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The supply of fresh air for underground rail transit systems is not as simple as opening windows, which is a conventional ventilation (CV) measure adopted in aboveground vehicles. This study aims to improve contaminant dilution and air purification in subway car ventilation systems and the safety of rail transit post-coronavirus disease pandemic era. We designed an air conditioning (AC) terminal system combined with stratum ventilation (SV) to enable energy consumption reduction for subway cars. We experimentally tested the effectiveness of a turbulence model to investigate ventilation in subway cars. Further, we compared the velocity fields of CV and SV in subway cars to understand the differences in their airflow organizations and contaminant removal efficiencies, along with the energy savings of four ventilation scenarios, based on the calculations carried out using computational fluid dynamics. At a ventilation flow rate of 7200 m3/h, the CO2 concentration and temperature in the breathing areas of seated passengers were better in the SV than in the CV at a rate of 8500 m3/h. Additionally, the energy-saving rate of SV with AC cooling was 14.05%. The study provides new ideas for reducing the energy consumption of rail transit and broadens indoor application scenarios of SV technology.

The cutting force spectrum of the CNC lathe is the basic data for the reliability design, reliability test, and reliability evaluation of the CNC lathe and its components. Due to the complex and changeable turning conditions and different cutting processes, the cutting load presents multi-peak characteristics. At the same time, grouping the counted load cycles when parameter modeling will produce certain errors. As a result, the parameter distribution model cannot meet the modeling requirements. Thus, a compilation method based on kernel density estimation (KDE) of goodness-smoothness comprehensive evaluation (G-SCE) is proposed. The KDE is used to establish the dynamic cutting force distribution of the CNC lathe in which grouping the counted load cycles is not needed. For the bandwidth-determining methods, the rule of thumb method (ROT) and the least-squares cross-validation method (LCV) do not take into account the influence of different bandwidths on the goodness estimation and the smoothness of the estimated curve, and the G-CSE for KDE is proposed to determining the optimal bandwidth. It includes the estimation accuracy test method based on multiple goodness-of-fit tests and the smoothness test method based on the envelope curve, and the entropy method is used to comprehensively weights the estimated goodness index and the smoothness index to determine the optimal bandwidth. The results of the case analysis indicate that the method proposed can solve the problem of too large estimation error of parameter distribution for multimodal distribution. At the same time, it can better comprehensively evaluate the KDE under different bandwidths. In short, a new method of optimal bandwidth selection is proposed in the original method.

The load spectrum of machining center (MC) is the data basis for fatigue life prediction. A novel compiling method of dynamic cutting load spectrum of MC spindle is proposed, and then applied to the fatigue life prediction. Typical process parameters were determined based on the data collected in the user field by establishing the characteristic load distribution, and dynamic cutting load was measured using the load test platform. Mean-frequency and amplitude-frequency matrices of the load were obtained by the rainflow counting method, and mixture Weibull distribution (MWD) was used to establish the mean and amplitude distribution. Thus, the two-dimensional dynamic cutting load spectrum of spindle was compiled. The eight-level program load spectrum was established, and then applied to the spindle fatigue life prediction. The accuracy of load spectrum is improved because of the MWD, instead of single distribution, and the complete load spectrum compilation process also improves the life prediction accuracy.

Recently, transition metal dichalcogenide quantum dots have attracted widespread attention among researchers as fluorescent materials. In this work, a facile bottom–up route is proposed to synthesize water-soluble molybdenum disulfide quantum dots (MoS2 QDs) by using sodium molybdate and cysteine as precursors. The as-prepared spherical MoS2 QDs with an average size of 3.5 nm exhibit intense PL emission, whose quantum yield (QY) is 6.5% (cal.). In particular, the as-synthesized MoS2 QDs show highly sensitive and selective detection of H2Q. The lowest limit of detection can reach 0.3 μM in water, far lower than the estimated wastewater discharge limit of the United States Environmental Protection Agency (EPA) (0.5 mg L−1). What is more, the as-prepared MoS2 QDs exhibited applicability in cell imaging with lower cytotoxicity. Therefore, the as-prepared MoS2 QDs show potential applications in environmental monitoring, bioanalysis, disease diagnosis and treatment, etc.

