Search Results (10,698)

Amid the intensification of demographic aging, home robots based on intelligent technology have shown great application potential in assisting the daily life of the elderly. This paper proposes a multimodal human–robot interaction system that integrates EEG signal analysis and visual perception, aiming to realize the perception ability of home robots on the intentions and environment of the elderly. Firstly, a channel selection strategy is employed to identify the most discriminative electrode channels based on Motor Imagery (MI) EEG signals; then, the signal representation ability is improved by combining Filter Bank co-Spatial Patterns (FBCSP), wavelet packet decomposition and nonlinear features, and one-to-many Support Vector Regression (SVR) is used to achieve four-class classification. Secondly, the YOLO v8 model is applied for identifying objects within indoor scenes. Subsequently, object confidence and spatial distribution are extracted, and scene recognition is performed using a Machine Learning technique. Finally, the EEG classification results are combined with the scene recognition results to establish the scene-intention correspondence, so as to realize the recognition of the intention-driven task types of the elderly in different home scenes. Performance evaluation reveals that the proposed method attains a recognition accuracy of 83.4%, which indicates that this method has good classification accuracy and practical application value in multimodal perception and human–robot collaborative interaction, and provides technical support for the development of smarter and more personalized home assistance robots. Full article

In this paper, the problem of aero-engines ensemble modeling under sparse data is addressed. Firstly, the Makima method is used to interpolate and complement the sparse data by analyzing the experimental data of a specific real aero-engine. In this way, the data sparsity problem due to sampling or transmission is solved equally well. Secondly, the Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network is brought in as the computational structure of the model. Based on the Automatic Neural Network Architecture Search (ANAS) method, the hyperparameters of the model can be searched efficiently, and the performance is improved. Third, a novel ensemble modeling method based on the Makima method, the NARX model, and the ANAS method is proposed to realize high-precision modeling throughout the entire operation process of the aero-engine from the idle state to the full throttle state. Finally, the proposed method is validated by simulations and experiments, and the results illustrate the innovation and correctness. Full article

It has become a global challenge to realize sustainable regional development in the context of demographic transition. Based on the panel data of the Beijing-Tianjin-Hebei region from 2017 to 2022, this paper analyzes in depth the impact mechanism of labor market integration on export quality and its sustainable development effect by using various econometric methods. It is found that labor market integration enhances regional export quality, and every 1% increase in the integration level can bring 0.184% improvement in export quality. Mechanism analysis shows that labor market integration works mainly through two channels: innovation synergy effect (27%) and labor cost effect (8%). Heterogeneity analysis shows that the elasticity coefficients of general trade and high-income nations are 0.155 and 0.208, respectively, but the elasticity coefficients for processing trade, low-income, lower-middle-income and upper-middle-income nations are not significant. Furthermore, feature fact analysis reveals that the three regions of Beijing, Tianjin, and Hebei have varying degrees of labor market integration: Beijing (0.038) > Tianjin (0.037) > Hebei (0.034); nevertheless, the export product quality gradient is reversed: Beijing (0.617) < Tianjin (0.665) < Hebei (0.669). The evaluation of sustainable development impacts reveals that labor market integration not only mitigates internal labor shortages but also effectively counteracts the external shock of U.S. tariff increases on China. This study provides important theoretical support and policy insights for building a sustainable regional development model in the context of demographic transition. Full article

Traumatic injuries to the brain and spinal cord remain among the most challenging conditions in clinical neuroscience due to the complexity of repair mechanisms and the limited regenerative capacity of neural tissues. Nanotechnology has emerged as a transformative field, offering precise diagnostic tools, targeted therapeutic delivery systems, and advanced scaffolding platforms that are capable of overcoming the biological barriers to regeneration. This review summarizes the recent advances in nanoscale diagnostic markers, functionalized nanoparticles for drug delivery, and nanostructured scaffolds designed to modulate the injured microenvironment and support axonal regrowth and remyelination. Emerging evidence indicates that nanotechnology enables real-time, minimally invasive detection of inflammation, oxidative stress, and cellular damage, while improving therapeutic efficacy and reducing systemic side effects through targeted delivery. Electroconductive scaffolds and hybrid strategies that integrate electrical stimulation, gene therapy, and artificial intelligence further expand opportunities for personalized neuroregeneration. Despite these advances, significant challenges remain, including long-term safety, immune compatibility, the scalability of large-scale production, and translational barriers, such as small sample sizes, heterogeneous preclinical models, and limited follow-up in existing studies. Addressing these issues will be critical to realize the full potential of nanotechnology in traumatic brain and spinal cord injury and to accelerate the transition from promising preclinical findings to effective clinical therapies. Full article

