Non-Visible Light Data Synthesis and Application: A Case Study for Synthetic Aperture Radar Imagery

We explore the "hidden" ability of large-scale pre-trained image generation models, such as Stable Diffusion and Imagen, in non-visible light domains, taking Synthetic Aperture Radar (SAR) data for a case study. Due to the inherent challenges in capturing satellite data, acquiring ample SAR training samples is infeasible. For instance, for a particular category of ship in the open sea, we can collect only few-shot SAR images which are too limited to derive effective ship recognition models. If large-scale models pre-trained with regular images can be adapted to generating novel SAR images, the problem is solved. In preliminary study, we found that fine-tuning these models with few-shot SAR images is not working, as the models can not capture the two primary differences between SAR and regular images: structure and modality. To address this, we propose a 2-stage low-rank adaptation method, and we call it 2LoRA. In the first stage, the model is adapted using aerial-view regular image data (whose structure matches SAR), followed by the second stage where the base model from the first stage is further adapted using SAR modality data. Particularly in the second stage, we introduce a novel prototype LoRA (pLoRA), as an improved version of 2LoRA, to resolve the class imbalance problem in SAR datasets. For evaluation, we employ the resulting generation model to synthesize additional SAR data. This augmentation, when integrated into the training process of SAR classification as well as segmentation models, yields notably improved performance for minor classe

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Chronic Lead (Pb) Exposure Increased Dura Mater Meningeal Macrophages in C57BL/6j Young Mice

Chronic low-level lead (Pb) exposure is detrimental for child neurocognitive development and health. Mouse studies modeling exposure in children have suggested that chronic low-level Pb exposure causes olfactory memory deficits, decreases hippocampal microglia cell numbers, and increases neuroinflammation. The specific underlying mechanisms driving these deficits remain unknown. The dura mater meninges is important for modulating neuroinflammation and macrophages residing both in the brain and dura mater meninges might regulate Pb exposure- induced deficits. It was hypothesized that chronic low- and high-level Pb exposed mice would have increased numbers of meningeal macrophages as compared to controls. Stereological analyses revealed that the high-dose Pb exposed group had significantly increased numbers of meningeal macrophages as compared to controls. Additionally, males had a significantly higher number of meningeal macrophages as compared to females. Future studies should examine the functional relevance of these findings and examine potential treatment interventions to rescue pb-induced brain and behavioral damages

Lane-aware image enhancement for lane detection in rain (part B - communications)

In recent years, auto-driving is becoming a hot topic. Auto-driving cars utilize in-vehicle cameras to capture the surrounding environment images and use algorithms to extract useful information from images. One of the most critical issues to be solved in auto-driving is lane detection and road marking recognition. By implementing lane and road marking detection algorithms, surrounding traffic symbols can be recognized and used to help the human driver avoid accidents. Two central problems remain in such process: 1) the curved road is not easy to detect; 2) the rainy condition may distort the image captured by cameras, thus hard to recognize. In this dissertation, a network structure RVPGNet, based on previous work VPGNet, is defined to address above mentioned problems. Novel ways of combining the information and efficiently employing the abstract training data are proposed. Inspired by VPGNet, the RVPGNet employs multi-task to do lane detection and road marking classification tasks simultaneously, and it also utilizes the vanishing point to guide the lane prediction. Besides, RVPGNet features in four innovative combination layers and algorithms: 1) To save computational resources, a new information combination layer, called the 4-tiling layer, was proposed and applied; Two new feeding schemes, called 2) N-map layer and 3) 2-D Gaussian feeding layer, were designed to utilize the vanishing point better and avoid training being trapped at the saddle point; 4) The network is implemented in Caffe framework and currently under construction in PyTorch framework. The experimental results are significant. In the Caffe implementation, a 97:19% accuracy is achieved in multi-label classification. In the test of rainy conditions, the network achieves as high as 93:35% to 99:73% F1 score in the blurry and low-brightness images. Currently, we are transplanting the network from Caffe to the state-of-the-art PyTorch framework. The overall structure has been constructed and debugging on test metric and backpropagation is in progress. Based on this dissertation’s work, many valuable improvements can be made in the future. 1) The PyTorch implementation’s main structure is finished, so future research only needs to refine the backpropagation, implement the 2-D Gaussian in Torch tensors. Besides, it is worthy to refine the selection of the initialization function. 2) A loss function measuring the offset of Vanishing Point and ground truth can be constructed; 3) Re-scale the network from low-definition to high resolution for pixel-to-pixel classification. All the diagrams are drawn in vector graph format, so readers can zoom in to check the details.Master of Science (Communications Engineering

Effect of calcination temperature on rare earth tailing catalysts for catalytic methane combustion

