Abstract
Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resourceconstrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge. © 2023 Reyna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract
Achieving carbon neutral power systems is pushing for higher penetration of distributed energy resources (DER) in existing distribution systems. Accordingly, sophisticated, yet, practical tools for the optimal operation and management of active distribution systems (ADS) are in high need. In response to this necessity, this paper presents a novel and scalable tool for ancillary services procurement by distribution system operators (DSOs). The developed tool takes into consideration the inter-temporal and variable nature of DER in an uncertainty-aware approach. This tool is also suited for real-world implementation with large ADS, as it adopts a sequential linearization approach. As such, it allows DSOs to procure flexibility optimally from DERs embedded in ADS in the day-ahead operation planning timeframe, where congestion and voltage issues are managed.

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.

Abstract
Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge and classifications. It typically processes user-generated data streams to provide and refine popular services such as wikis, collaborative maps, e-commerce sites, and social networks. Nevertheless, this modus operandi raises severe concerns regarding ill-intentioned data manipulation in adver-sarial environments. This paper presents a simulation, modelling, and classification approach to automatically identify human and non-human (bots) as well as benign and malign contributors by using data fabrication to balance classes within experimental data sets, data stream modelling to build and update contributor profiles and, finally, autonomic data stream classification. By employing WikiVoyage - a free worldwide wiki travel guide open to contribution from the general public - as a testbed, our approach proves to significantly boost the confidence and quality of the classifier by using a class-balanced data stream, comprising both real and synthetic data. Our empirical results show that the proposed method distinguishes between benign and malign bots as well as human contributors with a classification accuracy of up to 92%.

Abstract
In this paper we define several notions of term expansion, used to define terms with less sharing, but with the same computational properties of terms typable in an intersection type system. Expansion relates terms typed by associative, commutative and idempotent intersections with terms typed in the Curry type system and the relevant type system; terms typed by non-idempotent intersections with terms typed in the affine and linear type systems; and terms typed by non-idempotent and non-commutative intersections with terms typed in an ordered type system.

Abstract
A typical example of a Brain-Computer Interface (BCI) is a system that allows a person to move a ball displayed on a computer screen to the left or to the right, simply by imagining the movement of the left or right hand, respectively. Since the term Brain-Computer Interface was coined in 1973, the interest and efforts in this field have grown tremendously and there are now thought to be several hundred laboratories worldwide developing research in this topic. This paper aims at summarizing its resulting knowledge in a way that allows for a quick and clear consultation, highlighting the research lines, technologies and the most relevant cases of applications, so that policy makers, professionals and consumers can make effective use of the findings. With this in mind, a Brain-Computer Interface toolkit is proposed with a focus on different target audiences (e.g., children, seniors, people with intellectual disabilities) that can take advantage of this resource and promote an independent life routine. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.