Distributed Data Sets Research Articles

Partition refinement of WorldPop population spatial distribution data method: A case study of Zhuhai, China.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.

Environmental and anthropogenic drivers of invasive plant diversity and distribution in the Himalaya

Invasive alien species (IAS) are currently considered as one of the major causes of global environmental change. To manage the IAS, it is crucial to identify the different environmental and associated anthropogenic drivers that contribute to invasion of alien species in non-native regions. Although multiple drivers of invasion have been identified at a global scale, the relative roles of these are known to vary considerably at regional scales. Here, we investigate the role of key environmental and anthropogenic drivers in determining the diversity and distribution of selected invasive alien plant species in Kashmir Himalaya. We generated an extensive distribution dataset of these species through field sampling across the region and supplemented it with novel herbarium records. We also extracted data on the relevant environmental (climatic, soil and topographic) and anthropogenic drivers for the study region. The random forest model was employed to quantify the relative contribution of these drivers to determine the two common diversity metrics (species richness and abundance) of selected invasive alien plants. We found that soil water content followed by distance to city, the maximum air temperature, soil pH, soil temperature and human population density exerted the greatest influence on species richness of the invasive plants. Species abundance was significantly influenced by the maximum air temperature followed by soil temperature, distance to city, slope, soil pH and human population density. Overall, our findings help in disentangling the individual and interactive roles of multiple drivers of plant invasions, with wide-ranging implications for management in this Himalayan region and similar landscapes elsewhere.

2021–2022年鄱阳湖丰水期水体富营养化指标空间分布数据集

Poyang Lake, the largest lake connected to the Yangtze River, plays an important role in preserving the ecological balance of the Yangtze River Basin. However, due to the escalating effects of climate change and human activities, the eutrophication status of the lake has been increasing year by year, resulting in a gradual deterioration of its ecological function. In order to study the spatio-temporal variation characteristics of eutrophication in Poyang Lake and protect its water resources and environment, we systematically collected water samples from 72 sites across the lake during the peak algae growth periods of the summers from 2021 to 2022. In the laboratory, the determination of eutrophication indicators, including total nitrogen, total phosphorus, permanganate index and chlorophyll-a at sampling sites was conducted in strict accordance with the methods stipulated by national standards and industrial standards. Based on this, we obtained the dataset of spatial distribution of water eutrophication indicators in Poyang Lake during high flow period from 2021 to 2022. This dataset can provide precise insights into the water pollution and eutrophication status, as well as the spatial distribution characteristics of Poyang Lake during high flow season, which has important data supporting significance for the pollution control of Poyang Lake basin and water environment protection of the Yangtze River basin.

Mechanical Field Guiding Structure Design Strategy for Meta-Fiber Reinforced Hydrogel Composites by Deep Learning.

Fiber-reinforced hydrogel composites are widely employed in many engineering applications, such as drug release, and flexible electronics, with more flexible mechanical properties than pure hydrogel materials. Comparing to the hydrogel strengthened by continuous fiber, the meta-fiber reinforced hydrogel provides stronger individualized design ability of deformation patterns and tunable stiffness, especially for the elaborate applications in joint, cartilage, and organ. In this paper, a novel structure design strategy based on deep learning algorithm is proposed for hydrogel reinforced by meta-fiber to achieve targeted mechanical properties, such as stress and displacement fields. A solid mechanic model for meta-fiber reinforced hydrogel is first developed to construct the dataset of fiber distribution and the corresponding mechanical properties of the composite. Generative adversarial network (GAN) is then trained to characterize the relationship between stress or displacement field, and meta-fiber distribution. The well-trained GAN is implemented to design meta-fiber reinforced hydrogel composite structure under specific operation conditions. The results show that the deep learning method may efficiently predict the structure of the hydrogel composite with satisfied confidence, and has great potential for applications in drug delivery and flexible electronics.

Global spatial dataset of mangrove genus distribution in seaward and riverine margins

Mangroves are nature-based solutions for coastal protection however their ability to attenuate waves and stabilise and accrete sediment varies with their species-specific architecture and frontal area. Hydrodynamic models are typically used to predict and assess the protection afforded by mangroves, but without species or genus distribution information, the results can be significantly different from reality. Data on the frontal genus of mangroves exposed to waves and tides can provide information that can be used in hydrodynamic models to more accurately forecast the protection benefit provided by mangroves. Globally, frontal species were identified from existing mangrove zonation diagrams to create a global mangrove genus distribution map. This dataset aims to improve the accuracy of hydrodynamic models. Data may be of interest to researchers in coastal engineering, marine science, wetland ecology and blue carbon.

