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Significance of key distribution using quantum cryptography
The main challenge to the cryptosystems is providing secrecy in distributing key. This challenge is explained through key distribution problem. The key distribution in classical cryptosystems is based on classical information or bits. As bits can be replicable, there will be scope for an eavesdropper to make copies of information. The classical key distribution methods rely on computational assumptions which are not potential to offer anticipated results. Consequently, it is solved using laws of quantum mechanics, and the solution is Quantum Key Distribution (QKD). In QKD, the bits are encoded into quantum states or qubits using photon polarization. The qubits cannot be replicated as per the laws of quantum mechanics. An attempt to replication will introduce errors. Thus an eavesdropping will inevitably lead to detectable traces and then the legitimate entities will decide upon discarding a particular qubit. BB84 protocol is the first QKD protocol evolved in 1984. This paper notifies the significance of QKD over key distribution performed using classical methods. It is evidently shown that the time taken to distribute a secret key through BB84 QKD protocol is comparatively less than the classical methods of key distribution. 2018 ICIC International. -
Provably secure quantum key distribution By applying quantum gate
The need for Quantum Key Distribution (QKD) is strengthening due to its inalienable principles of quantum mechanics. QKD commences when sender transforms bits into qubits or quantum states by applying photon polarization and sends to the receiver. The qubits are altered when measured in incorrect polarization and cannot be reproduced according to quantum mechanics principles. BB84 protocol is the primary QKD protocol announced in 1984. This paper introduces a new regime of secure QKD using Hadamard quantum gate named as PVK16 QKD protocol. Applying quantum gate to QKD makes tangle to the eavesdroppers to measure the qubits. For a given length of key, it is shown that the error rate is negligible. Also, the authentication procedure using digital certificates prior to QKD is being performed which confers assurance that the communicating entities are legitimate users. It is used as a defensive mechanism on man in the middle attack. The Japan Society for Analytical Chemistry. -
AI-Driven Predictive Analytics for Sustainable Restaurant Operations and Waste Minimization
There is mounting pressure in the restaurant business to minimize the waste of their operations and use of resources, and still be able to make a profit. Unreasonable forecasting, over-procurement, and poor management of resources are the key causes of environmental and financial waste. As a potential solution to the issue, it presents an AI-based Predictive Analytics Framework (AID-PAF), which combines both a Temporal-Fusion Neural Architecture (TFNA), which is an asset demand prediction framework, and a waste-conscious linear programming model used to solve an inventory and resources allocation problem. Real restaurant operational datasets were used to test the system in a hybrid AnyLogic-MATLAB simulator. Experimental findings show that the proposed framework achieved 40%, 18%, 15%, and 21% reductions in the food waste, energy use, water use, and costs, respectively, and in addition enhanced the accuracy of the forecast, MAPE of 6.5%, the customer fill-rate 96.2%, and the Sustainability Score 78.7. The results prove that predictive analytics based on AI can greatly contribute to the sustainability, efficiency, and profitability of restaurant operations by making intelligent decisions with the assistance of data. 2025 IEEE. -
The hope and the dilemma of the urban poor /
Economic Political Weekly, Vol.53, Issue 40, pp.40-46, ISSN No. 0012-9976. -
Impact of New CSR Bill on Indian Standard & Poor 50
Research Revolution, Vol-1 (3), pp. 1-4. -
Impact of Aggressive and Conservative Working Capital Management Policy on Firms Profitability
The International Journal's Research Journal of Social Science & Management, Vol-4 (1), pp. 105-110. ISSN-2251-1571 -
Work-life balance amongst dental professionals during the COVID-19 pandemic -A structural equation modelling approach
