Search Results (46)

The digitisation of cultural heritage has transformed how GLAM (Galleries, Libraries, Archives and Museums) institutions manage and share collections. Digital catalogues are indispensable for documenting and granting public access to cultural assets. However, integrating spatial data remains challenging due to the ambiguity, uncertainty, granularity, and heterogeneity of historical data. This study addresses these issues through a case study on the Museo de América’s “Place of Provenance” data, proposing a methodology for data cleaning and evaluating geocoding accuracy using Nominatim, ArcGIS, and GeoNames APIs. We assess these APIs by quantifying geocoding errors through a “balance sheet” method, identifying instances of over-representation, under-representation, or neutral results for geographical regions. The effectiveness of each API is analysed using confusion matrices and interactive cartograms, offering insights into misallocations. Our findings reveal varying accuracy among the APIs in processing heterogeneous historical spatial data. Nominatim achieved a 40.91% neutral result in correctly geocoding countries, underscoring challenges in spatial data representation. This research provides valuable methodological experiences and insights for researchers and GLAM institutions working with cultural heritage datasets. By enhancing spatial dispersion visualisation, this work contributes to understanding cultural circulations and historical patterns. This interdisciplinary work was developed as part of the ClioViz project, integrating Data Science, data Visualisation, and art history. Full article

Traditional detection methods and security defenses are gradually insufficient to cope with evolving attack techniques and strategies, and have coarse detection granularity and high memory overhead. As a result, we propose Sylph, a lightweight unsupervised APT detection method based on a provenance graph, which not only detects APT attacks but also localizes APT attacks with a fine event granularity and feeds possible attacks back to system detectors to reduce their localization burden. Sylph proposes a whole-process architecture from provenance graph collection to anomaly detection, starting from the system audit logs, and dividing subgraphs based on time slices of the provenance graph it transforms into to reduce memory overhead. Starting from the system audit logs, the provenance graph it transforms into is divided into subgraphs based on time slices, which reduces the memory occupation and improves the detection efficiency at the same time; on the basis of generating the sequence of subgraphs, the full graph embedding of the subgraphs is carried out by using Graph2Vec to obtain their feature vectors, and the anomaly detection based on unsupervised learning is carried out by using an autoencoder, which is capable of detecting new types of attacks that have not yet appeared. After the experimental evaluation, Sylph can realize the APT attack detection with higher accuracy and achieve an accuracy rate. Full article

Additive manufacturing has reached a level of reliability and credibility that has already been integrated into specific industries producing final parts or tooling. Among Material Extrusion (ME) techniques, the Fused Granular Fabrication (FGF) method has enabled the development of Large Format Additive Manufacturing (LFAM) using polymeric materials, which has also established its presence in industries working with large prototypes, molds, and tools. This cost-efficient process has proven its applicability and success in manufacturing molds for composites, particularly in short and medium production runs, significantly reducing production times and costs. This paper presents two experiments designed to optimize process parameters when producing molds using the combined FGF and milling approach. These experiments identified optimal extrusion temperatures and extrusion multipliers to minimize defects at both the macro- and microscales for ASA 20 wt.% carbon fiber (CF) material; additionally, a correlation between milling speed, milling strategy, and surface roughness was established. These findings are valuable for industries adopting this innovative production method, as they provide guidance for defining process parameters to achieve the desired surface roughness of a specific part. A case study of the design of an automobile carter mold is presented, concluding that a specific range of milling speeds is required for conventional or climbing milling strategies to achieve a defined surface roughness range. Full article

This paper presents two innovative procedures developed for rural electrification planning. To address the challenges of processing vast geospatial data, handling complex and computationally intensive network design, and making detailed yet accessible economic assessments, this work introduces a Buffering plugin for community identification and a Grid Routing and Cost Allocation plugin for network design and economic assessment, both integrated into the open-source QGIS platform. The first enables the identification of potential electrification zones through dual methodologies, while the second introduces three key processes: hierarchical clustering, a modified minimum spanning tree, and a novel cost allocation methodology that provides village-specific LCOE calculations. Testing in Zambia has proven that this approach is not only effective but also—compared to existing tools—offers significant advantages in terms of computational efficiency and accessibility, while providing practical solutions to large-scale challenges. This synergistic approach enables planners to move from granular geospatial data to actionable electrification decisions through a streamlined process. The analysis covered over 3 million buildings, grouped into 162,142 settlement clusters, and subsequently determined optimal electrification strategies for 3025 villages—40.4% connected to grid extensions and 59.6% to mini-grids—serving a total population of 18 million people. Full article

