Search Results (238)

Aerobic composting shows state-dependent dynamics in key parameters such as temperature, moisture content, oxygen concentration, and pH, and these variables are strongly coupled over time. This coupling makes accurate state identification and process regulation challenging when relying on single-parameter thresholds or experience-based control. To enable robust recognition of composting states throughout the process, we propose an IRRTO-optimized CNN–LSTM–attention model (IRRTO–CNN–LSTM–attention). The model uses a convolutional neural network (CNN) to extract discriminative multivariate features, a long short-term memory (LSTM) network to model temporal dependencies, and an attention module to adaptively emphasize informative features. To address the hyperparameter selection challenge, the Rapidly-exploring Random Tree Optimizer (RRTO) was introduced and further enhanced via four strategies (fluctuating attenuation adaptive regulation, dual-mode guided update, dynamic dimension adaptive perturbation, and dual-mechanism adaptive perturbation regulation), forming the improved IRRTO. The proposed approach was validated using sensor data from windrow composting of pig manure and corn straw. The IRRTO–CNN–LSTM–attention model achieved an overall accuracy of 98.31% in classifying the four states (mesophilic/heating, thermophilic, cooling, and abnormal) on the independent test set, which was 3.39 percentage points higher than the RRTO-based model. These results suggest that the proposed method can accurately identify composting states and support early warning and state-specific regulation in practical aerobic composting systems. Full article

The high penetration of photovoltaic and wind power introduces strong non-stationarity and multi-scale fluctuations into power system load profiles, challenging the accuracy of short-term load forecasting (STLF). To address this, we propose a hybrid forecasting framework, IMVMD-XLSTM, which synergistically integrates an optimized multivariate decomposition with an advanced neural network. First, to address the critical issue that MVMD performance is highly sensitive to its parameter settings, which impacts decomposition quality, a multi-strategy Improved Fruit Fly Optimization Algorithm (IFOA) is developed to task-oriented adaptively tune the key parameters of MVMD, forming an Improved MVMD (IMVMD). This optimization aims to ensure decomposition stability and maximize the relevance for the subsequent forecasting task. Second, to fully leverage the characteristics of the frequency-aligned, multi-channel sub-sequences generated by IMVMD, an Extended LSTM (XLSTM) network is designed. Its serially arranged BisLSTM and mLSTM units are specifically tailored to capture the bidirectional long-term dependencies within each stable sub-sequence and the complex high-dimensional interactions across the aligned sub-sequences, respectively. Evaluated on 15 min resolution data from the Austrian grid, the proposed IMVMD-XLSTM framework achieves a day-ahead forecasting Mean Absolute Percentage Error (MAPE) of 2.45% (±1.41%). This study provides a verifiable and effective solution that couples data-adaptive signal processing with a purpose-built neural architecture to enhance forecasting reliability in renewable-rich power systems. Full article

This paper presents the design and industrial implementation of an adaptive discrete control system for a rotary dryer operating in potash fertilizer production. The drying process is characterized by high inertia, multivariable interactions, transport delay, and non-stationary behavior resulting from variations in raw material properties and external disturbances, which significantly reduce the effectiveness of conventional fixed-parameter controllers. A discrete-time mathematical model of the rotary drying process was developed using industrial experimental data collected from a full-scale production plant. The process was modeled as a coupled 2 × 2 multivariable system with pronounced time-delay effects in the main control channels. System identification was carried out using statistical and frequency-domain methods to capture the dominant dynamic characteristics required for controller synthesis. Based on the identified model, an adaptive discrete controller with online parameter adjustment was developed to regulate outlet moisture content and exhaust gas temperature. Simulation and industrial results confirmed stable operation under varying conditions, improved regulation accuracy, enhanced process stability, and an average production efficiency increase of approximately 1.8%, accompanied by reduced fuel consumption. Full article

This paper revisits and extends the information-theoretic dual adaptive control framework initially developed by the author for single-input single-output systems to multiple-input multiple-output (MIMO) systems, with specific application to fault-tolerant control (FTC). The core contribution is a MIMO formulation that preserves the essential dual property, i.e., balancing control performance against parameter learning, while addressing the increased complexity of coupled multivariable systems. A convexity condition is derived for the MIMO optimization problem, generalizing the original SISO condition. The framework naturally handles actuator faults through a parameter vector that includes effectiveness factors, with fault detection achieved via monitoring of information gain. Control reconfiguration strategies ensure graceful performance degradation under faults. Simulation results demonstrate the effectiveness of this dual approach to FTC methods in balancing detection speed, identification accuracy, and tracking performance, while maintaining computational feasibility for real-time implementation. Full article

