A novel conducting nanocomposite obtained by p-anisidine and aniline with titanium(IV) oxide nanoparticles: Synthesis, Characterization, and Electrochemical properties

Nanocomposites were successfully synthesized by the oxidative polymerization of p-anisidine and/or aniline monomers (at initial “p-anisidine:aniline” mole ratios of “100 : 0,” 50 : 50,” and “0 : 100”) with titanium(IV) oxide nanoparticles, in the presence of hydrochloric acid as a dopant with ammonium persulfate as an oxidant. The morphological, structural, conductivity, and electrochemical properties of the synthesized nanocomposites were studied using Transmission Electron Microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and UV–vis spectroscopies. The presence of polymer on TiO2 nanoparticles in samples nanocomposites was confirmed by the Transmission Electron Microscopy coupled with Energy Dispersive X-ray Spectroscopy. The thermal stability of samples nanocomposites were evaluated using the Thermogravimetric Analysis. Electrical conductivity of nanocomposites obtained is in the range of 0.08 − 0.91 S cm−1. The electrochemical behavior of the polymers extracted from the nanocomposites has been analyzed by cyclic voltammetry. Good electrochemical response has been observed for polymer films; the observed redox processes indicate that the polymerization on TiO2 nanoparticles produces electroactive polymers. These composite microspheres can potentially be used in commercial applications as fillers for antistatic and anticorrosion coatings.National Assessment and Planning Committee of the University Research (CNEPRU); contract grant number: E-03720130015; contract grant sponsor: MINECO; contract grant number: MAT2013-42007-P; contract grant sponsor: Generalitat Valenciana; contract grant number: PROMETEO2013/038; contract grant sponsor: Directorate General of Scientific Research and Technological Development (DGRSDT) of Algeria

Cross-Domain Cooperative Technology of Intelligent Unmanned Swarm Systems: Current Status and Prospects

As intelligent technologies and unmanned systems develop rapidly, the concept of cross-domain cooperative technology of intelligent unmanned swarm systems has emerged, received widespread attention, and gradually become the high ground in the competition of unmanned system technologies among countries worldwide. Based on the development demand for the cross-domain cooperative technology of intelligent unmanned swarm systems in China, this study summarizes the research status of the crossdomain cooperative technology in typical unmanned scenarios such as sea – air, air – ground, and sea – ground/sea – ground – air, and thoroughly analyzes the current status, technological demand, and key research directions of the technology. Additionally, countermeasures and suggestions are proposed to promote the steady and rapid development of the cross-domain cooperative technology from the perspectives of overall concept, system architecture, theoretical innovation, and technological breakthroughs, with the aim of facilitating the sustained development of unmanned systems in China

Multiple stress fractures: A case report and discussion

Stress fracture is the result of bone destruction with prolonged and repetitive loading. It usually occurs among various groups, including athletes, military recruits, and others. Early stress fractures often undergo undiagnosed or misdiagnosed because of atypical symptoms and effective medical examination. Here, we report a rare clinical case about the multiple stress fractures in one adolescent. Expect for the pathological biopsy, it hardly gets confirm diagnosis. With the increasing population of sports lover, healthcare institutions should be enhanced their understanding of stress fractures and enable effective management at an early stage

Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey

Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a category to each pixel in remote sensing images, plays a vital role in a broad range of applications, such as environmental monitoring, urban planning, and land resource utilization. Recently, with the successful application of deep learning in remote sensing, a substantial amount of work has been aimed at developing SSRSI methods using deep learning models. In this survey, we provide a comprehensive review of SSRSI. First, we review the current mainstream semantic segmentation models based on deep learning. Next, we analyze the main challenges faced by SSRSI and comprehensively summarize the current research status of deep-learning-based SSRSI, especially some new directions in SSRSI are outlined, including semisupervised and weakly-supervised SSRSI, unsupervised domain adaption in SSRSI, multimodal data-fusion-based SSRSI, and pretrained models for SSRSI. Then, we examine the most widely used datasets and metrics and review the quantitative results and experimental performance of some representative methods of SSRSI. Finally, we discuss promising future research directions in this area

Learning Adversarially Robust Object Detector with Consistency Regularization in Remote Sensing Images

Object detection in remote sensing has developed rapidly and has been applied in many fields, but it is known to be vulnerable to adversarial attacks. Improving the robustness of models has become a key issue for reliable application deployment. This paper proposes a robust object detector for remote sensing images (RSIs) to mitigate the performance degradation caused by adversarial attacks. For remote sensing objects, multi-dimensional convolution is utilized to extract both specific features and consistency features from clean images and adversarial images dynamically and efficiently. This enhances the feature extraction ability and thus enriches the context information used for detection. Furthermore, regularization loss is proposed from the perspective of image distribution. This can separate consistent features from the mixed distributions for reconstruction to assure detection accuracy. Experimental results obtained using different datasets (HRSC, UCAS-AOD, and DIOR) demonstrate that the proposed method effectively improves the robustness of detectors against adversarial attacks

Identification and verification of a PANoptosis-related long noncoding ribonucleic acid signature for predicting the clinical outcomes and immune landscape in lung adenocarcinoma

PANoptosis is a type of programmed cell death (PCD) characterised by apoptosis, necroptosis and pyroptosis. Long non-coding ribonucleic acids (lncRNAs) are participating in the malignant behaviour of tumours regulated by PCD. Nevertheless, the function of PANoptosis-associated lncRNAs in lung adenocarcinoma remains to be investigated. In this work, a PANoptosis-related lncRNA signature (PRLSig) was developed based on the least absolute shrinkage and selection operator algorithm. The stability and fitness of PRLSig were confirmed by systematic evaluation of Kaplan–Meier, Cox analysis algorithm, receiver operating characteristic analysis, stratification analysis. In addition, ESTIMATE, single sample gene set enrichment analysis, immune checkpoints and the cancer immunome database confirmed the predictive value of the PRLSig in immune microenvironment and helped to identify populations for which immunotherapy is advantageous. The present research provides novel insights to facilitate risk stratification and optimise personalised treatment for LUAD

Erythrocyte membrane-camouflaged nanoworms with on-demand antibiotic release for eradicating biofilms using near-infrared irradiation

The increase in the number of resistant bacteria caused by the abuse of antibiotics and the emergence of biofilms significantly reduce the effectiveness of antibiotics. Bacterial infections are detrimental to our life and health. To reduce the abuse of antibiotics and treat biofilm-related bacterial infections, a biomimetic nano-antibacterial system, RBCM-NW-G namely, that controls the release of antibiotics through near infrared was prepared. The hollow porous structure and the high surface activity of nanoworms are used to realize antibiotic loading, and then, biomimetics are applied with red blood cell membranes (RBCM). RBCM-NW-G, which retains the performance of RBCM, shows enhanced permeability and retention effects. Fluorescence imaging in mice showed the effective accumulation of RBCM-NW-G at the site of infection. In addition, the biomimetic nanoparticles showed a longer blood circulation time and good biocompatibility. Anti-biofilm test results showed damage to biofilms due to a photothermal effect and a highly efficient antibacterial performance under the synergy of the photothermal effect, silver iron, and antibiotics. Finally, by constructing a mouse infection model, the great potential of RBCM-NW-G in the treatment of in vivo infections was confirmed

S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate the use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms