On some properties of g-frames and g-coherent states

After a short review of some basic facts on g-frames, we analyze in details the so-called (alternate) dual g-frames. We end the paper by introducing what we call {\em g-coherent states} and studying their properties.Comment: In press in Il Nuovo Cimento

Modeling the optimal factors affecting combine harvester header losses

Combine header loss comprises more than 50% of wheat harvesting losses.  Therefore, decline in this part of the loss to the extent allowed amount is an important step in reducing of crop wastes.  Combine header is a complex system in which several factors are involved in its work.  And, if these factors can be adjusted and controlled to suit the working conditions, to a large extent of crop loss can be prevented during the harvest.  In this study, reel index, cutting height of crop and horizontal and vertical distance of reel from cutter bar were selected as the effective factors in header loss.  In response surface method, central composite design was used to modeling and finding optimal levels of mentioned factors.  The results showed that power model was the best model to describe the dependence of the independent variables and the dependent variable.  The optimum conditions for minimum combine header loss (103 kg/ha) were obtained 1.2, 25 and 5 for reel index, cutting height of crop and horizontal and vertical distances of reel from cutter bar, respectively

Numerical Simulation of Turbulent Flow Past a Cylinder Placed Downstream of a Step

This study investigates the effect on the flow structure in a backward-facing step (BFSF) due to a cylinder placed downstream of the step. Numerical simulations were carried out using OpenFOAM with several turbulence models (standard k-ɛ, RNG k-ɛ, standard k-ω, and SST k-ω). The recirculating flow, the skin friction coefficient (Cf), and the pressure coefficient (Cp) of the bottom wall were comparatively analyzed. The added cylinder modified the structure of flow and increased the skin friction coefficient (Cf) in the recirculation zone. Also, the pressure coefficient of the bottom wall increased immediately downstream of the cylinder and farther downstream of the reattachment point remained stable in the flow recovery process

Influence of a rigid cylinder on flow structure over a backward-facing step

In the present study, laminar and turbulent flow over a backward-facing step (BFSF) where a cylinder was placed immediately downstream of the step was investigated through numerical simulation using OpenFOAM. In laminar flow mean errors between numerical and literature experimental data for velocity profiles and reattachment lengths were lower than 8.1% and 18%, respectively. The cylinder significantly modified the structure of recirculating flow over the BFSF. In addition, the cylinder increased the skewness of the velocity profiles, and the location of the maximum velocity shifted towards the upper wall. In turbulent flow, the results from several RANS models (standard k-ɛ, RNG k-ɛ, standard k-ω, SST k-ω, and RSM (SSG)) were compared with literature experimental data. The average error in predicting reattachment length and velocity profiles ranged from 2.2% to 28.5% and from 7.8% to 14.5%, respectively. The most accurate model in predicting reattachment length and velocity profiles was the standard k-ɛ and SST k-ω models respectively. The cylinder modified flow structure and the distribution of turbulent kinetic energy, whose largest value was found downstream of a cylinder in the separated shear laye

Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed intelligence network for Internet of Things (IoT) devices. The heterogeneous nature of clients and data presents challenges for effective federated learning (FL) techniques, prompting our exploration of federated transfer learning (FTL) on Raspberry Pi, Odroid, and virtual machine platforms. Our study provides a detailed examination of the design, implementation, and evaluation of the FTL framework, specifically adapted to the unique constraints of various IoT platforms. By measuring the accuracy of FTL across diverse clients, we reveal its superior performance over traditional FL, particularly in terms of faster training and higher accuracy, due to the use of transfer learning (TL). Real-world measurements further demonstrate improved resource efficiency with lower average load, memory usage, temperature, power, and energy consumption when FTL is implemented compared to FL. Our experiments also showcase FTL’s robustness in scenarios where users leave the server’s communication coverage, resulting in fewer clients and less data for training. This adaptability underscores the effectiveness of FTL in environments with limited data, clients, and resources, contributing valuable information to the intersection of edge computing and DL for the 6G IoT