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A Multi-Phase DRL-Driven SDN Migration Framework Addressing Budget, Legacy Service Compatibility, and Dynamic Traffic
No full textSoftware-Defined Networking (SDN) is a network architecture that offers enhanced flexibility,
programmability, and more efficient traffic load management by decoupling the control plane from the data
plane. However, complete migration to SDN is challenging for most organizations due to costs and operational complexities. Hybrid SDN has emerged as a practical incremental path where legacy and SDN-enabled
nodes coexist, yet existing migration strategies typically address only individual challenges such as dynamic
traffic patterns, legacy service compatibility, and budget constraints. This paper introduces SMART (SDN
Migration Assisted by a Deep Reinforcement Learning (DRL) Technique), a comprehensive framework
that simultaneously addresses dynamic traffic patterns, legacy service compatibility, and phased migration
under budget constraints. By integrating a DRL model with a clustering algorithm, SMART determines the
migration sequence to minimize link utilization and reduce the number of SDN-enabled nodes required for
effective traffic load distribution under dynamic traffic patterns. Extensive evaluations on the Abilene and
GEANT network topologies demonstrate that SMART outperforms three existing approaches, achieving
most of the SDN benefits by migrating only 36% and 52% of legacy nodes, respectively. This approach
can potentially lower migration costs by up to 64% while achieving network optimization objectives. These
insights provide both a foundation for future research in network migration strategies and practical guidance
for organizations planning cost-effective transitions from legacy to SDN-based architectures
A review of few-shot fine-grained image classification
No full textFew-shot fine-grained image classification presents a significant challenge in computer vision due to its need for
distinguishing subtle differences among visually similar categories with limited labeled data. This review paper
provides a comprehensive overview of current methodologies and advances in this field. It examines various
approaches including metric-based, data augmentation-based, knowledge distillation, self-supervised learning,
and hybrid-based approaches that integrate multiple strategies. Metric-based methods focus on optimizing
similarity metrics to enhance classification accuracy with few samples. Data augmentation approaches generate
synthetic samples to expand training datasets and address data scarcity. Knowledge distillation leverages the
transfer of knowledge from large teacher models to smaller student models, improving their performance.
Self-supervised learning utilizes unlabeled data to pre-train models, thereby reducing dependence on labeled
datasets. Hybrid approaches combine these techniques to address their individual limitations and enhance
model robustness and adaptability. In addition, this paper also discusses the current limitations of these approaches, such as data scarcity, interpretability issues, and challenges in domain adaptation. Furthermore, key
areas for future research, including multimodal learning, scalability and efficiency, domain adaptability, novel
data augmentation techniques, and the interpretability and explainability of few-shot fine-grained models,
are identified. The review highlights the broader implications of advancements in this field, emphasizing
the potential impact on applications like object recognition, medical imaging, and species identification. By
summarizing the state-of-the-art techniques and suggesting directions for future work, this paper aims to
contribute to the advancement of few-shot fine-grained image classification and its practical applications
Quad-band split ring resonator-based sensor for microwave sensing application
No full textThis study offers a compact size, highly sensitive, and reliable split ring resonator-based sensor
for microwave sensing applications. The designed unit cell is assembled on a 1.575 mm width of
low-cost dielectric substrate Rogers RT5880. CST software is employed to design and analyze the
proposed sensor. The size of the sensor is 8 × 8 mm2 which is very small and it’s a low price. Also,
the CST-simulated model was validated using ADS software. The MATLAB is used to extract the
effective parameters of the suggested unit cell. Then the prototype is fabricated, and the laboratory
measurements are done to validate the simulated results. The obtained resonances from the designed
sensor are 2.77, 5.78, 9.82, and 12.29 GHz. Sensing performance is examined by using various
materials and thicknesses of FR-4 material. After analysis, the sensor’s EMR, quality factor, and figure
of merit (FoM) are found to be 13.54, 325, and 6.15 respectively which are effective. The sensitivity
of the sensor is 12.03% which means the sensor performance is optimum. The resonances are shifted
to 210, 600, and 810 MHz due to permittivity change and 290, 270, and 560 MHz due to materials
thickness change. All laboratory results are perfectly matched with the simulated results. Due to its
small size, low cost, high sensitivity, and superior performance, the suggested sensor can be used for
sensing material thickness as well as glass, plastic, and substrate material
Characterization and machine learning analysis of hybrid alumina-copper oxide nanoparticles in therminol 55 for medium temperature heat transfer fluid
