Browse Items (14421 total)
Sort by:
-
An investigation of the business level strategies in Zimbabwe food manufacturing sector (2006 -2013) /
International Journal Of Science And Research, Vol.3, Issue 6, pp.1052-1063, ISSN No: 2319-7064. -
Effectual Energy Optimization Stratagems for Wireless Sensor Network Collections Through Fuzzy-Based Inadequate Clustering
Wireless Sensor Networks (WSNs) are crucial in the burgeoning Internet of Things (IoT) landscape, serving as a backbone technology that enables myriad applications across various industries. Originating as a simple methodology, WSNs have evolved significantly, propelled by rapid advancements in sensor technology and hardware capabilities. These networks play a pivotal role in collecting and transmitting data, which is essential for the infrastructure of most IoT systems. WSNs operate by deploying sensor nodes across diverse locations to gather environmental data. This scalability and adaptability of WSNs were demonstrated in studies where network coverage was expanded to include 100 and 200 nodes. Notably, the implementation of the innovative FLECH (Fuzzy Logic Energy-efficient Clustering Hierarchy) protocol significantly enhanced energy efficiency, reducing consumption by 12.69% in networks with 100 nodes and by 36.85% in those with 200 nodes, compared to the traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. This work innovatively combines fuzzy logic and Particle Swarm Optimization (PSO) for efficient Cluster Head selection in Wireless Sensor Networks. The evaluation of these protocols involved numerous simulations and communication tests to ascertain the First Node Die (FND) pointindicative of when a network begins to lose efficacy due to energy depletion. Results indicated that the LEACH protocol reached the FND point faster than FLECH, suggesting that FLECH may offer better longevity and durability for IoT applications, aligning with the needs for sustainable and efficient operation in expanding technological ecosystems. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Novel approaches for nonlinear Sine-Gordon equations using two efficient techniques
In this work, we obtained a new functional matrix using Clique-polynomials of complete graphs (Formula presented.) with (Formula presented.) vertices and considered a new approach to solving the SineGordon (SG) equation. The clique polynomial method transforms this equation into a system of algebraic equations. The solution will be drawn with the help of Newton Raphsons method. Also, we employed the q-homotopy analysis transform method (q-HATM), which is the proper collision of the Laplace transform and the q-homotopy analysis method (q-HAM). To witness the reliability and accuracy of the considered schemes, some illustrations of the SG equation and double SG equation are considered. Here, the SG equation is solved easily and elegantly without using discretization or transformation of the equation by using the q-HATM. Also, in q-HATM, the presence of homotopy and axillary parameters allows us to have a large convergence region. The 3D surfaces of acquired solutions are drawn effectively. The tables of error analysis demonstrate the success of these methods. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms
Stock market prediction is one of the critical issues in fiscal market. It is important issue for the traders and investors. Artificial Neural Networks (ANNs) associated with nature inspired algorithms are playing an increasingly vital role in many areas including medical field, security systems and stock market. Several prediction models have been developed by researchers to forecast stock market trend. However, few studies have focused on improving stock market prediction accuracy especially when utilizing artificial neural networks to perform the analysis. This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Time Delay Neural Network (TDNN) and nature inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC).The developed models were named as GA-BPNN, PSO-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN. Nature inspired algorithms are employed for optimizing the parameters of ANNs. Technical indicators calculated from historical data are fed as input to developed models. Proposed hybrid models validated on four datasets representing different sectors in NSE. Four statistical metrics, Root Mean Square Error (RMSE), Hit Rate (HR), Error Rate (ER) and prediction accuracy were utilized to gauge the performance of the developed models. Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. The other models, GA-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN produced lower performance with mean prediction accuracy of 97.24%, 98.37%, 84.01%, 85.15%, 84.01%, 83.87%, 89.95% and 78.61% respectively. 2021 -
Systemless authoritarianism, counter-archives, and literary witnessing: a New Historicist and cultural-political reading of Arundhati Roys The Ministry of Utmost Happiness
