bluuebunny/crossref_metadata_2025 huggingface dataset:
A Sociocultural Perspective on Computer Science Capital and Its Pedagogical Implications in Computer Science Education
Authors: Vrieler Tina , Salminen-Karlsson Minna
Published: April 2022

The aim of this conceptual article is to provide a framework and a lens for educators in diversifying and making CS education more inclusive. In this article, we conceptualize the notion of computer science capital (CSC), which extends Bourdieu’s sociological theory of capital and Archer et al.’s work on “science capital.” The CSC concept was developed by contrasting the concept of science capital with a literature review on key factors affecting students’ aspirations in CS. We argue that there is a need to distinguish between science capital and CSC, because the types of capital that are considered legitimate vary between the field of natural science and computer science. The CSC concept uses a sociocultural perspective on learning and can be understood as a form of symbolic capital that is influential in facilitating students’ possibility to fully participate in, engage with, and form aspirations in CS. The CSC concept consists of three main components, each with associated subcomponents. We believe our CSC framework, along with the self-reflection prompts included in this article, will offer support for reflections for educators in their daily pedagogical work. By taking students’ various levels of social and cultural capital into consideration, educators can plan didactic activities with a focus to strengthen students’ various types of capital. This includes reflection on how implicit and explicit norms, beliefs, thoughts, expectations, values, and ideas can affect the pedagogical practices and ultimately the students. Only when we are reflective about our teaching practices can we be better positioned to construct a more inclusive teaching and learning environment.
The Different Types of Contributions to Knowledge (in CER): All Needed, But Not All Recognised
Authors: Draper Steve , Maguire Joseph
Published: April 2022

The overall aim of this article is to stimulate discussion about the activities within CER, and to develop a more thoughtful and explicit perspective on the different types of research activity within CER, and their relationships with each other. While theories may be the most valuable outputs of research to those wishing to apply them, for researchers themselves there are other kinds of contributions important to progress in the field. This is what relates it to the immediate subject of this special journal issue on theory in CER. We adopt as our criterion for value “contribution to knowledge”.

This article’s main contributions are

–

A set of 12 categories of contributions which together indicate the extent of this terrain of contributions to research.

–

Leading into that is a collection of ideas and misconceptions which are drawn on in defining and motivating “ground rules”, which are hints and guidance on the need for various often neglected categories. These are also helpful in justifying some additional categories which make the set as a whole more useful in combination.

These are followed by some suggested uses for the categories, and a discussion assessing how the success of the article might be judged.
An Agile New Research Framework for Hybrid Human-AI Teaming: Trust, Transparency, and Transferability
Authors: Caldwell Sabrina , Sweetser Penny , O’Donnell Nicholas , Knight Matthew J. , Aitchison Matthew , Gedeon Tom , Johnson Daniel , Brereton Margot , Gallagher Marcus , Conroy David
Published: April 2022

We propose a new research framework by which the nascent discipline of human-AI teaming can be explored within experimental environments in preparation for transferal to real-world contexts. We examine the existing literature and unanswered research questions through the lens of an Agile approach to construct our proposed framework. Our framework aims to provide a structure for understanding the macro features of this research landscape, supporting holistic research into the acceptability of human-AI teaming to human team members and the affordances of AI team members. The framework has the potential to enhance decision-making and performance of hybrid human-AI teams. Further, our framework proposes the application of Agile methodology for research management and knowledge discovery. We propose a transferability pathway for hybrid teaming to be initially tested in a safe environment, such as a real-time strategy video game, with elements of lessons learned that can be transferred to real-world situations.
Algorithm 1026: Concurrent Alternating Least Squares for Multiple Simultaneous Canonical Polyadic Decompositions
Authors: Psarras Christos , Karlsson Lars , Bro Rasmus , Bientinesi Paolo
Published: April 2022

Tensor decompositions, such as CANDECOMP/PARAFAC (CP), are widely used in a variety of applications, such as chemometrics, signal processing, and machine learning. A broadly used method for computing such decompositions relies on the Alternating Least Squares (ALS) algorithm. When the number of components is small, regardless of its implementation, ALS exhibits low arithmetic intensity, which severely hinders its performance and makes GPU offloading ineffective. We observe that, in practice, experts often have to compute multiple decompositions of the same tensor, each with a small number of components (typically fewer than 20), to ultimately find the best ones to use for the application at hand. In this article, we illustrate how multiple decompositions of the same tensor can be fused together at the algorithmic level to increase the arithmetic intensity. Therefore, it becomes possible to make efficient use of GPUs for further speedups; at the same time, the technique is compatible with many enhancements typically used in ALS, such as line search, extrapolation, and non-negativity constraints. We introduce the Concurrent ALS algorithm and library, which offers an interface to MATLAB, and a mechanism to effectively deal with the issue that decompositions complete at different times. Experimental results on artificial and real datasets demonstrate a shorter time to completion due to increased arithmetic intensity.
Weighted programming: a programming paradigm for specifying mathematical models
Authors: Batz Kevin , Gallus Adrian , Kaminski Benjamin Lucien , Katoen Joost-Pieter , Winkler Tobias
Published: April 2022

