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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. -
HEES-Based IFVR for Energy-Saving Application Using DCDC Converter
The rapid response capabilities of high-conducting electromagnetic energy storage (HEES) devices are advantageous for mitigating sudden fluctuations in voltage and power. However, the cost of HEES coils significantly exceeds that of traditional battery energy storage solutions. To enhance the efficiency of energy use and diminish the costs associated with energy storage across multiline power distribution systems, this study presents an innovative approach involving an interline dc flexible voltage restorer (IFVR) configuration. This approach utilizes a single HEES coil connected to several compensating circuits. The innovation introduces a currentvoltage (VI) chopper assembly with multiple input/output power connections, enabling the connection of one HEES coil to various power lines. This setup ensures the independent management of energy exchanges for any compensated line. Importantly, when multiple power lines require compensation simultaneously, the HEES coil can be selectively activated to prioritize compensation based on the designated order of importance of the lines. The practicality of this method is confirmed through technical verification, demonstrating its ability to sustain transient voltage stability during voltage increases and decreases on multiple lines. These scenarios may arise from fluctuations in output voltage from power external supplies or variations in load demand from locally connected loads. 2021 IEEE. -
Government Support Mechanism in Perishable Food Supply Chain: A Transition from Sustainability to Circularity
The transition from sustainability to circularity within the food supply chain (FSC) is intricate and multifaceted. Governmental efforts involve educating the perishable food sector about the advantages of circularity. The transition to circularity necessitates a reassessment of current business models and an emphasis on innovative practices. Therefore, the purpose of this article is to identify the potential enablers of government support mechanism in the transition from sustainability to circularity in perishable FSC. Furthermore, the study ranks the enablers according to their respective significance, adopting fuzzy simple additive weighting (SAW) method. Fuzzy SAW approach is selected as it can handle uncertainty and vagueness in the decision-making process, which is common when dealing with qualitative factors and subjective judgments. The study evaluates seven alternatives in relation to four criteria using the fuzzy SAW method. The findings from the study highlight fostering collaborative partnerships, innovative infrastructural support, and enforcing regulations and standards as the top three ranked enablers. The study contributes to the existing literature on sustainability and circularity in FSCs. The results from the study can assist the industry in focusing efforts on circularity and help businesses align practices with government policies. 1973-2011 IEEE. -
Provably Adaptive Trust Dynamics in Context-Aware Zero-Trust Systems: A Formal Framework for Continuous Verification
Zero-Trust (ZT) requires continuous, context-aware evaluation of authentication and authorization decisions. This paper introduces Zero-Trust Hybrid Adaptive Authentication (ZeTHAA), a continuous authentication and authorization framework integrating contextual attributes, authentication strength, behavioral evidence, and retry dynamics. ZeTHAA utilizes a probabilistic risk model and dual-policy thresholds to partition outcomes into allow, step-up, and block regions, enabling precise control over security-usability trade-offs. The system introduces a global admissibility predicate to distinguish hard violations from probabilistic soft violations. Attribute importance is dynamically derived from entropy and Beta-posterior distribution, enabling robust cold-start initialization and online recalibration. ZeTHAA presents a unified composite attack surface covering credential compromise, attribute forgery, and post-grant hijacking, modeling retry behavior with exponential risk escalation and temporal decay. A large-scale synthetic dataset capturing realistic authentication flows, adversarial and temporal patterns, was used to evaluate ZeTHAA against heuristic, logistic regression, random forest, XGBoost, and isolation forest baselines. ZeTHAA produced a more expressive risk distribution and significantly higher attack detection and efficiency while minimizing user friction. ZeTHAA outperformed baseline models, with Recall and Area Under the Curve (AUC) exceeding 79% and 15.1%, respectively. F1-Score showed increases of 48%-147%, with efficiency boost of 20-65%, while reducing the cost per attack by up to 39.6%. Benchmarks against frameworks from Dasu et al. and Matiushin et al. showed a 57.5% lead in F1-Score, more than double increase in detection rate, while blocking 70.78% more attacks. Additional analysis shows that ZeTHAA provides a mathematically grounded foundation for Zero-Trust systems, aligns with NIST standards, offering improved security guarantees and adaptive enforcement. 2013 IEEE. -
