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It is an attempt to interpret the role of the universal constant 'c ' the velocity of light, in fundamental physics. It cannot be just be setting an upper limit to velocities of material particles. It must have much more crucial contribution in foundations. In addition, it is also important to recognize the new phenomenon discovered from Dirac's relativistic quantum theory, namely, 'zitterbewegung'. It shows fundamental particles such as electrons must have an 'internal structure' which we have failed to recognize in spite of overwhelming evidences so far. It leads to a fact that electrons travel along helical trajectories and its representation come out naturally as 'complex' exponent. Thus it gives a new interpretation to the complex wavefunction!

Visual Question Answering (VQA) is a multi disciplinary challenging problem involving various fields such as Natural Language Processing, Computer Vision, Deep Learning and Knowledge Representation, which has garnered much interest among researchers, especially with the recent advancements in machine perception. The problem at hand not only involves reasoning over visual elements present in the image or natural language understanding of the input query, but also may involve outside world knowledge in order to infer the answer. In this paper, we convert the VQA problem to a factoid question answering task over a set of natural language facts extracted from images in the Visual Genome Dataset [1]. Recent literatures in textual question answering have established the effectiveness of End to End Memory Networks (MemN2N) over the standard LSTMs. Inspired by the approaches incorporated by researchers in this direction, as a first step to create an explainable VQA model, this paper proposes the incorporation of MemN2N with soft attention for inferring the answer from a set of regional facts extracted from the image. We also experiment with the addition of a Bayesian Neural layer for posterior reasoning of the answer from a fixed vocabulary, as proposed in [2] which shows a significant improvement in the accuracy score compared to the other models tested.

���Quantum Reconstruction��� attempts to rebuild the highly successful Quantum Mechanics (QM) from scratch to understand the ���real��� meaning of its mathematical structure. In addition, perhaps, we must re-look at the role the constant ���c��� plays in physics. It would be shown that this constant has a more crucial role at the foundations than what Relativity envisaged. It was Einstein, who postulated in his Special Theory of Relativity (SR), that the velocity of light is invariant for all inertial observers! This is counter-intuitive. Another mystery from QM is Schrodinger���s ���zitterbewegung��� (ZB) phenomenon which is a mathematical extension of Dirac���s free electron theory. By integrating these two concepts into physics at the foundational level we can rebuild a fairly consistent model which seems to unify SR and Quantum Mechanics (QM) by giving a geometrical interpretation to the ���complex wave-function��� as representing a helical trajectory of particles like electrons. Helix being a geodesic on a cylinder accommodates ���quantization of energy��� and is a three-dimensional wave having all the properties that we are familiar with the 2D wave. Thus by postulating an internal structure to these fundamental particles consistent with ZB, many of the results of QM and SR which are at present purely based on intuitive mathematics, can be understood in a simple and ���realistic��� way.

Background: Biological macromolecules, namely, DNA, RNA, and protein, have their building blocks organized in a particular sequence and the sequential arrangement encodes the evolutionary history of the organism (species). Hence, biological sequences have been used for studying evolutionary relationships among the species. This is usually carried out by Multiple Sequence Algorithms (MSA). Due to certain limitations of MSA, alignment-free sequence comparison methods were developed. The present review is on alignment-free sequence comparison methods carried out using the numerical characterization of DNA sequences. Discussion: The graphical representation of DNA sequences by chaos game representation and other 2-dimensional and 3-dimensional methods are discussed. The evolution of numerical characterization from the various graphical representations and the application of the DNA invariants thus computed in phylogenetic analysis are presented. The extension of computing molecular descriptors in chemometrics to the calculation of a new set of DNA invariants and their use in alignment-free sequence comparison in an N-dimensional space and construction of phylogenetic trees are also reviewed. Conclusion: The phylogenetic tress constructed by the alignment-free sequence comparison methods using DNA invariants were found to be better than those constructed using alignment-based tools such as PHLYIP and ClustalW. One of the graphical representation methods is now extended to study viral sequences of infectious diseases for the identification of conserved regions to design peptidebased vaccines by combining numerical characterization and graphical representation.

