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Ultraviolet Communication (UVC) faces the challenge of increased Bit Error Rate (BER) due to signal attenuation caused by atmospheric scattering. In recent years, wireless optical communication technologies have made significant progress in both Ultraviolet (UV) and Visible Light (VL) communication domains. However, traditional channel equalization methods still exhibit limitations when handling complex nonlinear channels. This study proposes a Long Short-Term Memory - Deep Neural Network (LSTM-DNN)-based channel equalization approach to enhance signal recovery accuracy. The model leverages LSTM to process temporal dependencies and combines it with DNN for nonlinear feature extraction, thereby improving its adaptability to single-scattering channels. Experimental results demonstrate that the LSTM-DNN model shows significant advantages in improving signal recovery accuracy and transmission quality compared to conventional methods. These methods include Least Mean Squares (LMS), Recursive Least Squares (RLS), Particle Swarm Optimization (PSO), Support Vector Machine (SVM), and Minimum Mean Squared Error (MMSE). Specifically, the LSTM-DNN model outperforms traditional methods across key performance metrics such as BER and Mean Squared Error (MSE). When the Signal-to-Noise Ratio (SNR) is 0 dB, the LSTM-DNN model achieves a BER of 0.135, significantly lower than LMS (0.45), RLS (0.38), PSO (0.35), SVM (0.25), and MMSE (0.20). As SNR increases, the LSTM-DNN model’s BER further decreases, demonstrating strong robustness. When the SNR is 20 dB, the BER of the LSTM-DNN model drops to 0.015, substantially outperforming conventional methods. Additionally, the LSTM-DNN model exhibits the smallest MSE values, with 0.035 at 0 dB SNR and decreasing to 0.004 with higher SNR. On average, the LSTM-DNN model reduces BER by approximately 67.8% and MSE by about 70.8% compared to traditional methods. These results confirm that the LSTM-DNN model significantly improves signal recovery accuracy and transmission quality in UVC systems. Overall, the LSTM-DNN model demonstrates superior performance in UVC applications compared to conventional methods, offering higher precision and stability. This study effectively addresses signal attenuation issues in UVC, significantly enhancing signal recovery accuracy and transmission quality, thus possessing important theoretical value and practical significance.

China's technology is developing rapidly, and the number of patent applications has surged. Therefore, there is an urgent need for technical managers and researchers that how to apply computer technology to conduct in-depth mining and analysis of lots of Chinese patent documents to efficiently use patent information, perform technological innovation and avoid R&D risks. Automatic term extraction is the basis of patent mining and analysis, but many existing approaches focus on extracting domain terms in English, which are difficult to extend to Chinese due to the distinctions between Chinese and English languages. At the same time, some common Chinese technical terminology extraction methods focus on the high-frequency characteristics, while technical domain correlation characteristic and the unithood feature of terminology are given less attention. Aiming at these problems, this paper proposes a Chinese technical terminology method based on DC-value and information entropy to achieve automatic extraction of technical terminology in Chinese patents. The empirical results show that the presented algorithm can effectively extract the technical terminology in Chinese patent literatures and has a better performance than the C-value method, the log-likelihood ratio method and the mutual information method, which has theoretical significance and practical application value.

Inland waters are large sources of methane to the atmosphere. However, considerable uncertainty exists in estimating the emissions of this potent greenhouse gas from global streams and rivers due, in part, to a lack of direct measurements in the high-altitude cryosphere and poor accounting for ebullition. Here we present methane concentrations and fluxes over three years in four basins on the East Qinghai–Tibet Plateau. Methane ebullition rates decrease exponentially whereas diffusion declines linearly with increasing stream order. Nonetheless, the average ebullition rate (11.9 mmolCH4 m−2 d−1) from these streams and rivers—which have large organic stocks in surrounding permafrost, abundant cold-tolerant methanogens, shallow water depths, and experience low air pressure—were six times greater than the global average and reached a maximum of 374.4 mmolCH4 m−2 d−1. Upscaled total emissions from sampled third- to seventh-order waterways of the East Qinghai–Tibet Plateau are estimated to be 0.20 TgCH4 yr−1, 79% of which was attributed to ebullition. These methane emissions are approximately 20% of CO2 emissions (2.70 TgCO2 yr−1) in terms of carbon release and two times greater in terms of CO2-equivalent emissions. When upscaled to first- to seventh-order waterways, we estimate emissions of 0.37–1.23 TgCH4 yr−1. Our findings demonstrate that high-elevation rivers on the Qinghai–Tibet Plateau are hotspots of methane delivery to the atmosphere. The large ebullitive fluxes, which constitute a substantial fraction of global fluvial methane emissions, reveal a positive feedback between climate warming, permafrost thaw and methane emissions.High-elevation rivers in permafrost of the East Qinghai–Tibet Plateau are hotspots of methane emissions, according to measurements of methane fluxes in the region.

Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.

Risk communication is critical to emergency management. The objective of this paper is to illustrate the effective process and attention points of risk communication reflecting on the COVID-19 (2019-nCoV) outbreak in Wuhan, China. We provide the timeline of risk communication progress in Wuhan and use a message-centered approach to identify problems that it entailed. It was found that the delayed decision making of the local government officials and the limited information disclosure should be mainly responsible for the ineffective risk communication. The principles for effective risk communication concerning Wuhan’s outbreak management were also discussed. The whole communication process is suggested to integrate the accessibility and openness of risk information, the timing and frequency of communication, and the strategies dealing with uncertainties. Based on these principles and lessons from Wuhan’s case, this paper employed a simplified Government–Expert–Public risk communication model to illustrate a collaborative network for effective risk communication.

