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The time-varying mesh stiffness excitation of helical gears impacts the vibration state of the rotor–bearing systems, while the existence of mechanical dynamic eccentricity makes the rotor–bearing dynamics equation a system of parametric excitation. To address this situation, the time-varying mesh stiffness of the helical gear is substituted into the coupled bending–torsion–axial dynamic equation of the rotor–bearing system. By considering dynamic eccentricity, the rotor’s vibration displacement response is calculated. The unified strength theory is introduced to compute the complex stress state. The study’s results indicate that time-varying stiffness significantly influences the system’s vibration characteristics, with the equivalent stress values exceeding those under twin-shear stress. This finding demonstrates the advantage of using the unified strength theory under high-load conditions, providing an essential reference for optimizing the dynamic performance of high-speed helical gear transmission systems.

Excessive nitrogen accumulation from aquaculture poses a significant threat to water quality in river–lake systems. This study investigated the Taipu River and five interconnected lakes to analyze the forms, spatial distribution, and ecological impact of nitrogen in both water and surface sediments. Sediment total nitrogen (TN), ammonium nitrogen (NH4+-N), and nitrate nitrogen (NO3−-N) were measured, with aquaculture-dominated lakes such as Xueluoyang Lake and Caodang Marsh exhibiting significantly higher sedimentary TN concentrations than the Taipu River. In Xueluoyang Lake, the average TN content reached 1037.3 mg/kg—1.87 times higher than in the river—highlighting the legacy effect of historical intensive aquaculture. Correlation analyses showed strong associations between sediment NH4+-N and NO3−-N and nitrogen levels in overlying water, confirming sediments as a major endogenous nitrogen source. Multivariate statistical methods, including Pearson’s correlation, hierarchical clustering, and principal component analysis, were applied to elucidate spatial patterns and key influencing factors. Water quality evaluation indices and sediment organic pollution assessments revealed widespread TN exceedance, particularly in dry seasons, with water quality deteriorating to Class V or worse. These results underscore the need for strengthened control of sedimentary nitrogen release and effective management of agricultural non-point source pollution to restore and protect water quality in river–lake systems.

The current multi-source fusion fault diagnosis algorithm rarely considers the information correlation of multi-sensor networks and the important difference between multi-sensors. Aiming at this challenge, we propose an intelligent fault identification method for high-speed railway bogie based on the graph neural network embedded with prior knowledge, which brings the spatial information of the sensor network into the diagnosis algorithm and re-weights each sensor according to the diagnosis results. Firstly, the time–domain correlation of vibration signals between bogie sensor networks is calculated as the prior knowledge. Then, based on the spatial topological relationship of the sensors, the graph correlation matrix of the network is established. Further, the importance of each sensor is dynamically analyzed and updated together with the training process. The proposed method is tested on a high-precision bogie test bed, and the experimental results demonstrate the effectiveness and superiority of the proposed method.

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.

In this paper, magnetic cotton textile wastes pyrolyzed by ferric cerium oxide (FexCey oxide/PC) were synthesized for degradation of p-nitrophenol by catalytic ozonation, and the optimal Fe-Ce ratio was 10:1. Compared to Fe10Ce1 oxide, the Fe10Ce1 oxide/PC not only greatly improved the degradation efficiency of PNP, but also reduced the dosage of catalyst. Through the BET test, the Fe10Ce1 oxide/PC has a high specific surface area to absorb part of the pollutants. VSM test shows that the material is magnetic and easy to recycle. Response surface methodology (RSM) was applied to optimize the experimental condition, and the optimal removal rate was 90% when the initial pH was 9, the catalyst dosage was 0.4 g/L, and the ozone addition was 1.77 L/min (5.9 mg/L). Finally, the mechanism of PNP degradation was explored utilizing inhibitor and ESR free radical detection. The adsorption capacity of the material and electron-absorbing property of PNP jointly determined the high catalytic efficiency with Fe10Ce1 oxide/PC in catalytic ozonation.

The ammonia removal as well as the current efficiency during electrolysis was investigated by using a vermiculite-packed electrochemical reactor under continuous mode. Experimental results showed that adsorption of ammonia by vermiculite and electrolytic desorption of ammonia simultaneously existed in the reactor, leading to 89% removal of initial 30 mg N/L ammonia and current efficiency of 25% under the condition of 2.0 A, 6.0 min hydraulic retention time with 300 mg Cl/L chloride as the catalyst. The ammonia removal capacity had a linear relationship with the products of hydraulic retention time, current and chloride concentration within experimental conditions. The treatment results of secondary effluent indicated that 29.9 mg N/L ammonia can be reduced to 4.6 mg N/L with 72% removal of total nitrogen and a current efficiency of 23%, which was 2% less than synthetic wastewater due to the reducing components in the real wastewater.

