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Effect of quick lime addition for improving compressive strength of fly ash based (cement free) geopolymer concrete
In this current investigation, a comprehensive exploration has been undertaken to advance the development of Class F fly ash-based Geopolymeric mortar and concrete, utilizing fly ash that has been sourced from the Satpura Thermal Power Station in Sarni, District Betul, Madhya Pradesh, India. The study has systematically investigated the influence of Quick Lime addition in the formulation of fly ash-based Geopolymeric mortar and concrete, with a particular focus on enhancing compressive strength within the constraints of ambient atmosphere conditions. The Geopolymeric binder has been meticulously crafted using sodium hydroxide and sodium silicate as alkaline activators. Systematic variations of Quick Lime dosages (ranging from 0% to 10% by weight of fly ash) have been introduced, and the ensuing specimens have undergone scrutiny for standard consistency and setting time under ambient temperature curing. The outcomes have underscored a discernible trend wherein the judicious addition of 7 to 9 wt.% calcium oxide (Quick Lime) to the fly ash matrix has precipitated a noteworthy reduction in setting time at room temperature, concurrently manifesting a substantial enhancement in compressive strength for Geopolymeric mortar and concrete formulations. The elucidation of the binder's microstructural phases and their chemical characteristics has been pursued through rigorous analytical methodologies, encompassing X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), and field emission scanning electron microscopy (FE-SEM). Cost analysis has also been conducted for 1m of concrete, comparing conventional concrete (M25) and fly ash-based Geopolymer concrete. 2025, National Institute of Science Communication and Policy Research. All rights reserved. -
Resilience Amidst Neglect: Analyzing Government Failures and Community Strengths in the Wayanad Crisis
Disaster-prone areas are geographically, environmentally, or climatically vulnerable to natural disasters and require strong preparedness and adaptation. Collaboration, indigenous knowledge, and resource mobilization help communities recover and rebuild after crises, reducing vulnerability. This study investigates the catastrophic landslide in Wayanad, Kerala, in July 2024, to analyze governmental inadequacies in disaster preparedness and the notable resilience strategies exhibited by the local community. The landslide, intensified by unregulated development in ecologically vulnerable areas, underscores the failure to execute essential environmental recommendations, including those specified in the Madhav Gadgil and Kasturirangan reports. Notwithstanding these challenges, the community's swift response via local organization, resource mobilization, and local leadership was instrumental in alleviating additional losses and facilitating recovery. The research highlights the relationship among crisis management, community empowerment, and environmental sustainability in areas susceptible to disasters. This research analyzes the Wayanad disaster and proposes a model that integrates proactive policy measures with community-driven strategies to bolster resilience and diminish vulnerability in ecologically sensitive areas. The RESTORE Model Framework is a comprehensive methodology for disaster management, highlighting Resilience, Early Warning, Sustainability, and Strategic Collaboration as fundamental components. The results underscore the imperative for collaborative frameworks that incorporate government entities, local stakeholders, and environmental specialists to enhance disaster management systems. 2026, IDRiM Society. All rights reserved. -
Unfolding the aggression and locus of control paradigm in sportspersons and non-sportspersons
The present study investigated Aggression and Locus of Control on Combat Sports Persons, Non-Combat Sports Persons, and Non-Sports Persons. In this study, a sample of 240 individuals (80 Combat sports, 80 Non-Combat Sports & 80 Non-Sportspersons) was used through purposive sampling. The tools administered were the Buss and Perry Aggression Questionnaire by Arnold H. Buss and Mark Perry and Rotters Locus of Control Scale by Julian Rotter respectively. The objective of the study was to investigate Aggression and Locus of Control in males and females from Combat, Non-Combat, and Non-Sports persons. This research also aims to explore the relationship between Aggression and Locus of Control. Mean, t-test, F-value (ANOVA), and correlation have been computed over SPSS-16. Results suggest that males from Combat have higher Aggression than people from non-sports and non-combat sports. There is also a significant difference between non-sports persons and sports people over the Locus of Control, sports persons showed internal locus of control compared to non-sports persons who were higher on external locus of control. The result also indicates a significant relationship between the anger dimension of the Aggression and Locus of Control. 2025 ARD Asociaci Espala. -