The fourth member of the Triassic in the Tahe Oilfield, as one of the key strata for clastic rock reservoirs, poses significant challenges to oil and gas exploration due to unclear identification of its depositional environments and sedimentary microfacies. Based on the guidance of sequence stratigraphy and sedimentological theories, this study comprehensively analyzed well logging data from more than 130 wells, core analysis from 9 coring wells (including lithology, sedimentary structures, and facies sequence characteristics), 3D seismic data (covering an area of 360 km²), and regional geological background. Combined with screening and settling method granularity experiments, the sedimentary characteristics of the sand body in the fourth member were systematically characterized. The results indicate the following: (1) In the Tahe Oilfield, the strata within the fourth member of the Triassic are predominantly characterized by marginal lacustrine subfacies deposits, with delta-front subfacies deposits developing in localized areas. (2) From the planar distribution perspective, influenced by the northwestern provenance, a small deltaic depositional system developed in the early stage of the fourth member in the northwestern part of the Triassic Akekule Formation. This system was dominated by subaqueous distributary channel sand bodies, which were subjected to erosion and reshaping by lake water, leading to the formation of several stable sand bars along the lake shoreline. In the later stage of the fourth member, as the lake level continued to recede, the area of deltaic deposition expanded westward, and deltaic deposits also developed in the central to slightly eastern parts of the study area. Based on this, a depositional model for the fourth member of the Triassic in the Tahe Oilfield has been established. (3) In the Tahe Oilfield, the sand bodies within the fourth member of the Triassic system gradually pinch out into mudstone, forming lithological pinch-out traps. Among these, the channel sand bodies and long belt sand ridges, due to their good sorting and high permeability, become favorable reservoirs for oil and gas accumulation. This study clarifies the sedimentary model of the fourth member and reveals the spatial differentiation mechanism of sand bodies under the control of lake-level fluctuations and ancient structures. It can provide exploration guidance for delta lake sedimentary systems similar to the edge of foreland basins, especially for efficient development of complex lithological oil and gas reservoirs controlled by multistage lake invasion–lake retreat cycles. Full article

As soon as a granular sediment has been set in motion by the currents of droppable fluids or by wind, sand waves form as smaller ripples or larger dunes. The relevance of this phenomenon lies in the roughness effect against the currents and the influence on sediment loads. Likewise, their physical understanding helps us to estimate past flow conditions by means of fossilized sand waves, as well as those on distant planets with proven ripples and dunes, such as Mars and Titan. In the literature, diagrams exist based on observations for the conditions under which the various forms of sand waves develop. However, the cause of their formation is unclear. Various theories have been discussed regarding the further development of ripples once they have formed, but none of them explains the fundamental mechanism that generates the very first ripples. These occur simultaneously over a large area and almost instantly, with a fairly even distance from crest to crest. This contribution presents a solution for how this is possible based on hydrodynamic instability. Full article

As a pivotal research method in the field of granular computing (GrC), fuzzy rough sets (FRSs) have garnered significant attention due to their successful overcoming of the limitations of traditional rough sets in handling continuous data. This paper is dedicated to exploring the application potential of FRS models within the framework of multi-source complex information systems, which undoubtedly holds profound research significance. Firstly, a novel multi-source mixed information system (MsMIS), encompassing five distinct data types, is introduced, thereby enriching the dimensions of data processing. Subsequently, a similarity function, designed based on the unique attributes of the data, is utilized to accurately quantify the similarity relations among objects. Building on this foundation, fuzzy T-norm operators are employed to integrate the similarity matrices derived from different data types into a cohesive whole. This integration not only lays a solid foundation for subsequent model construction but also highlights the value of multi-source information fusion in the analysis of the MsMIS. The integrated results are subsequently utilized to develop FRS models. Through rigorous examination from the perspective of information granularity, the rationality of the FRS model is proven, and its mathematical properties are explored. This paper contributes to the theoretical advancement of FRS models in GrC and offers promising prospects for their practical implementation. Full article

