Situation prediction, as a critical component of the situation awareness framework, plays a vital role in decisionmaking systems such as traffic management and cybersecurity. However, most existing studies still focus primarily on univariate time series, lacking effective modeling and optimization mechanisms for multivariate situation data with exogenous variables.In view of the above research status, this paper proposes a novel prediction method called Improved Grey Wolf Optimizer -Neural Basis Expanded Analysis of Time Series with Exogenous Variables (CWGWO-NBEATSx), specifically designed for time series situation prediction tasks involving multivariate exogenous variables. The proposed approach integrates the powerful modeling capability of N-BEATSx with an improved grey wolf optimization algorithm (CWGWO), aiming to achieve joint optimization of model architecture, critical hyperparameters, and exogenous variable selection. Within the CWGWO-N-BEATSx framework, N-BEATSx is, for the first time, applied to situation prediction. Key hyperparameters influencing the performance of NBEATSx are identified and quantified, and a CWGWO algorithm—enhanced through the incorporation of chaotic mapping and adaptive weighting mechanisms—is introduced to optimize these hyperparameters. A multi-objective fitness function is constructed by jointly considering prediction accuracy and model complexity, and extensive empirical evaluations are conducted on real-world situation and time series datasets.Experimental results demonstrate that CWGWO outperforms eight mainstream metaheuristic algorithms. Compared with five state-of-the-art (SOTA) methods, CWGWO-N-BEATSx reduces the average MAE on situation datasets and time series datasets by 17.614% and 17.55%, respectively; the average SMAPE by 16.27% and 8.932%; and the MSE by 23.488% and 43.87%, respectively. In addition, CWGWO-N-BEATSx maintains relatively low model complexity, validating its superior performance and strong potential for practical application.

MOTIVATION: Glycans play a central role in many essential biological processes. Glycan Reader was originally developed to simplify the reading of Protein Data Bank (PDB) files containing glycans through the automatic detection and annotation of sugars and glycosidic linkages between sugar units and to proteins, all based on atomic coordinates and connectivity information. Carbohydrates can have various chemical modifications at different positions, making their chemical space much diverse. Unfortunately, current PDB files do not provide exact annotations for most carbohydrate derivatives and more than 50% of PDB glycan chains have at least one carbohydrate derivative that could not be correctly recognized by the original Glycan Reader. RESULTS: Glycan Reader has been improved and now identifies most sugar types and chemical modifications (including various glycolipids) in the PDB, and both PDB and PDBx/mmCIF formats are supported. CHARMM-GUI Glycan Reader is updated to generate the simulation system and input of various glycoconjugates with most sugar types and chemical modifications. It also offers a new functionality to edit the glycan structures through addition/deletion/modification of glycosylation types, sugar types, chemical modifications, glycosidic linkages, and anomeric states. The simulation system and input files can be used for CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Glycan Fragment Database in GlycanStructure.Org is also updated to provide an intuitive glycan sequence search tool for complex glycan structures with various chemical modifications in the PDB. AVAILABILITY AND IMPLEMENTATION: http://www.charmm-gui.org/input/glycan and http://www.glycanstructure.org. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In the Google Play store, an introduction page is associated with every mobile application (app) for users to acquire its details, including screenshots, description, reviews, etc. However, it remains a challenge to identify what items influence users most when downloading an app. To explore users’ perspective, we conduct a survey to inquire about this question. The results of survey suggest that the participants pay most attention to the app description which gives users a quick overview of the app. Although there exist some guidelines about how to write a good app description to attract more downloads, it is hard to define a high quality app description. Meanwhile, there is no tool to evaluate the quality of app description. In this paper, we employ the method of crowdsourcing to extract the attributes that affect the app descriptions’ quality. First, we download some app descriptions from Google Play, then invite some participants to rate their quality with the score from one (very poor) to five (very good). The participants are also requested to explain every score’s reasons. By analyzing the reasons, we extract the attributes that the participants consider important during evaluating the quality of app descriptions. Finally, we train the supervised learning models on a sample of 100 app descriptions. In our experiments, the support vector machine model obtains up to 62% accuracy. In addition, we find that the permission, the number of paragraphs and the average number of words in one feature play key roles in defining a good app description.