Search Results (1,223)

The temperature regulation of nonlinear continuous stirred tank reactor (CSTR) processes remains a challenging control problem due to strong nonlinearities, time-delay effects, and sensitivity to disturbances and parameter variations. Conventional proportional–integral–derivative (PID)-based control strategies often fail to provide the robustness and precision required under such conditions, motivating the use of more flexible controller structures and advanced optimization techniques. In this study, an enhanced joint-opposition artificial lemming algorithm (JOS-ALA) is proposed for the optimal tuning of a fractional-order PID (FOPID) controller applied to CSTR temperature control. The proposed JOS-ALA incorporates a joint opposite selection mechanism into the original ALA to improve population diversity, convergence stability, and resistance to local optima stagnation. A nonlinear CSTR model is linearized around a stable operating point, and the resulting model is employed for controller design and optimization. The FOPID controller parameters are tuned by minimizing a composite cost function that simultaneously accounts for tracking accuracy, overshoot suppression, and instantaneous error behavior. The effectiveness of the proposed approach is assessed through extensive simulation studies and benchmarked against state-of-the-art and high-performance metaheuristic optimizers, including ALA, electric eel foraging optimization (EEFO), linear population size reduction success-history based adaptive differential evolution (L-SHADE), and the improved artificial electric field algorithm (iAEFA). The benchmarking set is further extended with the success rate-based adaptive differential evolution variant (L-SRTDE) to broaden the comparative evaluation. Simulation results demonstrate that the JOS-ALA-based FOPID controller consistently achieves superior performance across multiple criteria. Specifically, it attains the lowest mean cost function value of 0.1959, eliminates overshoot, and yields a normalized steady-state error of 4.7290 × 10⁻⁴. In addition, faster transient response and improved robustness under external disturbances and measurement noise are observed when compared with competing methods. Statistical reliability of the observed performance differences is additionally examined using a Wilcoxon signed-rank test conducted over 25 independent runs. The resulting p-values confirm that the improvements achieved by the proposed approach are statistically significant at the 5% level across all pairwise algorithm comparisons. These findings indicate that the proposed JOS-ALA provides an effective and reliable optimization framework for high-precision temperature control in nonlinear CSTR systems and offers strong potential for broader application in complex process control problems. Full article

High-fidelity 3D reconstruction of human-sized objects typically requires multi-sensor scanning systems that are expensive, complex, and rely on proprietary hardware configurations. Existing low-cost approaches often rely on handheld scanning, which is inherently unstructured and operator-dependent, leading to inconsistent coverage and variable reconstruction quality. This limitation necessitates the need for a controlled, repeatable, and affordable scanning method that can generate high-quality data without requiring multi-sensor hardware or external tracking markers. This study presents a marker-less scanning platform designed for human-scale reconstruction. The system consists of a single structured-light sensor mounted on a vertical linear actuator, synchronised with a motorised turntable that rotates the subject. This constrained kinematic setup ensures a repeatable cylindrical acquisition trajectory. To address the geometric ambiguity often found in vertical translational symmetry (i.e., where distinct elevation steps appear identical), the system employs a sensor-assisted initialisation strategy, where feedback from the rotary encoder and linear drive serves as constraints for the registration pipeline. The captured frames are reconstructed into a complete model through a two-step Iterative Closest Point (ICP) procedure that eliminates the vertical drift and model collapse (often referred to as “telescoping”) common in unconstrained scanning. To evaluate system performance, a modular anthropometric benchmark object representing a human-sized target (1.6 m) was scanned. The reconstructed model was assessed in terms of surface coverage and volumetric fidelity relative to a CAD reference. The results demonstrate high sampling stability, achieving a mean surface density of $0.760{\mathrm{points}/\mathrm{mm}}^2$ on front-facing surfaces. Geometric deviation analysis revealed a mean signed error of −1.54 mm ($\sigma =$ 2.27 mm), corresponding to a relative volumetric error of approximately 0.096% over the full vertical span. These findings confirm that a single-sensor system, when guided by precise kinematics, can mitigate the non-linear bending and drift artefacts of handheld acquisition, providing an accessible yet rigorously accurate alternative to industrial multi-sensor systems. Full article

