1,623 research outputs found
The Remarkable Role of Hydrogen in Conductors with Copper and Silver Nanoparticles by Mixed Convection Using Viscosity Reynold’s Model
No full textThis article includes an analysis of the influence of mixed convection and variable viscosity under the effect of a transverse magnetic field on a stretching surface. Nano-fluid viscosity is supposed to be dependent on temperature. The effect of variable viscosity on the transversal magnetic field and hybrid convection can be seen by using Reynold’s model. The resulting nonlinear system of partial differential
equations is transformed into a nonlinear system of first-order ordinary differential equations by the Lobatto IIIA approach, simplifying physical flow problems. Moreover, the impact of different parameters on temperature and velocity is shown graphically and tabulated results are also presented. The numerical findings obtained in this study are validated and very well in line with some previous literature findings. This
research has helped to minimize the fluid flow and increases the fluid temperature and associated thermal boundary thickness by increasing the amount of Hartmann (parameter). In addition, the effect of the mixed convection and applied magnetic transverse fields are studied
sj-docx-1-wmr-10.1177_0734242X221124069 – Supplemental material for Identification and selection of suitable landfill sites using GIS-based multi-criteria decision analysis in the Peshawar District, Pakistan
No full textSupplemental material, sj-docx-1-wmr-10.1177_0734242X221124069 for Identification and selection of suitable landfill sites using GIS-based multi-criteria decision analysis in the Peshawar District, Pakistan by Iftikhar Ali, Aneeza Islam, Syeda Maria Ali and Syed Adnan in Waste Management & Research</p
Design of stochastic computational Levenberg Marquardt backpropagation-based technique to investigate temperature distribution of longitudinal moving porous fin
No full textAbstract The improvement of thermal exchange is of utmost interest in a wide range of engineering areas. The current study focuses on thermal evaluation involving natural radiation and convection in a fractionally arranged moving longitudinal fin model placed under a magnetic field. We implement the Levenberg Marquardt backpropagation (LMB) algorithm for investigating an innovative use of stochastic numerical computation for analyzing the efficiency of the temperature distribution in a porous moving longitudinal fin. The datasets for LMB have been created using a shooting approach for dynamic systems with varying ranges of different parameters. The validation, testing, and training processes are used to simulate networks using the LMB approach for diverse scenarios of moving porous fin models. The reliability of results is assessed based on the regression measures, absolute error, error histograms, mean square error, and other metrics for fuller numerical modeling of the suggested LMB to investigate the thermal efficiency and effectiveness of porous moving fin
sj-doc-1-rcp-10.1177_14782715221131409 – Supplemental material for Comparative analysis of mRNA and inactivated COVID-19 vaccines: A study from Faisalabad district of Pakistan
No full textSupplemental material, sj-doc-1-rcp-10.1177_14782715221131409 for Comparative analysis of mRNA and inactivated COVID-19 vaccines: A study from Faisalabad district of Pakistan by Syed Ata Ul Munamm, Iftikhar Nadeem, Noor Mahdi, Muhammad Saqlain, Zaid Khalid Rana, Usman Feroze Khatana, Umer Mustansir Bhatty, Visakan Navayogaarajah, Fatimah Mahsal Khan and Masood Ur Rasool in Journal of the Royal College of Physicians of Edinburgh</p
Mediapipe based Preprocessed VGGFace2 Dataset
No full textVGGFace2 Dataset and Face Mesh PreprocessingIntroductionThe VGGFace2 dataset is a large-scale face recognition dataset containing over 3.31 million images of 9,131 identities, with an average of 362 images per identity. The dataset is designed to include extensive variations in pose, age, illumination, ethnicity, and profession, making it one of the most diverse and challenging face recognition datasets available. For more details, please refer to the original publication:VGGFace2: A dataset for recognizing faces across pose and age - DOI: 10.48550/arXiv.1710.08092
Preprocessing Using MediaPipe 3D Face MeshOn this dataset, we applied the MediaPipe-based 3D face mesh algorithm to accurately detect faces while removing all background elements, including hair. Our preprocessing strictly retained facial landmarks, ensuring that only the essential facial features were preserved. This approach significantly enhanced the accuracy and generalization of our model, as the model was trained exclusively on landmark-based facial data.
