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Abstract The continuously reinforced concrete pavement (CRCP) system grapples with challenges such as non-uniform transverse crack patterns and the need for substantial reinforcement. Field research on the Belgian CRCP sections along motorway E313 indicates that active cracking induced by partial surface saw-cuts consistently leads to transverse crack patterns. This study introduces an innovative modification to the CRCP: the actively reinforced concrete pavement design (ARCP). The ARCP leverages partial surface saw-cuts to reduce reinforcement needs by replacing continuous-length steel bars with partial-length counterparts. The main objective of the present study is to develop a 3D finite element (FE) model capturing the active cracking behavior of ARCP under realistic external temperature variations. Comparative analysis with CRCP considers early-age crack patterns, crack strain development, and the distribution of maximum steel stress for different steel ratios (0.67%, 0.75%, and 0.85%). FE simulation results align with field data, indicating that ARCP exhibits similar early-age cracking behavior to CRCP but with a significant 24 to 42% reduction in total reinforcement. This innovation presents a promising avenue for addressing CRCP challenges while optimizing material usage in pavement construction.

Although scientific inquiry with simulations may enhance learning, learners often face challenges creating high demand for self-regulation due to an abundance of information in simulations and supplementary instructional texts. In this research, participants engaged in simulation-based inquiry about principles of electric circuits supplemented by domain-specific expository text provided on-demand. They received just-in-time inquiry prompts for inquiry behaviors, guidance to self-explain electrical principles, both, or neither. We examined how these interventions influenced participants’ access of text information and achievement. Undergraduates (N = 80) were randomly assigned to one of four groups: (1) inquiry prompts and principle-based self-explanation (SE) guidance, (2) inquiry prompts without principle-based SE guidance, (3) principle-based SE guidance without inquiry prompts, or (4) control. Just-in-time inquiry prompts facilitated learning rules. However, there was no main effect of principle-based self-explanation guidance nor an interaction involving both interventions. Effects of just-in-time inquiry prompts were moderated by prior knowledge. Although principle-based self-explanation guidance promoted re-examination of text-based domain information, reading time did not affect posttest scores. These findings have important implications for instructional design of computer-based adaptive guidance in simulation-based inquiry learning.

This study primarily emphasizes the effect of alkaline treatment on the surface morphology, and the physical properties of extracted bamboo fibers were investigated in detail. The bamboo fibers were extracted from raw bamboo culms using a mechanical extraction process followed by roller-milling techniques. The physical properties of the extracted bamboo fibers and their chemical composition were examined based on standard requirements. The extracted and sun-dried bamboo fibers were subjected to surface modifications by treating them with a 5 wt.% NaOH solution. The process involved soaking the extracted bamboo fibers for 1 day at ambient temperature; subsequently, the alkali-treated fibers were washed with distilled water several times to remove alkali content from the fiber surface until it became neutral. Finally, the fibers were dried under the sun for a week. The alkali-treated and untreated extracted bamboo fibers underwent characterization using Fourier transform infrared spectroscopy (FTIR), Thermogravimetric analysis (TGA), X-Ray diffraction (XRD) analysis, and Scanning electron microscopy (SEM). The investigation revealed an improvement in the surface morphology of the alkali-treated extracted bamboo fibers, with a significant reduction in surface roughness, as illustrated by SEM. Test results from FTIR, XRD, and TGA indicated that the alkali-treated fibers had removed lignin and hemicellulose from their surface. This study strongly suggests that bamboo fibers prepared using these techniques could be utilized as reinforcing material in composite production.

We investigated the effects of just-in-time guidance at various stages of inquiry learning by novice learners. Thirteen participants, randomly assigned to an intervention (n = 8) or control (n = 5) group, were observed as they learned about DC electric circuits using a web-based simulation. Just-in-time instructional prompts to observe, predict, explain, systematically test, collect evidence, and generate rules were strongly associated with diagnosing and correcting misconceptions, and constructing correct scientific concepts. Students' repeated use of predictions, systematic testing, and evidence-coordinated reasoning often led to formulating new principles, generalizing from observed patterns, verifying comprehension, and experiencing "Aha!" moments. Justin-time prompts helped learners manage embedded cognitive challenges in inquiry tasks, achieve a comprehensive understanding of the model represented in the simulation, and show significantly higher knowledge gain. Just-in-time prompts also promoted rejection of incorrect models of inquiry and construction of robust scientific mental models. The results suggest ways of customizing guidance to promote scientific learning within simulation environments.

