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An air-to-ground downlink communication network consisting of a reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) is proposed. In conjunction with a resource allocation strategy, the system’s energy efficiency is improved. Specifically, the UAV equipped with a RIS starts from an initial location, and an energy-efficient unmanned aerial vehicle deployment (EEUD) algorithm is deployed to jointly optimize the UAV trajectory, RIS phase shifts, and BS transmit power, so as to obtain a quasi-optimal deployment location and hence improve the energy efficiency. First, the RIS phase shifts are optimized by using the block coordinate descent (BCD) algorithm to deal with the nonconvex inequality constraint, and then integrated with the Dinkelbach algorithm to address the resource allocation problem of the BS transmit power. Finally, for solving the UAV trajectory optimization problem, the complex objective function is transformed into a convex function, and the optimal UAV flight trajectory is obtained. Our simulation results show that the quasi-optimal deployment location obtained by the EEUD algorithm is superior to other deployment strategies in energy efficiency. Moreover, the instantaneous energy efficiency of the UAVs along the trajectory of searching the deployment location is better than other comparison trajectories. Furthermore, the RIS-assisted multi-user air-to-ground communication network can offer up to 145% improvement in energy efficiency over the traditional amplify-and-forward (AF) relay.

ObjectiveTo explore the therapeutic effect and mechanism of low-frequency repetitive transcranial magnetic stimulation on the speech function of patients with non-fluent aphasia after stroke.MethodsAccording to the inclusion and exclusion criteria, 60 patients with post-stroke non-fluent aphasia were included and randomly divided into treatment group (rTMS group) and sham stimulation group (S-rTMS group). Patients in rTMS group were given low-frequency rTMS + ST training. Patients in the S-rTMS group were given sham low-frequency rTMS + ST training. Once a day, 5 days a week, for a total of 4 weeks. The Western Aphasia Battery and the short-form Token test were used to evaluate the language function of the patients in the two groups before and after treatment. Part of the enrolled patients were subjected to functional magnetic resonance imaging examination, and the morning fasting venous blood of the enrolled patients was drawn before and after treatment to determine the content of BDNF and TNF-α.ResultsIn the comparison before and after treatment within the group, all dimensions of the WAB scale of the patients in the rTMS group increased significantly. Only two dimensions of the WAB scale of the patients in the S-rTMS group improved significantly after treatment. The results of the short-form Token test showed that patients in the rTMS group improved significantly before and after treatment. The resting state functional magnetic resonance imaging of the two groups of patients before and after treatment showed: the activation of multiple brain regions in the left hemisphere of the rTMS group increased compared with the control group. The serum BDNF content of the patients in the rTMS group was significantly higher than that of the patients in the S-rTMS group after treatment.ConclusionLow-frequency rTMS combined with conventional speech training can significantly improve the speech function of patients with non-fluent aphasia after stroke.

A visible-light-engaged 2-fold site-selective alkylation of indole derivatives with aliphatic ethers or alcohols has been accomplished for easy access to symmetric 3,3'-bisindolylmethane derivatives. The experimental data suggest a sequential photoredox catalysis induced radical addition and proton-mediated Friedel-Crafts alkylation mechanism. [ABSTRACT FROM AUTHOR]

