An urban functional zone (UFZ) serves as the planning and implementation unit in urban development and management strategies. Previous works on multi-modal UFZ representation learning have integrated socio-economic attributes from points-of-interest (POIs) with visual features from remote sensing images. However, the inherent sampling bias and spatial inequality in POIs can impede the model’s discriminative capacity. To address the problems of insufficient data coverage and incomplete representation of physical-semantic sensing, we propose an interconnected, consistent and scalable framework within the physical-spatial-semantic representation space that we term TUF-Sensing. TUF-Sensing models building footprints and POIs as graph nodes, respectively, and applies a symmetrical graph convolutional architecture to capture the topology of the constructed graph, and the neighborhood influence between entities. To enhance the expressivity of nodes and stimulate neighborhood aggregation, the input features of buildings and POIs are constructed differently, using the polygonal attributes of buildings and one-hot encoding that reflects the categorical identity of POIs. The conducted experiments compared the performance of TUF-Sensing and six other methods on different scales of grids and blocks in Wuhan, China. The results demonstrate that TUF-Sensing yields significant improvements in both probability distribution- and categorical performance-based metrics, indicating its adaptability in large-scale and fine-grained UFZ recognition.

As the use of GPUs in collaborative computing environments increases, the demand for memory capacity continues to grow. The introduction of Compute Express Link (CXL) technology enables GPUs to access extended memory but also introduces new security challenges. Expressly, data transmission between secure memory regions incurs overhead due to ciphertext conversion, driven by independent security meta data for each memory region, which results in additional memory access overhead and degrades performance. Existing solutions alleviate this issue by sharing security metadata, thus reducing overhead from additional memory accesses. However, transmitting security metadata still consumes significant bandwidth, limiting overall system performance. To address this limitation, we propose a data transmission optimization method based on secure metadata reconstruction tailored to the unique characteristics of GPU multi-memory architectures in collaborative computing. Our method minimizes the amount of security metadata transmitted and reconstructs complete metadata at the receiver's end. This approach significantly reduces bandwidth usage and enhances system performance. Experimental results demonstrate that our method outperforms the Salus approach, achieving an 11.0% improvement in IPC (Instructions Per Cycle) performance.

With the increasing adoption of GPUs in neural networks, memory security concerns have become more pronounced. As a widely used memory integrity protection mechanism, the Integrity Tree effectively defends against replay attacks. However, the access and processing overhead associated with Integrity Trees has emerged as a significant performance bottleneck. Existing optimization strategies primarily rely on runtime prediction of frequently accessed regions to adjust the Integrity Tree structure, thereby reducing node access and processing costs. Nevertheless, due to the spatial accumulation and temporal locality characteristics of GPU memory access, these methods suffer from limitations in prediction accuracy and responsiveness: the influence of historical data can lead to inaccurate predictions, while the dynamic evolution of frequently accessed regions is challenging to detect in real-time, ultimately impacting the optimization effectiveness of the Integrity Tree. To address these challenges, this paper proposes an Integrity Tree optimization method based on a gradient-threshold mechanism to improve the access efficiency of secure GPU memory. The proposed approach introduces a gradient-based access weighting mechanism to mitigate the influence of historical data, enabling predictions to capture current memory access patterns more accurately. Additionally, a threshold-based real-time detection strategy is employed to continuously monitor changes in frequently accessed regions and adapt the Integrity Tree structure accordingly. Experimental results demonstrate that the proposed method significantly reduces the node traversal overhead associated with Integrity Tree.