Es krim termasuk produk pangan semi padat, memiliki tekstur lembut, rasa yang enak dan manis sehingga disukai oleh banyak orang. Namun, pada umumnya es krim masih rendah nilai aktivitas antioksidan. Oleh sebab itu, diperlukan pendekatan yang sesuai untuk meningkatkan aktivitas antioksidan es krim agar menjadi produk yang bernilai fungsional. Daun kersen kaya dengan kandungan komponen-komponen bioaktif seperti flavonoid, tanin, triterpen, saponin, dan polifenol. Bubuk daun kersen (BDK) diyakini dapat diaplikasikan sebagai bahan tambahan untuk meningkatkan aktivitas antioksidan pada es krim. Penelitian ini bertujuan untuk mengetahui pengaruh penambahan BDK terhadap sifat fisikokimia es krim. Rancangan Acak Lengkap (RAL) satu faktor penambahan BDK telah digunakan dalam penelitian ini. Beberapa komposisi BDK yang digunakan adalah 0%(F0), 0,5%(F1), 1%(F2), 1,5%(F3), dan 2%(F4). Karakteristik fisikokimia es krim yang dievaluasi antara lain overrun, waktu leleh, komposisi air, komposisi protein total, komposisi gula total, komposisi flavonoid, dan aktivitas antioksidan (IC50). Karakteristik fisikokimia es krim dengan penambahan BDK menunjukkan nilai overrun 50,27-40,73%, waktu leleh 27,52-36,13 menit/g, kadar air 64,82-62,79%, kadar lemak 5,88-4,74%, kadar protein total 2,00-2,70%, kadar gula total 29,63-33,71%, kadar flavonoid 0,01-0,08%, dan aktivitas antioksidan IC50 71,16-30,02 ppm. Secara keseluruhan, karakteristik fisikokimia es krim dengan penambahan BDK menunjukkan perbedaan yang siginifikan (p

This special issue's editors invited discussion of three broad questions. Slightly rephrased, they are: How well do self-report data represent theoretical constructs? How should analyses of data be conditioned by properties of self report data? In what ways do interpretations of self-report data shape interpretations of a study's findings? To approach these issues, I first recap the kinds of self-report data gathered by researchers reporting in this special issue. With that background, I take up a fundamental question. What are self-report data? I foreshadow later critical analysis by listing facets I observe in operational definitions of self-report data: nature of the datum, topic, property, setting or context, response scale, and assumptions setting a stage for analyzing data. Discussion of these issues leads to a proposal that ameliorates some of them: Help respondents become better at self reporting.

Metacognition is the engine of self-regulated learning. At the object level, learners seek information and choose learning tactics and strategies they forecast will develop knowledge. At the meta level, learners gather and analyze data about learning events to draw conclusions, such as: Is this tactic a good fit to conditions? Was it effective? Was effort required reasonable? Is my ability publicly exposed? As data accumulate, learners shape, re-shape and refine a personal theory about optimal learning. Thus, self-regulating learners are learning scientists. However, without training and tools on which "professional" learning scientists rely, learners' N = me research programs are naïve and scruffy. Merging models of tasks, cognition, metacognition and motivation, I describe software tools, approaches to analyzing data and learning analytics designed to serve three goals: supporting self-regulating learners' metacognition in N = me research, accelerating professional learning scientists' research, and boosting synergy among learners and learning scientists to accelerate progress in learning science.

Abstract Within the global health field, progress is being made to adopt a justice and sustainability-centred approach by advancing what has been named a planetary health agenda. Meanwhile, an increasing number of global health scholars argue for the decolonisation of the field. Yet, amongst these collective efforts to ‘transform’ global health thinking, a thorough analysis of political economy dimensions is often missing. ‘Growthism’, the belief that more production is necessarily good, continues to prevail. Truly committing to a decolonial eco-just global health agenda requires addressing the continuation of colonial arrangements within the structure of the global economy, removing growth dependencies and ushering in post-growth policies.

