Search Results (1,684)

This study proposes a novel and improved numerical approximation of the simulation of Gaussian process autoregressive models. As a Bayesian nonparametric regression method, Gaussian process models offer the unique advantage of providing closed-form uncertainty quantification. When Gaussian process models are used for autoregressive models, the validation procedure requires the model’s simulation or multi-step-ahead prediction. However, simulating dynamical Gaussian process models is complex due to the intractable propagation of uncertain inputs through the nonlinear model. Numerical approximation, namely Monte Carlo simulation, is one of the most frequent options for simulating dynamical models based on Gaussian processes. The computational burden of Monte Carlo simulation algorithms increases cubically with data size, representing a challenge. This paper introduces a unified simulation framework invariant to sparse and variational approximations to obtain a static sample from the pseudo-point posterior. Furthermore, we propose an innovative method for simulating Gaussian process dynamical models. A single parameter is proposed to regulate the trade-off between computational complexity and algorithmic accuracy. This innovation demonstrates the potential to replace the conditionally independent Monte Carlo method with no additional computational burden, thereby enhancing estimates of latent responses. The proposed simulation method is demonstrated using two synthetic examples and a realistic case study. Full article

Background: Proteasome inhibitors (PIs) are integral in multiple myeloma (MM) treatment but carry a substantial risk of cardiovascular adverse events (CVAEs). The Heart Failure Association–International Cardio-Oncology Society (HFA-ICOS) risk score was designed to identify patients at risk of cardiotoxicity, but its performance in MM remains uncertain. Methods: We retrospectively evaluated 98 patients with MM or primary amyloidosis treated with PIs between 2019 and 2024 to externally validate and refine this score. Results: CVAEs occurred in 22 patients (22.5%), predominantly heart failure. The original HFA-ICOS model demonstrated modest predictive accuracy (AUC 0.66) and sensitivity (50%), with frequent risk overclassification. Carfilzomib exposure (HR 4.68, 95% CI 1.47–14.90; p = 0.009) and elevated NT-proBNP before cycle 2 (>300 pg/mL; HR 3.13, 95% CI 1.10–8.93; p = 0.033) independently predicted CVAEs. A dynamic model incorporating these parameters and adjusting the age threshold to ≥65 years was associated with improved discrimination (AUC 0.72, p = 0.032), model fit (ΔAIC = −4), and CVAE-free survival stratification (p = 0.026), achieving 90.9% sensitivity. Conclusions: These findings indicate that the original HFA-ICOS score has limited prognostic value in PI-treated MM patients. Incorporating early NT-proBNP monitoring, carfilzomib exposure, and refined age categorization may improve risk prediction and support more personalized cardiovascular surveillance strategies in cardio-oncology. However, this refined dynamic model should be regarded as exploratory and requires validation in larger independent cohorts before it can be considered for clinical application. Full article

This paper extends the active disturbance rejection control (ADRC) approach to a class of nonlinear hybrid systems with uncertain fractional disturbances and unknown parameters, aiming to investigate the applicability of the ADRC approach in the presence of abrupt state changes and complex noise structures. By employing the fractional Wick–Itô–Skorohod (fWIS) integral, an extended state observer (ESO) together with an ESO-based control strategy is developed. It is shown that the resulting closed-loop hybrid stochastic system achieves mean-square stability under fractional noise. Furthermore, the proposed approach is generalized to enable the observation, tracking, and compensation of white noise disturbances with abrupt changes. Numerical simulations are presented to demonstrate the effectiveness of the proposed method. Full article

