bluuebunny/crossref_metadata_2025_split huggingface dataset:
Making the case for project‐based learning: An examination of research evidence translation and mobilisation in education
Authors: Malin Joel R. , Rind Gul Muhammad 
Published: January 2022

AbstractEvidence about how to translate and mobilise research evidence for use in schools remains quite limited. The present study sought to address this gap by leveraging an opportunity to examine such processes. In February 2021, Edutopia (a large US‐based, practice‐focused educational intermediary organisation) hosted a large virtual event, with 6526 participants, to communicate/highlight just‐released findings from four large‐scale, funded causal studies into the efficacy of project‐based learning and to encourage educators to adopt this practice. Examining this event, this primarily qualitative study addressed one main research question: How were research evidence and other forms of evidence used to justify and motivate the adoption of project‐based learning in schools? This study also drew upon secondary data to examine certain self‐reported impacts. This study applied a two‐layered analytic frame to broadly examine the selling and gelling of educational research evidence and other forms of evidence, and to uncover if/how five main factors of innovation adoption were addressed. Findings provided detail and analysis along each of these dimensions and, further, revealed certain apparent tensions associated with making the case for project‐based learning—for example, some instances of possible research ‘over‐selling’, and a focus on answering certain questions that could not be addressed solely via research evidence, necessitating a reliance on other evidence sources. Survey/summary results suggested participants were strongly convinced of this innovation's positive impacts and were eager to discuss it further with colleagues, though given data limitations we were unable to discern the extent/ways participants’ subsequent practices subsequently shifted.
What We Know and Need to Know about Literacy Interventions for Elementary Students with Reading Difficulties and Disabilities, including Dyslexia
Authors: Al Otaiba Stephanie , McMaster Kristen , Wanzek Jeanne , Zaru Mai W. 
Published: January 2022

AbstractThe purpose of this paper is to describe what we know and what we still need to learn about literacy intervention for children who experience significant difficulties learning to read. We reviewed 14 meta‐analyses and systematic reviews of experimental and quasi‐experimental studies published in the last decade that examined the effects of reading and writing interventions in the elementary grades, including research focused on students with reading difficulties and disabilities, including dyslexia. We attended to moderator analyses, when available, to further refine what we know and need to learn about interventions. Findings from these reviews indicate that explicit and systematic interventions focusing on the code and meaning dimensions of reading and writing, and delivered one‐to‐one or in small groups, are likely to improve foundational code‐based reading skills, and to a lesser extent, meaning‐based skills, across elementary grade levels. Findings, at least in the upper elementary grades, indicate that some intervention features including standardized protocols, multiple components, and longer duration can yield stronger effects. And, integrating reading and writing interventions shows promise. We still need to learn more about specific instructional routines and components that provide more robust effects on students’ ability to comprehend and individual differences in response to interventions. We discuss limitations of this review of reviews and suggest directions for future research to optimize implementation, particularly to understand for whom and under what conditions literacy interventions work best.
Recent Advances in ZIF‐Derived Atomic Metal–N–C Electrocatalysts for Oxygen Reduction Reaction: Synthetic Strategies, Active Centers, and Stabilities
Authors: Gao Chen , Mu Shengdong , Yan Rui , Chen Fan , Ma Tian , Cao Sujiao , Li Shuang , Ma Lang , Wang Yinghan , Cheng Chong 
Published: January 2022

AbstractExploring highly active, stable electrocatalysts with earth‐abundant metal centers for the oxygen reduction reaction (ORR) is essential for sustainable energy conversion. Due to the high cost and scarcity of platinum, it is a general trend to develop metal–N–C (M–N–C) electrocatalysts, especially those prepared from the zeolite imidazolate framework (ZIF) to replace/minimize usage of noble metals in ORR electrocatalysis for their amazingly high catalytic efficiency, great stability, and readily‐tuned electronic structure. In this review, the most pivotal advances in mechanisms leading to declined catalytic performance, synthetic strategies, and design principles in engineering ZIF‐derived M–N–C for efficient ORR catalysis, are presented. Notably, this review focuses on how to improve intrinsic ORR activity, such as M–Nx–Cy coordination structures, doping metal‐free heteroatoms in M–N–C, dual/multi‐metal sites, hydrogen passivation, and edge‐hosted M–Nx. Meanwhile, how to increase active sites density, including formation of M–N complex, spatial confinement effects, and porous structure design, are discussed. Thereafter, challenges and future perspectives of M–N–C are also proposed. The authors believe this instructive review will provide experimental and theoretical guidance for designing future, highly active ORR electrocatalysts, and facilitate their applications in diverse ORR‐related energy technologies.
Mapping Techniques for the Design of Lithium‐Sulfur Batteries
Authors: Li Chunyuan , Li Yaoyao , Fan Yuxin , Wang Fan , Lei Tianyu , Chen Wei , Hu Anjun , Xue Lanxin , Huang Jianwen , Wu Chunyang , Yang Chengtao , Hu Yin , Yan Yichao 
Published: January 2022

AbstractMapping technique has been the powerful tool for the design of next‐generation energy storage devices. Unlike the traditional ion‐insertion based lithium batteries, the Li‐S battery is based on the complex conversion reactions, which require more cooperation from mapping techniques to elucidate the underlying mechanism. Therefore, in this review, the representative works of mapping techniques for Li‐S batteries are summarized, and categorized into the studies of lithium metal anode and sulfur cathode, with sub‐sections based on shared characterization mechanisms. Due to specific features of mapping techniques, various aspects such as compositional distribution, in‐plain/cross section characterization, coin cell/pouch cell configuration, and structural/mechanical analysis are emphasized in each study, aiming for the guidance for developing strategies to improve the battery performances. Benefited from the achieved progresses, suggestions for future studies based on mapping techniques are proposed to accelerate the development and commercialization of the Li‐S battery.
Tailoring Ordered Porous Carbon Embedded with Cu Clusters for High‐Energy and Long‐Lasting Phosphorus Anode
Authors: Xiao Jiajia , Cai Zihe , Muhmood Tahir , Hu Xiaojun , Lin Shengxuan , Hu Xiaobin 
Published: January 2022

AbstractThe natural insulating property and notorious pulverization of volume variation‐induced materials during cycling pares the electrochemical activity of red phosphorous (RP) for lithium/sodium‐ion batteries (LIBs/SIBs). To work out these issues, a tailored trimodal porous carbon support comprising highly ordered macropores and micro‐mesoporous walls embedded with copper (Cu) nanoclusters (Cu‐OMC) is proposed to confine RP. The construction of highly conductive copper–carbon wall facilitates fast electrons and ions transportation, while the interconnected and ordered porous structure not only creates enough space to resist the expansion effect of RP but also minimizes the ion diffusion length and enhances ion accessibility (the ion migration coefficient is ten times that of disordered porous carbon). Consequently, the resulting Cu‐OMC@RP anode delivers a high reversible capacity (2498.7 mAh g–1 at 0.3 C for LIBs; 2454.2 mAh g–1 at 0.1 C for SIBs), superb rate properties (824.7 mAh g–1 at 10 C for LIBs; 774.2 mAh g–1 at 5 C for SIBs), and outstanding cycling stability (an ultralow decay rate of 0.057% per cycle after 1000 cycles at 10 C for LIBs and 0.048% per cycle at 5 C over 500 cycles for SIBs).
In Situ Detection of Lithium‐Ion Battery Pack Capacity Inconsistency Using Magnetic Field Scanning Imaging
Authors: Wang Hang , Dai Lang , Mao Lei , Liu Yongbin , Jin Yi , Wu Qiang 
Published: January 2022