Background: Guidance during inquiry learning plays an important role in developing conceptual understanding and inquiry skills. This study analysed learner-tutor interactions in a simulation-assisted learning environment to investigate how tutor guidance enabled knowledge construction and fostered epistemic practice. Objectives: This research aimed to illuminate challenges learners encounter in the inquiry process and forms of guidance that support learning in both conceptual and epistemic aspects. Methods: This study uses a mixed methods approach. We analysed video recordings in which nine participants asked 72 questions and the microsequences of interactions immediately surrounding and including each question. We coded properties of each question and whether the tutors' utterances were intended to increase (upregulate) or decrease (downregulate) the complexity of the inquiry processes, and used a two-step cluster analysis to explore groupings emerged from tutors' regulation guidance and learners' questions. Results and Conclusions: The regulatory intent of tutors' utterances depended on various characteristics of student questions. The microsequences clustered in five categories: 1) upregulated investigation and inference, 2) upregulated evidence-based justification, 3) downregulated cognitive load, 4) downregulated procedural uncertainties, and 5) downregulated perceptual dissonance. Our findings suggest tutors offering guiding prompts should consider dual processes in the inquiry and, by strategically prompting them, strike a balance between the goals of guiding learners to discover scientific knowledge and grounding their conceptual understanding in concepts, data, and procedures. Implications: We emphasize conceptual and epistemic learning should be concurrently guided in scientific inquiry. We propose a bidirectional guidance model as a pedagogical approach to guide instructional practice.

Data used in learning analytics rarely provide strong and clear signals about how learners process content. As a result, learning as a process is not clearly described for learners or for learning scientists. Gaševic, Dawson, and Siemens (2015) urged data be sought that more straightforwardly describe processes in terms of events within learning episodes. They recommended building on Winne's (1982) characterization of traces -- ambient data gathered as learners study that more clearly represent which operations learners apply to which information -- and his COPES model of a learning event -- conditions, operations, products, evaluations, standards (Winne, 1997). We designed and describe an open source, open access, scalable software system called nStudy that responds to their challenge. nStudy gathers data that trace cognition, metacognition, and motivation as processes that are operationally captured as learners operate on information using nStudy's tools. nStudy can be configured to support learners' evolving self-regulated learning, a process akin to personally focused, self-directed learning science.

AbstractObjectives To investigate possible persistent performance deficits after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in elite athletes.Methods A prospective cohort study in three Belgian professional male football teams was performed during the 2020 − 2021 season. Participants were submitted to strength, jump, and sprint tests and an aerobic performance test (the Yo-Yo Intermittent Recovery test (YYIR)). These tests were repeated at fixed time intervals throughout the season. Assessment of SARS-CoV-2 infection was performed by a polymerase chain reaction (PCR) test before each official game.Results Of the 84 included participants, 22 were infected with SARS-CoV-2 during follow-up. At the first testing after infection (52.0 ± 11.2 days after positive PCR testing) significantly higher percentages of maximal heart rate (%HRmax) were seen – within the isolated group of infected players- during (p = .006) and after the YYIR (2 min after, p = .013), compared to pre-infection data. This increase in %HRmax was resolved at the second YYIR testing after infection (127.6 ± 33.1 days after positive PCR testing). Additionally, when comparing the first test after infection in formerly infected to non-infected athletes, significantly higher %HRmax were found during (p

This review covers the synthetic applications of 1,4-dithianes, as well as derivatives thereof at various oxidation states. The selected examples show how the specific heterocyclic reactivity can be harnessed for the controlled synthesis of carbon–carbon bonds. The reactivity is compared to and put into context with more common synthetic building blocks, such as 1,3-dithianes and (hetero)aromatic building blocks. 1,4-Dithianes have as yet not been investigated to the same extent as their well-known 1,3-dithiane counterparts, but they do offer attractive transformations that can find good use in the assembly of a wide array of complex molecular architectures, ranging from lipids and carbohydrates to various carbocyclic scaffolds. This versatility arises from the possibility to chemoselectively cleave or reduce the sulfur-heterocycle to reveal a versatile C2-synthon.

