Source Distribution Research Articles

Capability of big data to capture threatened vertebrate diversity in protected areas.

Protected areas (PAs) are an essential tool for conservation amid the global biodiversity crisis. Optimizing PAs to represent species at risk of extinction is crucial. Vertebrate representation in PAs is assessed using species distribution databases from the International Union for Conservation of Nature (IUCN) and the Global Biodiversity Information Facility (GBIF). Evaluating and addressing discrepancies and biases in these data sources are vital for effective conservation strategies. Our objective was to gain insights into the potential constraints (e.g., differences and biases) of these global repositories to objectively depict the diversity of threatened vertebrates in the global system of PAs. We assessed differences in species richness (SR) of threatened vertebrates as reported by IUCN and GBIF in PAs globally and then compared how biased this information was with reports from independent sources for a subset of PAs. Both databases showed substantial differences in SR in PAs (t=-62.35, p≤0.001), but differences varied among regions and vertebrate groups. When these results were compared with data from independent assessments, IUCN overestimated SR by 575% on average and GBIF underestimated SR by 63% on average, again with variable results among regions and groups. Our results indicate the need to improve analyses of the representativeness of threatened vertebrates in PAs such that robust and unbiased assessments of PA effectiveness can be conducted. The scientific community and decision makers should consider these regional and taxonomic disparities when using IUCN and GBIF distributional data sources in PA assessment. Overall, supplementing information in these databases could lead to more robust and reliable analyses. Additional efforts to acquire more comprehensive and unbiased data on species distributions to support conservation decisions are clearly needed.

Spatial and temporal patterns of floating litter in shallow habitats: Insights from high-tourism tropical areas in Northeastern Brazil

We investigated the composition, quantity, distribution and potential sources of floating litter in the subtidal zones of tropical beaches. Marine sampling sites were selected based on the presence (protected/sheltered) or absence (non-protected) of reefs. Plastic was the most abundant material, and the presence of floating litter from land-based sources was confirmed, which was likely associated with domestic and tourism-related activities, owing to irregular waste disposal. Marine debris was more abundant during the rainy season, and marine beaches had more floating litter than estuarine beaches. Coastal reefs served as a barrier, impeding the transport of marine litter by coastal currents, explaining the greater abundance of floating litter on protected/sheltered marine beaches. Thus, three mechanisms seemed to drive the amount of floating litter in the subtidal zone of the tropical beaches: 1) the availability of floating litter in the water, 2) coastal hydrodynamics favoring the accumulation of floating litter, and 3) a beach’s capacity to retain floating litter. Our study demonstrates that marine litter is an important pollution source in sensitive areas that play a key role as nurseries for marine life, especially in beaches close to reef formations.

Optimal control strategies for energy storage systems for HUB substation considering multiple distribution networks

With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices.

Integrated modeling of anisotropic neutron yields of classical and spherical tokamaks

Estimations of counting rates of neutron spectrometers in experiments on controlled fusion with magnetic confinement, as well as calculations of energy resolved flux densities of fusion neutrons from plasma to the walls of a reactor require spatial integration of the local, usually anisotropic function of the neutron source. The integrated modeling consists of three main stages. First, sources of fast particles in beam- or wave-heated plasma are calculated. The next stage deals with spatial, energetic, and angular velocity distributions of plasma ions. Finally, double differential rate coefficients of nuclear fusion reactions are computed. This article describes calculations of spatial distributions of nuclear fusion reaction rates in classical and spherical tokamaks and the anisotropy of the neutron yield and spectra. The results are based on analytical formulas for energetic and angular distributions of the local source of fusion products in plasma. Examples of energetic spectral densities of neutron fluxes on first walls are presented, as well as energy resolved counting rates of collimated neutron spectrometers for perpendicular and tangential lines of sight.

Resolving Twin Jets and Twin Disks with JWST and ALMA: The Young WL 20 Multiple System

