Search Results (1,199)

This paper proposes a predefined time transient coordinated control strategy based on an adaptive nonlinear extended state observer (ANLESO) to address the adaptability challenges of mode transition control in power-split hybrid electric vehicles (PS-HEVs). Firstly, building upon a conventional dynamic coordinated control framework, the influence of varying acceleration conditions and external disturbances on mode transition performance is analyzed. To enhance disturbance estimation under both positive and negative as well as large and small errors, an ANLESO is developed, which not only improves the speed and accuracy of disturbance observation but also guarantees symmetric convergence performance with respect to estimation errors. Subsequently, a predefined time feedback controller is developed based on the theory of predefined time control. Theoretical stability analysis demonstrates that the convergence time of the system is independent of the initial state and can be guaranteed within a predefined time. Finally, the feasibility and superiority of the proposed control strategy are validated through Hardware-in-the-Loop (HIL) testing and vehicle experimentation. The results show that, compared with PID control based on a linear expansion state observer, the proposed strategy reduces the mode transition time by 45.7% and mitigates drivability shock by 59.2%. Full article

Urban noise levels that exceed the World Health Organization (WHO) recommendations have become a growing concern due to their adverse effects on public health. In Bogotá, Colombia, studies by the District Department of Environment (SDA) indicate that 11.8% of the population is exposed to noise levels above the WHO limits. This research aims to identify and categorize environmental noise sources in real time using an embedded intelligent system. A total of 657 labeled audio clips were collected across eight classes and processed using a 60/20/20 train–validation–test split, ensuring that audio segments from the same continuous recording were not mixed across subsets. The system was implemented on a Raspberry Pi 2W equipped with a UMIK-1 microphone and powered by a 90 W solar panel with a 12 V battery, enabling autonomous operation. The TinyML-based model achieved precision and recall values between 0.92 and 1.00, demonstrating high performance under real urban conditions. Heavy vehicles and motorcycles accounted for the largest proportion of classified samples. Although airplane-related events were less frequent, they reached maximum sound levels of up to 88.4 dB(A), exceeding the applicable local limit of 70 dB(A) by approximately 18 dB(A) rather than by percentage. In conclusion, the results demonstrate that on-device TinyML classification is a feasible and effective strategy for urban noise monitoring. Local inference reduces latency, bandwidth usage, and privacy risks by eliminating the need to transmit raw audio to external servers. This approach provides a scalable and sustainable foundation for noise management in smart cities and supports evidence-based public policies aimed at improving urban well-being. This work presents an introductory and exploratory study on the application of TinyML for acoustic environmental monitoring, aiming to evaluate its feasibility and potential for large-scale implementation. Full article

Methane pyrolysis produces hydrogen (H₂) with solid carbon black as a co-product, eliminating direct CO₂ emissions and enabling a low-carbon supply when combined with renewable or low-carbon heat sources. In this study, we propose a hybrid geothermal pyrolysis configuration in which an enhanced geothermal system (EGS) provides base-load preheating and isothermal holding, while either electrical or solar–thermal input supplies the final temperature rise to the catalytic set-point. The work addresses four main objectives: (i) integrating field-scale geothermal operating envelopes to define heat-integration targets and duty splits; (ii) assessing scalability through high-pressure reactor design, thermal management, and carbon separation strategies that preserve co-product value; (iii) developing a techno-economic analysis (TEA) framework that lists CAPEX and OPEX, incorporates carbon pricing and credits, and evaluates dual-product economics for hydrogen and carbon black; and (iv) reorganizing state-of-the-art advances chronologically, linking molten media demonstrations, catalyst development, and integration studies. The process synthesis shows that allocating geothermal heat to the largest heat-capacity streams (feed, recycle, and melt/salt hold) reduces electric top-up demand and stabilizes reactor operation, thereby mitigating coking, sintering, and broad particle size distributions. High-pressure operation improves the hydrogen yield and equipment compactness, but it also requires corrosion-resistant materials and careful thermal-stress management. The TEA indicates that the levelized cost of hydrogen is primarily influenced by two factors: (a) electric duty and the carbon intensity of power, and (b) the achievable price and specifications of the carbon co-product. Secondary drivers include the methane price, geothermal capacity factor, and overall conversion and selectivity. Overall, geothermal-assisted methane pyrolysis emerges as a practical pathway to turquoise hydrogen, if the carbon quality is maintained and heat integration is optimized. The study offers design principles and reporting guidelines intended to accelerate pilot-scale deployment. Full article