The Coronavirus disease (COVID-19) outbreak in 2019, has shocked the entire world. As an effort to control the disease spread, the Indian government declared a nationwide lockdown on March 25th, 2020. As dental treatment was considered high risk in the spread of COVID-19, dentistry became one of the most vulnerable professions during this time. Dental professionals had to face job layoffs, salary cuts in professional colleges, closure of private clinics resulting in huge psychological, moral, and financial crises. Studies during the previous and present pandemics have shown mental issues among health care workers necessitating institutional reforms, along with early care and support. A balance in the work-life amongst professionals is the key to better efficiency and, was majorly affected during the COVID-19 pandemic lockdown due to sudden unexpected changes. Hence this study was conducted to understand the changes they underwent both at home and professional front with a hypothesis that physical and mental health, activities, relationship status, and workplace influence the work-life balance. Methods A pre-validated questionnaire survey was done on dentists across India. Structural Equation Modelling and path analysis were applied to the data collected. Results The results of the study supported the hypothesis that factors like physical and mental health, activities, relationship status, and workplace influenced the work-life balance directly. A significant imbalance was seen amongst the female dentists. Conclusion The present study proved the unpreparedness among dental professionals. Hence an evolutionary phase in every field with better working protocols, robust mental health support, and a focus on strategies to face future such emergencies is required. 2021 Pai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Relationship Between Interpersonal Conflict, Stress and Burnout in Nurses using ML Classification
Nurses often face challenges due to heavy workloads, ambiguous roles, and hierarchical pressures. This makes them particularly susceptible to stress and burnout. These factors impair their mental health, productivity, and interpersonal dynamics, resulting in emotional exhaustion, dissatisfaction, and high turnover rates. Therefore, the association between interpersonal conflict, stress, and burnout, among nurses is examined in this study. Data were collected from 636 registered nurses in Karnataka. An Artificial Neural Network was used to classify nurses on low/high interpersonal conflict levels based on their stress/burnout levels. It was found that stress and burnout strongly predict interpersonal conflicts. Factors like impaired judgment and altered behavior contribute heavily to these challenges. AI and IoT can be used to manage stress and burnout. Predictive AI analytics can monitor physiological and behavioral indicators, which can help provide an early intervention. 2025 IEEE. -
ETL and Business Analytics Correlation Mapping with Software Engineering
Large information approach can't be effectively accomplished utilizing customary information investigation strategies. Rather, unstructured information requires specific information demonstrating methods, apparatuses, and frameworks to separate experiences and data varying by associations. Information science is a logical methodology that applies scientific and measurable thoughts and PC instruments for preparing large information. At present, we all are seeing an exceptional development of data created worldwide and on the web to bring about the idea of large information. Information science is a significant testing zone because of the complexities engaged with consolidating and applying various strategies, calculations, and complex programming procedures to perform insightful investigation in huge volumes of information. Thus, the field of information science has developed from enormous information, or huge information and information science are indistinguishable. In this article we have tried to create bridge between ETL and software engineering. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Development of a natural product-based selective fluorescent sensor for Cu2+ and DNA/protein: insights from docking, DFT, cellular imaging and anticancer activity
The natural product seselin (SS), was synthesized and characterized spectroscopically for the selective detection of Cu2+ and biomolecules such as ct-DNA and BSA. The probe exhibits strong bluish emission in a MeOH-H2O (7 : 3, v/v) HEPES buffer solution (pH 7.4) at 453 nm. Upon exposure to Cu2+, the SS solution shows a selective fluorescence turn-off with a binding constant of 2.13 105 M?1 and a detection limit of 3.48 10?8 M. The HOMO-LUMO energy gap of the probe SS decreases from ?E = 7.97 eV to ?E = 7.77 eV upon binding with Cu2+, indicating enhanced stability due to ligand-metal complex formation. Significantly, the ligand SS exhibits fluorescence enhancement in the presence of ct-DNA and BSA, resulting in a visible fluorescence change from colorless to blue, with binding constants of 4.8 104 M?1 and 4.7 104 M?1, respectively. The binding interactions of SS with biomacromolecules have been explored through molecular docking studies, revealing that the probe can serve as a promising anti-cancer and anti-viral agent. Furthermore, the probe SS demonstrates potent anticancer activity in treatments involving MCF-7 and HLC cells. Additionally, the probe SS is capable of detecting intracellular Cu2+ in live MCF-7 cell lines. 2025 The Royal Society of Chemistry. -
Investigation of the therapeutic potential of daphnetin, derived from Daucus carota leaves, against colorectal cancer: insights from in-silico, DFT, in-vitro, and fluorescence studies on its interactions with proteins