In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several critical issues that need to be addressed in the HSIC task, such as the lack of labeled training samples, which constrains the classification accuracy and generalization ability of CNNs. To address this problem, a deep multi-scale attention fusion network (DMAF-NET) is proposed in this paper. This network is based on multi-scale features and fully exploits the deep features of samples from multiple levels and different perspectives with an aim to enhance HSIC results using limited samples. The innovation of this article is mainly reflected in three aspects: Firstly, a novel baseline network for multi-scale feature extraction is designed with a pyramid structure and densely connected 3D octave convolutional network enabling the extraction of deep-level information from features at different granularities. Secondly, a multi-scale spatial–spectral attention module and a pyramidal multi-scale channel attention module are designed, respectively. This allows modeling of the comprehensive dependencies of coordinates and directions, local and global, in four dimensions. Finally, a multi-attention fusion module is designed to effectively combine feature mappings extracted from multiple branches. Extensive experiments on four popular datasets demonstrate that the proposed method can achieve high classification accuracy even with fewer labeled samples. Full article

The sandstone-type uranium deposit of the Kelulun Depression is the first industrially valuable uranium deposit discovered in the Hailar Basin. This study performed a systematic examination of 17 sandstone samples from the Yimin Formation in the Kelulun Depression based on various analytical techniques. The findings of the current study were synthesized with previous research to investigate the impact of the redox conditions and the tectonic background of the source area, as well as the paleoclimatic evolution of the Yimin Formation on uranium mineralization. The elemental Mo, U/Th, V/Cr, Ni/Co, and V/(V + Ni) ratios indicate that the paleowater was in an oxygen-rich environment during the deposition of the Yimin Formation. Additionally, the C-value, Sr/Cu, Al₂O₃/MgO, and Rb/Sr ratios indicate that the Yimin Formation was formed in a paleoclimate characterized by arid-to-semi-arid conditions. The geochemical characteristics of the observed elements indicated that the sediment source of the Yimin Formation was mainly felsic rocks from the upper continental crust, the weathering of the rock was weak, and the tectonic background was a passive continental margin. Coffinite is distributed in the form of cementation and stellates within or around pyrite crystals, and uranium-titanium oxide is mostly distributed in an irregular granular distribution in the biotite cleavage fractures of the study area. In summary, the findings of this study reveal that the tectonic settings, provenance, uranium source, paleoclimate, and oxygen-rich paleowater of the Yimin Formation have important geological significance for the large-scale uranium mineralization of the Kelulun Depression. Full article

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)—a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines’ scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration. Full article

Granular activated carbon (GAC) has proven to be an effective adsorbent for removing the chemical warfare agent sarin (GB) and simulants like Dimethyl methylphosphonate (DMMP). However, it comes with certain limitations, including inadequate contact efficiency, notable mass transfer resistance, and lower bed utilization efficiency. This study synthesized steel fiber-entrapped activated carbon composites (SFEACs), which exhibited a maximum adsorption capacity of 285.3 mg/g at 303 K. Compared with the packed bed (PB) filled with GAC, while the adsorption capacity of SFEACS decreased, there was a substantial increase in the adsorption mass transfer rate. These SFEACs were combined with GAC to create a structural fixed bed (SFB), which demonstrated excellent performance in DMMP removal. Under identical experimental conditions, the DMMP breakthrough curve of SFB exhibited a steeper profile compared to the packed bed (PB) filled with GAC at the same bed height, and the breakthrough time against DMMP vapor could be extended by 13.8%. Furthermore, the adsorption rate constant of the Yoon-Nelson model increased by more than 17.6%, and the unused bed length, according to the Wheeler–Jonas model, decreased by more than 14%. Full article

This study aims to investigate the performance of a two-stage anaerobic digestion system using a hybrid methane reactor to deal with biodegradable municipal solid waste. The reactor allowed both suspended sludge and granular sludge to work together. The feedstock was fermented in one continuous stirred tank at different pH conditions for 5 d. Furthermore, the liquid hydrolysate was diluted and pumped into a methane reactor with different organic loading rates. In the fermentative reactor, raising the pH condition from 4.5 to 6.5 caused a sharp increase in volatile fatty acids concentration, mainly due to the increase in acetate and propionate. The efficiency of the methane reactor was proven by the results of hydrodynamic analysis and biogas production. The relationship between biogas production and operating parameters in this reactor was modeled using a quadratic multivariate regression model. Overall, by maintaining the fermentative reactor at a pH of 6.0–6.5, the methane reactor was able to achieve an organic loading rate of 7.6 g-TS.L⁻¹·d⁻¹ with outstanding biogas quality and yield. In terms of microbiology, the most dominant phyla in the reactor included Firmicutes, Bacteroidetes, Proteobacteria, Euryarchaeota, Synergistetes, and Chloroflexi. Among them, the species with the highest relative abundance in granular sludge was Firmicutes, while that in suspended sludge was Bacteroidetes. Full article