Addressing the carbon footprint in pig production is a fundamental technical basis for achieving carbon neutrality and peak carbon emissions. Only by systematically studying the carbon footprint can the goals of carbon neutrality and peak carbon emissions be effectively realized. This study aims to reduce the carbon footprint through optimized feeding strategies based on minimizing carbon emissions. To this end, this study conducted a full-lifecycle monitoring of the carbon footprint during pig growth from December 2024 to May 2025, optimizing feeding strategies using a real-time pig weight estimation model driven by deep learning to reduce resource consumption and the carbon footprint. We introduce EcoSegLite, a lightweight deep learning model designed for non-contact real-time pig weight estimation. By incorporating ShuffleNetV2, Linear Deformable Convolution (LDConv), and ACmix modules, it achieves high precision in resource-constrained environments with only 1.6 M parameters, attaining a 96.7% mAP50. Based on full-lifecycle weight monitoring of 63 pigs at the Pianguan farm from December 2024 to May 2025, the EcoSegLite model was integrated with a life cycle assessment (LCA) framework to optimize feeding management. This approach achieved a 7.8% reduction in feed intake, an 11.9% reduction in manure output, and a 5.1% reduction in carbon footprint. The resulting growth curves further validated the effectiveness of the optimized feeding strategy, while the reduction in feed and manure also potentially reduced water consumption and nitrogen runoff. This study offers a data-driven solution that enhances resource efficiency and reduces environmental impact, paving new pathways for precision agriculture and sustainable livestock production. Full article

Rapid and green detection of soil nutrients is essential for soil fertility and plant growth. However, traditional methods cannot meet the needs of rapid detection, and the reagents easily cause environmental pollution. Hence, we proposed a multivariable output weighting-network (MW-Net) combined with laser-induced breakdown spectroscopy (LIBS) to achieve rapid and green detection for three soil nutrients. For a better spectral signal-to-background ratio (SBR), the two important parameters of delay time and gate width were optimized. Then, the spectral noise was removed by the near-zero standard deviation method. Three common quantitative models were investigated for single-element prediction, which are usually applied in LIBS analysis. Also, multi-element prediction was investigated using MW-Net. The results showed that MW-Net outperformed other models generally with very good quantification for soil total N and K (the determination coefficients in the prediction set (R_p²) of 0.75 and 0.83 and the relative percent difference in the prediction sets (RPD) of 2.05 and 2.43) and excellent indirect determination for soil exchangeable Ca (R_p² of 0.93 and RPD of 3.91). Finally, the interpretability was realized through feature extraction from MW-Net, indicating its design rationality. The preliminary results indicated that MW-Net combined with LIBS technology could quantify the three soil nutrients simultaneously, improving the detection efficiency, and it could possibly be deployed on a LIBS portable instrument in the future for precision agriculture. Full article

In Chinese language semantic analysis, the processed languages often reveal similar representations in models for different application scenarios, resulting in similar language models. With that characteristic, evolutionary multitask optimization (EMTO) algorithms, which realize the synergy optimization for multiple tasks, have the potential to optimize such models for different scenarios. EMTO is an emerging topic in evolutionary computation (EC) for solving multitask optimization problems (MTOPs) with the help of knowledge transfer (KT). However, the current EMTO algorithms often involve two limitations. First, many KT methods usually ignore the distribution information of populations to evaluate task similarity. Second, many EMTO algorithms often directly transfer individuals from the source task to target task, which cannot guarantee the quality of the transferred knowledge. To overcome these challenges, an auxiliary–population–based multitask optimization (APMTO) is proposed in this paper, which will be further applied to Chinese semantic understanding in our future works. We first propose an adaptive similarity estimation (ASE) strategy to exploit the distribution information among tasks and evaluate the similarity of tasks, so as to adaptively adjust the KT frequency. Then, an auxiliary-population-based KT (APKT) strategy is designed, which uses auxiliary population to map the global best solution of the source task to target task, offering more useful transferred information for the target task. APMTO is tested on multitask test suite CEC2022 and compared with several state–of–the–art EMTO algorithms. The results show that APMTO outperforms the compared state–of–the–art algorithms, which fully reveals its effectiveness and superiority. Full article

The half-through arch bridge short suspender is more prone to damage due to its high linear stiffness and special force characteristics. To analyze the vehicle-induced vibration characteristics of the short suspender during service, a half-through arch bridge finite element model and a three-axis vehicle model were established to realize the coupled vibration of the suspender axle under bridge deck unevenness excitation. The suspender was simulated using LINK element and BEAM element and separated along its axial and radial directions, and its tension-bending response characteristics was studied. The study found that the short suspender’s amplitude and frequency are higher than those of the long suspender as vehicle critical duration increases. Influenced by the tensile bending effect, the vibration, cross-section equivalent force amplitude, and impact coefficient at the anchorage end are larger than those at the center, and the lower anchorage end’s cross-section peak stress is biased towards the direction of the side column. The internal force of the short suspender is consistent with the deformation trend; its internal force coincides with the deformation trend; and its axial alternating load is generated by the axial relative deformation between the arch rib and the bridge deck, while the bending alternating load originates from the rotational deformation of the short suspender. Full article