Aim: This study analyzed the concurrent validity of the Optojump infrared photocell system for estimating lower limb peak power by comparing it with the 15 s Wingate anaerobic test (WAnT) and examining relationships with sprint performance indicators. Methods: Twelve physically active university students (ten males, two females; age: 23.39 ± 1.47 years; body mass: 73.08 ± 9.19 kg; height: 173.67 ± 6.97 cm; BMI: 24.17 ± 1.48 kg·m⁻²) completed a cross-sectional validation protocol. Participants performed WAnT on a calibrated Monark ergometer (7.5% body weight for males, 5.5% for females), 30 s continuous jump tests using the Optojump system (Microgate, Italy), and 30 m sprint assessments with 10 m and 20 m split times. Peak power was expressed in absolute (W), relative (W·kg⁻¹), and allometric (W·kg^−0.67) terms. Results: Thirty-second continuous jump testing produced systematically higher peak power values across all metrics (p < 0.001). Mean differences indicated large effect sizes: relative power (Cohen’s d = 0.99; 18.263 ± 4.243 vs. 10.99 ± 1.58 W·kg⁻¹), absolute power (d = 0.86; 1381.71 ± 393.44 vs. 807.28 ± 175.45 W), and allometric power (d = 0.79). Strong correlations emerged between protocols, with absolute power showing the strongest association (r = 0.842, p < 0.001). Linear regression analysis revealed that 30 s continuous jump-derived measurements explained 71% of the variance in Wingate outcomes (R² = 0.710, p < 0.001). Sprint performance showed equivalent predictive capacity for both tests (Wingate: R² = 0.66; 30 s continuous jump: R² = 0.67). Conclusions: The Optojump infrared photocell system provides a valid and practical alternative to laboratory-based ergometry for assessing lower limb anaerobic power. While it systematically overestimates absolute values compared with the Wingate anaerobic test, its strong concurrent validity (r > 0.80), large effect sizes, and equivalent predictive ability for sprint performance (R² = 0.66–0.71) confirm its reliability as a field-based assessment tool. These findings underscore the importance of sport-specific, weight-bearing assessment technologies in modern sports biomechanics, providing coaches, practitioners, and clinicians with a feasible method for monitoring performance, talent identification, and training optimization. The results further suggest that Optojump-based protocols can bridge the gap between laboratory precision and ecological validity, supporting both athletic performance enhancement and injury prevention strategies. Full article

Currently, the customs inspection of wood-boring pests in timber still primarily relies on manual visual inspection, which involves observing insect holes on the timber surface and splitting the timber for confirmation. However, this method has significant drawbacks such as long detection time, high labor cost, and accuracy relying on human experience, making it difficult to meet the practical needs of efficient and intelligent customs quarantine. To address this issue, this paper develops a rapid identification system based on the peristaltic signals of wood-boring pests through the PyQt framework. The system employs a deep learning model with multi-attention mechanisms, namely the Residual Denoising Vision Network (RDVNet). Firstly, a LabVIEW-based hardware–software system is used to collect pest peristaltic signals in an environment free of vibration interference. Subsequently, the original signals are clipped, converted to audio format, and mixed with external noise. Then signal features are extracted through three cepstral feature extraction methods Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) and input into the model. In the experimental stage, this paper compares the denoising module of RDVNet (de-RDVNet) with four classic denoising models under five noise intensity conditions. Finally, it evaluates the performance of RDVNet and four other noise reduction classification models in classification tasks. The results show that PNCC has the most comprehensive feature extraction capability. When PNCC is used as the model input, de-RDVNet achieves an average peak signal-to-noise ratio (PSNR) of 29.8 and a Structural Similarity Index Measure (SSIM) of 0.820 in denoising experiments, both being the best among the comparative models. In classification experiments, RDVNet has an average F1 score of 0.878 and an accuracy of 92.8%, demonstrating the most excellent performance. Overall, the application of this system in customs timber quarantine can effectively improve detection efficiency and reduce labor costs and has significant practical value and promotion prospects. Full article

