Combination Of Metrics Research Articles

Lopsided distribution of MATLAS and ELVES dwarf satellite systems around isolated host galaxies

The properties of satellite dwarf galaxies provide important empirical insights for verifying cosmological models on galaxy scales. Their phase-space correlations, in particular, offer interesting insights into a broad range of models, since they are dominated by gravity and are largely independent of the specific dark matter flavor or baryonic processes that are considered. Next to the much-debated planes-of-satellites phenomenon, the lopsided distribution of satellites relative to their host galaxy has been studied observationally and in cosmological simulations. The degree to which observed lopsidedness is consistent with expectations from simulations is still unclear. We quantified the level of lopsidedness in isolated observed satellite systems under six different metrics. We studied 47 systems from the MATLAS survey beyond the local volume (LV) as well as 21 LV satellite systems from the ELVES survey. The satellite systems are complete to an estimated absolute magnitude of M ∼ −9. We find that the so-called wedge metric, counting the number of dwarfs in wedges with varying opening angles, is best suited to capture a system’s overall lopsidedness. Under this metric, our analysis reveals that ∼16 percent of the tested satellite systems exhibit a statistically significant degree of lopsidedness when compared to systems with randomly generated satellite position angles. This presents a notable excess over the expected 5% (2σ level) of significantly lopsided systems in a sample with no overall inherent lopsidedness. To gain the most rounded picture, however, a combination of metrics that are sensitive to different features of lopsidedness should be used. Combining all tested metrics, the number of significantly lopsided systems increases to ∼21 percent. Contrary to recent results from the literature, we find more lopsided systems among the red early-type galaxies in the MATLAS survey compared to the mostly blue late-type hosts in ELVES. We further find that satellite galaxies at larger distances from the host, potentially recently accreted, are likely the primary contributors to the reported excess of lopsidedness. Our results set the groundwork that allows a comparison with similar systems in cosmological simulations to assess the consistency with the standard model.

Pathologically based criteria to distinguish essential tremor from controls: analyses of the human cerebellum.

Essential tremor is among the most prevalent neurological diseases. Diagnosis is based entirely on neurological evaluation. Historically, there were few postmortem brain studies, hindering attempts to develop pathologically based criteria to distinguish essential tremor from control brains. However, an intensive effort to bank essential tremor brains over recent years has resulted in postmortem studies involving >200 brains, which have identified numerous degenerative changes in the essential tremor cerebellar cortex. Although essential tremor and controls have been compared with respect to individual metrics of pathology, there has been no overarching analysis to derive a combination of metrics to distinguish essential tremor from controls. We asked whether there is a constellation of pathological findings that separates essential tremor from controls, and how well that constellation performs. Analyses included 100 essential tremor brains from the essential tremor centralized brain repository and 50 control brains. A standard tissue block from the cerebellar cortex was used to quantify 11 metrics of pathological change. Three supervised classification algorithms were investigated, with data divided into training and validation samples. Using three different algorithms, we illustrate the ability to correctly predict a diagnosis of essential tremor, with sensitivity and specificity >87%, and in the majority of situations, >90%. We also provide a web-based application that uses these metric values, and based on specified cutoffs, determines the likely diagnosis. These analyses set the stage for use of pathologically based criteria to distinguish clinically diagnosed essential tremor cases from controls, at the time of postmortem.

Exploring the atypia of undetermined significance: Malignant ratio, ThyroSeq v3 positive call rate, molecular-derived risk of malignancy, and risk of malignancy as possible quality metric tools in thyroid cytology.

