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Conventional cancer chemotherapies are not fully efficacious and do not target tumors, leading to significant treatment-related morbidities. A number of genetically attenuated cancer-targeting bacteria are being developed to safely target tumors in vivo. Here we report the toxicological, tumor-targeting, and efficacy profiles of Salmonella enterica serovar Typhimurium CRC2631 in a syngeneic and autochthonous TRAMP model of aggressive prostate cancer. CRC2631 preferentially colonize primary and metastatic tumors in the TRAMP animals. In addition, longitudinal whole genome sequencing studies of CRC2631 recovered from prostate tumor tissues demonstrate that CRC2631 is genetically stable. Moreover, tumor-targeted CRC2631 generates an anti-tumor immune response. Combination of CRC2631 with checkpoint blockade reduces metastasis burden. Collectively, these findings demonstrate a potential for CRC2631 in cancer immunotherapy strategies.

Summary Background Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment. Methods In this economic analysis modelling study, using 39 006 consecutive patients with diabetes in a national diabetic retinopathy screening programme in Singapore in 2015, we used a decision tree model and TreeAge Pro to compare the actual cost of screening this cohort with human graders against the simulated cost for semi-automated and fully automated screening models. Model parameters included diabetic retinopathy prevalence rates, diabetic retinopathy screening costs under each screening model, cost of medical consultation, and diagnostic performance (ie, sensitivity and specificity). The primary outcome was total cost for each screening model. Deterministic sensitivity analyses were done to gauge the sensitivity of the results to key model assumptions. Findings From the health system perspective, the semi-automated screening model was the least expensive of the three models, at US$62 per patient per year. The fully automated model was $66 per patient per year, and the human assessment model was $77 per patient per year. The savings to the Singapore health system associated with switching to the semi-automated model are estimated to be $489 000, which is roughly 20% of the current annual screening cost. By 2050, Singapore is projected to have 1 million people with diabetes; at this time, the estimated annual savings would be $15 million. Interpretation This study provides a strong economic rationale for using deep learning systems as an assistive tool to screen for diabetic retinopathy. Funding Ministry of Health, Singapore.

Although deep learning for Diabetic Retinopathy (DR) screening has shown great success in achieving clinically acceptable accuracy for referable versus non-referable DR, there remains a need to provide more fine-grained grading of the DR severity level as well as automated segmentation of lesions (if any) in the retina images. We observe that the DR severity level of an image is dependent on the presence of different types of lesions and their prevalence. In this work, we adopt a multi-task learning approach to perform the DR grading and lesion segmentation tasks. In light of the lack of lesion segmentation mask ground-truths, we further propose a semi-supervised learning process to obtain the segmentation masks for the various datasets. Experiments results on publicly available datasets and a real world dataset obtained from population screening demonstrate the effectiveness of the multi-task solution over state-of-the-art networks.

The present research was conducted to systematically review existing research that examined the relationships of the aspects of self-construal and social network sites (SNS) usages. A total of 12 research articles met the inclusion criteria for the present review. The reviewed research articles mainly supported the significant relationship of the interdependent self-construal and SNS-related outcomes. The present review highlighted that the reviewed relationships differed. Some findings supported the direct effect of self-construal on SNS-related outcomes, whereas some supported the indirect effects of intervening variables on these relationships. The reviewed findings supported the influence of self-construal on cognition, emotion, and motivation. Implications of the present systematic review were discussed in the manuscript.

Background Aortic dissection is a life-threatening syndrome that sometimes requires emergency intervention, and endovascular aortic aneurysm repair (EVAR) is a treatment option. Long-term image follow-up is also required for patients after EVAR due to possible complications. Case presentation We present the case of a 73-year-old male with underlying chronic renal disease diagnosed with a type A aortic dissection who underwent EVAR. Four-dimensional (three spatial dimensions combined with time) phase-contrast magnetic resonance imaging (4D PC-MRI) was performed during regular follow-up in preference to contrast-enhanced computed tomography or simple MRI while taking his poor renal function into consideration. Conclusions We considered this preferable given his issues with renal function.

Deep learning-based models are developed to automatically detect if a retina image is `referable' in diabetic retinopathy (DR) screening. However, their classification accuracy degrades as the input images distributionally shift from their training distribution. Further, even if the input is not a retina image, a standard DR classifier produces a high confident prediction that the image is `referable'. Our paper presents a Dirichlet Prior Network-based framework to address this issue. It utilizes an out-of-distribution (OOD) detector model and a DR classification model to improve generalizability by identifying OOD images. Experiments on real-world datasets indicate that the proposed framework can eliminate the unknown non-retina images and identify the distributionally shifted retina images for human intervention., Accepted at IEEE ICIP 2021

Interventions engaging community health workers (CHW) for diabetes management aim to improve diabetes care and self-management behaviors among patients. We evaluated the effects of nonsurgical periodontal treatment (NSPT) with the CHW strategy on oral self-care behaviors, periodontal status and oral health-related quality of life (OHQoL) in patients with type 2 diabetes mellitus (T2DM). The participants were randomly assigned to experimental (EG, n = 35) and control (CG, n = 33) groups. All participants received NSPT, whereas the patients in the EG also received one-on-one 30 min lessons from a CHW over 4 weeks. The EG exhibited greater improvement in the probing pocket depth (β = −0.2, effect size [ES] = 0.61) and clinical attachment level (β = −0.2, ES = 0.59) at 1-month follow-up than the CG did. The ES increased over the 1-, 3- and 6-month follow-ups, indicating an increase in OHQoL (ES = 0.19, 0.60, and 0.62, respectively) in the EG. The patients in the EG were more likely to change their oral self-care behaviors than patients in the CG were. The NSPT with CHW strategy had a positive effect on 1-month periodontal treatment outcomes, long-term OHQoL and oral self-care behaviors in patients with T2DM.

Patient similarity analytics has emerged as an essential tool to identify cohorts of patients who have similar clinical characteristics to some specific patient of interest. In this study, we propose a patient similarity measure called D3K that incorporates domain knowledge and data-driven insights. Using the electronic health records (EHRs) of 169,434 patients with either diabetes, hypertension or dyslipidaemia (DHL), we construct patient feature vectors containing demographics, vital signs, laboratory test results, and prescribed medications. We discretize the variables of interest into various bins based on domain knowledge and make the patient similarity computation to be aligned with clinical guidelines. Key findings from this study are: (1) D3K outperforms baseline approaches in all seven sub-cohorts, (2) our domain knowledge-based binning strategy outperformed the traditional percentile-based binning in all seven sub-cohorts, (3) there is substantial agreement between D3K and physicians (κ = 0.746), indicating that D3K can be applied to facilitate shared decision making. This is the first study to use patient similarity analytics on a cardiometabolic syndrome-related dataset sourced from medical institutions in Singapore. We consider patient similarity among patient cohorts with the same medical conditions to develop localized models for personalized decision support to improve the outcomes of a target patient.

