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The purpose of this paper is to investigate the design method of partial observer canonical form (POCF), which is one of the important research tools for industrial plants.

Motivated by the two-steps method proposed in Xu et al. (2020), this paper extends this method to the case of Multi-Input Multi-Output (MIMO) nonlinear system. It decomposes the original system into two subsystems by observable decomposition theorem first and then transforms the observable subsystem into OCF. Furthermore, the necessary and sufficient conditions for the existing of POCF are proved.

The proposed method has a wide range of applications including completely observable nonlinear system, noncompletely observable nonlinear system, autonomous nonlinear system and forced nonlinear system. Besides, comparing to the existing results (Saadi et al., 2016), the method requires less verified conditions.

The new method concerning design POCF has better plants compatibility and less validation conditions.

The purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability.

This paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end.

This paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data.

This paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability.

In order for a software system to better serve the user, it should be able to adjust its behavior according to the changing needs in the environment. Oftentimes, selecting a particular action may depend upon various non‐functional requirements (NFRs) such as safety, cost, and so on. In the past, the many possible alternatives for an adaptation action by and large have not been considered systematically and rationally, keeping various NFRs in mind, hence, resulting in low‐level of confidence that such an action is indeed a best possible one that is really desirable. The purpose of this paper is to present a goal‐oriented approach to select alternative(s) based on a particular contextual event, while considering important NFRs.

The paper proposes a goal‐oriented approach in which various NFRs are treated as softgoals to be satisficed and used in exploring, analyzing and selecting among possible adaptation alternatives, in consideration of the particular contextual event.

Without the goal‐oriented methodology, which offers an ontology enriched with the notion of goals for contextual information and also integrates rules for triggering adaptation, the authors feel, through their scenario study applied to their smart‐phone application, that some critical issues might not have been considered in building a usable, useful system.

The concepts introduced in this paper provide a systematic and rational approach to select adaptation alternative(s), considering NFRs along with detecting a contextual event.

This paper aims to apply the spacing effect of capacitive imaging (CI) sensors to inspect and differentiate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module in oil and gas pipelines simultaneously. Through experimental verification, a method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors has been demonstrated.

A 3Dimensions (3D) model for simulating the inspection of these flaws was established by using COMSOL. A novel CI sensor with adjustable working electrode spacing was designed, and a modular CI system was developed to substantiate the theoretical findings with experimental evidence. A method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors was established.

The results indicate that the method can successfully discriminate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module using CI sensors.

The method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors is vital for keeping the transportation safety of oil and gas pipelines.

Loss of control and air crashes are frequently caused by aircraft faults. Therefore, a practical control strategy can prevent aircrafts from losing control without control laws reconstruction. The purpose of this paper is to propose a sideslip trim fault-tolerant control strategy for wing damage and aileron stuck.

The six degree of freedom model of the damaged aircraft is constructed by using the non-center-of-mass approach on the basis of aerodynamic database, which is calculated in XFlow. This paper adopts the sideslip command for trim, combining with the adaptive nonlinear dynamic inversion control to achieve fault-tolerant control.

This strategy can effectively improve the control margin of the remaining control surface and guarantee maneuverability of the aircraft after serious faults.

The original and wing-damaged aircraft models are reconstructed in CATIA, and the aerodynamic data is calculated in XFlow. Sideslip angle is adopted to compensate additional roll moment caused by wing damage or aileron stuck. Adaptive nonlinear dynamic inversion control, combined with sideslip trim, is applied to achieve fault-tolerant control.

This study aims to review the current one-step electrodeposition of superhydrophobic coatings on metal surfaces.

One-step electrodeposition is a versatile and simple technology to prepare superhydrophobic coatings on metal surfaces.

Preparing superhydrophobic coatings by one-step electrodeposition is an efficient method to protect metal surfaces.

Even though there are several technologies, one-step electrodeposition still plays a significant role in producing superhydrophobic coatings.

The purpose of this paper is to analyze the influencing factors of patients’ financial burden through a case study of hospital on public‒private partnerships (PPP) model (Chaonan Minsheng Hospital of Guangdong Province) and provide some useful information to policymakers for better development of hospitals on PPP model.

There are total six indicators that are defined as patients’ financial burden, basing on the policy of “indicators of medical quality management and control on the third level large general hospital (2011 edition),” issued by Chinese Government. In total, 23 potentially influencing factors of patients’ financial burden for hospital on PPP model were chosen from the above policy. The five-year (2007‒2011) data for the above 29 indicators come from statistic department of hospital on PPP model. Grey relational analysis (GRA) was applied to analyze the influencing factors of patients’ financial burden for hospital on PPP model.

A clear rank of influencing factors of patients’ financial burden is obtained and suggestions are provided from results of GRA, which provide reference for policymakers of hospital on PPP model. The five main influencing factors of patients’ financial burden for hospital on PPP model, in sequence, are rescuing critical ill patients on emergency, rescuing critical ill inpatients, inpatient bed occupancy rate, working days per bed and medical building area.