FINANCIAL DECISION-MAKING POWER AND RISK-TAKING BEHAVIOUR IN INDIAN HOUSEHOLDS
This study aims to examine the impact of decision-making power on risk-taking behaviour in household economies in India. It further explores the relationship between decision-making power, perceived risk-taking behaviour, and actual risk-taking behaviour. Further, the study employs the primary data collected through a structured questionnaire. The snowball sampling method was adopted to gather data from 312 retail investors in the study area. The response rate for the sample size is 91.50%. An OLS regression model was constructed to measure the frequency of trading habits as a proxy for the respondents' risk-taking behaviour. The results indicate that decision-making power significantly impacts investors' risk-taking behaviour in Indian household economies. Additionally, decision-making power has a significant impact on perceived risktaking behaviour. The findings of this study show how decision-making power influences the risk-taking behaviour of retail investors. This study adds value to the literature on behavioural finance and household economies. The results will pertinently support retail investors' decision-making skills in unbiased investment decision-making. 2025 by the author(s). -
The role of audiobooks in developing English listening proficiency: A study of undergraduate learner in Tamil Nadu
Listening is one of the inevitable yet often overlooked skills in the process of learning a second language, especially for Indian undergraduate students learning English as a Second Language (ESL). Even after years of studying English in school, many students still struggle to understand spoken English. This occurs because students do not get sufficient exposure to real English as spoken in daily life, and they often feel nervous or uncomfortable in classroom settings. Various apps and tools are used to facilitate language learning, but there remains limited understanding of how audiobooks specifically contribute to enhancing listening skills. This study examines the effectiveness of audiobooks in developing listening skills through a twelve-week program involving 66 undergraduate students from Tamil Nadu. Students participated in a pre-test before the intervention and a post-test after completing the twelve-week audiobook listening program, which involved daily sessions of one hour. They listened to audiobooks with teacher support initially and then independently. The results indicated a significant improvement in listening scores, with an average increase of 25%. Additionally, students reported feeling less anxious about listening, better retention of new vocabulary, and improved ability to follow spoken English. The findings suggest that audiobooks are a vital component of language learning, providing autonomous, low-pressure, and long-term benefits in listening development. Consequently, audiobooks enhance confidence and foster consistent listening skills among ESL learners. The study recommends integrating audiobooks into regular classroom activities to support language acquisition. Contribution/Originality: This study contributes to the existing literature by validating audiobooks as effective ESL tools. It uses a new estimation methodology through SPSS analysis, originates a formula linking Krashens Affective Filter with audiobook learning, and is one of the few studies on Tamil Nadu undergraduates. The paper contributes quantitative and emotional factors, documents progress, and finds that audiobooks enhance proficiency. 2025 AESS Publications. All Rights Reserved. -
Developing a psychometric scale to measure motherhood stress among newly working mothers in Indias IT sector
While becoming a mother is an incredible blessing, it can also be a very stressful time for women. India has a unique cultural and economic diversity, with women facing distinct challenges in their role as new mothers, including family obligations and a tendency toward a patriarchal mindset. This research aims to identify the unique stress factors related to motherhood stress for a new working mother in the IT sector and to create a scale to measure these factors. The study employed a mix of qualitative methods, such as focus group discussions, and quantitative research methods, including a pilot survey among 115 mothers, to develop a new scale for motherhood stress. Four main factors were identified through exploratory factor analysis: career-related stress, adequacy of support systems, maternal guilt, and self-efficacy. A new 31-item scale is proposed. Internal consistency, reliability, and validity were established for all items on the scale, with a KMO value of 0.826 and a Cronbach's alpha of 0.840. This new scale will be a useful tool for organizations to understand motherhood stress among newly working mothers and to adopt practices that reduce stress and address prejudices through interventions such as anti-discrimination policies, managerial sensitization, flexible work options, career counseling, and peer support. For policymakers, this study highlights the need for an industrial policy that recognizes the cultural setting and the unique challenges faced by Indian working mothers, as well as the importance of rigorous enforcement of maternity policies to ensure equitable treatment for them. 2026 AESS Publications. All Rights Reserved. -
