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Electromagnetic Radiation-Driven Plastic Degradation and Energy Recovery for Sustainable Waste Management
The persistent accumulation of plastic waste presents a severe global environmental challenge. This study presents a non-thermal photodegradation and energy-recovery system that selectively cleaves 82 5% of CC/CH bonds in polyethene (PE), polypropylene (PP), and polystyrene (PS) within 30 min of UVC (254 nm) exposure. The bond-dissociation energy is harvested via thermoelectric generators (TEGs), delivering 10 W, and via photoelectric cells, yielding 5 W (10 mA.cm- at ? < 2 eV), for a combined recovery of 15 W. Emissions are held below 0.5 ppm VOCs and 0.1 mg.m- microplastics. A lab-scale prototype processes 0.5 kg.h-1 of mixed plastic per 0.1 m reaction area equivalent to 30 Wh.kg-1 of electrical energy and is scalable to 5 kg.h-1 in a pilot module. Real-time FTIR, Raman, and UV-VIS spectroscopy, integrated with an IoT-PID feedback loop, ensures autonomous optimization. Life-cycle assessment indicates a 25% reduction in greenhouse gas emissions compared to conventional recycling methods. A circular-economy framework envisions recovering oligomeric and monomeric fragments for direct reintegration into polymer production. Feature work will implement digital-twin simulations to refine process control, maximize throughput, and ensure long-term system reliability. 2026 by the authors Licensee: Technoscience Publications. -
Performance Evaluation Frameworks in the Context of Indian Microfinance Institutions
The paper conducts a detailed examination of the existing evaluative frameworks for microfinance institutions to gauge the differences and similarities. Efficiency evaluates how MFIs are meeting the performance standards considering time and budget constraints. Outreach evaluates the effectiveness of MFIs in reaching the beneficiaries. Relative efficiency scores were calculated using data envelopment analysis and outreach was measured in five different dimensions (pentagon model). Further, cluster analysis assisted in categorizing the MFIs into five value clusters. The study compares both outreach performance and relative efficiency scores employing ANOVA and correlation analysis. The study was conducted among the Indian context when the sector was hit by crisis during 2010. Paper brought out important insights about the sample. Indian MFIs were found to be more socially efficient, since the social dimension taken into consideration was number of female clients and majority of Indian MFIs has exclusive female focus. The correlation tests found that relative efficiency scores are positively related to depth (poor focus) and length (sustainability) outreach. The results showed that cluster analysis model basing outreach scores was more comprehensive and captured more information compared to the data envelopment model relative efficiency scores. The study is original in its approach in using cluster analysis for outreach performance and in the objective of comparing the two different models. 2019 Aruna Balammal et al., published by Sciendo 2019. -
A study on prediction of health care data using machine learning
Every clinical-decision relies on the doctors experience and knowledge. Perhaps this conventional practice may look appropriate, but it may lead to unpredictable errors, biases, and maximized costs that may affect QoS (Quality-of-Service) given to patients. To help the doctor to save time, the conventional practice to analyze the data for clinical-decision support has to be updated. Machine Learning (ML) and Data Mining (DM) algorithms have applied to have greater and higher predictions. This paper studies a set of ML algorithms by which clinical-predictions are going to be more appropriate and cost-effective. IJSTR 2020. -
Secured Cloud Computing for Medical Database Monitoring Using Machine Learning Techniques
A growing number of people are calling on the health-care industry to adopt new technologies that are becoming accessible on the market in order to improve the overall quality of their services. Telecommunications systems are integrated with computers, connectivity, mobility, data storage, and information analytics to make a complete information infrastructure system. It is the order of the day to use technology that is based on the Internet of Things (IoT). Given the limited availability of human resources and infrastructure, it is becoming more vital to monitor chronic patients on an ongoing basis as their diseases deteriorate and become more severe. A cloud-based architecture that is capable of dealing with all of the issues stated above may be able to provide effective solutions for the health-care industry. With the purpose of building software that would mix cloud computing and mobile technologies for health-care monitoring systems, we have assigned ourselves the task of designing software. Using a method devised by Higuchi, it is possible to extract stable fractal values from electrocardiogram (ECG) data, something that has never been attempted previously by any other researcher working on the development of a computer-aided diagnosis system for arrhythmia. As a result of the results, it is feasible to infer that the support vector machine has attained the best classification accuracy attainable for fractal features. When compared to the other two classifiers, the feed forward neural network model and the feedback neural network model, the support vector machine excels them both. Furthermore, it should be noted that the sensitivity of both the feed forward neural network and the support vector machine yields results that are equivalent in quality (92.08% and 90.36%, respectively). 