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This paper aims to investigate the impact of aerofoil camber on the performance of micro-air-vehicle-scale cycloidal propellers.

First, experiments were conducted to validate the numerical methodology. After that, three turbulent models were compared to select the most accurate one. Then, 2D numerical simulation was carried out on 11 aerofoils with different cambers, including five cambered aerofoils, one symmetrical aerofoil and five inverse cambered aerofoils. The inverse cambered aerofoils are symmetrical about the chord line to the corresponding cambered ones.

The cycloidal propeller with large cambered aerofoil gives the lowest hovering efficiency, but with symmetrical aerofoil or small inverse cambered aerofoil shows the highest. Also, blades with large cambered aerofoil display high performance at the upper part of its trajectory, while with symmetrical aerofoil or the inverse cambered aerofoil have their best at the lower part. In addition, intensified downwash can be observed in the rotor cage for all cases. When a blade runs through the top-left part of its circle path, all cases display the feature of deep dynamic stall. When the blade travels through the nadir of its path, the actual angle of attack is close to zero due to the strong downwash. Furthermore, there exits intensified blade-vortex interaction induced by the preceding blade for large cambered aerofoils at the lower-right part of its trajectory.

This paper develops a new cycloidal propeller which is more efficient than the one already present.

This paper discovers that the aerofoil camber is a vital design parameter in the performance of cycloidal propeller, and the authors expect that the rotor with deformable aerofoil on camber would achieve much higher efficiency.

The purpose of this paper is to investigate the mechanism and performance of a potential strategy, which is to enhance turbulence to carry out drag reduction for heavy trucks.

Enhancing turbulence deflector (ETD) was placed on the roof surface of an ground transportation system (GTS) to investigate the drag reduction mechanism of enhancing turbulence. Transition shear-stress transport improved delay detach eddy simulation model was adopted to simulate the unsteady small-scale flow around the ETD.

By optimizing the three influencing factors, diameter, streamwise length and streamwise position, the optimized ETD has achieved a maximum drag reduction of 7.04%. The analysis of flow field results shows that enhancing turbulence can effectively suppress flow separation and reduce the negative pressure intensity in the wake region of GTS.

The present work provides another potential possibility for the improvement of the aerodynamic performance of heavy trucks.

This paper investigates the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets and explores the potential explanations.

In this paper, the authors use the panel data regressions and the dynamic tests of two-way Granger causality in the panel VAR model to examine the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets.

The authors find that the institutional ownership in the Chinese (the USA) stock market is significantly and positively (negatively) related to idiosyncratic volatility through various tests. This paper indicates that institutional investors in the USA are more prudent and risk-averse, while the Chinese institutional investors are not because of high risk-bearing capacity.

This paper deepens the authors’ understanding on the relationship between institutional ownership and idiosyncratic volatility and in the USA and the Chinese stock markets. This paper explains the opposite relationships between institutional ownership and idiosyncratic volatility in the stock markets in China and USA.

While the economic benefits of the exhibition industry for the hotel sector have been addressed, the impact of exhibitions on individual hotels is unknown, especially when individual hotels’ star classification and locations are considered. This study aims to provide a better understanding of how room rates of different hotels change during different stages of the Canton Fair in China from a spatial-temporal perspective.

Room rates of 681 star-hotels within the city of Guangzhou before, during and after the Fair were extracted from websites. Through spatial interpolation and autocorrelation analysis and geographical detector (GeoDetector) technique, spatial and temporal patterns of hotel room rates and the interdependence between the convention center and the hotels with different star classification and locations were examined.

An inverse-U shape of room rate change was identified before, during and after the Fair, and the five-star hotels had the sharpest increase. Moreover, the distribution of hotel room rates followed the law of distance decay. The variation of hotel rates became larger when the distance to the convention center was larger. Spatial high-high clusters varied among hotels with different star classification.

This study contributed to the hotel literature by providing empirical evidence regarding how hotels with different star classification and locations were affected by events. This study also advanced the event literature by introducing GeoDetector. The findings of this study offered insights into the hotel location selection, pricing strategies and hotel collaboration with events.

