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Since 2019 the breakout of Covid-19, the pandemic has strongly impacted individuals’ lives and economic activities. Although there was much research on the influence of Covid-19, fewer researchers studied the relationship between the impact of the pandemic and companies’ compensation policies. Therefore, the main goal of this essay is to analyze the influence of Covid- 19 on technology companies’ compensation policies. We chose three technology companies: Intel, Amazon, and Microsoft, and studied their proxy statement to collect the data on their director compensation and executive compensation from 2019 to 2021. After the comparison, we find that, for Intel, director compensation expenditure rises first in 2019 but drops dramatically in the following two years, with the executive compensation expenditure constantly decreasing from 2019 to 2021; for Microsoft, changes in director compensation are subtle during the pandemic and those in executive compensation are in the same direction with Intel; for Amazon, its expenditure on both director compensation and executive compensation drops rises magnificently from 2020 to 2021, showing a completely different trend from other two companies. This result shows that some technology corporates suffer loss from COVID-19, while some do not. The result also suggests that for those who want to serve as directors and executives in tech companies during the pandemic, a compensation policy with a several-year-long vesting period might be more beneficial and risk averse.

To achieve superior thermal and mechanical properties of copper-bonded (Cu-bonded) Si3N4 substrate, a pressure-assisted direct bonded Cu (DBC) technique was applied to bond Cu foil with Si3N4 plate. The effects of oxide layer (SiO2) thickness of Si3N4 plate on the microstructure, thermal and mechanical properties of the Si3N4-DBC samples were investigated. The successful bonding of Cu foil to Si3N4 plate was confirmed by the presence of the interfacial products of Cu2MgSiO4 and CuYO2. Additionally, it was demonstrated that a thin SiO2 layer can result in a discontinuous distribution of interfacial products while a thick one can lead to the formation of pores in SiO2 layer. Notably, the sample prepared by Si3N4 plate with 5-μm-thickness SiO2 layer and Cu foil with 5.9-μm-thickness oxide layer (Cu2O) exhibited the optimally comprehensive properties with thermal conductivity of 92 W·m−1·K−1 and shearing strength of 102 MPa, which demonstrates significant promise for application in power electronic modules.

Amorphization is considered to be an effective way to enhance the electrochemical performances of electrode materials due to the existence of isotropy and numerous defects. Herein, an amorphous hierarchically structured MnO2/acetylene black (a-MnO2/AB) composite is successfully fabricated via a redox method and subsequent mechanical ball milling. The a-MnO2/AB composite is composed of approximately 300 nm flower-like amorphous MnO2 submicron spheres and acetylene black particles with a diameter of about 50 nm. The a-MnO2/AB electrode exhibits an initial coulombic efficiency of 73.2%, excellent rate capabilities of 318 mA h g-1 at 9.6 A g-1, and high specific capacity retention of 1300 mA h g-1 after 300 cycles at 1 A g-1. The amorphous structure can provide more channels for rapid lithium-ion transmission due to the disorder and defects, and the ion-diffusion coefficient (∼5 × 10-7 cm2 s-1) is higher than those of crystalline materials. Due to the strong interactions (Mn-O-C bonds) between MnO2 and AB as a result of the ball milling, the composite shows low charge transport resistance and small volume changes during the discharging/charging process. This work provides a facile route for the construction of amorphous hierarchically structured Mn-based oxides as anodes for lithium-ion batteries (LIBs).

Wide-bandgap (WBG) semiconductors devices such as silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) can handle much higher voltage and current in a relatively small die size than their Si counterpart. However, the shrinkage in die size poses new challenges to the thermal management systems for SiC MOSFET power modules. As a two-phase cooling component, vapor chamber (VC), has extremely high thermal conductivity with benefits including light weight, low cost and uniform temperature distribution, making it promising in SiC power module packaging. This paper proposes a new packaging design in which SiC bare dies are directly integrated on the top of vapor chambers. In this design, the vapor chamber not only is a heat spreader, but also conducts the drain current of SiC MOSFETs. The VCs in this study are optimized to improve the thermal performance and reduce the thermal stress in the die solder layer. SiC power module prototypes directly integrated with VCs are built using a new fabrication process. The modules integrated with VCs can operate under 200 W of power dissipation per die (632 W/cm2) without exceeding the maximum rated junction temperature. This paper reveals the potential of directly integrating phase change cooling components inside power modules, providing a new solution to improve the thermal performance and reliability of SiC power modules without adding complexity and energy consumptions to external cooling systems.

This paper aims at the inspection problems faced by photovoltaic power plants in the long-term operation of photovoltaic power plants in harsh environments such as Qinghai and Tibet plateaus under low air pressure, high cold and hypoxia, large temperature differences, centered on robot technology, fusion sensor application technology, image recognition and processing Technology and artificial intelligence and other advanced technologies, research and development and design of intelligent robot inspection system for photovoltaic power plants. The system is used to monitor the operating status of photovoltaic panels and meteorological parameters in real time to establish a prediction model, estimate the instantaneous power generation of photovoltaic power plants, and establish an image recognition model based on convolutional neural networks, which can accurately detect the failure of photovoltaic power plant equipment. Analysis and discrimination provide innovative technical testing methods for unattended photovoltaic power stations to achieve safe and stable operation of high-altitude photovoltaic power stations and good operational benefits.

