Spatial Resolution Requirements Research Articles

Effect of Spatial Resolution on Accurate Detection and Localization of Arrhythmia Rotors in Human Right Ventricular Tachycardia.

The goal of this study was to identify the spatial resolution requirements for accurate rotor detection and localization in human right ventricular tachyarrhythmias. Poor spatial resolution is often cited as a reason for the inaccuracy of cardiac mapping catheters in detecting and localizing arrhythmia rotors. High-resolution (0.7 mm) arrhythmia data from optical recordings obtained from human donor hearts (n = 12) were uniformly downsampled to lower resolutions (1.4-7 mm) to approximate the spatial resolution (4 mm) of clinical mapping catheters. Rotors were tracked at various subresolutions and compared to the rotors in the original data by computing F1-scores to create accuracy profiles for both rotor detection and localization. Further comparisons were made according to arrhythmia type, donor sex, anatomical region, and mapped surface: endocardium or epicardium. For a spatial resolution of 4.2 mm, the accuracies of rotor detection and localization were 57% ± 4% and 61% ± 7%, respectively. Arrhythmia type affected the accuracy of rotor detection (monomorphic ventricular tachycardia, 58% ± 4%; ventricular fibrillation, 56% ± 8%) and localization (monomorphic ventricular tachycardia, 70% ± 4%; ventricular fibrillation, 54% ± 13%). However, donor sex, anatomical region (right ventricular outflow tract, mid, and apical), and mapped surface (epicardium and endocardium) did not significantly affect rotor detection or localization accuracy. To achieve rotor detection accuracy of 80%, a spatial resolution of 1.4 mm or better is needed. The accuracy profiles provided here serve as a guideline for future mapping device development.

10Boron-film-based gas detectors at ESS

The development of detectors for the European Spallation Source is an important parallel element to the effort put into the design and construction of the neutron source and instruments. The 10Boron-film-based detector developments that started over a decade ago as basic detector concepts have now reached technical maturity after intense prototyping work and numerous testing campaigns. Several of the ESS beamlines that will soon enter the commissioning phase started welcoming the detector systems built with the 10B-film converter technology. The real-size demonstrators evolved in the last 2–3 years into a diverse suite of gas proportional counters for use in diffraction, reflectometry and small-angle scattering studies in spite of the operational complexity posed by the requirements for large-area coverage, low material budget and robustness for operation in the high-flux and high-radiation environment of ESS. The detectors are now being shipped to ESS or undergoing the last performance and calibration tests before being handed over to the instrument teams for installation in the host beamlines. A common feature of these detector systems is the very large number of readout channels that they are instrumented with in order to fulfill the demanding requirements for sensitivity, spatial resolution and count-rate capability. Across all of the detector types that will operate at ESS, the integration, testing and commissioning of the read-out technologies and software tools for data reduction, calibration and analysis are the focus of the detector and integration teams. These will yield a wealth of knowledge about their operation as well as initial results on the in-situ performance, a very important asset to ensure rapid commissioning of the detectors when neutrons from the ESS source become available. In this paper we will give an overview of the 10B-film-based detector technologies included in the ESS detector suite that are currently facing the transition from the production phase to installation and integration with the other beamline components, which comes with its own specific challenges of both organizational and technical nature.

Deep Learning-Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study.

Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning-optimized sequence using T1-weighted imaging. Clinical and optimized deep learning-based image reconstruction T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) were evaluated, comparing noncontrast sequences in 10 healthy controls and postcontrast sequences in 5 consecutive patients. Images were reviewed on a Likert-like scale by 4 fellowship-trained neuroradiologists. Scores (range, 1-4) were separately assigned for 11 vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness, and homogeneous CSF signal. Segment-wise scores were compared using paired samples t tests. The scan time for the clinical and deep learning-based image reconstruction sequences were 7:26 minutes and 5:23 minutes respectively. Deep learning-based image reconstruction images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in most vessel segments on both pre- and postcontrast images. Deep learning-based image reconstruction had lower background noise, higher image sharpness, and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the deep learning-based image reconstruction. Our preliminary findings suggest that deep learning-based image reconstruction-optimized intracranial vessel wall imaging sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of intracranial vessel wall imaging in clinical practice and should be further validated on a larger cohort.

