high energy-spectral efficiency of the time-division synchronization OFDM (TDS-OFDM), it is a significant attractive candidate for underwater multicarrier communication. However, wasting the power transmission of long guard intervals in the battery-based underwater communication is represented as one of the TDS-OFDM main drawbacks. Harvesting energy and improving the energy efficiency of acoustic-based TDS-OFDM-NOMA represent high achievement goal battery recharging challenges due to the ocean environment. This paper proposes time switching simultaneous wireless information and power transfer (TS-SWIPT) to harvest the energy of transmitted power over the guard interval in the TDS-OFDM-NOMA scheme. The proposed energy harvested scheme harvests the energy from the wasted power in the long guard interval and improves the energy efficiency of the TDS-OFDM multicarrier scheme. This study demonstrates the superiority of the proposed TDS-OFDM-NOMA over the underwater acoustic channel by revealing high energy efficiency, high spectral efficiency, better bit error rate performance, and high system data throughput.

Suanyu is a traditional natural fermented fish product from Southwest China that contains very complex microflora. The main purpose of this study was to explore the fungal community and its relationship with the physicochemical properties of Suanyu. The fungal community structure of Suanyu from the main provinces (Guizhou and Hunan) was studied via high-throughput sequencing. The correlation between dominant fungi and physicochemical characteristics was analyzed via Spearman’s correlation coefficient. The results showed that the pH value, total volatile base nitrogen content, and thiobarbituric acid reactive substance content ranges of Suanyu samples were 4.30–5.50, 17.11–94.70 mg/100 g, and 0.61 to 3.62 mg/kg, respectively. The average contents of total volatile base nitrogen, thiobarbituric acid reactive substance, and total BAs in Suanyu from Guizhou were lower than those from Hunan. The main BAs were phenethylamine, putrescine, cadaverine, histamine, and tyramine. Ascomycota was the dominant fungal phylum, and Kodamaea, Debaryomyces, Wallemia, Zygosaccharomyces, and unclassified Dipodascaceae were the dominant fungal genera in different samples. Moreover, high abundance levels of Kodamaea and Zygosaccharomyces were found in Suanyu from Guizhou. According to the correlation analysis, Kodamaea and Zygosaccharomyces were negatively correlated with TBARS (R2 = −0.43, −0.51) and TVBN (R2 = −0.37, −0.29), and unclassified Dipodascaceae was significant negatively correlated with tyramine (R2 = −0.56). This study expands the understanding of the fungal community and the fermentation characteristics of the dominant fungi in Suanyu.

Aim Cerebrovascular disease is the leading cause of death and disability in China, causing a huge burden among patients and their families. Hence, stroke prevention is critical, especially in the high-risk population. Here, we present the evidence-based guideline suitable for the Chinese population.Methods Literature search of PubMed and Cochrane library (from January 1964 to June 2019) was done. After thorough discussion among the writing group members, recommendations were listed and summarised. This guideline was reviewed and discussed by the fellow writing committees of the Chinese Stroke Association’s Stroke.Results This evidence-based guideline was written in three parts: controlling the risk factors of stroke, utilisation of antiplatelet agents and assessing the risks of first-ever stroke. All recommendations were listed along with the recommending classes and levels of evidence.Conclusions This guideline provides recommendations for primary prevention of cerebrovascular disease among high-risk population in China. Controlling related risk factors, appropriately using antiplatelet agents, assessing the risk of developing first-ever stroke should help reduce the rate of cerebrovascular disease in China.

