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A Paradigmatic Shift: Telehealth Counselling's Expansion and Challenges in India
Background: This study provides a comprehensive analysis of the rapid expansion and transformative impact of telehealth counselling in India, a trend significantly propelled by the challenges posed by the COVID-19 pandemic. Methodology: This paper presents a perspective on the current telehealth landscape, synthesizing insights from an extensive literature review. The investigation integrates qualitative insights from health care practitioners and clients, allowing for a multifaceted understanding of the emerging obstacles linked to telehealth implementation. The synthesis is structured around several key concepts identified in the literature, including the efficacy of telehealth counselling services compared to traditional face-to-face interactions, the resilience of mental health services during crises, and the growing acceptance of digital modalities among patients. Additionally, it explores significant challenges such as disparities in technological access, the need for comprehensive regulatory frameworks, varying levels of patient receptivity, infrastructural limitations, and the readiness of health care professionals to adopt telehealth technologies. Results: By focusing on these areas, the paper elucidates the complex interplay of technical, regulatory, and cultural factors shaping the telehealth ecosystem in India. It advocates for urgent policy enhancements and the continuous integration of technology to effectively address these barriers. Discussion: This perspective underscores the potential for telehealth counselling to evolve into a permanent and essential component of India's mental health service delivery model, ultimately contributing to a more resilient and accessible health care system. Conclusion: The conclusions drawn emphasize the necessity for targeted policy interventions and the establishment of robust technological infrastructures to foster a more inclusive and effective telehealth environment, ensuring mental health services reach all segments of the population. 2025 John Wiley & Sons Ltd. -
Out of South America Into India: Unusual Long Distance Dispersal of a Plant GenusLepidagathis (Acanthaceae)
Background: Lepidagathis Willd. (Barlerieae, Acanthoideae, Acanthaceae) is a pantropical plant genus with about 156 species. Classification of Lepidagathis and Lophostachys has been under debate for long. The genus is mainly Asian in distribution, followed by Africa, with an odd distribution of over a quarter of its diversity in the Neotropics. Aims: Given its pantropical distribution, we hypothesised that this pattern may be due to long distance dispersal followed by radiation in either the Old World or New World. Therefore, we aimed to test the monophyly of the Old World and New World species and trace its ancestral area using molecular dating and biogeographical analysis. Materials and Methods: We used 28 Lepidagathis sensu lato (s.l.) species, with seven molecular markers (ITS, trnL-LF, trnSG, trnGR, psbA-trnH, rps16 and rbcL), and conducted single-gene phylogenies. Later, we used a concatenated dataset of five markers (ITS, trnL-LF, trnSG, trnGR and rps16) to perform molecular dating based on secondary calibration as well as primary calibration. For historical biogeography, we applied two schemes: one treating peninsular Indiahome to about 35 speciesas a distinct region, and another grouping it with Southeast Asia. Results: Our study of the 28 species resolved Lepidagathis into three well-supported clades and supported its broad circumscription (sensu lato), including the former Lophostachys. The combined gene phylogeny places the African monotypic genus Schaueriopsis variabilis and the Asian genus Chroesthes within Lepidagathis s.l. Historical biogeography under the DIVALIKE+J model identified Africa as the most likely ancestral area for the genus, with Indian endemic species derived from Neotropical ancestors. In an alternative scheme, which included India within Asia, it was inferred that Lepidagathis s.l. has evolved from Asia, with all three lineages within Lepidagathis s.l. also with Asian ancestry. Discussion: The timing of the dispersal events out of Africa into the neotropics, and dispersal into the paleotropics, was during the late Oligocene, suggesting that it must have been long-distance dispersal, as there were no land bridges connecting the Americas with the Old World. Conclusion: This phylogenetic study, together with the biogeographical analysis demonstrates that Lepidagathis and Lophostachys represent independently evolving lineages that can be recognised as distinct subgenera. Moreover, Lepidagathis s.l. provides strong evidence for long-distance intercontinental dispersal events, inferred to have occurred during the late Oligocene. Our biogeographical analysis, along with the genus's greatest species diversity in Asia, supports an Asian origin for Lepidagathis as the more plausible scenario. 2026 John Wiley & Sons Ltd. -
Defect-Controlled Charge-Carrier Dynamics in M- and W-Type Hexaferrites
