Abstract
Physical models and probabilistic applications often guide the study and characterization of natural phenomena in engineering. Such is the case of the study of air change rates (ACHs) in buildings for their complex mechanisms and high variability. It is not uncommon for the referred applications to be costly and impractical in both time and computation, resulting in the use of simplified methodologies and setups. The incorporation of airtightness limits to quantify adequate ACHs in national transpositions of the Energy Performance Building Directive (EPBD) exemplifies the issue. This research presents a roadmap for developing an alternative instrument, a compliance tool built with a Machine Learning (ML) framework, that overcomes some simplification issues regarding policy implementation while fulfilling practitioners' needs and general societal use. It relies on dwellings' terrain, geometric and airtightness characteristics, and meteorological data. Results from previous work on a region with a mild heating season in southern Europe apply in training and testing the proposed tool. The tool outputs numerical information on the air change rates performance of the building envelope, and a label, accordingly. On the test set, the best regressor showed mean absolute errors (MAE) below 1.02% for all the response variables, while the best classifier presented an average accuracy of 97.32%. These results are promising for the generalization of this methodology, with potential for application at regional, national, and European Union levels. The developed tool could be a complementary asset to energy certification programmes of either public or private initiatives. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract
As the world's population grows, there is a need to find new sources of energy that are more sustainable. Photovoltaic (PV) energy is one of the renewable energy sources (RES) expected to have the greatest margin for growth in the near future. Given their intermittency, RES bring uncertainty and instability to the management of the power system, therefore it is essential to predict their behavior for different time frames. This paper aims to find the most effective forecasting method for PV energy production that could be applied to different time frames. PV energy production is directly dependent on solar radiation and temperature. Several forecasting approaches are proposed in this paper. A multiple linear regression (MLR) model is proposed to predict the monthly energy production based on the climatic parameters of the previous year. Different approaches are proposed based on first predicting the temperature and radiation and then applying the PV mathematical models to predict the produced energy. Three methods are proposed to predict the climatic parameters: using the average values, the additive decomposition, or the Holt-Winters method. Comparing the errors of the four proposed forecasting methods, the best model is the Holt-Winters, which presents smaller errors for radiation, temperature, and produced energy. This method is close to additive decomposition. © 2023 IEEE.

Abstract
ydata-profiling is an open-source Python package for advanced exploratory data analysis that enables users to generate data profiling reports in a simple, fast, and efficient manner, fostering a standardized and visual understanding of the data. Beyond traditional descriptive properties and statistics, ydata-profiling follows a Data-Centric AI approach to exploratory analysis, as it focuses on the automatic detection and highlighting of complex data characteristics often associated with potential data quality issues, such as high ratios of missing or imbalanced data, infinite, unique, or constant values, skewness, high correlation, high cardinality, non-stationarity, seasonality, duplicate records, and other inconsistencies. The source code, documentation, and examples are available in the GitHub repository: https://github.com/ydataai/ydata-profiling.

Abstract
The ocean has a huge impact on our way of life; therefore, there is a need to monitor and protect its biodiversity [...].

Abstract
To address metadata with researchers it is important to use models that include familiar domain concepts. In the Social Sciences, the DDI is a well-accepted source of such domain concepts. To create FAIR data and metadata, we need to establish a compact set of DDI elements that fit the requirements in projects and are likely to be adopted by researchers inexperienced with metadata creation. Over time, we have engaged in interviews and data description sessions with research groups in the Social Sciences, identifying a manageable DDI subset. A recent Clinical Psychology project, TOGETHER, dealing with risk assessment for hereditary cancer, considered the inclusion of a DDI subset for the production of metadata that are timely and interoperable with data publication initiatives in the same domain. Taking a DDI subset identified by the data curators, we make a preliminary assessment of its use as a realistic effort on the part of researchers, taking into consideration the metadata created in two data description sessions, the effort involved, and overall metadata quality. A follow-up questionnaire was used to assess the perspectives of researchers regarding data description.

Abstract
Over the past decade, molecular imprinting (MI) technologyhasmade tremendous progress, and the advancements in nanotechnology havebeen the major driving force behind the improvement of MI technology.The preparation of nanoscale imprinted materials, i.e., molecularlyimprinted polymer nanoparticles (MIP NPs, also commonly called nanoMIPs),opened new horizons in terms of practical applications, includingin the field of sensors. Currently, hydrogels are very promising forapplications in bioanalytical assays and sensors due to their highbiocompatibility and possibility to tune chemical composition, size(microgels, nanogels, etc.), and format (nanostructures, MIP film,fibers, etc.) to prepare optimized analyte-responsive imprinted materials.This review aims to highlight the recent progress on the use of hydrogelMIP NPs for biosensing purposes over the past decade, mainly focusingon their incorporation on sensing devices for detection of a fundamentalclass of biomolecules, the peptides and proteins. The review beginsby directing its focus on the ability of MIPs to replace biologicalantibodies in (bio)analytical assays and highlight their great potentialto face the current demands of chemical sensing in several fields,such as disease diagnosis, food safety, environmental monitoring,among others. After that, we address the general advantages of nanosizedMIPs over macro/micro-MIP materials, such as higher affinity towardtarget analytes and improved binding kinetics. Then, we provide ageneral overview on hydrogel properties and their great advantagesfor applications in the field of Sensors, followed by a brief descriptionon current popular routes for synthesis of imprinted hydrogel nanospherestargeting large biomolecules, namely precipitation polymerizationand solid-phase synthesis, along with fruitful combination with epitopeimprinting as reliable approaches for developing optimized protein-imprintedmaterials. In the second part of the review, we have provided thestate of the art on the application of MIP nanogels for screeningmacromolecules with sensors having different transduction modes (optical,electrochemical, thermal, etc.) and design formats for single use,reusable, continuous monitoring, and even multiple analyte detectionin specialized laboratories or in situ using mobiletechnology. Finally, we explore aspects about the development of thistechnology and its applications and discuss areas of future growth.