Explanatory Model Research Articles

Machine learning (ML) modelling techniques for mobile technology-integrated vocabulary learning on Chinese universities EFL students’ adoption

<p class="Abstract">Studying new vocabulary is an important goal for language students, as is making the learning of vocabulary a student-centered process. Of course, the goal of investigating what variables affect people’s willingness to embrace new technologies is to better adopt such technologies. The present study will thus look at the elements that have an impact on Chinese EFL college students’ adoption and usage of mobile technology-integrated vocabulary acquisition as a means to promote more learner-centric education. To better forecast human behavior, we successfully included the following elements from the technology adoption literature into the model: attitude toward change brought on by technology usage; attitude toward technology; desire; financial ramifications; aims; past behavior; perceived consistency; positive expected feelings; visibility; and so on. Furthermore, the characteristics, all of which are external factors that describe the traits of optimal technology design, are included in the development of <a href="https://open.ncl.ac.uk/theories/2/unified-theory-of-acceptance-and-use-of-technology/" target="https://www.google.com.hk/_blank"><span style="text-decoration: underline;">Unified Theory of Acceptance and Use of Technology</span> (</a>UTAUT) using a machine-learning. All these elements help UTAUT progress. Among its many uses, ML-based modeling may be put to good use in enhancing pre-existing explanatory statistical models (UTAUT) by identifying and analyzing hidden patterns and correlations between their many aspects. This is possible because ML-based modeling may enhance traditional statistical explanations (UTAUT).</p>

The significance of information variables in polydrug use by adolescents: insights from a cross-sectional study in Tarragona (Spain).

Substance use, especially among adolescents, is a significant public health concern, with profound implications for physical and psychological development. This study aimed to evaluate the quantity and sources of information available to adolescents regarding polydrug use. A cross-sectional survey was conducted in Tarragona involving adolescents with an average age of 16.44 years. This study assessed the number of substances used (alcohol, cigarettes, and cannabis) in the past month, along with information sources related to substance use. Monitored sources (e.g., schools, parents, and mass media) and unmonitored sources (e.g., peers, siblings, internet) were distinguished. In addition, four individual and four environmental control variables were considered. Multinomial logistic regression analysis revealed that incorporating variables related to adolescents' substance use information and its sources enhanced the explanatory model, surpassing control variables. The degree of information about substance use did not significantly explain consumption patterns, but the number of information sources, both monitored and unmonitored, did. The unmonitored sources were associated with increased polydrug use. Conversely, greater reliance on supervised sources for information was linked to reduced single-substance and polydrug use. This protective effect increased with an increase in the number of substances used. In conclusion, information obtained from monitored sources acts as a deterrent to substance consumption, consistent with findings suggesting that greater health literacy among adolescents discourages substance use. Conversely, this study suggests that information from more informal sources may encourage heavier polydrug use, aligning with reports indicating that adolescents with a more comprehensive understanding of substance use consequences tend to engage in heavier drug use.

Deep convolutional neural networks are not mechanistic explanations of object recognition

Given the extent of using deep convolutional neural networks to model the mechanism of object recognition, it becomes important to analyse the evidence of their similarity and the explanatory potential of these models. I focus on one frequent method of their comparison—representational similarity analysis, and I argue, first, that it underdetermines these models as how-actually mechanistic explanations. This happens because different similarity measures in this framework pick out different mechanisms across DCNNs and the brain in order to correspond them, and there is no arbitration between them in terms of relevance for object recognition. Second, the reason similarity measures are underdetermining to a large degree stems from the highly idealised nature of these models, which undermines their status as how-possibly mechanistic explanatory models of object recognition as well. Thus, building models with more theoretical consideration and choosing relevant similarity measures may bring us closer to the goal of mechanistic explanation.

Dropout in Andalusian universities: prediction and prevention

Problem and objectiveUniversity dropout is a major problem that affects more than 31,000 students each year in Andalusian universities, with serious personal and social consequences and an economic cost of more than 222 million euros for the region’s public administration. As concluded from the review of explanatory models we reviewed, dropout has a multicausal origin. The purpose of our work is to test the efficacy of the use of a screening for the early detection of the risk of academic dropout in Higher Education in Andalusian universities.ProcedureWe applied a screening instrument adapted for incoming students in public universities in Andalusia. The survey was applied at the beginning of the second semester. In this article we present data from a sample composed of 976 subjects from the universities of Granada UGR, Jaén UJA and Pablo de Olavide de Sevilla UPO.ResultsWith the data obtained we have established the dropout risk group, which includes those students who do not reach an average score of 3.00 in the total screening. There are 34 students representing 3.48% of the sample. Of these 34 students, 26 are women and 8 are men; 20 belong to the UGR, 8 to the UJA and 5 to the UPO. The detection of the risk group will allow the universities to apply preventive measures in a personalized and adjusted way to avoid possible dropout.