Background: Vitamin K2 (menaquinone) has been studied as a molecule with important effects on bone metabolism and has been proposed as a potential adjuvant in fracture healing, particularly under osteoporotic conditions. However, its functional impact on osteoporotic fracture healing remains largely undefined. The aim of this study was to evaluate the effects of vitamin K2 supplementation, in the form of menaquinone-4 (MK-4) and menaquinone-7 (MK-7), on fracture healing in an ovariectomized rat model of osteoporosis. Methods: Forty Wistar rats were included in this study and allocated to four equal groups: Sham control, ovariectomized control, MK-4, and MK-7. Osteoporosis was induced by bilateral ovariectomy, and 12 weeks after ovariectomy, a femoral fracture was produced and fixed by intramedullary nailing. Starting on postoperative day 2, the MK-4 group received 5 mg/kg/day of MK-4, while the MK-7 group received MK-7 at a dose of 0.05 mg/kg/day. Fracture healing was assessed primarily by biomechanical testing using a three-point bending test and was further analyzed by histological and biochemical parameters, including CTXI, PINP, ucOC, BALP, and ALT. Results: Vitamin K2 supplementation did not improve functional fracture healing. In both treatment groups, fractures showed nonunion-like mechanical behavior, precluding meaningful quantitative biomechanical comparison. Although histological and biochemical findings, particularly in the MK-4 group, showed some degree of biological activity, these changes did not translate into mechanically competent bone union. Both treatment groups showed a tendency toward impaired healing, with progression toward nonunion-like behavior under the present experimental conditions. No significant hepatic toxicity was observed. Conclusions: In this ovariectomized rat femoral fracture model, vitamin K2 supplementation with either MK-4 or MK-7 did not enhance functional fracture healing despite evidence of biological activity of the treatment. These findings suggest a discrepancy between molecular or histological effects and biomechanical outcomes, indicating that, under the conditions tested, vitamin K2 is insufficient to overcome impaired healing in osteoporotic bone and may adversely influence fracture repair under these experimental conditions, although the mechanism remains uncertain. Full article

The paper presents a proposed clinical intelligence system, which is called NLP-Bayesian Optimization Clinical Model (NBO-CM), to study large-scale unstructured electronic health record narratives in the MIMIC-IV discharge and radiology note datasets with the help of a structured pipeline of clinical text preprocessing, feature extraction and probabilistic modeling to solve the linguistic variability, missing information and uncertainty in clinical decisions. Text preprocessing methods are first applied in the standardization of clinical narratives, such as text tokenization, text normalization, text lemmatization, text segmentation, and text negation detection. Next, the unstructured text is converted into structured clinical variables with the help of feature extraction techniques, including named entity recognition, medical concept normalization using UMLS/SNOMED ontologies, generating contextual embeddings, and vectorizing text using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. Bayesian Networks are then used to model these features, with dependency structure learning based on the Poor-and-Rich Optimization algorithm (PRO), having the ability to explore probabilistic relationships efficiently, and parameter estimation based on expectation–maximization, providing robust learning with incomplete and uncertain conditions of data. Lastly, probabilistic reasoning and inference are used to predict diseases, prioritize risks and make clinical inferences with clear measures of uncertainty and interpretability. Experimental analysis of real-world MIMIC-IV clinical notes indicates that the framework is more effective in terms of diagnostic accuracy, predictive strength, and clinical explainability than traditional machine learning methods, resulting in a scaled and explainable framework of intelligent clinical decision support systems in complex care settings. Full article

Background/Objectives: Maintaining oral hygiene in infants in neonatal and pediatric intensive care is essential for preventing ventilator-associated pneumonia (VAP). Chlorhexidine (CHX) is widely used in adults but its safety and efficacy in infants remain uncertain, and it is not recommended for children under 6 years due to rinsing difficulties and mucosal irritation risk. Expressed breast milk (EBM), rich in immunological and antimicrobial components, has been explored as a biologically appropriate alternative. This review synthesizes evidence on EBM effectiveness and safety and contextualizes it against limited indirect evidence for CHX, as no head-to-head comparative trials were identified. Methods: A systematic search of PubMed, EMBASE, Cochrane Library, CINAHL, and Web of Science (January 2015–January 2026) identified randomized and non-randomized studies involving infants ≤ 12 months receiving EBM, colostrum, or CHX for oral care. Risk of bias was assessed using RoB 2 for RCTs and ROBINS-I for non-RCTs. Due to substantial clinical and methodological heterogeneity (differing populations, dosages, frequencies, delivery methods, and outcome definitions), a narrative synthesis was performed. Results: Seventeen studies met inclusion criteria (11 RCTs, n = 1185; 6 non-RCTs, n > 3000). EBM and oropharyngeal colostrum were associated with trends toward lower VAP incidence trends (0–4%), reduced bacterial colonization, improved oral health indices, shorter mechanical ventilation time, and reduced ICU/hospital stays, with no reported adverse events. Evidence for CHX in infants was limited to a single paediatric RCT and bundled interventions, showing no significant VAP reduction and associations with mucosal irritation. The risk of bias was generally low to moderate. Conclusions: Indirect evidence suggests EBM is a potentially beneficial option for infant oral hygiene, with favourable trends for infection-related outcomes and recovery parameters. However, all EBM–CHX comparisons are indirect, and CHX evidence in infants is limited by the risk of bias and heterogeneity. High-quality head-to-head randomized controlled trials are needed to determine optimal strategies and inform guidelines. Full article

