Statistical Representation Research Articles

Video‐based modeling examples and comparative self‐explanation prompts for teaching a complex problem‐solving strategy

AbstractBackgroundIn example‐based learning, examples are often combined with generative activities, such as comparative self‐explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self‐explanations. While static text‐based examples can be compared easily, this is challenging for transient video‐based modelling examples used in complex domains because simultaneous processing of two videos is not feasible.ObjectivesTo allow for such comparisons, we combined video‐based modelling examples with static representations (i.e., summarizing tables) of the observed optimal and a suboptimal solution of the problem‐solving process. A comparative self‐explanation prompt asked learners to compare the different solution approaches. Our study investigated the impact of video‐based modelling examples versus independent problem‐solving on cognitive load and problem‐solving skill development. Moreover, we investigated the effects of comparative versus sequential self‐explanation prompts, depending on learners' prior knowledge.MethodsIn an experiment, 118 automotive apprentices learned a car malfunction diagnosis strategy. Apprentices were divided into three groups: (1) modelling examples with comparative self‐explanation prompts, (2) modelling examples with sequential prompts, and (3) no examples or prompts. Diagnostic knowledge and skills were assessed before and after the intervention. Cognitive load was measured retrospectively.Results and conclusionsDespite no observed effects on cognitive load, modelling examples enhanced diagnostic knowledge and diagnostic skills with scaffolds, though not independent diagnostic skills without scaffolds. The need for more practice opportunities to foster independent diagnostic skills is assumed. Additionally, comparative prompts seem promising for learners with higher prior knowledge.TakeawaysVideo‐based modelling examples were more beneficial for learning than practising to apply the diagnostic strategy. Static representations allow for comparisons of video examples and comparative prompts are promising for learners with higher prior knowledge (cf. expertise‐reversal effect). Further research, especially on the effects on cognitive load, is necessary.

To Animate or Not to Animate Usability Scales: The Effect of Animation on Questionnaire Experience and Psychometric Properties

The Hybrid Usability Inventory (HUI) is a usability questionnaire that uses a combination of pictorial and verbal information to express the meaning of its items. This study aimed to extend the static pictorial representation by using animations. Previous research has not yet addressed the positive or negative outcomes of using animations in pictorial questionnaires. We hypothesized that an animated questionnaire would have an additional positive effect on respondents’ questionnaire experience, motivation, and preferences without impinging psychometric properties. The goal of the present study was to compare the static HUI with an animated version (AniHUI) in an online test setting. Respondent-centered aspects (questionnaire experience) and psychometric properties (sensitivity, validity, reliability) were assessed. Participants (N = 192) interacted with a website prototype (either high or low usability) and subsequently assessed the website’s usability either with HUI or AniHUI, the System Usability Scale (SUS), and further measures of interest. Results suggest that AniHUI did not differ substantially from HUI. However, both the static and animated scales were superior to the SUS regarding respondent-centred measures. Findings suggest that the HUI and the AniHUI are engaging and reliable scales that can be used in research and practice.

Validation of a Suprasystolic Cuff System for Static and Dynamic Representation of the Central Pressure Waveform.

