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Tissue-Specific Profile and Activity Patterns of Glycosyl Hydrolases from Trichosanthes Anguina (Snake Gourd)
Plant glycosyl hydrolases (GH) and their function have been extensively studied using biochemical and molecular genetic approaches. GHs are involved in metabolism of various glycoconjugates specifically by the hydrolysis of glycosidic bonds and also in N-glycan processing. Several GHs have been extensively characterized from various plant sources and their diverse functional roles in cell wall polysaccharide metabolism, glycan biosynthesis and remodulation, signaling, symbiosis, secondary metabolism, etc. have been studied. However, information on tissue specific distribution of these enzymes, which is crucial for further understanding their physiological roles in plants is highly limited. In these lines, the present study was aimed at qualitative analysis of selected GHs from different tissues of a model plant, Snake Gourd (Trichosanthes anguina). The qualitative analysis of GHs such as ?-mannosidase, ?-hexosaminidase, ?-galactosidase, ?-glucosidase, ?-glucuronidase, ?-glucosidase, ?-galactosidase, ?-mannosidase and ?-fucosidase from seeds, sprouts, roots, stem, leaves, flowers and fruits of the Snake Gourd plant was carried out. Activities of different GHs varied in a tissue specific manner. The ?-mannosidase activity was maximum in ripened fruits whereas ?-hexosaminidase showed highest activity in roots. Interestingly, flowers had maximum activities of ?-glucuronidase and ?-fucosidase. The correlation analysis suggested significant correlations between various GHs which altered in tissue specific manner. (2023) Association of Carbohydrate Chemists and Technologists. -
An Investigation of the Effects of Chronic Stress on Attention in Parents of Children with Neurodevelopmental Disorders
Prolonged exposure to stress can cause impairments in various brain functions including cognition. Attention is one such important cognitive function that is required for our daily life and work-related activities. Chronic stress can have an impact on attention networks such as alerting, executive control, and orienting. The effects of naturalistic, persistent psychosocial stress on several attention networks were explored in this study. Parents of children with neurodevelopmental disorders (NDD) and parents of children with typical development (TD) were given an attention network test (ANT). Overall the stressed group (M= 564.623, SD= 75.484) was found to have a quicker reaction time in all the target and cue conditions whencompared to the non-stressed group (M= 588.874, SD= 101.575). Both groups had similar accuracy in all the conditions. When comparing the three attention network scores, no significantdifference was found in either group. However, in the stressed group, there was a significant beneficial relationship between the alerting and orienting networks (p=.006) and a high negative correlation between the alerting and executive control networks (p=.028). No significant correlation was found between the attention networks in the non-stressed group. Copyright2024 by authors, all rights reserved. -
Narrative Therapy with Dalit Female Survivors of Violence
Narrative therapy is an evidence-based therapeutic intervention that can help address trauma experienced by women who have experienced violence. Narrative therapists open up new perspectives for their clients by examining moments of strength, vitality, and autonomy, which are often hidden in stories about oppression, suffering, and marginalization. Dalit women who participated in the research revealed how the stories opened up new possibilities for constructing unique narratives. A multiple case study design was used to elicit the responses of female survivors with severe mental illness to physical, sexual, and psychological abuse perpetrated by Dalit and higher caste men. 2024 Mary Ann Liebert Inc.. All rights reserved. -
Profiles of Victimized Outpatients with Severe Mental Illness in India
Persons with severe mental illness (PwSMI) are at risk of being victimized due to persistent cognitive, emotional, and behavioral symptoms, which can become potential threats for effective reintegration into the community. A total of 217 PwSMI, receiving outpatient psychiatric treatment from a tertiary hospital, were screened for abuse, and if they were identified as abuse, then information about contextual factors contributing to abuse, sociodemographic, family, and clinical and legal profiles was created. Overall, 150 PwSMI were victimized, of which 56% were females, 50.7% were married, 20.7% were educated up to middle school, and 31.4% were homemaker. The most common form of diagnosis was schizophrenia (43.3%), with a mean duration of illness of 14 years. All the victimized PwSMI were subjected to emotional abuse. PwSMI were more likely to be victimized by multiple family members due to poor knowledge and understanding about illness (24%). The majority of the PwSMI had disclosed abuse (62.7%) to nonformal sources (33.3%) with no documentation in the clinical file (82.7%). PwSMI experience ongoing abuse and are more likely to be re-victimized, which increases the need for regular screening and culturally sensitive and comprehensive community-coordinated care and support. 2023 Indian Journal of Community Medicine. -
A Qualitative Study to Understand the Nature of Abuse Experienced by Persons with Severe Mental Illness
