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The UNICEF-Akelius Digital Learning Initiative ('the Initiative') is one of the largest and longest-standing digital learning initiatives implemented by UNICEF. Launched in 2018 and continuing through 2024, this initiative spans various educational settings across 12 countries. Its primary focus is to improve education outcomes through digital learning, with a particular emphasis on second- and foreign-language acquisition for marginalized children and adolescents aged 6-18 years. This report synthesizes six years of experiences and insights from the Initiative, focusing on the factors and conditions that maximize the effectiveness of digital learning. It specifically addresses the often-overlooked challenges and best practices related to technology management in schools and classrooms. By doing so, the report offers five core messages for the implementation of digital learning at a global level, contributing to a broader understanding of the key factors that influence the successful adoption of digital learning in classrooms, including: (1) Digital tools must be relevant and easy to use; (2) Schools need support with managing technology; (3) Teachers require skills for pedagogical integration and training for blended teaching pedagogy; (4) Evidence and learning are essential to improve implementation; and (5) Building partnerships is key for sustainability. This report serves as a guide for implementing agencies (including UNICEF country offices), ministries of education, school leaders and other stakeholders to adopt effective digital learning practices that support teachers and enhance children's time spent learning. [This report was jointly developed with UNICEF Sweden.]

Global progress in child labour reduction has stalled since 2016. In South Asia, the negative consequences of the COVID-19 pandemic, recurrent extreme weather events and the cost-of-living crisis further threaten to slow or even reverse the progress made. This synthesis report addresses a timely need for new research to help decision-makers and practitioners identify policy and programmatic actions that can support prevention and reduction of child labour in South Asia, with a focus on the role of education. Drawing on quantitative data and qualitative primary evidence from UNICEF Innocenti's research in Bangladesh and India, it offers an accessible and rigorous overview of recent trends and patterns in child labour and explores how these relate to children's schooling experiences in both countries. Implications of COVID-19 for education and labour, and the role of child marriage as a key related outcome, are also assessed. The report also explores children's perspectives of their labour and schooling experiences, including in migration contexts as well as during the COVID-19 pandemic and resulting school closures. The report concludes with programmatic and research recommendations for further action at regional, country, and global levels.

India has made rapid progress towards the universalization of school education, hand in hand with a decline in child labour. Despite progress, child labour persists in the country, just as school attendance and completion rates reveal gaps in educational attainment. This report addresses a timely need for new research to help decision-makers and practitioners build an improved understanding of how to?strengthen the role of education in the elimination of child labour across India. Drawing on quantitative secondary data analysis and qualitative primary data analysis, it offers an accessible and rigorous overview of recent patterns in child labour in India and their linkages with children's school participation. The report also explores children's perspectives of their labour and schooling experiences, including in migration contexts as well as during the COVID-19 pandemic and resulting school closures. The report concludes with programmatic and research recommendations for further action.

In the past two decades, Bangladesh experienced a substantial reduction in the prevalence of child labour, associated with improvements in school enrolment and completion. Despite progress, child labour persists in the country, also driven by household earning losses and school closures due to the COVID-19 pandemic. This report addresses a timely need for new research to help decision-makers and practitioners identify policy and programmatic actions that can support prevention and reduction of child labour, with a focus on the role of education. Drawing on quantitative secondary data analysis and qualitative primary data analysis, it offers an accessible and rigorous overview of recent trends and patterns in child labour and explores how these relate to children's schooling experiences. Implications of COVID-19 for education and labour, and the role of child marriage as a key related outcome, are also assessed. The report concludes with programmatic and research recommendations for further action. [This report was prepared by the Economic Research Group (ERG).]

In 2019, children with disabilities in Cambodia were three times less likely to access educational opportunities than their peers without disabilities. Several barriers impede their ability to access inclusive educational opportunities. These include limited capacity of mainstream schools to enroll children with disabilities, lack of teachers who are trained in pedagogical strategies and approaches to teach children with various types of functional difficulties, and community perceptions towards inclusive education. A holistic approach is required to ensure necessary changes are made at all levels of the education system to ensure that children with disabilities can access inclusive learning in Cambodia. This is the first report under the Learning is For Everyone (LiFE) project and offers key recommendations to address barriers. Leaning is For Everyone (LiFE) research was implemented in Cambodia through a partnership between UNICEF and the Ministry of Education, Youth and Sport (MoEYS).

Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcare applications. Nevertheless, the practical implementation of FL presents technical and organizational challenges, as it generally requires complex communication infrastructures. In this context, consensus-based learning (CBL) may represent a promising collaborative learning alternative, thanks to the ability of combining local knowledge into a federated decision system, while potentially reducing deployment overhead. Methods. In this work we propose an extensive benchmark of the accuracy and cost-effectiveness of a panel of FL and CBL methods in a wide range of collaborative medical data analysis scenarios. The benchmark includes 7 different medical datasets, encompassing 3 machine learning tasks, 8 different data modalities, and multi-centric settings involving 3 to 23 clients. Findings. Our results reveal that CBL is a cost-effective alternative to FL. When compared across the panel of medical dataset in the considered benchmark, CBL methods provide equivalent accuracy to the one achieved by FL.Nonetheless, CBL significantly reduces training time and communication cost (resp. 15 fold and 60 fold decrease) (p < 0.05). Interpretation. This study opens a novel perspective on the deployment of collaborative AI in real-world applications, whereas the adoption of cost-effective methods is instrumental to achieve sustainability and democratisation of AI by alleviating the need for extensive computational resources.

We introduce JPC, a JAX library for training neural networks with Predictive Coding. JPC provides a simple, fast and flexible interface to train a variety of PC networks (PCNs) including discriminative, generative and hybrid models. Unlike existing libraries, JPC leverages ordinary differential equation solvers to integrate the gradient flow inference dynamics of PCNs. We find that a second-order solver achieves significantly faster runtimes compared to standard Euler integration, with comparable performance on a range of tasks and network depths. JPC also provides some theoretical tools that can be used to study PCNs. We hope that JPC will facilitate future research of PC. The code is available at https://github.com/thebuckleylab/jpc., Comment: 9 pages, 7 figures

This study investigates the interactions between tides, storm surge, river flow, and power peaking in the microtidal Neretva River estuary, Croatia. Based on the existing NS_Tide tool, the study proposes a new non-stationary harmonic model adapted for microtidal conditions, which incorporates linear storm surge, as well as linear and quadratic river discharge terms. This model enhances the NS_Tide's ability to accurately predict water levels from tide-dominated sections downstream to discharge-dominated areas upstream. River discharge was identified as the dominant factor for predicting stage levels at most stations, while the influence of storm surge, though consistent, decreased upstream. Strong tide-river interactions were observed throughout the study domain, with the stationary tidal component consistently contributing to water level fluctuations at all locations, and minimal influence from the tide-surge interaction component. Simulations using the STREAM numerical model were also used to isolate the variability in water levels caused by power peaking. These simulations demonstrated that high-frequency discharge fluctuations due to hydropower plant operations amplify the $S_1$ constituent in upstream river sections and modulate the amplitudes of other tidal constituents in the estuarine and tidal river sections. The proposed method proved highly effective in the microtidal context of the Neretva River and shows potential for adaptation to mesotidal and macrotidal systems.

Despite modular conditions to guarantee stability for large-scale systems have been widely studied, few methods are available to tackle the case of networks with multiple equilibria. This paper introduces small-gain like sufficient conditions for 2-contraction of large-scale interconnected systems on the basis of a family of upper-bounds to the $L_2$ gains that arise from the gains computed on individual channels of the second additive variational equation. Such a condition guarantee the 2-additive compound of the system's Jacobian to be exponentially contractive, thus implying convergence towards equilibria of the system's solutions. The gains are obtained by solving suitable Linear Matrix Inequalities. Three interconnected Thomas' systems are considered in order to illustrate the application of the theory and the degree of conservatism.

