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Evaluation of Corrosion Mitigation Performance of 1-(3,4,5-Trimethoxyphenylmethylidene)-2-Naphthylamine (TMPNA) Schiffs Base on Carbon Steel Using Electrochemical, Thermodynamic and Theoretical Approaches
A novel Schiff base,1-(3,4,5-trimethoxyphenylmethylidene)-2-naphthylamine(TMPNA) has been synthesized using naphthylamine and 3,4,5-trimethoxybenzaldehyde.The effective corrosion resistance and inhibition effect of TMPNA was studied at different concentrations in water medium on carbon steel by electrochemical techniques. The protective behavior of the passive film formed by the inhibitor was characterized through electrochemical impedance spectroscopy with an increased charge transfer resistance of 954 ?.cm2. The inhibition efficiency exhibited a gradual increase up to 92% with increase in schiff base concentration. Potentiodynamic polarization studies revealed that corrosion current decreased to 0.35 10?5A/cm2 with the addition of the inhibitor, TMPNA. Through various electrochemical studies such as impedance, polarization and electrochemical noise analysis (ENA), the concentration of TMPNA was optimized to 300ppm at which the maximum corrosion resistance was observed. Inhibition efficiency was found to decrease with increase in temperature. Also, the increased activation energy (Ea) value of 27kJ/mol confirmed that the inhibitor hindered the metal dissolution reaction. Adsorption of TMPNA on carbon steel/electrolyte interface was found to obey Langmuir adsorption isotherm. Scanning electron microscope (SEM) was used to evaluate the surface morphology. The Quantum chemical analysis (QCA) revealed that there was an electron transfer between TMPNA and the metal surface at ? 6.340eV. Molecular dynamic simulation study was carried out to investigate the adsorption of TMPNA on Fe (1 1 0) surface and adsorption energy value for the gaseous form was found to be ? 4197cal/mol. 2020, Springer Nature Switzerland AG. -
Explainable AI and computational intelligence in healthcare: Application to clinical decision support and personalized medicine
Human intelligence system simulation has made significant strides in several areas, including clinical decision-making using medical imaging and electronic health records, health referral systems, discovering recommended medications and vaccines, recognizing prescribed errors, and real-time data analysis. Therefore it is essential to discover patterns and transfer knowledge in the medical domain. The obstacles at the level of data collection, data analysis, model development, decision-making, and ethical concerns need to be addressed. It is vital that clinical interpretation tools associated with both hardware and software employed by medical professionals be precisely examined when rendering decisions regarding diagnoses and therapies related to the diagnosis. Computer scientists generally lack training in medical concepts specific to their field. Another crucial aspect is that black box algorithms based on artificial and computational intelligence are opaque and devoid of logical justification. Owing to these limitations, the technique of eXplainable Artificial Intelligence (XAI) models is explored in this chapter, primarily focusing on improving the interpretability of computational models. Specific objectives of this chapter are to: a) discuss the role that CI techniques and methods in the construction of an intelligent health prediction system; b) demonstrate the multiple CI paradigms utilized in medical prediction; and c) present recent case studies to showcase the performance of the computational intelligent models. 2026 Elsevier Inc. All rights reserved. -
An enhancement of machine learning model performance in disease prediction with synthetic data generation
The challenges of handling imbalanced datasets in machine learning significantly affect the model performance and predictive accuracy. Classifiers tend to favor the majority class, leading to biased training and poor generalization of minority classes. Initially, the model incorrectly treats the target variable as an independent feature during data generation, resulting in suboptimal outcomes. To address this limitation, the model was adjusted to more effectively manage target variable generation and mitigate the issue. This study employed advanced techniques for synthetic data generation, such as synthetic minority oversampling (SMOTE) and Adaptive Synthetic Sampling (ADASYN), to enhance the representation of minority classes by generating synthetic samples. In addition, data augmentation strategies using Deep Conditional Tabular Generative Adversarial Networks (Deep-CTGANs) integrated with ResNet have been utilized to improve model robustness and overall generalizability. For classification, TabNet, a model tailored specifically for tabular data, proved highly effective with its sequential attention mechanism that dynamically processes features, making it well suited for handling complex and imbalanced datasets. Model performance was evaluated using a novel approach of training synthetic data and testing on real data (TSTR). The framework was validated on the COVID-19, Kidney, and Dengue datasets, achieving impressive testing accuracies of 99.2%, 99.4%, and 99.5%, respectively. Furthermore, similarity scores of 84.25%, 87.35%, and 86.73% between the real and synthetic data for the COVID-19, Kidney, and Dengue datasets, respectively, confirmed the reliability of the synthetic data. TabNet consistently showed substantial improvements in F1-scores compared to other models, such as Random Forest, XGBoost, and KNN, emphasizing the importance of selecting the right synthetic data augmentation techniques and classifiers. Additionally, SHapley Additive exPlanations (SHAP)-based explainable AI tools were used to interpret model performance, providing insights into feature importance and its impact on predictions. These findings confirm that the proposed approach enhances the accuracy, robustness, and interpretability, offering a valuable solution for addressing data imbalance in classification tasks. The Author(s) 2025. -
Surface adsorption and anticorrosive behavior of benzimidazolium inhibitor in acid medium for carbon steel corrosion /
Journal of Applied Electrochemistry, Vol.52, Issue 11, pp.1659–1674, ISSN No: 0021-891X (Print) 1572-8838 (Online).
