Accurate drug-target interaction prediction is vital for drug discovery and optimization. Traditional experimental methods, while effective, are time-intensive and costly. HyperGCN-DTI, a novel framework that explicitly advances beyond existing models such as CHL-DTI and HHDTI by leveraging hypergraph neural networks with a multimodal feature fusion strategy. While exsiting methods primarily focuses on low-order graph representations and fixed heterogeneous network structures, HyperGCN-DTI incorporates richer multimodal fused features including embeddings from pretrained language models and diverse biological networks and build robust hypergraphs that capture high-order multi-entity relationships within drug-target pairs. This dual-channel architecture effectively captures both local topological connections and higher-order structural dependencies. HyperGCN-DTI outperforms state-of-the-art DTI prediction models across multiple datasets and remains robust under imbalanced and large-scale real-world datasets, demonstrating its superior predictive power. The model demonstrates significant improvements when using multimodal features and hypergraph-based message passing, with sensitivity analysis confirming stability across hyperparameter variations. Top-ranked predictions are validated through biomedical literature and molecular docking, underscoring the reliability and practical relevance of our approach. HyperGCN-DTI is the first DTI prediction model to jointly integrate such a wide range of heterogeneous information sources with hypergraph representation, significantly enhancing accuracy and robustness, particularly in sparse or noisy settings. The proposed model offers a powerful and generalizable tool for accelerating drug development and target identification.

Accurately assessing power systems’ ability to accommodate wind power is an important basis for systems to participate in the day-ahead wind power market and make reasonable trading plans. However, existing assessment methods do not consider frequency security of systems, which leads to frequency security issues during the execution process of trading results. This paper proposes an assessment method for day-ahead wind power admissibility (DWPA) of power systems considering frequency constraints (FCs). First, a FCs model is established through the oscillation equation of frequency deviation dynamics after system disturbances, and embedded in the assessment of day-ahead DWPA. Then, a robust feasible region definition for wind power output is proposed, and a system operation risk model is developed based on it. Finally, based on the two-stage robust optimization theory, an overall assessment model for the DWPA of power systems considering FCs is proposed, and the column-and-constraint generation algorithm is used to solve the problem. The effectiveness of the proposed method was verified through simulations conducted on the IEEE 9-bus and 118-bus power systems. The results emphasized the importance of taking FCs into account and the enthusiasm of wind farms to provide virtual inertia.

Log-Structured Merge Tree (LSM-Tree) is widely employed in key-value (kv) store owing to its exceptional performance in write-intensive workloads. LSMTree appends kv pairs to memory initially, and only when the memory capacity reaches a threshold does it flush the data to disk. However, in comparison to write, read operations often entail more disk access, resulting in relatively a slower response speed. Although LSM-Tree utilizes an index structure to expedite read operations, its performance enhancement remains limited. In addition, LSM-Tree fails to consider the read hotness of each sorted string table (SSTable) during compaction, leading to significant fluctuations in performance. This paper introduces HL-LSM, a novel LSM-Tree. HL-LSM utilizes learned index to replace original index to save system overhead and improve read performance. Moreover, a lightweight read hotness statistics is devised to optimize the compaction and assess the feasibility of each SSTable in constructing a learned index. In order to evaluate the performance of HL-LSM, we constructed a system prototype, HLDB, based on WiscKey. Experiment demonstrates that in read-intensive scenarios, HLDB is able to reduce the overall system latency by nearly 10% compared to WiscKey. Furthermore, it was observed that the optimization effect of HL-LSM improves as the variance of hotness value distribution increases.

Wide swath altimeters are a new generation of radar altimeters, which can provide elevations of both the ocean and the inland surface waters with unprecedented swath and spatial resolution. Interferometric calibration that gives estimates of the unknown variations of the baseline length, the baseline roll angle, and the phase offset, are essential to deriving topography accurately. Current methods are mainly developed on a cross-track height error model for systems with a horizontal baseline. However, for altimeters with a deliberately-inclined baseline, the height error model turns into a complicated form. This paper proposes a calibration method for wide swath altimeters with an inclined baseline using phase measurements from large inland lakes. The validity of the method has been confirmed by simulated buoy samples and together with real data acquired by Interferometric Imaging Radar Altimeter (InIRA), which was equipped on the Tiangong-2 space laboratory of China.

