Analysis of longitudinal data in high-dimensional gene–environment interaction studies have been extensively conducted using variable selection methods. Despite their success, these studies have been consistently challenged by the lack of uncertainty quantification procedures to identify main and interaction effects under longitudinal phenotypes that follow heavy-tailed distributions due to disease heterogeneity. In this article, to improve statistical rigor of variable selection-based G × E analysis, we propose to apply the robust Bayesian linear mixed-effect model with a false discovery rate (FDR) control procedure to tackle these challenges. The Bayesian mixed model adopts a robust likelihood function to account for skewness in longitudinal phenotypic measurements, and it imposes spike-and-slab priors to detect important main and interaction effects. Leveraging the parallelism between spike-and-slab priors and the Bayesian approach to hypothesis testing, we perform variable selection and uncertainty quantification through a Bayesian false discovery rate (FDR)-assisted procedure. Numerical analyses have demonstrated the advantage of our proposal over alternative approaches. A case study of a longitudinal cancer prevention study with high-dimensional lipid measures yields main and interaction effects with important biological implications.

Record linkage is an essential task in data integration in the fields of healthcare, law enforcement, fraud detection, transportation, biology, and supply chain management. The problem of record linkage is to cluster records from various sources such that each cluster belongs to a single entity. Scalability in record linking is limited by the large number of pairwise comparisons required. Blocking addresses this challenge by partitioning data into smaller parts, substantially reducing the computational cost. With the advancement of Large Language Models (LLMs), there are several possibilities to improve record linkage by leveraging their semantic understanding of textual attributes. LLM-based record linkage algorithms in the literature have very large runtimes. In this paper, we show that the employment of blocking can result in significant improvements not only in the runtime but also in the accuracy. Specifically, we propose a record linkage algorithm that combines LLMs with blocking. Experimental evaluation demonstrates that our algorithm achieves lower runtimes while simultaneously improving F1 scores compared to the approaches relying solely on LLMs. These findings demonstrate the importance of blocking even in the era of advanced machine learning models.

The digital transformation in the treatment of mental health and emotional disharmony requires artificial intelligence architectures that overcome the limitations of purely neural approaches, such as temporal inconsistency, opacity, and lack of theoretical foundations. Assuming the existence and use of generalist LLMs currently used in clinical settings and considering the appropriate limitations indicated by experts, this article aims to offer clinicians an alternative Neuro-symbolic-Psychological multi-agent architecture (NSPA-AI), which integrates archetypal symbolic reasoning with neurobiological modelling, based on our established framework of artificial neurotransmitters for the modelling and analysis of affective-emotional stimuli to enable interpretable AI-assisted psychological intervention. The system implements a hub-and-spoke topology that coordinates five specialized agents (symbolic, psychological, neurofunctional, decision fusion, learning) that process heterogeneous information via SPADE protocols. Seven archetypal constructs from Jungian psychology and narrative identity theory provide stable symbolic frameworks for longitudinal therapeutic consistency. An empirical study of 156 university students demonstrated significant improvements in depression (Cohen’s d = 1.03), stress (d = 0.89), and narrative identity integration (d = 0.75), which were maintained at a 12-week follow-up and superior to GPT-4 controls (d = 0.34). Neurofunctional correlations—downregulation of cortisol (r = 0.71 with stress reduction), increase in serotonin (r = −0.68 with depression improvement)—validated the neurobiological basis of the entropy-energy framework. Qualitative analysis revealed the following four mechanisms of improvement: symbolic emotional support (93%), increased self-awareness through neurotransmitter visualization (84%), non-judgmental AI interaction (98%), and archetypal narrative organization (87%). The results establish that neuro-symbolic architectures are viable alternatives to large language models for digital mental health, providing the interpretability and clinical validity essential for adoption in the healthcare sector.