This research examined the relationship between social network site (SNS) intensity, SNS addiction, and the severity of obsessive-compulsive disorder (OCD), alongside its factors of obsession and compulsion. The overlap of SNS intensity and SNS addiction was controlled in the study to predict the measured severity of OCD. In this study, 204 Malaysian undergraduate students were recruited to complete the revised Facebook Intensity Scale, the revised Bergen Facebook Addiction Scale, and the self-report version of the Yale-Brown Obsessive-Compulsive Scale. The overlap of SNS intensity and SNS addiction was supported by their significant positive correlation. Furthermore, SNS addiction significantly correlated with the measured OCD and its corresponding factors. The hierarchical regression analysis revealed that the entry of SNS intensity enhanced the facilitative effect of SNS addiction on OCD and its factors. Therefore, the role of SNS intensity as a suppressor was supported. In the same regression model, SNS intensity predicted the severity of OCD and its compulsion subscale negatively. Implications and directions for future research were also discussed in this manuscript.

Background Sepsis causes death and morbidity in young infants. Globally, an estimated 1.3 – 3.9 million young infants experience sepsis and 400,000 – 700,000 die from sepsis-related conditions annually. Even though there have been significant progress over the past twenty years in reducing young infant mortality, sepsis currently accounts for up to 15% of all young infant deaths. A thorough understanding of young infant sepsis can inform strategies that span prevention, diagnosis and intervention for young infant sepsis. Objectives We aimed to perform a systematic review and meta-analysis to investigate the case fatality rates (CFRs) among young infants less than 90 days with sepsis globally. Methods We used the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2009 guidelines. We searched PubMed, Cochrane Central, Embase and Web of Science for randomized clinical trials and observational studies in English language, published between 2010 to 2019. Studies involving young infants less than 90 days old with sepsis and reported CFRs were included. We obtained pooled CFRs estimates using the random effects model. Additional stratifications by gestation, birth weight, onset of sepsis (early onset was defined as Results Among 6314 articles screened, 240 studies with a total of 437,796 patients met the inclusion criteria and were included in our analysis. 99 came from high income countries, 44 from upper middle income countries, 82 from lower middle income countries, 6 from low income countries and 9 were conducted in multiple countries. Overall, the pooled CFR was 0.18 (95% CI, 0.17–0.19). The CFR was the highest in low income countries (0.25 [95% CI, 0.07–0.43]), followed by lower middle (0.24 [95% CI, 0.21–0.26]), upper middle (0.21 [95% CI, 0.18–0.24]) and lastly high income countries (0.12 [95% CI, 0.11–0.13]). Other factors associated with higher CFRs included prematurity (0.23 [95% CI, 0.19–0.26] vs term CFR 0.10 [95% CI, 0.08–0.13]), low birth weight (0.21 [95% CI, 0.19–0.24] vs normal birth weight 0.19 [95% CI, 0.18–0.20]), early onset sepsis (0.20 [95% CI, 0.17–0.24] vs combined (0.16 [95% CI, 0.14–0.18]) and hospital acquired infection (0.23 [95% CI, 0.17–0.30] vs community acquired infections 0.21 [95% CI, 0.10–0.33]). Time trend analysis showed higher CFRs in the low income countries than the middle and high income countries. A decreasing trend in CFRs over time was observed in high and upper middle income countries, as compared to an increasing trend in lower middle and low income countries. Conclusions While we saw a declining trend of young infant sepsis CFRs among high and upper middle income countries across the years, the increasing trend amongst lower middle and low income countries highlights a disparity in infant sepsis outcomes based on resource availability. We highlight specific vulnerable patient populations that should be further studied in order to reduce the global burden of young infant sepsis.

Link prediction is a fundamental challenge in network science. Among various methods, local similarity indices are widely used for their high cost-performance. However, the performance is less robust: for some networks local indices are highly competitive to state-of-the-art algorithms while for some other networks they are very poor. Inspired by techniques developed for recommender systems, we propose an enhancement framework for local indices based on collaborative filtering (CF). Considering the delicate but important difference between personalized recommendation and link prediction, we further propose an improved framework named as self-included collaborative filtering (SCF). The SCF framework significantly improved the accuracy and robustness of well-known local indices. The combination of SCF framework and a simple local index can produce an index with competitive performance and much lower complexity compared with elaborately-designed state-of-the-art algorithms., 19 pages, 3 tables, 1 figure

Though deep learning systems have achieved high accuracy in detecting diseases from medical images, few such systems have been deployed in highly automated disease screening settings due to lack of trust in how well these systems can generalize to out-of-datasets. We propose to use uncertainty estimates of the deep learning system’s prediction to know when to accept or to disregard its prediction. We evaluate the effectiveness of using such estimates in a real-life application for the screening of diabetic retinopathy. We also generate visual explanation of the deep learning system to convey the pixels in the image that influences its decision. Together, these reveal the deep learning system’s competency and limits to the human, and in turn the human can know when to trust the deep learning system.