FORTIFICATION OF LAUNDRY WATER WITH BACTERIA CAPABLE OF SODIUM DODECYL SULFATE (SDS) REMEDIATION AND PLANT GROWTH PROMOTION. A SUSTAINABLE WAY TO REUSE WATER FOR IRRIGATION
Anionic surfactant sodium dodecyl sulfate (SDS) is used in cosmetics and cleaning goods. It discharges into the environment and waterways due to its extensive use. Basal media with 0.05% SDS as the sole carbon source was used to isolate bacteria that can utilize SDS. The isolates survived nitrogen-free medium and solubilized potassium and phosphate. Using 16S rRNA sequencing, Enterobacter cloacae strain MSK86 (OR136425) was identified. Stains-all dye was used to test the bacteria's SDS-utilizing capability. A 49% drop in SDS levels in the broth was observed after 7 days of 24-hour analysis. The bacteria exhibited tolerance to heavy metals like Cd (II), Ar (III), and Zn (II) at concentrations up to 2000 ppm, whereas they were susceptible to Cu (II), Cr (II), and Pb (II) at minimum concentrations of 200, 600, and 1000 ppm, respectively. The bacteria effectively reduced SDS levels in the laundry wash water. The treated water was reused for the irrigation of Capsicum annum L. and Solanum lycopersicum L. until the 45th day of growth. The plants' morphological and phytochemical properties were also analyzed. The potential of bacteria for SDS degradation and plant growth enhancement has been extensively explored independently; however, these traits have not been studied together in a single bacterial strain. In the present study, multifaceted Enterobacter cloacae MSK86 was isolated with these capabilities together, which may help in SDS remediation, making the water reusable for irrigation. (2025), (Slovak University of Agriculture). All Rights Reserved. -
Evaluating social media content's effect on consumer engagement in the context of digital marketing
The advancement of social media platforms in promoting consumer participation in brand development and sustainable consumption has been substantial. Social media's popularity has increased significantly in the twenty-first century. To enhance sales performance, enterprises consistently seek novel strategies to integrate these platforms into their promotional initiatives. Social media functions as a platform for networking and communication; consequently, organizations must imbue their brands with personality to connect with consumers. Despite extensive academic research on corporate social media marketing techniques, the influence of these activities on consumer purchase choices remains largely unexplored. Organizations have recently embraced influencer marketing as a tactic to promote and publicize their content by leveraging the support of influential individuals. The growing frequency of product endorsements on social media highlights the importance of understanding the impact that these influencers have on customers. This research aims to analyze the influence of social media content and its characteristics on consumer engagement in the digital domain. Additionally, this study will serve as a foundation for future investigations in this area. The insights regarding the content elements of social media marketing that foster consumer engagement were contributed by seventy-five unique social media users. 2025 by the authors; licensee Learning Gate. -
Data privacy in blockchain management scheme with Nudge Theory for banking sector
Blockchain is an emerging digital transformation technique for processing and storing information. The study explores how blockchain technology can transform the banking sector by improving efficiency, transparency, and security. The main goal is to understand how blockchain can modernize traditional banking operations and address key challenges such as fraud, high transaction costs, and slow processing times. The study uses a qualitative approach, drawing insights from existing research, real-world examples, and current trends in financial technology. Findings show that blockchain offers clear advantages, including faster and more secure transactions, reduced operational costs, and improved record-keeping. It holds strong potential in areas like payments, trade finance, and compliance. However, the paper also highlights significant obstacles such as unclear regulations, difficulties in integrating with existing systems, and technical limitations related to scalability and interoperability. Blockchain is seen as a promising solution for many of the inefficiencies in current banking practices. Still, successful implementation will require careful planning, regulatory support, and collaboration across the financial ecosystem. The study offers practical insights for banks, technology developers, and regulators, recommending a gradual and strategic approach to blockchain adoption to ensure long-term value and sustainability. 2025 by the authors; licensee Learning Gate. -