Arundhati Roys The Ministry of Utmost Happiness offers a significant literary response to the evolving forms of authoritarianism in postcolonial India. This article uses a New Historicist and cultural-political framework to analyse the idea of systemless authoritarianism. Instead of using overt state force, the idea refers to a diffused and socially acceptable type of authority that functions through common institutions, cultural norms, and unofficial practices. Roys multi-voiced narrative focuses on marginalised characters such as Anjum, Tilo, and Musa, whose interwoven stories show how repression is intertwined with caste, gender, bureaucracy, and territorial warfare. Using theoretical concepts from Antonio Gramsci, Marlies Glasius, Michel Foucault, and Stephen Greenblatt, the study looks at Roys creation of a counter-archive that challenges official narratives and offers a voice to those who have been silenced. By examining spaces such as the militarised Kashmir Valley and the Jannat Guest House, the study demonstrates how authoritarian authority operates through internalised discipline and cultural consensus inside democratic communities. Ultimately, the novels fractured narrative structure, documentary style, and intertextual elements function as literary witnessing acts that resist authoritarian erasure and recreate various forms of belonging. 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Model independent analysis in (?, n) reactions using deuterium targets
Photonuclear reactions play an important role in nuclear physics, astrophysics and in various applications such as non-destructive measurement of nuclear materials (NDT). The study of (?, n) reactions using deuterium targets i.e., photodisintegration of deuterons in addition to all the other (?, n) reactions, is of considerable interest to these fields. In this contribution, we have studied the photodisintegration of deuterons with unpolarized photons. The angular dependence of the differential cross section is studied by expressing it in terms of Legendre polynomials. The analysis of differential cross-section is presented using the model-independent irreducible tensor formalism. 2021 -
Publication stress amongst scholars and faculties: a concern of mental health
Purpose: The purpose of this paper is to explore the impact of the seemingly entrenched culture of publish or perish on academics and lecturers mental health in academia. From an autoethnographic perspective, personal experiences of stress, anxiety and burnout are articulated and considered in terms of broader system issues within academia. Design/methodology/approach: Using personal reflections on publication pressure and combining that with the broader existing literature on mental health in academia, this paper, like the ones mentioned above, has been written with autoethnography as the research mode. Autoethnography is a research method that allows for profoundly exploring personal experiences but frames them in a broader academic context, thereby allowing for a qualitative analysis of academics mental health challenges. Findings: The pressure to publish in high-impact journals puts a person under a level of mental health stress that includes feeling anxious, feeling like an impostor and suffering from burnout. Therefore, this very unfitting competitive environment requires institutional support and strategies to mitigate the stress associated with publication. Originality/value: This paper offers an autoethnographic view of the mental health difficulties in academia, providing a firsthand account of the emotional toll of academic publishing. This paper fleshes out the burgeoning discourse surrounding mental health within higher education by connecting personal experiences with systemic issues, pointing to changes in culture and structure. 2024, Emerald Publishing Limited. -
Mapping of groundwater availability in dry areas of rural and urban regions in India using IOT assisted deep learning classification model
Groundwater is a crucial resource for fulfilling the water requirements of India's rural and urban areas. The heterogeneous nature of geological, hydrological, and climatic factors results in substantial variability in the accessibility of groundwater across disparate regions. The present investigation centers on the cartography of groundwater accessibility in arid zones of rural and urban Indian areas using a Deep Learning Classification Model (DL-GWCM) supported by the Internet of Things (IoT). The introductory section underscores the importance of groundwater in India, where groundwater sources cater to around 80% of rural and 50% of urban water demands. The text highlights statistical data derived from surveys that indicate a notable decrease in groundwater levels. This underscores the pressing necessity for implementing effective monitoring and management strategies. The DL-GWCM is a proposed solution that aims to enhance the precision and effectiveness of groundwater availability mapping by incorporating IoT technology and Deep Learning Classification. The DL-GWCM comprises multiple constituent elements, such as Groundwater Prediction, Water Quality Index, and Conventional Neural Network- Bidirectional Long Short-Term Memory (CNNBi LSTM) classification. The process of Groundwater Prediction involves the utilization