Testing the Validity of the Multi-Factor Model in the Chinese Stock Market

In the wake of the burgeoning field of quantitative finance, factor models have risen to considerable prominence within the Chinese financial landscape. This research undertakes a comprehensive analysis employing data spanning the years 2014 to 2022, encompassing a total of 108 monthly observations sourced from the Chinese A-share stock market. The principal objective of this study revolves around the evaluation of the effectiveness of an array of common factors. These factors include the market risk premium, market value, book-to-market ratio, profitability, investment, momentum, and liquidity factors. Moreover, our inquiry extends beyond the conventional boundaries of the Fama-French three-factor and five-factor models. It introduces the pivotal elements of momentum and liquidity factors, effectively formulating an enriched model poised to offer a more robust framework for understanding and explaining returns. The discerned findings shed valuable light on the model configurations most apt for dissecting the excess returns exhibited within the sample stocks. Notably, the amalgamation of the three-factor model with the strategic incorporation of the liquidity factor emerges as the most comprehensive explanatory model for the observed excessive returns in the context of the Chinese A-share stock market. It is important to highlight that the introduction of the momentum factor, while explored, does not impart a significant augmentation in the model's capacity to clarify excess returns, a noteworthy departure from expectations within the Chinese A-share market. These insights not only advance our understanding of the Chinese financial landscape but also underscore the intricate dynamics at play in the realm of quantitative finance.

Factors associated with variability in skeletal muscle radiodensity in patients with metastatic cancer

ObjectivesThis study aimed to explore factors associated with skeletal muscle radiodensity (SMD) variability in patients with metastatic cancer. MethodsThis study included 393 patients (median age 61 y, 70% women) who had computed tomography (CT) scans within 30 days of inclusion in the study. SMD was evaluated from CT by averaging the Hounsfield unit value of the total muscle area. Skeletal muscle index (SMI), visceral adipose tissue index (VATI), subcutaneous adipose tissue index (SATI), and total adipose tissue index (TATI) were also assessed by CT. Additionally, age, sex, race/skin color, disease characteristics, comorbidities, inflammatory markers, handgrip strength (HGS), and body mass index (BMI) were recorded and evaluated in the linear regression analysis to identify factors associated with SMD variability. ResultsMultivariate explanatory models having SMD as an independent variable were performed and included BMI (model 1, r2 = 0.699), TATI (model 2, r2 = 0.712) or VATI and SATI (model 3, r2 = 0.706) in addition to age, race/skin color, tumor site, kidney disease, serum albumin, HGS, and SMI as dependent variables. For all models, lower SMD was associated with higher age, BMI, and adiposity measurements, kidney disease, White race/skin color, and lower serum albumin, HGS, and SMI. The primary tumor site also contributed to changes in SMD in all models, specifically those located in the gastrointestinal tract, gynecologic, and bone and connective tissue. Conclusion: In this group of patients with metastatic cancer, lower SMD was associated with older age, White race/skin color, and an overall worse clinical condition.

Usual light touch osteopathic treatment versus simple light touch without intent in the reduction of infantile colic crying time: A randomised controlled trial