Two-dimensional (2-D) water surface elevations (WSEs) can be measured for the first time using a new generation of radar altimeter, which uses the interferometric synthetic aperture radar (InSAR) technique on near-nadir swath to measure elevations of both the inland surface waters and the ocean with unprecedented swath and spatial resolution. The interferometric imaging radar altimeter (InIRA) in the Tiangong-2 space laboratory of China is the first spaceborne interferometric radar altimeter, which was launched in 2016. Calibration is essential to a guarantee of the measurement accuracy. Not only the cross-track errors, but also the along-track errors have to be estimated and corrected. This letter proposes a correction method to remove the azimuth time shift of the radar system using geographic positions of the surrounding lakeshores and riverbanks. Real acquisitions of InIRA over the largest inland lake of China, i.e., the Qinghai Lake, have been selected to validate the method. Both azimuth and range system time errors, the media delays, and the cross-track phase errors have been estimated and corrected on the level-1 complex products. The derived 2-D WSEs over an area of ~400 square kilometers have been compared with the high-resolution data of Sentinel-3A synthetic aperture radar altimeter (SRAL). The validation results have shown good agreements in both along-track and cross-track directions with a root mean square error of 28.1 cm and a mean absolute error of 22.7 cm.

Compact polarimetric (CP) synthetic aperture radar (SAR) systems are attracting increasing interest as they possess the ability to construct quad-polarimetric information with advantages of simpler system, lower data rate, and wider swath, compared to a fully polarimetric (FP) system. In order to get undistorted polarimetric information, calibration of CP measurements has to be performed. Freeman and Chen both proposed methods for CP SAR calibration using merely calibrators, which require at least three calibrators at a range line and would require many more calibrators when monitoring the change of the system across the swath is necessary. It has been proved that FP SAR calibration methods exploring scattering characteristics of natural distributed targets (DTs) can reduce the requirement of the number of deployed calibrators. However, it is much more complicated in the case of CP SAR calibration, as only two channels of CP measurements are available. This paper proposes a numerical CP calibration method using natural DTs and only one corner reflector (CR) for a reciprocal CP SAR system. The validity of the method for three typical CP modes is confirmed by simulations with Advanced Land Observing Satellite Phased Array L-band SAR data. Through sensitivity analysis, a dihedral at 0° is selected as the best CR for the calibration algorithm.

A compact polarimetric (CP) synthetic aperture radar (SAR) has an advantage of reconstructing quad-polarimetric (quad-pol) information from measured dual-polarized scattering vectors. In order to fully explore CP data, calibration of CP measurements is important. However, among different system errors, the crosstalk on transmit is not possible to be corrected in measured scattering vectors even when it is already estimated and known. Except scattering vectors, reconstructed quad-pol data are widely used. Three reconstruction algorithms have been published in the literature: one by Souyris et al., a refined algorithm by Nord, and another refined one by Collins et al. However, the removal of transmit crosstalk is still left unresolved. In this letter, we proposed a calibration algorithm to correct the transmit crosstalk in reconstructions of quad-pol data from CP data. We conducted simulations with fully polarimetric Advanced Land Observing Satellite Phased Array L-band SAR data. Simulation results confirmed that the algorithm performed effectively and stably under certain conditions.

Describes a system that predicts significant short-term price movement in a single stock utilizing conservative strategies. We use preprocessing techniques, then train a probabilistic neural network to predict only price gains large enough to create a significant profit opportunity. Our primary objective is to limit false predictions (known in the pattern recognition literature as false alarms). False alarms are more significant than missed opportunities, because false alarms acted upon lead to losses. We can achieve false alarm rates as low as 5.7% with the correct system design and parameterization.