Miracle-dz/UniFAR-FTUs huggingface dataset:
FABRICATION OF AN AC MAGNETIC SUSCEPTIBILITY MEASUREMENT SYSTEM

The fabrication of an AC magnetic susceptibility measurement system for the characterization of superconductors has been developed. Magnetic susceptibility describes the magnetic characteristic of materials, it is a measure of the ease with which certain materials are magnetized when subjected to a magnetic field. The mutual inductance method and the lock-in technique served as the mechanism for the equipment. The setup consists of a primary coil that is driven by a current to generate a magnetic field inside. Two pairs of secondary coils are wound oppositely inside the primary coil to cancel their mutual inductances. The primary coil is wound using a gauge # 36 magnetic wire with 1105 turns and a resistance of 118. 43Ω. The secondary coils were wound with a gauge #40 magnetic wire with 1625 turns and have the resistance of 338. 41Ω and 338. 63Ω respectively. The presence of a superconductor inside the coil will produce a change in the flux at its superconducting state and thus result in a drop in the induced voltage. This voltage is directly proportional to the magnetic susceptibility. To obtain accurate measurements, it was necessary to phase tune the reference signal with the physical setting of the secondary coils at room temperature. This was done through the help of phasor diagrams. The reference signals used were at 400Hz, 5V at 1.2 kHz, 5V, at 800 Hz, in 5V, 3V and 1V. The resultant vector obtained at 400Hz, 5V was 0.003V∠15.69o while at 1.2 kHz, 5V was 0.022V∠-56.31o and at 800Hz, 5V was 0.012V∠-34.99o. This resultant vector was then adjusted by 180o to be located in the second quadrant. The reference signal’s phase setting was adjusted to be either perpendicular to resultant vector or parallel to one of the secondary coil’s vector depending on the placement of the phasors. A standard sample was used to determine the efficiency of the apparatus. YBCO sample was used to serve as the standard. The results of the test runs had yielded a curve directly proportional to the real and imaginary parts of the magnetic susceptibility when the adjusted phase setting was along the adjusted resultant vector.
Job stress, psychological empowerment, and job satisfaction among the IT employees in Coimbatore

Psychological empowerment was defined from the perspective of individual employees which was characterized by a sense of perceived control, perceptions of competence, and internalization of the goals and objectives of the organization (Menon, S.T 1999) [9] . Psychological empowerment is a multi-faceted construct reflecting the different dimensions of being psychologically enabled, and is conceived of personal control, a proactive approach to life, and a critical understanding of the socio-political environment, which is rooted firmly in a social action framework. The role played by the software industry in the contemporary world aroused the need for understanding the relationship between job stress, psychological empowerment and job satisfaction with special reference to software industry. The tool for the study was a questionnaire comprising of twelve items on psychological empowerment, fifteen items on job stress and twenty items on job satisfaction on a five point scaling ranging from strongly agree (5) to strongly disagree (1). Data was collected from a sample of 64 respondents from four software companies in Coimbatore. Being satisfied with the reliability of the research instrument the researcher carried out parson T-test, ANOVA, and regression to understand the relationship between Job stress, psychological empowerment and job satisfaction
on Ma

: PageRank is a most popular link analysis algorithm for measuring the relative importance of web pages. A lot of research has been done on page rank to improve its efficiency and performance by using parallelizing techniques. In this paper we have proposed a novel parallel k-means PageRank Algorithm based on MapReduce. Previous implementation of PageRank on MapReduce framework generates lot of internal results which in turn produces huge shuffling of bytes for Hadoop task. The proposed algorithm lowered the amount of shuffled bytes by processing a sub graph in a mapper function rather than processing an edge in the mapper function. We have used K-means clustering technique so that there is a proper communication between different sub graphs. The proposed work performs faster PageRank calculation by employing map cache memory to load prior PageRank scores from HDFS, so that only one MapReduce job is required for a particular PageRank iteration. Experimental results show that the proposed K means PageRank algorithm provides better results in terms of execution time as compared to traditional PageRank and Local Iterative PageRank on MapReduce.
Centroid Neural Network With Directional Spawning of New Weights

— This paper proposes Centroid Neural Network with Directional Spawning for efficient data clustering. The proposed algorithm locates each new neuron for cluster center by considering the location trajectory of previously added neurons during training process. Experiments on different data sets are performed in order to evaluate the performance of training error, training speed, and test error. The results show that the proposed algorithm outperforms conventional Centroid Neural Network and Fuzzy c-Means algorithm in terms of clustering errors and training speed.
Weighted Round-Robin Load Balancing Using Software Defined Networking

