Search Results (46,931)

The integration of mechanics-based analysis and materials design procedures has become central to the development of multi-material structures with tailored mechanical and dynamic performance. In this study, the dynamic and flexural behaviour of multi-material FDM sandwich beams composed of PETG face sheets and an ABS core is experimentally investigated. Seven different infill patterns Grid, Line, Wavy, Honeycomb, Gyroid, Cubic, and Triangle were implemented in the core layer to assess their influence on damping and natural frequency behaviour. Experimental modal analysis was performed using impact testing to identify the first three vibration modes. Natural frequencies were extracted from Frequency Response Functions (FRFs), and modal damping ratios were determined using the half-power bandwidth method. The reliability of the damping results was evaluated through statistical analysis. Additionally, quasi-static three-point bending tests were conducted to assess flexural strength and load-carrying capacity. The results demonstrate that infill topology has a significant impact on both dynamic and mechanical responses. In particular, geometrically complex infill patterns exhibit enhanced stiffness, higher natural frequencies, and improved damping performance. Among the investigated designs, the Triangle infill exhibited the highest natural frequency values across the first three vibration modes (f₁ ≈ 24.910 Hz, f₂ ≈ 162.609 Hz, f ≈ 466.595 Hz), indicating its superior stiffness characteristics. In terms of damping behaviour, the Cubic infill showed the highest loss factor in the first vibration mode (0.0426), while the Line and Gyroid patterns exhibited the highest damping in the second (0.0439) and third modes (0.0354), respectively. Moreover, the force–displacement results revealed that the Triangle infill exhibited the highest load-bearing capacity, further confirming its superior structural stiffness among the investigated designs (SEA = 110.83 J/kg). These findings highlight the potential of multi-material FDM for designing polymer-based sandwich structures with tailored vibration and energy dissipation characteristics. Full article

Floods are among the most devastating natural hazards, intensified by climate change and rapid urbanization. This study introduces a novel deep learning framework, Transfer Learning-Enhanced FEDformer (TLE-FEDformer), designed for accurate and temporally consistent flood inundation mapping. The framework integrates pre-trained Xception backbones for robust multi-sensor feature extraction from Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery, a cross-modal fusion module to align heterogeneous modalities, and the Frequency Enhanced Decomposed Transformer (FEDformer) for efficient frequency-domain temporal modeling. This architecture effectively captures long-range dependencies and flood dynamics including onset, peak, duration, and recession, while addressing challenges such as cloud contamination, speckle noise, and limited labeled data. Comprehensive experiments demonstrate superior performance, achieving an overall accuracy of 98.12%, an F1-score of 98.55%, and an Intersection over Union (IoU) of 97.38%, outperforming baselines including Convolutional Neural Networks, Capsule Networks, and transfer learning alone. Ablation studies validate the contributions of each component, while sensitivity analyses confirm robustness across hyperparameters. Uncertainty quantification via Monte Carlo dropout highlights high confidence in core flooded regions. Preliminary generalization tests on independent events yield IoU > 94%, indicating strong transferability. TLE-FEDformer advances operational flood monitoring by providing reliable, scalable, and temporally consistent mapping from multi-sensor remote sensing data. This approach offers significant potential for real-time disaster response, early warning systems, and damage assessment in flood-prone regions worldwide. Full article

