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A study on socio-economics impact of remittances on forward migrants household of the Tibetan refugees in India /
Migration and Development is an agenda of every country’s economic policy in recent time. Migration has been linked to the flow of remittances influencing socio-economic development particularly of developing countries. Studies on remittances have also reflected its positive side having potential effect at all levels including micro (households), macro (country) and meso (community) levels. The existing literature on remittance manifested the prominent role of remittance in enhancing livelihood of receiving households. Empirical study conducted on developing economies concluded that households receiving remittances are better off than those of non-receiving households. -
A study on socio-economic impact of remittances on forward migrants household of tibetan refugees in india
Migration and Development is an agenda of every country s economic policy in recent time. Migration has been linked to the flow of remittances influencing socio-economic development particularly of developing countries. Studies on remittances have also reflected its positive side having potential effect at all levels including micro (households), macro (country) and meso newline(community) levels.The existing literature on remittance manifested the prominent role of remittance in enhancing livelihood of receiving households. Empirical study conducted on developing economies concluded that households receiving remittances are better off than those of non-receiving newlinehouseholds. International remittance has a direct role on household s economy by raising newlinehousehold s standard of living. Remittances were used for household consumption activities including education, health, housing, accumulating assets leading to human capital development. Likewise, literature pointed out the potential role of remittance inducing investment in business and entrepreneurship development by employing households in becoming self-reliant. Further, remittance improves trust and network within households and community which indirectly helps poor in the community. Thus, it is evident from the previous literature that newlineremittances have enhanced human and financial and social capital development. However, the existing literature lacks information on remittance affecting livelihood in Tibetan newlinecontext. Hence, there is a need of in-depth study in this area of research which is latent and unexplored. In this study, it has made an attempt to understand the role of remittance on Tibetan refugee communities in India who rely on remittance as one of the major sources of income. The study focuses on the impact of remittances from forward migrants who migrated from India towards newlinewestern and European countries. They send remittances back home leading to socio-economic development in the country of origin. -
Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
Recent collaborations in medical diagnostic systems are based on data private collaborative learning using Federated Learning (FL). In this approach, multiple organizations train a machine-learning model at the same time eventually leading to global model generation. This paper reviews the fundamentals of FL and its evolution path in Healthcare. The objective of this review is to scope a wide variety of healthcare applications in FL. Exactly what research direction is moving in interesting for research communities to guide their future course. This review uniquely focuses on examining numerous FL-based healthcare implementations, detailing their core methodologies and performance metrics, which, to our knowledge, have not been previously available. Privacy-preserving collaborative distributed learning through federated learning in healthcare enhances research collaborations, thereby resulting in better-performing models. This comprehensive review will act as a valuable reference for researchers exploring new FL applications in the healthcare domain. 2024 IEEE. -
Privacy-Preserving Federated Learning for Prognostic Modeling in Rare Diseases: A Scalable Case Study on Kawasaki Disease
Predictive modeling in rare diseases faces major challenges, including data scarcity, class imbalance, and strict privacy regulations that limit cross-border collaboration. These challenges are particularly critical in Kawasaki disease (KD)a rare vasculitis in childrenwhere 10% to 20% of patients are resistant to intravenous immunoglobulin (IVIG), the standard first-line treatment. This significantly increases the risk of coronary artery abnormalities (CAA), making early and accurate prediction of resistance to IVIG essential for improving patient outcomes. Our work proposes a federated learning (FL) approach to address the constraints imposed by security and privacy concerns. We investigate convolutional neural networks (CNN) as the shared model, collaboratively trained across clients. Coupled with strategies to address class imbalance resulting from the rarity of the condition, the federated approach yielded promising results when evaluated against conventional machine learning (ML) models. The proposed approach demonstrated strong performance, achieving 94% accuracy, 93% precision, 89% recall, and 91% F1 score. To ensure robustness and generalizability, an independent dataset was also used, where the proposed model excelled similarly. These results highlight the potential of FL to overcome data privacy barriers and provide a scalable, secure solution for predictive modeling in rare diseases, supporting its integration into medical prediction workflows. 2025 by the authors of this article. -