Analyzing the Seasonal Vertical Displacement Fluctuations Using the Global Navigation Satellite System and Hydrological Load: A Case Study of the Western Yunnan Region

The non-tectonic deformation caused by hydrological loads is an important influencing factor in GNSS vertical displacement. Limited by the temporal and spatial resolution of global models and model errors, the hydrological load results calculated by traditional methods are difficult to meet the high temporal and spatial resolution requirements of small to medium-scale regions. This paper introduces the idea of the remove–restore method, assimilates regional high-resolution hydrological data, and obtains higher temporal and spatial-resolution hydrological load results. Subsequently, utilizing data from 12 CORS observed in the western Yunnan region between January 2018 and December 2020, the quantitative relationship and variation characteristics between GNSS vertical displacement and hydrological load displacement were analyzed in detail. Furthermore, the annual signals of both were extracted using the SSA method for comparative analysis. After removing the effects of atmospheric load and non-tidal ocean load, the average correlation coefficient between GNSS vertical displacement and hydrological load displacement is 0.84, with an average reduction of WRMS (%) reaching 37.17%. The average correlation coefficient of the annual signals between GNSS vertical displacement and hydrological load deformation is 0.94, with an average reduction of WRMS (%) reaching 46.5%, indicating that the contribution of hydrological load to the GNSS non-tectonic vertical displacement annual signal is close to 50%. The research results provide scientific support and important references for studying surface tectonic deformation by removing non-tectonic deformations such as hydrological loads from GNSS vertical displacement. Additionally, it helps to explore the mechanisms of interaction between water storage migration and surface deformation.

Distributed ultra large strain measurement range sensing based on spectral shift adjacent point difference method in OFDR

Compared with the time-domain distributed optical fiber sensing technology, optical frequency domain reflectometry (OFDR) has the advantages of high spatial resolution and low detection optical power requirements. However, in the large strain measurement, OFDR encountered such a problem: the correlation of the Rayleigh scattering spectra between strain signals and reference signals is relatively low if the loading strain is too large, resulting in incorrect demodulation. To address this issue, we propose a new method called the spectral shift adjacent point difference method. In our approach, the cross-correlation operations using two sets of non-strain signals are performed, and the average value of the cross-correlation’s spectral shift is selected as a reference standard. Then the spectral shift of the entire sensing fiber is obtained by re-sweeping frequency when the strain is loaded along the sensing fiber. The data points with a spectral shift adjacent point difference less than the reference standard will be retained, while other data points with spectral shift adjacent point difference greater than the reference standard will be considered as noisy fake spikes and removed. Finally, interpolation fitting is performed on the retained data points along the entire sensing fiber to obtain the demodulated spectral shifts realize large strain measurement. The experimental results show that the maximum measurable strain by the proposed method can reach 10,800 µε with a spatial resolution of 7.84 mm, which is an order of magnitude higher than the conventional method. The experiment demonstrates the high stability and excellent repeatability of the proposed processing method. The proposed method has the advantage of simple algorithm and easy implementation, and making it more competent in the field of large strain measurement.

End-to-end simulations to optimize imaging spectroscopy mission requirements for seven scientific applications

CNES is currently carrying out a Phase A study to assess the feasibility of a future hyperspectral imaging sensor (10 m spatial resolution) combined with a panchromatic camera (2.5 m spatial resolution). This mission focuses on both high spatial and spectral resolution requirements, as inherited from previous French studies such as HYPEX, HYPXIM, and BIODIVERSITY. To meet user requirements, cost, and instrument compactness constraints, CNES asked the French hyperspectral Mission Advisory Group (MAG), representing a broad French scientific community, to provide recommendations on spectral sampling, particularly in the Short Wave InfraRed (SWIR) for various applications.This paper presents the tests carried out with the aim of defining the optimal spectral sampling and spectral resolution in the SWIR domain for quantitative estimation of physical variables and classification purposes. The targeted applications are geosciences (mineralogy, soil moisture content), forestry (tree species classification, leaf functional traits), coastal and inland waters (bathymetry, water column, bottom classification in shallow water, coastal habitat classification), urban areas (land cover), industrial plumes (aerosols, methane and carbon dioxide), cryosphere (specific surface area, equivalent black carbon concentration), and atmosphere (water vapor, carbon dioxide and aerosols). All the products simulated in this exercise used the same CNES end-to-end processing chain, with realistic instrument parameters, enabling easy comparison between applications. 648 simulations were carried out with different spectral strategies, radiometric calibration performances and signal-to-noise Ratios (SNR): 24 instrument configurations × 25 datasets (22 images + 3 spectral libraries).The results show that spectral sampling up to 20 nm in the SWIR range is sufficient for most applications. However, 10 nm spectral sampling is recommended for applications based on specific absorption bands such as mineralogy, industrial plumes or atmospheric gases. In addition, a slight performance loss is generally observed when radiometric calibration accuracy decreases, with a few exceptions in bathymetry and in the cryosphere for which the observed performance is severely degraded. Finally, most applications can be achieved with a realistic SNR, with the exception of bathymetry, shallow water classification, as well as carbon dioxide and methane estimation, which require the optimistic SNR level tested. On the basis of these results, CNES is currently evaluating the best compromise for designing the future hyperspectral sensor to meet the objectives of priority applications.