In recent years, SiC power modules are getting more and more attention in the field of Electrical vehicle. In order to aid the design, a design and evaluation method for electrical vehicle SiC module is proposed in this paper. The method considers the impact of the operation condition of electrical vehicle on the efficiency and reliability of SiC Power modules by the accurate modelling and simulation on the motor drive system in Simulink. Moreover, the multiphysics effects in power module, including the coupling relationship among power loss, temperature and mechanical stress fields, are emphasized by the co-simulation between MATLAB and simulation tools COMSOL. By the method, the power loss, temperature and stress distribution of power modules with different chip specification, packaging and layout can be accurately calculated, so that the cost, efficiency, safety and reliability of SiC power modules in the electrical vehicle can be comprehensively evaluated. The proposed evaluation procedure can be regarded as an effective support for the design of power modules, which greatly reduces the design period and cost. In this paper, two 1200V SiC modules with the same packaging and layout but different chips are fully evaluated with the method.

As a large amount of renewable energy is injected into the power grid, the source side of the power grid becomes extremely uncertain. Traditional static safety analysis methods based on pure physical models can no longer quickly and reliably give analysis results. Therefore, this paper proposes a deep learning-based static security analytical method. First, the static security assessment index of the power grid under the N-1 principle is proposed. Secondly, a neural network model and its input and output data for static safety analysis problems are designed. Finally, the validity of the proposed method was verified by IEEE grid data. Experiments show that the proposed method can quickly and accurately give the static security analysis results of the source-side high uncertainty grid.

As an important structure connecting rooms and atrium in large shopping malls, corridors have a huge impact on smoke movement but are rarely mentioned in previous studies. The purpose of this study is to investigate the impact of changes in the corridor’s physical boundary (corridor width and keep stand height) on the development of fire within a typical room-corridor-atrium model. A series of cases were carried out numerically by using Fire Dynamic Simulator (FDS).The result shows that: the keep stand height has a huge effect on the smoke migration path, asymmetric distribution of temperature, and the mass flow rate. With a certain heat release rate, a critical width was found to determine the maximum value of the temperature and spill plume of the atrium. Based on the above analyses, some smoke control measures were put forward and discussed. It indicates that the exhaust vent position and the keep stand height should better be given position 3 and non-dimensional height with 0.375 respectively. Besides, the keep stand was also introduced into Poreh’s spill plume model to verify the relationship between mass flow rates and keep stand height, the modified model shows a good linear relationship.

In view of the problem that the complex structure of distribution network and the low measurement data cause the low accuracy of distribution network state estimation, a distribution network weighted least square state estimation method based on phase measurement unit and network topology partitions is proposed.Firstly, based on the analysis of the network connectivity and the equilibrium degree of each partition, the topology partition model of the distribution network is established, and the distribution network is divided into several sub-regions to reduce the Jakobi matrix scale. Secondly, the synchronous phase measurement device is introduced to enrich the state estimation measurement types, improve the accuracy of measurement, and simplify the Jakobi matrix in each sub-region.Finally, through the power distribution system of IEEE33, the validity of the proposed method is verified from the aspects of state estimation accuracy, calculation time and convergence. The results show that the proposed method combines the advantages of network topology partition and phasor measurement unit, and improves the accuracy of state estimation while reducing the time of state estimation.

Blended teaching mode is one of the trends in higher education reform in recent years. The article studies the connotation of blended teaching, analyzes the organizational form and requirements of blended teaching, explores the problems in the implementation of blended teaching, and proposes relevant measures to strengthen the reform of blended teaching.

By introducing the transformation of NOC to SOC, this paper analyzes the main contents of SOC system, and discusses the five general thoughts of changing operation thinking, determining construction objectives, setting up a team and formulating rules and procedures, and building a technology platform for the construction of SOC system.

Accuracies of high voltage AC overhead transmission line parameters are important for power system analysis and calculation. In order to obtain accurate transmission line parameters, field measurements of transmission line parameters are often conducted in practice. However, for a large power system, there are many transmission lines that needed to be measured. To determine the priority and significance of transmission line parameters to be measured and reduce the measurement cost accordingly, a selection method is proposed based on the parameter error influences on power system calculation including power flow and static voltage stability. In this paper, the line parameter error sensitivity on power flow is analyzed by the definition of derivation and local linearization, to investigate the positive sequence parameter influences. Then, the L-index is adopted to study the line parameter error impacts on system voltage stability. Based on the two indexes above, a selection method for transmission line parameters to be measured is proposed. The method synthesis the influences of parameter error and obtains the priority of transmission line measurements. Simulation case studies conducted on the IEEE 39 node system have verified the effectiveness of the proposed method for practical engineering applications.

