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Unmanned aerial vehicles (UAVs) can provide wireless access to terrestrial users, regardless of geographical constraints, and will be an important part of future communication systems. In this article, a multi-user downlink dual-UAVs enabled covert communication system was investigated, in which a UAV transmits critical information to ground users in the presence of multiple wardens as well as a friendly jammer UAV transmits artificial jamming signals to fight with the wardens. The scenario of wardens being outfitted with a single antenna is considered, and the detection error probability (DEP) of wardens with finite observations is researched. Then, considering the uncertainty of wardens' location, a robust optimization problem with worst-case covertness constraint is formulated to maximize the average covert rate by jointly optimizing power allocation and trajectory. To cope with the optimization problem, an algorithm based on successive convex approximation methods is proposed. Thereafter, the results are extended to the case where all the wardens are equipped with multiple antennas. After analyzing the DEP in this scenario, a tractable lower bound of the DEP is obtained by utilizing Pinsker's inequality. Subsequently, the nonconvex optimization problem was established and efficiently coped by utilizing a similar algorithm as in the single-antenna scenario. Numerical results indicate the effectiveness of our proposed algorithm.

Graphitized carbonitride (g-C3N4) is widely used in CO2reduction, hydrogen production, and degradation of toxic chemical dyes and antibiotics. It is a kind of photocatalytic material with excellent performance, and it has the advantages of being safe and nontoxic, having a suitable band gap (2.7 eV), and having a simple preparation and high stability, but because of its fast optical recombination speed and low visible light overutilization, the multifunctional application of g-C3N4is seriously hindered. Compared with pure g-C3N4, MWCNTs/g-C3N4have a red-shift in the visible range and a strong absorption in the visible region. Melamine and carboxylated multiwalled carbon nanotubes were used as raw materials to successfully prepare CMWCNT modified g-C3N4doped with P, Cl by a high temperature calcination method. The effect of the addition amount of P, Cl on the photocatalytic performance of modified g-C3N4was studied. The experimental results show that the multiwalled carbon nanotubes can accelerate the electron migration, and the doping of P, Cl elements can change the energy band structure of g-C3N4and reduce the band gap. Through fluorescence analysis and photocurrent analysis, it is known that the incorporation of P, Cl reduces the recombination efficiency of photogenerated electron–hole pairs. In order to explore the application in the degradation of chemical dyes, the photocatalytic degradation efficiency of RhB under visible light was studied. The photocatalytic performance of the samples was evaluated by photodecomposition of aquatic hydrogen. The results showed that when the amount of ammonium dihydrogen phosphate was 10 wt %, the photocatalytic degradation efficiency was the highest, which was 21.13 times higher than that of g-C3N4.

The progress of membrane-based applications is hindered by widespread membrane fouling, which leads to a significant decrease in separation effectiveness and lifespan, even though membrane separations have shown great potential in the treatment of dye wastewater. The metal-organic framework MOFs (ZIF-8) display strong thermal and chemical stability and an exceptional capacity to degrade organic molecules under visible light, making them extremely promising for the development of self-cleaning membranes. In this investigation, a ZIF-8/L-DOPA/PVDF mixed matrix membrane with excellent self-cleaning properties was constructed using the in-situ growth technique. The characterization results confirmed the formation of a continuous ZIF-8 coating layer, considerably increasing the surface roughness of the composite membrane from 22.9 nm to 76.3 nm compared to L-DOPA/PVDF substrates. The ZIF-8 layer deposited on the L-DOPA/PVDF membrane surface reduced the contact angle from 75.71° to 40.94°. The highest-performing ZIF-8/L-DOPA/PVDF membranes show a higher water permeability of 159.63 (L·m−2h−1bar−1) and superior dye rejection rates (Congo red: 97.3 %, Methylene Blue: 98.32 %, Methyl Blue: 98.62 %), along with enhanced removal of antibiotics (e.g., Tetracycline: 95.3 % and Ciprofloxacin: 93.34 %), and long-term nanofiltration stability. Furthermore, the treated membrane recovers almost all of its initial separation efficacy after exposure to visible light due to the successful removal of attached dyes from the membrane surface through photocatalysis. This work presents the potential to fabricate membranes with advantageous photocatalytic properties and effectively treat dye wastewater using self-cleaning membranes.