Spectroscopic and Photometric Study of the Low-luminous AGN NGC 4395

The study of intermediate mass black holes (IMBHs) and estimating the black hole mass could potentially elucidate the origin of supermassive black hole (SMBH) seeds present at high redshifts and how they co-evolve with their host galaxy. The difficulty in detecting these IMBHs and the requirement of high spatial resolution to measure BHs mass makes this task challenging. In this work, we have performed photometric and spectroscopic analyses of an extremely low luminosity Seyfert 1 galaxy NGC4395. Our observations revealed strong emission lines in the spectra, and we observed a fractional variability in the V-band of 12%. Utilizing the broad-line region (BLR) size-luminosity relation for active galactic nuclei (AGNs), we estimated the size of the BLR to be 74±14 light minutes, based on a continuum luminosity of 1038 erg s−1 at 5100 Å. Additionally, the gas clouds are found to be revolving with a velocity dispersion of 384 km s−1 around the central engine, leading to a black hole mass estimation of ∼3.87−1.1+1.1 ×105 M⊙.

Upscaling peatland mapping with drone-derived imagery: impact of spatial resolution and vegetation characteristics

ABSTRACT Northern peatland functions are strongly associated with vegetation structure and composition. While large-scale monitoring of functions through remotely sensed mapping of vegetation patterns is therefore promising, knowledge on the interdependency between spatial resolution of acquired imagery, spatial vegetation characteristics, and resulting mapping accuracy needs to be improved. We evaluated how classification accuracy of commonly used vegetation mapping units (microforms and plant functional types) was affected by spatial resolution of acquired imagery and several spatial characteristics of the vegetation itself (size, shape, configuration, and diversity). To this end, we collected very high-resolution drone imagery (<0.05 m) from eight Irish peatlands differing in vegetation composition and pattern complexity in September 2021 and 2022. We then resampled this imagery from pixel sizes of 0.027–1 m and classified both mapping units at all unique spatial resolutions. Hereafter, we computed spatial vegetation characteristics for each of the eight classified images at 0.027 m spatial resolution to determine their role in defining minimum spatial resolution requirements for both microforms and plant functional types. We found that overall classification accuracy of microforms and plant functional types was consistently high (>90%) for all studied peatlands until average spatial resolutions were reached of 0.5 m ± 0.2 m and 0.25 m ± 0.1 m, respectively. However, variability within mapping units was considerable, with overall minimum spatial resolution ranging between 0.25 and 0.7 m for microforms and between 0.15 and 0.35 m for plant functional types. Individual classes even varied from 0.05 to 1 m. Furthermore, spatial vegetation characteristics were important drivers of minimum spatial resolution for microforms, but not for plant functional types. Particularly, peatlands with larger, compacter, and more clustered microform patches could be classified with coarser spatial resolution imagery (up to 0.7 m), while peatlands with small, complex, diverse, and more finely distributed patches required higher spatial resolutions (minimally 0.25 m). Based on these findings, we conclude that spatial vegetation characteristics strongly determine minimum required spatial resolution and thus affect upscaling of peatland vegetation mapping beyond the landscape scale by constraining the use of specific remote sensing platforms.