Systematic synthesis of REVO4 nano/microcrystals (RE = La-Lu lanthanides) was performed via hydrothermal reaction under 200 °C and pH = 12 for 30 h, and the effects of malate (Mal2−) chelate and lanthanide contraction on phase structure and crystal morphology were revealed. With RE = Y0.95Eu0.05 as representative, the influence of solution pH, reaction temperature/duration and VO43- concentration was also systematically investigated. It was shown that, under the optimized VO43-:RE3+:Mal2− molar ratio of 1.5:1.0:2.0, the products are tetragonal (t-) structured REVO4 for RE = Ce-Lu while a mixture of monoclinic (m-) and tetragonal (∼10.8 wt%) phases for RE = La. Mal2− was demonstrated to promote crystallization of phase-pure REVO4 through suppressing RE3+ hydrolysis to form hydroxyl nitrate impurity. The t-REVO4 crystals were identified to be single-crystalline quasi-squares significantly exposed with high energy {001} facets (∼80% coverage for t-LuVO4) owing to the capping effect of Mal2-, and the lateral size of the crystals gradually increased from ∼20 to 850 nm with decreasing RE3+ size from Ce3+ to Lu3+. Aside from structure details and IR response of the full series of REVO4, photoluminescence features were analyzed for RE = Y0.95Eu0.05, Eu, Dy, Er and Tm, and the 600 °C calcined (Y0.95Eu0.05)VO4 red phosphor was determined to have favorably-high external and internal quantum efficiencies of ∼56% and 80%, respectively, a fluorescence lifetime of ∼1.45 ± 0.01 ms and color coordinates of around (0.67, 0.33) under 313 nm excitation.

Tetragonal structured LaVO4 (t-LaVO4) is superior to its thermodynamically stable monoclinic polymorph for luminescence, but is difficult to synthesize without using chelating molecules/ions. With Ln2(OH)4SO4·2H2O layered hydroxide as an original template for phase conversion, well-crystallized t-(La0.95Eu0.05)VO4 nanocrystals (∼40–80 nm) of favorable dispersion and size uniformity were directly produced via hydrothermal reaction at 200 °C for 24 h without using any organic additive. Luminescence analysis showed that the as-made nanocrystals have an absolute quantum yield of ∼38.2%, fluorescence lifetime of ∼1.52 ms and color coordinates of around (0.33, 0.65) under 304 nm UV excitation at room temperature (RT), and the 620 nm main emission can retain as high as ∼66% of its RT intensity at 150 °C. The activation energies for thermal quenching were analyzed to be ∼0.321, 0.286 and 0.291 eV for the 537 nm (5D1 → 7F1 transition), 593 nm (5D0 → 7F1) and 620 nm (5D0 → 7F2) emissions of Eu3+, respectively. Application of the nanocrystals in LED lighting found electric-field enhanced 5D0 → 7F4 emission of Eu3+ (∼697 nm) and increasing brightness but relatively stable color correlated temperature (∼3270–3400 K) at a higher driving current (30–100 mA) for the lamp. The nanocrystals were also demonstrated to be well capable of temperature sensing with the I537/I593 fluorescence intensity ratio, whose maximum absolute sensitivity, maximum relative sensitivity and minimum temperature resolution are ∼0.27% K−1, 2.01% K−1, and 0.015 K at 298 K, respectively.

Layered hydroxide sulfate (Ln2(OH)4SO4·2H2O) was employed as a new template for (La0.97Yb0.02RE0.01)F3 nanocrystal synthesis via fluorination (RE=Ho, Er), with the course of phase/morphology evolution being studied in detail via systematically varying the reaction temperature (up to 180 °C) and reaction duration (up to 24 h). It was shown that phase conversion can be completed by reaction at room temperature for 24 h or at 180 °C for ∼2 h and the hexagonal structure of LaF3:Yb/RE preferentially develops with ab instead of (00l) basal planes. The upconversion (UC) performance under 978 nm laser excitation was examined with the nanocrystals synthesized via 24 h of reaction at 180 °C (average crystallite size ∼31 nm), and it was found that LaF3:Yb/Ho exhibits luminescence at ∼540 nm (5S2 → 5I8 transition, medium strong), 642 nm (5F5 → 5I8, strong) and 749 nm (5F4 → 5I7, weak), with chromaticity coordinates of about (0.47, 0.52), and LaF3:Yb/Er shows strong green (∼520/540 nm, 2H11/2,4S3/2 → 4I15/2) and even stronger red (∼656/668 nm, 4F9/2 → 4I15/2) luminescence, with color coordinates of around (0.45, 0.54). Analyzing the UC intensity against excitation power (0.6-1.5 W) via logarithmic data transformation found approximately two laser photons are needed to populate the emitting state, implying a two-photon mechanism for the observed UC luminescence of the two types of fluoride nanocrystals. The 642 and 656 nm main emissions of Ho3+ and Er3+ were found to decay in a single exponential manner and have fluorescence lifetimes of ∼742 and 1453 μs, respectively.

