Identifying complementary-determining regions (CDRs) and antigen-binding regions (ABRs) requires accurate antibody numbering, which is essential for therapeutic antibody development. AbNumPro is a comprehensive offline toolkit developed for antibody numbering and ABRs prediction, addressing the limitations of existing tools, which often lack comprehensiveness and rely solely on online services. By integrating five established numbering schemes—Kabat, Chothia, IMGT, Aho, and Martin—AbNumPro provides precise delineation of CDRs and ABRs, offering both compatibility with diverse research applications and the assurance of data security. • Identifying CDRs and ABRs requires accurate antibody numbering, which is essential for therapeutic antibody development. • AbNumPro integrates multiple numbering schemes and provides offline software to ensure data security. • AbNumPro is a pioneering software specifically designed to predict ABRs.

Telegram’s growing role as a digital communication platform creates opportunities and challenges for analyzing public discourse. TeleCatch, an open-source tool, simplifies access to data from public Telegram groups and channels, requiring no programming skills. Built with FastAPI and Telethon, it enables collection management, rapid sampling, and retrieval of text and media, offering a privacy-focused, decentralized approach. TeleCatch has proven valuable in studies on human mobility and food security, supporting diverse research fields. Future updates will enhance search capabilities and visualization features, further expanding its applicability for digital communication and social media analysis. • Open-source and accessible tool for filtering and extracting data from Telegram. • Enables large-scale data collection without coding skills. • Decentralized data streaming ensures data privacy and control. • Supports retrieval of both text messages and media files for comprehensive analysis.

BioMASS is an innovative multi-agent spatial model designed to enhance computational efficiency in simulations involving complex sensory and locomotion functions. Traditional agent-based modeling (ABM) platforms suffer from performance degradation as the number of agents and their perception ranges increase, resulting in a quadratic growth in computational cost. BioMASS addresses this issue employing a quadruply linked list structure, which allows constant-time neighborhood search and movement. This feature allows BioMASS to simulate large populations in dynamic environments efficiently. The model has been successfully applied to marine ecosystem simulations, demonstrating its ability to track species interactions across multiple trophic levels in real-time, outperforming existing platforms. • BioMASS model uses a quadruply linked list to optimize agent-based spatial simulations. • The model enables constant-time search and movement for large-scale simulations. • BioMASS is an open-source tool for scalable simulations of multi-agent systems.

Multi-level capacitated lot-sizing problems with linked lot sizes and backorders (MLCLSP-L-B) are used in pharmaceutical tablets manufacturing processes to right-size material production lots so that costs are kept at a minimum, production resource capacities are not exceeded, and customer demand is fulfilled. Uncertain demand behavior characterizes today’s global tablets market. Pharmaceutical companies request solution approaches that solve the MLCLSP-L-B with probabilistic demand. Implementing this model in industrial applications for tablets manufacturing systems requires efficient data processing due to the amount of data and the capability to store simulated demand scenarios. This paper covers the first integration of the MLCLSP-L-B with probabilistic demand and Relational Database Structures (RDS). Modeling techniques for the RDS to process massive data are outlined. A virtual environment provides the implementation software PostgreSQL and infrastructure environment. Additionally, numerical experiments with research data are used to evaluate the agility and efficiency of the developed RDS.

The increasing prevalence of high-speed trains necessitates robust analysis tools to ensure the safety and reliability of railway bridges. This paper presents a user-friendly software application designed for the dynamic analysis of railway bridges subjected to high-speed train loadings. Leveraging the semi-analytical modal method, the software offers a balanced approach that combines computational efficiency with high accuracy. Key features include an intuitive interface, rapid analysis capabilities, and reliable prediction of bridge responses, facilitating design optimization and maintenance planning. This software is poised to become an indispensable tool for structural engineers, researchers, and infrastructure planners.

The study and analysis of drifter trajectories, such as buoys and floating devices, have become fundamental to understanding oceanographic phenomena. The detailed analysis of oceanographic parameters using specialized software allows researchers to study environmental changes on different temporal and spatial scales. This article explores the importance and functionalities of this type of software, highlighting its role in improving research methodologies and generating scientific knowledge applicable to oceanography and meteorology. drifViewer is a MATLAB package designed to optimize the search for drift trajectories and analyze oceanographic parameters. It allows researchers to search for drift trajectories in the AOML dataset quickly, create summary tables, and export data to MATLAB and NetCDF formats. driftViewer contributes to scientific research in oceanography and marine geology by improving the ability to model and predict drifting currents, which is essential for studies on climate change, ocean current dynamics, and marine habitat conservation. It is also a useful tool for studying the trajectories of oil slicks, microplastics, and chemical and living species.

Differential expression analysis of biomarkers followed by the enrichment tests identifies the roles of enriched pathways or functional terms in a univariate manner. Cross-talks among enriched pathways are also key to better deciphering the complexity of tumor micro-environment and underlying mechanisms of its metastasization and/or drug resistance. Here we develop the rXTalkViz R package that facilitates an unmet need for quantifying and producing publication-ready visualization for functional cross-talk in a novel manner. We hypothesize that using rXTalkViz may enhance our understanding of the contribution of functional cross-talks in cancer progression and seamlessly aid further downstream analysis of biomarker.

This manuscript delineates the code developed for a published scholarly article aimed at supporting researchers in addressing plant leaf disease detection and classification (PLDC) challenges while evaluating the efficacy of various deep learning models. Furthermore, the research incorporates preprocessing strategies, correlation, segmentation employing the UNet model, feature extraction methods and EfficientNet model. The software model generates graphs such as confusion matrix, ROC curve (Receiver Operating Characteristic), and visual representations of loss and accuracy graphs. The initial research was disseminated in the Multimedia Tools and Applications journal, and the accompanying dataset was also introduced in the Data in Brief journal.