Microscopy-based technique has been widely used in the detection of Blastocystis sp. This study was conducted to compare the techniques used for screening of Blastocystis sp., namely in vitro cultivation of stool specimens in Jones’ medium (IVC) followed by Wheatley Trichrome staining and direct examination of stool samples preserved with polyvinyl alcohol (PVA) followed by Wheatley Trichrome staining with single-round polymerase chain reaction (PCR) as the reference technique. The study was performed on 466 stool samples obtained from the aboriginal community in Pahang, Malaysia. IVC showed higher detection rate of Blastocystis sp. (35.6%) than PVA (20.0%). Single-round PCR detected Blastocystis sp. in 41.0% of the stool specimens. The sensitivity and specificity of PVA and IVC in comparison to the reference technique were 75.3% (95% CI: 65.2-83.6) and 68.5% (CI: 63.7-73.3) and 88.6% (CI: 82.7-93.0) and 86.3% (CI: 81.9-90.0), respectively. The agreement between the reference technique and PVA showed statistically significant fair agreement by Cohen Kappa statistics of (K=0.318, p

Introduction: Although collagen is widely used in various forms as a functional ingredient in skin care products, the effect of oral supplementation of collagen tripeptides (CTPs) on human skin is unclear. Moreover, the majority of the positive outcomes of CTP reported so far have not considered the effect of weather conditions. Therefore, we tested the effect of CTP and adjusting for climate change on skin properties in middle-aged women.Materials and Methods: A randomized controlled trial was conducted with 84 women between 40 and 60 years of age. Participants were randomized to receive placebo or 1,000 mg CTP daily for 12 weeks. CTP was prepared from the skin of Nile Tilapia by the digestion method using collagenase from non-pathogenic bacteria of the genus Bacillus. Skin hydration, wrinkling, and elasticity were assessed at baseline and after 6 and 12 weeks with adjustments for temperature, humidity, and ultraviolet A exposure during the evaluation time using weather data from the regional meteorological office.Results: Of the 82 participants, 74 completed the trial without adverse effects. Compared with the control group, trans-epidermal water loss was reduced more in the CTP group after 12 weeks (P < 0.05). At 12 weeks, even after adjustment for humidity, temperature, and UVA in the region, the difference of the two groups in TEWL remained statistically significant (adjusted for humidity and temperature, P = 0.024; adjusted for UVA, P = 0.032; adjusted for temperature, high temperature, and ultraviolet A, P = 0.031). In terms of skin hydration, more improvement was evident in the CTP group than in the control group. In the subgroup analysis, subjects under 50 years of age showed a significant improvement in total score and moisture in the subjective skin improvement questionnaire after taking CTP for 12 weeks. Application of CTP was well-tolerated, and no notable adverse effect was reported from both groups.Discussion: Our findings suggest that oral ingestion of CTP from the Skin of Nile Tilapia (Oreochromis niloticus) is well-tolerated and helps reduce water loss in in middle-aged women.Clinical Trial Registration:www.clinicaltrials.gov/, Identifier: NCT03505684.

Background Clinical trials have demonstrated that either initiating or up-titrating a statin dose substantially reduce Low-Density Lipoprotein-Cholesterol (LDL-C) levels. However, statin adherence in actual practice tends to be suboptimal, leading to diminished effectiveness. This study aims to use real-world data to determine the effect on LDL-C levels and LDL-C goal attainment rates, when selected statins are titrated in Asian patients. Methods A retrospective cohort study over a 5-year period, from April 2014 to March 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of Dyslipidaemia in a primary care clinic in Singapore. The statins were classified into low-intensity (LI), moderate-intensity (MI) and high-intensity (HI) groups according to the 2018 American College of Cardiology and American Heart Association (ACC/AHA) Blood Cholesterol Guidelines. Patients were grouped into “No statin”, “Non-titrators” and “Titrators” cohorts based on prescribing patterns. For the “Titrators” cohort, the mean percentage change in LDL-C and absolute change in LDL-C goal attainment rates were computed for each permutation of statin intensity titration. Results Among the cohort of 11,499 patients, with a total of 266,762 visits, there were 1962 pairs of LDL-C values associated with a statin titration. Initiation of LI, MI and HI statin resulted in a lowering of LDL-C by 21.6% (95%CI = 18.9–24.3%), 28.9% (95%CI = 25.0–32.7%) and 25.2% (95%CI = 12.8–37.7%) respectively. These were comparatively lower than results from clinical trials (30 to 63%). The change of LDL-C levels due to up-titration, down-titration, and discontinuation were − 12.4% to − 28.9%, + 13.2% to + 24.6%, and + 18.1% to + 32.1% respectively. The improvement in LDL-C goal attainment ranged from 26.5% to 47.1% when statin intensity was up-titrated. Conclusion In this study based on real-world data of Asian patients in primary care, it was shown that although statin titration substantially affected LDL-C levels and LDL-C goal attainment rates, the magnitude was lower than results reported from clinical trials. These results should be taken into consideration and provide further insight to clinicians when making statin adjustment recommendations in order to achieve LDL-C targets in clinical practice, particularly for Asian populations.

Background There is increasing evidence supporting coronavirus disease 2019 (COVID-19)-related coagulopathy. In the available literature, only 2 cases of superior mesenteric vein thrombosis have been described. Methods We present a peculiar case of high-grade small bowel obstruction in a patient with COVID-19 infection. Results Exploratory laparotomy revealed a congenital adhesion band with associated focal bowel ischemia contributed by superior mesenteric vein thrombosis and positive lupus anticoagulant. Conclusions It is important to consider the rare differential of mesenteric vein thrombosis and its related sequelae of mesenteric ischemia in a patient with COVID-19 who presents with abdominal pain.

Learning concepts that are consistent with human perception is important for Deep Neural Networks to win end-user trust. Post-hoc interpretation methods lack transparency in the feature representations learned by the models. This work proposes a guided learning approach with an additional concept layer in a CNN- based architecture to learn the associations between visual features and word phrases. We design an objective function that optimizes both prediction accuracy and semantics of the learned feature representations. Experiment results demonstrate that the proposed model can learn concepts that are consistent with human perception and their corresponding contributions to the model decision without compromising accuracy. Further, these learned concepts are transferable to new classes of objects that have similar concepts., Comment: Accepted to IJCNN 2021

Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. We describe a three-stage automated fact-checking system, named Quin+, using evidence retrieval and selection methods. We demonstrate that using dense passage representations leads to much higher evidence recall in a noisy setting. We also propose two sentence selection approaches, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. Quin+ is able to verify open-domain claims using results from web search engines.

Smart city and smart hospital development comprise a mainstream strategy worldwide to enhance the achievement of Sustainable Development Goals (SDGs) and Corporate Social Responsibility (CSR) practices. The smart healthcare industry needs to optimize its smart healthcare information strategies, creating the process-activity value and reducing the cost of healthcare without sacrificing the quality of patient care. This study proposes an decision model of Activity-Based Costing (ABC) concept and Multi-Criteria Decision-Making (MCDM) techniques that integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Processes (ANP), the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Zero-One Goal Programming (ZOGP) to achieve optimal smart healthcare information system portfolio strategy decisions. The resulting data shows that the perspective of government policy support and the criterion of Minimize Equipment Maintenance Costs are the most significant evaluation factors, and that the Health Data Informational System and Hospital Device and Drug Management System comprise by far the optimal portfolio of smart healthcare information system which strengthen the connection between ABC and MCDM techniques in evaluation process. The major contributions of this study are as follows: (1) the proposed model contributes to the management accounting innovation development of a sustainable city and Operation Research (OR) application, (2) the integrated model can promote the smart healthcare industry development and help decision makers to more accurately understand how to allocate resources and planning for intelligent-related activities to each smart healthcare information system through appropriate cost drivers.