Objectives The primary objectives of this study are to determine efficacy of Siddha medicine, Kabasura kudineer in reduction of SARS-CoV-2 viral load and reducing the onset of symptoms in asymptomatic COVID-19 when compared to Vitamin C and Zinc (CZ) supplementation. In addition, the trial will examine the changes in the immunological markers of the Siddha medicine against control. The secondary objectives of the trial are to evaluate the safety of the Siddha medicine and to document clinical profile of asymptomatic COVID-19 as per principles of Siddha system of Medicine. Trial design A single centre, open-label, parallel group (1:1 allocation ratio), exploratory randomized controlled trial. Participants Cases admitted at non-hospital settings designated as COVID Care Centre and managed by the State Government Stanley Medical College, Chennai, Tamil Nadu, India will be recruited. Eligible participants will be those tested positive for COVID-19 by Reverse Transcriptase Polymerase Chain reaction (RT-PCR) aged 18 to 55 years without any symptoms and co-morbidities like diabetes mellitus, hypertension and bronchial asthma. Those pregnant or lactating, with severe respiratory disease, already participating in COVID trials and with severe illness like malignancy will be excluded. Intervention and comparator Adopting traditional methods, decoction of Kabasura kudineer will be prepared by boiling 5g of KSK powder in 240 ml water and reduced to one-fourth (60ml) and filtered. The KSK group will receive a dose of 60ml decoction, orally in the morning and evening after food for 14 days. The control group will receive Vitamin C (60000 IU) and Zinc tablets (100mg) orally in the morning and evening respectively for 14 days. Main outcomes The primary outcomes are the reduction in the SARS-CoV-2 load [as measured by cyclic threshold (CT) value of RT-PCR] from the baseline to that of seventh day of the treatment, prevention of progression of asymptomatic to symptomatic state (clinical symptoms like fever, cough and breathlessness) and changes in the immunity markers [Interleukins (IL) 6, IL10, IL2, Interferon gamma (IFNγ) and Tumor Necrosis Factor (TNF) alpha]. Clinical assessment of COVID-19 as per standard Siddha system of medicine principles and the occurrence of adverse effects will be documented as secondary outcomes. Randomisation The assignment to the study or control group will be allocated in equal numbers through randomization using random number generation in Microsoft Excel by a statistician who is not involved in the trial. The allocation scheme will be made by an independent statistician using a sealed envelope. The participants will be allocated immediately after the eligibility assessment and informed consent procedures. Blinding (masking) This study is unblinded. The investigators will be blinded to data analysis, which will be carried out by a statistician who is not involved in the trial. Numbers to be randomised (sample size) Sample size could not be calculated, as there is no prior trial on KSK. This trial will be a pilot trial. Hence, we intend to recruit 60 participants in total using a 1:1 allocation ratio, with 30 participants randomised into each arm. Trial status Protocol version 2.0 dated 16th May 2020. Recruitment is completed. The trial started recruitment on the 25th May 2020. We anticipate study including data analysis will finish on November 2020. We also stated that protocol was submitted before the end of data collection Trial registration The study protocol was registered with clinical trial registry of India (CTRI) with CTRI/2020/05/025215 on 16 May 2020. Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines (Additional file 2).

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing.

UV light triggers thymine to form thymine dimers inducing cell death. Though the sun provides heat and light, which are essential for life on Earth, ultraviolet (UV) rays in sunlight can cause damage to DNA.In this science fair project, I will experiment with a strain of yeast that is super-sensitive to UV light.The goal for this project is to find out what percent of yeast colony growth has been killed. Bakers yeast, or saccharomyces cerevisiae, is a eukaryotic unicellular organism. Cerevisiae is used in many laboratories as a model organism because it has internal organs such as a nucleus and a mitochondria. Since cerevisiae’s genes have been well-studied, researchers are able to separate genes of interest from others, called knockout genes. In this project, a knockout strain of yeast will be used. This modified yeast is designed to be DNA-repair deficient which means that this strain of yeast does not have the enzymes needed to repair damaged cells while regular yeast and human cells do. When UV light destroys DNA the light initiates a reaction with thymine creating a thymine dimer. If the thymine dimer does not repair properly there are two paths it can follow, become a cancer cell if the thymine dimers are not widespread, or die, if they are widespread. In this project, there are many thymine dimers that will be formed when the modified yeast is exposed to UV light causing the yeast to die. There will be two dishes next to each other with grown modified yeast. One dish will have aluminum foil on the top and the other one will not have aluminum foil. Then both of them will be exposed to UV light. This is the equation that is used to find out what percent of the yeast colony has died: $100 \times$ (1-colonies on exposed plate/colonies on control plate) =% killed Two more tests will be done on the effects of pure UV light and the effects of regular light with no UV rays on yeast cells. This will show that the light is not effecting the yeast but the UV rays are. This project will demonstrate how DNA in yeast cells are damaged by UV light, causing yeast cells to die. Similarly, UV rays cause human cells to mutate by destroying DNA, which leads to skin cancer. Although modified yeast does not have the enzymes that unmodified baker’s yeast and human cells have, it will still show how UV rays affect eukaryotic cells’ DNA. A future application for this project would be using skin cells to see how they interact with UV rays and by doing this more research can be done on skin cancer. When I find out what percent of yeast died when exposed to UV lights I will compare it to the effects of skin cancer and see how the enzymes react differently to UV light and look at the difference between the modified yeast and the skin cell.