A Hybrid Deep-ensemble Decision-Support Framework for Reliable Early Breast Cancer Detection: A Cross-validated Outcome Analysis
OBJECTIVE The necessity to diagnose breast cancer early and correctly is the need to minimize the diagnostic uncertainty and unwarranted clinical procedures. This paper assesses the reliability of a hybrid deep-ensemble decision-support model in terms of diagnostic reliability, stability of outcome, and translational feasibility of the model via structured clinical data to detect early breast cancer. METHODS The Wisconsin Diagnostic Breast Cancer dataset which consisted of 569 cases of benign and malignant tumors was analyzed retrospectively. The framework proposed combines the deep learning of latent representations with stacked classification, ensemble-based feature selection, and stacked classification. Performance evaluation was performed based on sensitivity, specificity, accuracy, F1-score, and area under the curve (AUC) performed using stratified 10-fold cross-validation. The statistical stability across folds and the comparison with baseline models were determined with the help of non-parametric tests (p<0.05). RESULTS The model had good diagnostic performance with an accuracy of between 91.2-100 (Mean 96), Sensitivity of 76.2-100, good specificity value, and AUC 0.973-1.000. Variability in performance between folds was low, and statistically significant enhancement as compared to baseline classifiers were present. CONCLUSION The hybrid deep-ensemble model is highly diagnostic, has robust discriminative ability, and ultimately remains stable, which demonstrates the methodological robustness and diagnostic reliability of the proposed framework as a proof-of-concept decision-support model for early breast cancer detection, with potential translational relevance subject to further external clinical validation. 2026, Turkish Society for Radiation Oncology. -
MALL-Based Writing Instruction: Assessing the Effectiveness of Digital Platforms Among ESL Learners
Nowadays, mobile-assisted language learning (MALL) has emerged as a globally adopted approach that builds on the earlier development of computer-assisted language learning (CALL) by utilizing the accessibility and flexibility of mobile devices to promote independent and self-directed learning. It enables learners to extend lan guage practice beyond classroom boundaries and provides authentic opportunities to engage with English as a Second Language (ESL). This study investigates the potential of digital platforms, specifically WordPress and Hem ingwayEditor, in enhancing the writing skills of non-native English learners within a MALL framework. WordPress offers a collaborative digital space where learners can publish, share, and receive feedback on their writing, while Hemingway Editor provides real-time analytical feedback to improve readability, grammar, and stylistic accuracy. The research adopted a quantitative design with both control and experimental groups to examine the effective ness of these platforms. Participants included ESL learners who engaged in structured writing tasks, with their progress assessed through pre- and post-tests. The findings of the study reveal that learners using WordPress and Hemingway Editor demonstrated notable improvements in writing performance when compared to the control group. The integration of these tools not only improved grammatical accuracy and stylistic clarity but also encour aged active participation, reflection, and learner autonomy. The results emphasize the pedagogical value of incor porating MALL strategies into language instruction, particularly for developing essential writing skills among ESL learners. In conclusion, this research affirms that mobile technologies, when strategically integrated into teaching, significantly enhance learning outcomes and offer sustainable pathways for improving ESL writing proficiency. 2025, Digital Technologies Research and Applications. All rights reserved. -
Anti-epileptic medication induced disturbed calcium-vitamin D metabolism: A behavioral analysis using association rule mining technique
BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy, despite the growing recognition of the importance of bone health in individuals with epilepsy. Associations one statistical method for finding correlations between variables in big datasets is called association rule mining (ARM). This technique finds patterns of common items or events in the data set, including associations. Through the analysis of patient data, including demographics, genetic information, and reactions with previous treatments, ARM can identify harmful drug reactions, possible novel combinations of medicines, and trends which connect particular individual features to treatment outcomes. AIM To investigate the evidence on the effects of anti-epileptic drugs (AEDs) on calcium metabolism and supple-menting with vitamin D to help lower the likelihood of bone-related issues using ARM technique. METHODS ARM technique was used to analyze patients behavior on calcium metabolism, vitamin D and anti-epileptic medicines. Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study. There were three patient groups: Group 1 received one AED, group 2 received two AEDs, and group 3 received more than two AEDs. The researchers analyzed the alkaline phosphatase, ionized calcium, total calcium, phosphorus, vitamin D levels, or parathyroid hormone values. RESULTS A total of 150 patients, aged 12 years to 60 years, were studied, with 50 in each group (1, 2, and 3). 60% were men, this gender imbalance may affect the studys findings, as women have different bone metabolism dynamics influenced by hormonal variations, including menopause. The results may not fully capture the distinct effects of AEDs on female patients. A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs. 86 patients had generalized epilepsy, 64 partial. 42% of patients had AEDs for > 5 years. Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy. Polytherapy elevated alkaline phosphatase and phosphorus levels. CONCLUSION ARM revealed the possible effects of variables like age, gender, and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication. The Author(s) 2025. -
Implantable Chip Revolutionizing Early-Stage Liver Cancer Detection with Advanced Diagnosis System
Millions of people die from cancer annually. Advanced metastatic cancers may not respond to traditional therapy. The importance for early diagnosis is highlighted by the difficulty of treating cancers in later stages. Enhancing patient outcomes using tissue-engineered cancer diagnosis and therapy is gaining popularity. Cancer and associated immune problems burden healthcare systems, making efficient, high-throughput drug development strategies essential. Thus, implanted chips may solve these issues. A revolutionary technique for early liver cancer identification is the Machine Learning-based Liver Cancer Diagnosis System (ML-LCDS). K-Nearest Neighbour (KNN) identifies liver tumors precisely in ML-LCDS. The performance evaluation reports sensitivity=97.2%, specificity=91.3%, precision=93.5%, FPR=8.7%, and accuracy=94.1%, computed from the confusion matrix derived through 10-fold cross-validation. Experimental findings validate its consistent performance, establishing ML-LCDS as an efficient and reliable diagnostic tool for early-stage liver cancer detection. The Author(s) 2025. The text of this article is open access and licensed under a Creative Commons Attribution 4.0 International License. -
Enhancing Glaucoma Detection in Fundus Images: A ResNet based Segmentation and Advanced ML Algorithms with Duck Pack Optimizer
Untreated glaucoma, a chronic eye illness, can cause irreversible vision loss if not caught early. The condition begins with abnormalities in the eye's drainage flow, leading to a rise in intraocular pressure. As the disease progresses, the optic nerve head deteriorates, resulting in vision loss. Ophthalmologists need extensive training and expertise to interpret findings accurately during medical follow-ups to examine the retina. To address this challenge, deep learning-based algorithms have been developed to screen for and diagnose glaucoma using images of the optic nerve, retinal structures, and retinal fundus. This research explores the use of classification and segmentation algorithms based on ResNet to identify glaucoma in fundus images. We fine-tuned the classifier using the DuckPack optimizer and employed XGBoost, LightGBM, and CatBoost algorithms for classification. The results were promising. The segmentation model based on ResNet effectively extracted features, aiding the classification models in accurately identifying glaucoma. All three algorithms performed admirably, though further fine-tuning is needed to determine the best one. Enhancing the model's performance was straightforward after using the DuckPack optimizer for fine-tuning. This study highlights the promising applications of deep learning and sophisticated machine learning algorithms in glaucoma detection. Its findings could inform the development of future diagnostic tools. The Author(s) 2025. -
Narrating Trauma as Victims of Human Trafficking in China: A Study on Select North Korean Memoirs