Bayesian approaches have been successfully applied in social network analysis to study group behaviors such as online information dissemination and voting pattern. The focus has been on estimating the structure and strength of peer influence and its impact on the decisions of an individual. Less attention has been given to incorporating contextual information and individuals' hidden characteristics (or bias). In this work, we examine the social dynamics where social influence and contextual information play pivotal roles in driving one's decision. We design a probabilistic graphical model called CLAP to understand users' decision behavior in a social network, with an emphasis on both social-level and individual-level factors. To this end, the proposed model introduces hidden bias states associated with each actor and jointly estimates each actor's hidden bias state together with the social influence network. We demonstrate the effectiveness of CLAP on two types of social networks, a real-world US Congress network where senators vote on new bills, and online Twitter networks where users debate on the effectiveness of vaccine and lockdown policy during COVID-19. The experiment results show that CLAP outperforms state-of-the-art game theoretic approaches in predicting user decision. Further, the estimated social influence networks by CLAP has high edge homogeniety ratios.

Background: South-East Asia has seen a dramatic increase in type 2 diabetes (T2D). Risk prediction models for Major adverse cardiovascular events (MACE) identify patients who may benefit most from intensive prevention strategies. Existing risk prediction models for T2D were developed mainly in Caucasian populations, limiting their generalizability to Asian populations. We developed a Lasso-Cox regression model to predict the 5-year risk of incident MACE in Asian patients with T2DM using data from the largest diabetes registry in Singapore. Methodology: The diabetes registry contained public healthcare data from 9 primary healthcare centers, 4 hospitals and 3 national specialty centers. Data from 120,131 T2D subjects without MACE at baseline, from 2008 to 2018, were used for model development and validation. Patients with less than 5 years of follow-up data were excluded. Lasso-Cox, a semi-parametric variant of the Cox Proportional Hazard Model with l1-regularization, was used to predict individual survival distribution of incident MACE. A total of 69 features within electronic health records, including demographic data, vital signs, laboratory tests, and prescriptions for blood pressure, lipid and glucose-lowering medication were supplied to the model. Regression shrinkage and selection via the lasso method was used to identify variables associated with incident MACE. Identified variables were used to generate individual survival probability curves. Incident MACE was defined as the first occurrence of nonfatal myocardial infarction, nonfatal stroke, and CV disease-related death. Results: A total of 12,535 (10.4%) subjects developed MACE between 2008 and 2018. Model performance was evaluated by time-dependent concordance index and Brier score at 1, 2 and 5 years. The results of 5-fold cross validation shows that the model displayed good discrimination, achieving time-dependent C-statistics of 0.746±0.005, 0.742±0.003 and 0.738±0.002 at 1, 2 and 5 years respectively. The model demonstrated low Brier scores of 0.0355±0.0004, 0.0601±0.0011, 0.104±0.004 at 1, 2 and 5 years respectively, indicating good calibration. Factors most predictive of MACE were age and a history of hypertension and hyperlipidemia. Conclusions: We have developed a risk prediction model for MACE in Asian T2D using a large Singaporean T2D cohort, which can be used to support clinical decision-making. The individual survival probability estimates achieve an average C-statistics of 0.742 and are well-calibrated at 1, 2 and 5 years.

The purpose of this paper is to create a smart operating roadmap, which shows the entire process of a strategic business plan, including functions, methods, and tools, to link IFRS 8 (International Financial Reporting Standards No.8) to ABSC (Activity-Based Standard Costing), and to integrate ERP (Enterprise Resource Planning), MES (Manufacturing Execution System) under an Industry 4.0 environment. The IFRS is a global accounting framework that provides high-quality global accounting standards and governance principles for companies. Using the ABSC production decision model can support the Chief Operating Decision Maker (CODM) in planning Product&ndash, Business Unit (Product-BU) organization, which complies with the definition of the IFRS8 operating segments. The case study of the steel group uses the organizational design of the ERP system to achieve the systematization of reportable segments financial statements. In this process, the mathematical programing methods and ABSC can be used to obtain the optimal solutions for sales, costs, and profits. An international steel group case is used to demonstrate how to apply the methodology proposed in this paper for operating planning and control. The sensitivity analysis on the carbon emission reduction goal of environmental sustainability is also presented for the steel group case.

Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p p p

Summary Background Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies. Methods We used data from three population-based, multiethnic, cross-sectional studies in Singapore and China. The Singapore Epidemiology of Eye Diseases study (SEED, patients aged ≥40 years) was used to develop (5188 patients) and validate (1297 patients) the DLA. External testing was done on two independent datasets: the Singapore Prospective Study Program (SP2, 3735 patients aged ≥25 years) and the Beijing Eye Study (BES, 1538 patients aged ≥40 years). Chronic kidney disease was defined as estimated glomerular filtration rate less than 60 mL/min per 1·73m2. Three models were trained: 1) image DLA; 2) risk factors (RF) including age, sex, ethnicity, diabetes, and hypertension; and 3) hybrid DLA combining image and RF. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC). Findings In the SEED validation dataset, the AUC was 0·911 for image DLA (95% CI 0·886 −0·936), 0·916 for RF (0·891–0·941), and 0·938 for hybrid DLA (0·917–0·959). Corresponding estimates in the SP2 testing dataset were 0·733 for image DLA (95% CI 0·696–0·770), 0·829 for RF (0·797–0·861), and 0·810 for hybrid DLA (0·776–0·844); and in the BES testing dataset estimates were 0·835 for image DLA (0·767–0·903), 0·887 for RF (0·828–0·946), and 0·858 for hybrid DLA (0·794–0·922). AUC estimates were similar in subgroups of people with diabetes (image DLA 0·889 [95% CI 0·850–0·928], RF 0·899 [0·862–0·936], hybrid 0·925 [0·893–0·957]) and hypertension (image DLA 0·889 [95% CI 0·860–0·918], RF 0·889 [0·860–0·918], hybrid 0·918 [0·893–0·943]). Interpretation A retinal image DLA shows good performance for estimating chronic kidney disease, underlying the feasibility of using retinal photography as an adjunctive or opportunistic screening tool for chronic kidney disease in community populations. Funding National Medical Research Council, Singapore.

