Super-Resolution Exciton Imaging of Nanobubbles in 2D Semiconductors with Near-Field Nanophotoluminescence Microscopy.

Two-dimensional (2D) semiconductors, such as transition metal dichalcogenides, have emerged as important candidate materials for next-generation chip-scale optoelectronic devices with the development of large-scale production techniques, such as chemical vapor deposition (CVD). However, 2D materials need to be transferred to other target substrates after growth, during which various micro- and nanoscale defects, such as nanobubbles, are inevitably generated. These nanodefects not only influence the uniformity of 2D semiconductors but also may significantly alter the local optoelectronic properties of the composed devices. Hence, super-resolution discrimination and characterization of nanodefects are highly demanded. Here, we report a near-field nanophotoluminescence (nano-PL) microscope that can quickly screen nanobubbles and investigate their impact on local excitonic properties of 2D semiconductors by directly visualize the PL emission distribution with a very high spatial resolution of ∼10 nm, far below the optical diffraction limit, and a high speed of 10 ms/point under ambient conditions. By using nano-PL microscopy to map the exciton and trion emission intensity distributions in transferred CVD-grown monolayer tungsten disulfide (1L-WS2) flakes, it is found that the PL intensity decreases by 13.4% as the height of the nanobubble increases by every nanometer, which is mainly caused by the suppression of trion emission due to the strong doping effect from the substrate. In addition to the nanobubbles, other types of nanodefects, such as cracks, stacks, and grain boundaries, can also be characterized. The nano-PL method is proven to be a powerful tool for the nondestructive quality inspection of nanodefects as well as the super-resolution exploration of local optoelectronic properties of 2D materials.

Seasonal variations of intensity of avian malaria infection in the Thousand Island Lake System, China.

BACKGROUND: Migratory birds play an important part in the spread of parasites, with more or less impact on resident birds. Previous studies focus on the prevalence of parasites, but changes in infection intensity over time have rarely been studied. As infection intensity can be quantified by qPCR, we measured infection intensity during different seasons, which is important for our understanding of parasite transmission mechanisms. METHODS: Wild birds were captured at the Thousand Island Lake with mist nets and tested for avian hemosporidiosis infections using nested PCR. Parasites were identified using the MalAvi database. Then, we used qPCR to quantify the infection intensity. We analyzed the monthly trends of intensity for all species and for different migratory status, parasite genera and sexes. RESULTS: Of 1101 individuals, 407 were infected (37.0%) of which 95 were newly identified and mainly from the genus Leucocytozoon. The total intensity trend shows peaks at the start of summer, during the breeding season of hosts and during the over-winter season. Different parasite genera show different monthly trends. Plasmodium causes high prevalence and infection intensity of winter visitors. Female hosts show significant seasonal trends of infection intensity. CONCLUSIONS: The seasonal changes of infection intensity is consistent with the prevalence. Peaks occur early and during the breeding season and then there is a downward trend. Spring relapses and avian immunity are possible reasons that could explain this phenomenon. In our study, winter visitors have a higher prevalence and infection intensity, but they rarely share parasites with resident birds. This shows that they were infected with Plasmodium during their departure or migration and rarely transmit the disease to resident birds. The different infection patterns of different parasite species may be due to vectors or other ecological properties.

Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization.

In the traditional particle swarm optimization algorithm, the particles always choose to learn from the well-behaved particles in the population during the population iteration. Nevertheless, according to the principles of particle swarm optimization, we know that the motion of each particle has an impact on other individuals, and even poorly behaved particles can provide valuable information. Based on this consideration, we propose Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization, called LFIACL-PSO. In the LFIACL-PSO algorithm, First, when the particle is trapped in the local optimum and cannot jump out, inverse learning is used, and the learning step size is obtained through the Lévy flight. Second, to increase the diversity of the algorithm and prevent it from prematurely converging, a comprehensive learning strategy and Ring-type topology are used as part of the learning paradigm. In addition, use the adaptive update to update the acceleration coefficients for each learning paradigm. Finally, the comprehensive performance of LFIACL-PSO is measured using 16 benchmark functions and a real engineering application problem and compared with seven other classical particle swarm optimization algorithms. Experimental comparison results show that the comprehensive performance of the LFIACL-PSO outperforms comparative PSO variants.