Daphnetin (NAP) has a sophisticated structure containing two oxygen-linked rings, which have long attracted medicinal chemists in the drug discovery process. Daphnetin features a coumarin structure with two hydroxy groups positioned side by side at the 7th and 8th carbons, making it an essential component for developing a novel triple point co-prodrug. We have isolated NAP from the leaves of Daucus carota (commonly known as carrot). NAP showed a good docking score with the EGFR protein obtained from network pharmacology, while molecular dynamics simulations evaluated the stability. ROS generation assays, MTT assays, etc. confirmed that NAP exhibits significant cytotoxic effects on HCT-116 cells. An investigation of the reverse phase interaction of NAP with BSA and ovalbumin, where proteins are added to the ligand solution, demonstrated fluorescence enhancement, ensuing in a observable colour transformation from neutral to blue, through binding-constants of 4.8 104 M?1 and 2.08 104 M?1. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Deciphering the Nature and Dynamics of Gig-Platform Jobs: Workers Hidden Precarity
The technology-driven gig-platform sector has emerged as a new source of employment generation both globally as well domestically. This recent transformation in the labour market is reshaping the nature of labour practices, labour relations, workers rights, and contracts. The sector has huge potential to generate millions of job opportunities by leveraging the use of digital technology. As this sector continues to generate more jobs, such jobs are portrayed as fostering economic growth, while creating meaningful jobs, which are mutually beneficial to workers and employers in terms of providing flexibility and freedom, better earning opportunity, and promoting social inclusion, by which it implies that women are increasingly equipped to find better jobs. This article critically examines the developmental roles of platform jobs which are being particularly highlighted within the policy circle, in academic literature, and tech companies through workers lens. It delves deeper into the discussion on those very aspects of platform jobs just listed, including the flexibility and freedom debate, workers income, and the gender aspect of jobs. In doing so, it carefully examines these aspects with respect to their implications on workers in terms of working conditions and regulatory aspects. The article brings out the workers precarity hidden within those developmental aspects of gig-platform jobs. 2024 CSD. -
Smart UAV Surveillance Platform with Onboard Object Detection and Geofencing for Public Safety
Modern public safety operations are also changing due to the incorporation of artificial intelligence (AI) into the use of Unmanned Aerial Vehicles (UAVs) enabling maneuverability, open area, and continuous surveillance. The apparently exact plan and assessment of a clever UAV watchdog platform with on-board article locating, autonomous navigation using geofences, and edge processing will be introduced in this paper. The proposed system will be composed of a Pixhawk flight controller, a u-blox M10 GNSS module to provide navigation capabilities, and NVIDIA Jetson to run high-definition aerial video processing, specifically to recognize and use a YOLO-based detection pipeline. A simulated environment of a public event was used to test the accuracy of detection, inference latency, detection of anomalies and geofencing compliance across different environmental and crowd circumstances. The results of experiments also show average detection accuracy rates of 93.42%, inference delays of less than 60 ms, and few boundary violations, confirming the use of the system in responsive and secure use of aerial monitoring. The open-source modular nature of the platform permits the addition of other sensors and its subsequent expansion ability into multi-drone collaborative applications to note the prospects of such low-cost systems to influence large scale event tracking, disaster relief and urban security applications. 2025 IEEE. -
Object Detection Framework for Identifying Suspicious Items in School Environments using YOLOv8
The issue of unattended bags, metallic items, and concealed weapons at schools has made school safety a growing issue worldwide. This paper presents a software-driven, deep learning framework, implementing automatic identification of suspicious items within a school environment, using the new YOLOv8 neural net architecture. A proprietary 5,000 image dataset of simulated school corridors and classrooms, with 5 annotated threat classes, was developed. The system attained a mAP of 95.6%, precision of 96.8%, and recall of 94.5%, with 38 fps inference speed using a single GPU. YOLOv5 and Faster RCNN comparisons showed a mAP improvement of 12-15%, along with nearly 2x faster frame throughput for the proposed approach YOLOv8, resulting in lower latency and faster responsiveness. The system works in a real-time framework, producing annotated alert logs and frames with mAP scores above 0.5. Experiments conducted with different levels of clutter and illumination show the system has sufficient robustness for school surveillance use cases. 2025 IEEE. -
