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Today, the emergence of the industry revolution systems such as Industry 4.0, Internet of Things, and big data frameworks poses new challenges in terms of storage and processing of real-time data. As systems scale in humongous sizes, a crucial task is to administer the variety of different subsystems and applications to ensure high performance. This is directly related with the identification and elimination of system failures and errors, while the system runs. In particular, database systems may experience abnormalities related with decreased throughput or increased resource usage, that in turn affects system performance. In this article, we focus on not only SQL (NoSQL) database systems that are ideal for storing sensor data in the concept of Industry 4.0. This typically includes a variety of applications and workloads that are difficult to online monitor, thus making anomaly detection a challenging task. Creating a robust platform to serve such infrastructures with minimum hardware or software failures is a key challenge. In this article, we propose RADAR, an anomaly detection system that works on real time. RADAR is a data-driven decision-making system for NoSQL systems, by providing process information extraction during resource monitoring and by associating resource usage with the top processes, to identify anomalous cases. In this article, we focus on anomalies such as hardware failures or software bugs that could lead to abnormal application runs, without necessarily stopping system functionality, e.g., due to a system crash, but by affecting its performance, e.g., decreased database system throughput. Although different patterns may occur through time, we focus on periodic running workloads (e.g., monitoring daily usage) that are very common for NoSQL systems, and Internet of Things scenarios where data streams are forwarded to the Cloud for storage and processing. We apply various machine learning algorithms such as autoregressive integrated moving average (ARIMA), seasonal ARIMA, and long–short-term memory recurrent neural networks. We experimentally analyze our solution to demonstrate the benefits of supporting online erroneous state identification and characterization for modern applications.

The field of assisted reproductive technology is shaped and changed constantly by advances in science and cutting-edge innovations. In a quest to maximise outcomes, add-on interventions are often adopted and utilised prematurely while the principles of evidence-based medicine seem to be less strictly adhered to. In this review we will attempt to summarise the latest evidence about some of the adjuvants.

Cloud computing has emerged as a new paradigm that offers on-demand availability and flexible pricing models. However, cloud applications are being transformed into large scale systems where managing and monitoring cloud resources becomes a challenging task. System administrators are in need of automated tools to effectively detect abnormal system behaviour and ensure the Service Level Agreement (SLA) between the service user and the service provider. In this work, we propose a framework for online anomaly detection based on cloud application metrics. We utilize Recurrent Neural Networks for learning normal sequence representations and predict future events. Then, we use the predicted sequence as the representative sequence of normal events and based on the Dynamic Time Warping algorithm we classify future time series as normal or abnormal. Furthermore, to create a real world scenario and validate the proposed method, we used Yahoo! Cloud Serving Benchmark as a state-of-the-art benchmark tool for cloud data serving systems. Our experimental analysis shows the ability of the proposed approach to detect abnormal behaviours of NoSQL systems on-the-fly with minimum instrumentation.

Cloud computing technology is on the rise as it provides an easy to scale environment for Internet users in terms of computational resources. At the same time, cloud providers manage this demand for computational power by offering a pay per use model for virtualized resources. Yet, it is a challenging issue to administer the variety of different cloud applications and ensure high performance by identifying failures and errors on runtime. Distributed applications are error-prone, and creating a platform to support minimum hardware and software failures is a key challenge. In this work, we focus on anomaly detection of data storage systems, and we propose a solution for detecting performance degradation of cloud deployed systems in real time. We use Long Short-Term Memory (LSTM) Autoencoders for learning the normal representations and reconstruct the input sequences. Then, we used the reconstructed errors of the LSTM Autoencoders on unseen time series data to detect abnormal behaviours. We used state-of-the-art benchmarks such as TPCx-IoT and YCSB to evaluate the performance of HBase and MongoDB systems. Our experimental analysis shows the ability of the proposed approach to detect abnormal behaviours in cloud systems.

Objectives To test the hypothesise that the treatment protocol used for preparation of the endometrium for frozen embryo transfer (ET) has a beneficial effect on the disorganised endometrium in women with endometriosis and leads to a higher pregnancy rate. Study design We performed a retrospective, database-searched cohort study. Relevant information was collected from the electronic records of women who underwent IVF/ICSI between 1/1/2000 and 31/12/2008 in our unit. Endometriosis patients formed the study group. The rest of the women formed the control group. The two groups were subdivided, depending on whether they had fresh or frozen ET. The main outcome was live birth rate (LBR). Secondary outcomes were clinical pregnancy rate (CPR) and miscarriage rate (MR). Comparisons were performed by Chi-square and Mann–Whitney tests (SPSS 16.0). Results A total of 3763 fresh and 3523 frozen ET IVF cycles were included in our study, of which 415 (5.7%) were due to endometriosis related subfertility. In the non-endometriosis group, fresh ET had significantly higher LBR, CBR and MR than frozen ET. In women with endometriosis, down-regulated frozen ET cycles had a markedly high LBR and CPR (16.9%, 18.2%), comparable to the LBR and CPR of fresh ET cycles in the same group (19.5%, 20.2%). No significant differences were found in the LBR and CPR in fresh ET cycles between the study and the control group. In frozen ET, however, the CPR was significantly higher in the endometriosis group (18.2% versus 12.7%, P =0.048). Conclusion Unlike the general IVF population, in women with endometriosis undergoing IVF, the preparation of the endometrium for frozen ET with GnRH agonists compared to fresh cycles is associated with higher LBR (16.9% versus 11.9%) and a significantly higher CPR (18.2% versus 12.7%, P =0.048). These results suggest that, in cases of endometriosis, the combined effect of GnRHa on the endometrium and the low level of ovarian steroids may simultaneously offer a better endometrial environment for implantation which may lead to better outcomes.

The loss of three or more consecutive pregnancies, known as recurrent miscarriage (RM), affects approximately 1 % of couples. Aetiology can be varied but in up to 50 % of the cases the cause remains unexplained. Progestogen supplementation has been used for more than half a century for the treatment of recurrent miscarriage in different doses and routes of administration. Progesterone activity is mediated via specific progesterone receptors, and is considered crucial for the development of an endometrial environment suitable for implantation. Progesterone regulates endometrial receptivity, and induces uterine quiescence. In addition, the immunomodulatory properties of progesterone, controlled by progesterone induced blocking factor (PIBF), seem to be also important for the maintenance of early pregnancy. In women with RM there may be a degree of immunological intolerance, progesterone may benefit these women by altering the immune response to the embryo. Despite favourable meta-analyses on the use of progestogens in RM, the evidence is not considered to be solid and data from ongoing research are awaited.

There are inherent difficulties in defining infertility and unexplained infertility (UI), and the scientific community is yet to reach a consensus opinion. These difficulties stem from the differences in the clinical, epidemiological and demographic approach in defining infertility.