BackgroundMany parents seek osteopathic care for their infants with colic. Our aim was to test the effectiveness of usual light touch osteopathic treatment on crying time for infants with ‘colic’. MethodsA superiority, two arm, single blinded (parent) multi-centre (UK, Australia and Switzerland), randomised controlled trial, included healthy infants between 1 and 69 days of age who excessively cried, fussed, or were distressed and difficult to console. The Test intervention consisted of usual light touch osteopathic treatment, the Control intervention simple light touch to random body locations with no treatment intent. Both groups received best practice advice and guidance. The primary outcome was the daily crying time, reported hourly by parents in a diary, for two-weeks. Secondary outcomes were parenting confidence, global change, satisfaction, and experience of care. ResultsSixty-six infants were recruited (32 Test: 34 Control group). Mean average daily crying time in the Test group was 124 min (SD = 69, n = 26) and in the Control 115 min (SD = 49, n = 29). After adjustment, infants in the Test group cried 2.2 min more per day than those in the Control group (CI95 % −20 to 25 min, p = 0.849). Parents’ perceptions of global change in symptoms, satisfaction with, and experience of care were high and similar in both groups. There were no serious adverse events related to the treatments or the trial. ConclusionUsual light touch osteopathic treatment was not superior to simple light touch without treatment intent. The biomechanical explanatory models and underpinning assumptions about the mechanisms of osteopathic intentional light touch care may require reconsideration. Trial registrationACTRN12620000047998 (January 22, 2020).

Optimization of the Teaching Process of Physical Education and Training in Colleges and Universities Based on Explanatory Structural Models

Abstract This study examines the explanatory structural model’s inter-level division technique and how it relates to the directed graphs representing the system’s constituent parts. The directed graphs without loops are successfully identified by analyzing these directed graphs, and the critical elements in the set of highest-level elements are found accordingly. The reduced reachability matrix is used in the study to directly locate the corresponding sinks, and this approach not only optimizes the hierarchical effect of the elements of the complex system, but also significantly improves the computational efficiency of the explanatory structural model. We also used this refined model to examine the main variables influencing how well colleges and universities teach physical training. To maximize the effectiveness of the physical exercise teaching method, a strategy of moving from the deep to the surface was chosen, considering the hierarchical link between these components. The study’s findings demonstrated that applying this optimization method raised the subjects’ overall test scores before and after training by an average of 20.14 points. In addition, the subjects’ lung capacity increased from 4.21 liters to 4.86 liters, and the left ventricular end-diastolic volume and output per beat in terms of cardiac function increased by 11.7% and 18.9%, respectively. The study’s findings offer a significant theoretical and practical foundation for advancing physical education techniques and developing college students’ athletic potential.

Primacy of the real and interdisciplinarity: contradictions in theoretical production

The purpose of this text is to assert the thesis that scientific work, manifested in the production of analyses, theories, and concepts, irrespective of their disciplinary, multidisciplinary, or interdisciplinary origins, only unfolds when rooted in the primacy of the real. The complexity of scientific objects cannot be fully addressed exclusively within disciplinary confines. Discussions centered solely on theoretical, conceptual, or explanatory models, while assisting in transcending disciplinary limitations, do not propel the advancement of scientific knowledge. Contradictions in theoretical-conceptual and analytical production, explicit within disciplinary fields, arise not only from the limitations of disciplines but from how reality is conceptualized as concrete thought. Regardless of the discipline, theory and concept divorced from reality amount to mere speculation.

Dealing with overwhelming life situations - young people's experiences of becoming depressed.

Depression is common and increasing in young people, who seem especially vulnerable, both in the probability of developing depression, and in the resulting negative consequences across the lifespan. Unfortunately, available treatments rarely lead to full remission and even in cases of remission relapse rates are high. Different explanatory models have been proposed, and research indicates a multifaceted etiology. The descriptive DSM-5 has low diagnostic validity in this age-group, especially for depressive disorders, and limited attention has been given to young people's own experiences of becoming depressed. Hence, there is a risk of missing clinical information that is important for the therapeutic alliance and treatment. This study aimed to explore young people's experiences of becoming depressed. A qualitative study was performed. Six participants with clinical depression, currently in treatment at child and adolescent psychiatric outpatient clinics in northern Sweden were recruited. Interviews followed a semi-structured manual, and data was analyzed with inductive qualitative content analysis. Participants described different reasons for their depression, and from their stories four categories were identified: "Being subjected to violence", "Suffering separation and loss", "Feeling abandoned", and "Feeling burdened and vulnerable". These categories were interpreted in the theme: "Dealing with an overwhelming life situation". The participants presented mainly stressful external and relational events preceding their depression. A combination of overwhelming stressors, lack of support and lack of time for recovery was described. This points to the importance of validating the narratives of young patients with depression and to offer trauma-informed treatment approaches in mental health care.