Noninvasive pulse waveform analysis is valuable for central cardiovascular assessment, yet controversies persist over its validity in peripheral measurements. Our objective was to compare waveform features from a cuff system with suprasystolic blood pressure hold with an invasive aortic measurement. This study analyzed data from 88 subjects undergoing concurrent aortic catheterization and brachial pulse waveform acquisition using a suprasystolic blood pressure cuff system. Oscillometric blood pressure (BP) was compared with invasive aortic systolic BP and diastolic BP. Association between cuff and catheter waveform features was performed on a set of 15 parameters inclusive of magnitudes, time intervals, pressure-time integrals, and slopes of the pulsations. The evaluation covered both static (subject-averaged values) and dynamic (breathing-induced fluctuations) behaviors. Peripheral BP values from the cuff device were higher than catheter values (systolic BP-residual, 6.5 mm Hg; diastolic BP-residual, 12.4 mm Hg). Physiological correction for pressure amplification in the arterial system improved systolic BP prediction (r2=0.83). Dynamic calibration generated noninvasive BP fluctuations that reflect those invasively measured (systolic BP Pearson R=0.73, P<0.001; diastolic BP Pearson R=0.53, P<0.001). Static and dynamic analyses revealed a set of parameters with strong associations between catheter and cuff (Pearson R>0.5, P<0.001), encompassing magnitudes, timings, and pressure-time integrals but not slope-based parameters. This study demonstrated that the device and methods for peripheral waveform measurements presented here can be used for noninvasive estimation of central BP and a subset of aortic waveform features. These results serve as a benchmark for central cardiovascular assessment using suprasystolic BP cuff-based devices and contribute to preserving system dynamics in noninvasive measurements.

ANALYSIS OF THE INTEGRAL EFFICIENCY OF MEDICAL AID FOR NEWBORN IN UKRAINE

Introduction. Preservation of life and quality of health of newborns is an urgent problem for formation of the future of society and mankind, defense capability, labor reserve and intellectual potential of the country. The fundamental basis for the formation and maintenance of health of newborns is the existence of eff ective health care systems. Objective information about the eff ectiveness of medical care of newborns and its dynamics is the basis for evaluation of achievement of planned results and development of necessary medical organizational technologies. The aim of the study is to analyze and evaluate the integrated eff ectiveness of medical care for newborns in Ukraine at the national and regional levels.Material and Methods. In order to carry out the research, statistical reporting materials on the activities of health care institutions providing medical care to mothers and newborns in all periods of their development for 2018, 2022 were used. The analysis of the integral eff ectiveness of medical care for newborns was carried out using a modifi ed model of the fi nal results of health care institutions. I with the calculations of coeffi cients of comprehensive assessment (CCA) and coeffi cients of achievement of normative values (CANV) of individual indicators with their gradation from high (0,8-1) to low (0,1-0,49) levels. The methods of system approach, bibliographic, statistical data processing and graphical representation are applied.Results. A comprehensive study of the integrated eff ectiveness of medical care for newborns at the national and regional levels conducted at the population level showed a suffi cient, above- average level of achievement of results CCA 0.75 in 2018 and 0.76 in 2022) without clear geological patterns in 2018 and signifi cant diff erences in 2022. – From a high level of CCA (0.82-0.94) in Volyn, Zakarpattia, Ivano- Frankivsk, Lviv, Mykolaiv, Ternopil, Kharkiv and Khmelnytsky regions to a low level of CCA (0.41 and 0.35) in Donetsk and Kherson regions. The study of the main indicative indicators of the eff ectiveness of medical care for newborns in 2022 proved a suffi cient achievement. The normative values of coverage of newborns by screening programs, BCG vaccination, survival and mortality rates of newborns, neonatal transfer index (CANV =0.96, 0.82, 0.94, 0.82 and 0.76, respectively). The most problematic indicators of medical care for newborns with low and below- average levels of the coeffi cient of achievement of results, which are signifi cantly associated with the deterioration of the health of pregnant women and the course of childbirth, are the frequency of low weight, sick newborns (CANV = 0.48, 0.51) and the frequency of transmission of HIV infection from mother to child (CANV = 0.4), which is associated with the fullapplication of an eff ective 4-component strategy for prevention of HIV transmission from mother to child.Conclusions. A toolkit for integral assessment of the eff ectiveness of medical care for newborns as a measure of viability and health quality of the off spring at all stages of ontogenesis has been developed. makes it possible to ensure on a unifi ed basis objective assessment and monitoring of it as a whole, by region and by individual indicators with the subsequent development of measures to infl uence the identifi ed problems and their implementation in the activities of maternal and child health care. The model of integral eff ectiveness of medical care of newborns is a pilot project and has dynamic properties, therefore, in the future, when the normative values of indicators of comprehensive assessment are reached, their revision is planned.