© 2020 Elsevier Ltd Background: Observational studies have suggested that accelerated surgery is associated with improved outcomes in patients with a hip fracture. The HIP ATTACK trial assessed whether accelerated surgery could reduce mortality and major complications. Methods: HIP ATTACK was an international, randomised, controlled trial done at 69 hospitals in 17 countries. Patients with a hip fracture that required surgery and were aged 45 years or older were eligible. Research personnel randomly assigned patients (1:1) through a central computerised randomisation system using randomly varying block sizes to either accelerated surgery (goal of surgery within 6 h of diagnosis) or standard care. The coprimary outcomes were mortality and a composite of major complications (ie, mortality and non-fatal myocardial infarction, stroke, venous thromboembolism, sepsis, pneumonia, life-threatening bleeding, and major bleeding) at 90 days after randomisation. Patients, health-care providers, and study staff were aware of treatment assignment, but outcome adjudicators were masked to treatment allocation. Patients were analysed according to the intention-to-treat principle. This study is registered at ClinicalTrials.gov (NCT02027896). Findings: Between March 14, 2014, and May 24, 2019, 27 701 patients were screened, of whom 7780 were eligible. 2970 of these were enrolled and randomly assigned to receive accelerated surgery (n=1487) or standard care (n=1483). The median time from hip fracture diagnosis to surgery was 6 h (IQR 4–9) in the accelerated-surgery group and 24 h (10–42) in the standard-care group (p

Introduction This study aimed to evaluate compliance with and performance of the Canadian Computed Tomography Head Rule (CCHR), and its applicability to the Singapore adult population with minor head injury. Methods We conducted a retrospective study over six months of consecutive patients who presented to the adult emergency department (ED) with minor head injury. Data on predictor variables indicated in the CCHR was collected and compliance with the CCHR was assessed by comparing the recommendations for head computed tomography (CT) to its actual usage. Results In total, 349 patients satisfied the inclusion criteria. Common mechanisms of injury were falls (59.3%), motor vehicle crashes (16.9%) and assault (12.0%). 249 (71.3%) patients underwent head CT, yielding 42 (12.0%) clinically significant findings. 1 (0.3%) patient required neurosurgical intervention. According to the CCHR, head CT was recommended for 209 (59.9%) patients. Compliance with the CCHR was 71.3%. Among the noncompliant group, head CT was overperformed for 20.1% and underperformed for 8.6% of patients. Multivariate logistic regression analysis revealed that absence of retrograde amnesia (odds ratio [OR] 4.1, 95% confidence interval [CI] 1.8-9.7) was associated with noncompliance to the CCHR. Factors associated with underperformance were absence of motor vehicle crashes as a mechanism of injury (OR 6.6, 95% CI 1.2-36.3) and absence of headache (OR 10.8, 95% CI 1.3-87.4). Conclusion Compliance with the CCHR for adult patients with minor head injury remains low in the ED. A qualitative review of physicians' practices and patients' preferences may be carried out to evaluate reasons for noncompliance.

Vascular occlusion is a rare but severe complication of dermal filler injections. Early treatment of this complication produces better outcomes. Current diagnostic methods for vascular occlusion in the skin are subjective and imprecise; these include capillary refill time, skin color, and reports of pain. This study aimed to assess the use of laser Doppler imaging (LDI) in the evaluation and treatment of vascular complications caused by dermal filler injections. This retrospective study used laser Doppler imaging (LDI) in 13 patients who developed vascular occlusion after facial dermal filler injections, with subsequent follow-up. The precise areas of perfusion observed on LDI were compared with the findings of clinical and photographic evaluation. The results showed that LDI accurately identified areas of vascular occlusion and improved treatment precision among these thirteen patients. The procedure was more precise than visual inspection or photographic evidence. Satisfactory outcomes were achieved for all patients, and no procedure-related complications were reported. Collectively, LDI provides fast, noninvasive, and accurate delineation of areas of vascular occlusion caused by complications of dermal filler injections and avoids several subjective shortcomings of visual and photographic evaluations. Thus, LDI effectively tracks treatment outcomes. However, large-scale studies are required to confirm the present findings.

The studies of nanostructure-based aluminum sensors have attracted huge attention because aluminum is a more cost-effective plasmonic material. However, the intrinsic properties of the aluminum metal, having a large imaginary part of the dielectric function and a longer electromagnetic field decay length and problems of poor long-term chemical stability, limit the surface-sensing capability and applicability of nanostructures. We propose the combination of capped aluminum nanoslits and a thin-capped dielectric layer to overcome these limitations. We show that the dielectric layer can positively enhance the wavelength sensitivities of the Wood's anomaly-dominant resonance and asymmetric Fano resonance in capped aluminum nanoslits. The maximum improvement can be reached by a factor of 3.5. Besides, there is an optimal layer thickness for the surface sensitivity because of the trade-off relationship between the refractive index sensitivity and decay length. We attribute the enhanced surface sensitivity to a reduced evanescent length, which is confirmed by the finite difference time-domain calculations. The protein-protein interaction experiments verify the high-surface sensitivity of the structures, and a limit of quantification (LOQ) of 1 pg/mL anti-bovine serum albumin is achieved. Such low-cost, highly sensitive aluminum-based nanostructures can benefit various sensing applications.

A chip-based ultrasensitive surface plasmon resonance (SPR) sensor in a checkerboard nanostructure on plastic substrates is presented for digital detection. The sensing elements on the checkerboard are composed of silver-capped nanoslit arrays, which were fabricated using the thermal-embossing nanoimprint method, to meet the demand for low-cost and rapid fabrication. Sharp Fano resonances in the optimized nanoslit arrays provide high-intensity sensitivities (20,000% per refractive index unit), with an element size of 12.5µm. The polarization-dependent transmission in the checkerboard pattern produces optical isolation between sensing elements and results in a crosstalk lower than 1%. Protein-antibody experiments demonstrated that the digital detection limit was up to 1pg/mL, which is approximately 1000 times lower than that of conventional analog detection. For a 140µm×140µm checkerboard pattern, the dynamic range was approximately 100 times higher than that of conventional surface plasmon resonance measurements. This new digital detection method is very useful for detecting ultralow concentrations of analytes with a nonuniform distribution on the sensor surface.

