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The purpose of this paper is to develop an effective approach to significantly improve the thermomechanical properties of shape memory polymer (SMP) nanocomposites that show fast thermally responsive shape recovery.

Hexagonal boron nitrides (h-BNs) were incorporated into polymer matrix in an attempt to improve the thermal conductivity and thermally responsive shape recovery behaviour of SMP, respectively. Thermally actuated shape recovery behaviour was recorded and monitored instrumentally.

The results show that both glass transition temperature (T_g) and thermomechanical properties of the SMP nanocomposites have been progressively improved with increasing concentration of h-BNs. Analytical results also suggest that the fast-responsive recovery behaviour of the SMP nanocomposite incorporated with h-BNs was due to the increased thermal conductivity.

A simple way for fabricating SMP nanocomposites with enhanced thermally responsive shape recovery based on the incorporation of h-BNs was developed.

The outcome of this study may help fabrication of SMP nanocomposites with fast responsive recovery behaviour.

The high probability of the occurrence of separation bubbles or shocks and early transition to turbulence on surfaces of airfoil makes it very difficult to design high-lift and high-speed Natural-Laminar-Flow (NLF) airfoil for high-altitude long-endurance unmanned air vehicles. To resolve this issue, a framework of uncertainty-based design optimization (UBDO) is developed based on an adjusted polynomial chaos expansion (PCE) method.

The γ ̄Re-θt transition model combined with the shear stress transport k-ω turbulence model is used to predict the laminar-turbulent transition. The particle swarm optimization algorithm and PCE are integrated to search for the optimal NLF airfoil. Using proposed UBDO framework, the aforementioned problem has been regularized to achieve the optimal airfoil with a tradeoff of aerodynamic performances under fully turbulent and free transition conditions. The tradeoff is to make sure its good performance when early transition to turbulence on surfaces of NLF airfoil happens.

The results indicate that UBDO of NLF airfoil considering Mach number and lift coefficient uncertainty under free transition condition shows a significant deterioration when complicated flight conditions lead to early transition to turbulence. Meanwhile, UBDO of NLF airfoil with a tradeoff of performances under both fully turbulent and free transition conditions holds robust and reliable aerodynamic performance under complicated flight conditions.

In this work, the authors build an effective uncertainty-based design framework based on an adjusted PCE method and apply the framework to design two high-performance NLF airfoils. One of the two NLF airfoils considers Mach number and lift coefficient uncertainty under free transition condition, and the other considers uncertainties both under fully turbulent and free transition conditions. The results show that robust design of NLF airfoil should simultaneously consider Mach number, lift coefficient (angle of attack) and transition location uncertainty.

The noise measurement on magnetoresistive (MR) sensors is generally conducted by techniques including single-channel data sampling and fast Fourier transform (FFT) analysis as well as two-channel cross-correlation. The single-channel method is easy to implement and is widely used in the noise measurement on MR sensors, whereas the two-channel method can only eliminate part of the system noise. This study aims to address two key issues affecting measurement accuracy: calibration of the measurement system and the elimination of system noise.

The system is calibrated by using a low-noise metal film resistor in that the system noise is eliminated through power spectrum subtraction. Noise measurement and analysis are conducted for both thermal noise and detectivity of magnetic tunnel junction (MTJ) sensor.

The thermal noise measurement error is less than 2%. The detectivity of the MTJ sensor reaches 27 pT/Hz^1/2 at 2 kHz.

This study provides a more practical solution for noise measurement and system calibration on MR sensors with a bias voltage and magnetic field.

It has been noted that the spin-valve sensor exhibits lower sensitivity with higher temperature because of the variation of GMR ratio, which could lead to the measurement error in applications where working temperature changes largely over seasons or times. This paper aims to investigate and compensate the temperature effect of the spin-valve sensor.

A spin-valve sensor is fabricated based on microelectronic process, and its temperature relevant properties are investigated, in which the transfer curves are acquired within a temperature range of −50°C to 125°C with a Helmholtz coil and temperature chamber.

It is found that the sensitivity of spin-valve sensor decreases with temperature linearly, where the temperature coefficient is calculated at −0.25 %/°C. The relationship between sensitivity of spin-valve sensor and temperature is well-modeled.

The temperature drift model of the spin-valve sensor’s sensitivity is highly correlated with tested results, which could be used to compensate the temperature influence on the sensor output. A self-compensation sensor system is proposed and built based on the expression modeled for the temperature dependence of the sensor, which exhibits a great improvement on temperature stability.