Urban areas require context-specific bioenergy solutions to advance toward circular and sustainable energy systems. In Bogotá, urban pruning and grass-cutting residues constitute a relatively stable biomass stream; however, the absence of district-scale valorization infrastructure leads to their direct disposal in landfill. This study develops and applies a GIS-based planning methodology to support the territorial design of a small-scale anaerobic digestion plant using urban green waste. In this study, “small-scale” is understood as an early-stage urban facility concept compatible with the available pruning stream of approximately 1200–1300 t/month of valorizable biomass, corresponding only to an order-of-magnitude energy range of a few hundred kW${}_e$/kW${}_t$, rather than to a final engineering design. The approach integrates official geospatial data with logistical, environmental, and institutional criteria to characterize biomass availability and evaluate location alternatives under real urban constraints. A continuous location model based on the Weber problem is first applied to estimate a theoretical lower bound of spatial effort, using public schools weighted by enrollment as a proxy for sensitive urban demand. Subsequently, a GIS-assisted Analytic Hierarchy Process (AHP) is implemented to incorporate environmental exclusions, territorial compatibility, and the operational structure of exclusive waste service areas. Results show that the optimal geometric location diverges from the territorially feasible alternative once environmental restrictions and biomass supply coherence are explicitly considered. The findings highlight that urban bioenergy infrastructure planning is governed less by pure spatial efficiency than by the integration of supply, demand, and institutional constraints. The proposed methodology provides a reproducible decision-support tool for urban bioenergy planning and contributes to sustainable waste management, circular economy strategies, and local energy resilience in cities of the Global South. Full article

The structural reliability of pistons operating under severe thermo-mechanical loading strongly depends on the properties of the selected Al–Si alloy. This study presents an integrated experimental–numerical investigation of hypereutectic Al–Si alloys intended for piston applications. Phase constitution and silicon morphology were characterized by metallography and X-ray diffraction, while tensile testing provided mechanical properties for finite element modeling. The experimentally determined parameters were implemented in a three-dimensional piston model to evaluate stress distribution, deformation, and safety margins under maximum combustion pressure and maximum engine speed. The simulations revealed maximum von Mises stresses up to 150 MPa, with inter-alloy differences below 0.3%, indicating geometry-dominated stress behavior. The maximum displacement did not exceed 76 µm, varying by approximately 3% between alloys. In contrast, the minimum factor of safety ranged from 1.20 to 1.35, showing differences of up to 12%, primarily governed by yield strength and microstructural homogeneity. The results demonstrate that piston performance under combustion-dominated loading is strength-controlled rather than stiffness-controlled. The study provides quantitative insight into the structure–properties–performance relationship of hypereutectic Al–Si alloys and supports informed material selection for preliminary piston design. Full article

With the rapid advancement of deep learning, data acquisition remains a persistent challenge, as model effectiveness heavily relies on the quality and quantity of training data. To address the difficulties of time-consuming and labor-intensive data collection, data augmentation techniques are commonly adopted. In this study, the proposed WACGAN-GP, a Generative Adversarial Network (GAN) architecture, serves as an effective data augmentation tool designed to augment training datasets and bolster model performance. This method integrates the advantages of the Auxiliary Classifier GAN and the Wasserstein GAN with gradient penalty to generate diverse and realistic samples. Experiments were conducted on three image datasets—MNIST, CIFAR-10, and a ship classification dataset—under limited training data conditions. By incorporating WACGAN-GP generated synthetic samples into the original training sets, classification performance was evaluated in both balanced and imbalanced scenarios. The results demonstrate that the proposed GAN-based approach significantly improves recognition accuracy and outperforms conventional augmentation methods, such as horizontal and vertical flipping. Full article

In the transition toward the circular economy and high-rate manufacturing, thermoplastic composites (TPCs) are increasingly outperforming conventional thermosets due to their superior fracture toughness, recyclability, and rapid processing capabilities. Among available joining techniques, fusion bonding stands as the main mechanism for structural integration, as it bypasses the fundamental limitations of traditional assembly: the weight penalties and stress concentrations inherent in mechanical fastening, as well as the long cycle times and interfacial weaknesses often associated with adhesive bonding. This paper provides a comprehensive evaluation of welded TPC joints through a dual-methodological approach: a historical narrative review tracing the evolution of fusion bonding principles, and an in-depth literature review of 25 key articles published since 2015. The analysis focuses on the intersection of experimental characterization—quantifying interfacial strength and fracture energy—and numerical modeling techniques, such as Cohesive Zone Modeling (CZM) and progressive damage analysis. By categorizing recent advancements into specific thematic pillars, this study correlates process-induced phenomena with macro-scale mechanical performance and virtual predictive accuracy. The findings synthesize decades of foundational knowledge with cutting-edge research trends, highlighting the transition from empirical testing to computational design. This work serves as a roadmap for achieving standardized, high-performance thermoplastic assemblies in safety-critical applications. Full article