Examining the usability of mobile applications among undergraduate students using SUS and data mining techniques

Mobile Applications offer a new style to service sectors, for instance, in higher education, mobile applications are utilized to provide access to academic resources and academic services. Despite the wealth of mobile applications, they encounter various challenges that have attracted the interest of academia and software developers. The usability issues of mobile applications may cause performance degradation, resulting in the company's loss in terms of cost. This study aims to investigate the usability of the Prince Sattam bin Abdulaziz University (PSAU) mobile application by adopting data mining as a descriptive and predictive process. The first step was gathering data of the usability of the PSAU mobile application using the system usability scale. Afterwards, data was preprocessed into a suitable format to apply data mining methods. Specifically, the explanatory model has been employed to describe and investigate insights related to the usability factors and features of the PSAU mobile application. Furthermore, this study adopted the Four Clustering methods to segment the usability levels of the PSAU mobile application into homogenous groups based on user behavior. Additionally, the predictive model was used to build models for predicting the usability level and Grade and five classification algorithms were employed to predict the usability level and Grade. Most algorithms have given positive results in all performance indicators, where the accuracy rate achieved is 98% to 95% for most methods. The results revealed that the PSAU mobile application has an acceptable usability level, and the data mining methods helped to discover hidden patterns. Furthermore, the findings will help the developers and policymakers understand users' and stakeholders' behavior to find the most common usability problems for each group, and customize the PSAU mobile application.

Developing a spectrum model of engagement in services for first episode psychosis: beyond attendance.

Early intervention services (EIS) for psychosis have proven highly effective in treating first episode psychosis. Yet, retention or "engagement" in EIS remains highly variable. Dis/engagement as a contested concept and fluid process involving relationships between service providers and service users remains poorly understood. This study aimed to critically evaluate and explain the dynamic interplay of service provider-user relationships in effecting dis/engagement from an early intervention program for psychosis. Forty study participants, 16 service providers and 24 service users (19 current and 5 disengaged) from a Canadian EIS program, were administered semi-structured interviews. Qualitative analysis was conducted using grounded theory methods, with findings captured and reconceptualized in a novel explanatory model. A model of engagement with eight major domains of engagement in EIS positioned along a control-autonomy spectrum was developed from the findings, with Clinical engagement (attendance) and Life engagement (life activities) at opposite ends of the spectrum, interspersed by six intermediate domains: Medication/treatment, Symptoms/illness, Mental health, Physical health/wellness, Communication, and Relationships, each domain bearing uniquely on engagement. An examination of service user and service provider perspectives on the various domains identified in the spectrum model, and their dynamic interplay, reveals the complexity of choices faced by service users in engaging and not engaging with services.

Socio-emotional competencies of Colombian high school students in face-to-face and virtual environments

Introduction: Socio-emotional competencies in face-to-face communication are an essential tool for personal and social adjustment from an early age. Online communication has become equally important, especially in adolescence. It is known that its characteristics require specific skills, but there is a lack of studies that analyse the relationship that exists between generally acquired socio-emotional skills and those necessary for online communication. Hence, this research is focused on analysing how offline socio-emotional skills predict these online competencies in girls and boys in a differentiated way. Method: This study included 478 Secondary Education students between 10 and 14 years of age. This is a cross-sectional study that used Path Analysis differentiated by sex, with intentional and non-probabilistic sampling. To assess socio-emotional competencies, we used the ESCQ-21 instrument for generally assessed socio-emotional competencies and the e-COM questionnaire to quantify online socio-emotional competencies. Results: The explanatory model indicates that socio-emotional competencies are partially transferred to the online environment in both sexes, especially Perception and comprehension. Furthermore, it is suggested that E-self-control of impulsiveness and E-emotional autonomy require specific development in that environment. Conclusions: It seems reasonable to include online experiences that encourage development in online spaces. The repercussions for the educational field are analysed.

Study on the Strategy of Red Literature and Tourism Brand Communication Helping Study Tourism Economic Development in Liaoning Province under the Background of Internet

