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Due to its ideal optoelectronic qualities for photovoltaic response, quantum dot solar cells are termed as solar cells of the third generation. Opportunities for dimensions and adaptability formation make QDs suitable suction devices to match the broad spectrum of the sun effectively. The potential for generations of multiple electron-hole pairs by a single photon number, i.e., generation of multiple charge carriers, demonstrates the ability to get beyond the theoretical limitations of a single conversion capability. Quantum solar dots show a dynamic efciency of up till Twelve percent energy, remarkably similar to their solar dye-sensitive counterparts. Although, the efciency of the Quantum Dot Solar Cells lags behind the standard solar single-junction photovoltaic. We'll talk about the rst emergence of quantum solar dots in minimalist terms in this review. The study will also look at how critical building blocks are being developed and features like the several interoperability areas in the Quantum Dot Solar Cell, movement of charge transporter, and re-integration into all different visual connections, which affect the efciency of power conversion. Additionally, carrier multiplication's fundamental notions and numerous exciton production are discussed in terms of their inuence on the efcient quantum dot solar cells conversion.

Introduction: Facial aesthetic surgery requires a thorough preoperative analysis of face and identification of problems to frame a comprehensive surgical plan. The conceptions of an attractive face must be analyzed beforehand. Powell and Humphrey had defined facial angles and had formulated range which would be perceived as attractive. Objective: To compare the change in pre- and post-operative nasolabial, nasofrontal and nasofacial angles in patients undergoing Rhinoplasty. Materials and Methods: A prospective observational study was conducted from October 2014 to October 2019 in ENT department of a tertiary care hospital. Twenty-one patients undergoing Rhinoplasty and fulfilling the inclusion criteria were considered as study population. Their pre- and post-operative profile photographs were taken for morphometric analysis. The nasolabial, nasofrontal and nasofacial angles were measured and compared, before and one month after the surgery in profile pictures. Results: A significant change in the nasolabial and nasofacial angles were seen following Rhinoplasty. The mean nasofrontal angle showed an increase following Rhinoplasty but was not found significant. Conclusions: Rhinoplasty results in significant change in nasofacial and nasolabial angles and in expert hands, is expected to restore the various facial angles to their acceptable range. Keywords: Rhinoplasty, Nasolabial, Nasofrontal, Nasofacial angles, Morphometric measurements.

Purpose: This study aims to build a conceptual model based on the S-O-R (Stimulus-Organism-Response) framework to understand how Augmented Reality influences brand equity. The proposed model is intended to look into the influence of AR attributes like interactivity, vividness, modality, novelty, and media richness on consumers’ experiential values and brand equity in e-commerce. Theoretical framework: This study developed the conceptual model by following the Stimulus-Organism-Response (S-O-R) Model (Mehrabian & Russell, 1974). Design/methodology/approach: To advance the conceptual and managerial understanding of AR as an experiential marketing tool, this study followed the systematic literature review approach to build the integrated conceptual model. Findings: Results of the study has developed a conceptual model for studying the application of AR technology as an experiential marketing tool on customer purchasing experience and brand equity in the e-commerce setting. Research, Practical & Social implications: This study contributes towards the available literature by studying the AR and its various dimensions for building brand equity. This study will not only assist the e-commerce firms in their decision-making process about adopting this technology but also the marketers to develop the effective marketing strategies for consumer experience of AR uses on e-commerce platforms. Originality/value: This study introduces modality of AR as a media characteristic in its interface in creating seamless user experience in the proposed model.

Water has become a major threat in today’s world. Collection of heavy metals, a few of them, is potentially toxic and these get distributed to different areas through different pathways. With an increase in the earth’s population, development and industrialization are taking place rapidly and these get the major source of water contamination. With heavy metals in lakes, rivers, groundwater, and various water sources, water gets polluted by the increased concentration of heavy metals and metalloids through release from the suddenly mine tailings, disposal of high metal wastes, growing industrial areas, leaded gasoline and paints, usage of fertilizers inland, animal manures, E-waste, sewage sludge, pesticides, wastewater irrigation, coal, etc. Exposure to heavy metals has been linked to chronic and acute toxicity, which develops retardation; neurotoxicity can damage the kidneys, lead to the development of different cancers, damage the liver and lungs; bones can become fragile; and there are even chances of death in case of huge amount of exposure. This chapter mainly focuses on heavy metal pollution in water and its toxic effect on living organisms.