The flexibility of industrial power consumption plays a key role in the transition to renewable energy systems, contributing to grid stability, cost reduction and decarbonization efforts. This paper presents a novel methodology to quantify and optimize the flexibility of electricity consumption in manufacturing plants. The proposed model is applied to actual cement and steel plant configurations. Comparative simulations performed with the model reveal significant differences in flexibility and cost-effectiveness, driven by factors such as production capacity, downstream process demand, storage capacity, and operational constraints. A comprehensive sensitivity analysis further clarifies the impact of various parameters on production optimization and flexibility savings. Specifically, as demand approaches production levels, flexibility decreases. Although increasing storage capacity typically reduces production costs, the benefits diminish above a certain threshold. The results provide valuable information for industrial operators wishing to improve operational efficiency, reduce costs and increase the flexibility of their operations.

Two-dimensional particle-in-cell (PIC) simulations explore the collisionless tearing instability developing in a Harris equilibrium configuration in a pair (electron-positron) plasma, with no guide field, for a range of parameters from non-relativistic to relativistic temperatures and drift velocities. Growth rates match predictions of Zelenyi & Krasnosel'skikh (1979) modified for relativistic drifts by Hoshino (2020) as long as the assumption holds that the thickness $a$ of the current sheet is larger than the Larmor radius $\rho_L$, with the fastest growing mode at $ka \approx 1/\sqrt{3}$. Aside from confirming these predictions, we explore the transitions from thick to thin current sheets and from classical to relativistic temperatures. We show that for thinner current sheets ($a< \rho_L$), the growth rate matches the prediction for the case $a=\rho_L$. We also explore the nonlinear evolution of the modes. While the wave number with the fastest growth rate initially matches the prediction of Zelenyi & Krasnosel'skikh (1979), these modes saturate moving the dominant mode to lower wave numbers (especially for thick current sheets with low growth rates). Furthermore, at a late, non-linear stage, the growth rate (initially following the growth rate prediction proportional to $(\rho_L/a)^{3/2} < 1$) increases faster than exponentially, reaching a maximum growth rate equivalent to the linear growth rate prediction at $\rho_L/a = 1$, before eventually saturating., Comment: 25 pages, 11 figures

Early childhood education (ECE) is becoming a global policy priority, especially in the Pacific Island Countries and Territories (PICTs) where there is commitment to invest in the youngest learners. Building on system-level mappings in 2015 and 2017, this report delves into the status of ECE systems in the Pacific region as of 2022. Based on a comprehensive desk review and primary data collection involving ECE focal points from the ministries of education across the 15 PICTs, this research report highlights key strengths, gaps, and opportunities for enhancing ECE service delivery. Explore the findings and recommendations to see how these insights can inform advancements in early childhood education systems in the region.

Botswana will need to invest in human capital and strengthen its skills base to transition into a knowledge-based economy. However, low foundational learning levels remain a key challenge for the education sector. While Botswana has invested heavily in teacher supply, teacher deployment has not always reflected school-level teacher needs. The uneven distribution of teachers -- both across and within schools -- contributes to disparities in class sizes and learning conditions. Moreover, female teachers are particularly underrepresented in the sciences despite comprising much of the teaching force. This report aims to provide policymakers with a granular overview of teacher distribution in the country and inform ongoing efforts to improve teacher allocation. [This research project was made possible by the Botswana Ministry of Education and Skills Development.]

The Data Must Speak (DMS) Positive Deviance research aims to improve the equity and quality of education through the use of data by studying the practices and behaviours of 'positive deviant' schools -- schools that outperform others despite operating in similar contexts and with equivalent resources. The analysis aims to inform practical recommendations on how to scale these practices and behaviours to lower-performing schools. This report presents the results of the first stage of the DMS research in Maranhão state in Brazil, which analyses different factors related to educational performance. The main findings and areas for further exploration based on the analysis are presented in this report. [This report was co-published by the State Department of Education in Maranhão (SEDUC-MA) and UNICEF Brazil.]

