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Hybrid AODV: An Efficient Routing Protocol for Manet Using MFR and Firefly Optimization Technique
A MANET is a category of ad hoc protocol that could vary positions and track itself on the flutter. It utilizes wireless connections that are attached to several networks. They include wirelessly in a self-configured, self-healing network while not having permanent communication linked in a collection of mobile networks. The network topology of nodes typically varies in MANET, and nodes are free to stir errantly and independently as a router as they accelerate traffic to more nodes within the network. Ad hoc on-demand distance vector (AODV) was employed for node selection to attain the shortest path strategy in existing techniques. In the proposed system, the hybrid AODV (HAODV) technique incorporates the MFR (Most Forward within Radius) technique to detect the shortest path routing algorithm. The MFR method was deployed for selecting the neighbor node, while HAODV was deployed to find the shortest path. To find the shortest path based on the updating equation, the Firefly algorithm is also implemented into the Hybrid AODV. The proposed work's performance is calculated by different network parameters like the end to end delay, average routing overhead, throughput, and packet delivery ratio. After comparing AODV and DSR algorithms, the proposed algorithm (HAODV) shows improvement in packet delivery ratio, end-To-end delay, Routing overhead, and throughput. 2021 World Scientific Publishing Company. -
Reliability, maintainability and sensitivity analysis of physical processing unit of sewage treatment plant
India is facing radical change in perspective of inadequate water resources which can reduce by using treated water. In this direction, sewage treatment plants play a key role. Sewage treatment plant comprises three units namely physical processing, chemical processing and biological process. The physical process is the most important part and it has five component arranged in series configuration. It becomes necessary to perform this process with high efficiency and reliability, availability, maintainability, and dependability (RAMD) is the methodology to analyze the performance. The failure and repair rates of the subsystems has been considered exponentially distributed. ChapmanKolmogorov differential equations are derived using Markovian birthdeath process and several measures like mean time between failures, mean time to repair and dependability ratio are derived. The sensitivity analysis of reliability of the plant has also been performed. RAMD investigation shows that: availability of system is 0.952177, reliability of the system after 20days is 0.2018 and after 60days 0.00823, maintainability of the plant is 0.999948, dependability ratio is 0.9541 and raw sewage sump is the most sensitive subsystem of the plant with reliability 0.382893. This work is projected to support as an informative exertion in steering a RAMD analysis of physical processing unit and vary few work is available in literature related to the performance features of physical processing units of the sewage treatment plants. The main findings may be very useful for sewage treatment plants designers. 2019, Springer Nature Switzerland AG. -
Identification and structure-activity relationship studies of small molecule inhibitors of the human cathepsin D
Cathepsin D, an aspartyl protease, is an attractive therapeutic target for various diseases, primarily cancer and osteoarthritis. However, despite several small molecule cathepsin D inhibitors being developed, that are highly potent, most of them show poor microsomal stability, which in turn limits their clinical translation. Herein, we describe the design, optimization and evaluation of a series of novel non-peptidic acylguanidine based small molecule inhibitors of cathepsin D. Optimization of our hit compound 1a (IC50 = 29 nM) led to the highly potent mono sulphonamide analogue 4b (IC50 = 4 nM), however with poor microsomal stability (HLM: 177 and MLM: 177 ?l/min/mg). To further improve the microsomal stability while retaining the potency, we carried out an extensive structureactivity relationship screen which led to the identification of our optimised lead 24e (IC50 = 45 nM), with an improved microsomal stability (HLM: 59.1 and MLM: 86.8 ?l/min/mg). Our efforts reveal that 24e could be a good starting point or potential candidate for further preclinical studies against diseases where Cathepsin D plays an important role. 2020 Elsevier Ltd -
Unique helical conformation of the fourth cytoplasmic loop of the CB1 cannabinoid receptor in a negatively charged environment
