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Co-MoS2 nanoflower coated carbon fabric as a flexible electrode for supercapacitor
Cobalt doped MoS2 (Co-MoS2) nanoflowers have been successfully synthesized via a simple one-step hydrothermal method for supercapacitor applications. To identify the crystalline nature and morphology, the as-prepared material is characterized by XRD, SEM, and TEM measurements. The material exhibits a specific capacitance value of 86 F g-1 at a current density of 1 Ag-1 in symmetric two-electrode configuration with excellent cyclic stability of 98.5% even after 10,000 chargedischarge cycles. The results suggest the suitability of Co-MoS2 as an efficient electrode material for supercapacitors. 2021 Elsevier Ltd. All rights reserved. -
Design Optimization of Electrical Connector Assembly using FEA
Due to the increasing number of devices and systems connected to an electric system, the need for reliable and high-quality electrical connectors has become more prevalent. This project aims to optimize the design of an electrical connector during its two most critical stages: insertion and retention of housing using FEA. A structural analysis is performed during the insertion and retention stages of housing. This process involves calculating the dimensional deformations and maximum strains developed during the steps mentioned above to determine the reliable functioning of electrical contacts. The input geometry is fed to the finite element analysis. The forces applied on the connectors latch on their respective connection are ensured to be under the limit. The analysis and simulation results are reflected to validate the safe forces in the connector assembly and a proper justification for an experimental set up in the laboratory. 2022, Books and Journals Private Ltd.. All rights reserved. -
Towards Visibility: Subaltern Counterpublics in Paul Chirakkarodes Pulayathara
Christianity has always been celebrated as a catalyst towards modernity for the Dalits of Kerala. Though missionary accounts and ethnographic studies confirm the progress of the community, there was rampant casteism and separatism too. This is succinctly revealed in Dalit Christian texts. Pulayathara by Paul Chirakkarode stands as a testimony to the Dalit Christian dilemma and traces the history of the Kuttanadan Pulaya community in the pre- and post-conversion scenarios. Conversions could not change the existing public sphere of Kerala, where upper castes were the dominant party. They (Dalits) continued to be marginalized and subordinated and lacked a class consciousness. The article highlights the limitations in the public sphere that emerged in Kerala as part of the missionary endeavours in accommodating the converted Dalits. The article attempts to trace the emergence of subaltern counterpublics among the Dalit Christians to oppose the continued oppression and casteism by situating Pulayathara at the centre of the analysis. 2022 Indian Institute of Management, Ahmedabad. -
What Numbers Never Revealed: Tracing Dalit Christian Modernity Through Malayalam Literature
Kerala has a long-standing history of Christianity as well as conversions. Conversions can be dated back to the fifteenth and sixteenth centuries, which saw a large number of slave caste conversions. For the slave castes of KeralaPulayas and ParayasChristianity offered a salvation from the circle of pollution. Scriptures provided the slave castes new vistas of knowledge which they encultured to form a counter discourse against the public sphere set up by the dominant castes. The public sphere of the Malayalee psyche was formed by the ideas of caste pollution, which restricted the slave castes from accessing the social space. A new Dalit perspective on the religious consciousness of the converted Christians will show the role of the Bible, Original Sin, and Repentance on their daily lives. Dalit Christian literature becomes the primary source where Christianity metamorphoses into an oppositional force in resisting oppression as well as in creating a social space with agency. 2022 Indian Institute of Management, Ahmedabad. -
New biomarkers for the detection of fetal death derived from large-scale proteomic analysis of maternal plasma