Federated Learning with Adaptive Intermediate Model Selection for Predicting IVIG Resistance in Kawasaki Disease
Kawasaki disease (KD), a rare pediatric illness affecting children under five, is treated with intravenous immunoglobulin (IVIG). But 1020% of patients are resistant to IVIG, and these resistant kids face a higher risk of coronary artery abnormalities. Identifying resistance early is vital, yet data scarcity, class imbalance, and the diseases rarity necessitate nationwide collaboration, which is often hindered by country-specific privacy policies. Federated learning (FL) provides a practical way for different parties to collaborate on training a model while keeping their raw data private and secure. To enhance model adaptability across diverse clinical populations, we propose an adaptive intermediate model selection strategy in federated learning. Each client retains the versionglobal or locally fine-tunedthat performs best on its own data, using customizable performance metrics such as F1-score or recall. The system was implemented using the Flower FL framework, with three simulated clients and a shared convolutional neural network (CNN) architecture. Experiments demonstrated that the global model achieved stronger performance than conventional models, and several clients obtained further gains by selecting intermediate models aligned with their data. This approach introduces a novel balance between worldwide collaboration and local personalization in FL, offering a flexible and clinically meaningful solution for IVIG resistance prediction. 2026 by the authors of this article. -
Privacy-Preserving Federated Learning: Foundations andAlgorithmic Directions
Federated Learning (FL) stands at the forefront of decentralized machine learning, revolutionizing collaborative model training among distributed devices while maintaining stringent privacy standards. FL requires multiple algorithms to handle issues with model initialization, synchronization, and convergence in remote environments. This paper comprehensively examines FL algorithms, focusing on pivotal techniques such as client-side training, server-side aggregation, and FedAvg. Detailed analysis elucidates these algorithms intricate workings, showcasing how they harmonize the aggregation of local model updates with global parameter refinement, thereby striking a delicate equilibrium between privacy preservation and model accuracy. The foundations of FL and the specifics of its sophisticated algorithms are covered in this study. By providing researchers with a roadmap for delving into FL algorithm development, this paper catalyzes unlocking new avenues of innovation and advancing the frontiers of privacy-preserving machine learning. For experimental learning, the federated learning implementation is carried out using the Flower framework on the well-known iris flower classification problem, with performance metrics thoroughly evaluated. Moreover, this paper represents, to our knowledge, the first work that extends the algorithmic directions presented in a review paper with detailed implementation on a sample problem, further encouraging exploration of various algorithms in FL implementation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Titania Doped CDs as Effective CT-DNA Binders: A Novel Fluorescent Probe via Green Synthesis
Carbon dots (CDs), which belong to the class of zero-dimensional carbon-based nanomaterials, have garnered significant interest owing to their wide array of applications spanning from the electronics industry to the healthcare sector. This work employs a facile, inexpensive approach to synthesize green luminescent carbon dots (J-10) from a potential medicinal plant named Justicia Wynaadensis by the one-step hydrothermal method. A nanocomposite (JT-10) of the CDs is prepared by adding TiO2 nanoparticles derived from green synthesis of Lavandula leaves. The J-10 and JT-10 are further characterized by X-ray Diffraction spectroscopy (XRD), Transmission Electron Microscopy (TEM), Raman analysis X-ray Photoelectron Spectroscopy (XPS), and Fourier transform infrared techniques (FTIR), UVvis spectroscopy, Photoluminescence (PL), and Fluorescence or PL lifetime analysis. The average size of synthesized CDs is 1.85 nm and exhibits an excitation-dependent fluorescence nature at 320 nm. PL lifetime analysis of J-10 and JT-10 is calculated to be 5.80 and 2.84 ns respectively. Offering these unique optical properties and biocompatibility, the synthesised material is suitable for investigating their binding affinity and interaction mechanisms with DNA. The use of JT-10 in DNA binding studies contributes to the development of sustainable and efficient nanomaterials for applications in biosensors, drug delivery, and gene therapy. 2024 Wiley-VCH GmbH. -