In today's environment our networks have to handle enormous amount of traffic, serve thousands of clients. For a single server it is very troublesome to handle such huge load. The solution is to use multiple servers with load balancer acting as a front end. The load balancer will receive the request from multiple clients. Depending upon various load balancing strategy the purpose of load balancer is to forward these incoming requests of the client to different servers. Load balancer use customized hardware. These traditional load balancers are exorbitant and not flexible to change. As traditional load balancer are vendor locked and due to its non programmable nature network administrators does not have capability to build their algorithms by own. But in case of SDN load balancers, it provide the facility to the programmers to build and design our own load balancing strategy thus makes the SDN load balancer programmable and flexible. Another strong point of using Software Defined Networking load balancer is that it does not require any separate hardware that behaves as a load balancer. The simple dumb silicon device can be converted to a powerful load balancer by the application that is built on top of controller without the need of network administration. In this paper we highlighted the implementation detail as well as experimentation results of weighted Round-Robin load balancing strategy using an OpenFlow switch connected to a POX controller that are based on
Enhanced Memory Reliability against Multiple Cell Upsets Using Decimal Matrix Code for 32-Bit Data

— Transient multiple cells upsets (MCUs) is an important issue if consideration is the reliability of memories exposed to radiation environment . Many superior packaging technique are available which protect the memory data from radiations and transients. However, a particular packaging provides protection from a limited variation of radiations. Due to the increasing demand in the application in wireless communication field the devices are exposed to a very wide range of environment radiations. Consequently some supplementary data protection techniques are always preferred for authenticating the data before it is processed. A number of these techniques use encoded data to be stored in memories. Error correction codes (ECCs) are used to encode the data which is used to be stored in the memory. A less number of redundant bits to be stored and a minimized delay overhead in data correction are always chosen to implement an error correction code. This paper presents an FPGA based execution of memory data error detection and correction code that involves simple decimal addition algorithm in the encoding of data that is to be stored in memory and The Hamming code is used for decoding of the data for error detection and correction. Symbolic codes are composed by divide-symbol concept to represent the linear data in groups. The length of the symbol is inversely proportional to the delay overhead of the code.
Generation of defocus map of single image using defocus blur

the depth information of any image is crucial part of image processing, projecting 3D images requires this information, and capturing 2D images has not 3D information. So this will lead to create algorithm which will extract depth map from 2D images. This 2D image along with depth map reconstructed from this image will generate 3D image. This depth map could be obtained by various cues present in the images. Getting depth map from 2D images with the help of defocus cue will use blurring of image at edge locations.at edge locations of different segment discontinuity in image pixels observed. We have created different regions of image propagation for good defocus map. Segmentation of images along with Edge detection will target detection of near and far object present in image. This accuracy of defocus map could be improved with the better segmentation ability. Segmenting image into multiple parts is called as a superpixel creation. We have used this superpixel technique.
damage caused by cryopreservation of spermatozoa using a modified Single cell gell electrophoresis in the freshwater catfish

Pangasianodon hypophthalmus (P. hypophthalmus) is a freshwater fish species of considerable commercial importance in the aquaculture industry in Southeast Asia. Overfishing and pollution have substantially reduced the wild populations[1]. Reliable methods for sperm cryopreservation could particularly be beneficial and improve the breeding of this species. Another index of sperm quality is DNA stability[2]. However, there is little information available on the effects of cryopreservation on the DNA of fish spermatozoa. Hagedorn and Carter used in vitro fertilization to increase sperm fertility after cryopreservation in zebrafish[3]. Beirão et al. introduced antifreeze proteins to improve sperm cryopreservation in seabream[4]. Lin and You used glycerol as cryoprotectants for the successful storage of large yellow croaker sperm[5]. Lin et al. discovered effects of the freeze-thaw process on sperm ultrastructure of large yellow croaker[6].
Identifying Keywords and Key Phrases

: Keywords and key phrases are widely used in large document collections. They describe the content of single documents and provide a kind of semantic metadata that is useful for a variety of purposes. Text mining is powerful tool to find useful and needed information from huge data set. For context based text mining, key phrases are used. Key phrases provide brief summary about the contents of documents. In document clustering, number of total cluster is not known in advance. In K-means, if prespecified number of clusters modified, the precision of each result is also modified. Therefore Kea, is algorithm for automatically extracting key phrases from text is used. In this kea algorithm, number of clusters is automatically determined by using extracted key phrases. Keameans clustering algorithm provide easy and efficient way to extract test document from large quantity of resources. Key phrase play important role in text indexing, summarization and categorization. Key phrases are selected manually. Assigning key phrases manually is tedious process that requires knowledge of subject. Therefore automatic extraction techniques are most useful.
A REVIEW: Distributed File System