The dynamics of Long Reach Manipulators (LRM) produce undesirable vibrations creating disturbances in the mounting base. Such disturbances hamper proper mission tasks and hence controlling them has been and is an active area of research. This paper presents a numerical study incorporating eddy current (EC) damping for controlling the base disturbance of LRM applications. The LRM system has been idealized as a Double Pendulum. A short link augmented on the rigid link swings as a secondary pendulum so that the magnet, housed in it, continuously intercepts the coils mounted on the rigid link causing eddy currents and the consequent damping effect. The disturbances—in terms of the damping of oscillations, and angular displacement of the flexibly mounted base link, and damping of the angular displacement of the rigid link—are compared considering the absence and presence of EC damping. A parametric analysis involving the ratio of the lengths of the secondary link to the rigid link is also investigated. The results indicate the suitability of the damping arrangement in controlling the disturbances. Based on these results, mechanisms can be suitably developed for actual applications.

A bilayered nanofibrous scaffold with rapid wound healing properties is found to be suitable for tissue regeneration applications. The objective of this study is to reveal the fabrication of a poly(3-hydroxybutyric acid) (P)–gelatin (G) nanofibrous mat through electrospinning, with a horn keratin–chitosan-based biosheet (KC) as a bilayered nanofibrous scaffold. The mupirocin (D)-loaded horn KC biosheet (KCD) acts as the primary layer over which PG nanofibers were electrospun to act as the secondary layer. It is shown that this engineered bilayered nanofibrous scaffold material (KC–PG) should fulfill the functions of the extracellular matrix (ECM) by elucidating its function in vitro and in vivo. The bilayered nanofibrous scaffold was designed to exhibit improved physiochemical, biological and mechanical properties, with better swelling and porosity for enhanced oxygen permeability, and it also exhibits an acceptable antibacterial property to prevent infection at the wound site. The bilayered nanofibrous scaffold assists in better biocompatibility towards fibroblast and keratinocyte cell lines. The morphology of the nanofibrous scaffold aids increased cell adhesion and proliferation with cell material interactions. This was elucidated with the help of in vitro fluorescence staining against both cell lines. The bilayered KCD–PG nanofibrous scaffold material gives accelerated wound healing efficiency during in vivo wound healing. The results showed the regulation of growth factors with enhanced collagen synthesis, thereby helping in faster wound healing.

In this work, we propose to leverage the advantages of both the Artificial Neural Network (ANN) based Second Order Reliability Method (SORM) and Importance sampling to yield an Adaptive Importance Sampling based ANN, with specific application towards failure probability and sensitivity estimates of Variable Stiffness Composite Laminate (VSCL) plates, in the presence of multiple independent geometric and material uncertainties. The performance function for the case studies is defined based on the fundamental frequency of the VSCL plate. The accuracy in both the reliability estimates and sensitivity studies using the proposed method were found to be in close agreement with that obtained using the ANN based brute-force Monte Carlo Simulations (MCS) method, with a significant computational savings of 95%. Moreover, the importance of taking into account the randomness in ply thickness for failure probability estimates is also highlighted quantitatively under the sensitivity studies section.

This research demonstrates the feasibility of filler rod fabrication to develop nanocomposite weldment to enhance the joint strength via ARB technique. Al 3003 alloys were joined through GTAW by melting fabricated nanocomposite filler metal. TiO2 nanoparticles were chosen as reinforcement from 0 to 3 wt%. Roll bonding is desired owing to the proper distribution of TiO2 particles. SEM depicted the distribution of reinforcement particles in the grain boundaries. TEM disclosed the uniformity in particulates distribution, peening of dislocation and strain fields in 12 wt% TiO2 reinforced nanocomposite weldment. EBSD portrayed the grain refinement occurred in the weld zone due to reinforcement addition. The improvement in impact and bending strength were due to excellent bonding between the Al and reinforcement particles and excellent load transfer ability provided by reinforcement particles.

Over last few decades, mathematics has played a crucial role in developing efficient algorithms for Face Recognition (FR) used in biometric systems. FR using Machine Learning (ML) techniques has impacted FR systems tremendously, towards efficient and accurate models for FR. Existing FR systems used in biometrics use ML techniques to learn patterns in the images by extracting various features from them and often require pre-processed face image data for the learning process. In this paper, we have used various pre-processing techniques and compared them in the deployed FR framework. It was observed that the Steerable Pyramid (SP) filter was the most efficient pre-processing technique among all techniques used for pre-processing in this work. Though existing feature extraction methods such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF) have been used in the past, they have not been accurate enough in various vision based biometric systems. Hence, a novel PSI (Pose Scale and Illumination) invariant SURF-RootSIFT technique is proposed by extending the well known SIFT-RootSIFT feature extraction technique which is achieved by calculating the Bhattacharya Coefficient between the feature vectors. A framework which uses the proposed novel feature extraction technique is deployed in this work. This paper demonstrates that the novel SURF-RootSIFT based framework is proven to perform more accurately and efficiently than the other techniques, with 99.65, 99.74 and 97.93% accuracy on the Grimace, Faces95 and Faces96 databases respectively.