Downscaling of Oceanic Chlorophyll-a with a Spatiotemporal Fusion Model: A Case Study on the North Coast of the Yellow Sea

Chlorophyll-a concentration (Chl-a) is an important indicator of coastal eutrophication. Remote sensing technology provides a global view of it. However, different types of sensors are subject to design constraints and cannot meet the requirements of high temporal and spatial resolution on nearshore engineering simultaneously. To obtain high-spatiotemporal-resolution images, this study examines the performance of the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) on GOCI and Landsat Chl-a data fusion. Considering the rapidly changing rate and consistency of oceanic Chl-a, the ESTARFM was modified via segmented fitting and numerical conversion. The results show that both fusion models can fuse multiple data advantages to obtain high-spatiotemporal-resolution Chl-a images. Compared with the ESTARFM, the modified solution has a better performance in terms of the root mean square error and correlation coefficient, and its results have better spatial consistency for coastal Chl-a. In addition, the new solution expands the data utilization range of data fusion by reducing the influence of the time interval of original data and realizes better monitoring of nearshore Chl-a changes.

Real-time water/fat imaging at 0.55T with spiral out-in-out-in sampling.

To develop an efficient and flexible water/fat separated real-time MRI (RT-MRI) method using spiral out-in-out-in (OIOI) sampling and balanced SSFP (bSSFP) at 0.55T. A bSSFP sequence with golden-angle spiral OIOI readout was developed, capturing three echoes to allow water/fat separation. A low-latency reconstruction that combines all echoes was available for online visualization. An offline reconstruction provided water and fat RT-MRI in two steps: (1) image reconstruction with spatiotemporally constrained reconstruction (STCR) and (2) water/fat separation with hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (HIDEAL). In healthy volunteers, spiral OIOI was acquired in the wrist during a radial-to-ulnar deviation maneuver, in the heart without breath-hold and cardiac gating, and in the lower abdomen during free-breathing for visualizing small bowel motility. We demonstrate successful water/fat separated RT-MRI for all tested applications. In the wrist, resulting images provided clear depiction of ligament gaps and their interactions during the radial-to-ulnar deviation maneuver. In the heart, water/fat RT-MRI depicted epicardial fat, provided improved delineation of epicardial coronary arteries, and provided high blood-myocardial contrast for ventricular function assessment. In the abdomen, water-only RT-MRI captured small bowel mobility clearly with improved water-fat contrast. We have demonstrated a novel and flexible bSSFP spiral OIOI sequence at 0.55T that can provide water/fat separated RT-MRI with a variety of application-specific temporal resolution and spatial resolution requirements.

Secondary flow and thermal field dynamics in a T-junction with an upstream elbow: A POD-aided, eddy-sensitized Reynolds-stress modeling study

The thermal mixing of the flow-crossing streams in a T-shaped pipe junction configuration with the main branch inflow forming an elbow is presently simulated by applying a RANS-based eddy-resolving model of turbulence. The low-temperature main stream enters the T-junction with a Reynolds number of Rem=107893 after passing through a 90o-turned elbow located upstream. The high temperature branch stream is injected perpendicularly into the main stream at a Reynolds number of Rem=5422. A significant difference between the flow velocities of the two streams leads to the formation of a characteristic mixing process resembling a reattached jet configuration. Of particular interest is the interference of the branch flow with the main flow characterized by secondary vortices – the so – called Dean vortices, that are convected from the curved main inlet generating a low-rank mixing structures in its wake. The reference LES-based (Large-Eddy Simulation) database is provided by Tunstall et al. (2016). The computational framework relying on the scale-adaptive turbulence modeling approach, introduced principally by Menter and Egorov (2010), is used to simulate the dynamics of large-scale turbulent structures with moderate spatial and temporal resolution requirements. The motion of unresolved subscale structures is described by an appropriately sensitized differential near-wall Reynolds stress model according to Jakirlić and Maduta (2015). In addition, Proper-Orthogonal Decomposition (POD) is used to analyze the prevailing flow and thermal field mechanisms. The results obtained show a reasonable level of agreement with the LES reference, both from the standpoint of first-order statistics and spectral reproduction of the dynamics of coherent structures.