This paper presents the design of an event-triggered state estimator for neural sliding mode control (SMC) in networked nonlinear Markov jump systems (MJSs) with incomplete mode information. To improve communication efficiency, a simulated annealing-based event-triggering mechanism is introduced for observer design over the network. This mechanism is enhanced by a neural-based adaptive compensator that effectively addresses unknown nonlinearities. An integral sliding surface is then constructed in the state estimation space, serving as the foundation for deriving the sliding mode dynamics and ensuring robustness to uncertainties. In light of uncertain transition rates (TRs), a unified sliding mode controller is developed to accommodate various mode types, ensuring both the reachability condition and the maintenance of sliding motion. The stochastic stability of the system is analyzed for each transition rate scenario. Finally, simulation results are provided to validate the effectiveness and performance of the proposed approach. Full article

To address the limitations of conventional anaerobic digestion (AD), this study explored the integration of microbial electrolysis cells (MECs) with AD to improve biogas production and process stability. While AD is a proven technology for renewable energy recovery from waste, it can suffer from volatile fatty acid accumulation and reduced efficiency. The hybrid MEC–AD system leverages electro-methanogenesis to enhance methane yields and overall system performance. This research evaluated the effects of different electrode materials (graphite plate vs. graphite felt) and applied voltages (0.5 V and 0.7 V) on biogas output, methane content, and operational stability. Results showed that MEC–AD systems significantly outperformed conventional AD, with the highest biogas production reaching 239 ± 3 mL/gVS·d—an increase of up to 162% using graphite felt electrodes at 0.5 V. Internal resistance was also markedly lower with graphite felt (19 Ω/m²) compared to graphite plates (1120 Ω/m²). Furthermore, the pH of the MEC–AD system with graphite felt electrodes was maintained within the optimal range (6.8–7.0), avoiding the acidification seen in control systems. These findings underscore the promise of MEC–AD systems for advancing circular bio-economy initiatives and carbon neutrality. Further work is needed to refine electrode materials and reactor design for improved scalability and efficiency. Full article

Neuromorphic computing seeks to mimic structure and function of biological neural systems to enable energy-efficient, adaptive information processing. A critical component of this paradigm is neural encoding—the translation of analog or digital input data into spike-based representations suitable for spiking neural networks (SNNs). This paper provides a comprehensive overview of major neural encoding schemes used in neuromorphic systems, including rate and temporal encoding, as well as latency, interspike interval, phase, and multiplexed encoding. The purpose of this paper is to explore the use of encoding techniques for deep learning applications. We discussed the underlying principles of spike encoding approaches, their biological inspiration, computational efficiency, power consumption, integrated circuit design and implementation, and suitability for various neuromorphic applications. We also presented our research on a hardware-and-software co-design platform for different encoding schemes and demonstrated their performance. By comparing their strengths, limitations, and implementation challenges, we aim to provide insights that will guide the development of more efficient and application-specific neuromorphic systems. We also performed an encoder performance analysis via Python 3.12 simulations to compare classification accuracies across these spike encoders on three popular image and video datasets. The performance of neural encoders working with both deep neural networks (DNNs) and SNNs is analyzed. Our performance data is largely consistent with the benchmark data on image classification from other papers, while limited performance data on the University of Central Florida’s 101 (UCF-101) video dataset were found in comparable studies on spike encoders. Based on our encoder performance data, the Interspike Interval (ISI) encoder performs well across all three datasets, preserving continuous, detailed spike timing and richer temporal information for standard classification tasks. Further, for image classification, multiplexing encoders outperform other spike encoders as they simplify timing patterns by enforcing phase locking and improve stability and robustness to noise. Within the SNN testbenches, the ISI-Phase encoder achieved the highest accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, surpassing the Time-To-First Spike (TTFS) encoder by 1.9%. On the Canadian Institute For Advanced Research (CIFAR-10) dataset, the ISI encoder achieved the highest accuracy. This ISI encoder had 22.7% higher accuracy than the TTFS encoder on the CIFAR-10 dataset. The ISI encoder performed best on the UCF-101 dataset, achieving 12.7% better performance than the TTFS encoder. Full article

