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Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of −10–40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors. [ABSTRACT FROM AUTHOR]

In this work, a complete beneficiation technical route combining physical and chemical methods, namely a roasting-flotation-leaching scheme, is proposed to produce a qualified grade Ti-concentrate from altered Vanadium titanomagnetite (VTM) ore. Based on the character of the ore sample, it is recommended to recover the Ti-bearing minerals, ilmenite and anatase, as composite mineral. Pretreatment experiments indicate that the oxidation roasting (800 °C) and acid washing methods increase the flotation indexes significantly. Flotation condition tests show that the optimal conditions are a grinding fineness of −0.045 mm 83%, sulfuric acid dosage of 2000 g/t, water glass dosage of 1500 g/t, oxalic acid dosage of 200 g/t, and EM328 dosage of 1500 g/t. An open flotation circuit test obtains a flotation concentrate with a TiO2 grade and recovery of 38.30% and 25.99%, respectively. A leaching exploration test shows that the TiO2 grade of the flotation concentrate can be improved to 53.90%. XRD analyses reveal that the ilmenite in the VTM ore is converted into anatase and rutile during the roasting process at 600–800 °C, but pseudobrookite begins to form at 900 °C. Compared to the flotation concentrate, it is confirmed that the content of Ti-bearing minerals is increased significantly in the leaching residue. [ABSTRACT FROM AUTHOR]

The sustainable competitiveness of an organization is largely dependent upon its effectiveness in developing and maintaining high levels of socializees' work engagement. Based on COR (conservation of resources) theory, the present study proposes an integrative model of work engagement pathway to organizational socialization. LMX (leader–member exchange) is seen to create fertile or infertile ground for the creation or limitation of six adjustment-specific resources (e.g., task mastery), which in turn affect work engagement. SmartPLS 3.0 is employed to analyze the data with 455 respondents from 15 luxury hotels on China's Hainan Island. As a result, the six adjustment-specific resources collectively and fully mediate the LMX–engagement relation. LMX positively influences all six adjustment-specific resources, which then either directly or conditionally affect work engagement. While engagement's relationship with task mastery is moderated by income, its relationship with fitting in is moderated by line vs. staff department. The foregoing findings are exploratory and insightful, particularly considering that the work engagement pathway to organizational socialization has become a new paradigm with important implications for theory, research, and practice. [ABSTRACT FROM AUTHOR]

Temperature, depth, conductivity, and turbulence are fundamental parameters of marine dynamics in the field of ocean science. These closely correlated parameters require time-synchronized observations to provide feedback on marine environmental problems, which requires using sensors with synchronized power supply, multi-path data solving, recording, and storage performances. To address this challenge, this work proposes a hardware system capable of synchronously processing temperature, depth, conductivity, and turbulence data on marine dynamics collected by sensors. The proposed system uses constant voltage sources to excite temperature and turbulence sensors, a constant current source to drive a depth sensor, and an alternating current (AC) constant voltage source to drive a conductivity sensor. In addition, the proposed system uses a high-precision analog-digital converter to acquire the direct current (DC) signals from temperature, depth, and turbulence sensors, as well as the AC signals from conductivity sensors. Since the sampling frequency of turbulence sensors is different from that of the other sensors, the proposed system stores the generated data at different storage rates as multiple-files. Further, the proposed hardware system manages these files through a file system (file allocation tab) to reduce the data parsing difficulty. The proposed sensing and hardware logic system is verified and compared with the standard conductivity-temperature-depth measurement system in the National Center of Ocean Standards and Metrology. The results indicate that the proposed system achieved National Verification Level II Standard. In addition, the proposed system has a temperature indication error smaller than 0.02 °C, a conductivity error less than 0.073 mS/cm, and a pressure error lower than 0.8‰ FS. The turbulence sensor shows good response and consistency. Therefore, for observation methods based on a single point, single line, and single profile, it is necessary to study multi-parameter data synchronous acquisition and processing in the time and spatial domains to collect fundamental physical quantities of temperature, salt, depth, and turbulence. The four basic physical parameters collected by the proposed system are beneficial to the in-depth research on physical ocean motion, heat transfer, energy transfer, mass transfer, and heat-energy-mass coupling and can help to realize accurate simulation, inversion, and prediction of ocean phenomena. [ABSTRACT FROM AUTHOR]

In China, food has become safer over the past five years, especially commonly consumed foods. Food supervision sampling has played an important role in improving food safety. However, consumer acceptance of the results of food safety supervision have not kept pace. Communicating actual food safety risks to consumers and improving the public trust in food safety supervision sampling inspection has become an important issue. This study focused on food safety surveillance sampling of commonly consumed foods. In total, 4408 adult consumers were surveyed between August and October 2021. Structural equation modeling was performed for data analysis. This study found significant differences along gender lines and across different cities and levels of education with respect to evaluating competence trust and care trust on food supervision sampling inspection. This study identified the public's competence trust, care trust, and perception of food safety as factors that significantly affect one's attitude toward supervision sampling inspection. Care trust showed a more pronounced effect on trust enhancement than competence trust. The present study also provides some practical measures for food safety supervisors to improve public trust in the national food inspection. Specifically, the sampling process should be open and transparent. [ABSTRACT FROM AUTHOR]

Microelectromechanical system (MEMS) pressure sensors have a wide range of applications based on the advantages of mature technology and easy integration. Among them, piezoresistive sensors have attracted great attention with the advantage of simple back-end processing circuits. However, less research has been reported on the performance of piezoresistive pressure sensors in dynamic environments, especially considering the vibrations and shocks frequently encountered during the application of the sensors. To address these issues, this paper proposes a design method for a MEMS piezoresistive pressure sensor, and the fabricated sensor is evaluated in a series of systematic dynamic environmental adaptability tests. After testing, the output sensitivity of the sensor chip was 9.21 mV∙bar−1, while the nonlinearity was 0.069% FSS. The sensor overreacts to rapidly changing pressure environments and can withstand acceleration shocks of up to 20× g. In addition, the sensor is capable of providing normal output over the vibration frequency range of 0–5000 Hz with a temperature coefficient sensitivity of −0.30% FSS °C−1 over the temperature range of 0–80 °C. Our proposed sensor can play a key role in applications with wide pressure ranges, high-frequency vibrations, and high acceleration shocks, as well as guide MEMS-based pressure sensors in high pressure ranges and complex environmental adaptability in their design. [ABSTRACT FROM AUTHOR]

A series of supports were prepared through the method of hydrothermal synthesis, and copper–iron catalysts supported on ceria nanorods modified by different amounts of manganese were prepared by the liquid phase co-reduction method. The effect of the catalytic performance after Mn addition mainly on higher alcohols synthesis (HAS) was evaluated. Different techniques, such as BET, ICP-AES, XRD, H2-TPR, CO-TPD, TEM, FESEM, XPS and MES, were performed for catalyst characterization. The results indicated that the abilities of CO chemical desorption and carbon chain growth were promoted with appropriate Mn addition, and higher ratio of Cu0/Cu+ species facilitated the methanol homologous reaction and the C2+OH formation. The Ce4+ species were reduced into Ce3+ species during HAS process, providing a large amount of oxygen vacancies. Proper Mn content promoted the formation of χ-Fe5C2 and leaded to the Fe 2p binding energy shift, causing the electron transformation between Fe and Mn species. The largest weight selectivity of C2+OH appeared in the reaction over CuFe/3.6MnCe catalyst with CO conversion 41.43%, and weight fraction of C2+OH 84.41 wt% in the alcohols distribution. [ABSTRACT FROM AUTHOR]

Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of -10-40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors.