Model Approach Research Articles

Employing an Interpretive Structural Modeling–System Dynamics Approach for Deep Foundation Pit Risk Assessment Model

Employing an Interpretive Structural Modeling–System Dynamics Approach for Deep Foundation Pit Risk Assessment Model

Load cascades in spatial networks: A sandpile model approach

In the real world, the construction of infrastructure networks is influenced by spatial factors. For such complex networks, We refer to them as spatial networks. This study investigates, based on the Bak–Tang–Wiesenfeld sandpile model, the impact of spatial factors on sandpile cascading avalanches. Through simulations, we discover that spatial factors not only facilitate the occurrence of moderate-scale avalanches in the system but also have a suppressing effect on the occurrence of the largest cascading avalanches. Using a multiplicative branching process, we analyze the influence of triangular structures composed of nodes on sandpile avalanches and find that nodes within triangular structures are relatively resistant to topples. Because of the relatively stable nature within triangular structures, more load tends to stay within this kind of structures. Once a cascading avalanche occurs in the system, the avalanche scale is often larger than that in the non-spatial counterpart. This increase in avalanche scale results in more load being released by the system, making the spatial networks more relaxed. And their probability of very-large-scale cascading avalanches is lower than that of non-spatial networks.

Influence of spatiotemporal and meteorological variation on Norwegian atmospheric pollen seasonality

Climate change is and will continue to alter plant responses to their environment. This is especially prominent concerning the adaptive tracking in reproductive phenology. For wind pollinated plants, this will substantially influence their pollen seasonality, yet there are gaps in knowledge about how environmental variation influences pollen seasonality. To investigate this, we monitored daily atmospheric pollen concentrations of seven pollen types from ecologically, economically and allergenically important plants (alder, hazel, willow, birch, pine, grass and mugwort) in twelve Norwegian locations spanning the entire country for up to 28 years. Six daily meteorological variables (maximum temperature, precipitation, wind speed, relative humidity, solar radiation and atmospheric pressure) was obtained from the MET Nordic dataset with full data cover. The pollen seasonality was then modelled using four spatial, three temporal and the six meteorological variables in a generalized linear model approach with a negative binomial distribution to investigate how each variable group thematically and individually contribute to variation in pollen seasonality. We found that the full models explained the most variation, ranging from R2 = 20.3 % to 59.5 %. The models were also highly accurate, being able to predict 54.5 % to 99.1 % of daily pollen concentrations to within 20.1 pollen grains/m3. Overall, the temporal variables were able to explain more variation than spatial and meteorological variables for most pollen types. Month, altitude and maximum temperature were the most important single variables for each category. The importance of each variable could be traced back to their individual effects of reproductive phenology, plant metabolism, species distributions and pollen release processes. We further emphasise the importance of source maps and atmospheric regional transport models in further model improvements. By understanding the relevance of environmental variation to pollen seasonality we can make better predictions regarding the consequences of climate change on plant populations.

Enhancing speech emotion recognition with deep learning using multi-feature stacking and data augmentation

This study evaluates the effectiveness of data augmentation on 1D convolutional neural network (CNN) and transformer models for speech emotion recognition (SER) on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset. The results show that data augmentation has a positive impact on improving emotion classification accuracy. Techniques such as noising, pitching, stretching, shifting, and speeding are applied to increase data variation and overcome class imbalance. The 1D CNN model with data augmentation achieved 94.5% accuracy, while the transformer model with data augmentation performed even better at 97.5%. This research is expected to contribute better insights for the development of accurate emotion recognition methods by using data augmentation with these models to improve classification accuracy on the RAVDESS dataset. Further research can explore larger and more diverse datasets and alternative model approaches.

