Purpose The purpose of this paper is to investigate the relationship between green initiatives, green performance, and a firm’s financial performance in the world. The existing literature on environmental initiatives and their impacts is limited to the context of a particular country. This gap points to a lack of clarification of variations in environmental regulation and in economic disparity which may affect the impact of green initiatives on green performance and on financial performance. Design/methodology/approach Data on the world top 500 publicly traded companies are collected from Compustat, a database of financial, statistical and market information on global companies, and from Newsweek, an information gatekeeper that enables consumers to access a list of environmentally friendly companies. The paper adopts linear regression to test the relationships between variables. Findings The results show that green initiatives have a positive impact on green performance, which in turn has a positive impact on financial performance. However, the impact of green initiatives varies by country. The study revealed that companies in European countries and Canada lead in the green initiatives and green performance, followed by the USA and Japan. China and Hong Kong lag behind compared to other countries. Research limitations/implications The small sample size in some of the countries used in this study may impact the validity of the results. Practical implications This study suggests that companies that seek financial benefits of pursuing green initiatives should have a long-term orientation when implementing these initiatives and should consider the country where they operate. Originality/value The current study provides a global understanding of the relationship between green initiatives, green performance, and financial performance, and contributes to the literature by highlighting variation among countries and by year.

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The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.

BackgroundEmbryo selection has been based on developmental and morphological characteristics. However, the presence of an important intra-and inter-observer variability of standard scoring system (SSS) has been reported. A computer-assisted scoring system (CASS) has the potential to overcome most of these disadvantages associated with the SSS. The aims of this study were to construct a prediction model, with data mining approaches, and compare the predictive performance of models in SSS and CASS and to evaluate whether using the prediction model would impact the selection of the embryo for transfer.MethodsA total of 871 single transferred embryos between 2008 and 2013 were included and evaluated with two scoring systems: SSS and CASS. Prediction models were developed using multivariable logistic regression (LR) and multivariate adaptive regression splines (MARS). The prediction models were externally validated with a test set of 109 single transfers between January and June 2014. Area under the curve (AUC) in training data and validation data was compared to determine the utility of the models.ResultsIn SSS models, the AUC declined significantly from training data to validation data (p < 0.05). No significant difference was detected in CASS derived models. Two final prediction models derived from CASS were obtained using LR and MARS, which showed moderate discriminative capacity (c-statistic 0.64 and 0.69 respectively) on validation data.ConclusionsThe study showed that the introduction of CASS improved the generalizability of the prediction models, and the combination of computer-assisted scoring system with data mining based predictive modeling is a promising approach to improve the selection of embryo with the highest implantation potential.Electronic supplementary materialThe online version of this article (doi:10.1186/s12958-016-0145-1) contains supplementary material, which is available to authorized users.