This study proposes the first ground motion prediction equation (GMPE) for the parameter I_Np, an intensity measure based on the spectral shape. A Machine Learning Algorithm based on Support Vector Machines (SVMs) was employed due to its robustness towards outliers, which is a key advantage over ordinary linear regression. I_Np also offers a more robust measure of the ground motion intensity than the traditionally used spectral acceleration at the first mode of vibration of the structure Sa(T₁). The SVM algorithm, configured for regression (SVR), was applied to derive the prediction coefficients of I_Np for diverse vibration periods. Furthermore, the complete dataset was analyzed to develop a unified, generalized expression applicable across all the periods considered. To validate the model’s reliability and its ability to generalize, a cross-validation analysis was performed. The results from this rigorous validation confirm the model’s robustness and demonstrate that its predictive accuracy is not dependent on a specific data split. The numerical results show that the newly developed GMPE reveals high predictive accuracy for periods shorter than 3 s and acceptable accuracy for longer periods. The generalized equation exhibits an acceptable coefficient of determination and Mean Squared Error (MSE) for periods from 0.1 to 5 s. This work not only highlights the powerful potential of machine learning in seismic engineering but also introduces a more sophisticated and effective tool for predicting ground motion intensity. Full article

Strong coupling between plasmons and excitons in two-dimensional materials offers a powerful route for manipulating light–matter interactions at the nanoscale, with potential applications in quantum optics, nanophotonics, and polaritonic devices. Here, we design and numerically investigate a low-loss coupling platform composed of a silver nanocuboid dimer and monolayer of WS₂ using finite-difference time-domain (FDTD) simulations. The dimer supports a subradiant bonding plasmonic mode with a linewidth as narrow as 60 meV. This ultralow-loss feature enables strong coupling with monolayer WS₂ at relatively low coupling strengths. FDTD simulations combined with the coupled oscillator model reveal a Rabi splitting of ~60 meV and characteristic anticrossing behavior in the dispersion relations. Importantly, we propose and demonstrate two independent tuning mechanisms—loss engineering through nanocuboid tilt and coupling-strength modulation through the number of WS₂ layers—that enable transitions between weak and strong coupling regimes. This work provides a low-loss and tunable plasmonic platform for studying and controlling strong light–matter interactions in plasmon-two-dimensional material systems, with potential for room-temperature quantum and optoelectronic devices. Full article

Detecting small split pins on transmission lines poses significant challenges, including low accuracy in complex backgrounds and slow inference speeds. To address these limitations, this study proposes a novel two-stage collaborative detection framework. The first stage utilizes a Yolo11x-based model to localize and crop components containing split pins from high-resolution images. This procedure transforms the difficult small-object detection problem into a more manageable, conventional detection task on a simplified background. For the second stage, a new high-performance detector, Mamba-YOLO-SPDC, is introduced. This model enhances the Yolo11 backbone by incorporating a Vision State Space (VSS) block, which leverages Mamba—a State Space Model (SSM) with linear computational complexity—to efficiently capture global context. Furthermore, a Space-to-Depth Convolution (SPD-Conv) module is integrated into the neck to mitigate the loss of fine-grained feature information during downsampling. Experimental results confirm the efficacy of the two-stage strategy. On the cropped dataset, the Mamba-YOLO-SPDC model achieves a mean Average Precision (mAP) of 61.9%, a 238% improvement over the 18.3% mAP obtained by the baseline Yolo11s on the original images. Compared to the conventional SAHI framework, the proposed method provides superior accuracy with a substantial increase in inference speed. This work demonstrates that the ‘localize first, then detect’ strategy, powered by the Mamba-YOLO-SPDC model, offers an effective balance between accuracy and efficiency for small object detection. Full article

