Search Results (24,014)

This paper addresses the problem of observer-based control (OBC) for nonlinear systems with time delay (TD). A novel hybrid modeling framework for nonlinear TD systems is first introduced by synergistically combining TD Takagi–Sugeno (TDTS) fuzzy and Lipschitz approaches. The proposed methodology broadens the range of representable systems by enabling Lipschitz nonlinearities to fulfill dual functions: they may describe essential dynamic behaviors of the system or represent aggregated uncertainties, depending on the specific application. The proposed TDTS–Lipschitz (TDTSL) model class features measurable premise variables while accommodating Lipschitz nonlinearities that may depend on unmeasurable system states. Then, through the construction of an appropriate Lyapunov–Krasovskii (L-K) functional, we derive sufficient conditions to ensure exponential stability of the augmented closed-loop model. Subsequently, through a decoupling methodology, these stability conditions are reformulated as a set of linear matrix inequalities (LMIs). Finally, the proposed OBC design is validated through application to a continuous stirred tank reactor (CSTR) with lumped uncertainties. Full article

In order to establish a general design methodology for water-based electromagnetic metamaterial absorber microstructures, a topology optimization method for water-based metamaterial absorber microstructures design was proposed in this paper. According to Mie resonance and impedance matching theory, the realization mechanism and physical model of the broadband water-based metamaterial absorber were constructed. The highest average in-band absorption rate was taken as the design object; the topological optimization model for water-based metamaterial absorber design was established. A metamaterial absorber microstructure with 16 discretized water columns inside the unit cell was designed as an example. The obtained structure exhibited a very high average in band absorption rate in the specific frequency band. The proposed method was a collaborative optimization approach that employed a single type of design variable, namely water column height, to simultaneously adjust surface impedance matching and specific resonant modes. It provided a feasible method for achieving the highest average absorption rate within a specific band. Full article

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

With the large-scale integration of renewable energy into distribution networks, the intermittency and uncertainty of renewable generation pose significant challenges to the voltage security of the power grid under extreme scenarios. To address this issue, this paper proposes an optimal scheduling method for power grids under extreme scenarios, based on an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By simulating potential extreme scenarios in the power system and formulating targeted secure scheduling strategies, the proposed method effectively reduces trial-and-error costs. First, the time series clustering method is used to construct the extreme scene dataset based on the principle of maximizing scene differences. Then, a mathematical model of power grid optimal dispatching is constructed with the objective of ensuring voltage security, with explicit constraints and environmental settings. Then, an interactive scheduling model of distribution network resources is designed based on a multi-agent algorithm, including the construction of an agent state space, an action space, and a reward function. Then, an improved MADDPG multi-agent algorithm based on specific information fusion is proposed, and a hybrid optimization experience sampling strategy is developed to enhance the training efficiency and stability of the model. Finally, the effectiveness of the proposed method is verified by the case studies of the distribution network system. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

Magnetocardiography (MCG) offers a noninvasive method for early screening and precise localization of cardiovascular diseases by measuring picotesla-level weak magnetic fields induced by cardiac electrical activity. However, in unshielded magnetic environments, geomagnetic disturbances, power-frequency electromagnetic interference, and physiological/motion artifacts can significantly overwhelm effective magnetocardiographic components. To address this challenge, this paper systematically constructs an integrated denoising framework, termed “AOA-VMD-WT”. In this approach, the Arithmetic Optimization Algorithm (AOA) adaptively optimizes the key parameters (decomposition level K and penalty factor α) of Variational Mode Decomposition (VMD). The decomposed components are then regularized based on their modal center frequencies: components with frequencies ≥50 Hz are directly suppressed; those with frequencies <50 Hz undergo wavelet threshold (WT) denoising; and those with frequencies <0.5 Hz undergo baseline correction. The purified signal is subsequently reconstructed. For quantitative evaluation, we designed performance indicators including QRS amplitude retention rate, high/low frequency suppression amount, and spectral entropy. Further comparisons are made with baseline methods such as FIR and wavelet soft/hard thresholds. Experimental results on multiple sets of measured MCG data demonstrate that the proposed method achieves an average improvement of approximately 8–15 dB in high-frequency suppression, 2–8 dB in low-frequency suppression, and a decrease in spectral entropy ranging from 0.1 to 0.6 without compromising QRS amplitude. Additionally, the parameter optimization exhibits high stability. These findings suggest that the proposed framework provides engineerable algorithmic support for stable MCG measurement in ordinary clinic scenarios. Full article

Data preprocessing and feature engineering play key roles in data mining initiatives, as they have a significant impact on the accuracy, reproducibility, and interpretability of analytical results. This review presents an analysis of state-of-the-art techniques and tools that can be used in data input preparation and data manipulation to be processed by mining tasks in diverse application scenarios. Additionally, basic preprocessing techniques are discussed, including data cleaning, normalisation, and encoding, as well as more sophisticated approaches regarding feature construction, selection, and dimensionality reduction. This work considers manual and automated methods, highlighting their integration in reproducible, large-scale pipelines by leveraging modern libraries. We also discuss assessment methods of preprocessing effects on precision, stability, and bias–variance trade-offs for models, as well as pipeline integrity monitoring, when operating environments vary. We focus on emerging issues regarding scalability, fairness, and interpretability, as well as future directions involving adaptive preprocessing and automation guided by ethically sound design philosophies. This work aims to benefit both professionals and researchers by shedding light on best practices, while acknowledging existing research questions and innovation opportunities. Full article