This paper provides a comprehensive survey of Human–Robot Interaction (HRI) for indoor mobile robots operating in human-centered environments such as hospitals, laboratories, offices, and homes. We review interaction modalities—including speech, gesture, touch, visual, and multimodal interfaces—and examine key user experience factors such as usability, trust, and social acceptance. Implementation challenges are discussed, encompassing safety, privacy, and regulatory considerations. Representative case studies, including healthcare and domestic platforms, highlight design trade-offs and integration lessons. We identify critical technical challenges, including robust perception, reliable multimodal fusion, navigation in dynamic spaces, and constraints on computation and power. Finally, we outline future directions, including embodied AI, adaptive context-aware interactions, and standards for safety and data protection. This survey aims to guide the development of indoor mobile robots capable of collaborating with humans naturally, safely, and effectively. Full article

The Fourth Industrial Revolution has accelerated the adoption of advanced digital technologies in construction, with Digital Twin (DT) emerging as a data-driven framework for enhancing project performance, efficiency, and sustainability. Despite these advantages, DT adoption in construction remains limited due to high implementation costs, data integration challenges, and a lack of standardized practices, especially in real-time data utilization and lifecycle management. This study presents a PRISMA-guided systematic literature review of DT applications across the construction lifecycle. The study addresses three main objectives: (1) to analyze DT’s adoption across construction lifecycle phases, (2) to identify barriers and benefits to DT adoption, and (3) to explore research gaps and potential advancements. Peer-reviewed journal articles published between 2003 and 2024 were retrieved from the Scopus and Web of Science databases using structured keyword combinations related to Digital Twin and the built environment. From an initial pool of 3109 records, 53 studies met predefined inclusion criteria. They were analyzed using a lifecycle-oriented thematic coding framework examining application domains, enabling technologies, reported benefits, and implementation constraints. Unlike prior reviews that focus on specific technologies or lifecycle segments, this study provides a lifecycle-wide synthesis of DT maturity across design, construction, operation, and demolition phases. The findings indicate that DT applications are most developed in the design and operation phases, particularly through integration with Building Information Modeling (BIM) and Internet of Things (IoT) systems for simulation, monitoring, and predictive maintenance. In contrast, construction-phase adoption is constrained by challenges in real-time data integration, while demolition and end-of-life applications remain largely conceptual. Overall, current DT implementations are predominantly phase-specific rather than lifecycle-integrated, therefore emphasizing the need for standardized data frameworks, scalable architectures, and cross-phase governance strategies to enable end-to-end lifecycle digitalization in construction. Full article

This study re-evaluates the hydrogeological framework of the Bohemian Cretaceous Basin (Czech Republic), where preliminary surveys unexpectedly identified old groundwater in several springs and abstraction wells. Traditional distinction into a Cenomanian (A) and a single Turonian (C) aquifer failed to explain the observed hydraulic head discrepancies and the occurrence of old groundwater. By integrating the spatial correlations of hundreds of well logs with hydraulic head data, environmental tracers (chemistry, ²H, ³H, ¹³C, ¹⁴C, ¹⁸O, ³⁹Ar, ⁸⁵Kr, CFCs, SF₆, and noble gases), and field measurements, we objectively delineated the hydrostratigraphic architecture of the basin. The results demonstrate three distinct aquifers (A, Ca, and Cb), challenging long-standing interpretations. Several flow systems were identified, with mean residence times of the old water exceeding 300 years. The hydrogeochemical and isotopic evidence confirmed mixing of Holocene groundwater between Ca and Cb aquifers while excluding Last Glacial Period fossil groundwater that is typical of the A aquifer. These findings highlight the necessity of a multi-proxy approach to validate conceptual models in seemingly “well-understood” regions. The newly characterized subdivision of Turonian aquifers is critical for protecting old groundwater resources, optimizing the design of geothermal and water supply wells to prevent hydraulic short-circuiting, and identifying previously unrecognized groundwater resources currently discharging to the Jizera River. Full article