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of Systematic Trade-offs between Military and Healthcare Expenditure alongside GDP Growth of Select Asian and Western Exporting Economies in the 21st Century
This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality between GDP growth and healthcare expenditure, reflecting economic stability and balanced allocation patterns. In contrast, developing Asian nations display fragmented and volatile relationships due to resource constraints and inefficiencies. Outlier analysis reveals country-specific dynamics, such as conflict-driven spending in Afghanistan and Myanmar and growth-focused strategies in China. Temporal trends show that economic crises, like the COVID-19 pandemic, significantly disrupt GDP growth but have limited long-term effects on healthcare or military expenditures. Clustering analysis identifies distinct groups of nations, shaped by economic capacity and geopolitical pressures. The findings emphasize the need for tailored policy frameworks to balance national security and human well-being, particularly in developing nations facing structural challenges. For sustainable development, policies must align resource allocation with economic priorities, geopolitical contexts, and societal needs. 2025, Binghamton University Libraries. All rights reserved. -
Complex Systems Mapping of Fiscal Growth Dynamics at Strategic Maritime Chokepoints Using Time-Series Slopes
This study examines how maritime and trading states allocate public resources between defence, health, and economic growth around three strategic chokepoints the Strait of Malacca, the Strait of Hormuz, and the Suez Canal. The analysis extends the classic guns versus butter framing by treating defence and health spending as co-evolving components of an interconnected fiscal-growth system. Using World Development Indicators data (1999-2024), trend slopes are estimated for military spending (% of GDP), healthcare spending (% of GDP), and GDP growth (annual %). Two derived indicators are computed, a defence-to-health slope ratio (military slope/health slope) and a fiscal-balance proxy (health slope - military slope). Augmented Dickey-Fuller tests are used to assess stationarity (unit-root behaviour), and Granger causality tests to examine whether GDP growth temporally precedes changes in spending shares. Hormuz chokepoint states show non-negative health slopes (e.g., UAE +0.1199) alongside negative GDP growth slopes in some cases (e.g., Qatar -0.4754). Suez chokepoint states exhibit negative defence slopes (e.g., Egypt -0.0899) with comparatively small or negative health slopes (e.g., Egypt -0.0211). The United States is included as an external benchmark because it is the largest trading nation by monetary trade volume and is directly or indirectly coupled to chokepoint flows; it shows health +0.1758 and military -0.0116 (ratio -0.0657). These quantified configurations support chokepoint-specific fiscal regimes and provide a compact visual map of security, health, growth dynamics in a small integrated complex systems. @ Binghamton (The ORB), 2026. -
Charting the Complexity of Diabetes Risk using Network-based Exploration of Nonlinear Interactions
Diabetes mellitus is a global health challenge shaped by complex clinical, demographic, and socioenvironmental factors. Traditional linear models often overlook the non-linear dependencies that drive diabetes risk. This study adopts a systems-thinking approach by integrating mutual information (MI)-based network modeling with machine learning to improve prediction, interpretability, and fairness. Using a nationally representative CDC dataset, we build a weighted undirected network where variables are nodes connected by MI-derived edges. Centrality analysis identifies age, HbA1c, and BMI as key hubs. Community analysis reveals clinical, demographic, and racial modules, reflecting the multidimensional nature of diabetes risk. These network insights inform feature selection for training logistic regression, random forest, and XGBoost models. XGBoost achieves the highest accuracy (95.3%) and AUC (0.939), while logistic regression offers the best calibration (Brier score = 0.045), enhancing clinical usability. Subgroup analysis shows stable predictions across racial groups, supporting fairness. This integrated framework uncovers latent, non-linear associations and offers a robust, interpretable, and equitable tool for precision diabetes risk modeling. 2025 IEEE. -