A surface molecularly imprinted ratiometric fluorescent sensor (Eu/CDs@SiO₂@IIPs) was constructed for the selective and visual detection of Cu²⁺. The sensor integrates blue-emitting carbon dots as an internal reference and a custom-designed Eu(III) complex, Eu(MAA)₂(2,9-phen), as both the functional and fluorescent monomer within a surface-imprinted polymer layer, enabling efficient ratiometric fluorescence response. This structural design ensured that all fluorescent monomers were located at the recognition sites, thereby reducing background fluorescence interference and enhancing the accuracy of signal changes. Under optimized conditions, the sensor exhibited a detection limit of 2.79 nM, a wide linear range of 10–100 nM, and a rapid response time of 3.0 min. Moreover, the uncoordinated nitrogen atoms in the phenanthroline ligand improved resistance to interference from competing ions, significantly enhancing selectivity. Practical applicability was validated by spiked recovery tests in deionized and river water, with results showing good agreement with ICP-MS analysis. These findings highlight the potential of Eu/CDs@SiO₂@IIPs as a sensitive, selective, and portable sensing platform for on-site monitoring of Cu²⁺ in complex water environments. Full article

Reproducibility is a cornerstone of scientific progress, yet in X-ray computed tomography (CT) reconstruction, it remains a critical and unresolved challenge. Current benchmarking practices in CT are hampered by the scarcity of openly available datasets, the incomplete or task-specific nature of existing resources, and the lack of transparent implementations of widely used methods and evaluation metrics. As a result, even the fundamental property of reproducibility is frequently violated, undermining objective comparison and slowing methodological progress. In this work, we analyze the systemic limitations of current CT benchmarking, drawing parallels with broader reproducibility issues across scientific domains. We propose an extended data model and formalized schemes for data preparation and quality assessment, designed to improve reproducibility and broaden the applicability of CT datasets across multiple tasks. Building on these schemes, we introduce checklists for dataset construction and quality assessment, offering a foundation for reliable and reproducible benchmarking pipelines. A key aspect of our recommendations is the integration of virtual CT (vCT), which provides highly realistic data and analytically computable phantoms, yet remains underutilized despite its potential to overcome many current barriers. Our work represents a first step toward a methodological framework for reproducible benchmarking in CT. This framework aims to enable transparent, rigorous, and comparable evaluation of reconstruction methods, ultimately supporting their reliable adoption in clinical and industrial applications. Full article

Because of the increased prevalence of chronic diseases, older adults frequently take many medications. However, adhering to a medication treatment tends to be difficult. The lack of medication adherence can cause health problems or even patient death. This paper describes the methodology used in developing a mobile augmented reality (MAR) pill box. The proposal supports patients in adhering to their medication treatment. First, we explain the design and construction of the automatic pill box, which includes alarms and uses QR codes recognized by the MAR system to provide medication information. Then, we explain the development of the MAR system. We conducted a preliminary survey with 30 participants to assess the feasibility of the MAR app. One hundred older adults participated in the survey. After one week of using the proposal, each patient answered a survey regarding the proposal functionality. The results revealed that 88% of the participants strongly agree and 11% agree that the app is a support in adhering to medical treatment. Finally, we conducted a study to compare the time elapsed between the scheduled time for taking the medication and the time it was actually consumed. The results from 189 records showed that using the proposal, 63.5% of the patients take medication with a maximum delay of 4.5 min. The results also showed that the alarm always sounded at the scheduled time and that the QR code displayed always corresponded to the medication that had to be consumed. Full article

Water supply networks in both developed and developing major cities worldwide were constructed many years ago. Currently, these systems face numerous challenges, including population growth, climate change, emerging technologies, and the policies implemented by local governments. Such factors can impact the design life of water infrastructure, leading to service pressure deficiencies. Consequently, water infrastructure must be reinforced to ensure an adequate and reliable service. This research presents the development of an analytical formulation for hydraulic installations with a pumping station, enabling the calculation of requirements for a new parallel pipeline within an existing water system without altering the current pipe resistance class. To implement the proposed solution, it is essential to maintain the initial pump head by adjusting the impeller size. A construction cost assessment is also undertaken to identify the most cost-effective reinforcement strategy, acknowledging that pipe costs vary significantly with diameter and material, and are proportional to the square of the diameter. The proposed methodology is applied to a 30 km pipeline with a 10% increase in demand, showing that a new parallel pipe of the same diameter as the existing hydraulic installation must be installed to minimise construction costs. A multi-parametric analysis was conducted employing machine learning presets with 309 dataset points. Full article