Bayan Obo tailings are rich in rare earth elements (REEs), iron, and other catalytic active substances. In this study, mine tailings were calcined at different temperatures and tested for the catalytic combustion of low-concentration methane. Upon calcination at 600°C, high catalytic activity was revealed, with 50% CH4 conversion at 587°C (space velocity of 12,000 mL/g h). The physicochemical properties of catalysts were characterized using thermogravimetric analysis, X-ray diffraction, scanning electron microscopy, hydrogen temperature-programmed reduction (H2-TPR), and X-ray photoelectron spectroscopy (XPS). Compared to the raw ore sample, the diffraction peak intensity of Fe2O3 increased post calcination, whereas that of CeCO3F decreased. A porous structure appeared after the catalyst was calcined at 600°C. Additionally, Fe, Ce, Ti, and other metal elements were more highly dispersed on the catalyst surface. H2-TPR results revealed a broadening of the reduction temperature range for the catalyst calcined at 600°C and an increase in the reduction peak. XPS analysis indicated the presence of Ce in the form of Ce3+ and Ce4+ oxidation states and the coexistence of Fe in the form of Fe2+ and Fe3+. Moreover, XPS revealed a higher surface Oads/Olatt ratio. This study provides evidence for the green reuse of Bayan Obo mine tailings in secondary resources

Potato (Solanum tuberosum L.) tuber-root modeling method based on physical properties.

The development of tuber-root models based on the physical properties of the root system of a plant is a prominent but complicated task. In this paper, a method for the construction of a 3D model of a potato tuber-root system is proposed, based on determining the characterization parameters of the potato tuber-root model. Three early maturing potato varieties, widely planted in Northeast China, were selected as the research objects. Their topological and geometric structures were analyzed to determine the model parameters. By actually digging potatoes in the field, field data measurement and statistical analysis of the parameters were performed, and a model parameter database was established. Based on the measured data, the root trajectory points were obtained by simulating the growth of the root tips. Then MATLAB was used to develop a system that would complete the construction of the potato tuber-root 3D visualization model. Finally, the accuracy of the model was verified experimentally. Case studies for the three different types indicated an acceptable performance of the proposed model, with a relative root mean square error of 6.81% and 15.32%, for the minimum and maximum values, respectively. The research results can be used to explore the interaction between the soil-tuber-root aggregates and the digging components, and provide a reference for the construction of root models of other tuber crops

Multi-replicas integrity checking scheme with supporting probability audit for cloud-based IoT

Nowadays, more people are choosing to use cloud storage services to save space and reduce costs. To enhance the durability and persistence, users opt to store important data in the form of multiple copies on cloud servers. However, outsourcing data in the cloud means that it is not directly under the control of users, raising concerns about security and integrity. Recent research has found that most existing multicopy integrity verification schemes can correctly perform integrity verification even when multiple copies are stored on the same Cloud Service Provider (CSP), which clearly deviates from the initial intention of users wanting to store files on multiple CSPs. With these considerations in mind, this paper proposes a scheme for synchronizing the integrity verification of copies, specifically focusing on strongly privacy Internet of Things (IoT) electronic health record (EHR) data. First, the paper addresses the issues present in existing multicopy integrity verification schemes. The scheme incorporates the entity Cloud Service Manager (CSM) to assist in the model construction, and each replica file is accompanied with its corresponding homomorphic verification tag. To handle scenarios where replica files stored on multiple CSPs cannot provide audit proof on time due to objective reasons, the paper introduces a novel approach called probability audit. By incorporating a probability audit, the scheme ensures that replica files are indeed stored on different CSPs and guarantees the normal execution of the public auditing phase. The scheme utilizes identity-based encryption (IBE) for the detailed design, avoiding the additional overhead caused by dealing with complex certificate issues. The proposed scheme can withstand forgery attack, replace attack, and replay attack, demonstrating strong security. The performance analysis demonstrates the feasibility and effectiveness of the scheme

Probabilistic ecological risk assessment of heavy metals in western Laizhou Bay, Shandong Province, China.

Considering the serious land-based pollution and the weak water exchange ability of western Laizhou Bay, it is essential to conduct an ecological risk assessment of the pollutants in this area. In this study, the ecological risk caused by heavy metals deposited in the surface sediments and those resuspended in the seawater of western Laizhou Bay was evaluated using probabilistic approaches. First, the concentrations of seven heavy metals, namely As, Cd, Cr, Cu, Hg, Pb, and Zn, in the surface sediments and seawater of western Laizhou Bay were detected during the spring and autumn of 2016. The concentrations of As, Cd, Cr, Cu, and Pb were found to be at levels comparable to those in the other global coastal systems, while those of Hg and Zn were lower than those in other coastal areas. Next, an ecological risk assessment of heavy metals in the surface sediments was performed using a typical potential ecological risk index and refined by using a Monte Carlo simulation. The results suggested low risk for the heavy metals detected in the sediments of western Laizhou Bay, with the exception of Hg in September 2016, which showed a probability (0.03%) of moderate risk. Meanwhile, the aquatic ecological risk assessment of the heavy metals was performed by applying a combination of hazard quotient (HQ) and joint probability curve. While the ecological risk of Cd, Hg, and Pb was found to be acceptable, the HQs for Cr, Cu, and Zn were greater than 1, and the overall risk probability of their adverse effects was higher than 0.05, suggesting certain ecological risk. Specifically, in the case of As, the overall risk probability was lower than 0.05, suggesting that its ecological risk was acceptable, although its HQ was greater than 1. Thus, by applying the probabilistic approaches, the ecological risk of the heavy metals in western Laizhou Bay was better characterized in this study, avoiding both overestimation and underestimation of ecological risk