The proximal portion of the C-terminus of the CB1 cannabinoid receptor is a primary determinant for G-protein activation. A 17 residue proximal C-terminal peptide (rodent CB1 401-417), the intracellular loop 4 (IL4) peptide, mimicked the receptor's G-protein activation domain. Because of the importance of the cationic amino acids to G-protein activation, the three-dimensional structure of the IL4 peptide in a negatively charged sodium dodecyl sulfate (SDS) micellar environment has been studied by two-dimensional proton nuclear magnetic resonance (2D 1H NMR) spectroscopy and distance geometry calculations. Unambiguous proton NMR assignments were carried out with the aid of correlation spectroscopy (DQF-COSY and TOCSY) and nuclear Overhauser effect spectroscopy (NOESY and ROESY) experiments. The distance constraints were used in torsion angle dynamics algorithm for NMR applications (DYANA) to generate a family of structures which were refined using restrained energy minimization and dynamics. In water, the IL4 peptide prefers an extended conformation, whereas in SDS micelles, 310-helical conformation is induced. The predominance of 310-helical domain structure in SDS represents a unique difference compared with structure in alternative environments, which can significantly impact global electrostatic surface potential on the cytoplasmic surface of the CB1 receptor and might influence the signal to the G-proteins. 2007 Elsevier Inc. All rights reserved. -
Solution structure of the tachykinin peptide eledoisin
Both the aqueous and the lipid-induced structure of eledoisin, an undecapeptide of mollusk origin, have been studied by two-dimensional proton nuclear magnetic resonance spectroscopy and distance geometry calculations. Unambiguous nuclear magnetic resonance assignments of protons have been made with the aid of correlation spectroscopy experiments and nuclear Overhauser effect spectroscopy experiments. The distance constraints obtained from the nuclear magnetic resonance data have been utilized in a distance geometry algorithm to generate a family of structures, which have been refined using restrained energy minimization and dynamics. These data show that, while in water and dimethyl sulfoxide, eledoisin prefers to be in an extended chain conformation, whereas in the presence of perdeuterated dodecylphosphocholine micelles, a membrane model system, helical conformation is induced in the central core and C-terminal region (K4-M11) of the peptide. N terminus, though less defined, also displays some degree of order and a possible turn structure. The conformation adopted by eledoisin in the presence of dodecylphosphocholine micelles is similar to the structural motif typical of neurokinin-2 selective agonists and with that reported for kassinin in hydrophobic environment. -
Sexual Selection, Signaling and Facial Hair: US and India Ratings of Variable Male Facial Hair
Objective: The objective of this study was to address the putative ancestral social signaling value of male facial hair, in concert with variable cultural meaning. The ability to grow facial hair might have served as an honest ancestral signal of male age, social dominance, strength and health. Male facial hair may also have had signaling value for attractiveness, though these might be less strong than effects tied to male-male competition. Male facial hair can also be modified, giving rise to cultural variation in its potential signaling function. Methods: We surveyed N= 252 US men and women and N= 280 Indian men and women, ages 1825, about sociodemographics and attitudes toward male facial hair. Participants rated a randomized series of nine images of a composite male model with facial hair with respect to: preferred style, estimated age, attractive to potential partners, assertive, physically strong, friendly, and healthy. Types of facial hair were group into three categories: clean shaven, partial (e.g., Van Dyke, soul patch, stubble) and beard. Results: Supporting hypothesized differences, results show that more male facial hair was positively associated with age estimates and negatively with friendliness, and positively related to assertiveness and physical strength. Supporting hypotheses, women preferred less facial hair and rated less facial hair as more attractive. Some sample differences arose, such as Indian participants perceiving greater age range estimates than US respondents. Conclusion: These data indicate patterned variation in evaluations of male facial hair that can be situated within an evolutionary and culturally evolved signaling framework. 2020, Springer Nature Switzerland AG. -
Grandparenting in Urban Bangalore, India: Support and Involvement From the Standpoint of Young Adult University Students