Digital Twins for predictive maintenance of Production and Machines: A Comprehensive Review
Digital Twin (DT) technology has matured from concept to practice across factories and critical assets, enabling new capabilities in condition-based and predictive maintenance, resilient production planning, and life-cycle decision-making. This review synthesizes current knowledge on DT usage for production and predictive machine maintenance, with concise notes on structural maintenance where SHM (structural health monitoring) increasingly adopts twin concepts. We first consolidate enabling architectures (standards, ontologies, F?FMI-based co-simulation, and hybrid modelling) and then critically survey applications spanning CNC cutting tools, bearings and gearboxes, robotic cells, and production lines. We highlight evidence that hybrid (physics + data-driven) twins reduce remaining useful life (RUL) prediction error compared to single-strategy approaches, improve energy-aware scheduling, and shorten diagnosis-to-action loops. Industrial deployments demonstrate up to 20-30% reduction in unplanned downtime when DT-enabled predictive maintenance is integrated into operational workflows Finally, we surface open challenges - data governance, model validation, uncertainty quantification, interoperability, and work-force adoption - and propose a practical roadmap to make DT predictive maintenance projects production-ready. 2025 IEEE. -
A Context-Aware Finite State Machine for Gesture-Driven UAV Control
Gesture based Unmanned Aerial Vehicles (UAVs) is a very intuitive way to control drones (UAVs). Current methods tend to associate one gesture to one action, a practice which is rigid and inflexible. In this paper, we propose the Finite State Machine (FSM)-based gesture control framework, which allows triggering several actions of the UAV with a single gesture depending on the current state of the drone. MediaPipe hand gestures recognition and integration with ROS2 and PX4 allows the system to automatically takeoff, land, hover, automated ascents, and directional speed variation. Experiments in a ROS2 simulation environment test the system in terms of gesture-to-state latency, the rate of successful commands, the extent of the FSM that the system is capable of controlling, and the rate of false positives (spurious transitions). The results indicate that the suggested method has robust and responsive control of the UAV, which forms the basis to establish more intuitive and adaptive human-UAV interaction in limited spaces. 2025 IEEE. -
The International Capital Flows and Domestic Savingsdomestic Investment Nexus: A Comparative Evidence Between Heterogeneous Developing Regions
Drawing inspiration from Feldstein and Horiokas (1980) (FH) puzzle, our study elucidates the impact of remittances and Foreign Direct Investment (FDI) on domestic savings and investment in two disparate yet globalized developing regions: Latin America and the Caribbean and South Asia. Utilizing an extensive dataset spanning from 1984 to 2021 and employing diverse methodologies, including Dynamic System generalized method of moment, DriscollKraay standard error, fully modified ordinary least squares, and dynamic ordinary least squares, our findings reveal that remittances exert a positive influence on both domestic investment and savings across both regions. However, South Asia predominantly directs remittance inflows towards investment, while Latin America and the Caribbean exhibit a propensity towards saving these funds. As for FDI, the primary developing region predominantly channels these funds into investment, whereas the lower region prioritizes savings. The impact of control variables manifests varied effects across both regions. Ultimately, our study underscores the pivotal role of foreign remittances in supporting investment and savings, underscoring the profound influence of economic growth on these dynamics. This accentuates the imperative for governments to proactively allocate financial resources to optimize economic growth and fortify financial frameworks. Moreover, focused strategies are indispensable for adeptly managing foreign inflows while navigating external shocks such as international repayments, external debt, and aid. Additionally, enhancements in monetary and fiscal policies are imperative to sustain competitive interest rates and foster stable macroeconomic conditions, thereby fostering conducive environments for both public and private domestic savings. JEL Classification: F24; F3; P33; C23; O18 2024 The Author(s). -
Do economic globalization and the level of education impede poverty levels? A non-linear ARDL approach