This paper is concerned withthe problem of paraphrase detection. For a number of applications, the ability to detect similar sentences, such as text mining, summary text, plagiarism detection, authorship authentication, and question answering, is important. Given two phrases, the goal is to detect whether they are identical semantically. This work involves a novel model namely, ParaCap, which uses capsule networks for the investigation of sentences. Capsule networks understand the spatial information (context, language, length of sentences and others) by using the instantiation parameters for the better results as compared to CNNs. For the objective, the Quora Question Pair dataset containing 404291 pairs of Quora Questions is being used. The ParaCap model outperforms many state-of-art methods, and also proves to be comparable to other techniques by achieving the accuracy of 89.19%.

Direct metal laser sintering (DMLS) of Ti6Al4V is being widely used to manufacture biomedical implants. The rapid solidification process can result in undesired microstructure that imparts poor duc...

In this investigation, the superalloy IN718 has been prepared by additive manufacturing (AM) following a selective laser melting technique, and the post-AM heat treatments have been optimized. The microstructure of additively manufactured (AM) IN718 is characterized by the presence of dendritic and cellular features with large spatial heterogeneity along and across the build plane. Along the build direction, the fiber texture dominates. Heat treatment involving two-step solution treatment, and subsequently, two-step aging treatment was specifically designed to facilitate the precipitation of d phase at the grain boundaries to make the material resistant to grain boundary sliding (GBS). The AM IN718 showed dynamic strain aging (DSA) at three different temperatures, while the critical strain for the onset of serration was extended to a higher value after the heat treatment.

Braided composites have seen substantial industrial uptake for structural applications in the past decade. The dependence of their properties on braid angle provides opportunities for lightweighting through structure-specific optimisation. This paper presents an integrated approach, combining finite element (FE) simulations and a genetic algorithm (GA) to optimise braided beam structures in the spaceframe chassis of a rail vehicle. The braid angle and number of layers for each beam were considered as design variables. A set of 200 combinations of these variables were identified using a sampling strategy for FE simulations. The results were utilised to develop a surrogate model using genetic programming (GP) to correlate the design variables with structural mass and FE-predicted chassis displacements under standard loads. The surrogate model was then used to optimise the design variables using GA to minimise mass without compromising mechanical performance. The optimised design rendered approximately 15.7% weight saving compared to benchmark design.

Smart cities are composed of interlinked components with constant data transfer and services targeted at increasing the life style of the people. The chapter focuses on diverged smart city components as well as the security models designed to be implemented. The four major paradigms discussed in this chapter are smart grids, building automation system (BAS), unmanned aerial vehicle (UAV), and smart vehicles. Apart from addressing the security concerns of every component, the major highlights of this chapter are architecture, smart environment, industry, lifestyle, services, and digital lifestyle quality. Finally, the chapter focuses on privacy preserving mechanisms, its essence over smart cities, strong architecture related to privacy, preserving mechanism, and various approaches available that can retaliate these issues in a smart city environment.

Background Telemedicine Practice Guidelines were promulgated in India in 2020. There is little information about the awareness, knowledge, attitude and skills (AKAS) of the healthcare professionals regarding telemedicine and its practice guidelines a year after. The aim of the study was to study the awareness, knowledge, attitudes and skills of the healthcare workers of the country about telemedicine especially in the context of telemedicine practice guidelines 2020. Methods An online survey was conducted over 12 weeks from active healthcare workers in India. A standard AKAS Questionnaire for telemedicine and other close and open ended questions were used. Results A total of 602 active healthcare workers participated in the study. It was seen that a majority of participants had high scores for awareness, knowledge, and attitude toward telemedicine but a mere 39.53% scored high for skills related to telemedicine. A majority of the respondents had no knowledge of the existence of telemedicine practice guidelines and very few had seen or read them. A majority of the respondents were keen to attend a course or training to enhance their understanding and practice of telemedicine. Conclusion The results of the study show that the basic awareness and background knowledge regarding telemedicine exists in the healthcare professionals in the country. The requirement is to train and educate them about the skills and provisions of the telemedicine practice guidelines 2020 to ensure optimal use of telemedicine and to avoid medico-legal issues.