Background Normal pregnancy involves the modulation of thousands of maternal plasma proteins, and protein values not within the normal range may indicate the development of adverse pregnancy outcomes. A decrease in placental growth factor and an increase in soluble fms-like tyrosine kinase 1 in maternal plasma were shown to be associated with fetal death at the time of diagnosis and to predict this devastating pregnancy outcome at 24 to 28 weeks of gestation. However, these proteomic dysregulations are also present in other obstetrical syndromes, and more specific and sensitive biomarkers are needed to implement preventive strategies. Objective This study aimed to identify candidate protein biomarkers that can improve the prediction of fetal death relative to placental growth factor and soluble fms-like tyrosine kinase 1. Study Design This retrospective case-control study included 38 patients who experienced fetal death (cases) and 23 patients with uncomplicated pregnancies (controls). Plasma samples were collected at the time of diagnosis (2041 weeks of gestation) from cases and during routine care from gestational agematched controls. An aptamer-based multiplex assay was used to measure the abundance of >7000 protein analytes. Differential protein abundance was assessed using linear models with adjustment for gestational age at sample collection. Significance was inferred using a moderated t test adjusted P value of <.1 and a fold change of >1.25. Hypergeometric tests were performed to identify gene ontology biological processes enriched among proteins with significant changes in abundance. Random forest models were trained and evaluated via cross-validation to distinguish between fetal death cases and controls and to pinpoint the most salient predictors. Results Among the 7146 protein assays tested, 97 assays (1.4%) corresponding to 87 unique proteins differed significantly in abundance between fetal death cases and controls: 63 of 87 proteins (72%) were less abundant in fetal death cases, and 24 of 87 proteins (26%) were more abundant in fetal death cases. Dysregulated proteins were involved in pregnancy-related processes, such as angiogenesis and lactation. Random forest models effectively differentiated fetal death cases from controls, achieving an area under the receiver operating characteristic curve of 72% for the combination of placental growth factor and soluble fms-like tyrosine kinase 1, which increased to 86% when up to 50 additional proteins were included in the models (Delong test: P =.004). In addition, the point estimate of sensitivity increased from 53% to 74% (false-positive rate of approximately 10% for both). Glycoprotein hormones alpha chain (CGA), DnaJ homolog subfamily B member 9 (DNAJB9), and DNA-directed RNA polymerase III subunit RPC10 (POLR3K) emerged as the top 3 candidates to improve discrimination relative to placental growth factor and soluble fms-like tyrosine kinase 1. The significant proteomic changes in a subset of fetal death cases diagnosed first with preeclampsia relative to controls were highly correlated ( r =0.78; P <.001) with those reported in late preeclampsia cases leading to live births. On average, for each 2-fold change in protein abundance in late preeclampsia leading to live birth, there was an 8.6-fold change in preeclampsia leading to fetal death. Despite this overall correlation, transcobalamin 2, glucose-6-phosphate 1-dehydrogenase, and hepcidin, among others, demonstrated dysregulation only in preeclampsia leading to fetal death, suggesting both shared and distinct pathways perturbed in the 2 syndromes. Conclusion Our findings suggest that new maternal plasma proteins improve the discrimination of fetal death from controls relative to known biomarkers and that, although the signatures of fetal death and of preeclampsia are correlated, fetal death not only represents a much heightened disease state but also involves distinct perturbed pathways. Future studies are needed to determine whether the biomarkers can predict fetal death. 2026 Elsevier Inc. -
Health Microinsurance-Challenges and Strategies
Golden Research Thoughts, Vol-2 (5), pp. 19-23. ISSN-2231-5063 -
Enhancing Human Resource Management Practices in Marketing Companies Using Dual Graph Attention Networks
Marketing organization features and strategy implementation have been studied for over 30 years. These include organizational structure, culture, leadership, and processes. HR regulations can motivate marketing professionals to support group and individual goals when correctly implemented, but this part of HR has gotten little attention. Model preparation, feature extraction, and training comprise the suggestive technique. It reviewed data quality, evaluated dataset structure, and described data types during pre-processing. Principal component analysis (PCA) ranked and evaluated decision-making units to reduce dimension. Model training used MGGAN. In comparison to GAN and CNN, the proposed model performed well. With an average accuracy rate of 94.36%, it surpassed earlier approaches and captured all dataset peculiarities. MGGAN modeling can increase predictive performance, and marketing organizations should integrate HR regulations, according to this study. This study opens up new organizational analysis and strategy execution methods. 2025 IEEE. -