Corrosion inhibition property of a newly synthesized 3-(4-chlorobenzoylmethyl) benzimidazolium bromide inhibitor against carbon steel corrosion in 1 N hydrochloric acid solution was studied and analyzed utilizing various electrochemical methods. Electrochemical impedance study inferred that the inhibition efficiency increased with increasing inhibitor concentration and give 93.5% at 250 ppm. Potentiodynamic polarization study emphasized that inhibitor acted as a mixed type inhibitor and the adsorption of inhibitor on the metal surface followed Langmuir adsorption isotherm. The noise results were in good correlation with other electrochemical results obtained. -
A modern approach of swarm intelligence analysis in big data: Methods, tools, and applications
Swarm intelligence is one of the most modern and less discovered artificial intelligence types. Until now it has been proven that the most comprehensive method to solve complex problems is using behaviours of swarms. Big data analysis plays a beneficial role in decision making, education domain, innovations, and healthcare in this digitally growing world. To synchronize and make decisions by analysing such a big amount of data may not be possible by the traditional methods. Traditional model-based methods may fail because of problem varieties such as volume, dynamic changes, noise, and so forth. Because of the above varieties, the traditional data processing approach will become inefficient. On the basis of the combination of swarm intelligence and data mining techniques, we can have better understanding of big data analytics, so utilizing swarm intelligence to analyse big data will give massive results. By utilizing existing information about this domain, more efficient algorithm can be designed to solve real-life problems. 2023, IGI Global. All rights reserved. -
Impact of Leverage on Valuation of Non-Financial Firms in India under Profitabilitys Moderating Effect: Evidence in Scenarios Applying Quantile Regression
The firms valuation (FV) is the key element for all stakeholders, particularly the investors, for their investment decisions. The main impetus of this research is to estimate the effects of the debt ratio (DR, i.e., leverage) on the FV (i.e., assets and market capitalisation) of the non-financial firms listed in India. The quantile panel data regression (QPDR) on the secondary data of 76 non-financial BSE-100 listed firms in India is employed. This study also checks the effect of the net profit margin (NPM) as profitability on the association between DR and FV. The QPDR estimates result in multiple quantiles and provide evidence in scenarios. The findings reveal a positive relationship of DR to assets only in higher quantiles, i.e., 90%ile), and a negative association of DR is found with a market capitalisation in all quantiles. Under the interaction effect, profitability (NPM) does not affect the association of DR with assets but negatively affects the association of debt ratio with market capitalisation in the middle (50%) quantile. The findings indicate that leverage (DR) affects a firms value. The studys outcomes are helpful to all stakeholders, particularly investors, to realise the leverage (DR) as a critical indicator of FV before making any investment decisions. Managers should also consider lower debt ratios for better firm value. The present analysis is original and holds novelty in the form of the moderating role of the net profit margin, i.e., the profitability of the firm between DR and FV in the non-financial firm in India. To the best of our knowledge, no such studies have been performed to look for the association of the debt ratio with a firms value under the effect of profitability in different quantiles using quantile regression. 2023 by the authors. -
A Diabetes Detection Framework Based on Datadriven Predictive Technologies
Diabetes is a chronic disease spreading worldwide with major health challenges. It is not only caused by medical factors but other factors too such as genetic, demographics and lifestyle factors. With traditional or manual diagnosis methods, timely diagnosis becomes challenging due to complex and fragmented datasets. Recent advancements in machine learning (ML) models have greatly enhanced the efficiency and accuracy in disease diagnosis and risk evaluation. This review synthesizes the findings from the recent studies in the field of diabetes, major contributions and limitations, identifies the directions for the future work. This review has included the articles from three databases: Scopus, IEEE Xplore and PubMed; published between 2017 and 2025. The study has employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model for the review process. The scope of this work includes datadriven predictive technologies for diabetes detection, risk of complications and disease progression. It also sheds light on ongoing challenges such as data imbalance, limited interpretability, and population generalizability, while pointing to future opportunities in explainable AI and more personalized approaches to diabetes care. The review highlights that hybrid or ensemble models performing better than classical single models for risk prediction. 2025 IEEE. -
