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This paper introduces a novel channel estimation method for Orthogonal Time Frequency Space (OTFS) systems affected by nonlinear distortion from High-Power Amplifiers (HPA). The method integrates a Bidirectional Gated Recurrent Unit (Bi-GRU) with a dynamic gating mechanism driven by the Input Back-Off (IBO) parameter of the HPA, combined with a multi-head attention network. The dynamic gating mechanism adaptively adjusts the update gate of the Gated Recurrent Unit (GRU) based on real-time IBO values, optimizing the trade-off between historical memory and current input during training. The multi-head attention module further captures long-range dependencies in the channel response. Theoretical analysis indicates that the proposed IBO-driven dynamically gated Bi-GRU achieves a computational complexity reduction of 20–46.7% compared to a Bi-GRU architecture. Simulation results demonstrate the superior performance of the proposed method across both bit error rate (BER) and normalized mean square error (NMSE) metrics under high mobility and nonlinear distortion. It achieves up to 22.6 quantified in decibels (dB) lower NMSE and, at a signal-to-noise ratio (SNR) of 30 dB, a 15.2 dB reduction in logarithmic BER compared to conventional methods, along with a 3–4 dB improvement over deep learning baselines at the same SNR. It also provides over 7 dB peak-to-average power ratio (PAPR) reduction over traditional methods, confirming strong robustness and accuracy in challenging communication scenarios.

Decision-making for engineering systems like multi-path parallel transmission is plagued by ambiguous and asymmetric information. While q-rung orthopair fuzzy sets offer expressive power, their decision-making frameworks face three intertwined gaps: (1) existing ranking methods yield unstable results under varying parameter q; (2) conventional distance measures fail to preserve higher-order structural information; (3) the prospect theory-based TODIM method, crucial for modeling risk-prone decisions, remains underdeveloped in q-rung orthopair fuzzy environments. To bridge these gaps, this paper introduces an integrated TODIM extension for q-rung orthopair fuzzy information. The core innovations are threefold. We first develop a geometric visualization-based ranking method that maps q-rung orthopair fuzzy numbers onto a coordinate plane and uses arc-length aggregation to integrate membership, non-membership, and hesitation degrees. This method inherently enhances interpretability and ranking stability against q-value fluctuations. Second, we propose a novel higher-order distance measure specifically designed to capture the nuanced structural information of q-rung orthopair fuzzy numbers, ensuring higher fidelity in difference quantification. Third, we seamlessly embed these enhanced components into the classical TODIM framework, creating the first comprehensive q-rung orthopair fuzzy TODIM method that retains behavioral realism while processing complex fuzzy information. A case study on multi-path transmission scheme selection demonstrates the method’s practicality. Comparative experiments and a detailed sensitivity analysis confirm that our framework delivers superior ranking consistency and robustness compared to existing q-rung orthopair fuzzy MCDM methods.

The allocation of communication resources is extremely important for the device-to-device (D2D) communication network with unmanned aerial vehicle (UAV) relays, but this field currently mainly focuses on the communication between UAVs and ground devices. While neglecting the comprehensive optimization of air-to-ground communication and internal ground communication. This paper studies the optimization problem of communication for ground D2D devices based on UAV relays in urban environment. We study the comprehensive optimization considering the air-to-ground communication and the mobile ground communication devices. We construct the D2D communication neighbor table, and the terrestrial D2D devices will sort and select the best D2D link for pairing based on the Euclidean distance and gain set around them. We construct a communication model for this scenario and propose an improved Dual-Actor Network Softmax Twin Delayed Deep Deterministic Policy Gradient algorithm based on deep reinforcement learning. We perform transmit power optimization to address the interference caused by D2D communication using frequency division multiplexing technology. By designing the reward function and training mechanism, we can achieve good power control and reduce communication interference. The results of the simulation analysis performed in this scenario show an effective reduction in communication latency and an increase in D2I (device-to-infrastructure) channel capacity, D2D connectivity success, and energy efficiency. The feasibility and effectiveness of the algorithm is verified by comparing the baseline algorithm.