Face-Based Kinship Verification using Deep Embeddings for Low-Cost Health Record Linkage
Precise linkage of health records is essential for continuity of care, reducing duplicate health records, and accurately documenting family medical histories. Genomic testing offers the evidence-based biological 'gold standard' for verifying kinship; however, access to testing is either impossible or unavailable in most low-resourced environments due to prohibitive costs, long timelines, and/or lack of infrastructure. This study provides a low cost and interpretable pipeline for kinship verification in the form of Siamese deep embeddings. The processed facial image embeddings produced by a ResNet-18 backbone using 256-dimensional and L2-normalized embeddings, are then compared using cosine similarity. A validation-based calibration process selects the logit polarity and decision threshold that support stable deployment decisions. Grad-CAM visualizations can be interpreted frame-by-frame and allow for pair-specific attributions of faces that were more relevant or important in decisions of similarity. In experiments on the Families in the Wild (FIW) dataset (family-disjoint splits), we report ROC-AUC of 0.834, target balanced accuracy of ?0.88, with similar precision, recall, and specificity. The confusion matrices also illustrate a near symmetric distribution of errors by family and both Grad-CAM explanations highlight how the model came to a decision for true cases and hard cases. The above results illustrate how we can deploy a lightweight, explainable, and face-based kinship verification pipeline on a CPU-only system. Our study therefore provides a feasible assistive tool for health record linkage where genomic validation is not possible. 2025 IEEE. -
Transforming towards 6G: Critical Review of Key Performance Indicators
With the experiences acquired upon the successful implementation of 5G networks academia, researchers, and industry are envisioning the need for 6G networks. The vision of the 6G communication network is supposed to completely assist the creation of a Ubiquitous Intelligent Mobile Society. Already 5G technologies are in place and still few extended features of 5G are continuously being introduced. Even though the 6G communication network is expected to have greater capabilities than the existing 5G, there are no clear specifications on how far these capabilities shall be capitalized in 6G. The 6G technologies shall move past ordinary mobile internet services and advance to support ubiquitous Artificial Intelligent (AI) services from the network's core to end-to-end service devices/applications. The architecture, protocols, and operations which are the primary constituents of the 6G network shall implement AI technologies for self-optimization and actualization. This article brings an all-inclusive deliberation of 6G based on an assessment of preceding generations' evolving technology developments. 2022 IEEE. -
Resource Aware Weighted Least Connection Load Balancing Technique in Cloud Computing
Cloud computing became a pivotal for the most of the real time applications. In cloud computing, the customer demands the services with the best performance even when the application is expanding rapidly. Therefore, it is essential to manage the resources effectively because the number of users and services growing proportionately. The main aim of the load balancing technique is to allocate the customers' requests with the large pool of resources efficiently. The problem is how to evenly distribute the load of requests among the compute nodes according to their capacity. Therefore, there is a need for an effective load balancing technique for smooth continuity of operations in a distributed environment with a heterogeneous server configuration. This paper presents a novel load balancing technique, namely, Resource aware weighted least connection load balancing which addresses the above said problem efficiently. The essence of this work is to assign the requests across multiple servers based on the requested resource and the status of the number of connections presently served by each server. This work used standard score technique to enumerate the weight of each node. Experiments were conducted using Cloud Analyst, a famous cloud simulator breed from CloudSim. Appropriate performance parameters were analysed to measure the effectiveness of the proposed technique. Future directions for the extension of the implemented technique also identified. 2023 IEEE. -
Research Perspectives on Load Balancing Strategies in Serverless Computing
