Scientific Reports最新文献

The use of textile fibre-reinforced composite materials for many alternative applications has significantly increased in the twenty-first century due to their lightweight nature and high strength-to-weight ratio. In this study, false banana fibres were used as reinforcement and unsaturated polyester resin as the matrix. The optimal ratio of fibre to matrix was established through an analysis of physico-mechanical parameters, including tensile, compressive, and flexural strengths, water absorption, and void fraction, utilising Design Expert software. Additionally, deformation, Von Mises stress, Von Mises strain, and velocity were analyzed using ANSYS simulation software. The composite exhibited water absorption of 1.5% over 24 to 48 h, a void fraction of 1.02%, a tensile strength of 33.15 MPa, a compressive strength of 29.69 MPa, and a bending or flexural strength of 28.85 MPa. Furthermore, the ANSYS results showed a maximum deformation of 0.60887 mm, a maximum equivalent elastic strain of 0.0018815, a minimum value of 1.0375 × 10^-10, a maximum equivalent stress of 22.27 MPa, a minimum of 1.3877 × 10^-5 MPa, and a velocity streamline of 14.97 m/s at 21 rad/s. The simulated stresses were well below the material's measured strength limits, indicating a safe design under the analysed conditions. The weight of the developed composite blade was 31% lower than that of a conventional aluminum blade.

We employ density functional theory (DFT) to investigate how Stone-Wales (SW) defects modulate the electronic and electrochemical properties of two-dimensional silicon carbide (SiC) monolayer for sodium (Na)-, potassium (K)-, and magnesium (Mg)-ion batteries. The SW-SiC structure is energetically feasible and dynamically stable, with defect formation reducing the bandgap by ~ 70% and enhancing electronic conductivity. Compared to pristine SiC, SW-SiC exhibits stronger adsorption for Na (- 0.89 eV) and K (- 1.52 eV) with pronounced charge transfer at the adatom-substrate interface. Theoretical capacities of 300 and 600 mAh g⁻¹ for Na and K, respectively, are achieved, along with low diffusion barriers (0.88 eV for Na, 0.54 eV for K) and favorable open-circuit voltages (0.44 V, 0.70 V). Minimal structural distortion upon ion insertion confirms structural stability. These results elucidate the defect-property interplay in 2D SiC and establish SW defect engineering as a viable approach for optimizing condensed-phase anode materials beyond lithium systems.

Accurately estimating peak width and wave duration (WD) is a significant challenge across various scientific disciplines. Traditional methods, such as Full Width at Half Maximum (FWHM), often encounter difficulties with overlapping peaks, asymmetric patterns, noisy data, and, especially, multi-channel data where these challenges multiply. This paper introduces a novel approach for estimating peak width and WDs in curve-fitting applications, leveraging the oscillatory nature of signals through Frequency Modulated Möbius (FMM) decomposition. We derive a parametric expression for FWHM and propose a novel WD measure. Beyond being both mathematically and physiologically sound, this method offers several advantages, including a straightforward parametric formulation, robust estimation, and flexibility to handle single and overlapping peaks, as well as peaks recorded across multiple channels. While potential applications extend across disciplines, we demonstrate this method's effectiveness in addressing critical challenges in electrocardiogram (ECG) signal and spectroscopic analysis. In ECG analysis, the WD measure effectively estimates ECG segments that capture critical aspects of cardiac electrical activity. In spectroscopy, we evaluate the new FWHM estimator, a key parameter for determining spectral resolution and material properties. Extensive testing confirms the suitability and robustness of the new measures, outperforming standard techniques in both applications.

With the development of sports rehabilitation, accurate assessment of the patient's rehabilitation process has become the key to enhance the rehabilitation effect. To solve the problems of inaccurate recognition and poor real-time performance of the rehabilitation human pose recognition model for traditional sports in complex environments, this study proposes an integrated framework for efficient and accurate human pose recognition and automated scoring in sports rehabilitation. The study constructs a human pose recognition model using a human pose tracking algorithm and achieves pose classification by extracting key points of the human skeleton and combining them with a random forest algorithm. Meanwhile, a siamese neural network and similarity metric algorithm are introduced to optimize the automated score detection model, accurately assessing the quality of rehabilitation movements. The outcomes indicated that the automatic scoring detection system achieved 98% accuracy in human body pose recognition. In terms of joint angle error, the error rate of the detection model designed in the study was below 6%, which was significantly better than the comparison method. In rehabilitation score correlation test, the correlation of the model was maintained at 92-98%, demonstrating higher scoring accuracy. The outcomes reveal that the model designed in the study has high recognition accuracy and evaluation stability. This makes it an efficient and accurate assessment tool for rehabilitation therapy. It can also effectively improve the effectiveness of rehabilitation training and the quality of life of patients.

False Data Injection Attacks (FDIAs) represent a significant cybersecurity threat to smart grids (SGs), compromising both system stability and operational reliability. Conventional detection approaches frequently prove inadequate, largely due to challenges such as data imbalance and suboptimal model parameterisation. To overcome these limitations, this study proposes a proactive detection framework that integrates ensemble learning, adaptive oversampling, and a novel metaheuristic optimization algorithm, termed FalsEye. At the core of the proposed framework is a Voting Classifier ensemble, which strategically combines heterogeneous base learners, including ExtraTrees, CatBoost, and LightGBM. The performance of this ensemble is further enhanced through the IceCube Optimization (IO) algorithm, a physics-inspired metaheuristic technique employed to fine-tune the hyperparameters of the individual base models. Additionally, the framework incorporates adaptive oversampling using the Adaptive Synthetic method to effectively mitigate class imbalance within the dataset, thereby improving the detection rate of minority FDIA instances. Experimental results demonstrate that the IO Voting Classifier achieves superior F1-scores and exhibits a more balanced precision-recall trade-off compared to conventional ensemble approaches. The optimized framework attains an accuracy of 99%, with a precision of 92%, a recall of 98%, and an F1-score of 95%, marking a substantial improvement over traditional methods. These findings highlight the considerable potential of combining metaheuristic optimization with ensemble learning to develop robust and cyber-resilient SG infrastructures.

To investigate the changes in hand morphology among young females, researchers employed 3D hand scanning to perform anthropometric measurement of 111 Chinese young women (20-26 years), enabling hand morphology classification for ergonomic applications. A total of 32 hand parts were measured and analyzed based on these models. The findings reveal that variables describing hand morphology are predominantly categorized into four types: finger width, finger circumference, finger length, and hand length. The typical indicators reflecting hand morphological characteristics include hand length, middle finger width, proximal circumference of the index finger, and ring finger length. Results revealed five distinct hand types: short/thin, short/wide, standard, long/thin, and long/wide. Compared to current national standards in China (GB/T 16252 - 1996), modern hand morphology showed significant increases in hand length (+ 3.3%) and metacarpal breadth (+ 8.3%). We propose a novel sizing system (5-size-5-fit) with 180/86 as the predominant type, optimized for ergonomic glove design. This study provides critical data references for the industrial design of hand appliances, while also offering potential implications for ergonomics and hand injury prevention.