The purpose of this paper is to define a new method (grey relational analysis (GRA)) for extracting pattern samples of dissolved gases in power transformer oil, then a hybrid algorithm of the back‐propagation (BP) network and fuzzy genetic algorithm‐artificial neural network (FGA‐ANN) is used to power transformer fault diagnosis based on extracted pattern samples.

The existing manners (e.g. international electro technical commission triple‐ratio method), in practice, have certain faultiness due to the ambiguity of the inference and insufficient standard for judgment. So GRA method is chosen to solve a problem of optimal pattern samples data, then a hybrid algorithm of the BP network and FGA‐ANN is developed to optimize initial weights and to enable fast convergence of the BP network, and lastly, this algorithm is applied to the classification of dissolved gas analysis (DGA) data and power transformer fault diagnosis.

If possible, the results should be accompanied by significance. For comparative studies, the proposed scheme does not require the three ratio code and high diagnosis accuracy is obtained. In addition, useful information is provided for future fault trends and multiple faults analysis.

Accessibility and availability of data are the main limitations which model will be applied.

This paper provides useful advice for power transformer fault diagnosis method based on DGA data.

The new method of optimal choice of options of pattern samples due to GRA. The paper is aimed at optimized samples data classified and abandons the traditional ratio method.

The customer enables online reviews, discusses product features and enhances the user's experiences in online activities. Users generated product innovation and product reviews effect as market competition. This research study explains deep learning, online reviews and product innovation empirical evidence used by mobile apps.

Online reviews and product innovation are very important for every organization and firms to achieve a competitive advantage in a large business environment. When the authors see past traditional history, customers are not involved in product creating and innovating processes. Due to new technology changes, online systems and web 2.0 increase this ability.

For this research purpose, the authors use different analytical software to measure the impact among variables. This study is established on primary data; this study collected data from online customers and its users. For data collection, the authors use some questionnaires, and these questions are filled from 200 respondents.

This research study used data from the Google app store – Google product selling application – and gathered customers' online reviews. Research found that customers' online reviews and deep learning positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.

This research study used data from the Google app store Google product selling application and gathered customers' online reviews. Research founded that customers' online reviews and deep learning are positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.

The purpose of this paper is to examine how well local planners have recognized the issues surrounding climate change, the analysis that jurisdictions have conducted on climate change, and policies that have been implemented to address climate change.

This study conducted a mail questionnaire survey for 214 counties ' planning directors in the USA and received 53 effective responses. This survey examined how well local planning directors have been prepared for climate change, including awareness, analysis scope, and implementation strategy.

The descriptive results indicate that the directors who responded to this survey had a relatively high (79.87 percent) level of awareness for climate change; but they had limited (34.94 percent) analysis scopes to assess the sources, impacts, and risk of climate change in their jurisdictions. These directors had partially but not fully (51.51 percent) developed local land use planning implementation strategies to mitigate or adapt climate change. The regression model indicates that the political commitment and planning personnel resources have significant influence on local planning directors ' actions for climate change.

This paper provides policy implications to improve local land use planning ability for climate change mitigation and adaptation.

This research outlines the Hong Kong film industry with examination of key actors, directors, films, and production companies within the martial arts genre of Hong Kong Action Cinema. Hong Kong Film Award winners and nominees, core films within genres, and core reference works both general and theoretical from experts in the field of Hong Kong martial arts film research have been highlighted. Web sites are suggested that provide reviews of Hong Kong martial arts films, biographical information on a variety of actors and actresses as well as comprehensive bibliographic information on select films. Also included are commercial Web sites that provide Hong Kong martial arts films.

Aerodynamic shape optimisation is complex because of the high dimensionality of the problem, the associated non-linearity and its large computational cost. These three aspects have an impact on the overall time of the design process. To overcome these challenges, this paper aims to develop a method for transonic aerodynamic design with dimensionality reduction and multifidelity techniques.

The developed methodology is used for the optimisation of an installed civil ultra-high bypass ratio aero-engine nacelle. As such, the effects of airframe-engine integration are considered during the optimisation routine. The active subspace method is applied to reduce the dimensionality of the problem from 32 to 2 design variables with a database compiled with Euler computational fluid dynamics (CFD) calculations. In the reduced dimensional space, a co-Kriging model is built to combine Euler lower-fidelity and Reynolds-averaged Navier stokes higher-fidelity CFD evaluations.

Relative to a baseline aero-engine nacelle derived from an isolated optimisation process, the proposed method yielded a non-axisymmetric nacelle configuration with an increment in net vehicle force of 0.65% of the nominal standard net thrust.

This work investigates the viability of CFD optimisation through a combination of dimensionality reduction and multifidelity method and demonstrates that the developed methodology enables the optimisation of complex aerodynamic problems.