Spatial-explicitly mapping of the hotspots and coldspots is a vital link in the priority setting for ecosystem services （ES） conservation. However, little research has identified and tested the compactness and efficiency of their ES hotspots and coldspots, which may weaken the effectiveness of ecological conservation. In this study, based on the RUSLE model and Getis-Ord Gi＊ statistics, we quantified the variation of annual soil conservation services （SC） and identified the statistically significant hotspots and coldspots in Shaanxi Province of China from 2000 to 2013. The results indicate that, 1） areas with high SC presented a significantly increasing trend as well, while areas with low SC only changed slightly; 2） SC hotspots and coldspots showed an obvious spatial differentiation--the hotspots were mainly spatially ag- gregated in southern Shaanxi, while the coldspots were mainly distributed in the Guanzhong Basin and Sand-windy Plateau; and 3） the identified hotspots had the highest capacity of providing SC, with 29.6% of the total area providing 59.7% of the total service. In contrast, the coldspots occupied 46.3% of the total area, but only provided 17.2% of the total SC. In addition to conserving single ES, the Getis-Ord Gi＊ statistics method can also help identify multi-functional priority areas for conserving multiple ES and biodiversity.

Incorporating sustainable design practices into creative economy businesses is increasingly vital amidst growing global environmental concerns and shifting market demands. This research examines the factors that promote or hinder the adoption of sustainable design from a strategic business perspective. Utilizing data from a detailed survey, it adopts a multi-faceted approach, incorporating descriptive analysis, exploratory and confirmatory factor analyses, structural equation modeling (SEM), cluster analysis, and machine learning methods. Findings indicate that leadership vision, innovative capacity, and customer engagement are primary motivators, whereas internal inefficiencies and limited resources present significant obstacles. Cluster analysis reveals three strategic profiles: Innovation-Driven, Strategically-Aligned, and Barrier-Dominated, offering meaningful insights for designing targeted strategies. The study delivers a validated framework that enhances sustainability theory and supports strategic decision-making within the creative sector.

Background Dihydroartemisinin (DHA) has been shown to exert anticancer activity through iron-dependent reactive oxygen species (ROS) generation, which is similar to ferroptosis, a novel form of cell death. However, whether DHA causes ferroptosis in glioma cells and the potential regulatory mechanisms remain unclear. Methods Effects of DHA on the proliferation, cell death, ROS and lipid ROS generation as well as reduced gluthione consumption were assessed in glioma cells with or without ferroptosis inhibitor. The biological mechanisms by which glioma cells attenuate the pro-ferroptotic effects of DHA were assessed using molecular methods. Results DHA induced ferroptosis in glioma cells, as characterized by iron-dependent cell death accompanied with ROS generation and lipid peroxidation. However, DHA treatment simultaneously activated a feedback pathway of ferroptosis by increasing the expression of heat shock protein family A (Hsp70) member 5 (HSPA5). Mechanistically, DHA caused endoplasmic reticulum (ER) stress in glioma cells, which resulted in the induction of HSPA5 expression by protein kinase R-like ER kinase (PERK)-upregulated activating transcription factor 4 (ATF4). Subsequent HSPA5 upregulation increased the expression and activity of glutathione peroxidase 4 (GPX4), which neutralized DHA-induced lipid peroxidation and thus protected glioma cells from ferroptosis. Inhibition of the PERK-ATF4-HSPA5-GPX4 pathway using siRNA or small molecules increased DHA sensitivity of glioma cells by increasing ferroptosis both in vitro and in vivo. Conclusions Collectively, these data suggested that ferroptosis might be a novel anticancer mechanism of DHA in glioma and HSPA5 may serve as a negative regulator of DHA-induced ferroptosis. Therefore, inhibiting the negative feedback pathway would be a promising therapeutic strategy to strengthen the anti-glioma activity of DHA.

DNA replication is a tightly regulated process that ensures the precise duplication of the genome during the cell cycle 1 . In eukaryotes, the licensing and activation of replication origins are regulated by both DNA sequence and chromatin features 2 . However, the chromatin-based regulatory mechanisms remain largely uncharacterized. Here we show that, in HeLa cells, nucleosomes containing the histone variant H2A.Z are enriched with histone H4 that is dimethylated on its lysine 20 residue (H4K20me2) and with bound origin-recognition complex (ORC). In vitro studies show that H2A.Z-containing nucleosomes bind directly to the histone lysine methyltransferase enzyme SUV420H1, promoting H4K20me2 deposition, which is in turn required for ORC1 binding. Genome-wide studies show that signals from H4K20me2, ORC1 and nascent DNA strands co-localize with H2A.Z, and that depletion of H2A.Z results in decreased H4K20me2, ORC1 and nascent-strand signals throughout the genome. H2A.Z-regulated replication origins have a higher firing efficiency and early replication timing compared with other origins. Our results suggest that the histone variant H2A.Z epigenetically regulates the licensing and activation of early replication origins and maintains replication timing through the SUV420H1–H4K20me2–ORC1 axis. DNA replication in eukaryotes requires the histone variant H2A.Z, which binds the enzyme SUV420H1 to promote the dimethylation of histone H4, in turn recruiting the origin-recognition complex to activate early replication origins.