Search Results (6,073)

This paper comprehensively reviews structural vibration control systems for earthquake mitigation in civil engineering structures. Structural vibration control is vital for enhancing the resilience and safety of infrastructure subjected to seismic activity. This study examines various control strategies, including passive, active, and hybrid methods, with a focus on the advantages of semi-active systems, which offer a balance of energy efficiency and adaptive capabilities. Semi-active devices, such as magnetorheological dampers, are highlighted for their ability to offer adaptive control without the high energy demands of fully active systems. The review discusses challenges like time delays, sensor placement, and model uncertainties that can impact the practical implementation of these systems. Experimental studies and real-world applications demonstrate the effectiveness of semi-active systems in reducing seismic responses. This paper emphasizes the need for further research into optimizing control algorithms and addressing practical challenges to enhance the reliability and robustness of these systems. It concludes that semi-active control systems are a promising solution for enhancing structural resilience in earthquake-prone areas, offering a practical alternative that strikes a balance between performance and energy requirements. Full article

Robots are increasingly being used across industries, healthcare, and service sectors to perform a wide range of tasks. However, as these tasks become more complex and environments more unpredictable, the need for adaptable robots continues to grow—bringing with it greater technological challenges. Artificial intelligence (AI), driven by large datasets and advanced algorithms, plays a pivotal role in addressing these challenges and advancing robotics. AI enhances robot design by making it more intelligent and flexible, significantly improving robot perception to better understand and respond to surrounding environments and empowering more intelligent control and decision-making. In summary, AI contributes to robotics through design optimization, environmental perception, and intelligent decision-making. This article explores the driving role of AI in robotics and presents detailed examples of its integration with fields such as embodied intelligence, humanoid robots, big data, and large AI models, while also discussing future prospects and challenges in this rapidly evolving field. Full article

Resilience refers to an enduring and yet fluid characteristic that enhances children’s adaptation. It is a dynamic developmental process that is highly promoted by individuals’ internal characteristics, such as self-efficacy and locus of control. The present study examined whether self-efficacy mediates the relationship between locus of control and resilience among Greek primary school students. Participants were 690 students aged 9–12 years who were enrolled at primary schools in Greece in Grades 4, 5, and 6. Participants completed a questionnaire including measures assessing resilience, locus of control, and self-efficacy. Structural equation modeling using AMOS 26.0 was used to analyze the data. The results indicated that locus of control and self-efficacy function as significant predictors for all dimensions of resilience, while demographic characteristics such as gender and grade only predict some dimensions of resilience. The hypothesized model was a good fit to the data, and self-efficacy partially mediates the relationship between locus of control and resilience. Psychologists, instructors, and practitioners can develop and apply intervention programs in order to strengthen children’s resilience by enhancing their self-efficacy and helping them adopt an internal locus of control. Full article

Hybrid modeling techniques are increasingly important for improving predictive accuracy and control in biomanufacturing, particularly in data-limited conditions. This study develops and experimentally validates a hybrid deep learning model predictive control (MPC) framework for recombinant P. pastoris fed-batch fermentations. Bayesian optimization and grid search techniques were employed to identify the best-performing hybrid model architecture: an LSTM layer with 2 hidden units followed by a fully connected layer with 8 nodes and ReLU activation. This design balanced accuracy (NRMSE 4.93%) and computational efficiency (AICc 998). This architecture was adapted to a new, smaller dataset of bacteriophage Qβ coat protein production using transfer learning, yielding strong predictive performance with low validation (3.53%) and test (5.61%) losses. Finally, the hybrid model was integrated into a novel MPC system and experimentally validated, demonstrating robust real-time substrate feed control in a way that allows it to maintain specific target growth rates. The system achieved predictive accuracies of 6.51% for biomass and 14.65% for product estimation, with an average tracking error of 10.64%. In summary, this work establishes a robust, adaptable, and efficient hybrid modeling framework for MPC in P. pastoris bioprocesses. By integrating automated architecture searching, transfer learning, and MPC, the approach offers a practical and generalizable solution for real-time control and supports scalable digital twin deployment in industrial biotechnology. Full article