Financial time-series data often exhibit statistically significant skewness and heavy tails, and numerous flexible distributions have been proposed to model them. In the context of the Log-linear Realized GARCH model with Skew-t (ST) distributions, our objective is to explore how the choice of prior distributions in the Adaptive Random Walk Metropolis method and initial parameter values in the Generalized Reduced Gradient (GRG) Solver method affect ST parameter and log-likelihood estimates. An empirical study was conducted using the FTSE 100 index to evaluate model performance. We provide a comprehensive step-by-step tutorial demonstrating how to perform estimation and sensitivity analysis using data tables in Microsoft Excel. Among seven ST distributions—namely, the asymmetric, epsilon, exponentiated half-logistic, Hansen, Jones–Faddy, Mittnik–Paolella, and Rosco–Jones–Pewsey distributions—Hansen’s ST distribution is found to be superior. This study also applied the GRG method to estimate new approaches, including Realized Real-Time GARCH, Realized ASHARV, and GARCH@CARR models. An empirical study showed that the GARCH@CARR model with the feedback effect provides the best goodness of fit. Out-of-sample forecasting evaluations further confirm the predictive dominance of models incorporating real-time information, particularly Realized Real-Time GARCH for volatility forecasting and Realized ASHARV for 1% VaR estimation. The findings offer actionable insights for portfolio managers and risk analysts, particularly in improving volatility forecasts and tail-risk assessments during market crises, thereby enhancing risk-adjusted returns and regulatory compliance. Although the GRG method is sensitive to initial values, its presence in the spreadsheet method can be a powerful and promising tool in working with probability density functions that have explicit forms and are unimodal, high-dimensional, and complex, without the need for programming experience. Full article

The current research aims to discover applications of QML approaches in realizing liabilities within smart contracts. These contracts are essential commodities of the blockchain interface and are also decisive in developing decentralized products. But liabilities in smart contracts could result in unfamiliar system failures. Presently, static detection tools are utilized to discover accountabilities. However, they could result in instances of false narratives due to their dependency on predefined rules. In addition, these policies can often be superseded, failing to generalize on new contracts. The detection of liabilities with ML approaches, correspondingly, has certain limitations with contract size due to storage and performance issues. Nevertheless, employing QML approaches could be beneficial as they do not necessitate any preconceived rules. They often learn from data attributes during the training process and are employed as alternatives to ML approaches in terms of storage and performance. The present study employs four QML approaches, namely, QNN, QSVM, VQC, and QRF, for discovering susceptibilities. Experimentation revealed that the QNN model surpasses other approaches in detecting liabilities, with a performance accuracy of 82.43%. To further validate its feasibility and performance, the model was assessed on a several-partition test dataset, i.e., SolidiFI data, and the outcomes remained consistent. Additionally, the performance of the model was statistically validated using McNemar’s test. Full article

Long-distance water conveyance systems require controlled filling after initial operation or maintenance. This process is complex and challenging to manage accurately. It involves transient two-phase flow with rapid velocity and pressure changes, which can risk pipeline damage. Studying the filling process is thus essential to ensure the safe and efficient operation of the system. Combining a specific engineering case, this work investigates gas–liquid two-phase flow in tunnel sections during filling. We employ a coupled Volume of Fluid (VOF) multiphase model and a Realizable k-ε turbulence model for our simulations. Hydraulic parameters (flow patterns, pressure, velocity) are analyzed using the results. Key findings indicate that higher filling flow rates destabilize the process. Gas retention behavior in low-pressure caverns varies, and gas–liquid eruptions occur at shaft water surfaces. Increased flow rates also intensify phase–pattern transitions, elevate peak pressure and velocity values, and amplify pressure pulsations and velocity fluctuations. Furthermore, faster gas transport in low-pressure caverns triggers flow instability, compromising exhaust efficiency. Full article

