Mapping Of Sources Research Articles

Aquacritical Vistula: the deep mapping of literary sources within the emerging historiography of flooding (1934)

This article focuses on an aquacritical reading of literary sources within the emerging historiography of flooding. In 1934 the largest flood in the history of the Second Polish Republic (1918–1939) happened. After summer torrential rains the tributaries of the main river in Poland, the Vistula River, disrupted the functioning of the centrally-managed and newly recovered state. By using a deep mapping tool for river-related discourse analysis and for historical river management approach, the authors discuss different accounts of the 1934-flood: firstly, works from the historical period (J. Kurek, K. I. Gałczyński, relevant newspapers) and secondly, a contemporary reference to 1934 and flood narrative in prose (M. Płaza). All these literary sources contain numerous renamings of the Polish flood management dictionary but have one historical feature in common: they anchor the modern militarization of language in flood narratives (the fight against the river) and the symptomatic discourse of power and control (ruling over the river). Finally, these sources led the authors to the conclusion that both the Polish experience of World War I and later subsequent armed conflicts, as well as the impact of militarized state policy left their stamp on the flood lexicon and deepened the divide between humans and disempowered rivers.

Design and implementation of firefighting reconnaissance robot control system based on master-slave control

In response to issues related to casualties and property damage caused by fires, this paper primarily investigates a deformable wheeled firefighting reconnaissance robot with functions such as fire source detection, obstacle avoidance, and map transmission. The control system of the robot is elaborated, with the main motion control system utilizing STM32 technology. Tests were conducted on the robot’s two motion capabilities (wheeled and legged), obstacle avoidance ability, reconnaissance capability, etc. The results indicate compliance with the design requirements.

Correction to ‘Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches’

Correction to ‘Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches’

Multi-open-path laser dispersion spectroscopy combined with Bayesian state estimation for localizing and quantifying methane emissions

A global effort towards improved quantitative understanding of greenhouse gas emissions is taking pace. This includes developing source identification, quantification, and apportionment in an attempt to understand global budget and trends, but also developing monitoring systems making emission reduction commitment verifiable. In this context, we demonstrate a novel approach to continuous methane emission monitoring at the spatial scale of an industrial facility. By combining multi-directional measurements of path-integrated methane concentrations with Bayesian state estimation, we show a realistic tomographic gas plume reconstruction, its evolution in time, and the associated estimation of the source map. The method is validated using measurements from controlled methane releases over a domain of area 120×40 m2. For the first demonstration, a two dimensional geometry has been used in the gas flow model; nevertheless, sources are located within 3–12 meters, and mass emission rates are estimated within <30% for 80% of the cases.

High-Resolution localization of broadband sound sources in a duct using out-duct array measurements

This paper investigates using an Iterated Bayesian Focusing Algorithm and out-duct array measurements to identify multiple broadband sources placed in a cylindrical duct. The problems of large main lobe and low resolution due to localization of sources using conventional methods are investigated in this paper. To solve these problems, an Iterated Bayesian Focusing (IBF) method is utilized to localize the broadband sources. To evaluate and confirm the efficacy of the method, simulation tests and experimental validation are performed. In our experiments, we used a 56-channel dobby spiral array for out-duct measurements and beamforming method, equivalent source method (ESM), deconvolution approach for the mapping of acoustic sources (DAMAS), and Iterated Bayesian Focusing (IBF) method to localize broadband sources inside the duct. Simulation and experimental results demonstrate that the Iterated Bayesian Focusing (IBF) method significantly reduces the main lobe width and minimizes the number of minor lobes. This leads to improved accuracy in localizing double sources.

Integrating spatial-temporal soundscape mapping with landscape indicators for effective conservation management and planning of a protected area