The atypia of undetermined significance (AUS) category is heterogeneous, leading to variations in its use. To prevent excessive usage, the AUS rate should be≤10%. Although this recommendation aims to maintain diagnostic quality, it lacks supporting data. The AUS:Malignant (AUS:M) ratio has been proposed as a metric tool to evaluate AUS use. Furthermore, integrating ThyroSeq v3 (TSV3) positive call rate (PCR) and the molecular-derived risk of malignancy (MDROM) have been put forward as performance improvement tools. The authors reviewed their AUS:M ratios, TSV3 PCR, MDROM, and ROM. Thyroid aspirates evaluated in the laboratory (from August 2022 to September 2023) by seven cytopathologists (CPs) were identified. AUS:M ratio, MDROM, ROM, and TSV3 PCR results for the laboratory and each CP were recorded and analyzed. A total of 2248 aspirates were identified (462 AUS and 80 malignant). The AUS:M ratio for the laboratory was 5.8 (CPs range, 2.8 to 7.3). The TSV3 PCR for the laboratory was 23% (CPs range, 11% to 41%). The MDROM for the laboratory was 19% (CPs range, 9% to 31%), whereas the ROM was 36% (CPs range, 29% to 50%). Linear regression analysis of AUS:M ratio versus TSV3 PCR and MDROM demonstrated a moderate positive correlation but a weak negative correlation to the ROM. Deviations from established targets were attributed to multiple factors. The findings of this study underscore the importance of using a combination of metrics to evaluate diagnostic practices. By dissecting the practice patterns of each CP, the authors can measure different aspects of their performance and provide individualized feedback.

Myofascial trigger point (MTrP) size and elasticity properties can be used to differentiate characteristics of MTrPs in lower back skeletal muscle

Myofascial trigger points (MTrPs) are localized contraction knots that develop after muscle overuse or an acute trauma. Significant work has been done to understand, diagnose, and treat MTrPs in order to improve patients suffering from their effects. However, effective non-invasive diagnostic tools are still a missing gap in both understanding and treating MTrPs. Effective treatments for patients suffering from MTrP mediated pain require a means to measure MTrP properties quantitatively and diagnostically both prior to and during intervention. Further, quantitative measurements of MTrPs are often limited by the availability of equipment and training. Here we develop ultrasound (US) based diagnostic metrics that can be used to distinguish the biophysical properties of MTrPs, and show how those metrics can be used by clinicians during patient diagnosis and treatment. We highlight the advantages and limitations of previous US-based approaches that utilize elasticity theory. To overcome these previous limitations, we use a hierarchical approach to distinguish MTrP properties by patients’ reported pain and clinician measured palpation. We show how US-based measurements can characterize MTrPs with this approach. We demonstrate that MTrPs tend to be smaller, stiffer, and deeper in the muscle tissue for patients with pain compared to patients without pain. We provide evidence that more than one MTrP within a single US-image field increases the stiffness of neighboring MTrPs. Finally, we highlight a combination of metrics (depth, thickness, and stiffness) that can be used by clinicians to evaluate individual MTrPs in combination with standard clinical assessments.

A novel computational pipeline for var gene expression augments the discovery of changes in the Plasmodium falciparum transcriptome during transition from in vivo to short-term in vitro culture

The pathogenesis of severe Plasmodium falciparum malaria involves cytoadhesive microvascular sequestration of infected erythrocytes, mediated by P. falciparum erythrocyte membrane protein 1 (PfEMP1). PfEMP1 variants are encoded by the highly polymorphic family of var genes, the sequences of which are largely unknown in clinical samples. Previously, we published new approaches for var gene profiling and classification of predicted binding phenotypes in clinical P. falciparum isolates (Wichers et al., 2021), which represented a major technical advance. Building on this, we report here a novel method for var gene assembly and multidimensional quantification from RNA-sequencing that outperforms the earlier approach of Wichers et al., 2021, on both laboratory and clinical isolates across a combination of metrics. Importantly, the tool can interrogate the var transcriptome in context with the rest of the transcriptome and can be applied to enhance our understanding of the role of var genes in malaria pathogenesis. We applied this new method to investigate changes in var gene expression through early transition of parasite isolates to in vitro culture, using paired sets of ex vivo samples from our previous study, cultured for up to three generations. In parallel, changes in non-polymorphic core gene expression were investigated. Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour.

A novel computational pipeline for var gene expression augments the discovery of changes in the Plasmodium falciparum transcriptome during transition from in vivo to short-term in vitro culture.

