Polymerization of Diphenylcarbodiimide Catalysed by Fullerene-60

In the presence of fullerene-60 as a catalyst, the formation of diphenylcarbodiimide (DPCD) from phenylisocyanate followed by its polymerization into a hexamer in xylene at refluxing temperature was carried out. The product was analysed by FAB-Mass spectrometry, FT-IR, UV-Vis and HPLC techniques.     

A SiC MEMS Resonant Strain Sensor for Harsh Environment Applications

In this paper, we present a silicon carbide MEMS resonant strain sensor for harsh environment applications. The sensor is a balanced-mass double-ended tuning fork (BDETF) fabricated from 3C-SiC deposited on a silicon substrate. The SiC was etched in a plasma etch chamber using a silicon oxide mask, achieving a selectivity of 5:1 and etch rate of 2500 Aring/min. The device resonates at atmospheric pressure and operates from room temperature to above 300degC. The device was also subjected to 10 000 g shock (out-of-plane) without damage or shift in resonant frequency. The BDETF exhibits a strain sensitivity of 66 Hz/muepsiv and achieves a strain resolution of 0.11 muepsiv in a bandwidth from 10 to 20 kHz, comparable to state-of-the-art silicon sensors     

Prediction of Heat of Vapourization of Moisture from Cereal Grains - A Modelling Approach

The Henderson's EMC model in modified form was used to fit the EMC data given by Engels et al (1987) for brown, parboiled rice. The constants in the model were related to temperature. A method to find an expression for heat of vapourization of moisture from grain was developed and demonstrated using the EMC data of brown, parboiled rice. The heat of vapourization of moisture from rice was high at low moisture contents. For moisture contents above 25% (db), the moisture inside the grain behaved almost like free water.     

Fabrication of Architectured Biomaterials by Multilayer Co-Extrusion and Additive Manufacturing

Tissue engineering benefits from advances in 3D printing and multi-material assembly to attain certain functional benefits over existing man-made materials. Multilayered tissue engineering constructs might unlock a unique combination of properties, but their fabrication remains challenging. Herein, a facile process is reported to manufacture biomaterials with an engineered multilayer architecture, via a combination of co-extrusion and 3D printing. Polymer filaments containing 5, 17, or 129 alternating layers of poly(lactic acid)/thermoplastic polyurethane (PLA/TPU) are produced, and explored for their use in fused deposition modeling (FDM) to fabricate scaffolds for cardiomyocyte culture. The co-extruded filaments exhibit a layered architecture in their cross-section with a continuous interface, and the integrity and alignment of the layers are preserved after 3D printing. The 17 alternating layers PLA/TPU composites exhibit excellent mechanical properties. It is envisaged that the multilayered architecture of the fabricated scaffolds can be beneficial for aligning cardiomyocytes in culture. It is found that the 17 alternating layers PLA/TPU significantly improve cardiomyocyte morphology and functionality compared to single phase materials. It is believed that this biomaterials fabrication scheme, combining a top-down and bottom-up approach, offers tremendous flexibility in producing a broad class of novel-architectured materials with tunable structural design for tissue engineering applications and beyond.     

Dual-Atom-Site Sn-Cu/C3N4 Photocatalyst Selectively Produces Formaldehyde from CO2 Reduction

The solar-driven catalytic reduction of CO₂ to value-added chemicals is under intensive investigation. The reaction pathway via *OCHO intermediate (involving CO₂ adsorbed through O-binding) usually leads to the two-electron transfer product of HCOOH. Herein, a single-atom catalyst with dual-atom-sites featuring neighboring Sn(II) and Cu(I) centers embedded in C₃N₄ framework is developed and characterized, which markedly promotes the production of HCHO via four-electron transfer through the *OCHO pathway. The optimized catalyst achieves a high HCHO productivity of 259.1 µmol g⁻¹ and a selectivity of 61% after 24 h irradiation, which is ascribed to the synergic role of the neighboring Sn(II)–Cu(I) dual-atom sites that stabilize the target intermediates for HCHO production. Moreover, adsorbed *HCHO intermediate is detected by in situ Fourier transform infrared spectroscopy (CO stretches at 1637 cm⁻¹). This study provides a unique example that controls the selectivity of the multi-electron transfer mechanisms of CO₂ photoconversion using heteronuclear dual-atom-site catalyst to generate an uncommon product (HCHO) of CO₂ reduction.     