We report the discovery of jets emanating from pre-main-sequence objects exclusively at mid-infrared wavelengths, enabled by the superb sensitivity of JWST’s Mid-Infrared Instrument Medium-Resolution Spectrometer. These jets are observed only in lines of [Ni ii], [Fe ii], [Ar ii], and [Ne ii]. The H2 emission, imaged in eight distinct transitions, has a completely different morphology, exhibiting a wide-angled, biconical shape, symmetrically distributed about the jet axes. Synergistic high-resolution Atacama Large Millimeter/submillimeter Array (ALMA) observations resolve a pair of side-by-side edge-on accretion disks lying at the origin of the twin mid-infrared jets. Assuming coevality of the components of the young multiple system under investigation, the system age is at least (2–2.5) × 106 yr, despite the discrepantly younger age inferred from the spectral energy distribution of the combined edge-on disk sources. The later system evolutionary stage is corroborated by ALMA observations of CO(2−1), 13CO(2−1), and C18O(2−1), which show no traces of molecular outflows or remnant cavity walls. Consequently, the observed H2 structures must have their origins in wide-angled disk winds, in the absence of any ambient, swept-up gas. In the context of recent studies of protostars, we propose an outflow evolutionary scenario in which the molecular gas component dominates in the youngest sources, whereas the fast, ionized jets dominate in the oldest sources, as is the case for the twin jets discovered in the WL 20 system.

Photoacoustic differential attenuation spectrum for bone assessment

Abstract Photoacoustic (PA) technology shows great potential in bone microstructure assessment. Due to the solid-liquid two-phase characteristics of cancellous bone, the PA signal received by transducers is aliasing of distributed PA source characteristics and acoustic propagation characteristics of cancellous bone. The contribution of various multi-scale distributed PA sources to the PA signal complexity leads to difficulty in identifying the sound propagation characteristics, making it difficult for quantitative assessment of bone microstructure. To eliminate the spectrum contribution of the PA source to the PA signal, we proposed an eccentric excitation-differential detection system and the acoustic propagation characteristics of cancellous bone can be obtained by calculating the PA differential attenuation spectrum (PA-DAS). The PA-DAS quantization parameter-slope is further extracted to quantify the attenuation of different frequency components. Simulation results show that slope strongly correlates to the porosity of cancellous bone, demonstrating the eccentric excitation-differential detection system combined with the PA-DAS method can realize the quantitative bone assessment.

Telescope imaging beyond the Rayleigh limit in extremely low SNR

Abstract The Rayleigh limit and low Signal-to-Noise Ratio (SNR) scenarios pose significant limitations to optical imaging systems used in remote sensing, infrared thermal imaging, and space domain awareness. In this study, we introduce a Stochastic Sub-Rayleigh Imaging (SSRI) algorithm to localize point objects and estimate their positions, brightnesses, and number in low SNR conditions, even below the Rayleigh limit. Our algorithm adopts a maximum likelihood approach and exploits the Poisson distribution of incoming photons to overcome the Rayleigh limit in low SNR conditions. In our experimental validation, which closely mirrors practical scenarios, we focus on conditions with closely spaced sources within the sub-Rayleigh limit (0.49-1.00R) and weak signals (SNR less than 1.2). We use the Jaccard index and Jaccard efficiency as a figure of merit to quantify imaging performance in the sub-Rayleigh region. Our approach consistently outperforms established algorithms such as Richardson-Lucy and CLEAN by 4X in the low SNR, sub-Rayleigh regime. Our SSRI algorithm allows existing telescope-based optical/infrared imaging systems to overcome the extreme limit of sub-Rayleigh, low SNR source distributions, potentially impacting a wide range of fields, including passive thermal imaging, remote sensing, and space domain awareness.

Source identification, characteristics, and spatial distribution of airborne microplastic deposition in Lahore City, Pakistan.

A large volume of atmospheric microplastics (MPs) has been observed worldwide and is considered an emerging global environmental issue. Nevertheless, no significant assessment of the atmospheric deposition of MPs in Pakistan has been reported yet. The present study was designed to highlight the source, type, and spatial distribution of MPs in atmospheric fallout in Lahore, Pakistan. A total of 23 sites were sampled in Lahore with a heterogeneous background of human activities. All samples were collected in a box with a one-foot depth fitted with a steel tray of 0.1 m2 at the bottom. Fenton's reagent and hydrogen peroxide were used to remove organic matter, and sodium chloride for density separation, while the MPs were quantified through a stereomicroscope, and polymers were identified through ATR-FTIR spectroscopy. The highest deposition rate (particles/m2/day) was observed at Badami Bagh, i.e., 6819, followed by Mall Road 4414, and Chung 4263, while the lowest was in Sabzazaar (i.e., 524) and Township (1047) with an average deposition rate of 2340 ± 1392. Among the MPs, the major portion was fiber, i.e., 96%, while fragment was 1.9%, sheets 0.78%, foams 1.12%, beads 0.04%, and other MPs 0.06%. At all sampling sites, 20 different types of polymers were identified with different percentages, of which polyester fibers were predominant with an abundance of 96.09% associated with clothes and textiles. A high frequency of MPs was found in populated areas with dense traffic, plastic wastes, household plastic materials, and mismanagement of wastes, which accelerates the atmospheric deposition of airborne microplastics. Evaluation and characterization of MP help assess health and environmental impacts, cleanup efforts, and guiding regulations. It also provides valuable information for waste management innovation to reduce plastic pollution.

