In view of the complex and variable nature of photovoltaic power generation, a short-term photovoltaic power prediction method based on a deep hybrid learning model is proposed. This model accurately extracts key variables that significantly influence output power based on meteorological data of the area to be measured, and incorporates satellite cloud image features. It combines a deep hybrid learning model to achieve short-term accurate photovoltaic power prediction. Firstly, variational mode decomposition is used to decompose the original photovoltaic power data and related meteorological parameters. Secondly, a bidirectional long short-term memory network is used to construct a hybrid prediction architecture that integrates deep features. The selection, crossover, and mutation mechanism of the genetic algorithm is introduced into the northern goshawk algorithm to optimize the hyperparameters of the deep hybrid learning model, thereby effectively avoiding the problem of the model falling into local optimum. On this basis, a “single training+multiple prediction” prediction method is proposed, which effectively solves the information leakage problem existing in traditional variational mode decomposition. Experiments under different seasons and weather conditions show that compared with other algorithms, the deep hybrid learning model shows significant advantages. After incorporating cloud image feature values, the prediction accuracy is greatly improved.

To address the inefficiency of existing deep learning denoising algorithms when processing large-scale scanning data, a mesh denoising algorithm based on moving least squares surface fitting is proposed. First, an optimal normal estimation for non-feature surfaces is achieved by fitting a quadratic polynomial surface to the local neighborhood. Next, normal signals are filtered using surface fitting residuals and normal deviations, effectively preserving geometric features and enhancing smoothness. Finally, the vertex positions are iteratively updated based on the denoised normal information to further optimize the mesh geometry. The proposed algorithm requires only one surface fitting and filtering step, resulting in low computational complexity and high efficiency. Experimental results show that compared with latest deep learning denoising algorithms, this algorithm is more than three orders of magnitude faster , while achieving similar denoising accuracy.

To meet the requirements of high accuracy and high availability for dynamic gesture switching in gesture recognition, this paper proposes a dynamic gesture recognition method for hand interaction. This method obtains gesture images through a standard RGB camera, and extracts the 2.5D coordinate information of 21 hand keypoints based on the Mediapine Hands framework. A multi- classifier model combining Fully Connected Network (FCNN), Support Vector Machine (SVM), and Light Gradient Boosting Machine (LGBM) is employed for hand shape recognition. The accuracy is improved through vertical feature extension and horizontal score voting, with an average recognition accuracy of 98.81% . Based on hand shape serialization and action feature modeling, dynamic gestures are categorized into instantaneous and continuous types, enabling the recognition of 10 types of dynamic gestures, with an average accuracy of 92.4% for continuous gestures. To further validate the method's practicality, we have designed and developed a gesture recognition system for shopping mall scenery, which is used forcontrolling PPT interaction through gestures. The average response time of the system is 76.39 ms , and experimental results demonstrate the effectiveness and practicality of the method in non- contact interaction, and the research findings provide new insights for dynamic gesture recognition research.

In the electronic market, third- party reviews are increasingly important as a critical channel for consumers to obtain product information. Moreover, frequent manipulation of perceived product quality by merchants has become a common issue. Although platforms have implemented anti- manipulation policies, their effectiveness is limited. This study focuses on the electronic market, aiming to explore the impact of third- party reviews on merchants' perceived quality manipulation strategies and platform anti- manipulation policies. By constructing a game theory model, the equilibrium outcomes for merchants and platforms' under different scenarios are analyzed, identifying three effects of manipulation; the demand expansion effect, the third- party review premium effect, and the cost suppression effect. The research results indicate that while perceived quality manipulation by merchants always increases product demand, it only leads to an increase in the equilibrium product price when the manipulation cost is low, or when the manipulation cost is high but the product quality revealed by third- party reviews is also high. Consumer trust in merchant and platform information exhibits an inverted U- shaped relationship with merchant profits. The decisions of both merchants and platforms are significantly influenced by the manipulation cost and the quality revealed by third- party reviews.

Accurately assessing the distributed power carrying capacity of distribution networks is crucial for enhancing the integration of renewable energy sources. However, the large- scale integration of distributed generation(DG) can impact parameters such as voltage, current, and fault protection, while the stochastic nature of DG output further complicates the calculation of its carrying capacity. Therefore, a distributed power source carrying capacity calculation method for distribution networks based on an improved decoupled linear power flow was proposed to determine the carrying capacity of distributed power sources in the distribution network. The method first analyzes the typical power flow in the distribution network, followed by decoupling and linearization based on its characteristics. It incorporates constraints related to voltage, lines, and reverse power flow to calculate the carrying capacity of distributed power sources in the distribution network. The proposed method is validated through various distribution network case studies, demonstrating its effectiveness in maintaining high computational efficiency while ensuring accuracy.

