Cloud Computing is an essential paradigm of computing services based on the “elasticity” property, where available resources are adapted efficiently to different workloads overtime. In elastic platforms, the forecasting component can be considered by far the most important element and the differentiating factor when comparing such systems, with workload forecasting one of the problems to solve if we want to achieve a truly elastic system. When properly addressed the cloud workload forecasting problem becomes a really interesting case study. As there is no general methodology in the literature that addresses this problem analytically and from a time series forecasting perspective (even less so in the cloud field), we propose a combination of these tools based on a state-of-the-art forecasting methodology which we have enhanced with some elements, such as: a specific cost function, statistical tests, visual analysis, etc. The insights obtained from this analysis are used to detect the asymmetrical nature of the forecasting problem and to find the best forecasting model from the viewpoint of the current state of the art in time series forecasting. From an operational point of view the most interesting forecast is a short-time horizon, so we focus on this. To show the feasibility of this methodology, we apply it to several realistic workload datasets from different datacenters. The results indicate that the analyzed series are non-linear in nature and that no seasonal patterns can be found. Moreover, on the analyzed datasets, the penalty cost as usually included in the SLA can be reduced to a 30 percent on average.

Advances in artificial intelligence (AI) are transforming assisted reproductive technologies by significantly enhancing fertility diagnostics. This review focuses on integrating AI with Computer-Aided Sperm Analysis (CASA) systems to improve assessments of sperm motility, morphology, and DNA integrity. By employing a spectrum of techniques, from classic machine learning (ML), often valued for its interpretability and efficiency with structured data, to deep learning (DL), which excels at extracting intricate features directly from image and video data, the field now achieves more accurate, automated, and high-throughput evaluations. These advanced systems offer significant advantages, including enhanced objectivity, improved consistency over manual methods, and the ability to detect subtle predictive patterns not discernible by human observation. The emergence of extensive open datasets and big data analytics has enabled the development of more robust models. However, limitations persist, such as the dependency on large, high-quality annotated datasets for training DL models, potential challenges in model generalizability across diverse clinical settings, and the “black-box” nature of some complex algorithms, alongside crucial needs for rigorous clinical validation, data standardization, and ethical management of sensitive information. Despite promising progress, these challenges must be addressed. Overall, this review outlines current innovations and future research directions essential for advancing personalized, efficient, and accessible fertility care.

Anomaly detection, vital for identifying deviations from normative data patterns, is particularly crucial in sensor‐driven real‐world applications, which predominantly involve temporal data in the form of time series. Traditional evaluation of anomaly detection methods has relied on public benchmark datasets. Yet, recent revelations have uncovered inherent flaws and inadequacies in these datasets, casting doubt on the perceived progress in the field. To address this challenge, the UCR Time Series Anomaly Archive has been recently proposed—a meticulously curated database comprising 250 time series—designed to provide a robust and error‐free benchmark for anomaly detection research. This paper comprehensively evaluates state‐of‐the‐art anomaly detection techniques using the UCR Time Series Anomaly Archive. Our findings demonstrate the efficacy of current methods in accurately detecting anomalies across an important portion of datasets without additional optimization, underscoring the archive's utility as a foundational baseline for future research and development in anomaly detection methodologies.