Jian-Qiang Wang

In engineering R&D organizations, resource contention among autonomous projects creates complex scheduling challenges in distributed multi-project environments. Traditional coordination mechanisms often prioritize global efficiency while overlooking inter-project fairness, which can lead to stakeholder resistance and execution delays. This study proposes a transparent decision support approach for the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). We develop a two-stage scheduling mechanism that integrates Multi-Criteria Decision-Making (MCDM) into the global coordination process. First, an enhanced Chaotic Genetic Algorithm (CGA) with elitism generates local schedules. Second, global resource conflicts are resolved using MCDM methods. Inter-project fairness is implemented by limiting each project’s relative objective deterioration and by evaluating the dispersion of the resulting project-level burdens. Validation through an Unmanned Aerial Vehicle R&D case study and extensive experiments shows that the TOPSIS-based mechanism achieves a significantly lower standard deviation than the auction-based mechanism under high resource contention. The approach supports transparent and fairness-aware conflict resolution by making trade-offs among project-level outcomes explicit to managers.

Evaluating credit risk is crucial for financial institutions because it determines whether loans should be granted to applicants, impacting potential returns and losses for the institution. However, modeling the heterogeneous information of applicants while simultaneously balancing institutional gains and losses faced by the institution is a complex challenge. To address the issues, this study proposes a credit risk evaluation framework with heterogeneous information: combining three‐way decision (TWD) theory and graph sample and aggregate (GraphSAGE) learning. This framework (i) integrates a fuzzy similarity relation based on fuzzy theory, constructing a GraphSAGE model for graph‐structured data, (ii) combines the results of GraphSAGE with the TWD theory to divide loan applications into three regions: positive, negative, and boundary, and (iii) adds additional information for instances of the boundary for final evaluations, providing new perspectives for research in credit risk evaluation. The experiment results demonstrate the excellent effectiveness of the method in credit risk evaluation, coping well with data imbalances while balancing losses and gains.

Grey plastic is a representative traditional architectural decoration craft in the Lingnan region in China, carrying rich historical and cultural values as well as distinctive regional artistic characteristics. However, the grey plastic craft is currently facing problems such as inheritance gaps and a shortage of craftsmen, and its restoration projects impose extremely high professional requirements on contractors. Existing contractor selection methods are mostly applicable to ordinary construction projects and are difficult to adapt to its particularity, which may easily lead to risks such as substandard restoration quality. Therefore, this paper proposes a contractor selection method for grey plastic decoration projects of cultural relic buildings based on the BWM-TODIM method. Firstly, an evaluation system covering six core criteria is constructed; secondly, the BWM is adopted to determine the criteria weights; thirdly, the TODIM method is used to characterize the decision-makers’ loss aversion psychology and rank the candidate contractors; finally, an empirical analysis is conducted with a grey plastic restoration project in Lingnan as a case to verify the feasibility and effectiveness of the method. This study can provide decision support for the scientific selection of contractors for grey plastic decoration projects and contribute to the sustainable protection of cultural heritage. The scope of this study is limited to contractor selection for grey plaster decoration engineering of cultural relic buildings.

Credit rating serves as a crucial instrument for lenders to evaluate borrowers' creditworthiness and mitigate the risk of nonperforming loans. However, credit rating tasks often face significant challenges due to multiclass distributions and severe class imbalances. Given the advantages of ensemble learning methods in addressing these challenges, this study presents a novel multiclass imbalance classification framework that integrates the Error Correcting Output Codes (ECOC) decomposition approach with diverse dichotomizer imbalance algorithms to enhance credit ratings. Nevertheless, selecting and quantifying the uncertainty of dichotomizer sets poses challenges. To this end, we introduce a dynamic ensemble selection strategy and evidence theory within the ECOC setup. By tailoring specific dichotomizers to individual samples and consolidating uncertain binary outcomes using belief functions, a resilient ensemble classifier is developed. Extensive experiments on nine KEEL benchmark datasets and two real credit datasets demonstrate its effectiveness in handling severe imbalance in credit rating tasks.

Modelling patient trajectories from longitudinal electronic health records (EHRs) is crucial for early chronic disease prediction. Foundation models (FMs), benefiting from the computational power and generalization abilities, offer a promising direction towards understanding patient health progression. However, key challenges of adopting FMs in clinical decisions remain in (1) incorporating multi-modal EHR data into an FM effectively for unified patient representations and (2) ensuring model generalizability across various clinical domains with distribution shifts. To address these challenges, we propose MsHeCare, an FM-based, two-stage learning framework integrating multi-modal representation learning and multi-source domain adaptation (MSDA). In the pretraining stage, MsHeCare performs self-supervised contrastive learning and masked language modelling tasks to mitigate semantic biases across text-based diagnostic and treatment sequences. A cross-attention mechanism further fuses these temporal features with structured static demographic information, enhancing personalized patient representations. In the fine-tuning stage, MsHeCare incorporates an MSDA framework with a novel source importance estimation method, facilitating adaptive knowledge transfer across domains and improving model generalizability. Experiments on two real-world EHR datasets demonstrate that MsHeCare significantly outperforms single-domain baselines and state-of-the-art MSDA methods. Furthermore, we validate the robustness of MsHeCare across varying targetdomain data sizes and demonstrate its alignment with clinical practices through case studies, underscoring its potential for multi-modal, domain-adaptive predictive healthcare systems.