Sufficient Information Research Articles

Development of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide

People often use infographics (also called visual or graphical abstracts) as a substitute for reading the full text of an article. This is a concern because most infographics do not...

Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering.

In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP.

Attitude and Practices of Paediatric Dentists towards Parental Guidance on Dental Trauma. A cross-sectional survey.

There is a lack of sufficient information about the protocols followed by paediatric dentists in informing parents about traumatic dental injuries as a part of anticipatory guidance. Hence, the aim of this study was to assess paediatric dentists' attitudes and practices about parental guidance regarding these injuries. This was a cross-sectional survey conducted using a validated questionnaire emailed through Google form to approximately 2500 paediatric dentists in various world regions. The sampling method used was a list-based sampling frame followed by simple random sampling. Participants were recruited through national member societies of the International Association of Paediatric Dentistry, personal contacts and social media groups. Only paediatric dentists with at least three years of experience after their post-graduation were only included in the study. Their attitudes and practices towards parental education on dental trauma during the child's first and recalled dental visits were assessed as per their age, gender, country of post-graduation qualification and years of experience in the profession. Chi-Square test was used to evaluate the association between the paediatric dentist response and the continent of practice. Kruskal-Wallis H test was used to assess the level of significance within each variable in relation to the continent of practice. A 95% confidence interval with a significance level of α = 0.05 was used. The overall attitude and practice of paediatric dentists toward parental education on traumatic dental injuries were not satisfactory. Many paediatric dentists do not impart education on emergency care and dental trauma prevention in primary teeth. Parents should be informed about oral hygiene instructions and prevention-oriented interventions during the first visit and about managing traumatic dental injuries.

Shared and unique features of epistemic emotions: Awe, surprise, curiosity, interest, confusion, and boredom.

Epistemic emotions are hardly ever studied together, making it difficult to predict what features are shared versus unique to each emotion. To address this, we conducted two autobiographical recall experiments. We compared awe, surprise, curiosity, interest, confusion, and boredom in terms of elicitors, subjective experience components, and action tendencies. Ratings were analyzed using network analyses, to describe the central features for the whole group of epistemic emotions. In addition, ratings were compared per emotion, to identify key features for each individual emotion. Results showed that valence, arousal, coping potential, and avoidance are central features of all epistemic emotions. Awe, surprise, and interest were relatively positive emotions, which together with curiosity, were associated with arousal, high coping potential, and approach. Confusion and boredom were relatively negative emotions, which were associated with low arousal, low coping potential, and avoidance. Further analyses revealed unique features of (groups of) emotions. For example, awe was associated with exceeded expectancies, while surprise was associated with both exceeded and disconfirmed expectancies. Moreover, curiosity and confusion were associated with having (too) little information, while awe and interest were associated with having sufficient information. All emotions except boredom were associated with exploration, but this was particularly high for curiosity and interest. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Semantic-enabled architecture for auditable privacy-preserving data analysis

Small and medium-sized organisations face challenges in acquiring, storing and analysing personal data, particularly sensitive data (e.g., data of medical nature), due to data protection regulations, such as the GDPR in the EU, which stipulates high standards in data protection. Consequently, these organisations often refrain from collecting data centrally, which means losing the potential of data analytics and learning from aggregated user data. To enable organisations to leverage the full-potential of the collected personal data, two main technical challenges need to be addressed: (i) organisations must preserve the privacy of individual users and honour their consent, while (ii) being able to provide data and algorithmic governance, e.g., in the form of audit trails, to increase trust in the result and support reproducibility of the data analysis tasks performed on the collected data. Such an auditable, privacy-preserving data analysis is currently challenging to achieve, as existing methods and tools only offer partial solutions to this problem, e.g., data representation of audit trails and user consent, automatic checking of usage policies or data anonymisation. To the best of our knowledge, there exists no approach providing an integrated architecture for auditable, privacy-preserving data analysis. To address these gaps, as the main contribution of this paper, we propose the WellFort approach, a semantic-enabled architecture for auditable, privacy-preserving data analysis which provides secure storage for users’ sensitive data with explicit consent, and delivers a trusted, auditable analysis environment for executing data analytic processes in a privacy-preserving manner. Additional contributions include the adaptation of Semantic Web technologies as an integral part of the WellFort architecture, and the demonstration of the approach through a feasibility study with a prototype supporting use cases from the medical domain. Our evaluation shows that WellFort enables privacy preserving analysis of data, and collects sufficient information in an automated way to support its auditability at the same time.