We study weighted programming, a programming paradigm for specifying mathematical models. More specifically, the weighted programs we investigate are like usual imperative programs with two additional features: (1) nondeterministic branching and (2) weighting execution traces. Weights can be numbers but also other objects like words from an alphabet, polynomials, formal power series, or cardinal numbers. We argue that weighted programming as a paradigm can be used to specify mathematical models beyond probability distributions (as is done in probabilistic programming). We develop weakest-precondition- and weakest-liberal-precondition-style calculi à la Dijkstra for reasoning about mathematical models specified by weighted programs. We present several case studies. For instance, we use weighted programming to model the ski rental problem — an optimization problem. We model not only the optimization problem itself, but also the best deterministic online algorithm for solving this problem as weighted programs. By means of weakest-precondition-style reasoning, we can determine the competitive ratio of the online algorithm on source code level.
Complexity-guided container replacement synthesis
Authors: Wang Chengpeng , Yao Peisen , Tang Wensheng , Shi Qingkai , Zhang Charles
Published: April 2022

Containers, such as lists and maps, are fundamental data structures in modern programming languages. However, improper choice of container types may lead to significant performance issues. This paper presents Cres, an approach that automatically synthesizes container replacements to improve runtime performance. The synthesis algorithm works with static analysis techniques to identify how containers are utilized in the program, and attempts to select a method with lower time complexity for each container method call. Our approach can preserve program behavior and seize the opportunity of reducing execution time effectively for general inputs. We implement Cres and evaluate it on 12 real-world Java projects. It is shown that Cres synthesizes container replacements for the projects with 384.2 KLoC in 14 minutes and discovers six categories of container replacements, which can achieve an average performance improvement of 8.1%.
Finding real bugs in big programs with incorrectness logic
Authors: Le Quang Loc , Raad Azalea , Villard Jules , Berdine Josh , Dreyer Derek , O'Hearn Peter W.
Published: April 2022

Incorrectness Logic (IL) has recently been advanced as a logical theory for compositionally proving the presence of bugs—dual to Hoare Logic, which is used to compositionally prove their absence. Though IL was motivated in large part by the aim of providing a logical foundation for bug-catching program analyses, it has remained an open question: is IL useful only retrospectively (to explain existing analyses), or can it actually be useful in developing new analyses which can catch real bugs in big programs?

In this work, we develop Pulse-X, a new, automatic program analysis for catching memory errors, based on ISL, a recent synthesis of IL and separation logic. Using Pulse-X, we have found 15 new real bugs in OpenSSL, which we have reported to OpenSSL maintainers and have since been fixed. In order not to be overwhelmed with potential but false error reports, we develop a compositional bug-reporting criterion based on a distinction between latent and manifest errors, which references the under-approximate ISL abstractions computed by Pulse-X, and we investigate the fix rate resulting from application of this criterion. Finally, to probe the potential practicality of our bug-finding method, we conduct a comparison to Infer, a widely used analyzer which has proven useful in industrial engineering practice.
Functional collection programming with semi-ring dictionaries
Authors: Shaikhha Amir , Huot Mathieu , Smith Jaclyn , Olteanu Dan
Published: April 2022

This paper introduces semi-ring dictionaries, a powerful class of compositional and purely functional collections that subsume other collection types such as sets, multisets, arrays, vectors, and matrices. We developed SDQL, a statically typed language that can express relational algebra with aggregations, linear algebra, and functional collections over data such as relations and matrices using semi-ring dictionaries. Furthermore, thanks to the algebraic structure behind these dictionaries, SDQL unifies a wide range of optimizations commonly used in databases (DB) and linear algebra (LA). As a result, SDQL enables efficient processing of hybrid DB and LA workloads, by putting together optimizations that are otherwise confined to either DB systems or LA frameworks. We show experimentally that a handful of DB and LA workloads can take advantage of the SDQL language and optimizations. SDQL can be competitive with or outperforms a host of systems that are state of the art in their own domain: in-memory DB systems Typer and Tectorwise for (flat, not nested) relational data; SciPy for LA workloads; sparse tensor compiler taco; the Trance nested relational engine; and the in-database machine learning engines LMFAO and Morpheus for hybrid DB/LA workloads over relational data.
Hardware Trojan Detection Using Unsupervised Deep Learning on Quantum Diamond Microscope Magnetic Field Images
Authors: Ashok Maitreyi , Turner Matthew J. , Walsworth Ronald L. , Levine Edlyn V. , Chandrakasan Anantha P.
Published: April 2022