A novel deep learning framework based swin transformer for dermal cancer cell classification

Recent studies have demonstrated the efficacy of deep learning architectures in enhancing the interpretation of skin images, thereby aiding in the classification and segmentation of skin cancer. However, the existing deep learning techniques predominantly focus on either segmentation or classification and are designed for normally distributed data. Addressing this limitation, the present study introduces a hybrid ensemble deep learning approach for skin cancer classification and segmentation. This proposed model amalgamates Residual Learning Machines, Swin Transformers, and Fast Neural Networks (FNN) to proficiently manage diverse sets of non-uniformly distributed data, thereby augmenting diagnostic accuracy. The efficacy of this approach was evaluated using various skin cancer datasets, including ISIC-2008, PH-2, and HM007. The assessment involved several performance metrics, such as the Matthew correlation coefficient (MCC), recall, F1-score, specificity, accuracy, and precision. Moreover, to underscore the superiority of the proposed model, its performance was juxtaposed with that of previous efforts. Results from repeated trials indicate that the proposed model achieved 98.78% classification accuracy, 98.7% precision, 98.7% F1-score, 98.64% average recall, and an MCC of 0.9863 across different skin disease datasets. Demonstrating consistent superiority over existing methods, the proposed approach shows considerable potential in revolutionizing the diagnostics of skin cancer.

SpuBase: Solar Wind Ion Sputter Database for Modeling Purposes

We supply the modelers with a database, SpuBase (doi:10.5281/zenodo.10783295), that is based on the latest approach for obtaining solar wind ion sputter yields in agreement with experimental sputter data outlined in Jäggi et al. We include an overview of sputter results for typical Lunar and Hermean surfaces. To obtain total sputter yields for any given surface, we perform a mass balance of individual mineral sputter yields. For a set of impact angles, the angular and energy distribution data are scaled according to the sputter yield, summed up and fitted to obtain one probability distribution for each chemical element involved. Comparison of the results from different geochemical terranes on the Moon and Mercury has shown that variations in the abundance of silicates result in comparable energy and angular distribution data owing to the underlying model assumptions. The inclusion of sulfides relevant for Mercury, however, significantly affects the energy and angular distributions of sputtered particles. The application of the damage-driven sulfur diffusion rate in FeS in all sulfur-bearing minerals results in 35 times lower sulfur yields on average and a less prominent forward sputtering of sulfur at grazing incidence angles.

A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020

We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated from publicly available Sentinel-2 imagery. Meanwhile, topographic features consisting of slope and aspect that are sensitive to PV locations are also included, aiming to construct a multi-dimensional and discriminative feature space. Finally, the trained random forest model is adopted to predict PV power stations of China parallelly on GEE. Technical validation has been carefully performed across China which achieved a satisfactory accuracy over 89%. Above all, as the first publicly released 10-m national-scale distribution dataset of China’s ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.

Accurate and Rapid Extraction of Aquatic Vegetation in the China Side of the Amur River Basin Based on Landsat Imagery

Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km2 within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km2 to 3412 km2, while the vegetation area decreased from 745 km2 to 687 km2. These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation.

Elastic distributed dataset RDD (Resilient Distributed Datasets)

In recent years, the amount of data to be processed has become larger and larger, and it is difficult for a single processor to handle data, so we need to work hard to develop the expansion mode of cluster mode. Mapreduce, Storm, etc. are a solution for clusters to process data. However, we still have parallel processing, user resource sharing and other issues to be addressed. RDD data model is the key to solve these problems. In this paper, RDD is applied to Spark for detailed expansion, and project experiments are carried out to study the RDD data model. The programming language used in this article is python. The advantage of studying distributed iterative computing is that during the calculation process, the task calculation fails and can be recovered. The default is to allow four failures recoverable. If the stage fails, one can recover, and if the partition fails, one can also recover. Multiple partitions can improve parallelism and efficiency. After a shuffle, a partition failed because the many-to-one relationship needs to be recalculated.