As large language models (LLMs) are increasingly used as digital respondents and generative agents in computational social science, prior work has primarily focused on the fidelity of their expressed opinions, often overlooking a fundamental question: the behavioral stability of outputs across repeated runs of the same model when the persona specification and task conditions remain unchanged. This paper proposes a behavioral stability evaluation framework for role-playing tasks, using the Political Compass questionnaire as the testbed. The questionnaire maps responses onto a two-dimensional coordinate system defined by an economic axis and a social axis, enabling political orientations to be directly quantified and compared in a continuous space. To ground the simulation in realistic user behaviors, we construct personas from publicly available social media texts and stratify them based on Political Signal Clarity. Across three LLMs, we compare repeated questionnaire completions under different decoding temperatures and prompting strategies. We characterize it along two complementary dimensions: dispersion of the resulting two-dimensional coordinates across runs, measured by an Overall Stability Score (OSS), and dispersion of per-item choices across runs, quantified by response entropy. We further use linear mixed-effects models to account for persona-level heterogeneity and to estimate the effects of key factors on stability. Our results show that coordinate drift and item-level dispersion do not always move in tandem. Increasing temperature typically amplifies variability, although models differ in their sensitivity. Contrary to its success in reasoning tasks, Chain-of-Thought (CoT) prompting failed to enhance stability in this value-laden context. Instead, it frequently amplified coordinate drift by introducing stochasticity into intermediate reasoning steps. Results show that LLMs exhibit greater behavioral stability when role-playing personas with clearer political signals. These findings suggest that stability should be treated as a pre-study benchmark before deploying LLM-based role-playing simulations, and that key generation settings and stability statistics should be reported alongside substantive conclusions. Full article

Visible-infrared image fusion is crucial for applications like autonomous driving and nighttime surveillance, yet it remains challenging due to the inherent limitations of existing deep learning models. Convolutional Neural Networks (CNNs) are constrained by their local receptive fields, while Transformers suffer from quadratic computational complexity. To address these issues, this paper investigates the application of the Mamba model—a novel State Space Model (SSM) with linear-complexity global modeling and selective scanning capabilities—to the task of visible-infrared image fusion. Building upon Mamba, we propose a novel fusion framework featuring two key designs: (1) A Multi-Path Mamba (MPMamba) module that orchestrates parallel Mamba blocks with convolutional streams to extract multi-scale, modality-specific features; and (2) a Dual-path Mamba Attention Fusion (DMAF) module that explicitly decouples and processes shared and complementary features via dual Mamba paths, followed by dynamic calibration with a Convolutional Block Attention Module (CBAM). Extensive experiments on the MSRS benchmark demonstrate that our framework achieves state-of-the-art performance, outperforming strong baselines such as U2Fusion and SwinFusion across key metrics including Information Entropy (EN), Spatial Frequency (SF), Mutual Information (MI), and edge-based fusion quality (Qabf). Visual results confirm its ability to produce fused images that saliently preserve thermal targets while retaining rich texture details. Full article

Accurate prediction of separated flows remains a critical challenge for Reynolds-Averaged Navier–Stokes (RANS) simulations, primarily due to the tendency of standard turbulence models to overpredict separation. To address this limitation, this study develops and validates a helicity-augmented variant of Menter’s Shear Stress Transport (SST) model within a high-fidelity, data-guided framework. First, a scale-resolving database, capturing the physics of corner separation, is established via an improved Delayed Detached Eddy Simulation (DDES) of a linear compressor cascade. Insights from this database directly inform the integration of a normalized helicity parameter into the SST formulation, enabling dynamic modulation of the turbulent eddy viscosity to account for non-equilibrium turbulence and energy backscatter in three-dimensional (3D) vortical flows. The enhanced SST model is subsequently validated against experimental data for two benchmark aerodynamic configurations: ARA M100 wing–fuselage and DLR-F6 aircraft models. Results demonstrate that the proposed correction significantly improves the prediction of separation topology and aerodynamic coefficients, delays the predicted onset of stall, and achieves closer agreement with measurements. These findings confirm the DDES-guided helicity correction as an effective strategy for enhancing the predictive fidelity of RANS models in simulating the complex separated flows encountered in practical aeronautical applications. Full article