DynamicAug: Enhancing Transfer Learning Through Dynamic Data Augmentation Strategies Based on Model State

Transfer learning has made significant advancements, however, the issue of overfitting continues to pose a major challenge. Data augmentation has emerged as a highly promising technique to counteract this challenge. Current data augmentation methods are fixed in nature, requiring manual determination of the appropriate intensity prior to the training process. However, this entails substantial computational costs. Additionally, as the model approaches convergence, static data augmentation strategies can become suboptimal. In this paper, we introduce the concept of Dynamic Data Augmentation (DynamicAug), a method that autonomously adjusts the intensity of data augmentation, taking into account the convergence state of the model. During each iteration of the model’s forward pass, we utilize a Gaussian distribution based sampler to stochastically sample the current intensity of data augmentation. To ensure that the sampled intensity is aligned with the convergence state of the model, we introduce a learnable expectation to the sampler and update the expectation iteratively. In order to assess the convergence status of the model, we introduce a novel loss function called the convergence loss. Through extensive experiments conducted over 27 vision datasets, we have demonstrated that DynamicAug can significantly enhance the performance of existing transfer learning methods.

THE INNOVATION OF GOVERNMENT RISK MANAGEMENT MITIGATION FOR DISASTER MANAGEMENT IN INDONESIA

Purpose: The dynamics and complexity of governance in disaster management in Indonesia require disaster management through the implementation of GRC (Governance, Risk, Compliance). integrated. The aim of this research is to assess the readiness of GRC innovation in disaster management through risk management mitigation, supporting infrastructure, and developing an integrated GRC implementation development plan. Theoretical reference and Method: Types of qualitative and quantitative research through surveys, distribution of questionnaires, collection of regulatory documents and GRC system policies in an integrated manner, reinforced by interviews with respondents, use of GRC system infrastructure design through infrastructure preparedness in facing disasters which are formulated in the form of policy proposal frameworks and guidelines that refer to against the ISO Handbook Integrated Use of Management System Standard, an action examination (PDCA) sample of 124 respondents. Results and Conclusion: The research results can be explained: 1) The creation of risk management guidelines resulting from integrated GRC practices in disaster risk mitigation has not been standardized. 2) The infrastructure design resulting from a study regarding readiness to implement integrated GRC in government in disaster management in Indonesia has not been implemented in general. 3) Based on the results of the Delphi method, the implementation of GRC has not been integrated. Implications of research: The implications of the research results can be used as recommendations for risk management stakeholders in making decisions based on the implementation of integrated G/R/C. Measuring the level of effectiveness uses a maturity model approach (Maturity Assessment) according to ISO standards. Originality/value: Originality: This research contributes to the implementation of integrated GRC 1) risk management guidelines resulting from integrated GRC practices 2) Infrastructure design resulting from studies regarding disaster management in Indonesia. 3) Delphi method, GRC implementation is not yet integrated.

Effect of source credibility and consumer ethnocentrism on halal purchase intentions in the UK: an elaboration likelihood model approach

PurposeThis study aims to examine the mediating role of product judgement in the relationship between electronic word-of-mouth (eWOM) and purchase intention. Additionally, it explores the moderating effects of source credibility and consumer ethnocentrism on the relationship between eWOM and product judgement, with a specific focus on Halal products. We utilise the Elaboration Likelihood Model (ELM) to investigate how individuals navigate the processing of information, distinguishing between central and peripheral routes. This exploration aims to enhance our understanding of how the ELM framework influences product judgement and purchase intention in the context of eWOM, with a focus on Halal products.Design/methodology/approachTo achieve these objectives, an online survey was conducted in the United Kingdom. The study employed a moderated-mediation model, analysed using PLS-SEM.FindingsThe findings highlight the significant role of source credibility in the central route of information processing and purchase judgements. This study confirms that Halal product judgement fully mediates the relationship between eWOM and purchase intention. Additionally, it reveals that higher source credibility amplifies the impact of eWOM on consumer judgement. However, no significant moderating effect of consumer ethnocentrism on the relationship between eWOM and product judgement was observed in this context.Originality/valueThis study enhances our understanding of how Halal products are adopted in non-Muslim societies, shedding light on persuasive processes. Additionally, it refines the ELM in the context of cross-cultural consumer behaviour. The findings underscore the importance of prioritising source credibility in communication to shape information evaluation and persuasion.

