Explore the vast range of titles available.

The slow propagation speed of acoustic waves in water leads to significant variations and random fluctuations in communication delays among underwater acoustic sensor network (UASN) nodes. Conventional deep reinforcement learning (DRL)-based underwater acoustic network access methods can adaptively adjust their parameters and improve network communication efficiency by effectively utilizing inter-node delay differences for concurrent communication. However, they still suffer from shortcomings such as not accounting for random delay fluctuations in underwater acoustic links and low learning efficiency. This paper proposes a DRL-based delay-fluctuation-resistant underwater acoustic network access method. First, delay fluctuations are integrated into the state model of deep reinforcement learning, enabling the model to adapt to delay fluctuations during learning. Then, a double deep Q-network (DDQN) is introduced, and its structure is optimized to enhance learning and decision-making in complex environments. Simulations demonstrate that the proposed method achieves an average improvement of 29.3% and 15.5% in convergence speed compared to the other two DRL-based methods under varying delay fluctuations. Furthermore, the proposed method significantly enhances the normalized throughput compared to conventional Time Division Multiple Access (TDMA) and DOTS protocols.

Inducing cancer differentiation is a promising approach to treat cancer. Here, we identified chlorogenic acid (CA), a potential differentiation inducer, for cancer therapy, and elucidated the molecular mechanisms underlying its differentiation-inducing effects on cancer cells. Cancer cell differentiation was investigated by measuring malignant behavior, including growth rate, invasion/migration, morphological change, maturation, and ATP production. Gene expression was analyzed by microarray analysis, qRT-PCR, and protein measurement, and molecular biology techniques were employed for mechanistic studies. LC/MS analysis was the method of choice for chemical detection. Finally, the anticancer effect of CA was evaluated both and Results: Cancer cells treated with CA showed reduced proliferation rate, migration/invasion ability, and mitochondrial ATP production. Treating cancer cells with CA resulted in elevated SUMO1 expression through acting on its 3'UTR and stabilizing the mRNA. The increased SUMO1 caused c-Myc sumoylation, miR-17 family downregulation, and p21 upregulation leading to G /G arrest and maturation phenotype. CA altered the expression of differentiation-related genes in cancer cells but not in normal cells. It inhibited hepatoma and lung cancer growth in tumor-bearing mice and prevented new tumor development in naïve mice. In glioma cells, CA increased expression of specific differentiation biomarkers Tuj1 and GFAP inducing differentiation and reducing sphere formation. The therapeutic efficacy of CA in glioma cells was comparable to that of temozolomide. CA was detectable both in the blood and brain when administered intraperitoneally in animals. Most importantly, CA was safe even at very high doses. CA might be a safe and effective differentiation-inducer for cancer therapy. \"Educating\" cancer cells to differentiate, rather than killing them, could be a novel therapeutic strategy for cancer.

Emerging evidence indicates the critical roles of N 6 -methyladenosine (m 6 A) modification in human cancers. Herein, our work reported that a novel m 6 A-modified circRNA from the MYO1C gene, circMYO1C, upregulated in the pancreatic ductal adenocarcinoma (PDAC). Our findings demonstrated that circMYO1C is highly expressed in PDAC tissues. Functionally, circMYO1C promoted the proliferation and migration of PDAC cells in vitro and its silencing reduced the tumor growth in vivo. Mechanistically, circMYO1C cyclization was mediated by m 6 A methyltransferase METTL3. Moreover, methylated RNA immunoprecipitation sequencing (MeRIP-seq) unveiled the remarkable m 6 A modification on PD-L1 mRNA. Moreover, circMYO1C targeted the m 6 A site of PD-L1 mRNA to enhance its stability by cooperating with IGF2BP2, thereby accelerating PDAC immune escape. In conclusion, these findings highlight the oncogenic role of METTL3-induced circMYO1C in PDAC tumorigenesis via an m 6 A-dependent manner, inspiring a novel strategy to explore PDAC epigenetic therapy.

Spin thermoelectrics represents a new paradigm of thermoelectricity that has a potential to overcome the fundamental limitation posed by the Wiedmann-Franz law on the efficiency of conventional thermoelectric devices. A typical spin thermoelectric device consists of a bilayer of a magnetic insulator and a high spin-orbit coupling (SOC) metal coated over a non-magnetic substrate. Pt is the most commonly used metal in spin thermoelectric devices due to its strong SOC. In this paper, we found that an alloy of Cu and Pt can perform much better than Pt in spin thermoelectric devices. A series of CuPt alloy films with different Pt concentrations were deposited on yttrium iron garnet (YIG) films coated gadolinium gallium garnet (GGG) substrate. Through spin Seebeck measurements, it was found that the Cu 0.4 Pt 0.6 /YIG/GGG device shows almost 3 times higher spin Seebeck voltage compared to Pt/YIG/GGG under identical conditions. The improved performance was attributed to the higher resistivity as well as enhanced spin hall angle of the CuPt layer.

