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The Impact of International Remittances on Public Debt Sustainability in Kerala: Evidence from the FMOLS Approach
Remittances from Keralas migrant workers and professionals constitute a vital financial inflow, surpassing their relative significance in most other Indian states. With public debt nearing 60 % of NSDP, these transfers offer a potential buffer for debt sustainability. Applying Oates theory within Bohns debt-sustainability framework using an FMOLS model, and validated through Johansen testing, this study examines the remittancedebt nexus from 1980 to 2023. FMOLS results indicate a deterioration of debt sustainability despite substantial remittance inflows. The findings emphasize the need to reduce market borrowings, enhance the fiscal role of remittances, and promote policies supporting formal transfer channels, source diversification, remittance-linked investment, and financial literacy among migrant households to strengthen Keralas long-term fiscal resilience and enable more productive use of remittances. 2025 the author(s), published by De Gruyter. -
Memes as ensemble of illocutionary acts
As digital communication continues to shape discourse, memes have emerged as a potent tool for conveying messages. Previous studies on internet memes have focused on various aspects such as humor generation, speech acts, and political communication. Although there are studies on speech acts and memes, research specifically examining speech acts within subcultures is scarce. This paper aims to fill that gap by examining the illocutionary acts in political memes within a subculture. To understand how illocutionary acts function in memes, 50 political memes that appeared during the Kerala state assembly election were analyzed using the framework of speech acts. The analysis revealed that memes often contain multiple illocutionary acts. Additionally, it was observed that a single meme can encompass several illocutionary acts simultaneously. This study highlights the complexity and richness of political memes as a form of communication within subcultures, demonstrating how they can convey layered and multifaceted messages through the use of illocutionary acts. 2025 the author(s), -
Sonographic measurement of placental volume: a comparative analysis of two-dimensional and three-dimensional sonographic techniques
To evaluate the correlation between a simple two-dimensional (2D) sonographic method and the gold-standard three-dimensional (3D) method for estimating placental volume during early to mid-pregnancy. Placental volume was measured using both 2D and 3D ultrasound techniques in 58 pregnant patients between 11 and 22 weeks of gestation. All participants had normal term pregnancy outcomes. The correlation between the two methods was analyzed using Pearson's correlation coefficient. A strong positive correlation was observed between the 2D and 3D placental volume estimates (Pearson's r=0.93, p<0.001), indicating high agreement between the two measurement approaches. The simpler 2D sonographic method shows excellent correlation with the 3D gold-standard technique and may serve as a feasible alternative for placental volume assessment, particularly in low-resource settings where access to advanced equipment is limited. 2026 the author(s) -
Maternal circulating sFlt-1/placental growth factor is a biomarker of fetal death associated with placental lesions of maternal vascular malperfusion
Objectives: Fetal death is a major pregnancy complication, with rates of 5.5 per 1,000 births in the United States and substantially higher in India (24.7/1,000) and Pakistan (44.5/1,000). Maternal vascular malperfusion (MVM) is the most frequent placental lesion associated with fetal death, occurring in 58 % of fetal deaths and 31 % of preterm neonatal deaths in South Asia. Angiogenic imbalance, characterized by a low placental growth factor (PlGF) to soluble fms-like tyrosine kinase-1 (sFlt-1) ratio, has been associated with MVM and fetal death in high-income countries. We examined whether maternal serum concentrations of PlGF, sFlt-1, and their ratio differ between mothers with and without MVM among stillbirths and preterm neonatal deaths in India and Pakistan. Methods: This retrospective cohort analysis used data from the PURPOSe study (Project to Understand and Research Preterm Pregnancy Outcomes and Stillbirths in South Asia). Maternal blood was collected at delivery, and placental histopathology was classified according to the Amsterdam criteria. Serum PlGF and sFlt-1 were measured using Elecsys immunoassays, with analyses stratified by gestational age. Results: Placental MVM was present in 44-57 % of stillbirths and 31-38 % of preterm neonatal deaths. Between 28 and 36 weeks, women with MVM had significantly lower PlGF and higher sFlt-1 and sFlt-1/PlGF ratios (p<0.001). A tenfold decrease in PlGF or increase in the ratio was associated with MVM (OR 0.5 and 1.7, respectively). Conclusions: The maternal sFlt-1/PlGF ratio identifies pregnancies with fetal or neonatal death associated with placental MVM, particularly between 28 and 36 weeks' gestation. 2025 the author(s) -
The Paradigm of Party Autonomy: Towards a Viable AI-Driven Pre-Arbitration Framework in India
Arbitration proceedings in India are adversely affected by multiple adjournments and procedural uncertainties in the courts. To ensure limited judicial intervention, certain procedural tasks can be automated via artificial intelligence (AI) to improve the efficiency of the arbitration process. The objective of the paper is to examine the current state of the Indian judicial framework and assess the procedural feasibility of incorporating AI to address arbitration-related limitations. The research employs a thematic review of the literature to assess the use of AI in dispute resolution proceedings. In addition, using a comparative research methodology, the study critically analyzes the technological and policy-related initiatives taken by both India and China in their respective judicial systems. Findings suggest that reliance on the party autonomy principle in arbitration is a solution to the limitations that disincentivize governments from implementing AI-powered automation in dispute resolution. The paper, therefore, recommends an integrative approach to AI technology that partially automates arbitration proceedings in the courts. 2026 Walter de Gruyter GmbH, Berlin/Boston 2026. -
