Search results

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.

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 Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.

The idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.

From the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.

Compared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

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.

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.

This paper aims to study the interplay between a risk-averse national brand manufacturer's (NBM) selling mode decision and a risk-neutral e-platform's private brand (PB) introduction decision.

A game theory model is used to solve selling mode decision, that is whether transform the selling mode from the wholesale mode to the marketplace mode, and PB introduction decision, that is, whether introduce the PB.

The results show that for the NBM, under certain condition, the NBM's selling mode decision is not affected by the e-platform's PB introduction decision. High revenue-sharing rate is conducive only when the difference in consumer preference between the PB and the national brand (NB) is small. The NBM's risk aversion will improve the applicability of the marketplace mode. For the e-platform, high PB preference of consumers and risk-averse behavior of the NBM is not conducive to PB introduction. For the supply chain, scenarios that the NB monopolizes the market under the wholesale mode and PB introduction under the marketplace mode should be prevented. PB introduction under the wholesale mode will become the only equilibrium with the increase of risk aversion of the NBM. Finally, the authors extend the scenario that consumers prefer the PB and the e-platform is risk-averse enterprise and find that PB introduction under the wholesale mode is detrimental to the NBM but beneficial to the supply chain. The impact of consumers' PB preference on the e-platform's PB introduction is opposite to the basic model. The impact of the e-platform's risk aversion on game equilibrium is opposite to that of the NBM's risk aversion.

This paper is first to study selling mode decision and PB introduction decision when considering enterprises' risk-averse attitude.

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.