In this paper, an efficient approach toward face recognition is presented. The proposed model is invariant to pose, expression, scale, illumination, and translation with the application of different techniques and implementation of their algorithms. The images are first preprocessed using different techniques like CS-LBP, XCS-LBP, CS-LDMP, etc. to remove noise and make it illumination invariant. In one implementation, these are fed as input to different descriptors. The keypoints detected are invariant to lighting condition. A wide variety of descriptors are applied to the detected keypoints in order to compare the quality of features computed. These descriptors are quantized to a single vector, representing the most salient features of the facial image. The vectors are then fed into the stacking classifier. In the other method, these preprocessed images are directly fed as input into the residual network. A comparison of the results by these two pipelines is studied on two benchmark face datasets, namely FACES94 and Grimace.

Face Recognition is immensely proliferating as a research area in the paradigm of Computer Vision as it provides an extensive choice of applications in surveillance and commercial domains. This paper throws light upon the comparison of various dense feature descriptors (Dense SURF, Dense SIFT, Dense ORB) with each other and also with their classical counterparts (SURF, SIFT, ORB) using a novel technique for recognition. This proposed technique uses Laplacian of Gaussian filter for enhancement of the image. It applies various dense and classical feature descriptors on the enhanced image and outputs a feature vector. In order to achieve high performance, this feature vector is given to Fisher vector since Fisher Vector is a feature patch-aggregation method. Finally, extended nearest neighbor Classifier is used for classification over the orthodox k-nearest classifier. Experiments were carried out on three diverse datasets—ORL, Faces94, and Grimace. On scrutinizing the results, Dense SIFT and Dense ORB were found to be preeminent as measured by various performance metrics. 98.44 on Grimace, 98.15 on Faces94.

In recent years, there has been an increase in online shopping resulting in an increased number of online reviews. Customers cannot delve into the huge amount of data when they are looking for specific aspects of a product. Some of these aspects can be extracted from the product reviews. In this paper we introduced SimsterQ - a clustering based system for answering questions that makes use of word vectors. Clustering was performed using cosine similarity scores between sentence vectors of reviews and questions. Two variants (Sim and Median) with and without stopwords were evaluated against traditional methods that use term frequency. We also used an n-gram approach to study the effect of noise. We used the reviews in the Amazon Reviews dataset to pick the answers. Evaluation was performed both at the individual sentence level using the top sentence from Okapi BM25 as the gold standard and at the whole answer level using review snippets as the gold standard. At the sentence level our system performed slightly better than a more complicated deep learning method. Our system returned answers similar to the review snippets from the Amazon QA Dataset as measured by the cosine similarity. Analysis was also performed on the quality of the clusters generated by our system.

Security and identity have become one of the primary concerns of the people in this digital world. Person authentication and identification is transforming the way these services are provided. Earlier it was mainly achieved through passwords and patterns but with significant advancements in face recognition technologies, it has been exploited in providing authentication in smart phones and computers. Face Recognition (FR) extends its use in applications like face tagging in social media, surveillance system at theaters, airports and so on. The proposed mathematical model employs linear algebra and mathematical simulations for the task of person identification. Kernel singular value decomposition is used to denoise the facial image which is then passed to a feature detector and descriptor based on nonlinear diffusion filtering. The obtained descriptors are quantized into a vector using an encoding model called VLAD which uses k-means++ for clustering. Further, classification is done using Gradient boosting decision trees. The pipeline proposed aims at reducing the average computational power and also enhancing the efficiency of the system. The proposed system has been tested on the three benchmark datasets namely Face 95, Face 96 and Grimace.

Using high throughput tandem mass tag (TMT) based tagging technique, we identified 4,172 proteins in three developmental stages: early, mid, and late seed filling. We mapped the identified proteins to metabolic pathways associated with seed filling. The elevated abundance of several kinases was observed from the early to mid-stages of seed filling, indicating that protein phosphorylation was a significant event during this period. The early to late seed filling stages were characterized by an increased abundance of proteins associated with the cell wall, oil, and vacuolar-related processes. Among the seed storage proteins, 7S (β-subunit) and 11S (Gy3, Gy4, Gy5) steadily increased in abundance during early to late stages of seed filling, whereas 2S albumin exhibited a decrease in abundance during the same period. An increased abundance of proteases, senescence-associated proteins, and oil synthesis proteins was observed from the mid to late seed filling stages. The mid to late stages of seed filling was also characterized by a lower abundance of transferases, transporters, Kunitz family trypsin, and protease inhibitors. Two enzymes associated with methionine synthesis exhibited lower abundance from early to late stages. This study unveiled several essential enzymes/proteins related to amino acid and protein synthesis and their accumulation during seed development. All data can be accessed through this link: https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=38784ecbd0854bb3801afc0d89056f84. (Accession MSV000087577) This article is protected by copyright. All rights reserved.

