Search

The purpose of this research project was to study how students in the first years of elementary school (children from 7 to 10 years of age) are initiated into the construction of explanations of physical phenomena in the teaching of science. With this purpose in mind, we organized classes based on the proposition of investigative problems, where children, working in groups, could solve problems by raising and testing their own hypotheses. They would then attempt, by means of general discussion organized by the teacher, to discuss how each problem was solved and why it worked. We videotaped a series of classes in which the students solved 15 different investigative problems. We also analysed the teacher/student interactions that took place (in this paper, we present data on two of these classes). Based on our data we found that students construct their own causal explanations by following a sequence of stages that includes the appearance of novelties. We also discuss how our data relate to the teacher's role in the classroom and to the organization of science teaching at this level.

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

Introduction: This study aimed to develop a learning-based model of organizational entrepreneurship, utilizing structural equation modeling, within the context of Iran’s health insurance organization.Methods: The study’s statistical population consisted of all managers and employees of the Iran Health Insurance Organization, estimated to be 500 individuals. Using the Cochran equation, 217 individuals were randomly selected as a statistical sample. Data was collected via a researcher-made questionnaire consisting of 86 items. Thirty experts confirmed the questionnaire’s face and content validity, and its reliability exceeded 0.7 in a preliminary test with 30 subjects across all variables under investigation. Descriptive statistics methods were employed for data analysis using SPSS version 22 software, while inferential statistics were conducted using structural equation modeling in Smart PLS version 2 software.Results: The study’s findings showed that causal factors significantly influence the central phenomenon and that the background factors, the central phenomenon, and intervening factors all impact the strategies employed. Furthermore, these organizational strategies influence the consequences. Specifically, an increase of 1 unit in strategies results in an increase of 0.919 in consequences.Conclusion: Based on the results obtained, it can be inferred that organizational learning enhances creativity and fosters the generation of knowledge and new ideas. Therefore, it is crucial to consider the impact of causal factors on the central phenomenon, the role of contextual factors, and the influence of intervening factors on strategies, which subsequently affect the consequences.

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debris-flow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debris-flow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m–2500 m elevation difference, 25°–30° average slope, and 0.13–0.15 land use index is the most favorable conditions for debris-flow occurrence; The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to develop a learning-based model of organizational entrepreneurship, utilizing structural equation modeling, within the context of Iran’s health insurance organization. Methods: The study’s statistical population consisted of all managers and employees of the Iran Health Insurance Organization, estimated to be 500 individuals. Using the Cochran equation, 217 individuals were randomly selected as a statistical sample. Data was collected via a researchermade questionnaire consisting of 86 items. Thirty experts confirmed the questionnaire’s face and content validity, and its reliability exceeded 0.7 in a preliminary test with 30 subjects across all variables under investigation. Descriptive statistics methods were employed for data analysis using SPSS version 22 software, while inferential statistics were conducted using structural equation modeling in Smart PLS version 2 software. Results: The study’s findings showed that causal factors significantly influence the central phenomenon and that the background factors, the central phenomenon, and intervening factors all impact the strategies employed. Furthermore, these organizational strategies influence the consequences. Specifically, an increase of 1 unit in strategies results in an increase of 0.919 in consequences. Conclusion: Based on the results obtained, it can be inferred that organizational learning enhances creativity and fosters the generation of knowledge and new ideas. Therefore, it is crucial to consider the impact of causal factors on the central phenomenon, the role of contextual factors, and the influence of intervening factors on strategies, which subsequently affect the consequences. [ABSTRACT FROM AUTHOR]

BackgroundThe risk factors of cardiovascular disease (CVD) in end-stage renal disease (ESRD) with hemodialysis remain not fully understood. In this study, we developed and validated a clinical-longitudinal model for predicting CVD in patients with hemodialysis, and employed Mendelian randomization to evaluate the causal 6study included 468 hemodialysis patients, and biochemical parameters were evaluated every three months. A generalized linear mixed (GLM) predictive model was applied to longitudinal clinical data. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the model. Kaplan-Meier curves were applied to verify the effect of selected risk factors on the probability of CVD. Genome-wide association study (GWAS) data for CVD (n = 218,792,101,866 cases), end-stage renal disease (ESRD, n = 16,405, 326 cases), diabetes (n = 202,046, 9,889 cases), creatinine (n = 7,810), and uric acid (UA, n = 109,029) were obtained from the large-open GWAS project. The inverse-variance weighted MR was used as the main analysis to estimate the causal associations, and several sensitivity analyses were performed to assess pleiotropy and exclude variants with potential pleiotropic effects.ResultsThe AUCs of the GLM model was 0.93 (with accuracy rates of 93.9% and 93.1% for the training set and validation set, sensitivity of 0.95 and 0.94, specificity of 0.87 and 0.86). The final clinical-longitudinal model consisted of 5 risk factors, including age, diabetes, ipth, creatinine, and UA. Furthermore, the predicted CVD response also allowed for significant (p