The pathogenesis of severe Plasmodium falciparum malaria involves cytoadhesive microvascular sequestration of infected erythrocytes, mediated by P. falciparum erythrocyte membrane protein 1 (PfEMP1). PfEMP1 variants are encoded by the highly polymorphic family of var genes, the sequences of which are largely unknown in clinical samples. Previously, we published new approaches for var gene profiling and classification of predicted binding phenotypes in clinical P. falciparum isolates (Wichers et al., 2021), which represented a major technical advance. Building on this, we report here a novel method for var gene assembly and multidimensional quantification from RNA-sequencing that outperforms the earlier approach of Wichers et al., 2021, on both laboratory and clinical isolates across a combination of metrics. Importantly, the tool can interrogate the var transcriptome in context with the rest of the transcriptome and can be applied to enhance our understanding of the role of var genes in malaria pathogenesis. We applied this new method to investigate changes in var gene expression through early transition of parasite isolates to in vitro culture, using paired sets of ex vivo samples from our previous study, cultured for up to three generations. In parallel, changes in non-polymorphic core gene expression were investigated. Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour.

Towards a Combination of Metrics for Machine Translation

In this scholar, we compare three metrics for machine translation, from English toFrench and vice versa, and we give some combination formulas based on someschemes, algorithms, and machine learning tools. As an experimental dataset, weconsider 10 English and French theses abstracts published in the web with four free incharge machine translation systems. Five combinations, with the same implicit weights,are considered namely: (BLEU+NIST), (BLEU+ (1-WER)), (NIST+(1-WER)),(BLEU+NIST+(1-WER)), and (FR(BLEU)+FR(NIST)+FR(WER)). Thesecombinations are also considered differently through generating weights parameters onthe basis of regression. The results of 12 formulas are computed and compared then intotal. According to the obtained results, average regression combinations based onmachine learning step are the best, especially with the three basic metrics, followed byaverage WER metric in the case of English to French. For French to English,(FR(BLEU)+FR(NIST)+FR(WER)) combination is the best followed respectively bythe average regression combination with both first parameters (Reg(α,β)) and averageBLEU basic metric. Another performance criterion is considered here, in the secondposition, namely: the number of times, over the 10 abstracts, where the formula is thebest. Based on the obtained results, combination with regression based on the first andthe last parameters (Reg(α,γ)) outperforms the others, in the case of English to French,with 3 times followed by Reg(β,γ), Reg(α,β,γ), NIST+(1-WER), and the basic metrics(BLEU, NIST, and WER) with 2 times for each of them. For French to English, thebasic WER metric outperforms the others with three times followed by BLEU, (BLEU+(1-WER)), (FR(BLEU)+FR(NIST)+FR(WER)), and Reg(α,γ) with 2 times for each ofthem. To note that there is a room of improvement for the combinations with1.0914 inthe case of English to French and 1.01 in the case of French to English.

Kinematic, Kinetic, and Temporal Metrics Associated With Golf Proficiency.

McHugh, MP, O'Mahoney, CA, Orishimo, KF, Kremenic, IJ, and Nicholas, SJ. Kinematic, kinetic, and temporal metrics associated with golf proficiency. J Strength Cond Res 38(3): 599-606, 2024-The biomechanics of the golf swing have been studied extensively, but the literature is unclear on which metrics are indicative of proficiency. The purpose of this study was to determine which metrics identified golf proficiency. It was hypothesized that discrete kinematic, kinetic, and temporal metrics would vary depending on proficiency and that combinations of metrics from each category would explain specific proficiency metrics. Kinematic, kinetic, and temporal metrics and their sequencing were collected for shots performed with a driver in 33 male golfers categorized as proficient, average, or unskilled (based on a combination of handicap, ball velocity, and driving distance). Kinematic data were collected with high-speed motion analysis, and ground reaction forces (GRF) were collected from dual force plates. Proficient golfers had greater x-factor at ball impact and greater trunk deceleration before ball impact compared with average ( p < 0.05) and unskilled ( p < 0.01) golfers. Unskilled golfers had lower x-factor at the top of the back swing and lower peak x-factor, and they took longer to reach peak trunk velocity and peak lead foot GRF compared with average ( p < 0.05) and proficient ( p < 0.05) golfers. A combination of 2 kinematic metrics (x-factor at ball impact and peak pelvis velocity), 1 kinetic metric (peak lead foot GRF), and 2 timing metrics (the timing of peak trunk and arm velocity) explained 85% of the variability in ball velocity. The finding that x-factor at ball impact and trunk deceleration identified golf proficiency points to the potential for axial trunk rotation training to improve performance.