Stipulation of fresh water for domestic use without any microbial, organic and inorganic contaminants is of high need. Sustainable, efficient, cost-effective and robust water purification technologies is of high need and it can be achieved using nanomaterials and their composite. Nanostructured graphene has unique properties like high surface to volume ratio, higher absorbability, reusability with minimal chemical alterations, and low cytotoxicity. From the validation of these properties, we have developed PLLA-Ag@graphene sandwich structures as an effective adsorbate for water purification application. As the real water bodies have lot of bacterial contaminants, the material is also designed as efficient adsorbate with antibacterial efficacy. In view of achieving these objectives, we have synthesized PLLA fibre mats by electrospinning method, followed by PLLA-Graphene and Ag decorated PLLA-graphene mats. The crystallite size for graphite and Ag@graphene was calculated as 30.82 nm and 43.79 nm, respectively. Furthermore, the UV analysis of Ag@graphene shows two peaks corresponding to graphene and Ag NP at 285 nm and 407 nm respectively. The layers were assembled in the order of polymeric fibre, as-fired biomass graphite, Ag@graphene for methodical filtration process. The filtration efficacy of the filtrate was tested using sewage water and the results shows higher contamination removal percentage of 87 % with TDS values in the drinking water standards after filtration. The antibacterial efficacy results also evidence of the potentialities of the hybrid system towards water purification application.

Novel Al–3 wt.% TiO2 (fillers) nanocomposite has been successfully fabricated by accumulative roll bonding method to join Al 3003 alloy with the parameters of gas tungsten arc welding process such ...

Manufacturing copper metal matrix composite (CMMCs) through stir casting method results in numerous desirable benefits. This research is dedicated on fabricating Cu with 0, 3, 6 and 9 wt% TiO2 and 1% Gr CMMCs. The reinforcement particles were added to Cu molten metal at 1200 °C. The cast CMMCs have been analysed using advanced characterization method viz. XRD, FESEM and EDAX. XRD peaks confirm the presence of hard ceramic TiO2 particles in the matrix. Grain refinement takes place in the composite due to TiO2 particles. Two kinds of distribution are observed in the micrographs. By increasing the content of TiO2 and Gr, agglomeration occurs within the grain boundaries and reduces the grain size. As a result, hardness and ultimate tensile strength are improved by the incorporation of TiO2 particles and the corresponding strengthening mechanisms involved have been discussed.

The slowness of legal proceedings in the common law legal system is a widely known fact. Any tool which could help reduce the time taken for the resolution of a case is invaluable. Common legal systems place a great importance on precedents and retrieving the correct set of precedents is considerably time consuming. Hence, for any case whose proceedings are in progress, if there are suitable prior cases, then the court has to follow the same interpretations that were passed in the prior cases. This is to ensure that similar situations receive similar treatment, thus maintaining uniformity amongst the legal proceedings across all courts at all times. Hence, precedent cases are treated as important as any other written law (a statute) in this legal system. In this paper, we propose two new approaches to solve this information retrieval problem wherein the system accepts the current case document as the query and returns the relevant precedent cases as the result. The first approach is to calculate the document similarity using Wordnet, which is a lexical database that could be leveraged to quantify the semantic relatedness between two documents, using a semantic network. The second approach is the use of a Siamese Manhattan Long Short Term Memory network, which is a supervised model trained to understand the underlying similarity between two documents.

Al 3003 alloy is well known for its industrial use as heat exchangers, radiators, oil tanks and household utensils. In case of heat exchangers, fabrication becomes mandatory by a welding process which is carried out by gas tungsten arc welding (GTAW). It is preferred due to its advantages of superior mechanical properties. Thus by involving GTAW process and reinforcing with suitable nanoparticles, the strength of Al 3003 alloy can be improved at the joints. Hence this technical paper deals with the fabrication of Al100–x – x wt% TiO2 (x = 0, 0.75, 1.5, 2.25 and 3) nanocomposite filler metal through accumulative roll bonding (ARB) technique to weld Al 3003 alloy through GTAW. Further characterization studies have been made through various electron microscopic techniques besides X‐ray diffraction analysis (XRD), vicker's microhardness and tensile testing. It was observed that the incorporation of TiO2 nanoparticles reduced the grain size much due to the formation of more nucleation sites and deceleration growth. XRD results revealed the presence of TiO2 peaks in the composite. FESEM confirmed the distribution of second phase nanoparticles regularly in the Al matrix. TEM analysis showed that the nanoscale TiO2 distribution and strain fields due to thermal mismatch between matrix and reinforcement. The improvement in mechanical properties was owing to good interfacial bonding between the Al matrix and ceramic nanoparticles.