Persons with Severe Mental Illness (PwSMI) living in the community are considered high-risk groups for victimization. However, the nature of violence experienced by PwSMI is not well understood in India, which limits the effectiveness of clinical interventions to prevent revictimization. The Key Informant Interviews (KIIs) and Focused Group Discussion guides were developed, content validated, and pilot tested. A total of 27 KIIs and 5 focus groups were conducted with 14 PwSMIs, 19 experts, and 18 caregivers. Thematic analysis was done using Braun and Clarke's six stages of thematic analysis. The saturation of themes was determined using the Comparative Method for Themes Saturation (CoMeTS). Some of the themes and subthemes that emerged were (1) Physical Abuse (physical restraining, hitting, spitting), (2) Psychological Abuse (living in a controlling environment, criticized, neglected, scapegoated, symptomization of emotions and behavior),(3) Sexual Abuse (sexual assault, reproductive coercion, sexual exploitation), (4) Social Abuse (teased or labeled, social deprivation, abandonment, discrimination, and exclusion), and (5) Trauma in formal care (Coercive treatment practices, seclusion, negative attitude of staff, surreptitious prescribing of medicines, patronizing behavior). Abuse experienced by PwSMI has significant treatment and health care costs and an increased burden on families and society, so comprehensive psychosocial care and support are needed to prevent revictimization. Copyright 2023, Mary Ann Liebert, Inc., publishers 2023. -
Enhancing stochastic optimization: investigating fixed points of chaotic maps for global optimization
Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Improved Bald Eagle Search for Optimal Allocation of D-STATCOM in Modern Electrical Distribution Networks with Emerging Loads
Currently, modern electrical distribution networks (EDNs) are experiencing high demand with emerging electric vehicle loads and are being planned for specific load requirements such as agricultural loads. In this connection, characterization and optimization of their performance become essential in planning studies. In this paper, optimal reactive power compensation using a distribution-static synchronous compensator (D-STATCOM) is proposed with the aim of loss reduction, voltage profile improvement and voltage stability enhancement different types of loads including agricultural and electric vehicle loads. A recent efficient meta-heuristic approach, improved bald eagle search (IBES), is implemented for solving the proposed optimization problem considering different operational and planning constraints. The simulation results are performed on IEEE 33-bus for different types of load modelling. The computational efficiency of IBES is compared with basic BES and other literature works. From the results, IBES has shown superior computational characteristics than all compared works. On the other hand, the optimal location and size of D-STATCOM caused significant loss reduction, voltage profile improvement and voltage stability enhancement for kinds of loads as experiencing in the modern EDNs 2022,International Journal of Intelligent Engineering and Systems.All Rights Reserved -
Adaptive Grasshopper Optimization Algorithm for Multi-Objective Dynamic Optimal Power Flow in Renewable Energy Integrated Microgrid
Global warming has prompted several governments to adopt more sustainable policies in all areas. Incorporating renewable energy sources (RES) and adopting electric vehicles (EVs) are examples of such practises. Today's electrical distribution networks (EDNs) are becoming more reliable microgrids (MG) that can operate grid-connected or self-healing. As a result, the fluctuating nature of RES and EVs has raised numerous technical and economic concerns. This research proposes a novel multi-objective dynamic optimum power flow (OPF) addressing total load dispatch cost minimization and network security margin maximisation for various load profiles. A composite load model is proposed considering residential, industrial, commercial, EVs, agricultural loads. The proposed optimization issue is tackled using an adaptive grasshopper optimization algorithm (AGOA), a metaheuristic grasshopper optimization technique with adaptive control parameter (AGOA). A modified IEEE 33-bus benchmark test system with PV units and reactive power compensation devices is used for simulation over 24-hour horizon. The suggested AGOA's computing efficiency is compared for two scenarios. By combining good exploration and exploitation features with adaptive regulating variables, the AGOA outperformed in terms of global optima. Also, the techno-economics of MG operation and control are improved significantly. In scenario 1, the network is configured in a radial topology, with average operational costs, distribution losses, voltage variation, and transmission loadability of 1117.72 $/h, 82.4803 kW, 0.0058 p.u., and 0.7910 p.u., respectively, over a 24-hour period. In scenario 2, the network is run as a meshed network, with network performance of 1113.36 $/h, 43.15 kW, 0.0019 p.u., and 0.8524 p.u., respectively. This suggests that switching from radial to meshed configuration can result in lower losses, a better voltage profile, and increased loadability, as well as the applicability of the suggested methodology for managing uncertainty in modern EDNs. 2022. All Rights Reserved. -
Cutting across the Durand: Water dispute between Pakistan and Afghanistan on river Kabul