The memoirs titled In Order to Live; A North Korean Girl's Journey, to Freedom and; A Thousand Miles to Freedom: My Escape from North Korea are written by Yeonmi Park and Eunsun Kim two women who managed to escape from North Korea. They went through an experience of being forced into labour in China as victims of trafficking. In their memoirs these authors vividly depict the pain that comes with being exploited. The main aim of this study is to analyse how memoirs can effectively address the issue of trafficking. These remarkable women skilfully use the memoir genre to make a personal plea for action. They strategically make choices appeal to readers emotions openly share their distressing experiences and support their stories with research and evidence that connect their experiences with the broader problem of human trafficking in China. This study clearly shows that both these memoirs emphasize the importance of the memoir genre in advocating for rights. It also highlights how survivor memoirs have the potential to inspire advocacy and involvement, in combating trafficking. 2025 Sciedu Press. All rights reserved. -
Silenced, Scarred & Shattered: Unmasking the Wounds of Child Sexual Abuse in Select American Memoirs
The research brings to light the marginalized voices of three American women who have written about their sexual abuse in their respective memoirs Roxane Gay, Hunger: A Memoir of my Body (2017), Nikki Dubose, Washed Away: From Darkness to Light (2016) and Neesha Arter Controlled: The worst Night of my Life and its Aftermath (2015). Using these memoirs as primary data and using thematic analysis the study identified three themes which were further classified into different subthemes. Firstly, the research discovered the challenges faced by the survivors in expressing and communicating about sexual abuse due to fear and shame, the survivors do not come forward because of threats, because of rape stereotypes that permeate the society and the fear of what parents and others might think. Secondly, the research explores the various impact of trauma that is caused by sexual abuse which include shame, guilt and self blame, unworthy self, uncontrollable rage, disruption of safety and trust, isolating themselves from everyone, hostility towards body, destructive behaviours which include eating disorder from Anorexia Nervosa to Binge eating disorder, it also includes self harm and substance abuse. Thirdly, the research focuses on the recovery aspect on how the survivors learn to live with the wounds caused by sexual abuse. It focuses on how the survivors came in terms with the abuse, the conflicting feelings of forgiveness and revenge and how they sought redemption through writing their journey. 2025 Sciedu Press. All rights reserved. -
An Effective IoT based Vein Recognition Using Convolutional Neural Networks and Soft Computing Techniques for Dorsal Vein Pattern Analysis
In this research, we provide a CNN-based system that can reliably identify the dorsal veins of the hand. In order to get better results on different picture quality datasets, the suggested model makes use of refined variants of the pre-trained VGG Net-16 and VGG Net-19 designs. We use the BOSPHORUS dataset, which provides medium-quality photos, in addition to two self-constructed datasets that provide good-and low-quality images. By using state-of-the-art augmenting image methods, streamlined pre-processing procedures, and meticulously designed CNN designs, the fine-tuned VGG Net-16 model achieves superior performance in comparison to all other models. Using ROI pictures with a resolution of 22424 pixels, a multi-class technique is employed for arranging the vein patterns. Improving data quality during training makes the approach more broad, which helps prevent over fitting. On every dataset, the proposed method achieves better results than standard ML models like K-NN and SVM, and the experimental outcomes demonstrate significant improvements in accuracy. The modifying process led to a considerable decrease in the equal error rates (EER) when compared to benchmark methods. The structure enhances efficiency in computing with GPU-accelerated studying. It was built with the help of Python extensions like as OpenCV, Keras, and TensorFlow. Results from extensive testing of the proposed method show an accuracy of 99.98%, a precision of 98.98%, and a recall of 98.8%. From what we can see, the technique is both adaptable and dependable; making it well suited for use in practical biometrics vein recognition applications. 2025, American Scientific Publishing Group (ASPG). All rights reserved. -
Significance of Suffering: A Neuroscience Perspective