Adversarial Shadows in Digital Forensics: New Insights Into File Fragment Classification Vulnerabilities and Defenses
The paper is a comprehensive survey of adversarial attacks on file fragment classification (FFC) models - a relatively unexplored area in digital forensics, given the increasing application of machine learning techniques. Unlike image or text classification adversarial attacks, adversarial attacks on FFC exploit statistical and structural properties at the byte level in systems that lack semantic or perceptual knowledge. Such properties necessitate the use of domain-specific defense strategies, as the defense strategies adopted from other domains are typically not effective for the problems of FFC. The survey comprehensively evaluates attack mechanisms relevant to FFC, including evasion and poisoning attacks, and discusses their impact on forensic reliability. It highlights the absence of domain-specific benchmarks, robust evaluation protocols, and systematic research on the adversarial robustness of FFC. The paper also discusses the different types of byte level perturbations that can happen in fragment data, and it sets specific research priorities for raising the reliability of machine learning-based digital evidence recovery and security. The paper provides building blocks for future work, offering practical insights for development in ensuring file fragment classification systems utilized in forensics are secure. 2013 IEEE. -
A New Versatile Discrete Distribution for Censored Data: Frequentist and Bayesian Methods With Real-Life Applications
This study introduces a novel and highly flexible class of discrete probability distributions tailored to model the diverse monotonic failure-rate patterns frequently observed in stock-market data. The proposed distribution accommodates outliers effectively and serves as a discrete analogue of the exponential law, enabling analysts to derive robust and interpretable insights into market dynamics. Fundamental mathematical characteristics of the distributionsuch as the probability-generating function, mean, and varianceare thoroughly derived. The model is further extended to handle Type-II censored data, enhancing its applicability to real-world scenarios where incomplete observations are common. Parameter estimation is performed using both maximum-likelihood and Bayesian approaches, with a special focus on techniques suitable for censored samples. The performance and reliability of the estimators are examined through extensive simulation studies. To validate the practical utility of the model, it is applied to five real stock-market datasets obtained from Indiastat. The results demonstrate a superior empirical fit, affirming the models relevance in capturing the underlying patterns of financial time series. This distribution provides a valuable tool for analysts and researchers in the fields of financial statistics, risk modeling, and market behavior analysis. 2013 IEEE. -
Thermodynamic Modeling of Hashtag Dynamics for Social Media Clustering: A Maxwell-Boltzmann Approach
Social media hashtags function as critical organizational markers in digital discourse, yet traditional weighting methods fail to capture their dynamic significance across temporal and contextual dimensions. This paper presents a novel thermodynamic framework that conceptualizes social network activity as system 'temperature', applying statistical mechanics principles to model hashtag importance as process innovation. We establish mathematical foundations based on the Maxwell-Boltzmann distribution, providing an information-theoretic justification for dynamic hashtag weighting. Our approach incorporates activation thresholds and power-law scaling behaviors through a temperature-dependent function, with Simple Moving Average techniques implemented to stabilize temperature estimation, mathematically reducing variance by a factor of 1/N. Empirical evaluation using Twitter discourse from the US Presidential Election demonstrates unprecedented improvements in clustering performance: Silhouette Scores increased from 0.0126 to 0.9070 for Trump-related content and from 0.0105 to 0.8220 for Biden-related content, while Calinski-Harabasz Scores improved from 65.51 to nearly 98 million. These findings establish a rigorous mathematical bridge between thermodynamic systems and social media behavior, contributing to computational social science by providing a theoretical framework that significantly enhances discourse community detection in politically polarized environments. The approach enables more accurate identification of topic clusters, revealing distinct discourse patterns that conventional methods fail to capture. 2025 The Authors. -
A Novel SHiP Vector Machine for Network Intrusion Detection