Thermoplastic braided composite tubular beams were manufactured using commingled hybrid yarn with braid angles 30°, 45° and 60° and tested in static three-point flexure. Two principal deformation modes were observed during the flexural loading: global flexure and localised crush. The extent of each mode occurring in the three braid angle variants was measured using a linear deflectometer as well as 3D digital image correlation. Localised crushing was found to decrease significantly with increasing braided angle, accounting for 63%, 45% and 19% of the total applied deflection for the 30°, 45° and 60° beams respectively. Further, surface strain measurements obtained from 3D DIC showed increasing the braid angle led to a global flexure-dominated deformation. In addition, the stiffness and peak load increased with increasing braid angle. The observed differences in deformation modes were due to a combination of multiple braid angle-dependent properties of braided composites such as modulus, thickness etc.

Technical Appendix for the paper "Approximate Novelty Search" - ICAPS-2021.

Candida auris is a nosocomial pathogen responsible for an expanding global public health threat. This ascomycete yeast has been frequently isolated from hospital environments, representing a significant reservoir for transmission in healthcare settings. Here, we investigated the relationships among C. auris isolates from patients with chronic respiratory diseases admitted in a chest hospital and from their fomites, using whole-genome sequencing (WGS) and multilocus microsatellite genotyping. Overall, 37.5% (n = 12/32) patients developed colonisation by C. auris including 9.3% of the screened patients that were colonised at the time of admission and 75% remained colonised till discharge. Furthermore, 10% of fomite samples contained C. auris in rooms about 8.5 days after C. auris colonised patients were admitted. WGS and microsatellite typing revealed that multiple strains contaminated the fomites and colonised different body sites of patients. Notably, 37% of C. auris isolates were resistant to amphotericin B but with no amino acid substitution in ERG2, ERG3, ERG5, and ERG6 as compared to the reference strain B8441 in any of our strains. In addition, 55% of C. auris isolates likely had two copies of the MDR1 gene. Our results suggest significant genetic and ecological diversities of C. auris in healthcare setting. The WGS and microsatellite genotyping methods provided complementary results in genotype identification.

In this paper, we compare four state-of-the-art gradient boosting algorithms viz. XGBoost, CatBoost, LightGBM and SnapBoost. All these algorithms are a form of Gradient Boosting Decision Trees(GBDTs). They find wide usage across competitive machine learning contests like Kaggle, due to their flexibility and considerably faster training times. Since a typical vanilla GBDT is usually implemented as a black box model, our research makes an attempt to help improve the explainability of GBDTs. We performed an exhaustive 360 degree comparative analysis of each of the four algorithms by training and testing them on diverse datasets leveraging IBM's PowerAIR AC922 CPU. The analysis was performed using two approaches; One was by training the baseline algorithms on the datasets, and the other was by performing systematic hyperparameter optimization (HPO) using the HyperOpt framework. Although the HPO process is resource-intensive, the Power System architecture facilitated lower training times without compromising the algorithm's accuracy for each of the datasets. We present the accuracy scores and training times across the four datasets for both the aforementioned approaches. The results imply that despite interesting trends observed across all the datasets, there is no clear winner that excels equally in every aspect of performance

Cloud Computing is one of the fastest emerging technology which is in a massive growth to transform the definition of computing with its service oriented paradigm. It has overcome the issue related accruing the own IT infrastructure by proving the service through pay and use model. As a resultant reduce the cost of ownership, headache of manageability and saving of time of or execution of the process. However, the cloud services could be deterring due to the security breaches. Data security might be the major issue in cloud as the entire service is provided through internet only. Current data encryption mechanism might not be so strong to provide true privacy and reliability. Here in this paper we proposed a novel solution to data security with data encryption along with salted password hashing technique. Efficacy of the proposed method is validated through comparison with the current traditional security approach on the basis of the security parameters.