The blast furnace ironmaking process (BFIP) is a highly complex and dynamic industrial system where strong spatiotemporal coupling and severe data imbalance pose substantial challenges for fault diagnosis. To address these issues, this study proposes a Transformer–BiLSTM Double Deep Q-Network (TBDDQN) framework for intelligent fault diagnosis. The framework employs a dual-branch architecture that integrates a Transformer-based spatial encoder with a BiLSTM-attention temporal extractor to capture global dependencies and dynamic patterns from multivariate time-series data. To mitigate class imbalance and asymmetric fault costs, a cost-sensitive reinforcement learning scheme based on Double DQN is incorporated, featuring prioritized experience replay and adaptive misclassification penalties. Experiments on real blast furnace datasets show that TBDDQN achieves a macro-averaged precision of 0.970 and a macro-averaged F1-score of 0.929, outperforming conventional CNN, LSTM, and DQN-based baselines. These results demonstrate that TBDDQN offers a robust and interpretable solution for imbalanced industrial fault diagnosis in the BFIP. Full article

Extrusion-based three-dimensional (3D) bioprinting has enabled the fabrication of complex, cell-laden constructs; however, process parameter selection remains largely empirical and system-specific. As biofabrication workflows scale in complexity and translational ambition, trial-and-error optimization increasingly limits reproducibility, transferability, and informed decision-making. In this work, a formal, optimization-oriented design framework is proposed to structure extrusion-based bioprinting as a constrained, multivariable design problem. Rather than introducing a system-specific predictive model, the framework organizes process parameters, material descriptors, scaffold architecture, and biological feasibility into a unified formulation based on objective functions and admissible constraints. Symbolic coupling relationships are employed to make parameter dependencies, trade-offs, and constraint interactions explicit without imposing restrictive assumptions on material behavior or biological response. A demonstrative computational case study is presented to illustrate how qualitative predictive reasoning emerges through constraint-driven design space analysis and multi-objective considerations. The framework reveals how feasible operating regions are shaped by competing biological, mechanical, and manufacturing limitations, emphasizing robustness-aware parameter selection over isolated optimization. The proposed approach is intended as a transferable methodological foundation that supports structured reasoning, experimental planning, and future integration with numerical models, data-driven tools, and closed-loop biofabrication systems. Full article

Spatio-temporal features are crucial for maritime trajectory forecasting, especially in scenarios involving curved waterways or abrupt changes in ship motion patterns. Although Automatic Identification System (AIS) data, which are widely used for trajectory prediction, inherently include temporal and spatial information, effectively strengthening these features and integrating them into prediction models remains challenging. To address this challenge, we propose a Convolutional Neural Network (CNN)-Series-cOre Fused Time Series forecaster (SOFTS)-based framework that explicitly couples spatial and temporal features to achieve high-fidelity maritime trajectory forecasting, especially in scenarios with complex spatial patterns. We first employ a CNN-based spatial encoder to hierarchically abstract spatial density distributions through convolution and pooling operations, thereby learning global spatial structure patterns of ship movements. This encoder emphasizes overall spatial morphology rather than precise individual trajectory points. Second, we employ the SOFTS model to incorporate angular velocity, acceleration, and angular acceleration as input features to characterize ship motion states, which can capture the temporal dependencies of ship motion states from multivariate time series. Finally, the spatial embedding features extracted by the CNN are concatenated with the temporal feature representations learned by SOFTS along the feature dimension to form a joint spatiotemporal representation. This representation is then fed into a fusion regression module composed of fully connected layers to predict future ship trajectories. Experimental results on the validation dataset show that the proposed method achieves an MSE of 0.020 and an MAE of 0.060, outperforming several advanced time series forecasting models in prediction accuracy and computational efficiency. The introduction of angular velocity, acceleration, and angular acceleration features reduces the MSE and MAE by approximately 10.22% and 9.49%, respectively, validating the effectiveness of the introduced dynamic features in improving trajectory prediction performance. These results underscore the proposed method’s potential for intelligent navigation and traffic management systems by effectively enhancing inland river navigation safety and strengthening waterborne traffic monitoring capabilities. Full article

To address the nonlinear dynamics and strong multivariate coupling inherent in complex industrial data, while overcoming the high computational costs and deployment challenges of deep learning, this paper proposes a Channel-Independent Anchor Graph-Regularized Broad Learning System (CI-GBLS). First, a Channel Independence (CI) strategy is introduced: by constructing physically isolated feature channels, multivariate inputs are orthogonally decomposed, enabling the model to mine the intrinsic temporal evolutionary patterns of each variable. Building upon this, enhancement nodes are constructed using Radial Basis Functions (RBFs) to capture nonlinear dynamics; moreover, RBF cluster centers are reused as graph anchors to design an efficient manifold regularization algorithm. This algorithm embeds the intrinsic geometric structure of the data into the learning objective via reduced rank approximation, thereby guiding output weights to explicitly reconstruct spatial coupling relationships while preserving manifold consistency. Experimental results on the IndPenSim process demonstrate that CI-GBLS effectively balances prediction accuracy and efficiency. It completes training within seconds, validating its effectiveness for complex time-series data and offering an efficient solution for real-time, high-precision industrial modeling. Full article