Background: Gene fusions play a pivotal role in the pathogenesis and classification of hematologic malignancies. RNA sequencing (RNA-seq) has emerged as a powerful tool for detecting gene fusions; however, many clinical studies have focused on targeted RNA-seq, and optimal parameters for whole transcriptome RNA-seq remain uncertain. Methods: We retrospectively analyzed whole RNA-seq data from 301 patients diagnosed with acute leukemia between October 2022 and May 2025 to characterize the landscape of pathogenic gene fusions. Fusions were identified using the Arriba algorithm, and subsampling analyses were performed on cases with recurrent fusions to determine the minimum sequencing output required for reliable detection. Results: Pathogenic gene fusions were identified in 113 of 301 patients (37.5%). Whole RNA-seq detected fusions that were not identifiable by conventional assays, including UBTF::ATXN7L3, and highlighted frequent fusion events, such as ZNF384 rearrangements. Subsampling analysis demonstrated that a sequencing output ≥ 100 million reads (moderate confidence) or ≥300 million reads (high confidence) was sufficient for 100% detection of recurrent fusions. Conclusions: Whole RNA-seq reliably detects clinically relevant gene fusions in acute leukemia, aligns well with conventional karyotyping results, and surpasses targeted RNA-seq in comprehensiveness. A sequencing output of at least 100 million reads is recommended for clinical fusion detection. Full article

This study proposes a geometric procedure for robust controller tuning under parametric uncertainty, based on root-contour analysis of the closed-loop control system. For a fixed candidate controller tuning, the set of possible pole locations induced by the admissible variations of the control plant parameters is constructed. Robust admissibility is formulated as a geometric set-inclusion problem, requiring this set to remain inside a prescribed dynamic performance region in the complex s-plane. A distinction is introduced between nominal admissibility, robust stability, and robust admissibility, showing that stability over the entire uncertainty set is not sufficient to guarantee the desired dynamic performance. To quantify the root contours, several indices are defined, including the dispersion along the real and imaginary axes, the maximum pole displacement with respect to the nominal pole locations, and the geometric margin to the boundary of the performance region. The procedure is applied to the selection and verification of PI controller tunings for an uncertain single-input–single-output (SISO) control system and is further validated through examples with different structures of parametric uncertainty, including a system with a single uncertain parameter and a PID-controlled system with several uncertain control plant parameters. The results show that root-contour analysis can distinguish tunings that are only robustly stable from tunings that preserve the prescribed dynamic performance over the entire uncertainty set. Thus, the method can be used as a practical tool for the diagnosis, comparison, and selection of controller tunings under parametric uncertainty. Full article

Uncertain dynamic games have recently emerged as a rigorous and versatile framework for the analysis and synthesis of multi-agent decision-making processes in complex, stochastic, and dynamically evolving environments. By integrating foundational concepts from dynamic game theory with neural network-based function approximation techniques, these methodologies facilitate the development of adaptive, data-driven strategies for agents whose interactions unfold over time and are subject to both state and control constraints. Notwithstanding these advances, practical implementations are invariably influenced by model inaccuracies, exogenous disturbances, and parametric uncertainties, all of which may substantially impair system performance and jeopardize stability if left unmitigated. In this context, the present study examines dynamic game formulations defined on perturbed and uncertain system models, explicitly incorporating state and control constraints, with the objective of ensuring robustness and reliability in both competitive and cooperative settings. We consider a broad class of nonlinear dynamic games characterized by system dynamics affected by unknown disturbances and uncertain parameters. Within this framework, Dynamic Neural Networks (DNNs) are employed to approximate feasible solutions to the associated robust control problem, thereby enabling the characterization of $\epsilon$-Nash equilibria through learning mechanisms driven by worst-case trajectory realizations. A comprehensive theoretical analysis is developed to elucidate the effects of perturbations and uncertainties on equilibrium existence, convergence behavior, and closed-loop stability properties. Furthermore, sufficient conditions are established under which the neural learning dynamics ensure boundedness and convergence to approximate Nash or saddle-point equilibria, despite the presence of modeling imperfections. The proposed framework effectively synthesizes principles from robust control theory and learning-based game-theoretic approaches, yielding formal guarantees that are often absent in purely data-driven methodologies. Finally, numerical simulations conducted on representative dynamic game scenarios substantiate the efficacy of the proposed approach, demonstrating enhanced robustness relative to nominal neural game formulations. These findings contribute to the advancement of dependable dynamic game architectures, with potential applications spanning autonomous systems, robotics, and networked control systems operating under uncertainty. Full article