AbstractOne of the main obstacles for the reliability and safety of a lithium‐ion battery pack is the difficulty in guaranteeing its capacity consistency at harsh operating conditions, while the key solution is accurate detection of cell capacity inconsistency within the battery pack without taking it apart for destructive testing. Here, an in situ and nondestructive technology is proposed for this purpose, by imaging the magnetic field of the battery pack during its operation, the minor current imbalance within the pack can be identified without strong interference of the magnetic susceptibility due to state of charge change, and the corresponding location can also be determined. The feasibility of employing this technique in the battery pack with multicells is also demonstrated, which is beneficial in practical systems for evaluating the battery pack reliability, with which mitigation and maintenance can be taken to extend its remaining useful life.
Rheological properties and dynamic thermal stability ofPVC/La L2/Ca‐Zncomposite stabilizer
Authors: Li Deling , Luo Congcong , Yao Bing , Ding Yue , Ren Jizhen 
Published: January 2022

AbstractLanthanum complex with curcumin (La L2)/Ca‐Zn composite stabilizer was prepared using La L2and Ca‐Zn stabilizer, in which Ca‐Zn stabilizer was obtained from calcium stearate and zinc stearate at their mass ratio of 5:1. The rheological properties and dynamic thermal stability of polyvinyl chloride (PVC) stabilized by La L2/Ca‐Zn composite stabilizers were estimated through a torque rheometer. The degraded fragments obtained from the torque experiments were measured by Fourier‐transform infrared spectroscopy (FTIR) and ultraviolet–visible (UV‐vis) spectroscopy. La L2had an effective synergistic effect with Ca‐Zn stabilizer. Compared with La L2, La L2/Ca‐Zn composite stabilizer could more significantly improve the rheological properties and dynamic thermal stability of PVC. An optimal synergistic effect could be observed when the mass ratio of La L2with Ca‐Zn stabilizer was about 8:2, in which the plasticizing torque and equilibrium torque of PVC was 33.3 N m and 22.5 N m, respectively, and its dynamic thermal stabilizing time was 25.1 min. The possible thermal stability mechanism of La L2/Ca‐Zn composite stabilizer for PVC was suggested based on the evaluation of FTIR spectroscopy and UV‐vis spectroscopy.
Applied citizen science in freshwater research
Authors: Metcalfe Anya N. , Kennedy Theodore A. , Mendez Gabriella A. , Muehlbauer Jeffrey D. 
Published: January 2022

AbstractWorldwide, scientists are increasingly collaborating with the general public. Citizen science methods are readily applicable to freshwater research, monitoring, and education. In addition to providing cost‐effective data on spatial and temporal scales that are otherwise unattainable, citizen science provides unique opportunities for engagement with local communities and stakeholders in resource management and decision‐making. However, these methods are not infallible. Citizen science projects require deliberate planning in order to collect high data quality and sustain meaningful community partnerships. Citizen science practitioners also have an ethical responsibility to ensure that projects are not putting the safety of participants at stake. We discuss here how citizen science is being applied in freshwater research, emerging challenges in project planning and implementation, as well as how citizen science is shaping public understanding, policy, and management of freshwaters.This article is categorized under:
Science of Water > Water Quality
Water and Life > Conservation, Management, and Awareness
Human Water > Water Governance
Water and Life > Methods
libwfa: Wavefunction analysis tools for excited and open‐shell electronic states
Authors: Plasser Felix , Krylov Anna I. , Dreuw Andreas 
Published: January 2022

