The rise in violent crime across major U.S. cities, fueled mainly by gang members using social media to broadcast messages of loss and aggression, poses an urgent challenge. Although prior research has examined gang-affiliated social media content, there remains a crucial gap in identifying which posts serve as credible signals of impending violence. Addressing this gap is essential for enhancing community safety, improving resource allocation, and optimizing law enforcement strategies. This study introduces a novel research model grounded in a contextualized adaptation of signaling theory. The model identifies key indicators of credible signals, such as follower count, specific hashtags, and retweet counts, which correlate with gang-related aggression. Environmental factors, such as temperature, are also examined for their influence on violent crime escalation. Using this contextualized theory, we designed a machine learning model to predict violent crime counts, training it on a dataset of 143,700 gang-affiliated tweets and their accompanying text and metadata. This approach enables automated identification of credible social media signals related to gang violence. The findings contribute to theory and practice by offering new insights into social media credibility and its link to violent crime, and by demonstrating how such signals can be used for prediction. Furthermore, the predictive model provides law enforcement with advanced tools to anticipate crime and inform community-based prevention strategies and policy development.

Ride-sharing apps have both positive and negative impact in our daily lives. To study the negative impacts especially personal safety issues, this study explores the reaction of ride-sharing app users to such safety issues. We contextualise our study using a scenario based survey. We found empirical support to all the hypotheses by surveying 421 individuals. This study contributes to the information systems literature in providing ways to apply dissonance reduction techniques that are a strong predictor of satisfaction. In addition, this study extends the dimensions of service quality to better suit ride-sharing apps.

The emergence of Cross-Border E-commerce (CBeC) has brought substantial changes to both businesses and consumers. Although CBeC businesses have existed for less than a decade, many academic researchers addressed important issues in this context. It is essential to evaluate what has been studied through a structured review of the literature and derive meaningful insights given that research on this topic is new and largely fragmented. Therefore, this study conducts a review of CBeC literature to find the current gaps and fragmentation to provide guidelines for future research. The review shows that research in this domain needs more attention and enforcement to address the current research gaps. Addressing the current gaps helps academia build a rigorous body of knowledge and enables practitioners to solve challenging business problems.

Reverse engineering mechanistic gene regulatory network (GRN) models with a specific dynamic spatial behavior is an inverse problem without analytical solutions in general. Instead, heuristic machine learning algorithms have been proposed to infer the structure and parameters of a system of equations able to recapitulate a given gene expression pattern. However, these algorithms are computationally intensive as they need to simulate millions of candidate models, which limits their applicability and requires high computational resources. Graphics processing unit (GPU) computing is an affordable alternative for accelerating large-scale scientific computation, yet no method is currently available to exploit GPU technology for the reverse engineering of mechanistic GRNs from spatial phenotypes. Here we present an efficient methodology to parallelize evolutionary algorithms using GPU computing for the inference of mechanistic GRNs that can develop a given gene expression pattern in a multicellular tissue area or cell culture. The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation and selection operators and launching GPU kernels asynchronously. Kernels can run in parallel in a single or multiple GPUs and each kernel simulates and scores the error of a model using the thread parallelism of the GPU. We tested this methodology for the inference of spatiotemporal mechanistic gene regulatory networks (GRNs)-including topology and parameters-that can develop a given 2D gene expression pattern. The results show a 700-fold speedup with respect to a single CPU implementation. This approach can streamline the extraction of knowledge from biological and medical datasets and accelerate the automatic design of GRNs for synthetic biology applications.

SUMMARY: Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources. AVAILABILITY AND IMPLEMENTATION: GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper the authors proposed a database sampling framework that aims to minimize the time necessary to produce a sample database. They argue that the performance of current relational database sampling techniques that maintain the data integrity of the sample database is low and a faster strategy needs to be devised. The sampling method targets the production environment of a system under development that generally consists of large amounts of data computationally costly to analyze. The results have been improved due to the fact that the authors have selected the users that they had more information about them and they have made the data table denser. Therefore, by increasing the data and making the rating more comprehensive for all the users they can help to produce the more and better association rules. The obtained results were not that much suitable for Jester dataset but with their proposed methods the authors have tried to improve the quantity and quality of the rules. These results indicate that the effectiveness of the system greatly depends on the input data and the applied dataset. In addition, if the user rates more number of the items the system efficiency will be more increased.