Education plays a pivotal role in Zambia's vision of achieving prosperity as a middle-income nation by 2030. The Government is committed to delivering high-quality, relevant education and lifelong skills for all children. Despite progress in reading and mathematics scores, learning outcomes in Zambia remain low. Regional disparities within the country persist in primary school completion rates, with girls facing a higher likelihood of dropping out before reaching senior secondary levels. The 2017-2021 Education and Skills Sector Plan underscores the Government's focus on enhancing learning outcomes by addressing gaps in education quality, accessibility, equity and efficiency. In this context, the Ministry of Education (MoE) is leveraging an innovative research partnership with UNICEF to identify and scale local solutions already improving learning in Zambian primary schools. This report presents the local behaviours and practices found in positive deviant schools in Zambia, with the aim of helping to inform future education policy. This mixed-methods research has been co-created and co-implemented with MoE since 2021. [The Ministry of Education Zambia and UNICEF Zambia collaborated with this report.]

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quantifies how the spread of quantum information into correlations makes it irretrievable from local measurements. Here, we explore the tight relation between QIS and the predictive power of QELMs. In particular, we show efficient state estimation is possible even beyond the scrambling time, for many different types of dynamics -- in fact, we show that in all the cases we studied, the reconstruction efficiency at long interaction times matches the optimal one offered by random global unitary dynamics. These results offer promising venues for robust experimental QELM-based state estimation protocols, as well as providing novel insights into the nature of QIS from a state estimation perspective.

Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the study of secure aggregation (SA) schemes to provide privacy guarantees over the model's parameters transmitted by the clients. Nevertheless, the practical availability of SA in currently available FL frameworks is currently limited, due to computational and communication bottlenecks. To fill this gap, this study explores the implementation of SA within the open-source Fed-BioMed framework. We implement and compare two SA protocols, Joye-Libert (JL) and Low Overhead Masking (LOM), by providing extensive benchmarks in a panel of healthcare data analysis problems. Our theoretical and experimental evaluations on four datasets demonstrate that SA protocols effectively protect privacy while maintaining task accuracy. Computational overhead during training is less than 1% on a CPU and less than 50% on a GPU for large models, with protection phases taking less than 10 seconds. Incorporating SA into Fed-BioMed impacts task accuracy by no more than 2% compared to non-SA scenarios. Overall this study demonstrates the feasibility of SA in real-world healthcare applications and contributes in reducing the gap towards the adoption of privacy-preserving technologies in sensitive applications., Comment: Accepted at the 5-th MICCAI Workshop on Distributed, Collaborative and Federated Learning in Conjunction with MICCAI 2024

In the last few years the literature has witnessed a remarkable surge of interest for chaotic maps implemented by discrete-time (DT) memristor circuits. This paper investigates on the reasons underlying this type of chaotic behavior. To this end, the papers considers the map implemented by the simplest memristor circuit given by a capacitor and an ideal flux-controlled memristor or an inductor and an ideal charge-controlled memristor. In particular, the manuscript uses the DT flux-charge analysis method (FCAM) introduced in a recent paper to ensure that the first integrals and foliation in invariant manifolds of continuous-time (CT) memristor circuits are preserved exactly in the discretization for any step size. DT-FCAM yields a two-dimensional map in the voltage-current domain (VCD) and a manifold-dependent one-dimensional map in the flux-charge domain (FCD), i.e., a one-dimensional map on each invariant manifold. One main result is that, for suitable choices of the circuit parameters and memristor nonlinearities, both DT circuits can exactly embed two classic chaotic maps, i.e., the logistic map and the tent map. Moreover, due to the property of extreme multistability, the DT circuits can simultaneously embed in the manifolds all the dynamics displayed by varying one parameter in the logistic and tent map. The paper then considers a DT memristor Murali-Lakshmanan-Chua circuit and its dual. Via DT-FCAM these circuits implement a three-dimensional map in the VCD and a two-dimensional map on each invariant manifold in the FCD. It is shown that both circuits can simultaneously embed in the manifolds all the dynamics displayed by two other classic chaotic maps, i.e., the Henon map and the Lozi map, when varying one parameter in such maps. In essence, these results provide an explanation of why it is not surprising to observe complex dynamics even in simple DT memristor circuits.