AbstractAn open‐source software library for wavefunction analysis, libwfa, provides a comprehensive and flexible toolbox for post‐processing excited‐state calculations, featuring a hierarchy of interconnected visual and quantitative analysis methods. These tools afford compact graphical representations of various excited‐state processes, provide detailed insight into electronic structure, and are suitable for automated processing of large data sets. The analysis is based on reduced quantities, such as state and transition density matrices (DMs), and allows one to distill simple molecular orbital pictures of physical phenomena from intricate correlated wavefunctions. The implemented descriptors provide a rigorous link between many‐body wavefunctions and intuitive physical and chemical models, for example, exciton binding, double excitations, orbital relaxation, and polyradical character. A broad range of quantum‐chemical methods is interfaced with libwfa via a uniform interface layer in the form of DMs. This contribution reviews the structure of libwfa and highlights its capabilities by several representative use cases.This article is categorized under:
Software > Quantum Chemistry
Theoretical and Physical Chemistry > Spectroscopy
Assessing the Evaluability of Adaptation-Focused Interventions: Lessons from the Adaptation Fund
Authors: MacPherson Ronnie , Jersild Amy , Bours Dennis , Holo Caroline 
Published: January 2022

AbstractEvaluability assessments (EAs) have differing definitions, focus on various aspects of evaluation, and have been implemented inconsistently in the last several decades. Climate change adaptation (CCA) programming presents particular challenges for evaluation given shifting baselines, variable time horizons, adaptation as a moving target, and uncertainty inherent to climate change and its extreme and varied effects. The Adaptation Fund Technical Evaluation Reference Group (AF-TERG) developed a framework to assess the extent to which the Fund’s portfolio of projects has in place structures, processes, and resources capable of supporting credible and useful monitoring, evaluation, and learning (MEL). The framework was applied on the entire project portfolio to determine the level of evaluability and make recommendations for improvement. This chapter explores the assessment’s findings on designing programs and projects to help minimize the essential challenges in the field. It discusses how the process of EA can help identify opportunities for strengthening both evaluability and a project’s MEL more broadly. A key conclusion was that the strength and quality of a project’s overall approach to MEL is a major determinant of a project’s evaluability. Although the framework was used retroactively, EAs could also be used prospectively as quality assurance tools at the pre-implementation stage.
Evaluation for Transformational Change: Learning from Practice
Authors: Naidoo Indran A. 
Published: January 2022

AbstractThe COVID-19 crisis has challenged the evaluation profession by altering the framework within which it operates. Evaluators must embrace new realities and respond to changes while not altering their principles, norms, and standards.A review of how evaluation networks and offices have responded to changing demands showed lack of recognition for the long-term implications to the profession. Commissioners and users of evaluation now have new priorities, and nontraditional actors have entered the traditional evaluation space, offering similar expertise and meeting the demands of evaluation commissioners and users. The extensive development challenges posed by COVID-19 require a comprehensive response capacity from evaluation if it is to be transformative as a profession.This chapter draws on national and international case studies, examining the concept of transformation from a contextual perspective and noting the relativism in the concept. It draws links between aspects, suggesting that this period is an opportunity for evaluators to learn from practice around transformation, and suggests that flexibility provides an opportunity to remain relevant and advance transformational goals.
Sustainability After Project Completion: Evidence from the GEF
Authors: Negi Neeraj Kumar , Sohn Molly Watts 
Published: January 2022

AbstractThis chapter examines the extent to which completed GEF projects are sustainable and the factors affecting sustainability. We considered only those projects that were covered through postcompletion evaluation at least 2 years after implementation completion, and where the evaluation reports provided adequate information related to observed sustainability during the postcompletion period. We assessed 62 projects to meet the selection criteria, then completed a desk review of the postcompletion evaluation reports and other relevant documents for these projects to assess the extent to which the project outcome was sustainable.We found that the projects covered through postcompletion evaluations were generally sustainable, with the sustainability outlook deteriorating for some projects while improving for others. The incidence of the catalytic processes that enhance sustainability—sustaining, mainstreaming, replication, scaling-up, and market change—was higher at postcompletion evaluation, as the passage of time allows long-term project outcomes to manifest. At the project level, we observed these catalytic processes in a wider set of activities at postcompletion evaluation than at implementation completion. Factors such as financial support for follow-up, political support, follow-up by and capacities of the executing agency, stakeholder buy-in, and project design seem to play a crucial role in determining project sustainability.
Making Space for Qualitative Evidence in Global Maternal and Child Health Policymaking
Authors: Colvin Christopher J. 
Published: January 2022