Assessment of Battery Technologies for Future of Electro-Mobility in Emerging Markets
In the outset of economic growth, the emerging country like India faces challenges due to rapid urbanization, infrastructure and city-congestion. The increased demand for mobility and a pivotal role of internal combustion engines from decades in the transportation segment have led to two influencing factors i.e., increased dependency on the oil import from fuel rich countries and alarming levels of emission. Hence it is essential for a country like India to venture into newer technologies to reform the transportation segment, reduce the dependency on the oil import and also has a positive impact on the pollutants. There are few technological barriers for the development of electric vehicles over internal combustion (IC) engines in terms of cost and performance of the vehicle. Along with the reduction of emissions, the electric vehicles should exhibit considerably good specific energy density and specific power density to emulate over the conventional (IC) engines. The three major constituents of electric vehicles are the battery, electric engine and the controller. The energy storage device forms the crux of the electric vehicle and has a significant role in its performance as well as forms the expensive component of the vehicle. Hence this paper involves the evaluation of various battery technologies, their performance requirements and options feasible for electric vehicles of the future. 2018 IEEE. -
Joint algorithm for energy-conservation and secure key generation in wireless sensor network
From more than a decade, wireless sensor network is one of the active area of research area. However, the energy dissipation as well as security loopholes are still unanswered question inspite of massive research work. Hence, the proposed system implements a novel algorithm that cumulatively addresses the unwanted energy dissipation problem along with secure authentication process. The prime attempt of this paper was to maintain a well-balance relationship between energy efficiency and security robustness in large scale wireless sensor network. The proposed security process allows one node to authenticate another node using quadratic approach. Implemented over first order radio energy model, the outcome of the proposed system was found to outperform the conventional SecLEACH routing algorithm with respect to processing time and energy. Research India Publications. -
SARDS: Secured anonymous routing with digital signature in wireless sensor network
A Wireless Sensor Network has witnessed a massive research towards security as well as energy efficiency in past decades. However, there are few studies that have witnessed a cost effective secure routing technique with energy effectiveness till date. Objectives: Our objective is to use public key cryptography for ensuring energy-efficient routing technique in Wireless Sensor Network. Method/Analysis: The proposed paper presents a technique called as SARDS (Secured Anonymous Routing with Digital Signature) that performs verification of the routing information exchanged among the sensors in Wireless Sensor Network. SARDS uses elliptical curve cryptography as the backbone of security formulations and performs authentication of all the communicating nodes present in the network. Findings: The system also allows a dual layer of security by introducing a novel signature based scheme towards public key encryption policy. The outcome of the study shows SARDS to excel best in performance in comparison of existing security and energy efficient routing schemes. Application/Improvements: Proposed SARDS technique offers 1) A novel public key encryption, 2) A novel digital signature scheme, and 3) A novel privacy or anonymous scheme. The outcome of the proposed system is also found to be superior as compared to existing protocols e.g. SecLEACH, LEACH and PEGASIS. -
STREE: A Secured Tree based Routing with Energy Efficiency in Wireless Sensor Network
The Wireless Sensor Network (WSN) applications are today not only limited to the research stage rather it has been adopted practically in many defense as well as general civilians applications. It has been witness that extensive research have been conducted towards energy efficient routing and communication protocols and it has been reached to an acceptable stages, but without having a secure communications wide acceptance of the application is not likely. Due to unique characteristics of WSN, the security schemes suggested for other wireless networks are not applicable to WSN. This paper introduces an novel tree based technique called as Secure Tree based Routing with Energy Efficiency or STREE using clustering approximation along with lightweight key broadcasting mechanism in hierarchical routing protocol. The outcome of the study was compared with standard SecLEACH to find that proposed system ensure better energy efficiency and security. 2015 IEEE. -