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multidimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause. [ABSTRACT FROM AUTHOR]

In order to improve the assessment reliability of defects thermophysical and geometric parameters of industrial objects and products in the process of IR thermography testing, an analysis of causal factors and errors that affect the results of the thermography procedure was carried out. The classes and the influence of the main uncertainties on the parameters of thermography are defined. [ABSTRACT FROM AUTHOR]

Under the backdrop of the robust growth of the global economy, the water transport industry is experiencing rapid development, resulting in an increase in ship collisions and a critical water traffic safety situation. This study uses text mining techniques to gather a corpus of data. The corpus includes human factors, ship factors, natural environmental factors, and management factors, which are used as target data to obtain a high-dimensional sparse original feature vector space set comprising eigenvalues and eigenvalue weight attributes. Chi-square statistics are utilised to reduce dimensionality, resulting in a final set of 33-dimensional text feature items that determine the causal factors of ship collision risk. Taking the four steps involved in the collision process as the primary focus, a Bayesian network structure for ship collision risk is constructed based on the “human-ship-environment-management” system. By incorporating existing ship collision accident/danger reports, conditional probability tables are computed for each node in the Bayesian network structure, enabling the modelling of ship collision risk. The model is validated through an example, revealing that, under relevant conditions, the probability of collision exceeds 90 %. This finding demonstrates the validity of the model and allows one to deduce the primary cause of ship collision accidents.

Accidents occur due to a series of interactions between deficiencies within the various levels of a sociotechnical system. Quantifying the relationship between upper and lower levels helps develop accident countermeasures focusing on significant organisational latent conditions. This study explores the relationship between the causal factors of accidents within Ghanaian mines using SEM. Data obtained from the analysis of incident reports using HFACS-GMI were quantified to enable its use in the SEM software, as SEM calculations cannot be done using a 0/1 description. The study also tests five hypotheses, including the basic assumption of the HFACS model. The case study results showed that organisational factors significantly influence workplace/individual conditions; upper causal categories do not only influence adjacent immediate lower causal categories, and partial correlations exist between causal categories with a particular level. Based on the SEM model from LISERL, an accident causation path diagram was developed. The diagram reveals that leadership flaws, the technological environment and adverse physiological/mental states were the mediating factors in accident causation within the mines. The operational process has a prominent position in the organisational factors tier and is an essential factor in the entire accident system. Therefore, accident countermeasures should be directed to addressing operational deficiencies. [ABSTRACT FROM AUTHOR]

To determine the impact implementation of Emergency Department Clinical Emergency Response System (EDCERS) on inpatient deterioration events and identify contributing causal factors. EDCERS was implemented in an Australian regional hospital, integrating a single parameter track and trigger criteria for escalation of care, and emergency, specialty and critical care clinician response to patient deterioration. In this controlled pre-post study, electronic medical records of patients who experienced a deterioration event (rapid response call, cardiac arrest or unplanned intensive care admission) on the ward within 72 h of admission from the emergency department (ED) were reviewed. Causal factors contributing to the deteriorating event were assessed using a validated human factors framework. Implementation of EDCERS reduced the number of inpatient deterioration events within 72 h of emergency admission with failure or delayed response to ED patient deterioration as a causal factor. There was no change in the overall rate of inpatient deterioration events. This study supports wider implementation of rapid response systems in the ED to improve management of deteriorating patients. Tailored implementation strategies should be used to achieve successful and sustainable uptake of ED rapid response systems and improve outcomes in deteriorating patients. [ABSTRACT FROM AUTHOR]

The new normal has shifted high school teachers' jobs from face-to-face to modular distance learning which requires more paperwork. This research article investigated job burnout among secondary teachers in Ormoc City Division, Leyte, Philippines, and elucidated the statistically significant factors affecting it. Primary data were gathered from a simple random sample of 132 high school teachers in the City of Ormoc. To summarise the acquired survey results, typical descriptive metrics such as frequency tables, percentages, mean average, and standard deviation were used to summarize the gathered survey data. The Chi-square test of independence was employed to capture the different factors influencing job burnout. The findings revealed that, on average, secondary teachers are "highly burnout" in modular and distance learning amidst the COVID-19 pandemic setup. The Chi-square test showed that marital status and department/school assignment are the factors influencing teachers' burnout levels. In particular, the cross-tabulation of marital status and burnout category revealed that married teachers are mostly highly burnout. Finally, responsibilities and duties in their household contribute to the teachers' working stress and tiredness. Furthermore, different schools have varying job assignments, with some schools assigning heavier loads, resulting in severe teacher burnout. According to the report, school leaders should prioritize teachers' well-being by reducing their responsibilities and implementing leisure school activities to alleviate stressful circumstances. [ABSTRACT FROM AUTHOR]

