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Due to safety problems caused by the use of organic electrolytes in lithium-ion batteries and the high production cost brought by the limited lithium resources, water-based zinc-ion batteries have become a new research focus in the field of energy storage due to their low production cost, safety, efficiency, and environmental friendliness. This paper focused on vanadium dioxide and expanded graphite (EG) composite cathode materials. Given the cycling problem caused by the structural fragility of vanadium dioxide in zinc-ion batteries, the feasibility of preparing a new composite material is explored. The EG/VO2 composites were prepared by a simple hydrothermal method, and compared with the aqueous zinc-ion batteries assembled with a single type of VO2 under the same conditions, the electrode materials composited with high-purity sulfur-free expanded graphite showed more excellent capacity, cycling performance, and multiplicity performance, and the EG/VO2 composites possessed a high discharge ratio of 345 mAh g−1 at 0.1 A g−1, and the Coulombic efficiency was close to 100%. The EG/VO2 composite has a high specific discharge capacity of 345 mAh g−1 at 0.1 A g−1 with a Coulombic efficiency close to 100%, a capacity retention of 77% after 100 cycles, and 277.8 mAh g−1 with a capacity retention of 78% at a 20-fold increase in current density. The long cycle test data demonstrated that the composite with expanded graphite effectively improved the cycling performance of vanadium-based materials, and the composite maintained a stable Coulombic efficiency of 100% at a high current density of 2 A/g and still maintained a specific capacity of 108.9 mAh/g after 2000 cycles. [ABSTRACT FROM AUTHOR]

In this paper, a field effect transistor (FET)-type sensor with Pt-decorated In2O3 (Pt-In2O3) nanoparticles is fabricated for detecting H2 gas at room temperature. A pulsed measurement method is adopted to continuously alternate between pre-biasing the gate and reading the drain current of the FET-type sensor. This method effectively reduces the drift in the sensing signal. It is also found that negative pre-bias voltages can dramatically shorten the recovery time of the sensor after sensing H2, while positive pre-bias voltages have the opposite effect. The H2 sensing performance of the sensor is characterized under the enhancement of a pulsed negative pre-bias. By calculating and comparing the root mean square, signal-to-noise ratio, and detection limit of the sensor under different operating regions, it is found that the sensor has the best sensing performance in the subthreshold region, which is suggested to be the optimum operating region for FET-type sensors. In addition, the presence of oxygen significantly consumes the hydrogen molecules and reduces the room-temperature H2 sensitivity of the sensor. The proposed sensor presents promising H2 sensing properties, and this research could be a guide for the use of FET-type sensors in more gas detection applications. [ABSTRACT FROM AUTHOR]

Yb:CALYGLO crystals with a dopant concentration of 5 at.% were successfully grown using the Czochralski method. The crystal samples were extensively studied to analyze their structure, room temperature and low temperature spectra, and laser properties. The highest absorption cross-section at 977 nm was calculated to be 1.83 × 10−20 cm2 for σ polarization and 5.32 × 10−20 cm2 for π polarization. Similarly, the emission cross-section was determined to be 1.38 × 10−20 cm2 at 980 nm for σ polarization and 2.28 × 10−20 cm2 at 981 nm for π polarization, with a full width at half maximum (FWHM) of 50.3 nm and 89.5 nm, respectively. The fluorescence lifetime of the 5 at.% Yb:CALYGLO crystal at 2F5/2 was measured to be 1.10 ms. Additionally, gain cross-sections were calculated for different β values. In the continuous laser experiment, the crystal demonstrated a laser output of 20.15 W at 1057 nm, with a slope efficiency of 53.3%. These experimental findings indicate that the lattice of Y3+ in the crystal is partially replaced by Lu3+ and Gd3+, resulting in a broader spectrum. Consequently, this crystal shows promising potential as a gain medium for ultrashort pulse laser crystals. [ABSTRACT FROM AUTHOR]

This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer's body, thus enhancing both the fit and aesthetic value of the clothing. [ABSTRACT FROM AUTHOR]

To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection. [ABSTRACT FROM AUTHOR]