Fabrication of LaCoO3/g-C3N5 Z-scheme photo catalyst for Allura Red dye degradation, ascorbic acid sensing and hydrogen evolution studies
Production of hydrogen by water splitting through photocatalytic process under visible light from waste water is one of the potential green energy technologies. In this study, LaCoO3, g-C3N5, LaCoO3/g-C3N5 (1:1), (1:2) and (2:1) weight ratio nanocomposites (NCs) have been successfully synthesized using a solution combustion, hydrothermal and probe sonication method. The X-Ray Diffraction (XRD) confirmed the compound formed with the crystal size range 20 ?30?nm. The studies of Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) verify the morphology of the particles; band gap of the range 1.52?eV identified from Ultraviolet-Visible (UVvis) studies. The findings demonstrate that Z-scheme heterostructures have developed on the interfaces between the layered flake-like g-C3N5 and the perovskite-type oxides LaCoO3, which improve the absorption of visible light, the separation of photogenerated electron-hole pairs, and the transformation of photogenerated electrons. From the different ratio of synthesized nanoparticles (NPs), the LaCoO3/g-C3N5 (2:1) shows enhanced photocatalytic activity of 99.87?% for degradation of Allura red dye in visible light irradiation. For the first time, the produced nanomaterials were tested for ascorbic acid sensing at extremely low concentrations with a 0.12??M detection limit. The prepared nanomaterials were assessed for their electrocatalytic water splitting operation. Especially, the nanomaterial, LaCoO3/g-C3N5 (2:1) reveal exceptional hydrogen evolution reaction (HER) and also oxygen evolution reaction (OER) capabilities with overpotential of 79?mV and 450?mV, respectively. Hence, the prepared nanomaterials are used for multifunctional applications. 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Fabrication of Kaempferol Loaded Zein Nanoparticles: Investigation of in Vitro Cytotoxicity and Apoptosis Induction in Oral Cancer Cells
Oral cancer remains a significant health challenge, necessitating innovative therapeutic strategies to enhance treatment efficacy and minimize side effects. This study investigates the potential of kaempferol-loaded zein nanoparticles (KZNPs) for this purpose. Kaempferol, a flavonoid with anticancer properties, has poor water solubility, limiting its effectiveness. Zein nanoparticles (ZNPs) offer a promising delivery system for such bioactive compounds. UV-Vis spectroscopy identified Kaempferols absorption peaks at 347 and 253nm, which shifted to 338nm when encapsulated in ZNPs, indicating a change in ??* conjugation. Dynamic light scattering (DLS) and scanning electron microscopy (SEM) confirmed that sodium caseinate (SC) stabilizes ZNPs, resulting in spherical particles with optimal size and stability. Fourier transform infrared (FTIR) spectroscopy suggested enhanced hydrogen bonding between Kaempferol and zein. Differential scanning calorimetry (DSC) revealed the absence of Kaempferols crystalline peaks in KZNPs. The encapsulation efficiency (EE) was 98.39%, and drug release studies showed a controlled release of 79% kaempferol over 8h. In vitro assays demonstrated that KZNPs significantly increased Kaempferols cytotoxicity against PCI-13 oral cancer cells without affecting normal NIH3T3 cancer cells. Overall, these results demonstrate that our KZNPs enhanced biocompatibility and anticancer properties for oral cancer cells. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Wound healing efficacy of curcumin-loaded sandalwood bark-derived carbon nanosphere/PVA nanofiber matrix
The present investigation deals with the evaluation of the wound healing efficacy of sandalwood bark-derived carbon nanospheres loaded with curcumin-embedded polyvinyl alcohol (PVA) nanofiber membranes (NF). Carbon nanospheres (CNS) were prepared by pyrolyzing sandal wood bark powder at 750 C. The morphology was confirmed by field emission scanning electron micrographs and a rich amount of carbon was confirmed by the energy dispersive X-ray technique. Curcumin, an active wound healing drug was loaded onto synthesized CNS and confirmed by ATR-IR studies. Drug-loaded CNS were anchored in a PVA matrix via electrospun nanofiber fabrication. The fabricated nanofiber membranes were characterized and evaluated for wound healing efficiency. The cytotoxicity assay proved the non-toxic nature of the prepared PVA/CNS-curcumin-loaded NF. Membranes with active CNS/drug showed better antimicrobial activity against S. aureus and E. coli, which was estimated using the zone of inhibition (ZOI) test. The in vitro scratch wound healing assay of prepared PVA/CNS-curcumin nanofibers was efficient enough and showed 92 to 98% wound closure, which was greater than the control (without drug) nanofiber membranes. The PVA nanofiber matrix with interconnected structure and carbon nanostructures together enhanced the wound healing efficacy of the considered wound healing membrane, which is a promising novel approach for future wound healing patches. 2023 The Royal Society of Chemistry. -
