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Climate change and water security are among the grand challenges of the 21st century, but literacy on these matters among high-school students is often unsystematic and/or detached from the real world. This study aims to introduce the educational objectives, methods, and early results of "Water and Us", a three-module initiative that can contribute to advancing water education in a warming climate by focusing on the natural and anthropogenic water cycle, climate change, and emerging water conflicts. The method of Water and Us revolves around storytelling to aid understanding and generate new knowledge, learning by doing, a flipped-classroom environment, and a constant link to examples from the real world (such as ongoing droughts across the world or seeds of conflict regarding transnational river basins). Water and Us was established in 2021–2022 and, during that school year, involved ≥200 students as part of a proof of concept to test the complete didactic approach using small-scale experiments. Results from ≥40 h of proof-of-concept events confirmed the effectiveness of this approach with respect to conveying the essential elements of the natural and anthropogenic water cycle, the most commonly recurring concepts related to climate change and water as well as the possible conflicts and solutions related to water scarcity in a warming climate. The Water and Us team remains interested in networking with colleagues and potential recipients to upscale and further develop this work. [ABSTRACT FROM AUTHOR]

Electrical machines cover a very wide range of applications in many industrials sectors and the research to improve the performance of those applications is recently leading to the development of new solutions. Those devices are generally equipped with magnetic circuits made of laminated ferromagnetic steel, but in the last decade, new magnetic materials have been developed to realise magnetic circuits: Soft Magnetic Composites (SMC). The Authors have investigated SMCs with organic layer obtained through the adoption of phenolic and epoxy resins; in previous research activities several mixture compositions have been produced and analysed with different percentages of binder and compacting pressures. Promising results regarding magnetic and mechanical performances have been obtained using a very low binder content. The paper aims to investigate the lower limit of the binder to be used, still keeping good mechanical properties. Appropriate magnetic tests have been performed on toroidal specimens: good magnetic characteristics have been obtained, maintaining on the other side proper mechanical strength.

The optimization of the motor cores, coupled with new core shapes as well as powering the motor at high frequency are the primary reasons for the use of new materials. The utilization of new materials, like SMC (soft magnetic composite), reduce the core loss and/or provide quasi-isotropic core’s properties in any magnetization direction. Moreover, the use of SMC materials allows for avoiding degradation of the material portions, resulting from punching process, thereby preventing the deterioration of operating parameters of the motor. The authors examine the impact of technological parameters on the properties of a new type of SMC material and analyze the possibility of its use as the core of the fractional power motor. The result of the work is an indication of the shape of the rotor core made of a new SMC material to achieve operational parameters similar to those that have a motor with a core made of laminations.

State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models. [ABSTRACT FROM AUTHOR]

ABSTRACT DRIHM and its companion project DRIHM2US have developed a prototype research infrastructure for simulating the complete process involved in extreme hydrometeorological events, enabling a step change in how scientists [...]

The increasing lexicalization of syntactic theories poses new difficulties for incremental models of language processing. In this paper, we describe an incremental interpreter that makes use of knowledge on categories to keep the syntactic structure always connected. This, in turn, guarantees a fine-grained syntax-semantics interaction. The paper introduces the general problem of formalizing the notion of incremental interpretation, and analyzes the current approaches in the cognitive literature.

Nowadays, the circular economy is gaining more and more attention in sectors where the raw material supply is critical for both cost and geo-political reasons. Moreover, the environmental impact issue calls for recycling. From this perspective, the recovery of rare earth elements represents a strategic point. On the other hand, the high cost and the dangerous standard recovery methods that apply to NdFeB magnets limits options for traditional recycling. A new mechanical method is proposed, not requiring hydrogen, high temperature, or chemical processes, but instead using an impact mill designed to operate in vacuum. A traditional impact mill operating in a glove box filled with Ar atmosphere has also been used for comparison. The obtained NdFeB powders were analyzed in terms of magnetic properties and chemical composition, particularly in terms of the oxygen content. [ABSTRACT FROM AUTHOR]