Multilevel inverters are very popular because of rise the overall staging of the network with low harmonic distortion, less stresses and give higher staging in industrial and electrical vehicle applications. This paper demonstrates, a single phase multilevel inverter (MLI) consists less count of switches and drive circuits compare to conventional MLI. It has the capability to operate as a Symmetrical 5-Level (5-L) inverter and by adding one bidirectional switch to the above inverter it is able to act a asymmetrical 7-Level (7-L) inverter, under normal running conditions. The control of inverter is implemented by Variable Amplitude Sinusoidal Pulse Width Modulation (VASPWM) Techniques. In this technique a single carrier signal is divided into three equal carriers with different magnitudes with same frequency, then it reduce the volt-sec of output. So that it gives less conduction period and provides less Total Harmonic Distortion (THD) in both the waveforms of the voltage and current compare to other exiting PWM techniques and Conventional Space Vector Pulse Width Modulation (CSVPWM). The ease of conversion capability with less count of switches and operation with less THD shows the converter\'s ability to convert. The converter is modeled in MATLAB/SIMULINK, and the results are discussed.

The High Intensity Focused Ultrasound (HIFU) is a non-invasive therapeutic technology that uses ultrasound wave to induce sonication in target tissue with minimal injury to adjacent tissue. The method is widely adopted as it ensures a short patient recovery time. Moreover, HIFU has an added advantage of high ability to deliver thermal energy into the target with accuracy. In contrast, HIFU causes an instantaneous temperature rise; while maximum temperature is induced at the focal point, and the neighboring tissues are exposed to various heat levels depending on their physiological characteristics [2]. The resulting effects include thermal, mechanical, chemical and optical reactions. Mechanical effects, more specifically, may consist of acoustic cavitation, radiation force, shear stress, and acoustic streaming/micro streaming. The thermal effect is caused by the absorption of Ultrasound (US) into biological tissue. US waves cause vibration or rotation of molecules or part of macromolecules in the tissue, and this movement results in frictional heat. Depending on the temperature and the duration of contact, the tissue may become more susceptible to chemotherapy or radiotherapy (> 43⁰C, 1 hr) or alternatively, protein denaturation may occur (coagulation necrosis) (56⁰C, 1 sec) [3, 4]. Also, If an US wave, more intense than a specific threshold, is insonated into biological tissue, negative pressure representing the rarefaction of an US wave, may be large enough to draw gas out of the tissue solution to form a bubble. Considering all these factors, it is utmost necessary to estimate and control the heat levels so that undesirable heating of tissue is avoided.

Internet of Things (IoT) has renowned due to tremendous advancement in IoT applications. IoT is being used in our daily life in terms of smart services such as (smart healthcare, smart homes, and its utilities, entertainment and sports, smart mobility, smart water management, etc). Some of the emerging and advanced areas relevant to IoT are Edge, Fog, and Cloud Computing. The massive data sharing will produce new challenges for privacy. IoT, usually, has limited processing and storage capacity with some challenging issues such as reliability, security, privacy, and performance. In this research, Fog computing (fog layer) is under discussion because privacy-preserving policies will be on this layer for users. The fog layer plays a vital role in data sharing which causes privacy issues. The utmost property under discussion is the privacy threat due to the user’s data sharing from IoT devices to the clouds. The idea of the matrix is to frame the model while quantification of the matrix with access levels will result in privacy-preserving policies for users. The proposed solution has been discussed privacy matrix which instantiation of privacy-preserving policies and will be provided access to privacy parameters (public, private, protected) according to access levels (full trust, compliance-based trust, no trust). The Ontology Engineering Stages use to develop the privacy matrix by applying semantic constraints and access levels to use cases. These privacy-preserving policies will develop by using Protégé. SPARQL queries are used to verify the results. These privacy-preserving policies benefits to users for preserving their privacy during data sharing from IoT devices to the clouds on their own.