Policies and technical safeguards for artificial intelligence (AI) governance have implicitly assumed that AI systems will continue to operate via massive power-hungry data centers operated by large companies like Google and OpenAI. However, the present cloud-based AI paradigm is being challenged by rapidly advancing software and hardware technologies. Open-source AI models now run on personal computers and devices, invisible to regulators and stripped of safety constraints. The capabilities of local-scale AI models now lag just months behind those of state-of-the-art proprietary models. Wider adoption of local AI promises significant benefits, such as ensuring privacy and autonomy. However, adopting local AI also threatens to undermine the current approach to AI safety. In this paper, we review how technical safeguards fail when users control the code, and regulatory frameworks cannot address decentralized systems as deployment becomes invisible. We further propose ways to harness local AI’s democratizing potential while managing its risks, aimed at guiding responsible technical development and informing community-led policy: (1) adapting technical safeguards for local AI, including content provenance tracking, configurable safe computing environments, and distributed open-source oversight; and (2) shaping AI policy for a decentralized ecosystem, including polycentric governance mechanisms, integrating community participation, and tailored safe harbors for liability. Full article

Power hardware-in-the-loop (PHIL) creates a safe test environment to connect simulations with real hardware under test (HuT). Therefore, an interface algorithm (IA) must be chosen. The ideal transformer method (ITM) and the partial circuit duplication (PCD) are popular IAs, where a distinction is made between voltage- (V-) and current-type (C-) IAs. Depending on the sample time of the simulator and further delays, simulation accuracy is reduced and instability can occur due to negative feedback in the V-ITM and C-ITM control loops, which makes PHIL operation impossible. In the case of positive feedback, such as with the V-PCD and C-PCD, the delay causes destructive interference, which results in a phase shift and attenuation of the output signal. In this article, a novel damped Smith predictor (SP) for positive feedback PHIL IAs is presented, which significantly reduces destructive interference while allowing stable operation at low linking impedances at V-PCD and high linking impedances at C-PCD, thus reducing losses in the system. Experimental results show a reduction in phase shift by 21.17° and attenuation improvement of 24.3% for V-PCD at a sample time of 100 µs. The SP transfer functions are also derived and integrated into the listed negative feedback IAs, resulting in an increase in the gain margin (GM) from approximately one to three, which significantly enhances system stability. The proposed methods can improve stability and accuracy, which can be further improved by calculating the HuT impedance in real-time and dynamically adapting the SP model. Stable PHIL operation with SP is also possible with SP model errors or sudden HuT impedance changes, as long as deviations stay within the presented limits. Full article

Food quality and safety are essential for ensuring public health, preventing foodborne illness, reducing food waste, maintaining consumer confidence, and supporting regulatory compliance and international trade. This has led to the emergence of many research works that focus on automating and streamlining the assessment of food quality. Electronic noses have become of paramount importance in this context. We analyze the current state of research in the development of electronic noses for food quality and safety. We examined research papers published in three different scientific databases in the last decade, leading to a comprehensive review of the field. Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. Despite these encouraging results, key challenges remain, particularly in diversifying the sensor response across complex substances, improving odor differentiation, compensating for sensor drift, and ensuring real-world reliability. These limitations indicate that a complete device mimicking the flexibility and selectivity of the human olfactory system is not yet available. To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. We introduce benchmark comparisons and a future roadmap for electronic noses that demonstrate their potential to evolve from controlled studies to scalable industrial applications. In doing so, this review aims not only to assess the state of the field but also to support its transition toward more robust, interpretable, and field-ready electronic nose technologies. Full article

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed and torque signals as inputs while leveraging physics-derived torsional torque as a feedback input to refine estimation accuracy and robustness. While model-based methods provide insight into system dynamics, they lack predictive capability—an essential feature for proactive control. Conversely, standalone NARX NNs often suffer from error accumulation and overfitting. The proposed hybrid architecture synergises the adaptive learning of NARX NNs with the fidelity of physics-based feedback, enabling proactive vibration damping. The method was implemented and evaluated on a two-mass drive system using an IP controller and additional torsional torque feedback. Results demonstrate high accuracy and reliability in one-step-ahead torsional torque estimation, enabling effective proactive vibration damping. MATLAB 2024a/Simulink and dSPACE 1103 were used for simulation and hardware-in-the-loop testing. Full article

