This paper investigates the optimal control problem for a class of parabolic equations where the diffusion coefficient is influenced by a control function acting nonlocally. Specifically, we consider the optimization of a cost functional that incorporates a controlled probability density evolving under a Fokker-Planck equation with state-dependent drift and diffusion terms. The control variable is subject to spatial convolution through a kernel, inducing nonlocal interactions in both drift and diffusion terms. We establish the existence of optimal controls under appropriate convexity and regularity conditions, leveraging compactness arguments in function spaces. A maximum principle is derived to characterize the optimal control explicitly, revealing its dependence on the adjoint state and the nonlocal structure of the system. We further provide a rigorous financial application in the context of mean-variance portfolio optimization, where both the asset drift and volatility are controlled nonlocally, leading to an integral representation of the optimal investment strategy. The results offer a mathematically rigorous framework for optimizing diffusion-driven systems with spatially distributed control effects, broadening the applicability of nonlocal control methods to stochastic optimization and financial engineering.
Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts. Although recent vision–language models provide strong pretrained visual representations, adapting them to longitudinal ecological settings remains challenging, particularly under identity and temporal distribution shifts. We present a parameter-efficient CLIP adaptation framework for animal ReID and introduce a continuous metadata-conditioning mechanism that incorporates numerical attributes directly into the prompt representation during training. While low-rank visual adaptation, prompt-based supervision, and cross-modal alignment provide the adaptation framework, the proposed metadata-conditioning strategy constitutes the primary methodological contribution. By preserving the continuous structure of numerical metadata rather than discretizing it into textual categories, the proposed approach enables smooth modulation of the embedding space during training while maintaining a purely visual inference pipeline. Experiments on a seven-year longitudinal fish dataset and multiple wildlife benchmarks demonstrate improved performance under closed-set, open-set, and time-aware evaluation protocols. The results demonstrate that continuous metadata conditioning improves robustness to longitudinal appearance variation and temporal distribution shifts, while parameter-efficient adaptation enables a purely visual inference pipeline without requiring metadata at test time. Code and evaluation splits can be found at: https://github.com/AnilOsmanTur/MetaPrompt-ReID.
Personality traits and emotion regulation (ER) are key antecedents of emotions in learning contexts. Nevertheless, little is known about the possible mediating role of ER in the relation between the Big Five (BF) traits and achievement emotions (AEs). Considering the Control-Value Theory as the main framework, we examined the mediation of behavioral (Actively Approaching, Withdrawal) and cognitive (Refocus on Planning, Catastrophizing) ER strategies in the relation between the BF traits and 10 study-related AEs. We recruited 754 university students from Italy and the United Kingdom. Using path analyses (PAs), we found that Actively Approaching mediated the relation of Extraversion, Agreeableness, Negative Emotionality, and Open-Mindedness with pride and relaxation. Withdrawal mediated the relation of Extraversion and Negative Emotionality with boredom. Catastrophizing mediated the relation between Negative Emotionality and hope, anxiety, anger, and hopelessness. Through multi-group PAs, we observed some country differences in the relation of the BF traits with, respectively, Actively Approaching and Catastrophizing, and three negative emotions. These findings can serve as the basis for developing evidence-based interventions that take into account personality differences and/or influence ER strategies to improve students' AEs.
PurposeThis study investigates how healthcare professionals make complex adoption decisions that transform service encounters and value co-creation processes. While most research has focused on patient-side technology adoption, this study advances the understanding of the configurational conditions that drive provider-side adoption of digital service innovations.Design/methodology/approachEmploying fuzzy-set qualitative comparative analysis (fsQCA), this study analyzes data from 315 self-employed otolaryngologists (ENTs) who interacted with a virtual-reality-based healthcare service. The configurational approach reveals how combinations of technological, professional, and contextual factors jointly shape professionals' intentions to adopt service innovations.FindingsFour conditions are necessary for adoption: performance expectancy, hedonic motivation, price value, and social influence (consistency = 0.90). When combined with effort expectancy, facilitating conditions and low Anxiety, they form a sufficient configuration explaining 63% of high adoption cases (consistency = 0.97). This pattern shows professional service innovation adoption needs cognitive evaluation, intrinsic motivation, economic viability and social legitimacy, unlike consumer technology adoption where single factors may suffice.Originality/valueBeyond methodological innovation through fsQCA application, this study advances professional service theory by revealing how adoption requires multiple psychological mechanisms: expectancy-value calculations, intrinsic motivation, social validation and emotional regulation. The necessity of these conditions distinguishes professional service innovation from consumer technology adoption and IT implementation. These insights extend professional service firm theory by showing how knowledge intensity, low capital intensity and workforce shape digital transformation patterns. The study provides guidance for designing provider-centered digital healthcare services in post-pandemic ecosystems.
Pancreatic cystic lesions (PCLs) are increasingly detected due to the widespread use of cross-sectional imaging and represent a significant diagnostic challenge because of their heterogeneous biological behavior, ranging from benign lesions to neoplasms with malignant potential. Accurate characterization and risk stratification are essential to guide appropriate management and avoid unnecessary surgical interventions. Conventional imaging modalities, including computed tomography (CT), magnetic resonance (MR) imaging, and endoscopic ultrasound (EUS), remain central to the diagnostic work-up; however, their ability to reliably differentiate cyst subtypes and predict malignant transformation remains limited. In recent years, artificial intelligence (AI) and radiomics have emerged as promising approaches for improving the non-invasive characterization of PCLs by extracting quantitative imaging features beyond those appreciable through visual assessment. This narrative review summarizes the current evidence regarding CT- and MR-based radiomics and AI in pancreatic cyst characterization, focusing on their role in differentiating mucinous from non-mucinous cysts, identifying high-risk intraductal papillary mucinous neoplasms (IPMNs), and supporting clinical decision-making. The potential advantages of these techniques are discussed alongside main methodological limitations, including variability in imaging acquisition protocols, segmentation reproducibility, small and often retrospective datasets, limited external validation, and interpretability of AI-based models. Further multicenter studies, standardized radiomic pipelines, and prospective validation are required before these tools can be reliably integrated into routine clinical practice.
