Abstract
Machine Learning has emerged in the last years as the main solution to many of nowadays' data-based decision problems. However, while new and more powerful algorithms and the increasing availability of computational resources contributed to a widespread use of Machine Learning, significant challenges still remain. Two of the most significant nowadays are the need to explain a model's predictions, and the significant costs of training and re-training models, especially with large datasets or in streaming scenarios. In this paper we address both issues by proposing an approach we deem predictive and user-centric. It is predictive in the sense that it estimates the benefit of re-training a model with new data, and it is user centric in the sense that it implements an explainable interface that produces interpretable explanations that accompany predictions. The former allows to reduce necessary resources (e.g. time, costs) spent on re-training models when no improvements are expected, while the latter allows for human users to have additional information to support decision-making. We validate the proposed approach with a group of public datasets and present a real application scenario.

Abstract
The search for accurate and sensitive methods to detect chemical substances, namely cations and anions, is urgent and widely sought due to the enormous impact that some of these chemical species have on human health and on the environment. Here, we present a new platform for the efficient sensing of Cu2+ and Li+ cations. For this purpose, two novel photoactive diketopyrrolopyrrole-rhodamine conjugates were synthesized through the condensation of a diketopyrrolopyrrole dicarbaldehyde with rhodamine B hydrazide. The resulting chemosensors 1 and 2, bearing one or two rhodamine hydrazide moieties, respectively, were characterized by H-1 and C-13 NMR and high-resolution mass spectrometry, and their photophysical and ion-responsive behaviours were investigated via absorption and fluorescence measurements. Chemosensors 1 and 2 displayed a rapid colorimetric response upon the addition of Cu2+, with a remarkable increase in the absorbance and fluorescence intensities. The addition of other metal ions caused no significant effects. Moreover, the resulting chemosensor-Cu2+ complexes revealed to be good probes for the sensing of Li+ with reversibility and low detection limits. The recognition ability of the new chemosensors was investigated by absorption and fluorescence titrations and competitive studies.

Abstract
While research has mostly focused on the pragmatics of variable tag questions, fewer studies have explored invariable tags, either for their own sake or in contrast with other tag types within and across languages. It will be argued that invariable tag questions are as much part of the tag question system as variable tag questions, and that a unified functional model needs to consider both types in order to compare function-to-form mappings and reveal language variation, as well as the factors motivating their use. This study proposes one such model comprising eight functional types of tag questions, i.e., informational, affective, challenging, hortatory, facilitative, focusing, phatic and regulatory, in relation to four clusters of grammatical, dialogic, generic and sociolinguistic features. Based on the analysis of 539 invariable tag questions in British English and European Portuguese, results show that the constructions are more frequent and functionally varied in Portuguese (N = 397 vs. 142). In addition, based on statistical analyses, corresponding multi-feature prediction models are obtained for the proposed functional types of invariable tag questions in the two languages under inspection, thereby uncovering novel contributions to the pragmatics of invariable tag questions within the tag question paradigm. (c) 2022 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
Analysis of intra- and inter-population diversity has become important for defining the genetic status and distribution patterns of a species and a powerful tool for conservation programs, as high levels of inbreeding could lead into whole population extinction in few generations. Microsatellites (SSR) are commonly used in population studies but discovering highly variable regions across species' genomes requires demanding computation and laboratorial optimization. In this work, we combine next generation sequencing (NGS) with automatic computing to develop a genomic-oriented tool for characterizing SSRs at the population level. Herein, we describe a new Python pipeline, named Micro-Primers, designed to identify, and design PCR primers for amplification of SSR loci from a multi-individual microsatellite library. By combining commonly used programs for data cleaning and microsatellite mining, this pipeline easily generates, from a fastq file produced by high-throughput sequencing, standard information about the selected microsatellite loci, including the number of alleles in the population subset, and the melting temperature and respective PCR product of each primer set. Additionally, potential polymorphic loci can be identified based on the allele ranges observed in the population, to easily guide the selection of optimal markers for the species. Experimental results show that Micro-Primers significantly reduces processing time in comparison to manual analysis while keeping the same quality of the results. The elapsed times at each step can be longer depending on the number of sequences to analyze and, if not assisted, the selection of polymorphic loci from multiple individuals can represent a major bottleneck in population studies.

Abstract
This paper presents an algorithm for optimal resilient allocation of Mobile Energy Storage Systems (MESSs) for an active distribution system considering the microgrids coordinated bidding process. The main contribution of this paper is that the impacts of coordinated biddings of microgrids on the allocation of MESSs in the day-ahead and real-time markets are investigated. The proposed optimization framework is another contribution of this paper that decomposes the optimization process into multiple procedures for the day-ahead and real-time optimization horizons. The active distribution system can transact active power, reactive power, spinning reserve, and regulating reserve with the microgrids in the day-ahead horizon. Further, the distribution system can transact active power, reactive power, and ramp services with microgrids on the real-time horizon. The self -healing index and coordinated gain index are introduced to assess the resiliency level and coordination gain of microgrids, respectively. The proposed algorithm was simulated for the 123-bus test system. The method reduced the average value of aggregated operating and interruption costs of the system by about 60.16% considering the coordinated bidding of microgrids for the worst-case external shock. The proposed algorithm successfully increased the self-healing index by about 49.88% for the test system.

Abstract
A production system constrained by workers and machines, where machines are not fully staffed and workers can be transferred between machines, is here considered. Previous simulation research on this type of dual resource constrained production systems assumes that machines are fully reliable. However, this is questionable in most practical situations. Discrete event simulation is used as research method to assess the impact of machine failures on where to transfer workers. Experimentation was carried out for different levels of machine availability, worker utilization and worker assignment rules. Results show that the modified operation due date rule for worker assignment improves tardiness related performance for all production situations considered. This rule shifts between a focus on completing jobs on dune and a focus on speeding up jobs with short processing time. Results further show that ignoring the machine state at worker assignment may lead to significant performance deterioration. For a machine availability of 97% and a worker utilization of 90%, a deterioration between 101% and 416% on the percentage of tardy jobs was observed, compared to scenarios where machines are always available. However, if the machine state is considered, deterioration ranges only between 11% and 30% under the same conditions. 'This highlights the need to considered machine availability at worker assigment.