An unmanned aerial vehicle (UAV) swarm together with a large-scale heterogeneous Internet of Things (IoT) network consisting of macrocells and energy-constrained IoT transmitters (IoT-Ts) is investigated. The UAVs are utilized as flying robot swarms that intelligently transfer energy to the energy-constrained IoT-Ts on the ground. Each IoT-T has an associated IoT device (IoT-D) that is placed at a fixed distance in a random direction. The transmission probability of the energy-constrained IoT-Ts is derived by considering one-slot charging and two-slot charging according to three dimensional (3D) locations, respectively. The coverage probability of each type of IoT-D is investigated. The energy efficiency is derived by considering the transmission power of the active IoT-Ts and the effect of the association biasing factor, and the energy efficiency is also maximized by deploying the optimal density of IoT-Ts. Simulation results are examined to validate the accuracy of our theoretical analysis. Results illustrate the insightful effects of the network parameters, and the helpful guidelines for practical UAV swarms and IoT system design.

Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to integrate these two tasks, with the benefit of reducing the overall computational complexity and improving the classification accuracy of each task. In this paper, we propose a dual-task neural network named AMSCN that simultaneously classifies the modulation and the transmitter of the received signal. In the AMSCN, we first use a combination of DenseNet and Transformer as the backbone network to extract the distinguishable features; then, we design a mask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To train the AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropy loss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our method achieves performance gains for the SEI task with the aid of additional information from the AMC task. Compared with the traditional single-task model, our classification accuracy of the AMC is generally consistent with the state-of-the-art performance, while the classification accuracy of the SEI is improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN.

With the strengths of quickness, low cost, and adaptability, unmanned aerial vehicle (UAV) communication is widely utilized in the next-generation wireless network. However, some risks and hidden dangers such as UAV “black flight” disturbances, attacks, and spying incidents lead to the necessity of the real-time supervision of UAVs. A compressed sensing-based genetic Markov localization method is proposed in this paper for two-dimensional trajectory tracking of the mobile transmitter in a finite domain, which consists of three modules: the multi-station sampling module, the reconstruction module, and the localization module. In the multi-station sampling module, multiple stations are deployed to receive the signal transmitted by the UAV using compressed sensing, and the motion model of the mobile transmitter is the constant turn rate and acceleration (CTRA) model. In the reconstruction module, we propose a direct reconstruction method to extract the joint cross-spatial spectrum. In the genetic Markov localization module, we propose a two-step localization method to genetically correct the inaccurate points in the preliminary results and generate the tracking result. Extensive simulations are conducted to verify the effectiveness of the proposed method. The results show that the proposed method is superior to the particle filter method and the Markov Monte Carlo method at all sampling moments. Specifically, when SNR = 15dB, the root-mean-square error (RMSE) of the proposed method is 39% and 60% lower than that of the other two methods, respectively. Moreover, under the premise that the RMSE of the localization result is less than 30 m, the reconstruction module can reduce the running time of the proposed method by 33.3%.

An efficient methodology for the stereoselective synthesis of cis-2,5-disubstituted pyrrolidines using copper catalyst was developed. The corresponding cis-2,5-disubstituted pyrrolidines could be obtained in reasonable yields and with good stereoselectivities in the presence of 4,4′-di- tert-butyl-2,2′-bipyridine as ligand and 1-methyl-2-pyrrolidinone as solvent. [ABSTRACT FROM AUTHOR]