In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibit constraints in effectively detecting and classifying multifarious cyber threats. Specifically, the surge in high-dimensional network traffic data and the imbalanced distribution of classes exacerbate the predicament of ideal classification performance. Notably, the presence of redundant information within network traffic data undermines the accuracy of classifiers. To address these challenges, this study introduces a novel approach for intrusion detection classification which integrates advanced techniques of feature engineering and model optimization. The method employs a feature engineering approach that leverages mutual information maximum correlation minimum redundancy (mRMR) feature selection and synthetic minority class oversampling technique (SMOTE) to process network data. This transformation of raw data into more meaningful features effectively addresses the complexity and diversity inherent in network data, enhancing classifier accuracy by reducing feature redundancy and mitigating issues related to class imbalance and the detection of rare attacks. Furthermore, to optimize classifier performance, the paper applies the Optuna method to fine-tune the hyperparameters of the Catboost classifier, thereby determining the optimal model configuration. The study conducts binary and multi-classification experiments using publicly available datasets, including NSL_KDD, UNSW-NB15, and CICIDS-2017. Experimental results demonstrate that the proposed method outperforms traditional approaches regarding accuracy, recall, precision, and F-value. These findings highlight the method's potential and performance in network intrusion detection. [ABSTRACT FROM AUTHOR]

In the process of open-roller conveying, magnesium alloy plates easily produce temperature drops and uneven temperatures. Ignoring the heat dissipation caused by the contact between the plate and the roller table, the process can be described as a process of air cooling. The present study aimed to investigate the temperature distribution and evolution of a magnesium alloy plate during the process. The air-cooling process of the AZ31B magnesium alloy plate was investigated and analyzed in detail under different initial temperatures and plate thicknesses, with a specific focus on the temperature distribution along both the width and thickness directions. The results show that the temperature-difference curves between the end face and the center under different air-cooling conditions appeared to have four stages: rapid increase, slow increase, basic stable and slow decline. To facilitate the establishment of the temperature model, the whole air-cooling process was approximately divided into two independent one-dimensional heat-conduction processes in the thickness direction and the width direction. Subsequently, one-dimensional steady-state heat-conduction models were developed in various directions, based on the fundamental principles of heat transfer and assuming that the adjacent temperature-drop layer satisfied the quadratic function distribution. Through the superposition of cooling with time in two directions, the temperature evolution at different positions in the process of air cooling can be solved accurately. [ABSTRACT FROM AUTHOR]

Sensitive H2 sensors at low concentrations and room temperature are desired for the early warning and control of hydrogen leakage. In this paper, a resistive sensor based on Pt-doped In2O3 nanoparticles was fabricated using inkjet printing process. The H2 sensing performance of the sensor was evaluated at low concentrations below 1% at room temperature. It exhibited a relative high response of 42.34% to 0.6% H2. As the relative humidity of 0.5% H2 decreased from 34% to 23%, the response decreased slightly from 34% to 23%. The sensing principle and the humidity effect were discussed. A dynamic current sensing model for dry H2 detection was proposed based on Wolkenstein theory and experimentally verified to be able to predict the sensing behavior of the sensor. The H2 concentration can be calculated within a short measurement time using the model without waiting for the saturation of the response, which significantly reduces the sensing and recovery time of the sensor. The sensor is expected to be a promising candidate for room-temperature H2 detection, and the proposed model could be very helpful in promoting the application of the sensor for real-time H2 leakage monitoring. [ABSTRACT FROM AUTHOR]

The rapid growth of China's economy in recent years has greatly improved its citizens' living standards, but economic growth consumes many various energy sources as well as produces harmful air pollution. Nitrogen oxides, SO2 (sulfur dioxide), and other polluting gases are damaging the environment and people's health, with a particular spike in incidences of many air pollution-related diseases in recent years. While there have been many documents discussing China's energy and environmental issues in the past, few of them analyze economic development, air pollution, and residents' health together. Therefore, this study uses the modified undesirable dynamic two-stage DEA (data envelopment analysis) model to explore the economic, environmental, and health efficiencies of 30 provinces in China. The empirical results show the following: (1) Most provinces have lower efficiency values in the health stage than in the production stage. (2) Among the provinces with annual efficiency values below 1, their energy consumption, CO2 (carbon dioxide), and NOx (nitrogen oxide) efficiency values have mostly declined from 2013 to 2016, while their SO2 efficiency values have increased (less SO2 emissions). (3) The growth rate of SO2 efficiency in 2016 for 10 provinces is much higher than in previous years. (4) The health expenditure efficiencies of most provinces are at a lower level and show room for improvement. (5) In most provinces, the mortality rate is higher, but on a decreasing trend. (6) Finally, as representative for a typical respiratory infection, most provinces have a high level of tuberculosis efficiency, indicating that most areas of China are highly effective at respiratory disease governance. [ABSTRACT FROM AUTHOR]