The pore structure of SCC and of all cement-based materials plays a crucial role on the mechanical and durability characteristics of the material. The pore structure is affected by mix design, water–binder ratio and the incorporation of SCM and/or nanomaterials, all of which can improve mechanical and durability characteristics by decreasing porosity. Computed tomography (CT) is a powerful, non-destructive imaging technique to investigate the internal pore structure of concrete. The main advantage compared to other investigation techniques used to assess the pore structure is in terms of sample size. More specifically, industrial CT can be used to scan large concrete samples and be able to assess the internal pore structure without damaging the specimen. CT provides accurate measurements of pore diameters, volumes and shapes and enables the assessment of the total porosity. The paper presents the results of an experimental program on the characterization of self-compacting concrete (SCC) at a very long age (7 years) in terms of static and dynamic elastic properties and compressive and splitting tensile strength, all of which are correlated with the internal pore structure assessed via the use of an industrial Nikon XTH 450 CT. The results highlight the influence of pore volume, maximum pore diameter and sphericity on the strength and elastic properties of SCC at the age of 7 years. Both the compressive strength and the static modulus of elasticity values tend to decrease with the increase in the internal total porosity, with stronger influence on the former. Full article

Additive manufacturing involves numerous independent parameters, often leading to inconsistent print quality and necessitating costly trial-and-error approaches to optimize input variables. Machine learning offers a solution to this non-linear problem by predicting optimal printing parameters from a minimal set of experiments. Using Fused Deposition Modeling (FDM) as a case study, this work develops a machine learning-powered process to predict porosity defects. Specimens in two geometrical scales were 3D-printed and CT-scanned, yielding raw datasets of grayscale images. A machine learning image classifier was trained on the small-cube dataset (~2200 images) to distinguish exploitable images from defective ones, averaging over 97% accuracy and correctly classifying more than 90% of the large-cube exploitable images. The developed preprocessing scripts extracted porosity features from the exploitable images. A repeatability study analyzed three replicate specimens printed under identical conditions, and quantified the intrinsic process variability, showing an average porosity standard deviation of 0.47% and defining an uncertainty zone for quality control. A multi-layer perceptron (MLP) was independently trained on 1709 data points derived from the small-cube dataset and 3746 data points derived from the large-cube dataset. Its accuracy was 54.4% for the small cube and increased to 77.6% with the large-cube dataset, due to the larger sample size. A rigorous grouped k-fold cross-validation protocol, relying on splitting data per cube, strengthened the ML algorithms against data leakage and overfitting. Finally, a dimensional scalability study further assessed the use of the pipeline for the large-cube dataset and established the impact of geometrical scaling on defect formation and prediction in 3D-printed parts. Full article

In laser wireless power transmission systems, the powersphere serves as a spherical enclosed receiver that performs photoelectric conversion, achieving uniform light distribution within the cavity through infinite internal light reflection. However, in practical applications, the high level of light absorption displayed by photovoltaic cells leads to significant disparities in light intensity between directly irradiated regions and reflected regions on the inner surface of the powersphere, resulting in poor light uniformity. One approach aimed at addressing this issue uses a spectroscope to split the incident beam into multiple paths, allowing the direct illumination of all inner surfaces of the powersphere and reducing the light intensity difference between direct and reflected regions. However, experimental results indicate that light transmission through lenses introduces power losses, leading to improved uniformity but reduced output power. To address this limitation, this study proposes a method that utilizes multiple incident laser beams combined with a centrally positioned spherical reflector within the powersphere. A wireless power transmission system model was developed using optical simulation software, and the uniformity of the intracavity light field in the system was analyzed through simulation. To validate the design and simulation accuracy, an experimental system incorporating semiconductor lasers, spherical mirrors, and a powersphere was constructed. The data from the experiments aligned with the simulation results, jointly confirming that integrating a spherical reflector and distributed incident lasers enhances the uniformity of the internal light field within the powersphere and improves the system’s efficiency. Full article

The aviation wet clutch, as an indispensable component in helicopters, is particularly vulnerable to performance deterioration due to temperature rises, especially in high-power-density and high-torque conditions. Consequently, a comprehensive thermal-fluid-dynamic model, coupled with a dynamic model considering the spline friction and split spring and a thermal model considering the heat transfer parameters in friction pair gaps, was proposed in this work. The effects of operating parameters on the transient thermal behaviors of friction discs were investigated. A rise in rotation speed from 2000 rpm to 2400 rpm facilitates a 10.1% increase in the maximum temperature of the friction discs. An increase in control oil pressure from 1.5 MPa to 1.9 MPa rises the maximum temperature of the friction disc by 19.4%. Moreover, increased lubrication oil flow not only depresses the maximum temperature of the friction disc by 14.5% but also significantly narrows the temperature gradient by 16.7% and improves the temperature field uniformity. Therefore, reasonably increasing lubricant oil flow and decreasing control oil pressure can effectively reduce temperature rises and improve the temperature field uniformity. These results contribute to designing and developing optimal control strategies to enhance the comprehensive performance of helicopter transmission. Full article