Integrated Parameter Identification Based on a Topological Structure for Servo Resonance Suppression

In the dual-inertia servo system, the servo resonance in which the characteristics are closely related to the mechanical parameters is usually suppressed by adding a notch filter. As a result, accurate mechanical parameter identification facilitates adjusting the notch parameters and avoiding the failed servo resonance suppression caused by the mechanical parameter mismatch. In this paper, a topological structure expanded by five improved Sliding-Mode Observers (ISMOs) is proposed to simultaneously identify the load speed, the motor inertia, the load inertia, the stiffness coefficient, and the load disturbance, where the load speed and the load disturbance are considered as intermediate variables to provide sufficient load-side information to ensure the identification accuracy of the mechanical parameters. Furthermore, according to the natural knowledge of the dual-inertia servo system, a time-varying interconnected gain with an equal derivative to the identification parameter in each ISMO is designed to promote the global convergence of the topological structure. Finally, the identified mechanical parameters are utilized to adjust the notch parameters for the servo resonance suppression. Simulation and experimental results are presented to indicate the superiority of the topological structure and the effectiveness of the servo resonance suppression.

Beyond green borders: an innovative model for sustainable transportation in supply chains

Modern requirements necessitate the establishment of sustainable transportation systems, considering the substantial growth in transportation activities over recent years, which is expected to continue. Companies are facing the challenge of modeling their system transport to align with green principles. Sustainable transport relied on involving diverse stakeholders, particularly scientific research, in the development of this field. In light of this, maintaining sustainable transport quality involves conducting thorough investigations into an innovative study focusing on an uncertain interval programming model for a multi-stage, multi-objective, multi-product transportation challenge within budget constraints and safety measures in a green supply chain. Human languages often contain imperfect or unknown information, inherently lacking certainty; achieving precision in describing existing states or future outcomes is frequently unattainable. In probability theory, sufficient historical information is crucial for estimating probability distributions; while in fuzzy theory, determining a reliable membership function proves challenging; hence, there is often a hesitant estimation of the degree of belief in the occurrence of each condition. Addressing such uncertainties, the theory of uncertain intervals proves highly valuable. Given these considerations, the elements of the specified problem are recognized as uncertain intervals. To manage this lack of assurance, a fusion of interval theory and methods from uncertain programming is used to formulate two distinct models: an expected value model and a chance-constrained model. The equivalent deterministic models are then formulated and solved utilizing Weighted Sum Method, fuzzy programming, and goal programming. Following this, a numerical example is utilized to assess the model’s performance, and the results obtained are compared. Finally, the document concludes with a sensitivity analysis and outlines future directions.

Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review

The problem arises from the lack of sufficient and comprehensive information about the necessary computer techniques. These techniques are crucial for developing information systems that assist doctors in diagnosing breast cancer, especially those related to positron emission tomography and computed tomography (PET/CT). Despite global efforts in breast cancer prevention and control, the scarcity of literature poses an obstacle to a complete understanding in this area of interest. The methodologies studied were systematic mapping and systematic literature review. For each article, the journal, conference, year of publication, dataset, breast cancer characteristics, PET/CT processing techniques, metrics and diagnostic yield results were identified. Sixty-four articles were analyzed, 44 (68.75%) belong to journals and 20 (31.25%) belong to the conference category. A total of 102 techniques were identified, which were distributed in preprocessing with 7 (6.86%), segmentation with 15 (14.71%), feature extraction with 15 (14.71%), and classification with 65 (63.73%). The techniques with the highest incidence identified in each stage are: Gaussian Filter, SLIC, Local Binary Pattern, and Support Vector Machine with 4, 2, 7, and 35 occurrences, respectively. Support Vector Machine is the predominant technique in the classification stage, due to the fact that Artificial Intelligence is emerging in medical image processing and health care to make expert systems increasingly intelligent and obtain favorable results.

