The Nature-Based Solutions Case-Based System: A hybrid expert system

Evaluating the effectiveness of coastal protection structures in flood mitigation using hydrodynamic modeling: A case study in a tropical environment

The coastal zone is complex and vulnerable to flooding, especially in low-lying areas. This research aimed to assess the effectiveness of coastal protection structures as mitigating factors through the application of the hydrodynamic model, MIKE 21. The model utilizes numerical methods to analyze scenarios involving bathymetry, tidal effects, river discharge, digital elevation model (DEM), and sea level rise (SLR). Three scenarios were outlined: the first, K1, represents a simulation without a coastal protection structure; the second, K2, includes the existing coastal protection structure; and the third, K3, presents a modified coastal protection structure designed for the study location. The K3 scenario was segmented into two regions: the northern region (NA) and the southern region (SA), both of which utilize hard and soft engineering coastal protection approaches, respectively. The results demonstrated that K2 and K3 significantly reduced flood areas, decreasing the potential inundation zones in the NA from 228 ha to 304 ha and in the SA from 31 ha to 10 ha, respectively, compared to K1. Statistical analysis (Friedman Test) showed a significant difference between K1 and K2 (chi-square = 4.0, p < 0.05), which validated the models' inclusion of coastal structures for flood risk assessment. In brief, hydrodynamic modeling that incorporates coastal structures can effectively evaluate the risk of coastal flooding in impacted areas while also enhancing the accuracy and reliability of the output.

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

AI explainability framework for environmental management research

Deep learning networks powered by AI are essential predictive tools relying on image data availability and processing hardware advancements. However, little attention has been paid to explainable AI (XAI) in application fields, including environmental management. This study develops an explainability framework with a triadic structure to focus on input, AI model and output. The framework provides three main contributions. (1) A context-based augmentation of input data to maximize generalizability and minimize overfitting. (2) A direct monitoring of AI model layers and parameters to use leaner (lighter) networks suitable for edge device deployment, (3) An output explanation procedure focusing on interpretability and robustness of predictive decisions by AI networks. These contributions significantly advance state of the art in XAI for environmental management research, offering implications for improved understanding and utilization of AI networks in this field.

Methodology for development of an expert system to derive knowledge from existing nature-based solutions experiences

Learning from past experiences is essential for the adoption of Nature-Based Solutions (NBS). There is a growing number of knowledge repositories sharing the experience of NBS projects implemented worldwide. These repositories provide access to a large amount of information, however, acquiring knowledge from them remains a challenge. This paper outlines the technical details of the NBS Case-Based System (NBS-CBS), an expert system that facilitates knowledge acquisition from an NBS case repository. The NBS-CBS is a hybrid system integrating a black-box Artificial Neural Network (ANN) with a white-box Case-Based Reasoning model. The system involves:•a repository that stores the information of past NBS projects, and an input collection component, guiding the collection and encoding of the user's inputs;•a classifier that predicts solutions (i.e., generates a hypothesis), based on user input (target case), drawing on a pre-trained ANN model to guide the case retrieval, and a case retrieval engine that identifies cases similar to the target case;•a case adaption and retainment process in which the user assesses the provided recommendations and retains the solved problem as a new case in the repository.