The new normal has shifted high school teachers' jobs from face-to-face to modular distance learning which requires more paperwork. This research article investigated job burnout among secondary teachers in Ormoc City Division, Leyte, Philippines, and elucidated the statistically significant factors affecting it. Primary data were gathered from a simple random sample of 132 high school teachers in the City of Ormoc. To summarise the acquired survey results, typical descriptive metrics such as frequency tables, percentages, mean average, and standard deviation were used to summarize the gathered survey data. The Chi-square test of independence was employed to capture the different factors influencing job burnout. The findings revealed that, on average, secondary teachers are “highly burnout” in modular and distance learning amidst the COVID-19 pandemic setup. The Chi-square test showed that marital status and department/school assignment are the factors influencing teachers’ burnout levels. In particular, the cross-tabulation of marital status and burnout category revealed that married teachers are mostly highly burnout. Finally, responsibilities and duties in their household contribute to the teachers’ working stress and tiredness. Furthermore, different schools have varying job assignments, with some schools assigning heavier loads, resulting in severe teacher burnout. According to the report, school leaders should prioritize teachers’ well-being by reducing their responsibilities and implementing leisure school activities to alleviate stressful circumstances.

BackgroundPostpartum depression (PPD) is a type of depressive episode related to parents after childbirth, which causes a variety of symptoms not only for parents but also affects the development of children. The causal relationship between potential risk factors and PPD remains comprehensively elucidated.MethodsLinkage disequilibrium score regression (LDSC) analysis was conducted to screen the heritability of each instrumental variant (IV) and to calculate the genetic correlations between effective causal factors and PPD. To search for the causal effect of multiple potential risk factors on the incidence of PPD, random effects of the inverse variance weighted (IVW) method were applied. Sensitivity analyses, including weighted median, MR-Egger regression, Cochrane’s Q test, and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO), were performed to detect potential Mendelian randomization (MR) assumption violations. Multivariable MR (MVMR) was conducted to control potential multicollinearity.ResultsA total of 40 potential risk factors were investigated in this study. LDSC regression analysis reported a significant genetic correlation of potential traits with PPD. MR analysis showed that higher body mass index (BMI) (Benjamini and Hochberg (BH) corrected p = 0.05), major depression (MD) (BH corrected p = 5.04E-19), and schizophrenia (SCZ) (BH corrected p = 1.64E-05) were associated with the increased risk of PPD, whereas increased age at first birth (BH corrected p = 2.11E-04), older age at first sexual intercourse (BH corrected p = 3.02E-15), increased average total household income before tax (BH corrected p = 4.57E-02), and increased years of schooling (BH corrected p = 1.47E-11) led to a decreased probability of PPD. MVMR analysis suggested that MD (p = 3.25E-08) and older age at first birth (p = 8.18E-04) were still associated with an increased risk of PPD.ConclusionIn our MR study, we found multiple risk factors, including MD and younger age at first birth, to be deleterious causal risk factors for PPD.

Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progress of equipment faults is non-stationary. More specifically, capturing the time point at which a fault first appears is more important for improving the accuracy of fault prediction. Moreover, the progress of the different faults of equipment varies significantly. Therefore, taking feature differences and time information into consideration, we propose a Causal-Factors-Aware Attention Network, CaFANet, for equipment fault prediction in the Internet of Things. Experimental results and performance analysis confirm the superiority of the proposed algorithm over traditional machine learning methods with prediction accuracy improved by up to 15.3%. [ABSTRACT FROM AUTHOR]

Cork spot disorder has affected the fruit of Asian pear since the 1990s and has become serious in recent years with increasingly affected cultivars and areas. The commodity value of affected fruit is greatly decreased, resulting in severe economic losses. Cork spot disorder of pear fruit is a physiological disorder, and the factors responsible are relatively complex. Research on the cause of cork spot disorder is still at an early stage and, thus, further investigations are needed to elucidate the underlying mechanism of the disorder. In this review, current knowledge of the factors associated with the incidence of cork spot disorder in Asian pear fruit is summarized, including fruit growth and development, fruit nutrient status, and environmental factors. Potential preventive measures and priorities for future research are outlined. [ABSTRACT FROM AUTHOR]