Serverless computing, a groundbreaking trend in cloud computing, has transformed how applications are deployed and managed by abstracting the infrastructure layer. Serverless computing enables developers to concentrate exclusively on their code while cloud providers care for server provisioning, maintenance, and scaling. Services like AWS Lambda, Google Cloud Functions, and Azure Functions exemplify this model, offering s ubstantial advantages in terms of reduced operational complexity and cost. However, one persistent challenge in this domain is load balancing. Effective load balancing in serverless computing ensures efficient resource utilization, optimal performance, and cost-effectiveness. Unlike traditional load balancing, which typically relies on long-lived server instances, load balancing in serverless environments must accommodate the stateless and ephemeral nature of serverless functions. Traditional techniques are not directly applicable because serverless architectures functions that are instantiated on-demand in response to incoming requests. This paper surveys various strategies and approaches developed to address the unique load balancing challenges in serverless computing, providing a comprehensive overview of the current state of research and practice. The paper extends further research on serverless computing by analyzing the survey papers. The paper highly focuses the research areas in the field of edge computing, hybrid cloud models and distributed load balancing for the future usage. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A frequent itemset generation approach in data mining using transaction-labelling dynamic itemset counting method
A significant amount of data is generated, gathered, stored, and evaluated in real-world applications as a result of technology breakthroughs. Data mining (DM) combines a number of disciplines to efficiently discover hidden patterns from vast archives of historical information. To significantly reduce complexities associated with data, the proposed method, transaction-labelling dynamic itemset counting (TL-DIC), utilises a labelling approach on the given transactional database to logically arrange and process the underlying transactions. This method generates frequent itemsets thereby improving the performance of conventional dynamic itemset counting (DIC) method. Based on experimental findings, the average scan count in DIC and M-Apriori is 4% and 3.66%, respectively higher than TL-DIC, for different support counts. TL-DIC executes 20% and 16% quicker than DIC and M-Apriori, respectively, in terms of execution time. These results validate the proposed approachs efficacy in creating frequent itemsets from large datasets. Copyright 2025 Inderscience Enterprises Ltd. -
Exosomes as an emerging nanoplatform for functional therapeutics
The release of a division of extracellular entities with 40-100 nm dimension from both tumor and varied mesenchymal stem cells during pathologic conditions is termed as exosomes that are shown to actively participate in chemical signaling events occurring in multicellular organisms infection. Exosomes act as a vehicle for shifting amino acids, lipids, and genetic components that are readily engulfed by far-flanging cells (or adjacent cells) at the site of release for remolding the receptor cell functions once the biological contents get activated. Furthermore, pathogens too display the dispense of exosomes to temper the hosts immune response and trigger the infection rate, making them apt investigation markers for diseases. Additionally, aiding in antigen presentation and immune response stimulation, exosomes are significant in showing contrasting role as initiating anticarcinogenic responses and involving in promoting tumorigenesis as they are released from tumor cells. Due to their site specificity, cell lineage property, and encapsulation of specific constituents, exosomes can be potentially utilized as a precious investigatory and prognostic tool along with a possible carrier of drugs and gene shipment for curative goal. Since exosome groundwork is at the infancy stage, deeper insight is required to know its composition, formation, and targeting mechanism along with its significant role in disease diagnosis and treatment. This states, as mentioned previously, we have tried to focus solely on the functional and clinical implications of exosomes in-depth in this review. 2021 Elsevier B.V. All rights reserved. -
Customer preferences to select a restaurant through smart phone applications: An exploratory study
The increasing number of Smart Phone Applications (SPA) user and fast growing restaurant industry proves the great potential of using SPA as business marketing opportunity in Malaysia. The constant growth in mobile technology has created a prospect for the restaurant industry to use SPA as a restaurant promotion tool. The growing attention of use of SPA among the Malaysian customer, marketing research remains understudied in the field of SPA based restaurant promotion activities. The aim of this study is to explore the increase in customer acceptance to use SPA based restaurant promotion and to identify the customer preference to use SPA to select the restaurant. Thus, this paper mainly focuses on restaurant information on product and promotion as antecedents of