Detecting Deepfake Voices Using a Novel Method for Authenticity Verification in Voice-Based Communication
The widespread use of deepfake technology in recent years has given rise to grave worries about the alteration of audio-visual material. The integrity of voice-based communication is particularly vulnerable to the threat posed by deepfake voice synthesis. The development of cutting-edge methods for the identification of deepfake voices is examined in this paper, which also offers a thorough analysis of current approaches, their advantages, and disadvantages. The research presents a novel method for detecting deepfakes in voice recordings that uses signal processing, machine learning, and audio analysis to separate synthetic voices from authentic voices. By achieving superior accuracy in differentiating between real and deepfake voices, and proposed method supplies a strong barrier against the misuse of voice synthesis technology for malicious purposes, also go over the research some of the possible uses for this technology, like voice authentication system security and social media platform content moderation. The paper's insights will support continued efforts to strengthen the authenticity of voice communication in the digital age and reduce the risks associated with deepfake voice synthesis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Machine Learning Model Enabled with Data Optimisation for Prediction of Coronary Heart Disease
Cardiovascular disorders remain leading cause for mortality worldwide, necessitating robust early risk assessment. Although machine learning models show promise, most rely on conventional preprocessing, which lacks model portability across datasets. We propose an integrated preprocessing pipeline enhancing model generalizability. Our methodology standardises features solely based on training statistics and then transforms test data identically to prevent leakage. We handle class imbalance through synchronised oversampling, enabling consistent performance despite distribution shifts. This framework was evaluated on an open-source dataset of clinical parameters from an African cohort using classifiers like support vector machines and gradient boosting. All models achieved upto 80% accuracy. Remarkably, evaluating the identical models on five external European and Asian datasets maintains 80% - 86% accuracy. Our reproducible data conditioning strategy enables precise and transportable heart disease risk prediction, overcoming population variability. The framework provides the flexibility to readily retrain models on new data or update risk algorithms for clinical implementation in diverse locales. Our work accelerates the safe translation of machine learning to guide cardiovascular screening worldwide. 2024 IEEE. -
A Survey on Enhancing System Performance of Wireless Sensor Network by Secure Assemblage Based Data Delivery
To provide secure data transmission in Cluster Wireless Sensor Networks (CWSNs), the challenging task is to provide an efficient key management technique. To enhance the performance of sensor networks, clustering approach is used. Wireless Sensor Network (WSN) comprises of large collection of sensors having different hardware configurations and functionalities. Due to limited storage space and battery life, complex security algorithms cannot be used in sensor networks. To solve the orphan node problem and to enhance the performance of the WSN, authors introduced many secure protocols such as LEACH, Sec-LEACH, GS-LEACH and R-LEACH, which were not secure for data transmission. The energy consumption in existing approach is more due to overhead incurred in computation and communication in order to achieve security. This paper studies about different schemes used for secure data transmission. We are proposing new methodology called IBDS and EIBDS that will increase the performance of WSN by reducing computational overhead and also increases resilience against the adversaries. 2017 IEEE. -
An energy efficient authentication scheme based on hierarchical ibds and eibds in grid-based wireless sensor networks
Wireless sensor network is a peculiar kind of ad hoc network, consists of hundreds of tiny, resource constrained as sensor nodes. Clustering is a demanding task in such environment mainly due to the unique constraints such as energy efficiency and dynamic topology. In this paper, a novel energy efficient cluster-based routing algorithm is proposed on which hierarchical identity-based digital signature (IBDS) and enhanced-identity-based digital signature (EIBDS) scheme is concerning in grid-based wireless sensor networks. Firstly we form clusters using multi-parameters-based type-2 fuzzy logic algorithm. This paper proposes an improved ant colony optimisation algorithm, which optimises the energy consumption on data transfer in a WSN. Each node in a sensor network is authenticated using elliptic curve cryptography (ECC). After a set of simulation tests on NS-3 simulator, our proposed work achieves good performance for various metrics. Copyright 2020 Inderscience Enterprises Ltd. -
Enhancing performance of WSN by utilising secure QoS-based explicit routing
