Coastal Disasters and Remote Sensing Monitoring Methods

Coastal disaster is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Remote sensing technology has real-time and large-area advantages in promoting the monitoring and forecast ability of coastal disaster. Relative to natural disasters, ones caused by human factors are more likely to be monitored and prevented. In this paper, we use several remote sensing methods to monitor or forecast three kinds of coastal disaster cause by human factors including red tide, sea-level rise and oil spilling, and make proposals for infrastructure based on the research results. The chosen method of monitoring red tide by inversing chlorophyll-a concentration is improved OC3M Model, which is more suitable for the coastal zone and higher spatial resolution than the MODIS chlorophyll-a production. We monitor the sea-level rise in coastal zone through coastline changes without artificial modifications. The improved Lagrangian model can simulate the trajectory of oil slick efficiently. Making the infrastructure planning according the coastal disasters and features of coastline contributes to prevent coastal disaster and coastal ecosystem protection. Multi-source remote sensing data can effectively monitor and prevent coastal disaster, and provide planning advices for coastal infrastructure construction

PathMLP: Smooth Path Towards High-order Homophily

Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph neural networks (GNNs) and impeding their performance. Intriguingly, we observe that certain high-order information on heterophilous data exhibits high homophily, which motivates us to involve high-order information in node representation learning. However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: 1) over-smoothing due to excessive model depth and propagation times; 2) high-order information is not fully utilized; 3) low computational efficiency. In this regard, we design a similarity-based path sampling strategy to capture smooth paths containing high-order homophily. Then we propose a lightweight model based on multi-layer perceptrons (MLP), named PathMLP, which can encode messages carried by paths via simple transformation and concatenation operations, and effectively learn node representations in heterophilous graphs through adaptive path aggregation. Extensive experiments demonstrate that our method outperforms baselines on 16 out of 20 datasets, underlining its effectiveness and superiority in alleviating the heterophily problem. In addition, our method is immune to over-smoothing and has high computational efficiency

Analytical model for nonlinear vibration of flexible rotor system

An analytical model is proposed to analyze a series of typical nonlinear behaviors of flexible rotor system, such as resonance, oscillation, whirl and whip. The model is constructed by introducing a defined nonlinear scale factor ε, nonlinear stiffness and nonlinear damping. Based on multi-scale method, the analytical solutions of steady-state and transient-state are derived, and the nonlinear natural frequency and Frequency Response Equation (FRE) are obtained. A transient time scale factor t1 is defined to reflect the transient-state influence on steady-state solution. The experimental result also verifies the rationality and validity of the analytical model and the analytical solutions

FPGA accelerated model predictive control for autonomous driving

Purpose – The purpose of this paper is to reduce the difficulty of model predictive control (MPC) deployment on FPGA so that researchers can make better use of FPGA technology for academic research. Design/methodology/approach – In this paper, the MPC algorithm is written into FPGA by combining hardware with software. Experiments have verified this method. Findings – This paper implements a ZYNQ-based design method, which could significantly reduce the difficulty of development. The comparison with the CPU solution results proves that FPGA has a significant acceleration effect on the solution of MPC through the method. Research limitations implications – Due to the limitation of practical conditions, this paper cannot carry out a hardware-in-the-loop experiment for the time being, instead of an open-loop experiment. Originality value – This paper proposes a new design method to deploy the MPC algorithm to the FPGA, reducing the development difficulty of the algorithm implementation on FPGA. It greatly facilitates researchers in the field of autonomous driving to carry out FPGA algorithm hardware acceleration research

An agricultural drought index for assessing droughts using a water balance method: a case study in Jilin Province, Northeast China

Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed

Resveratrol Ameliorates Glucocorticoid-Induced Bone Damage in a Zebrafish Model

Resveratrol (Res) is a multi-functional polyphenol compound that has protective functions in cardiovascular and neurodegenerative diseases. This study aimed to determine the effect of Res on osteogenic differentiation and bone mineralization in zebrafish (Danio rerio) with dexamethasone (Dex)-induced bone damage. Our results showed that Dex exposure (15 μmol/l) decreased the green fluorescence areas and the integrated optic density (IOD) values in the skull bones of zebrafish larvae of the TG(SP7:EGFP) strain in a dose-dependent manner (p < 0.01). Furthermore, Dex exposure decreased the alizarin red S-stained areas (bone mineralization area) in the skeleton and spinal bones of zebrafish larvae of the AB strain in a dose-dependent manner (p < 0.01). By contrast, Res treatment (150 μmol/l) significantly increased both the green fluorescence and bone mineralization area in Dex-exposed zebrafish larvae. Thus, our data show that Res improves bone mineralization after glucocorticoid-induced bone damage in a zebrafish model. Res may be a candidate drug for the prevention of osteoporosis