English teachers’ beliefs and practices in integrating digital literacy in the language classroom

This case study investigates teachers’ beliefs (i.e., behavioral, normative, and control beliefs) and practices in integrating digital literacy in English as foreign language (EFL) classroom. A descriptive study on EFL teachers is used in this research. Four participants were purposively selected to give sufficient information to answer the research questions. Data from the participants were collected by using interviews, observation, and documentation. In terms of the teachers’ beliefs, the finding shows that the behavioral belief was related to the benefits of using digital literacy; normative belief dealt with the expectation in the social context of both superiors and peers; while control belief was about the availability of particular digital technology to be implemented in the classroom and the ability of teachers. With regard to the teachers’ practices, it was found that the teachers used several digital tools for searching for information, communication, collaboration, creativity, critical thinking, cultural understanding, and e-safety. It was evident that teachers’ strong beliefs in integrating digital literacy influence the implementation of digital literacy in the classroom. The findings of this study highlight the importance of cultivating teachers’ beliefs toward the actual implementation of the integration of digital literacy in the EFL classroom.

758 An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery

INTRODUCTION: Overwhelming evidence indicates that the quality of reporting of predictive models is poor, often being confounded by small datasets, inappropriate statistical methods, a lack of validation, and reproducibility. METHODS: Articles reporting prediction models published in the top five neurosurgery journals by SJR2-rank (Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of NeuroInterventional Surgery, and Journal of Neurology, Neurosurgery, and Psychiatry) between January 1st, 2018, and January 1st, 2023, were identified via a PubMed search strategy that combined terms related to machine learning and prediction models. The identified original research articles were analyzed against the TRIPOD criteria. RESULTS: 110 articles were included and analyzed against the TRIPOD checklist. Among the articles screened, the median compliance was 57.4% (50.0-66.7%). Models utilizing machine learning-based models exhibited lower compliance on average compared to conventional learning-based models (57.1%, 50.0-66.7% vs. 68.1%, 50.2-68.1%, p = 0.472). Among the TRIPOD criteria, the lowest compliance was observed in blinding the assessment of predictors and outcomes (n = 7, 12.7% and n = 10, 16.9%, respectively), including an informative title (n = 17, 15.6%) and reporting model performance measures including confidence intervals (n = 27, 24.8%). Few studies provided sufficient information to allow for the external validation of results (n = 26, 25.7%). CONCLUSIONS: Published machine learning models predicting outcomes in neurosurgery often fall short of meeting the established guidelines laid out by TRIPOD for their development, validation, and reporting. This lack of compliance may indicate the extent to which these models have been subjected to external validation or adopted into routine clinical practice in neurosurgery.

The potential and limitations of large language models in identification of the states of motivations for facilitating health behavior change.

The study highlights the potential and limitations of the Large Language Models (LLMs) in recognizing different states of motivation to provide appropriate information for behavior change. Following the Transtheoretical Model (TTM), we identified the major gap of LLMs in responding to certain states of motivation through validated scenario studies, suggesting future directions of LLMs research for health promotion. The LLMs-based generative conversational agents (GAs) have shown success in identifying user intents semantically. Little is known about its capabilities to identify motivation states and provide appropriate information to facilitate behavior change progression. We evaluated 3 GAs, ChatGPT, Google Bard, and Llama 2 in identifying motivation states following the TTM stages of change. GAs were evaluated using 25 validated scenarios with 5 health topics across 5 TTM stages. The relevance and completeness of the responses to cover the TTM processes to proceed to the next stage of change were assessed. 3 GAs identified the motivation states in the preparation stage providing sufficient information to proceed to the action stage. The responses to the motivation states in the action and maintenance stages were good enough covering partial processes for individuals to initiate and maintain their changes in behavior. However, the GAs were not able to identify users' motivation states in the precontemplation and contemplation stages providing irrelevant information, covering about 20%-30% of the processes. GAs are able to identify users' motivation states and provide relevant information when individuals have established goals and commitments to take and maintain an action. However, individuals who are hesitant or ambivalent about behavior change are unlikely to receive sufficient and relevant guidance to proceed to the next stage of change. The current GAs effectively identify motivation states of individuals with established goals but may lack support for those ambivalent towards behavior change.