Defect engineering provides an effective means of tuning the charge transport in ferrimagnetic oxides. Here, we present a comparative study of M-type (BaFe12O19, SrFe12O19) and W-type (BaCo2Fe16O27, BaZn2Fe16O27) hexaferrites synthesized via solgel auto-combustion. Using XRD, SEM, TEM, dielectric measurements, and currentvoltage measurements, lattice defects are linked to charge-carrier conduction pathways. XRD confirmed phase-pure hexagonal structures with nanocrystallite sizes of 3336nm. High-resolution TEM revealed edge dislocations and planar strain fields in BaFe12O19, along with stacking-fault arrays in BaCo2Fe16O27, which create localized strain-induced potential fluctuations. The presence of dislocations and oxygen-vacancy defects promotes field-assisted thermionic emission with trap densities of ?1016 cm?3 and earlier onset of space-charge-limited conduction. Dielectric spectroscopy revealed MaxwellWagner relaxation, while the JE analysis indicated that Schottky emission dominates, with secondary space-charge-limited conduction occurring at high electric fields. The results demonstrate that oxygen-vacancy and strain-related defects serve as active transport mediators, providing a pathway to tune the electrical properties of ferrites for multifunctional electronic and energy applications. 2026 The American Ceramic Society. -
Role of Digitalization and Government Effectiveness in Sustainable Energy Transition: Evidence From Asian Economies
This study explores how digitalization, through resident and non-resident innovation initiatives, along with government effectiveness, affects the transition to renewable energy generation in five Advanced (Australia, Hong Kong, Japan, New Zealand and Singapore) and seven Emerging (China, India, Indonesia, Malaysia, Philippines, Thailand and Vietnam) Asian economies. The research uses annual data from 1985 to 2022 and applies several econometric methods to analyse the impact of these factors on renewable energy generation in a panel setup while also considering economic growth and human capital as key control variables. The findings reveal that residential innovation negatively impacts renewable energy generation in Advanced Asia but has a positive effect in Emerging Asia. Additionally, government effectiveness and non-residential innovation hinder renewable energy generation in Emerging Asia while contributing positively in Advanced Asia. Economic growth and human capital show a positive association with renewable energy generation in both Advanced and Emerging Asian economies. These findings are robust to an alternative method used. Besides, additional robust results further indicate that artificial intelligence patents used as an alternative measure of digitalization hinder renewable energy generation in Emerging Asia and promote it in Advanced Asia. These findings provide valuable guidance for policymakers and stakeholders, highlighting the need for tailored strategies to drive sustainable energy transition in different economic contexts. 2025 John Wiley & Sons Ltd. -
Driving AR Experience to Purchase Intention: Examining the Role of Users' Immersiveness, Perceived Innovativeness and Engagement in Virtual Try-On
This study examined Augmented Reality Virtual Try-On (AR-VTO) experiences in luxury jewellery retail by integrating Flow Theory with the S-O-R framework to investigate how AR experiences (stimulus) enhance user immersion (organism), shape attitudes towards the brand, and drive user engagement and luxury product purchase intentions (responses) among high-involvement consumers in an emerging market. Despite growing AR-VTO research in apparel and cosmetics, its influence on immersion, engagement, and purchase intentions in luxury jewellery remains underexplored in emerging markets. This study addresses this gap by examining 488 Indian luxury jewellery consumers through a sequential mixed-methods approach: thematic interviews (n = 28) identified consumer perceptions, followed by a survey analyzed using SEM-ANN with a multilayered perceptron feed-forward back-propagation (FFBP) algorithm. Results demonstrate that perceived innovativeness moderates key organism-to-response relationships. The findings contribute to literature by embedding Flow Theory and the S-O-R framework in AR-based luxury experiences and provide practical insights for AR developers and luxury brands on designing high-fidelity virtual interfaces to enhance engagement and conversion. 2026 John Wiley & Sons Ltd. -
Evaluating Allocations of Opportunities
This paper provides a robust criterion for comparing lists of probability distributionsinterpreted as allocations of opportunitiesfaced by different social groups. We axiomatically argue in favor of comparing those lists of probability distributions on the basis of a uniformamong groupsvaluation of their expected utility. We identify an empirically implementable criterion for comparing allocations of opportunities that coincides with the unanimity of all such uniform valuations of expected utility that exhibit aversion to inequality of opportunity. We illustrate our criterion by evaluating allocations of educational opportunities among castes and genders in 14 Indian states. 2025 The Author(s). International Economic Review published by Wiley Periodicals LLC on behalf of The Economics Department of the University of Pennsylvania and the University of Osaka Institute of Social and Economic Research Association. -
Beyond the talk: Parentadolescent sexual socialization in a culture that silences discourse about adolescent sexuality