The rapid growth of vehicle ownership has led to increasingly congested road networks, which significantly reduces the energy efficiency of electric vehicles (EVs) and intensifies user range anxiety. To address these challenges, a dynamic EV route planning process is proposed by integrating traffic flow (TF) prediction, charging service modelling, and time-varying path optimization within a unified framework. First, future TF is predicted using a data-driven forecasting module based on the iTransformer model, which captures multivariate temporal dependencies across road links and provides accurate inputs for downstream decision-making. Based on the predicted traffic states, a time-dependent queuing process is formulated to estimate charging station waiting times by modelling the dynamic interaction between vehicle arrivals and service capacity. These components are then embedded into a time-varying shortest path optimization process that explicitly considers mid-journey charging constraints, with the objective of minimizing total travel time and economic cost. The proposed framework establishes a closed-loop decision-making process that couples traffic evolution, charging service dynamics, and routing behaviour. Extensive comparative experiments against classical Time-Dependent Shortest Path (TDSP) methods under different network scales, together with a real-world case study, demonstrate that the proposed approach achieves higher computational efficiency and improved routing performance under dynamic conditions. The results indicate that the proposed process-oriented method provides an effective and practical solution for EV routing in intelligent transportation systems characterized by time-varying traffic and service processes. Full article

Closed-basin lakes are highly sensitive to climatic variability, yet for the Lake Van Basin (Türkiye), the dynamic and spatially heterogeneous linkages among atmospheric drivers and lake-level changes (particularly their lag structure and predictive directionality) remain insufficiently quantified in a unified multivariate setting. This study examines how temperature and precipitation jointly influence hydrological behavior in the Lake Van Basin using a multi-station Vector Autoregression (VAR) framework. By integrating long-term observations from multiple meteorological stations, the analysis explicitly captures the spatial heterogeneity that characterizes this complex endorheic system and provides a consistent basis for comparing station-specific dynamics. The results show strong persistence in lake-level dynamics across specifications, with lagged lake-level coefficients of 0.2595 to 0.3685 (p < 0.01), indicating a buffered endorheic response. Temperature exhibits a highly consistent seasonal dependence across stations, reflected by a uniformly negative and significant four-month temperature lag in the temperature equations (−0.34 to −0.42, p < 0.01). Granger-causality tests further indicate robust bidirectional coupling between temperature and precipitation in all station specifications (p < 0.01 and typically p ≤ 0.05), while climate-to-lake-level linkages remain spatially heterogeneous but are statistically supported across both Tatvan-based and Gevas-based specifications (Tatvan-Tatvan: p < 0.01 for both climate variables; Tatvan-Ahlat: temperature p = 0.000; Gevas-Van, Gevas-Ercis, and Gevas-Muradiye: temperature p = 0.000 and precipitation p = 0.013, 0.008, and 0.015, respectively). Distinct station-level patterns further demonstrate that topographical differences modulate the strength and direction of climate–hydrology linkages across the basin. By providing a coherent, causally consistent understanding of these interactions and explicitly incorporating season-specific VAR and Granger-causality evidence, this study offers a transferable methodological framework for analyzing climate-sensitive lake systems and highlights the need to incorporate temperature-driven processes into water-management and climate-adaptation strategies in endorheic basins. 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

Modeling forced nonlinear multivariable dynamical systems remains challenging, particularly when first-principles models are unavailable or strong nonlinear couplings are present. In recent years, data-driven approaches grounded in the Koopman operator theory have gained attention for their ability to represent nonlinear dynamics via linear evolution in appropriately lifted spaces. This work presents a data-driven modeling framework for forced nonlinear multiple-input multiple-output (MIMO) systems based on Hankel Dynamic Mode Decomposition with control and lifting functions (HDMDc+Lift). The proposed methodology exploits Hankel matrices to encode temporal correlations and employs lifting functions to approximate the Koopman operator’s action on observable functions. As a result, an augmented-order linear state-space model is identified exclusively from input–output data, without relying on explicit knowledge of the system’s governing equations. The effectiveness of the proposed approach is demonstrated using operational data from a real multivariable tank system that was not used during the identification stage. The identified model achieves a coefficient of determination exceeding 0.87 in multi-step prediction tasks. Furthermore, spectral analysis of the resulting linear operator reveals that the dominant dynamical modes of the physical system are accurately captured. At the same time, additional modes associated with nonlinear interactions are also identified. These results highlight the HDMDc+Lift framework’s ability to provide accurate and interpretable linear representations of forced nonlinear MIMO dynamics. Full article

