Search

The recent research was done to assess the hematological and biochemical disorders in nephrotoxicity induced by gentamicin (100 mg/kg) in Wistar rats. The green-formulated silver nanoparticles (AgNPs) by Tribulus terrestris leaf were analyzed by XRD, energy-dispersive X-ray spectrometer, and field emission scanning electron microscopy. The shape of the recent nanoparticles was spherical in size of 48 nm. In the in vivo, the classical and novel kidney injury parameters were used to assess the nephroprotective properties of AgNPs in animal model. Gentamicin decreased significantly (P ≤ 0.01) the value of PCV, concentration of HB, and count of RBC. The value of serum iron, erythropoietin, Cr, and urea raised in the gentamicin group. Leucogram revealed thrombocytopenia, granulocytosis, and leukocytosis. AgNPs improved the iron, erythropoietin, thrombocytes, leukogram, and erythrogram. The levels of creatinine, urea, GGT, ALT, AST, and ALP significantly reduced and albumin and total protein increased in group treated with AgNPs. Also, AgNPs significantly raised the anti-inflammatory cytokines, i.e., IL3, TGFβ, IL10, IL5, and IL4, and decreased the pro-inflammatory cytokines, i.e., TNFα, IL18, IL12, IL6, and IL1. These findings may offer AgNPs as a nephroprotective agent that could be a suitable therapeutic supplement in blood disorders conditions.

In order to use the existing hardware sources to improve the line selection performance of single-phase grounding fault of small resistance grounding systems， a method for line selection of single-phase grounding fault based on the global steady-state monitoring information of the substation automation system is investigated. The zero-sequence voltage of the busbar and the zero-sequence current of each line in the case of single-phase grounding faults are deduced. The ratio of zero-sequence current of lines with single-phase grounding faults to that of non-fault lines under extreme conditions is analyzed. Moreover， the line selection method of single-phase grounding fault is proposed. The zero-sequence current is used as the starting criterion for line selection of single-phase grounding fault， and the setting principle of zero-sequence principle is introduced to calculate the maximum transition resistance that can be started under the setting value of zero-sequence current. An example is given to illustrate the proposed method， and the results show that the steady-state monitoring information of the substation automation system can remarkably improved the detection performance of single-phase grounding fault of small resistance grounding systems.

This study investigates the evolutionary game dynamics among electric vehicle distribution entities in the context of shared charging facilities, addressing the critical issue of inadequate charging resources. To understand the behavior of different stakeholders under government incentive policies, we develop an evolutionary game model involving a government department and two logistics enterprises (A and B). Through stability analysis, we explore equilibrium conditions of evolutionarily stable strategies (ESSs) for the tripartite evolutionary game. To ensure the robustness of our findings, we conduct a MATLAB simulation analysis to validate the analytical results. Our findings highlight that government subsidies, the costs incurred by logistics enterprises to share charging facilities, and the additional distribution income derived from this sharing are critical in determining whether the evolutionary game can achieve a stable equilibrium state. This research enables logistics companies to optimize the use of charging resources, lower operating costs, and enhance delivery efficiency. Additionally, government subsidy policies play a crucial role in encouraging logistics enterprises to engage in charging facility sharing, thereby fostering the sustainable development of the entire logistics industry. Based on these insights, the paper offers practical recommendations to further promote the sharing of charging facilities in electric vehicle distribution. [ABSTRACT FROM AUTHOR]

Three-dimensional perception in intelligent virtual and augmented reality (VR/AR) and autonomous vehicles (AV) applications is critical and attracting significant attention. The self-supervised monocular depth and ego-motion estimation serves as a more intelligent learning approach that provides the required scene depth and location for 3D perception. However, the existing self-supervised learning methods suffer from scale ambiguity, boundary blur, and imbalanced depth distribution, limiting the practical applications of VR/AR and AV. In this article, we propose a new self-supervised learning framework based on superpixel and normal constraints to address these problems. Specifically, we formulate a novel 3D edge structure consistency loss to alleviate the boundary blur of depth estimation. To address the scale ambiguity of estimated depth and ego-motion, we propose a novel surface normal network for efficient camera height estimation. The surface normal network is composed of a deep fusion module and a full-scale hierarchical feature aggregation module. Meanwhile, to realize the global smoothing and boundary discriminability of the predicted normal map, we introduce a novel fusion loss which is based on the consistency constraints of the normal in edge domains and superpixel regions. Experiments are conducted on several benchmarks, and the results illustrate that the proposed approach outperforms the state-of-the-art methods in depth, ego-motion, and surface normal estimation. [ABSTRACT FROM AUTHOR]