Shandong Police College

Pseudocapacitive materials employ rapid, non-diffusion-limited faradaic processes to store charge, demonstrating significant potential for fast-charging batteries and supercapacitors. However, the high redox potentials of existing pseudocapacitive anodes substantially lower the overall cell voltage and energy density. Here, a cation-disordered rock-salt lithium titanium oxyfluoride (DRX-LixTiOF2, 0 < x < 2) is reported to reversibly accommodate approximately 1.19mol of Li+ (~ 310mAhg-1) and delivers high-rate performance (exceeding 64.4C) via pseudocapacitive Li+ storage within a low potential window extending down to 0.1V vs. Li+/Li. This pseudocapacitive behavior is characterized by several structural and electrochemical features: the absence of phase transformation during Li+ intercalation, quasi-rectangular cyclic voltammetry curves, sloping charge/discharge profiles and a surface-controlled current response. We further reveal that the pseudocapacitive characteristics originate from a three-dimensional percolation network that facilitates fast Li+ migration with low energy barriers, enabled by a cation/anion-disordered structure arising from the mixed occupancy of Li/Ti cations and O/F anions. Owing to its low operating potential and high-rate capability, DRX-LixTiOF2 allows a lithium-ion capacitor to attain 4.0V cell voltage and achieve energy and power densities several times higher than those obtained with conventional anodes, such as battery-type Li4Ti5O12 or pseudocapacitive materials like Nb2O5 and TiO2.

As utilization of generative artificial intelligence increases, understanding user behavioral intentions that influence its use is crucial. We surveyed 359 ChatGPT users and analyzed their responses using partial least squares structural equation modeling to test hypotheses based on the unified theory of acceptance and use of technology. We found that performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived enjoyment were all positively related to the behavioral intention to use ChatGPT, and perceived risk was negatively related to the behavioral intention to use ChatGPT. The results of fuzzy-set qualitative comparative analysis were categorized into the following antecedent configurations affecting the behavioral intention to use ChatGPT: influence-driven performance, influence-facilitating enjoyment, and hybrid. These findings not only enrich and develop the unified theory of acceptance and use of technology framework but also provide insights for ChatGPT designers to promote the product.

Energy consumption optimization in Computer Numerical Control milling operations represents a critical challenge in modern manufacturing, where energy efficiency directly impacts both economic sustainability and environmental footprint. This study presents a comprehensive deep learning framework for energy consumption pattern recognition and prediction in milling processes. An experimental dataset of 5000 samples was collected, encompassing 13 process parameters, including spindle speed, feed rate, cutting forces, tool wear, and vibration signatures. We developed a deep neural network (DNN) architecture consisting of four dense layers (256–32 neurons) with batch normalization and dropout regularization. The proposed DNN model achieved performance with R2 = 0.5902, RMSE = 104.77 W, and MAPE = 10.76%, outperforming traditional machine learning methods, including ridge regression (R2 = 0.5869), random forest (R2 = 0.5580), and gradient boosting (R2 = 0.4931). Feature importance analysis revealed that tool wear (25.12%), cutting forces (42.3% combined), and spindle speed (17.29%) are the most significant contributors to power consumption. Energy efficiency classification achieved 89.33% accuracy with 96.24% recall for high-efficiency operations, enabling real-time monitoring and optimization. This study demonstrates that deep learning approaches can effectively capture complex nonlinear relationships in machining dynamics, providing a foundation for intelligent energy management systems in smart manufacturing.

This research presents an empirical analysis of the association between ambient air quality and mental health outcomes within the police force of Henan Province, China. It utilizes Air Quality Index (AQI) data from all 28 cities in the province, along with mental health survey data obtained from a sample of 40,790 officers using the 12-item General Health Questionnaire (GHQ-12). The analysis employs spatial econometric modeling to investigate these associations. The results demonstrate significant spatial autocorrelation in both air pollution levels and mental health risks, with cities exhibiting higher AQI values and elevated GHQ positivity rates showing pronounced spatial clustering. Estimates from a Spatial Durbin Model confirm a significant association between local air quality and mental health among police officers. Furthermore, the results indicate that air quality in neighboring areas is also correlated with the mental health risk levels of officers in a given locale. These findings point to both local and regional pollution exposure as significant correlates of psychological well-being among police officers, a group that experiences high levels of occupational exposure. The study underscores the need for inter-regional environmental policy coordination and the integration of mental health support into occupational health frameworks, particularly for public safety personnel in heavily polluted regions.