Situating censorship: A study of the politics of state and self in literary translations in Iran /
A nation’s culture flourishes by interacting with other cultures” (Razmjou). Cultural variety not only enriches our knowledge, but also acts as a guide towards the growth of a nation. It gives an insight about the basic human right practices of different cultures. The standard of culture of any particular nation can be gauged through various tools, but importance is given to literature, as it acts as a barometer to measure the cultural growth of the nation. To interpret any culture it is important to understand the beginnings of that particular culture and make an in-depth study of the progress of its civilization. The cultural evolution of any place is continuous and is a combination of many factors like geographic location, weather conditions prevalent, suitable food crops grown, its political policies, religious influences, its history and its present circumstances. The above factors directly or indirectly become responsible to blend and give shape to a culture. -
Deep Learning-Based Signal Detection Techniques for Real-Time Communication in Fading Channels
Dependable signal detection has also been a major concern in real-time wireless communication especially in the case of fading channels that cause non-adaptive distortion and deteriorate the overall performance drastically. The conventional detection meth-ods, like the maximum likelihood detection, are not always adaptive in the circumstances of dynamic and therefore unpredictable channel conditions, and particularly in the cases when the statistical profiles are unknown or vary too quickly. In order to address these shortcomings, the papers introduce a new paradigm of deep learning signal detection trained to learn hierarchies and temporal patterns of raw received signals, which by their pas integrate convolutional neural networks (CNN) and recurrent neural networks (RNN). The trained architecture is end-to-end that is able to map the noisy distorted inputs to their symbols which are inherently transmitted in the context of channel state informa-tion. Heavy simulation over Rayleigh and Rician fading channels with different Doppler spreads and SNR values shows that the suggested approach shows substantial improve-ment over the traditional maximum likelihood and classical machine learning-based detec-tors regarding bit error rate (BER), inference latency and computational overhead. Such results emphasize the performance as well as the flexibility of deep learning model in very dynamic propagation conditions. On the whole, this paper draws the conclusion that deep learning is a perspective direction to solve the problem of real-time detection of a signal in next-generation wireless networks, such as a 6G or IoT edge setup. 2025, Society for Communication and Computer Technologies. All rights reserved. -
Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Residual-based MEWMA control charts in the presence of multicollinearity
Statistical Process Control has been performing a critical role in attaining quality assurance from historic times to the modern era. Examining and governing the process variables involves rigorous stages and several control charts. The multivariate process is considered for a more comprehensive understanding of handling multiple correlated variables of the process. The study here focuses on the unique creation and deployment of residual-based Multivariate Exponentially Weighted Moving Average control charts in the presence of multicollinearity, specially constructed and evaluated for Phase I and Phase II. The chart offers a reliable framework for understanding shifts in multivariate processes across time from minor to moderate changes in process parameters. Agro-Economy data of Indian States for the years 2019 and 2020 are utilized in an application example. The proposed residual-based MEWMA control charts detect out-of-control circumstances with few false alarms and this is critical for rapid interventions, resulting in optimal crop management and production. 2025, Prince of Songkla University. All rights reserved. -
Biocompatible Sodium Alginate Modified BaO2H2O2 Nanoparticles With Improved Therapeutic Efficacy Against Multidrug-Resistant Pathogens and Cancer Cells