This article presents a method for hardware trojan detection in integrated circuits. Unsupervised deep learning is used to classify wide field-of-view (4 × 4 mm
2
), high spatial resolution magnetic field images taken using a Quantum Diamond Microscope (QDM). QDM magnetic imaging is enhanced using quantum control techniques and improved diamond material to increase magnetic field sensitivity by a factor of  4 and measurement speed by a factor of  16 over previous demonstrations. These upgrades facilitate the first demonstration of QDM magnetic field measurement for hardware trojan detection. Unsupervised convolutional neural networks and clustering are used to infer trojan presence from unlabeled data sets of 600 × 600 pixel magnetic field images without human bias. This analysis is shown to be more accurate than principal component analysis for distinguishing between field programmable gate arrays configured with trojan-free and trojan-inserted logic. This framework is tested on a set of scalable trojans that we developed and measured with the QDM. Scalable and TrustHub trojans are detectable down to a minimum trojan trigger size of 0.5% of the total logic. The trojan detection framework can be used for golden-chip-free detection, since knowledge of the chips’ identities is only used to evaluate detection accuracy.
Investigation on the Effect of Nanoparticle Diameter and Concentration on the Performance of Nanofluid Solar Pond
Authors: Ting Shania Ying Ying , Liaw Kim Leong , Kurnia Jundika Candra
Published: April 2022

The usage of brine water in a solar pond has triggered concern on its potential to pollute surrounding land ecosystems. Thus, non-toxic, non-polluting nanofluid is proposed to replace brine water in a solar pond. No study on it has been reported. This study is therefore conducted to evaluate the performance of a density gradient nanofluid solar pond. An experimental test rig is built to conduct this evaluation. The nanoparticle used in this study is the widely available non-toxic silicon oxide (SiO2). The effect of nanoparticle diameter and concentration on the solar pond performance is evaluated. The results firmly indicate the potential of SiO2 nanofluid in replacing the brine water. In addition, it was found that both nanoparticle diameter and concentration affect the thermal storage performance.
Use of 3D Coronal and Sagittal Images to Improve the Diagnosis of Brain Tumor
Authors: Mishra Shailendra Kumar , Singh Kunal , Kumar Raushan , Kumar Praveen
Published: April 2022

A brain tumour is a type of cancer which is very hard to detect by a doctor in the starting stages. Generally, the shape and size of the tumour are unidentified. The Brain tumour classification is performed by serologic analysis and is not usually conducted before conclusive brain surgery. Normally Brain tumour is predicted by Magnetic Resonance Imaging (MRI) images, however, it is time-consuming and high cost. Nowadays a lot of data sets are available for identifying the several stages of brain tumour such as Glioma, Meningioma and a Pituitary tumour to train the Machine Learning (ML) model. The conventional ML models logistic regression, support vector machine (SVM), Convolution Neural Network (CNN) and Residual Neural Network (RNN) are used to predict the location of tumour present in the brain and also able to create tumour pattern mask. However, their accuracy is very less. In this paper, an effective ML model 3D U-NET has been developed that can generate a tumour pattern mask for any type of tumour present in the brain. The proposed model provides better accuracy as compared to the conventional method. Simulation results shows that, 85% accuracy.
Role of Green Buildings in Sustainable Living: Implementation and Impacts
Authors: Ganesh Viswanathan , V.M Ajay Krishna , S Senthilmurugan , R.R Ajit Ram
Published: April 2022

The rise in the number of carbon footprints has led to various new challenges and opportunities to reduce the rate of global warming and carbon footprints present in the atmosphere. Similarly, the amount of global warming caused by the modern buildings and electrical devices which are required for the purpose of cooling and heating have played a key role in the higher concentration of temperature in the urban areas. On the other hand, the temperature levels in rural areas are comparatively lower than the urban areas due to large bare land available for the free movement of air and the effect of trees have reduced the temperature with the help of natural shades and oxygen production. Therefore, the concept of green building and net zero building have been incorporated to enable the concept of nature into the buildings where the people can be more interconnected to the nature and get benefited to reduce the overall temperature and global warming effect in the coming future.
Towards a Smarter Connected Society By Enhancing Internet Service Providers' Qos Metrics Using Data Envelopment Analysis
Authors: S Gracia , Soundarabai P.Beaulah , Raj Pethuru
Published: April 2022

This paper analyses wireline broadband Quality of Service (QoS) metrics of India’s small and medium Internet Service Providers (ISPs). Key Performance Indicators (KPIs) used in this analysis are – Fault repair (>90% in 1 working day and >=99% in 3 working days), Response time to customer for voice-to-voice operator assistance (in 60 sec. >60% and in 90 sec. >90%), Broadband connection speed from ISP to node (Download speed) and Service availability/uptime. Benchmarks are arrived at, using the Slack Based Measure (SBM) in Data Envelopment Analysis (DEA). Twenty Decision Making Units (DMUs – ISPs) were used in the analysis with eight of them needing to improve their QoS on some of the mentioned parameters. Relative benchmark providers for all providers needing improvement with their weightage are found and optimal targets by each QOS metric is mathematically arrived at.
Analysis of Landsat 8 Imagery for Landform Classification and Change Detection
Authors: Dhakar Narendra Kumar , Pahuja Roop
Published: April 2022