Protected areas (PAs) possess generous biodiversity, making them great potential for human and wildlife well-being. Nevertheless, rising anthropogenic sounds may pose a serious challenge and threat to the habitats. Therefore, understanding the acoustic environments of PAs and implementing proper conservation strategies are essential for maintaining species richness within the territory. In this study, we investigate the spatial-temporal variations of soundscape distribution in the Dashanbao Protected Area (DPA) of China, ultimately discussing the planning and management strategies. Firstly, to systematically analyse the spatial-temporal soundscape distribution of the reserve, we generated single and multi-acoustic source maps by classifying geographical, biological, and anthropogenic sounds. In the region, we installed 35 recording points and collected sounds using the synchronic recording method. Secondly, we conducted Spearman correlation analyses to examine the relationships between the sound sources and i) temporal variations, ii) landscape feature indicators. Thirdly, we identified the dominant sound sources in the region and their conflict areas through the cross-analysis module of Grass Geographic Information Systems (GIS). Finally, we provided sound control strategies by discussing landscape indicators and land-use management policies. The results show that even though there is conservation planning in the DPA, anthropogenic sounds dominate in certain parts of the reserve depending on diurnal and seasonal cycles. This reveals deficiencies in the DPA's current planning concerning the soundscape and highlights the effectiveness of spatial-temporal mapping. Additionally, our correlation analyses demonstrate that landscape feature indicators can represent how sound environment is affected by landscape. The patch diversity (PD), landscape shape index (LSI), Shannon's Diversity Index (SHDI), woodland, shrubland, and water distance (WD) were identified as the primary predictors for both biological and anthropogenic sounds. None of the indicators exhibited a significant positive or negative correlation with geological sounds. Consequently, to enhance and conserve the acoustic quality of the region, spatial-temporal mapping with landscape indicators can be employed in the management and planning processes.

Heuristic optimal transport in branching networks

Optimal transport aims to learn a mapping of sources to targets by minimizing the cost, which is typically defined as a function of distance. The solution to this problem consists of straight line segments optimally connecting sources to targets, and it does not exhibit branching. These optimal solutions are in stark contrast with both natural, and man-made transportation networks, where branching structures are prevalent. Here, we discuss a fast heuristic branching method for optimal transport in networks. We also provide several numerical applications to synthetic examples, a simplified cardiovascular network, and the “Santa Claus” distribution network which includes 141[Formula: see text]182 cities around the world, with known location and population.

Experimental and numerical comparison of multiple passive beamformers for separating intra- and extra-canal cavitation activity during transvertebral spinal cord therapy

Distinguishing intra-canal cavitation activity during transvertebral focused ultrasound sonication of the spinal cord is a challenge due to the strong prefocal cavitation emissions in the spinalis musculature overwhelming emissions originating in the canal. To achieve mapping of all cavitation sources simultaneously, two methods for enhancing detection sensitivity are investigated: (1) multiple dynamic ranges within a reconstructed volume and (2) utilizing alternative beamformers to delay-and-sum (DAS) during map reconstruction. The performance of DAS beamforming is compared to a delay-multiply and-sum beamformer (DMAS), with and without a paired multiplicative compounding method (pDMAS). Experiments and simulations were performed on a 128-element, dual-aperture transvertebral array, through stacks of ex vivo human vertebra. Numerically and experimentally obtained point spread functions were compared in 3D, producing voxel-wise cross correlation values of 0.84, 0.89, 0.97 (N = 1) for the beamformers listed above, respectively, in water. Experimental, transvertebral localizations of canal sources in isolation produced localization error of 2.8 ± 1.2, 2.9 ± 1.4, 2.7 ± 1.3 mm for a single vertebral target (N = 30 sonications), respectively. A large numerical data set investigating the prefocal cavitation problem (N &amp;gt; 160) is presented and compared with (N = 9) experimental data demonstrating enhancement of intracanal sensitivity in cavitation maps.

Automotive Security: Threat Forewarning and ECU Source Mapping Derived From Physical Features of Network Signals

Automotive Security: Threat Forewarning and ECU Source Mapping Derived From Physical Features of Network Signals

Mapping on Islamic Microfinance Research: Lesson Learn from Indonesia and Turkey

This study reviews the scientific literature on Islamic microfinance in Scopus-indexed journals. This analysis uses descriptive statistical analysis based on 345 papers from 2007 to 2023 related to the theme of Islamic microfinance. The export data was then processed and analyzed using the R-Biblioshiny application program to determine bibliometric visualization maps and topic trends in scientific literature. This bibliometric mapping research shows the mapping of sources, authors, affiliations, countries, documents, keywords, and thematic maps. This research finds that Islamic microfinance is essential in financial relations and sustainable development because it aligns with Islamic principles and encourages inclusive economic growth. In addition, this research also discusses more about Islamic microfinance from Indonesia and Turkey.

Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches.

