[Objective] The extraction process of total flavonoids from Sanghuangporus vaninii and its inhibitory effect on α-glucosidase and α-amylase activities were studied. [Methods] On the basis of single factor experiment, response surface methodology was used to optimize the extraction process of total flavonoids from S. vaninii by using enzyme-assisted microwave method. The inhibitory activities of the total flavonoids extract from S. vaninii were determined. [Results] The optimized process was as follows: solid-liquid ratio 1:25 g/mL, microwave time 160 s, enzyme amount 1%, extraction yield of total flavonoids from S. vaninii was 1.933%, which was identical to the predicted value of 1.938%. The results showed that the half inhibitory concentration (IC50) of total flavonoids on α-glucosidase was 0.56 mg/mL, and the inhibitory rate was 80.70% at the concentration of 1.0 mg/mL. The IC50 value of α-amylase inhibitory activity was 0.79 mg/mL, and the inhibitory rate was 64.31% at the concentration of 1.0 mg/mL. [Conclusion] The extraction process optimized in this study could be used for prepared total flavonoids from S. vaninii. The obtained total flavonoids extract showed good hypoglycemic activity, which could provide scientific basis for the development of hypoglycemic drugs and functional foods.

Due to their unique electrical and optical properties, gold nanomaterials can be used in optical fiber communication, high-speed measurement, laser or medical fields. In this paper, we synthesized gold nanowires (GNWs) and studied the synthesis mechanism of gold nanowires. Based on the plasmon resonance properties of gold nanomaterials, the optical response range of gold nanomaterials can cover the visible-near-infrared wavelength range at the maximum, which provides an important application in the field of optics. We characterize the properties of gold nanowires, such as X-ray diffraction (XRD), transmission electron microscopy and high-resolution transmission electron microscopy, and test the absorption spectra of gold nanowires, and find that their absorption peaks can cover the near-infrared band, which lays the foundation for their application in the field of optics.

This study used Fourier Transform Infrared Spectroscopy (FT-IR) and Second Derivative Infrared Spectroscopy (SD-IR) to differentiate citrus peels (Chenpi) from various geographical origins. The FT-IR results revealed common absorption peaks of stretching vibrations across all samples at 3400~3429 cm-1 (-OH), 2925 cm-1 (C-H), and 1743 cm-1 (C=O), indicating the presence of hydroxyl compounds, alkyl chains, and ester/ketone components (e.g., hesperidin derivatives)?. Notably, S5, S12, and S13 distinctive fingerprint regions with characteristic peaks at 3427~3429 cm-1, 1242 cm-1, and 536 cm-1, respectively, enabling preliminary differentiation from other samples?. The SD-IR technique amplified peak intensity variations and identified unique low-wavenumber peaks (e.g., 1606 cm-1 for S6 and 816 cm-1 for S8), while leveraging differential peak intensities (e.g., S2's strong peak at 1647 cm-1 and S1's prominent peak at 2925 cm-1) to achieve precise discrimination among remaining samples?. This work demonstrates that the combination of FT-IR and SD-IR provides an efficient, non-destructive analytical approach for origin tracing and quality control of ChenPi.

As an emerging pollutant, microplastics have been shown to cover widely in the global marine and freshwater environments. In order to clarify the microplastic pollution in the Jilin section of the Songhua River, 17 sampling points from the Songhua Lake in the upstream to the Jiuzhan Bridge in the downstream of the Jilin section of the Songhua River were selected as the research objects, and the occurrence characteristics, correlation and ecological risks of microplastics in the Songhua River were studied. The results showed that the microplastic pollution in the Jilin section of Songhua River was serious, and the abundance of microplastics in the water body was 401.64 n·m-3~4242.02 n·m-3, and the average abundance of microplastics was 1011.86 n·m-3. It mainly includes five shapes: block, sheet, foam, filament and membrane, among which the lumpy is the most (40%), followed by filament (29.5%), foam (23.3%), flake (4.9%), and membrane (0.2%). A total of 11 colors of microplastics were found, and the white and colorless contents accounted for the majority, and the particle size of microplastics was mainly less than 0.5mm in the water body. There was no significant correlation between pH, ammonia nitrogen content, and total phosphorus content and microplastic abundance for environmental factors, and a strong correlation between lumpy and foamy microplastics for shape. The pollution load index (PLI) ranged from 0.24~0.80, and the Nemeiro index was 0.46, both of which belonged to the low pollution level. Studies have shown that human activities are a major reason for microplastics to enter the Songhua River, and most of the microplastics come from the degradation and rupture of large plastic products. It fills the gap in the understanding of the distribution of microplastics in Jilin City, Songhua River, and provides new data support and direction for the study of microplastic pollution in Songhua River and the control and treatment of microplastic pollution sources in Songhua River.