Predictive coding (PC) is an energy-based learning algorithm that performs iterative inference over network activities before updating weights. Recent work suggests that PC can converge in fewer learning steps than backpropagation thanks to its inference procedure. However, these advantages are not always observed, and the impact of PC inference on learning is not theoretically well understood. Here, we study the geometry of the PC energy landscape at the inference equilibrium of the network activities. For deep linear networks, we first show that the equilibrated energy is simply a rescaled mean squared error loss with a weight-dependent rescaling. We then prove that many highly degenerate (non-strict) saddles of the loss including the origin become much easier to escape (strict) in the equilibrated energy. Our theory is validated by experiments on both linear and non-linear networks. Based on these and other results, we conjecture that all the saddles of the equilibrated energy are strict. Overall, this work suggests that PC inference makes the loss landscape more benign and robust to vanishing gradients, while also highlighting the fundamental challenge of scaling PC to deeper models., Comment: 35 pages, 12 figures

This study is motivated by recent observations from the Parker Solar Probe (PSP) mission, which have been identified as the ion-acoustic waves from 15 to 25 solar radii. These observations reveal characteristic sequences of narrow-band, high-frequency bursts exceeding 100 Hz embedded into a slower evolution around 1 Hz, persisting for several hours. To explore the potential role of the ion-acoustic instability (IAI) in these phenomena, we begin by reviewing classical findings on the IAI within the framework of linear kinetic theory. Focusing on proton distributions comprising both a core and a beam component, we analyze the IAI instability range and growth rates within the parameter regime relevant to PSP observations. Our findings indicate that the IAI can indeed occur in this regime, albeit requiring electron-to-core and beam-to-core temperature ratios slightly different from reported values during electrostatic burst detection. Furthermore, employing 1D kinetic plasma simulations, we validate the growth rates predicted by linear theory and observe the saturation behavior of the instability. The resultant nonlinear structures exhibit trapped proton beam populations and oscillatory signatures comparable to those observed, both in terms of time scales and amplitude.

This policy analysis examines regional and national policy and normative documents, and existing literature related to digital learning in Bosnia and Herzegovina, Kosovo, Montenegro, and North Macedonia. The report captures the state of the education recovery after the COVID-19 school closures and explores trends, promising practices, challenges, and gaps in digital learning systems and policies. The purpose of the report is to support government partners in the development of national digital learning policies, plans and roadmaps. The preliminary findings from this research have been instrumental in shaping the UNICEF Europe and Central Asia Digital Learning Strategy for 2022-2025.

In Mauritania, many learners struggle with French, one of the official languages of instruction at school. This language barrier hinders student progress with the curriculum, increasing the chances of leaving school. This risk disproportionately affects adolescent girls due to discriminatory social norms undermining their continued education, even in urban areas of the capital, Nouakchott. To address these challenges, UNICEF Mauritania, in collaboration with the Akelius Foundation and national partners, has initiated a blended course to improve French proficiency for girls in a disadvantaged neighbourhood of Nouakchott. The blended course, which takes place at a community-based facility during afternoons, integrates regular teaching with the offline use of the Akelius Digital Learning app. This research brief presents evidence on how the blended course supported vulnerable girls' learning and other socio-emotional skills. It highlights lessons learnt for practitioners in implementing digital learning in resource-constrained environments, including the reliance on offline solutions and the co-design of digital content with the teachers.

Electron-only reconnection is a process recently observed in the Earth's magnetosheath, where magnetic reconnection occurs at electron kinetic scales with only electrons involved. Electron-only reconnection is likely to have a significant impact on the energy conversion and dissipation of turbulence cascades at kinetic scales. This paper investigates electron-only reconnection under different intensities of strong guide fields via two-dimensional fully kinetic Particle-in-Cell (PIC) simulations, focusing on electron heating. The simulations are initialized with a force-free current sheet equilibrium under various intensities of strong guide fields. The quadrupolar structures of the out-of-plane magnetic field and asymmetric structures of density are found to become narrower in space as the guide field increases. Electron velocities are considerably larger than ion velocities, and the motion of both species is notably affected by the strength of guide fields. Similarly to previous experiments studies, electron temperature anisotropy along separatrices is observed, which is found to be mainly caused by the variations of parallel temperature. Both regions of anisotropy and parallel temperature increase/decrease along separatrices become thinner with increasing guide fields. Besides, we find a transition from a quadrupolar to a six-polar to an eight-polar structure in temperature anisotropy and parallel temperature as the guide field intensifies. Non-Maxwellian electron velocity distribution functions (EVDFs) at different locations in the three simulations are observed. Our results show that parallel electron velocity varies notably with different guide field intensities and strong parallel electron heat flux is observed.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