Satellite imagery plays a dominant role in assessment of geographical features of earth, vegetation and landform change detection, monitoring of weather, air pollution and water body, etc. In this work, recently available Landsat 8 Level 2 reliable products are used to extract and analyze NDVI for landform, vegetation change detection, and classification. NDVI is the quantitative measure that has sub-ranges to broadly classify the landform into five classes, such as water, soil and rock, grassland and agriculture area, dense vegetation, and very dense vegetation. NDVI based different approaches, such as histogram formation, colour coded images, confusion matrix generation are applied to classify landform and estimate the change in vegetation and landcover of a region over certain time span. The software tool in Python is developed for the methods of Landsat 8 retrieval to classify the landform and estimate the change in vegetation of certain regions of India.
Detection and Classification of Thoracic Diseases in Medical Images Using Artificial Intelligence Techniques: A Systematic Review
Authors: Prakash Shubhra , B Ramamurthy
Published: April 2022

Artificial Intelligence is at the leading edge of innovation and is developing very fast. In recent studies, it has played a progressive and vital role in computer-aided diagnosis. The chest is one of the large body parts of human anatomy and contains several vital organs inside the thoracic cavity. Furthermore, chest radiographs are the most commonly ordered and globally used by physicians for diagnosis. An automated, fast, and reliable detection of diseases based on chest radiography can be a critical step in radiology workflow. This study presents the conduction and results of a systematic review investigating Artificial Intelligence techniques to identify thoracic diseases in medical images. The systematic review was performed according to PRISMA guidelines. The research articles published in English were filtered based on defined inclusion and exclusion criteria.
Prediction Error Identification for Closed Loop System with Errors-in-Variables
Authors: Zhang Yun Feng
Published: April 2022

In order to study whether the predictive error method can also obtain satisfactory results in the closed-loop system with variable errors, the closed-loop situation is deduced by imitating the predictive error method in the open-loop system. The results show that the prediction error method can be used to identify the classical closed-loop system. When the input and output noise are all white noise with mean 0 and variance 1, the closed-loop system with errors-in-variables can be used to identify the prediction error.
Investigation of Behavior of Flat Slab with Openings Adjacent to Column of Different Shapes Using CSI-Safe
Authors: Gurnani Jaicky
Published: April 2022

It is necessary in building to pass pipes and ducts through slab to accommodate essential public services, such as gas pipeline, water supply, and computer networking. However, presence of opening abrupt the path of load to column. This will increase effect of critical forces like moments, deflection, and punching shear. This paper represents numerical analysis and studies the behavior of different types of flat slabs having different types of openings for serviceability and collapse states. The numerical analysis includes group A and group B. Group A and group B are further subdivided into five parts of models with no opening, square opening, circular opening at varying distance of opening from the face column, square opening, and circular opening at the center of slab. This study is conducted to know the behavior of different type of flat slabs. As per analytical result, various conclusion and recommendation are proposed in the study.
Identification of Most Suitable Dielectrics Substrate for UWB Bandpass Filter
Authors: Gopalakrishnan Ramakrishnan , Rajagopal Sivakumar , Reddy Sai Viswanth , R Anirudh E
Published: April 2022

Identification of a suitable substrate is a critical parameter in the design of microstrip bandpass filters. In this work, the performance of an Ultra Wideband (UWB) bandpass filter based on Electromagnetic Bandgap (EBG) resonators are designed, fabricated, and analyzed. Printed Circuit Board (PCB) with suitable dielectric substrates are used extensively to develop microstrip lines. The filter is fabricated using RT Duroid substrate material and compared with FR4 and Alumina dielectric substrates and the performance results show that the best result is obtained with RT Duroid.
100 Mev Ag and 25 Kev He Induced Defects in 4H-SiC
Authors: Chakravorty Anusmita , Kabiraj Debdulal
Published: April 2022

We demonstrate the formation of radiative defects, known as color centres, in 100 MeV Ag and 25 keV He irradiated semi-insulating 4H-SiC using low-temperature (~77 K) photoluminescence (PL). In PL, the luminescence resulting from radiative recombination of photo-excited carriers (electrons and holes) is studied. In our work, a 266 nm deep UV excitation source is utilized for performing PL measurements. In the range, 850-1150 nm, the photoluminescent peaks ascribed to silicon vacancies (V
Si) and unknown defect-related bands (UD3 and UD4) are observed after both ion irradiations. It has also been observed that till an optimum irradiation fluence there is an enhancement of photoluminescence from the ion irradiation-induced defects but further increase in irradiation fluence results in accumulation of photo-absorbing defects, providing pathways for non-radiative processes. In addition, ion irradiation has been shown to suppress the undesirable luminescence in the range 300-600 nm caused by the intrinsic defects in as-grown 4H-SiC.
Investigations of Turbulence in the IIAEM Wind Tunnel Using Computational Fluid Dynamics
Authors: Luis Samuel , More Pooja , Satuluri Sridatta , Seeni Aravind
Published: April 2022