High cost imported components of seamless steel tube manufacturing plants wear frequently and need replacement to ensure the quality of the product. Hard chrome plating, which is time consuming and hazardous, is conventionally used to restore the original dimension of the worn-out surface of the machine components. High Velocity Oxy-Fuel (HVOF) thermal spray coatings with NiCrBSi super alloy powder and Cr3C2 NiCr75/25 alloy powder applied on a 50CrMo4 (DIN-1.7228) chromium molybdenum alloy steel, the material of the wear prone machine component, were evaluated for use as an alternative for hard chrome plating in this present work. The coating characteristics are evaluated using abrasive wear test, sliding wear test and microscopic analysis, hardness test, etc. The study results revealed that the HVOF based NiCrBSi and Cr3C2NiCr75/25 coatings have hardness in the range of 800–900 HV0.3, sliding wear rate in the range of 50–60 µm and surface finish around 5 microns. Cr3C2 NiCr75/25 coating is observed to be a better option out of the two coatings evaluated for the selected application.

Plasma-sprayed NiCrBSi–graphite coatings have been subjected to solid particle erosion at elevated temperatures. The work reports the erosion resistance of NiCrBSi coatings with 4, 6 and 8 wt-% add...

In this paper, we employ the virtual element method for the numerical solution of linear thermo-elastic problems in two dimensions. The framework is implemented within the commercial software Abaqus using its user element feature. The implementation details of the virtual element method in Abaqus-Matlab software framework are described. The corresponding details on the input data format, which forms the core of the analysis, are given. Both linear and quadratic elements are used within the virtual element framework. A few benchmark problems from linear thermo-elasticity are solved to validate the implementation.

Cat thyme is an important medicinal plant used for treating many diseases. With renewed interest as arthropod deterrent and repellents, there is immediate need to cultivate cat thyme on large scale in the laboratory. To improve cat thyme seed germination, obstacles involved in its seed dormancy must be investigated. To address such challenges, mechanical abrasion and chemical treatments were used to enhance the rate of seed germination. The application of mechanical abrasion with low speed grinding using walnut husk to improve the germination of cat thyme seeds was studied. In addition, to improve the seed germination percentage, the seeds were further treated with different concentrations of hydrogen peroxide (2.5, 5, 10 and 15%). The results show that the treatment of 5% hydrogen peroxide concentration promoted the seed germination rate. After the aforementioned different treatments, Baggy method was used and it achieved 50% germination rate within 30 days. In conclusion, the germination tests showed that cat thyme seed germination improved with combination of mechanical abrasion and 5% hydrogen peroxide treatments. Key words: Cat thyme, seed germination percentage, hydrogen peroxide.

The paper proposes an efficient and accurate model for face recognition using an attentive local feature descriptor extracted from Convolutional Neural Network referred to as DEep Local Feature (DELF). The algorithm mentioned formerly is used for extracting descriptors from the images using a fully convolutional network which are trained with weak supervision and using image level classes, neglecting the usage of patch and object level annotations. The physical characteristics such as colour, texture, etc are represented in the form of 40 dimensional vectors using DELF. Further, such descriptors are quantized to represent them into the compact form using Vector of Locally Aggregated Descriptors and Fisher kernels. Subsequently, such vectors are used for multi-class image classification using ensemble learning methods including Rotation Forest and Random Forests. Comparative study between both the classifiers and feature aggregation methods are performed and tabulated in the paper.

In this paper we propose a method for person identification. The proposed method is invariant to illumination, scale, pose, camera exposure and translation of the head. In order to make the model illumination invariant, a linear transform is applied. Binary affine features are used to extract facial features from each image. The facial features obtained are compressed to form a vector which is then passed to a Bayesian classifier. This method was tested on three benchmark datasets to show as to how the method overcomes of all the hurdles such as variation of illumination, change of scale, motion of head, change in expression and more. The error rate obtained is in the neighbourhood of 18%.

The paper presents several thresholds obtained by heuristic approach for face verification using Locally Adaptive Regression Kernel (LARK) descriptors for euclidean, cosine and chebyshev distance metrics. The absence of a threshold for several distance metrics possess several setbacks such as increased computational complexity and escalated runtime. The proposed method has significantly higher influence when the verification process is computationally intensive. The proposed approach requires minimal training and face verification is accomplished based on the threshold value obtained during training. The whole process is modest and nearly all of the time spent is solely on quantifying any of the distance metrics between the LARKs obtained from the two faces for verification. LARK descriptors compute a measure of resemblance on the basis of “signal-induced distance” between a pixel and its nearby pixels. We assess the interspace between the LARKs from these faces and analogize the resemblance from the threshold resulting in a binary decision.