Urban heat islands, defined as specific zones exhibiting substantially higher temperatures than their immediate environs, pose significant threats to environmental sustainability and public health. This study introduces a novel machine-learning model that amalgamates data from the Sentinel-3 satellite, meteorological predictions, and additional remote sensing inputs. The primary aim is to generate detailed spatiotemporal maps that forecast the peak temperatures within a 24-hour period in Turin. Experimental results validate the model's proficiency in predicting temperature patterns, achieving a Mean Absolute Error (MAE) of 2.09 degrees Celsius for the year 2023 at a resolution of 20 meters per pixel, thereby enriching our knowledge of urban climatic behavior. This investigation enhances the understanding of urban microclimates, emphasizing the importance of cross-disciplinary data integration, and laying the groundwork for informed policy-making aimed at alleviating the negative impacts of extreme urban temperatures., Comment: 4 pages, submitted to IEEE MetroLivEnv 2024 conference

Very-High Resolution (VHR) remote sensing imagery is increasingly accessible, but often lacks annotations for effective machine learning applications. Recent foundation models like GroundingDINO and Segment Anything (SAM) provide opportunities to automatically generate annotations. This study introduces FMARS (Foundation Model Annotations in Remote Sensing), a methodology leveraging VHR imagery and foundation models for fast and robust annotation. We focus on disaster management and provide a large-scale dataset with labels obtained from pre-event imagery over 19 disaster events, derived from the Maxar Open Data initiative. We train segmentation models on the generated labels, using Unsupervised Domain Adaptation (UDA) techniques to increase transferability to real-world scenarios. Our results demonstrate the effectiveness of leveraging foundation models to automatically annotate remote sensing data at scale, enabling robust downstream models for critical applications. Code and dataset are available at \url{https://github.com/links-ads/igarss-fmars}., Comment: Accepted at IGARSS 2024, 5 pages. Revised and corrected version

Air pollution poses a significant threat to public health and well-being, particularly in urban areas. This study introduces a series of machine-learning models that integrate data from the Sentinel-5P satellite, meteorological conditions, and topological characteristics to forecast future levels of five major pollutants. The investigation delineates the process of data collection, detailing the combination of diverse data sources utilized in the study. Through experiments conducted in the Milan metropolitan area, the models demonstrate their efficacy in predicting pollutant levels for the forthcoming day, achieving a percentage error of around 30%. The proposed models are advantageous as they are independent of monitoring stations, facilitating their use in areas without existing infrastructure. Additionally, we have released the collected dataset to the public, aiming to stimulate further research in this field. This research contributes to advancing our understanding of urban air quality dynamics and emphasizes the importance of amalgamating satellite, meteorological, and topographical data to develop robust pollution forecasting models., Comment: 5 pages, 2 figures, submitted to IEEE MetroLivEnv 2024

Partially-ionized plasmas consist of charged and neutral particles whose mutual collisions modify magnetic reconnection compared with the fully-ionized case. The collisions alter the rate and locations of the magnetic dissipation heating and the distribution of energies among the particles accelerated into the non-thermal tail. We examine the collisional regimes for the onset of fast reconnection in two environments: the partially-ionized layers of the solar atmosphere and the protoplanetary disks that are the birthplaces for planets around young stars. In both these environments, magnetic nulls readily develop into resistive current sheets in the regime where the charged and neutral particles are fully coupled by collisions, but the current sheets quickly break down under the ideal tearing instability. The current sheets collapse repeatedly, forming magnetic islands at successively smaller scales, till they enter a collisionally-decoupled regime where the magnetic energy is rapidly turned into heat and charged-particle kinetic energy. Small-scale, decoupled fast reconnection in the solar atmosphere may lead to preferential heating and energization of ions and electrons that escape into the corona. In protoplanetary disks such reconnection causes localized heating in the atmospheric layers that produce much of the infrared atomic and molecular line emission observed with the Spitzer and James Webb Space Telescopes., Comment: 16 pages, 8 figures