Wireless sensor networks (WSN) are infrastructure less and self-configured a wireless network that allows monitoring the physical conditions of an environment. Many researchers focus on enhancing the performance of WSN in order to provide effective delivery of data on the network, but still results in lower quality of services like energy consumption, delay and routing. We tackle this problem by introducing a new routing algorithm, QoS-based explicit routing algorithm which helps in transmitting the data from source node to destination node on WSN. We also involve clustering process in WSN based on genetic algorithm and particle swarm optimisation (GA and PSO) algorithm. We proposed identity-based digital signature (IBDS) and enhanced identity-based digital signature (EIBDS) that involves reduction of computation overhead and also increasing resilience on the WSN. We also use advanced encryption standard (AES), for ensuring the security between nodes and avoid hacking of data by other intruders. Copyright 2020 Inderscience Enterprises Ltd. -
Within-School Socioeconomic Disparities in Academic Achievement: A Qualitative Case Study of Study-Regulation Supports among Indian Secondary Students
Objective: This study explored how socioeconomic contexts shape students study strategies and how these differences relate to academic achievement within the same school setting. Methods and Materials: A single-site qualitative case study was conducted in a private, unaided English-medium CBSE school in Bengaluru, India, enrolling students from diverse socioeconomic status (SES) groups. Thirty students in Grades 89 (aged 1315) were selected through purposive sampling, representing all achievement levels and residence types (day scholars and residential/hostel students). SES classification was informed by parental education/occupation and the Modified Kuppuswamy Scale (2019). Data were collected through semi-structured individual interviews, audio recorded, transcribed verbatim, and analyzed iteratively using line-by-line and focused coding guided by Charmazs grounded theory approach, leading to theme development. Findings: Three themes explained within-school achievement disparities: (1) parental engagement and access to cultural/social capital varied by SES, shaping monitoring, subject support, and study regulation at home; (2) hostel routines and mentoring provided compensatory structures resembling middle-class concerted cultivation, supporting academic regulation for some low-SES residential students; and (3) for low-SES day scholars, teachers and remedial support served as the primary learning resource, often framed in skill-deficit terms rather than culturally responsive pedagogy. Conclusion: Equal access to school resources does not necessarily produce equal outcomes because study regulation develops within unequal family and institutional support ecologies. Equity-oriented, culturally responsive, and relational school practicesalongside targeted academic mentoringmay help reduce persistent achievement gaps. 2025 the authors. -
Impact of Innovative Technology on Quality Education: The Resourceful Intelligence for Smart Society Development
Quality education is the systematic learning and execution road map that build confidence among the learners and develop employability skills. Innovative techniques are the central facilitators of providing quality education for the younger generation. The economists, scientists, management experts, and research initiators are putting their efforts to develop a certain sustainable system in quality education through innovative technology. This is about digital equity, customized education, activity-based classroom, where the young mind is to be in synchronizing with the technology to explore new possibilities of learning and accomplishment. The research initiative reveals the system of implementing the innovative technology for quality education that has a direct impact on smart society development. The principal outcomes of the research initiative include the innovative ideas that transform the traditional education system into a dynamic education framework. The framework includes the integration of tools and techniques for standard mode of operations that reflects the productive and realistic education system. The researchers gracefully interconnected the concepts, methods, and applications of a quality education system that will open up new vistas for future research initiatives in the area of digital education, industry-institution collaboration, developing smart society, and economy of a nation at large. There is significant level of impact of innovative technology on quality education that leads to independent employability skills, creative, and innovative projects for facilitating future generation. All the influencing factors of resourceful intelligence together have great impact on smart society development that leads to provide modern facilities for the residents of smart society and create favorable environment for the future generations. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data visualization and toss related analysis of IPL teams and batsmen performances