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994.

GMS-3DQA: Projection-Based Grid Mini-patch Sampling for 3D Model Quality Assessment

Nowadays, most three-dimensional model quality assessment (3DQA) methods have been aimed at improving accuracy. However, little attention has been paid to the computational cost and inference time required for practical applications. Model-based 3DQA methods extract features directly from the 3D models, which are characterized by their high degree of complexity. As a result, many researchers are inclined towards utilizing projection-based 3DQA methods. Nevertheless, previous projection-based 3DQA methods directly extract features from multi-projections to ensure quality prediction accuracy, which calls for more resource consumption and inevitably leads to inefficiency. Thus, in this article, we address this challenge by proposing a no-reference (NR) projection-based G rid M ini-patch S ampling 3D Model Q uality A ssessment (GMS-3DQA) method. The projection images are rendered from six perpendicular viewpoints of the 3D model to cover sufficient quality information. To reduce redundancy and inference resources, we propose a multi-projection grid mini-patch sampling strategy (MP-GMS), which samples grid mini-patches from the multi-projections and forms the sampled grid mini-patches into one quality mini-patch map (QMM). The Swin-Transformer tiny backbone is then used to extract quality-aware features from the QMMs. The experimental results show that the proposed GMS-3DQA outperforms existing state-of-the-art NR-3DQA methods on the point cloud quality assessment databases for both accuracy and efficiency. The efficiency analysis reveals that the proposed GMS-3DQA requires far less computational resources and inference time than other 3DQA competitors. The code is available at https://github.com/zzc-1998/GMS-3DQA .

Learning to Order for Inventory Systems with Lost Sales and Uncertain Supplies

We consider a stochastic lost-sales inventory control system with lead time L over a planning horizon T. Supply is uncertain, and it is a function of the order quantity (because of random yield/capacity, etc.). We aim to minimize the T-period cost, a problem that is known to be computationally intractable even under known distributions of demand and supply. In this paper, we assume that both the demand and supply distributions are unknown and develop a computationally efficient online learning algorithm. We show that our algorithm achieves a regret (i.e., the performance gap between the cost of our algorithm and that of an optimal policy over T periods) of [Formula: see text] when [Formula: see text]. We do so by (1) showing that our algorithm’s cost is higher by at most [Formula: see text] for any [Formula: see text] compared with an optimal constant-order policy under complete information (a widely used algorithm) and (2) leveraging the latter’s known performance guarantee from the existing literature. To the best of our knowledge, a finite sample [Formula: see text] (and polynomial in L) regret bound when benchmarked against an optimal policy is not known before in the online inventory control literature. A key challenge in this learning problem is that both demand and supply data can be censored; hence, only truncated values are observable. We circumvent this challenge by showing that the data generated under an order quantity q2 allow us to simulate the performance of not only q2 but also, q1 for all [Formula: see text], a key observation to obtain sufficient information even under data censoring. By establishing a high-probability coupling argument, we are able to evaluate and compare the performance of different order policies at their steady state within a finite time horizon. Because the problem lacks convexity, commonly used learning algorithms, such as stochastic gradient decent and bisection, cannot be applied, and instead, we develop an active elimination method that adaptively rules out suboptimal solutions. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Funding: This work is supported by the National Science Foundation [Grant CCF-2312205]. Z. Zhou also acknowledges the New York University’s 2024 Center for Global Economy and Business [Faculty Research Grant] and New York University [Research Catalyst Prize]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.02476 .

