Search Results (5,760)

This study investigates the foundational causes of unsustainability that obstruct efforts to address global challenges such as climate change, environmental degradation, water crises, and public health deterioration. Using qualitative research with in-depth expert interviews from education, environmental studies, and business, it finds that these global challenges, while visible on the surface, are deeply rooted in worldviews that shape human behavior, societal structures, and policies. Building on this insight, the thematic analysis manifests three interrelated systemic paradigms as the fundamental drivers of unsustainability: a crisis of wholeness, reflected in fragmented identities and collective disorientation; a disconnection from nature, shaped by human-centered perspectives; and the influence of dominant political-economic systems which prioritize growth logics over ecological and social concerns. These paradigms underlie both structural and cognitive barriers to systemic transformation, which influence the design and implementation of education for sustainability. By clarifying a body of knowledge and systemic paradigms regarding unsustainability, this paper calls for transformative education that promotes a holistic, value-based approach, eco-empathy, and critical thinking, aiming to equip future generations with the tools to challenge and transform unsustainable systems. Full article

The present paper proposes a three-dimensional (3D) spherical shell model for the magneto-electro-elastic (MEE) free vibration analysis of simply supported multilayered smart shells. A mixed curvilinear orthogonal reference system is used to write the unified 3D governing equations for cylinders, cylindrical panels and spherical shells. The closed-form solution of the problem is performed considering Navier harmonic forms in the in-plane directions and the exponential matrix method in the thickness direction. A layerwise approach is possible, considering the interlaminar continuity conditions for displacements, electric and magnetic potentials, transverse shear/normal stresses, transverse normal magnetic induction and transverse normal electric displacement. Some preliminary cases are proposed to validate the present 3D MEE free vibration model for several curvatures, materials, thickness values and vibration modes. Then, new benchmarks are proposed in order to discuss possible effects in multilayered MEE curved smart structures. In the new benchmarks, first, three circular frequencies for several half-wave number couples and for different thickness ratios are proposed. Thickness vibration modes are shown in terms of displacements, stresses, electric displacement and magnetic induction along the thickness direction. These new benchmarks are useful to understand the free vibration behavior of MEE curved smart structures, and they can be used as reference for researchers interested in the development of of 2D/3D MEE models. Full article

Background/Objectives: Neuromodulation is under investigation as a possibly effective add-on therapy in Alzheimer’s disease (AD). While transcranial pulse stimulation (TPS) has shown positive short-term effects, long-term effects have not yet been fully explored. This study aims to evaluate the long-term feasibility, safety, and potential cognitive benefits of TPS over one year in patients with Alzheimer’s disease, focusing on domains such as memory, speech, orientation, visuo-construction, and depressive symptoms. Methods: We analyzed preliminary data from the first ten out of thirty-five patients enrolled in a prospective TPS study who completed one year of follow-up and were included in a dedicated long-term database. The protocol consisted of six initial TPS sessions over two weeks, followed by monthly booster sessions delivering 6000 pulses each for twelve months. Patients underwent regular neuropsychological assessments using the Alzheimer Disease Assessment Scale (ADAS), Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Beck Depression Inventory (BDI-II). All adverse events (AEs) were documented and monitored throughout the study. Results: Adverse events occurred in less than 1% of stimulation sessions and mainly included mild focal pain or transient unpleasant sensations, as well as some systemic behavioral or vigilance changes, particularly in patients with underlying medical conditions, with some potentially related to the device’s stimulation as adverse device reactions (ADRs). Cognitive test results showed significant improvement after the initial stimulation cycle (ADAS total improved significantly after the first stimulation cycle (M_pre = 28.44, M_post = 18.56; p = 0.001, d = 0.80, 95% CI (0.36, 1.25)), with stable scores across all domains over one year. Improvements were most notable in memory, speech, and mood. Conclusions: TPS appears to be a generally safe and feasible add-on treatment for AD, although careful patient selection and monitoring are advised. While a considerable number of participants were lost to follow-up for various reasons, adverse events and lack of treatment effect were unlikely primary causes. A multifocal stimulation approach (F-TOP2) is proposed to enhance effects across more cognitive domains. Full article

Titanium is widely recognized as an interesting material for electrodes due to its excellent corrosion resistance, mechanical strength, and biocompatibility. However, further functionalization is often necessary to impart advanced interfacial properties, such as selective ion transport or stimuli responsiveness. In this context, the integration of smart polymers, such as poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA)—noted for its dual pH- and thermo-responsive behavior—has emerged as a promising approach to tailor surface properties for next-generation devices. This work compares two covalent immobilization strategies for PDMAEMA on titanium: the “graft-to” method, involving the attachment of pre-synthesized polymer chains, and the “graft-from” method, based on surface-initiated polymerization. The resulting materials were characterized with size exclusion chromatography (SEC) for molecular weight, Fourier-transform infrared spectroscopy (FTIR) for chemical structure, scanning electron microscopy (SEM) for surface morphology, and contact angle measurements for wettability. Electrochemical impedance spectroscopy and polarization studies were used to assess electrochemical performance. Both strategies yielded uniform and stable coatings, with the mode of grafting influencing both surface morphology and functional stability. These findings provide valuable insights into the development of adaptive, stimuli-responsive titanium-based interfaces in advanced electrochemical systems. Full article

Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field. Traditional radiation mapping methods often fail to accurately capture these complex variations, making the rapid and precise localization of radiation sources a significant challenge in emergency response scenarios. To address this issue, based on standard Gaussian process regression (GPR) models that primarily utilize a single Gaussian kernel to reflect the inverse-square law in free space, a novel multi-kernel Gaussian process regression (MK-GPR) model is proposed for high-fidelity radiation mapping in environments with physical obstructions. MK-GPR integrates two additional kernel functions with adaptive weighting: one models the attenuation characteristics of intervening materials, and the other captures the energy-dependent penetration behavior of radiation. To validate the model, gamma-ray distributions in complex, shielded environments were simulated using GEometry ANd Tracking 4 (Geant4). Compared with conventional methods, including linear interpolation, nearest-neighbor interpolation, and standard GPR, MK-GPR demonstrated substantial improvements in key evaluation metrics, such as MSE, RMSE, and MAE. Notably, the coefficient of determination (R²) increased to 0.937. For practical deployment, the optimized MK-GPR model was deployed to an RK-3588 edge computing platform and integrated into a mobile robot equipped with a NaI(Tl) detector. Field experiments confirmed the system’s ability to accurately map radiation fields and localize gamma sources. When combined with SLAM, the system achieved localization errors of 10 cm for single sources and 15 cm for dual sources. These results highlight the potential of the proposed approach as an effective and deployable solution for radiation source investigation in post-disaster environments. Full article

Self-regulated learning (SRL) has been widely recognized as a critical skill for academic success in online and blended learning contexts. However, many students experience difficulty in effectively applying SRL strategies in the absence of structured instructional guidance. To address this challenge, this study developed and implemented a learning management system integrated with SRL support (SRL-LMS), specifically designed for the online component of a blended learning course. The SRL-LMS consisted of two sections: a conventional course content section and a SRL training section designed to support students in applying SRL strategies. A quasi-experimental design was adopted with 69 college students assigned to either an experimental group, with access to both course and SRL sections, or a control group, which accessed only the course section. Results indicated that students in the experimental group reported higher levels of self-regulation and showed more frequent and diverse application of SRL strategies compared to the control group. In terms of academic performance, the experimental group performed significantly better than the control group on the first exam, though no significant difference was observed on the second exam. These results highlight the effectiveness of structured SRL interventions within digital learning platforms for improving students’ self-regulatory behaviors. Future implementations should address cognitive load and incorporate strategic approaches to sustain student motivation. This study advances current SRL intervention designs and offers valuable insights for educators and instructional designers aiming to integrate targeted SRL supports in online and blended learning environments. Full article

This study presents a comprehensive analysis of CO₂ trapping mechanisms in subsurface reservoirs by integrating numerical reservoir simulations, geochemical modeling, and machine learning techniques to enhance the design and evaluation of carbon capture and storage (CCS) strategies. A two-dimensional reservoir model was developed to simulate CO₂ injection dynamics under realistic geomechanical and geochemical conditions, incorporating four primary trapping mechanisms: residual, solubility, mineralization, and structural trapping. To improve computational efficiency without compromising accuracy, advanced machine learning models, including random forest, gradient boosting, and decision trees, were deployed as smart proxy models for rapid prediction of trapping behavior across multiple scenarios. Simulation outcomes highlight the critical role of hysteresis, aquifer dynamics, and producer well placement in enhancing CO₂ trapping efficiency and maintaining long-term storage stability. To support the credibility of the model, a qualitative validation framework was implemented by comparing simulation results with benchmarked field studies and peer-reviewed numerical models. These comparisons confirm that the modeled mechanisms and trends align with established CCS behavior in real-world systems. Overall, the study demonstrates the value of combining traditional reservoir engineering with data-driven approaches to optimize CCS performance, offering scalable, reliable, and secure solutions for long-term carbon sequestration. Full article

