Mixed oxide supported hydrodesulfurization catalysts—a review

Support effects form important aspect of hydrodesulfurization (HDS) studies and mixed oxide supports received maximum attention in the last two decades. This review will focus attention on studies on mixed oxide supported Mo and W catalysts. For convenience of discussion, these are divided into Al₂O₃ containing mixed oxide supports, TiO₂ containing mixed oxide supports, ZrO₂ containing mixed oxide supports and other mixed oxide supports containing all the rest. TiO₂ containing mixed oxides received maximum attention, especially TiO₂–Al₂O₃ supported catalysts. A brief discussion about their prospects for application to ultradeep desulfurization is also included. An overview of the available literature with emphasis on research carried out in our laboratory form the contents of this publication.     

Analysis of spring softening effect on the collapse voltage of capacitive MEMS ultrasonic transducers

Microsystem Technologies - This paper explained the dependency of collapse voltage on semiconductor device structural features (membrane diameter, membrane thickness and the vertical distance...     

Effect of phosphorus on activity of hydrotreating catalyst of Maya heavy crude

The effect of phosphorus on physical properties of the catalyst and on activity of hydrotreating of Maya crude was studied in this work. Catalysts were prepared by the co-impregnation method. Alumina-titania binary oxide was used as a support material. The presence of phosphorus in the catalyst decreases the percentage of micropores, and it results in a decrease of specific surface area. Temperature program reduction (TPR) shows that phosphates reduce metal support interaction. It leads to the formation of polymolybdate phases in expense of strongly bonded tetrahedral molybdates. At higher P loading, polymolybdates may be present with quasi crystalline MoO₃. However, the TPR experiment is not sufficiently sensitive to distinguish several phases present on the catalysts used by the authors. A slight increment of HDM activity is observed, but HDS activity is lower in the P containing catalyst compared with the P free catalyst. The changes of physical properties of the spent catalysts are mainly due to the coke formation on the catalyst. The presence of phosphorus on hydrotreating catalysts inhibits coke formation during the hydrotreating reaction.     

Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization

In the medical field, image segmentation is a paramount and challenging task. The head and vertebral column make up the central nervous system (CNS), which control all the paramount functions. These include thinking, speaking, and gestures. The uncontrolled growth in the CNS can affect a person’s thinking of communication or movement. The tumor is known as the uncontrolled growth of cells in brain. The tumor can be recognized by MRI image. Brain tumor detection is mostly affected with inaccurate classification. This proposed work designed a novel classification and segmentation algorithm for the brain tumor detection. The proposed system uses the Adaptive fuzzy deep neural network with frog leap optimization to detect normality and abnormality of the image. Accurate classification is achieved with error minimization strategy through our proposed method. Then, the abnormal image is segmented using adaptive flying squirrel algorithm and the size of the tumor is detected, which is used to find out the severity of the tumor. The proposed work is implemented in the MATLAB simulation platform. The proposed work Accuracy, sensitivity, specificity, false positive rate and false negative rate are 99.6%, 99.9%, 99.8%, 0.0043 and 0.543, respectively. The detection accuracy is better in our proposed system than the existing teaching and learning based algorithm, social group algorithm and deep neural network.

     

Feature tracking and matching in video using programmable graphics hardware

This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1,024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation extracts about 800 features from 640 × 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation.     

A review of power electronics interfaces for distributed energy systems towards achieving low-cost modular design

Due to increased attention towards clean and sustainable energy, distributed energy (DE) systems are gaining popularity all over the world. Power electronics are an integral part of these energy systems being able to convert generated electricity into consumer usable and utility compatible forms. But the addition of power electronics adds costs to the DE capital investments along with some reliability issues. Therefore, widespread use of distributed energy requires power electronics topologies that are less expensive and more dependable. Use of modular power electronics is a way to address these issues. Adoption of functional building blocks that can be used for multiple applications results in high volume production and reduced engineering effort, design testing, onsite installation and maintenance work for specific customer applications. In this paper, different power electronics topologies are reviewed that are typically used with distributed energy systems. The integrated power electronics module (IPEM) based back-to-back converter topologies are found to be most suitable interface that can operate with different DE systems with small or no modifications. Also the requirements for a hierarchical control structure with standardized power and communication interfaces are addressed in the paper along with some discussion on future design possibilities for the IPEM-based power electronics topologies. It is expected that modular and flexible power electronics and standardized controls and interfaces, will provide commonality in hardware and software for the power electronics interfaces, thus will enable their volume production and decrease their cost share in distributed energy applications.     

Analysis of Temperature Effect in Quadruple Gate Nano-scale FinFET

Silicon - Quadruple gate FinFET is a promising candidate among other multi-gate MOS devices due to it’s better scalability and higher short channel effect suppression capability in advanced...     

Binary liquid–liquid equilibria of aniline–paraffin and furfural–paraffin systems

Binary liquid-liquid-equilibria data for several aniline-paraffin and furfural-paraffin systems have been taken. These data along with data for other aniline-hydrocarbon and furfural-hydrocarbon systems from literature have been correlated using UNIFAC model. The UNIFAC group interaction parameters have been found to have a linear temperature dependence. The CH₂ groups in cyclo and non cyclo paraffins require different interaction parameters. It was also found that a scaling of the combinatorial term is necessary for higher molecular weight hydrocarbons.     

Comparative study on the structural make-up and mechanical behavior of silicon and silver doped amorphous carbon films

Silicon - Silicon (Si) and silver (Ag) doped amorphous carbon (a-C) thin film were deposited on chrome nitrided 316 LVM stainless steel using filtered cathodic vacuum arc (FCVA) deposition...     

Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images. Furthermore, the proposed framework also encompasses the residual network technique, which subsequently enhances the performance and displays the improved training process. Over and above, the performance of ResU-Net has experimentally been analyzed with conventional U-Net, FCN8, FCN32. Algorithm performance is evaluated in the form of dice coefficient and MIoU (Mean Intersection of Union), accuracy, loss, sensitivity, specificity, F1score. Experimental results show that ResU-Net achieved validation accuracy & dice coefficient value of 73.22% & 85.32% respectively on the Rider Breast MRI dataset and outperformed as compared to the other algorithms used in experimentation.