Objective: This systematic review examined how parents socialize their adolescents' sexual behaviors and attitudes through indirect communication processes. Background: Sexual socialization between parents and adolescents extends beyond formal conversations about sexuality. Understanding indirect sexual socialization can identify messages that may be helpful or harmful to adolescents living in the United States, a culture where family conversations about sex are often uncomfortable and infrequent. Method: We systematically reviewed 41 articles from 20122022 that addressed how U.S. parents indirectly socialize their adolescents' sexuality. Results: Parents conveyed messages about sexuality through their behaviors (modeling behaviors and enforcing rules), words (and silence) in everyday conversations, and emotional reactions to sexual topics. These processes reflected parents' own sexualization within contexts in which sexual behavior during adolescence is seen as inappropriate, embarrassing, or private. Some adolescents internalized parents' indirect sexual socialization. However, parents and adolescents are also agentic and sometimes resisted problematic socialization processes. Conclusion: Findings suggest that a cultural discourse of silence on adolescent sexuality leaves parents unprepared for everyday interactions that present socializing opportunities. Overcoming this lack of preparation requires conscious effort on the part of parents and adolescents. Implications: Understanding indirect sexual socialization processes allows researchers and practitioners to identify strategies to interrupt problematic intergenerational cycles and to challenge the harmful effects of macrolevel political divisiveness on families and children. 2025 The Author(s). Family Relations published by Wiley Periodicals LLC on behalf of National Council on Family Relations. -
A Novel Blockchain-Integrated Deep Learning Framework for Securing Smart Healthcare Communication Networks
With the rapid expansion of intelligent medical equipment and their interconnectedness through the Internet of Things (IoT), addressing safety issues in the communicating system has become increasingly critical. A learning mechanism is proposed for an intelligent healthcare-based communication system that uses blockchain for secure network communication and incorporates a data evaluation layer based on cloud which actively segregates and ranks transactions into three main categories: Good, Moderate, and Malware. Fog servers are utilized to route the communicating nodes via Rician and Rayleigh channels. The learning mechanism employs a deep neural network to instruct and classify categories, thereby improving the blockchain layer's decision-making process. This paper introduces several significant contributions, such as the development of a secure blockchain framework for user authentication and a protected digital ledger for communication. Additionally, it incorporates a cloud-driven data analysis layer combined with a neural network to improve training accuracy and category classification. The developed algorithm surpassed the existing works in terms of quality of service (QoS) parameters with low latency, bit error rate (BER), higher signal to inference plus noise ratio (SINR), packet delivery ratio (PDR), true detection rate (TDR), false detection rate (FDR), and throughput. Also, a thorough comparison of consensus mechanisms like practical Byzantine fault tolerance (pBFT), proof of work (PoW), Raft, and Paxos is done to ensure which consensus helps optimize the proposed system in terms of security and fault tolerance with low latency and energy-efficient operations. It also establishes a secure and efficient communication network for smart healthcare, aimed at enhancing the overall quality of life for individuals. 2025 Wiley Periodicals LLC. -
Fueling a greener tomorrow: The impact of energy diversification on green growth
Motivated by the necessity of attaining carbon neutrality and striking a balance between clean and conventional energy sources, the emphasis is on how urgent it is to combat climate change and make the switch to sustainable energy systems. In this context, the currentstudy aims to examine the effects of energy diversification on green growth, considering such complementary factors as green technology, human capital, remittance inflows, foreign direct investment inflows, trade openness, and gross fixed capital formation. An empirical analysis is conducted by framing a green growth function in the panel data framework which is analyzed using the dynamic standard correlated model for the BRICS nations (Brazil, Russia, India, China, and South Africa) for the period between 1995 and 2020. The results show that (i) energy diversification exerts a dampening effect on the trajectory of green growth, (ii) the process of green growth was affected due to green technology and foreign direct investment inflows, (iii) underscores the pivotal role of human capital and gross fixed capital formation in bolstering the green growth trajectory, (iv) despite their potential relevance, remittance inflows and trade openness exhibit negligible impact within the framework of the green growth function, thus underscoring their limited contribution to the overarching sustainable development. The practical policy recommendations and invaluable insights provided by these empirical findings are instrumental in fostering green growth among the BRICS countries. Moreover, it contributes to the discourse on sustainable development by providing a solid foothold for informing the development of relevant policies in similar situations around the world. 2024 United Nations. -
