Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery

Potpuno iskorištavanje taloga preostalog nakon priprave zeolitskog katalizatora

A novel utilization method of filter residue from the preparation process of zeolite-based catalysts was investigated. Y zeolite and a fluid catalytic cracking (FCC) catalyst were synthesized from filter residue. Compared to the Y zeolite synthesized by the conventional method, the Y zeolite synthesized from filter residue exhibited better thermal stability. The catalyst possessed wide-pore distribution. In addition, the pore volume, specific surface area, attrition resistance were superior to those of the reference catalyst. The yields of gasoline and light oil increased by 1.93 and 1.48 %, respectively. At the same time, the coke yield decreased by 0.41 %. The catalyst exhibited better gasoline and coke selectivity. The quality of the cracked gasoline had been improved.Proučena je upotreba taloga preostalog nakon priprave katalizatora baziranog na zeolitima. Od taloga su pripravljeni zeolit Y i katalizator za katalitičko krekiranje u fluidiziranom sloju (FCC). Ovako sintetiziran zeolit Y termički je stabilniji u odnosu na zeolit Y sintetiziran konvencionalnom metodom. Katalizator ima široku distribuciju veličine pora. U odnosu na referentni katalizator pokazuje veći obujam pora i specifičnu površinu te bolju otpornost na atriciju. Iskorištenje benzina i lakog ulja povećalo se za 1,93 i 1,48 %, a koksa smanjilo za 0,41 %. Katalizator je pokazao bolju selektivnost s obzirom na benzin i koks, a poboljšala se kvaliteta krekiranog benzina

Humanin Rescues Cultured Rat Cortical Neurons from NMDA-Induced Toxicity Not by NMDA Receptor

Excitatory neurotoxicity has been implicated in many pathological situations and there is no effective treatment available. Humanin is a 24-aa peptide cloned from the brain of patients with Alzheimer’s disease (AD). In the present study, excitatory toxicity was induced by N-methyl-D-aspartate (NMDA) in primarily cultured rat cortical neurons. MTT assessment, lactate dehydrogenase (LDH) release, and calcein staining were employed to evaluate the protective activity of humanin on NMDA induced toxicity. The results suggested that NMDA (100 μmol/L, 2.5 hr) triggered neuronal morphological changes, lactate dehydrogenase (LDH) release (166% of the control), reduction of cell viability (about 50% of the control), and the decrease of living cell density (about 50% of the control). When pretreated with humanin, the toxicity was suppressed. The living cells’ density of humanin treated group was similar to that of control. The cell viability was attenuated dose-dependently (IC50 = 0.132 nmol/L). The LDH release was also neutralized in a dose-dependent manner. In addition, the intracellular Ca2+ overloading triggered by NMDA reverted quickly and humanin could not inhibit it. These findings indicate that humanin can rescue cortical neurons from NMDA-induced toxicity in rat but not through interfering with NMDA receptor directly