Road network detection based on improved FLICM-MRF method using high resolution SAR images

The automatic detection of road network from satellite and aerial images is highly significant in many actual applications, for instance, urban traffic measurement, military emergency response, and vehicle target tracking. Compared with other high-resolution satellite remote sensing images, high-resolution synthetic aperture radar (SAR) has become a popular research perspective for road detection owing to its insensitivity to the atmosphere and sun-illumination. However, the method of road network detection is still lagging due to the strong multiplicative speckle noise and complex background interference, causing the loss and break in the road segment extraction results. Aiming to solve this problem, a three-step road network detection framework is proposed. In the first step, the road segment candidates are extracted by the Fuzzy Local Information C-Means (FLICM) algorithm based on the gray-level co-occurrence matrix(GLCM) with Markov Random Fields (MRF), and it contains an adaptive parameter selection procedure which is presented for adjusting joint clustering parameters. In order to reduce false segments, we perform the local processing which combines the morphological operation, linearity index, and local Hough transform in the second step. Finally, as for the global road segment connection, we propose an improved region growing algorithm which fully considering the rationality of road elements to gain the road network. Compared with the traditional region growing algorithm, the proposed method can effectively promote the improvement of the integrity of the road network detection. Moreover, the performance of the proposed method is evaluated by comparing the results with the ground truth road map and the evaluation index including the completeness, correctness, and quality factor. In experiments, the algorithm has been verified with the SAR images from the different resolutions of the GF-3 satellite SAR image. The results of the various real images demonstrate that the proposed algorithm has improved considerably the adaptability and efficiency of road detection compared with other methods

Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management

The harsh environment imposes a unique set of challenges on networking strategies. In such circumstances, the environmental impact on network resources and long-time unattended maintenance has not been well investigated yet. To address these challenges, we propose a flexible and adaptive resource management framework that incorporates the environment awareness functionality. In particular, we propose a new network architecture and introduce the new functionalities against the traditional network components. The novelties of the proposed architecture include a deep-learning-based environment resource prediction module and a self-organized service management module. Specifically, the available network resource under various environmental conditions is predicted by using the prediction module. Then based on the prediction, an environment-oriented resource allocation method is developed to optimize the system utility. To demonstrate the effectiveness and efficiency of the proposed new functionalities, we examine the method via an experiment in a case study. Finally, we introduce several promising directions of resource management in harsh environments that can be extended from this paper.Comment: 8 pages, 4 figures, to appear in IEEE Network Magazine, 202

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices

The α-mating factor secretion signals and endogenous signal peptides for recombinant protein secretion in Komagataella phaffii

Abstract Background The budding yeast Komagataella phaffii (Pichia pastoris) is widely employed to secrete proteins of academic and industrial interest. For secretory proteins, signal peptides are the sorting signal to direct proteins from cytosol to extracellular matrix, and their secretion efficiency directly impacts the yields of the targeted proteins in fermentation broth. Although the α-mating factor (MF) secretion signal from S. cerevisiae, the most common and widely used signal sequence for protein secretion, works in most cases, limitation exists as some proteins cannot be secreted efficiently. As the optimal choice of secretion signals is often protein specific, more secretion signals need to be developed to augment protein expression levels in K. phaffii. Results In this study, the secretion efficiency of 40 α-MF secretion signals from various yeast species and 32 endogenous signal peptides from K. phaffii were investigated using enhanced green fluorescent protein (EGFP) as the model protein. All of the evaluated α-MF secretion signals successfully directed EGFP secretion except for the secretion signals of the yeast D. hansenii CBS767 and H. opuntiae. The secretion efficiency of α-MF secretion signal from Wickerhamomyces ciferrii was higher than that from S. cerevisiae. 24 out of 32 endogenous signal peptides successfully mediated EGFP secretion. The signal peptides of chr3_1145 and FragB_0048 had similar efficiency to S. cerevisiae α-MF secretion signal for EGFP secretion and expression. Conclusions The screened α-MF secretion signals and endogenous signal peptides in this study confer an abundance of signal peptide selection for efficient secretion and expression of heterologous proteins in K. phaffii

Design and Study of a Novel Thermal-Resistant and Shear-Stable Amphoteric Polyacrylamide in High-Salinity Solution