Risk of introduction and establishment of alien vertebrate species in transboundary neighboring areas

Cross-border neighboring areas could be particularly vulnerable to biological invasions due to short geographic distances and frequent interactions, although the invasion risk remains unevaluated worldwide. Here, based on global datasets of distributions of established alien vertebrates as well as vectors of introduction and establishment, we show that more than one-third of the world’s transboundary neighboring areas are facing high invasion risk of alien vertebrates, especially in Europe, North America, South Asia, and Southeast Asia. The most important predictors of high introduction and establishment risk are bilateral trade, habitat disturbance and the richness of established alien vertebrates. Interestingly, we found that border fences may have limited effects in reducing the risk, as only 7.9% of border fences spatially overlap with hotspots of biological invasion even in the Eurasia areas (13.7% overlap) where physical border barriers are mainly located. We therefore recommend the implementation of immediate and proactive prevention and control measures to cope with cross-border invasions in response to continued globalization.

The global distribution of angiosperm genome size is shaped by climate.

Angiosperms, which inhabit diverse environments across all continents, exhibit significant variation in genome sizes, making them an excellent model system for examining hypotheses about the global distribution of genome size. These include the previously proposed largegenome constraint, mutational hazard, polyploidy-mediated, and climate-mediated hypotheses. We compiled the largest genome size dataset to date, encompassing 16 017 (> 5% of known) angiosperm species, and analyzed genome size distribution using a comprehensive geographic distribution dataset for all angiosperms. We observed that angiosperms with large range sizes generally had small genomes, supporting the large genome constraint hypothesis. Climate was shown to exert a strong influence on genome size distribution along the global latitudinal gradient, while the frequency of polyploidy and the type of growth form had negligible effects. In contrast to the unimodal patterns along the global latitudinal gradient shown by plant size traits and polyploid proportions, the increase in angiosperm genome size from the equator to 40-50°N/S is probably mediated by different (mostly climatic) mechanisms than the decrease in genome sizes observed from 40 to 50°N northward. Our analysis suggests that the global distribution of genome sizes in angiosperms is mainly shaped by climatically mediated purifying selection, genetic drift, relaxed selection, and environmental filtering.

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructed and used to construct and optimize polarization characteristics. A new SAR index named VH-sum is built, which is defined as the summation of VH backscattering intensity for specific time periods based on the temporal changes in VH polarization characteristics of different land cover types. Then comes feature selection, optimization, and reconstruction of optical data. Finally, the PFO-HKMAR classification method is established based on Simple Non-Iterative Clustering. PFO-HKMAR can achieve early-season rice mapping one month before harvest, with overall accuracy, Kappa, and F1 score reaching 0.9114, 0.8240 and 0.9120, respectively (F1 score is greater than 0.9). Compared with the two crop distribution datasets in Northeast China and ARM-SARFS, overall accuracy, Kappa, and F1 scores of PFO-HKMAR are improved by 0.0507–0.1957, 0.1029–0.3945, and 0.0611–0.1791, respectively. The results show that PFO-HKMAR can be promoted in Northeast China to enable early-season rice mapping, and provide valuable and timely information to different stakeholders and decision makers.

Modeling the Spatial Distribution of Population Based on Random Forest and Parameter Optimization Methods: A Case Study of Sichuan, China

Spatial population distribution data is the discretization of demographic data into spatial grids, which has vital reference significance for disaster emergency response, disaster assessment, emergency rescue resource allocation, and post-disaster reconstruction. The random forest (RF) model, as a prominent method for modeling the spatial distribution of population, has been studied by many scholars, both domestically and abroad. Specifically, research has focused on aspects such as multi-source data fusion, feature selection, and data accuracy evaluation within the modeling process. However, discussions about parameter optimization methods during the modeling process and the impact of different optimization methods on modeling accuracy are relatively limited. In light of the above circumstances, this paper employs the RF model to conduct research on population spatialization with multi-source spatial information data. The study primarily explores the differences in model parameter optimization achieved through random search algorithms, grid search algorithms, genetic algorithms, simulated annealing algorithms, Bayesian optimization based on Gaussian process algorithms, and Bayesian optimization based on gradient boosting regression tree algorithms. Additionally, the study investigates the influence of different optimization algorithms on the accuracy of population spatialization modeling. Subsequently, the model with the highest accuracy is selected as the prediction model for population spatialization. Based on this model, a spatial population distribution dataset of Sichuan Province at a 1 km resolution is generated. Finally, the population dataset created in this paper is compared and validated with open datasets such as GPW, LandScan, and WorldPop. Experimental results indicate that the spatial population distribution dataset produced by the Bayesian optimization-based random forest model proposed in this paper exhibits a higher fitting accuracy with real data. The Coefficient of Determination (R2) is 0.6628, the Mean Absolute Error (MAE) is 12,459, and the Root Mean Squared Error (RMSE) is 25,037. Compared to publicly available international datasets, the dataset generated in this paper more accurately represents the spatial distribution of the population.

Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning with Neural Architecture Search.

Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model. While a centralized training model is effective in mitigating this problem, it raises concerns about privacy protection. Federated learning is a distributed training paradigm that can utilize multi-institutional data for collaborative training without sharing data. However, existing federated learning MR image reconstruction methods rely on models designed manually by experts, which are complex and computationally expensive, suffering from performance degradation when facing heterogeneous data distributions. In addition, these methods give inadequate consideration to fairness issues, namely ensuring that the model's training does not introduce bias towards any specific dataset's distribution. To this end, this paper proposes a generalizable federated neural architecture search framework for accelerating MR imaging (GAutoMRI). Specifically, automatic neural architecture search is investigated for effective and efficient neural network representation learning of MR images from different centers. Furthermore, we design a fairness adjustment approach that can enable the model to learn features fairly from inconsistent distributions of different devices and centers, and thus facilitate the model to generalize well to the unseen center. Extensive experiments show that our proposed GAutoMRI has better performances and generalization ability compared with seven state-of-the-art federated learning methods. Moreover, the GAutoMRI model is significantly more lightweight, making it an efficient choice for MR image reconstruction tasks. The code will be made available at https://github.com/ternencewu123/GAutoMRI.

Secure multi-party computation in deep learning : Enhancing privacy in distributed neural networks

Ensuring data privacy while applying Deep Learning (DL) on distributed datasets represents an essential task in the current period of critical data security. Data privacy and accuracy of models are typically impacted by traditional methods. Data privacy is of the most tremendous significance in distributed data settings, and the current research presents a novel model for DL termed Secure Multi-Party Computation (SMPC). The accuracy of the mathematical models and confidentiality of the data are frequently compelled to coexist in conventional methods. In order to enable collaborative DL without compromising private information, the recommended system uses the Paillier Homomorphic Encryption Scheme (PHES). By using innovative cryptographic methods, this decentralized method secures the confidentiality of data without utilizing a Trusted Authority (TA). By performing a thorough assessment of the CIFAR-10 and IMDB datasets, the present study demonstrates that the system the author uses is simple and scalable and that it offers accuracy on par with conventional methods. By presenting an approach for achieving a balance between the two competing demands of data security and computational performance, this method signifies a vast advance forward with a confidentiality DL.

Experimental method for warm dense matter ionization distribution based on X-ray fluorescence pectroscopy

Warm dense matter (WDM), a state of matter that lies at the frontier between condensed matter and plasma, is one of the main research objects of high energy density physics (HEDP). Comparing with the isolated atom, the electron structure of WDM will change because of the influence of density and temperature effect. Both the accurate theoretical representation and the accurate experimental study of WDM electron structure are challenging, as it is strongly coupled and partially degenerated. In this work, an experimental method of studying the ionization distribution of WDM based on X-ray fluorescence spectroscopy is developed. In the experiment, in a specially designed hohlraum, warm and dense titanium with several tens of electron volts and nearly solid density is produced by simultaneous driving of high-energy X-ray heating and shock compression. Then, using the characteristic line spectrum emitted by the laser irradiation on pump material (Vanadium) as a pump source, the titanium emits fluorescence. The X-ray fluorescence spectra of titanium with different states (cold sample, 1.8–4.5 g/cm<sup>3</sup> and 1–25 eV) are diagnosed by changing the experimental strategy. The experimental results indicate that the line profiles of K<sub><i>α</i></sub> and K<sub><i>β</i></sub> fluorescence spectrum of the heated sample change obviously compared with those of the cold sample. According to the theoretical calculation of the two-step Hartree-Fock-Slater (TSHFS) method, the main reason for the change of the line profile is the change of ionization distribution caused by temperature rising. The future work will focus on optimizing the experimental method of X-ray fluorescence spectrum, such as improving the spectrum resolution, characterizing the temperature and density experimentally, obtaining a set of ionization distribution data, and then studying the influence of dense environment on electronic structure.