Climate neutrality goals in the building sector require a large-scale estimation of environmental impacts for various stakeholders. Life Cycle Assessment (LCA) is a viable method for this purpose. However, its high granularity, and subsequent data requirements and effort, hinder its propagation, and potential employment of Machine Learning (ML) applications on a larger scale. The presented paper outlines the current state of research and practice on district-scale building LCA in terms of standards, software and certifications, and data availability. For this matter, the authors present the development and application of two district-scale LCA tools, Teco and DisteLCA, to determine the Global Warming Potential (GWP) of three different residential districts. Both tools employ data based on (including, but not limited to) CityGML, TABULA, and ÖKOBAUDAT. The results indicate that DisteLCA’s granular approach leads to an overestimation of environmental impacts, which can be derived from the statistical approach to operational energy use and related emissions. While both tools lead to substantial time savings, Teco requires less manual effort. The linkage of the aforementioned data sources has proven laborious and could be alleviated with a common data framework. Furthermore, large-scale data analysis could substantially increase the viability of the presented approach. Full article

The most common technology for the recovery of energy and valuable materials from sewage sludge is anaerobic digestion (AD). Ensuring thermophilic conditions during AD has been proven to cause process intensification and an improvement in its final outcomes. Nonetheless, the search is underway for other methods to bolster the effectiveness of the AD of aerobic granular sludge (AGS), which is characterized by a compact and complex structure. A prospective AGS pre-treatment technology entails the use of solidified carbon dioxide (SCO₂). The present study focused on an evaluation of the AGS pre-treatment with SCO₂ on the thermophilic AD technological effects. It evaluated the effect of the SCO₂ pre-treatment method on changes in the concentrations of organic and biogenic compounds in the dissolved phase and the yield and kinetics of biogas and methane production in periodical reactors, as well as enabled the development of an empirical organizational model of biogas production. SCO₂ introduced to AGS caused an increase in the content of COD, N-NH₄⁺, and P-PO₄³⁻ in the AGS dissolved phase at SCO₂/AGS volumetric ratios ranging from 0 to 0.3. A further increase in the SCO₂ dose did not cause any statistically significant differences in this respect. The highest biogas and methane yields were obtained at SCO₂/AGS of 0.3 and reached 482 ± 21 cm³/gVS and 337 ± 14 cm³/gVS, respectively. The higher SCO₂ doses used led to a significant decrease in the pH value of the AGS, which, in turn, contributed to a decreasing CH₄ concentration in the biogas. Full article

It has been proven that the biocenosis of microalgae and bacteria improves the chemical properties of biomass for its use in anaerobic digestion. However, this anaerobic digestion can be limited by the strong, compact, and complex structure of granulated biomass. Therefore, there is a need to search for an effective method for microalgal–bacterial granular sludge pretreatment, which has not been undertaken in previous scientific works. In this study, ultrasonic pretreatment was used to determine the effects of sonication on anaerobic digestion efficiency. Anaerobic digestion was performed in batch respirometric reactors. It was found that the ultrasonic pretreatment enhanced the biomass solubility; thus, the organic matter concentration increased more than six times compared to the variant without pretreatment. The study showed a positive effect of sonication on the kinetics of the anaerobic process and methane production. The highest methane yield was found in the variants in which the ultrasonication lasted from 150 s to 200 s, and this yield was from 534 ± 16 mL CH₄/g VS to 561 ± 17 mL CH₄/g VS. The data analysis confirmed strong correlations between the pretreatment time, the amount of biogas and methane production, and the gross energy gain. The highest net energy output and net energy gain were obtained for 150 s of sonication, and, respectively, were 4.21 ± 0.17 Wh/g VS and 1.19 ± 0.18 Wh/g VS. Full article