Search Results (48,792)

Efficient spectrum utilization is critical for meeting the growing data demands of modern wireless communication networks. Automatic Modulation Classification (AMC) plays a key role in enhancing spectrum efficiency by accurately identifying modulation schemes in received signals—an essential capability for dynamic spectrum allocation and interference mitigation, particularly in cognitive radio (CR) systems. With the increasing deployment of smart edge devices, such as IoT nodes with limited computational and memory resources, there is a pressing need for lightweight AMC models that balance low complexity with high classification accuracy. In this study, we propose a low-complexity, lightweight deep learning (DL) AMC model optimized for resource-constrained edge devices. We introduce a dual-path deep residual shrinkage network (DP-DRSN) with garrote thresholding for effective signal denoising, and we designed a compact hybrid CNN-LSTM architecture comprising only 27,072 training parameters. The proposed model achieved average classification accuracies of 61.20%, 63.78%, and 62.13% on the RML2016.10a, RML2016.10b, and RML2018.01a datasets, respectively, demonstrating a strong balance between model efficiency and classification performance. These results highlight the model’s potential for enabling accurate and efficient AMC on edge devices with limited resources, despite not surpassing state-of-the-art accuracy owing to its deliberate emphasis on computational efficiency. Full article

Location-based games offer innovative approaches for health promotion among older adults, but their effectiveness depends on understanding complex contextual factors beyond technological design. In our study, we aimed to adapt a location-based game in the form of a smartphone application which originally targeted younger people. We employed ethnographic observations in a field test under real-world conditions for identifying the needs and preferences of older adults in this regard. Field notes of one co-creative workshop were analyzed using thematic analysis. Four key contextual factor categories emerged that significantly influenced user engagement: (1) temporal/spatial factors including weather conditions, topography, and traffic safety that impacted screen visibility and cognitive function; (2) virtual-physical orientation challenges requiring high cognitive load to transfer abstract digital maps to real environments; (3) individual factors such as technical competence, mobility levels, and prior accessibility experiences that shaped usage patterns; and (4) social dynamics that provided motivation and peer support while potentially creating exclusionary practices. Successful digital health innovations for older adults require a socio-technical systems approach that addresses environmental conditions, reduces cognitive transfer demands between virtual and physical navigation, leverages social elements while preventing exclusion, and accounts for heterogeneity among older adults as contextually interactive factors rather than merely individual differences. Full article

The rapid development of the Internet of Things (IoT) has enabled the implementation of interconnected intelligent systems in extremely dynamic contexts with limited resources. However, traditional paradigms, such as those using ECC-based heuristics and centralised decision-making frameworks, cannot be modernised to ensure resilience, scalability and security while taking quantum threats into account. In this case, we propose a modular architecture that integrates quantum-inspired cryptography (QI), epistemic uncertainty reasoning, the multiscale blockchain MuReQua, and the quantum-inspired decentralised storage engine (DeSSE) with fragmented entropy storage. Each component addresses specific cybersecurity weaknesses of IoT devices: quantum-resistant communication on epistemic agents that facilitate cognitive decision-making under uncertainty, lightweight adaptive consensus provided by MuReQua, and fragmented entropy storage provided by DeSSE. Tested through simulations and use case analyses in industrial, healthcare and automotive networks, the architecture shows exceptional latency, decision accuracy and fault tolerance compared to conventional solutions. Furthermore, its modular nature allows for incremental integration and domain-specific customisation. By adding reasoning, trust and quantum security, it is possible to design intelligent decentralised architectures for resilient IoT ecosystems, thereby strengthening system defences alongside architectures. In turn, this work offers a specific architectural response and a broader perspective on secure decentralised computing, even for the imminent advent of quantum computers. Full article

Using the newly introduced, by the author, basic complex and hybrid complex rheologic models of the fractional type, the dynamics of a series of mechanical rheologic discrete dynamical systems of the fractional type (RDDSFT) of rheologic oscillators (ROFTs) or creepers (RCFTs), with corresponding independent generalized coordinates (IGCs) and external (IGCEDF) and internal (IGCIGF) degrees of freedom of movement, were studied. Laplace transformations of solutions for independent generalized coordinates (IGCs), as well as external (IGCEDFs) and internal (IGCIDF) degrees of freedom of system dynamics, were determined. On the studied specimens, it was shown that rheologic complex models of the fractional type introduce internal degrees of freedom into the dynamics of rheologic discrete dynamical systems. New challenges appear for mathematicians, such as translating the Laplace transformations of solutions for independent generalized coordinates (LTIGCs) into the time domain. A number of translations of Laplace transformations of solutions into the time domain were performed by the author of this paper. A series of characteristic surfaces of elongations of Laplace transformations of independent generalized coordinates (IGCs) of the dynamics of rheologic discrete dynamic systems of the rheologic oscillator type, i.e., the rheologic creeper type, is shown as a function of fractional order differentiation exponent and Laplace transformation parameter. This manuscript presents the scientific results of theoretical research on the dynamics of rheologic discrete dynamic systems of the fractional type that was conducted using new models and a rigorous mathematical analytical analysis with fractional-order differential equations (DEFOs) and Laplace transformations (LTs). These results can contribute to new experimental research and materials technologies. A separate section presents the theoretical foundations of the methods and methodologies used in this research, without the details that can be found in the author’s previously published works. Full article