A comparative study on the parametrization of a time-variable geopotential model from GRACE monthly solutions

AbstractThe gravity field of the Earth is time-dependent due to several types of mass variations which take place on different time scales. Usually, the time-variability of the gravitational potential of the Earth is expressed by the monthly determination of a static geopotential model based on data from gravity field missions. In this paper, the variability of the potential is parameterized by a functional approach which contains a polynomial trend and periodic contributions. The respective parameters are estimated based on the monthly solutions derived from the GRACE and GRACE-FO gravity field mission up to a maximum degree of expansion $$n_\text {max}=96$$ n max = 96 . As a preliminary data analysis, a Fourier analysis is performed on selected potential coefficients from the available monthly solutions of the GFZ. The indicated frequency components are then used to formulate a time-dependent analytical approach to describe each Stokes coefficient’s temporal behaviour. Different approaches are presented that include both polynomial and periodic components. The respective parameters for modelling the temporal variability of the coefficients are estimated in a Gauss-Markov model and tested for significance by statistical methods. Extensive comparative numerical studies are carried out between the newly generated model variants and the existing monthly GRACE, GRACE-FO and the existing time dependent EIGEN-6S4 solutions. The numerical comparisons make it clear that estimated models based on all available monthly solutions describe the essential periods very well, but such monthly events that deviate strongly from the mean behaviour of the signal show less precision in the space domain. Models that are estimated based on fourteen consecutive monthly solutions, covering one selected year, represent the amplitudes much more precise. The statements made apply to four initial data used, which are filtered to varying degrees. In particular, DDK2, DDK5 and DDK8, as well as unfiltered coefficients were used. For all the model approaches used, it can be seen that the potential coefficients contain up to about $$n\approx 40$$ n ≈ 40 in case of DDK5 periodically signals with annual, semi-annual or quarterly, as well as Luna nodal periods and do not vary significantly beyond that degree. Only an offset can be estimated significantly for all Stokes coefficients.

Development of an innovation ecosystem model in handling the covid-19 in Indonesia

Technological innovation in the medical area is vital when the COVID-19 pandemic strikes, including in Indonesia. One of the essential innovations is a health facility for infection testing. Through the TFRIC-19 program, several Indonesian researchers have developed a mobile laboratory named Mobile Lab Biosafety Level 2 (MBSL2). MBSL2 is one of the medical innovations from the TFRIC-19 program as a measure to handle the COVID-19 pandemic in Indonesia. TFRIC-19 is an innovation system initiated by government institutions in the RnD area. Establishing TFRIC-19 was the initial step in handling the COVID-19 pandemic in Indonesia. This research aims to identify the actor's role in developing MBSL2. The technique uses the Ecosystem Pie Model (EPM) approach for analysis. It was found that the most essential aspect of creating innovation is collaboration between involved actors.

Pengaruh Indeks Pembangunan Manusia Terhadap Indeks Pertumbuhan Ekonomi Inklusif Provinsi Gorontalo

This research aims to examine the influence of the human development index (average years of schooling, open unemployment rate, percentage of poor people) on the inclusive economic growth index of Gorontalo Province. The Central Statistics Agency obtained the quantitative methodology, covering six districts/cities in Gorontalo Province from 2012-2021. This research uses a panel data regression analysis method using the Fixed Effect Model approach to determine the influence of the dependent variable on the independent variable. The findings of this research show that the human development index variable, namely the average number of years of schooling, has a positive and significant value, the open unemployment rate has a positive and insignificant value, the percentage of poor people has a negative and important value on the inclusive economic growth index.

Analisis Determinan Pembangunan Manusia Berbasis Gender Dan Desentralisasi Fiskal Pada Inklusifitas Pertumbuhan Ekonomi Di Provinsi Nusa Tenggara Barat

This research aims to examine the influence of gender and fiscal decentralization on inclusive economic growth in West Nusa Tenggara Province. This research uses a panel data regression analysis method using the Fixed Effect Model approach to determine the influence of the dependent variable on the independent variable. As a result, the gender-based development index variable, namely the ratio of average years of schooling, has a positive and significant value; the ratio of life expectancy and the open unemployment rate ratio has a negative value and is not substantial; the variable of the degree of fiscal decentralization, namely the ratio of the degree of fiscal decentralization and the capital expenditure ratio, has a value positive and significant towards inclusive economic growth in West Nusa Tenggara Province.