This study sought to assess the influence of causal factors related to anthropogenic activities on landslide occurrence in Bukit Antarabangsa, a township northeast of Kuala Lumpur in Ampang Jaya Municipal Council. Anthropogenic factors were chosen based on the township’s rapid growth, numerous landslide records and intensity of hillside development. The study used a data-driven statistical model to identify factors most predictive of landslide occurrence based on an inventory of 20 landslides, and to evaluate the extent to which susceptibility was driven by factors related to urban development. A total of 17 factors were categorized into four clusters (geological, geomorphological, hydro-tographical and anthropogenic). Factor maps were classified to derive factor classes for each parameter. The dataset was then processed using a weight-of-evidence statistical model to determine the contrast value of each factor class. Contrast value (C) reflects the extent to which each factor class predicts landslide occurrence. The C-weighted factor maps were then combined to derive the landslide susceptibility index (LSI). The LSI enabled visualization of the spatial distribution of susceptibility based on a given combination of factors. Susceptibility maps were prepared for combinations containing only non-anthropogenic parameters and all landslide parameters. The study comparedthese combinations to determine the influence of anthropogenic factors on total LSI. Similar analyses were conducted to determine the effect of each anthropogenic factor on LSI. The results indicated that lineament density, distance to lineament and distance to road had a significant influence on landslide occurrence. A strong correlation with landslide occurrence was observed for land use/land cover, especially in high susceptibility zones, followed closely by the influence of one distance to road factor class. The results could be useful in planning infrastructure corridors in densely built-up landslide-prone areas.

The virtualization of university teaching in Spain caused by the pandemic was a success, but the urgent nature of the transition from face-to-face teaching to online teaching meant that some peculiarities that should have been taken into account were omitted. Thus, in order to find out and analyze the factors causing the success of this virtualization and take measures to address the weak points of this process as teachers may want to maintain the use of online teaching as support for their face-to-face teaching, these factors were located through a literature review and were classified according to their level of importance in this success through a causal study carried out with the methodology of fuzzy cognitive maps. The results achieved indicated the determining factors in students, involving their attitude, aptitude and predisposition, which are factors where the work of the teacher is of main relevance (as it exerts the greatest influence on these factors). For this reason, technological attitude and aptitude, teaching style, and the creation and structuring of content should be reinforced in teachers.

The vital role of container liner shipping in international trade suggests that understanding why container liner firms go bankrupt is crucial to the sustainability of the maritime supply chain to improve resilience. Considering the insolvency of Hanjin Shipping as a case study, this paper investigates the probabilistic relationships among the bankruptcy causal factors that are disclosed qualitatively and quantitatively, exploiting a fuzzy Bayes network approach. A sensitivity analysis is conducted to increase the accuracy of the findings. Outcomes of the paper reveal that an integrated approach comprising of both endogenous and exogenous causal factors is a more powerful approach to explain the demise of Hanjin Shipping. Compared to exogenous factors, endogenous factors account more for the collapse of the firm. Furthermore, it is found that government support would have been a more influential measure to mitigate the negative effects of the demise compared to the merging and acquisition practice. Competitor liner operators, policymakers, and stakeholders in the maritime supply chain ecosystem can utilize the outcomes of this research to mitigate the bankruptcy risk and improve the maritime supply chain resilience capacity.

Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progress of equipment faults is non-stationary. More specifically, capturing the time point at which a fault first appears is more important for improving the accuracy of fault prediction. Moreover, the progress of the different faults of equipment varies significantly. Therefore, taking feature differences and time information into consideration, we propose a Causal-Factors-Aware Attention Network, CaFANet, for equipment fault prediction in the Internet of Things. Experimental results and performance analysis confirm the superiority of the proposed algorithm over traditional machine learning methods with prediction accuracy improved by up to 15.3%.

Information technology equipment (ITE) processing sensitive information can have its security compromised by unintentional electromagnetic radiation. Appropriately assessing likelihood of a potential compromise relies on radio frequency (RF) engineering expertise—specifically, requiring knowledge of the associated causal factors and their interrelationships. Several factors that can cause unintentional electromagnetic emanations that can lead to the compromise of ITE have been found in the literature. This paper confirms the list of causal factors reported in previous work, categorizes the factors as belonging to threat, vulnerability, or impact, and develops an interpretive structural model of the vulnerability factors. A participatory modelling approach was used consisting of focus groups of RF engineers. The resulting hierarchical structural model shows the relationships between factors and illustrates their relative significance. The paper concludes that the resulting model can motivate a deeper understanding of the structural relationship of the factors that can be incorporated in the RF engineers' assessment process. Areas of future work are suggested. [ABSTRACT FROM AUTHOR]