customer acceptance of smart phone apps by underpinning the Unified theory of acceptance and use of technology (UTAUT) model. A conceptual model and hypotheses are tested with a sample of 116 students from a private university at Selangor district, Malaysia. The findings indicate that there is a positive relationship to increase customer acceptance level through SPA based restaurant product information and also strong relationship with the restaurant promotion information. It also indicates that customer acceptance of SPA through experience and satisfaction has a positive significant effect on customer preference to select a restaurant. Based on the results, this paper rounds off with conclusion, recommendations for future marketing research and provides a new marketing strategy to formulate among the restaurant business sector. 2015 American Scientific Publishers. All rights reserved. -
RF-ShCNN: A combination of two deep models for tumor detection in brain using MRI
The tumor in the brain is the reason for jagged cell enlargement in the brain. Magnetic resonance imaging (MRI) is a common scheme to identify tumor existence in the brain. With these MRIs, the medical practitioner can examine and detect the abnormal growth of tissues and corroborate if the brain is influenced by a tumor or not. Due to the appearance of artificial intelligence models, the discovery of brain tumor is performed by adapting different models which thereby help in making decisions and selecting the most suitable diagnosis for patients. The main motivation of this work is to reduce the death rate. If they are not adequately treated, the survival rate of the patient decreases. The correct diagnoses help patients receive accurate treatments and survive for a long time. This paper develops a hybrid model, namely the Residual fused Shepherd convolution neural network (RF-ShCNN) for discovering tumor in the brain considering MRI. Thus, the Adaptive wiener filtering is adapted to filter image-commencing noise. Thereafter, Conditional Random Fields-Recurrent Neural Networks (CRF-RNN) are adapted for segmentation followed by the mining of essential features. Lastly, the features employed in RF-ShCNN for making effective brain tumor detection by means of MRI. Thus, the RF-ShCNN is built by unifying the deep residual network and Shepherd convolution neural network. The hybridization is done by adding a regression layer wherein the regression is fused with Fractional calculus (FC) to make effective detection. The RF-ShCNN provided better accuracy of 94%, sensitivity of 95% and specificity of 94.9%. 2023 -
Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection
In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches. 2022. Balasubramanian et al. -
Microlearning and Learning Performance in Higher Education: A Post-Test Control Group Study
This study aimed at evaluating the effectiveness of microlearning in higher education. The sample consisted of first-year MBA students, and a post-test control group design was used to assess the effectiveness of a microlearning module. The results indicated that the use of microlearning was significantly related to learning performance and participants' reactions to the module. Moreover, the microlearning group scored significantly higher than the control group. The findings suggest that microlearning has the potential to improve learning outcomes and enhance participant engagement. However, the study has certain limitations, and future research is needed to gain a comprehensive understanding of the optimal design and delivery of microlearning modules. The study supports the use of microlearning in higher education as an effective instructional strategy. 2024, Commonwealth of Learning. All rights reserved. -
Jugaad in organizational settings: exploring the Jugaad leadership competencies
The Hindi term 'jugaad' is closely linked to frugal innovation. In resource-scarce environments, organizations can thrive by developing jugaad-related leadership abilities. Previous research on jugaad has focused primarily on individual problem-solving and overlooked the leadership skills necessary to implement it in organizational settings. This study employs a theoretical lens of leadership competency models, interpretive phenomenology, purposive sampling, and an inductive data-driven coding approach to explore the jugaad leadership competencies of 28 Indian business leaders and managers. The study presents the Jugaad Leadership Competency (JLC) model, identifying ten competency clusters exhibited by jugaad leaders. This is the first study to develop a model for jugaad leadership in organizational settings. In environments characterized by scarcity and intense competition, the JLC model can aid individuals and organizations in acquiring the necessary competencies for frugal innovation. The study evaluates the theoretical and practical implications of the findings, their transferability, and limitations and offers suggestions for future research. 2023, Springer Nature Limited.