Riverbed siltation in estuaries affects ecosystem functioning, water quality, and navigation. This study investigates the flow-regulated Osellino Canal, a freshwater tributary of the Venice Lagoon that crosses a largely urbanized area and is undergoing progressive siltation. High-resolution measurements of discharge (Q) and suspended sediment concentration (SSC) were performed using hydroacoustic instrumentation from September 2019 to December 2021. The analysis examined discharge dynamics, sediment transport, and rainfall-runoff relationships. Results indicate a mean annual discharge of 2.1 m³ s⁻¹ and an average annual suspended sediment load of ~2900 ± 330 t. Discharge patterns were strongly influenced by water management, resulting in anomalous runoff coefficients (δ > 1) during dry periods. Sediment export proved to be strongly event-driven: episodic high-flow events accounted for about 23% of the total load despite representing only a small fraction of the study period. Furthermore, a strong linear relationship between runoff and sediment load (R² = 0.94) confirms an advection-dominated regime, where net export is regulated primarily by hydrodynamic volume rather than fluctuations in sediment supply. Bathymetric comparisons (2011–2019) reveal a mean annual sediment retention of 400 ± 100 t yr-1, corresponding to a trapping efficiency of approximately 12 ± 3% relative to the gross sediment input. These findings, supported by SSL–runoff regression residuals, consistently indicate net sediment accumulation associated with the long-term malfunction of a miter-gate system that impedes efficient sediment export. This study provides a critical pre-rehabilitation baseline, establishing a benchmark to evaluate the effectiveness of ongoing restoration efforts initiated in March 2022 and the future hydromorphological recovery of the canal. Full article

Postprandial plasma amino acid (AA) kinetics serve as essential indicators of digestive efficiency and systemic metabolic status in pigs. Traditional kinetic analysis relies on Non-Linear Least Squares (NLS) regression using compartmental models, yet these methods typically demand repeated blood sampling and precise initialization to ensure convergence. In this study, we developed a Physics-Informed Neural Network (PINN) framework by integrating mechanistic Ordinary Differential Equations (ODEs) directly into the deep learning loss function. The framework was evaluated using a benchmark dataset. Specifically, we performed a retrospective analysis by downsampling the original high-frequency data to simulate dense and sparse sampling strategies. The results demonstrate that while both models exhibit high fidelity under dense sampling, PINN maintains superior robustness and predictive accuracy under data-constrained conditions. Under the sparse sampling scenario, PINN reduced the Root Mean Square Error (RMSE) compared to NLS in key metabolic profiles, such as Methionine in the FAA group (p < 0.01) and Lysine in the HYD group (p < 0.05). Unlike NLS, which is sensitive to initial guesses, PINN successfully utilized physical laws as a regularization term to robustly solve the inverse problem, demonstrating superior parameter identification stability and predictive consistency under data-constrained conditions compared to NLS. We concluded that the PINN framework provides a reliable and consistent alternative for modeling the AA dynamics. In the future, it may be possible to reconstruct highly accurate physiological trajectories under optimized sparse sampling conditions. Full article