A Reference Process for Assessing the Reliability of Predictive Analytics Results

Organizations employ data mining to discover patterns in historic data in order to learn predictive models. Depending on the predictive model the predictions may be more or less accurate, raising the question about the reliability of individual predictions. This paper proposes a reference process aligned with the CRISP-DM to enable the assessment of reliability of individual predictions obtained from a predictive model. The reference process describes activities along the different stages of the development process required to establish a reliability assessment approach for a predictive model. The paper then presents in more detail two specific approaches for reliability assessment: perturbation of input cases and local quality measures. Furthermore, this paper describes elements of a knowledge graph to capture important metadata about the development process and training data. The knowledge graph serves to properly configure and employ the reliability assessment approaches.

A Model Validatory Approach in Determining Solar Panel Tilting Angles and Orientations at the Brikama Environment of The Gambia

This paper determines the solar panel optimal tilt angle and orientations at the Brikama environment of The Gambia. This was achieved by comparing mathematical model results with experimental data collected using a PV module (Model AP-120) manufactured by AstroPower, USA, and a single-axis tracker technology at the same location and months of the year. The experimental tilt angles obtained range from 5.1°-28.2° with an average angle of 14.8° while the mathematical model approach obtained the tilt angle to be within the range of 9.1°-22.3° with a mean value of 15.5°. The generated power analysis revealed that the peak power produced by the solar panel was recorded at an angle of 30° due South and the mean power gain from the orientation of the PV panel ranged from 3.6-48.1W. The mean power result suggests that, in comparison to a horizontal position, solar collecting efficiency is higher at the optimal tilt angle.

The Role of Family Functioning in the Prevention of Depressive Symptoms and Drug Use Among Hispanic Youth

ABSTRACT Background There is an established link between depression and drug use among adolescents. This relationship holds true for Hispanic youth, for whom familial relationships are especially impactful toward health outcomes. Purpose Our purpose was to examine the relationships between family functioning and adolescent and parent depressive symptoms, and the indirect effects of depressive symptoms on the relationship between family functioning and adolescent drug use. Methods Participants were 231 Hispanic adolescents (52.9% female, Mage = 13.90 ± 1.37) and their parents (93.0% female, Mage = 43.24 ± 6.64) enrolled in an RCT. Data on depressive symptoms, drug use, and family functioning were collected and a latent variable was specified using four indicators of family functioning (positive parenting, monitoring, communication, and involvement). A structural equation model approach was utilized. Results Youth family functioning predicted youths’ depressive symptoms which predicted their own drug use. Youths’ depressive symptoms mediated the relationship between youth family functioning and drug use. Discussion Findings suggest that adolescents’ perceptions of family functioning plays a role in preventing depressive symptoms and drug use among Hispanic youth. Translation to Health Education Practice Teaching students of the role that family support and depressive symptoms play in the manifestation of drug use may be an effective approach to preventing drug use.

An Intelligent Model and Methodology for Predicting Length of Stay and Survival in a Critical Care Hospital Unit

This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions.

Using Unmanned Aerial Vehicles and Multispectral Sensors to Model Forage Yield for Grasses of Semiarid Landscapes

Forage yield estimates provide relevant information to manage and quantify ecosystem services in grasslands. We fitted and validated prediction models of forage yield for several prominent grasses used in restoration projects in semiarid areas. We used field forage harvests from three different sites in Northern Utah and Southern California, USA, in conjunction with multispectral, high-resolution UAV imagery. Different model structures were tested with simple models using a unique predictor, the forage volumetric 3D space, and more complex models, where RGB, red edge, and near-infrared spectral bands and associated vegetation indices were used as predictors. We found that for most dense canopy grasses, using a simple linear model structure could explain most (R2 0.7) of the variability of the response variable. This was not the case for sparse canopy grasses, where a full multispectral dataset and a non-parametric model approach (random forest) were required to obtain a maximum R2 of 0.53. We developed transparent protocols to model forage yield where, in most circumstances, acceptable results could be obtained with affordable RGB sensors and UAV platforms. This is important as users can obtain rapid estimates with inexpensive sensors for most of the grasses included in this study.