of past data and environmental factors to make precise forecasts of groundwater levels. The Water Quality Index evaluates the quality of subsurface water resources, guaranteeing their secure and enduring utilization. The Deep Learning Classification Model with IoT technology was implemented for groundwater accessibility mapping in Indian arid zones. It integrates Groundwater Prediction, Water Quality Index, and CNNBi LSTM classification. The model makes precise forecasts using past data and environmental factors, ensuring secure water quality. Using the CNNBi LSTM classification model improves the precision of groundwater availability mapping due to its resilient classification capabilities. These findings suggest that the DL-GWCM outperforms conventional approaches. The mean values of all five metrics for the proposed method are presented as follows: The performance metrics of the model are as follows: Root Mean Square Error (RMSE) of 0.77%, Mean Absolute Error (MAE) of 2.13%, Relative Absolute Error (RAE) of 8.72%, Root Relative Squared Error (RRSE) of 0.92%, and Correlation Coefficient (CC) of 0.92. The results of the proposed methodology facilitate the discernment of regions with abundant or scarce groundwater accessibility, thereby supporting the sustainable management and planning of groundwater resources. 2024 Elsevier B.V. -
Actor-critic guided CDBN with GAN augmentation for robust facial emotion recognition
Facial emotion recognition (FER) remains challenging under limited data, noise, and occlusion. This study introduces an ActorCritic Convolutional Deep Belief Network (ACCDBN) that unifies Generative Adversarial Network (GAN)based augmentation, deep probabilistic feature learning, and reinforcement-driven optimization. Conditional GANs expand minority emotion classes, enhancing data diversity, while the CDBN extracts hierarchical texture features through convolutional and restricted Boltzmann layers. An ActorCritic module dynamically refines representations by rewarding accurate emotion classification and penalizing uncertain predictions. Trained and validated on the CK+ dataset with five-fold cross-validation, the proposed model achieves higher accuracy and stability than CNN, LSTM, and ResNet-50 baselines, maintaining strong performance under noise and occlusion. The approach demonstrates how reinforcement-guided generative learning can improve both accuracy and robustness in FER tasks.1. To implement this, the research utilised the publicly available Cohn-Kanade+ dataset, consisting of eight classes with samples of 920 grey-scale images.2. An improved ACCDBN model outperformed with 90.4% accuracy and 0.69 MCC (Mathews Correlation Coefficient) in 5-fold cross-validation using the cGAN-generated dataset and 87% on the CK+ dataset3. The main objective is to present an advanced facial emotion recognition (FER) system that combines a Convolution Deep Belief Network (CDBN) with a model-free reinforcement learning technique, namely the actor-critic approach. 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Economic impact of micro loans on the rural women through self help group-bank linkage programme(SBLP) /
Zenith International Journal Of Business Economics And Management Research, Vol.6, Issue 2, pp.98-112, ISSN: 2249-8826. -
Recent advances in electrochemical and optical sensing of the organophosphate chlorpyrifos: A review /
Critical Reviews in Toxicology, Vol.52, Issue 6, ISSN No: 1040-8444.
Chlorpyrifos (CP) is one of the most popular organophosphorus pesticides that is commonly used in agricultural and nonagricultural environments to combat pests. However, several concerns regarding contamination due to the unmitigated use of chlorpyrifos have come up over recent years. This has popularized research on various techniques for chlorpyrifos detection. Since conventional methods do not enable smooth detection, the recent trends of chlorpyrifos detection have shifted toward electrochemical and optical sensing techniques that offer higher sensitivity and selectivity. -
Government spending and lower secondary education completion in Asia: A cross-national analysis
This paper examines the influence of government expenditure on lower secondary education completion rates across 35 Asian countries, using 2019 data from the UNESCO Institute for Statistics. Despite global commitments to equitable education, regional disparities in funding and outcomes persist. Employing a cross-sectional correlational design, the study identifies a weak, statistically nonsignificant association (r = 0.26, p = 0.122) between government education spending and completion rates. These findings suggest that while funding remains a critical input, its impact may be limited without concurrent investments in education quality, governance, and equity. Key limitations include reliance on single-year data, absence of control variables, and structural inefficiencies across national systems. The study advocates for more nuanced public investment strategies that emphasize targeted interventions, data-driven policymaking, and inclusive financing to align national efforts with Sustainable Development Goals. These insights are relevant for ministries of education, international organizations, and donors seeking to strengthen education systems and promote equitable access across Asia. 2025 Elsevier Ltd -
