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While cost allocation decisions attract considerable attention in the management accounting literature, many studies are contradicting and inconclusive. They often seek to develop product or service weights in order to make operating decisions with the sole objective of maximizing the firm’s profitability. But before developing these weights, the studies must first rank these products – which is a complex endeavor that is often driven by many hierarchical financial and nonfinancial goals and objectives. Ranking is also difficult due to using such complex concepts as time, uncertainty, cost, and interdependencies between accounting systems and manufacturing systems and among the products of the product mix. These concepts are inherently fuzzy and coextensively applied often with a confluence of variables operating simultaneously.

This paper applies an advanced mathematical model to account for a hospital cost allocation decisions in treating spinal cord injuries (SCI). The model combines the powers of fuzzy set theory (Zadeh, 1965) and the analytic hierarchy process (Saaty, 1978). The precise ratings required in the conventional analytic hierarchy process but practically hard to obtain are replaced by naturally semantic variables by using the fuzzy set concept. de Korvin and Kleyle’s (1999) fuzzy-analytic-hierarchical process (FAHP) then develop these ambiguous variables. FAHP can help to optimize decisions involving ambiguous variables and the web of prioritized strategies and goals of cost leadership, product differentiation, financial objectives of earnings, cash flows, and market share and nonfinancial goals such as tradition and owners’ convictions and philosophies.

We use data from seven Michigan SCI facilities in applying the FAHP model to rank and otherwise develop more optimal strategies and goals and compare our results to the decisions of hospital management.

The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

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.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

Between 1840s and 1940s, 27 occidental concessions have been created in several cities in China which represented difficult signs and memories for Chinese. Nowadays, these territories are experiencing a joint phenomenon of heritagization and tourismification which makes them experimental theaters for modern urban life and identity. Taking the former concessions of Tianjin as place study, the purpose of this study is to analyze the role of the heritage and tourism in the former concessions in city branding and more specifically the actors, approaches and products of this phenomenon.

This research draws on the comparison and analysis of two place studies in China. The authors base their analysis on semi-structured interviews in Chinese with previously identified stakeholders. In all, 20 individuals, including developers, public authority representatives, business owners, academics and conservation association members, were interviewed. This research was completed, updated and triangulated by content analysis of Web-based materials; official documents such as urban plans, guidelines and urban and tourism strategies collected during the fieldwork, as well as non-intrusive spatial observations of the concession and its various developments.

The results of this study show that the heritage in the former concessions has become an attractive tool for the city branding through tourism development, often led by the public actors with the participation of private entrepreneurs.

This study looks at the hybrid dimensions of the former concessions in China. It provides a better understanding of the co-action of heritage and tourism in the processes of territorial rehabilitation, which contributes to both the practitioners and researchers in this domain.

The purpose of this study is to prolong the service life of the Al–Si alloy cylinder and achieve the objective of energy saving and emission reduction by the composite treatments.

Chemical etching + laser texturing + filled MoS₂ composite treatment was applied to the friction surface of aluminum–silicon (Al–Si) alloy cylinder. The friction coefficient and wear loss were measured to characterize the tribology property of cylinders.

The composite-treated Al–Si alloy cylinder had the lowest friction coefficient and weight loss. The friction coefficient and weight loss of the composite treatment were approximately 27.08% and 54.17% lower than those of the untreated sample, respectively. The laser micro-textures control the release of solid lubricant to the interface of friction pairs slowly, which prolongs the service life of cylinders.

The synergistic effect of the chemical etching + laser texturing + filled MoS2 modified the tribology properties of Al–Si alloy cylinder. The chemical etching raised the silicon particles to bear the load, and laser micro-textures control the release of solid lubricant to improve the lubrication property.

This paper aims to analyze high-quality papers on the research of electronic word-of-mouth (eWOM) for product and service quality improvement from 2009 to 2022, in order to fully understand their historical progress, current situation and future development trend.

This paper adopts the bibliometrics method to analyze the relevant literature, including publishing trend and citation status, regional and discipline area distribution, and influential publications. Secondly, the VOSviewer is used for literature co-citation analysis and keyword co-occurrence analysis to obtain the basic literature and research hotspots in this research field.

Firstly, the study finds that the number of publications basically shows an increasing trend, and those publications are mainly published in tourism journals. In addition, among these papers, China has the largest number of publications, followed by the USA and South Korea. Through co-citation analysis of literature and keyword co-occurrence analysis, 22 foundational papers and six main research topics are obtained in this paper. Finally, this paper elaborates on the development trend of the research topic and future research directions in detail.

This is the first paper that uses bibliometrics to analyze and review relevant researches on eWOM for product and service quality improvement, which is helpful for researchers to quickly understand its development status and trend. This review also provides some future research directions and provides a reference for further research.

The purpose of this paper is to investigate the relationship between inventory leanness and venture survival, and demonstrate the role of organizational environments in moderating this relationship from three dimensions: environmental complexity, dynamism and munificence.

Using a large panel data of more than 150,000 new Chinese small- and medium-sized enterprises between 2000 and 2007 in the manufacturing sector, the authors employ the method of survival analysis via an accelerated failure time model to explore the non-linear relationship between inventory leanness and the likelihood of survival. Moreover, the moderation model is applied to examine the moderating role of organizational environments.

At its core, this paper demonstrates the inverted U-shaped relationship between inventory leanness and the likelihood of survival. Furthermore, the authors find that environmental complexity and dynamism can negatively moderate this relationship, whereas environmental munificence acts the exact opposite.

Managers need to realize the trade-off between inventory leanness and venture survival. Collectively, more than 90 percent of new Chinese ventures have great potential to improve the likelihood of survival by implementing inventory leanness management. In addition, firms ought to be fully aware of the internal management and the external environments.

This is the first study to confirm the inverted U-shaped relationship between inventory leanness and the likelihood of survival, and empirically verify the moderating role of environmental complexity, dynamism and munificence on this relationship.

Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach.

In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations.

One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost.

The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty.

The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.