Data collection in the world are growing and expanding. This capability, it is important that the infrastructure must be able to store a huge collection of data on their number grows every day. The conventional methods are used in data centers for capacity building, costs of software, hardware and management of this process will be very high, today. File system architecture is that, independent of the data stored in its metadata. All of Distributed File System (DFS) are used for processing, storing and analyzing very large amount of unstructured data. The most common file system can be noted Google File System (GFS) and Hadoop Distributed File System (HDFS). In this paper, we present a review on design of distributed file system. File systems compared by scalability, availability, compatibility, extensibility, autonomy and etc.
Test Driven Development of Model Driven Embedded Systems

— Good tested designing of the system maximizes its performance and lowers the development cost. This paper focuses on the model-based test-driven development of embedded systems using modeling languages. The test driven embedded system development using modeling languages is more efficient to reduce cost of testing. Testing early in the design phase ensure that the software uses the full capability of the hardware and thus avoids the redesign of hardware. Model driven designing and test driven development using modeling languages can be used to create reliable embedded software code for safety-critical and security-critical embedded systems.
Review of Particle Swarm Optimization Techniques

— Particle Swarm Optimization (PSO) is a population-based, self-adaptive search optimization technique. PSO attracts researchers around the world due to its powerful searching ability and simplicity. PSO is computationally faster than other optimization technique and requires very less memory for implementation. Considerable researches have been carried out to improve the performance of PSO. This paper reviews the development of the particle swarm optimization method in recent years. Modifications to adapt to different and complex environments are reviewed.
Enhanced Beta Trust Model for Identifying Insider Attacks in Wireless Sensor Networks

Wireless sensor networks (WSN) are more prone to insider and outsider attacks as the sensor nodes are deployed in open environment for collecting data. The traditional cryptography based security mechanisms such as authentication and authorization are able to sort out issues of outside attacker, but they are not effective against insider attacks. Trust based approaches are used to defend against insider attacks in wireless sensor network. A trust model provides a way to quantify the trustworthiness of a sensor node. Watchdog is a popular mechanism to collect the information regarding the behavior of nodes. The existing trust models are having vulnerabilities for insider attacks in wireless sensor networks. In this paper, we discuss several security vulnerabilities in a Beta trust model for identifying insider attack and then propose countermeasures for defending against insider attacks in wireless sensor networks.
Tetra-primer Amplification Refractory Mutation System for Screening T2D, TCF7L2 Variant

Tetra-primer amplification refractory mutation system PCR (T-ARMS PCR) is a fast and economical means to analyze single nucleotide polymorphisms (SNPs). It requires only PCR amplification and electrophoresis afterwards for the determination of genotypes. Despite the reliability of the technique T-ARMS PCR, only fewer studies involving this have been published to date. This is because of the amplicons number which increases per reaction which is tough at times to separate on gel electrophoresis and also it is difficult to find such primers with similar annealing temperature. Considering expenditure of high throughput genotyping in the developing world, this study was designed to optimize an efficient technique, T-ARMSPCR for studying type 2 diabetes (T2D) susceptible gene using TCF7L2 SNP. A pair of non-allele-specific outer forward, reverse primers (OF, OR) and two pairs of allele-specific inner primers (IF1, IF2, IR1, IR2) were designed and optimized in separate reactions after troubleshooting of PCR conditions particularly, annealing temperature. Total four primers, a pair of outer and inner T-ARMS-PCR primers (OF, OR, IF1 and IR1), were pooled together eventually, to amplify the two target alleles of a specific SNP, rs12255372 (G>T). A second pair of inner primers (IF2 and IR2) was optimized to recheck the results of genotyping. Optimization of the T-ARMS PCR method resulted in likelihood of genotyping the two alleles of the TCF7L2 SNP rs12255372, G and T in a single, rapid and cost-effective PCR reaction. In this study T-ARMS PCR protocol was successively and thoroughly optimized in terms of PCR cycling conditions and reaction components. However, these results highlight the advantages of T-ARMS PCR in SNP genotyping, and have led to the incorporation of this method in the routine molecular diagnostic workup.
The third world war: man versus drug resistant virus and bacteria

Unlimited commercial production of antibiotics and their ever increasing use in our day to day life has led to the evolution of a class of organism known as multi-drug resistance organisms (MDRO). Multi drug resistance (MDR) is a condition in which a particular disease causing microorganism (virus, bacteria, fungi and parasites) becomes resistant to antimicrobials (antivirals, antibiotics or antimicrobics). The present Chapter will focus on the cause and mechanism of MDR in bacteria and their prevention, if any, such as combination antibiotic therapy. These bacteria range from urgent threat level such as Neisseria gonorrhoeae to serious threat level such as Streptococcus pneumonia and also includes microorganism with a concerning threat level such as Group A Streptococcus. The discussion also includes MDR in viruses (such as Hepatitis B) and their prevention, if any. Keyword: Multi drug resistance; Antibiotics; Antiviral drugs; R plasmids; Efflux pumps; WHO.
Brain Tumor Detection and Classification in MRI Images