Expectation versus reality: How crisis statement essentials are evaluated by Gen Z stakeholders and crisis communication practitioners

AbstractAt the onset of a crisis, an initial organizational statement sets the tone for subsequent discussion. While existing literature provides strategic guidance, it offers limited insight into the message‐level execution of such statements. This study synthesizes insights from literature and trade publications to complement strategic literature by proposing a practical set of ‘crisis essentials’ which practitioners can leverage in composing an initial crisis statement. Upon identification of these elements from scholarly and industry literature, two online surveys presenting a hypothetical crisis scenario are released to Gen Z stakeholders and crisis communication practitioners in the United States; respondents rate the relative importance of each element. Gen Z was selected as the stakeholder population of interest due to their high activity on social media, their role as the talent which will be entering the workforce and eventually leadership roles, and the more limited existing analysis of this segment. Comparative analysis is conducted, based on two online surveys among adult individuals from these two populations, to evaluate whether gaps exist in stakeholder expectations and practitioner approaches. Findings reveal: the proposed elements (e.g., statement of the crisis, attribution, expert quote; 11 in total) were of nonzero importance to both Gen Z stakeholder respondents and crisis communication practitioner respondents, essentials related to the assuring stakeholders (e.g., action steps) were rated more highly than informational essentials (e.g., timeline), and importance ratings across survey samples were generally consistent. Practitioners should consider each of these proposed essentials when developing communication materials, and that while stakeholders expect sufficient information to understand a crisis event, they ultimately care about what the crisis means for the future of the organization. This study, grounded in crisis communication theory, provides a relevant, practical contribution to the implementation of effective strategic crisis communication in situations concerning Gen Z stakeholders.

Effects of using audience response systems (Kahoot and Google Forms and Sheets) on learning of medical students in a large class: A questionnaire-based retrospective study

AbstractThe effectiveness of implementing various audience response system (ARS) formats in large classes to engage students, positively influence academic outcomes, and align with their preferences for different types of activities lacked sufficient information. This study aimed to (1) evaluate students’ perspectives on different ARS formats, including Kahoot for individual (Kahoot-I) and team (Kahoot-T) play, and Google Forms and Google Sheets for multiple-choice questions (Google-MCQ) and for open-ended questions (Google-OEQ) on different aspects with/without subgroup analysis of students into quartiles (Q1-lowest-Q4-highest) of summative scores, and (2) determine correlations between students’ perspectives and academic outcomes. At the course’s final class, a QR code for a 5-point Likert scale questionnaire was displayed for all enrolled medical students. 269/312 students (86.22%) provided written consent and completed the questionnaire constructed based on dimensions of student engagement. Across all ARS formats, students rated scores ≥ 4 for almost all aspects, with participation in class receiving the highest scores. For each aspect, Kahoot-T was rated highest in various aspects and Google-MCQ for “understanding” and Google-OEQ for “asking/answering question”. Higher academic achievers expressed more “enthusiasm” for Kahoot-I than lower achievers (P < 0.05). Formative and summative scores were positively correlated with many aspects. Students reported high engagement with all ARS formats, with higher academic achievers rating them with higher scores. Kahoot-T was the most favored for multiple aspects, Google-MCQ for “understanding” and Google-OEQ for interactive “asking/answering question”. Thus, selecting ARS in alignment with learning objective could enhance students’ engagement and learning outcomes.

Risk Groups by the Needs for Help: Asian American Breast Cancer Survivors.