Sports play a very significant role in the development of the human persona. Getting involved in games like Cricket and other various sports help us to build character, discipline, confidence and physical fitness. Indian Premier League, IPL provides the most successful form of cricket as it gives opportunities to young and talented players to show case their talents on various pitch. Decision-makers are the utmost customers for all fundamentals in the sports analytics framework. Sports analytics has been a smash hit in shaping success for many players and teams in various sports. Sports analytics and data visualization can play a crucial role in selecting the best players for a team. This paper is about the Toss Related analysis and the breadth of data visualization in supporting the decision makers for identifying inherent players for their teams. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Combined weighted feature extraction and deep learning approach for chronic obstructive pulmonary disease classification using electromyography
The COVID-19 outbreak has led to a rise in respiratory disease-related deaths, including Chronic Obstructive Pulmonary Disease (COPD). Early diagnosis of COPD is crucial, but it can be challenging to distinguish between different chronic pulmonary diseases due to their similar symptoms, leading to misdiagnosis and time-consuming manual inspections. To address this issue, this paper explores the use of a deep learning model to differentiate COPD from other lung diseases using lung sound captured during Electromyography (EMG). The model includes steps such as noise removal, data augmentation, combined weighted feature extraction, and learning. The model's efficacy was evaluated using various metrics, including accuracy, precision, recall, F1-score, kappa coefficient, and Matthews correlation coefficient (MCC), with and without augmentation. The results show that the model achieved 93% accuracy and outperformed other existing state-of-the-art deep learning models, increasing the robustness of clinical decision-making. The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. -
Bridging Science and Spirituality: Investigating the Effects of OM Chanting on Brain Waves
In Hindu tradition, the syllable "OM" holds significant spiritual and cultural value in Hindu tradition and is believed to produce positive psychological and physiological effects. Despite its prominence in spiritual practices, the neurophysiological basis for these benefits remains underexplored. In this study, electroencephalography (EEG), a non-invasive method for measuring electrical activity in the cerebral cortex, was employed to investigate the physical changes in brain wave patterns that occur when listening to OM chanting. Five frequency bands, namely delta, theta, alpha, beta, and gamma, are associated with brainwaves recorded through EEG, which define different states of cognitive and emotional nature. With these, this research analyzes EEG signals before and after chanting to identify and quantify changes, and to discuss the therapeutic implications. Several signal processing techniques, such as time and frequency domain analysis, assess variations in amplitude, frequency, and coherence across different brain regions. These findings show an increase in alpha amplitudes (34.2%) and an 85.4% improvement in the theta/beta ratio, related to relaxation, emotional regulation, and additional focus, as well as a decrease in beta waves, linked to stress and cognitive overload. This would show stronger neural integration between the brain hemispheres. The OM chanting evoked these results as a possible neurotherapeutic tool for stress management and cognitive enhancement. In bridging ancient spiritual practices with modern neuroscience, this study provides information on how such seemingly nonsensical meditations as OM chanting can enhance brain function, which is favorable for the third Sustainable Development Goal (SDG) of the United Nations, regarding the goal of healthy life and wellbeing throughout all ages. Further research should be done into these effects in different populations and over long periods to confirm that this is a long-term therapeutic effect. 2025, Sakarya University. All rights reserved. -
Optimized trimetallic CoNiFe phospho-boride electrocatalyst for overall seawater electrolysis
Utilizing abundant seawater for hydrogen production by electrolysis poses new challenges to electrocatalyst performance, demanding effectiveness, resilience, and selectivity for oxygen evolution reactions (OER) over undesired reactions in harsh saline conditions. Herein, trimetallic phospho-boride, CoNiFePB, was synthesized via a chemical reduction method by carefully tuning the concentrations of all elements for overall seawater splitting. The optimized CoNiFePB demonstrated superior OER activity, requiring only 239 mV to achieve 10 mA/cm2 in alkaline simulated seawater, outperforming bimetallic configurations (CoNiPB and CoFePB). The enhancement in hydrogen evolution reaction (HER) activity was attained by adjusting the B/P molar ratio in CoNiFePB, resulting in a low overpotential of 137 mV. A comprehensive kinetic analysis revealed that Ni and Fe play crucial roles in enhancing the adsorption and