Assessing and Exploring Machine Learning Techniques for Cardiovascular Disease Prediction using Cleveland and Framingham Datasets
Heart disease prediction using machine learning has garnered significant attention due to its potential for early diagnosis and intervention. This study presents an analysis of various machine learning algorithms applied to HD prediction across multiple research papers. The goal of this study is to analyze the performance and predictive capabilities of various machine learning algorithms in predicting heart disease across different datasets and research papers. Algorithms such as Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, Naive Bayes, and Gradient Boosting were evaluated using diverse datasets and parameters. In the Cleveland dataset, both Random Forest and Decision Tree classifiers achieved perfect accuracy 100%. Conversely, in the Framingham dataset, Random Forest exhibited the highest accuracy at 94%, followed by SVM at 87.45%, and Decision Tree at 85.23%. While specific algorithm performance varies depending on the dataset and parameters considered, ensemble methods like Random Forest often demonstrate superior performance. These findings underscore the effectiveness of machine learning in HD prediction and emphasize the significance of algorithm selection in developing accurate predictive models for cardiovascular health. 2024 IEEE. -
Microscale screen printing of large-area arrays of microparticles for the fabrication of photonic structures and for optical sorting
There are a limited number of methods applicable to the large-scale fabrication of arrays of discrete microparticles; however, such methods can be applied to the fabrication of structures applicable to photonics, barcoding, and optoelectronics. This manuscript describes a universal method, "microparticle screen printing" (?SP), for the rational patterning of micron-scale particles onto a variety of 2D substrates with diverse mechanical and chemical properties. Specifically, an array of microparticles of different sizes and compositions were patterned onto an array of materials of varying chemistry and stiffness using ?SP yielding a diversity of homo/heterogeneous microparticle-based structures. Further, this manuscript reports how the Young's moduli of the substrate can be used to calculate contact area and thus interaction energies (quantified using Hamaker constants) between the particle/substrate during ?SP. Generally, ?SP is most effective for substrates with low Young's moduli and large Hamaker constants (A132) with the target particles, as confirmed by the performance (quantified using yield and accuracy metrics) of ?SP for the different empirically investigated particle/substrate combinations. These understandings allow for the design of optimal surface/particle pairing for ?SP and were applied to the fabrication of a diversity of heterogeneous structures, including those with periodic vacancies in HCP (hexagonally closed packed) 2D photonic crystal useful to structural optics, optical particle screening useful to chemical assays, and the fabrication of structural barcodes useful for labeling and anticounterfeiting. 2018 The Royal Society of Chemistry. -
Chromatic Zagreb and irregularity polynomials of graphs
Graph coloring is an assignment of colors, labels or weights to elements of a graph subject to certain constraints. Coloring the vertices of a graph in such a way that adjacent vertices are having different colors is called proper vertex coloring. A proper vertex coloring using minimum parameters of colors is studied extensively in recent literature. In this paper, we define new coloring related polynomials, called chromatic Zagreb polynomials and chromatic irregularity polynomials, in terms of minimal parameter coloring and structural characteristics of graphs such as distances and degrees of vertices. 2021 World Scientific Publishing Company. -
On certain chromatic topological indices of some Mycielski graphs
As a coloring analogue of different Zagreb indices, in the recent literature, the notion of chromatic Zagreb indices has been introduced and studied for some basic graph classes in trees. In this paper, we study the chromatic Zagreb indices and chromatic irregularity indices of some special classes of graphs called Mycielski graphs of paths and cycles. 2020 Yarmouk University. All rights reserved.