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Background: In complex health settings, care coordination is required to link patients to appropriate and effective care. Although articulated as system and professional values, coordination and cooperation are often absent within and across levels of service, between facilities and across sectors, with negative consequences for clinical outcomes as well as service load. Aim: This article presents the results of an applied research initiative to facilitate the coordination of patient care. Setting: The study took place at three hospitals in the sub-district 3 public health complex (Tshwane district). Method: Using a novel capability approach to learning, interdisciplinary, clinician-led teams made weekly coordination-of-care ward rounds to develop patient-centred plans and facilitate care pathways for patients identified as being stuck in the system. Notes taken during threestage assessments were analysed thematically to gain insight into down referral and discharge. Results: The coordination-of-care team assessed 94 patients over a period of six months. Clinical assessments yielded essential details about patients’ varied and multimorbid conditions, while personal and contextual assessments highlighted issues that put patients’ care needs and possibilities into perspective. The team used the combined assessments to make patient-tailored action plans and apply them by facilitating cooperation through interprofessional and intersectoral networks. Conclusion: Effective patient care-coordination involves a set of referral practices and processes that are intentionally organised by clinically led, interprofessional teams. Empowered by richly informed plans, the teams foster cooperation among people, organisations and institutions in networks that extend from and to patients. In so doing, they embed care coordination into the discharge process and make referral to a link-to-care service.

In this paper, a novel reliability-based topology optimization (RBTO) framework integrating fail-safe is first presented to boost reliability levels and load path redundancy for complex marine structures. The sequential optimization and reliability assessment (SORA) approach using the conjugate gradient (CG) algorithm (SORACG) is proposed to decouple the RBTO procedure into sequential deterministic topology optimization (DTO) loops and reliability assessment (RA) loops. The computational efficiency and solution accuracy are enhanced benefiting from the decoupling feature of SORA. A popular fail-safe model simulating the local material failure using damaged zones with prescribed shape and size is introduced into DTO. Non-differentiable fail-safe worst-case problem is transformed into an equivalent bound formulation via the β -method. Combing the three-stage continuation technique (3SCT) which considers both iterative efficiency and global optimality, a multi-model optimization strategy is suggested to address the fail-safe model. In RA, the CG algorithm is developed to derive the most probable point (MPP) for the optimal fail-safe DTO design. Numerical cases concerning a cantilever beam and engineering applications for a long-span open deck and 10,000-ton container ship demonstrate the effectiveness of the framework.

PurposeThe existing three-stage network Data Envelopment Analysis (DEA) models with shared input are self-assessment model that are prone to extreme efficiency scores in pursuit of decision-making units (DMUs) efficiency maximization. This study aims to solve the sorting failure problem of the three-stage network DEA model with shared input and applies the proposed model to evaluate innovation resource allocation efficiency of Chinese industrial enterprises.Design/methodology/approachA three-stage network cross-DEA model considering shared input is proposed by incorporating the cross-efficiency model into the three-stage network DEA model. An application of the proposed model in the innovation resource allocation of industrial enterprise is implemented in 30 provinces of China during 2015–2019.FindingsThe efficiency of DMU would be overestimated if the decision-maker preference is overlooked. Moreover, the innovation resource allocation performance of Chinese industrial enterprises had a different spatial distribution, with high in eastern and central China and low in western China. Eastern China was good at knowledge production and technology development but not good at commercial transformation. Northeast China performed well in technology development and commercial conversion but not in knowledge production. The central China did not perform well in terms of technology development.Originality/valueA three-stage network DEA model with shared input is proposed for the first time, which makes up for the problem of sorting failure of the general three-stage network model.

The increasing demand of humankind has caused a large number of land use changes, which pose a direct or indirect threat to the environment while promoting economic growth. The lack of risk-oriented land use changes may increase the disaster risk in the region. Therefore, how to study the relationship between land use change and disaster risk deserves attention. In this study, a research framework with quantitative relationship between land use change and disaster risk was constructed from the perspective of efficiency. The framework integrated land use change, disaster losses and environment variable (runoff increment) into a three-stage data envelopment analysis (DEA) assessment model to dynamically evaluate the impact of land use changes on disasters. The main conclusions include: (I) after the influence of runoff increment and random error was excluded, the overall risk score of counties and cities in Taiwan is 0.643, which represents a relatively high level, indicating that land use changes have caused high disaster risk; and (II) the vulnerability of land development in each county and city can be obtained through the comprehensive score of disaster risk the amount of unused input. The results of this study can help government agencies to rank various types of land development and then determine the acceptable risk level and incorporate disaster risk into land development.

Based on tension–tension fatigue tests, this paper investigated the mechanical property degradation of carbon fiber reinforced polymer (CFRP) tendons from a macroscopic perspective. According to the degradation regularity, this paper proposed a normalized phenomenological fatigue model based on the residual stiffness/strength of CFRP tendons during the fatigue loading process. In this paper, the residual stiffness of CFRP tendons were tested at five stress ranges, while the residual strength was tested at four stress ranges. In order to validate the reliability and applicability of proposed fatigue damage model, the predictions of proposed model and cited models from the literature are discussed and compared. Furthermore, experimental results from literatures were adopted to verify the accuracy of the proposed model. The results showed that the proposed model is applicable to predict both residual stiffness and residual strength throughout fatigue life cycle and has a better accuracy than models from the literature. Moreover, the three-stage degradation can be observed from the degradation processes of stiffness and strength at each stress level.