GPUs have become critical accelerators in applications such as scientific computing and deep learning. The demand for more robust security measures has significantly increased as their usage expands across a broader range of applications. To address this need, Trusted Execution Environments (TEEs) have been integrated into GPU systems to safeguard applications and data. However, with technologies like Compute Express Link expanding the memory capacity of GPUs, cross-memory data transfers now pose new security challenges. Existing methods attempt to optimize the performance overhead associated with these transfers by sharing security metadata. However, this shared metadata is unreliable, raising concerns about data integrity and freshness and making systems vulnerable to replay attacks. In response to these issues, this paper introduces the trusted shared security metadata concept. Leveraging the tamper-resistant of MoveCtr, the paper develops a trusted shared security metadata scheme and a secure cross-memory integrity tree, ensuring the integrity and freshness of transferred data and protecting against replay attacks. Experimental results demonstrate that this method provides strong security guarantees while maintaining performance overhead within an acceptable range.

Urban function mapping serves as a vital role in urban management and planning tasks. To generate fine-grained recognition at spatial and semantic scale, a contrastive manner integrating comprehensive representation from multi-modal descriptions of urban functional zones (UFZs)is proposed. Abstract physical features from VHR images are obtained from the founder deep convolutional model. Spatial pattern and semantic features are extracted from geographical entities including urban buildings and POIs, respectively. The proposed model is validated in the downtown Wuhan, China, where resources and sensing data citywide are concentrated. Rich information is provided but also the challenges due to the high complexity are posed for urban functions recognition. The superior performances demonstrate that the multidimensional, especially the integration with spatial pattern of primary urban materials, enhances the exact and robust recognition on sophisticated functions of urban land.

Improving the spatial resolution of satellite-based precipitation data is crucial in expanding the application of quantitative precipitation estimation. Many machine learning-based techniques have been proposed for the downscaling of satellite-derived precipitation. However, these methods often highly rely on the availability of observation data, posing limitations on their applicability in data-scarce regions. To address this issue, a novel approach based on deep transfer learning (TL) was introduced to improve the precipitation downscaling in data-scarce areas. The proposed framework implemented the fine-tuning to transfer the pre-trained convolutional neural network-based downscaling model from the source domain to two target domains. Results showed that compared with the original satellite data, the correlation coefficient of the downscaled precipitation using fine-tuning increased to 0.715 and 0.622 in the two target domains, respectively. The transferred models with three fine-tuned layers achieved the highest performance, and models with one fine-tuned layer was the most efficient. The simplest transferred models with four frozen layers obtained the worst performance (with CC values of 0.586 and 0.519 in two target domains). By incorporating at least one fine-tunable layer, the TL models demonstrated significant improvements. Meanwhile, the transferred downscaling models had better performance under higher precipitation intensity, and worse for low precipitation intensity. They received more accurate downscaled results during the wet season than the dry season. This study provided a promising transfer leaning-based approach in generating high-resolution precipitation in data-sparse regions, which has great significance to the meteorological, hydrological and ecological research in such areas.

Numerous statistical downscaling techniques were proposed to improve the spatial resolution of satellite-based precipitation data. However, these downscaling methods generally required observation data, making it difficult to apply in data-scarce areas. To address this issue, this study presented a framework based on transfer learning (TL), in which the pretrained convolutional neural network model in source domain was fine-tuned and implemented in target domains. Results showed that the pretrained model cannot be directly applied in transferred regions due to its poor performance, but the TL model with at least one fine-tunable layers achieved significant improvements and can be employed successfully. It was notable that the fine-tuning model obtained even higher accuracy than the model trained independently with data of target domains. Results also showed that TL model attained a higher performance with more fine-tunable layers, and different fine-tunable layers would impact the downscaling results and should be selected during TL.

