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In this paper, an intelligent reflecting surface (IRS) is introduced to assist an unmanned aerial vehicle (UAV) communication system based on non-orthogonal multiple access (NOMA) for serving multiple ground users. We aim to minimize the average total system energy consumption by jointly designing the resource allocation strategy, the three dimensional (3D) trajectory of the UAV, as well as the phase control at the IRS. The design is formulated as a non-convex optimization problem taking into account the maximum tolerable outage probability constraint and the individual minimum data rate requirement. To circumvent the intractability of the design problem due to the altitude-dependent Rician fading in UAV-to-user links, we adopt the deep neural network (DNN) approach to accurately approximate the corresponding effective channel gains, which facilitates the development of a low-complexity suboptimal iterative algorithm via dividing the formulated problem into two subproblems and address them alternatingly. Numerical results demonstrate that the proposed algorithm can converge to an effective solution within a small number of iterations and illustrate some interesting insights: (1) IRS enables a highly flexible UAV's 3D trajectory design via recycling the dissipated radio signal for improving the achievable system data rate and reducing the flight power consumption of the UAV; (2) IRS provides a rich array gain through passive beamforming in the reflection link, which can substantially reduce the required communication power for guaranteeing the required quality-of-service (QoS); (3) Optimizing the altitude of UAV's trajectory can effectively exploit the outage-guaranteed effective channel gain to save the total required communication power enabling power-efficient UAV communications.

As the most prevalent primary CNS tumor, glioma is characterized by high mortality and morbidity. This research aims to investigate glioma-associated microRNAs (miRNAs) and their target mRNAs, as well as to explore their biological functions in gliomas. The Gene Expression Omnibus (GEO) database was applied to acquire the GSE112264 miRNA microarray dataset and the GSE15824 mRNA dataset. We selected samples from the GSE112264 dataset and the GSE15824 to identify differently expressed miRNAs (DE-miRNAs) as well as differentially expressed mRNAs (DEGs), respectively. Next, the intersections of mRNA and target mRNAs of miRNA were selected, and we constructed miRNA-mRNA regulation networks. These DEGs were selected for Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses by conducting the package clusterProfiler. After conducting Cytoscape software, a protein-protein interaction (PPI) network was created. Next, survival analysis of the miR-423-3p was confirmed by conducting TCGA database. Subsequently, Quantitative real-time PCR (qRT-PCR) was conducted to verify miR-423-3p's expression. Finally, miR-423-3p's biological functions of in effecting the cell proliferative, migratory, and invasive capabilities of glioma were investigated by performing Cell Counting Kit-8 (CCK-8) and Transwell assays. Our analysis elucidated a novel miRNA-mRNA regulatory network related to glioma carcinogenesis, which may be considered as future therapeutic biomarkers for glioma.

The physical and heat limits of semiconductor technology require the adaptation of heterogeneous architectures in supercomputers, such as graphics processing units (GPUs) with many-core accelerators and many-core processors with management and computing cores, to maintain a continuous increase in computing performance. The transition from homogeneous multi-core architectures to heterogeneous many-core architectures can produce non-bit-for-bit reproducibility that leads to numerical perturbations and uncertainties in simulation results, which could blend with errors due to coding bugs. The development of a methodology to identify computational perturbations and secure model correctness is a critically important step in model development on computer systems with new architectures. Thus, we have developed a methodology to characterize the uncertainties in the heterogeneous many-core computing environment. This methodology contains a simple multi-column atmospheric model consisting of typical discontinuous physical parameterizations defined by the selection programming structure, an efficient ensemble-based test approach, and an application to the GPU-based high-performance computing (HPC) and Sunway systems. Statistical distributions from ensembles of the heterogeneous systems show quantitative analyses of computational perturbations and acceptable error tolerances. The methodology aims to enable one to fully distinguish between perturbations caused by platforms and discrepancies caused by software bugs, and it provides encouraging references for verifying the reliability of supercomputing platforms and discussing the sensibility of Earth system modeling to the adaptation of new heterogeneous many-core architectures.

This study was aimed to prepare a silk fibroin (SF)/chitosan (CS)/nano-Hydroxyapatite (n-HAp) composite scaffold for repair of cartilage damage. A four-layer bionic scaffold SF/CS/n-HAp was constructed by cryogenic freezing and forming. Sodium Tripolyphosphate (STPP) was used as a cross-linking agent to post-process the scaffold to improve the mechanical strength of the scaffold. The various materials prepared were characterized. The SF/CS/n-HAp scaffold prepared in this study was cream-colored cylindrical and elastic. It can show variability after external force was applied, and can be restored to its original appearance after elimination of the external force. The diameter of the scaffold was 5 cm, and the total thickness was about 1 cm. The scanning electron microscope (SEM) results showed that the surface of the scaffold material was smooth, the pore size changed gradually, and the connectivity among the holes was good. The pore size distribution of the entire scaffold material was between 100 μm∼300 μm, and the pore size range was suitable for the adhesion, expansion, and migration of chondrocytes and osteoblasts. The mechanical performance test results showed that the composite scaffold had a compression modulus of 706 kPa, which can meet the mechanical performance requirements for repair of smaller articular cartilage damage. In addition, the porosity of each layer of the scaffold was more than 91%, which was conducive to the adhesion of cells on the surface of the material. The cell experiment results revealed that the composite scaffold material was beneficial to the growth and proliferation of cells. Under osteogenic and chondrogenic induction culture, bone marrow stromal cells (BMSCs) can differentiate in the direction of osteogenesis and cartilage. The results of animal experiments revealed that the SF/CS/n-HAp material group showed better effect than the blank control group in repairing rat cartilage defects, and the Wayne score and O’Driscoll score were also significantly higher than the blank control group. This suggested that the biomimetic scaffold SF/CS/n-HAp prepared in this study showed good physical properties and biocompatibility, and had certain prospects in the application of repairing cartilage damage.