Objectives To investigate the rates and influencing factors of transient ischaemic attack (TIA) inpatient admissions and outpatient visits in China.Setting A door-to-door survey of 178 059 families from 155 urban and rural areas in 31 provinces in China, 2013.Participants Total 596 536 people were assessed in the annual rate analysis, and 829 TIA patients were assessed in the influencing factor analysis.Main outcome measures The weighted annual rates of TIA inpatient admissions and outpatient visits and the factors influencing inpatient admissions and outpatient visits for TIA patients.Results The weighted annual inpatient admission rate per TIA patient was 25.8 (95% CI: 18.4 to 36.2) per 100 000 in the population, whereas the weighted annual inpatient admission rate for patients with TIAs was 32.5 (95% CI: 23.3 to 38.9) per 100 000 in the population. The weighted annual outpatient visit rate per TIA patient was 34.4 (95% CI: 26.2 to 45.1) per 100 000 in the population, whereas the weighted annual outpatient visit rate for patients with TIAs was 149.6 (95% CI: 127.0 to 165.5) per 100 000. The inpatient rate was higher for men than for women (OR: 2.24; 95% CI: 1.40 to 3.59; p=0.001), for TIA patients with stroke than for patients with isolated TIAs (2.93; 2.01 to 4.25; p

Fourier ptychographic microscopy (FPM) is a potential imaging technique, which is used to achieve wide field-of-view (FOV), high-resolution and quantitative phase information. The LED array is used to irradiate the samples from different angles to obtain the corresponding low-resolution intensity images. However, the performance of reconstruction still suffers from noise and image data redundancy, which needs to be considered. In this paper, we present a novel Fourier ptychographic microscopy imaging reconstruction method based on a deep multi-feature transfer network, which can achieve good anti-noise performance and realize high-resolution reconstruction with reduced image data. First, in this paper, the image features are deeply extracted through transfer learning ResNet50, Xception and DenseNet121 networks, and utilize the complementarity of deep multiple features and adopt cascaded feature fusion strategy for channel merging to improve the quality of image reconstruction; then the pre-upsampling is used to reconstruct the network to improve the texture details of the high-resolution reconstructed image. We validate the performance of the reported method via both simulation and experiment. The model has good robustness to noise and blurred images. Better reconstruction results are obtained under the conditions of short time and low resolution. We hope that the end-to-end mapping method of neural network can provide a neural-network perspective to solve the FPM reconstruction.

Time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) has been attractive due to its fast synchronization and efficient spectral efficiency over conventional cyclic prefix orthogonal frequency division multiplexing (OFDM) and zero padding OFDM. However, inter-block interference (IBI) affects its performance because of delay over multipath channels. To evade IBI, dual pseudo-random noise (DPN) sequences have been introduced that causes to reduce spectral and energy efficiency. But, DPN is unprepared for underwater acoustic (UWA) communication because of battery-based nature and limited bandwidth. To overcome these issues, this paper exploits compressive sensing theory algorithm for obtaining the time-varying channel state information by utilizing sparse property of UWA channels. In this paper, the IBI free region is utilized to estimate accurate UWA channel impulse response and mitigate its interference. Look-ahead backtracking orthogonal matching pursuit-based sparse channel estimation technique is proposed for underwater TDS-OFDM in a real sparse time-varying multipath channel (channel taps are randomly distributed). Furthermore, Doppler-shift of UWA channel is estimated and compensated by PN sequence in time domain. The performance of the proposed technique is evaluated and demonstrated through numerical computation of bit error rate (BER) and mean square error (MSE) using Monte Carlo iterations. Simulation analysis confirms the superiority of the proposed scheme not only in terms of BER and MSE over the conventional ones but also achieve high energy and spectral efficiency.

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial’s communication, using multi-input multi-output (MIMO) technologies to overcome the bandwidth limitation problem is highly restricted in underwater acoustic communication by high inter-channel interference (ICI) and the channel multipath effect. Recently, the spatial modulation techniques (SMTs) have been presented as an alternative solution to overcome these issues by transmitting more data bits using the spatial index of antennas transmission. This paper proposes a new scheme of SMT called spread-spectrum fully generalized spatial modulation (SS-FGSM) carrying the information bits not only using the constellated data symbols and index of active antennas as in conventional SMTs, but also transmitting the information bits by using the index of predefined spreading codes. Consequently, most of the information bits are transmitted in the index of the transmitter antenna, and the index of spreading codes. In the proposed scheme, only a few information bits are transmitted physically. By this way, consumed power transmission can be reduced, and we can save the energy of underwater nodes, as well as enhancing the channel utilization. To relax the receiver computational complexity, a low complexity deep learning (DL) detector is proposed for the SS-FGSM scheme as the first attempt in the underwater SMTs-based communication. The simulation results show that the proposed deep learning detector-based SS-FGSM (DLSS-FGSM), compared to the conventional SMTs, can significantly improve the system data rate, average bit error rate, energy efficiency, and receiver’s computational complexity.

Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater environment. This paper proposes a TDOA-based localization algorithm for an underwater acoustic sensor network using the maximum-likelihood (ML) ratio criterion. To relax the complexity of the proposed localization complexity, we construct an auxiliary function, and use the majorization-minimization (MM) algorithm to solve it. The proposed localization algorithm proposed in this paper is called a T-MM algorithm. T-MM is applying the MM algorithm to the TDOA acoustic-localization technique. As the MM algorithm iterations are sensitive to the initial points, a gradient-based initial point algorithm is used to set the initial points of the T-MM scheme. The proposed T-MM localization scheme is evaluated based on squared position error bound (SPEB), and through calculation, we get the SPEB expression by the equivalent Fisher information matrix (EFIM). The simulation results show how the proposed T-MM algorithm has better performance and outperforms the state-of-the-art localization algorithms in terms of accuracy and computation complexity even under a high presence of underwater noise.

Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN signals. Firstly, the IVMD method is employed to decompose the SRN signals into a number of finite intrinsic mode functions (IMFs). The noise IMFs are then filtered out by a denoising method before PE extraction. Next, the MIC between each retained IMF and the raw SRN signal and PE of retained IMFs are calculated, respectively. After this, the norMICs are used to weigh the PE values of the retained IMFs and the sum of the weighted PE results is regarded as the classification parameter. Finally, the feature vectors are fed into the particle swarm optimization-based support vector machine multi-class classifier (PSO-SVM) to identify different types of SRN samples. The experimental results have indicated that the classification accuracy of the proposed method is as high as 99.1667%, which is much higher than that of other currently existing methods. Hence, the method proposed in this paper is more suitable for feature extraction of SRN signals in practical application.

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

Background: The leading cause of death in China is stroke, a condition that also contributes heavily to the disease burden. Nontraumatic intracerebral hemorrhage (ICH) is the second most common cause of stroke. Compared to Western countries, in China the proportion of ICH is significantly higher. Standardized treatment based on evidence-based medicine can help reduce ICH's burden. In the present study we aimed to explore the agreement between the management strategies during ICH's acute phase and Class I recommendations in current international practice guidelines in Beijing (China), and to elucidate the reasons underlying any inconsistencies found.Method: We retrospectively collected in-hospital data from 1,355 ICH patients from 15 hospitals in Beijing between January and December 2012. Furthermore, a total of 75 standardized questionnaires focusing on ICH's clinical management were distributed to 15 cooperative hospitals. Each hospital randomly selected five doctors responsible for treating ICH patients to complete the questionnaires.Results: Numerous approaches were in line with Class I recommendations, as follows: upon admission, all patients underwent radiographic examination, about 93% of the survivors received health education and 84.5% of those diagnosed with hypertension were prescribed antihypertensive treatment at discharge, in-hospital antiepileptic drugs were administered to 91.8% of the patients presenting with seizures, and continuous monitoring was performed for 88% of the patients with hyperglycemia on admission. However, several aspects were inconsistent with the guidelines, as follows: only 14.2% of the patients were initially managed in the neurological intensive care unit and 22.3% of the bedridden patients received preventive treatment for deep vein thrombosis (DVT) within 48 h after onset. The questionnaire results showed that imaging examination, blood glucose monitoring, and secondary prevention of ICH were useful to more clinicians. However, the opposite occurred for the neurological intensive care unit requirement. Regarding the guidelines' recognition, no significant differences among the 3 education subgroups were observed (p > 0.05).Conclusions: Doctors have recognized most of ICH's evidence-based practice guidelines. However, there are still large gaps between the management of ICH and the evidence-based practice guidelines in Beijing (China). Retraining doctors is required, including focusing on preventing DVT providing a value from the National Institutes of Health Stroke Scale and Glasgow Coma Scalescores at the time of admission.