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In the present study, 108-week-old Hyline W-98 laying hens, reared in a cage system in a farm allocated in Tepatitlán de Morelos Jalisco, México, showed cyanosis of the comb and wattles, and greenish-brown diarrhea. Macroscopic lesions revealed diffuse inflammation of the caecum with thinning walls and the presence of numerous small white worms of almost 2 cm, identified as Heterakis gallinarum. The liver was severely congested. Histopathological examination of the liver revealed the presence of necrosis and numerous forms of Histomonas trophozoites. Due to the unavailability of commercial vaccines and therapeutics against H. meleagridis, field surveillance in poultry is proviral to understanding the epidemiology and pathogenesis of H. meleagridis and identifying solutions for histomoniasis. Additionally, hence, the advantage of production in cages system is the control of parasitic infections; the subject of research is how birds were infested with Heterakis gallinarum and Histomonas meleagridis in this type of production.

Tuning magnetic properties in layered magnets is an important route to realize novel phenomenon related to two-dimensional (2D) magnetism. Recently, tuning antiferromagnetic (AFM) properties through substitution and intercalation techniques has been widely studied in $M\mathrm{P}{X}_{3}$ compounds. Interesting phenomena, such as diverse AFM structures and even the signatures of ferrimagnetism, have been reported. However, long-range ferromagnetic (FM) ordering has remained elusive. In this work, we explored the magnetic properties of the Cr-substituted $\mathrm{NiP}{\mathrm{S}}_{3}$. We found that Cr substitution is extremely efficient in controlling spin orientation in $\mathrm{NiP}{\mathrm{S}}_{3}$. Our study reveals a field-induced spin polarization in lightly (9%) Cr-substituted $\mathrm{NiP}{\mathrm{S}}_{3}$, which is likely attributed to the attenuation of AFM interactions and magnetic anisotropy due to Cr doping. Our work provides a possible strategy to achieve FM phase in AFM $M\mathrm{P}{X}_{3}$, which could be useful for investigating 2D magnetism as well as potential device applications.

The high edge rates of wide bandgap (WBG) semiconductors are known to excite resonances within device packaging, resulting in increased voltage overshoot and electromagnetic signatures. Simulation tools based on finite-element analysis (FEA) are commonly used during design to optimize device geometry and minimize parasitics. However, developing an accurate model to extract these parameters is challenging and requires an extensive understanding of the simulation environment. This article presents three custom power module geometries that are measured empirically and evaluated in COMSOL multiphysics. The included sensitivity analysis demonstrates that even a well-designed FEA model may offer poor predictions of module parasitics compared to empirical measurements. A virtual compensation procedure is then proposed that greatly improves the accuracy of the COMSOL FEA predictions by removing the contribution of the return path. This approach does not require empirical measurements to implement and can be applied generally to a variety of geometries. Finally, the technique is applied to a high-performance WBG power module, for which good agreement is demonstrated between empirical measurements and simulation predictions.

In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-readable relationships between input features with the help of nilpotent fuzzy logic and multi-criteria decision making (MCDM). In the neural network a parameterized, differentiable activation function is used, where the parameter is determined by gradient descent. The goal is to find the optimal regularization value by applying the deep learning model to classification problems from the UCI Machine Learning Repository.