Extraction of 3D distribution of potato plant CWSI based on thermal infrared image and binocular stereovision system.

As the largest component of crops, water has an important impact on the growth and development of crops. Timely, rapid, continuous, and non-destructive detection of crop water stress status is crucial for crop water-saving irrigation, production, and breeding. Indices based on leaf or canopy temperature acquired by thermal imaging are widely used for crop water stress diagnosis. However, most studies fail to achieve high-throughput, continuous water stress detection and mostly focus on two-dimension measurements. This study developed a low-cost three-dimension (3D) motion robotic system, which is equipped with a designed 3D imaging system to automatically collect potato plant data, including thermal and binocular RGB data. A method is developed to obtain 3D plant fusion point cloud with depth, temperature, and RGB color information using the acquired thermal and binocular RGB data. Firstly, the developed system is used to automatically collect the data of the potato plants in the scene. Secondly, the collected data was processed, and the green canopy was extracted from the color image, which is convenient for the speeded-up robust features algorithm to detect more effective matching features. Photogrammetry combined with structural similarity index was applied to calculate the optimal homography transform matrix between thermal and color images and used for image registration. Thirdly, based on the registration of the two images, 3D reconstruction was carried out using binocular stereo vision technology to generate the original 3D point cloud with temperature information. The original 3D point cloud data were further processed through canopy extraction, denoising, and k-means based temperature clustering steps to optimize the data. Finally, the crop water stress index (CWSI) of each point and average CWSI in the canopy were calculated, and its daily variation and influencing factors were analyzed in combination with environmental parameters. The developed system and the proposed method can effectively detect the water stress status of potato plants in 3D, which can provide support for analyzing the differences in the three-dimensional distribution and spatial and temporal variation patterns of CWSI in potato.

A multi-sample particle swarm optimization algorithm based on electric field force.

Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.

Computerized Tomography Radiomics Features Analysis for Evaluation of Perihematomal Edema in Basal Ganglia Hemorrhage.

To evaluate the edema area around basal ganglia hemorrhage by the application of computerized tomography (CT)-based radiomics as a prognostic factor and improve the diagnosis efficacy, a total of 120 patients with basal ganglia hemorrhage were analyzed retrospectively. The texture analysis software Mazda 3.3 was used to preprocess the CT images and manually sketch the region of interest to extract the texture features. The extracted texture features were selected by Fisher coefficient, POE+ACC and mutual information. The texture discriminant analysis uses the B11 module in the Mazda 3.3 software. The data were randomly divided into a training dataset (67%) and test dataset (33%). To further study the texture features, the training dataset can be divided into groups according to the median of GCS score, NIHSS score, and maximum diameter of hematoma. Random forest model, support vector machine model, and neural network model were built. AUC of the receiver operating characteristics curve was used to assess the performance of models with test dataset. Among all texture post-processing methods, the lowest error rate was 2.22% for the POE+ACC/nonlinear discriminant. For the maximum diameter of hematoma, GCS score, and NIHSS score group, the lowest error rate were 26.66%, 23.33%, and 30.00%, respectively. The values of AUCs were 0.87, 0.81, and 0.76, for random forest model, support vector machine model, and neural network model in the test dataset, respectively. Radiomic method with proper model may have a potential role in predicting the edema area around basal ganglia hemorrhage. It can be used as a secondary group in the diagnosis of edema area around basal ganglia hemorrhage.

Single-channel UV/vis dual-band detection with ZnCdS:Mn/ZnS core/shell quantum dots.

In the ultraviolet detection system, the Si-based photodetector could be sensitised with different kinds of fluorescent material to enhance its response in the short-wavelength range. Thick-shell ZnCdS:Mn/ZnS core/shell quantum dots (QDs) exhibit unique advantages in UV signal sensitisation due to their long PL lifetime, as well as stable emission matched with CCD's response. Herein, a single-channel UV panoramic detection system based on these Mn-doped QDs has been proposed. The QDs@PMMA film was attached on a Si-based CCD camera versus a tapered fibre, and an optical chopper was mounted before the QDs@PMMA film. The long lifetime fluorescence originating from UV signal could be still collected by the CCD camera when the chopper is in the 'off' state, hence the UV/vis signal ratio is significantly enhanced.