Effective containment of contaminant plumes in heterogeneous aquifers is critically challenged by the inherent uncertainty in hydraulic conductivity (K). Conventional, deterministic optimization approaches for pump-and-treat (P&T) system design often fail when confronted with real-world geological variability. This study proposes a novel robust simulation-optimization framework to design reliable hydraulic containment systems that explicitly account for this subsurface uncertainty. The framework integrates the Karhunen–Loève Expansion (KLE) for efficient stochastic representation of heterogeneous K-fields with a Genetic Algorithm (GA) implemented via the pymoo library, coupled with the MODFLOW groundwater flow model for physics-based performance evaluation. The core innovation lies in a multi-scenario assessment process, where candidate well configurations (locations and pumping rates) are evaluated against an ensemble of K-field realizations generated by KLE. This approach shifts the design objective from optimality under a single scenario to robustness across a spectrum of plausible subsurface conditions. A structured three-step filtering method—based on mean performance, consistency (pass rate), and stability (low variability)—is employed to identify the most reliable solutions. The framework’s effectiveness is demonstrated through a numerical case study. Results confirm that deterministic designs are highly sensitive to the specific K-field realization. In contrast, the robust framework successfully identifies well configurations that maintain a high and stable containment performance across diverse K-field scenarios, effectively mitigating the risk of failure associated with single-scenario designs. Furthermore, the analysis reveals how varying degrees of aquifer heterogeneity influence both the required operational cost and the attainable level of robustness. This systematic approach provides decision-makers with a practical and reliable strategy for designing cost-effective P&T systems that are resilient to geological uncertainty, offering significant advantages over traditional methods for contaminated site remediation. Full article

In order to scientifically and reasonably evaluate the digital transformation and upgrading level of “emerging industry” innovation ecosystems, this paper firstly uses the grounded theory to extract the factors influencing the digital transformation and upgrading of the emerging industry innovation ecosystems. Secondly, a cloud model is introduced to evaluate the importance of the influencing factors, select the important factors, and construct an evaluation index system. Thirdly, the projection pursuit model based on the quantum genetic algorithm is used to search for the optimal projection direction and determine the weight and comprehensive evaluation value of each index. Finally, the digital transformation and upgrading levels of 506 innovation subjects are divided into a budding level (I), growth level (II), and mature level (III) based on K-means and the SVM—most of which are at a medium–low level. Therefore, countermeasures and suggestions for promoting the digital transformation and upgrading of the emerging industry innovation ecosystems are put forward. This paper provides a systematic and complete method for the evaluation of digital transformation and upgrading of the emerging industry innovation ecosystems. Further, this paper promotes the combination of qualitative and quantitative analysis and realizes the effective integration of the overall logic chain of theoretical demonstrations, method design, and data analysis. Full article

A permanent fault identification scheme based on LCC signal injection for high-voltage direct current (HVDC) systems is proposed to avoid secondary damage when it recloses to a permanent fault. Firstly, using the fault control ability of LCC, the additional control strategy is applied to the trigger angle of LCC to realize signal injection. The frequency, duration, and amplitude of the injection signal are analyzed and determined, and a signal injection strategy based on LCC is proposed. Secondly, the differences in voltage after signal injection under different fault properties are analyzed under the distributed parameter model. There is a significant difference in the amplitude of the measured voltage at the local end and the calculated voltage at the remote end under different fault properties due to differences in line models. Finally, a normalized area differential is constructed based on the above amplitude difference to realize permanent fault identification. PSCAD/EMTDC simulation results show that the proposed scheme utilizes single end data and is not affected by data communication. There is no need to set a threshold through simulation, and it can reliably identify permanent faults under 400 Ω fault resistance and 40 dB noise. It is suitable for line lengths of 1500 km and below. Full article

The development of the digital economy has become a significant driving force for the innovation of green technology in the manufacturing sectors. Green technology innovation in the manufacturing sectors is not only a key engine for realizing economic green transformation and achieving the goal of achieving peak carbon emissions by 2030 and carbon neutrality by 2060, but also an important path for cultivating new quality productivity. Based on Schumpeter’s endogenous growth theory, in this study, we constructed an analytical model with a unified framework of digital economic development and environmental regulation, systematically explored the mechanism of digital economic development with respect to green technological innovation in the manufacturing sectors and the moderating effect of environmental regulation, and carried out empirical research based on panel data at the provincial level and the level of the subdivided manufacturing sectors in China. We found that the development of the digital economy promotes green technology innovation in the manufacturing industry. However, according to the theory of increasing marginal information costs, it shows a significant nonlinear relationship. Absorptive capacity is the key means of support that manufacturing enterprises can leverage to improve their level of green technological innovation. Environmental regulation plays a crucial role in guiding green technological innovation in the manufacturing sectors. A further heterogeneity analysis showed that the development of the digital economy exerts a stronger positive impact on green technological innovation in cleaner-production-oriented manufacturing sectors and those located in regions with more advanced financial regions and in technology-intensive industries. This study provides theoretical support for understanding the driving mechanisms of green technological innovation in the manufacturing sector against the backdrop of the digital economy, offering practical implications for optimizing environmental regulation policies and enhancing the level of green development in manufacturing. Full article