Turbulence is important to be assessed in the design of wind tunnels. Undesired turbulence levels undermine the quality of fluid flow and accordingly the quality of scientific data. At the test section, the level of turbulence is critical. The amount of turbulence present in the test section of a subsonic wind tunnel has not been investigated in earlier researches. In the present research, a subsonic open-section wind tunnel installed at the authors’ facility is used as a testbed for turbulence investigation. Computational Fluid Dynamics that solves Reynolds Averaged Navier Stokes equations (RANS) is used as the scientific method for calculating the turbulent flow parameters. RANS instead of Large Eddy Simulation or Direct Numerical Simulation is adopted as a first step to identify large-scale eddies. The characterization of turbulence is performed though decomposition of fluctuating velocity components (u and v) in two-dimension.
Aerodynamic Analysis and Optimization of Wings for the Jain University Sailplane Using XFLR5
Authors: Kakade Sayali , Chikkala Deekshitha , Reghunath Keerthana , Seeni Aravind
Published: April 2022

The wing of a sailplane must be designed to conform to specified mission and design requirements. High aerodynamic efficiency and good stall characteristics are some of the design requirements, while achieving level flight at a particular altitude could be a mission requirement. The aerospace engineering students of Jain University set out the task of designing a sailplane capable of flying at an altitude of 7,000 feet. The design task was undertaken using the computational code XFLR5. In the first step, the aerodynamic characteristics of five different airfoils are analyzed considering the effect of altitude. Secondly, the effect of chord length and span on rectangular wing aerodynamics is investigated. The design convergence is obtained for a wing of chord length 1 m and span 20 m. The effect of wing twist on the initially converged design to obtain a more elliptical lift distribution is then performed to obtain optimized designs.
Correlation of Trace Elemental Content in Selected Anti-Viral Medicinal Plants as Source of Active Molecules Used Against COVID-19 by ED-XRF Technique
Authors: Jyothsna S , Sammaiah D
Published: April 2022

Coronavirus is a positive sense RNA and has an envelope and has spread and effected globally leading to an increase in death tools. Currently, there is no effective drug for treatment and prevention of this disease. In this scenario, the only way to prevent the disease is the following certain safety guidelines and to boost up the immune system of body. Trace elements are essential for cellular process, growth, development, and boost up immune system. According to the previous studies, COVID-19 patients serum diagnostics tests illustrated that decreasing or increasing some elements in COVID-19 patients serum and plasma causing pathogenesis of corona disease. Alteration in the levels of these trace elements causes inhibits the activity of RNA dependent, impairment of immune system, oxidative stress that increase the risk of mutation in the viral genome and which are finally acquire COVID-19 properties. In the present work, elemental analysis was carried out in the 2 anti-viral plants O. sanctum and C. papaya by ED-XRF technique. 13 elements, i.e. P, S, Cl, K, Ca, Mn, Fe, Cu, Zn, Se, Br, Rb, and Sr concentrations, were estimated. Present research work revealed that six elements, such as Zn, Se, Fe, K, and Cu, were found at high and notable content in both plant samples, whereas low amount of Ca was found in C. papaya when compared with NIST 1515. The obtained results justify that the presence of some of these trace elements is correlated with the COVID-19 curative properties of these plants.
Role of Women Empowerment in Sustainable Development
Authors: Sharma Meenakshi
Published: April 2022

Sustainable development relies upon an impartial appropriation of assets for now and for what's to come. It can't be accomplished without sex fairness. Women's strengthening is a vital factor for accomplishing sustainable financial development, social development, and ecological maintainability. Women play an imperative part in ecological administration and development. Their full interest is thusly fundamental to accomplish sustainable development. There is a double reasoning for advancing sexual orientation correspondence. Initially, that correspondence among women and men, equivalent rights, openings, and obligations, involves common liberties and social equity. Besides that, more noteworthy uniformity among women and men is additionally a precondition for (and compelling marker of) sustainable individuals focused development. The insights, interests, requirements, and needs of all kinds of people should be thought about as an issue of social equity as well as on the grounds that they are important to enhance development measures.
Comparative Study of Various DC-DC Converter Topologies for PV Powered EV Charging Stations
Authors: Vijayan Maya , Udumula Ramanjaneya Reddy , Mahto Tarkeshwar
Published: April 2022

There is a drift in automotive industry from conventional internal combustion engines (ICE) to Electric Vehicles (EVs). This drift from ICE to EVs counts to the reduced carbon emission and thus reducing environmental pollution. EVs also find a solution for increasing fossil fuel costs. When it comes to renewable energy sources, typically to solar energy, it is affluent and reliable. The usefulness of solar energy is maximized by the incorporation of advanced power converter topologies along with their advanced controls. This paper aims to compare some of the boost converter topologies that are used in EV applications with solar photovoltaic powered charging stations. The comparative study is conducted on various parameters, such as DC voltage gain, duty-cycle, efficiency, voltage stress, merits, and demerits. Simulation results are analyzed and compared using MATLAB/Simulink platform.
A Novel Transfer Learning Technique for Detecting Breast Cancer Mammograms Using VGG16 Bottleneck Feature
Authors: Prusty Sashikanta , Dash Sujit Kumar , Patnaik Srikanta
Published: April 2022