The increasing problem of multidrug-resistant pathogens and the limitations of conventional therapies for cancer treatments require designing new therapeutic agents. BaO2H2O2 and BaSA nanoparticles were prepared and characterized to determine their antimicrobial, antifungal, and anticancer activities. The XRD confirmed the crystallite sizes to be 34 nm for BaO2H2O2 and 25 nm for BaSA. The UVvisible analysis confirmed the band gap energies as 4.13 and 4.11 eV for BaO2H2O2 and BaSA, respectively. A shift in the blue-green PL emission from 488510 nm in BaO2H2O2 to 535 nm in BaSA indicated increased oxygen vacancies. EDAX analysis demonstrated elemental variations due to SA modification, whereas DLS measurements showed a decrease in the mean size of the nanoparticles from 116.70 nm (BaO2H2O2) to 111.90 nm (BaSA). Antimicrobial activity was shown against Klebsiella pneumoniae, Shigella dysenteriae, Escherichia coli, Pseudomonas aeruginosa, and Proteus vulgaris, while a considerable enhancement of antifungal activity against Candida albicans was observed in BaSA. Against MG-63 osteosarcoma cells, BaSA exhibited lower IC50 values (21.5, 20.2, 18.7 ?g mL?1 at 24, 48, and 72 h) when compared with BaO2H2O2 (23.4, 22.5, 21.3 ?g mL?1). Zebrafish embryos tolerated BaSA at 0.5 mg mL?1, with developmental abnormalities observed only at 1.0 mg mL?1. 2025 John Wiley & Sons Ltd. -
Economic Insights: The Computational Intelligence Perspective on Finance
Using technological advancements and shifting risk landscapes as a driving force, this abstract investigates the revolutionary approaches that have reshaped risk mitigation in contemporary contexts. Introducing a new era of proactive risk management has been made possible by the combination of artificial intelligence (AI), machine learning (ML), and predictive analytics. Organizations are able to recognize patterns and anticipate potential risks with an accuracy that has never been seen before, thanks to these technologies, which analyze vast datasets. By extracting valuable insights from unstructured data sources, natural language processing (NLP) and sentiment analysis broaden the scope of risk assessment with their respective capabilities. Blockchain technology improves both transparency and security, particularly in the realm of financial transactions, thereby lowering the likelihood of fraudulent activity. Cloud computing makes dynamic risk modeling easier to accomplish, which in turn makes it possible to simulate real-time scenarios. The cumulative effect of these innovations not only improves the efficiency of risk reduction, but it also helps organizations develop risk management frameworks that are more agile and resilient. When navigating the complexities of a risk landscape that is constantly shifting, it is essential to strike a balance between technological advancements, ethical considerations, and transparency. 2026 by Apple Academic Press, Inc. -
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Thermorheological effect on RayleighBard magnetoconvection in a biviscous Bingham fluid with rough boundary condition on velocity and Robin boundary condition on temperature
The thermorheological effect on the onset of RayleighBard convection in a biviscous Bingham fluid in the presence of a horizontal magnetic field is investigated considering rough boundary conditions on velocity and Robin boundary conditions on temperature. The viscosity of the electrically conducting fluid is assumed to be sensitive to temperature variation. Linear and global nonlinear stability analyses are performed using the Chebyshev pseudospectral method to determine the existence of instability or otherwise. A general interpretation is made from the results to show the effects of the magnetic field and the variable viscosity on the system's stability. The biviscous Bingham parameter and the Chandrasekhar number are shown to have a delay in the onset of convection, while the effect of temperature sensitivity is to advance the onset. It is found that the results of linear and global nonlinear stability are not in agreement, so the region of subcritical instability exists. Also, the results obtained for RayleighBard convection agree pretty well with those of Platten and Legros and Siddheshwar et al. for the limiting cases. 2023 Wiley Periodicals LLC. -
Study of Influence of Combustion on DarcyBard Convection with Inherent Local Thermal Non-equilibrium Between Phases
This work deals with a DarcyBard convection problem in the presence of combustion and with local thermal non-equilibrium between the fluid and the solid phases. The effects of combustion and local thermal non-equilibrium on the onset of convection is studied in the linear and nonlinear regimes. Unlike all reported local thermal non-equilibrium problems reported so far, the present problem has a unique situation of having thermal non-equilibrium not only in the perturbed state but also in the basic state. Further, we observe that local thermal non-equilibrium does not, under any circumstance, lead to local thermal equilibrium except in an approximate sense when the combustion is quite weak. The effect of combustion is to advance the onset of convection compared to that in its absence. The effect of local thermal non-equilibrium is to reinforce the effect of combustion. In the presence of both these effects, sub-critical instability exists. The results are obtained numerically and have implication in practical porous medium convection problems. 2022, The Author(s), under exclusive licence to Springer Nature B.V. -