The incidence of papillary thyroid carcinoma (PTC) has increased significantly in recent years, and for patients with metastatic and recurrent PTC, the options for treatment currently available are insufficient. To date, the exact molecular mechanism underlying PTC is still not fully understood. 5-Methylcytosine (m5C) RNA methylation is associated with the prognosis of a variety of tumors. However, the molecular mechanisms and biomarkers associated with m5C in the diagnosis, treatment, and prognosis of this disease have not been fully elucidated. Ten m5C regulators with significantly different expression levels were included in this study. Immune infiltration analysis revealed significant negative correlations between most of these regulators and regulatory T cells. TRDMT1, NSUN5, and NSUN6 had high weights and strong correlations in the protein–protein interaction network. Using gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis, 1489 differentially expressed genes were screened from The Cancer Genome Atlas messenger RNA matrix, indicating that these differentially expressed genes were significantly enriched in various pathways and functions related to cancers. Four m5C regulators, NSUN2, NSUN4, NSUN6, and DNMT3B, were screened as prognostic markers by least absolute shrinkage and selection operator regression analysis, and NSUN2 and NSUN6 were identified as risk factors for poor prognosis. We found that the prognostic prediction model constructed using the m5C regulators NSUN2, NSUN4, NSUN6, and DNMT3B showed good prognostic prediction ability and diagnostic ability. This model was applied to predict the survival probability of patients with PTC, the prediction ability of 5-year survival was the best. The multi-factor prognostic prediction model combined with the tumor node metastasis stage and risk score grouping showed better prognostic predictive power.

Clustering is widely exploited in data mining. It has been proved that embedding weak label prior into clustering is effective to promote its performance. Previous researches mainly focus on only one type of prior. However, in many real scenarios, two kinds of weak label prior information, e.g., pairwise constraints and cluster ratio, are easily obtained or already available. How to incorporate them to improve clustering performance is important but rarely studied. We propose a novel constrained Clustering with Weak Label Prior method (CWLP), which is an integrated framework. Within the unified spectral clustering model, the pairwise constraints are employed as a regularizer in spectral embedding and label proportion is added as a constraint in spectral rotation. To approximate a variant of the embedding matrix more precisely, we replace a cluster indicator matrix with its scaled version. Instead of fixing an initial similarity matrix, we propose a new similarity matrix that is more suitable for deriving clustering results. Except for the theoretical convergence and computational complexity analyses, we validate the effectiveness of CWLP through several benchmark datasets, together with its ability to discriminate suspected breast cancer patients from healthy controls. The experimental evaluation illustrates the superiority of our proposed approach.

A highly bright two-photon fluorescent probe Lyso-OSC, was developed for highly sensitive real-time detection of autophagy process in living cells.

Background:Abnormal blood pressure (BP) is one of the most common modified risk factors for the development of cognitive impairment (CI) with increasing aging population.Objective:This study aims to investigate the association between risk factors and CI in elderly patients based on artificial intelligence (AI) analysis of BP measurements and brain magnetic resonance angiography (MRA).Methods:A total of 3221 elderly individuals undergone brain MRA were recruited into the nested case-control study from 2007 to 2021. Patients were divided into two groups according to diagnosis with or without CI. Data were collected before diagnosing CI. The stenosis rate of cerebral arteries was calculated by AI analysis of brain MRA. Four machine learning modalities were applied to establish predictive models.Results:A total of 416 cases (mean age 84.47 ± 6.50 years old) with or without CI were finally recruited into the study after matching with age and gender. Mean follow-up time was 3.46 ± 3.19 years. The number of BP measurements was 56.98 ± 77.94 times. In the univariate logistic regression model, high systolic blood pressure (SBP) (OR 1.029, 95% CIs 1.010-1.049), pulse pressure (PP) (OR 1.043, 95% CIs: 1.021-1.065) and pulse pressure index (PPI) (OR 3588.920, 95% CIs: 44.392-290148.336) increased the risk of CI. Based on various mean BP parameters, the predictive ability of the support vector machine (SVM) was improved after adding cerebral arterial stenosis rate (AUC 0.781, 95% CIs 0.639~0.923).Conclusion:Abnormal SBP, PP and PPI were risk factors for CI in the elderly Chinese population. Adding the cerebral arterial stenosis rate of internal carotid artery and posterior cerebral artery the predictive ability of SVM model of various mean BP parameters for the risk of CI was improved.

To effectively improve the ride comfort of heavy-duty trucks, and relieve the driver's fatigue and ensure the integrity of the cargo. In this study, first, according to the requirements of GB/T 7031-2005/ISO 8608, the random sine wave superposition method is used to determine the random excitation of a road surface and establish an FRC tyre model based on the elastic roller theory. Second, the Jiefang-6P truck is considered as an example to establish the linear model of heavy-duty trucks, conducting virtual experiments by establishing vehicle–tyre–road closed-loop system. Finally, orthogonal tests with the control parameters as variables are designed to determine the optimal combination of control parameters for best smooth driving. Thereafter, range analysis is used to determine the degree of impact of each control parameter. The effects rules of load, vehicle speed, tyre stiffness and tyre damping on ride comfort are obtained, and the results show that the best ride comfort can be obtained when the tyre stiffness is 60,000 N/m and the tyre damping is about 600 N s/m, and the tyre stiffness has the highest influence on the ride comfort.