The study on the influencing factors of patients’ financial burden for hospital on PPP model not only provides decision-making for policymaker of hospital and controlling of medical expenditure but also contributes to release patients’ financial burden for hospitals on PPP model.

This paper answers, in the Chinese stock market, who can realize the “spot value” of corporate social responsibility (CSR).

The authors use event-study to build the research framework. Using CSR report content analysis, the authors measure the specification level of CSR disclosure. Applying the Baidu index, the authors mine Chinese investors’ profiles data to investigate retail investor heterogeneity closely.

The authors find strong evidence that the measure captures a behavioral bias in CSR pricing: firms that choose to disclose CSR report experience positive abnormal return more among retail investors than institutional investors, more among young investors than older, but no difference between female and male investors.

For Chinese public firms, the authors give them evidence that they can realize positive abnormal returns by applying certain CSR disclosure strategies. For Chinese investors, especially retail investors and youths, the authors ask them to rethink whether their positive evaluation of CSR is a rational trade-off choice or whether they are fooled by the “hedging mask” and “attention-grabbing.”

The findings can give some suggestions to regulators: encouraging voluntary disclosure and reducing mandatory disclosure can drive enterprises to engage in more CSR activities because the voluntarily CSR disclosure can realize both long-term value and “spot value.” Complementarily, a more rigorous CSR report auditing regulation can suppress the “greenwash” by increasing the “lying cost.”

Using behavioral finance theory, the authors connect the gap between neoclassical research on the “U-shaped” value realization of CSR and the increasing voluntary CSR disclosure in the Chinese market. The authors find that heuristic reason and emotionality orientation results in the Chinese “CSR-friendly” market.

To utilize Bluetooth as a layer 2 multi‐hop subnet for global IP network, there are two missing protocols in current Bluetooth stack: network formation and routing. In this paper, we propose our network formation and routing protocols optimized for such Bluetooth subnset usage scenarios to fill this gap. In this paper, we first present some observations on performance degradations of Bluetooth PAN due to network topologies and topology change when radio independant protocols are implemented. And then we analyze the reason of performance degradation. Based on our analysis, we first propose a flexible scatternet formation algorithm under conference scenario for multi‐hop communication. By using proposed method, scatternet can be formed flexibly with star, mesh, or linked line based on several parameters like number of maximum piconets that a gateway Bluetooth device can participate, and whether loops are needed in the resulting scatternet to achieve better network performance. To utilize topology information in multi‐hop communication, we propose a Cross‐layer Optimized Routing for Bluetooth (CORB) CORB is a QoS‐extended AODV routing protocol with mainly two optimizations between networking layer and underlying Bluetooth MAC layer. The first optimization is to use a new load metric. (LM) in QoS routing protocol instead of number of hops in conventional best effort routing. LM is derived from estimation of nodes’ link bandwidth, which reflects the different roles of nodes in Bluetooth scatternet. This proposal helps routing protocol to bypass heavily loaded nodes, and find route with larger bandwidth. The second optimization is to adjust LM and some MAC layer parameters in response to the unstable network topology caused by movement and change of indoor radio condition. Finally, We present some simulation and experiment results based on implementation, which prove the effectiveness of our protocols.

Money laundering is the process of concealing unlawfully obtained funds by presenting them as coming from a legitimate source. Criminals use crypto money laundering to hide the illicit origin of funds using a variety of methods. The most simplified form of bitcoin money laundering leans hard on the fact that transactions made in cryptocurrencies are pseudonymous, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. With the motive to curb these illegal activities, there exist various rules, policies and technologies collectively known as anti-money laundering (AML) tools. When properly implemented, AML restrictions reduce the negative effects of illegal economic activity while also promoting financial market integrity and stability, but these bear high costs for institutions. The purpose of this work is to motivate the opportunity to reconcile the cause of safety with that of financial inclusion, bearing in mind the limitations of the available data. The authors use the Elliptic dataset; to the best of the authors' knowledge, this is the largest labelled transaction dataset publicly available in any cryptocurrency.

AML in bitcoin can be modelled as a node classification task in dynamic networks. In this work, graph convolutional decision forest will be introduced, which combines the potentialities of evolving graph convolutional network and deep neural decision forest (DNDF). This model will be used to classify the unknown transactions in the Elliptic dataset. Additionally, the application of knowledge distillation (KD) over the proposed approach gives finest results compared to all the other experimented techniques.

The importance of utilising a concatenation between dynamic graph learning and ensemble feature learning is demonstrated in this work. The results show the superiority of the proposed model to classify the illicit transactions in the Elliptic dataset. Experiments also show that the results can be further improved when the system is fine-tuned using a KD framework.

Existing works used either ensemble learning or dynamic graph learning to tackle the problem of AML in bitcoin. The proposed model provides a novel view to combine the power of random forest with dynamic graph learning methods. Furthermore, the work also demonstrates the advantage of KD in improving the performance of the whole system.