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Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance.

The research on how to effectively extract the instantaneous characteristic components of non-stationary signals continues to be both a research hotspot and a very challenging topic. In this paper, a new method of multi-component decomposition is proposed to decompose a signal into finite mono-component signals and extract their Instantaneous Amplitude (IA), Instantaneous Phase (IP), and Instantaneous Frequency (IF), which is called Sinusoidal Fitting Decomposition (SFD). The proposed method can ensure that the IA extracted from the given signal must be positive, the IP is monotonically increasing, and the signal synthesized by both IA and IP must be mono-componential and smooth. It transforms the decomposition process into a synthesis iterative process and does not rely on any dictionary or basis function space or carry out the sifting operation. In addition, the proposed method can describe the instantaneous-frequency-amplitude characteristics of the signal very well on the time-frequency plane. The results of numerical simulation and the qualitative analysis of the amount of calculation show that the proposed method is effective.

Orthogonal frequency division multiplexing (OFDM) is a promising solution for underwater acoustic communication (UWA); however, it requires careful handling of the challenges of large multipath and severe Doppler effects inherent in underwater acoustic communication. This paper proposes a novel feedforward backpropagated neural network (FBNN) implementation for Doppler scaling estimation using UWA cyclic-prefix (CP) OFDM communication. A two-layered input-output feedforward network is utilized with three different backpropagated training algorithm variants: Fletcher-Reeves Conjugate Gradient (CGF), Polak-Ribiére Conjugate Gradient (CGP), and Conjugate Gradient with Powell/Beale Restarts (CGB). The proposed approach calculates the Doppler scale factor by combining the neural computational power with the accuracies offered by the three training algorithms. To evaluate the effectiveness of the proposed FBNN implementation, root mean square error (RMSE) is used as a performance metric for different multipath and signal-to-noise ratio (SNR) channel conditions. The paper also presents a comparison of the proposed FBNN-based training algorithms’ performance with that of the benchmark offered by conventional methods.

Objectives This study sought to examine the factors associated with postoperative complications following the Bentall procedure by conducting a retrospective single-center cohort analysis. In addition, the study aimed to develop a nomogram model for postoperative risk prediction. Methods A total of 193 patients who underwent Bentall surgery in our hospital from May 2022 to May 2024 were included consecutively, of which 16 developed complications within 30 days after surgery. Preoperative demographic characteristics, preoperative cardiac function, intraoperative parameters, and other data were collected. Single-factor analysis was used to screen potential-related factors ( P  < 0.10), and modeling variables were determined based on clinical evidence and the principle of \"at least 10 positive events per variable\". Firth penalty logistic regression was used to analyze independent risk factors and construct a nomogram. Internal validation was performed using the Bootstrap method with 1000 resamples, and the calibrated AUC, calibration slope, intercept, and Brier score were calculated. Loess calibration curve and decision curve were also plotted. A risk scoring system based on regression coefficient weights and clinical guidelines was established, and patients were divided into low, medium, and high-risk groups. The incidence of complications between above groups was analyzed. Results Multivariate Firth regression showed that preoperative NYHA functional classification (OR = 2.185, 95% CI 1.201–3.964), intraoperative aortic occlusion time (OR = 1.014, 95% CI 1.002–1.026), and pure intraoperative bleeding volume (OR = 1.002, 95% CI 1.000–1.004) were independent risk factors for postoperative complications in Bentall (all P  < 0.05). The nomogram constructed based on the above variables, after Bootstrap correction, had an AUC of 0.817 (95% CI 0.765–0.889), a calibration slope of 0.95 (95% CI 0.81–1.09), an intercept of 0.05 (95% CI − 0.15–0.25), and a Brier score of 0.078, indicating good discrimination and calibration. The decision curve showed a clinical net benefit within the threshold range of 5–65%. The risk scoring system (0–18 points) divided patients into low-risk group (0–5 points, 102 cases), medium-risk group (6–12 points, 65 cases), and high-risk group (≥ 13 points, 26 cases). The incidence of complications in the three groups was 2.94%, 13.85%, and 53.85%, respectively, and the overall difference between the groups was statistically significant ( P  < 0.001). Pairwise comparison showed that the incidence of complications in the high-risk group was significantly higher than that in the medium and low-risk groups (both P  < 0.05), and the medium-risk group was significantly higher than the low-risk group ( P  < 0.05). Conclusions Preoperative NYHA cardiac function classification, intraoperative aortic occlusion time, and pure intraoperative bleeding volume are independent risk factors for postoperative complications in Bentall. The nomogram and risk stratification model constructed based on this exhibit good discriminative ability and calibration in this cohort, which can identify high-risk patients early before/during surgery and provide reference for personalized interventions. The performance of the model still requires external validation from multiple centers.

