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In this paper, a set of safe adaptive genetic algorithms (sGAs) is proposed based on the Splicing/Decomposable encoding scheme and the efficient speed‐up strategies developed by Xu et al.. The proposed algorithms implement the self‐adaptation of the problem representation, selection and recombination operators at the levels of population, individual and component which commendably balance the conflicts between “reliability” and “efficiency”, as well as “exploitation” and “exploration” existed in the evolutionary algorithms. It is shown that the algorithms converge to the optimum solution in probability one. The proposed sGAs are experimentally compared with the classical genetic algorithm (CGA), non‐uniform genetic algorithm (nGA) proposed by Michalewicz, forking genetic algorithm (FGA) proposed by Tsutsui et al. and the classical evolution programming (CEP). The experiments indicate that the new algorithms perform much more efficiently than CGA and FGA do, comparable with the real‐coded GAs — nGA and CEP. All the algorithms are further evaluated through an application to a difficult real‐life application problem: the inverse problem of fractal encoding related to fractal image compression technique. The results for the sGA is better than those of CGA and FGA, and has the same, sometimes better performance compared to those of nGA and CEP.

Shape memory polyurethanes (SMPUs) are typically synthesized using polyols of low molecular weight, Mw, and high hydroxyl number as it is believed that high density of cross-links in these polyols are essential for high performance shape memory polymers. In this study, polyethylene glycol (PEG-6000) with Mw ~ 6000 g/mol and low hydroxyl number (OH ~ 18 mg K OH/g) as the soft segment and diisocyanate as the hard segment were used to synthesize SMPUs. It revealed that although the PEG-6000 based SMPUs have lower maximum elongation at break (425%) and recovery stress than those of PCL-2000 polyol based SMPUs, they have much better shape recovery ratio (98%) and shape fixity (95%). Furthermore, these SMPUs showed a much shorter actuation time of <10sec for up to 85% shape recovery, much shorter than those low Mw SMPUs, clearly demonstrated their great potential for applications.

Genetic algorithms have been extensively used in different domains as a type of robust optimization method. They have a much better chance of achieving global optima than conventional gradient‐based methods which usually converge to local sub‐optima. However, convergence speeds of genetic algorithms are often not good enough at their current stage. For this reason, improving the existing algorithms becomes a very important aspect of accelerating the development of the algorithms. Three improved strategies for genetic algorithms are proposed based on Holland’s simple genetic algorithm (SGA). The three resultant improved models are studied empirically and compared, in feasibility and performance evaluation, with a set of artificial test functions which are usually used as performance benchmarks for genetic algorithms. The simulation results demonstrate that the three proposed strategies can significantly improve the SGA.

This paper aims at the problem of attaching the data of doctors, patients and the real-time sensor data of the exoskeleton to the cloud in intelligent rehabilitation applications. This study designed the exoskeleton cloud-brain platform and validated its safety assessment.

According to the dimension of data and the transmission speed, this paper implements a three-layer cloud-brain platform of exoskeleton based on Alibaba Cloud's Lambda-like architecture. At the same time, given the human–machine safety status detection problem of the exoskeleton, this paper built a personalized machine-learning safety detection module for users with the multi-dimensional sensor data cloned by the cloud-brain platform. This module includes an abnormality detection model, prediction model and state classification model of the human–machine state.

These functions of the exoskeleton cloud-brain and the algorithms based on it were validated by the experiments, they meet the needs of use.

This thesis innovatively proposes a cloud-brain platform for exoskeletons, beginning the digitalization and intelligence of the exoskeletal rehabilitation process and laying the foundation for future intelligent assistance systems.

The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

This chapter explores Prospect Theory — a descriptive model of modelling individual choice making under risk and uncertainty, and its applications to a range of travel behaviour contexts.

The chapter provides background on Prospect Theory, its basic assumptions and formulations, and summarises some of its theoretical developments, applications and evidence in the field of transport research.

A body of empirical evidence has accumulated showing that the principle of maximisation of expected utility provides limited explanation of travel choices under risk and uncertainty. Prospect Theory can be seen as an alternative and promising framework for travel choice modelling (although not without theoretical and practical controversy). These findings are supported by empirical observations reported in the literature reviewed in this chapter.

The chapter provides a detailed account of the design and results of accumulated research in travel behaviour research that is based on Prospect Theory’s observations, insights and formulations. The potential of Prospect Theory for particular decision-making in travel behaviour research is articulated, main findings are presented and discussed, and limitations are identified, leading to further research needs.