LNG storage tanks are essential facilities for large-scale storage and transportation of cryogenic energy. Because of the flammable, explosive, and ultra-low-temperature characteristics of liquefied natural gas, failures in such systems may result in serious consequences for operational safety and the surrounding environment. Effective identification and prioritization of potential failure modes are therefore crucial for safe operation. Failure mode and effects analysis (FMEA) has been widely applied in risk assessment, yet conventional FMEA methods still show limited capability in describing uncertain linguistic evaluation information, reflecting the reliability of expert judgments, and representing high-order coupling relationships among failure modes. To address these issues, this study develops a modified FMEA framework that integrates confidence-enhanced probabilistic linguistic modeling with weighted hypergraph propagation for LNG storage tank risk assessment. In the proposed framework, confidence-enhanced probabilistic linguistic term sets are employed to represent the fuzziness, probabilistic preference, and reliability differences contained in expert assessments. A confidence-adaptive scoring function is further constructed to strengthen the discrimination of risk quantification by capturing structural differences in probability distributions without introducing externally specified parameters. Meanwhile, the importance of risk factors is determined through a combined subjective–objective weighting strategy, and a weighted hypergraph propagation mechanism is established to characterize high-order structural associations among failure modes and to revise baseline risk levels through a node–hyperedge–node transmission process. A case study of a large LNG storage tank system in Tangshan, China, is carried out to examine the applicability and effectiveness of the proposed framework. The results demonstrate that the proposed method can effectively integrate complex expert evaluation information with structural coupling effects, while sensitivity and comparative analyses further confirm its robustness and suitability for failure risk prioritization in LNG storage tanks. Full article

An emotional neural network-based global predefined-time sliding mode control (ENN-GPTSMC) method is proposed for an uncertain hybrid mechanism. To estimate and compensate for the lumped uncertainty including discontinuous friction, an emotional neural network is developed. Simultaneously, a predefined-time terminal sliding mode control (PTTSMC) uses the estimation value. The adjustable predefined-time performance parameters are then incorporated into the PTTSMC law to extend its attractiveness for the system states to the global domain, thereby solving the limitation of the existing PTTSMC that can only locally achieve the predefined-time convergence of the system states during the reaching phase. The fast convergence of system states is subsequently achieved by embedding an integer-power linear term and its derivative into the sliding manifold and PTTSMC law, respectively. Based on these, an ENN-GPTSMC algorithm is designed. Furthermore, the saturation function of a dynamic boundary layer with an adjustable thickness is designed to avoid the singularity of ENN-GPTSMC, thereby achieving no-singularity fast global predefined-time convergence of the system. Theoretical analysis shows the Lyapunov stability of the system. Finally, simulation and prototype experiments are used to verify the effectiveness of the proposed method. Full article

Togolese agriculture is vulnerable to climate variability. In this context, this study aims to analyze the seasonal variability of climatic parameters and its effects on food production in the western Plateaux region. To achieve this, climatic data (from stations in Agou-Gare, Adéta, Amou, Badou, Kouma Konda, and Atakpamé) and agricultural data (yields, production, and areas of maize, rice, cowpea, cassava, and yam) from 1991 to 2020 were processed using RStudio 4.4.0. A methodology integrating both daily rainfall and changes in available soil water (ASW) was used to determine the rainy seasons and their durations. Seasonal rainfall totals were used to analyze spatial variability. Finally, an ordinary least squares (OLS) regression model with a threshold of 10% was used to assess the effect of climate parameters on food production. The results reveal a transition from a bimodal rainfall regime to a monomodal regime, characterised by a dry season of 4–5 months and a rainy season of 7–8 months. This transition is accompanied by an increase in temperatures ranging from 24.69 °C to 34.7 °C. The results also reveal an uncertain start to the long rainy season (early or late), an extension of the short season and dry spells lasting between 11 and 34 days that affect crops. Finally, spatial variability in precipitation remains significant during the long rainy season. Agroclimatic analysis reveals that maximum temperature positively influences cowpea yields (p = 0.0079) but negatively influences cassava (p = 0.00013) and rice (p = 0.050) yields. These results could inform the development of effective adaptation strategies tailored to this environment, helping to maintain and increase food production in the context of climate change. Full article