An amorphous MnO2 and acetylene black composite (AMOA) was synthesized through a facile redox method. The composite presents a heterogeneous structure of MnO2 nanosheets anchoring on the surface of acetylene black tightly. The AMOA anode shows excellent stability during initial-stage cycles at different current densities for lithium-ion batteries. The discharge capacity can maintain at 968 mAh g−1 and 410 mAh g−1 at 1 and 5 A g−1 after 40 cycles, respectively. The excellent performance can be attributed to amorphous materials, which mitigate volume expansion and promote phase transition. Moreover, the amorphous material can remain amorphous structure after 40 cycles.

In order to accurately evaluate the influence of the correlation and sequential feature of wind power, PV power, and load on the economic operation of a power system, a sequential probabilistic optimal power flow calculation method based on multi-uncertain source-load scenarios generated by a multi-generative adversarial network (MGAN) is proposed. First, the historical datasets of wind power, PV power, and load in adjacent areas were collected, and their probability distributions in 24 time periods were analyzed. The results showed that the probability distribution of different periods had obvious differences, which were defined as time sequence differences in this paper. Second, a novel neural network called MGAN for scenario generation was designed. Based on the historical data of each time period, the correlated wind power, photovoltaic (PV) power, and load scenarios were generated by MGAN. Finally, in the modified IEEE 118-bus system, in which two wind power sources, two PV power sources, and two loads with correlation were integrated, probabilistic optimal power flow calculations were carried out for 24 periods with the goal of the lowest power generation cost of thermal power units. The experimental results showed that the power generation cost fluctuation ranges that considered the time sequence difference were reduced by 60% on average compared with the nonconsidered. The proposed method can effectively reduce the power generation cost budget.

With the shortage of transmission channel resources and the increase of human activities, the inspection of transmission channel environment changes is of great significance to the protection of line breakage, the management of transmission channel resources and the detection of hidden dangers in advance. In order to explore a fast and intelligent extraction method of environmental change information in the transmission channel, this paper takes a transmission line section in Tongzhou District of Beijing as the experimental area, takes two high-resolution satellite images of Beijing No.2 as the data source, and conducts principal component analysis and vegetation index processing analysis on the two satellite image data. An intelligent patrol method for transmission channel environmental change based on multi-source feature multi-scale segmentation technology and object-oriented classification algorithm is proposed. The results show that the satellite remote sensing environmental change intelligent patrol method proposed in this paper is sensitive and effective to the human activity change patrol of the transmission channel, and can be extended to the large-scale and normal intelligent patrol operation of the transmission channel against external breach.

The use of portable X-ray stress analyzers, which utilize an area detector along with the newly adopted 'cosα' or full-ring fitting method, has recently attracted increasing interest. In laboratory conditions, these measurements are fast, convenient and precise because they employ a single-exposure technique that does not require sample rotation. In addition, the effects of grain size and orientation can be evaluated from the Debye ring recorded on the area detector prior to data analysis. The accuracy of the measured stress, however, has been questioned because in most cases just a single reflection is analyzed and the sample-to-detector distances are relatively short. This article presents a comprehensive analysis of the uncertainty associated with a state-of-the-art commercial portable X-ray device. Annealed ferrite reference powders were used to quantify the instrument precision, and the accuracy of the stress measurement was tested by in situ tensile loading on 1018 carbon steel and 6061 aluminium alloy bar samples. The results show that the precision and accuracy are sensitive to the instrument (or sample) tilt angle (ψ) as well as to the selected hkl reflection of the sample. The instrument, sample and data analysis methods all affect the overall uncertainty, and each contribution is described for this specific portable X-ray system. Finally, on the basis of the conclusions reached, desirable measurement/analysis protocols for accurate stress assessments are also presented.

The sustained penetration of wind and solar generation is conducive to alleviating the energy crisis and environmental pollution. However, the finite capacity of transmission corridor limits the delivery of remote renewables and leads to curtailment. Energy storage plays an important role in renewables accommodation and improving equipment utilization, and shared energy storage can magnify the benefits through a temporal and spatial complementary. This paper proposes an online dispatch approach of energy storage shared by multiple renewable plants. The uncertainty and non-anticipativity issue are addressed via a two-stage decision-making process. The day-ahead scheduling determines the allowable energy and power levels of shared energy storage; the real-time online dispatch decides the wait-and-see strategies of charging and discharging power based on a single-period optimization model constrained by the pre-scheduled bounds. The case study verifies the effectiveness of the proposed method.

In the era of wireless communication, the security of transmission, especially physical layer security, has become a big concern. In this paper, we propose an efficient physical layer encryption scheme for the OFDM based wireless systems. The scheme employs the time domain encryption to protect the entire physical layer frame in two steps. First, transmitted data in Presentation Protocol Data Unit (PPDU) is protected with an iterative encryption algorithm that can effectively deal with known plaintext and ciphertext attacks. Second, Long Training Sequence (LTS) is also encrypted to prevent the adversary from performing correct channel synchronization. We analyze the security of the proposed scheme under different attack scenarios, and evaluate the performance of our scheme with the simulation experiments.