To supply proper and sufficient information and coaching through cancer education, it is important to decide who are risk groups among a target population. A decision tree analysis could help decide the characteristics of the risk groups. This study aimed to identify the combined characteristics of Asian American breast cancer survivors that were closely linked to high needs for help during their breast cancer survivorship process. The data on the needs for help among 185 Asian American breast cancer survivors from a parent clinical trial were included in this analysis. The instruments included the Supportive Care Needs Survey-Short Form 34 (SCNS-SF34) and multiple scales to measure the factors influencing the women's needs for help. The data were analyzed using latent profile analyses and decision tree analyses. The characteristics of the group with the highest needs for help were different depending on the types of needs. For instance, the group with the highest patient care/support needs for help score had high global symptom distress scores (cut point = 2.25) and high physical symptom distress (cut point = 1.57) and did not have regular access to health care (mean = 95.00; node 10). The findings suggest several risk groups to target in future interventions for cancer education to reduce the needs for help among this specific population. Multiple factors that could influence the needs for help among Asian American breast cancer survivors need to be considered in future intervention development for cancer education.

Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information.

Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints. Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable. Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance. This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.

Drivers’ Steering Behavior in Curve by Means of New Indicators

The design of road geometry is based on a rather elementary assumption that the user strictly follows the lane axis. Based on this hypothesis, the ideal trend of some factors related to the driver’s performance, such as steering angle and speed, can be derived to optimize the most appropriate design choices. In practice, driving behavior differs from the assumed one and produces trends in these variables, which are very different from the ideal functions. The purpose of this research is therefore to propose synthetic performance indicators useful for highlighting the real characteristics of users’ driving behavior during road travel. Toward this aim, some driving experiments along four different curves in a simulated environment were studied in order to evidence possible criticisms. The proposed indicators showed a remarkable ability to represent and synthesize even very complex performance function trends. The proposed performance indicators can have multiple uses, such as, for example, in statistical analyses—which are generally carried out at a later stage—or constitute sufficient information to guide the decisions of infrastructure managers. In the long term, in a “smart road” perspective, they can be used by road administrators for information exchange among users (with each other and with the infrastructure) to improve road operation and safety.

Validation of the Russian version of the Social Media Disorder Scale (SMDS) questionnaire in adolescents

Introduction. An urgent medical and social problem at present is the need to develop nationally adapted versions of the questionnaire for assessing dependence on social networks, which is due to the avalanche-like increase in the prevalence of this phenomenon, especially among adolescents and youth, and often associated with disorders of the neuropsychic and somatic spectrum.Aim. Validation of the English-language Social Media Disorder scale (SMDS) questionnaire based on 9 criteria, including psychopathological aspects of engagement with social networking sites.Materials and methods. After the procedures of direct and reverse translation of the questionnaire, the psychometric validation of the test was carried out on a Russian-speaking sample of 3074 adolescents aged 11–19 years (46.1% of boys and 53.9% of girls, median age 14 (13–16) years) – students of 10 educational institutions in Krasnoyarsk. The external validity of the SMDS- RU questionnaire was assessed using the following methods: Chen Internet Addiction Scale (CIAS) – to assess the presence of Internet addicted behavior; the Strengths and Challenges Questionnaire (SDQ) – to analyze the mental health of the subjects.Results. Expiratory and confirmatory factor analyzes demonstrated good agreement between the test components. The results of confirmatory factor analysis confirmed its single-factor structure (CFI = 0.9, TLI = 0.9, RMSEA = 0.06), an acceptable Cronbach’s Alpha value (Cronbach’s Alpha = 0.7) indicates its sufficient internal consistency and reliability. The external validity of the SMDS-RU questionnaire was confirmed by established associations with the results of the Chen Internet Addiction Test (CIAS) and the Strengths and Challenges Questionnaire (SDQ) by R. Goodman. Test-retest reliability when measured at 6-month intervals also demonstrated acceptable results: the Spearman correlation coefficient between the sums of scores of two measurements was 0.66, p < 0.001.Conclusion. The Russian-language version of the Social Network Addiction Questionnaire (SMDS-RU) developed for teenagers has sufficient information content, reliability, internal and external validity and can be actively used in the Russian adolescent population.