Mean orientation discrimination based on proximal stimuli.

Ensemble perception refers to the ability to accurately and rapidly perceive summary statistical representations of specific features from a group of similar objects. However, the specific type of representation involved in this perception within a three-dimensional (3-D) environment remains unclear. In the context of perspective viewing with stereopsis, distal stimuli can be projected onto the retina as different forms of proximal stimuli based on their distances, despite sharing similar properties, such as object size and spatial frequency. This study aimed to investigate the effects of distal and proximal stimuli on the perception of summary statistical information related to orientation. In our experiment, we presented multiple Gabor patches in a stereoscopic environment, allowing us to measure the discrimination threshold of the mean orientation. The object size and spatial frequency were fixed for all patches regardless of depth. However, the physical angular size and absolute spatial frequency covaried with the depth. The results revealed the threshold elevation with depth expansion, especially when the patches formed two clusters at near and far distances, leading to large variations in their retinotopic representations. This finding indicates a minor contribution of similarity of the distal stimuli. Subsequent experiments demonstrated that the variability in physical angular size of the patches significantly influenced the threshold elevation in contrast to that of binocular disparity and absolute spatial frequency. These findings highlight the critical role of physical angular size variability in perceiving mean orientations within the 3-D space.

Investigating the Metastability-Triggered Reactivity of Pt7,8 Clusters on Graphene: Unraveling Statistical Ensemble Representation for ORR in Gas and Implicit Solvent Phases

Investigating the Metastability-Triggered Reactivity of Pt_7,8 Clusters on Graphene: Unraveling Statistical Ensemble Representation for ORR in Gas and Implicit Solvent Phases

Contrastive representation learning on dynamic networks

Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying excessive attention to network details, and disregarding distinctions between snapshots with larger time intervals, resulting in less robustness for sparse or noisy networks. To alleviate these challenges, this paper proposes a contrastive mechanism for temporal representation learning on dynamic networks, inspired by the success of contrastive learning in visual and static network representation learning. This paper proposes a novel Dynamic Network Contrastive representation Learning (DNCL) model. Specifically, contrast objective functions are constructed using intra-snapshot and inter-snapshot contrasts to capture the network topology, node feature information, and network evolution information, respectively. Rather than estimating or generating ground-truth network features, the proposed approach maximizes mutual information between nodes from different time steps and views generated. The experimental results of link prediction, node classification, and clustering on several real-world and synthetic networks demonstrate the superiority of DNCL over state-of-the-art methods, indicating the effectiveness of the proposed approach for dynamic network representation learning.

Suffix sorting via matching statistics

We introduce a new algorithm for constructing the generalized suffix array of a collection of highly similar strings. As a first step, we construct a compressed representation of the matching statistics of the collection with respect to a reference string. We then use this data structure to distribute suffixes into a partial order, and subsequently to speed up suffix comparisons to complete the generalized suffix array. Our experimental evidence with a prototype implementation (a tool we call sacamats) shows that on string collections with highly similar strings we can construct the suffix array in time competitive with or faster than the fastest available methods. Along the way, we describe a heuristic for fast computation of the matching statistics of two strings, which may be of independent interest.

Examining Exposure Fires from the United States National Fire Incident Reporting System between 2002 and 2020