The objectives of this research are to 1) study the causal factors of customer satisfaction (SAT) and performance (PER) of retail businesses in Khon Kaen, 2) study the direct, indirect and total effects of causal factors influencing PER of retail businesses in Khon Kaen, and 3) examine the fit to empirical data of causal model of factors influencing PER of retail businesses in Khon Kaen. For methodology, the data was collected by questionnaires from 380 retailers in Khon Kaen Province. It was analyzed by descriptive statistics, confirmatory factor analysis (CFA), Pearson's correlation coefficient and structural equation model (SEM). The results could be summarized as follows. 1. Operational factors (OPE) and management (MAN) were at a moderate level, while SAT and PER were at a high level. 2. For highest effect, total positive effect of MAN on PER was 0.70, with positive direct effect of 0.45 and indirect effect mediated by SAT of 0.25, with statistically significant level of 0.01, followed by OPE that had positive total effect of 0.36, direct positive effect of 0.27, and indirect effect mediated by SAT of 0.09, with a statistically significant level of 0.01. Likewise, SAT had total effect of 0.32 on PER, with all direct effect and a statistically significant level of 0.01. 3. The SEM fit to the empirical data by considering the following indices: 2/df = 0.83, GFI = 0.99, AGFI = 0.97, and RMSEA = 0.01. [ABSTRACT FROM AUTHOR]

Distance Interpreting (DI) is a form of technology-mediated interpreting which has gained traction due to the high demand for multilingual conferences, live-streaming programs, and public service sectors. The current study synthesized the DI literature to build a framework that represents the construct and measurement of cognitive load in DI. Two major areas of research were identified, i.e., causal factors and methods of measuring cognitive load. A number of causal factors that can induce change in cognitive load in DI were identified and reviewed. These included factors derived from tasks (e.g., mode of presentation), environment (e.g., booth type), and interpreters (e.g., technology awareness). In addition, four methods for measuring cognitive load in DI were identified and surveyed: subjective methods, performance methods, analytical methods, and psycho-physiological methods. Together, the causal factors and measurement methods provide a multifarious approach to delineating and quantifying cognitive load in DI. This multidimensional framework can be applied as a tool for pedagogical design in interpreting programs at both the undergraduate and graduate levels. It can also provide implications for other fields of educational psychology and language learning and assessment. [ABSTRACT FROM AUTHOR]

The virtualization of university teaching in Spain caused by the pandemic was a success, but the urgent nature of the transition from face-to-face teaching to online teaching meant that some peculiarities that should have been taken into account were omitted. Thus, in order to find out and analyze the factors causing the success of this virtualization and take measures to address the weak points of this process as teachers may want to maintain the use of online teaching as support for their face-to-face teaching, these factors were located through a literature review and were classified according to their level of importance in this success through a causal study carried out with the methodology of fuzzy cognitive maps. The results achieved indicated the determining factors in students, involving their attitude, aptitude and predisposition, which are factors where the work of the teacher is of main relevance (as it exerts the greatest influence on these factors). For this reason, technological attitude and aptitude, teaching style, and the creation and structuring of content should be reinforced in teachers. [ABSTRACT FROM AUTHOR]

Copyright of Campus Virtuales is the property of Campus Virtuales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The risk factors of cardiovascular disease (CVD) in end-stage renal disease (ESRD) with hemodialysis remain not fully understood. In this study, we developed and validated a clinical-longitudinal model for predicting CVD in patients with hemodialysis, and employed Mendelian randomization to evaluate the causal 6study included 468 hemodialysis patients, and biochemical parameters were evaluated every three months. A generalized linear mixed (GLM) predictive model was applied to longitudinal clinical data. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the model. Kaplan-Meier curves were applied to verify the effect of selected risk factors on the probability of CVD. Genome-wide association study (GWAS) data for CVD ( n  = 218,792,101,866 cases), end-stage renal disease (ESRD, n  = 16,405, 326 cases), diabetes ( n  = 202,046, 9,889 cases), creatinine ( n  = 7,810), and uric acid (UA, n  = 109,029) were obtained from the large-open GWAS project. The inverse-variance weighted MR was used as the main analysis to estimate the causal associations, and several sensitivity analyses were performed to assess pleiotropy and exclude variants with potential pleiotropic effects., Results: The AUCs of the GLM model was 0.93 (with accuracy rates of 93.9% and 93.1% for the training set and validation set, sensitivity of 0.95 and 0.94, specificity of 0.87 and 0.86). The final clinical-longitudinal model consisted of 5 risk factors, including age, diabetes, ipth, creatinine, and UA. Furthermore, the predicted CVD response also allowed for significant ( p  < 0.05) discrimination between the Kaplan-Meier curves of each age, diabetes, ipth, and creatinine subclassification. MR analysis indicated that diabetes had a causal role in risk of CVD ( β  =   0.088, p  < 0.0001) and ESRD ( β  =   0.26, p =  0.007). In turn, ESRD was found to have a causal role in risk of diabetes ( β  =   0.027, p  = 0.013). Additionally, creatinine exhibited a causal role in the risk of ESRD ( β  =   4.42, p  = 0.01)., Conclusions: The results showed that old age, diabetes, and low level of ipth, creatinine, and UA were important risk factors for CVD in hemodialysis patients, and diabetes played an important bridging role in the link between ESRD and CVD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Tian, Xu, Wang, Zhu, Huang, Liu, Li and Li.)