Background: Accurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Many computational methods based on protein-protein interaction (PPI) networks have been proposed to predict the function of proteins. However, the precision of these predictions still needs to be improved, due to the incompletion and noise in PPI networks. Integrating network topology and biological information could improve the accuracy of protein function prediction and may also lead to the discovery of multiple interaction types between proteins. Current algorithms generate a single network, which is archived using a weighted sum of all types of protein interactions. Method: The influences of different types of interactions on the prediction of protein functions are not the same. To address this, we construct multilayer protein networks (MPN) by integrating PPI networks, the domain of proteins, and information on protein complexes. In the MPN, there is more than one type of connections between pairwise proteins. Different types of connections reflect different roles and importance in protein function prediction. Based on the MPN, we propose a new protein function prediction method, named function prediction based on multilayer protein networks (FP-MPN). Given an un-annotated protein, the FP-MPN method visits each layer of the MPN in turn and generates a set of candidate neighbors with known functions. A set of predicted functions for the testing protein is then formed and all of these functions are scored and sorted. Each layer plays different importance on the prediction of protein functions. A number of top-ranking functions are selected to annotate the unknown protein. Conclusions: The method proposed in this paper was a better predictor when used on Saccharomyces cerevisiae protein data than other function prediction methods previously used. The proposed FP-MPN method takes different roles of connections in protein function prediction into account to reduce the artificial noise by introducing biological information. [ABSTRACT FROM AUTHOR]

A Lewis-acid catalyzed intermolecular Diels-Alder reaction between multisubstituted acyclic dienes and the E and Z isomers of α,β-enals was studied. It was found that the diene reacted selectively with the Z-isomer of the α,β-enal. [ABSTRACT FROM AUTHOR]

Abstract We report a new minimally invasive technique utilizing interventional ultrasound for precise external intracerebral hemorrhage drain (EICHD) placement in pigs.

With plentiful spectrum resources, millimeter-wave (mmWave) band has been the forefront candidate to enable the requirements of lower latency and faster data rate for future automotive systems. However, the incorporation of mmWave directional transmission and diversely vehicular mobility triggers frequent beam realignment, thus largely increasing the beamforming overhead and leaving less time for data transmission. In this paper, an energy-angle domain access and transmission frame structure is investigated for mmWave vehicle-to-everything communications, which consists of two phases, initial access and data transmission. Considering the transmission interruption issue caused by blockage, we propose an energy-angle domain initial access scheme, by which the signals are labeled by different directions with multi-power level. Several performance metrics are subsequently obtained to evaluate the proposed scheme. Next, we formulate the access time-throughput tradeoff problem mathematically and prove that there indeed exists the optimal access time which yields the highest throughput for data transmission. Moreover, a binary-decision beam tracking scheme is designed to maintain the directional link connection in the data transmission phase. Numerical evaluations and simulations have been conducted to verify the correctness of our point of view and the superiority of our proposed methods.

The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.

An efficient Pd(II)-catalyzed difunctionalizationreaction of internalalkynes is reported. In this reaction, various enynoates and electron-deficientalkenes were used for the syntheses of useful highly substituted pyronederivatives via a 6-endocyclization and subsequentalkenylation pathway. The corresponding products could be obtainedin moderate to good yields under very mild reaction conditions. Thehigh regioselectivity and wide compatibility with different functionalgroups found in the desired products demonstrate this method to bea general and useful tool for the synthesis of highly substitutedpyrone derivatives. [ABSTRACT FROM AUTHOR]

Objective To summarize the clinical and pathological features of patients with cardiac lymphoma. Methods The general conditions, clinical features, pathological types, and prognostic indices of 37 patients with cardiac lymphoma treated in our hospital were analyzed. Results Among the 37 patients, only one had primary cardiac lymphoma, and the other 36 patients had secondary cardiac lymphoma. The cardiac manifestations were mainly chest tightness, shortness of breath, increased heart rates, and electrocardiographic abnormality caused by pericardial effusion, but myocardial enzyme levels were normal in all patients. Only three patients displayed solid heart-occupying manifestations. These lesions were mainly located in the right atrium, and the masses were all larger than 5 cm. The pathological type was diffuse large B cell lymphoma that did not arise from the germinal center in all three patients. Conclusions Cardiac lymphoma was mostly secondary, and pericardial effusion was the primary objective sign. Moreover, cardiac lymphoma was characterized by a high international prognostic index, late stage, and high rates of T and NK cell lymphoma. Most cases were accompanied by serous cavity effusion, extranodal involvement of important organs, elevated lactate dehydrogenase levels, hypoalbuminemia, and normal myocardial enzyme levels.

