基于动态二值化技术的交通流量实时微机检测方法的研究

本语文介绍了一种基于ＰＣＩ总线的微机图像处理和分析系统，提出了一种新的动态二值化技术，并运用于该系统。这种技术不仅能够实时采集、显示序列图像，而且能够实时检测、显示车流量。研究结果显示了这种方法的可行性和实用性。     

对现代教学媒体在高校武术专业教学运用中的若干思考

采用文献资料法、实践法等研究方法,分析现代教学媒体在高校武术专业教学中的作用及优势,并探讨其运用过程中所存在的问题;旨在优化高校武术教学手段与方法,进而提高教学质量和丰富教学内容.     

Impact of Internal Factors on Information Technology Adoption: An Empirical Investigation of Chinese Firms

Organizations differ significantly in their inclination to deploy information technology (IT). The factors that determine the organizational inclination to deploy IT need to be classified to help firms formulate an appropriate e-strategy. This paper discusses a framework that explains the influence of internal factors on IT adoption. The framework is based on quantitative data on IT adoption from 95 firms in China. The results highlight that the internal factors of leadership, organizational learning, and IT resources positively impact IT adoption. The study also shows that these factors are correlated.     

粉煤灰刨花板工艺的初步研究

论述了热压法制造粉煤灰（ｆｌｙ－ａｓｈ）刨花板工艺。试验选取热压温度、热压时间、粉煤灰／（粉煤灰＋水泥）、灰木比、水灰比、添加剂和板子密度等工艺变量进行了研究。结果表明：（１）用速生杨和南就热电厂Ⅰ级电除尘粉煤灰作主要原料制造粉煤灰刨花板是可行的。（２）粉煤灰用量占总灰量（即粉煤灰／（粉煤灰＋水泥）的６０％－７０％时制板仍然能达到冷压法水泥刨花板国家行业标准要求。但随着粉煤灰用量所占比例的增加，板     

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.     

Effect of ultrasound and chlorine dioxide on Salmonella Typhimurium and Escherichia coli inactivation in poultry chiller tank water

This study evaluated the application of ultrasound alone or combined with chlorine dioxide (ClO₂) for Salmonella Typhimurium and Escherichia coli inactivation in poultry processing chiller tank water. A Full Factorial Design (FFD) 2² was conducted for each microorganism to evaluate the effect of ultrasound exposure time (x₁: 1 to 9 min; fixed: 37 kHz; 330 W; 25 °C) using a bath, and ClO₂ concentration (x₂: 1 to 17 mg L⁻¹) on microorganism count expressed in log CFU mL⁻¹ in distilled water. Variable x₂ had a negative effect on Salmonella Typhimurium (-5.09) and Escherichia coli (-2.00) count, improving the inactivation; while a x₁ increase present no inactivation improvement, explaining the use of x₁ lower level (1 min) and x₂ higher level (17 mg L⁻¹). The best condition for microorganism inactivation based on FFD was evaluated in chiller tank water (with organic matter) at 25, 16, and 4 °C; x₁ was kept (1 min), however x₂ was adjusted to obtain the same residual free chlorine (2.38 mg L⁻¹) considering the ClO₂ consumption by organic matter, achieving the value of 30 mg L⁻¹. An inactivation of 49% and 31% were observed for Salmonella Typhimurium and Escherichia coli. When ultrasound was replaced by a simple agitation in the presence of ClO₂, there was no inactivation for both microorganisms. Moreover, at poultry carcass pre-chilling (16 °C) and chilling (4 °C) conditions, the synergism of ultrasound combined with ClO₂ was more pronounced, with microorganisms’ reductions up to 100%.     

Particle separation in microfluidics using different modal ultrasonic standing waves

Microfluidic technology has great advantages in the precise manipulation of micro and nano particles, and the separation of micro and nano particles based on ultrasonic standing waves has attracted much attention for its high efficiency and simplicity of structure. This paper proposes a device that uses three modes of ultrasonic standing waves to continuously separate particles with positive acoustic contrast factor in microfluidics. Three modes of acoustic standing waves are used simultaneously in different parts of the microchannel. According to the different acoustic radiation force received by the particles, the particles are finally separated to the pressure node lines on both sides and the center of the microchannel. In this separation method, initial hydrodynamic focusing and satisfying various equilibrium constraints during the separation process are the key. Through numerical simulation, the resonance frequency of the interdigital transducer, the distribution of sound pressure in the liquid, and the relationship between the interdigital electrode voltage and the output sound pressure are obtained. Finally, the entire separation process in the microchannel was simulated, and the separation of the two particles was successfully achieved. This work has laid a certain theoretical foundation for the rapid diagnosis of diseases in practical applications.     

Recent innovations of ultrasound green technology in herbal phytochemistry: A review

Ultrasound (US) has become one of the most important techniques in green chemistry and emerging technologies. Many research investigations documented the usefulness of US in a wide range of applications in food science, nanotechnology, and complementary medicine, where effective extraction of natural products is important. However, as with all novel technologies, US has advantages and limitations that require clarification for full adaptation at an industrial scale. The present review discusses recent applications of US in herbal phytochemistry with the emphasis on US effects on chemical structures of bioactive compounds extracted from herbs and their bioactivities. The impact of different US processing conditions such as frequency, intensity, duration, temperature, and pressure on the effectiveness of the extraction process and the properties of the extracted materials are also discussed. Different frequencies and intensities of US have demonstrated its potential applications in modifying, determining, and predicting the physicochemical properties of herbs and their extracts. US has important applications in nanotechnology where it supports the fabrication of inexpensive and eco-friendly herbal nanostructures, as well as acoustic-based biosensors for chemical imaging of the herbal tissues. The application of US enhances the rates of chemical processes such as hydrolysis of herbal fibers, which reduces the time and energy consumed without affecting the quality of the final products. Overall, the use of US in herbal science has great potential to create novel chemical constructions and to be used as an innovative diagnostic system in various biomedical, food, and analytical applications.     

Effect of ultrasound on the properties of rice bran protein and its chlorogenic acid complex

Ultrasound technology was used to treat rice bran protein (RBP), and the structural and functional properties of ultrasonically treated RBP (URBP) and its chlorogenic acid (CA) complex were studied. When ultrasonic power of 200 W was applied for 10 min, the maximum emission peak λ_max of the URBP-CA complex in the fluorescence spectrum was red-shifted by 3.6 nm compared to that of the untreated complex. The atomic force microscope (AFM) analysis indicated that the surface roughness of the complex was minimized (3.89 nm) at the ultrasonic power of 200 W and treatment time of 10 min. Under these conditions, the surface hydrophobicity (H₀) was 1730, the contents of the α-helix and β-sheet in the complex were 2.97% and 6.17% lower than those in the untreated sample, respectively, the particle size decreased from 106 nm to 18.2 nm, and the absolute value of the zeta-potential increased by 11.0 mV. Therefore, ultrasonic treatment and the addition of CA changed the structural and functional properties of RBP. Moreover, when ultrasonic power of 200 W was applied for 10 min, the solubility, emulsifying activity index (EAI), and emulsion stability index (ESI) were 68%, 126 m²/g, and 37 min, respectively.     

Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.