In scientific inquiry learning, within-task guidance tailored to the learner's domain knowledge and inquiry skill may be essential to promote intended learning outcomes. However, due to dynamic complexity across the timeline of inquiry learning, principles for designing tailored guidance are elusive. In this study, experienced tutors provided just-in-time guidance to 11 learners. We analysed tutor-learner interactions to investigate how tutors adapted guidance. We found tutors provided five types of guidance: prompts, support for domain knowledge, assessments, hints, and feedback. Guidance was provided when learners made errors, expressed difficulties, or asked questions; or when the tutor judged a learner successfully demonstrated a skill and was ready to progress to a follow-on skill. Based on these results, we propose a model for tailored, just-in-time guidance in simulation-assisted inquiry learning environments.

This research utilises statistical modelling to explore the impact of roadway infrastructure elements, primarily those related to cross-section design, on crash occurrences in urban areas. Cross-section design is an important step in the roadway geometric design process as it influences key operational characteristics like capacity, cost, safety, and overall functionality of the transport system entity. Evaluating the influence of cross-section design on these factors is relatively straightforward, except for its impact on safety, especially in urban areas. The safety aspect has resulted in inconsistent findings in the existing literature, indicating a need for further investigation. Negative binomial (NB) models are typically employed for such investigations, given their ability to account for over-dispersion in crash data. However, the low sample mean and under-dispersion occasionally exhibited by crash data can restrict their applicability. The generalised Poisson (GP) models have been proposed as a potential alternative to NB models. This research applies GP models for developing crash prediction models for urban road segments. Simultaneously, NB models are also developed to enable a comparative assessment between the two modelling frameworks. A six-year dataset encompassing crash counts, traffic volume, and cross-section design data reveals a significant association between crash frequency and infrastructure design variables. Specifically, lane width, number of lanes, road separation, on-street parking, and posted speed limit are significant predictors of crash frequencies. Comparative analysis with NB models shows that GP models outperform in cases of low sample mean crash types and yield similar results for others. Overall, this study provides valuable insights into the relationship between road infrastructure design and crash frequency in urban environments and offers a statistical approach for predicting crash frequency that maintains a balance between interpretability and predictive power, making it more viable for practitioners and road authorities to apply in real-world road safety scenarios.

Resumo: Em meio à pandemia provocada pela COVID-19,as instituições de ensino no Brasil colocaram em ação o Ensino Remoto Emergencial - ERE, solução temporária e estratégica, que possibilitou a comunidade escolar realizar, dentro das circunstâncias possíveis, as atividades de ensino. Os professores, em caráter de urgência, trabalharam com conteúdos até então desenvolvidos presencialmente, em uma realidade totalmente nova, tornando as Tecnologias Digitais de Informação e Comunicação - TDIC como um importante recurso durante este período pandêmico. Objetivo deste relato é analisar o uso do aplicativo WhatsApp como recurso pedagógico no ERE, na Educação Infantil, os desafios e aprendizados para os docentes. Optamos metodologicamente por um relato de experiência, com abordagem qualitativa, tendo como instrumento para construção dos dados observações registradas durante um estágio extracurricular, numa turma de pré-escola da rede pública, no interior do Estado da Bahia, com crianças do Pré 1, da faixa etária de 04 a 05 anos. Concluímos que o uso do WhatsApp durante o Ensino Remoto Emergencial com crianças pequenas evidenciou dificuldades que há tempos permeiam a realidade da Educação Infantil pública e a necessidade do investimento não só na infraestrutura das escolas, mas, na formação continuada dos docentes.

Hospitals can be overburdened with large numbers of patients with severe infectious conditions during infectious disease outbreaks. Such outbreaks or epidemics put tremendous pressure on the admission capacity of care facilities in the concerned region, negatively affecting the elective program within these facilities. Such situations have been observed during the recent waves of the coronavirus disease pandemic. Owing to the imminent threat of a “tripledemic” by new variants of the coronavirus disease (such as the new Omicron XBB.1.16 strain), influenza, and respiratory syncytial virus during future winter seasons, healthcare agencies should take decisive steps to safeguard hospitals' surge capacity while continuing to provide optimal and safe care to a potentially large number of patients in their trusted home environment. Preparedness of health systems for infectious diseases will require dynamic interaction between a continuous assessment of region-wide available hospital capacity and programs for intensive home treatment of patients who can spread the disease. In this viewpoint, we describe an innovative, dynamic coupling system between hospital surge capacity and cascading activation of a nationwide system for remote patient monitoring. This approach was developed using the multi-criteria decision analysis methodology, considering previously published real-life experiences on remote patient monitoring.