We present an algebraic framework for constructing challenge–response authentication protocols based on powers of non-diagonalizable matrices over finite fields. The construction relies on upper triangular Toeplitz matrices with a single Jordan block and on their structured power expansions, which induce nonlinear relations between matrix parameters and exponents through an autopotency phenomenon. The protocol is built from a cyclic family of matrix products derived from secret matrices ${\left({\mathbf{A}}_i\right)}_{i=1}^n\subset G{L}_k\left({\mathbb{F}}_p\right)$: for each index i, a product ${\mathbf{P}}_i={\mathbf{A}}_i{\mathbf{A}}_{i+1}\dots {\mathbf{A}}_{i+n-1}$ is formed (indices modulo n), and its power ${\mathbf{P}}_i^{\left(x\right)}$ is published for a secret exponent x. The resulting family of powered products is linked by conjugation via the unknown factors ${\mathbf{A}}_i$, enabling an interactive authentication mechanism in which the prover demonstrates the knowledge of selected factors by satisfying explicit conjugacy relations. We formalize the underlying algebraic problems in terms of factor recovery and conjugacy identification from powered products, and analyze how the enforced non-diagonalizable structure and Toeplitz constraints lead to coupled multivariate polynomial systems. These systems arise naturally from the algebraic design of the construction and do not admit immediate reductions to classical discrete logarithm settings. The framework illustrates how non-diagonalizable matrix structures and structured conjugacy relations can be used to define concrete authentication primitives in noncommutative algebraic settings, and provides a basis for further cryptanalytic and cryptographic investigation. Full article

This paper presents the design and optimization, based on electromagnetic simulation, of a fifth-order bandpass filter centered at 30 GHz, implemented using Gap Waveguide (GWG) technology and pole-type cylindrical resonators, intended for applications in 5G communication systems and high-frequency satellite links. Unlike conventional Chebyshev designs reported in the literature, this study proposes an integrated methodology that combines theoretical synthesis, full-wave electromagnetic modeling, and multivariable optimization, while accounting for manufacturing constraints. The developed method encompasses the electromagnetic characterization of individual resonators, the extraction of cavity–cavity coupling parameters, and the complete implementation of the filter using full-wave electromagnetic simulations. A distinctive aspect of the proposed approach is the explicit incorporation of practical manufacturing effects, such as rounded corners induced by machining processes, alongside analytical and numerical geometric compensation procedures that preserve the device’s electrical response. Furthermore, a combination of gradient-based optimization algorithms and evolutionary strategies is employed to align the electromagnetic response with the target theoretical behavior. The results obtained through electromagnetic simulation indicate that the designed filter achieves return losses exceeding 20 dB and a fractional bandwidth of 4.95%, consistent with the reference Chebyshev response. Finally, the potential extension of the proposed approach to higher frequency bands is discussed conceptually, laying the groundwork for future work that includes experimental validation. Full article

Maca (Lepidium meyenii Walp.), a Brassicaceae species native to the high Andes of Peru, has gained global attention as a functional food and herbal medicinal product due to its endocrine-modulating, fertility-enhancing, and neuroprotective properties. Although numerous studies have addressed its biological effects, a systematic and up-to-date summary of its chemical constituents and analytical methodologies is lacking. This review aims to provide a critical overview of the chemical constituents of L. meyenii and to evaluate analytical studies published between 2000 and 2025, focusing on recent advances in extraction strategies and qualitative and quantitative analytical techniques for quality control. Major compound classes include macamides, macaenes, glucosinolates, and alkaloids, each contributing to maca’s multifaceted activity. Ultra-(high-)performance liquid chromatography (U(H)PLC), often coupled with ultraviolet, diode array, or mass spectrometric detection, is the primary and most robust analytical platform due to its sensitivity, selectivity, and throughput, while ultrasound-assisted extraction improves efficiency and reproducibility. Emerging techniques such as metabolomics and chemometric approaches enhance quality control by enabling holistic, multivariate assessment of complex systems and early detection of variations not captured by traditional univariate methods. As such, they provide complementary, predictive, and more representative insights into maca’s phytochemical complexity. The novelty of this review lies in its integration of conventional targeted analysis with emerging approaches, comprehensive comparison of analytical workflows, and critical discussion of variability related to phenotype, geographic origin, and post-harvest processing. By emphasizing analytical standardization and quality assessment rather than biological activity alone, this review provides a framework for quality control, authentication, and safety evaluation of L. meyenii as a functional food and dietary supplement. Full article