Fires resulting from antecedent fires, known as exposure fires, can manifest across diverse environments, including suburban, urban, and rural areas. Notably, exposure fires represented by structure-destroying fires within the wildland–urban interface (WUI) can extend into non-WUI suburban and urban regions, presenting significant challenges. Leveraging data from the United States National Fire Incident Reporting System (NFIRS) spanning 2002 to 2020, this study investigates 131,739 exposure fire incidents impacting 348,089 features (incidents). We analyze reported economic costs, affected feature types, and property utilization patterns for these exposure fires. We also compare these exposure fires to information documented in other databases. Finally, we examine structure separation distance at residential dwellings and describe ignition pathways for selected fires. Reported property losses for some fire incidents amounted to USD 5,647,121,172, with content losses totaling USD 1,777,345,793. Prominent fire incident categories include buildings, vehicles, and natural vegetation fires, predominantly occurring in residential, outdoor, and storage areas. While the NFIRS lacked information on most major structure-destroying WUI fires, highlighting this analysis’s lack of statistical representation, it did provide insights into less extensive exposure fires, both WUI and non-WUI, unrecorded elsewhere. Our study reveals significant distinctions in the distribution of separation distances between damaged-to-damaged structures (average separation of 6.5 m) and damaged-to-not-damaged structures (average separation of 18.1 m). Notably, 84% of the incidents in exposure fires involved fire suppression defensive actions. These defensive actions contributed to the differences in structure separation distance distributions, highlighting the often-neglected role of these measures in assessing structure responses during WUI fires. We examined ignition pathways at select exposure fires, highlighting some common features involved in fire spread and challenges in documenting these pathways. Finally, we propose a set of idealized attributes for documenting exposure fires, accentuating the inherent difficulties in collecting such data across expansive geographical areas, particularly when striving for statistical representation. Our findings yield valuable insights into the multifaceted nature of exposure fires, informing future research and database development to aid in mitigating their impact on vulnerable communities.

Experimental study of bonded, bolted, and hybrid bonded-bolted single lap shear joints with woven CFRP adherends

This paper presents an extensive experimental performance study of bonded joints (BJs), only bolted joints (OBJs) and hybrid bonded-bolted joints (HJs). For each joint type, three test configurations are considered: namely, short, medium and long overlap lengths. In each case, the adherends comprise quasi-isotropic twill woven CFRP. For each joint type/overlap length combination, three specimens are tested for statistical representation. HJs demonstrate 1.4, 1.5 and 1.5 times higher failure load and 1.5, 1.6 and 1.9 times higher stiffness than OBJs, for short, medium and long overlap, respectively. In all test cases, HJs outperform BJs except for short overlaps where BJs outperform both HJs and OBJs. OBJs perform poorly in terms of failure load and Hooke’s stiffness. Nevertheless, due to bearing deformation at bolt hole locations, OBJs experience higher failure displacements than BJs and HJs leading to a desirable energy absorption mechanism compared to HJs and BJs. This is thanks to the bearing failure mode of the joint despite much lower failure load in OBJs. It was found that increasing the overlap length generally benefits BJs. However, for medium length overlap specifically, HJs show better performance than BJs. Stress–strain behaviours show a linear behaviour for all test groups with significant joint rotation for OBJs compared to BJs and HJs. Failure mechanism studies presented in the paper show that BJs fail in cohesive failure mode for all test groups. OBJs fail in bearing mode, which is followed by net tension failure. OBJs experience matrix cracking and delamination at bolt hole locations. On the contrary, HJs experience considerably less bearing failure at bolt holes due to the load bearing contribution from the adhesive.

Detecting periodic time scales of changes in temporal networks

Abstract Temporal networks are commonly used to represent dynamical complex systems like social networks, simultaneous firing of neurons, human mobility or public transportation. Their dynamics may evolve on multiple time scales characterizing for instance periodic activity patterns or structural changes. The detection of these time scales can be challenging from the direct observation of simple dynamical network properties like the activity of nodes or the density of links. Here, we propose two new methods, which rely on already established static representations of temporal networks, namely supra-adjacency and temporal event graphs. We define dissimilarity metrics extracted from these representations and compute their power spectra from their Fourier transforms to effectively identify dominant periodic time scales characterizing the changes of the temporal network. We demonstrate our methods using synthetic and real-world data sets describing various kinds of temporal networks. We find that while in all cases the two methods outperform the reference measures, the supra-adjacency-based method identifies more easily periodic changes in network density, while the temporal event graph-based method is better suited to detect periodic changes in the group structure of the network. Our methodology may provide insights into different phenomena occurring at multiple time scales in systems represented by temporal networks.