A comparative study on the moderating impact of renewable energy and innovation on environmental quality
This study explores the complex interactions between renewable energy production, innovation, economic growth, institutional quality, economic globalization, and CO2 emissions in OECD countries and emerging economies from 1996 to 2021. Results from DriscollKraay standard error and feasible generalized least square reveal distinct trends: renewable energy production leads to increased CO2 emissions in emerging economies but significantly reduces emissions in OECD countries. Besides, residential and non-residential innovation, along with total innovation, show similar effects. Notably, technology-moderated renewable energy production effectively lowers CO2 emissions in both country groups. Similarly, economic growth enhances environmental quality in both sets of countries. However, institutional quality needs improvement in emerging economies, while current levels suffice in OECD nations to maintain environmental quality. Moreover, the study emphasizes the importance of considering globalization's impact on CO2 emissions, advocating for international agreements to leverage globalization for environmental benefits. Overall, these findings provide valuable insights for shaping renewable energy policies, fostering innovation, promoting economic growth, enhancing institutional quality, and harnessing globalization efforts to reduce CO2 emissions and enhance environmental quality. 2024 United Nations. -
Understanding the disconnect: A study on competency of special educators in autism education in India
Special educators are at the forefront of the Indian special education system in providing tailored instruction and specialized training for students with autism. There are evidence-based strategies that exist to enhance social, communication, cognitive, and mental health skills among students with autism. However, their implementation in the classroom is inconsistent or inadequate. Globally, there is a lack of empirical data on the quality of services provided to students with autism and the competency of educators teaching them in special education schools. This brief study provides a focused review of two articles from India, analysing the responses of 95 special educators gathered through a descriptive survey to assess their perceived competence in autism education. The findings indicate that in India, the special educators have limited knowledge and inadequate skills in autism education. The results will contribute to educational change at multiple levelsindividual, institutional, and systemicto foster better outcomes for students with autism. 2025 National Association for Special Educational Needs. -
We wear multiple hats: Exploratory study of role of special education teachers of public schools in India
The role of special education teachers (SETs) is multifaceted. A gap was recognised in the literature in the lack of studies on the roles and responsibilities of SETs in India and the field realities of carrying out the role. The aim was to explore to what extent the special education teachers fulfil their roles and responsibilities. The following is an exploratory study, using open-ended questions that interviewed 12 SETs from five public schools in Delhi, India. The policy documents shared that the SETs were responsible for direct instruction to special needs students, parentteacher collaboration and documentation, including IEPs for students with special needs. But in practice, there were not any clear-cut boundaries, the SETs played multiple rolesSubject teacher, taking substitution periods, para teachers, these were keeping the SETs away from their core responsibilities. The results of the study demonstrated an undervaluation of the work of SETs and lack of support from the principal and regular teachers. The results concluded with recommendations for policy proposal with regards to defining the role of all stakeholders in an inclusive education school and improvements for the teacher education program. 2024 National Association for Special Educational Needs. -
Novel Pooling-Based VGG-Lite for Pneumonia and Covid-19 Detection From Imbalanced Chest X-Ray Datasets
This paper proposes a novel pooling-based VGG-Lite model in order to mitigate class imbalance issues in Chest X-Ray (CXR) datasets. Automatic Pneumonia detection from CXR images by deep learning model has emerged as a prominent and dynamic area of research, since the inception of the new Covid-19 variant in 2020. However, the standard Convolutional Neural Network (CNN) models encounter challenges associated with class imbalance, a prevalent issue found in many medical datasets. The innovations introduced in the proposed model architecture include: (I) A very lightweight CNN model, VGG-Lite, is proposed as a base model, inspired by VGG-16 and MobileNet-V2 architecture. (II) On top of this base model, we leverage an Edge Enhanced Module (EEM) through a parallel branch, consisting of a negative image layer, and a novel custom pooling layer 2Max-Min Pooling. This 2Max-Min Pooling layer is entirely novel in this investigation, providing more attention to edge