Innovation of Media Business Models in the Digital Economy Era

This research delves into the evolution of media business models in the digital economy period, with a specific emphasis on examining the key aspects of technology-centered and customer-focused. The research defined how essential components like value proposition, customer segments, and revenue streams are uniquely implemented in the media sector's business model. It has considered real contributions in big data, artificial intelligence, and blockchain that augment efficiency with respect to content creation, user experience, and equality with concern to revenue sharing in the media industry. The current piece of research at the same time emphasizes analyzing users' behavior and gauging users' involvement to enhance their level of engagement and satisfaction ratio. After this, it plunges deep into two details of new critical media platforms in TikTok and Facebook, describing successful business models detailing user growth trends, sources of revenue for firms, user distribution, and other dataset details. The findings recommend how, in this era, where the digital economy is growing tremendously, media companies should keep working persistently to innovate business models through an amalgamation of technology-driven with customer-oriented methods to make it sterling in such a stiff market. This study offers theoretical backing and practical guidance for the development of new media business models, with significant practical implications.

青藏高原色林错2000–2021年湖冰数据集

Lake ice is a sensitive indicator of climate change. Research on the response mechanism between lake ice and climate offer valuable insights into the mechanisms driving global climate change and enhance our ability to predict climate patterns. Using the Landsat surface albedo products, we delineated the boundaries of Lake Selin Co from 2000 to 2021; and by leveraging the MODIS Terra surface reflectance data with a 250 m resolution and daily revisit time, we generated a dataset of lake ice distribution in Lake Selin Co from 2000 to 2021. We selected the results of the freezing period and the complete freezing period respectively, compared them with the visual results to perform accuracy verification and calculate the confusion matrix. The overall accuracy of the user reached 94%, and the Kappa coefficient exceeded 0.89. Continuous monitoring data for lake ice phenology in Selin Co are of great significance for tracking climate change, which help fill gaps in measured data and provide valuable support for related research.

Bycatch-threatened seabirds disproportionally contribute to community trait composition across the world

Human pressures in the ocean are restructuring biological communities, driving non-random extinctions, and disrupting marine ecosystem functioning. In particular, fisheries bycatch, the incidental mortality of non-target species, is a major threat to seabirds worldwide. Direct bycatch data are often scarce. Instead, leveraging trait-based analyses with fine-scale fisheries data could answer fundamental questions about spatial patterns of bycatch-threatened species and facilitate targeted conservation strategies. Here, we combine a dataset of species' traits and distribution ranges for 361 seabird and sea duck species with spatially resolved fishing effort data for gillnet, longline, trawl, and purse seine gears. First, we quantify geographic patterns of seabird community traits. Second, we describe how community traits could shift under local extinction scenarios in areas where bycatch-threatened seabirds spatially overlap with fishing activities. These objectives allow us to highlight the collective contribution of species currently threatened from bycatch to ecosystem functioning. We reveal distinct spatial variation in the community weighted mean of five seabird traits (body mass, generation length, clutch size, diet guild, and foraging guild) are evident. Moreover, our results show that fisheries bycatch is selectively removing a distinct suite of traits from the community within particular oceanic regions. Specifically, fisheries bycatch is threatening species with larger body masses, slower reproductive speeds (smaller clutch sizes and longer generation lengths), and specialised diet and foraging guilds. The spatial non-uniformity of the community trait shifts suggests that within specific marine regions, communities have limited redundancy and therefore may have less insurance to buffer against declines in ecosystem functioning. Our extinction scenario warns that seabirds currently threatened from fisheries bycatch substantially contribute to community functional composition. Management actions that incorporate species’ traits and fine-scale fisheries datasets as tools for marine spatial planning will add an important dimension when evaluating the success of conservation initiatives.