Background/Aims: Insufficient vitamin D status has been associated with adverse pregnancy outcomes; thus, it has been recognized as a public health concern. There have been no published data on vitamin D status in Bangladeshi pregnant women. The objective of this study was to determine the prevalence and risk factors of vitamin D deficiency among pregnant women in rural Bangladesh. Methods: A cross-sectional study was conducted with 522 pregnant women, with a gestational age of

Summary Background Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a population-based diabetic retinopathy screening programme in Zambia, a lower-middle-income country. Methods We adopted an ensemble AI model consisting of a combination of two convolutional neural networks (an adapted VGGNet architecture and a residual neural network architecture) for classifying retinal colour fundus images. We trained our model on 76 370 retinal fundus images from 13 099 patients with diabetes who had participated in the Singapore Integrated Diabetic Retinopathy Program, between 2010 and 2013, which has been published previously. In this clinical validation study, we included all patients with a diagnosis of diabetes that attended a mobile screening unit in five urban centres in the Copperbelt province of Zambia from Feb 1 to June 31, 2012. In our model, referable diabetic retinopathy was defined as moderate non-proliferative diabetic retinopathy or worse, diabetic macular oedema, and ungradable images. Vision-threatening diabetic retinopathy comprised severe non-proliferative and proliferative diabetic retinopathy. We calculated the area under the curve (AUC), sensitivity, and specificity for referable diabetic retinopathy, and sensitivities of vision-threatening diabetic retinopathy and diabetic macular oedema compared with the grading by retinal specialists. We did a multivariate analysis for systemic risk factors and referable diabetic retinopathy between AI and human graders. Findings A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable diabetic retinopathy was found in 697 (22·5%) eyes, vision-threatening diabetic retinopathy in 171 (5·5%) eyes, and diabetic macular oedema in 249 (8·1%) eyes. The AUC of the AI system for referable diabetic retinopathy was 0·973 (95% CI 0·969–0·978), with corresponding sensitivity of 92·25% (90·10–94·12) and specificity of 89·04% (87·85–90·28). Vision-threatening diabetic retinopathy sensitivity was 99·42% (99·15–99·68) and diabetic macular oedema sensitivity was 97·19% (96·61–97·77). The AI model and human graders showed similar outcomes in referable diabetic retinopathy prevalence detection and systemic risk factors associations. Both the AI model and human graders identified longer duration of diabetes, higher level of glycated haemoglobin, and increased systolic blood pressure as risk factors associated with referable diabetic retinopathy. Interpretation An AI system shows clinically acceptable performance in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, and diabetic macular oedema in population-based diabetic retinopathy screening. This shows the potential application and adoption of such AI technology in an under-resourced African population to reduce the incidence of preventable blindness, even when the model is trained in a different population. Funding National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.

Aims: This study investigated whether there is a link between severe hypoglycemia and progression into end-stage renal disease (ESRD) in patients with type 2 diabetes. Methods: Tapping into Taiwan&rsquo, s Health Insurance Research Database, we identified all type 2 diabetes patients between 1996 and 2013 and identified those diagnosed with a severe hypoglycemia episode during an emergency department visit and those who were not. Controls were then matched 1:1 for age, sex, index year, and medication. Results: We identified 468,421 type 2 diabetes patients diagnosed as having severe hypoglycemia in an emergency department visit. Compared with controls, these patients with SH had a higher risk of all-cause mortality (Hazard Ratio (HR), 1.76, 95% confidence interval, 1.61&ndash, 1.94) and progressed into ESRD within a shorter period of time. Results were similar after controlling for competing risk. Conclusion: Severe hypoglycemia is significantly associated with worsening renal dysfunction in patients with type 2 diabetes and hastened progression into ESRD.

The industrial revolution has grown to the fourth generation, or so-called Industry 4.0. The literature on Industry 4.0 is quite extensive and involves many different dimensions, however, production costs under Industry 4.0 have seldom been discussed. On the other hand, environmental problems are increasingly serious nowadays. Activity-Based Costing is a mature accounting method that can easily trace direct and indirect product costs, based on activities, as well as trace the carbon tax to products, which may lead to different product combinations, in order to reduce environment problems. Thus, the purpose of this paper is to propose a green activity-based costing production planning model under Industry 4.0. In order to make the paper more realistic, we suggest three models with five possible scenarios: normal and material cost fluctuation, material cost discount, and carbon tax with the related cost function. The Aluminum-Alloy Wheel industry was chosen as the illustrative industry to present the results. The model provides managers with a way to deal with the cost problem under Industry 4.0 and to be able to handle the environmental issues in making production decisions. This paper also provides suggestions for governments that have not considered carbon taxation.

In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes. This is a multi-ethnic (5 races), multi-site (8 datasets from Singapore, USA, Hong Kong, China and Australia), cross-sectional study involving 18,912 patients (n = 93,293 images). We compared these results and the time taken for DR assessment by DLS versus 17 human assessors – 10 retinal specialists/ophthalmologists and 7 professional graders). The estimation of DR prevalence between DLS and human assessors is comparable for any DR, referable DR and vision–threatening DR (VTDR) (Human assessors: 15.9, 6.5% and 4.1%; DLS: 16.1%, 6.4%, 3.7%). Both assessment methods identified similar risk factors (with comparable AUCs), including younger age, longer diabetes duration, increased HbA1c and systolic blood pressure, for any DR, referable DR and VTDR (p > 0.05). The total time taken for DLS to evaluate DR from 93,293 fundus photographs was ~1 month compared to 2 years for human assessors. In conclusion, the prevalence and systemic risk factors for DR in multi-ethnic population could be determined accurately using a DLS, in significantly less time than human assessors. This study highlights the potential use of AI for future epidemiology or clinical trials for DR grading in the global communities.

Purpose: The purpose of this study was to identify novice nurses' experience of turnover. Methods: Data were collected from June to August, 2014 through in-depth interview and E-mail. Twelve novice nurses with turnover experiences were selected through snowball sampling method. All interviews were recorded with MP3 recorder and transcribed for analyzing by Van Kaam method of phenomenology. Results: The results were divided into 4 categories, 20 themes, and 119 formulated meaning related to turnover experience. These four categories were 'Excessively heavy working environment', 'Relationship problem', 'Physical/emotional withdrawal', and 'Realizing the absence of goal in job'. Conclusion: The result of this study will offer anticipatory information to understand of novice nurses' difficulties and to prevent their turnover. And these findings can be used for nursing administrators to design a turnover prevention program more effectively for nurses.