components within pneumonia CXR images. Thus, it works as an efficient spatial attention module (SAM). We have implemented the proposed framework on two separate CXR datasets. The first dataset is obtained from a readily available source on the internet, and the second dataset is a more challenging CXR dataset, assembled by our research team from three different sources. Experimental results reveal that our proposed framework has outperformed pre-trained CNN models, and three recent trend existing models Vision Transformer, Pooling-based Vision Transformer (PiT) and PneuNet, by substantial margins on both datasets. The proposed framework VGG-Lite with EEM, has achieved a macro average of 95% accuracy, 97.1% precision, 96.1% recall, and 96.6% F1 score on the Pneumonia Imbalance CXR dataset, without employing any pre-processing technique. 2017 IEEE. -
Intelligent Retrieval and Secure Content Generation in Consumer Healthcare Electronics Using Quantum Blockchain and Edge-Fog-Cloud Intelligence
To address the growing need for intelligent retrieval and personalized content generation in consumer healthcare electronic devices, this work proposes a secure, scalable, and AI-enhanced framework integrating wearable IoMT devices with edgefogcloud infrastructures. The system leverages quantum blockchain with Quantum Key Distribution (QKD) for tamper-proof storage of sensor data and applies a hybrid Practical Byzantine Fault Tolerance (pBFT) and Proof of Work (PoW) consensus for low-latency validation. At the edge layer, consumer medical devices, such as smart watches, smart patches, and mobile health assistants perform preliminary anomaly detection using lightweight BiLSTM-CNN models integrated with Quantum Neural Networks (QNN). When emergencies or anomalies are detected, the fog layer handles intelligent data retrieval and prioritization based on task urgency, network quality, and energy constraints. The cloud layer supports long-term storage and AI-driven content generation, such as personalized health summaries, alerts, and predictive reports. The architecture enables fast retrieval of user-specific biomedical data across consumer platforms and generates real-time decision support notifications through smartphones, wearables, and connected home healthcare centers. The simulation results demonstrate improved responsiveness, security, and retrieval efficiency compared to traditional IoMT architectures. This framework positions consumer healthcare electronic devices as intelligent, context-aware, and secure systems capable of real-time predictive assistance, data retrieval, and adaptive content generation for smart living environments. 2026 IEEE. All rights reserved. -
DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme. 1975-2011 IEEE. -
BORCAE: Bayesian Optimized Residual Convolutional Autoencoder for Efficient Feedback Compression in RIS-Assisted Time-Varying IoT Networks
Reconfigurable Intelligent Surfaces (RIS) have strong potential to improve the performance of time-varying Internet of Things (IoT) networks. However, a major challenge in operating RIS effectively is the need for frequent Quantized Phase Configuration (QPC) feedback bits from the Base Station (BS) to the controller. This challenge becomes more serious asthe RIS size grows, since the feedback bandwidth is limited. As a result, efficient compression of control signals is crucial for the practical deployment of RIS. In this work, we propose Bayesian Optimized Residual Convolutional AutoEncoder (BORCAE), a lightweight and noise-resilient feedback compression framework based on a 1D Convolutional Autoencoder with residual connections. The model is designed to reduce QPC feedback size while preserving high reconstruction fidelity. To ensure adaptability across varying deployment conditions, we employ Bayesian hyperparameter optimization using Optuna, which enables automatic tuning of key architectural hyperparameters. This optimization ensures that the architecture generalizes effectively across a wide range of operating scenarios. Additionally, we integrate the Limited Memory Broyden Fletcher Goldfarb Shanno (LBFGS) optimizer during the final training epochs, which accelerates convergence and improves stability. For performance evaluation, we use Normalized Mean Squared Error (NMSE) as the reconstruction metric. Extensive testing across different Signal-to-Interference-plus-Noise Ratio (SINR) levels demonstrates that BORCAE consistently achieves lower NMSE compared to DL-CsiNet and CsiNet. The results highlight the practical viability of BORCAE for RIS-assisted communication, offering improved efficiency, and scalability for real-world IoT and Sixth-Generation (6G) applications. 2020 IEEE. -
Unified Multimodal Information Flows for Immersive and Context-Aware 6G Systems
