Search Results (2,294)

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

Carbon deposits on the crown of engine pistons can markedly reduce combustion efficiency and shorten service life. Conventional cleaning techniques often fail to simultaneously ensure a high carbon removal efficiency and maintain optimal surface integrity. To enable efficient and precise carbon removal, this study proposes the application of hybrid laser cleaning—combining continuous-wave (CW) and pulsed lasers—to piston carbon deposit removal, and employs response surface methodology (RSM) for multi-objective process optimization. Using the N52B30 engine piston as the experimental substrate, this study systematically investigates the combined effects of key process parameters—including CW laser power, pulsed laser power, cleaning speed, and pulse repetition frequency—on surface roughness (Sa) and carbon residue rate (RC). Plackett–Burman design was employed to identify significant factors, the method of the steepest ascent was utilized to approximate the optimal region, and a quadratic regression model was constructed using Box–Behnken response surface methodology. The results reveal that the Y-direction cleaning speed and pulsed laser power exert the most pronounced influence on surface roughness (F-values of 112.58 and 34.85, respectively), whereas CW laser power has the strongest effect on the carbon residue rate (F-value of 57.74). The optimized process parameters are as follows: CW laser power set at 625.8 W, pulsed laser power at 250.08 W, Y-direction cleaning speed of 15.00 mm/s, and pulse repetition frequency of 31.54 kHz. Under these conditions, the surface roughness (Sa) is reduced to 0.947 μm, and the carbon residue rate (RC) is lowered to 3.67%, thereby satisfying the service performance requirements for engine pistons. This study offers technical insights into the precise control of the hybrid laser cleaning process and its practical application in engine maintenance and the remanufacturing of end-of-life components. Full article

It is demonstrated that neuromorphic materials designed for computational tasks can be effectively implemented by drawing an analogy with trigger-based systems built upon classical binary elements. Among the most promising approaches in this context are systems that perform computations based on the Residue Number System (RNS). A specific implementation of a trigger-based adder employing the proposed methodology is presented and tested through simulation modeling. This adder utilizes the representation of natural numbers as elements of a subtraction ring modulo P, where P is the product of Mersenne prime numbers. This configuration enables component-wise, independent execution of arithmetic operations. It is further shown that analogous trigger-based systems can be realized using recurrent neural network analogs, particularly those implemented with neuromorphic materials. The study emphasizes that it is possible to construct a neural network, especially one based on neuromorphic substrates, that can perform logical operations equivalent to those executed by conventional binary circuitry. A key challenge in the proposed approach lies in implementing an operation analogous to the carry mechanism employed in classical binary adders. An algorithm addressing this issue is proposed, based on the transition from computations modulo P to computations modulo 2P. Full article

This paper introduces a novel method for selecting parameters of finite fields formed by 2 × 2 matrices over a finite field of integers modulo a prime p. The method aims to simultaneously determine both the field parameters and primitive elements, thereby optimizing the construction of cryptographic algorithms. The proposed approach leverages the properties of quadratic residues and non-residues, simplifying the process of finding matrix field parameters while maintaining computational efficiency. The method is particularly effective when the prime number p is either a Mersenne prime or (p + 1)/2 is also a prime. This study demonstrates that the resulting matrix fields can be practically computed, offering a high degree of flexibility for cryptographic protocols such as key agreement and secure data transmission. Compared to previous methods, the new method reduces the parameter search space and provides a structured way to identify primitive elements without the need for a separate search procedure. The findings have significant implications for the development of efficient cryptographic systems using matrix-based finite fields. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

To address the issue of low accuracy in identifying the transition states of rolling bearing performance degradation when relying solely on vibration signals, this study proposed a vibration–temperature fusion-based adaptive method for bearing performance degradation assessments. First, a multidimensional time–frequency feature set was constructed by integrating vibration acceleration and temperature signals. Second, a novel composite sensitivity index (CSI) was introduced, incorporating the trend persistence, monotonicity, and signal complexity to perform preliminary feature screening. Mutual information clustering and regularized entropy weight optimization were then combined to reselect highly sensitive parameters from the initially screened features. Subsequently, an adaptive feature fusion method based on auto-associative kernel regression (AFF-AAKR) was introduced to compress the data in the spatial dimension while enhancing the degradation trend characterization capability of the health indicator (HI) through a temporal residual analysis. Furthermore, the entropy weight method was employed to quantify the information entropy differences between the vibration and temperature signals, enabling dynamic weight allocation to construct a comprehensive HI. Finally, a dual-criteria adaptive bottom-up merging algorithm (DC-ABUM) was proposed, which achieves bearing life-stage identification through error threshold constraints and the adaptive optimization of segmentation quantities. The experimental results demonstrated that the proposed method outperformed traditional vibration-based life-stage identification approaches. Full article