虽然展览业对酒店业的经济效益已经得到解决, 但展览对单个酒店的影响尚不清楚, 尤其是在考虑单个酒店的星级和位置时。本研究旨在从时空角度更好地了解中国广交会不同阶段不同酒店的房价变化情况。

网站提取了广交会前、中、后广州市内681家星级酒店的房价。通过空间插值和自相关分析以及地理探测器（GeoDetector）技术, 研究了酒店房价的时空格局以及会议中心与不同星级和位置的酒店之间的相互依赖关系。

会前、会中、会后房价变化呈倒U型, 其中五星级酒店涨幅最大。此外, 酒店房价的分布遵循距离衰减规律。到会展中心的距离越远, 酒店价格的变化就越大。不同星级酒店的空间高-高集群存在差异。

该研究通过提供关于不同星级和位置的酒店如何受到事件影响的经验证据, 为酒店文献做出了贡献。这项研究还通过引入 GeoDetector 推进了事件文献。研究结果为酒店选址、定价策略和酒店与活动的合作提供了见解。

The purpose of this paper is to present an actuator fault detection method for unmanned aerial vehicles (UAVs) based on interval observer and extended state observer.

The proposed algorithm has very little model dependency. Therefore, a six-degree-of-freedom linear equation of UAVs is first established, and then, combined with actuator failure and external disturbances in flight control, a steering gear model with actuator failure (such as stuck bias and invalidation) is designed. Meanwhile, an extended state observer is designed for fault detection. Moreover, a fault detection scheme based on interval observer is designed by combining fault and disturbances.

The method is testified on the extended state observer and the interval observer under the failure of the steering gear and bounded disturbances. The simulation results show that the two types of fault detection schemes designed can successfully detect various types of faults and have high sensitivity.

This research paper studies the failure detection scheme of the UAVs’ actuator. The fault detection scheme in this paper has better performance on actuator faults and bounded disturbances than using regular fault detection schemes.

This paper aims to investigate the mechanisms lying behind the cycloidal rotor under hovering status.

Experiments were conducted to validate the numerical simulation results. The simulations were based on unsteady Reynolds-averaged Navier–Stokes (URANS) equations solver and the sliding mesh technique was used to model the blade motion. 2D and 2.5D simulations were made to investigate the 3D effects of turbulence. The effects of pressure and viscosity were compared to study the significance of the blade motion on force generation.

The 2.5D numerical simulation cannot produce more accurate results than the 2D counterpart. The pitching motion of the blade results in dynamic stall. The dynamic stall vortices induce parallel blade vortex interaction (BVI) upon downstream blades. The interactions between the blades delay the stall of the blade which is beneficial to the thrust generation. The blade pitching motion is the dominant contributor to the force generation and the turbulence is the secondary. Strong downwash in the rotor cage varied the inflow velocity as well as the effective angle of attack (AOA) of the blade.

Cycloidal rotor is a propulsion device that can provide omni-directional vectored thrust with high efficiency and low noise. To understand the mechanisms lying behind the cycloidal rotor helps the authors to design efficient cycloidal rotors for aircraft.

The authors discovered that the blade pitching motion plays primary role in force generation. The effects of the dynamic stall and BVI were studied. The reason why cycloidal rotor can be more efficient was discussed.

American depository receipts (ADRs) listed on the New York Stock Exchange during the 1990s and 2000s are compared to determine how well they performed versus the US index and respective regional indexes utilizing three-year holding period excess returns. Results suggest that ADRs listed in the 2000s perform better than those in the 1990s. Also, seasoned equity offerings performed better than initial public offerings. Regression analysis indicated the best predictors of ADR performance are the returns of the respective regional index where the ADR-listing firm is headquartered, the date of issue (2000s vs 1990s), and whether the ADR was from an emerging economy.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.

Power amplifiers (PAs) play an important role in wireless communications because they dominate system performance. High-linearity broadband PAs are of great value for potential use in multi-band system implementation. The purpose of this paper is to present a cascode power amplifier architecture to achieve high power and high efficiency requirements for 4.2∼5.4 GHz applications.

A common emitter (CE) configuration with a stacked common base configuration of heterojunction bipolar transistor (HBT) is used to achieve high power. T-type matching network is used as input matching network. To increase the bandwidth, the output matching networks are implemented using the two L-networks.

By using the proposed method, the stacked PA demonstrates a maximum saturated output power of 26.2 dBm, a compact chip size of 1.17 × 0.59 mm² and a maximum power-added efficiency of 46.3 per cent. The PA shows a wideband small signal gain with less than 3 dB variation over working frequency. The saturated output power of the proposed PA is higher than 25 dBm between 4.2 and 5.4 GHz.

The technology adopted for the design of the 4.2-to-5.4 GHz stacked PA is the 2-µm gallium arsenide HBT process. Based on the proposed method, a better power performance of 3 dB improvement can be achieved as compared with the conventional CE or common-source amplifier because of high output stacking impedance.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.