The structure of a protein is of great importance in determining its functionality, and this characteristic can be leveraged to train data-driven prediction models. However, the limited number of available protein structures severely limits the performance of these models. AlphaFold2 and its open-source data set of predicted protein structures have provided a promising solution to this problem, and these predicted structures are expected to benefit the model performance by increasing the number of training samples. In this work, we constructed a new data set that acted as a benchmark and implemented a state-of-the-art structure-based approach for determining whether the performance of the function prediction model can be improved by putting additional AlphaFold-predicted structures into the training set and further compared the performance differences between two models separately trained with real structures only and AlphaFold-predicted structures only. Experimental results indicated that structure-based protein function prediction models could benefit from virtual training data consisting of AlphaFold-predicted structures. First, model performances were improved in all three categories of Gene Ontology terms (GO terms) after adding predicted structures as training samples. Second, the model trained only on AlphaFold-predicted virtual samples achieved comparable performances to the model based on experimentally solved real structures, suggesting that predicted structures were almost equally effective in predicting protein functionality.

The research of evaporation duct is of fundamental importance in the radar and signal communication industry. Particularly, in a real atmosphere environment, most of the radar holes cannot be corrected in time because of the persistent evaporation duct phenomenon. Thus, conducting evaporation duct research is a major concern of both academic and industrial communities. However, there is no descriptive research on the mobile communication in the marine and atmospheric environment. Numerous scholars and institutions have integrated atmospheric duct into multidisciplinary knowledge overview of the atmospheric duct in the existing published articles. The objective of this article is to explore the focus and status of atmospheric duct in-depth investigation and to grasp the frontiers and prospects of atmospheric duct research. A visual presentation of co-citation was adopted to realize the influence of authors, contributing institutions, keyword research hotspots, and frontier research based on co-citation network analysis. In addition, to identify the direction of atmospheric duct more accurately, the deep learning algorithm was adopted to conduct a comprehensive analysis of the future development trend of atmospheric duct.

This paper considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For DTM, we characterize a throughput-backlog trade-off of [O}(1/V), O(V)] to indicate that the system throughput goes up as the queue backlog increases. However, as V goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog. To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of ${[O(1/V), O((\log(V))^2)]}$ between the system throughput and the queue backlog.

In this paper, we propose a novel integrated sensing and communication (ISAC) transmission framework based on the spatially-spread orthogonal time frequency space (SS-OTFS) modulation by considering the fact that communication channel strengths cannot be directly obtained from radar sensing. We first propose the concept of SS-OTFS modulation, where the key novelty is the angular domain discretization enabled by the spatial-spreading/de-spreading. This discretization gives rise to simple and insightful effective models for both radar sensing and communication, which result in simplified designs for the related estimation and detection problems. In particular, we design simple beam tracking, angle estimation, and power allocation schemes for radar sensing, by utilizing the special structure of the effective radar sensing matrix. Meanwhile, we provide a detailed analysis on the pair-wise error probability (PEP) for communication, which unveils the key conditions for both precoding and power allocation designs. Based on those conditions, we design a symbol-wise precoding scheme for communication based only on the delay, Doppler, and angle estimates from radar sensing, without the a priori knowledge of the communication channel fading coefficients, and also introduce the power allocation for communication. Furthermore, we notice that radar sensing and communication requires different power allocations. Therefore, we discuss the performances of both the radar sensing and communication with different power allocations and show that the power allocation should be designed leaning towards radar sensing in practical scenarios. The effectiveness of the proposed ISAC transmission framework is verified by our numerical results, which also agree with our analysis and discussions.

With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-consuming and costly process. Deep learning has been applied to the de novo design of AMPs and address AMP classification with high efficiency. In this study, several natural language processing models were combined to design and identify AMPs, i.e. sequence generative adversarial nets, bidirectional encoder representations from transformers and multilayer perceptron. Then, six candidate AMPs were screened by AlphaFold2 structure prediction and molecular dynamic simulations. These peptides show low homology with known AMPs and belong to a novel class of AMPs. After initial bioactivity testing, one of the peptides, A-222, showed inhibition against gram-positive and gram-negative bacteria. The structural analysis of this novel peptide A-222 obtained by nuclear magnetic resonance confirmed the presence of an alpha-helix, which was consistent with the results predicted by AlphaFold2. We then performed a structure–activity relationship study to design a new series of peptide analogs and found that the activities of these analogs could be increased by 4–8-fold against Stenotrophomonas maltophilia WH 006 and Pseudomonas aeruginosa PAO1. Overall, deep learning shows great potential in accelerating the discovery of novel AMPs and holds promise as an important tool for developing novel AMPs.