No full textEfficient heat dissipation is crucial for various industrial and technological applications, ensuring system reliability and performance. Advanced thermal management systems rely on materials with superior thermal conductivity and stability for effective heat transfer. This study investigates the thermal conductivity, viscosity, and stability of hybrid Al2O3-CuO nanoparticles dispersed in Therminol 55, a medium-temperature heat transfer fluid. The nanofluid formulations were prepared with CuO-Al2O3 mass ratios of 10:90, 20:80, and 30:70 and tested at nanoparticle concentrations ranging from 0.1 wt% to 1.0 wt%. Experimental results indicate that the hybrid nanofluids exhibit enhanced thermal conductivity, with a maximum improvement of 32.82% at 1.0 wt% concentration, compared to the base fluid. However, viscosity increases with nanoparticle loading, requiring careful optimization for practical applications. To further analyze and predict thermal conductivity, a Type-2 Fuzzy Neural Network (T2FNN) was employed, demonstrating a correlation coefficient of 96.892%, ensuring high predictive accuracy. The integration of machine learning enables efficient modeling of complex thermal behavior, reducing experimental costs and facilitating optimization. These findings provide insights into the potential application of hybrid nanofluids in solar thermal systems, heat exchangers, and industrial cooling applications
Developing Motion Templates of Sport Training Using R-GDL Approach for Evaluating Extrinsic Feedback of Penalty Kicks
No full textThe study developed Motion Templates (MTs) using
the Reverse-Gesture Description Language (R-GDL) method to
evaluate extrinsic feedback in football penalty kick training.
Traditional coaching methods often rely on subjective and
qualitative assessments. To address this, motion capture (MoCap)
technology was employed to collect kinematic data from two
university football players (right- and left-footed) performing
penalty kicks toward left (Set 1) and right (Set 2) goalpost and
Score Rubric Assessment (SRA) form was used by professional
coach to evaluate the performance. From the collected MoCap
data, 40 successful penalty kicks were selected, converted into SKL
format and generate MTs through Gesture Description Language
(GDL) system using R-GDL, which standardized movement
patterns through adaptive machine-learning-derived rules. The
MTs incorporated features such as joint angles and limb
trajectories, producing five rules per template for comparative
analysis. Results demonstrated that MTs effectively differentiated
players’ techniques across sets (e.g., Player A required fewer
attempts in Set 1 than Player B in Set 2). Cross-validation against
coach-evaluated Score Rubric Assessment (SRA) outcomes
revealed that extrinsic feedback scores from MTs did not surpass
SRA benchmarks, confirming the uniqueness of each player’s
motion patterns. This highlights MTs’ reliability in providing
objective, granular feedback for skill improvement. The study
concludes that R-GDL-based MTs offer a robust tool for
enhancing sports training analytics, enabling data-driven
coaching strategies. Future work will focus on scalability, cost
reduction, and extending this approach to other sports
Empowering Social Sciences with Data Visualization - An Insights and Explorations into Behavioral Patterns and Urban Dynamics
No full textVisual data is an integral and essential aspect of any science, a powerful tool for description and exploration in social sciences005. Abstract: In this study, we apply new advanced ways of visualization to bring light on steps helping policy makers better understand multifaceted social phenomena using datasets publicly available. Through combining geospatial, temporal and network visualizations, the framework provides an intimate understanding of urban growth patterns of i-Social networks and behavior characteristics. Findings show that attractive visualizations make interpreting and revealing otherwise hidden patterns easier and provide insight into policy. Such an approach would finally close the gap between data complexity and actionable insights, offering social scientists a solid foundation for research with real impact
Identification of Depression Patients Using LIF Spiking Neural Network Model From the Pattern of EEG Signals
No full textInterpreting electroencephalography signals and the abnormality of the signals can help to find the specific pattern for specific diseases like depression. A Spiking Neural Network is a machine learning approach that emphasizes the data value and manipulates the value to find the particular signal feature. Finding the specific abnormal features of electroencephalography signals can help to detect depression patients. Since a vast number of individuals are suffering from depression and the treatment of depression is possible by detecting depression patients earlier, different deep learning and conventional machine learning approaches were proposed. But speed, accuracy, and reality with less time and space complexity are essential factors in detecting depression patients in our society. We have proposed a leaky integrate and fire spiking neural network model for interpreting the electroencephalography signals of depression patients. The electroencephalography signals of a sixty-channel dataset of 121 subjects are taken for the experiment where frequency for each channel of a subject is recorded for 2 mins in 2-second time intervals, and the dataset contains 4,35,600 data with 121 instances and 3600 attributes. A leaky integrate and fire model is applied to the electroencephalography signals to find the spike sequences and potentials. Then, a three-layered neural network approach is stacked to generate a classifier. The performance of the classifier is shown to be approximately 98% accuracy. Generating a noble classifier and implementing it with a mask of metal disk benefited society for easily and quickly detecting a depression patient, and corresponding treatment can be started. Besides, more experiments are needed on different and more depression datasets with spiking neural network models to identify depression patients and finalize a robotic classifier