To address the low- speed, high- torque requirements of a 48- slot 52- pole two- wheel electric hub motor, a parametric simulation model of the motor was built using Maxwell software. Sensitivity analysis of the stator slot structural parameters was performed using Isight software based on optimal Latin hypercube sampling, identifying four key parameters that significantly affect the motor's load torque and cogging torque peak. Based on the Radial Basis Function(RBF) neural network theory, a surrogate model with four inputs and two outputs is constructed and combined with the NSGA- II algorithm for multi- objective optimization. After optimization, the cogging torque peak decreased from 5.56 N·m to 5.28 N·m , a reduction of 5.15% compared to before optimization; the load torque improved under various operating conditions, with an average increase of 1.98 N·m . The results demonstrate that this optimization method, through the efficient coupling of the surrogate model and the genetic algorithm, effectively reduces computational complexity and improves optimization accuracy, providing a reference for the development of low- speed two- wheeled electric vehicle hub motors.

Neonicotinoid pesticides (NEOs) are widely detected across multiple environmental media and pose potential risks to human health. However, studies on human exposure and the in vivo metabolic processes of NEOs remain limited. In this study, the mass concentrations of nine parent NEOs and seven NEO metabolites were quantified in a total of 168 urine samples collected from 12 non- occupationally exposed individuals under different dietary conditions over two consecutive days. The results showed detection rates ranging from 41.07\\% to 100.00\\% . The total mass concentrations of parent NEOs and their metabolites were 5.06\\mathrm{ng / mL} and 3.69\\mathrm{ng / mL} , respectively. A significant negative correlation was observed between the urinary concentrations of parent NEOs and their octanol- water partition coefficients ( \\log K_{\\mathrm{ow}} ) in females. The metabolite- to- parent concentration ratios ranged from 0.11 to 2.06, indicating that certain NEOs, such as acetamiprid (ACE), imidacloprid (IMI), and thiamethoxam (THIA), were predominantly present in metabolized forms within the human body. Higher concentrations of parent NEOs and their metabolites were observed in morning urine samples. Additionally, following the intake of light- colored vegetables and meat, a decreasing trend in the total mass concentration of parent NEOs in urine was observed within 1- 5 hours, suggesting that some NEOs were excreted in their parent form without prior metabolism. Comparison of internal and external exposure levels indicated that vegetable consumption was the primary pathway of exposure to IMI, clothianidin (CLO), and ACE. These findings contribute to a better understanding of the occurrence levels and exposure sources of NEOs in the urine of non- occupationally exposed populations.

This study focuses on the microaggregates of surface sediments in the river- estuary- gulf continuum, and examines the influence of iron oxides in the 1 - 5 \\mu \\mathrm{m} , 5 - 50 \\mu \\mathrm{m} and 50 - 250 \\mu \\mathrm{m} size fractions on the stability of organic matter. The results show that iron oxides predominantly occur as amorphous, hydrated forms, and the content of organic carbon is closely related to the content of crystalline iron oxides. Fine sediment particles ( 1 - 5 \\mu \\mathrm{m} ) have a larger specific surface area, allowing them to provide more reaction sites, thereby interacting more effectively with organic matter and enhancing its stability within the river- estuary- bay continuum. Meanwhile, the stability of organic matter in aggregates is significantly influenced by changes in salinity and oxygen availability. The research provides a new perspective for in- depth understanding of the burial and preservation mechanisms of organic carbon in microaggregates of different particle sizes in marine sediments.

Chloride ingress and carbonation are the primary cause for the performance deterioration of concrete structures. As slag has been widely used in engineering structures, it is important to study the properties of chloride binding and carbonation of slag cement paste for the durability assessment of structures. By taking the water/binder ratio and the slag content as the primary variables, the properties of chloride binding and carbonation of slag cement paste are studied through experiment. Based on the experimental results, the effects of free chloride ion concentration, slag content, and water/binder ratio on the chloride binding capacity are analyzed in detail. It is found that for a given free chloride concentration, the larger the slag content and the water/binder ratio are, the stronger the chloride binding capacity is. The chloride binding capacity of slag cement paste was described using the Freundlich and Langmuir models, with the relevant parameters reported. The results show that both models provide a good fit to the experimental data. For the carbonation of slag cement paste with an initial free chloride ion concentration of 0.5\\mathrm{mol / L} , the effects of carbonation time, slag content, and the water/binder ratio on the \\mathrm{pH} value of slag cement paste are discussed in detail. It is found that for a given carbonation time, the larger the slag content and the water/binder ratio are, the lower the \\mathrm{pH} value is. The fundamental properties of chloride binding and carbonation of slag cement paste are revealed through study, which provides a reference for the design of engineering structures.