Intelligent predictive hiring model and personality assessment
Selecting the right candidates is essential for organisational success, yet traditional hiring methods often fall short. This research introduces an advanced approach integrating natural language processing (NLP), personality assessment, and deep learning to improve candidate selection. NLP extracts key attributes from job descriptions and resumes, while personality assessments evaluate candidate suitability. A fusion of LSTM and RNN models predicts job fit using a dataset of job roles, resumes, and MBTI personality types. Pre-processing includes tokenisation, encoding, and data splitting for training and testing. The model architecture combines embedding layers with LSTM units, optimised using binary cross-entropy loss and accuracy metrics. Results show that this fusion model outperforms traditional algorithms, improving job matching accuracy. This research enhances recruitment by leveraging AI-driven insights. Future work will refine predictive models, integrate additional data, and address ethical concerns to ensure fairness and transparency, fostering a more efficient and equitable hiring process. Copyright 2026 Inderscience Enterprises Ltd. -
Relationship between psychological contract and organisational commitment: an empirical investigation on airline cabin crew
The study explores the link between organisational commitment (OCO) and psychological contract (PCO) among airline cabin crews. The study also investigates the moderation effect of the type of organisation and work experience in the relationship between PCO and OCO. A theoretical model with hypothesised relationship is developed and tested empirically using structural equation modelling with the data generated by means of a questionnaire survey conducted among the airline cabin crew. The study finds that: 1) There is a direct connection between PCO and OCO in the case of both employee PCO and employer PCO; 2) type of the organisation moderates the relation between: a) employers PCO and employees PCO; b) employers PCO and OCO. We also found that the relationship between the PCO and OCO is moderated by the organisation type in the case of cabin crew working for private airlines but not for government-owned airlines. Since a favourable PCO is favourably correlated with OCO, the studys findings would assist airline operators in identifying the critical PCO dimensions that influence their OCO and helping them implement appropriate steps to increase their commitment to their staff. Copyright 2025 Inderscience Enterprises Ltd. -
How tourist motivations shape perceptions of service quality at pilgrimage sites
Previous research has not adequately examined how various tourist motivations affect perceived service quality at pilgrimage destinations. This study seeks to investigate the effect of various motives religious pilgrimage, votive offerings, leisure, and meditation on service quality perceptions at seven Jyotirlinga pilgrimage destinations in North India. A cross-sectional survey of 1047 visitors was carried out, and data were analysed through one-way ANOVA to determine significant differences between visitor groups. For multiple comparisons, Bonferroni and Games-Howell post hoc tests were used depending on the homogeneity of variances. The results show differences in service quality perceptions, specifically in desired facilities, safety and security, and transportation. Pilgrims interested in religious devotion emphasised safety, whereas leisure travellers gave more importance to the quality of facilities available and transportation. These findings have practical implications for pilgrimage site management, highlighting the importance of making targeted improvements in service delivery to meet the expectations of various visitor segments. Copyright 2025 Inderscience Enterprises Ltd. -
Regulatory data protection for global economy of biopharmaceuticals: comparative legal analysis with focus on innovative biopharma in India
This provides a new global economy of biopharmaceuticals with an exclusive right over clinical data, meaning that no other person or persons may use them for a specified period. This study, therefore, offers a critical analysis of complementary protection granted to biopharmaceuticals by patents and regulatory data protection (RDP) globally with respect to innovation, competition, and access to medicines. This study probes the effectiveness and the challenge RDP is making using statistical analysis, financial modelling, and comparative analysis of the regulatory framework in Central Drugs Standard Control Organization (CDSCO), Food and Drug Administration (FDA), and Emergency Market Authorization (EMA). The justification for this combination is that RDP fosters innovation due to the protection of clinical trial investments, which provides a drive for the introduction of innovative biologics but does not inhibit the launch of biosimilars. With RDP, though they are very different in what they do, patents have created an enabling environment to make sustainable innovation in biopharmaceuticals accessible. International regulatory hurdles have to emerge so that a balance that advances both innovation and affordability becomes the norm within biopharma. Copyright 2025 Inderscience Enterprises Ltd. -
The influence of sustainability risk management on supply chain sustainability and profitability of medical technology firms
The primary objective of this study is to determine how sustainability spending can help build sustainable supply chains. This study also assesses the sustainability of five firms with respect to four dimensions; profitability, sustainability spending, risk rating, and sustainability risk management. Data was collected through unstructured interviews with biotechnology and medical technology industry personnel. Key sustainability risk factors that created a ripple effect along the supply chain were identified. The supply chain surplus and risk management strategies were evaluated. Technique for order of preference by similarity to ideal solution (TOPSIS) was used to rank the firms on their sustainability and risk management practices. The critical sustainability risks identified are inventory management, logistics/ transportation, waste management, energy consumption, supplier sustainability, material handling, and plant or facility management. Findings reveal that firms investing in sustainability risk management face better prospects of developing supply chain sustainability and profitability. Copyright 2025 Inderscience Enterprises Ltd. -