desorption of the reactant and product, respectively, while Co serves as the active site for intermediate formation, collectively boosting the activity of the trimetallic CoNiFePB. While the electron modulation achieved by B and P triggers the formation of active sites and avoids chloride ion oxidation. The bifunctional CoNiFePB catalyst deposited on Ni foam (NF) demonstrated excellent durability for 10,000 cycles and maintained performance for 70 h in chronoamperometric testing at a high current density of 0.7 A/cm2, emphasizing its long-term stability in alkaline seawater. When integrated into an advanced seawater electrolyzer with a zero-gap assembly, CoNiFePB/NF achieved a current density of 2 A/cm2 at a cell voltage of approximately 2.43 V in alkaline natural seawater. These findings provide significant insights into electrocatalysis for seawater splitting with promising implications for commercial applications. 2025 Elsevier B.V. -
Highly Luminescent MOF and Its in Situ Fabricated Sustainable Corn Starch Gel Composite as a Fluoro-Switchable Reversible Sensor Triggered by Antibiotics and Oxo-Anions
Frequent use of antibiotics and the growth of industry lead to the pollution of several natural resources which is one of the major consequences for fatality to human health. Exploration of smart sensing materials is highly anticipated for ultrasensitive detection of those hazardous organics. The robust porous hydrogen bonded network encompassing a free-NH2 moiety, Zn(II)-based metal-organic framework (MOF) (1), is used for the selective detection of antibiotics and toxic oxo-anions at the ppb level. The framework is able to detect the electronically dissimilar antibiotic sulfadiazine and nitrofurazone via fluorescence "turn-on"and "turn-off"processes, respectively. The antibiotic-triggered reversible fluoro-switching phenomena (fluorescence "on-off-on") are also observed by using the fluorimetric method. An extensive theoretical investigation was performed to establish the fluoro-switching response of 1, triggered by a class of antibiotics and also the sensing of oxo-anions. This investigation reveals that the interchange of the HOMO-LUMO energy levels of fluorophore and analytes is responsible for such a fluoro-switchable sensing activity. Sensor 1 showed the versatile detection ability which is reflected by the detection of a carcinogenic nitro-group-containing drug "roxarsone". In view of the sustainable environment along with quick-responsive merit of 1, an in situ MOF gel composite (1@CS; CS = corn starch) is prepared using 1 and CS due to its useful potential features such as biocompatibility, toxicologically innocuous, good flexibility, and low commercial price. The MOF composite exhibited visual detection of the above analytes as well as antibiotic-triggered reversible fluoro-switchable colorimetric "on-off-on"response. Therefore, 1@CS represents a promising smart sensing material for monitoring of the antibiotics and oxo-anions, particularly appropriate for the real-field analysis of carcinogenic drug molecule "roxarsone"in food specimens. 2022 American Chemical Society. -
The linkage of sustainable development and spirituality at workspace from the perspectives of university teachers
Purpose Sustainability and spirituality are interrelated concepts that hold immense importance in todays world. The purpose of this study is to explore the influence of spirituality (and its dimensions) on sustainability in the educational sector, in addition to examining the role of socio-demographic factors. Design/methodology/approach A quantitative research design was employed using a structured questionnaire. The data were analysed through independent t-tests, analysis of variance, correlation analysis and structural equation modelling (SEM) to assess relationships between socio-demographic factors, workspace spirituality and sustainability. Findings The study found no significant differences in sustainability levels across gender, age or years of work experience. However, significant differences in workspace spirituality were observed between males and females, and across different age groups and experience levels. Positive correlations were found between workspace spirituality and dimensions such as compassion, transcendence and meaningful work, while mindfulness showed a negative correlation. SEM results further indicated that compassion and meaningful work positively influence sustainability, while mindfulness negatively affects it. Transcendence, however, showed no significant impact. Research limitations/implications The study highlights the deep interconnection between spirituality and sustainability and how socio-demographic factors shape this relationship. It provides insights for educational institutions to foster spiritually enriching environments that not only enhance academic outcomes but also promote ethical awareness, personal growth and environmental responsibility. Originality/value This research uniquely bridges gaps between spirituality, sustainability and employee demographics, offering practical implications for creating spiritually fulfilling and sustainable workspaces in the educational sector. 2026 Emerald Publishing Limited