With the improvement and development of micro-mechanical manufacturing technology, people can produce an increasing variety of micro-electromechanical systems in recent years, such as micro-satellite thrusters, micro-sensors, micro-aircrafts, micro-medical devices, micro-pumps, and micro-motors. At present, these micro-mechatronic systems are driven by traditional energy power systems, but these traditional energy power systems have such disadvantages as short endurance time, large size, and low energy density. Therefore, efforts were made to study micro-energy dynamical systems with small size, light gravity, high density and energy, and long duration so as to provide continuous and reliable power for these systems. In general, the micro-thermal photoelectric system not only has a simple structure, but also no moving parts. The micro-thermal photoelectric system is a micro-energy power system with good application prospects at present. However, as one of the most important structural components of micro-thermal photoelectric systems, the microburner, is the key to realize the conversion of fuel chemical energy to electric energy in micro-thermal photoelectric system. The studies of how to improve the flame stability and combustion efficiency are very necessary and interesting. Thus, some methods to improve the performance of micro-burners were introduced and summarized systematically, hoping to bring some convenience to researchers in the field.

The heavy metal release experiments were conducted in the laboratory to examine the effects of 3 factors - pH, dissolved oxygen (DO), and temperature on the metal release from sediments taken from the Huangpu River. The metal concentrations in the dry sediments ranged from 0.030 to 0.296 mg g for Cr, 0.021 to 0.097 mg g for Ni, 0.014 to 0.219 mg g for Cu, 0.035 mg to 0.521 mg g for Zn, 0.0002 to 0.001 mg g for Cd and 0.023 to 0.089 mg g for Pb. Most of the metals found in the sediments were in the form of residual fraction, the exchangeable fraction consisted of only a small portion of total metals. The average dissolved metal concentrations in the overlying water during the 13-day period under different conditions were ranging from 0.82 to 1.93 μg L for Cr, 1.08 to 4.19 μg L for Ni, 40.79 to 82.28 μg L for Cu, 20.30 to 29.96 μg L for Zn, 1.57 to 4.07 μg L for Cd, and 22.26 to 75.50 μg L for Pb, respectively. Statistical interpretation of the data indicated that pH (7, 8, 9), dissolved oxygen DO (1.0 and 5.0 mg L ) and temperature (4, 16, 25°C) had no significant effects on the heavy metal release under the studied conditions. Cu and Pb had the highest release flux, while Cd, Pb and Cu had higher mobility. The main factors controlling the metals release might be the inherent characters of metals and sediments.

This study evaluated the effectiveness of pressure-assisted ozonation (PAO) in Chlorella vulgaris (C. vulgaris) cell disruption, and compared the disruption result with that of the ultrasonication (US) by using four quantification indicators: cell counting, ultra violet (UV) absorbance, turbidity and visible light absorbance. It was found that under the condition of 0.8 MPa and 80 cycles, PAO treatment achieved cell rupture of 80.3%, with the power of 1080 W and treatment time of 60 min, US achieved cell rupture of 83.8%. Cell counting was a reliable indicator and applicable to both PAO and US treatments. Turbidity and visible light absorbance gave similar results and featured as the simplest operation. UV absorbance reflected the metabolite release due to cell breakage; however, it was less reproducible when it was applied to quantify the cell rupture by PAO. Its trend indicated that during cell disruption metabolite degradation occurred, especially after significant rupture in the case of excessive PAO treatment. The cellular morphology of C. vulgaris cells during PAO and US treatments was investigated by scanning electron microscope (SEM) which certified that the cells damage was caused by both physical and chemical attack.

In this research, nano Cu/Al–HTLCs, Co/Al–HTLCs and Cu/Co/Al–HTLCs were synthesized, characterized, and tested in electrolytic reduction nitrate. Experimental results showed that Cu/Al–HTLCs were less stable than Co/Al–HTLCs due to the Jahn–Teller effect. However, electrocatalytic activity of copper was superior to that of cobalt; thus, Cu/Co/Al–HTLCs were selected based on their stable crystalline structure and electrochemical activity. The optimized Cu2CoAl–HTLC was highly active in nitrate reduction, with two peaks for nitrate and nitrite reduction, respectively. Ammonia, nitrite and N-containing gases were found to be the final products of constant potential electrolysis at −0.54 and −0.74 V.