Quantum technologies outreach and AI
Quantum computing is one of the buzzing technologies in this modern computational era. Quantum computing is purely based on quantum mechanics as few dormant applications and advanced studies of quantum computers are integrated with quantum mechanics. This paper highlights the seven perspectives of quantum computing which is essential to get deep insights of quantum computing. The seven prerequisites are superposition, decoherence, entanglement, linear algebra, classical mechanics, quantum Fourier analysis and many body systems. The main objective of this paper is to find few stupendous impacts of this computing which will complement and explore various applications of artificial intelligence like weather pattern identification, traffic prediction, e-mail spam filtering, logistics optimisation, etc. This paper discusses visions of the top quantum computing companies and their contributions to quantum technologies. In this paper, a comparative analysis has been presented between quantum computing and classical computing. The major challenges which quantum computing faces have been addressed. Copyright 2025 Inderscience Enterprises Ltd. -
Improved Indian currency recognition: neighbourhood-centred image processing and CNNs with region of pixel selection techniques
The paper proposes an improved approach for Indian currency recognition using neighbourhood-centred image processing and convolutional neural networks (CNNs) with region of pixel selection techniques. The method includes image pre-processing steps such as noise reduction, contrast enhancement, and resizing. A neighbourhood-centred image processing technique is applied to capture contextual information from local neighbourhoods around each pixel. A CNN-based model is then trained on the pre-processed images to learn discriminative features for currency recognition. To enhance accuracy and efficiency, a region of pixel selection technique is introduced to select only relevant regions of interest for CNN training and inference, reducing computational overhead. Experimental results demonstrate the effectiveness of the proposed approach, achieving high accuracy in currency recognition and improved efficiency in terms of computational time and memory requirements. The proposed method has potential applications in automated cash-handling machines, vending machines, and mobile payment systems where reliable currency recognition is essential. Copyright 2025 Inderscience Enterprises Ltd. -
Eco-market dynamics: deciphering Indias agricultural pricing in the context of carbon emissions and inflation
This study explores the intricate relationship between carbon emissions, agricultural commodity prices, and inflation in India. Using monthly data from January 2014 to March 2022 and structural vector auto regression (SVAR) modelling, the analysis reveals diverse dynamics among key; commodities. An inverse relationship is found between wheat prices and inflation, suggesting consumer benefits. Turmeric shows a strong negative correlation, indicating unique market behaviour, while refined soybean oil and cotton prices exhibit minimal negative impacts. In contrast, crude palm oil prices positively influence inflation. A noteworthy finding is the negative correlation between carbon emissions and inflation, highlighting the environmental-economic linkage. These insights enhance understanding of how specific agricultural prices interact with inflation, and how environmental variables play a role. The findings can guide evidence-based policies for agricultural stability, environmental sustainability, and economic growth in India. The implications extend globally, offering valuable insights for developing economies facing similar challenges. Copyright 2025 Inderscience Enterprises Ltd. -
AMAA-GMM: adaptive Mexican axolotl algorithm based enhanced Gaussian mixture model to segment the cervigram images
Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance. Before segmenting the cervical region, specular reflection removal is an efficient approach. Cervical cancer can be found using a visual check with acetic acid that turns precancerous and cancerous areas white and these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-white areas and should therefore be removed. So, in this paper, specular reflection removal with segmenting the cervix region in a colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican axolotl optimisation (AMAO) algorithm. The performance of the proposed approach is analysed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. Copyright 2026 Inderscience Enterprises Ltd. -
Cyberdeviance among students a multidimensional scaling approach
Cyberdeviance off late has been gaining a lot of attention because of the increased use. Educational institutions have also made the internet available to its student to improve their exposure to various educational information. Hence, it becomes essential to identify a model that helps understand college factors to cyberdivert. The study focused on assessing whether college students are involved in cyberdeviation and the demographic effect on internet behaviour and cyberdeviance. The multidimensional approach was used to understand cyberdeviance. Data were collected from 264 students using convenience sampling in Bengaluru city, India. The study found that the respondents prefer to use the internet mainly for games and prefer least for theft, harassment, adult content, and hacking. They misused the internet due to the fear of unemployment and were involved in internet fraud to deploy knowledge. Copyright 2025 Inderscience Enterprises Ltd. -
Privacy over instant messaging platforms: are users making the right decisions?