A variety of caregivers, including grandparents, help raise children. Among grandparents, most Western samples evidence a matrilateral (i.e., mothers kin) bias in caregiving, and many studies show more positive impacts and stronger relationships with grandmothers than grandfathers. The aim of the present study is to test competing hypotheses about a potential laterality bias and explore contrasts between grandmothers and grandfathers in a sample of urban young adult university students in Bangalore, India. A sample of 377 (252 women) relatively mobile and high socioeconomic status individuals 17 to 25 years of age completed a survey consisting of sociodemographic and grandparenting questions. Results reveal generally little evidence of either a patrilateral or matrilateral bias, though findings varied for some outcomes. As illustrations, there were no differences in residential proximity or the most recent time when a participant saw matrilateral or patrilateral grandparents, whereas maternal grandmothers were more approving of ones choice of a life partner than were paternal grandmothers. In inductively coded responses to an open-ended item about the roles of grandparents, maternal grandmothers were more often identified as guides and less often deemed non-significant than paternal grandmothers, while paternal grandfathers were less often viewed as guardians and more often noted for their influence compared with maternal grandparents. Findings also revealed differences between grandmothers and grandfathers, such as grandmothers playing more prominent roles in community and religious festivals. Findings are interpreted within changing residential, work, education, and family dynamics in urban India as well as a primary importance on parents relative to grandparents. The Author(s) 2019. -
Panel data analysis of Indian textile exports in the post quota period /
Trade And Development Review, Vol. 11, Issue 1-2, pp.1-27. -
Floral waste as a potential feedstock for polyhydroxyalkanoate production using halotolerant Bacillus cereus TS1: optimization and characterization studies
The versatile properties and high degree of biodegradability of polyhydroxyalkanoates (PHA) have made them the ideal candidate for biomedical and other applications. Although extensive research on PHA-producing bacterial isolates from terrestrial environments is documented in the available literature, the potential of marine bacterial isolates in PHA production remains less explored and offers a great scope for future research. This research work primarily focuses on isolation and characterization of PHA-producing bacterial isolates from samples collected from coastal areas of Kerala, India. Furthermore, the possibility of PHA production from the most potential isolate Bacillus cereus TS1 using jasmine waste hydrolysate-based media was explored in this study. The utilization of floral waste hydrolysate (FWH) for PHA fermentation is not widely discussed in the available literature and is the major novelty factor of this research work. Under optimized conditions of glucose (1.2% w/v), yeast extract (0.15% w/v), NaCl (5.02% w/v), and incubation period (60h), a maximum PHA yield of 1.13g/L was achieved. The characterization of PHA polymer was done using Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA). Thus, this research work integrates floral waste valorisation with microbial biopolymer production and highlights an innovative approach for sustainable development. The scale of this method on an industrial scale in future may prove helpful in the cost-effective production of PHA using cheap raw materials. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Isolation and characterization of polyhydroxyalkanoate producing halotolerant Bacillus subtilis SG1 using marine water samples collected from Calicut coast, Kerala
Halotolerant bacterial strain isolated from the Calicut coast, Kerala, India, was screened for its potential ability to synthesize polyhydroxyalkanoates (PHA) using Sudan black B and Nile blue staining. The quantitative analysis for PHA production was done in modified M9 media and a PHA yield of about 1.52 g/L was observed with the most potential isolate SG1. Further, the biochemical and molecular characterization of the PHA-producing halotolerant bacteria was done using standard biochemical tests and 16 s ribosomal RNA sequencing respectively and the isolate was identified to be Bacillus subtilis SG1. Further, the PHA recovery was done using solvent extraction method employing acetone and diethyl ether followed by precipitation using chloroform with a maximum PHA yield of 1.52 g/L. Further, the material properties of the extracted polymer were studied using Fourier transform infrared, X-ray diffraction, nuclear magnetic resonance, thermo gravimetric analysis, and differential scanning calorimetry analysis. Further investigations are necessary to optimize PHA production and to carry out its application study in various fields. 2024 Sneha Grigary, et al. -
Exploring the Digital Revolution in Education in India during the COVID-19 Pandemic