Based on the theory of fractional order calculus, the viscoelastic Merchant model of unsaturated foundation is established. The Laplace transform is applied to the governing equations of the unsaturated foundation and the fractional-order Merchant model. The analytical solutions of excess pore water, excess gas pressures, and soil settlement in the transformed domain are obtained by combining the boundary conditions and initial conditions. Through numerical investigations, the effects of boundary permeability conditions, fractional-order Merchant model parameters, physical and mechanical parameters of soil on the consolidation and settlement characteristics of unsaturated foundations are analyzed. The results indicate that increasing the boundary permeability of the foundation accelerates the dissipation of pore water pressure and pore air pressures, thereby accelerating the consolidation settlement rate of soil. After considering the rheological properties of the Merchant model, the consolidation rate of soil layer always lags behind the dissipation rate of pore air pressure and water pressure. Additionally, the foundation settlement lags behind the pore pressure dissipation. The smaller the fractional order or the larger the viscous hysteresis coefficient, the more obvious this phenomenon is and the longer the time for consolidation to complete.

To study in-plane flexural behavior of the T-joints with square hollow section (SHS) braces and circular hollow section (CHS) chords, experiments of two T-joints with SHS braces and CHS chords (short for SHS-CHS T-joints) and one T-joint with H-steel brace and CHS chord (short for H-CHS T-joint) under in-plane bending were carried out. The results show that all joint specimens share the same final fail model: plastic softening of the chord wall near the brace/chord intersection. For each joint specimen, the test peak moment is close to the flexural capacity determined by the deformation criterion. Both the flexural capacity and flexural stiffness of the SHS-CHS T-joint specimens are higher than those of the corresponding H-CHS T-joint specimens with the same outer brace profile, with increases of approximately 11%. Furthermore, both flexural capacity and flexural stiffness of SHS-CHS T-joints increase significantly with an increasing brace width-to-chord diameter ratio. When the ratio increases from 0.68 to 0.91, both the flexural capacity and flexural stiffness more than double. Current specifications and design guidelines provide reasonable but conservative predictions of the flexural capacity of SHS-CHS T-joints.

To investigate environmental vibration issues induced by adjacent multi-source rail transit in high-density urban areas, an under-construction super high-rise building located near a high-speed railway hub and metro lines in Hangzhou was selected as the research object. In-situ vibration monitoring and theoretical analysis were conducted. The results indicate that the peak vibration response of the building induced by rail transit occurs at the lower floors. During vertical propagation, the vibration exhibits a pattern of rapid initial attenuation followed by slight amplification, while the dominant vibration frequency gradually shifts toward lower frequencies with increasing floor height. Floor slabs of different dimensions show varying degrees of resonance under train-induced vibration excitation. The vibration response at the slab center is significantly higher than that near the slab edges, and the resonance effect becomes more pronounced when the slab’s natural frequency approaches the dominant frequency range of train-induced vibrations. Furthermore, based on an approximate vibration level superposition method, the vibration response of the super high-rise building under multi-source excitation conditions was evaluated. The results show that the maximum frequency-dependent vibration level remains within the comfort limits specified by relevant national standards.

To investigate the nonlinear degradation mechanism and damage behavior of the small- strain shear modulus in coastal gassy clay, resonant column tests were conducted to examine the effects of initial pore water pressure ( u_{\\mathrm{wo}} ), effective confining pressure ( p_{\\mathrm{o}}^{\\prime} ), and gas content ( \\phi ) on the small- strain behavior of the soil. A shear modulus degradation model was subsequently established based on damage theory. The results indicate that the degradation of sheat modulus in gassy clay is a damage process arising from bubble- skeleton interaction, with the mechanical response controlled by the initial pore pressure is below the threshold, an increasing in u_{\\mathrm{wo}} brings the shear modulus close to that of saturated clay; when above the threshold, a futher increase in u_{\\mathrm{wo}} significantly reduces the sheat modulus. An increase in \\phi weakens the initial stiffness of the soil by interrupting interparticle contact paths. The maximum shear modulus G_{\\mathrm{max}} decreases with increasing void ratio and increases with p_{\\mathrm{o}}^{\\prime} . Compared with the conventional H- D model, the proposed model based on a modified Harris distribution provides a more accurate characterization of the shear modulus degradation law, especially under high \\phi conditions.

In order to reveal the hydration nucleation mechanism of carbon nanotubes (CNTs) on cement- based materials at early age, the exothermic rate and cumulative heat of CNTs modified cement paste at hydration temperature of 20^{\\circ}C were measured by isothermal calorimetry. The time range dominated by nucleation and the kinetic parameters of hydration product nucleation were simulated by Krstulovic- Dabic (K- D) model and nucleation growth model, respectively, to quantify the nucleation of CNTs. The incorporation of 0.06\\% CNTs extended the induction period by 0.09\\mathrm{h} and increased both the cumulative heat (by 4\\% ) and the number of C- S- H nuclei (by 51\\% ), indicating enhanced nucleation and accelerated hydration.