The extreme value of cutting force is one of the important factors affecting the reliability of machine tools. Correctly establishing its distribution is an important prerequisite for the preparation of CNC machine tool cutting force spectrum and the reliability design of CNC machine tools. In this paper, the cutting force data of the CNC lathe under light, medium and heavy load conditions were obtained by the actual cutting test. The cutting force was sampled by the Block Maximum Method (BMM), and the sample was distributed by the generalized extreme value (GEV) distribution. The chi-square test was utilized to test the goodness of the fitted GEV distribution, and then, the optimal number of blocks in BMM model was determined by comparing values of chi-square test. At last the optimal distribution function of the extreme values was obtained. A reliable distribution model of extreme value for the extrapolation of cutting force extremes and establishment of cutting force spectra is provided in this paper.

The load spectrum of numerical control machine tool (NCMT) is the basis data of reliability design, life prediction, and reliability bench test with simulated actual load conditions. Static cutting force spectrum, which can be compiled by empirical formula, cannot accurately reflect the real load conditions. Hence, a method for compiling the spectrum of dynamic turning force is proposed by using two-dimensional mixture models. Dynamic turning force is measured in the laboratory based on a large amount of cutting process data collected in the user field. Mean–frequency and amplitude–frequency matrices of dynamic turning force are obtained by the rainflow counting method. The probability distributions of mean and amplitude are established by using Weibull mixture models (WMM) according to the characteristics of the dynamic turning force, and the two main problems of this method are the determination of the number of mixture components and the estimation of parameters. Gray relation analysis is used to quantify the multi-objective evaluation matrix, and thus the number of mixture components is determined. Parameters are estimated using the improved expectation maximization (EM) algorithm, where the initial values of parameters are obtained by the K-means++ and Bayesian random classification. Finally, the joint probability distribution function between the mean and amplitude is established by the Copula function. Thus, the dynamic cutting force spectrum is compiled. Case analysis results show that the mean and amplitude distribution models with high fitting precision can be obtained using the proposed method. Furthermore, the precision of the load spectrum of NCMT is improved.

In this work, we first report Au nanoclusters/porous silica particles nanocomposites as fluorescence enhanced sensors for selective and sensitive detection of Cu (II). As red-emitting GSH-protected Au nanoclusters (Au NCs) were self-assemble into porous silica particles (PSPs) after ultrasonic treatment. As a result, the Au NCs can be immobilized in the nano-channels of PSPs, which leads to the observation of an immobilized induced emission enhancement phenomenon. The photoluminscence (PL) intensity of the nanocomposites can enhance dozens of times compared with Au NCs. As a result, we obtain a novel PL enhanced sensor of Au NCs/PSPs nanocomposites with excellent PL properties. The as-prepared Au NCs/PSPs nanocomposites show good water-solubility, high stability, low toxicity, and exhibit a high PL quenching for reliable, sensitive and selective detection of Cu2+. The limit of detection can reach as low as 1 ppb. What is more, the Au NCs/PSPs nanocomposites also show sensitive detection of Cu2+ in living cells. These properties provide the Au NCs/PSPs nanocomposites with promising PL sensors for Cu2+ detection in various environmental and biological systems.

The goal of this paper is to use digital holographic imaging for sensing marine plankton in recording sampling volume. The process stage of this approach includes: wavefront recording using in-line holographic recording set up and numerical reconstruction using Fresnel approximation and convolution algorithm. So, by capturing hologram of marine plankton and reconstructing hologram, the recorded optical field of marine plankton is retrieved. Digital holographic imaging is an extremely powerful technique for the study of marine plankton fields as it allows instantaneous, noninvasive, high-resolution recording of substantial volumes. Finally, this paper presents that it is possible for digital holographic imaging system to sense marine plankton according to laboratory results.

As the interconnection technology of intelligent devices in smart homes become more and more mature, the problems of resolving interoperability of devices in different home networks arises spontaneously. In this paper, we proposed a crossover interconnection model which is based on OSGi Framework, and it can solve the problems of interoperability of intelligent devices in different home networks. Consequently, it is very important in remote control and sharing of information. Finally, we designed an experiment in order to test the possibility of crossover interconnection and loading capacity of this model. The result shows that intelligent devices in different home networks could connect with each other, but reflecting delay is growing with the increasing number of online devices.

To achieve interconnection and interoperability among different devices in home network environment, such as TV, PAD, and computer. Several digital home network protocol standards, such as UPnP, Java Intelligent Network Infrastructure (Jini), IGRS, etc, are proposed. But, devices based on these different protocols cannot discover or control each other, so it is very important to achieve interoperability among these devices. A method of interconnection between IGRS network and UPnP network based on Open Service Gateway Initiative (OSGI) platform is proposed in this paper. A service agent on the OSGI platform is designed. This paper described the system architecture and implement process of this method. The experiment result of this method is also described. It is shown that the Service search and protocol conversion between UPNP and IGRS devices are implemented effectively.