Blending the old with the new through technology- sanskrit and e-learning /
Internation Journal Of English Language, Vol.5(10), pp.57-64. -
Detection of explosive picric acid via ESIPT-inhibited fluorescent chemosensor: theoretical insights, vapour phase detection and flexible indicator design
A fluorescent probe, (E)-2-((benzo[d]thiazol-2-ylimino)methyl)-5-(diethylamino)phenol (BMP), was designed and synthesized using 4-(diethylamino)-2-hydroxybenzaldehyde and benzothiazole-2-amine, and subsequently characterized for its selective turn-off response toward picric acid (PA). Upon the gradual addition of PA, significant changes in the absorption and fluorescence spectra were observed, marked by strong fluorescence quenching even in the presence of competing nitroaromatic compounds. BMP exhibited two absorption signals at 350 nm and 433 nm with a prominent emission band at 488 nm, attributed to excited-state intramolecular proton transfer (ESIPT), accompanied by a large Stokes shift of 138 nm. The interaction between PA and the hydroxyl group of BMP effectively suppressed the ESIPT process, leading to the observed spectral variations. The binding interactions were further confirmed through NMR spectroscopy and density functional theory (DFT) calculations. The ligand BMP has been utilized as a selective chemosensor for PA with a 2-fold reduction in fluorescence intensity and 19-fold increment in absorption intensity, showing a binding affinity of 2 104 M?1 and strong quenching efficiency toward picric acid, with a SternVolmer constant (Ksv) of 14.059 M?1 with a limit of detection (LOD) of 4.87 ?M. For practical implementation, BMP was successfully employed in a dipstick-based detection format for vapor-phase sensing. Moreover, BMP-embedded polymer films demonstrated excellent potential as solid-state fluorescent sensors, exhibiting visible fluorescence quenching upon exposure to PA. Their rapid, time-dependent emission response under UV light allows for convenient, on-site detection using devices such as smartphones, making them highly promising for real-world applications in explosives detection and environmental monitoring. This journal is The Royal Society of Chemistry, 2025 -
Digital micromirror device characterization in optical band for astronomical multi-object spectrograph
The Digital Micromirror Device (DMD), a micro-electro-mechanical system (MEMS) consisting of individually controllable micromirrors, has emerged as a versatile tool for astronomical instrumentation, particularly in multi-object spectroscopy (MOS). Unlike traditional slit masks or fiber-based systems, DMDs offer dynamic reconfigurability, enabling efficient light modulation and enhanced spectral acquisition. Their adaptability has led to widespread adoption in ground-based spectrographs (e.g., RITMOS, BATMAN, SAMOS, IRMOS) and feasibility studies for space missions (e.g., EUCLID, CASTOR, SUMO, SIRMOS). DMDs have demonstrated robustness in space qualification tests, including radiation exposure, thermal cycling, and mechanical stress, making them viable for space-based applications. Recent advancements, such as UV-transparent windows and enhanced coatings, further expand their potential for ultraviolet astronomy. In India, the success of AstroSats Ultra Violet Imaging Telescope (UVIT) has motivated the development of the next-generation INdian Spectroscopic and Imaging Space Telescope (INSIST), which includes a DMD-based MOS for UV/optical observations. To advance its Technology Readiness Level (TRL), we evaluated the Texas Instruments DLP9500 DMD (1920 1080 micromirrors, 10 m pitch) in the optical band, assessing key parameters such as diffraction efficiency, reflectivity, contrast, micromirror repeatability, and Point Spread Function (PSF) alignment. This study establishes a foundation for future UV-optimized DMD applications in INSIST and other astronomical missions. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Selection of tightened-normal-tightened sampling scheme under the implications of intervened poisson distribution /
Pakistan Journal of Statistics and Operation Research, Vol.15, Issue 1, pp.129-140 -
Harnessing technology for mitigating water woes in the city of Bengaluru /
Journal of Physics: Conference Series, Vol.1427, pp.1-12, ISSN No: 1742-6596. -
Effect of imposed time periodic boundary temperature on the onset of Rayleigh-Benard convection in a dielectric couple stress fluid /
International Journal of Applied Mathematics and Computation, Vol.5, Issue 4, pp.400-412, ISSN No: 0974-4665 (Print), 0974-4673 (Online) -
Linear and weakly non-linear analysis of gravity modulation and electric field on the onset of Rayleigh- Benard convection in a micropolar fluid /
Journal of Advances in Mathematics, Vol.9, Issue 3, pp.363-388, ISSN No: 2347-1921. -
Optimizing milk run and use of bin-packing in waste collection problems /
International Journal of Engineering & Technology, Vol.7, Issue 4.10, pp.577-579