Volatile anesthetics improve postischemic cardiac function and reduce infarction even when administered for only a brief time at the onset of reperfusion. A recent study showed that sevoflurane postconditioning (SPC) attenuated myocardial reperfusion injury, but the underlying mechanisms remain unclear. In this study, we examined the effects of sevoflurane on nitric oxide (NO) release and autophagic flux during the myocardial ischemia/reperfusion (I/R) injury in rats in vivo and ex vivo. Male rats were subjected to 30 min ischemia and 2 h reperfusion in the presence or absence of sevoflurane (1.0 minimum alveolar concentration) during the first 15 min of reperfusion. We found that SPC significantly improved hemodynamic performance after reperfusion, alleviated postischemic myocardial infarction, reduced nicotinamide adenine dinucleotide content loss, and cytochrome c release in heart tissues. Furthermore, SPC significantly increased the phosphorylation of endothelial nitric oxide synthase (NOS) and neuronal nitric oxide synthase, and elevated myocardial NOS activity and NO production. All these effects were abolished by treatment with an NOS inhibitor NG-nitro- l -arginine methyl ester ( l -NAME, 10 mg/kg, i.v.). We also observed myocardial I/R-induced accumulation of autophagosomes in heart tissues, as evidenced by increased ratios of microtubule-associated protein 1 light chain 3 II/I, up-regulation of Beclin 1 and P62, and reduced lysosome-associated membrane protein-2 expression. SPC significantly attenuated I/R-impaired autophagic flux, which were blocked by l -NAME. Moreover, pretreatment with the autophagic flux blocker chloroquine (10 mg/kg, i.p.) increased autophagosome accumulation in SPC-treated heart following I/R and blocked SPC-induced cardioprotection. The same results were also observed in a rat model of myocardial I/R injury ex vivo, suggesting that SPC protects rat hearts against myocardial reperfusion injury by restoring I/R-impaired autophagic flux via an NO-dependent mechanism.

Topology control is one of the most essential technologies in wireless sensor networks (WSNs); it constructs networks with certain characteristics through the usage of some approaches, such as power control and channel assignment, thereby reducing the inter-nodes interference and the energy consumption of the network. It is closely related to the efficiency of upper layer protocols, especially MAC and routing protocols, which are the same as underwater acoustic sensor networks (UASNs). Directional antenna technology (directional transducer in UASNs) has great advantages in minimizing interference and conserving energy by restraining the beamforming range. It enables nodes to communicate with only intended neighbors; nevertheless, additional problems emerge, such as how to guarantee the connectivity of the network. This paper focuses on the connectivity problem of UASNs equipped with tri-modal directional transducers, where the orientation of a transducer is stabilized after the network is set up. To efficiently minimize the total network energy consumption under constraint of connectivity, the problem is formulated to a minimum network cost transducer orientation (MNCTO) problem and is provided a reduction from the Hamiltonian path problem in hexagonal grid graphs (HPHGG), which is proved to be NP-complete. Furthermore, a heuristic greedy algorithm is proposed for MNCTO. The simulation evaluation results in a contrast with its omni-mode peer, showing that the proposed algorithm greatly reduces the network energy consumption by up to nearly half on the premise of satisfying connectivity.

Magneto-inductive (MI) waveguide technology is often proposed to increase the MI communication distance without adding significant cost and power consumption to the wireless sensor network. The idea is to add intermediate relaying nodes between transmitter (Tx) and receiver (Rx) to relay the information from Tx to Rx. Our study of MI wave-guides has realized that adding a relay node improves the communication distance, however, the performance is greatly dependent on the position of the relaying node in the network. We therefore, in this work have investigated the effect of placement of a relay node and have determined the optimal relay position. We have performed various sets of experiments to thoroughly understand the behavior and identified three main regions: (a) for region 1, when the distance between Tx and Rx is equal or less than the diameter of the coils ( d ≤ 2 r ), the optimal relay position is close to Tx, (b) for region 2, when the distance between Tx and Rx is greater than diameter of the coils but less than twice the diameter ( 2 r < d < 4 r ), the optimal relay position lies in the center of Tx and Rx, and (c) for region 3, when the distance between the Tx and Rx is equal or greater than twice the diameter of the coils ( d ≥ 4 r ), the optimal relay position is close to Rx.