Purpose: The purpose of this study is to develop a model for the successful implementation of public policy in state-owned, project-based oil companies in Iran. Methodology: This research was a qualitative study in terms of methodology. Also, since it intended to provide a model for the implementation of public policies in such companies in Iran, it was considered to be an exploratory research. The statistical population of the study included senior managers of Iranian state project-oriented companies active in the field of oil and gas as well as university professors in the field of policy-making. The size of the research sample was determined by snowball method and interview was used to collect the required data. Findings: Results show that successful implementation of public policy can be measured in the light of such criteria as efficiency, effectiveness, meeting expectations, adequacy in problem solving, justice distribution, and adaptation to the conditions of the oil industry. Value: According to the acquired framework, three clusters of variables were identified under the headings of causal, intervening, and underlying factors, which strongly influence the successful implementation of public policies. Implications: The acquired framework indicates that the successful implementation of public policies leads to increased public trust and satisfaction as well as promotion of corporate legitimacy among stakeholders.

The air traffic environment is a complex system that involves several players. Between the air navigation service provider (ANPS) and the final client, many different organizations act with different purposes but are strongly interfaced. Changes and modifications in terms of technology, predefined process, or personnel are constantly needed, requiring coordination among the stakeholders. However, due to the high level of interaction between the players, any change in a complex system like the air traffic environment requires risk management. This paper demonstrates the suitability and advantages of the System-Theoretic Accident Model and Processes/Systems-Theoretic Process Analysis (STAMP/STPA) method to be applied to the risk assessment of an operational air traffic modification. The method, which had never been used on an air traffic problem in Brazil before, was applied considering the implementation of segregated simultaneous operation at the Guarulhos International Airport. The results were proven to be effective in terms of deriving useful safety requirements. From such demonstration, STAMP/STPA can be considered as a feasible alternative to the brainstorming method currently applied for risk assessment and generation of safety requirements regarding modifications in the structure of the air traffic services (ATS) in Brazil.

This research aims to develop a causal relationship model on political management for sustainability policy formation under Thai environmental law by applying the best and valid model with a non-spurious property called the Covariance-based on Structural Equation Model with exogenous variables (Covariance-based SEMxi Model). This newly-developed model is in distinction with any past models as it is made effectively applicable to any sectors across areas. The model can also be utilized to design a long-term forecasting model with the ability to determine appropriate future scenarios. When assessing the covariance-based SEMxi model performance, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) are estimated at 1.19% and 1.30%, respectively, in comparison of other models, including Gray-Autoregressive Integrated Moving Average Model (GM-ARIMA), Gray Model (GM), Back Propagation Neural Network (BP), Artificial Neural Natural Model (ANN), and Multiple Regression Model (MR). As for the results, this research reveals a direct impact of economic factors on environmental and social factors. In the meanwhile, social factors have a direct impact on environmental and economic factors. The research also indicates a direct effect on the environment with a maximal magnitude of 67%. Whereas a direct effect of social factors on the environment is detected at the magnitude of 55%. These effects are perceived to exceed the specified carrying capacity set by Thailand. In addition, a causal relationship is observed between economic and social factors, where the environment is found with the lowest error correction capability of only 5 percent. At the same time, economic and social factors are noticed with greater correction capability of 59% and 31%, respectively. This finding implies that the ecosystem will experience slow recovery whenever it deteriorates. Hence, the government must place a higher concentration on the environment, while different measures on environmental legislation should be closely controlled to contain any future damage. Besides, energy consumption must be managed not to exceed the established carrying capacity by simultaneously implementing both proactive and reactive measures. This process can be strengthened by optimizing the newly-introduced model produced by this work for a scenario design in policy management to attain sustainability.