In the current situation of worldwide pandemic COVID-19, which has infected 62.5 Million people and caused nearly 1.46 Million deaths worldwide as of Nov 2020. The profoundly powerful and quickly advancing circumstance with COVID-19 has made it hard to get precise, on-request latest data with respect to the virus. Especially, the frontline workers of the battle medical services experts, policymakers, clinical scientists, and so on will require expert specific methods to stay aware of this literature for getting scientific knowledge of the latest research findings. The risks are most certainly not trivial, as decisions made on fallacious, answers may endanger trust or general well being and security of the public. But, with thousands of research papers being dispensed on the topic, making it more difficult to keep track of the latest research. Taking these challenges into account we have proposed COBERT: a retriever-reader dual algorithmic system that answers the complex queries by searching a document of 59K corona virus-related literature made accessible through the Coronavirus Open Research Dataset Challenge (CORD-19). The retriever is composed of a TF-IDF vectorizer capturing the top 500 documents with optimal scores. The reader which is pre-trained Bidirectional Encoder Representations from Transformers (BERT) on SQuAD 1.1 dev dataset built on top of the HuggingFace BERT transformers, refines the sentences from the filtered documents, which are then passed into ranker which compares the logits scores to produce a short answer, title of the paper and source article of extraction. The proposed DistilBERT version has outperformed previous pre-trained models obtaining an Exact Match(EM)/F1 score of 80.6/87.3 respectively.

The surge in the number of services being provided off cloud-based servers in recent times, has led to a concern in the security of these platforms. It is thus highly important to have in place systems which can efficiently recognize and alert the system administrators about malicious activities on the cloud platform. Further, it is desirable to know the kind of attack being made on the cloud server to better understand and mitigate the threats. The usage of Artificial Neural Networks(ANN) has been found in Intrusion Detection Systems(IDS). To bring these systems closer to the biological model of the neural system, in this paper, the authors have deployed Spiking Neural Networks (SNN) to create a highly efficient IDS. SNNs deploy spiking neurons which mimic the biological neuron more closely than the artificial neuron model which is an overly simplified mathematical model. In this paper, the authors propose a hybrid SNN-ANN model which achieves a high degree of accuracy in identifying and classifying malicious connections on the network into their type of attack using the NSL-KDD dataset.

Objective of current study is to analyse the effect of excitation frequency, magnetic field intensity and waveform on the hysteresis loop and Barkhausen Noise signals. As the property of ferromagnetic material especially the hysteresis loop and Barkhausen noise depends on microstructure, excitation frequency and magnetic field intensity. In this study the hysteresis loop and Barkhausen noise of the mild steel sample were experimentally measured by varying the frequency, intensity and waveform using magnetic Barkhausen noise analyser. Sinusoidal and triangular waveform were applied in varying frequency (0.1 Hz to 0.4 Hz) and intensity (250 Oe to 1000 Oe) for hysteresis loop whereas for Barkhausen noise analysis frequency varies from 20 Hz to 50 Hz and intensity varies from 250 Oe to 1000 Oe. Characteristics of hysteresis loop and magnetic Barkhausen noise depends upon magnetizing parameter and waveforms were explained with the help of micro magnetic theory.

Background There is a deluge of information and misinformation about COVID-19. The present survey was conducted to explore the sources of information /misinformation for healthcare professionals from India. Methods A cross-sectional online survey using snowballing technique was conducted from 24 Mar to 10 Apr 2020. The questionnaire was pretested and developed using standard techniques. It was circulated among medical students and physicians. Data were analysed using the STATA software. Results Data of 758 participants were analysed. A total of 255 (33.6%) medical students, 335 (44.2%) nonspecialists and 168 (22.1%) specialists participated. The most common source of formal and informal information was official government websites and online news, respectively. A total of 517 (68.2%) participants accepted receiving misinformation. Social media and family and friends were the most common sources of misinformation. Seventy-two percent of participants agreed that spread of information helped to contain COVID-19, but more than that 75% agreed to having received inaccurate information. Seventy-four percent of respondents felt the need for regulation of information during such times; 26% and 33% felt that information about COVID-19 made them feel uncomfortable and distracts routine decision-making, respectively, and 50% felt it was difficult to differentiate correct from incorrect information about COVID-19. Conclusion The study explored the sources of information and misinformation and found a high prevalence of misinformation, especially from social media. We suggest the need to better manage the flow of information so that it can be an effective weapon against SARS-CoV2. There is a need for doctors to adapt to the changing times of infodemics accompanying pandemics.