Communication is moving from being data-centric to being experience-centric and perception-aware with the advent of 6G wireless networks. Immersive applications such as digital twins, holographic telepresence, extended reality, tactile Internet, and visual-auditory-haptic data integration are essential to these applications. Unfortunately, the way communication is currently designed treats these modalities as separate data streams, leading to wasteful use of energy, bandwidth, and latency, and a poor user experience. For 6G systems that are both immersive and aware of their surroundings, this research presents a novel paradigm called Unified Multimodal Information Flow (UMIF). Underpinned by user intent and contextual awareness, UMIF combines multimodal data into coherent semantic flows, reimagining communication as a semantic experience. A semantic flow model with predictive state development and relevance-aware filtering, along with an event-driven semantic flow optimisation algorithm, enables scalable, energy-efficient, and ultra-low-latency communication. One way to improve the framework is by implementing distributed multi-agent orchestration at the network edge. Compared to bit-centric approaches, UMIF performs better across many areas, including transmission efficiency, semantic latency, user experience quality, and sustainability, according to several trials. 6G communication networks that are intelligent, immersive, and focused on humans can be built on top of this framework. 2026 The Authors. -
Blockchain-Based Model for Secure and Fair Data Provision in Crowdsourced Drone Services
Current centralized systems for crowdsourced drone services face problems in maintaining data integrity, fairness in data exchanges, and efficient resource allocation. These issues are critical in applications such as bushfire management, where accurate and timely data are essential. In response, we propose a blockchain-based model that creates a decentralized marketplace for secure data provisioning. In this system, drone operators send real-time environmental data to bushfire management authorities, and the data are recorded on a blockchain to ensure traceability. The model includes a time commitment-based mutually verifiable fairness mechanism to prevent dishonest behavior and to ensure that both data providers and consumers follow the agreed terms. Two new consensus mechanisms, Proof-of-Data Integrity (PoDI) and Proof-of-Service (PoSv), are introduced to confirm data authenticity and service quality. Additionally, a dynamic trust model that combines direct and indirect trust metrics is implemented to further support system reliability. Ethereum smart contracts are used to automate secure payment processing and to enforce transaction rules. This approach addresses the shortcomings of current systems and provides a clear framework for secure and fair data management in emergency response scenarios. 2020 IEEE. -
Toward Smart 5G and 6G: Standardization of AI-Native Network Architectures and Semantic Communication Protocols
Semantic communication and AI-native design are widely recognized as defining features of 6G, yet existing surveys often treat them conceptually or in isolation. This article provides a standards-oriented perspective that integrates these paradigms and evaluates their implications for architectural design and standardization. We make three concrete contributions: 1) we propose enriched KPI frameworks, security and privacy taxonomies, and interoperability prescriptions that extend beyond current 3GPP, ITU-T, and O-RAN activities; 2) we analyze implementation trade-offs such as computational overhead of semantic encoding and the scalability of federated learning in ultra-dense deployments; and 3) we demonstrate the potential of semantic communication through a UAV case study, highlighting measurable improvements in bandwidth, latency, and coordination efficiency. These contributions distinguish our work from prior surveys by moving beyond high-level vision toward feasibility analysis and concrete standardization pathways, thereby offering actionable insights for the evolution of semantic-aware 6G systems. 2017 IEEE. -
Electrochemical Oxidation of Hydrazine Hydrate Using Subja Seeds-Green Redox Chemistry-Impregnated Carbon-Modified Platform: Harmonizing Sustainable Sensing
The inclusion and interpretation of various phyto-based natural moieties embodying health gains is a critical and worthwhile scientific investigative focus. The descriptions comprehend their fundamental build-up, redox data, with significant electron shuttling, and strenuous obstacles in regard to the green-plant bioactives. Thus, a pressing and transformative undertaking toward simplistic electrocatalytic probe applications exploiting their reactive sites is a key focus, time demanding, or immediate call for top priority. Plant-sourced Basil or Subja seeds-redox entrapped within the mesoporous carbon spheres on a glassy carbon (GC) surface has been established (GC/graphitized mesoporous carbon [(GMC)@Subja] in this work. Unlike other established research constituting conventional approaches with limited access toward nonspecific or featureless voltammetric signals, we report a well-defined, sharp faradic response with an electrode potential E0' = 0.23 V (A1/C1) and 0.3 V (A2/C2) signals. The model Subja seeds-redox (GC/GMC@Subja) has been developed in an aqueous pH 7 phosphate buffer (PB) solution, contributing toward a sustainable and resilient strategy. This electrochemical methodology involved an underlying sp2-based mesoporous carbon framework for the ?-electron interaction and adsorption of the Subjaredox, leading to sp3 hybridization. The electrocatalytic function of GC/GMC@Subja showcased selective hydrazine (HZ) oxidation with a sensitivity and detection limit of 0.98 ?A mM-1 and 1.20 ?M (s/n = 3), respectively. Furthermore, the as-prepared system demonstrated HZ sensing in real samples with a recovery value of ?101.3%. 2026 IEEE.