One important response to COVID-19 was the intensification of the use of digital media to deliver education. However, the results are paradoxical, since the digital revolution did not lead to improvement of the social quality of teachers working circumstances. We ana-lyze internal or subjective oriented constitutional and external or objective orientated conditional factors related to teachers that determine the adaptation of digitalization, taking a social quality perspective. Through a case study in the most advanced educational hub of IndiaDelhiwe find that the digital revolution helped India to address the first-order problems in digital transformation, namely concerning objective infrastructural facilities. The second-order problems, particularly changing the subjective belief structures of teachers related to the integration of technologies, appear to remain a challenge. As India has recently adopted a new education policy (2020), the findings of our study have significant relevance to improving the accessibility and utilization of digital technology in educational spaces. The Author(s). -
Crystal and Molecular Structure of 1,1-Bis(methylthio)-5-(4-chlorophenyl)-1,4-pentadien-3-one
Journal of Crystallography, pp. 2014. -
K? to K? X-ray intensity ratio and KL vacancy transfer probability of Mn following electron capture decay
K? and K? X-rays of Mn following electron capture (EC) decay of55 Fe were detected using Amptek XR-100 T-CdTe X-ray detector spectrometer. Measured K? and K? X-ray intensities of Mn were used to determine the K? to K? intensity ratio and total KL total vacancy transfer probability. These values were compared with the theoretical, semiempirical, and others experimental values obtained via EC decay as well as photoionization. The X-ray intensity ratio of Mn was found to be higher by 1.5% from the relativistic Hartree-Slater theoretical value. This deviation may be attributed to the exchange interactions occurring between the 3p and 3d shell electrons as well as the recoil effect of the nucleus due to neutrino emission. 2023 The Author(s). -
A compilation of interstellar column densities
We have collated absorption line data toward 3008 stars in order to create a unified database of interstellar column densities. These data have been taken from a number of different published sources and include many different species and ionizations. The preliminary results from our analysis show a tight relation [N(H)/E(B - V)= 6.1210 21] between N(H) and E(B - V). Similar plots have been obtained with many different species, and their correlations along with the correlation coefficients are presented. 2012 The American Astronomical Society. All rights reserved. -
A Multi-Modal Approach to Digital Document Stream Segmentation for Title Insurance Domain
In the twenty-first century, storing and managing digital documents has become commonplace for all corporate and public sectors around the world. Physical documents are scanned in batches and stored in a digital archive as a heterogeneous document stream, referred to as a digital package. To make Robotic Process Automation (RPA) easier, it's necessary to automatically segment the document stream into a subset of independent, coherent multi-page documents by detecting the appropriate document boundary. It's a common requirement of a TI company's Automated Document Management Systems (ADMS), where business operations are automated using RPA and the goal is to extract information from digital documents with minimal user intervention. The current study proposes, evaluates, and compares a multi-modal binary classification network incorporating text and picture aspects of digital document pages to state-of-the-art baseline methodologies. Image and textual features are extracted simultaneously from the input document image by passing them through Visual Geometry Group 16 - Convolutional Neural Network (VGG16-CNN) and pre-trained Bidirectional Encoder Representations from Transformers (Legal-BERT {}_{base} ) model through transfer learning respectively. Both features are finally fused and passed through a fully connected layer of Multi Layered Perceptron (MLP) to obtain the binary classification of the pages as the First Page (FP) and Other Page (OP). Real-time document image streams from production business process archive were obtained from a reputed Title Insurance (TI) company for the study. The obtained F_{1} score of 97.37% and 97.15% are significantly higher than the accuracies of the considered two baseline models and well above the expected Straight Through Pass (STP) threshold defined by the process admin. 2013 IEEE. -
Hybrid Approach to Document Anomaly Detection: An Application to Facilitate RPA in Title Insurance