Sonovisual: Transforming Image Description into Dynamic Narratives

Sonovisual is an innovative framework aimed at bridging the gap between static image descriptions and immersive, dynamic narratives. Traditional image descriptions are often limited to static textual representations, lacking the ability to fully convey the visual essence and emotional depth embedded within an image. This project introduces a novel approach that combines cutting-edge image recognition technologies with advanced natural language processing techniques, facilitating the seamless transformation of image features into engaging and vivid narratives. The core of the Sonovisual framework lies in its integration of sophisticated deep learning algorithms, enabling the extraction of intricate visual details and contextual information from images. Leveraging state-of-the-art machine learning models, the system can recognize objects, scenes, and intricate visual patterns, and subsequently generate descriptive narratives that not only encapsulate the visual content but also evoke a multisensory experience for the audience. Through the integration of adaptive storytelling techniques, Sonovisual dynamically adapts its narrative style and tone to cater to different audiences and contexts. By incorporating sentiment analysis and contextual understanding, the framework ensures that the generated narratives resonate with the emotional nuances and cultural sensitivities associated with the depicted imagery. The Sonovisual project holds promise for various applications, including enriching accessibility features for the visually impaired, enhancing educational materials with immersive visual narratives, and augmenting multimedia content with engaging storytelling elements. Additionally, its potential extends to fostering new avenues for creative expression and facilitating enhanced human-computer interaction through visually enriched storytelling experiences.

MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps.

Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.

How Can We Improve the Seamless Representation of Climatological Statistics and Weather Toward Reliable Global K‐Scale Climate Simulations?

AbstractToward the achievement of reliable global kilometer‐scale (k‐scale) climate simulations, we improve the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) by focusing on moist physical processes. A goal of the model improvement is to establish a configuration that can simulate realistic fields seamlessly from the daily‐scale variability to the climatological statistics. Referring to the two representative configurations of the present NICAM, each of which has been used for climate‐scale and sub‐seasonal‐scale experiments, we try to find the appropriate partitioning of fast/local and slow/global‐scale circulations. In a series of sensitivity experiments at 14‐km horizontal resolution, we test (a) the tuning of terminal velocities of rain, snow, and cloud ice, (b) the implementation of turbulent diffusion by the Leonard term, and (c) enhanced vertical resolution. These tests yield reasonable convection triggering and convection‐induced tropospheric moistening, and result in better performance than in previous NICAM climate simulations. In the mean state, double Intertropical Convergence Zone bias disappears, and the zonal contrast of equatorial precipitation, top‐of‐atmosphere radiation balance, vertical temperature profile, and position/strength of subtropical jet are reproduced dramatically better. Variability such as equatorial waves and the Madden–Julian oscillation (MJO) is spontaneously realized with appropriate spectral power balance, and the Asian summer monsoon, boreal‐summer MJO, and tropical cyclone (TC) activities are more realistically simulated especially around the western Pacific. Meanwhile, biases still exist in the representation of low‐cloud fraction, TC intensity, and precipitation diurnal cycle, suggesting that both higher spatial resolutions and further model development are warranted.

A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study.

Adverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual information on the occurrences. Accurate classification of PSE reports is crucial for patient safety monitoring. However, this process faces challenges due to inconsistencies in classifications and the sheer volume of reports. Recent advancements in text representation, particularly contextual text representation derived from transformer-based language models, offer a promising solution for more precise PSE report classification. Integrating the machine learning (ML) classifier necessitates a balance between human expertise and artificial intelligence (AI). Central to this integration is the concept of explainability, which is crucial for building trust and ensuring effective human-AI collaboration. This study aims to investigate the efficacy of ML classifiers trained using contextual text representation in automatically classifying PSE reports. Furthermore, the study presents an interface that integrates the ML classifier with the explainability technique to facilitate human-AI collaboration for PSE report classification. This study used a data set of 861 PSE reports from a large academic hospital's maternity units in the Southeastern United States. Various ML classifiers were trained with both static and contextual text representations of PSE reports. The trained ML classifiers were evaluated with multiclass classification metrics and the confusion matrix. The local interpretable model-agnostic explanations (LIME) technique was used to provide the rationale for the ML classifier's predictions. An interface that integrates the ML classifier with the LIME technique was designed for incident reporting systems. The top-performing classifier using contextual representation was able to obtain an accuracy of 75.4% (95/126) compared to an accuracy of 66.7% (84/126) by the top-performing classifier trained using static text representation. A PSE reporting interface has been designed to facilitate human-AI collaboration in PSE report classification. In this design, the ML classifier recommends the top 2 most probable event types, along with the explanations for the prediction, enabling PSE reporters and patient safety analysts to choose the most suitable one. The LIME technique showed that the classifier occasionally relies on arbitrary words for classification, emphasizing the necessity of human oversight. This study demonstrates that training ML classifiers with contextual text representations can significantly enhance the accuracy of PSE report classification. The interface designed in this study lays the foundation for human-AI collaboration in the classification of PSE reports. The insights gained from this research enhance the decision-making process in PSE report classification, enabling hospitals to more efficiently identify potential risks and hazards and enabling patient safety analysts to take timely actions to prevent patient harm.

Sensitivity of the polar boundary layer to transient phenomena

Abstract. Numerical weather prediction and climate models encounter challenges in accurately representing flow regimes in the stably stratified atmospheric boundary layer and the transitions between them, leading to an inadequate depiction of regime occupation statistics. As a consequence, existing models exhibit significant biases in near-surface temperatures at high latitudes. To explore inherent uncertainties in modeling regime transitions, the response of the near-surface temperature inversion to transient small-scale phenomena is analyzed based on a stochastic modeling approach. A sensitivity analysis is conducted by augmenting a conceptual model for near-surface temperature inversions with randomizations that account for different types of model uncertainty. The stochastic conceptual model serves as a tool to systematically investigate which types of unsteady flow features may trigger abrupt transitions in the mean boundary layer state. The findings show that the incorporation of enhanced mixing, a common practice in numerical weather prediction models, blurs the two regime characteristic of the stably stratified atmospheric boundary layer. Simulating intermittent turbulence is shown to provide a potential workaround for this issue. Including key uncertainty in models could lead to a better statistical representation of the regimes in long-term climate simulation. This would help to improve our understanding and the forecasting of climate change in high-latitude regions.

Research on Multimodal Fusion of Temporal Electronic Medical Records.

The surge in deep learning-driven EMR research has centered on harnessing diverse data forms. Yet, the amalgamation of diverse modalities within time series data remains an underexplored realm. This study probes a multimodal fusion approach, merging temporal and non-temporal clinical notes along with tabular data. We leveraged data from 1271 myocardial infarction and 6450 stroke inpatients at a Beijing tertiary hospital. Our dataset encompassed static, and time series note data, coupled with static and time series table data. The temporal data underwent a preprocessing phase, padding to a 30-day interval, and segmenting into 3-day sub-sequences. These were fed into a long short-term memory (LSTM) network for sub-sequence representation. Multimodal attention gates were implemented for both static and temporal subsequence representations, culminating in fused representations. An attention-backtracking module was introduced for the latter, adept at capturing enduring dependencies in temporal fused representations. The concatenated results were channeled into an LSTM to yield the ultimate fused representation. Initially, two note modalities were designated as primary modes, and subsequently, the proposed fusion model was compared with comparative models including recent models such as Crossformer. The proposed model consistently exhibited superior predictive prowess in both tasks. Removing the attention-backtracking module led to performance decline. The proposed model consistently shows excellent predictive capabilities in both tasks. The proposed method not only effectively integrates data from the four modalities, but also has a good understanding of how to handle irregular time series data and lengthy clinical texts. An effective method is provided, which is expected to be more widely used in multimodal medical data representation.