The expanding solar wind plasma ubiquitously exhibits anisotropic non-thermal particle velocity distributions. Typically, proton Velocity Distribution Functions (VDFs) show the presence of a core and a field-aligned beam. Novel observations made by Parker Solar Probe (PSP) in the innermost heliosphere have revealed new complex features in the proton VDFs, namely anisotropic beams that sometimes experience perpendicular diffusion. In this study, we use a 2.5D fully kinetic simulation to investigate the stability of proton VDFs with anisotropic beams observed by PSP. Our setup consists of a core and an anisotropic beam populations that drift with respect to each other. This configuration triggers a proton-beam instability from which nearly parallel fast magnetosonic modes develop. Our results demonstrate that before this instability reaches saturation, the waves resonantly interact with the beam protons, causing perpendicular heating at the expense of the parallel temperature.

In 2021, Mexico introduced "Pasaporte al Aprendizaje," a localized version of the Learning Passport digital learning platform, to mitigate learning loss after widespread school closures due to the COVID-19 pandemic. Between October 2021 and February 2023, almost 100,000 students utilized it for upper-secondary courses. The research explores the key design and implementation steps undertaken for the successful deployment and use of the "Pasaporte al Aprendizaje." In addition, analysis of data from the digital learning platform shows that students assessment scores improved as they progressed through courses, in subjects such as mathematics, Spanish, chemistry and physics. The overall goal of this research is to inform improvements in the "Pasaporte al Aprendizaje" and provide key lessons learned for other countries implementing national digital learning programmes.

In Mozambique, the COVID-19 pandemic led to schools closing for 80 percent of the 2020 academic year, affecting 8.5 million students and nearly 15,000 schools across the country (United Nations, 2020). This brief seeks to address two crucial questions regarding Mozambique's Grade 3 and 4 students. First, what is the current state of reading outcomes of these students, and how have they been affected by the prolonged school closures? Second, how do children's individual, household and school characteristics relate to literacy outcomes? An analysis of pre- and post-pandemic reading skills, using data from the Avaliação Longitudinal da Desistência Escolar (ALDE, Longitudinal Assessment of School Dropout) reveals significant learning losses in basic literacy skills. Across the country, children in Grades 3-4 showed reduced proficiency in identifying letters in 2021, compared to students in the same grades in 2019.

Instruction time is a crucial component of student learning and is closely associated with attendance rates for students, teachers and directors. This report examines teacher and director absenteeism in Mozambique's schools, using data from surveys conducted in 2018, 2019, and 2021 as part of the Longitudinal Assessment of School Dropout or Avaliação Longitudinal da Desistência Escolar (ALDE). The main objective of this research is to: (1) provide an overview of teacher and director absenteeism prior to and after the COVID-19 pandemic; (2) compare teacher and director absenteeism rates based on factors such as individual teacher attributes and school-related variables; and (3) investigate the factors at both individual and school levels that influence teacher absenteeism. It seeks to inform policies and practices to strengthen the primary education system in Mozambique.

The equitable and effective deployment of teachers is critical to achieving quality education for all. However, the uneven distribution of teachers -- both across and within schools -- contributes to significant disparities in class sizes and learning conditions. Recognizing the critical role that teachers play in learning, the Government of Zambia has committed to addressing teacher allocation through large-scale teacher recruitments. This report aims to provide policymakers an overview of the magnitude and distribution of teacher shortages, and inform ongoing efforts to address teacher shortages and improve teacher allocation. [Michelle Mills and Ivan Mahardika contributed to this report.]

A method for identifying and quantifying the flexibility of electricity demand in a production plant is reported. The plant is equipped with electric machines, product storage silos, distributed generation, and electrical storage systems. The method aims to minimize production costs. To achieve this, the plant is mathematically modeled, and an economic optimization problem is formulated by managing these plant equipment. From this optimal schedule (base schedule), the feasibility of modifying it to sell or buy energy in the electricity balancing regulation markets is evaluated, thus obtaining the so called flexibility schedule. Finally, this method was successfully applied to a real case using data from a Spanish cement production plant.

