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This study investigates the possibility of utilizing online learning data to design face-to-face activities in a flipped classroom. We focus on heterogeneous group formation for effective collaborative learning. Fifty-three undergraduate students (18 males, 35 females) participated in this study, and 8 students (3 males, 5 females) among them joined post-study interviews. For this study, a total of 6 student characteristics were used: three demographic characteristics obtained from a simple survey and three academic characteristics captured from online learning data. We define three demographic group heterogeneity variables and three academic group heterogeneity variables, where each variable is calculated using the corresponding student characteristic. In this way, each heterogeneity variables represents a degree of diversity within the group. Then, a two-stage hierarchical regression analysis was conducted to identify the significant group heterogeneity variables that influence face-to-face group achievement. The results show that the academic group heterogeneity variables, which were derived from the online learning data, accounted for a significant proportion of the variance in the group achievement when the demographic group heterogeneity variables were controlled. The interviews also reveal that the academic group heterogeneity indeed affected group interaction and learning outcome. These findings highlight that online learning data can be utilized to obtain relevant information for effective face-to-face activity design in a flipped classroom. Based on the results, we discuss the advantages of this data utilization approach and other implications for face-to-face activity design.

Although empirical research often underpins practical visualization guidelines, it remains unclear how well these research-driven insights are reflected in the guidelines practitioners actually use. In this paper, we investigate the research-practice gap in visualization design guidelines through a mixed-methods approach. We collected 390 design guidelines from practitioner-facing sources and 235 empirical studies to quantitatively assess their alignment. To complement this analysis, we conducted surveys with 69 participants (33 practitioners, 36 researchers) and in-depth interviews with 20 experts to examine their experiences, perceptions, and challenges. Our findings reveal discrepancies: empirical evidence often contradicts or only partially supports widely used guidelines, and the two communities prioritize different attributes of design. Based on these insights, we derive a holistic guideline template (integrating Context, Approach, Problem, and Purpose) and discuss actionable strategies, such as a triadic knowledge model.

Predicting athletes' performance has relied mostly on statistical data. Besides the traditional data, various types of data, including video, have become available. However, it is challenging to use them for deep learning, especially when the size of the athletes' dataset is small. This research proposes a feature-selection strategy based on the criteria used by insightful people, which could improve ML performance. Our ML model employs features selected by people who correctly evaluated the athletes' future performance. We tested out a strategy to predict the LPGA players' next day performance using their interview video. We asked study participants to predict the players' next day score after watching the interviews and asked why. Using combined features of the facial landmarks' movements, derived from the participants, and meta-data showed a better F1-score than using each feature separately. This study suggests that the human-in-the-loop model could improve algorithms' performance with small-dataset.

We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory ( ) and inhibitory ( ) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset's utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.

Empirical research on perception and cognition has laid the foundation for visualization design, often yielding useful design guidelines for practitioners. However, it remains uncertain how well practitioners stay informed about such crucial visualization design knowledge. In this paper, we employed a mixed-method approach to explore the knowledge gap between visualization research and real-world design guidelines. We initially collected existing design guidelines from various sources and empirical studies from diverse publishing venues, analyzing their alignment and uncovering missing links and inconsistent knowledge. Subsequently, we conducted surveys and interviews with practitioners and researchers to gain further insights into their experiences and attitudes towards design guidelines and empirical studies, and their views on the knowledge gap between research and practice. Our findings highlight the similarities and differences in their perspectives and propose strategies to bridge the divide in visualization design knowledge.

Recent advances in high-resolution connectomics provide researchers access to accurate reconstructions of vast neuronal circuits and brain networks for the first time. Neuroscientists anticipate analyzing these networks to gain a better understanding of information processing in the brain. In particular, scientists are interested in identifying specific network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to the detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings of the involved neurons and synapses. To reduce visual clutter and simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by continuous visual abstractions that allows the user to transition from the highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly to form a longer synaptic chain. We evaluate Vimo in a user study with seven domain experts and an in-depth case study on motifs in the central complex (CX) of the fruit fly brain.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/jakobtroidl/neuronal-motifs

Unlike neutral species, ions are less susceptible to shadowing effects due to their acceleration by the sheath electric field, which leads to more directionality. Consequently, intra-cell depth loading can be reduced if the silicon trench formation were dominated by ions (Cl+ or Br+) instead of radicals. Ion dependent etching can be realized by increasing ion energy or ion density. To increase ion energy, one can elevate the bias power or lower the bias frequency.

This study examines the relationship between corporate commitment to business ethics and financial reporting quality. We posit that companies with a higher level of ethical commitment exhibit better quality financial reporting than those with a lower level of ethical commitment. Consistent with our prediction, we find that companies with a higher level of ethical commitment are engaged in less earnings management, report earnings more conservatively, and predict future cash flows more accurately than those with a lower level of ethical commitment. We also find that corporate commitment to business ethics has perpetuating effects on future financial reporting quality.

A variety of stakeholders have long been interested in the factors that are related to firm valuation. This article investigates why companies with more comprehensive corporate governance (CG) have a value premium over companies with less comprehensive CG. We posit and find that the cost of equity capital (COC) decreases with the strength of CG, suggesting that the value premium stems from the lower COC for more comprehensive CG. We also find that the COC is lower for companies with strong commitment to business ethics (BE) than for those with weak commitment to BE and that the beneficial effect of CG on the COC is more pronounced for companies with weak commitment to BE than for those with strong commitment to BE. Companies with more comprehensive CG tend to exhibit strong commitment to BE, but the beneficial effect of corporate ethical commitment is not completely subsumed by CG. Our results suggest that companies could lower their cost of equity capital and increase firm value by adopting more comprehensive CG practices and committing to higher standards of BE.

Platinum catalysts loaded on a hybrid support, composed of HZSM-48 and pseudoboehmite, were applied to the synthesis of benzene through methylcyclopentane (MCP) reforming in order to investigate the effect of the addition of pseudoboehmite to Pt/HZSM-48 for ring-enlargement reaction. A total of 0.5 wt% of platinum was impregnated on the hybrid support by using the incipient wetness method. Catalyst characterization was performed with nitrogen sorption, X-ray diffraction, temperature-programmed desorption of NH3, and infrared spectroscopy of adsorbed pyridine. It was found that mesoporous structures were well-developed in Pt/(HZSM-48 + pseudoboehmite) catalyst as a result of the pseudoboehmite addition, of which the average pore size was in the range of 7–8 nm. The presence of pseudoboehmite in the catalyst increases the total amount of acid sites and weakens the acid strength, compared with those of the Pt/HZSM-48 catalyst. Lewis acid sites were more abundant than Brönsted acid sites over the Pt/(HZSM-48+pseudoboehmite) catalysts. It was found that selectivity to the ring-enlargement reaction is dominant over selectivity to the ring-opening reaction over the Pt/(HZSM-48 + pseudoboehmite) catalysts. The benzene yield over Pt/(HZSM-48 + pseudoboehmite, 1:1) catalyst reached 65.1% at 450 °C and 0.3 h−1. As well as being influenced by the mesoporous structure, the higher activity and selectivity in MCP reforming was also determined by appropriate acidity of the Pt/(HZSM-48 + pseudoboehmite) catalysts.