To address wind power fluctuations causing curtailment and high costs, this study proposes an integrated method combining wind power forecasting with substation optimization. An enhanced Bidirectional Gated Recurrent Unit (BiGRU) model is developed by incorporating chaotic features (maximum Lyapunov exponent) and sliding-window statistical features (mean, standard deviation), significantly improving short-term prediction accuracy. Based on these high-precision forecasts, a dynamic transformer switching optimization model is established to maximize the wind farm’s net profit. This model finely balances power generation revenue, wind curtailment penalties, and transformer losses (no-load and load) at a 15 min timescale. Experimental results from a wind farm in Xinjiang demonstrate that the proposed method effectively enhances the economic efficiency of wind farm operations. The study provides a valuable framework for optimizing energy storage configuration and improving profitability by leveraging accurate forecasting.

Forest ecosystems are vital to sustainable development, contributing to economic, environmental and social well-being. However, the increasing frequency and severity of wildfires threaten these ecosystems, demanding more effective and integrated fire management (IFM) strategies. Current suppression efforts face limitations due to high resource demands and the need for timely, informed decision-making under uncertain conditions. This paper presents the SILVANUS project’s approach to developing an advanced Decision Support System (DSS) designed to assist incident commanders in optimizing resource allocation during wildfire events. Leveraging Geographic Information Systems (GIS), real-time data collection, AI-enhanced analytics and multicriteria optimization algorithms, the SILVANUS DSS component integrates diverse data sources to support dynamic, risk-informed decisions. The system operates within a cloud-edge infrastructure to ensure scalability, interoperability and secure data management. We detail the formalization of the resource allocation problem, describe the implementation of the DSS within the SILVANUS platform, and evaluate its performance in both controlled simulations and real-world pilot scenarios. The results demonstrate the system’s potential to enhance situational awareness and improve the effectiveness of wildfire response operations.

Network robustness optimization is crucial for enhancing the resilience of industrial networks and social systems against malicious attacks. Existing studies typically evaluate the robustness by simulating the sequential removal of nodes or edges and recording the residual connectivity at each step. However, the attack simulation is computationally expensive and becomes impractical for large-scale networks. Therefore, this paper proposes a multiobjective evolutionary algorithm assisted by a graph isomorphism network (GIN)-based surrogate model to efficiently optimize network robustness. First, the robustness optimization task is formulated as a multiobjective problem that simultaneously considers network robustness against attacks and the structural modification cost. Then, a GIN-based surrogate model is constructed to approximate the robustness, replacing the expensive simulation assessments. Finally, the multiobjective evolutionary algorithm is employed to explore promising network structures guided by the surrogate model, which is continuously updated via online learning to improve both prediction accuracy and optimization performance. Experimental results in various synthetic and real-world networks demonstrate that the proposed algorithm reduces the computational cost of the robustness evaluation by about 65% while achieving comparable or even superior robustness optimization performance compared with those of baseline algorithms. These results indicate that the proposed method is practical and scalable and can be applied to enhance the robustness of industrial and social networks.

To address the limitations of the single-modal electroencephalogram (EEG), such as its single physiological dimension, weak anti-interference ability, and inability to fully reflect emotional states, this paper proposes a gated multi-head cross-attention module (GMHCA) for multimodal fusion of EEG, electrooculography (EOG),and electrodermal activity (EDA). This attention module employs three independent and parallel attention computation units to assign independent attention weights to different feature subsets across modalities. Combined with a modality complementarity metric, the gating mechanism suppresses redundant heads and enhances the information transmission of key heads. Through multi-head concatenation, cross-modal interaction results from different perspectives are fused. For the backbone network, a multi-scale convolution and bidirectional long short-term memory network (MC-BiLSTM) is designed for feature extraction, tailored to the characteristics of each modality. Experiments show that this method, which primarily fuses eight-channel EEG with peripheral physiological signals, achieves an emotion recognition accuracy of 89.45%, a 7.68% improvement over single-modal EEG. In addition, in cross-subject experiments conducted on the SEED-IV dataset, the EEG+EOG modality achieved a classification accuracy of 92.73%. All were significantly better than the baseline method. This fully demonstrates the effectiveness of the innovative GMHCA module architecture and MC-BiLSTM feature extraction network proposed in this paper for multimodal fusion methods. Through the novel attention gating mechanism, higher recognition accuracy is achieved while significantly reducing the number of EEG channels, providing new ideas and approaches based on attention mechanisms and gated fusion for multimodal emotion recognition in resource-constrained environments.

