Proposed Model Framework Research Articles

Breaking Language Barriers: How E-Learning Enhances Metacognitive Awareness and Self-Efficacy in Foreign Language Acquisition

With its plethora of advantages, learning foreign language brings out a lot of benefits to students. Acquiring the foreign language has the potential to attract students to uplift the educational environment to be more productive and more strategised. Unfortunately, acquiring the foreign language can be troublesome since students were unaware of the skills and strategies needed. This paper explains the pilot test approach to a proposed framework model based on metacognitive awareness and self-efficacy before embarking on the extensive research necessary to develop best practices in foreign language learning. The content validity index and a pilot study were used to determine the usability and reliability of a survey questionnaire. Based on a simple random sampling plan, an online survey method was used to gather data from 163 respondents at the Universiti Malaya, Malaysia. The results indicate that the instrument's content validity index value is 0.9. The Cronbach alpha values for most of the constructs in the proposed framework model ranged between 0.9 and 0.7, indicating that the constructs are highly reliable. To contribute to the body of knowledge, the researchers provide an overview of the pilot test procedures as well as the methodology that was utilized. Based on the integration of the unified theory of acceptance and use of technology and the strategies needed to acquiring foreign language, a proposed framework model is presented to provide a more comprehensive framework model. It is concluded that the questionnaire items are deemed suitable and acceptable for further examination on a larger scale. As a result, the instrument can be investigated further in other contexts.

Flood risk investigation of pedestrians and vehicles in a mountainous city using a coupled coastal ocean and stormwater management model

AbstractTo examine the attributes, underlying mechanisms, and impacts of rainfall patterns on the vulnerability of pedestrians and vehicles to flood‐induced instability within mountainous urban areas, we introduced an integrated urban flood model that combined the Storm Water Management Model (SWMM) and Finite Volume Coastal Ocean Model (FVCOM). We implemented this model in the Yuelai New Town of Chongqing, China. Our findings indicated that in the case of early peak rainfalls, there was a rapid surge in flood volume during the initial stages of rainfall , while this increase was more gradual when the peak rainfall was delayed. Furthermore, for events with the same return period, flood peaks resulting from later peak rainfalls covered a larger area compared with those from earlier peak rainfalls; however, this effect diminished with increasing return periods. As the return period was extended, the exposed risk area for pedestrians and vehicles expanded. Analysis of instability indices revealed that pedestrians exhibit a lower index compared with vehicles, adults fare better than children, and SUVs outperform sedans. The efficacy of our proposed model framework was demonstrated through its successful application in assessing urban flood risk and evaluating the instability index for pedestrians and vehicles within a mountainous urban setting.

Nonparametric estimation of allocative efficiency using indirect production theory: Application to container ports in Norway

AbstractAdaption to prices is an important feature of productivity development. This paper proposes an extension of the StoNED model to accommodate estimation of allocative efficiency. It demonstrates how indirect production theory is suited for assessing allocative efficiency and helps alleviating the curse of dimensionality for stochastic nonparametric estimators compared to conventional measures of allocative efficiency. Furthermore, the paper elaborates on the appropriate cost of capital for the estimation of allocative efficiency. The proposed model framework is utilized to study allocative efficiency of Norwegian container ports, thereby adding to the literature on seaport terminal efficiency studies.

Geomorphologic risk zoning to anticipate tailings dams' hazards: A study in the Brumadinho's mining area, Minas Gerais, Brazil