Are they Twins or Only Friends? The Redundancy and Complementarity of Energy Poverty Indicators in Italy

A large body of literature has developed numerous unidimensional and multidimensional indicators to identify energy-poor households, also with the aim of targeting public policies. They all have pros and cons and may identify different populations. How do they relate to each other? How should they be used in a country such as Italy where energy poverty has not yet been officially defined? We frame these research questions in a context of general social vulnerability in which several dimensions of poverty intersect. Using SILC data (EU and IT modules) for the period 2004–2015 we compare four widely used unidimensional indicators and provide evidence—for the Italian case—that complementarity among the indicators prevails since the complexity of energy poverty requires a combination of metrics to capture various vulnerabilities. Our results confirm the set of relevant factors affecting the probability of being energy poor which are indicated in the related economics literature, regardless of which indicator is used. They may help tailor policies to tackle the phenomenon. Based on these findings, we strongly support the idea of using a dashboard of different metrics to measure energy poverty to design effective policies.

Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord.

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is widely used for MS diagnosis and clinical follow-up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball-and-Stick models. We analyzed spinal cord data acquired from multiple hospitals and extracted average diffusion MRI metrics per vertebral level using a collection of image processing methods and an atlas-based approach. We then performed a statistical analysis to evaluate the feasibility of these metrics for detecting lesions, exploring the usefulness of combining different metrics to improve accuracy. Our study demonstrates the sensitivity of each metric to underlying microstructure changes in MS patients. We show that selecting a specific subset of metrics, which provide complementary information, significantly improves the prediction score of lesion presence in the cervical spinal cord. Furthermore, the Ball-and-Stick model has the potential to provide novel information about the microstructure of damaged tissue. Our results suggest that diffusion measures, particularly combined measures, are sensitive in discriminating abnormal from healthy cervical vertebral levels in patients. This information could aid in improving MS diagnosis and clinical follow-up. Our study highlights the potential of the Ball-and-Stick model in providing additional insights into the microstructure of the damaged tissue.

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma

BackgroundOver the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported.ResultsWe highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use.ConclusionsWith this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.

Benchmark of multi-view Terrestrial Laser Scanning Point Cloud data registration algorithms

This study addresses multi-view Terrestrial Laser Scanning Point Cloud data registration methods. Multiple rigid point cloud data registration is mandatory for aligning all scans into a common reference frame and it is still considered a challenge looking from a large-scale surveys point of view. The goal of this work is to support the development of cutting-edge registration methods in geoscience and mobile robotics domains. This work evaluates 3 data sets of total 20 scenes available in the literature. This paper provides a novel open-source framework for multi-view Terrestrial Laser Scanning Point Cloud data registration benchmarks. The goal was to verify experimentally which registration variant can improve the open-source data looking from the quantitative and qualitative points of view. In particular, the following scanners provided measurement data: Z+F TLS Imager 5006i, Z+F TLS Imager 5010C, Leica ScanStation C5, Leica ScanStation C10, Leica P40 and Riegl VZ-400. The benchmark shows an impact of the metric e.g. point to point, point to projection onto a plane, plane to plane etc..., rotation matrix parameterization (Tait–Bryan, quaternion, Rodrigues) and other implementation variations (e.g. multi-view Normal Distributions Transform, Pose Graph SLAM approach) onto the multi-view data registration accuracy and performance. An open-source project is created and it can be used for improving existing data sets reported in the literature, it is the added value of the presented research. The combination of metrics, rotation matrix parameterization and optimization algorithms creates hundreds of possible approaches. It is shown that chosen metric is a dominant factor in data registration. The rotation parameterization and other degrees of freedom of proposed variants are rather negligible compared with chosen metric. Most of the proposed approaches improve registered reference data provided by other researchers. Only for 2 from 20 scenes it was not possible to provide significant improvement. The largest improvements are evident for large-scale scenes. The project is available and maintained at https://github.com/MapsHD/HDMapping.