The Shenhu sea area is rich in unconsolidated hydrate reserves, but the formation mineral particles are small, the rock cementation is weak, and the coupling mechanism of hydrate phase change, fluid seepage, and formation deformation is complex, resulting in unclear productivity change law under depressurization exploitation. Therefore, a thermal–fluid–solid–chemical coupling model for natural gas hydrate depressurization exploitation in the Shenhu sea area was constructed to analyze the variation law of reservoir parameters and productivity. The results show that within 0–30 days, rapid near-well pressure drop (13.83→9.8 MPa, 36.37%) drives peak gas production (25,000 m³/d) via hydrate dissociation, with porosity (0.41→0.52) and permeability (75→100 mD) increasing. Within 30–60 days, slower pressure decline (9.8→8.6 MPa, 12.24%) and fines migration cause permeability fluctuations (120→90 mD), reducing gas production to 20,000 m³/d. Within 60–120 days, pressure stabilizes (~7.6 MPa) with residual hydrate saturation < 0.1, leading to stable low permeability (60 mD) and gas production (15,000 m³/d), with cumulative production reaching 2.2 × 10⁶ m³. This study clarifies that productivity is governed by coupled “pressure-driven dissociation–heat limitation–fines migration” mechanisms, providing key insights for optimizing depressurization strategies (e.g., timed heat supplementation, anti-clogging measures) to enhance commercial viability of unconsolidated hydrate reservoirs. Full article

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article

Sepsis is a fetal disease that requires a clear diagnostic biomarker for timely antibiotic treatment. Recent research has identified a pyroptosis-inducing epitope known as P5-5 in tetranectin (TN), a plasma protein produced by monocytes. Previously, we produced a 12F1 monoclonal antibody against the P5-5 and discovered that it could not only diagnose the presence but also monitor the progress of sepsis in the clinic. In the current study, we further investigated the structure site of the P5-5 and the recognition mechanism between the 12F1 mAb and the P5-5 epitope. To this end, 10 amino acids (NDALYEYLRQ) in the P5-5 were individually mutated to alanine, and their binding to the mAb was tested to confirm the most significant antigenic recognition sites. In the meanwhile, the spatial conformation of 12F1 mAb variable regions was modeled, and the molecular recognition mechanisms in detail of the mAb to the P5-5 epitope were further studied by molecular docking. Following epitope prediction and experimental verification, we demonstrated that the motif “DALYEYL” in the epitope sequence position 2−8 of TN-P5-5 is the major binding region for mAb recognition, in which two residues (4L and 8L) were essential for the interaction between the P5-5 epitope and the 12F1 mAb. Therefore, our study greatly narrowed down the previously reported motif from ten to seven amino acids and identified two Leu as critical contact residues. Finally, a single-chain variable fragment (scFv) from the 12F1 hybridoma was constructed, and it was confirmed that the identified motif and residues are prerequisites for the strong binding between P5-5 and 12F1. Altogether, the data of the present work could serve as a theoretic guide for the clinical design of biosynthetic drugs by artificial intelligence to treat sepsis. Full article

With the increasing severity of urban noise pollution, its detrimental impact on public health has garnered growing attention. However, accurate identification and classification of noise sources in complex urban acoustic environments remain major technical challenges for achieving refined noise management. To address this issue, this study presents two key contributions. First, we construct a new urban noise classification dataset, namely the urban noise 15-category dataset (UN15), which consists of 1620 audio clips from 15 representative categories, including traffic, construction, crowd activity, and commercial noise, recorded from diverse real-world urban scenes. Second, we propose a novel deep neural network architecture based on a residual network and integrated with a time–frequency attention mechanism, referred to as residual network with temporal–frequency attention (ResNet-TF). Extensive experiments conducted on the UN15 dataset demonstrate that ResNet-TF outperforms several mainstream baseline models in both classification accuracy and robustness. These results not only verify the effectiveness of the proposed attention mechanism but also establish the UN15 dataset as a valuable benchmark for future research in urban noise classification. Full article

Global antibiotic use saturates ecosystems with selective pressure, driving mobile genetic element (MGE)-mediated antibiotic resistance gene (ARG) dissemination that destabilizes ecological integrity and breaches public health defenses. This review synthesizes the sources, environmental distribution, and ecological risks of antibiotics and ARGs, emphasizing the mechanisms of horizontal gene transfer (HGT) driven by MGEs such as plasmids, transposons, and integrons. We further conduct a comparative critical analysis of the effectiveness and limitations of antibiotics and ARGs remediation strategies for adsorption (biochar, activated carbon, carbon nanotubes), chemical degradation (advanced oxidation processes, Fenton-based systems), and biological treatment (microbial degradation, constructed wetlands). To effectively curb the spread of antimicrobial resistance and safeguard the sustainability of ecosystems, we propose an integrated “One Health” framework encompassing enhanced global surveillance (antibiotic residues and ARGs dissemination) as well as public education. Full article

Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions. Second, we reveal spatio-temporal correlations within multi-source feature topologies. By embedding multi-source spatio-temporal information into fault feature graphs via spatio-temporal collaborative perception, we establish high-dimensional spatio-temporal feature topology graphs based on spectral similarity, extending generalized feature representations into the spatio-temporal domain. Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. Experiments on variable/multi-condition datasets demonstrate the following: feature graphs seamlessly integrate multi-source information with operational variations; the methodology precisely captures spatio-temporal delays induced by vibrational direction/path discrepancies; and the proposed model maintains both high diagnostic accuracy and strong generalization capacity under complex operating conditions, delivering a highly reliable framework for rotating machinery fault diagnosis. Full article

This study presents a cost-effective, carbon-negative construction material for affordable housing, developed entirely from locally available agricultural wastes: rice husk ash, wood ash, and rice straw—materials often problematic to dispose of in many African regions. Rice husk ash provides high amorphous silica, acting as a strong pozzolanic agent. Wood ash contributes calcium oxide and alkalis to serve as a reactive binder, while rice straw functions as a lightweight organic filler, enhancing thermal insulation and indoor climate comfort. These materials undergo natural pozzolanic reactions with water, eliminating the need for Portland cement—a major global source of anthropogenic CO₂ emissions (~900 kg CO₂/ton cement). This process is inherently carbon-negative, not only avoiding emissions from cement production but also capturing atmospheric CO₂ during lime carbonation in the hardening phase. Field trials in Kenya confirmed the composite’s sufficient structural strength for low-cost housing, with added benefits including termite resistance and suitability for unskilled laborers. In a collaboration between the University of Tartu and Kenyatta University, a semi-automatic mixing and casting system was developed, enabling fast, low-labor construction of full-scale houses. This innovation aligns with Kenya’s Big Four development agenda and supports sustainable rural development, post-disaster reconstruction, and climate mitigation through scalable, eco-friendly building solutions. Full article

A steel modular building is a highly prefabricated form of steel construction. It offers rapid assembly, a high degree of industrialization, and an environmentally friendly construction site. To promote the application of multi-story steel modular buildings in earthquake fortification zones, it is imperative to conduct in-depth research on their seismic behavior. In this study, a seven-story modular steel building is investigated using shaking table tests. Three seismic waves (artificial ground motion, Tohoku wave, and Tianjin wave) are selected and scaled to four intensity levels (PGA = 0.035 g, 0.1 g, 0.22 g, 0.31 g). It is found that no residual deformation of the structure is observed after tests, and its stiffness degradation ratio is 7.65%. The largest strains observed during the tests are 540 × 10⁻⁶ in beams, 1538 × 10⁻⁶ in columns, and 669 × 10⁻⁶ in joint regions, all remaining below a threshold value of 1690 × 10⁻⁶. Amplitudes and frequency characteristics of the acceleration responses are significantly affected by the characteristics of the seismic waves. However, the acceleration responses at higher floors are predominantly governed by the structure’s low-order modes (first-mode and second-mode), with the corresponding spectra containing only a single peak. When the predominant frequency of the input ground motion is close to the fundamental natural frequency of the modular steel structure, the acceleration responses will be significantly amplified. Overall, the structure demonstrates favorable seismic resistance. Full article

Taking a composite goaf in goaf-side entry retaining as our research focus, a kilogram-level spontaneous combustion experiment was carried out, and limit parameters for coal spontaneous combustion characteristics were assessed. Combined with the key parameters of the site, a numerical model of a multi-area composite goaf was constructed, and the distribution features of the dangerous area for coal spontaneous combustion in the lower layer of in goaf-side entry retaining were determined by means of the upper and lower layer composite superposition division method. The results show that at a floating coal thickness in the goaf of 1.9 m, the lower limit of oxygen concentration C_min, upper limit of air leakage intensity, and corresponding seepage velocity are 6%, 0.282 cm⁻³·s⁻¹·cm⁻², and 11.28 × 10⁻³ m/s respectively. The dangerous area regarding residual coal on the intake side is 23~38 m away from the working face, while that on the return air side is concentrated amid the goaf at 23~75 m, and that on the flexible formwork wall is concentrated at 0~121 m. The research results are of crucial practical importance for the prevention and control of coal spontaneous combustion within a composite goaf. Full article