This article explores the impact of perceived vulnerability, self-efficacy, resistance to change, and habit on users perception of privacy over users intention to use messaging platforms. The conceptual model includes perceived vulnerability, self-efficacy, resistance to change, habit, and its impact on users perception of privacy over users intention to use messaging platforms. A structural equation and hierarchical regression model were used for data analysis. The results show that age and profession affect peoples decision of shifting to a different platform significantly. The study is based on a few specific instant messaging platforms at one particular point in time and is undertaken in India; hence, the findings cannot be extended/applicable to other countries. The paper discusses the factors impacting the users sensitivity to data privacy while using a communication application through an electronic device, especially a mobile phone. Copyright 2025 Inderscience Enterprises Ltd. -
Comparative study of benchmarking models for higher education institutions
Benchmarking is a systematic and ongoing process of assessing an organisations business processes against those of business process leaders to obtain data that will enable the firm to take corrective action to enhance performance (Pattison, 1993). Eight benchmarking models, namely the European Foundation for Quality Management (EFQM) excellence model, American Productivity and Quality Centre (APQC) consortium framework, Commonwealth Higher Education Management Service (CHEMS) model, Mckinnon model, Henderson-Smart et al. model, educational development efficiency (EDE) model, Tee benchmarking model, and fourth generation balanced scorecard method are being studied, analysed, evaluated and compared. While most models effectiveness depends on the cooperation and participation of benchmarking partners, few depends on secondary data are an exception. Most benchmarking models lack the implementation and are fluid and flexible models. This comparative benchmarking study helps an institution understand which benchmarking model needs to be used, as the study details each models essential features, advantages, and limitations. Copyright 2025 Inderscience Enterprises Ltd. -
Detection and classification of lung cancer using deep neural network
Lung cancers hold a critical spot among the reasons for most cancer deaths among humans. The better way to maximise the survival rate is the detection of cancer at the earliest. But existing traditional techniques are time-consuming and error-prone. This study is a significant and efficient method for the detection and classification of lung cancer into large cell carcinomas, small cell, adenocarcinoma, squamous cell carcinomas, or benign respectively. In the proposed technique, a novel algorithm is implemented to generate the Image patches from whole slide histopathological images. Then, histogram normalisation is carried out to remove noise and enhance the image. Then a novel extended Mobius transformation technique is used for image augmentation. Finally, Dense EfficientNetB7 is trained to extract the features for the detection and classification of lung cancer. The performance of the proposed technique is proved more efficient and par with histologists attaining an accuracy of 98.87%. Copyright 2025 Inderscience Enterprises Ltd. -
Exploring the impact of influencer marketing strategies on sustainability in the fashion industry
Social medias explosive expansion has forced firms to rethink their marketing tactics to communicate with a wider range of customers by providing value and enabling two-way dialogue. Influencers may contribute to increasing brand awareness and giving value to companies when they work with brands and the appropriate target audience. This study aims to evaluate the influence of source credibility dimensions such as trustworthiness, attractiveness, and perceived expertise on consumer attitudes toward fashion influencers and to assess how these attitudes impact consumers intentions to make purchases and provide recommendations. Also, determine the direct impacts of source credibility on these purchase and recommendation intentions. The research includes 342 individuals who follow a famous fashion influencer in India by using the convenient sampling method. Hierarchical regression analysis has been performed on data using SPSS. The outcome of the study shows the effect of trustworthiness and perceived expertise on attitudes toward influencers in the fashion industry. Copyright 2025 Inderscience Enterprises Ltd. -
Adoption barriers of blockchain technology in Indian automotive supply chain: an MCDM approach
The Indian automobile industry faces intense competition from international firms, making technological upgrades essential. Blockchain, known for powering cryptocurrencies, offers a transparent, immutable, and decentralised database beneficial for supply chains. Despite its potential and widespread use in other sectors, the Indian automobile industry has been slow to adopt it. This paper examines the barriers to blockchain adoption in this sector using Delphi and DEMATEL techniques. The study reveals that trust-building among partners and collaboration challenges due to blockchains complexity are the primary obstacles hindering adoption. These barriers make it difficult for firms to decide on implementing blockchain technology in their supply chains, with other obstacles being secondary but interconnected. Overcoming these obstacles requires transforming company cultures, establishing efficient governance systems, and ensuring transparent data disclosure. Governments can support this by stimulating innovation through legislation and creating blockchain sandboxes for safe testing, helping to develop standards with organisations like ISO and IEEE. 2025 Inderscience Publishers. All rights reserved.