Study on Electromagnetic Scattering of Cylinders Buried in a Half Space with Random Rough Surfaces of Finite/Infinite Length

Analysis of electromagnetic scattering of buried objects is a subject of great interest due to its practical importance in both military and civil applications, such as subsurface investigation and target detection. In reality, the earth is of layered structure of random rough interfaces, which leads to a greatly increased complexity of the analysis. However, it is necessary to incorporate the nature of random rough surface and the layered structure because they both have substantial impact on the scattered signature and hence affect the study of inverse scattering and detection of buried objects. In this dissertation, a Monte-Carlo multidomain pseudospectral time domain (MPSTD) method is developed for investigating the scattering from cylinders buried below a random rough surface separating two half spaces under various conditions. As a prelude, the formulation of multidomain PSTD algorithm is presented. Then, this formulation is extended and combined with the Monte-Carlo approach to analyze the scattering of an object buried below a random rough surface of finite length. In the analysis, special attention is paid to the treatments of the random rough surface including its profile generation, matching with CGL points, and subdomain patching. Next, the scattering of a cylinder buried below a random rough surface of infinite length is studied and a two-step computation model based on the Monte-Carlo MPSTD method is developed. Further, in order to better simulate the real situation, the analysis is then extended to study the scattering from one or more cylinders embedded in a layered half space with random rough surfaces. Finally, a near-zone field to far-zone field transformation technique is developed and presented. Sample numerical results under different conditions, involving random rough surface of various roughness, lower half space with different permittivities, and cylinders of circular and rectangular shapes are presented, validated, and analyzed. Throughout this research, a numerical technique based on Monte-Carlo method and MPSTD approach has been developed and validated for investigating cylinders buried in a half space with random rough surfaces. It is observed that the roughness of the random rough surface and the electromagnetic properties of the lower half space can significantly affect the scattered signature of the buried object

Raman spectroscopy-based prediction of ofloxacin concentration in solution using a novel loss function and an improved GA-CNN model

Abstract Background A Raman spectroscopy method can quickly and accurately measure the concentration of ofloxacin in solution. This method has the advantages of accuracy and rapidity over traditional detection methods. However, the manual analysis methods for the collected Raman spectral data often ignore the nonlinear characteristics of the data and cannot accurately predict the concentration of the target sample. Methods To address this drawback, this paper proposes a novel kernel-Huber loss function that combines the Huber loss function with the Gaussian kernel function. This function is used with an improved genetic algorithm-convolutional neural network (GA-CNN) to model and predict the Raman spectral data of different concentrations of ofloxacin in solution. In addition, the paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and gated recurrent units (GRU) models to conduct multiple experiments and use root mean square error (RMSE) and residual predictive deviation (RPD) as evaluation metrics. Results The proposed method achieved an $$R^2$$ R 2 of 0.9989 on the test set data and improved by 3% over the traditional CNN. Multiple experiments were also conducted using RNN, LSTM, BiLSTM, and GRU models and evaluated their performance using RMSE, RPD, and other metrics. The results showed that the proposed method consistently outperformed these models. Conclusions This paper demonstrates the effectiveness of the proposed method for predicting the concentration of ofloxacin in solution based on Raman spectral data, in addition to discussing the advantages and limitations of the proposed method, and the study proposes a solution to the problem of deep learning methods for Raman spectral concentration prediction

Seabed Terrain-Aided Navigation Algorithm Based on Combining Artificial Bee Colony and Particle Swarm Optimization

Position errors of inertial navigation systems (INS) increase over time after long-term voyages of the autonomous underwater vehicle. Terrain-aided navigation (TAN) can effectively reduce the accumulated error of the INS. However, traditional TAN algorithms require a long positioning time and need better positioning accuracy, and nonmatching and mismatching are prone to occur, especially when the initial position error is large. To solve this problem, a new algorithm combining the artificial bee colony (ABC) and particle swarm optimization (PSO) was proposed according to the principle of terrain matching, to improve the matching effect. Considering that PSO easily falls into a local optimum, the acceleration factor and inertia weight of PSO were improved. The improved PSO was called WAPSO. ABC was introduced based on WAPSO and could help WAPSO escape local optimum. The final algorithm was termed ABC search-based WAPSO (F-WAPSO). During the continuous iteration of particles, F-WAPSO seeks the optimal position for the particles. Simulation tests show that F-WAPSO can effectively improve the matching accuracy. When the initial position error is 1000 m, the matching error can be reduced to 93.5 m, with a matching time of only 13.7 s

HIPK1 Inhibition Protects against Pathological Cardiac Hypertrophy by Inhibiting the CREB‐C/EBPβ Axis

Abstract Inhibition of pathological cardiac hypertrophy is recognized as an important therapeutic strategy for heart failure, although effective targets are still lacking in clinical practice. Homeodomain interacting protein kinase 1 (HIPK1) is a conserved serine/threonine kinase that can respond to different stress signals, however, whether and how HIPK1 regulates myocardial function is not reported. Here, it is observed that HIPK1 is increased during pathological cardiac hypertrophy. Both genetic ablation and gene therapy targeting HIPK1 are protective against pathological hypertrophy and heart failure in vivo. Hypertrophic stress‐induced HIPK1 is present in the nucleus of cardiomyocytes, while HIPK1 inhibition prevents phenylephrine‐induced cardiomyocyte hypertrophy through inhibiting cAMP‐response element binding protein (CREB) phosphorylation at Ser271 and inactivating CCAAT/enhancer‐binding protein β (C/EBPβ)‐mediated transcription of pathological response genes. Inhibition of HIPK1 and CREB forms a synergistic pathway in preventing pathological cardiac hypertrophy. In conclusion, HIPK1 inhibition may serve as a promising novel therapeutic strategy to attenuate pathological cardiac hypertrophy and heart failure

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field