Background/Objectives: The PEER beamline at the ANSTO Australian Synchrotron has been developed to enable VHEE FLASH radiotherapy studies, both dosimetric and biological. Featuring a 100 MeV electron linac, it delivers single or multi-pulse irradiations consisting of 100 ps bunches with a 2 ns spacing, resulting in average dose-rates and instantaneous dose-rates as high as ${10}^8$ Gy/s and ${10}^9$ Gy/s, respectively. Much work has been conducted to realise a stable accelerator facility, complete with the tooling and diagnostics required to undertake such studies. However, to truly confirm its suitability required a successful biological benchmarking. Methods: Three cell lines were irradiated utilising real-time dosimetry to compare linear quadratic cell survival curves with other facilities. Also, mouse cadavers were transported and irradiated, mimicking live animals, to assess the feasibility and logistics of small animal experiments. Results: By comparing the trends of the linear quadratic model, evident in the $\alpha$ and $\beta$ parameters, the PEER cell survival results were shown to be in agreement with VHEE results from the ARES beamline at DESY, Hamburg, Germany. Evident in the survival trends, VHEE produced more cell sparing in all cell lines compared to 2 Gy/s X-rays delivered on the IMBL, another beamline at the Australian Synchrotron. The results of the mouse cadaver irradiations showed that PEER can safely and efficiently irradiate small animals. Conclusions: The PEER beamline is shown to possess suitable capabilities, including real-time dosimetry, repeatable alignment, and linac diagnostics, rendering it suitable for future in vivo VHEE UHDR FLASH radiotherapy investigations. Full article

Bipedal wheeled robots combine the advantages of wheeled mobility and legged agility, enabling high-speed locomotion and obstacle negotiation in complex environments. However, their dynamic behavior is inherently unstable and highly coupled, making robust control particularly challenging in the presence of task conflicts, external disturbances, and modeling uncertainties. This paper proposes an RBF–L1–WBC framework that integrates L1 adaptive control to compensate for model inaccuracies and disturbances, radial basis function (RBF) neural networks to approximate nonlinear variations in linear quadratic regulator (LQR) gains, and whole-body control (WBC) to coordinate multiple tasks while mitigating control conflicts. Experimental findings confirm that the proposed methodology yields statistically significant improvements in both attitude regulation precision and velocity tracking accuracy, surpassing the performance of benchmark controllers including classical LQR, adaptive LQR, and classical Virtual Model Control (VMC). Full article

Biopharmaceuticals are therapeutic drugs engineered to target specific sites within the body. Their manufacturing process comprises two primary stages: upstream processing (USP) and downstream processing (DSP). USP primarily involves cell culture and growth, whereas DSP focuses on purifying and packaging the final product. The recent literature only reports a few studies addressing production planning and scheduling in biopharmaceutical manufacturing. In this work, we address a long-term scheduling and midterm planning problem incorporating on-time or late delivery of final products with unknown finite delivery rates. Early delivery is prohibited, and late delivery incurs a penalty cost. Published models and evolutionary algorithms exhibit key limitations in areas such as shelf-life modeling, inventory management, and product delivery. To overcome these shortcomings, we propose a revised mixed-integer linear programming (MILP) model implemented using the General Algebraic Modeling System (GAMS). When applied to two illustrative examples, the model reduces optimum event counts by two to three, improving computational efficiency through fewer binary variables, continuous variables, and constraints. Furthermore, it achieves up to 7% improvement over two published benchmarks, underscoring its potential to enhance scheduling strategies for multiproduct biopharmaceutical facilities. Full article

Reliable multivariable control is critical for industrial sectors where processes exhibit severe nonlinearities and interactions. A Continuous Stirred Tank Reactor (CSTR) is a rigorous benchmark for testing control strategies addressing these complexities. This work first establishes a linear MIMO mathematical framework to define the specific structure of such interactive systems. Analysis via phase planes and steady-state analysis reveals low controllability, bistability, and strong coupling, leading to the collapse of traditional decoupled control schemes. To address these issues via multivariable control, we propose a centralized MIMO RST control structure synthesized via a Matrix Fraction Description (MFD) and the extended Bézout equation. Simulations for performance evaluation and comparison highlight the following key findings: (1) the centralized RST maintains stability and tracking precision in regions where decentralized RST loops fail; (2) it exhibits performance comparable to the Augmented State Pole Placement with Integral Action (ASPPIA) method and outperforms the standard Model-Based Predictive Control (MPC) baseline, particularly during critical equilibrium point transitions; and (3) it offers a robust yet computationally simple design that provides superior flexibility for pole placement, accommodating future identification-based models and adaptive tuning. These results validate our algebraic synthesis as a robust, computationally efficient solution for managing highly interactive nonlinear dynamics. Full article

Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of Vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN–Transformer–Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source GitHub repository to foster community-driven advancements. Full article

This study introduces a novel intraday volume-based uncertainty (IVU) proxy—the ratio of opening-half-hour volume to total volume of the preceding seven intervals—to predict final half-hour return direction in the Chinese stock market. Using threshold regression, we identify a statistically significant IVU critical value of 0.476225 (p < 0.001), which splits the sample into distinct uncertainty regimes. Logistic regression incorporating this threshold reveals that the joint condition of high opening volume and low IVU (high uncertainty) significantly amplifies the predictive power of initial returns, achieving 63.04% accuracy in the high-uncertainty, high-volume regime. XGBoost further captures complex non-linear interactions, with IVU-related features ranking among the most important predictors and achieving 71.43% out-of-sample accuracy under high-volume, high-uncertainty conditions. A machine learning trading strategy leveraging these predictions yields a total return of 117.99% with a Sharpe ratio of 3.02 over seven years, significantly outperforming benchmarks. Our findings highlight information uncertainty as a critical moderator of intraday momentum and a valuable source of actionable alpha. Full article

Fog computing extends cloud services to the network edge, enabling low-latency processing for Internet of Things (IoT) applications. However, this distributed approach is vulnerable to a wide range of attacks, necessitating advanced intrusion detection systems (IDSs) that operate under resource constraints. This study proposes integrating self-awareness (online learning and concept drift adaptation) into a lightweight RL (reinforcement learning)-based IDS for fog networks and quantitatively comparing it with non-RL static thresholds and bandit-based approaches in real time. Novel self-aware reinforcement learning (RL) models, the Hierarchical Adaptive Thompson Sampling–Reinforcement Learning (HATS-RL) model, and the Federated Hierarchical Adaptive Thompson Sampling–Reinforcement Learning (F-HATS-RL), were proposed for real-time intrusion detection in a fog network. These self-aware RL policies integrated online uncertainty estimation and concept-drift detection to adapt to evolving attacks. The RL models were benchmarked against the static threshold (ST) model and a widely adopted linear bandit (Linear Upper Confidence Bound/LinUCB). A realistic fog network simulator with heterogeneous nodes and streaming traffic, including multi-type attack bursts and gradual concept drift, was established. The models’ detection performance was compared using metrics including latency, energy consumption, detection accuracy, and the area under the precision–recall curve (AUPR) and the area under the receiver operating characteristic curve (AUROC). Notably, the federated self-aware agent (F-HATS-RL) achieved the best AUROC (0.933) and AUPR (0.857), with a latency of 0.27 ms and the lowest energy consumption of 0.0137 mJ, indicating its ability to detect intrusions in fog networks with minimal energy. The findings suggest that self-aware RL agents can detect traffic–dynamic attack methods and adapt accordingly, resulting in more stable long-term performance. By contrast, a static model’s accuracy degrades under drift. Full article

The quadratic multidimensional knapsack problem (QMdKP) is a combinatorial optimization problem that involves selecting a subset of items to maximize both linear and quadratic profits without exceeding the capacity constraints across multiple dimensions. Due to its NP-hard nature, this paper presents a framework that integrates machine learning to mitigate the high computational cost associated with its resolution. The proposed methodology employs a classification model to predict item inclusion in the optimal solution prior to the optimization process, effectively reducing the number of decision variables handled by the solver. Additionally, to address large-scale instances, we propose an iterated local search metaheuristic initialized via the predictive algorithm. These strategies were benchmarked against a standard solver, demonstrating their capability of finding optimal or near-optimal solutions with execution time improvements of up to 83%. Full article