The role of finger kinematics in the acquisition of number meaning in kindergarten: a pilot study

Several studies have shown that the acquisition of number meaning is related to finger motor representations. However, no study has tried to distinguish the respective roles of fingers representation and finger kinematics in this effect. Forty-three children (23 boys and 20 girls) enrolled in the middle class of kindergarten took part in the study. They were divided into two equivalent groups and compared on the acquisition of number meaning after a 2-week program designed to teach them a song about the numbers 1–9, scaffolded by visual supports. Each visual support included the symbolic representation of the number, the picture of an object mentioned in the song that rhymed with the number in French, and either a static image (static group) or an animated point-light display (animated group) of the finger representation. Results showed no difference between the two groups on the acquisition of numbers regarded as low (i.e., less than 5) but the animated group performed significantly better for numbers regarded as high (i.e., more than 5). Therefore, the present study shows the interest of using the observation of animated counting movement to teach number meaning in young children and suggests that finger kinematics may be more instrumental than static finger representation in the learning of number meaning. Future research is needed to confirm and specify this effect.

I sondaggi, i metodi e le analisi del progetto GeoDocuM. Alla ricerca delle tendenze locali e sovralocali del latino documentale dell’Italia meridionale

Abstract The GeoDocuM project deals with the lexical variation that characterizes the ancient legal documentation written in Latin of continental southern Italy (with particular reference to the area between Campania and central-northern Puglia in a period ranging from the 8th to the 11th century), by testing a geospatial and statistical representation of the textual data. The paper focuses on the methodological issues addressed to enhance the local dimension, which is central in the setting up of the project.

Demographic Profile of Patients with Cleft Lip and Palate Anomaly: 15-year Experience from a Tertiary Care Hospital and Teaching Institute in Wardha District of Maharashtra, India.

Cleft lip and palate (CLP) is considered to be a congenital defect involving the orofacial region. This defect affects the esthetics, speech as well and psychological well-being of a person. The study was performed to analyze the prevalence of different types of cleft deformity reported in a tertiary care hospital which would aid in spreading awareness and thereby reducing the prevalence of this congenital defect. To study the demographic profile of patients with CLP in the Wardha region. A single-center retrospective cross-sectional study was performed. The patients with cleft deformity report under the "Smile Train Project" under the Department of Orthodontics and Dentofacial Orthopedics along with the Department of Oral and Maxillofacial Surgery. The data retrieved included the name of the patient, age, gender, residential address, contact details, and type of cleft. This data was segregated focusing on the gender and type of cleft as per the concern of this study. The datasets were then entered in the Microsoft Excel sheet and the statistical graphical representation was done using Microsoft PowerPoint. In the present study we tried to find out the profile of CLP in the local population. We reported the high prevalence of this orofacial deformity among the population of this area. The data from the present study will help to provide a deeper insight into the burden of CLP anomaly. Based on the data obtained from the present study, future research can be conducted, and cleft-care improvement outcomes can be measured. There are several etiological factors that are responsible for the development of CLP. Also, due to a lack of awareness regarding this, there is an increased prevalence of this defect. In India, due to a lack of knowledge regarding cleft anomaly, poverty, and social stigma, many patients do not report to the hospitals, and hence, there is a need to spread awareness which would encourage people to access the healthcare facilities at an early age and would reduce the complications in later stages. Shivlani VI, Niranjane PP, Diagavane PS, et al. Demographic Profile of Patients with Cleft Lip and Palate Anomaly: 15-year Experience from a Tertiary Care Hospital and Teaching Institute in Wardha District of Maharashtra, India. Int J Clin Pediatr Dent 2023;16(S-3):S278-S282.