Risk Prioritization using A FUZZY BASED Approach in Software Development Design Phase

The success of a software project's objective is directly proportional to the degree to which it satisfies all of the stakeholders' concerns regarding the project's requirements, including the budget, schedule, and overall performance. Risks can occur throughout the software development lifecycle (SDLC) phases and affect every phase. The design phase of the SDLC yields an overview of the software and can be defined as the software's blueprint. Different types of software have their own unique design phases and have different types of risks. With the high number of interacting components, complex systems have a greater propensity to be more volatile, which increases the risk. It is necessary to prioritize the risks in order of their severity levels. The issue at hand is the lack of effective methods to prioritize and mitigate the risk. Recent studies have suggested several methods for prioritizing risks, but it is clear that few of these have been implemented. These methods are overly complicated, time-consuming, prone to inconsistency, and challenging to put into practice. This paper proposes a novel Fuzzy-based approach to risk prioritization in the software design phase using MATLAB software. Fuzzy-based models have been shown to be more accurate than other techniques when using standard datasets to prioritize risks. Fuzzy-based methods that have been proposed take into account the characteristics of risks by modelling those characteristics as fuzz

Next-generation sequencing (NGS) platforms: An exciting era of genome sequence analysis

© Springer Nature Singapore Pte Ltd. 2019. DNA referred to as blueprint of life codes for the diversity and function of all the living organisms. Determining DNA sequences of the living organisms not only gives an overview of their genetic makeup, but also provides information about their function. Nonetheless it was not easy to determine the genome sequencing of all the diversity around us especially with the technologies available before 2010. Therefore, determining the sequence of humans and some other organisms only was prioritized. Pioneering methods for DNA sequencing given discovered by Maxam and Gilbert, and Sanger although were very powerful and popular but were not high throughput and economic. Therefore, it was necessary to develop new economic and high-throughput methods that can sequence the biodiversity consequently providing better insights of their possible function. New methods were developed and commercialized by Roche Life Sciences, Thermo Fisher Scientific, Illumina, and Applied Biosystems. These methods generally referred to as next-generation sequencing methods have revolutionized the DNA sequencing. Many sequencing platforms employing NGS have been developed including pyrosequencing, Ion Torrent technology, Illumina/Solexa platform, and SOLiD (Sequencing by Oligonucleotide Ligation and Detection). Further optimization has led to innovative third and fourth-generation platforms as single molecule real-time (SMRT) sequencing by PacBio, nanopore sequencing, etc. As a consequence there is a sharp increase in the number of genomes being published and other genome-based studies since 2012. This has made it easy even to imagine of sequencing the genomes of individuals. Furthermore, scientists are now looking for third-generation sequencers that may be significantly different from the sequencers that are currently available

Indigenously produced biochar retains fertility in sandy soil through unique microbial diversity sustenance: a step toward the circular economy

IntroductionAgricultural productivity in the arid hot desert climate of UAE is limited by the unavailability of water, high temperature, and salt stresses. Growing enough food under abiotic stresses and decreasing reliance on imports in an era of global warming are a challenge. Biochar with high water and nutrient retention capacity and acid neutralization activity is an attractive soil conditioner. This study investigates the microbial community in the arid soil of Dubai under shade house conditions irrigated with saline water and the shift in the microbial community, following 1 year of amendment with indigenously prepared biochar from date palm waste.MethodsAmplicon sequencing was used to elucidate changes in bacterial, archaeal, and fungal community structures in response to long-term biochar amendment. Samples were collected from quinoa fields receiving standard NPK doses and from fields receiving 20 and 30 tons ha−1 of biochar, in addition to NPK for 1 year. Water holding capacity, pH, electrical conductivity, calcium, magnesium, chloride, potassium, sodium, phosphorus, total carbon, organic matter, and total nitrogen in the soil from biochar-treated and untreated controls were determined.Results and discussionThe results show that soil amendment with biochar helps retain archaeal and bacterial diversity. Analysis of differentially abundant bacterial and fungal genera indicates enrichment of plant growth-promoting microorganisms. Interestingly, many of the abundant genera are known to tolerate salt stress, and some observed genera were of marine origin. Biochar application improved the mineral status and organic matter content of the soil. Various physicochemical properties of soil receiving 30 tons ha−1 of biochar improved significantly over the control soil. This study strongly suggests that biochar helps retain soil fertility through the enrichment of plant growth-promoting microorganisms