Anomaly detection (AD) is an important aspect of various domains and title insurance (TI) is no exception. Robotic process automation (RPA) is taking over manual tasks in TI business processes, but it has its limitations without the support of artificial intelligence (AI) and machine learning (ML). With increasing data dimensionality and in composite population scenarios, the complexity of detecting anomalies increases and AD in automated document management systems (ADMS) is the least explored domain. Deep learning, being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI. We present a hybrid model for AD, using autoencoders (AE) and a one-class support vector machine (OSVM). In the present study, OSVM receives input features representing real-time documents from the TI business, orchestrated and with dimensions reduced by AE. The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range, regarding accuracy and performance. 2020, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature. -
A Deep Learning Model for Information Loss Prevention from Multi-Page Digital Documents
World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers' sensitive information of a reputed TI company securing it's one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization's information security monitoring team. 2013 IEEE. -
IIRM: Intelligent Information Retrieval Model for Structured Documents by One-Shot Training Using Computer Vision
Various information retrieval algorithms have matured in recent years to facilitate data extraction from structured (with a predefined template) digital document images, primarily to manage and automate different organizations invoice and bill reimbursement processes. The algorithms are designated either rule-based or machine-learning-based. Both approaches have respective advantages and disadvantages. The rule-based algorithms struggle to generalize and need periodic adjustments, whereas machine learning-based supervised approaches need extensive data for training and substantial time and effort for manual annotation. The proposed system attempts to address both problems by providing a one-shot training approach using image processing, template matching, and optical character recognition. The model is extensible for any structured documents such as closing disclosure, bill, tax receipt, besides invoices. The model is validated against six different structured document types obtained from a reputed title insurance (TI) company. The comprehensive analysis of the experimental results confirms entity-wise extraction accuracy between 73.91 and 100% and straight through pass 81.81%, which is within business acceptable precision for a live environment. Out of total 32 tested entities, 17 outperformed all state-of-the-art techniques, where max accuracy has been 93 % with only invoices or sales receipts. The system has been set operational to assist the robotic process automation of the TI mentioned above based on the experimental results. 2022, King Fahd University of Petroleum & Minerals. -
Computer Vision Based Automatic Margin Computation Model for Digital Document Images
Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve information automatically from the documents when necessary. Margin is one such non-textual information that becomes important for some business processes, and the demand for computing margins algorithmically mounts to facilitate RPA. We propose a computer vision-based text localization model, utilizing classical DIP techniques such as smoothing, thresholding, and morphological transformation to programmatically compute the top, left, right, and bottom margins within a digital document image. The proposed model has been experimented with different noise filters and structural elements of various shapes and size to finalize the bilateral filter and lines and structural elements for the removal of noises most commonly occurring due to scans. The proposed model is targeted towards text document images and not the natural scene images. Hence, the existing benchmark models developed for text localization in natural scene images have not performed with the expected accuracy. The model is validated with 485 document images of a real-time business process of a reputed TI company. The results show that 91.34 % of the document images have conferred more than 90 % IoU value which is well beyond the accuracy range determined by the company for that specific process. 2023, Crown. -
Analysis of nonlinear compartmental model using a reliable method
The goal of this work is to investigate nonlinear models and their complexity using techniques that are universal and have connections to historical and material aspects. Using the premise of a constant population that is uniformly mixed, a nonlinear compartmental model that depicts the movement between voter classes is taken into consideration. In the current work, we investigate the dynamical framework that supports the interactions between the three parties. It is discussed how rate change affects various metrics. The conditions for boundedness, stability, existence, and other dynamics are obtained. We derive the effects of generalizing the model in any order. The current study supports investigations into complex real-world issues and forecasts of necessary plans. 2023 The Author(s)