: Early and precise detection of tumors improve the quality of life of oncological patients. Keeping this in mind, in this paper we propose an effective method of brain tumor detection and classification. Simply, brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Brain tumors are having large variability in their characteristics compared to other cancer tissue. So that for effective classification the deep learning technique convolutional neural network (CNN) is used. This method pre-processes MRI image classifies and extracts tumors using MATLAB.
Automating the Computational Analysis of Exome Sequencing Data: A Prototype Methodology to Overcome Bottlenecks Observed with Operator Driven Clinical Interpretation for Known Pathogenic Mutations

Objective: Clinical exome sequencing produces between 90,000-100,00 variants per individual. Bottlenecks are manifested due to manual (operator based) interpretation of data. Given an increasing demand for genomic screening, automated computational methodologies are urgently required to meet both throughput and interpretation. Objective: determine if algorithms can be developed to identify and report specific pathogenic variants. Methods: Clinical exome sequencing was performed on 961 individuals presented for diagnostic analysis to King Fahad Medical City (KFMC). Variant Call Format (VCF 4.2) files from each patient were used for algorithm development. Perl (v5.28.1) was used as the construct language. 137 known pathogenic variants were used as a search test bed. A 10-step procedural workflow was implemented to automate the process of searching for targets. Where a positive identification was elicited, variants were annotated, merged with clinical data and output as a pdf report. Negative findings were output as a pdf report with clinical data only. Results: 961 VCF files were screened for 137 pathogenic variants of interest to KFMC. Target variants were compared against each variant within a patient’s VCF using logic operators. A total processing time including report production for 961 individuals was completed in 11.38 hours. 177 patients (18.4%) were positive for at least one variant and 15 patients had two variants (1.6%). All positive cases were verified manually in the originating VCF. The 137-target list of variants were “spiked” into a negative control patient VCF to act as a positive control (sensitivity). All variants were detected by the algorithm. 10 negative finding patients were chosen at random and manually checked for the absence (specificity) of the 137 variants. No variants were detected. Conclusion: Automated searching and production of reports for specific pathogenic variants using computational searching is feasible for diagnostic laboratories undertaking clinical exome sequencing.
Extent of antimicrobial resistance in Acinetobacter species in a Tertiary Care Teaching Hospital

Acinetobacter , gram negative coccobacillus, has become a frequent pathogen in hospitals and other health care settings. Acinetobacter species cause a wide variety of illness in debilitated and hospitalized patients especially in intensive care units (ICU). Carbapenems constitute the backbone of treatment of complicated Acinetobacter infections. However, resistance to Carbapenem is established and observed globally, leading to limited therapeutic options. The study was designed to understand the extent of resistance in Acinetobacter species in our hospital which is located in an island separated from mainland India. Isolates showing resistance to either Imipenem (IMP) and/or Meropenem by disc diffusion method was considered as Carbapenamase producing and further subjected to identification by phenotypic methods. Of the 160 isolates, 111 were resistant to either Imipenem and/or Meropenem. The susceptibility patterns of antibiotics tested suggests high resistance to 3rd/4th generation Cephalosporins (CTR 93.7%, CAZ 88.29%, CPM 85.59%) and least resistance to Colistin and Polymyxin B. MHT alone was positive in 66.98% isolates suggesting production of OXA type class D β-lactamase and DDST alone was positive in 7.55% isolates suggesting production of Metallo-β-lactamase /Group B β-lactamase. The present study adds to the literature available in respect to increasing instances of Carbapenem resistance and their presumptively mechanism of resistance.
Mallats Wavelet Transform for Human Face Recognition Using Artificial Neural Network

— This paper represents face recognition using Mallets Wavelet Transform and Artificial Neural Network. Various methods are implemented for the face recognition system since many years. Improving efficiency and creating a robust face recognition system is the main aim of implementing new single method or combination of methods. This paper represents hybrid method where Artificial Neural Networks (ANN) classifiers is combined with Mallets Wavelet Transform. Developing face recognition includes three stages image acquisition and preprocessing, feature extraction and classification. In the proposed method Mallats Wavelet Transform is implemented for feature extraction method and Artificial Neural Network for classification. ANN is better classifier as compared with other since it can be taught to take decisions as humans can take. It also adapt better with the changes in environment. ANN is also efficient as compared to the other classifiers.