Learner modeling systems so far formulated model learning in three main ways: a learner's "position" within a lattice of declarative and procedural knowledge about highly structured disciplines such as geometry or physics, a learner's path through curricular tasks compared to milestones, or profiles of a learner's achievements on a set of tasks relative to mastery criteria or a peer group. Opening these models to learners identifies for them factors and relations among factors. Open learner models tacitly invite learners to regulate learning. However, contemporary learner models omit data about how learners have and should process information to learn, understand, consolidate and transfer new knowledge and skills. What to do with information opened to learners is opaque. I propose incorporating trace data about learning processes in learner models. Trace data allow generating learning analytics that inform self-regulating learners about potentially productive adaptations to processes they have used to learn. In a context of big data, such elaborated learner models are positioned to collaborate with self-regulating learners. Together, they can coordinate symbiotically, creating a platform for the system to improve its models of learners and for learners to more productively self-regulate learning.

Developing knowledge-transforming skills in writing may help students increase learning by actively building knowledge, regardless of the domain. However, many undergraduate students struggle to transform knowledge when drafting essays based on multiple sources. Writing analytics can be used to scaffold knowledge transforming as writers bring evidence to bear in supporting claims. We investigated how to automatically identify sentences representing knowledge transformation in argumentative essays. A synthesis of cognitive theories of writing and Bloom's typology identified 22 linguistic features to model processes of knowledge transforming in a corpus of 38 undergraduates' essays. Findings indicate undergraduates mostly paraphrase or copy information from multiple sources rather than engage deeply with sources' content. Eight linguistic features were important for discriminating evidential sentences as telling versus transforming source knowledge. We trained a machine learning algorithm that accurately classified nearly three of four evidential sentences as knowledge-telling or knowledge-transforming, offering potential for use in future research.

Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an information stream. To this end, we designed an experiment inspired by pip-and-pop but more appropriate for eliciting attention and P3a-event-related potentials (ERPs). In this study, the aim was to distinguish between targets and distractors based on the subject’s electroencephalography (EEG) data. We achieved this objective by employing different machine learning (ML) methods for both individual-subject (IS) and cross-subject (CS) models. Finally, we investigated which EEG channels and time points were used by the model to make its predictions using saliency maps. We were able to successfully perform the aforementioned classification task for both the IS and CS scenarios, reaching classification accuracies up to 76%. In accordance with the literature, the model primarily used the parietal–occipital electrodes between 200 ms and 300 ms after the stimulus to make its prediction. The findings from this research contribute to the development of more effective P300-based brain–computer interfaces. Furthermore, they validate the EEG data collected in our experiment.

COVID-19 has accelerated the shift to blended or fully online learning environments, enforcing educational institutions to embrace technology and offer their students an online or at least blended learning experience [...]

This specific study investigates the chemical and morphological properties of naturally treated wheat straw fiber growing around in Jimma zone. The chemical composition properties including cellulose 45.43%, hemicellulose 36.50%, and lignin 21.61% were determined. The physical properties of naturally treated wheat straw fibers, such as bulk density, particle density, moisture content, and ash content, are 0.037 g/cm3, 0.194 g/cm3, 10.04%, and 1.44%, respectively. The chemical functional group of naturally treated wheat straw fiber was established by Fourier transform infrared spectroscopy (FTIR), crystallinity index is calculated by X-ray powder diffraction (×RD) analysis, and the thermogravimetric analysis (TGA) result ensures that the naturally treated wheat straw has thermally withstood up to 314.35°C. A fiber’s surface morphology shows the presence of shallow pores, which increase the surface roughness of the fiber; this contributes to increased contact surface area, which ensures better fiber-matrix adherence in composite fabrication. We conclude that naturally treated wheat straw fiber has a significant value in fiber reinforced concrete technology as reinforcement materials in concrete composites formation by incorporating optimum percentage to reduce the limitation of concrete and to enhance the mechanical properties of concrete.