SummaryThe impact of diabetes mellitus (DM) on hip fracture (HFx) is still controversial. We used nationwide population-based data in Taiwan to observe postoperative outcomes of HFx in patients with type 2 diabetes mellitus (T2DM) and found that the impact of T2DM may be related to medication of blood glucose control.ObjectivePublished studies evaluating diabetic patients with HFx have shown controversial outcomes. We assessed the impact of T2DM on postoperative outcomes after HFx in elderly patients using the nationwide population database in Taiwan.Research design and methodsWe used data from the National Health Research Institute in Taiwan to recruit patients who had undergone operations for HFx between 2000 and 2009. The recruited patients with T2DM were divided into the oral antidiabetic drug (OAD) cohort and the insulin cohort according to the use or non-use of insulin. Patients without DM were propensity score matched in a 1:1 ratio by four variables. We used the χ2 test, linear regression and Cox proportional hazards model to assess variables, including length of hospital stay, medical cost, complications, early readmission, and 1-year mortality.ResultsWe identified 5490 subjects in total. The insulin cohort exhibited prolonged hospital stay (11.8 days), higher medical costs, more complications within 30 and 90 after hip surgery, earlier readmission, and higher 1 year mortality rate (25.8%) than the OAD and non-DM cohorts. The OAD cohort had longer hospital stay (10.1 days) and higher readmission rate but fewer complications and mortality rates (14.9%) than the non-DM cohort.ConclusionsAfter matching confounding factors, the T2DM with OAD control groups were not associated with higher complication or mortality rates but were associated with higher readmission rates. However, diabetic patients with insulin control have poor outcome. The impact of T2DM on the postoperative outcomes of patients with HFx may be related to blood glucose control medication.

A spectral integration combined with a threshold method for the analysis of spectral scanning surface plasmon resonance (SPR) images can significantly increase signal recognition at low concentration of antibody solution. The 12-well SPR sensing plates consisted of gold-capped nanowire arrays with 500-nm period, 80-nm linewidth and 50-nm gold thickness which were used for generating multiple SPR images. A threshold method is introduced to eliminate background noises in spectral scanning images. Combining spectral integration and the threshold method, the detection limit of antibody concentration was 1.23 ng/mL. Using multiple-well SPR sensing plates and the proposed analytical method, multiple kinetic responses with spectral and spatial information on different sensing areas can be sensitively measured.

We propose a spectral contrast method to map the transmission images of surface plasmon resonance (SPR) in metallic nanostructures. Comparing the intensities between two neighboring wavelength bands near the SPR wavelength, the signal-to-noise ratio for biosensing applications obtained using the proposed method is found to be ten times higher than that obtained by conventional intensity analysis and 1.6 times better than that obtained by peak-wavelength fitting. The dynamic range and linearity of the refractive index are comparable to the peak-wavelength shift measurement. Based on the detection method, a spectral modulation system for the optical microscope is developed, combined with a gold-capped nanowire array, to measure the biointeractions in microfluidic devices. The experimental results show that the proposed method obtained multiple detections with a detection limit of 1.04 × 10−5 refractive index units. Two types of analysis methods for SPR images are used to study the protein-antibody interactions. The region-of-interest analysis supports multiplexing detections in a compact microfluidic sensor. The effective pixel analysis eliminates low-response pixels and enhances the signal-to-noise ratios for sensitive label-free detection.

Improving surface sensitivities of nanostructure-based plasmonic sensors is an important issue to be addressed. Among the SPR measurements, the wavelength interrogation is commonly utilized. We proposed using blue-shifted surface plasmon mode and Fano resonance, caused by the coupling of a cavity mode (angle-independent) and the surface plasmon mode (angle-dependent) in a long-periodicity silver nanoslit array, to increase surface (wavelength) sensitivities of metallic nanostructures. It results in an improvement by at least a factor of 4 in the spectral shift as compared to sensors operated under normal incidence. The improved surface sensitivity was attributed to a high refractive index sensitivity and the decrease of plasmonic evanescent field caused by two effects, the Fano coupling and the blue-shifted resonance. These concepts can enhance the sensing capability and be applicable to various metallic nanostructures with periodicities.

Cadmium arsenide (Cd3As2) has recently became conspicuous in solid-state physics due to several reports proposing that it hosts a pair of symmetry-protected 3D Dirac cones. Despite vast investigations, a solid experimental insight into the band structure of this material is still missing. Here we fill one of the existing gaps in our understanding of Cd3As2, and based on our Landau level spectroscopy study, we provide an estimate for the energy scale of 3D Dirac electrons in this system. We find that the appearance of such charge carriers is limited - contrary to a widespread belief in the solid-state community - to a relatively small energy scale (below 40 meV)., Comment: to be published in Phys. Rev. B

Objective Depression affect not only older people’ self-care ability and quality of life, but also increase the burden of their families and caregivers as well as health care resources. With consideration of the adverse effects of antidepressant medications and high medical costs of treating depression, it is important to use non-pharmacological interventions for managing depressive symptoms of older people. This study aimed to evaluate the effects of familiar music with alpha binaural beats on blood pressure, heart rate variability, and depression level of older people with depressive symptoms and to assess its feasibility for older people. Methods This study used a one-group pre-test and post-test design. Thirty-five older people residing in a retirement home participated in the study. Each participant listened to 30-minute familiar music with alpha binaural beats via a headphone individually while lying on the chair or bed in the afternoon in a private room. The music compact disk were Taiwanese old songs from the 1950-1970s which was familiar to most of the Taiwanese older people. Results The study results indicated that the participants after listening to 30-minute familiar music with embedded binaural beats of alpha frequencies at 10 Hz had shown immediate effects by a significant reduction on their heart rate, systolic blood pressure, and LF norm of heart rate variability (HRV), and a significant increase on HF norm of HRV indicating that the participants were in a relaxing state after listening to the music. Depression level was significantly decreased after listening to 30-minute familiar music with alpha binaural beats in the afternoon daily for five days. Conclusions Familiar music listening with alpha binaural beats can be a noninvasive and suitable intervention to promote relaxation and alleviate negative emotion in older people with depressive symptoms in long-term care settings.

