EM Wave Propagation Speed, Comments on “Measurement of Time Delay of Alternating Electrical Field in Wires” and “Physical Principles of Measuring the Speed of Alternating Electrical Field

The time shift of an electromagnetic wave at a single frequency between a transmitter and a receiver can be used to determine the phase velocity of the wave propagation only if there is no reflection at the receiver or the reflection is very small. The reflection adds additional phase shifts to the composted wave of an incident wave and a reflected wave so that the time difference of the composted wave is shifted between the transmitter and the receiver. This time difference may be either decreased or increased and even negative in a certain condition. Ignoring the phase shift and time shift induced by the reflection, the authors of two articles recently published on “Modern Physics” wrongly claim of “the speed of alternating electric field can be 20 times faster than the speed of light”. The two articles are: “Measurement of Time Delay of Alternating Electrical Field in Wires” (Modern Physics, 2015, 5, 29-34) and “Physical Principles of Measuring the Speed of Alternating Electrical Field” (Modern Physics, 2015, 5, 35-39). In this communication note, theory and experiments are presented to falsify their claim. 只有在没有反射或反射因素极小的情况下，在发射端和接收端电磁场的时间差可以用于计算电磁场从反 射端到接收端的传播相速度。由于反射，测量到的合成波的相位是入射波和反射波相位的合成，由反射 而引入的相位变化导致发射和接收端之间的同相位电磁波的时间差的移动。这种两点间的合成波的时间 差由于反射既能增加也可能减小，在一定的情况下，时间差还可以是负数。由于忽视因反射而引起的相 位差或时间差的变化，最近发表在《现代物理》上的两篇文章的作者们用带有反射的合成波的时间差计 算电场传播速度，并和光在自由空间里传播相速度比较，进而在两篇文章中错误地宣称：“交变电场的 速度超过光速20倍以上。”这两篇在《现代物理》上发表的文章是：“导线中交流电场时间延迟的测定” (现代物理，2015，5，29-34)和“交变电场速度测量的物理原理”(现代物理，2015，5，35-39)。此 评论文章用理论和实验数据推翻其文章“超光速20倍”的结论

Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking

Multi-Object Tracking (MOT) remains a vital component of intelligent video analysis, which aims to locate targets and maintain a consistent identity for each target throughout a video sequence. Existing works usually learn a discriminative feature representation, such as motion and appearance, to associate the detections across frames, which are easily affected by mutual occlusion and background clutter in practice. In this paper, we propose a simple yet effective two-stage feature learning paradigm to jointly learn single-shot and multi-shot features for different targets, so as to achieve robust data association in the tracking process. For the detections without being associated, we design a novel single-shot feature learning module to extract discriminative features of each detection, which can efficiently associate targets between adjacent frames. For the tracklets being lost several frames, we design a novel multi-shot feature learning module to extract discriminative features of each tracklet, which can accurately refind these lost targets after a long period. Once equipped with a simple data association logic, the resulting VisualTracker can perform robust MOT based on the single-shot and multi-shot feature representations. Extensive experimental results demonstrate that our method has achieved significant improvements on MOT17 and MOT20 datasets while reaching state-of-the-art performance on DanceTrack dataset

Discriminative Feature Learning with Foreground Attention for Person Re-Identification

The performance of person re-identification (Re-ID) has been seriously effected by the large cross-view appearance variations caused by mutual occlusions and background clutters. Hence learning a feature representation that can adaptively emphasize the foreground persons becomes very critical to solve the person Re-ID problem. In this paper, we propose a simple yet effective foreground attentive neural network (FANN) to learn a discriminative feature representation for person Re-ID, which can adaptively enhance the positive side of foreground and weaken the negative side of background. Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons. The resulting feature maps of encoder network are further fed into the body part subnetwork and feature fusion subnetwork to learn discriminative features. Besides, a novel symmetric triplet loss function is introduced to supervise feature learning, in which the intra-class distance is minimized and the inter-class distance is maximized in each triplet unit, simultaneously. Training our FANN in a multi-task learning framework, a discriminative feature representation can be learned to find out the matched reference to each probe among various candidates in the gallery. Extensive experimental results on several public benchmark datasets are evaluated, which have shown clear improvements of our method over the state-of-the-art approaches

Identification of a Novel Tumor Microenvironment–Associated Eight-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

Background: Lung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient's outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients' outcomes for LUAD. Methods: Dataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment. Results: An eight-gene signature was built, and it was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature. Conclusion: Identifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients' prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients

Virtual Inertia Adaptive Control of a Doubly Fed Induction Generator (DFIG) Wind Power System with Hydrogen Energy Storage

This paper presents a doubly fed induction generator (DFIG) wind power system with hydrogen energy storage, with a focus on its virtual inertia adaptive control. Conventionally, a synchronous generator has a large inertia from its rotating rotor, and thus its kinetic energy can be used to damp out fluctuations from the grid. However, DFIGs do not provide such a mechanism as their rotor is disconnected with the power grid, owing to the use of back-to-back power converters between the two. In this paper, a hydrogen energy storage system is utilized to provide a virtual inertia so as to dampen the disturbances and support the grid’s stability. An analytical model is developed based on experimental data and test results show that: (1) the proposed method is effective in supporting the grid frequency; (2) the maximum power point tracking is achieved by implementing this proposed system; and, (3) the DFIG efficiency is improved. The developed system is technically viable and can be applied to medium and large wind power systems. The hydrogen energy storage is a clean and environmental-friendly technology, and can increase the renewable energy penetration in the power network

Phonon Thermal Transport Through Tilt Grain Boundaries in Strontium Titanate

In this work, we perform nonequilibrium molecular dynamics simulations to study phonon scattering at two tilt grain boundaries (GBs) in SrTiO3. Mode-wise energy transmission coefficients are obtained based on phonon wave-packet dynamics simulations. The Kapitza conductance is then quantified using a lattice dynamics approach. The obtained results of the Kapitza conductance of both GBs compare well with those obtained by the direct method, except for the temperature dependence. Contrary to common belief, the results of this work show that the optical modes in SrTiO3 contribute significantly to phonon thermal transport, accounting for over 50% of the Kapitza conductance. To understand the effect of the GB structural disorder on phonon transport, we compare the local phonon density of states of the atoms in the GB region with that in the single crystalline grain region. Our results show that the excess vibrational modes introduced by the structural disorder do not have a significant effect on phonon scattering at the GBs, but the absence of certain modes in the GB region appears to be responsible for phonon reflections at GBs. This work has also demonstrated phonon mode conversion and simultaneous generation of new modes. Some of the new modes have the same frequency as the initial wave packet, while some have the same wave vector but lower frequencies