Training and PerformanceThe preprocessed data was utilized to train Xception model, which resulted in remarkably accurate outcomes due to the strictly landmark-based facial representation. The model demonstrated robust performance including explainable-AI, proving that eliminating unnecessary background elements contributed positively to its efficiency and reliability.
CitationIf you use this dataset or the preprocessed version in your work, please cite both of the following:
VGGFace2 Dataset:
@article{Cao2018VGGFace2, title={VGGFace2: A dataset for recognizing faces across pose and age}, author={Cao, Qiong and Shen, Li and Xie, Weidi and Parkhi, Omkar M and Zisserman, Andrew}, journal={arXiv preprint arXiv:1710.08092}, year={2018}}
DOI: [10.48550/arXiv.1710.08092](https://doi.org/10.48550/arXiv.1710.08092) Preprocessed Dataset using MediaPipe:@dataset{Shah2025_MediaPipe_FaceMesh, title={MediaPipe-based 3D Face Mesh Preprocessed VGGFace2 Dataset}, author={Shah, Syed Taimoor Hussain and Shah, Syed Adil Hussain and Zamir, Ammara and Qayyum, Kainat and Shah, Syed Baqir Hussain and Fatima, Syeda Maryam and Deriu, Marco Agostino}, year={2025}, doi={10.5281/zenodo.15078557}} DOI: [10.5281/zenodo.15078557](https://doi.org/10.5281/zenodo.15078557)
ContactFor any questions or further details, please feel free to contact us.Syed Taimoor Hussain ShahPolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, ItalyEmail: [email protected]: 0000-0002-6010-677
Design of evolutionary computational intelligent solver for nonlinear corneal shape model by Mexican Hat and Gaussian wavelet neural networks
No full textIn this study, an integrated computational intelligence algorithm is implemented for the numerical treatment of the two-point boundary value problems that arise in the nonlinear corneal shape (NCS) model through the exploitation of wavelet neural networks including Mexican-Hat (MHWNNs) and Gaussian-wavelet (GWNNs)
through global genetic algorithms (GAs) then hybridization with local sequential quadratic programming (SQP) solvers, i.e. MHWNNsGAs, GWNNs-GAs, MHWNNs-GA-SQP, and GWNNs-GA-SQP respectively. The GWNNs and MHWNNs are applied to calculate the mean squared error of mathematical modeling of the proposed problem through objective functions while optimization of the fitness functions is initially conducted with an efficiency of global search GAs and then the efficacy of local search technique SQP for fine-tuning. A comparison of the proposed solutions of MHWNNs-GAs, GWNNsGAs, MHWNNs-GA-SQP, and GWNNs-GA-SQP solvers with a reference solution of Adam’s method shows that the proposed schemes have better accuracy, stability, efficiency consistency on an independent number of runs analyzed through complexity analysis and different statistical operators
COVID-19 PANDEMIC: NEUROLOGICAL MANIFESTATIONS, COMPLICATIONS AND FUTURE PERSPECTIVE
No full textSyed Iftikhar Ur Hasaan*, Tooba Ali, Shafaq Taj, Fatema Tasnim, Warda Ali Naqvi, Moyosoreoluwa Onobun and Ahmed Al
A Grey Wolf-Driven Refinement of Fuzzy-Based Controller for Enhanced DC Microgrid Operation
No full textDistributed energy sources play a pivotal role in microgrids, addressing the stochastic nature of load demand. To maintain power stability, reduce computational time, and have minimal power fluctuations in a DC microgrid, a Grey Wolf Optimization (GWO) based fuzzy logic controller has been designed for a fuel cell, battery, and supercapacitor-based DC microgrid connected to a DC load. For this purpose, a fuzzy inference system (FIS) has been implemented for effective control signal generation. The main goal of this design is to eliminate the hit-and-trial method for selecting the spread of membership functions of the FIS through GWO while maintaining a stable power supply to the load and keeping the energy storage system within safe limits. The optimized controller's membership functions have been extensively evaluated through simulations carried out in MATLAB/Simulink (2020a). Lastly, a detailed comparative illustration of optimized and unoptimized fuzzy logic controllers for fuel cell, battery, and supercapacitor has been presented to demonstrate the superior performance of the GWO-fuzzy logic controller
Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal
No full textIrrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal
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