Antarctic research stations require reliable low-carbon power under extreme conditions. This study compiles a synchronized PV-meteorological time-series data set on Horseshoe Island (Antarctica) at 30 s, 1 min, and 5 min resolutions and compares four PV module types (monocrystalline, polycrystalline, flexible mono, and semitransparent) under controlled field operation. Model development adopts an interpretable, multi-objective framework: a modified SPEA-2 searches rule sets on the Pareto front that jointly optimize precision and recall, yielding transparent, physically plausible decision rules for operational use. For context, benchmark machine-learning models (e.g., kNN, SVM) are evaluated on the same splits. Performance is reported with precision, recall, and complementary metrics (F1, balanced accuracy, and MCC), emphasizing class-wise behavior and robustness. Results show that the proposed rule-based approach attains competitive predictive performance while retaining interpretability and stability across panel types and sampling intervals. Contributions are threefold: (i) a high-resolution field data set coupling PV output with solar radiation, temperature, wind, and humidity in polar conditions; (ii) a Pareto-front, explainable rule-extraction methodology tailored to small-power PV; and (iii) a comparative assessment against standard ML baselines using multiple, class-aware metrics. The resulting XAI models achieved 92.3% precision and 89.7% recall. The findings inform the design and operation of PV systems for harsh, high-latitude environments. Full article

This paper presents an analytical framework for the channel capacity evaluation of simultaneous wireless information and power transfer (SWIPT)-enabled opportunistic amplify-and-forward (OAF) relaying systems over Rayleigh fading channels. For the SWIPT, we employ a power splitter (PS) at the relay, which splits the received signal into the information transmission and the energy-harvesting parts. By modeling the partial channel state information (P-CSI)-based SWIPT OAF system as an equivalent non-SWIPT OAF configuration, a semi-lower bound and a new upper bound on the ergodic channel capacity are derived. A refined approximation is then obtained by averaging these bounds, yielding a simple yet accurate analytical estimate of the true capacity. Simulation results confirm that the proposed approximations closely track the actual performance across a wide range of signal-to-noise ratios (SNRs) and relay configurations. They further demonstrate that SR-based relay selection provides higher capacity than RD-based selection, primarily due to its direct influence on energy harvesting efficiency at the relay. In addition, diversity advantages manifest mainly as SNR improvements, rather than as gains in diversity order. The proposed framework thus serves as a practical and insightful tool for the capacity analysis and design of SWIPT-enabled cooperative networks, with direct relevance to energy-constrained Internet of Things (IoT) and wireless sensor applications. Full article

This study aimed to monitor the biomechanical development of an artistic swimming duet across a macrocycle through an individualised training approach. Two swimmers (17.5 ± 0.5 years), members of the Los Angeles 2028 National Olympic Project, were assessed in December 2023 (M1) and April 2024 (M2), corresponding to the beginning and the end of the macrocycle. Maximal (Fmax) and mean (Fmean) force in the prone sculling and kick pull action were measured using a 20 s tethered test. Split velocity (vSplit) was assessed in free format based on video recording. Dry-land strength included assessments of internal (IR) and external (ER) shoulder rotation strength of the dominant (D) and non-dominant (ND) limbs, and countermovement jump (CMJ) power. The standard duet choreography was analysed in competition at both time points. Percentage variation (∆%) between swimmers was calculated for M1 vs. M2. Results showed convergence (M1 vs. M2) in Fmean of the sculling (21.6% vs. 9.9%) and kick pull (45.1% vs. 29.1%), accompanied by greater similarity in vSplit (15.9% vs. 15.5%). Further convergence was observed in IR_ND (33.7% vs. 13.9%), ER_D (11.6% vs. 4.4%) and CMJ (7.4% vs. 3.6%). The duet’s competition score increased from 168.9943 to 190.7183 points. It can be concluded that individualised training was useful for the duet to become more homogeneous in in-water strength, in-water kinematics and dryland strength, resulting in improved competitive performance. Full article