Abstract
Haptic feedback in Virtual Reality is commonly provided through wearable or grounded devices adapted to specific scenarios and situations. Shape-changing devices allow for the physical representation of different virtual objects but are still a minority, complex, and usually have long transformation times. We present Shape-a-getti, a novel ungrounded, non-wearable, and graspable haptic device that can quickly change between different radially symmetrical shapes. It uses a single actuator to rotate several identical poles distributed along a radius to render the different shapes. The format of the poles defines the possible shapes, and in our prototype, we used one that could render concave, straight, and convex shapes with different radii. We conducted a user evaluation with 21 participants asking them to recognize virtual objects by grasping the Shape-a-getti. Despite having difficulties distinguishing between some objects with very similar shapes, participants could successfully identify virtual objects with different shapes rendered by our device. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Abstract
The development of more flexible and robust models for reasoning about systems in environments with potentially conflicting information is becoming more and more relevant in different contexts. In this direction, we recently introduced paraconsistent transition systems, i.e. transition systems whose transitions are tagged with a pair of weights, one standing for the degree of evidence that the transition exists, another weighting its potential non existence. Moreover, these structures were endowed with a modal logic [3] that was further formalised as an institution in [5]. This paper goes a step further, proposing an approach for the structured specification of paraconsistent transition processes, i.e. paraconsistent transition systems with initial states. The proposed approach is developed along the lines of [12], which introduced a complete methodology for (standard) reactive systems development building on the Sannella and Tarlecki stepwise implementation process. For this, we enrich the logic with dynamic modalities and hybrid features, and provide a pallet of constructors and abstractors to support the development process of paraconsistent processes along the entire design cycle.

Abstract
Retailers depend on accurate forecasts of product sales at the Store x SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model's parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives.

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.