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This study aims to promote the anti-disturbance and tracking accuracy of optoelectronic stabilized platforms, which ensure that optical detectors accurately track targets and acquire high-quality images.

An improved active disturbance rejection control (ADRC) strategy based on model-assisted double extended state observers (MDESOs) is proposed in this paper. First, by establishing an auxiliary model, the total disturbances are separated into two parts: inner and external disturbances. Then, MDESOs are designed to estimate the two parts by separately using two parallel ESOs, by which the controlled plant is adjusted to the ideal pure integral series. Simultaneously, combined with the nonlinear state error feedback, an overall control strategy is established.

Compared with the conventional ADRC and proportional derivative, the improved ADRC (IADRC) has stronger robustness and adaptability and effectively reduces the requirements for model accuracy and the gain of the ESO. The error of the auxiliary model is tolerated to exceed 50%, and the parameter values of the MDESOs are reduced by 90%.

The total disturbance rejection rate of the proposed strategy is only 3.11% under multiple disturbances, which indicates that the IADRC strategy significantly promotes anti-disturbance performance.

The modeling cost of the gradient-enhanced kriging (GEK) method is prohibitive for high-dimensional problems. This study aims to develop an efficient modeling strategy for the GEK method.

A two-step tuning strategy is proposed for the construction of the GEK model. First, an auxiliary kriging is built efficiently. Then, the hyperparameter of the kriging model is served as a good initial guess to that of the GEK model, and a local optimal search is further used to explore the search space of hyperparameter to guarantee the accuracy of the GEK model. In the construction of the auxiliary kriging, the maximal information coefficient is adopted to estimate the relative magnitude of the hyperparameter, which is used to transform the high-dimension maximum likelihood estimation problem into a one-dimensional optimization. The tuning problem of the auxiliary kriging becomes independent of the dimension. Therefore, the modeling efficiency can be improved significantly.

The performance of the proposed method is studied with analytic problems ranging from 10D to 50D and an 18D aerodynamic airfoil example. It is further compared with two efficient GEK modeling methods. The empirical experiments show that the proposed model can significantly improve the modeling efficiency without sacrificing accuracy compared with other efficient modeling methods.

This paper developed an efficient modeling strategy for GEK and demonstrated the effectiveness of the proposed method in modeling high-dimension problems.

This paper aims to investigate the causal inferences between mobile application adoption and changes in travelers’ purchasing behavior regarding services supported by the travel and tourism industry.

Using a quasi-experiment conducted by an airline, data sets from more than 10,000 travelers were collected, and hypotheses were tested using propensity score matching and difference-in-difference methods.

Mobile application adoption has a significant positive effect on the total purchasing frequency of services and a significant adverse effect on booking tickets in advance and purchasing frequency from self-owned websites. Besides, this finding also suggested that members or travelers who had high average purchases in the past tend to buy more air tickets on average after mobile application adoption, while the number of days to book tickets in advance and purchase auxiliary services declined after mobile application adoption. However, males purchased more auxiliary services via mobile applications.

The study is based on the causal effect of mobile application adoption on purchasing behavior. Nevertheless, the theoretical basis remains relatively weak. Furthermore, the underlying mechanisms that cause the changes in purchasing behavior via mobile applications need to be elucidated.

This study enriches the hospitality and tourism literature on mobile application adoptions, multichannel purchasing behavior and revenue management. First, a quasi-experimental design is used to verify a causal relationship between mobile applications’ adoption and travelers’ purchasing behavior in the travel and tourism industry. Second, this study adds to examining travelers’ multichannel purchasing behavior in the travel and tourism industry. Third, this work enriches the current literature that explores auxiliary services and revenue management in the travel industry.

Mobile application adoption significantly impacted the travel and tourism industry. Besides, To the best of the authors’ knowledge, this is one of the first empirical studies that examined changes in purchasing behavior due to mobile application adoption from the perspective of service type. The findings provide the first evidence of the impact of mobile application adoption on service purchasing in the travel industry.

This paper aims to present experimental experience of heavy fuelling of a spark ignition crankcase scavenged two-stroke cycle unmanned aerial vehicle (UAV) engine, particularly focusing on the effects of compression ratio variation, and to cross-correlate with the results of fluid dynamic modelling of the engine and fuels used.

One-dimensional modelling of the engine has been conducted using WAVE software supported by experimental dynamometer testing of a spark ignition UAV engine to construct a validated computational model using gasoline and kerosene JET A-1 fuels.

The investigation into the effects of compression ratio variation via fluid dynamic simulation and experimental testing has allowed an assessment of the approach for improving heavy fuel operation of UAV engines using auxiliary transfer port fuel injection. The power level achieved with reduced compression ratio heavy fuel operation is equal to 15.35 kW at 6,500 revolutions per minute compared to 16.27 kW from the standard gasoline engine or a reduction of 5.7%.

The studied engine is specifically designed for UAV applications. The validation of the computational models to explore the effects of compression ratio and heavy fuel injection on the solution and cost is supported by experimental tests.

The application of auxiliary port fuel injection of heavy fuel and associated compression ratio optimisation offers an alternative approach to achieve the safety and logistical challenges of the single fuel policy for UAVs. The application of WAVE to simulate crankcase scavenged two-stroke cycle engines has been applied in very few cases. This study shows further exploratory work in that context.