Recently, sheet metal forming industries have shown interest in characterization of anisotropy properties, tension-compression strength differential and deep drawing behavior of commercially pure titanium (CP Ti) material for successful fabrication of lightweight components. In present work, the formability of 1.0 mm thick CP Ti sheet metal was studied using a laboratory scale deep drawing test set-up, and the limiting drawing ratio (LDR) was found to be 2.143. Four ears were observed in the LDR cup with maximum cup height along diagonal direction (DD) and troughs along both rolling direction (RD) and transverse direction (TD), and the earing height was approximately 13.7% of the total cup height. Tensile and stack compression tests were carried out along different orientations to evaluate the anisotropy properties and tension-compression strength differential, and these data were used to develop the CPB06 constitutive yield model. Also, the developed CPB06 anisotropy plasticity theory was implemented in an FE model to predict the non-uniform material flow, earing defect and thickness distribution successfully. In order to reduce earing defect, a two-stage blank modification technique was proposed incorporating directionalities of both yield strength and plastic strain ratio. A significant 83% reduction in earing height was achieved through use of modified blank with simultaneous benefit of improvement in thickness distribution and reduction in peak load.

The current standard of care for surgical management of Otosclerosis is small fenestra stapedotomy, which can be done by CO2 Laser assisted as well as conventional techniques. Vertigo is the commonest complication after stapes surgery. The use of CO2 Laser has been rising recently owing to its no touch principle, high precision and possibly lower risk of vertigo post operatively. To compare the post-operative vestibular deficit in patients of Otosclerosis having undergone small fenestra stapedotomy by conventional versus CO2 Laser assisted technique. 80 clinically diagnosed Otosclerosis patients fulfilling the inclusion criteria were enrolled. They underwent small fenestra stapedotomy by either conventional or CO2 Laser assisted technique. Vestibular function was assessed objectively by measuring sway velocity using modified clinical test of sensory interaction on balance by static posturography. Subjective measurement of balance was done using Vestibular balance subscore of Vertigo Symptom Score (VSS-sf-V). The outcome measures were compared pre-operatively and at first and fourth week post-operatively. All patients had vestibular deficit 1 week post-operatively in the form of increased sway velocity and symptom scores, which reduced by 4 weeks after Stapedotomy. The vestibular deficit in the two groups was similar at 1 week after surgery. 4 weeks after surgery, the sway velocity in conventional group was significantly greater than Laser group though there was no significant difference in the symptom scores. The use of CO2 Laser for Stapedotomy results in lesser post-operative vestibular deficit as compared to conventional method.

Indian railways are one of the largest rail networks in the world, and millions of passengers travel daily through it, due to which there are also a vast number of complaints in front of Indian Railways coming every minute through various mediums like COMS (Complaint Management System) app, RailMadad, SMS etc. Given the top-down approach which is followed for the uncategorised complaints making official's work time-consuming. Therefore, faster complaint redressal becomes a critical factor for the passenger's satisfaction. Previous research has focused on traditional machine learning algorithms and Twitter dataset available publicly to tackle this problem. In this work, we have explored deep learning techniques on an official dataset of the COMS app from CRIS (Centre for Railways Information Systems) and proposed RailNeural: an Attention Based Bi-Directional Long Short-Term Memory (LSTM) model which analyses user's complaint input sequences, capturing the underlying character level feature and then classifies them into their respective departments of field units ensuring prompt and accurate redressal of complaints. Our model outperforms several baseline models achieving an accuracy of 93.25 per cent and an F1-Score of 0.93.