The aim is to propose SPHORB Face Recognition technique for Robotic Surveillance systems. Surveillance bots with efficient face recognition system becomes even more powerful and adds to the applications of the bots. In this paper we compare the results of face recognition using SPHORB (a new fast and robust binary feature detector and descriptor for spherical panoramic image) [1] algorithm with ORB (Oriented Fast and Rotated Brief) [4] algorithm for classifying 2D and 3D images Database. In the first part 2D face images are randomly selected from LFW [32] with good sample of images across gender and ethnicity. The number of keypoints that were identified and number of matching keypoints between the 2D images using SPHORB and ORB were compared. From the results we determine that the SPHORB algorithm has identified more keypoints for the same 2D image than ORB, it has processed more images but ORB gives better accuracy than SPHORB for 2D images. In the next part we investigated the image matching between a front face 2D image with a 3D image. The front face image and 3D images are randomly selected from the ThatsMyFace.com [2] with good sample of images across genders, ethnicity and age. The front face image is compared with a randomly selected 3D image from the sample class set using SPHORB and ORB algorithms. The number of keypoints that were identified and number of matching keypoints between images using SPHORB and ORB were compared. From the results we determine that the SPHORB algorithm has identified more keypoints for the same image than ORB, it has processed more images and has accuracy comparable to the ORB algorithm.

In this paper we propose a robust method for face recognition using a descriptor which has been designed to search for features using a grid pattern. This is different from popular key point descriptors which search images only in positions where they expect to extract the most features. An image segmentation technique to modify the representations of images of some standard datasets by enhancing its salient features. The descriptor provides us with a vector of key points of the image. The features obtained from the keypoints that are extracted are aggregated using a feature aggregator. A comparison study on the results obtained from classification of these aggregated features using different non-linear machine learning classifiers is performed.

In the current techno-savvy world, cybersecurity is a prime concern. Biometrics are being extensively used for authentication and authorization. Face recognition(FR) is a class of biometrics which has proved to be one of the most effective methods for identification and verification, which works even when the subject is unaware of being scanned. This paper gives insight into the use of Image Processing in Robotic Applications. It discusses the use of computer vision by surveillance robots. Surveillance applications prefer unsupervised learning over supervised learning as unsupervised learning doesn’t require true labels. The burgeoning demand for unsupervised learning in surveillance applications proffered the nub of this project. The paper coalesces the well-known similarity algorithm SSIM with Spectral Clustering to produce prodigious results. SSIM surpasses other techniques like MSE by extracting structural features from images. This leads to a significant improvement in performance because humans also extract structural information from visuals. SSIM eliminates the effect of illumination and then uses the attributes that depict the structure of objects to gain the desired structural information. The performance of the proposed model was compared with other similarity measures on the ORL(Olivetti Research Laboratory Cambridge), Caltech and Faces96 datasets. An accuracy of 89.5% was achieved on the ORL and 88.3% on the Caltech and 86.7% on the Faces96 dataset.

In Human-Robot Interaction (HRI), quick and efficient FR techniques are often required in service robots. In a real time scenario, it is absolute that face image patterns observed by robots depends often on variations such as pose, light conditions, location of the robots (view point), etc. In addition to these constraints, the service robots are expected to be quick enough for FR so that they can be deployed in applications such as counting people, security and surveillance, directing humans, etc. In this paper, ORB, a computational expensive and quick feature extraction technique is used, which has been a panacea for the above mentioned constraints. One of the dimensionality reduction techniques called PCA (a tool which reduces high dimensional data to lower dimension while keeping most of the data) with its sublime advantages of reduction of storage and time is often used. But, in the FR system, the linear uncorrelated components of PCA doesn’t consider the non-linear factors such as occlusion and in such cases PCA fails to find the good representative direction. Kernel PCA (KPCA) which uses kernel methods considers even the non-linear factors and is proven to be more suitable than PCA, thus producing better results. By considering all these factors, our paper proposes a novel technique ORB-KPCA for FR along with Threshold Based Filtering (TBF). The proposed technique is proven to be efficient in both time and space by experimenting on three benchmark datasets (ORL, Faces96 and Grimace).

Face recognition presents a challenging and interesting problem in the field of robotics and computer vision. Robot systems which support face recognition has been vastly used for surveillance, defense etc., Robots prominently rely on real time feedbacks from sensors. Robots with face recognition have numerous applications such as automation process, object detection, security and surveillance, defense, autonomous vehicles etc. A robot face recognition system is a computer application used to automatically identify or verify a person from a digital image or a video frame from a video source. This is usually achieved through a comparison of selected facial features from an image and a facial database. In order to recognize a face in real time the images captured by camera have to be stored and then processed by face recognition algorithm. In the entire face recognition process, the choice of feature extractor is very important. The constraint on the feature extractor limits the accuracy. SIFT, SURF and ORB are amongst the prominent feature extractors due to their insusceptibility to constraints such as illumination, pose and scale. In this paper, we demonstrate that proposed SRP model with ORB gives better percent accuracy of around 85% after preprocessing.

