Exploring the Potential of Self-Assessment for Teachers’ Development of ICT Competencies and Beliefs

We evaluate the effects of an online self-assessment tool on teachers’ competencies and beliefs about information and communication technologies (ICTs) in education. The causal impact of the tool is evaluated through a randomized encouragement design involving 7,391 lower secondary teachers across 11 European countries. Short-run impact estimates show that the use of the tool led teachers to critically revise their technology-enhanced teaching competencies (−0.14 standard deviations [SD]) and their beliefs about the use of ICT in education (−0.35 SD), while no impact on teachers’ ICT training is found. The effects are concentrated among teachers in the top-end tail of the distribution of pre-treatment outcomes. We provide suggestive evidence that the feedback score provided by the tool triggered such results by providing a negative information shock

Production scraps to raw materials: low-cost method for implementing lithium iron phosphate cathode scraps back to production lines

In recent years, the increased production of lithium-ion batteries (LIBs) has been causing significant amounts of production scraps that require efficient, economical, and environmentally viable recycling methods. This study investigates strategies for integrating low-temperature direct recycling of lithium iron phosphate (LFP) production scraps into battery manufacturing. Scrap LFP cathode active material (CAM) was direct recycled at 200 °C in air and 400 °C in N2. The recycled CAM was blended in different amounts (100, 50, 30%-wt) with commercial CAM. Two slurry compositions were considered based on CAM: polyvinylidene fluoride: carbon black ratios (80:10:10 and 92:5:3), and coin cells were manufactured and tested. Results indicate that recycled CAM can be directly reprocessed in new batteries exhibiting excellent electrochemical performance (154 mAh g−1, equivalent to pristine material) when the slurry included 30%-wt CAM recycled at 200 °C in air and 100%-wt CAM recycled at 400 °C in N2. Compared to virgin slurry material cost (9.06 €/kgslurry) and environmental impact (8.27 kg CO2/kgslurry), incorporating 30%-wt CAM recycled at 200 °C in air reduced costs to 6.59 €/kgSlurry and emissions to 6.21 kgCO2/kgslurry, and 100%-wt CAM recycled at 400 °C in N2 corresponded to 3.77 €/kgSlurry and 2.45 kgCO2/kgslurry. These findings clearly demonstrate that closed-loop integration of low-temperature direct recycling of LFP cathode scraps into cell manufacturing reduces material costs and environmental impact while maintaining high electrochemical performance

A multi-objective optimization approach in defining the decarbonization strategy of a district heating network: A case study of Oslo

This study investigates the decarbonization of the Oslo district heating network by, firstly, modeling its current framework using EnergyPLAN and, afterwards, exploring optimized solutions, in terms of CO2 emissions and total annual cost, combining EnergyPLAN with a multi-objective evolutionary algorithm. Alternative scenarios are explored based on nine decision variables (energy technologies), each constrained by upper and lower boundaries defined by a specific set of criteria. In this work, the exploration of 20,000 solutions is performed following a four-step approach that starting from a broad availability of the decision variables, step by step adds new constraints. In the first step, heat pumps and waste heat recovery were recognized as the most cost-effective decarbonization solutions. The second step, avoiding the use of heat pumps, shifted the focus to more costly electric boilers. The third step, excluding also electric boilers, resulted in favoring biomass boilers, but with an additional cost increase. In the final step, carbon capture and storage became the only feasible decarbonization option and highlighted the difficult decarbonization with a steep Pareto front. Overall, this study confirms that, in line with recent system-oriented literature but extending beyond technology- or case-specific studies, waste heat recovery and electrification-based sector coupling emerge as the dominant cost-optimal decarbonization pathways at the system level

Enhancing Student Engagement Through AI and Gamification: A Case Study of an Educational Platform

Integrating artificial intelligence (AI) and gamification in education opens new opportunities for personalized and engaging learning. This paper presents PolyGloT, an AI-powered platform enhanced with gamified elements to support the motivation and adaptability of learners. The system offers AI feedback and adaptive content in real time to maintain engagement. We outline its rationale, core characteristics and pedagogical foundations and report on an empirical study with 48 students using the UEQ-S questionnaire. The results showed moderately positive impressions, especially in hedonic quality, while pragmatic aspects such as clarity require improvement. Lessons learned stress the need to balance innovation with usability and point to future work on interface refinement, broader evaluation, and deeper analysis of the roles of AI and gamification

A Multitask Framework With Cross-Task Selective Feature Sharing for Remote Sensing Image Time-Series Analysis

Satellite image time series are used to solve dynamic tasks in Earth observation due to their rich information content, such as change detection and land cover classification. The amount of information in image time series and the interconnection among tasks may benefit from solving the tasks simultaneously in a unified framework taking advantage of a shared representation. However, in the literature, most of the methods perform a single task only. While methods for multiple tasks deal with single-date or bitemporal images, thus they fail to: first, model temporal dynamics in time series and, second, model temporal correlations across tasks. In this article, we propose a novel multitask deep learning framework to analyze remote sensing image time series (more than two images) in terms of various temporal relationships, handling both short-term and long-term temporal information. The framework aims to learn both task-common and task-specific representations to capture spatio-temporal task interdependencies. To this end, we use feature sharing in a multitask setting with a single encoder and a decoder for each task. We design a novel cross-task selective feature sharing mechanism in order to interexchange spatio-temporal task information. We flow down the framework into a model that deals with two tasks: frame-by-frame abrupt change detection and multitemporal semantic segmentation. The model is tested on three datasets of multispectral image time series, acquired by Landsat-8 (annual series of 12 images), Sentinel-2 (annual series of 12 images), and Gaofen-2 (3 images across years) sensors. Results show the effectiveness of the proposed framework in various settings and demonstrate the validity of the multitask model

Skills Training Programmes for Unemployed Workers in Developed Economies

This paper studies the effects of skills training courses on the labour market outcomes of unemployed workers in the north-east of Italy. We find a positive effect of about 5–6 percentage points in the share of days employed. This effect persists into the fourth year after the beginning of the course and is driven especially by participants younger than 30. From a methodological viewpoint, following Angrist & Rokkanen, we argue that the set-up is equivalent to a randomised controlled trial, given that the criterion which determines admission to the course is unrelated to the outcomes of interest

Distributed estimation of statistical parameters in an experimental network of single-transistor chaotic sensor nodes using frequency spectra and geometrical reconstruction

The possibility of performing useful computations on hardware substrates different from digital computers, such as networks of coupled nonlinear oscillators, represents an important perspective for increasing the pervasiveness of intelligent systems. Recent simulations have suggested that analog electronic circuits as simple as single-transistor chaotic oscillators could aggregate sensor readings over a wireless communication channel, offering direct readout of statistical parameters such as mean and variance without the need to interrogate individual nodes. In this paper, we present a comprehensive experimental demonstration of the viability of this concept using a benchtop apparatus comprising a complex network of coupled chaotic oscillators, each one controlled by a hypothetical sensor. By recording the ensemble-level activity corresponding to signals recorded by far-field and near-field antennas, it is shown that accurate estimation of the mean and variance is possible across a wide range of operating conditions. Modulating the coupling strength globally reveals transitions between distinct regimes, characterized by diverse collective responses to the distribution of sensor readings, including varying levels of coherent activity summation. Moreover, it is demonstrated that the statistical parameters of interest can be estimated from two independent representations of the signals: frequency spectra and time-lag reconstructions of the underlying geometry. Altogether, these results affirm the physical feasibility of distributed sensing in a network of elementary chaotic oscillators, paving the way to field experiments and eventually applications