Superconductivity in the Layered Cage Compound Ba_(3)Rh_(4)Ge_(16)

We report the synthesis and superconducting properties of a layered cage compound Ba_(3)Rh_(4)Ge_(16).Similar to Ba_(3)Ir_(4)Ge_(16),the compound is composed of 2 D networks of cage units,formed by noncubic Rh-Ge building blocks,in marked contrast to the reported rattling compounds.The electrical resistivity,magnetization,specific heat capacity,andμSR measurements unveiled moderately coupled s-wave superconductivity with a critical temperature T_(c)=7.0 K,the upper critical field μ_(0)H_(c2)(0)~2.5 T,the electron-phonon coupling strength λ_(e-ph)~0.80,and the Ginzburg-Landau parameterκ~7.89.The mass reduction with the substitution of Ir by Rh is believed to be responsible for the enhancement of T_(c) and coupling between the cage and guest atoms.Our results highlight the importance of atomic weight of framework in cage compounds in controlling the λ_(e-ph) strength and T_(c).

Vibration of an infinite inhomogeneous transversely isotropic viscoelastic medium with cylindrical hole

This paper investigates the influences of higher order viscoelasticity and the inhomogeneities of the transversely isotropic elastic parameters on the disturbances in an infinite medium, caused by the presence of a transient radial force or twist on the surface of a cylindrical hole with circular cross section. Following Voigt＇s model for higher order viscoelasticity, the nonvanishing stress components valid for a transversely isotropic and higher order viscoelastic solid medium have been deduced in terms of radial displacement component. Considering the power law variation of elastic and viscoelastic parameters, the stress equation of motion has been developed. Solving this equation under suitable boundary conditions, due to transient forces and twists, radial displacement and relevant stress components have been determined in terms of modified Bessel functions. The problem for the presence of transient radial force has been numerically analysed. Modulations of displacement and stresses due to different order of viscoelasticity and inhomogeneity have been graphically depicted. The numerical study of the disturbance caused by the presence of twist on the surface may be similarly done but is not pursued in this paper.

Lightweight convolutional neural network models for semanticsegmentation of in-field cotton bolls

Robotic harvesting of cotton bolls will incorporate the benefits of manual picking as well as mechanical harvesting. For robotic harvesting, in-field cotton segmentation with minimal errors is desirable which is a challengingtask. In the present study, three lightweight fully convolutional neural network models were developed for thesemantic segmentation of in-field cotton bolls. Model 1 does not include any residual or skip connections,while model 2 consists of residual connections to tackle the vanishing gradient problem and skip connectionsfor feature concatenation. Model 3 along with residual and skip connections, consists of filters of multiplesizes. The effects of filter size and the dropout rate were studied. All proposed models segment the cotton bollssuccessfully with the cotton-IoU (intersection-over-union) value of above 88.0%. The highest cotton-IoU of91.03% was achieved by model 2. The proposed models achieved F1-score and pixel accuracy values greaterthan 95.0% and 98.0%, respectively. The developed models were compared with existing state-of-the-art networks namely VGG19, ResNet18, EfficientNet-B1, and InceptionV3. Despite having a limited number of trainableparameters, the proposed models achieved mean-IoU (mean intersection-over-union) of 93.84%, 94.15%, and94.65% against the mean-IoU values of 95.39%, 96.54%, 96.40%, and 96.37% obtained using state-of-the-art networks. The segmentation time for the developed models was reduced up to 52.0% compared to state-of-theart networks. The developed lightweight models segmented the in-field cotton bolls comparatively faster andwith greater accuracy. Hence, developed models can be deployed to cotton harvesting robots for real-time recognition of in-field cotton bolls for harvesting.

Image processing algorithms for in-field cotton boll detection in natural lighting conditions

In developing countries,the cotton harvesting operation is currently being performed manually.Due to the monotonous nature of this task and the involvement of a considerable amount of labor,this operation becomes very tedious and costly.The harvesting robots can be a good alternative for the selective picking of cotton bolls from the field.In this study,an attempt has been made to develop the image processing algorithms for in-field cotton boll detection in natural lighting conditions for the cotton harvesting robot.Four image processing algorithms namely color difference,band ratio,YCbCr method,and chromatic aberration were proposed for the real-time segmentation of cotton bolls under natural outdoor light conditions.The performance of developed image processing algorithms was evaluated and the experimental results revealed that the chromatic aberration method outperforms as compared to other developed algorithms.The chromatic aberration method showed the highest identification rate of 91.05%with false positive and false negative rates of 6.99%and 4.88%respectively,among all the proposed algorithms.The highest sensitivity and specificitywere found to be 81.31%and 97.53%,respectively,using the chromatic aberration method.Overall,the chromatic aberration approach demonstrated a very promising performance for in-field cotton bolls detection under natural lighting conditions which confirms its applicability for the robotic cotton harvesters.