Community Detection Using Jaacard Similarity with SIM-Edge Detection Techniques

The structure and dynamic nature of real-world networks can be revealed by communities that help in promotion of recommendation systems. Social Media platforms were initially developed for effective communication, but now it is being used widely for extending and to obtain profit among business community. The numerous data generated through these platforms are utilized by many companies that make a huge profit out of it. A giant network of people in social media is grouped together based on their similar properties to form a community. Community detection is recent topic among the research community due to the increase usage of online social network. Community is one of a significant property of a network that may have many communities which have similarity among them. Community detection technique play a vital role to discover similarities among the nodes and keep them strongly connected. Similar nodes in a network are grouped together in a single community. Communities can be merged together to avoid lot of groups if there exist more edges between them. Machine Learning algorithms use community detection to identify groups with common properties and thus for recommendation systems, health care assistance systems and many more. Considering the above, this paper presents alternative method SimEdge-CD (Similarity and Edge between's based Community Detection) for community detection. The two stages of SimEdge-CD initially find the similarity among nodes and group them into one community. During the second stage, it identifies the exact affiliations of boundary nodes using edge betweenness to create well defined communities. Evaluation of proposed method on synthetic and real datasets proved to achieve a better accuracy-efficiency trade-of compared to other existing methods. Our proposed SimEdge-CD achieves ideal value of 1 which is higher than existing sim closure like LPA, Attractor, Leiden and walktrap techniques.     

An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments

Cloud computing is one of the distributed resource-sharing technology that offers resources on a pay-as-you-use basis. Platform as a service, Infrastructure as a service, and Software as a Service are services provided by the Cloud. Each end user's Quality of service must be ensured by the cloud service provider. In recent days, cloud utilization is rapidly increasing. To avoid congestion and to preserve the Service Level Agreement, the large workload must be balanced across the network. In this research work, a new load balancing approach is proposed for the dynamic resource allocation process to improve stability and to increase profit. PBMM algorithm is devised for an effective load balancing process through which, resource scheduling is performed. Task size and the bidding value coded by each customer are taken into account. To optimize the waiting time, resource tables and task tables are employed. The average waiting time and response time of the special users are minimized. The simulation results show that the proposed load balancing technique ensures the maximum profit and it enhances load balancing stability by increasing the number of special users.

     

Weakly-Supervised Action Localization,and Action Recognition Using Global–Local Attention of 3D CNN

International Journal of Computer Vision - 3D convolutional neural network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. However, due to the convolution and...     

Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images

Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.     

INVESTIGATION ON MAMMOGRAPHIC IMAGE COMPRESSION AND MICROCALCIFICATION ANALYSIS USING MULTIWAVELETS AND NEURAL NETWORKS

In digital mammography, the resulting electronic image is very large in size, which poses a significant challenge to the transmission, storage, and manipulation of images. Microcalcification is one of the earliest signs of breast cancer, and it tends to appear in small-sized, low-contrast radiopacities in the high-frequency spectrum of a mammographic image. Scalar wavelets excel multiwavelets in terms of peak-signal-to-noise ratio (PSNR), but fail to capture high-frequency information. This study proposes mammographic image compression and microcalcification detection in original and compressed reconstructed images using multiwavelets and neural networks. It also details the classification results obtained through the multiwavelet-based scheme in comparison with the existing scalar wavelet-based scheme. For a testing sample of 30 images, the proposed classification scheme outperforms the scalar wavelet-based classification by a sensitivity of 2.23% and specificity of 3.4% for original (uncompressed) images. Also, it increases the sensitivity by 2.72% and specificity by 8.4% for compressed reconstructed images. This increase in sensitivity and specificity demonstrates a better performance of the proposed detection scheme.