This paper develops GTPAN, an IoT-enabled temporal-pattern-aware neural framework for real-time shared mobility forecasting and dynamic pricing optimization. Within the Internet of Things (IoT) ecosystem of smart urban transportation, massive spatiotemporal data streams are continuously generated from bicycles, docking stations, and user mobile sensors. Traditional pricing and deployment schemes are often static and fail to adapt to this rapidly evolving IoT environment, leading to over-deployment and revenue loss. The proposed GTPAN framework integrates a Gated Recurrent Unit (GRU) for temporal encoding, a Temporal Pattern Attention (TPA) mechanism for adaptive feature weighting, and a Kolmogorov–Arnold Network (KAN) for nonlinear mapping, jointly enabling accurate demand prediction under complex IoT data dependencies. Furthermore, an improved domain-constrained simulated annealing algorithm is incorporated to achieve real-time dynamic pricing with low computational latency, making the model suitable for edge-intelligent deployment. Experimental results on real-world IoT-based shared bicycle datasets demonstrate that GTPAN improves mean prediction accuracy by 5.74% and revenue by 45% compared with state-of-the-art methods. The proposed framework provides an interpretable and scalable solution for IoT-driven smart mobility management, contributing to sustainable and efficient urban transportation systems.

We introduce an ultrasound speckle decorrelation-based time-lagged functional ultrasound technique (tl-fUS) for the quantification of the relative changes in cerebral blood flow speed (rCBF [Formula: see text]), cerebral blood volume (rCBV) and cerebral blood flow (rCBF) during functional stimulations. Numerical simulations, phantom validations, and in vivo mouse brain experiments were performed to test the capability of tl-fUS to parse out and quantify the ratio change of these hemodynamic parameters. The blood volume change was found to be more prominent in arterioles compared to venules and the peak blood flow changes were around 2.5 times the peak blood volume change during brain activation, agreeing with previous observations in the literature. The tl-fUS shows the ability of distinguishing the relative changes of rCBFspeed, rCBV, and rCBF, which can inform specific physiological interpretations of the fUS measurements.

Learning-based sketches have been widely studied as an improvement of traditional sketches that achieves high efficiency in terms of both time and space. It uses a learning model to reveal and exploit underlying patterns of input data for helping traditional sketches obtain accurate frequency estimation with memory efficient. However, recent studies only focus on the performance improvement of learning-based sketches and pay little attention to security. The potential security problems can be easily exploited by an adversary to make learning-based sketches inaccurate. In this paper, we firstly explore the security issues of learning-based sketches with regard to estimation accuracy and memory overhead. Some adversarial scenarios of learning model and backup sketch are modeled according to the knowledge and capabilities of an adversary. Then, we propose four attacks to deceive learning-based sketch, namely counterfeit attack, targeted point attack, memory occupation attack, and blind increment attack. We conduct a series of experiments based on real-world datasets and verify that the proposed attacks highly degrade the performance of learning-based sketch even when the adversary knows nothing about it.

We have designed and developed OceanBase, a distributed relational database system from the very basics for a decade. Being a scale-out multi-tenant system, OceanBase is cross-region fault tolerant, which is based on the shared-nothing architecture. Besides sharing many similar goals with alternative distributed DBMS, such as horizontal scalability, fault-tolerance, etc., our design has been driven by the demands of typical RDBMS compatibility as well as both on-premise and off-premise deployments. OceanBase has fulfilled its design goal. It implements the salient features of certain mainstream classical RDBMS, and most applications on them can run on OceanBase, with or without a few minor modifications. Tens of thousands of OceanBase servers have been deployed in Alipay.com as well as many other commercial organizations. It has also successfully passed the TPC-C benchmark test and seized the first place with more than 707 million tpmC. This paper presents the goals, design criteria, infrastructure, and key components of OceanBase including its engines for storage and transaction processing. Further, it details how OceanBase achieves the above leading TPC-C benchmark in a distributed cluster with more than 1,500 servers from 3 zones. It also describes lessons what we have learnt in building OceanBase for more than a decade.

