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A mathematical model and numerical method for water flow and solute transport in a tidal river network is presented. The tidal river network is defined as a system of open channels or rivers with junctions and cross sections. As an example, the Pearl River in China is represented by a network of 104 channels, 62 nodes, and a total of 330 cross sections with 11 boundary sections for one of the applications. The simulations are performed with a supercomputer for seven scenarios of water flow and/or solute transport in the Pearl River, China, with different hydrological and weather conditions. Comparisons with available data are shown. The intention of this study is to summarize previous works and to provide a useful tool for water environmental management in a tidal river network, particularly for the Pearl River, China.

A fully coupled numerical model to simulate the complex behaviour of soil deformation, water flow, airflow, and heat flow in porous media is developed. The following thermal effects are taken into account: heat transfer through conduction and convection, flow, as well as viscosity and density variation of the fluids due to temperature gradients. The governing equations in terms of soil displacements, water and air pressures, and temperature are coupled non‐linear partial differential equations and are solved by the finite element method. Two examples are presented to demonstrate the model performances.

Presents a fully coupled numerical model to simulate the slow transient phenomena involving heat and mass transfer in deforming partially saturated porous materials. Makes use of the modified effective stress concept together with the capillary pressure relationship. Examines phase changes (evaporation‐condensation(, heat transfer through conduction and convection, as well as latent heat transfer. The governing equations in terms of gas pressure, capillary pressure, temperature and displacements are coupled non‐linear differential equations and are discretized by the finite element method in space and by finite differences in the time domain. The model is further validated with respect to a documented experiment on partially saturated soil behaviour, and the effects of two‐phase flow, as compared to the one‐phase flow solution, are analysed. Two other examples involving drying of a concrete wall and thermoelastic consolidation of partially saturated clay demonstrate the importance of proper physical modelling and of appropriate choice of the boundary conditions.

This study aims to investigate the relationship between entrepreneurship and innovation efficiency (IE), as well as the moderating role of absorptive capacity.

This study uses a sample of industrial enterprises from Chinese provinces from 2005 to 2016, and it tests the research questions using the method of stochastic frontier analysis.

The results of this study indicate that entrepreneurship promotes IE, and that absorptive capacity plays a positive moderating role. In addition, the effect of entrepreneurship on IE differs between the central and eastern regions and the western region.

This research provides direct policy implications by demonstrating the role of entrepreneurship and absorptive capacity in IE, thereby guiding corporate management practices and the formulation of government innovation and entrepreneurship policies.

Although the value of artificial intelligence (AI) has been acknowledged by companies, the literature shows challenges concerning AI-enabled business-to-business (B2B) marketing innovation, as well as the diversity of roles AI can play in this regard. Accordingly, this study investigates the approaches that AI can be used for enabling B2B marketing innovation.

Applying a bibliometric research method, this study systematically investigates the literature regarding AI-enabled B2B marketing. It synthesises state-of-the-art knowledge from 221 journal articles published between 1990 and 2021.

Apart from offering specific information regarding the most influential authors and most frequently cited articles, the study further categorises the use of AI for innovation in B2B marketing into five domains, identifying the main trends in the literature and suggesting directions for future research.

Through the five identified domains, practitioners can assess their current use of AI and identify their future needs in the relevant domains in order to make appropriate decisions on how to invest in AI. Thus, the research enables companies to realise their digital marketing innovation strategies through AI.

The research represents one of the first large-scale reviews of relevant literature on AI in B2B marketing by (1) obtaining and comparing the most influential works based on a series of analyses; (2) identifying five domains of research into how AI can be used for facilitating B2B marketing innovation and (3) classifying relevant articles into five different time periods in order to identify both past trends and future directions in this specific field.

Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the AE form of CNC machining operations.

Experimental measurements were performed with three sensors on the CNC lathe to collect the data of the CNC machining. Adaptive neuro-fuzzy inference system (ANFIS) was applied for the fusion from the sensors’ signals to determine the strength of the signal periodic component among the sensors.

There were three inputs, namely, spindle speed, feed rate and depth of cut. ANFIS was also used to determine the inputs’ influence on the prediction of strength of the signal periodic component. Variable selection process was used to select the most dominant factors which affect the prediction of strength of the signal periodic component.

Results were shown that the spindle speed has the most dominant effect on the strength of the signal periodic component.

This paper aims to report small-signal parameter extraction and simulation of enhanced dual-channel dual-material gate AlGaN/GaN high electron mobility transistor (HEMT) for the first time for the characterization of a device in microwave range of frequency.

For parameter extraction, a standard and well-known direct parameter extraction methodology is applied. Extrinsic elements of small-signal circuit model are extracted from measured S-parameters obtained using pinch-off cold field effect transistor (FET) biasing in the first step at a low frequency range and at a higher frequency range in the second step to ensure higher extraction accuracy. Intrinsic elements are extracted from intrinsic Y-parameters that are obtained after de-embedding all the extrinsic parasitic elements of the device. Figure of merits of radio frequency are also derived from the measured results and S-parameters of the proposed device.

Small signal parameters of the proposed device circuit model are extracted using the standard direct parameter extraction technique. Analysis of microwave figure of merits for device include maximum oscillation frequency, cut-off frequency, current gain, transducer power gain, available power gain, maximum stable gain, transconductance, drain conductance, stern stability factor and time delay.

The paper bridges the gaps between theory and experimental practices by validating extracted results with reported results of structurally matching devices.

An enhanced device structure investigated for small signal parameters incorporates field plate over dual metal engineered gate to provide better electric field uniformity, effective suppression of short channel effect, reduction in current collapse, improvement in carrier transport efficiency and enhancement in drain current capabilities.