Assessing pollinator abundance and services to enhance agricultural sustainability and crop yield optimization in the Qilian Mountains

ContextPollination services are critical to crop production and human livelihoods, linking natural ecosystems directly to agricultural production systems. However, pollination services and pollinators are under constant threat from land-use changes and various other environmental pressures. ObjectiveIn this study, employing a case study of the entire Qilian Mountains in northwest China, the distribution of nectar sources in different land use types in the study area was determined through field survey and literature. MethodsBased on 1990–2020 land use data, crop yield data, crop prices, pollinating bee species and nectar plants data. We used the Gross Ecological Product (GEP) accounting and InVEST model of pollination services and assessed the status and trends of pollination services, the risks posed by land use changes and environmental pressures, and suggested potential solutions for mitigating identified risks. Results and conclusionsThe results of the study showed that (1) nectar sources and nesting areas' potential distribution closely correlates with land use types; (2) the Pollinator abundance index (PAI) is above 0.30, a high level, and the Pollination potential index (PPI) is between 0.15 and 0.30, a medium level, with both indices generally increasing over the past 40 years; (3) Human economic activities and land management policies had the most significant impact on pollination services, with 15.57 % and 14.02 %, respectively. Climate change (temperature, precipitation, extreme events) and invasive alien species had relatively minor impacts, accounting for 0.14 % and 0.15 %, respectively. (4) The Qilian Mountains will face a new risk, whether monoculture or the expansion of pollinator-dependent crops could lead to habitat homogenization issues, potentially affecting pollinator abundance and diversity. SignificanceWe recommend that future plans emphasize the provision of pollinator nesting resources along with floral resources, restoration of semi-natural and natural habitats adjacent to crops, adjustment of cropping patterns, and implementation of agricultural diversification, which will help to ensure pollinator diversity and sustainability of agroecosystem pollination services.

Shallow water source depth discrimination based on a vertical linear array using deep learning

This study aims to explore source depth discrimination in shallow water waveguides using a vertical line array. Due to the similarity in physical characteristics of features and environmental parameters, the feature distributions of shallow and deep sources overlap in feature space. This overlap is further exacerbated by strong background noise, reducing detection reliability. Therefore, a deep learning-based source depth discrimination (DL-SDD) scheme is proposed. Specifically, this scheme is based on a residual structure, embedding channel attention mechanisms into the deep structure to eliminate noise-related information gradually. Furthermore, a specially designed loss function considers inter-class and intra-class distances to achieve compact and mutually distant distributions of source features. When this loss function is applied, the overlap of source feature distributions is suppressed in end-to-end feature learning, leading to a high detection probability. The numerical simulations demonstrate that the proposed DL-SDD outperforms traditional method, achieving a 7 % increase in detection probability and a 15 % decrease in false alarm rate, even near the discrimination depth. Furthermore, the discrimination depth is reduced by nearly half. Experimental results from the South China Sea validate the effectiveness of the proposed DL-SDD.

A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach

AbstractThe uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.

Risk Assessment Method for Distributed Power Distribution Networks Considering Network Dynamic Reconstruction

A new safety assessment framework has been proposed to address the operational risks of the integration of wind power and photovoltaic grid, which integrates the characteristics of distributed power sources with the dynamic reconfiguration requirements of the distribution grid. The framework comprehensively considers the impacts of wind power and photovoltaic output uncertainties, as well as load fluctuations, on the stability of the distribution grid. It also evaluates the safety under different operational states of the distribution grid. Using Halton sequence sampling technology to accurately simulate the output of distributed power sources and the status of system components, combined with CPLEX optimisation for solving, a dynamic reconfiguration model is constructed to address potential faults in the distribution grid. Introducing the combined weighting method, a comprehensive risk assessment system for voltage violations, power flow violations, and load shedding has been constructed. The effectiveness of this method has been validated through simulations on the IEEE33 bus and IEEE118 bus systems, providing new insights to improve the safety and reliability of distribution grids.

Development of an Internet-based Product-related Child Injury Textual Data Platform (IPCITDP) in China.