The impact of feedback mechanisms on RayleighBard penetrative convection in micro-polar fluids
This study examines the effects of feedback control and internal heat sources on the onset criterion of RayleighBard convection (RBC) in a horizontal Boussinesq micropolar fluid layer. A linear stability analysis, employing the Chebyshev pseudospectral method, is conducted to compute the eigenvalues and assess the stability of the system under varying conditions. The analysis considers several parameters, including heat conduction, coupling, couple stress, scalar controller gain, and internal heat sources. The findings reveal that the introduction of internal heat sources destabilizes the system, while the scalar controller gain significantly delays the onset of convection, thereby enhancing system stability. Additionally, it is demonstrated that an increase in both the coupling and heat conduction parameters contributes positively to system stabilization, whereas an increase in the couple stress parameter hastens the onset of convection. Notably, the investigation indicates that the system demonstrates greater stability when the boundary is heated from above as opposed to from below. These results provide crucial insights for the control of heat transfer in micropolar fluids and suggest that optimizing the scalar controller gain, along with careful tuning of other system parameters, can significantly enhance stability. The implications of this research are substantial for the design of efficient fluid dynamical systems, particularly in scenarios requiring precise control over temperature, pressure, and flow, such as those encountered in chemical processing, power generation, and manufacturing. 2025 Elsevier Ltd -
THE MONOCHROME TAPESTRY OF SOLO EXISTENTIAL TRAVEL IN 21ST CENTURY HOLLYWOOD: A CRITICAL ANALYSIS
Solo existential travel films of Hollywood enjoyed their heyday in the first two decades of the 21st century with most of them emerging as cult classics that have inspired millions to venture out on backpacking trips. The solo travel beyond the margins of a materialistic society that promises the traveller some existential clarity, in theory, is a truly existential endeavour that lets the individual exercise their Sartrean freedom and responsibility. But a quick survey of the films produced by Hollywood over the decades reveals a rather stealthy racism within. Solo existential travellers in Hollywood films of the 21st century have predominantly been white Americans. Despite being a powerful tool to create ones meaning and authentic identity in society, solo travel is still an instrument of self-redemption that is kept away from people of colour, especially the black American community. The paper will look into the significance, relevance and consequences of this seemingly invisible omission. From an embodiment perspective, the paper will attempt to analyze the absence of racial diversity in the genre to shed light on why the coloured body is to find its space in Hollywoods tapestry of solo existential travel. Copyright 2024 Namitha Nandan. -
Cyanogenic glycosides: A sustainable carbon and nitrogen source for developing resilient Janus reversible oxygen electrocatalysts for metal-air batteries
Most of the transition metal based heteroatom doped carbon electrocatalysts, utilizes the fossil fuel derived commercially available precursors as source of nitrogen and carbon which may question our environmental generosity. Herein, we have developed Ni-based efficient bifunctional electrocatalysts using apple seeds (that contains cyanogenic glycosides) as the precursor for nitrogen and carbon. With tuning the temperature, we were able to optimize the nitrogen doping up to ?3 at.%. The optimized electrocatalyst catalyses the oxygen reduction reaction (ORR) process with muted peroxide generation (for 0.7500.1 V the % HO2 ? generation ?3 - 2%), preferential 4e? reduction pathways (n ? 3.93 to 3.98 in 0.750.1 V range) and electron transfer via inner-sphere electron transfer mechanism which ensures the maximum utilization of instituted active centres owing to the direct interaction of reactant species. Alike to ORR, the superior oxygen evolution reaction (OER) performance with smaller Eonset, EJ=10, Tafel slope and enduring accelerated stability test advocates its potential as a bifunctional oxygen electrocatalyst. Moreover, smaller potential gap ?E (EJ10_OER - E1/2_ORR) of 0.845 V further warrants the energy efficient OER/ORR process. A porotype of Al-air battery system using our catalysts as oxygen electrode and chocolate wafer as anode material is well capable of powering the light emitting diodes. This study hopefully opens a new avenue to explore cyanogenic glycosides plants product to develop multifunctional electrocatalysts. 2019 Elsevier Ltd