Exploring Influences of Leaves on Urban Species Identification Using Handheld Laser Scanning Point Cloud: A Case Study in Hong Kong

Urban tree species identification performs a significant role in tree management and the sustainable development of cities. Conventionally, multispectral or hyperspectral remote sensing images are applied to identify species. However, spectral profiles of trees on images are easily affected by surroundings and illuminations, resulting in urban trees of different species possibly having similar spectral features. The handheld laser scanning (HLS) technique can capture 3D structural information of trees and be confirmed to be effective in reducing the problem of spectral similarity through tree structural properties (TSP). TSP usually varies in different leaf conditions, especially TSP of tropical tree species. In this study, we investigated the effects of leaves on urban tropical tree species identification using HLS. A total of 89 metrics that characterized the TSP were evaluated, including 19 branches, 12 stems, 45 crowns, and 13 entire tree metrics. All metrics were derived under different leaf conditions. The correlation and importance of these metrics were further evaluated. Our results demonstrated that crown metrics perform the most important role in urban species identification in leaf-on and leaf-off conditions and that the combination of metrics derived in different leaf conditions can improve the identification accuracy. Furthermore, we discovered 9 robust metrics that perform well in all leaf conditions, including 3 crowns, 2 branches, 2 stems, and 2 entire tree metrics. These metrics give a deep understanding of numerous structural properties and provide a significant reference for the relevant structure-based classification of other tropical species. This study also illustrated that HLS could help to overcome the spectrum-related limitations and improve the efficiency of species identification and sustainable forest management.

A Comparison of Four Spatial Interpolation Methods for Modeling Fine-Scale Surface Fuel Load in a Mixed Conifer Forest with Complex Terrain

Patterns of spatial heterogeneity in forests and other fire-prone ecosystems are increasingly recognized as critical for predicting fire behavior and subsequent fire effects. Given the difficulty in sampling continuous spatial patterns across scales, statistical approaches are common to scale from plot to landscapes. This study compared the performance of four spatial interpolation methods (SIM) for mapping fine-scale fuel loads: classification (CL), multiple linear regression (LR), ordinary kriging (OK), and regression kriging (RK). These methods represent commonly used SIMs and demonstrate a diversity of non-geostatistical, geostatistical, and hybrid approaches. Models were developed for a 17.6-hectare site using a combination of metrics derived from spatially mapped trees, surface fuels sampled with an intensive network of photoload plots, and topographic variables. The results of this comparison indicate that all estimates produced unbiased spatial predictions. Regression kriging outperformed the other approaches that either relied solely on interpolation from point observations or regression-based approaches using auxiliary information for developing fine-scale surface fuel maps. While our analysis found that surface fuel loading was correlated with species composition, forest structure, and topography, the relationships were relatively weak, indicating that other variables and spatial interactions could significantly improve surface fuel mapping.

The impact of COVID-19 on global health journals: an analysis of impact factor and publication trends

BackgroundCOVID-19 has affected research productivity across all areas of knowledge. Current evidence suggests that COVID-19 has had a blockbuster effect on journal impact factors (JIFs) and publication trends, while little...

Acoustic speech parameter relationships with voice disorders and phrase differences

While acoustic speech analysis is non-invasive, the utility has been mixed due to the range of voice types. For vocal health practitioners to efficiently and quickly assess and document voice changes, knowing which voice parameter would be sensitive to vocal change is crucial. Using a database of 296 individual voices including 8 voice pathology types and typical voice samples, the sensitivity of a range of acoustic speech parameters to differentiate common voice pathology types was investigated. Both traditional and contemporary acoustic speech metrics were estimated for the samples using a custom MATLAB script and PRAAT (e.g., jitter, shimmer HNR, CPPS, Alpha ratio, PPE). Analysis then evaluate the predictability value of the metrics to discriminate pathology type. From the pool of parameters, 11 were able to identify pathological voices from normal controls and several of the parameters were more sensitive to some pathology. For example, CPPs and jitter values could discriminate neuropathological voices whereas HNR and Shimmer cold discriminate muscle-based pathologies. These results indicate how the sensitivity of acoustic speech metrics to the voice pathology types can allow for the identification of individual metrics (or combinations of metrics) which could be used to track changes in vocal health.