The aim of this study is to investigate how the positioning of outrigger systems affects the seismic performance of high-rise buildings with either reinforced concrete (RC) shear walls or buckling-restrained braces (BRBs) in the core. Two important questions emerge as the focus and direction of the study: (1) How does the structural performance change when outriggers are placed at various positions? (2) How do outrigger systems affect structural behavior under sequential earthquake scenarios? Nonlinear time history analyses were employed as the primary methodology to evaluate the seismic response of the two reinforced concrete buildings with 24 and 48 stories, respectively. Each building type was developed for two different core configurations: one with a reinforced concrete shear wall core and the other with a BRB core system. Each analysis model also includes outrigger systems constructed with BRBs positioned at different floor levels. Five sequential ground motion records were used to assess the effects of main- and aftershocks. The analysis results were evaluated not only based on displacement and force demands but also using a damage measure called the Park-Ang Damage Index. In addition, displacement-based metrics, particularly the maximum inter-story drift ratio (MISD), were also utilized to quantify lateral displacement demands under consecutive seismic loading. With the results obtained from this study, it is aimed to provide design-oriented insights into the most effective use of outrigger systems formed with BRB in high-rise RC buildings and their functions in increasing seismic resistance, especially in areas likely to experience consecutive seismic events. Full article

Passive microwave remote sensing provides indispensable observations for constructing long-term snow depth records, which are critical for climatology, hydrology, and operational applications. Nevertheless, despite decades of snow depth monitoring, systematic evaluations of how inter-sensor brightness temperature differences (TBDs) propagate into retrieval uncertainties are still lacking. In this study, TBDs between DMSP-F18/SSMIS, FY-3D/MWRI, and AMSR2 sensors were quantified, and the sensitivity of seven snow depth retrieval algorithms to these discrepancies was systematically assessed. The results indicate that TBDs between SSMIS and AMSR2 are larger than those between MWRI and AMSR2, likely reflecting variations in sensor specifications such as frequency, observation angle, and overpass time. In terms of algorithm sensitivity, SPD, WESTDC, FY-3B, and FY-3D demonstrate less sensitivity across sensors, with standard deviations of snow depth differences generally below 2 cm. In contrast, the Foster algorithm exhibits pronounced sensitivity to TBDs, with standard deviations exceeding 11 cm and snow depth differences reaching over 20 cm in heavily forested regions (forest fracion >90%). This study provides guidance for SWE virtual constellation design and algorithm selection, supporting long-term, seamless, and consistent snow depth retrievals. Full article

Wound healing remains a significant clinical challenge due to antibiotic-resistant pathogens, persistent inflammation, oxidative stress, and impaired tissue regeneration. Conventional therapies are often inadequate, necessitating alternative strategies. Plant bioactive compounds, including flavonoids, tannins, terpenoids, and alkaloids, offer antimicrobial, anti-inflammatory, antioxidant, and pro-angiogenic properties that directly address these challenges in wound healing therapy. However, their poor solubility, instability, and rapid degradation at the wound site limit clinical translation. Biomaterial-based scaffolds such as hydrogels, electrospun nanofibers, lyophilized dressings, and 3D-bioprinted constructs have emerged as promising delivery platforms to enhance bioavailability, stability, and sustained release of bioactive compounds while providing structural support for cell adhesion, proliferation, and tissue repair. This review was conducted through a structured literature search using PubMed, Scopus, and Web of Science databases, covering studies published between 1998 and 2025, with keywords including wound healing, phytochemicals, plant bioactive compounds, scaffolds, hydrogels, electrospinning, and 3D bioprinting. The findings highlight how incorporation of plant bioactive compounds onto scaffolds can combat resistant microbial infections, mitigate oxidative stress, promote angiogenesis, and accelerate tissue regeneration. Despite these promising outcomes, further optimization of scaffold design, standardization of bioactive formulations, and translational studies are needed to bridge laboratory research with clinical applications for next generation wound healing therapies. Full article

When a joined-wing configuration is applied to the design of solar-powered UAVs, the increasing span amplifies aeroelastic effects, while structure complexity poses greater challenges to computational effectiveness during the conceptual design phase. This paper focuses on a large-span joined-wing solar-powered UAV (LJS-UAV) engineering prototype. The structural finite element model of the whole system is constructed by developing the ‘Simplified beam-shell model’ (SBSM) and verified by a structural mode test. A numerical simulation approach is employed to comprehensively analyse and summarise the aeroelastic characteristics of the LJS-UAV from the perspectives of static aeroelasticity, flutter, and gust response. The mode test identified 30 global modes with natural frequencies below 10 Hz, indicating that the LJS-UAV possesses an exceptionally flexible structure and exhibits highly complex aeroelastic characteristics. The simulation results reveal that the structural elasticity induces significant variations in aerodynamic forces, moments, and derivatives during flight, which cannot be neglected. The longitudinal trim strategies can considerably influence the aeroelastic boundary of the LJS-UAV. Utilising the front-wing control surfaces for trim is beneficial in improving structural performance and expanding the flight envelope. Full article