Lignin-based microporous carbon nanofibers/S (LMCF@S) high performance cathode for superior room temperature Na–S batteries
No full textSodium-sulfur batteries (Na–S) present a compelling option for large-scale energy storage due to their significant storage capacity, coupled with the abundant and cost-effective nature of their constituent materials. However, their practical deployment is hindered by several critical issues, including the low conductivity of sulfur and its reduction products, volume expansion, the shuttling effect of polysulfides, and the formation of sodium dendrites, all of which can contribute to rapid capacity degradation. Herein, lignin-derived microporous carbon nanofibers/S (LMCF@S) were successfully produced by employing polyvinylpyrrolidone (PVP) and lignin as the precursor and zinc nitrate hexahydrate (ZNH) as an additive, combination of electrospinning, pre-oxidation, and carbonization techniques. The cell is assembled with LMCF@S cathode and Na foil anode, resulting in a remarkable capacity of 642 mAh g−1 over 100 cycles at a current density of 1 A g−1. The high density of micropores in the LMCF@S cathode facilitates robust chemical bonding and rapid redox kinetics during the conversion reaction, resulting in enhanced utilization of sodium polysulfides (NaPSs) for the advancement of next-generation sodium-sulfur (Na–S) batteries
The role of coating layers in gold nanorods' radioenhancement: a Monte Carlo analysis
No full textGold nanoparticles are promising radiosensitizing agents for nanoparticle-enhanced radiotherapy (NPRT). The coating layer on these nanoparticles can significantly influence their physicochemical characteristics and biological behavior. This study investigates the influence of various coating layers on the radioenhancement efficiency of gold nanorods by modeling the physical interactions and chemical reactions involved. We conducted Monte Carlo simulations using the TOPAS code to model secondary electron generation in gold nanorods exposed to 100 kVp X-rays. Through a multiscale approach, the dose contribution, electron spectrum, and G-values of radiolytic species were determined. Four conventional coating materials were examined and compared to a non-coated nanorod. The simulation results indicate that the addition of coating layers decreases the additional dose due to the gold nanorods by up to 7% across all materials. The assessment of electron spectra revealed that 1% to 8% of electrons with energies below 3.5 keV were absorbed within the various coating layers. In contrast, higher-energy electrons were mainly unaffected. The total G-values for all radiolytic species remained generally unchanged with the addition of the coating layer, regardless of the material used. However, increasing the coating thickness slightly increased the relative yield of chemical species at times beyond 10 ns post-irradiation. While the addition of a coating layer generally resulted in a decrease in electron fluence and dose contribution, the reduction was not as substantial as expected from results previously reported in the literature. This suggests that, from the physics perspective, the influence of coating layers on radioenhancement may be less pronounced than previously believed. Additionally, the observed increase in total G-values with thicker coatings emphasizes the need for further investigation into the effects of coatings on radiolytic species
YouTube advertising appeals on generation Z’s purchase intention of beauty products: a predictive approach
No full texthe study investigates Generation Z’s purchase intentions in the beauty industry that
are impacted by Youube advertising appeals. structured questionnaire and a
quantitative approach were used to collect data from a total of 205 respondents who
belonged to Generation Z. he study examined the impact of five types of advertising
appeals, namely emotional, rational, aesthetic, celebrity endorsement, and inclusivity on
purchase intention. he study utilized ristotle’s Rhetorical heory as the underpinning
theoretical framework. Results from Partial east Square Structural quation odeling
(PS-S) revealed that emotional appeal and celebrity endorsement significantly
predicted purchase intention among Generation Z consumers, while rational, aesthetic,
and inclusivity appeals did not. hese findings underscore the effectiveness of emotional
and celebrity endorsement appeals in Youube advertising strategies targeting
Generation Z within the beauty sector. he study contributes to both theoretical and
practical implications for marketers aiming to enhance digital advertising strategies
tailored to Generation Z’s unique consumer behaviors