Identification and mapping of larval sources are a prerequisite for effective planning and implementing mosquito larval source management (LSM). Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats by using multimodel ensemble modeling, which includes selection of models, ensembling method, and predictors, evaluation of variable importance, prediction of potential larval habitats, and assessment of prediction uncertainty. The models were built and validated based on multisite, multiyear field observations and climatic/environmental variables. Model performance was tested using independent field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors in western Kenya were elevation, geomorphon class, and precipitation for the 2 months prior. Additional predictors may be required to increase the predictive accuracy of the larva-positive habitats. This is the first study to provide a detailed framework for the process of multimodel ensemble modeling for malaria vector habitats. Mapping of potential habitats will be helpful in LSM planning.

Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.

The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity between EEG and MEG in measuring radial and tangential cortical sources, combined EEG/MEG is considered beneficial in improving the reconstruction performance of ESI algorithms. Traditional algorithms mainly emphasize incorporating predesigned neurophysiological priors to solve the ESI problem. Deep learning frameworks aim to directly learn the mapping from scalp EEG/MEG measurements to the underlying brain source activities in a data-driven manner, demonstrating superior performance compared to traditional methods. However, most of the existing deep learning approaches for the ESI problem are performed on a single modality of EEG or MEG, meaning the complementarity of these two modalities has not been fully utilized. How to fuse the EEG and MEG in a more principled manner under the deep learning paradigm remains a challenging question. This study develops a Multi-Modal Deep Fusion (MMDF) framework using Attention Neural Networks (ANN) to fully leverage the complementary information between EEG and MEG for solving the ESI inverse problem, which is termed as MMDF-ANN. Specifically, our proposed brain source imaging approach consists of four phases, including feature extraction, weight generation, deep feature fusion, and source mapping. Our experimental results on both synthetic dataset and real dataset demonstrated that using a fusion of EEG and MEG can significantly improve the source localization accuracy compared to using a single-modality of EEG or MEG. Compared to the benchmark algorithms, MMDF-ANN demonstrated good stability when reconstructing sources with extended activation areas and situations of EEG/MEG measurements with a low signal-to-noise ratio.

Synchronous Mapping of Neural Current Source and Sulcus With Acoustoelectric Brain Imaging

Synchronous Mapping of Neural Current Source and Sulcus With Acoustoelectric Brain Imaging

Geoecological and geochemical assessment of the impact of NMMC mining enterprises on the environment

Study of the dynamics of natural and man-made processes and their impact on the environment in the hills of Uzbekistan; study of ecological and geochemical processes occurring in technogenic and natural landscapes of mining enterprises of the NMMC; Conducting geochemical and analytical studies to identify the contents and distribution of toxic and other associated elements in technogenic waste from NMMC mining enterprises, soils, and plants in connection with their impact on the ecology of the environment. Study of geochemical features of the productive valuable and toxic element`s behavior in space and time in industrial waste area Geological and geochemical mapping of soil, plants and water sources. Study of the chemical composition, behavior and forms of occurrence of toxic elements (in soils, water streams, dust, gases, smoke) in the water area and in the vicinity of the Navoi Mining and Metallurgical Combine - geochemical ecology. As a result of investigations of initial samples by using mass spectrometric analysis, the limits of fluctuations and average contents of not only toxic elements, but also other associated elements were revealed.

Estimating dust source activities based on dust RGB images and wind speed

With the aim of improving the WRF/Chem model outputs for dust concentration, the soil erodibility as a representative for dust sources have been recalculated based on remote sensing (MSG-SEVIRI) and some relevant atmospheric data. The frequencies of dust episodes on specific regions have been modified by applying some weights with respect to atmospheric turbulence and wind speed. Several new regions of dust sources have been identified by the new dust source, and several formerly known dust sources have been identified as non-source areas in the new dust source map. The new dust source map have been used in the WRF/Chem model to simulated specific dust storms in the region. WRF/Chem results by the new dust sources were better performance in simulation of dust concentration over the region, in comparison to observations.

Prototype Design and Construction of 450 VA Permanent Magnetic Sync Generator for Vertical Axis Wind Power Generator House Scale with Low Wind Speed

