Sweet lemon mechanical damage detection using image processing technique and UV radiation

Non-destructive measurement of qualitative parameters of agricultural produce is quite beneficial in the postharvest operations. The consequential effect of mechanical damage in citrus fruits is rarely visible in their appearance compared to other commodities. The purpose of this study was to propose a fast, non-destructive method for sweet lemon mechanical damage detection using image processing technique and UV radiation. For this purpose, 135 sweet lemons were tested based on a completely randomized factorial design. In order to examine mechanical damage, the independent variables included drop height, fruit diameter and tempering period (holding time at room temperature after treatment). Fruits were dropped from heights of 2, 2.5 and 3 m onto the ground. Then images were captured under UV light having a wavelength of 365 nm, 1, 3 and 6 days after treatment. The images were sent to a PC and analyzed using MATLAB software. “Green Spot Index” or GSI was defined to show the extent of mechanical damage. Results of the analysis of variance showed that the percentage of green spots on fruit skin is significant at the 1% level considering the main and double interaction effects of drop height and fruit diameter. Green spot index significantly increases with the level of mechanical damage. Accuracy of the developed method in differentiating the bruised and undamaged fruits was found to be 100%.     

A note on multivariate non-normal process capability analysis using “mvtnorm” library in R

In this note we examine using Genzbretz and Miwa algorithms to improve estimation of proposition of non-conformance in multivariate normal distributions. This estimation is required in the procedure outlined in Abbasi and Niaki (Int J Adv Manuf Technol 50(5–8):823–830, 2010) to determine process capability index of multivariate non-normal processes.     

Joint routing and channel assignment using online learning in cognitive radio networks

Cognitive radio networks (CRNs) are the solution for the problem of underutilizing the licensed spectrum for which there are more requests in the last couple of decades. In CRNs, Secondary users (SUs) are permitted to access opportunistically the licensed spectrum owned by primary users (PUs). In this paper, we address the problem of joint routing and channel assignment for several flows generated by source SUs to a given destination. We consider a more realistic model based on Markov modulated Poisson process for modeling the PUs traffic at each channel and the SUs try to exploit short lived spectrum holes between the PUs packets at the selected channel. The SUs want to cooperatively minimize the end-to-end delay of source SUs flows meanwhile the quality of service requirements of the PUs would be met. To consider partial observation of SUs about PUs activity at all channels and quick adaptation of SUs decisions to environment changes and cooperative interaction of SUs, we use decentralized partially observable markov decision process for modeling the problem. Then, an online learning based scheme is proposed for solving the problem. Simulation results show that the performance of the proposed method and the optimal method is close to each other. Also, simulation results show that the proposed method greatly outperforms related works at control of interference to the PUs while maintains the end-to-end delay of SU flows in a low level.

     

MXene Composite and Coaxial Fibers with High Stretchability and Conductivity for Wearable Strain Sensing Textiles

The integration of nanomaterials with high conductivity into stretchable polymer fibers can achieve novel functionalities such as sensing physical deformations. With a metallic conductivity that exceeds other solution‐processed nanomaterials, 2D titanium carbide MXene is an attractive material to produce conducting and stretchable fibers. Here, a scalable wet‐spinning technique is used to produce Ti₃C₂T_x MXene/polyurethane (PU) composite fibers that show both conductivity and high stretchability. The conductivity at a very low percolation threshold of ≈1 wt% is demonstrated, which is lower than the previously reported values for MXene‐based polymer composites. When used as a strain sensor, the MXene/PU composite fibers show a high gauge factor of ≈12900 (≈238 at 50% strain) and a large sensing strain of ≈152%. The cyclic strain sensing performance is further improved by producing fibers with MXene/PU sheath and pure PU core using a coaxial wet‐spinning process. Using a commercial‐scale knitting machine, MXene/PU fibers are knitted into a one‐piece elbow sleeve, which can track various movements of the wearer's elbow. This study establishes fundamental insights into the behavior of MXene in elastomeric composites and presents strategies to achieve MXene‐based fibers and textiles with strain sensing properties suitable for applications in health, sports, and entertainment.     

Preparation of Au nanoparticles by Q switched laser ablation and their application in 4-nitrophenol reduction

Clean Technologies and Environmental Policy - This work reports the fabrication of Au nanoparticles (NPs) by laser ablation of a gold metal plate immersed in water in the absence of stabilizing...     

Photocatalytic decomposition of VOCs by AC–TiO2 and EG–TiO2 nanocomposites

Clean Technologies and Environmental Policy - In this study, TiO2 nanoparticles (NPs)-based catalysts were prepared for the photocatalytic removal of toluene as a model VOC from air under UV light....