Breast cancer represents the highest percentage of cancers and the second most common cancer overall that affect women with 87,090 deaths approximately as reported by ICMR, 2018 in India (1). Breast tumours are classified into two types as a) benign, which is not very harmful and would not cause breast cancer and b) malignant, the tumours are extremely dangerous and would form an abnormal cell that may cause cancer. One transfer learning model called VGG16 (Visual Geometry Group (16) has been implemented with a focus on breast cancer classification using mammography images taken from the MIAS dataset. Moreover, at the preprocessing step, all the breast images are enhanced by using CLAHE (Contrast Limited Adaptive Histogram Equalization) technique that specifically maintains adaptive techniques to remove the lines, letters, and other boxes irrelevant to the Breast Image. As the name suggests, this VGG16 has taken 13 convolutional layers and 3 fully connected layers to train the dataset. In the training period, this model could predict whether the breast images contain any type of cancerous cells or not. And finally, the model has successfully implemented and produced Network’s test score of 87.999 percent.
Conceptual Design of Jain University Sailplane Based on Concurrent Engineering Within Aerospace Engineering Curriculum
Authors: Satuluri Sridatta , Seeni Aravind
Published: April 2022

There is ever growing interest in the design of high-performance sailplanes since the start of the last century. Sailplanes or gliders form an example of sustainable engineering in aeronautics as there is practically little to no carbon footprint. Concurrent Engineering (CE) as a design methodology is adopted and implemented for the conceptual design phase of this project. Concurrent Engineering involves simultaneous sharing of data within various teams, thereby increasing productivity and reducing time for product design. As the project involves extensive data sharing and periodic design iterations, it was imperative that interrelationships between the different groups are handled properly through the implementation of flowcharts even before the start of the project. Efficient methods of sailplane design based on Concurrent Engineering methodology within an education curriculum have been applied. This paper reports ongoing progress made within the sailplane project management.
Smart Pre-Screening of Patients for COVID-19 Using Depth-Wise Separableconvolutional Neural Network
Authors: Bhatia Aviral , Desikan Shreyas , Tripathy Saroj Anand , Rajkumar Sujatha , Rajagopal Sivakumar , Soangra Rahul
Published: April 2022

Hospitals have become overcrowded and overburdened and at the same time have become a major source for viruses and pathogens to breed and spread. Even the testing mechanism and kits are limited in number and are very expensive to manufacture. We propose a mechanism using Machine Learning, Neural network techniques, and hardware to create a commercial inexpensive pre-screening device for such diseases. This device is designed for conditions that can be easily detected by analyzing the cough audio signals and have distinct cepstral coefficients. This device can then help us reduce the burden on the healthcare system as we can pre-screen and only send patients that are more likely infected to the hospital.
Automated Detection of Pneumothorax Using Frontal Chest X-rays
Authors: P Mathumetha , Rajagopal Sivakumar , Vaidya Shailly , Alhadidi Basim
Published: April 2022

Pneumothorax detection can assist doctors in treatment and diagnosis of diseases more accurately. In this paper automated method employed image segmentation techniques for the detection. The preprocessing methods are handled by image processing techniques using MATLAB 2020b software Support Vector machine is applied to classify normal and abnormal lung chest X-ray. Features are extracted from lung image with the texture based segmentation techniques. The rib boundaries are identified with sobel edge detection. The gray level incurrence matrices segmentation method increase accuracy rate with AUC 0.91.
EEG Signal Processing for Detection of Colour Vision Deficiencies
Authors: Hamid Mir Aamir , Singh Jaiteg
Published: April 2022

The clinical assessment methods of detection of colour blindness are prone to subjective bias, human errors, and lack of consistency. Also, heterogeneous nature of vision deficiencies makes manual detection difficult. Hence, objective methods based on physiological signals can be used. Various studies have been undertaken to analyze the effect that colour perceived has on EEG signals and as a result can be extended to detection of colour blindness. The results of this survey indicate that there are significant differences produced in EEG signals due to changes in colour, as well as due to differences in colour perception. EEG signals also differ between healthy and colour vision deficient individuals, thereby providing a biomarker for detection. This biomarker can be exploited using machine learning algorithms for detection of colour-related vision deficiencies.
A Stand Alone Building Integrated PV Tied Bidirectional Capability Direct DC Electric Vehicle Charging System Through Z-Source Inverter Impedance Network Capacitors
Authors: C Bharatiraja , G Ramanathan , G Saravana Ilango
Published: April 2022