This article employs the response surface design method to optimize the ammonia nitrogen removal efficiency using the MAP method. Significant influencing factors were identified through a PB experiment, and the center point of the BBD experiment was determined via a steepest ascent test. The BBD experiment was designed to ascertain the sequence of influence and optimal conditions for pH value, Mg:N, and P:N molar ratios on the removal of ammonia nitrogen pollutants from water. The findings revealed that the three factors affect the ammonia nitrogen removal rate in the following order: pH > Mg:N > P:N. The optimal conditions were determined as follows: pH 11.02, Mg:N molar ratio of 1.57, and P:N molar ratio of 1.41. Verification experiments confirmed that under these optimal conditions, the ammonia nitrogen removal rate can peak at 95.27%, closely matching the predicted value. This demonstrates the effectiveness of the MAP method in removing ammonia nitrogen pollutants and the high goodness of fit of the response surface regression model.

Utilizing agricultural waste, peanut shells, the CuFe2O4@BC-HS material was synthesized through a citric acid complexation method. This material effectively activates potassium monopersulfate (PMS), leading to the proficient degradation of sulfanilamide (SD). The study evaluated various factors influencing the degradation efficacy, including the dosage of CuFe2O4@BC-HS, PMS concentration, solution pH, initial SD concentration, and the presence of coexisting ions. The results indicate that at an initial SD concentration of 10 mg/L, a solution pH of 7, a CuFe2O4@BC-HS dosage of 0.1 g/L, and a PMS concentration of 0.225 mmol/L, the degradation rate of SD reached 93% within 90 minutes. Low concentrations of HCO3- enhance the degradation of sulfanilamide, while high concentrations exhibit inhibitory effects. High concentrations of Cl- slightly inhibit the degradation, whereas SO42- does not significantly affect the degradation process. Radical quenching experiments confirmed that 1O2, O2?-, and SO4?- are the principal reactive radicals responsible for the degradation of SD.

In the actual water treatment process, high energy consumption has always been the main limiting factor restricting the application of electrocatalytic oxidation technology in water treatment engineering. In order to reduce the energy consumption of electrocatalytic oxidation technology in treating organic pollutants in water, zirconium nitride intermediate layer was introduced by arc spraying technology, and the stability and service life of titanium-based PbO2 anode were improved by electrodeposition. The results show that by adding zirconium nitride intermediate layer, the crystal particles of PbO2 are significantly refined, the particle distribution is more uniform, and the crystal structure is more dense. This structural optimization significantly improves the resistance of the electrode surface to electrolyte erosion, thus enhancing the stability and durability of the electrode. The results of the treatment of wastewater containing 4-amino-7 chloroqualine showed that the removal rate of pollutants could reach 95.85% after electrolytic treatment with current density of 20mA/cm2 for 3 hours under neutral conditions. This research can effectively treat organic pollutants in water treatment engineering, and provide effective solutions for water bodies containing complex pollutants such as chemical wastewater.

Boiler is a kind of indispensably important power equipment in industrial process such as petro-chemical, thermal power generation. This paper proposes a composite control strategy for water level control based on triple-method and backstepping technique. Reference feedforward controller is designed to fasten the response velocity and error feedback controller is designed to enhance the control performance, and the whole system has Lyapunov stability. Simulation results show that the proposed scheme not only has smaller rise time and overshoot, but also can effectively reject vapor disturbance and water supply pressure fluctuation, and easy to engineering realization because of algorithm simple.

Aiming at the inherent volatility of wind power generation in power system and the problem of high frequency noise in power prediction. A short-term wind power prediction model based on VMD-WOA-ATLSTM is proposed. The Pearson correlation coefficient method is used in the model to obtain highly relevant data and determine the key impact characteristics of wind power. The variational mode decomposition (VMD) is used to decompose and reconstruct the data to highlight the main features related to power and reduce noise interference. Further the ATLSTM model combining long-term and short-term memory neural network and attention mechanism is built. The whale optimization algorithm (WOA) is used to globally optimize the parameters of ATLSTM network. Finally, the prediction results of each modal component are superimposed to improve the overall prediction performance of the model. The experimental results show that VMD-WOA-ATLSTM model has better prediction accuracy and robustness than LSTM, LSTM-Attention, VMD-ATLSTM and WOA-ATLSTM models in power prediction.