Background: Transition metal sulfide, especially Nickel cobalt sulfide (NiCo2S4), has been widely studied as electrode material based on its excellent electrochemical properties. It is found that increasing the conductivity and stability of the electrode material can greatly improve the performance of the supercapacitor. Method: Three-dimensional (3D) NiCo2S4 and NiCo2S4/rGO composite material are synthesized via two-step hydrothermal method for high-performance supercapacitor electrode material. Besides, the electrochemical properties of NiCo2S4/rGO have been analyzed by many characterization methods. Results: The experiment shows that pure NiCo2S4 samples exhibit a cubic spinel structure, and their morphologies are 3D flower-like structures. The ultra-pasteurized NiCo2S4 nanospheres have been successfully inserted into the surface of graphene through a hydrothermal method. A large specific surface of NiCo2S4/rGO has been observed from morphology and structure analysis. The specific electric capacity of the NiCo2S4/rGO electrode has reached 1002.9 F/g, when the current density is 1 A/g. Conclusion: It has been found that the capacitance retention rate of NiCo2S4/rGO composite electrode material has increased from 59.6% to 88.5% compared to NiCo2S4 after 2000 cycles at 5 A/g. Moreover, experiments denote that NiCo2S4/rGO electrode material has a larger ion diffusion rate and lesser solution resistance from the Electrochemical Independence Spectrum (EIS).

Recently proposed orthogonal time frequency space (OTFS) modulation has been considered as a promising candidate for accommodating various emerging communication and sensing applications in high-mobility environments. In this paper, we propose a novel cross domain iterative detection algorithm to enhance the error performance of OTFS modulation. Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation. In doing so, the proposed algorithm exploits the time domain channel sparsity and the DD domain symbol constellation constraints. We evaluate the estimation/detection error variance in each domain for each iteration and derive the state evolution to investigate the detection error performance. We show that the performance gain due to iterations comes from the non-Gaussian constellation constraint in the DD domain. More importantly, we prove the proposed algorithm can indeed converge and, in the convergence, the proposed algorithm can achieve almost the same error performance as the maximum-likelihood sequence detection even in the presence of fractional Doppler shifts. Furthermore, the computational complexity associated with the domain transformation is low, thanks to the structure of the discrete Fourier transform (DFT) kernel. Simulation results are consistent with our analysis and demonstrate a significant performance improvement compared to conventional OTFS detection methods., Comment: submitted to IEEE Transactions on Wireless Communications

In recent years, convolutional regression trackers have shown increasing attention for visual object tracking due to their favorable performance and easy implementation. However, most of them are restricted to features from a certain layer and hardly benefit from temporal spatial information, which limits the potential to significant appearance changes. In this work, we go beyond the traditional deep regression trackers and build a novel twofold tracking network, which exploits rich hierarchical features and incorporates both temporal and spatial information to boost the tracking performance. The proposed network is composed of two streams, i.e., an appearance stream and a semantic stream, each stream is independently learned from different convolutional layers. Specially, we propose temporal and spatial mechanism for robust target representation by considering historical information in previous frames as well as spatial information. By design, the proposed twofold convolutional regression tracking network with spatial and temporal mechanism can better tolerate the target appearance changes and improve the tracking accuracy. Extensive experimental results on the benchmarks OTB-2015, Temple-Color, UAV123, and VOT-2018 demonstrate the effectiveness of our method, as compared with a number of state-of-the-art trackers.

Based on the fact that the photo-Fenton process can directly use solar energy to degrade various pollutants, it has received widespread attention. However, it has attracted widespread attention due to the rapid recombination of photo-generated carriers and the low light response range. Therefore, the construction of a Z-scheme heterojunction in this paper can effectively enhance the electron-hole separation, increase the reduction and oxidation potential, and enhance the redox capability of the photocatalyst. This paper reports the successful preparation of visible-light-induced ZnFe2O4/BiOI composite photocatalyst. There is a Z-scheme heterojunction structure of ZnFe2O4 and BiOI. At the same time, the PL and UV absorption spectra showed that the light absorption performance of the composite nanomaterials was enhanced, the photo-generated carriers recombination rate was reduced, and the photo-Fenton performance was also significantly improved. And the photocurrent of ZnFe2O4/BiOI is more than 29 times that of pure ZnFe2O4. In addition, ZnFe2O4/BiOI can degrade the simulated pollutant RhB 100% within 20 min under simulated sunlight. It shows that ZnFe2O4/BiOI binary composite has excellent photo-Fenton properties. In addition, ZnFe2O4/BiOI still maintains a high photo-Fenton ability after three cycles. Therefore, it has potential application prospects of the industrial photodegradation of organic pollutants.