Xinglou Chengqi (XLCQ) decoction, composed of three botanical drugs and one inorganic drug, is used in clinics during the treatment of acute stroke complicated with Tanre Fushi (TRFS) syndrome in China. However, its active ingredients and the molecular mechanism have not been clarified. So, we aimed to preliminarily characterize its chemical constituents and investigate its pharmacological mechanisms using an integrative pharmacology strategy, including component analysis, network prediction, and experimental verification. We employed UPLC-QTOF-MS/MS to describe the chemical profile of XLCQ, Integrative Pharmacology-based Network Computational Research Platform of Traditional Chinese Medicine (TCMIP v2.0, http://www.tcmip.cn/ ), to assist in identifying the chemical components and predict the putative molecular mechanism against acute stroke complicated with TRFS, and LPS-stimulated BV-2 cells to verify the anti-neuroinflammatory effects of luteolin, apigenin, and chrysoeriol. Altogether, 197 chemical compounds were identified or tentatively characterized in the water extraction of XLCQ, 22 of them were selected as the key active constituents that may improve the pathological state by regulating 27 corresponding targets that are mainly involved in inflammation/immune-related pathways, and furthermore, luteolin, apigenin, and chrysoeriol exhibited good anti-neuroinflammatory effects from both protein and mRNA levels. In summary, it is the first time to employ an integrative pharmacology strategy to delineate 22 constituents that may improve the pathological state of stroke with TRFS by regulating 27 corresponding targets, which may offer a highly efficient way to mine the scientific connotation of traditional Chinese medicine prescriptions. This study might be a supplement for the deficiency of the basic research of XLCQ.

Affected by the complex underwater environment and the limitations of low-resolution sonar image data and small sample sizes, traditional image recognition algorithms have difficulties achieving accurate sonar image recognition. The research builds on YOLOv7 and devises an innovative fast recognition model designed explicitly for sonar images, namely the Dual Attention Mechanism YOLOv7 model (DA-YOLOv7), to tackle such challenges. New modules such as the Omni-Directional Convolution Channel Prior Convolutional Attention Efficient Layer Aggregation Network (OA-ELAN), Spatial Pyramid Pooling Channel Shuffling and Pixel-level Convolution Bilat-eral-branch Transformer (SPPCSPCBiFormer), and Ghost-Shuffle Convolution Enhanced Layer Aggregation Network-High performance (G-ELAN-H) are central to its design, which reduce the computational burden and enhance the accuracy in detecting small targets and capturing local features and crucial information. The study adopts transfer learning to deal with the lack of sonar image samples. By pre-training the large-scale Underwater Acoustic Target Detection Dataset (UATD dataset), DA-YOLOV7 obtains initial weights, fine-tuned on the smaller Smaller Common Sonar Target Detection Dataset (SCTD dataset), thereby reducing the risk of overfitting which is commonly encountered in small datasets. The experimental results on the UATD, the Underwater Optical Target Detection Intelligent Algorithm Competition 2021 Dataset (URPC), and SCTD datasets show that DA-YOLOV7 exhibits outstanding performance, with mAP@0.5 scores reaching 89.4%, 89.9%, and 99.15%, respectively. In addition, the model maintains real-time speed while having superior accuracy and recall rates compared to existing mainstream target recognition models. These findings establish the superiority of DA-YOLOV7 in sonar image analysis tasks.

Exploring and analyzing the effectiveness of an intelligent pacifying strategy information system based on assisted decision-making in reducing the sedation rate of children in short-duration magnetic resonance scans. A total of 125 children aged 3–5 years who underwent MRI scans at a children's hospital from July to December 2021 participated in this study, during which 62 children were assigned to a control group from July to September, and 63 children were assigned to an intervention group from October to December. In the intervention group, the pacifier used the intelligent pacifying strategy information system based on assisted decision-making to assess children's temperament, and utilization of a system-generated pacification plan according to assessment results. In the control group, the pacification plan was formulated by the pacifier based on their own experience and discussion with families of the participating children. The success rate of pacification, duration of pacification, and image quality of the two groups were compare. Compared with the control group, the intervention group had a higher success rate of pacification and lower duration of pacification, with statistically significant differences ( P  < 0.05). There was no difference in image quality between the two groups ( P  > 0.05). The intelligent pacifying strategy information system can help reduce the use of the sedative drugs in children aged 3–5 years who underwent a short-duration MRI scan.

In this paper, we aim at the problem that MIMO radar’s target detection performance is greatly reduced in the complex multi-signal-dependent interferences environment. We propose a joint design method based on semidefinite relaxation (SDR), fractional programming and randomization technique (JD-SFR) and a joint design method based on coordinate descent (JD-CD) to solve the actual transmit waveform and receive filter bank directly to reduce the loss of strong interference to the output signal-to-interference-plus-noise ratio (SINR) of the radar system. Therefore, the maximization of output SINR is taken as the criterion of the optimization problem. The designed waveforms take into account the radar transmitter’s hardware requirements for constant envelope waveforms and impose similarity constraints on the waveforms. JD-SFR uses SDR, fractional programming and randomization technique to deal with the non-convex optimization problems encountered in the solution process. JD-CD transforms the optimization problem into a function of the phase of the waveform and then solves the transmit waveform based on CD. Compared with other methods, the proposed method has lower output SINR loss under strong power interference and forms deep nulls on the direction beampattern of multiple interference sources, which indicates that it has better anti-interference performance.