Highly infectious and pathogenic viruses seriously threaten global public health, underscoring the need for rapid and accurate diagnostic methods to effectively manage and control outbreaks. In this study, we developed a comprehensive Surface-Enhanced Raman Scattering–Lateral Flow Immunoassay (SERS-LFIA) detection system that integrates SERS scanning imaging with artificial intelligence (AI)-based result discrimination. This system was based on an ultra-sensitive SERS-LFIA strip with SiO₂-Au NSs as the immunoprobe (with a theoretical limit of detection (LOD) of 1.8 pg/mL). On this basis, a negative–positive discrimination method combining SERS scanning imaging with a deep learning model (ResNet-18) was developed to analyze probe distribution patterns near the T line. The proposed machine learning method significantly reduced the interference of abnormal signals and achieved reliable detection at concentrations as low as 2.5 pg/mL, which was close to the theoretical Raman LOD. The accuracy of the proposed ResNet-18 image recognition model was 100% for the training set and 94.52% for the testing set, respectively. In summary, the proposed SERS-LFIA detection system that integrates detection, scanning, imaging, and AI automated result determination can achieve the simplification of detection process, elimination of the need for specialized personnel, reduction in test time, and improvement of diagnostic reliability, which exhibits great clinical potential and offers a robust technical foundation for detecting other highly pathogenic viruses, providing a versatile and highly sensitive detection method adaptable for future pandemic prevention. Full article

Graph-based multi-view clustering is a fundamental analysis method that learns the similarity matrix of multi-view data. Despite its success, it has two main limitations: (1) complementary information is not fully utilized by directly combining graphs from different views; (2) existing multi-view clustering methods do not adequately address redundancy and noise in the data, significantly affecting performance. To address these issues, we propose the Enhanced Similarity Matrix Learning (ES-MVC) for multi-view clustering, which dynamically integrates global graphs from all views with local graphs from each view to create an improved similarity matrix. Specifically, the global graph captures cross-view consistency, while the local graph preserves view-specific geometric patterns. The balance between global and local graphs is controlled through an adaptive weighting strategy, where hyperparameters adjust the relative importance of each graph, effectively capturing complementary information. In this way, our method can learn the clustering structure that contains fully complementary information, leveraging both global and local graphs. Meanwhile, we utilize a robust similarity matrix initialization to reduce the negative effects caused by noisy data. For model optimization, we derive an effective optimization algorithm that converges quickly, typically requiring fewer than five iterations for most datasets. Extensive experimental results on diverse real-world datasets demonstrate the superiority of our method over state-of-the-art multi-view clustering methods. In our experiments on datasets such as MSRC-v1, Caltech101, and HW, our proposed method achieves superior clustering performance with average accuracy (ACC) values of 0.7643, 0.6097, and 0.9745, respectively, outperforming the most advanced multi-view clustering methods such as OMVFC-LICAG, which yield ACC values of 0.7284, 0.4512, and 0.8372 on the same datasets. Full article

Amid the increasing complexity of urban risks, urban parks not only serve ecological and recreational functions but are increasingly becoming a critical spatial foundation supporting public psychological resilience and social recovery. This study aims to systematically evaluate the daily adaptability of urban parks in the context of micro-risks. The research integrates the theories of “restorative environments,” environmental safety perception, urban resilience, and social ecology to construct a five-dimensional framework for perceived resilience, encompassing resilience, safety, sociability, controllability, and adaptability. Additionally, a dynamic feedback mechanism of perception–behavior–reperception is introduced. Methodologically, the study utilizes the Fuzzy Delphi Method (FDM) to identify 17 core indicators, constructs a causal structure and weighting system using DEMATEL-based ANP (DANP), and further employs the VIKOR model to simulate public preferences in a multi-criteria decision-making process. Taking three representative urban parks in Guangzhou as empirical case studies, the research identifies resilience and adaptability as key driving dimensions of the system. Factors such as environmental psychological resilience, functional diversity, and visual permeability show a significant path influence and priority intervention value. The empirical results further reveal significant spatial heterogeneity and group differences in the perceived resilience across ecological, neighborhood, and central park types, highlighting the importance of context-specific and user-adaptive strategies. The study finally proposes four optimization pathways, emphasizing the role of feedback mechanisms in enhancing urban park resilience and shaping “cognitive-friendly” spaces, providing a systematic modeling foundation and strategic reference for perception-driven urban public space optimization. Full article

This paper presents an online efficiency optimization strategy for a six-phase induction generator (6PIG) operating in both healthy and faulty modes for micro-hydropower applications. The proposed method is based on an extended Loss Model Control (LMC) approach, in which the direct axis stator current Id is dynamically optimized in real time to minimize the total electrical losses. Unlike conventional LMC strategies, this method explicitly incorporates switching losses into the loss model, along with stator and rotor copper losses and iron losses. The optimization problem is solved using a numerical minimization routine, allowing the control system to adapt continuously to variations in torque requests. The proposed approach is validated under both healthy and faulty configurations of the 6PIG. It is implemented and tested through simulation in MATLAB/Simulink^® and experimentally validated on a 24 kW squirrel cage six-phase induction generator (SC6PIG). The results are compared in terms of power losses, energy saving, and efficiency. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article