ASAPA: a bioinformatic pipeline based on Iso-Seq that identifies the links among alternative splicing, alternative transcription initiation and alternative polyadenylation.

BACKGROUND: Although the events associated with alternative splicing (AS), alternative polyadenylation (APA) and alternative transcription initiation (ATI) can be identified by many approaches based on isoform sequencing (Iso-Seq), these analyses are generally independent of each other and the links between these events are still rarely mentioned. However, an interdependency analysis can be achieved because the transcriptional start site, splice sites and polyA site could be simultaneously included in a long, full-length read from Iso-Seq. RESULTS: We create ASAPA pipeline that enables streamlined analysis for a robust detection of potential links among AS, ATI and APA using Iso-Seq data. We tested this pipeline using Arabidopsis data and found some interesting results: some adjacent introns tend to be simultaneously spliced or retained; coupling between AS and ATI or APA is limited to the initial or terminal intron; and ATI and APA are potentially linked in some special cases. CONCLUSION: Our pipeline enables streamlined analysis for a robust detection of potential links among AS, ATI and APA using Iso-Seq data, which is conducive to a better understanding of transcription landscape generation.

Adaptable Hybrid Beamforming with Subset Optimization Algorithm for Multi-User Massive MIMO Systems.

The exploiting of hybrid beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems can enhance the system's sum rate while reducing power consumption and hardware costs. However, designing an effective hybrid beamformer is challenging, and interference between multiple users can negatively impact system performance. In this paper, we develop a scheme called Subset Optimization Algorithm-Hybrid Beamforming (SOA-HBF) that is based on the subset optimization algorithm (SOA), which effectively reduces inter-user interference by dividing the users set into subsets while optimizing the hybrid beamformer to maximize system capacity. To validate the proposed scheme, we constructed a system model that incorporates an intelligent reflecting surface (IRS) to address obstacles between the base station (BS) and the users set, enabling efficient wireless communication. Simulation results indicate that the proposed scheme outperforms the baseline by approximately 8.1% to 59.1% under identical system settings. Furthermore, the proposed scheme was applied to a classical BS-users set link without obstacles; the results show its effectiveness in both mmWave massive MIMO and IRS-assisted fully connected hybrid beamforming systems.

The study on mechanical model considering optimal self-adaption in the bottleneck area.

It aims to solve the problem that the evacuation state of pedestrians depicted by the traditional social force model in a crowded multiexit scenario has a relatively large difference with the actual state, especially the 'optimal path' considered by the self-driving force is the problem of shortest path, and the multiexit evacuation mode depicted by the 'herd behavior' is the local optimum problem. Through in-depth analysis of actual evacuation data of pedestrians and causes of problem, a new crowd evacuation optimization model is established in order to effectively improve the simulation accuracy of crowd evacuation in a multi-exit environment. The model obtains the direction of motion of pedestrians using a field model, fully considers the factors such as exit distance, distribution of pedestrians and regional crowding degree, makes a global optimization for the self-driving force in the social force model using a centralized and distributed network model, and makes a local optimization for it using an elephant herding algorithm, so as to establish a new evacuation optimization method for optimal self-adaption in the bottleneck area. The performance status is compared between the improved social force model and the new model by experiments, and the key factors that affect the new model are analyzed in an in-depth manner. The results show that the new model can optimize the optimal path choice at the early stage of evacuation and improve the evacuation efficiency of pedestrians at the late stage, so as to ensure relatively even distribution of pedestrians at each exit, and also make the simulated evacuation process be more real; and the improvement in overall evacuation efficiency is greater when the number of pedestrians to be evacuated is larger. Therefore, the new model provides a method to solve the phenomenon of disorder in overall pedestrian evacuation due to excessive crowd density during the process of multi-exit evacuation.