An Experimental Investigation on Four Stroke CI Engine with Diesel and Bio-Diesel Blend as fuel: Hazelnut

An experimental investigation was carried out to analyze non-edible oils (hazelnut) in blending with normal diesel fuel with approximate proportions of 5%, 10 % , 15%, 20% and 25% by volume in a mono cylinder, four stroke vertical, water cooled, Compression Ignition engine. Experimentation was performed by using the above fuel blends in the compression ignition engine operating at different loads. A comparative analysis was made on the output parameters such as emissions like oxides of nitrogen(NOx), total unburned hydro carbons, oxides of carbon(COx), and partially burned hydro carbons, Brake specific fuel consumption, Brake Thermal Efficiency, smoke density, temperature of exhaust gases, for all blends prepared with bio-fuels mixing with normal diesel fuel at different proportions mentioned above. It was known that engine is giving better performance by using a blend B20 with proportions of 20% hazelnut bio-fuel and 80% conventional diesel fuel without any modifying in design parameters and operating characteristics of the engine. Engine working on bio-diesels blend as fuel are showing considerable reduction in emissions like oxides of carbon and hydro carbons but with marginal increase in oxides of nitrogen without effecting brake thermal efficiency when compared with conventional neat diesel fuel. The corresponding neat diesel fuel operation, the bio-fuel oil blends show decrease in emissions of smoke with marginal increase in NOx with unchanged brake thermal efficiency. Hazelnut bio-diesel having better properties next to diesel fuel in comparison with other bio fuels

IDRCNN: A Novel Deep Learning Network Model for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Early identification of pancreatic ductal adenocarcinoma (PDAC) improves prognosis. Still, it is difficult since lesions are generally smaller and difficult to define on contrast-enhanced computed tomography images (CE-CT). Ineffective PDAC diagnosis has recently been achieved using deep learning models, but the output localized and identified images are of poor quality. This research focuses on small lesions and presents a new, efficient automatic deep-learning network model for PDAC detection. The Improved Deep Residual Convolutional Neural Network (IDRCNN) detects PDAC. The hyperparameter is optimized using the Tunicate Swarm Optimization Algorithm (TSOA) algorithm. A better diagnosis is made due to segmenting the surrounding anatomy structure effects, such as PD. We train a proposed IDRCNN model for segmenting and detecting lesions automatically using CE-CT images. Two more IDRCNN models are trained with the aim of investigating the effects of anatomy integration: (i) segmentation of tumor and pancreas (IDRCNN_TP), and (ii) segmentation of pancreatic Duct (IDRCNN_PD). The three networks\u27 performance was assessed using an external, publicly available test set. Due to its effective classification results, the proposed method produces improved identification results for automated preliminary diagnosis of PDAC in cervical cancer clinics and hospitals. The performance of the proposed method is evaluated using a publicly assessable CT image dataset. It outperforms the existing state-of-the-art methods and achieved 98.67% accuracy, 97.26% recall, 98.52% precision, 97.65% sensitivity, and 98.45% specificity for pancreatic tumor detection

AN APPROACH TOWARDS EFFICIENT VIDEO DATA HIDING USING PROHIBITED ZONE

The process of embedding information into a host medium is a data hiding. In general, due to their wide presence and the tolerance of human perceptual systems involved visual and arual media are preferred. The methods vary depending on the nature of such media and the general structure of data hiding process does not depend on the host media type. Due to the design complexities involved video data hiding is still an important research topic. A new video data hiding method that makes use of removal correction capabilities of replicate accumulate codes and advantage of prohibited zone data hiding is proposed in this paper. To determine host signal samples suitable for data hiding selective embedding is utilized in the proposed method. In order to withstand frame drop and insert attacks a temporal synchronization scheme is contained in this method. By typical broadcast material against MPEG- 2, H.264 compression, frame-rate renovation attacks, as well as other renowned video data hiding methods the proposed framework is tested here. For typical system parameters the decoding error values are reported. The imitation results specify that the frame can be effectively utilized in video data hiding applications

Observation of direct and indirect magnetoelectricity in lead free ferroelectric (Na 0.5Bi 0.5TiO 3)-magnetostrictive (CoFe 2O 4) particulate composite

A particulate composite consisting of 65 mol. % Na 0.5Bi 0.5TiO 3 and 35 mol. % CoFe 2O 4 was synthesized, and it's structure, microstructure, ferroelectric, magnetostrictive, magnetic, and direct/indirect magnetoelectric properties were studied. The composite showed different magnetization behaviour under electrically poled and un-poled conditions. The percentage change in magnetization as a result of poling is approximately -15% at 500 Oe magnetic field. Magnetostriction measurements displayed a value of λ 11 = -57 × 10 -6 and piezomagnetic coefficient δλ 11/δH = 0.022 × 10 -6 kOe -1 at 2.2 kOe for the composite. The maximum magnetoelectric output varied from 1350 mV/cm to 2000 mV/cm with change in the electrical poling condition

DATA VALIDATION AND RELIABILITY PROOF IN CLOUD STORAGE

To the rising storage costs of IT Enterprises, the cloud computing has been envisioned as the de-facto solution. For enterprises to frequently update their hardware with the high costs of data storage devices as well as the rapid rate at which data is being generated it proves costly. A part from decrease in storage costs data outsourcing to the cloud also helps in sinking the safeguarding. User does not have any control as they are remotely located and cloud storage moves the user’s data to large data centers. However, this exclusive feature of the cloud poses many new safety challenges which need to be clearly unspoken and resolved. To assure the customer of the integrity i.e. correctness of his data in the cloud is one of the important concerns that need to be addressed is. To check if the reliability of his data is maintained or is compromised as the data is physically not accessible to the user the cloud should provide a way for the user. In this paper, to check the correctness of data in the cloud, a scheme which gives a proof of data integrity in the cloud which the customer can employ is proposed here. Both the cloud and the customer and can be incorporated in the Service level agreement (SLA) and this proof can be agreed. This method ensures that the storage space at the client side is negligible which will be valuable for thin clients