Background and Methods: Acute myeloid leukemia (AML), which starts in the bone marrow, is a group of hematopoietic stem cell disorders. Chloride intracellular channel 4 (CLIC4) is regulated by p53, c-Myc, and TGF-β. It induces the NF-κB-dependent activation of HIF (hypoxia-inducible factor) and participates in tumor growth through its microenvironmental function. However, its prognostic value in AML remains unclear, as well as its co-expression biomarkers. In this study, we evaluated the prognostic significance of CLIC4 expression using two independent large cohorts of cytogenetically normal AML (CN-AML) patients. Multivariable analysis and multi-omics analysis with weighted correlation network analysis (WGCNA) in the CN-AML group were also presented. Based on CLIC4 and its related genes, microRNA–target gene interaction network analysis and downstream gene ontology analysis were performed to unveil the complex functions behind CLIC4.Results: We demonstrated that the overexpression of CLIC4 was notably associated with unfavorable outcome in the two independent cohorts of CN-AML patients [overall survival (OS) and event-free survival (EFS): P < 0.0001, n = 185; OS: P = 0.016, n = 232], as well as in the European LeukemiaNet (ELN) Intermediate-I group (OS: P = 0.015, EFS: P = 0.012, n = 115), the National Comprehensive Cancer Network Intermediate Risk AML group (OS and EFS: P < 0.0001, n = 225), and the non-M3 AML group (OS and EFS: P < 0.0001, n = 435). Multivariable analysis further validated CLIC4 as a high-risk factor in the CN-AML group. Multi-omics analysis presented the overexpression of CLIC4 as associated with the co-expression of the different gene sets in leukemia, up/downregulation of the immune-related pathways, dysregulation of microRNAs, and hypermethylation around the CpG islands, in open sea regions, and in different gene structural fragments including TSS1500, gene body, 5′UTR region, 3′UTR region, and the first exon. By further performing WGCNA on multi-omics data, certain biomarkers that are co-expressed with CLIC4 were also unveiled.Conclusion: We demonstrated that CLIC4 is a novel, potential unfavorable prognosticator and therapeutic target for CN-AML. As having a key role in CN-AML, the interactions between CLIC4 and other genomics and transcriptomics data were confirmed by performing microRNA–target gene interaction network analysis and gene ontology enrichment analysis. The experimental result provides evidence for the clinical strategy selection of CN-AML patients.

As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. Automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.

The hybrid Cartesian grid/gridless method is developed to simulate viscous flows. The flow field is basically discretized by Cartesian grids, and local gridless treatment is embedded in the region adjacent to the body. In order to simulate the thin boundary layer of the viscous flows interested in this paper, the cloud of anisotropic points is implemented in the local gridless region. A cloud overlap-free procedure is proposed which enables the hybrid method to treat complex flows involving close-coupled bodies. The unstructured Cartesian grid needed for the hybrid method is generated based on the quadtree data structure, which can help to control the quality of the clouds at the interface between the grid region and the gridless region. The resulting hybrid method is applied to solve two-dimensional compressible NS equations. The viscous flows past a NACA0012 airfoil and a RAE2822 airfoil are first simulated, and the numerical results obtained present a good agreement with available experimental data. The paper ends with a numerical example of the viscous flow past a two-element airfoil, which demonstrates the ability of the present method for treating more complex flows over multi-bodies. [ABSTRACT FROM AUTHOR]