Background Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists’ experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases. Methods We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively. Results The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95. Conclusion The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement.

China has been struck by earthquakes at all scales, and such quakes have resulted in enormous human and property losses. Previous studies have mainly focused on large-scale earthquakes. However, small-scale quakes can also have long-term impacts. This study sheds light on moderate earthquakes with magnitudes ranging from 5.0 to 7.0. It aims to evaluate county resilience to moderate earthquakes based on 102 moderate quakes that occurred in Mainland China during 2002–2014. To overcome the shortcomings of traditional data envelopment analysis (DEA) evaluation methods, this study adopts a three-stage super-efficient DEA model to evaluate the resilience of counties that have been struck by moderate earthquakes. Moreover, it identifies socioeconomic factors that can effectively affect county resilience. Results suggest that most counties in China that have been struck by moderate earthquakes exhibit low efficiency and resilience. The research uses Tobit regression to demonstrate that insurance intensity, hospital beds, teledensity, government financial expenditure, and disaster experience can efficiently improve county resilience to moderate earthquakes, which indicates the future improvement direction of local resilience. Moreover, a region with a high frequency of moderate quakes displays relatively low efficiency and resilience. Considerable attention and effort should be afforded to these areas.

Purpose – The purpose of this paper is to investigate the relationship between level of environmental disclosure and establishment of a legitimacy image of operation among Japanese firms after implementation of the Kyoto Protocol. Design/methodology/approach – This study uses a sample consisting of 208 firms listed in the Japan Nikkei Stock Index 500 and adopts three-stage least-squares (3SLS) to explore the relationship between environmental news exposure, environmental disclosure in corporate social responsibility (CSR) reports, and environmental legitimacy. Findings – Results indicate that firms from environmentally-sensitive industries can significantly improve their perceived legitimacy by releasing CSR reports; firms with better prior environmental legitimacy will be more active in environmental disclosure and establish better environmental legitimacy in the next period; firms with better carbon reduction performance tend to have higher levels of environmental disclosure. In terms of carbon reduction performance, Japanese firms in the sample may reduce carbon dioxide emissions by 49.636 tons by allocating one million yens (approximately 9,670.3 euros or 12,328 US dollars) to environmental expenditure. Practical implications – The top three items of environmental disclosure in most Japanese firms ' CSR reports are environmental management, development of alternative energies, and ecological information. These results reveal environmental behavior of sample firms in Japan to mitigate global warming. The managers should understand that the impact of substantive actions for environmental management on legitimacy is greater. Originality/value – Environmental management has become an important component of business management beliefs for most firms, and Japanese firms that belong to environmentally-sensitive industries are even more active in using CSR reports as an effective tool to establish their legitimacy image.

The assembly scheduling problem (ASP) and distributed assembly scheduling problem (DASP) have attracted much attention in recent years; however, the transportation stage is often neglected in previous works. Factory eligibility means that some products cannot be manufactured in all factories. Although it extensively exists in many real-life manufacturing processes, it is hardly considered. In this study, a distributed three-stage ASP with a DPm→1 layout, factory eligibility and setup times is studied, and a Q-learning-based artificial bee colony algorithm (QABC) is proposed to minimize total tardiness. To obtain high quality solutions, a Q-learning algorithm is implemented by using eight states based on population quality evaluation, eight actions defined by global search and neighborhood search, a new reward and an adaptive ε−greedy selection and applied to dynamically select the search operator; two employed bee swarms are obtained by population division, and an employed bee phase with an adaptive migration between them is added; a new scout phase based on a modified restart strategy is also presented. Extensive experiments are conducted. The computational results demonstrate that the new strategies of QABC are effective, and QABC is a competitive algorithm for the considered problem.

With the implementation of new round electricity system reform in China, the provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition and satisfy the requirements of the electricity industry reform. Therefore, it is essential to conduct operational efficiency evaluation on provincial EGEs. While considering the influences of exterior environmental variables on the operational efficiency of provincial EGEs, a three-stage data envelopment analysis (DEA) methodology is first utilized to accurately assess the real operational efficiency of provincial EGEs excluding the exterior environmental values and statistical noise. The three-stage DEA model takes the amount of employees, the fixed assets investment, the 110 kV and below distribution line length, and the 110 kV and below transformer capacity as input variables and the electricity sales amount, the amount of consumers, and the line loss rate as output variables. The regression results of the stochastic frontier analysis model indicate that the operational efficiencies of provincial EGEs are significantly affected by exterior environmental variables. Results of the three-stage DEA model imply that the exterior environmental values and statistical noise result in the overestimation of operational efficiency of provincial EGEs, and the exclusion of exterior environmental values and statistical noise has provincial-EGE-specific influences. Furthermore, 26 provincial EGEs are divided into four categories to better understand the differences of operational efficiencies before and after the exclusion of exterior environmental values and statistical noise.