Abstract: Water-soluble polymers are widely used in oilfields. The rheological behaviors of these polymers in high-salinity solution are very important for stimulation of high-salinity reservoirs. In this work, a novel thermal-resistant and shear-stable amphoteric polyacrylamide (PASD), prepared from acrylamide (AM), sodium styrene sulfonate (SSS), and acryloxyethyl trimethylammonium chloride (DAC) monomers, was prepared by free-radical polymerization in high-salinity solution. The amphoteric polyacrylamide was characterized by Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance spectroscopy (1H NMR), elemental analysis, thermogravimetric analysis (TG), and scanning electron microscopy (SEM). The amphoteric polyacrylamide exhibited excellent salinity tolerance. The slow increase in apparent viscosity of the polymer with increase in salinity was interesting. The amphoteric polyacrylamide showed perfect temperature resistance in high-salinity solution. The viscosity retention reached 38.9% at 120 °C and was restored to 87.8% of its initial viscosity when temperature was decreased to room temperature. The retention ratio of apparent viscosity reached 49.7% at 170 s−1 and could still retain it at 25.8% at 1000 s−1. All these results demonstrated that PASD had excellent thermal-resistance and shear-stability in high-salinity solution. We expect that this work could provide a new strategy to design polymers with excellent salinity-tolerance, thermal-resistance, and shear-stability performances

Can More Nanoparticles Induce Larger Viscosities of Nanoparticle-Enhanced Wormlike Micellar System (NEWMS)?

There have been many reports about the thickening ability of nanoparticles on the wormlike micelles in the recent years. Through the addition of nanoparticles, the viscosity of wormlike micelles can be increased. There still exists a doubt: can viscosity be increased further by adding more nanoparticles? To answer this issue, in this work, the effects of silica nanoparticles and temperature on the nanoparticles-enhanced wormlike micellar system (NEWMS) were studied. The typical wormlike micelles (wormlike micelles) are prepared by 50 mM cetyltrimethyl ammonium bromide (CTAB) and 60 mM sodium salicylate (NaSal). The rheological results show the increase of viscoelasticity in NEWMS by adding nanoparticles, with the increase of zero-shear viscosity and relaxation time. However, with the further increase of nanoparticles, an interesting phenomenon appears. The zero-shear viscosity and relaxation time reach the maximum and begin to decrease. The results show a slight increasing trend for the contour length of wormlike micelles by adding nanoparticles, while no obvious effect on the entanglement and mesh size. In addition, with the increase of temperature, remarkable reduction of contour length and relaxation time can be observed from the calculation. NEWMS constantly retain better viscoelasticity compared with conventional wormlike micelles without silica nanoparticles. According to the Arrhenius equation, the activation energy Ea shows the same increase trend of NEWMS. Finally, a mechanism is proposed to explain this interesting phenomenon

Investigation of Novel Triple-Responsive Wormlike Micelles

Smart wormlike micelles with stimuli-tunable rheological properties may be useful in a variety of applications, such as in molecular devices and sensors. The formation of triplestimuli-responsive systems so far has been a challenging and important issue. In this work, a novel triplestimuli (photo-, pH-, and thermoresponsive) wormlike micelle is constructed with <i>N</i>-cetyl-<i>N</i>-methylmorpholinium bromide and <i>trans</i>-cinnamic acid (CA). The corresponding multiresponsive behaviors of wormlike micellar system were revealed using cryogenic transmission electron microscopy, a rheometer, and ¹H NMR. The rheological properties of wormlike micellar system under different temperatures, pH conditions, and UV irradiation times are measured. As confirmed by ¹H NMR, chemical structure of a CA molecule can be altered by the multiple stimulation from an exotic environment. We expect it to be a good model for triple-responsive wormlike micelles, which is helpful to understand the mechanism of triple-responsiveness and widen their applications

Conversion to Resectability Using Transarterial Chemoembolization Combined With Hepatic Arterial Infusion Chemotherapy for Initially Unresectable Hepatocellular Carcinoma

Objective:. To evaluate whether this conversion rate to resectability could be increased when patients are treated with transarterial chemoembolization and hepatic arterial infusion chemotherapy (TACE-HAIC) using oxaliplatin plus fluorouracil/leucovorin. Background:. Conventional TACE (c-TACE) is a common regimen for initially unresectable hepatocellular carcinoma (HCC), which converts to curative-intent resection in about 10% of those patients. It is urgent need to investigated better regimen for those patients. Methods:. The data of 83 initially unresectable HCC patients were examined, including 41 patients in the TACE-HAIC group and 42 patients in the c-TACE group. Their response rate, conversion rate to resection, survival outcome, and adverse events were compared. Results:. The conversion rate was significantly better in the TACE-HAIC group than in the c-TACE group (48.8% vs 9.5%; P 0.05). Conclusions:. TACE-HAIC demonstrated a higher conversion rate and progression-free survival benefit than c-TACE and could be considered as a more effective regimen for patients with initially unresectable HCC. Future prospective randomized trials are needed to confirm it