All nations firmly believe in the absolute sovereignty over the waters flow in their areas and that only riparian states have any legal right, apart from an agreement, to use the water from the shared river. To address some of their water concerns, the co-riparian states compete to have more quantity of waters. Significantly, no water agreement exists between upper riparian Afghanistan and lower riparian Pakistan, despite sharing nine big and small rivers. The simmering water dispute between them on the River Kabul is rarely noted mainly because it is overshadowed by their political tensions, differences, and the dispute over the Durand Line. Using an analytical framework, this article examines three aspects of the River Kabul water dispute: its context, identifying the challenges that hinder a formalized bilateral agreement from being implemented, and its future. 2020 Policy Studies Organization -
A comparison of recommendation algorithms based on use of linked data and cloud
Recommendation generation is a critical need in today's time. With the advent of big data and the increasing number of users, generation of most suitable recommendation is essential. There are many issues already associated with recommendations such as data acquisition, scalability, etc.. Moreover, the users today look to get best recommendations at the minimum effort on their side. Thus it becomes difficult to manage such huge amount of information, extract the needed data and present it to the user with least user involvemen t. In this research, we surveyed some recommendation algorithms and analyze their applications on an open cloud server which uses linked data to generate automated recommendations. 2018 Authors. -
SUM SIGNED GRAPHS II
In this paper, the study of sum signed graphs is continued. The balancing and switching nature of the graphs are analyzed. The concept of rna number is revisited and an important relation between the number and its complement is established. 2023, Krasovskii Institute of Mathematics and Mechanics. All rights reserved. -
Sum Signed Graphs, Parity Signed Graphs and Cordial Graphs
Signed graphs are graphs with every edge is signed either positive or negative. Given an n vertex graph, the vertices are bijectively labelled from 1 to n. A signed graph is a sum signed graph if and only if every edge is signed negative whenever the sum of the vertex labels exceeds n and every edge is signed positive whenever the sum of the vertex labels does not exceed n. For a parity signed graph, an edge receives a negative sign, if the end vertices are of opposite parity and a positive sign otherwise. Cordial signed graphs are the ones with the difference between the total number of negative edges and the positive ones is at most 1. We discuss the connection between sum signed labeling with parity signed labeling and cordial labeling. The absolute cordial condition for graphs satisfying sum signed labeling will be analyzed 2023, IAENG International Journal of Applied Mathematics.All Rights Reserved. -
Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. 2022 River Publishers. -
Predicting Stock Market Movements Through Multisource Data Fusion Graphs: An Approach Employing Graph Convolutional Neural Network
The stock market plays an important role in the capital market, and investigating price fluctuations in the stock market has consistently been a prominent subject for researchers. The application of soft computing techniques to predict and categorize stock market movements is a significant research challenge that has gathered considerable attention from researchers. Although several studies highlight the significance of incorporating information from two sources in stock movement prediction, the potential of advanced graphical techniques for modeling and analyzing multi-source data remains an unattended research area. This study aims to address this gap by introducing a novel model that utilizes multi-source data fusion graphs to predict future market movements. The primary challenge involves establishing a model that can effectively gather the relationships among various data sources and employ this understanding to improve prediction performance. Compared to several existing methods relying only on historical data or sentiment data, which show limited predictive power and lack generality, the proposed approach seeks to overcome these limitations. The proposed model integrates various information sources, including historical prices, news data, Twitter data, and technical indicators for predicting future stock market trends. This presented method involves constructing a subgraph map for each data type to capture events from both rising and falling markets. Then, a Gated Recurrent Unit (GRU) is employed to aggregate the subgraph nodes. These aggregated nodes are then integrated with a Graph Convolutional Neural Network (GCNN) to classify the multi-source graph, therefore achieving stock market trend prediction effectively. To further validate its effectiveness, the presented model is applied to Indian stock market data, demonstrating its feasibility in fusing multi-source stock data and establishing its suitability for effectively predicting stock market movements. 2024 Seventh Sense Research Group -
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
An analytical model for a TFET with an n-doped channel operating in accumulation and inversion modes
