Generic and efficient framework for search trees on flash memory storage systems

Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Search Trees) as a generic framework for flash-aware tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

Design of backpropagated neurocomputing paradigm for Stuxnet virus dynamics in control infrastructure

Neural Computing and Applications - In the present study, a novel application of backpropagated neurocomputing heuristics (BNCH) is presented for epidemic virus model that portrays the Stuxnet...     

Cerebral Folate Metabolism in Post-Mortem Alzheimer’s Disease Tissues: A Small Cohort Study

We investigated the cerebral folate system in post-mortem brains and matched cerebrospinal fluid (CSF) samples from subjects with definite Alzheimer’s disease (AD) (n = 21) and neuropathologically normal brains (n = 21) using immunohistochemistry, Western blot and dot blot. In AD the CSF showed a significant decrease in 10-formyl tetrahydrofolate dehydrogenase (FDH), a critical folate binding protein and enzyme in the CSF, as well as in the main folate transporter, folate receptor alpha (FRα) and folate. In tissue, we found a switch in the pathway of folate supply to the cerebral cortex in AD compared to neurologically normal brains. FRα switched from entry through FDH-positive astrocytes in normal, to entry through glial fibrillary acidic protein (GFAP)-positive astrocytes in the AD cortex. Moreover, this switch correlated with an apparent change in metabolic direction to hypermethylation of neurons in AD. Our data suggest that the reduction in FDH in CSF prohibits FRα-folate entry via FDH-positive astrocytes and promotes entry through the GFAP pathway directly to neurons for hypermethylation. This data may explain some of the cognitive decline not attributable to the loss of neurons alone and presents a target for potential treatment.     

Synthesis of novel acrylic modified water reducible alkyd resin: Investigation of acrylic copolymer ratio effect on film properties and thermal behaviors

New four‐component water reducible acrylic modified alkyd resins that are based on 1,3‐propanediol and contain different ratios of acrylic copolymer (AC) were synthesized by using a novel four‐stage fatty acid method. The final content of solids in the water reducible acrylic modified alkyd resins was 60% by weight. After the modified alkyd resin films were cured at 150°C for 1 h, it was observed that the use of AC as the modifier component had improved their physical and chemical surface coating properties and thermal behaviors. Experimental results show that the optimum AC ratio is 40% of the equivalent amount of AC to alkyd resin. Low‐volatile organic compounds (VOC) content water reducible acrylic modified alkyd resins yielded soft and flexible films with high chemical/thermal resistance, suitable for manufacturing of surface coating binders. POLYM. ENG. SCI., 56:947–954, 2016. © 2016 Society of Plastics Engineers     

Low‐temperature synthesis of hierarchical structures of copper oxide and their superior biological activity

In this work, the authors report a facile low‐temperature wet‐chemical route to prepare morphology‐tailored hierarchical structures (HS) of copper oxide. The preparation of copper oxide collides was carried out using varying concentrations of copper acetate and a reducing agent at a constant temperature of 50°C. The prepared HS of CuO were characterised by powdered X‐rays diffraction that indicates phase pure having monoclinic structures. The morphology was further confirmed by field‐emission scanning electron microscope. It reveals a difference in shape and size of copper oxide HS by changing the concentration of reactants. In order to evaluate the effect of H₂ O₂ on CuO NPs, the prepared CuO are modified by treatment with H₂ O₂. In general trend, CuOH₂ O₂ collide showed enhanced protein kinase inhibition, antibacterial (maximum zone 16.34 mm against Staphylococcus aureus) and antifungal activities in comparison to unmodified CuO collides. These results reveal that CuO HS exhibit antimicrobial properties and can be used as a potential candidate in pharmaceutical industries.Inspec keywords: molecular biophysics, antibacterial activity, X‐ray diffraction, microorganisms, copper compounds, nanofabrication, nanoparticles, narrow band gap semiconductors, field emission scanning electron microscopy, enzymes, nanomedicine, particle size, semiconductor growthOther keywords: unmodified CuO collides, low‐temperature synthesis, morphology‐tailored hierarchical structures, copper acetate, reducing agent, monoclinic structures, copper oxide HS, CuO NPs, Staphylococcus aureus, biological activity, copper oxide, powdered X‐ray diffraction, field‐emission scanning electron microscopy, facile low‐temperature wet‐chemical method, protein kinase inhibition, antibacterial activity, antifungal activity, antimicrobial properties, pharmaceutical industries, temperature 50.0 degC, CuO     

Use of the task configuration system (TCS) for the design and on-line optimization of power plants operating with variable loads

The main purpose of this work is to show the ability of the TCS (task configuration system) to perform both the design and the operation optimization of power plants. The TCS is a module that permits to set the main task of each one of the equipments of the system and consequently setting how they respond to variable loads. In this work, the TCS was applied to a micro cogeneration plant of 60 kW in which both the electrical and the thermal loads were variable. Primarily, the design optimization of the nominal power of the equipments and of the TCS configuration was performed for the loads and electricity/fuel costs assumed in the design. After this operational optimizations in cases where the loads and electricity/fuel costs were doubled and then halved in relation to the standard case were performed. The results presented the TCS in a very robust way in most of the cases even the operational conditions being very different from the originally assumed. Based on the results, it is possible to defend the use of the TCS to decrease the risk of high initial investments made in cogeneration systems.     

Multifractal analysis of Mg-doped ZnO thin films deposited by sol–gel spin coating method

A multifractal analysis has been performed on the 3D (three-dimensional) surface microtexture of magnesium-doped zinc oxide (ZnO:Mg) thin films with doping concentration of 0, 2, 4, and 5%. Thin films were deposited onto the glass substrates via the sol–gel spin coating method. The effect of magnesium doping, on the crystal structure, morphology, and band gap for ZnO:Mg thin films has been analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and UV–Vis spectroscopy. It has been observed that the surface of ZnO thin films is multifractal in nature. However, multifractality and complexity observed to decrease with increasing content of Mg in ZnO thin films due to formation of islands on the surface in accordance with Volmer–Weber growth mechanism. The investigations revealed that crystallinity, microtexture, morphology, and optical properties of the thin films can be tuned by controlling the Mg content within the ZnO lattice. In particular, their optical band gap energies were 3.27, 3.31, 3.34, and 3.33 eV at 0, 2, 4, and 5%, respectively. The prepared thin films of ZnO:Mg with tuned characteristics would have promising applications in optoelectronic devices.