Disinfection by-products to obtain drinking water, such as CH2Cl2, CHCl3, and BrCHCl2, elicit cytotoxicity and hyperproliferation in human lung fibroblasts (MRC-5). Enzymes of their biotransformation modulate these damages and can generate toxic metabolites. However, it is unknown that the response to oxidative stress is involved in cellular hyperproliferation modulated by nuclear factor-kappa B (NF-κB). Hence, MRC-5 cells were treated with these compounds to evaluate ROS, lipid peroxidation, phospho-NF-κB/p65 (Ser536) levels, CAT, SOD, and GPx activities. Further, the interactions of halomethanes and ROS with IκBα/NF-κB/p65 complex were analyzed by molecular docking. The correlation among biomarkers showed positive relationships between the prooxidant damage with the antioxidant response, particularly in cells treated with CH2Cl2, and BrCHCl2. Furthermore, negative relationships among ROS with levels of NF-κB/p65 in cells treated with CH2Cl2, and CHCl3 were detected. The estimated relative free energy of binding using thermodynamic integration with the p65 subunit of NF-κB was -3.3 kcal/mol for BrCHCl2; -3.5 kcal/mol for both CHCl3 and O2•, and -3.6 kcal/mol for H2O2. The chloride and bromide are in close contact with the IPT domain residues, particularly in the RHD region involved in DNA binding. Ser281 is located in the same domain, allowing this protein's phosphorylation. Similarly, both ROS are in contact with IPT domain into region RHD and H2O2 as side-chain oxygen with Leu280, adjacent to the phosphorylation site of p65. However, a negative correlation among ROS with phospho- NF-κB/p65 suggests ROS's steric hindrance on the C-terminal domain of NF-κB/p65 is involved in antioxidant response.

The variation of mutual inductance and load parameters will affect the transmission power and efficiency of the inductive power transfer (IPT) system. The identification of mutual inductance and load parameters is an essential part of establishing a stable and reliable IPT system. This paper presents a joint identification method of load and mutual inductance for LCC-S IPT system, which does not require the establishment of primary and secondary communication and related control. Firstly, the resistance-inductance characteristics of the equivalent load of the rectifier are analyzed by simulation, and then the rectifier and system load are equivalent to the circuit model of resistance and inductance in series. Secondly, the characteristics of the reflected impedance are analyzed, and the functional relationship between the transmitter impedance and the rectifier impedance is established by using the ratio of the real part to the imaginary part of the reflected impedance, which realizes the decoupling of the load and the mutual inductance. Thirdly, the functional relationship between the equivalent impedance of the rectifier and the load resistance of the system is obtained by data fitting. Then, the equations of the above two functional relationships are combined. By measuring the voltage of the parallel compensation capacitor at the transmitting side, the current of the transmitting coil and the phase difference between the two, the battery load can be solved first, and then the mutual inductance can be calculated, so that the high-precision identification of the load and mutual inductance can be realized. Finally, an experimental platform of LCC-S IPT system is built for experimental verification. The experimental results show that the maximum identification errors of mutual inductance and load are 5.20 % and 5.53 %, respectively, which proves that the proposed identification method can achieve high precision identification.

The location and number data of individual fruit trees are critical for planting area investigation, fruit yield prediction, and smart orchard management and planning. These data are conventionally obtained through manual investigation and statistics with time-consuming and laborious effort. Object detection models in deep learning used widely in computer vision could provide an opportunity for accurate detection of individual fruit trees, which is essential for rapidly obtaining the data and reducing human operations errors. This study proposes an approach to detecting individual fruit trees and mapping their spatial distribution by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing collected high-resolution true-color images of fruit trees in the experimental pomelo tree orchards in Meizhou city, South China. An image dataset of deep learning samples of individual pomelo trees (IPTs) was constructed through visual interpretation and field investigation based on the fruit tree images captured by UAV remote sensing. Four different scales of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) for object detection were selected to train, validate, and test on the image dataset of pomelo trees. The results show that the average precision ([email protected]) of the four YOLOv5 models for validation reach 87.8%, 88.5%, 89.1%, and 90.7%, respectively. The larger the model scale, the higher the average accuracy of the detection result of validation. It suggests that YOLOv5x is a preferred high-accuracy model among the YOLOv5 family and is suitable to realize the detection of IPTs. The number of the IPTs in the study area was counted using YOLOv5x, and their spatial distribution map was made using the non-maximum suppression method and ArcGIS software. This study will provide primary data and technical support for smart orchard management in Meizhou city and other fruit-producing areas.

The article is focused on detailed framework process for hierarchized construction of periodic freight train paths (PFTPs) – allocation of pre-arranged railway capacity to freight rail operators. The framework process considers fluctuations in demand for capacity from freight rail opera-tors, so the quality of a freight train path is related with its construction priority. Introduced framework process aims to offer freight rail operators attractive train paths, with low number of scheduled stops, and this way enhance competitiveness and decrease energy consumption of freight railway as a factor for sustainable development. The proposed generic process is in-tended for all time horizons of capacity allocation. Correctness of the framework process is tested on the example of mainline Prague – Dresden, in the context of prospective (denser) mod-el passenger timetable.