Some oil and gas fields in China are located in the north, and in winter, oil fields face technical difficulties in separating oil and water from high viscosity condensate produced fluids. This article proposes a low-temperature oil-water separation scheme using a preheated hydrocyclone, with a focus on studying the separation effect of oil-water mixtures with different oil contents by varying the overflow diversion ratio and inlet flow rate of the hydrocyclone. The structure of the cyclone was optimized using response surface methodology (RSM) through three-dimensional modeling. The Euler multiphase flow model was used to study the distribution characteristics of the flow field and phase volume fraction of the oil-water two-phase medium inside the hydraulic cyclone under different injection parameters. The results show that when the oil content of the mixed liquid is 10% and the viscosity of the oil phase is 27-31mPa·s, an increase in the overflow diversion ratio is beneficial for the rapid separation of low-density phase media. If the diversion ratio is too high, it will cause the liquid flowing out of the overflow port to form an oily mixture again. In our experiment, the optimal overflow diversion ratio is 0.2, and the effective oil removal efficiency can reach 92%. The influence of the oil content in the mixed liquid on the oil-water separation efficiency of the cyclone is achieved through the interaction of viscous drag and centrifugal force on the radial partial pressure and tangential flow velocity. As the oil content and viscosity of the mixed liquid increase, the radial flow resistance experienced by the cylindrical and conical sections of the cyclone will increase. The radial partial pressure, pressure gradient, and radial flow velocity of the aqueous medium will also decrease, and the separation efficiency will also decrease accordingly.

Based on elementary quadratic chaotic flows with no equilibria, this paper proposes a novel four-dimensional hyperchaotic hidden attractor system. Compared with traditional chaotic systems, this system has more complex dynamic behaviors and higher degrees of freedom. Developing this novel system can further expand the research scope of chaos theory and reveal the intrinsic laws and characteristics of chaotic behaviors in higher dimensions.Through nonlinear dynamical analysis, the system exhibits diverse dynamic behaviors with varying parameters and possesses multiple hidden attractors, demonstrating the property of multistability. Subsequently, an analog circuit is constructed using Multisim 14.0 to further validate the system. Finally, image encryption is performed by employing different DNA encoding rules and corresponding operation rules based on chaotic sequences for diffusion. The cipher images are analyzed in terms of histograms, information entropy, and correlation. Experimental results indicate that the proposed algorithm achieves effective encryption and can withstand common attacks.

The traditional inference of thermal comfort relies mainly on either questionnaire surveys or invasive physiological signal monitoring. However, the use of these methods in real time is limited and they have a low accuracy; furthermore, they can cause an inconvenience to the daily work and life of indoor personnel. With the development of intelligent building technology, non-intrusive technology based on video analyses has gradually become a research hotspot. Not only does this type of technology avoid the limitations of traditional methods, but it can also be used to dynamically monitor thermal comfort. At present, the established and relatively complete non-intrusive recognition methods usually rely on additional equipment or cameras with specific angles, which limits their deployment and application in a wider range of scenarios. Therefore, in order to improve the non-intrusive prediction accuracy of the thermal comfort level of indoor personnel, it is necessary to establish a non-intrusive indoor personnel thermal comfort inference model. This study designed a cross-modal knowledge-distillation-based thermal adaptive behavior-recognition model. In order to avoid the difficulties of terminal deployment caused by the large model and the time-consuming nature of optical flow estimation, a multi-teacher network model was used to transfer the knowledge of different modes to a single student model. This reduced the number of model parameters and the computational complexity while improving the recognition accuracy. The experimental results show that the proposed vision-based thermal adaptation behavior-recognition model can non-invasively and accurately identify the thermal adaptation behavior of indoor personnel, which can not only improve the comfort of indoor environments, but also enable the intelligent adjustment of HVAC systems.