Efficient utilities usage and enhanced heat transfer are imperative in todays’ industrial and technological processes. Over these bases, a neuro-genetic procedure was proposed for optimization of the water flowrates distribution on a hydrogen sulphide gas coolers system. It relied on Genetic Algorithms, combined with an improved ɛ-NTU model for simulation of jacketed shell-and-tube heat exchangers. Artificial Neural Networks were furtherly applied to correlate the optimum water flowrates to predictive variables. The heat transfer incremental was estimated from 3695 to 10514 W, while reduction of the gas exit temperature was projected between 2.9 and 9.8 K. Calculated heat recovery averaged 12.44 %, varying from 3.90 to 22.16 %. The optimized water distribution scheme improved the system energy performance under a fixed network concept and unvaried overall feed water flowrate, thus effectively avoiding any additional cost incurred if topology modification is applied. This research work provided a technological solution to the studied problem, consisting on installation of automatic valves –at each water pipe feeding the heat exchangers– and programmable flow control-loops linked to a PLC.

The epidemiology of cryptosporidiosis in pigs of North- Eastern (NE) region of India is very little known. The objective of this study was to underscore the prevalence and characterize Cryptosporidium isolates from pigs in this region. A total of 725 pig faecal samples were examined from April, 2020 to March, 2021 for the present of Cryptosporidium oocysts by using modified Ziehl-Neelson staining technique. Shedding of oocysts was monitored at quarterly intervals in piglets (n= 310), starter (n= 180), fatteners (n=235). Fifty five samples (overall prevalence, 7.58%)were found positive by microscopic examination, while 76(overall prevalence, 10.48%) samples showed positive by Polymerase Chain Reaction (PCR). Prevalenceand intensity of infection varied significantly between different age groups (p<.01). Female showed significantly (p<.01) lower level of infection than male. The overall prevalence was not varied much throughout the year. Positive samples were further analyzed by amplifying 18S rRNA gene of Cryptosporidium by PCR. An825bp of the targeted gene was amplified by nested PCR. The purified PCR products were cloned, sequenced and a phylogenetic tree was made. The analysis revealed that pigs in this region of India are infected with Cryptosporidium\nparvum. The presence of Cryptosporidium parvum suggests that pigs may be a threat to public health in this area. However, more extensive studies are required to understand the transmission potential of cryptosporidiosis in between porcine and human hosts from this part of India.

Over the past decade, deep learning has drawn the interest of researchers and experts in various fields of artificial intelligence. Image processing tools such as convolutional neural networks, sequence processing models such as recurrent neural networks, and regularization tools such as dropout are widely used. However, in areas like autonomous driving systems and medical diagnosis, the representation of the model’s uncertainty is of significant importance. With the recent shift towards the use of Bayesian uncertainty, it is now possible to transform existing deep learning tools into Bayesian models without altering their architecture. In this work, we have implemented a Bayesian convolutional neural network using the variational inference algorithm, Bayes by Backprop. The proposed model was evaluated on an image classification task, with two benchmark datasets. The results’ review allowed to validate the Bayesian approach, and showed that it obtains comparable results to those of a non-Bayesian convolutional neural network. In addition, the uncertainty of the model was estimated, in terms of aleatory and epistemic uncertainty

Cryptographic analysis is always a fascinating field of research. It is understood that data security is the major concern in the Internet world. The process of encryption and decryption are cryptographic that are intended to provide data confidentiality over the network. Currently more number of organizations is working on huge databases in the real world to attain the best efficient data transfer security mechanism. Only information security can be maintained by conventional encryption methods. Data can be accessed by an unauthorized client for malicious purposes. Efficient encryption and decryption methods should be used to improve data security. The Proposed AFR Algorithm deals with 3 levels, namely Append, Formation of BST and Representation of Matrix which provides a strong encryption to provide security for transmission of data. This proposed novel AFR encryption algorithm creates the complexity in encryption to a huge extent; hence security is high in transmission of data.

The modeling and solving the optimization problems that come across our real life and business is one of the most important daily problems. Fully fuzzy linear programming problem is one of the most powerful tools to deal with the imprecise nature (not well-defined) of data. From studies it is noted that mathematical programming problem has a well defined objective function and set of constraints, the systematic determination of optimal solutions leads to the development of large numbers of methods and algorithms. Fully Fuzzy Linear Programming Problems are most suited way to express the real life optimization problems where the constraints are not always crisp or well defined this is why lots of research work is taking place to solve and find optimum solution fully fuzzy linear programming problems. In this paper various techniques that are emerged in a decade to solve fully fuzzy linear programming problems are discussed.