With rising renewable energy penetration, wind–hydrogen coupling systems are key to large-scale green hydrogen production and wind power integration. This paper proposes a multi-timescale power allocation measure and evaluation framework that executes scheduling planning, rolling updates and real-time control sequentially. First, an intelligent power allocation strategy based on model predictive control (MPC) and State of Health (SOH) prediction is designed, which pursues short-term operational efficiency while actively avoiding electrolyzer-damaging conditions. Second, a comprehensive evaluation model integrating dynamic hydrogen value and flexibility value is built, overcoming the limitations of traditional fixed-hydrogen-value and single-system-value evaluations to quantify operational strategy viability more accurately. Simulation results show that the proposed strategy boosts the system’s lifecycle Net Present Value (NPV) by ~12.7% versus conventional strategies, verifying the framework’s effectiveness and superiority in improving wind–hydrogen coupling system performance. Full article

Magnetic nonlinearity and multi-oscillator coupling are commonly employed to improve the performance of energy harvesters. This study integrates both mechanisms to propose a nested dual-oscillator coupled piezoelectric energy harvester with an external magnet, investigating both repulsive and attractive interactions between the two oscillators. The influence of parameters on static/dynamic characteristics and harvesting performance is analyzed. For the repulsive-type harvester, the response under weak excitation is characterized by small-amplitude in-phase motion within potential wells; under strong excitation, one oscillator exhibits a large-amplitude response while the other remains nearly quiescent, and non-periodic responses may occur. Large magnet spacings effectively enhance the bandwidth and output power. The attractive-type harvester primarily shows in-phase periodic motion, though non-periodic behavior may appear under strong excitation. Small moving-magnet spacing combined with large external-magnet spacing can significantly boost bandwidth and power output. In both configurations, performance declines as the external-magnet spacing exceeds an optimal range. The repulsive-type harvester features a wider potential well, performing well under weak excitation, whereas the attractive-type, with vibration modes aligned to the potential well profile, is more likely to generate large-amplitude responses under strong excitation. Experimental results show excellent agreement with simulation data, confirming the reliability of the proposed design. Full article

Background: Self-transcendence has been described in psychological literature as an orientation toward meaning beyond the individual self. However, because the present study does not directly measure transcendence as a psychological construct, we approach it cautiously as a candidate form of internally oriented attention, operationalized through EEG spectral dynamics. Although this construct has been linked to self-referential cognition and large-scale brain systems supporting internal mentation, electrophysiological evidence remains limited, especially in designs that compare spiritually oriented practices with non-spiritual internal-focus controls. Objective: We examined whether a candidate EEG-derived Transcendence Index (TI) is associated with EEG oscillatory activity across canonical frequency bands and whether prayer and relaxation show descriptively distinct oscillatory patterns. Methods: In a within-subject design, participants completed a psychological assessment battery including personality and anxiety measures and underwent EEG recording during two eyes-closed conditions (Prayer vs. Relaxation). Spectral power features were extracted for delta, theta, alpha (low/high), beta (low/high), and gamma (low/high, where signal quality permitted). We examined associations between TI and band-limited activity and explored condition-related oscillatory patterns across Prayer and Relaxation. Given the modest sample size (N = 39), the study was designed and interpreted as exploratory research. Results: Higher TI was associated with an oscillatory profile consistent with internally oriented attention and reflective self-processing, with the most consistent patterns observed in theta–alpha dynamics (and comparatively lower beta contribution). In addition, Prayer and Relaxation showed descriptively distinct oscillatory patterns, suggesting that prayer engages internal-focus processes that may not be fully captured by relaxation alone. Conclusions: These findings support the feasibility of examining internally oriented attentional dynamics potentially related to “transcendence” as a candidate construct through scalp EEG spectral activity. Integrating theory-informed indices with EEG features may help refine psychophysiological models of self-transcendence and inform digitally supported assessment approaches, pending further construct validation. These findings should therefore be interpreted as exploratory preliminary evidence supporting the feasibility of EEG-based indices of internally oriented attention. Full article