In this paper, network intrusion detection is proposed using an improved version of the support vector machine model to detect DoS attacks. Here, the SVM model considers the weight parameter along with the kernel to find the best decision boundary that separates the data into DoS and normal. The proposed model provides a novel kernel trick that reduces the overlapping of data. The intrusion detection system aims to construct an ideal system that can detect attacks with very high performance using a ShiP vector machine(Sophisticated High Performance Vector Machine). The framework comprises three major steps: data collection and preprocessing, Recursive Feature Elimination (RFE) based feature selection, and the ShiP Vector Machine classification strategy. The system is evaluated using the DoS dataset from UNSWNB15 and real time PSD-23 sniffer dataset. DoS data is generated by extracting the normal and DoS attacks from the UNSWNB15 dataset. Experimental results show that the proposed ShiP vector machine shows outstanding performance by achieving 96.44 % accuracy on the DoS dataset and 90.12 % accuracy for real time PSD-23 data. 2013 IEEE. -
A Quantum-Enhanced Artificial Neural Network Model for Efficient Medical Image Compression
The ability to effectively store and transmit high-resolution images such as MRI and CT scans without losing quality is critical to modernizing medical imaging. Traditional compression methods risk losing essential medical image data, which requires perfect detail for diagnosis. Quantum algorithms use superposition and entanglement to compress faster while preserving important information. This research presents a Quantum-enhanced Artificial Neural Network (QANN) model that combines quantum feature extraction with classical neural network topologies to improve image compression. Our approach consists of converting standardized classical data into quantum states, controlling these states using parameterized quantum circuits, and measuring the resulting states to produce enhanced feature vectors. The quantum-enhanced features are fed into a traditional neural network for image compression. The experimental results clearly show that our QANN framework outperforms standard models in terms of accurate reconstructed images, reduced size, and increased space-saving percentage, especially when dealing with large and complicated datasets. The QANN model demonstrates how quantum computing can significantly enhance the effectiveness of medical image processing solutions. Kaggle brain CT and MRI datasets and COVID-CXNet chest x-ray images are used. The proposed QANN model improves peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Using quantum technology, the image size is reduced for MRI (73.3 %), X-ray (74.1%), and CT-SCAN (71.8%) to save space. 2025 IEEE. -
Positive ageing: self-compassion as a mediator between forgiveness and psychological well-being in older adults
Purpose: Positive aging aims to promote the physical health and psychological well-being of older adults for them to age successfully. Under the domain of positive aging, this study aims to explore the mediating role of self-compassion between forgiveness and psychological well-being in older adults. Design/methodology/approach: It was based on a quantitative research design, with a sample of 250 individuals within the age group of 6075 years. Data was collected using Self-compassion Scale (2003), Heartland Forgiveness Scale (2005) and Psychological Well-being Scale. Analysis was performed using Pearsons correlation, linear regression, followed by the generalised linear model of mediation. Findings: The results revealed a significant (p ? 0.001), high and positive correlation between self-compassion and forgiveness (r = 0.821), forgiveness and psychological well-being (r = 0.852) and self-compassion and psychological well-being (r = 0.802). Linear regression suggested that self-compassion and forgiveness are significant (p ? 0.001) predictors of psychological well-being, causing a variance of 75.6%. Mediation revealed significant (p ? 0.001) direct, indirect and total effect between the variables, showing that self-compassion partially mediates the relationship between forgiveness and psychological well-being. Research limitations/implications: The findings provide valuable insights on how fostering self-compassion along with forgiveness can improve psychological well-being among the elderly, however, research on additional variables, drawing comparisons between gender, economic status and clinical populations can be further explored. Nevertheless, this study can be used to develop interventions and therapeutic techniques to enhance self-compassion and forgiveness to improve psychological well-being among older adults. Originality/value: As per the best knowledge of the researcher, this work is original as it is a primary research and no data has been collected of a similar nature from the participants. 2024, Emerald Publishing Limited.