This paper considers properties of an optimization-based sampler for targeting the posterior distribution when the likelihood is intractable. It uses auxiliary statistics to summarize information in the data and does not directly evaluate the likelihood associated with the specified parametric model. Our reverse sampler approximates the desired posterior distribution by first solving a sequence of simulated minimum distance problems. The solutions are then reweighted by an importance ratio that depends on the prior and the volume of the Jacobian matrix. By a change of variable argument, the output consists of draws from the desired posterior distribution. Optimization always results in acceptable draws. Hence, when the minimum distance problem is not too difficult to solve, combining importance sampling with optimization can be much faster than the method of Approximate Bayesian Computation that by-passes optimization.

Spatial autocorrelation in regression residuals is a major issue for the modeller because it disturbs parameter estimates and invalidates the reliability of conclusions drawn from models. The purpose of this paper is to develop an approach which generates new spatial predictors that can be mapped and qualitatively analysed while controlling for spatial autocorrelation among residuals.

This paper explores an alternate approach using a Fourier polynomial function based on geographical coordinates to construct an additional spatial predictor that allows to capture the latent spatial pattern hidden among residuals. An empirical validation based on hedonic modelling of sale prices variation using a large dataset of house transactions is provided.

Results show that the spatial autocorrelation problem is under control as shown by low Moran's I indexes. Moreover, this geo‐statistical approach provides coefficients on environmental amenities that are still highly significant by capturing only the remaining spatial autocorrelation.

The originality of this paper relies on the development of a new model that allows considering, simultaneously spatial and time dimension while measuring the marginal impact of environmental amenities on house prices avoiding competition with the weight matrix needed in most spatial econometric models.

The purpose of this paper is to investigate strategies and algorithms for expedited design optimization and explicit size reduction of compact ultra-wideband (UWB) antennas.

Formulation of the compact antenna design problem aiming at explicit size reduction while maintaining acceptable electrical performance is presented. Algorithmic frameworks are described suitable for handling various design situations and involving simulation models without and with response gradients available. Numerical and experimental case studies are provided demonstrating feasibility of solving real-world miniaturized antenna design tasks.

It is possible, through appropriate combination of the global and local optimization methods, surrogate modeling techniques and response correction methods, to find optimum dimensions of antenna structures that minimize antenna size while maintaining acceptable electrical performance. Design optimization can be performed at practically feasible computational costs.

The study summarizes recent advances in miniaturization-oriented design optimization of UWB antennas. The presented techniques reach far beyond the commonly used design approaches based on parameter sweeps and similar hands-on methods, particularly in terms of automation, reliability, and reduction of the computational costs of the design processes.

The proposed design problem formulation and algorithmic frameworks proved useful for rapid design of compact UWB antenna structures, which is extremely challenging when using conventional methods. To the knowledge, this is the first attempt to efficient solving of this type of design problems, especially in the context of explicit antenna size reduction.

This article analysis the impact of training on wages using combined survey‐register material. The objective is threefold: to combine survey and register information to estimate individual return from training on wages; to estimate the impact from both individual and firm level training on individual wages; and to confront the results with control for unobserved individual and firm level heterogeneity. Results using register data show that the return from individual training is approximately 3.1 per cent per year. The return from the average training level at the firm is also positive and significant. After control for unobservable individual and firm level heterogeneity a positive impact from individual training is found, but evidence of a positive relationship between the mean level of training at the firm level and individual wages is no longer found. Our interpretation of these findings is that training has a positive individual effect, but has no positive firm‐specific external effects on other workers at the plant.

The study aims to identify the prospects and challenges associated with current practices regarding digital data sets management in university libraries in Pakistan.

A cross-sectional survey approach was used to collect the data from library and information science (LIS) professionals working in public sector university libraries in Pakistan. A four-part questionnaire was used to collect the data from the respondents. The collected data from 371 participants were analyzed using a statistical package for social sciences (SPSS-24 version) and analysis of moment structure (AMOS-24).

LIS professionals are better placed to support digital data management practices, such as finding, collecting, assessing and analyzing digital data sets and making digital data publicly discoverable and accessible via open access. In spite of this, a lack of leadership support, interest and cooperation among university departments and the absence of a data management plan, policies and procedures were reported as significant challenges.

To meet the needs of data users, LIS professionals must become knowledgeable about managing and reusing digital data sets. Due to the demands of the information society, university librarians need to learn about data-centric practices that can enhance research outputs and provide new insights.

This research paper is extracted from a PhD dissertation to present a contemporary picture of library data management services and the challenges LIS professionals face to provide possible solutions.

In recent years, brands have increasingly engaged in corporate political advocacy (CPA; also termed brand activism or corporate sociopolitical activity) by taking positions on polarizing sociopolitical issues. Recent experimental research suggests that consumers respond to CPA based on its alignment with their own values, and that it typically induces an overall negative response. This study aims to provide additional insights by exploring consumer brand perceptions following CPA.

An event study of 106 CPA events and weekly consumer brand perception data was conducted. A regression model was used to investigate the moderating effects of CPA effort, concurrence and the strength of the online protests evoked by the CPA.

The results show that CPA had a negative effect on consumers’ brand perceptions and that the effect was stronger for customers relative to non-customers. The negative effect was attenuated by CPA concurrence and amplified by effort. Additionally, online protests were driven by the CPA effort and had a strong negative effect on brand perception. Online protests were stronger in the past, and, in turn, the negative effects of CPA on brand perceptions have slightly weakened in recent years.

This study contributes to the existing literature by highlighting the role of online protests following CPA and distinguishing consumer and customer responses. This study also provides converging evidence of the moderating effects of effort and concurrence identified in previous studies.