In the context of quantum objectivity, a standard way to quantify the classicality of a state is via the mutual information between a system and different fractions of its environment. Many of the tools developed in the relevant literature to quantify quantum objectivity via quantum mutual information rely on the assumption that information about the system leaks symmetrically into its environment. In this work, we highlight the importance of taking this assumption into account, and in particular, analyse how taking non-averaged quantum mutual information as a quantifier of quantum objectivity can be severely misleading whenever information about the system is encoded into the environment in a non-homogeneous way. On the other hand, the averaged mutual information always provides results with a clear operative interpretation., Comment: 7 pages, 4 figures, Comments welcome

In Italy, more than 700,000 asylum seekers and migrants arrived in the country between 2014-2020. Newly arrived children including refugees and migrants need to quickly acquire Italian skills to succeed in school and society. To help address this urgent need, the Akelius digital learning application was introduced in Bologna and Rome for Italian and English language learning in the 2021/22 school year. This research presents findings from the first year of implementation of the Akelius digital learning application in Italy. Results show the use of the application supported self-paced learning, boosted students' motivation and confidence and the use of the tool was especially beneficial for newly arrived children and children with disabilities. The report also explores challenges and good practices to inform improvements in the use of digital learning in classrooms.

In Bosnia and Herzegovina, the number of refugees and migrants arriving in the country has increased from just a few dozen arrivals annually prior to 2017 to 95,000 between 2018 and 2022. This increase has put incredible strain on the country's education systems. This research presents critical findings on the implementation and effectiveness of the Akelius digital learning application in Bosnia and Herzegovina, which was introduced as a tool to support refugee and migrant children's English and German language learning. The findings demonstrate that using the digital application in a blended learning approach in classrooms was especially useful for personalizing learning and supporting children with diverse learning levels and needs. The report also explores the challenges faced, and good practices to tangibly improve the implementation of digital learning in classrooms.

In 2019, the President of the European Commission announced the creation of the European Child Guarantee, an initiative that aimed to combat child poverty by making sure that every child who lives in poverty has access to free healthcare, free education, free childcare, decent housing and adequate nutrition. With "Delivering the EU Child Guarantee: Practical lessons for effective interventions," UNICEF Innocenti provides a synthesis of findings of operational research on 15 pilot models of intervention for children and families in Bulgaria, Croatia, Greece and Italy. The goal of the report is to learn from the experiences in these four countries to inform the rollout of the Child Guarantee programme throughout Europe. The study provides lessons learned in three themes: laying the foundations for the Child Guarantee work at the national level; developing interventions at the local level; supporting the Child Guarantee at the EU level.

The Kosovo Education Strategy (2022-2026) recognizes the need to scale up digital learning solutions to support individualized learning for children. Shkollat.org, Kosovo's version of the Learning Passport, combines the largest repository of curriculum-aligned video learning content in Kosovo with communication tools to engage students inside or outside the classroom. This research presents findings from the implementation of Shkollat.org in Kosovo, providing policy takeaways for the scale-up of digital learning solutions for education policymakers in the Western Balkans and beyond. Research findings are organized around six pillars of digital learning embedded within the Kosovo Education Strategy (2022-2026): digital learning platforms, content, teacher upskilling and support, connectivity, devices, and institutional support.

Nine out of every 10 refugees arriving in host countries from Ukraine are women and children. Only 1 in 3 of Ukrainian refugee children are enrolled in early childhood education and care services. This emphasizes the need for expanding and strengthening early childhood education systems to ensure sufficient access for all children, and the integration of Ukranian refugee children in the host-community. These briefs offer recommendations for policymakers on ways to expand services, and how to facilitate the integration of refugee children and their families. Strategies include limiting the barriers that may hinder refugee children's access to ECEC settings, capitalizing existing physical and human resources to address gaps in service delivery, in addition to the inclusion of refugees in national and sub-national plans, data systems and financing, adapting policies and programmes to ensure considerations are made for refugee children. This second brief focuses on expanding early childhood education and care for Ukrainian refugee children and caregivers. [For the first brief in the series, see ED627656.]