In response to the instability fluctuations and erosion characteristic changes in pantograph-catenary system (PCS) arcs induced by humidity variations in an open environment, a single-variable controlled experimental approach based on multi-source data fusion is proposed. This study innovatively establishes a humidity-controlled reciprocating current-carrying arc initiation test platform, integrating digital image processing with the dynamic analysis of multi-physics sensor signals (current, voltage, temperature). The study quantitatively evaluates the arc motion characteristics and the erosion effects on the frictional contact pair under different relative humidity levels (30%, 50%, 70%, and 90%) with a DC power supply (120 V/25 A). The experimental data and analysis reveal that increasing humidity results in higher contact resistance and accumulated arc energy, with arc stability first improving and then decreasing. At low humidity, arc behavior is more intense, and the erosion rate is faster. As humidity increases, the electrode wear transitions from adhesive wear to electrochemical wear, accompanied by copper transfer. The results suggest that the arc stability is optimal at moderate humidity (50% RH), with a peak current-carrying efficiency of 66% and a minimum loss rate of 14.5%. This threshold offers a vital theoretical framework for the optimization and risk assessments of PCS design. Full article

Small and medium-sized enterprises (SMEs) are increasingly targeted by cyber threats but often lack the financial and technical resources to implement advanced security systems. This paper presents HoneyLite, a lightweight and dynamic honeypot-based security solution specifically designed to meet the constraints and cybersecurity needs of SMEs. Unlike traditional honeypots, HoneyLite integrates real-time network traffic analysis with automated malware detection via the VirusTotal API, enabling it to identify a wide range of cyber threats, including TCP scans, FTP/SSH intrusions, ICMP flood attacks, and malicious file uploads. Developed using open-source tools, the system operates with minimal resource overhead and is validated within a simulated virtual environment. It also generates detailed threat reports to support incident analysis and response. By combining affordability, adaptability, and comprehensive threat visibility, HoneyLite offers a practical and scalable solution to help SMEs detect, analyze, and respond to modern cyberattacks in real time. Full article

Methane (CH₄) emissions from the wetlands of the Tibetan Plateau (TP) remain poorly quantified, particularly regarding their historical dynamics, spatial heterogeneity, and response to climate change. This study provides the high-resolution, observation-driven reconstruction of TP wetland CH₄ emissions over the past four decades, integrating a machine learning model with 108 flux measurements from 67 sites. This unique combination of field-based data and fine-scale mapping enables unprecedented accuracy in quantifying both emission intensity and long-term trends. We show that current TP wetlands emit 5.87 ± 1.43 g CH₄ m⁻² yr⁻¹, totaling 97.3 Gg CH₄ yr⁻¹, equivalent to 7.8% of East Asia’s annual wetland emissions. Despite a climate-driven increase in per-unit-area CH₄ fluxes, a 19.8% (8432.9 km²) loss of wetland area since the 1980s has reduced total emissions by 15%, counteracting the enhancement from warming and moisture increases. Our comparative analysis demonstrates that existing land surface models (LSMs) substantially underestimate TP wetland CH₄ emissions, largely due to the inadequate representation of TP wetlands and their dynamics. Projections under future climate scenarios indicate a potential 8.5–21.2% increase in emissions by 2100, underscoring the importance of integrating high-quality, region-specific observational datasets into Earth system models. By bridging the gap between field observations and large-scale modeling, this work advances understanding of alpine wetland–climate feedback, and provides a robust foundation for improving regional carbon budget assessments in one of the most climate-sensitive regions on Earth. Full article

Accurate and stable power-load forecasting is crucial for optimizing generation scheduling and ensuring the economic and secure operation of power grids. To address the issues of low prediction accuracy and poor robustness during abrupt load changes, this study proposes a Bayesian-optimized GCN-BiLSTM-Adaboost model (abbreviated as GCN-BiLSTM-AB). It combines Graph Convolutional Networks (GCN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and a Bayesian-optimized AdaBoost framework. Firstly, the GCN is employed to capture the spatial correlation features of the input data. Then, the BiLSTM is employed to extract the long-term dependencies of the data time series. Finally, the AdaBoost framework is used to dynamically adjust the base learner weights, and a Bayesian method is employed to optimize the weight adjustment process and prevent overfitting. The experiment results on actual load data from a regional power grid show the GCN-BiLSTM-AB outperforms other compared models in prediction error metrics, with $MAE$, $MAPE$, and $RMSE$ values of 1.86, 3.13%, and 2.26, respectively, which improve the prediction robustness during load change periods. Therefore, the proposed method shows that the synergistic effect of spatiotemporal feature extraction and dynamic weight adjustment improves prediction accuracy and robustness, which provides a new forecasting model with high precision and reliability for power system dispatch decisions. Full article