Investigation into the immune heterogeneity linked with atherosclerosis remains understudied. This knowledge gap hinders the creation of a robust theoretical framework essential for devising personalized immunotherapies aimed at combating this disease. Single-cell RNA sequencing (scRNA-seq) analysis was employed to delineate the immune cell-type landscape within atherosclerotic plaques, followed by assessments of cell-cell interactions and phenotype characteristics using scRNA-seq datasets. Subsequently, pseudotime trajectory analysis was utilized to elucidate the heterogeneity in cell fate and differentiation among macrophages. Through integrated approaches, including single-cell sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, we identified hallmark genes. A risk score model and a corresponding nomogram were developed and validated using these genes, confirmed through Receiver Operating Characteristic (ROC) curve analysis. Additionally, enrichment and immune characteristic analyses were conducted based on the risk score model. The model's applicability was further corroborated by and validation of specific genes implicated in atherosclerosis. This comprehensive scRNA-seq analysis has shed new light on the intricate immune landscape and the role of macrophages in atherosclerotic plaques. The presence of diverse immune cell populations, with a particularly enriched macrophage population, was highlighted by the results. Macrophage heterogeneity was intricately characterized, revealing four distinct subtypes with varying functional attributes that underscore their complex roles in atherosclerotic pathology. Intercellular communication analysis revealed robust macrophage interactions with multiple cell types and detailed pathways differing between proximal adjacent and atherosclerotic core groups. Furthermore, pseudotime trajectories charted the developmental course of macrophage subpopulations, offering insights into their differentiation fates within the plaque microenvironment. The use of machine learning identified potential diagnostic markers, culminating in the identification of RNASE1 and CD14. The risk score model based on these biomarkers exhibited high accuracy in diagnosing atherosclerosis. Immune characteristic analysis validated the risk score model's efficacy in defining patient profiles, distinguishing high-risk individuals with pronounced immune cell activities. Finally, experimental validation affirmed RNASE1's involvement in atherosclerotic progression, suggesting its potential as a therapeutic target. Our findings have advanced our understanding of atherosclerosis immunopathology and paved the way for novel diagnostic and therapeutic strategies.

Objective. The present study observed the therapeutic effect and possible side effects of Q-switch 1064-nm laser with large-spot and low-energy technology in the treatment of patients with combined freckles and chloasma. Methods. A Q1064-nm laser with a large-spot diameter of 6–8 mm, energy level of 2.0–3.3 J/cm2, frequency of 10 Hz, and pulse width of 10 ns was employed for the treatment. Each patient underwent treatment 10–15 times, with an interval of 10–15 days each time. Facial care was administered before and after treatment; attention was paid to cleaning and moisturizing, avoiding light, and using sunscreens strictly. The therapeutic effects were observed and evaluated. Results. Freckles basically subsided (effective rate = 100%) and chloasma faded (effective rate = 39.4%). Furthermore, whitening and delicacy improvement were observed in the surrounding normal skin area. After laser treatment, confocal laser scanning microscopy revealed a large number of melanin particles in the upper part of the granular layer. Moreover, the amount of melanin in the middle and lower parts of the basal layer and spinous layer was significantly decreased. None of the patients developed postinflammatory pigmentation. Conclusion. In the treatment of freckles with chloasma, Q 1064-nm laser large-spot, low-energy technology not only removed freckles and faded chloasma but, most importantly, also reduced the incidence of postinflammatory pigmentation and improved patient satisfaction. This provided new methods and ideas for freckle laser treatment.

A novel portable underwater acoustic modem is proposed in this paper for covert communication between divers or underwater unmanned vehicles (UUVs) and divers at a short distance. For the first time, real dolphin calls are used in the modem to realize biologically inspired Covert Underwater Acoustic Communication (CUAC). A variety of dolphin whistles and clicks stored in an SD card inside the modem helps to realize different biomimetic CUAC algorithms based on the specified covert scenario. In this paper, the information is conveyed during the time interval between dolphin clicks. TMS320C6748 and TLV320AIC3106 are the core processors used in our unique modem for fast digital processing and interconnection with other terminals or sensors. Simulation results show that the bit error rate (BER) of the CUAC algorithm is less than 10 − 5 when the signal to noise ratio is over ‒5 dB. The modem was tested in an underwater pool, and a data rate of 27.1 bits per second at a distance of 10 m was achieved.