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article

This paper proposes a pseudo full-channel signal integrity (SI) simulation method tailored for high-bandwidth memory (HBM) interconnects. In this approach, real interconnect models are applied to selected portions of the channel, while the remaining sections are replaced with synchronized current loads that emulate the electrical behavior of actual signal transmission. This technique enables accurate modeling of the HBM interface under full-channel parallel data transfer conditions. In addition to the simulation methodology itself, this study focuses on three specific implementation schemes for the synchronized current loads and explores their practical applications. Comparative analysis demonstrates the necessity and effectiveness of using synchronized current loads as substitutes for real transmission loads, offering a viable and efficient solution for SI analysis in HBM interconnect systems. Full article

Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article

The operational and indoor environmental quality of buildings has a significant impact on global energy consumption and human quality of life. One of the key directions for improving building performance is the optimization of building control systems. In modern buildings, the presence of numerous actuators and monitoring points makes manually designed control algorithms potentially suboptimal due to the complexity and human factors. To address this challenge, model predictive control based on artificial neural networks can be employed. The advantage of this approach lies in the model’s ability to learn and understand the dynamic behavior of the building from monitoring datasets. It should be noted that the effectiveness of such control models is directly dependent on the forecasting accuracy of the neural networks. In this study, we adapt neural network architectures such as GRU and TCN for use in the context of building model predictive control. Furthermore, we propose a novel hybrid architecture that combines the strengths of recurrent and convolutional neural networks. These architectures were compared using real monitoring data collected with a custom-developed device introduced in this work. The results indicate that, under the given experimental conditions, the proposed hybrid architecture outperforms both GRU and TCN models, particularly when processing large sequential input vectors. Full article

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article

The Furuta pendulum is a well-known benchmark in the field of underactuated mechanical systems due to its reduced number of control inputs compared to its degrees of freedom, and richly nonlinear behavior. This work addresses the challenge of accurately modeling and controlling such a system without relying on traditional linearization techniques. In contrast to the common approach based on Lagrangian analytical modeling and state–space linearization, we propose a methodology that integrates a high-fidelity multibody model developed in Simscape Multibody (MATLAB), capturing the complete nonlinear dynamics of the system. The multibody model includes all geometric, inertial, and joint parameters of the physical hardware and interfaces directly with Simulink, enabling realistic simulation and control integration. To validate the physical fidelity of the multibody model, we perform a frequency-domain analysis of the pendulum’s natural free response. The dominant vibration frequency extracted from the simulation is compared with the theoretical prediction, demonstrating accurate capture of the system’s inertial and dynamic properties. This validation strategy strengthens the reliability of the model as a digital twin. The classical analytical formulation is provided to validate the simulation model and serve as a comparative framework. This dual modeling strategy allows for benchmarking control strategies against a trustworthy nonlinear digital twin of the Furuta pendulum. Preliminary experimental results using a physical prototype validate the feasibility of the proposed approach and set the foundation for future work in advanced nonlinear control design using the multibody representation as a digital validation tool. Full article

Accurate evaluation of fish feeding intensity is crucial for optimizing aquaculture efficiency and the healthy growth of fish. Previous methods mainly rely on single-modal approaches (e.g., audio or visual). However, the complex underwater environment makes single-modal monitoring methods face significant challenges: visual systems are severely affected by water turbidity, lighting conditions, and fish occlusion, while acoustic systems suffer from background noise. Although existing studies have attempted to combine acoustic and visual information, most adopt simple feature-level fusion strategies, which fail to fully explore the complementary advantages of the two modalities under different environmental conditions and lack dynamic evaluation mechanisms for modal reliability. To address these problems, we propose the Adaptive Cross-modal Attention Fusion Network (ACAF-Net), a cross-modal complementarity learning framework with a two-stage attention fusion mechanism: (1) a cross-modal enhancement stage that enriches individual representations through Low-rank Bilinear Pooling and learnable fusion weights; (2) an adaptive attention fusion stage that dynamically weights acoustic and visual features based on complementarity and environmental reliability. Our framework incorporates dimension alignment strategies and attention mechanisms to capture temporal–spatial complementarity between acoustic feeding signals and visual behavioral patterns. Extensive experiments demonstrate superior performance compared to single-modal and conventional fusion approaches, with 6.4% accuracy improvement. The results validate the effectiveness of exploiting cross-modal complementarity for underwater behavioral analysis and establish a foundation for intelligent aquaculture monitoring systems. Full article

This comprehensive review explores the expansive design space of network architectures and their significant impact on the mechanical and viscoelastic properties of hydrogel systems. By examining the intricate relationships between molecular structure, network connectivity, and resulting bulk properties, we provide critical insights into rational design strategies for tailoring hydrogel mechanics for specific applications. Recent advances in sequence-defined crosslinkers, dynamic covalent chemistries, and biomimetic approaches have significantly expanded the toolbox for creating hydrogels with precisely controlled viscoelasticity, stiffness, and stress relaxation behavior—properties that are crucial for biomedical applications, particularly in tissue engineering and regenerative medicine. Full article