Improvement of mobility edge model by using new density of states with exponential tail for organic diode

The mobility edge （ME） model with single Gaussian density of states （DOS） is simplified based on the recent exper- imental results about the Einstein relationship. The free holes are treated as being non-degenerate, and the trapped holes are dealt with as being degenerate. This enables the integral for the trapped holes to be easily realized in a program. The J-V curves are obtained through solving drift-diffusion equations. When this model is applied to four organic diodes, an obvious deviation between theoretical curves and experimental data is observed. In order to solve this problem, a new DOS with exponential tall is proposed. The results show that the consistence between J-V curves and experimental data based on a new DOS is far better than that based on the Gaussian DOS. The variation of extracted mobility with temperature can be well described by the Arrhenius relationship.

Use of NMR relaxometry for determination of meat properties:a brief review

Nuclear magnetic resonance spectroscopy(NMR)is a non-destructive and rapid meat analytical technique.Since meat has high proton content,the quality of fresh meat can be assessed by exciting proton spin using NMR laboratory apparatus.NMR allows the assessment of intrinsic properties including animal development stages and muscle type,as well as extrinsic factors including processing and storage conditions that affect the technological traits of meat.It can be used to determine the water-holding capacity of meat,water distribution in muscles,meat authentication,fat content,post mortem aging,rigor mortis,and changes in metabolomes,among others.Changes in NMR relaxation times are associated with the changes in muscle components and their distribution as affected by various processing conditions.Since,industrial assessment is now demanding rapidity and real-time assessment,it is therefore essential to evaluate the potential of NMR in contrast to other techniques.Therefore,this review article presents the principle of working of NMR,various methods available,quality traits of meat that can be evaluated using this technique,and a brief history.This review article will help the industry to adopt novel NMR-based meat quality assessment tools for rapid quality determinations.

Defocus blur detection using novel local directional mean patterns(LDMP)and segmentation via KNN matting

Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information extraction.Existing defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera configuration.Hence,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned limitations.This paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur regions.We argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred regions.The fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the image.Additionally,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy images.Experimental results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur detection.Evaluation and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.