With the increasing prevalence of uncertainties and variability in modern energy systems, model predictive control (MPC) often faces the challenge of predictive model mismatch. This paper proposes a desired-dynamics-based predictive control (DDPC) framework, in which an inner shaping layer is introduced to transform the raw plant into a desired dynamic model for the outer MPC. A unified design methodology is developed, including equivalent-model construction, desired-dynamics selection, and two inner-layer realizations based on desired dynamic equation (DDE)-PID and active disturbance rejection control (ADRC). In this way, the prediction model used by MPC is no longer the original uncertain plant but an explicitly shaped equivalent model determined by inner-layer controller parameters. The proposed method is validated on linear and nonlinear benchmark plants, together with frequency-domain and Monte Carlo robustness analyses. Results show that DDPC improves disturbance-rejection ability and enhances robustness against model mismatch and parameter perturbations. Further evaluation on the superheated steam temperature loop of a high-fidelity 660 MW coal-fired boiler hardware-in-the-loop simulator shows that DDPC reduces the peak-to-peak temperature fluctuation from 22.88 °C to 11.39 °C in the deep peak shaving scenario, corresponding to a 50.2% reduction relative to standard MPC. Full article

The recycling of power batteries is a key measure for improving the new energy industry chain and achieving green circular economy goals. However, the process of evaluating and selecting recycling schemes is influenced by multiple complex factors and often involves a significant amount of ambiguous and uncertain decision-making information. As an important extension of intuitionistic fuzzy sets, picture fuzzy sets characterize fuzzy information through three distinct dimensions: membership, neutrality, and non-membership. This three-dimensional structure offers unique advantages in addressing uncertain and ambiguous decision-making problems, where traditional fuzzy sets may lose valuable information. Drawing on the Bray–Curtis distance measure, this paper proposes a novel picture fuzzy distance measure that captures differences across all three dimensions more comprehensively. By combining the weighted form of the proposed picture fuzzy distance measure with the classical TOPSIS method, a new multi-attribute decision-making model is established under the picture fuzzy framework. The effectiveness and feasibility of the proposed method are demonstrated through a case study on the recycling of power batteries for electric vehicles. A sensitivity analysis of relevant parameters is conducted, confirming the stability of the model against variations in parameter settings. Comparative results indicate that the proposed novel picture fuzzy distance measure exhibits superior robustness compared to existing similar distance measures. Furthermore, the constructed decision-making model can provide reliable and practical support for uncertain multi-attribute decision-making problems in real-world applications. Full article

Shared immersive environment sports venues, virtual classrooms, and collaborative workspaces require multiple users to stream 360° videos simultaneously over the same edge network, yet every existing adaptive bitrate system optimises each viewer in isolation. This self-interested behaviour triggers a bandwidth auction that chronically starves the most uncertain viewers: Jain’s Fairness Index for ten independently optimised agents routinely falls below 0.85. We present FairEdge360, a hierarchical multi-agent reinforcement learning framework that reformulates multi-user 360° streaming as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP) and proves, formally, that fairness and quality are complementary rather than competing objectives. Three tightly coupled innovations make this possible. First, a Lightweight Uncertainty Estimator (LUE) a compact 8385-parameter four-layer MLP evaluates per-device viewport prediction confidence $c_t^i=\sigma \left(w_4^{\top }{h}_3\right)$ in under approximately 2.1 ms on commodity smartphones (95th percentile, iPhone 12 A14 Bionic), enabling selective edge offloading that reduces device energy consumption by 38.9%. Second, a variational Graph Neural Network compresses each agent’s 256-dimensional GRU state into a 32-byte INT8 latent, transmitted over a dynamic RTT-gated neighbourhood graph at 96 bytes per agent per 500 ms 75% less overhead than competing approaches. Third, the edge coordinator maximises the Nash social welfare objective $\mathrm{NSW}={\left({\prod }_{i=1}^N{Q}_i\right)}^{1/N}$, whose gradient $\partial \mathrm{NSW}/\partial Q_i\propto 1/Q_i$ automatically prioritises the most disadvantaged viewer; a formal proof guarantees that every Pareto-optimal policy satisfies $Q_i/{\sum }_j{Q}_j\ge 1/N$. Counterfactual advantage estimation correctly attributes each agent’s marginal contribution to the global reward, eliminating the credit-assignment ambiguity inherent in standard multi-agent baselines. Evaluated on 284 users, 52 omnidirectional videos, and 10,000 real network traces spanning 4G LTE, 5G mmWave, HSDPA, and campus WiFi, FairEdge360 raises Jain’s Fairness Index from 0.934 to 0.976 (+4.5%), improves worst-case user quality-of-experience from MOS 2.54 to MOS 3.21 (+26.4%), and halves rebuffering rate from 2.1% to 1.1%, all within a 20 ms motion-to-photon budget on a commodity smartphone. Full article