Internet-based media stories provide valuable information for emerging risks of product-related child injury prevention and control, but critical methodological challenges and high costs of data acquisition and processing restrict practical use by stakeholders. We constructed a data platform through literature reviews and multi-round research group discussions. Developed components included standard search strategies, filtering criteria, textual document classification, information extraction standards and a keyword dictionary. We used ten thousand manually labelled media stories to validate the textual document classification model, which was established using the Bidirectional Encoder Representation from Transformers (BERT). Multiple information extraction methods based on natural language processing algorithms were adopted to extract data for 29 structured variables from media stories. They were evaluated through manual validation of 1000 media stories about product-related child injury. We mapped the geographic distribution of media sources and media-reported product-related child injury events. We developed an internet-based product-related child injury textual data platform, IPCITDP, consisting of four layers - automatic data search and acquisition, data processing, data storage, and data application - concerning product-related child injury online media stories in China. Each layer occurred daily. External validation demonstrated high performance for the BERT classification model we established (accuracy = 0.9703) and the combined information extraction strategies (accuracy >0.70 for 25 variables). As of 31 December 2023, IPCITDP collected 35 275 eligible product-related child injury reports from 13 261 news media websites or social media platform accounts which were geographically located across all 31 mainland Chinese provinces and covered over 97% of prefecture-level cities. The injury cases in IPCITDP were typically reported several months or years earlier than official announcements about the product-related child injury risks. Our data platform added data concerning 15 supplementary variables that the national product-related injury surveillance system lacks. Two examples demonstrate the value of IPCITDP in supplementing existing data and providing early epidemiological detection of emerging signals concerning product-related child injury: magnetic beads and electric self-balancing scooters. Our data platform provides injury data that can support early detection of new product-related child injury characteristics and supplement existing data sources to reduce the burden of product-related injury among Chinese children.

Some cases of explicit expression of the resulting field strength of magnets placed in the field of external sources

Formulas for calculating of the resulting magnetic field strength of magnets with a given magnetization distribution or given field of external sources are presented. We have collected exactly those cases where a user who does not have much experience in the field of computational mathematics and programming can perform calculations using these formulas. Namely, in these cases formulas for tension are mostly expressed in final form through elementary functions or require the use of one of the standard subroutines available in the library of any programming language. Such formulas can serve both as reference material and for solving corresponding problems with suitable real objects, as well as for determining optimal strategies and the optimal set of input parameters when using universal software packages and assessing the error of the results obtained.

The Formation Mechanism of Soil Interflow in Loess Hill Gully

To address the problems of salinization of the soil in gully control and land-making projects, the formation mechanism of soil interflow from a gully valley on the Loess Plateau was investigated, regarding its interface, water source, and spatial-temporal distribution characteristics, through field location monitoring and isotope tracer technique. The results showed the following: (1) there are two types of soil interflow in the Loess Plateau, namely soil interflow in slope and in gully, with interflow in gully being the main form; (2) adequate water supply, layered soil structure, and geographic disparity are conditions for the formation of soil interflow in the gully; (3) soil water is recharged by precipitation, surface water, and groundwater. Surface water is an important source of soil water recharge at the 0–100 cm depth, whereas groundwater is an important source of soil water recharge at the 100–200 cm depth. The results provide a basis for the regulation of the soil interflow, resource utilization, and land quality improvement in the Loess Plateau.

Simulation Study of Dynamic Rotation and Deformation for Plasmonic Electric Field-Skyrmions

The topological properties of optical skyrmions in confined electromagnetic fields are perfectly presented through spin vectors and electric-field vectors. However, currently, electric-field optical skyrmions in surface plasmon polaritons are mostly presented in the form of a Néel type. Most control strategies involve linear directional movement, and topological manipulation methods are monotonous. We specifically propose a multi-arc symmetric slit array, which generates skyrmions from the surface plasmon polariton (SPP) field under excitation of a linearly polarized Gaussian light-source array and exhibits strong dependence processes on the rotation, deformation, and phase distribution of the incident light source. We also discuss the independence and synthesis of deformation and rotation related to phase difference and positions of regulation, respectively, which provide the possibility for rich deformations under different rotation states. Our work extends new ideas for the dynamic control of plasmonic skyrmions, which is of great significance to fields such as spin photonics and nano-positioning.