Sound wave attenuation in stratified gas–liquid flows is crucial for pipeline monitoring and leak detection. This study uses computational fluid dynamics (CFD) to investigate acoustic wave propagation in pipelines, employing the Volume of Fluid (VOF) model with interfacial tension and a pressure-based solver. The effects of the gas volume fraction, pressure, frequency, and grid resolution are analyzed, with validation through mesh independence tests. The findings show that incorporating mesh refinement and boundary layer modeling improved attenuation prediction accuracy by approximately 25–30%. High-frequency waves (above 150 Hz) exhibited up to 30% greater attenuation when near-wall viscous effects were resolved, demonstrating the need for fine grid resolution in CFD-based multiphase diagnostic tools. This study highlights the importance of wave frequency, grid refinement, and boundary layer modeling for accurate attenuation predictions, offering insights for the improvement of CFD-based diagnostic tools in multiphase flow systems. Full article

This paper presents a comprehensive dynamical analysis of a nonlinear oscillator subjected to both deterministic and stochastic excitations. Utilizing a diverse suite of analytical tools—including phase portraits, Poincaré sections, Lyapunov exponents, recurrence plots, Fokker–Planck equations, and sensitivity diagnostics—we investigate the transitions between quasi-periodicity, chaos, and stochastic disorder. The study reveals that quasi-periodic attractors exhibit robust topological structure under moderate noise but progressively disintegrate as stochastic intensity increases, leading to high-dimensional chaotic-like behavior. Recurrence quantification and Lyapunov spectra validate the transition from coherent dynamics to noise-dominated regimes. Poincaré maps and sensitivity analysis expose multistability and intricate basin geometries, while the Fokker–Planck formalism uncovers non-equilibrium steady states characterized by circulating probability currents. Together, these results provide a unified framework for understanding the geometry, statistics, and stability of noisy nonlinear systems. The findings have broad implications for systems ranging from mechanical oscillators to biological rhythms and offer a roadmap for future investigations into fractional dynamics, topological analysis, and data-driven modeling. Full article

A miniaturised bionic electronic nose system was developed to solve the problems of expensive equipment and long response time for soil pesticide residue detection. The structure of the bionic electronic nasal cavity is designed based on the spatial structure and olfactory principle of the sturgeon nasal cavity. Through experimental study, the structure of the nasal cavity of the sturgeon was extracted and analyzed. The 3D model of the bionic electronic nasal cavity was constructed and verified by Computational Fluid Dynamics (CFD) simulation. The results show that the gas flow distribution in the bionic chamber is more uniform than that in the ordinary chamber. The airflow velocity near the sensor in the bionic chamber is lower than in the ordinary chamber. The eddy current intensity near the bionic chamber sensor is 2.29 times that of the ordinary chamber, further increasing the contact intensity between odor molecules and the sensor surface and shortening the response time. The 10-fold cross-validation method of K-Nearest Neighbor (K-NN), Random Forest (RF) and Support Vector Machine (SVM) was used to compare the recognition performance of the bionic electronic nasal cavity with that of the ordinary electronic nasal cavity. The results showed that, when the bionic electronic nose detection system identified the concentration of pesticide residues in soil, the recognition rate of the above three recognition algorithms reached 97.3%, significantly higher than that of the comparison chamber. The bionic chamber electronic nose system can improve the detection performance of electronic noses and has a good application prospect in soil pesticide residue detection. Full article

The excessive emission of cadmium (Cd²⁺) poses a serious threat to the aquatic environment due to its high toxicity and bioaccumulation potential. This study constructed three types of vertical-subsurface-flow constructed wetlands configured with iron–carbon–zeolite composite substrates, including an iron–carbon–zeolite constructed wetland (TF-CW), a zeolite–iron–carbon constructed wetland (FT-CW), and an iron–carbon–zeolite mixed constructed wetland (H-CW), to investigate the purification performance and mechanisms of constructed wetlands for cadmium-containing wastewater (0~6 mg/L). The results demonstrated that iron–carbon–zeolite composite substrates significantly enhanced Cd²⁺ removal efficiency (>99%) through synergistic redox-adsorption mechanisms, where the iron–carbon substrate layer dominated Fe-Cd co-precipitation, while the zeolite layer achieved short-term cadmium retention through ion-exchange adsorption. FT-CW exhibited superior NH₄⁺-N removal efficiency (77.66%~92.23%) compared with TF-CW (71.45%~88.05%), while iron–carbon micro-electrolysis effectively inhibited NO₃⁻-N accumulation (<0.1 mg/L). Under cadmium stress, Typha primarily accumulated cadmium through its root systems (>85%) and alleviated oxidative damage by dynamically regulating antioxidative enzyme activity, with the superoxide dismutase (SOD) peak occurring at 3 mg/L Cd²⁺ treatment. Microbial community analysis revealed that iron–carbon substrates promoted the relative abundance of Bacteroidota and Patescibacteria as well as the enrichment of Saccharimonadales, Thauera, and Rhodocyclaceae (genera), enhancing system stability. This study confirms that iron–carbon–zeolite CWs provide an efficient and sustainable technological pathway for heavy metal-contaminated water remediation through multidimensional mechanisms of “chemical immobilization–plant enrichment–microbial metabolism”. Full article