Fault Injection in Actuator Models for Testing of Automated Driving Functions

In this work, a simulation framework for virtual testing of autonomous driving functions under the influence of a fault occurring in a component is presented. The models consist of trajectory planning, motion control, models of actuator management, actuators and vehicle dynamics. Fault-handling tests in a right-turn maneuver are described, subject to an injected fault in the steering system. Different scenarios are discussed without and with a fault and without and with counteractions against the fault. The results of five scenarios for different criticality metrics are discussed. In the case of a fault without a counteraction, a pronounced lateral position deviation of the ego vehicle from the reference curve is observed. Furthermore, the minimal and hence most critical time-to-collision (TTC) and post-encroachment time (PET) values are calculated for each scenario together with a parameter variation of the initial position of a traffic agent. The minimum TTC values are lowest in the case of a fault without counteraction. For the lateral position deviation and the TTC, the counteractions cause reduced criticality that can become even lower than in the case without a fault, corresponding to a decrease in the dynamic behavior of the vehicle. For the PET, only in the case of a fault without counteraction, a non-zero value can be calculated. With the implemented testing toolchain, the automated vehicle and the reaction of the HAD function in non-standard conditions with reduced performance can be investigated. This can be used to test the influence of component faults on automated driving functions and help increase acceptance of implemented counteractions as part of the HAD function. The assessment of the situation using a combination of metrics is shown to be useful, as the different metrics can become critical in different situations.

WDN-tailored Complex Network Centrality Metrics for water distribution networks domain analysis

Water Distribution Networks (WDNs) are spatially organized infrastructures, whose hydraulic behavior greatly depends on their connectivity properties, i.e., the topological domain. The Complex Network Theory (CNT) provides a wide range of useful functions and metrics that have been tailored for identifying WDNs behavior, even before using hydraulic models. This work exploits the recent studies on the WDN domain, which couple the CNT metrics of centrality tailored for WDN with the intrinsic relevance of the spatial elements of the networks. The study accounts for various combinations of metrics and intrinsic relevance functions aimed at supporting WDN analysis for operational and management tasks. A Digital Water Service (DWS) is used to enable technicians and water companies to replicate the same domain analysis in the ongoing context of digital transformation in WDN sector.

Higher-order thinking skills assessment in 3D virtual learning environments using motifs and expert data

The research reported in this paper addresses the problem of assessing higher-order thinking skills, such as reflective and creative thinking, within the context of virtual learning environments. Assessment of these skills requires process-based observations and evaluation, as the output-based methods have been found to be insufficient. Virtual learning environments offer a wealth of data on the process, which makes them good candidates for process-based evaluation, but the existing assessment methods in these environments have shortcomings, such as reliance on large data sets, inability to offer specific feedback on actions, and the lack of consideration for how actions are integrated into bigger tasks. Demonstrating and confirming the ability of three-dimensional virtual learning environments to work with process metrics for assessment, we propose and evaluate the use of motifs as an assessment tool. Motifs are short and meaningful combination of metrics. Combining time-ordered motifs with a similarity analysis between expert and learner data, our proposed approach can potentially offer feedback on specific actions that the learner takes, as opposed to single output-based feedback. It can do so without the use of large training datasets due to reliance on expert data and similarity analysis. Through a user study, we found out that such a motif-based approach can be effective in the assessment of higher-order thinking skills while addressing the identified shortcomings of previous work. We also address the limited research on similarity-based analysis methods, compare their effectiveness, and show that utilizing different similarity measures for different tasks may be a more effective approach. Our proposed method facilitates and encourages the involvement of instructors and course designers through the definition of motifs and expert problem-solving paths.

Factorial Design with Simulation for the Optimization of the Level of Service in the Platform-Train Interface of Metro Stations—A Pilot Study

Metro stations are considered complex areas of pedestrian mobility due to the increasing congestion, due to the a high level of demand of different circulation spaces. Given this situation and the limited physical spaces remaining to develop transport systems in urban areas, railways acquire greater relevance given the need to mobilize pedestrians. Within the stations, the most problematic area is the platform-train interface (PTI) due to the high number of interactions between passengers boarding and alighting. The objective of this study is to identify the PTI configuration that maximizes the level of service for passengers, safeguards the operational continuity of the system and improves user experience by reducing dissatisfaction and delay times. For this, a pedestrian microsimulation model is used in order to recreate the reality of a generic metro station and its different scenarios given the combinations of two factors: the platform configurations (topology) and the traffic control elements. Subsequently, these scenarios are analyzed through a factorial design, looking for the situation that optimizes the combination of metrics chosen in a weighted way. Finally, it is found that the PTI configuration that maximizes the level of service for users is the mixed station with signaling. It is this which includes the factors with the greatest positive effect on the chosen metrics.