Synthesis & Vibrational Spectra of Dioxouranium(VI), Th(IV), Zr(IV) & Oxozirconium(IV) Chlorosulphates &Their Complexes with Some Organic Donors

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Cancellation of Contact Quenching : A Simple Concept for Selective Chemosensing of Basic Fluoride and Acetate Anions

A weakly fluorescent acid-base pair formed by reacting fluorescent acridine orange with the quencher picric acid is reported for the detection of basic fluoride and acetate anions. Deprotonation by these anions causes disengagement of the fluorescent acridine orange from the quencher, picric acid. This phenomenon cancels the quenching existing in the native probe, thereby allowing for the optical signalling of fluoride and acetate anions by color modulation as well fluorescence switch-on response. Anions such as Br-, I-, Cl-, NO3-, SCN-, HSO4-, and H2PO4- offer no detectable interferences even in excess concentrations

Advances in real time smart monitoring of environmental parameters using IoT and sensors

People who work in dangerous environments include farmers, sailors, travelers, and mining workers. Due to the fact that they must evaluate the changes taking place in their immediate surroundings, they must gather information and data from the real world. It becomes crucial to regularly monitor meteorological parameters such air quality, rainfall, water level, pH value, wind direction and speed, temperature, atmospheric pressure, humidity, soil moisture, light intensity, and turbidity in order to avoid risks or calamities. Enhancing environmental standards is largely influenced by IoT. It greatly advances sustainable living with its innovative and cutting-edge techniques for monitoring air quality and treating water. With the aid of various sensors, microcontroller (Arduino Uno), GSM, Wi-Fi, and HTTP protocols, the suggested system is a real-time smart monitoring system based on the Internet of Things. Also, the proposed system has HTTP-based webpage enabled by Wi-Fi to transfer the data to remote locations. This technology makes it feasible to track changes in the weather from any location at any distance. The proposed system is a sophisticated, efficient, accurate, cost-effective, and dependable weather station that will be valuable to anyone who wants to monitor environmental changes on a regular basis

Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images

Our proposed work, SculptorGAN, represents a novel advancement in the domain of medical imaging, for the accurate and automatic diagnosis of renal tumors, using the techniques and principles of Generative Adversarial Network (GAN). This dichotomous framework forms a contrast to the normal segmentation models like that of U-Net model but, instead, founded on a strategy that is aimed towards reconstruction and segmentation of CT images, particularly of renal malignancies. The core of the SculptorGAN methodology is a GAN-based approach for precise three-dimensional rendering of renal anatomies from CT scans, followed by a segmentation phase to correctly separate the neoplastic from non-neoplastic tissues. In fact, SculptorGAN was designed to circumvent limitations that come as inherent in the segmentation techniques, and in this case to eliminate them. In fact, by including such an advanced algorithmic architecture, accuracy of diagnosis in SculptorGAN has increased to 96.5%, which is the primary aspect behind early detection and thus proper curing of renal tumors. The better results were ascribed to more accurate and detailed reconstruction of renal structures that the framework allowed, apart from the better segmentation. The performance analyses show quantitative results with respect to the presented datasets, while the validation shows that SculptorGAN outperforms most of the traditional models such as U-Net. In particular, SculptorGAN decreased the time taken for 3D reconstruction by about 35% while increasing the accuracy of segmentation by 20% or more. The outcome, in their turn, may suggest this improvement in efficiency and the level of reliability for renal tumor diagnosis as of having far-reaching implications for the patient treatment and its outcomes. In conclusion, the framework deals with all the challenges with an accurate diagnosis of renal tumors and brings betterment in the overall field of medical image analysis by providing the abilities of GANs for the betterment in image reconstruction and segmentation

Deep learning approach for discovery of in silico drugs for combating COVID-19

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19. [Abstract copyright: Copyright © 2021 Nishant Jha et al.