Due to the rise of gasoline prices and depletion of fossil fuels, electric vehicles have gained a popularity. EVs can be charged at homes using the utility grid, but using a PV integrated system reduces the demand on grid and also contributes to the environmental sustainability. For the past few years there is a lot of interests shown in the building-integrated PV systems. They are designed and implemented to meet required building load demand. As the number of EV users increase in the commercial complexes, there is a need to provide adequate power requirement for EV fast charging. The power demand increases particularly in the daytime due to rush hour charging, so similar to rooftop solar energy system, building-integrated PV is a system which can provide power for EV charging. According to our proposed topology if PV system is offline, grid can act as a backup source. The Z-source inverter able to boost, buck ,and invert has got a lot of attention in PV grid applications. In this paper, a modified Z-source inverter-based charging system is proposed, and a step-up converter is used in the charging side of the system. This PV-Grid connected with MZSI topology. This topology demonstrates the bidirectional flow of power.
Standardization and Preliminary Phytochemical Screening of Polypodium vulgare Linn.
Authors: Singh Bahadur , Madaan Reecha , Arora Sandeep
Published: April 2022

Traditionally, Polypodium vulgare (family: Polypodiaceae) is used in the treatment of jaundice, dropsy, scurvy, and with the combination of mallows it is used to remove the hardness of spleen. The roots and rhizomes are also used as expectorant, laxative, stomachic, cholagogue, alternative and antistress. Current study has been performed to investigate the phytochemical and pharmacognostic properties of rhizomes of P. vulgare. Various physiochemical parameters evaluated were: Morphology, Microscopy, Ash value (4.54 ± 0.05), Water soluble ash (2.22 ± 0.06), acid soluble ash (1.25 ± 0.08), Petroleum ether extractive value (4.20 ± 0.10), Alcohol soluble extractive value (11.85 ± 0.18) and water-soluble extractive value (15.85 ± 0.12). Dried Powdered plant material were sequentially extracted via solvents in increasing order of polarity viz., n-hexane, chloroform, methanol and water and all extracts were screened for presence of different classes of phytoconstituents. Lipids were found in hexane extract, triterpenoids in chloroform extract, carbohydrates, proteins, flavonoids, saponins, and tannins in methanol extract, and carbohydrates, proteins, saponins, and tannins in aqueous extract, according to the phytochemical investigation.
Challenges and Issues of Working Women in 21st Century
Authors: Verma Mrinal , Srivastva Pragati , Sharma Piyush , Mishra Pradyumn , Gupta Prajjwal , Anisha Ms. , Dwivedi Prashant
Published: April 2022

This research is all about how women are facing difficulties to do job in corporate sector or corporate world and what measures will take place in future to reduced or overcome all the issues within the organization only. According to research there are some basic issues are their related to the safety, and harassment with the women who are working in the corporate sector, sometimes women are whiling to do the job in corporate sector due the above issues they are not doing 51.4% women are agreed that fell unsafe in the organization. And on the other side 40.7% women are agreed that they are not getting any kind of the appreciation while perform their activity in the organization and 73.6% women are agreed there is an inequality is present in the organization. Now it is a high time that government should make strict rules and regulation related to the safety of the women how are working in the corporate sector. KEYWORDS: Working women, corporate sector, Challenges and issues
Impact of COVID on Micro Finance Institutions
Authors: Balanagalakshmi B. , Kumari Sukhavasi Santha
Published: April 2022

COVID-19 pandemic has affected acutely the financial system and created several challenges to the financial institutions in terms of their existence and raised a question about its survival in future. Micro finance is playing a vital role in enhancing the financial strength of the society. Micro finance institutions offer financial services in the form of loans for various purposes to support low-income people and weaker sections of the society who have less access in commercial banks. The COVID-19 pandemic has worsened the liquidity conditions of low-income people and small business owners and they are unable to gather funds for their personal and business needs due to widespread lockdowns. The external private finance is falling due to the reason that the borrowers are unable to repay their loans and it has started to create pressure on institutions which cannot repay their deposits on time due to its poor liquidity conditions. Hence, this paper has made an attempt to study the impact of COVID-19 pandemic on micro finance institutions and measures taken to combat the situation.
Using H&E Stain, Compare the Cell Block Method to the Cytological Method in Body Fluid Cytology
Authors: Paul Rosy , Hrishi Das Kanishka , Sood Meenakshi
Published: April 2022

Sediment cells from cell blocks can be more useful than the routine cytological methods for body fluids. The present study was conducted to compare cell block and cytological methods of body fluids. Two-year study was done in a tertiary care hospital of Punjab. Samples were taken from 90 patients, comprising 30 Ascitic, 30 Broncho alveolar lavage (BAL), and 30 pleural fluids. Cytology smears were prepared & stained with H & E stain and cellblock preparations of centrifuged deposits were processed, then sections were cut with 5 micrometers and stained with H & E stain. Sensitivity of cell blocks was found to be nearly double that of routine cytology. Cell blocks are proved to be superior to the cytological smears, when there is need for Immunohistochemistry. Use of cell blocks plays a vital role than routine cytology smears of body fluids can increase the sensitivity.
Experimental Study on Shrinkage Characteristics of the Cotton Stalk in a Fluidized Bed Combustor
Authors: Sharma Rajeev Kamal
Published: April 2022

