Background: Infertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly dependent on the selection of a high-quality embryo for transfer. This is typically done manually by analysing embryos under a microscope. However, evidence has shown that the success rate of manual selection remains low. The use of new incubators with integrated time-lapse imaging system is providing new possibilities for embryo assessment. As such, we address this problem by proposing an approach based on deep learning for automated embryo quality evaluation through the analysis of time-lapse images. Automatic embryo detection is complicated by the topological changes of a tracked object. Moreover, the algorithm should process a large number of image files of different qualities in a reasonable amount of time. Methods: We propose an automated approach to detect human embryo development stages during incubation and to highlight embryos with abnormal behaviour by focusing on five different stages. This method encompasses two major steps. First, the location of an embryo in the image is detected by employing a Haar feature-based cascade classifier and leveraging the radiating lines. Then, a multi-class prediction model is developed to identify a total cell number in the embryo using the technique of deep learning. Results: The experimental results demonstrate that the proposed method achieves an accuracy of at least 90% in the detection of embryo location. The implemented deep learning approach to identify the early stages of embryo development resulted in an overall accuracy of over 92% using the selected architectures of convolutional neural networks. The most problematic stage was the 3-cell stage, presumably due to its short duration during development. Conclusion: This research contributes to the field by proposing a model to automate the monitoring of early-stage human embryo development. Unlike in other imaging fields, only a few published attempts have involved leveraging deep learning in this field. Therefore, the approach presented in this study could be used in the creation of novel algorithms integrated into the assisted reproductive technology used by embryologists.

Purpose: This paper aims to explore the development of sustainability accounting information systems through lens of contingency theory. In this digital age when companies are confronted with massive sets of data, integration of financial and non-financial data, little empirical evidence exists on how sustainability issues are integrated or linked within internal corporate information systems. Design/methodology/approach: A questionnaire-based survey, hypothesis testing, principal component methods and hierarchical clustering are used to provide original empirical evidence from major Lithuanian companies. Findings: The main findings reveal that most companies surveyed include a sustainability strategy in their core strategy, but there is a lack of linkage with measuring and integrating sustainability outcomes within the entirety of corporate financial results. Unexpectedly, the association between stakeholders' involvement and sustainability accounting information system design was not as strong as hypothesized theoretically. Therefore, it deserves further investigation, constituting an important implication for future research. Specifically, three profiles of sustainability accounting information systems were explained, namely, integrated, fragmented and compliance systems. Research limitations/implications: The limitations of this study relate to the small sample size, as sustainability-related information is still regarded quite confidential. Practical implications: This result could serve as a specific reference for companies to apply integrated sustainability accounting information systems that might serve as a good practice model for companies, however, fragmented and compliance profiles are the prevailing ones. Social implications: The findings are important for fostering corporate social responsibility by developing sustainability accounting information systems. Originality/value: This paper contributes to the sustainability accounting and information systems literature by providing empirical evidence linking contingent factors with the development of sustainability accounting information systems.

In stochastic programming and decision analysis, an important issue consists in the approximate representation of the multidimensional stochastic underlying process in the form of scenario tree. This paper presents the approach to generate the multistage multidimensional scenario tree out of a set of scenario fans. For this purpose, the multistage K-means clustering algorithm is developed. The presented scenario tree generation algorithm is motivated by the stability results for optimal values of a multistage stochastic program. The time complexity of developed multistage K-means clustering algorithm is proved to be linear in regard to the number of scenarios in the fan. The algorithm to determine the branches with nonduplicate information in the multistage scenario tree is also presented as an intermediate result of research.

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

The evolving crypto-currency market is seen as dynamic, segmented, and inefficient, coupled with a lack of regulatory oversight, which together becomes conducive to observing the arbitrage. In this context, a crypto-network is designed using bid/ask data among 20 crypto-exchanges over a 2-year period. The graph theory technique is employed to describe the network and, more importantly, to determine the key roles of crypto-exchanges in generating arbitrage opportunities by estimating relevant network centrality measures. Based on the proposed arbitrage ratio, Gatecoin, Coinfloor, and Bitsane are estimated as the best exchanges to initiate arbitrage, while EXMO and DSX are the best places to close it. Furthermore, by means of canonical correlation analysis, we revealed that higher volatility and the decreasing price of dominating crypto-currencies and CRIX index signal bring about a more likely arbitrage appearance in the market. The findings of research include pre-tax and after-tax arbitrage opportunities.