Artificial intelligence and machine learning have increasingly transformed optometry, enabling automated classification and predictive modeling of eye conditions. In this study, we introduce Optometry Random Forest, an artificial intelligence-based system for automated classification and forecasting of optometric data. The proposed methodology leverages Random Forest models, trained on academic optometric datasets, to classify key diagnostic categories, including Contactology, Dry Eye, Low Vision, Myopia, Pediatrics, and Refractive Surgery. Additionally, an autoRegressive integrated moving average based forecasting model is incorporated to predict future research trends in optometry until 2030. Comparing the one-shot and epoch-trained Optometry Random Forest, the findings indicate that the epoch-trained model consistently outperforms the one-shot model, achieving superior classification accuracy (97.17%), precision (97.28%), and specificity (100%). Moreover, the comparative analysis with Optometry Bidirectional Encoder Representations from Transformers demonstrates that the Optometry Random Forest excels in classification reliability and predictive analytics, positioning it as a robust artificial intelligence tool for clinical decision-making and resource allocation. This research highlights the potential of Random Forest models in medical artificial intelligence, offering a scalable and interpretable solution for automated diagnosis, predictive analytics, and artificial intelligence-enhanced decision support in optometry. Future work should focus on integrating real-world clinical datasets to further refine classification performance and enhance the potential for artificial intelligence-driven patient care.

This paper provides a detailed breakdown of a minimalist, fundamental Transformer-based architecture for forecasting univariate time series. It describes each processing step in detail, from input embedding and positional encoding to self-attention mechanisms and output projection. All of these steps are specifically tailored to sequential temporal data. By isolating and analyzing the role of each component, this paper demonstrates how Transformers capture long-term dependencies in time series. A simplified, interpretable Transformer model named ’minimalist Transformer’ is implemented and showcased using a simple example. It is then validated using the M3 forecasting competition benchmark, which is based on real-world data, and a number of data series generated by IoT sensors. The aim of this work is to serve as a practical guide and foundation for future Transformer-based forecasting innovations, providing a solid baseline that is simple to achieve but exhibits a stable forecasting ability not far behind that of state-of-the-art specialized designs.

Transmembrane proteins (TMPs) constitute approximately 30% of the mammalian proteome and are critical targets in biomedical research due to their involvement in signaling, transport, and drug interactions. However, their unique structural characteristics pose significant challenges for conventional multiple sequence alignment (MSA) methods, which are typically optimized for soluble proteins. In this paper, we propose TMP-M2Align, a novel topology-aware multiobjective algorithm specifically designed for the multiple alignment of TMPs. The method simultaneously optimizes two complementary objectives: (i) a topology-aware Sum-of-Pairs (SPs) score that integrates region-specific substitution matrices and gap penalties, and (ii) an Aligned Regions (ARs) score that rewards consistent alignment of functional and topological domains. By combining these objectives, TMP-M2Align generates Pareto front approximations of alignment solutions, enabling researchers to select trade-offs that best suit their biological questions. We evaluated TMP-M2Align on BAliBASE Reference Set 7 and on complete datasets of human G protein-coupled receptors (GPCRs) from classes A, B1, and C. Experimental results demonstrate that TMP-M2Align consistently outperforms both traditional alignment tools and specialized TM-specific methods in terms of SPs and Total Column metrics. Moreover, qualitative topological analyses confirm that TMP-M2Align preserves the integrity of transmembrane helices and loop boundaries more effectively than competing approaches. These findings highlight the effectiveness of integrating topology-aware scoring with multiobjective optimization for achieving accurate and biologically meaningful alignments of TMPs.