This paper presents the impact of fuel particle size and bed temperature on longitudinal shrinkage, transverse shrinkage, volumetric shrinkage, and weight loss of cotton stalk fuel particles in a lab-scale fluidized bed. Particle length and bed temperature have been varied from 2 cm to 8 cm and 300˚C to 500˚C respectively. It is found that linear shrinkage and transverse shrinkage are affected inversely by particle length and directly by bed temperature. Long particles are largely affected by the bed temperature. Volumetric shrinkage increases with an increase in bed temperature and decreases with increases in fuel particle length. At lower bed temperature, weight loss decreases with an increase in particle length and is almost independent of particle length at elevated bed temperature. Up to 500˚C bed temperature, approximately 27% of particle volume and 90% of particle weight decreases. Empirical correlations for estimating linear and transverse shrinkage have been presented.
Influence of App Notifications and Push Messages of Most Popular Food Delivering Applications Among the Users in Mysuru
Authors: K Sandeep T , B Moulya
Published: April 2022

The study is focusing on the influence of app notifications and push messages of food delivering applications among the users in Mysuru, a district in Karnataka state. The study was designed to examine the use of food delivering apps and the way the seller markets their ideas and strategies through app notifications and pop-up ads or push notifications. The research analyzed how the digital marketing trend is booming up in the field of the food industry and also tried to understand the user's comfortability as well as use and impact of push and pop-up notifications, specifically of food delivering apps. Using a structured questionnaire, in-depth interviews, and observations, the researcher collects the data. This primary data will be collected from around 300 app users in the city using non-probability convenience sampling method.
Detection of Metabolites in Latent Fingerprints Through Green Nanoparticles for Both Forensic Investigations and Medical Purposes
Authors: Aggarwal Pallavi , Chitkara Mansi
Published: April 2022

Fingerprints are of core importance to the forensic science for the case investigations. The latent fingerprints are the invisible fingerprints which needs different methods to be visualized. Many methods have been developed for the visualiztion of the latent fingerprints through which the unique ridge pattern of the individual can be identified, but there is a strong requirement for the detection of sweat residues present in the latent fingerprint ridges. The latent fingerprints opens a wide scope of unexplored area in research that is beyond the scope of forensics in detecting various illicit drugs or metabolites of clinical importance. These prints generally show surface-based phonemenon and the nanomaterials provides new opportunities in surface-based sciences. Therefore, the nanoparticles can play a vital role in the detection of molecules in the latent fingerprint ridges. The synthesis of nanoparticles from natural products like green plants provides non-toxic, eco-friendly, biodegradable materials which can be easily utilized in the environment.
Profitability Visualization in Catalog Management System
Authors: Anand Gurjapna , Saraswat Amar
Published: April 2022

In the past few years, the number of products has increased exponentially on e-commerce. Retailers and wholesalers have struggled to keep pace with the rise in the bulk of products. This research intends to carry out the possibilities for deciding the product to be used for promoting business or brand on any e-commerce platform. The most difficult task for a seller is to decide which products should be considered for selling on various online platforms. The method presented in this paper determines which product from a given category should be promoted on an online e-commerce platform, which further increases the brand value or product’s online face value. By analyzing the pattern of which product has the highest buyer or inclination towards the particular product, the model employed in this research for visualizing the product is successfully recorded and assessed using the data visualization tool Tableau. This model can be considered as an innovation in sustainable development goals.
Analysis of Irrigation Water Quality Indices for Chambal River at Kota, Rajasthan, India
Authors: Kuldeep Kuldeep , Kumar Porush , Mathur Anil K. , Kamboj Pawan
Published: April 2022

Surface water is an essential natural resource for the survival of biotic components on Earth and plays a vital role in every country's domestic, commercial, agricultural, and industrial sectors. River water is a primary source of surface water, and its quality is very important for crop production, maintenance of soil productivity, and protection of the environment; hence, the assessment of river water quality for irrigation is essential. The present study assessed the Chambal River water quality for irrigation purposes in upstream and downstream of Kota Dam, Rajasthan (India). Various irrigation water quality indices (IWQIs), such as SAR, KR, TH, Na%, PI, MH, RSC, and RSBC are evaluated to define overall irrigation water quality. Four sampling sites are selected to define IWQIs with the help of seven water quality parameters, namely, sodium, magnesium, potassium, calcium, carbonate, bicarbonate, and electrical conductivity. This study shows a significant variation in the values of different IWQIs for irrigation compared to 2019 in 2020. The values of IWQIs at Akelgarh indicate the water quality as "Good and Suitable" for irrigation purposes for both years. However, the scenario is different at Rangpur, SRRT, and Keshoraipatan sampling locations. The river water quality for irrigation is continuously deteriorating during the study period and presently has the status of "Moderately suitable" for irrigation. The research demonstrated the application of IWQI, which would be helpful to policymakers and stakeholders to provide large-scale management to control pollution in the Chambal River for economic and sustainable social development.