The lessons learned from each Traffic Collision (TC) will help safety practitioners to avoid similar occurrences in the future. However, few studies and methods have focused specifically on the similar features among different collisions. Thus, the development of a measurement method for investigating the best evidence on the causal factors of TCs was warranted. In this study, a similarity analysis method based on the Analytic Hierarchy Process (AHP) and Similarity (S) theory, the AHP-S method, was constructed. This method was designed to identify the similar elements and similar units of collision scenes according to the analysis criteria and sub-criteria and further to calculate the degree of similarity between recognized similar pairs among TCs. Six TC cases were randomly selected as examples, and the degrees of similarity between cases 1 to 5 and case 6 were calculated separately. The calculation results showed that out of the five collision cases (cases 1–5), case 1 provided the best evidence for analysing the causal factors of case 6. This study promotes the development of quantitative analysis methods for collision incidents and provides an effective evidence-based method for TC avoidance. First published online 17 March 2021

The air traffic environment is a complex system that involves several players. Between the air navigation service provider (ANPS) and the final client, many different organizations act with different purposes but are strongly interfaced. Changes and modifications in terms of technology, predefined process, or personnel are constantly needed, requiring coordination among the stakeholders. However, due to the high level of interaction between the players, any change in a complex system like the air traffic environment requires risk management. This paper demonstrates the suitability and advantages of the System-Theoretic Accident Model and Processes/Systems-Theoretic Process Analysis (STAMP/STPA) method to be applied to the risk assessment of an operational air traffic modification. The method, which had never been used on an air traffic problem in Brazil before, was applied considering the implementation of segregated simultaneous operation at the Guarulhos International Airport. The results were proven to be effective in terms of deriving useful safety requirements. From such demonstration, STAMP/STPA can be considered as a feasible alternative to the brainstorming method currently applied for risk assessment and generation of safety requirements regarding modifications in the structure of the air traffic services (ATS) in Brazil. [ABSTRACT FROM AUTHOR]

Anxiety has been considered as one of the obstacles in learning English and a common issue in the EFL teaching and learning. In speaking, it also debilitates the opportunity of English Foreign Language (EFL) students to speak English. Several studies have been conducted to discover the causes and straegies to overcome them in traditional classroom. To date, not many studies have focused on students' anxiety in online learning. Therefore, this study was designed to reveal the causal factors of the students' speaking anxiety and strategies to overcome them during online learning at Islamic senior secondary school setting. The study employed narrative inquiry approach and used interview as the main instrument of data collection. Four students of Islamic Senior Secondary School (MAN) Insan Cendekia Gowa whom selected to be the participants of the study using purposive sampling technique. The result of this study establishes two main findings. Firstly, the causal factors of students' speaking anxiety during online learning were divided into internal and external factors. The internal factors consisted of lack of confidence, shyness, fear of making mistake, and feeling insecure. The external factors consisted of lack of preparation, limited vocabulary, friends or classmates, embarrassment, teachers' teaching style. The study found that the external factors are indicated as the most dominant causes of students' speaking anxiety. Furthermore, the study highlights several strategies used by the students to overcome their speaking anxiety, namely: good preparation (lecture note), peer seeking, keeping silent or calm, practice speaking at home, turning the camera off, using 'google translator', and limiting speaking duration. [ABSTRACT FROM AUTHOR]

The vital role of container liner shipping in international trade suggests that understanding why container liner firms go bankrupt is crucial to the sustainability of the maritime supply chain to improve resilience. Considering the insolvency of Hanjin Shipping as a case study, this paper investigates the probabilistic relationships among the bankruptcy causal factors that are disclosed qualitatively and quantitatively, exploiting a fuzzy Bayes network approach. A sensitivity analysis is conducted to increase the accuracy of the findings. Outcomes of the paper reveal that an integrated approach comprising of both endogenous and exogenous causal factors is a more powerful approach to explain the demise of Hanjin Shipping. Compared to exogenous factors, endogenous factors account more for the collapse of the firm. Furthermore, it is found that government support would have been a more influential measure to mitigate the negative effects of the demise compared to the merging and acquisition practice. Competitor liner operators, policymakers, and stakeholders in the maritime supply chain ecosystem can utilize the outcomes of this research to mitigate the bankruptcy risk and improve the maritime supply chain resilience capacity. [ABSTRACT FROM AUTHOR]