Precise Short-Term Small-Area Sunshine Forecasting for Optimal Seedbed Scheduling in Plant Factories

Photosynthesis is one of the key issues for vertical cultivation in plant factories, and efficient natural sunlight utilization requires predicting the light falling on each seedbed in a real-time manner. However, public weather services neither provide sunshine data nor meet spatial resolution requirement. Facing these short-term and small-area weather forecasting challenges, we propose a cross-scale approach to infer seedbed-sized areas of sunshine from the city-level public weather services, and then design a seedbed rotation scheduling system for optimal natural sunlight utilization. First, an end-edge-cloud coordinated computing architecture was employed to concurrently aggregate the multi-scale data from weather satellites to sunshine sensors in the plant factory. Second, the small area of sunshine deterministically depends on the meteorological data given a fixed environment, and this correlation was described by a hybrid mapping model, which combined the long short-term memory (LSTM) and gradient boosting decision tree (GBDT) algorithms to form the LSTM-GBDT hybrid prediction algorithm (LGHPA). By training the LGHPA with historical local sensory sunshine and the city-scale meteorological data, the hourly sunshine on a seedbed can be predicted from the public weather forecasting service. Finally, a dynamic seedbed scheduling scheme was constructed to provide uniform solar energy absorption according to the one-hour-ahead radiation estimation. Experiment results show that the hourly sunshine prediction error was less than 18.44% over a seasonal period and the deviation for different solar absorption by seedbeds with rotation capability is less than 7.1%. Consequently, it was demonstrated that the application of short-term, small-area sunshine forecasting improved the performance of seedbed rotation for uniformly absorbed solar radiation. The proposed method verifies the feasibility of precisely predicting small-area sunshine down to the seedbed scale by leveraging a model-based approach and a cloud-edge-end merged cybernetic computing paradigm.

An exploratory approach to estimate point emission sources

The current atmospheric emission inventories do not fully meet the spatial and temporal resolution requirements of air quality modelling applications. Considering Portugal as a case study and focusing on combustion point emission sources (i.e., public power, refineries, manufacturers, and construction activities), this work proposes a methodological approach and dataset to estimate anthropogenic emissions suitable for different spatial scales (from regional to local). The obtained results were similar to the annual values reported by the Portuguese Environment Agency with the maximum emissions being estimated for manufacturing and construction activities. No significant differences were recorded between the temporal profiles developed in this and previous studies. However, the country-specific proxies from the developed database allowed us to better represent the temporal and spatial patterns of the Portuguese atmospheric emissions. The combination of the BigAir database with a comprehensive and standardized approach could help policymakers define mitigation and/or plan measures to reduce emissions from point sources, support countries worldwide (with a lack of data) to develop high-resolution emission inventories, and improve the current global and European inventories.

The Ultrasound Window Into Vascular Ageing: A Technology Review by the VascAgeNet COST Action.

Non-invasive ultrasound (US) imaging enables the assessment of the properties of superficial blood vessels. Various modes can be used for vascular characteristics analysis, ranging from radiofrequency (RF) data, Doppler- and standard B/M-mode imaging, to more recent ultra-high frequency and ultrafast techniques. The aim of the present work was to provide an overview of the current state-of-the-art non-invasive US technologies and corresponding vascular ageing characteristics from a technological perspective. Following an introduction about the basic concepts of the US technique, the characteristics considered in this review are clustered into: 1) vessel wall structure; 2) dynamic elastic properties, and 3) reactive vessel properties. The overview shows that ultrasound is a versatile, non-invasive, and safe imaging technique that can be adopted for obtaining information about function, structure, and reactivity in superficial arteries. The most suitable setting for a specific application must be selected according to spatial and temporal resolution requirements. The usefulness of standardization in the validation process and performance metric adoption emerges. Computer-based techniques should always be preferred to manual measures, as long as the algorithms and learning procedures are transparent and well described, and the performance leads to better results. Identification of a minimal clinically important difference is a crucial point for drawing conclusions regarding robustness of the techniques and for the translation into practice of any biomarker.

Exploring Mission Design for Imaging Spectroscopy Retrievals for Land and Aquatic Ecosystems

AbstractThe retrieval algorithms used for optical remote sensing satellite data to estimate Earth's geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal‐to‐noise ratio (SNR) performance to meet biogeoscience objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at &gt;10 nm spectral resolution, &gt;30 m spatial resolution, and &gt;8‐day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit, and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.