To address the three major challenges of poor path stability, low search efficiency, and high path cost when applying traditional RRT* algorithms to 6-DOF robotic arms in complex 3D environments, this paper proposes the DTSP-RRT* algorithm (RRT* Algorithm Incorporating Distance Threshold Classification and Shortest Path Requirements). The optimization is achieved through a three-stage technical approach: First, a DTSP (Distance Threshold evaluation and Shortest Path requirements) strategy is introduced to filter temporary target points, enhancing obstacle-avoidance stability. Second, an ellipsoid-constrained sampling space and dynamically adjusted extension step sizes based on obstacle detection are implemented to improve search efficiency. Finally, global parent node replanning reduces motion costs, while cubic B-spline curves eliminate path spikes. To validate algorithm performance, simulation comparison experiments were conducted on MATLAB platform comparing DTSP-RRT* with standard RRT, RRT*, and APF-RRT* algorithms. Results demonstrate that DTSP-RRT* significantly outperforms the other three algorithms in path search time, final path length, path node count, and sampling node count. This study provides novel insights for path planning of 6-DOF robotic arms in complex scenarios.

 The research focuses on detecting robotic arm grasping poses using RGB-D cameras. A novel framework combining YOLO segmentation and GSNet grasp detection is designed to identify and localize target objects for robotic arm grasping. First, the YOLOv8n-seg segmentation model extracts and segments target objects in RGB images. The segmented features are then mapped to the depth map, enhancing the 3D positioning accuracy of the target. Next, the Iterative Closest Point (ICP) algorithm registers the RGB and depth data into a fused point cloud, which is fed into the GSNet model for grasp pose detection. To address GSNet's limitation in handling multi-scale objects—particularly the biased sampling distribution that leads to unreliable grasping of small objects—a multi-scale cylindrical grouping method (RANSNC) is introduced. This optimization adaptively aggregates features across scales, improving the success rate of grasp pose detection for objects of varying sizes.

This study presents a self-powered piezoelectric energy harvesting system integrated into ski (ice) boots, which converts vibrational energy generated between the inner and outer boots during skiing into electrical energy through piezoelectric materials, subsequently powering various functional components. Initially, an analytical investigation was conducted to examine the harvestable energy forms during skiing and the voltage generation characteristics of circular piezoelectric vibrators. A 3D model was subsequently developed for simulation-based experimental validation. A prototype of the self-powered ski (ice) boot piezoelectric energy harvesting system was fabricated and experimentally tested. The experimental results confirm the system's efficacy, demonstrating a maximum output voltage of 204 V under three distinct alternating force application modes. This work provides a technical foundation for mechanical energy recovery in winter sports equipment.

To address the challenges of low efficiency and significant errors in parameter tuning of prediction horizon for traditional model predictive controllers (MPC), this paper proposes a dynamic horizon adaptive MPC controller integrated with a particle swarm optimization (PSO) algorithm (PSO-MPC). The method establishes a PSO-MPC collaborative framework that dynamically searches for optimal prediction horizon parameters based on the vehicle’s real-time state within each control cycle, thereby constructing a time-varying prediction model to enhance trajectory tracking adaptability. The simulation results show that in the double lane change and lane change scenarios, compared with the MPC method with a fixed prediction time domain, the trajectory tracking error of the proposed controller is reduced by 35% and 37% respectively, verifying that the dynamic prediction time domain optimization mechanism can significantly improve the tracking accuracy and dynamic adaptability of complex trajectories, providing a new technical path for the design of autonomous driving control strategies.

Based on the orthogonality of Banach spaces and analogous to the method of simplifying operator equations using Liapunov Schmidt, a Liapunov Schmidt scheme is derived for converting bifurcation equations into finite dimensional or low dimensional algebraic equations. Determine the bifurcation point of the simplified bifurcation equation, and calculate the partial derivative value at the bifurcation point to construct a smooth function strongly equivalent to the bifurcation equation .When the constructed smooth function satisfies the same condition as the partial derivative of the Pitchfork bifurcation equation at the bifurcation point, the smooth function is strongly equivalent to the Pitchfork bifurcation equation. The condition satisfied by the partial derivative is the necessary and sufficient condition for identifying Pitchfork bifurcations, enriching the identification methods of bifurcations in Banach spaces.