The aims of this study were to determine helmet use behavior (HUB) and to explore the causal factors influencing HUB. Four hundred and twenty two public health students of Pibulsongkram Rajabhat University were chosen as the population. The questionnaire with reliability (Cronbach’s alpha coefficient 0.94) was used as a research instrument. The results showed that 66.4% of all students claimed to wear a helmet, at least sometimes, while riding a motorcycle or sitting behind the motorcycle rider. With regard to causal factors influencing HUB among students, the structural equation model was valid and fitted the empirical data. This model indicated that the Chi-square goodness of fit to test was 227.64, df=200, Chi-square/df=1.138, p-value=0.08754, SRMR=0.038, GFI=0.99, AGFI=0.98 and RMSEA=0.018. The intention (r=0.47) and attitude (r=-0.45) toward behavior had direct influence on HUB of students. Both variables accounted for 64% (R2=0.64) of variance of those HUBs. Other variables which indirectly influenced students’ HUBvia intention, included attitude, perceived behavioral control on HUB, and subjective norms. Also, perceived behavioral control and subjective norms indirectly influenced students’ HUB via attitude. Therefore, academic staff, parents, and friends should encourage students to adopt a positive attitude toward helmet use and have perseverance to behave better. Subsequently, those students would eventually realize the good sense of wearing a helmet.

Drought is controlled and enhanced by changes in the local and global climate factors. The changes in the global factors mainly occur on the vast oceanic surfaces, while the changes in the local factors occur in the area of drought influence. In order to skilfully forecast and monitor drought occurrences, it is crucial that the specific causal factors be identified and their links studied. Based on this concept, this study tried to establish a relationship between the drought occurrences in Tigray region and the climate factors at the local and global scale. Monthly gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.01 was used to calculate the Standardized Precipitation and Evapotranspiration Index (SPEI). As potential global causal factors, SOI (Southern Oscillation Index), Sea Surface Temperature Anomalies (SSTAs) of the tropical oceanic surfaces, the Red Sea and Nino 3.4 regions were obtained from various sources. Vegetation cover and albedo data were also considered as local drought causal factors. The analyses results show that the zero-time-lag SSTA in the tropical Indian Ocean was identified as statistically significant (p

Empty container repositioning (ECR) is one of the major bottlenecks of maritime container transportation that should be minimized. To achieve this, it is crucial to reveal the leading causal factors. From a container liner firm perspective, this paper constructs a novel model of the causal mechanism of ECR with its reflections and prevention measures holistically. For this aim, this study examines the probabilistic relationships among the ECR causal factors, which are revealed qualitatively and quantitatively utilizing a fuzzy Bayes network method. Sensitivity and validation analyses subsequently are carried out to enhance the accuracy of findings. Outcomes of the research point out that the industry-specific and operational causal factors have the largest effect equally to explain the occurrence of ECR. Among the root factors, structural trade imbalance has the largest effect on the occurrence of ECR, and it is followed by seasonal demand. It is also observed that factors leading to equipment inefficiency have the least effect on ECR occurrence. Furthermore, the gray box pooling solution appears as the most effective solution to mitigate the negative impacts of ECR on both firm costs and the environment. What follows the gray box pooling are foldable containers and information technology solutions/ intermediaries in order. This paper provides insight into ECR as a way to enhance effectiveness in container operations, and container liner firms can utilize the outcomes of this paper to mitigate the accumulation of empty containers. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping plays an important role in integrated watershed management planning, especially in the field of land-use planning in landslide-prone areas. This study aims to evaluate the performance of widely used hybrid multi-criteria decision-making (MCDM) models including the integrated index method (IIM), AHP-TOPSIS, and AHP-VIKOR to produce landslide susceptibility maps (LSMs) in the Alamut watershed, Iran. Ten causal layers including slope angle, slope aspect, rainfall, lithology, altitude, land use, and the most common distance maps including distance to fault, distance to stream, and distance to road were used to produce LSMs. Meanwhile, the landslide distribution map was used to evaluate the accuracy of the produced LSMs. The weights of the causal factors were also calculated using the analytic hierarchy process (AHP) method based on the viewpoints of 14 experts. Finally, the quality sum (Qs) index as well as the receiver operating characteristic (ROC) curves were used to validate the performance of the MCDM models. The Qs values for AHP-TOPSIS, IIM, and AHP-VIKOR were calculated 0.355, 0.903, and 0.703, respectively. Therefore, IIM and AHP-VIKOR methods are more efficient than AHP-TOPSIS to create LSM. The area under the curve (AUC) values of the ROC curves for AHP-TOPSIS, IIM, and AHP-VIKOR were calculated 0.784, 0.853, and 0.844, respectively. Therefore, the accuracy of the MCDM models is acceptable for TOPSIS and excellent for IIM and AHP-VIKOR. In general, results of the performance assessment of MCDM models indicated the good performance of the models to produce LSMs, so that the IIM was introduced as the best model. Also, the performance of IIM and AHP-VIKOR are more than the AHP-TOPSIS model to produce LSMs. Overall, the application of the AHP method in combination with other MCDM models as a hybrid model to produce LSMs is recommended under the principles of group decision-making in AHP for land-use planning and landslide risk management. [ABSTRACT FROM AUTHOR]