Background The Guangxi government initiated two rounds of the Guangxi AIDS Conquering Project (GACP) in 2010 (Phase I) and 2015 (Phase II) to control human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemics. However, the effectiveness of GACP in HIV prevention and treatment has rarely been reported. This study aimed to assess the effectiveness of the GACP implemented in Guangxi, China and provide data for strategy and praxis improvements to achieve Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95 targets. Methods We used spatial approaches to trace the spatiotemporal distribution properties, epidemic trends, and correlation between macroscopic factors and HIV incidence using data from the Chinese HIV/AIDS case reporting system to explore the effects of the GACP. Results During the GACP era, the HIV epidemic stabilized in urban centers, showing a downward trend in the Hengzhou and Binyang Counties in the eastern region, whereas it continued to increase in rural areas of the northwest region, such as the Long’an, Mashan, Shanglin, and Wuming Districts. The linear directional mean (LDM) of HIV infection reported cases displayed a southeast–northwest direction, with an LDM value of 12.52°. Compared with that in Phase I, Hengzhou withdrew from the high-high clustering area, and the west–north suburban counties pulled out the low-low clustering area during Phase II. Significant HIV clusters were identified in the eastern region during Phase I, whereas these clusters emerged in the northwestern areas during Phase II. Regarding HIV, socioeconomic status, population mobility, and medical care levels were the key social drivers of heterogeneous spatial distribution. Conclusions The GACP assisted in effectively managing the HIV epidemic in urban and eastern areas of Nanning City. However, prevention and control efforts in rural regions, particularly those located in the northwest, may not have yielded comparable outcomes. To address this disparity, allocating additional resources and implementing tailored intervention measures for these rural areas are imperative.

Background Renal fibrosis is the late stage of many chronic kidney diseases (CKD). Clinically, there is almost no effective treatment for renal fibrosis except dialysis. Renshen Guben oral liquid (RSGB) is a Chinese patent medicine approved by National Medical Products Administration (NMPA), which is suitable for clinical patients with chronic nephritis. Currently, the chemical constituents of RSGB remains unclear, and its efficacy and mechanism on renal fibrosis have not been reported. Methods In our research, ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) was employed to describe the chemical profile of RSGB, unilateral ureteral obstruction (UUO) model in mice was established to evaluate the beneficial effect of RSGB on renal fibrosis by biochemical indexes, HE and Masson staining. RNA sequencing and “constituents-targets-pathways” multi-dimensional network was established to mine the mechanisms of RSGB. Key targets were verified by quantitative real-time PCR (qRT-PCR) and western bolt (WB). Results A total of 201 constituents were identified or tentatively characterized, 15 of which were confirmed with standards. The number of triterpenes was the highest with 49, followed by phenols with 46. RSGB ameliorated the blood urea nitrogen (BUN) and serum creatinine (Scr) levels in serum, normalizing pathological structure of kidney tissue. RNA sequencing revealed that RSGB regulates 226 differential genes, which were involved in kidney development. According to the “constituents-targets-pathways” network, 26 key active constituents may mainly regulate the inflammatory immune system through 88 corresponding targets. qRT-PCR and WB results showed that RSGB inhibited the activation of the Tgfβ1/Smad2/3 pathway, Wnt4/β-Catenin pathway and NGFR/NF-κB pathway. Conclusions Overall, our study, for the first time, characterized 201 chemical constituents in RSGB, and 26 of them were screened out to alleviates renal fibrosis mainly through Tgfβ1/Smad2/3 pathway, Wnt4/β-catenin pathway and NGFR/NF-κB pathway, which may provide a new research strategy for research on the mechanism of traditional Chinese Medicine.

Wang Bi tablet (WBT) is used to treat rheumatoid arthritis (RA) in China. We employed integrative pharmacology, including rapid analysis of chemical composition, pharmacological experiment, and network pharmacology analysis, to elucidate the active components and mechanism underlying the effect of WBT against RA. The chemical fingerprint of WBT was revealed by UPLC-QTOF-MS/MS, and the chemical composition was identified. The anti-inflammatory effect of WBT was evaluated in TNF-α-stimulated RAW264.7 cells by ELISA and transcriptome sequencing. Network pharmacology analysis, functional enrichment analysis, and network visualization were performed. A total of 293 chemical constituents were preliminarily identified or tentatively characterized in WBT extract, and they effectively inhibited inflammatory response in TNF-α-stimulated RAW264.7 cells. Forty-eight key active constituents were identified based on high-frequency binding to hub targets and their corresponding targets number. Next, 135 corresponding hub genes, which may be the putative targets of WBT in treating RA, were selected. Functionally, the putative targets were significantly associated with the inflammatory immune response regulation module, energy metabolism regulation module, and cell function regulation module, corresponding to the traditional efficacy of WBT. In summary, this study revealed, for the first time using integrative pharmacology, that WBT may attenuate RA through the inflammation-immune regulation system.