The role of noncognitive skills in educational mismatch: evidence from China

PurposeThe purpose of this study is to investigate the effect of noncognitive skills on educational mismatch in the context of China and to further explore the potential mechanisms concerning how noncognitive skills determine mismatch outcomes.Design/methodology/approachThis paper uses the Chinese Family Panel Survey of 2018, which provides the Big Five Personality Inventory to assess respondents' noncognitive skills and contains information on educational mismatch. The authors estimate the effects of noncognitive skills on educational mismatch by means of a probit model. Additionally, the correlated random effects (CRE) model and instrumental variable (IV) approach have also been exploited in the robust checks.FindingsThe findings show that the composite score of noncognitive skills reduces the probability of being overeducated and, conversely, increases the likelihood of being undereducated. When distinguishing the effects of different personality traits, the authors find significantly negative effects of agreeableness and openness on overeducation and a positive effect of openness on undereducation. With regard to heterogeneous analysis, the effects of noncognitive skills on educational mismatch exist mostly among white-collar employees and employees with fewer than 5 years of work experience. Finally, the authors provide two likely mechanisms related to job search effort and social capital, followed by the presentation of supporting evidence.Practical implicationsThe results of this paper underline the importance of noncognitive skills in raising the quality of jobs that individuals can obtain. This suggests that the development of noncognitive skills should be encouraged to be integrated into formal education systems and social job training programs in China.Originality/valueDespite a growing interest in its consequences in the labor market, the role of noncognitive skills in determining educational mismatch has rarely been discussed in developing countries. This study provides the first evidence regarding the effects of noncognitive skills on education mismatch in China. It contributes to the research on noncognitive skills' labor market outcomes and enhances the understanding of the factors driving educational mismatch.

Impedance-Based Method for Predictive Stability Assessment

Impedance-based analysis methods enable a more specific and earlier foundation for assessing harmonic stability in decentralized converter-based power plants compared to the conventional compliance testing in the grid connection process. Essentially, they can be implemented as black-box model approaches without the necessity to disclose internal control models. Initially, only knowledge of the input impedances of the planned grid connection point and the planned PV system is required for application. For this purpose, the method of impedance spectroscopy for inverters has already been developed as a means to determine the effective impedance profile and the internal harmonic sources of inverters, allowing for the description of the frequency-dependent behavior of individual units. The time- and frequency-dependent grid impedance at the grid connection point (GCP) has also been successfully measured in several campaigns on medium and low-voltage grids. Through the coordinated application of both measurement methods, a predictive harmonic assessment is intended in the future, ensuring high planning reliability and grid quality even in grids with a high penetration of power electronics-coupled systems. This paper provides an overview of the current state of research on impedance-based stability criteria and presents measurement methods for practical implementation. Furthermore, it outlines remaining open questions until application in the field.

CP factor model for dynamic tensors

Abstract Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional structure. In this paper, we present a factor model approach, in a form similar to tensor CANDECOMP/PARAFAC (CP) decomposition, to the analysis of high-dimensional dynamic tensor time series. As the loading vectors are uniquely defined but not necessarily orthogonal, it is significantly different from the existing tensor factor models based on Tucker-type tensor decomposition. The model structure allows for a set of uncorrelated one-dimensional latent dynamic factor processes, making it much more convenient to study the underlying dynamics of the time series. A new high-order projection estimator is proposed for such a factor model, utilizing the special structure and the idea of the higher order orthogonal iteration procedures commonly used in Tucker-type tensor factor model and general tensor CP decomposition procedures. Theoretical investigation provides statistical error bounds for the proposed methods, which shows the significant advantage of utilizing the special model structure. Simulation study is conducted to further demonstrate the finite sample properties of the estimators. Real data application is used to illustrate the model and its interpretations.

Random effects models of tumour growth for investigating interval breast cancer.

In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.