Extreme precipitation events have caused severe societal, economic and environmental impacts through the disasters of floods, flash-floods and landslides. However, the coarse-resolution of satellite-derived precipitation data makes it difficult to quantitatively capture certain fine-scale heavy rainfall process. Therefore, to improve the spatial resolution and accuracy of satellite-based precipitation extremes, a downscaling-calibration scheme based on eXtreme Gradient Boosting (XGBoost_DC) was proposed in this study, where the XGBoost algorithm was applied in both downscaling and calibration procedures. The performance of XGBoost_DC was evaluated with other two comparative methods, in which XGBoost was only used in either downscaling (XGBoost_Spline) or calibration (Spline_XGBoost) process. The results showed that: (i) XGBoost_DC achieved the best performance, as it obtained the highest accuracy and well reproduced the occurrence and the spatial distribution of precipitation during typhoon events. (ii) XGBoost_DC could capture the spatial variations of the precipitation. Although Spline_XGBoost obtained results only slightly worse than the XGBoost_DC, it significantly underestimated the spatial variability. (iii) the model assessment between the XGBoost_DC and Spline_XGBoost illustrated the essential contribution of XGBoost algorithm in downscaling process, and improved our understanding of the capability of machine learning algorithm in reproducing spatial variance of precipitation. These findings imply that our proposed downscaling-calibration scheme can be applied for generating high-resolution and high-quality precipitation extremes during typhoon events, which would benefit the water and flood management, as well as other various applications in hydrological and meteorological modelling.

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automaticin situobservations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI.

The justification of the spatial and temporal discretization schema is a critical step in the development of numerical hydrological models. Currently, the challenge remains in balancing the error and uncertainty induced by the algorithm and the mass calculation caused by the increase of the division of computational units. Thus, it is necessary to investigate an appropriate discretization scheme, which not only adequately represents the spatial heterogeneity characteristics, but also maintains a sufficiently high computational efficiency, with the constraints of the data validity and availability.

Internal tandem duplication (ITD) of FMS-like tyrosine kinase 3 (FLT3-ITD) constitutes an independent indicator of poor prognosis in acute myeloid leukaemia (AML). AML with FLT3-ITD usually presents with poor treatment outcomes, high recurrence rate and short overall survival. Currently, polymerase chain reaction and capillary electrophoresis are widely adopted for the clinical detection of FLT3-ITD, whereas the length and mutation frequency of ITD are evaluated using fragment analysis. With the development of sequencing technology and the high incidence of FLT3-ITD mutations, a multitude of bioinformatics tools and pipelines have been developed to detect FLT3-ITD using next-generation sequencing data. However, systematic comparison and evaluation of the methods or software have not been performed. In this study, we provided a comprehensive review of the principles, functionality and limitations of the existing methods for detecting FLT3-ITD. We further compared the qualitative and quantitative detection capabilities of six representative tools using simulated and biological data. Our results will provide practical guidance for researchers and clinicians to select the appropriate FLT3-ITD detection tools and highlight the direction of future developments in this field. Availability: A Docker image with several programs pre-installed is available at https://github.com/niu-lab/docker-flt3-itd to facilitate the application of FLT3-ITD detection tools.

Numerous recent works in side-channel attacks have experimentally shown that Simultaneous Multi-Threading (SMT) inherently has a broader attack surface as it exposes more microarchitecture components per-core than cross-core. Existing mechanisms that protect against these attacks either incur high execution costs or are ineffective against certain attack variants. In this paper, we propose Partial-SMT, a system based on core-scheduling that protects security-critical programs from all contention-based attacks due to SMT. Partial-SMT allocates some complete physical cores for the exclusive use of the individual applications and provides a user-level threading library linked into each application to control the placement of their threads on dedicated cores, thereby preventing the attacker from accessing shared CPU resources simultaneously on the victim's core. The key insight is that by limiting ourselves to SMT contention-based side channels, we can translate the protection into an allocation policy that allocates or frees computing resources with a granularity of one physical core. Security-critical applications can be implemented on-demand and coexist with existing applications. We demonstrate that Partial-SMT effectively defeats typical SMT contention-based attacks. We modify AES and SPEC 2006 to use Partial-SMT, and they all incur the slight negligible performance overhead.

Hydrological drought is a global issue that many countries face. In this paper, we present a new hydrological index (SGI) based on the Gravity Recovery and Climate Experiment (GRACE) product. For comparison, we compare it with the documented drought in July 2010 and calculate the correlation coefficient between it and the standardized precipitation index (SPI). The result shows the new index can reflect spatiotemporal distribution of dryness and wetness, and it has good agreement with SPI in different time scales, and the new index can be used in the hydrological drought measurement in the global scale.