Permanent magnet synchronous generator (PMSG) is a mechanical electrical machine that can convert motion energy into electrical energy which has two main parts, namely a rotor using permanent magnets and a stator without an iron core which the machine uses for low and low rotations. load torque used as a generator. The driving force for Wind Power Plants (PLTB) which we know is alternative and renewable power plants, the energy source is wind which continues to exist and is easily available, another reason for the need to develop permanent magnet generators for PLTB is the reduction of petroleum, other fossil energy, subsidies continue to cut materials fuel oil for power generation which increases the price of electricity per KWH as well as a global commitment to reduce greenhouse gas emissions. the need for electricity for the community and the high price of electricity as well as the difficulty of installing electricity with a capacity of 450 VA, this study aims to make a prototype of a laboratory-scale permanent magnet generator for wind power plants for residential homes that allows it to be placed close to the ground, no need to be directed towards the wind. blowing, can generate electricity with wind speed from 2.5 m/s, easy operation and low cost. The research method in this study is to conduct a literature study, conduct a wind source mapping survey from several sources to obtain wind speed data nationally, design a prototype and design a permanent magnet generator prototype and finally simulation with calculation capacity of the prototype. The design of the permanent magnet generator prototype for the Wind Power Plant has 12 poles with a rotor speed of 500 rpm (revolution per minute) with capacity of 450 VA.

Quantifying Mineral Dust Emissions on the Tibetan Plateau With a Modified Dust Source Map

AbstractThe region surrounding the Tibetan Plateau (TP) is widely considered a primary global dust source, with mineral dust comprising a significant proportion of aerosols over the TP. Current research on TP dust has mainly focused on transport from the surrounding deserts, with little focus on dust emissions from the TP's interior. The erodibility factor used by the WRF‐Chem (ERODDEF) is 0 for the TP, so the model cannot simulate the dust emissions inside the plateau. Thus, we constructed a high‐resolution erodibility data set (ERODSDS) based on a reliable dust source distribution and intensity map. Based on the modified EROD map, the WRF‐Chem model was used to simulate dust emissions and direct radiative forcing on the TP in 2018. With the modified EROD map, WRF‐Chem can well simulate the temporal variation and spatial pattern of mineral dust on the plateau, which greatly improves the model's dust emissions simulation accuracy on the TP.

Connections on Lie groupoids and Chern–Weil theory

Let [Formula: see text] be a Lie groupoid equipped with a connection, given by a smooth distribution [Formula: see text] transversal to the fibers of the source map. Under the assumption that the distribution [Formula: see text] is integrable, we define a version of de Rham cohomology for the pair [Formula: see text], and we study connections on principal [Formula: see text]-bundles over [Formula: see text] in terms of the associated Atiyah sequence of vector bundles. We also discuss associated constructions for differentiable stacks. Finally, we develop the corresponding Chern–Weil theory and describe characteristic classes of principal [Formula: see text]-bundles over a pair [Formula: see text].

Investigation on the time/frequency domain prediction model of radiation noise based on the source map

The main exterior noise sources of the high-speed train CRH380AM, such as pantograph and bogie, are studied by using the field test sound imaging data, and the equivalent point source spectrums are obtained here. Then, the prediction model of the exterior train noise is presented by using the moving point sound source radiation model in the time/frequency domain. The correctness and effectiveness of the model are verified by the field test data. The radiation contribution of local noise sources is analyzed by using this model, and the following conclusions are obtained: The largest noise sources are in the bogie areas, which provide the radiation contribution rate of about 80% when the train speed is above 300 km/h. The variation of the maximum radiation noise level can be ignored when the train formation or the pantograph lifting position is changed. The model can evaluate the radiated noise of similar vehicles quickly and accurately, which provides technical reference for acoustic optimization design of high-speed train.

A novel visual representation method for multi-dimensional sound scene analysis in source localization problem

A visual representation method, DoAgram, for multi-dimensional sound scene analysis is suggested. The visual representation of the sound source localization result gives intuitive information about the estimated one to the end user. Also, image-based deep learning is popularly used in the acoustic field nowadays, so such a visualized one can be used for data augmentation. To analyze the spatial sound scene for the moving source, the method displays the estimated azimuth angle and elevation angle of the source, and its corresponding time stamp and frequencies as RGB color channels and metadata by mapping the spatial coordinate to color space. Even though the suggested method is human-interpretable, decoding is needed for the quantitative analysis. Therefore, the time and frequency scanning method, and a histogram to estimate the DoA of the source are proposed. An experiment is conducted in an anechoic chamber to localize two quadcopter drones that have a mean angular velocity of 8°/s ± 9°/s (95 % CI) and 25°/s ± 31°/s (95 % CI), respectively, and the spatial sound scene analysis is implemented using the proposed methods. The test result shows that the trajectories with respect to the time of each source are well separated. Also, an additional test is conducted using an open-access audio dataset for machine learning. The cumulative source mapping method is adopted for the spatial sound scene analysis, and the decoded result shows that the DoAgram is feasible to adopt for machine learning applications.