Pain and suffering are inevitable realities of life. Not only do humans suffer from physical pain but animals too. Recently, the advent of the covid-19 pandemic has led to a global rise in suffering. The significance of physical pain and the emotional dimension of pain is long understood. Here we are trying to understand the significance of suffering pathway in the human brain. The recent advancement in neuroscience related to insights into pain perception, mirror neuron networks, suffering and compassion has created an appeal to revisit the pain and suffering from a contemporary neuroscience perspective. This article analyzes the benefits of suffering from an evolutionary and neuroscientific approach. Suffering affects people differently as some may become more compassionate and/or resilient while others develop depression. Here we are attempting to explain the underlying neural circuitry involved in suffering, empathy and compassion and to point out the interconnectedness among them. Subsequently, the article proposes a neuroscientific perspective to manage the emotional overdrive associated with suffering. 2025, Imprint Academic. All rights reserved. -
Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency
In the present era of data-driven organizational environment, the practice of Human Resource Management (HRM) has become increasingly reliant on intelligent Decision-Support Systems (DSS). This study develops a multifaceted two-pipeline model of Predictive Modelling (PM) and Sentiment Analysis (SA) to enhance workforce analytics capabilities. A publicly available HRM analytic dataset is used to train supervised classification models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), as well as an ensemble model that integrates these classifiers. These approaches use structured data to predict employee attrition based on features such as age, job role, experience, and job satisfaction. The unstructured textual data sources, including resumes and employee reviews, are handled using state-of-the-art Natural Language Processing (NLP) such as tokenization, Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations as Transformers (BERT)-based embeddings. The new Mathematically Modified Robustly Optimized BERT Pretraining (MM-RoBERTa) is proposed for extracting the PM and SA. All the models are evaluated using k-fold Cross-Validation (CV) and standard evaluation measures, namely Accuracy, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), and Mean Absolute Error (MAE). The ensemble model achieves a predictive accuracy of 91.3%, and MM-RoBERTa outperforms existing SA with an accuracy of 93.1 %. The combination of predictive and affective insights is of practical use in fine-tuning talent retention, empowering HRM professionals to make informed decisions based on objective performance indicators and subjective emotional states. 2025 The Authors. -
Ensuring Organizational Sustainability through HR Practices: Moderating Role of Leadership in the Banking Industry in the Context of SDGs
The study inspects the moderating role of leadership in the association between human resource (HR) practices and organizational sustainability, with a particular focus on Sustainable Development Goals (SDGs) 8 (Decent Work and Economic Growth) and 12 (Responsible Consumption and Production). It explores how leadership behaviors shape the effectiveness of HR practices in driving sustainability across economic, environmental, and social dimensions, while also situating these outcomes within the broader context of regional development and spatial planning. By analyzing the role of banks as institutional actors, the research highlights their contribution to financial inclusion, community well-being, and balanced urbanrural growth. A stratified random sample of 500 banking associates from urban, semi-urban, and rural branches was surveyed using a structured questionnaire, and data were analyzed through Structural Equation Modeling with SPSS and AMOS. HR practices, including recruitment, onboarding, performance management, compensation, and employee engagement, were assessed alongside leadership behaviors such as decision-making, resource allocation, empowerment, and vision. The findings indicate that leadership has a significant impact on the positive effects of HR practices on sustainability outcomes. In particular, leading by example and effective resource allocation emerge as strong moderators that advance SDG 8 and SDG 12. The findings underscore that sustainable HR leadership integration in banking not only improves organizational outcomes but also contributes to regional development and planning agendas by reinforcing equitable growth and sustainability across diverse spatial contexts. This study also situates banking institutions within the field of geography, planning, and development by showing how HR-leadership interactions contribute to territorial equity, financial inclusion, and spatial planning objectives. By linking organizational practices to regional sustainability trajectories, the findings highlight banks as critical institutional actors in advancing balanced urbanrural development. 2025, Green Publication. All rights reserved.