Background: Globally, depression and depressive disorder are common amongst people of all ages, especially amongst 15–29 years old. The prevalence of depression amongst medical students was studied on the World Health Day 2017 using the Brief Patient Health Questionnaire. Objective: The objective was to study the prevalence of depression and depressive symptoms amongst students of a class of MBBS based on responses collected anonymously in the year 2017. Materials and Methods: A class of students in a medical college was administered the questionnaire consisting of nine items, and 81 students participated in this study. Each item was explained, and the students were given time to think and mark their respective responses. The results of responses of 79 students who answered all questions are reported. Based on the cumulative score of their responses, depression was graded as minimal, mild, moderate, moderately severe and severe depression. Results and Discussion: Twenty-four male students and 55 female students constituted the study population. Overall, 91% of the students reported some degree of depression in the previous 2 weeks. Nearly 8.9% of the students did not have any depressive symptoms over the previous 2-week period. Almost 12.7% and 5.1% of the students reported moderately severe and severe depression, respectively. Minimal, mild and moderate depression were reported by 21.5%, 32.9% and 22.8% of the students, respectively. Students with depression were informed to consult with a psychiatrist for formal evaluation. Conclusion: The prevalence of depressive symptoms is very high amongst medical students, and a formal study with intervention is the need of the hour.

With recent advancements in machine perception and scene understanding, Visual Question Answering (VQA) has garnered much attraction from researchers in the direction of training neural models for jointly analyzing, grounding and reasoning over the multi-modal space of image visual context and natural language in order to answer natural language questions pertaining to the image contents. However, though recent works have achieved significant improvement over state-of-art models for answering questions that are answerable by solely referring to the visual context of the image, such models are often limited, being incapable of tackling questions involving external world knowledge beyond the visible contents. Though recently, research has been driven towards tackling external knowledge based VQA as well, there is significant room for improvement as limited studies exist in this area. Inspired by the aforementioned challenges involved, this paper is aimed at answering free form and open ended natural language questions, not limited to visual context of an image, but external world knowledge as well. With this motive, inspired by human cognitive abilities of comprehending and reasoning answers when given a set of facts, this paper proposes a novel model architecture to model VQA as a factoid question answering problem, leveraging state-of-the-art deep learning techniques for reasoning and inferring answers to free form questions, in an attempt of improving the state-of-art in open ended visual question answering.

With continually increasing demand for food accompanied by the constraints of climate change and the availability and quality of soil and water, the world’s farmers are challenged to produce more food per hectare with less water, and with fewer agrochemical inputs if possible. The ideas and methods of the system of rice intensification which is improving irrigated rice production are now being extended/adapted to many other crops: wheat, maize, finger millet, sugarcane, tef, mustard, legumes, vegetables, and even spices. Promoting better root growth and enhancing the soil’s fertility with organic materials are being found effective means for raising the yields of many crop plants with less water, less fertilizer, reduced seeds, fewer agrochemicals, and greater climate resilience. In this article, we review what is becoming known about various farmer-centred innovations for agroecological crop management that can contribute to agricultural sustainability. These changes represent the emerging system of crop intensification, which is being increasingly applied in Asian, African, and Latin American countries. More research will be needed to verify the efficacy and impact of these innovations and to clarify their conditions and limits. But as no negative effects for human or environmental health have been identified, making these agronomic options more widely known should prompt more investigation and, to the extent justified by results, utilization of these methodologies.

Purpose The aim of this paper is to study the hot corrosion behaviour of super 304H stainless steel for marine applications. Design/methodology/approach The investigation was carried out with three different combinations of salt mixture (Na2SO4, NaCl and V2O5) at two different temperatures (800 and 900°C). Findings The spalling and growth of oxide layer was observed more with the presence of V2O5 in the salt mixture at 900°C during experimentation than what was observed in 800°C. The mass change per unit area is calculated to study the corrosion kinetics and also the influence of salt mixture. Further, the samples are analysed through materials characterisation techniques using optical image, scanning electron microscope (SEM), energy dispersive X-ray (EDAX) and X-ray diffraction (XRD) analysis. The presence of V2O5 in the salt mixture was the most important influencing species for accelerating hot corrosion. Originality/value SEM, EDAX and XRD analysis confirmed the formation of Fe2O3 and Cr2O3 at 900°C showing contribution in corrosion protection.