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To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network.

First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model.

The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10^–3°/h and the angular velocity random walk value is 2.132 × 10^–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10^–2°/h/vHz of the original output signal of the gyro.

This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

After the drawbacks in the process management of China PE commercial real estate development project are analyzed, it is necessary to introduce a more integrated and advanced tool to solve them. The paper aims to discuss this issue.

A standard process model was proposed first. Based on the model, a coupled activity dependency model was also built by structural tool design structure matrix (DSM), the detailed matrix involving 119 activities and 322 information dependencies. Next, two large processes coupled blocks were generated by the sequencing algorithm and tearing algorithm. Then, the general simulation framework with rework probability and rework effect was introduced to do empirical research for one of the activities blocks due to the merit of solving rework of DSM.

These results showed that key activities were rearranged, that information interactive scope for them was optimized and that collaborative organization interface was built. And the expected duration after simulation was 131 days at 90 percent percentile, providing the objective basis of process arrangement to some extent and decreasing the risk of prospective yields during fund period.

This paper is a qualitative and quantitative practical research for the process management of PE commercial real estate development projects in China based on a real project which has begun construction in 2014.

This paper proposes a framework for director evaluation in the context of Chinese state-owned enterprises (SOEs), taking into account the influences of traditional and modern Chinese ideologies.

Following the Delphi method, a series of semi-structured interviews were conducted with Chinese SOE directors.

The framework used has been validated by examining seven dimensions of virtue and four dimensions of competence functions in Chinese SOEs. Effective and representative characteristics of each dimension are identified through interviews.

First, through this research, indicators of virtue have been materialized and those of competence have been specified in a broader range. Second, this research provides advice for training of candidate directors whose experience were in private firms before they step in as SOE directors.

The study explores how mobile governance affects the administrative burden on older adults, focusing on learning, psychological and compliance costs.

Using attribution theory, the research employs a quantitative research design, utilizing surveys to gather data from 516 older adults across three cities in China: Quzhou, Wuhan and Shanghai. The study examines how intrinsic factors and extrinsic factors of m-government interfaces impact older adults’ administrative burden.

Perceived complexity increases learning, psychological and compliance costs for older adults. Personalization and high-quality information decrease these costs, enhancing user satisfaction. Visual appeal decreases anxiety and psychological costs.

This research links attribution theory with m-government’s administrative burden on older adults, offering new insights into optimizing m-government to serve older adults better.

The purpose of this study is to develop luminescence sensors for the detection of nitroaromatic compounds (NACs) and metal ions to protect human health and prevent environmental pollution.

The composition and morphology of Eu-metal-organic frameworks (MOF) (1) were well characterized by powder X-ray diffraction, elemental analyses, Fourier-transform infrared spectroscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy and scanning electron microscopy. The emission spectrum displays that 1 has significant characteristic emission bands of Eu(III) ions. The authors further investigated the fluorescence sensing performances of 1 to NACs and metal ions.

The results show that Eu-MOF (1) exhibits significant fluorescence quenching effect toward p-nitroaniline and Fe³⁺ ions with good stability and recyclability. This means that 1 can be used as a multifunctional sensing material for the detection of p-nitroaniline and Fe³⁺ ions.

The authors have successfully synthesized a fluorescence Eu-based sensing material under hydrothermal conditions. In addition, the fluorescence property and sensing performances for detecting NACs and metal ions were studied. The results suggest that 1 has highly selective fluorescence quenching toward p-nitroaniline and Fe³⁺ ions with not only high sensitivity and selectivity but also excellent stability and recyclability. Furthermore, this study has confirmed that the multifunctional MOF material is very useful in environment pollutants’ detection and monitoring.

This paper aims to determine whether application of graphene layers to cuprammonium filaments affords the latter with excellent mechanical properties and improves their electrical properties. At the same time, a circuit model was established to explore the conductive mechanism of the filament. The actual model is used to verify the correctness of the model.

The cuprammonium filaments were desizing, the graphene oxide layer-by-layer sizing and reduction integration process by a continuous sizing machine. The electrical properties of mono- and multifilaments in the static condition, as well as the dynamic–mechanical properties of multifilaments, were analysed, and the related conductive mechanism of the filaments was deduced.

Cuprammonium filaments coated with graphene layers showed good electrical conductivity, and their volume resistance decreased to 4.35 O·cm with increasing number of graphene coats. The X-ray diffraction and thermogravimetric analysis results showed that the graphene layer treatment changed the crystallinity of the copperammonia filaments and improved the thermal stability of the filaments. In the dynamic case, filament resistance was calculated using the equivalent resistance model, and the fitting difference observed was small. This result confirmed the high fit of this circuit model.

Up to the knowledge from literature review, there are no reports on theoretical research on the relation between the electro-mechanical property and structure of conductive filaments.

The shield tunnels closely constructed near the foundations have an inevitable influence on the structures, even results in the large settlement or uplift of the structures.

The comparison of structural deformation of three different foundations is presented based on the field monitoring data.

Shield tunnelling parameters vary for the different types of foundations. For the long pile foundations, the recommended speed is 3 to 4 cm/min, the grouting pressure is about 0.3 MPa and the grouting rate ranges from 150 to 180.

The study based on the field monitoring data is rarely reported, especially the topic about the structural deformation of different types of the foundations.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.