A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions

Operating conditions reflect the mission evolution of rotating machinery in specific application scenarios. The monitoring data under different operating conditions exhibit disparate statistic distributions in terms of amplitude and frequency properties. In the modern operation and maintenance, domain adaptation approaches, which align the feature distributions of source and target domains, are commonly employed to address cross-domain diagnosis challenges. However, the availability of the target domain is serendipitous rather than guaranteed. To meet the engineering requirements, a domain feature decoupling network (DFDN) is proposed to achieve the fault diagnosis of rotating machinery under unseen operating conditions. The core concept of DFDN is to decouple the extracted features into domain-related features and fault-related features. These features are then adaptively integrated into domain-invariant features using a significance fusion method, thereby enabling the feature learning and knowledge transfer from seen source domains to unseen target domains. Furthermore, a self-learning joint optimization strategy is developed for DFDN to facilitate the efficient acquisition of generalized diagnosis knowledge. Two bearing datasets from different test rigs are considered to assess the domain generalization performance of DFDN. The experimental results demonstrate the superiority and potential of this method in coping with zero-shot fault diagnosis under unseen operating conditions.

Impact of manganese mining on potentially toxic elements pollution and bioaccumulation in Spirogyra varians and Hydrilla verticillata in the Xiaojiang River.

Potentially toxic elements (PTEs) pose a significant threat to aquatic ecosystems. This study investigated the content and potential sources of PTEs (Cr, Mn, Ni, Cu, Zn, Cd, Pb) in water, sediment, and dominant aquatic plants (Hydrilla verticillata and Spirogyra varians) in the Xiaojiang River, located near the Zhaiying manganese mine in Guizhou Province, China. Correlation analysis, principal component analysis (PCA), and cluster analysis were employed to assess PTE distribution and potential sources. Water PTE concentrations complied with the Class II standard (GB3838-2002), indicating no water pollution. However, sediment PTE levels exceeded background values, particularly Mn, which exhibited moderate to strong contamination. Cd also showed moderate contamination, posing a considerable ecological risk. Cd was the main potential pollutant with the highest contribution rate. Mn and Cd were therefore identified as priority pollutants requiring targeted abatement strategies. Mining activities likely represent the primary source, but combined pollution from vehicle traffic and agriculture might also contribute. Hydrilla verticillata demonstrated a higher capacity for PTE enrichment from sediment compared to Spirogyra varians, suggesting its potential for sediment remediation (except for Cu). A significant correlation existed between both plant species and sediment PTE content. PCA supported the association between S. varians and sediment PTEs. Linear regression analyses revealed better correlations between S. varians and sediment Mn, Ni, Cu, and Zn (0.77, 0.68, 0.82, and 0.79, respectively). Taken together, these findings suggest that S. varians serves as an effective bioindicator for monitoring sediment contamination with PTEs.

A multi-objective stochastic optimization model for combined heat and power virtual power plant considering carbon recycling and utilizing

In order to give full play to the energy supply potential of distributed energy resources, this paper studies the scheduling optimization of CHP-VPP. First, the CHP unit and various distributed energy sources are aggregated into VPP. Carbon recycling and utilizing are realized through carbon capture and power-to-gas devices. At the same time, carbon storage and hydrogen storage devices are added to decouple carbon capture and P2G procedures. Then, the risk of VPP real-time scheduling is quantified through uncertainty scenario generation and CVaR. Finally, with the goals of operating cost, carbon emission, and operation risk, a multi-objective stochastic scheduling optimization model of VPP is constructed, and the subjective and objective ensemble weighting method is used to solve the problem. The example results show that the proposed method can boost the wastage of wind and photovoltaic power, and also lower the carbon emissions of VPPs.

Towards Discriminability with Distribution Discrepancy Constrains for Multisource Domain Adaptation

Multisource domain adaptation (MDA) is committed to mining and extracting data concerning target tasks from several source domains. Many recent studies have focused on extracting domain-invariant features to eliminate domain distribution differences. However, there are three aspects that require further consideration. (1) Efforts should be made to ensure the maximum correlation in the potential subspace between the source and target domains. (2) While aligning the marginal distribution, the conditional distribution must also be considered. (3) Merely aligning the source distribution and target distribution cannot guarantee sufficient differentiation for classification tasks. To address these problems, we propose a novel approach named towards discriminability with distribution discrepancy constrains for multisource domain adaptation (TD-DDC). Specifically, TD-DDC first mines features of maximal relations learned from all domains while constructing domain data distribution mean distance metrics for interdomain distribution adaptation. Simultaneously, we integrate discriminability into domain alignment, which means increasing the distance among labels that are distinct from one another while reducing the distance among labels that are the same. Our proposed method not only reduces the interdomain distributional differences but also takes into account the preservation of interdomain correlation and inter-category discrimination. Numerous experiments have shown that TD-DDC performs much better than its competitors on three visual benchmark test databases.