Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Real-time fire localization in urban environments remains a significant challenge due to sparse IoT sensor deployments, measurement uncertainties, and the computational uses of AI-based estimation techniques. To address these limitations, this paper proposes a Chaotic Interval-Based Multi-Objective Honey Badger Algorithm (CI-MOHBA) designed to improve the accuracy and reliability of fire source localization under uncertain and limited sensor data. The approach formulates localization as a multi-objective optimization problem that simultaneously minimizes source estimation error, false alarm rates, and computation time. CI-MOHBA integrates a new chaotic map to improve global search capability and interval arithmetic to effectively manage sensor uncertainty within sparse measurement environments. Experimental evaluation of the proposed chaotic map, supported by entropy convergence analysis and Lyapunov exponent verification, demonstrates the stability and robustness of the proposed technique. Results indicate that CI-MOHBA achieves an average localization error of 0.73 m and a false alarm rate of 8.2%, while maintaining high computational efficiency. Results show that the proposed algorithm is well-suited for real-time fire localization in urban IoT-based monitoring systems....
Background: Recent research has challenged the viewpoint that pancreatic islets operate independently of surrounding exocrine tissues, revealing a bidirectional blood flow between the endocrine and exocrine pancreas. However, a methodology for simultaneous evaluation of pancreatic microhemodynamics and oxygen profiles remains elusive. Methods: To generate the common microcirculatory framework, we employed laser Doppler and diffuse reflectance spectroscopy to assess pancreatic microcirculation with concurrent acquisition of microhemodynamic and oxygen data as time- series measurements. The framework's analytical pipeline, featuring outlier adjustment using the boxplot algorithm and comparative normalization strategies (Z- score, min–max, L2, and median scaling), was subsequently validated in a T2DM mouse model with insulin and liraglutide- administered groups. Heat maps and chord plots were used to reveal the integrated dynamics of the associations between microcirculatory blood perfusion and oxygen saturation. Results: The established common microcirculatory framework effectively characterized integrated microhemodynamics and oxygen profiles, with min–max normalizing the microhemodynamic and oxygen. T2DM mice exhibited decreased blood perfusion, reduced red blood cell tissue fraction, diminished oxygen saturation, and lower hemoglobin concentration within the pancreatic microcirculation. Treatment with liraglutide significantly ameliorated these microcirculatory impairments, partially restoring the balance between blood perfusion and oxygen saturation and normalizing the disrupted coherence between oxygenated hemoglobin and speed- resolved blood perfusion. Conclusions: The common microcirculatory framework provides a novel methodology for monitoring, visualizing, and assessing integrated pancreatic microcirculatory function, with liraglutide demonstrating enhanced efficacy in ameliorating microcirculatory dysfunction in T2DM....
Dynamic process modeling is essential for simulating time-evolving biochemical systems, particularly those with multistate interactions and combinatorial complexity. Traditional Ordinary Differential Equation (ODE) models offer mechanistic clarity but struggle with scalability and context-sensitive encoding. Rule-Based Modeling (RBM) frameworks address these limitations through modular rule abstraction, yet require manual specification and lack adaptive learning. This study introduces algorithmic innovations within the Neural Ordinary Differential Equation (Neural ODE) paradigm to bridge the gap between mechanistic interpretability and scalable expressivity. Neural ODEs can be considered as a revolutionary approach in the field of modeling dynamic biochemical interactions. They have made it possible to create models of such interactions that are flexible enough to adapt to different scenarios and do so without requiring any manual intervention in terms of rule encoding or predefined reaction schemes. This is achieved by employing differential solvers within the framework of neural networks, thus enabling a learning process that is in accordance with the behavior of the system. Using the DARPP-32 signaling network—a benchmark system characterized by multivalent phosphorylation and dynamic perturbations—the proposed Neural ODE framework demonstrates the ability to replicate key dynamic behaviors observed in ODE and RBM models. Comparative simulations under baseline and perturbed conditions reveal that Neural ODEs maintain trajectory fidelity while offering enhanced modularity and computational efficiency. Feature importance analysis and latent space visualizations further validate the model’s interpretability and robustness. Unlike ODEs and RBMs, Neural ODEs adapt to structural mutations and binding schemes through latent trajectory learning, enabling flexible simulation of biochemical variability without manual rule encoding. This work establishes Neural ODEs as a viable and scalable alternative for modeling complex biochemical systems, combining the strengths of data-driven learning with the interpretability of differential equations....
Background: An international consensus is still lacking on the best operational definition of Sarcopenia in hospitalized older adults. The main objective of this study was to use the EWGSOP2 guidelines in hospitalized old subjects to test its predictivity for adverse clinical outcomes and to evaluate its step- by- step capability to predict unfavorable clinical events. Participants and Setting: Three hundred and seventeen men and two hundred and eighty seven women, aged 65 to 99 years, consecutively admitted to the Department of Geriatrics at the University Hospital of Verona. Methods: All patients underwent a complete geriatric assessment, clinical evaluation, and for the diagnosis of Sarcopenia, the EWGSOP2 guidelines were applied. As clinical outcomes, length of hospital stay, fall risk, and subjects' quality of life were considered. Results: Among 604 hospitalized older patients, 56.0% presented with a SARC- F score suggestive of a risk for Sarcopenia. Patients at risk for Sarcopenia, and with available handgrip strength data, in 85.5% of cases also presented probable Sarcopenia. Among patients with probable Sarcopenia, and with available body composition data, 83.1% were confirmed with Sarcopenia, with a general prevalence of Sarcopenia of 22%. The shortest average length of hospitalization was in non- sarcopenic patients, with a median of 11 days, whereas dynapenic and sarcopenic subjects have respectively a median of 12 and 13 days of hospitalization, with significant differences also after adjustment for age, nutritional status and comorbidity. After dividing the patients into negative or positive for each diagnostic step of the EWGSOP2 algorithm, we found, for each step of the algorithm, a progressively greater association with adverse clinical outcomes. Conclusions: EWGSOP2 algorithm is a valid tool even in hospitalized older patients, and each step enhances the predictivity of the algorithm; however, SARC- F and muscle strength can still be valuable tools for negative clinical outcomes when body composition data are not available....
This letter proposes an improved scheme for low-sidelobe beam broadening of spaceborne large-scale phased arrays, addressing the efficiency and performance limitations of typical algorithms in synergistically achieving controllable beamwidth, low sidelobe suppression and high solution robustness. By integrating a dynamic subarray partitioning mechanism into crossover and mutation operations and optimizing the fitness function with a robustness constraint term, the scheme enhances optimization capability. Comparative experiments under varied beam pointing directions confirm the scheme’s effectiveness: the SA-JADE algorithm stably meets beam broadening targets with excellent sidelobe suppression and solution robustness, offering reliable technical support for spaceborne phased array applications....
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