To enhance the adsorption capacity of biochar (BC), herein a novel multifunction modified biochar (SDMBC) was prepared by directly crosslinking of the nanomicelle of sodium dodecyl sulfate/sapindus-saponin (SDS/SAP) composite system onto the BC through a simple, environmental friendly approach. Result showed that the adsorption performance of SDMBC has been greatly improved, compared with BC or using alone SDS and SAP, adsorption ability increased by 48.83%, 29.50%, 36.44%, respectively, the best modified effect was appeared when the concentration of SAP to SDS was 0.8 and 0.8 CMC. SDMBC exhibited high adsorption abilities of 130.23, 108.43, 277.09 125.27, 112.78 mg/g for heavy metal ions lead Pb(II), Cadmium Cd(II) and organic pollutants with different chemical properties bisphenol A(BPA), Methylene blue (MB), P-nitrophenol (PNP), respectively, higher than most previously reported adsorbents, importantly, SDMBC can still efficient removal capabilities even in the binary competition. Subsequently, the SDMBC and BC was characterized by Fourier Transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), Zeta potential (Zeta), it found SDMBC has a more layered structure, richer functional groups and more amorphous structure compared with BC, which are closely related with improving its adsorption capacity. The adsorption behavior of SDMBC for MB show that process was found to be spontaneous, propitious, endothermic, the adsorption isotherms fitted Freundlich models well, pseudo-second-order best describes kinetics adsorption, suggesting that the process is multi-layer chemical adsorption. The little affected by ionic strength and coexisting substances, could remained removal rate over a wide pH range, SDMBC still keep high removal rates even after 5 reuses. Based on FT-IR analysis, plausible adsorption mechanism proposed, including hydrogen bond, electrostatic attraction and π-π bonding. Cost analysis manifests that the SDMBC are high efficiency and cheap eco-adsorbents compared with commercial activated carbon, and the SDMBC dosage required for the removal of 99% of a fixed amount of MB in different volumes of effluent was predicted. Seven machine learning (ML) models were used to predict the MB (60 mg/L) adsorption of the SDMBC, using Shapley Additive Explanations (SHAP) for model interpretation. Finding Extreme Gradient Boosting (XGBoost) exhibited best performance, the order of feature importance as time> Ratio> pH> concentration> temperature. Thus, SDMBC as a new cheap and eco-adsorbents, can be used to effectively remove various types of pollutants, has a great application potential in sewage treatment, while the accurate ML prediction model presented a valuable advice for designing efficient adsorbents and optimization operating conditions in the future.

The smoothing parameter on lattices is crucial for lattice-based cryptographic design. In this study, we establish a new upper bound for the lattice smoothing parameter, which represents an improvement over several significant classical findings. For one-dimensional integer lattices, under specific and optimized conditions, we have achieved a more precise upper bound compared to previous research. Regarding general high-dimensional lattices, when the lattice dimension is large enough and the error parameter is within a particular range, we have derived a new upper bound. In the practical applications of lattice-based cryptography, where the lattice dimension is typically large, our new bound enables a more natural and smaller setting for the error parameter, thereby improving the upper bounds on all known smoothing parameters.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

The motion planning and control method of automated vehicles, as the key technology of automated vehicles, directly affects the safety, comfort, and other technical indicators of vehicles. The planning module is responsible for generating a vehicle driving path. The control module is responsible for driving the vehicle. In this study, we review the main methods and achievements in motion planning and motion control for automated vehicles. The advantages and disadvantages of various planning and control methods are comparatively analyzed. Finally, some predictions and summaries based on the existing research results and trends are proposed. Through this analysis, it is believed that various types of algorithms will be further integrated in the future to complement each other’s strengths and weaknesses. The next area of research will be to establish more accurate vehicle models to describe vehicle motion, improve the generalization-solving ability of algorithms, and enhance the planning and control of integrated ‘human-vehicle-road’ traffic systems.

Herbaceous marsh is the most widely distributed type of marsh wetland ecosystem, and has important ecological functions such as water conservation, climate regulation, carbon storage and fixation, and sheltering rare species. The carbon sequestration function of herbaceous marsh plays a key role in slowing climate warming and maintaining regional environmental stability. Vegetation biomass is an important index reflecting the carbon sequestration capacity of wetlands. Investigating the biomass of marsh vegetation can provide a scientific basis for estimating the carbon storage and carbon sequestration capacity of marshes. Based on field survey data of aboveground biomass of herbaceous marsh vegetation and the distribution data set of marsh in China, we analyzed the aboveground biomass and its spatial distribution pattern of herbaceous marsh on a national scale for the first time. The results showed that in China the total area of herbaceous marsh was 9.7×10 4 km 2 , the average density of aboveground biomass of herbaceous marsh vegetation was 227.5±23.0 g C m −2 (95% confidence interval, the same below), and the total aboveground biomass was 22.2±2.2 Tg C (1 Tg=10 12 g). The aboveground biomass density of herbaceous marsh vegetation is generally low in Northeast China and the Tibetan Plateau, and high in central North China and coastal regions in China. In different marsh distribution regions of China, the average biomass density of herbaceous marsh vegetation from small to large was as follows: temperate humid and semi-humid marsh region (182.3±49.3 g C m −2 )

Journal Article

Share this book

Add to My Shelf