Rotating machinery is essential to modern industrial systems, where rolling bearings play a critical role in ensuring mechanical stability and operational efficiency. Failures in bearings can result in serious safety risks and significant financial losses, which highlights the need for accurate and robust methods for diagnosing bearing faults. Traditional diagnostic methods relying on single-source data often fail to fully leverage the rich information provided by multiple sensors and are more prone to performance degradation under noisy conditions. Therefore, this paper proposes a novel bearing fault diagnosis method based on a multi-level fusion framework. First, the Symmetrized Dot Pattern (SDP) method is applied to fuse multi-source signals into unified SDP images, enabling effective fusion at the data level. Then, a combination of RepLKNet and Bidirectional Gated Recurrent Unit (BiGRU) networks extracts multi-modal features, which are then fused through a cross-attention mechanism to enhance feature representation. Finally, information entropy is utilized to assess the reliability of each feature channel, enabling dynamic weighting to further strengthen model robustness. The experiments conducted on public datasets and noise-augmented datasets demonstrate that the proposed method significantly surpasses other single-source and multi-source data fusion models in terms of diagnostic accuracy and robustness to noise. Full article

This study presents the development of an open-source Driver-in-the-Loop simulation platform, specifically designed to test and analyze advanced automated driving functions. We emphasize the creation of a versatile system architecture that ensures seamless integration and interchangeability of components, supporting diverse research needs. Central to the simulator’s configuration is a hexapod motion platform with six degrees of freedom, chosen through a detailed benchmarking process to ensure dynamic accuracy and fidelity. The simulator employs a half-vehicle cabin, providing an immersive environment where drivers can interact with authentic human–machine interfaces such as pedals, steering, and gear shifters. By projecting complex driving scenarios onto a curved screen, drivers engage with critical maneuvers in a controlled virtual environment. Key innovations include the integration of a motion cueing algorithm and an adaptable, cost-effective open-source framework, facilitating collaboration among researchers and industry experts. The platform enables standardized testing and offers a robust solution for the iterative development and validation of automated driving technologies. Functionality and effectiveness were validated through testing with the ISO lane change maneuver, affirming the simulator’s capabilities. Full article

Monitoring and tracking small moving objects in cluttered environments remain a major challenge for artificial-intelligence-based motion vision systems. This difficulty is not only due to the limited features presented by small objects themselves but also because of the numerous fake features present in complex dynamic environments. Drawing inspiration from the efficient small target motion detection mechanisms in insects’ brains, researchers have developed various visual networks for detecting tiny moving objects within complex natural environments. Although these networks perform well in detecting small-object motion by leveraging motion information, their ability to distinguish true targets from background noise remains severely limited under low-light conditions, where the contrast of small targets drops sharply and they are more easily overwhelmed by false motion in the background. To resolve the aforementioned limitation, this research proposes a new visual neural network. The network achieves effective discrimination between small moving targets and false targets in the background in low-light environments by leveraging the motion information for the targets and the differences in the response gradients between real moving targets and fake objects from the background. The designed network is composed of two main components: a motion perception module and a response gradient analysis module. The motion information perception module is responsible for acquiring the motion and position information for small targets, while the response gradient detection module extracts the response gradients between a tiny object and a background object and integrates the motion information, thereby effectively distinguishing small targets from fake background objects. The experimental results demonstrate that the proposed model can effectively distinguish small targets and suppress background false alarms in low-light environments. Comparisons of the experimental performance show that under a fixed false alarm rate, our model achieved a detection rate of 0.8. In addition, the proposed method recorded an average precision of 0.1 and an average F1-score of 0.1888. In contrast, the highest average precision achieved by the other methods was only 0.0075, and the highest F1-score was 0.0151. These results clearly indicate that our method substantially outperforms previous approaches in both its average precision and F1-score. These results collectively validate the effectiveness and competitiveness of the proposed model in small target detection tasks under low-light conditions. Full article