Temporal Coordination of Gene Networks by Zelda in the Early Drosophila Embryo

In past years, much attention has focused on the gene networks that regulate early developmental processes, but less attention has been paid to how multiple networks and processes are temporally coordinated. Recently the discovery of the transcriptional activator Zelda (Zld), which binds to CAGGTAG and related sequences present in the enhancers of many early-activated genes in Drosophila, hinted at a mechanism for how batteries of genes could be simultaneously activated. Here we use genome-wide binding and expression assays to identify Zld target genes in the early embryo with the goal of unraveling the gene circuitry regulated by Zld. We found that Zld binds to genes involved in early developmental processes such as cellularization, sex determination, neurogenesis, and pattern formation. In the absence of Zld, many target genes failed to be activated, while others, particularly the patterning genes, exhibited delayed transcriptional activation, some of which also showed weak and/or sporadic expression. These effects disrupted the normal sequence of patterning-gene interactions and resulted in highly altered spatial expression patterns, demonstrating the significance of a timing mechanism in early development. In addition, we observed prevalent overlap between Zld-bound regions and genomic “hotspot” regions, which are bound by many developmental transcription factors, especially the patterning factors. This, along with the finding that the most over-represented motif in hotspots, CAGGTA, is the Zld binding site, implicates Zld in promoting hotspot formation. We propose that Zld promotes timely and robust transcriptional activation of early-gene networks so that developmental events are coordinated and cell fates are established properly in the cellular blastoderm embryo

Model Fusion from Unauthorized Clients in Federated Learning

A key feature of federated learning (FL) is that not all clients participate in every communication epoch of each global model update. The rationality for such partial client selection is largely to reduce the communication overhead. However, in many cases, the unselected clients are still able to compute their local model updates, but are not “authorized” to upload the updates in this round, which is a waste of computation capacity. In this work, we propose an algorithm FedUmf—Federated Learning with Unauthorized Model Fusion that utilizes the model updates from the unselected clients. More specifically, a client computes the stochastic gradient descent (SGD) even if it is not selected to upload in the current communication epoch. Then, if this client is selected in the next round, it non-trivially merges the outdated SGD stored in the previous round with the current global model before it starts to compute the new local model. A rigorous convergence analysis is established for FedUmf, which shows a faster convergence rate than the vanilla FedAvg. Comprehensive numerical experiments on several standard classification tasks demonstrate its advantages, which corroborate the theoretical results

A Hybrid MCDM Method Using Combination Weight for the Selection of Facility Layout in the Manufacturing System: A Case Study

Facility layout in the manufacturing system is an elaborate production activity since multiple, uncertain, and conflicting factors would affect facility layout decisions. Selecting the best facility layout among alternatives is considered as a problematic multicriteria decision-making (MCDM) problem. Generally, the facility layout is evaluated by the MCDM method with redundant factors, separate weights, and independent relationships between factors. To overcome this defect, this paper intends to propose a hybrid fuzzy MCDM method with combination weight (CW) based on integration with Delphi, fuzzy ANP, Entropy, and fuzzy PROMETHEE (CW-DFAE-FP) to select the most suitable facility layout alternative in an aircraft assembly workshop. The application results indicate that this method can effectively obtain the most suitable alternative for facility layout selection. Furthermore, the comparative analysis with other MCDM methods verifies the effectiveness of the proposed method and reveals the selection of weight has an impact on final priority ranking. Finally, sensitivity analysis highlights the performance of the proposed method and provides strategic insights to identify the critical criteria for the best alternative in facility layout

Atmospheric Anomaly Analysis Related to Ms > 6.0 Earthquakes in China during 2020–2021

The attention towards links of atmospheric parameter variation and earthquakes has increased exponentially by utilizing new methods and more accurate observations. Persistent research makes it possible to gain insight into the precursor mechanism of earthquakes. In this paper, we studied the universality of detecting atmospheric anomalies associated with earthquakes based on tidal force fluctuation in China for earthquakes of Ms > 6.0, and explored the influence of tidal force on tectonic stress. The data of air temperature, geopotential height, ozone mixing ratio, and relative humidity from the National Center for Environmental Prediction (NCEP) were analyzed to reveal the spatiotemporal variation of atmospheric anomalies at multiple isobaric surfaces. Furthermore, the coupling of atmospheric parameters was investigated. The results showed that continuous solicitation exerted by tidal forces could change the strength of tectonic stress that causes earthquakes. The evolution pattern of air temperature, geopotential height, and relative humidity could be supported by atmospheric thermal vertical diffusion, while the anomalies of ozone mixing ratio was not evident. This verified the feasibility of detecting multi-parameter atmospheric anomalies associated with earthquakes based on tidal force fluctuation. Our results provide more evidence for understanding the atmospheric precursor characteristics of earthquakes