High-performance simulation of flow dynamics remains a major challenge in the use of physical-based, fully distributed hydrologic models. Parallel computing has been widely used to overcome efficiency limitation by partitioning a basin into sub-basins and executing calculations among multiple processors. However, existing partition-based parallelization strategies are still hampered by the dependency between inter-connected sub-basins. This study proposed a particle-set strategy to parallelize the flow-path network (FPN) model for achieving higher performance in the simulation of flow dynamics. The FPN model replaced the hydrological calculations on sub-basins with the movements of water packages along the upstream and downstream flow paths. Unlike previous partition-based task decomposition approaches, the proposed particle-set strategy decomposes the computational workload by randomly allocating runoff particles to concurrent computing processors. Simulation experiments of the flow routing process were undertaken to validate the developed particle-set FPN model. The outcomes of hourly outlet discharges were compared with field gauged records, and up to 128 computing processors were tested to explore its speedup capability in parallel computing. The experimental results showed that the proposed framework can achieve similar prediction accuracy and parallel efficiency to that of the Triangulated Irregular Network (TIN)-based Real-Time Integrated Basin Simulator (tRIBS).

Accurate removal of atmospheric interference and precise retrieval of water-leaving reflectance is decisive for subsequent water color applications. As follow-up satellite of Envisat, Sentinel-3 will provide valuable observations of the earth. This study aims to adapt the shortwave infrared extrapolation (SWIRE) atmospheric correction algorithm for Sentinel-3 to derive remote sensing reflectance of turbid waters, and validation it using our in -situ data in Pearl River Estuary. Results showed that SWIRE algorithm could effectively remove atmospheric perturbations, and produced more accurate remote sensing reflectance over complex waters of PRE than NIR and SWIR algorithms.

Coastal water quality conditions have great influences on the ecological environment of tropical coastal regions. Conventional in-situ measuring approach is time consuming and laborious. The growing remote sensing data and techniques have become more widely used in estimating water quality parameters based on the reflection and absorption characteristics. The reflected spectral signals from the water body are easy to be disturbed or confused due to the uncertainty of cloud cover before they are recorded by remote sensors. Moreover, the atmospheric correction of remotely sensed data has always been one of the most difficult steps in quantitative remote sensing application research, especially in tropical cloudy environment. This project tries to improve the satellite-based water quality monitoring framework with the assistance of low-altitude remote remote platform, and the present study will preliminarily explore the feasibility and superiority of low-altitude spectral characteristics for monitoring water quality in coastal cloudy regions.

This paper proposes a flow-path network (FPN) model to simulate complex surface flow based on a drainage-constrained triangulated irregular network (TIN). The TIN was constructed using critical points and drainage lines extracted from a digital terrain surface. Runoff generated on the surface was simplified as ‘water volumes’ at constrained random points that were then used as the starting points of flow paths (i.e. flow source points). The flow-path for each ‘water volume’ was constructed by tracing the direction of flow from the flow source point over the TIN surface to the stream system and then to the outlet of the watershed. The FPN was represented by a set of topologically defined one-dimensional line segments and nodes. Hydrologic variables, such as flow velocity and volume, were computed and integrated into the FPN to support dynamic surface flow simulation. A hypothetical rainfall event simulation on a hilly landscape showed that the FPN model was able to simulate the dynamics of surface flow over time. A real-world catchment test demonstrated that flow rates predicted by the FPN model agreed well with field observations. Overall, the FPN model proposed in this study provides a vector-based modeling framework for simulating surface flow dynamics. Further studies are required to enhance the simulations of individual hydrologic processes such as flow generation and overland and channel flows, which were much simplified in this study.

