SUMMARY: Nanopore sequencing technology enables real-time sequencing and is widely used in rapid detection applications. However, in clinical scenarios, existing structural variant (SV) detection tools typically separate sequencing from computation, limiting their timeliness for clinical applications. To address this, we introduce cuteSV-OL, a novel framework designed for real-time SV discovery, which can be embedded within nanopore sequencing instruments to analyze data concurrently with its generation. Additionally, cuteSV-OL features a real-time SV detection rate evaluation module, allowing users to terminate sequencing early when appropriate, thereby reducing time and cost. Experimental results show that on a standard desktop computer, cuteSV-OL can perform real-time analysis during sequencing and complete SV calling within min after sequencing ends, achieving performance comparable to offline methods. This approach has the potential to enhance rapid clinical diagnostics. AVAILABILITY AND IMPLEMENTATION: cuteSV-OL is released under the MIT license and is available at https://github.com/gwmHIT/cuteSV-OL. It can also be installed via Bioconda or accessed through https://doi.org/10.5281/zenodo.17777436.

In order to use remote wind power/solar energy, the scale of power grid interconnection grow rapidly. If transient stability can be increased when faults happen on tie lines, the transmission capacity can be increased too. Dynamic braking can increase transient stability by increasing deceleration area, but it's difficult to coordinate several braking devices and calculate braking amount in real time. Things are no longer so after rotor angle droop (RAD) controllers are deployed across the power system. Since all rotor angles of generators will be fixed in rotating coordination system determined by global position system (GPS) pulse per second (PPS) signal, dynamic braking amount can be calculated by RAD controller by using local speed and rotor measurement. This paper shows the revised equal area criteria (EAC) when this kind of dynamic braking is used. The relationships between RAD controller parameter (saturation threshold) and the braking effect are also investigated. Simulation results in IEEE 68 nodes system show that, by applying dynamic braking on several generators simultaneously, critical clearing time can be increased dramatically when faults happen on tie lines.

Support vector machine(SVM) has become a powerful and widely used machine learning method in resent years. Gaussian kernel is the most commonly used kernel function. However, model selection including setting the width parameter sigma in kernel function and the regularization parameter C is essential to generalization performance of SVM. In this paper we proposed a new parameter selection method for Support Vector Machine. The key idea of our method MSKD in selecting the Gaussian kernel parameter is that convergent character between pattern's similarity measurement in feature space will decrease the classification ability of SVM. In addition, We combined MSKD algorithm with one-dimension search strategy based on cross-validation and developed a complex parameters selection method named MSKD-GS. Experiments on eight real world data sets from UCI have been carried out to demonstrate the effectiveness and efficiency of this method.