Terrestrial laser scanning (TLS) is a highly effective and noninvasive technology for retrieving the structural and biophysical attributes of trees using 3-D high-accuracy and high-density point clouds. The separation of leaf and wood points in TLS data is a prerequisite for the accurate and reliable derivation of these attributes. In this study, a new method is proposed to separate the leaf and wood points of individual trees by combining the TLS radiometric (intensity) and geometric (density) data. The leaf points are separated from the wood ones through three steps. First, the corrected intensity data are used to separate a part of the leaf points preliminarily given the differences in reflectance characteristics. Second, the density data are adopted for the further separation of another part of the leaf points because the density of the remaining leaf points is smaller than that of the wood points. Finally, a connectivity clustering algorithm is conducted to form several clusters with different sizes (points) and the remaining leaf points are separated in accordance with the cluster sizes. Eight different trees are selected to evaluate the performance of the proposed method. The averaged overall accuracy and kappa coefficient of the eight trees are approximately 95% and 0.81, respectively. The results suggest that the combination of TLS intensity and density data can perform a superior separation of leaf and wood points in terms of efficiency and accuracy, and the proposed separation method can be accurately and robustly used for various trees with different species, sizes, and structures.

The intensity data recorded by a terrestrial laser scanner (TLS) are significantly influenced by the distance between the scanned point and the scanner center. In this letter, we propose a new method to estimate the TLS distance-intensity relationship by using naturally homogeneous targets (NHT). Since the original intensity data of the NHT are simultaneously influenced by the incidence angle and distance, the incidence angle effect is first eliminated to obtain a corrected intensity that merely depends on distance. Then, the distance-intensity relationship is derived by analyzing the incidence angle corrected intensity data of the NHT. Results indicate that compared with existing methods the proposed method is accurate and easily implementable. The coefficient of variation for the intensity data can be reduced by 52% after correction by the proposed method.

Terrestrial laser scanning (TLS) can accurately and thoroughly document the actual geometrical deformations and surface conditions of metro tunnel structures using high-density point clouds. In addition, TLS can record the backscattered intensity values of each point simultaneously. Backscattered intensity values are significant measures of the spectral property of scanned objects in the near-infrared region of the electromagnetic spectrum. The intensity values of water leakage regions are theoretically lower than those of the background because of the high water absorption coefficient in the near-infrared spectrum. Thus, the intensity data can be used in water leakage detection and quantitative analysis. This letter investigates the feasibility of a new mobile system called Faro Focus3DX330 for data collection, processing, and intensity correction to detect and extract water leakage spots in metro tunnels. A case study of the Shanghai Metro was conducted. Results show that the corrected intensity value is an effective physical criterion to detect water leakages in metro tunnels. By combing the 3-D point cloud, the location, area, and even water content of the leakage regions can be accurately determined.

This letter presents an effective approach to reduce the ambiguity of matching results for image registration based on a coarse-to-fine strategy. In the coarse registration stage, we compute initial transformation parameters via the descriptors. In the fine registration stage, we propose a new matching strategy for an iterative closest point framework, in which the matching pairs are determined by a bidirectional matching criterion in terms of feature similarity and spatial consistency. In this letter, the spatial consistency includes not only spatial distance but also local structure constraints on reference and sensed images. Comparative experiments on multispectral and viewpoint-altered images show that the proposed algorithm achieves higher performance in accuracy and robustness.

Axiom pinpointing plays an important role in the development and maintenance of ontologies. It helps the user to comprehend an unwanted entailment of an ontology by presenting all minimal subsets of the ontology which are responsible for the entailment (called MinAs). In this paper, we consider the problem of axiom pinpointing in description logic EL+, which underpins OWL 2 EL, a profile of the latest version of Web Ontology Language (OWL). We propose a novel method to compute all MinAs that utilizes the hierarchy information obtained from the classification of an EL+ ontology. The advantage of our method over an existing labeled classification based method is that we do not attach labels to entailed subsumptions, which can be memory exhaustion for large scale ontologies. We further consider axiom pinpointing in EL+ when ontologies change. An incremental algorithm is given to compute all MinAs by reusing MinAs previously computed.