A scale-adaptive digital elevation model (S-DEM) method is proposed for multi-scale terrain analysis using a single high-resolution digital elevation model (DEM) database. The motivation is to construct a DEM that is self-adaptive to a given scale of an application, rather than letting the application fit into the built-in scale of the DEM. The method is based on an adaptive compound point extraction (CPE) algorithm that extracts surface ‘significant points’ from a high-resolution DEM according to their degree of importance (DOI) to the scale of an application. A data structure can be established to match the demand from an application at a coarser scale. Based on the data structure, a triangulated irregular network (TIN) model can be generated to support the terrain analysis at the desired scale. The aim of the S-DEM is to support multi-scale applications in three aspects, namely, ‘one database for all scales and scale-adaptive’ (i.e. matching any application scale using a single high-resolution DEM), ‘consistent measurement’ (i.e. delivering more constant measurements of terrain parameters with changing scales), and ‘skeleton preservation’ (i.e. preserving basic streamlines with changing scales). Compared with the raster resampling algorithm and the maximum z-tolerance algorithm, we find that the proposed method offers better performance, providing values that meet the accuracy requirements set by DEM data standards for different scales, and producing analytical derivatives that retain terrain features with consistent measurements of terrain parameters.

Terrain analysis (or geomorphometry) is ‘the science of quantitative land-surface analysis’ (cited in Pike et al. (2009, p. 3)). It collects, analyzes, evaluates, and interprets geographical inform...

The objective of this research is to study the relationship between terrain complexity and terrain analysis results from grid‐based digital elevation models (DEMs). The impact of terrain complexity represented by terrain steepness and orientation on derived parameters such as slope and aspect has been analysed. Experiments have been conducted to quantify the uncertainties created by digital terrain analysis algorithms. The test results show that (a) the RMSE of derived slope and aspect is negatively correlated with slope steepness; (b) the RMSE of derived aspect is more sensitive to terrain complexity than that of derived slope; and (c) the uncertainties in derived slope and aspect tend to be found in flatter areas, and decrease with increasing terrain complexity. The study shows that although primary surface parameters can be well defined mathematically, the implementation of those mathematical models in a GIS environment may generate considerable uncertainties related to terrain complexity. In general, when terrain is rugged with steep slopes, the uncertainty of derived parameters is quite minimal. While in flatter areas, the DEM‐based derivatives, particularly the aspect, may contain a great amount of uncertainty, causing significant limitation in applying the analytical results.

This paper reports an investigation on the accuracy of grid-based routing algorithms used in hydrological models. A quantitative methodology has been developed for objective and data-independent assessment of errors generated from the algorithms that extract hydrological parameters from gridded DEM. The generic approach is to use artificial surfaces that can be described by a mathematical model, thus the ‘true’ output value can be pre-determined to avoid uncertainty caused by uncontrollable data errors. Four mathematical surfaces based on an ellipsoid (representing convex slopes), an inverse ellipsoid (representing concave slopes), saddle and plane were generated and the theoretical ‘true’ value of the Specific Catchment Area (SCA) at any given point on the surfaces could be computed using mathematical inference. Based on these models, tests were made on a number of algorithms for SCA computation. The actual output values from these algorithms on the convex, concave, saddle and plane surfaces were compared with the theoretical ‘true’ values, and the errors were then analysed statistically. The strengths and weaknesses of the selected algorithms are also discussed.

In the real estate business, it is a common understanding that the value and potential of a property are fundamentally determined by its location. This emphasises the significance of spatial factors in decision making in real estate. A geographical information system (GIS) is undoubtedly useful in this decision making. This paper reports on the development of a prototype real estate GIS (REGIS) by integrating fuzzy set (FZ) theory, a rule-based system (RBS) and GIS. The role of this system in the real estate business is in assisting decision makers for sellers and buyers, as well as property managers. For real estate agencies, the system can be used as an aid to selling and managing properties. For the buyers, the REGIS can function as a consultant in their decision making in purchasing properties. Methodologies are demonstrated using case studies. Developed as a generic tool with capabilities to deal with uncertainties, this prototype REGIS can also be applied to other fields, which involves optimal spatial decision making.