Abstract Liaoning Province has a deep red cultural background and rich red tourism resources, and has an enormous potential for the development of red study tour. This paper establishes the evaluation system of the competitiveness of study tour development in Liaoning Province based on the explanatory structural model, solves the index weights through the coefficient of variation method, and gives the calculation of the competitiveness of study tour economic development. The regional differences in the competitiveness of research and study tourism economic development were measured by using the Thiel index, the spatial autocorrelation Moran index verified the regional correlation, and the TF-IDF algorithm was introduced to improve the LDA theme model thereby analyzing the emotional expression of the research and study tourism network text. Taking the tourism data of Liaoning Province from 2015 to 2019 as the research object, the data were quantitatively analyzed regarding development competitiveness evaluation, regional differences, spatial autocorrelation and emotional expression. The results of the study found that the weight of the red culture tourism market size in the economic development competitiveness of study tourism reached 29.64%, the peak and valley values of the regional differences in competitiveness from 2015 to 2019 differed by 0.0587, and significant differences were presented at the 1% level between cities. The frequency of red characteristic words reached 2,536 times. This indicates that the effective integration of red culture and study tourism in Liaoning Province effectively reduces the development differences between cities, and enhances the satisfaction of study students with red culture study tourism bases.

Novel explanatory hybrid model for classifying deepfake images

Novel explanatory hybrid model for classifying deepfake images

СИСТЕМНОСТЬ И ВАРИАТИВНОСТЬ МОДЕЛИ И СТРАТЕГИИ ПЕРЕВОДА

From the viewpoint of systematic and variational approach the article outlines the main provisions of the discourse and communication translation model and the procedural understanding of translation / interpreting strategy. We provide a framework for translation modeling at large and put forward the typology of explanatory translation models. Further we describe the structure of the discourse and communication translation model, the relations between its categories and parameters, and the distributed nature of the model components. We then explain how a translation / interpreting strategy is developed basing on the model and what factors influence the translation and interpreting strategies.

Analysis of reading comprehension in underachieving high school students: exploring neuropsychological, metacognitive and motivational factors

Introduction: the ability to comprehend written text is of utmost importance for educational progress in high school, given that most academic content is presented in this format, both in print and digital versions. Recent theoretical approaches and empirical studies in psychology and neuroscience have directed their interest towards explanatory models of reading comprehension that include motivational, neuropsychological and metacognitive elements. Objective: analyze, by means of structural equations, the multivariate effects of motivational, neuropsychological and metacognitive factors on reading comprehension in a group of adolescent high school students with reading difficulties. Methods: a quantitative, observational, cross-sectional study was conducted with a sample of 200 students between 11 and 18 years of age. Measures used: Academic Motivation and Attributional Styles Scale, Metacognitive Awareness of Reading Strategies Inventory, Regression Digit Retention Subtest, d2 Attention Test, and Inferential Reading Comprehension Test. Results: only working memory showed a statistically significant direct effect on reading comprehension. Although academic motivation, selective attention, and metacognition did not have statistically significant effects on reading comprehension, there were statistically significant effects on reading comprehension.Conclusions: although only working memory had an effect on reading comprehension, a circuit in which other motivational, cognitive and metacognitive variables are associated could be revealed

Use of Parallel Explanatory Models to Enhance Transparency of Neural Network Configurations for Cell Degradation Detection.

In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly found, though, that accuracy gains diminished as we added layers to the RNN. To investigate this, in this article, we build a parallel model to illuminate and understand the internal operation of neural networks (NNs), such as the RNN, which store their internal state to process sequential inputs. This model is widely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looking at RNN processing from a probability density function (pdf) perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection accuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. To demonstrate the fidelity of the model, we validate it against each stage of RNN processing and output predictions. As a result, we have been able to explain the reasons for RNN performance limits with useful insights for future designs for RNNs and similar types of NN.

A Suggested Curriculum based on (NGSS) Dimensions to develop Explanatory Models of Scientific Phenomena For Preparatory School students

A Suggested Curriculum based on (NGSS) Dimensions to develop Explanatory Models of Scientific Phenomena For Preparatory School students

Modelling Precipitation as a Function of Several Variables in Northern Madagascar Using the ANFIS Network

This article presents a study on the modeling of precipitation in northwest Madagascar as a function of several variables. The method is based on the use of the Granger test to assess the causal relationship between precipitation, temperature, atmospheric pressure, wind at 925 hPa and Outgoing Longwave Radiation (OLR). The model is developed using the multiple linear regression method, followed by the use of the Sobol index to identify the most influential of these variables. Forecasts are based on the ANFIS model. The results show that precipitation in this area (Sofia, Diana and Sava regions) is influenced by pressure, wind and OLR. The explanatory model obtained highlights the relationship between precipitation and these climatic variables, showing a 7 mm decrease in precipitation according to the ANFIS model developed.