Web services composition engineering (WSCE) is a big challenge because of the increasing complexity, openness and extensibility of services based on the information system (IS). In the absence of an adequate framework for IS engineering, the authors assume that the overall problem is consequently no longer easy to resolve. This paper aims to explore some of the issues underlying WSCE through a framework, which is built based on the state of the art. The proposed framework is structured around five views that mainly highlight aspects of personalization and security concerns. The main objectives of this framework are: to help understand and clarify the basics of the WSCE domain; to evaluate web services composition (WSC) methods; to analyze and compare existing personalization, secure methods and identify new research axes; and to identify the main criteria of the ongoing approach for the design of a secure IS based on personalized WSC.

This work develops a framework that is used as an analytical study to compare the existing WSCE methods and come up with research issues. Then, the proposed framework is considered as an abstract model for the new WSCE approach.

A set of criteria that the proposed framework should consider when developing a new approach to a secure IS based on personalized WSs composition.

The paper has theoretical implications as the personalization and security issues provide a research roadmap toward the realization of an approach for the design of a secure IS based on personalized WSs composition.

As proof, the authors are interested in a web services repository of a real mall. To do this, the authors deployed the application in a cloud environment and observed the results of personalization and security concepts in WSCE.

None of the existing comparison frameworks has raised both personalization and security issues in WSs composition, while personalization and security must be present in the whole composition process.

Microsyn signal generators have been used in high-precision inertial sensors for their good structural stiffness and high sensitivity. However, as the stator and the rotor of the microsyn are both constructed of silicon-steel laminations with high permeability, an extremely small non-concentricity between the stator and rotor of microsyn will cause two random reaction torques acting on the output axis. As a result, difficulty arises in compensating for these random reaction torques. This study aims to investigate the electromagnetic fields of a novel angular transducer characterized by high sensitivity.

Based on the operation principles of the new transducer, the output voltage is decided by the time rate of change of the net magnetic flux of each output pole. The transient analysis of the electromagnetic field of the transducer is carried out by ANSYS Maxwell-3D.

The distributions of the magnetic flux of the transducer’s interior and eddy current on the rotor are consistent with the results of theory analysis. Moreover, the leakage flux mainly distributes nearby the excitation poles. The novel small-angle transducer also possesses a remarkably low reaction torque and power loss.

Study on the electromagnetic fields of the new transducer not only provides a powerful basis to further improve the precision of the new transducer but also expands the scope of applications of the new transducer.

This new transducer is not only characterized by a high sensitivity, high linearity and fast response but also extremely low reaction torque and power losses. Thus, the new transducer is suitable for high-precision inertial sensors.

This paper aims to assess the asymmetric effects of oil price shocks and the impact of oil price volatility on the Eurozone’s supersector returns, with a particular emphasis on the impact of the subprime crisis and the euro debt crisis (EDC) on this relationship.

Empirical data consist of daily observations of the 19 EURO STOXX supersector indices and the Brent crude oil price index for the period January 2001 to August 2015. This paper uses a non-linear multifactor market model. This model accounts for heteroscedasticity and breakpoints that are identified by the Bai and Perron (1998, 2003) tests.

The results show that supersector returns are sensitive to oil price shocks. However, in most cases, their responsiveness to oil price volatility is not significant. The relationship between oil price shocks and supersector returns changes through time and depends on the sector. Financial turbulence affects the oil-stock market nexus. In most cases, the subprime crisis has had a positive impact on the oil-stock market relationship, whereas the EDC has had an overall negative effect. Before the subprime crisis, there is an evidence of asymmetric effects for some supersectors. Meanwhile, for most sectors, the asymmetric effects disappear after 2008.

The study improves understanding of the interaction between oil price risk and the Eurozone sector indices returns. Furthermore, it enables global investors to manage the risk inherent to the portfolio managers’ positions.

This study aims to examine the impact of employee treatment on stock price crash risk in emerging markets. The study further sheds light on the economic channels and boundary conditions between employee treatment and crash risk.

This study employs a large-scale archival dataset of Chinese A-share listed firms covering 2010 to 2021. To establish causality, the study leverages multi-way fixed effects, Oster’s test, change regression and instrumental variable methods to alleviate endogeneity concerns.

The results reveal that employee-friendly treatment leads to a lower crash risk. Moreover, improving internal control quality and enhancing firm reputation appear to be the two plausible economic channels through which employee treatment mitigates crash risk. Cross-sectionally, the documented impact is more evident for human-capital-intensive firms, firms with weaker external monitoring and those operating in fiercely competitive industries.

This study is among the first to show that employee treatment has a favorable consequence for shareholder benefit through reducing crash risk. The study thus adds to the ongoing debate regarding the relationship between employee treatment and shareholder wealth. The study also extends the nascent literature on the role of rank-and-file employees in shaping corporate information landscapes.