AbstractThe success of health interventions often hinges on complex processes of implementation, the impact of sociopolitical and cultural contexts, resource constraints and opportunity costs, and issues of equity and accountability. Qualitative research offers critical insights for understanding these issues. “Qualitative evidence syntheses” (or QES)—modeled on quantitative systematic reviews—have recently emerged as an important vehicle for integrating insights from qualitative evidence into global health policy. However, it is challenging to integrate QES into policymaking in ways that are bothacceptableto the often-conservative health policy world andconsonantwith social science’s distinctive methodologies and paradigms. Based on my experiences participating in and observing numerous guideline working group meetings and interviews with key informants, this chapter offers an auto-ethnographic account of an effort to integrate QES into the World Health Organization’s global OptimizeMNH guidelines for task shifting in maternal and newborn health (MNH). It is based on my experiences participating in and observing numerous guideline working group meetings as well as interviews with several key informants. Advocates of QES were successful in helping to make a place for qualitative evidence in this global guideline. Their work, however, required a delicate balance between adopting quantitatively inspired methods for evidence synthesis and innovating new methods that would both suit the project needs and be seen as legitimate by qualitative researchers. This case study of the development of one WHO guideline does not signal a revolution in knowledge production, but it does show there remains room—perhaps growing room—for a more expansive vision of what forms of knowledge need to be on the table when developing global health policy.
Fundamentals of Artificial Neural Networks and Deep Learning
Authors: Montesinos López Osval Antonio , Montesinos López Abelardo , Crossa Jose 
Published: January 2022

AbstractIn this chapter, we go through the fundamentals of artificial neural networks and deep learning methods. We describe the inspiration for artificial neural networks and how the methods of deep learning are built. We define the activation function and its role in capturing nonlinear patterns in the input data. We explain the universal approximation theorem for understanding the power and limitation of these methods and describe the main topologies of artificial neural networks that play an important role in the successful implementation of these methods. We also describe loss functions (and their penalized versions) and give details about in which circumstances each of them should be used or preferred. In addition to the Ridge, Lasso, and Elastic Net regularization methods, we provide details of the dropout and the early stopping methods. Finally, we provide the backpropagation method and illustrate it with two simple artificial neural networks.
Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes
Authors: Montesinos López Osval Antonio , Montesinos López Abelardo , Crossa Jose 
Published: January 2022

AbstractThis chapter provides elements for implementing deep neural networks (deep learning) for continuous outcomes. We give details of the hyperparameters to be tuned in deep neural networks and provide a general guide for doing this task with more probability of success. Then we explain the most popular deep learning frameworks that can be used to implement these models as well as the most popular optimizers available in many software programs for deep learning. Several practical examples with plant breeding data for implementing deep neural networks in the Keras library are outlined. These examples take into account many components in the predictor as well many hyperparameters (hidden layer, number of neurons, learning rate, optimizers, penalization, etc.) for which we also illustrate how the tuning process can be done to increase the probability of a successful application.
Convolutional Neural Networks
Authors: Montesinos López Osval Antonio , Montesinos López Abelardo , Crossa Jose 
Published: January 2022

AbstractWe provide the fundamentals of convolutional neural networks (CNNs) and include several examples using the Keras library. We give a formal motivation for using CNN that clearly shows the advantages of this topology compared to feedforward networks for processing images. Several practical examples with plant breeding data are provided using CNNs under two scenarios: (a) one-dimensional input data and (b) two-dimensional input data. The examples also illustrate how to tune the hyperparameters to be able to increase the probability of a successful application. Finally, we give comments on the advantages and disadvantages of deep neural networks in general as compared with many other statistical machine learning methodologies.