The tunnel field-effect transistor (TFET) is an ambipolar device that conducts current with the channel in both accumulation and inversion modes. Analytical expressions for the channel potential and current in a TFET with an n-doped channel when operating in the accumulation and inversion modes are proposed herein. The potential model is derived by solving the two-dimensional (2D) Poisson equation using the superposition principle while considering the charges present in the channel due to electron or hole accumulation along with the depletion charges. An expression for the tunneling current corresponding to the maximum tunneling probability is also derived. The tunneling current is obtained by analytically calculating the minimum tunneling length in a TFET when operating in the accumulation or inversion mode. The results of the proposed potential model is compared with technology computer-aided design (TCAD) simulations for TFET with various dimensions, revealing good agreement. The potential and current in an n-type TFET (nTFET) obtained using the proposed models are also analyzed. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Ionic strength and phase systems influence nanotubular material functionality
We synthesized novel thiacyanine chromonic liquid crystals (CLCs) and structurally characterized using NMR and mass spectrometry. The impact of distinct substitution at the para position of aromatic counter anions, aliphatic counter ion chain length, and varied spacer parity of thiacyanine dyes on CLC formation is investigated. Liquid crystal properties of the synthesized dyes are characterized by polarizing optical microscopy (POM) and X-ray diffraction (XRD) studies. Dyes exhibit nematic (N), lamellar (L?), columnar rectangular (Colr), and columnar oblique (Colob) CLCs at different concentrations in the water. Electronic absorption spectra reveal Scheibe aggregation in all the dyes. Cylicvoltametry studies confirm redox behaviour in TC-1a and TC-5e dyes. Chromonic LCs hybrid nano-materials are synthesized using solgel method. Scanning electron microscopy employed to confirm nano tubular fiber structure of the hybrid nanomaterilals. 2024 Elsevier B.V. -
Impact of terminal group on azobenzene liquid crystal dimers for photo-responsive optical storage devices
Three new series of photo responsive dimers bearing different terminal functional groups (-CN, -COOEt, -OMe) and variable aliphatic spacers have been synthesized and investigated in detail. The molecular structures of the new materials were proved using different spectroscopic techniques and their liquid crystal self-assembly was characterized using differential scanning calorimetry (DSC), polarized light microscope (POM) and X-ray diffraction (XRD). Moreover, their photo switching behaviour was investigated in details in solution. The results revealed that, almost all members of the dimeric materials are mesomorphic exhibit either nematic or both smectic A and nematic phases. Under UV illumination the materials show interesting photo-switching properties, reaching a photo-stationary state in 40 s and a thermal back-relaxation time of approximately 35 h in solution. Finally, a fabricated device authenticates the potential of the reported materials for optical storage devices. 2023 Elsevier B.V. -
Facile fabrication of 3D-?-Fe2O3@2D-g-C3N4 heterojunction composite materials: Effect of iron oxide loading on the electrochemical performance
Designing heterojunction nanocomposites is crucial for optimizing supercapacitor electrodes. This study addresses the challenge by introducing a facile synthesis method for creating 3D-?-Fe2O3@2D-g-C3N4 heterojunctions through a bulk carbon nitride-assisted hydrothermal process. During this process, the growth of ferric oxide particles coincides with the exfoliation and deposition of carbon nitride, leading to simultaneous structural changes in both iron oxide and carbon nitride phases. The resulting composite's properties strongly correlate with the iron oxide loading. Comprehensive characterization using XRD, FTIR, SEM-EDAX, XPS and TEM identified three distinct structures for ?-Fe2O3/g-C3N4 composites based on iron oxide loading: low, medium, and high. The medium-loaded sample demonstrated superior electrochemical performance, attributed to better interfacial contact and the formation of 3D-Fe2O3@2D-g-C3N4 heterojunctions. This composite, with an optimized 22 wt% iron oxide loading, exhibited a maximum specific capacitance of 925.1 Fg?1 at 5 mVs?1 and 498.6 Fg?1 at 6 Ag?1 in charge-discharge analysis, with stable performance over 2000 cycles. Overall, this research presents an enhanced hydrothermal method for facile preparation of effective ?-Fe2O3/g-C3N4 heterojunction materials. 2024 Elsevier B.V. -
Finite Element Analysis of Hybrid Skin Sandwich Composite
Sandwich structured composite is a particular classification in composite materials. This type of structure has been mainly used in recent studies because of its high specific strength, low density, and stiffness. It is increasingly more commonly employed in structural designs due to its features and performance. The sandwich composites used in this investigation are made of aluminium alloys and areca fibre. The sandwich composites face sheet comes in a variety of thicknesses. The adhesive skin layer is also varied to investigate the effect of using natural fibre. The sandwich composite is subjected to 3 point bend test. The modal analysis is investigated using the finite element method. The 3D model of sandwich composites is modelled using solid works 2020. Using Altair Hyper Works, the boundary conditions and meshing is carried out. ANSYS Mechanical APDL is used to analyse the sandwich composites. This investigation analyses the behaviour of composite sandwich beams. 2022, Books and Journals Private Ltd.. All rights reserved.