Deep Learning Optical Flow with Compound Loss for Dense Fluid Motion Estimation

Flow motion with complex patterns, such as vortex, stagnant flow, and seepage, put forward higher spatial resolution requirements for particle image velocimetry (PIV). With the development of deep learning technology in optical flow estimation, many attempts have been made to introduce deep learning-based optical flow (DLOF) into PIV. Compared with the traditional optical flow method, the DLOF method has the advantages of higher precision, faster calculation speed, and avoiding manual parameter adjustment. However, DLOF research is generally developed based on the basic characteristics of rigid body motion, and its key loss function part still generally uses the L1 (mean absolute error, MAE, L1) or L2 (mean square error, MSE, L2) loss functions, which lack consideration of fluid motion characteristics. Therefore, the current DLOF research has the problems of large angular error and serious curl-divergence loss in fluid motion estimation scenarios with smaller spatial scales than rigid bodies. Based on the prior knowledge of the traditional fluid motion characteristics, this study proposes a fluid loss function for describing the fluid motion characteristics, and combines this loss function with Flownet2. The compound loss (CL) function is combined with the displacement error, angular error, and div-curl smooth loss. The method combined with the loss function in this paper is called FlowNet2-CL-PIV. In order to verify that the compound loss function proposed in this study has a positive impact on the model training results, this paper uses the cosine similarity measure to demonstrate its effectiveness. In addition, the parameter selection of the compound loss function is analyzed and compared, and it is verified that the best training effect can be achieved by adjusting the parameter so that the order of magnitude of each part of the compound loss function is consistent. In order to test the calculation effect of the Flownet2-CL-PIV method proposed in this study, synthetic particle images are used for model training and performance analysis. Simulation results in various flow fields show that the root mean square error (RMSE) and average angular error (AAE) of Flownet2-CL-PIV reach 0.182 pixels and 1.7°, which are 10% and 54% higher than the original model, respectively.

A Review of Physics-Informed Machine Learning in Fluid Mechanics

Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Soil texture is one of the most important soil characteristics that affect soil properties. Rapid acquisition of soil texture information is of great significance for accurate farmland management. Traditional soil texture analysis methods are relatively complicated and cannot meet the requirements of temporal and spatial resolution. This research introduced a self-developed vehicle-mounted in-situ soil texture detection system, which can predict the type of soil texture and the particle composition of the texture, and obtain real-time data during the measurement process without preprocessing the soil samples. The detection system is mainly composed of a conductivity measuring device, a camera, an auxiliary mechanical structure, and a control system. The soil electrical conductivity (ECa) and the texture features extracted from the surface image were input into the embedded model to realize real-time texture analysis. In order to find the best model suitable for the detection system, measurements were carried out in three test fields in Northeast and North China to compare the performance of different models applied to the detection system. The results showed that for soil texture classification, ExtraTrees performed best, with Precision, Recall, and F1 all being 0.82. For particle content of soil texture prediction, the R2 of ExtraTrees was 0.77, and RMSE and MAPE were 74.72 and 39.58. It was observed that ECa, Moment of inertia, and Entropy had larger weights in the drawn model influence weight map, and they are the main contributors to predicting soil texture. These results showed the potential of the vehicle-mounted in-situ soil texture detection system, which can provide a basis for fast, cost-effective, and efficient soil texture analysis. Keywords: soil texture, soil sensor, electrical conductivity, soil surface image DOI: 10.25165/j.ijabe.20231601.6918 Citation: Meng C, Yang W, Ren X J, Wang D, Li M Z. In-situ soil texture classification and physical clay content measurement based on multi-source information fusion. Int J Agric & Biol Eng, 2023; 16(1): 203–211.

Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images

With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses.

Design and Performance of Photon Imaging Detector Based on Cross-Strip Anode with Charge Induction

Imaging detectors based on a microchannel plate (MCP) and charge division anode with charge induction have broad applications in particles (photons, neutrons, ions, and electrons) detection. However, the application of a charge induction readout mode is mostly focused on planar anodes, and there are few reports on double-layer cross-strip (XS) anodes with high spatial resolution and low gain requirements. In this paper, we design the parameters of the resistive layer and XS anode by theoretical derivation and the three-dimensional finite element method, including the sheet resistance, substrate thickness, strip width, and insulation thickness. The performance of the XS detector is characterized with the help of a resolution target and full field illumination using three different centroid algorithms. We conclude that the modified center of gravity (MCoG) has best imaging performance, which achieves a spatial resolution of 44.2 μm with a periodic distortion about 25.8%.