This study empirically examines whether economic globalization reduces (enhances) the level of poverty in the top (bottom) globalized region by controlling economic growth, urbanization, government expenditure, and public expenditure on education. This issue has taken Europe and Central Asia (ECA) as the top (16) and South Asia (SA) as the bottom (7) economic globalized developing region for the empirical analysis for the period of 1991-2020. Two empirical models, non-linear ARDL and PMG-ARDL, estimate the impact of globalization (trade and financial openness) and education on poverty. This study also segregates economic globalization into de jure and de facto to critically analyze the impact on poverty reduction. The long-run results suggest that economic globalization has a negative (positive) effect on poverty in the top (bottom) globalized region. Apart from globalization, primary education is insufficient for reducing poverty in the ECA region, while primary education is enough to reduce poverty in the SA region. After replacing economic globalization with trade and financial openness, the results reveal that more trade openness is difficult for reducing poverty in top globalized developing countries. On the contrary, financial openness reduces (enhances) poverty in the top (bottom) globalized region. Additionally, the impact of de jure and de facto economic globalization are similar throughout the regions. The effects of control variables are mixed in nature. From a policy perspective, the government of these two regions should use education as a weapon to lower poverty vulnerability by improving its quality and giving extensive focus on trade and financial openness to find out the leakage of the financial flows. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Role of Digitalization and Government Effectiveness in Sustainable Energy Transition: Evidence From Asian Economies
This study explores how digitalization, through resident and non-resident innovation initiatives, along with government effectiveness, affects the transition to renewable energy generation in five Advanced (Australia, Hong Kong, Japan, New Zealand and Singapore) and seven Emerging (China, India, Indonesia, Malaysia, Philippines, Thailand and Vietnam) Asian economies. The research uses annual data from 1985 to 2022 and applies several econometric methods to analyse the impact of these factors on renewable energy generation in a panel setup while also considering economic growth and human capital as key control variables. The findings reveal that residential innovation negatively impacts renewable energy generation in Advanced Asia but has a positive effect in Emerging Asia. Additionally, government effectiveness and non-residential innovation hinder renewable energy generation in Emerging Asia while contributing positively in Advanced Asia. Economic growth and human capital show a positive association with renewable energy generation in both Advanced and Emerging Asian economies. These findings are robust to an alternative method used. Besides, additional robust results further indicate that artificial intelligence patents used as an alternative measure of digitalization hinder renewable energy generation in Emerging Asia and promote it in Advanced Asia. These findings provide valuable guidance for policymakers and stakeholders, highlighting the need for tailored strategies to drive sustainable energy transition in different economic contexts. 2025 John Wiley & Sons Ltd. -
Role of Globalization and Innovation Pattern in Growth of Bank Credit: Evidence From Emerging and Advanced Asia
This study examines the role of globalization and innovation pattern (i.e., innovation by the residents and non-residents) in the growth of domestic bank credit across emerging and advanced Asian economies spanning from 1996 to 2022. The bank credit growth model includes economic growth and real interest rate as important control variables. This study employs Cross Sectional-Autoregressive Distributed Lag (CS-ARDL) as an appropriate baseline method because of the cointegration, endogeneity, and cross-sectional dependency present in the data. The long-run results for emerging Asian economies indicate that globalization exhibits a negative impact on banking credit, contrasting with the positive influence observed in advanced Asian economies due to heightened economic growth and increased credit demand. Residential innovation consistently bolsters banking credit in both sets of economies, albeit with mixed effects stemming from non-resident innovation. The long-run results further indicate the positive (negative) impact of economic growth (real interest rate) on bank credit in emerging and advanced Asian economies. These findings are reliable due to the similar results obtained from using Driscoll-Kraay Robust Standard Errors (DKSEs) as robust method. For policymakers in emerging economies, the imperative policy lies in striking a delicate balance between economic openness and bank credit, while counterparts in advanced economies are poised to bolster bank credit accessibility through foreign innovation while upholding stringent regulatory oversight. 2024 John Wiley & Sons Ltd.