Fairness and efficiency are the two basic objectives of the health care system design and reform. The problems such as the uneven distribution of medical resources, inadequate investment in hospitals and the lag of health care prevention have led to the problems of “expensive medical treatment”, “prolonged delay in disease”, “prominent medical disputes”, and other social phenomena, making the reform of the medical system something like “press the gourd floating ladle”. The “tripartite system reform” of medical care, medical insurance and medicine is aiming to clarify the functions of the hospital, guarantee the income of physicians to be commensurate with their input, stimulate the innovation competition of pharmaceutical enterprises, and exert medical insurance to medical treatment and compensation. The establishment of physicians’ multi-site practice as the core of the income distribution system, medical evaluation system, medical development system, medical insurance and relief system, is the key to achieve tripartite system reform.

Background: Obesity is a global public health problem. A systematic review showed that intensive behavioural counselling is effective in weight management amongst patients with obesity, but little is known if primary care physicians (PCPs) are involved in delivering such counselling. Studies revealed that patients had weight reduction if they were counselled by PCPs who used the 5As (Ask, Assess, Advise, Agree and Assist) method, but PCPs varied in their use of this tool. We aimed to explore the local PCP modalities of obesity counselling and if their approaches and methods corresponded to the 5As tool. Methods: Qualitative data were obtained from interviews with 50 PCPs from public and private primary care practices during six focus group discussions and seven in-depth interviews. The interviews were audio-recorded, transcribed, audited and analysed iteratively based on the grounded theory. Emergent themes were first externally validated, and then finalized after rounds of deliberations amongst the investigators. Results: PCPs varied in their approach in obesity counselling, focusing predominantly on “Ask”, “Assess” and “Advise” in the 5As tool. “Asking” was indirect and “Assessment” rarely covered the effects of obesity on psychosocial functioning. Dietary and lifestyle modifications were the main foci in “Advise”. “Agree” was least performed. Polyclinic doctors tended to “Assist” patients with referral to other healthcare workers for further weight management, but few deliberately “Arrange” to review progress in weight management, citing barriers. Conclusion: PCPs varied in their method of obesity counselling, pending on context and setting of their practices. In contrast to “Ask’, “Assess” and “Advise”, the use of “Agree” and “Arrange” was uncommon.

A complete product design cycle should include the following three stages: target setting, product design and development, and product validation. In the first target setting stage, it is required to understand the voice of customers (VOC), define the extreme customer usage conditions, acquire the duty cycle or customer usage data for the target, and cascade the top-down VOC targets from vehicle to system and component levels. In the second stage of product design and development, the multi-objective design optimization in the virtual analysis domain needs to be performed, where the analytical tools to design the products for various design criteria should have been developed and in place for analyses. In the final product validation stage, the test methods with a limited sample size are required to demonstrate the reliability of the product population for meeting the specific design functional and performance requirements. Since the test results exhibit significant statistical variations, these test methods (named the reliability demonstration test methods) are the most important mechanism to ensure a successful product launch for durability, reliability and quality. Again, due to the limited sample size for testing, a specific reliability and confidence target should be predetermined to be demonstrated by these test methods. The focus of this paper is to review the theoretical background and discuss the pros and cons of commonly used reliability demonstration test methods. Depending on the failure criterion in product validation testing, these reliability demonstration test methods can be categorized into the methods for non-repairable and repairable systems. For a non-repairable system, the test is considered complete, censored or suspended as the system fails, while a repairable system allows the actions required to restore/renew a failed system to operational status. The methods of interest for non-repairable systems include the test-to-failure method, the test-to-bogey method, the extended life test bogey method, and the step-stress accelerated life test method. Repairable systems are often modeled with the non-homogeneous Poisson process which includes the Duane and the Crow reliability growth models.

The economy of China has been rapidly growing in recent years. One of the important reasons is that there are a large number of foreign capital investments made in China. Creating a good investment environment in order to continuously attract foreign capitals is therefore an important topic for discussion. Taiwanese businessmen play an important role in the investments made in China, so this study investigates which aspects of China?s investment environment concerns Taiwanese businessmen more. Whether in the engineering or management field, AHP is a popular method on multi-criteria decision-making issues. However, with AHP, one must consider the problem of inconsistency. Later on, the Consistent Fuzzy Preference Relation method was developed. However, since this method does not have the easy-to-use software, this study is to construct and introduce the software using this method. From the results, the following items top the list: number of specialized manpower, quality and quantity of skilled manpower, electronic capabilities, electronic service, administrative efficiency, efficiency of finance and steady financial policies.

This study involved developing a natural disaster risk assessment framework based on the consideration of three phases: a pre-disaster phase, disaster impact phase, and post-disaster recovery phase. The exposure of natural disasters exhibits unique characteristics. The interactions of numerous factors should be considered in risk assessment as well as in monitoring environment to provide natural disaster warnings. In each phase, specific factors indicate the relative status in the area subjected to risk assessment. Three types of natural disaster were assessed, namely debris flows, floods, and droughts. The Chishan basin in Taiwan was used as a case study and the adequacy of the relocation of Xiaolin village was evaluated. Incorporating resilience into the assessment revealed that the higher the exposure is, the higher the resilience becomes. This is because highly populated areas are typically allocated enough resources to respond to disasters. In addition, highly populated areas typically exhibit high resilience. The application of this analysis in the policy of relocation of damaged village after disaster provides valuable information for decision makers to achieve the sustainability of land use planning.

Carbon emissions are an increasingly important consideration in sustainable environmental development. In the green building industry, green construction cost controls and low-carbon construction methods are considered to be the key barriers encountered. Based on Corporate Social Responsibility (CSR) policy, management of carbon emissions from green building projects contributes to the acquisition of accurate building cost information and to a reduction in the environmental impact of these projects. This study focuses on the CO2 emission costs and low-carbon construction methods, and proposes a 0–1 mixed integer programming (0–1 MIP) decision model for integrated green building projects, using an Activity-Based Cost (ABC) and life cycle assessment (LCA) approach. The major contributions of this study are as follows: (1) the integrated model can help construction company managers to more accurately understand how to allocate resources and funding for energy saving activities to each green building through appropriate cost drivers; (2) this model provides a pre-construction decision-making tool which will assist management in bidding on environmentally-friendly construction projects; and (3) this study contributes to the innovation operation research (OR) literature, especially in regard to incorporating the life cycle assessment measurement into construction cost management by utilizing a mixed decision model for green building projects.