The use of tailings dams in the mining industry is recurrent and a matter of concern given the risk of collapse. The planning of tailings dam's emplacement usually attends construction design criteria and site geotechnical properties, but often neglects the risk of installing the depositional facilities in potentially unstable landscapes, namely those characterized by steep slopes and(or) high drainage densities. In order to help bridging this gap, the present study developed a framework model whereby geomorphologic vulnerability is assessed by a set of morphometric parameters (e.g., drainage density; relief ratio; roughness coefficient). Using the Ribeirão Ferro-Carvão micro-basin (3265.16 ha) as test site, where six dams currently receive tailings from the mining of iron-ore deposits in the Brumadinho region (Minas Gerais, Brazil) and one has collapsed in 25 January 2019 (the B1 dam of Córrego do Feijão mine of Vale, S.A.), the risk of dam instability derived from geomorphologic vulnerability was assessed and alternative suitable locations were highlighted when applicable. The results made evident the location of five dams (including the collapsed B1) in high-risk regions and two in low-risk regions, which is preoccupying. The alternative locations represent 58 % of Ribeirão Ferro-Carvão micro-basin, which is a reasonable and workable share. Overall, the study exposed the fragility related with tailings dams' geography, which is not restricted to the studied micro-basin, because dozens of active tailings dams exist in the parent basin (the Paraopeba River basin) that can also be vulnerable to geomorphologically-dependent hydrologic hazards such as intensive erosion, valley incision or flash floods. Attention to this issue is therefore urgent to prevent future tragedies related with tailings dams' breaks, in the Paraopeba River basin or elsewhere, using the proposed framework model as guide.

Fusing joint distribution and adversarial networks: A new transfer learning method for intelligent fault diagnosis

As integral components within rotating machinery, bearings and gears pose a critical challenge in fault diagnosis. Presently, data-driven fault diagnosis stands out as a viable approach. However, real-world operational variations readily induce domain shifts, complicating transfer learning and diminishing the diagnostic efficacy of models. The process of re-labeling the fault categories of model demands substantial time and financial resources. Consequently, to surmount these challenges, this study introduces a novel unsupervised transfer learning framework that leverages the amalgamation of joint distribution and adversarial networks for diagnosing faults in bearings and gears within rotating machinery.The joint adaptation network facilitates the learning of the transfer network by aligning the joint distribution across multiple specific domain layers. This alignment is achieved through the application of joint maximum mean discrepancy (JMMD) within the joint network. Simultaneously, the adversarial network employs a domain classifier to minimize the domain classification loss, treating it as the difference in domain distribution to mitigate domain shift effectively. The integration of these two methodologies accomplishes domain alignment, reduces model training time, and enhances the accuracy and stability of the diagnostic model.Validation of the proposed model framework is conducted using four sets of bearing faults and six sets of gear faults. The results confirm the superior accuracy and stability of the new model framework in addressing bearing and gear faults within the realm of rotating machinery.

Addressing six standard model problems with Technically Natural Higgs models

We aim to study the potential of the recently proposed model framework, called Technically Natural Higgs (TNH), in addressing six fundamental problems in particle physics and cosmology. These questions encompass the electroweak (EW) naturalness problem, neutrino mass generation, nature of the inflaton, the matter-antimatter asymmetry problem, origin of dark matter (DM) and the strong CP problem. We investigate various solutions within the TNH framework for three inflation scenarios — Higgs, Starobinsky and scale-independent inflation. In the minimal TNH model, the Higgs is a mixture of an elementary and a composite state, with a compositeness scale far exceeding the EW scale. Traditionally, this has required an unnatural small vacuum misalignment, but in the TNH framework a novel mechanism enables a technically natural large compositeness scale, even up to the Planck scale. In this model framework, we demonstrate that a scale-invariant version of the minimal TNH model, featuring a special energy scale of around mathcal{O} (1012) GeV, loop-induced by the inflaton, simultaneously yields a technically natural 125-GeV Higgs boson, scotogenic neutrinos, a scale-invariant inflaton and a QCD axion DM candidate. These components dynamically generate the Planck scale and collectively have the potential to address all six open questions.

An adaptive modeling for bifacial solar module levelized cost and performance analysis for mining application

AbstractPower density and efficiency typically dominate design approaches for power electronics. However, cost optimality is in no way guaranteed by these strategies. A design framework that minimizes the (i) levelized cost of electricity (LCOE), (ii) collection of light, and (iii) irradiance of the generation system is proposed as a solution to this flaw. From an improvement of the swarm behavior optimization model to get a minimum LCOE of solar panel, we design to optimize height, tilt angle, azimuth angle, and some parameters to solve the objective function and LCOE improvement problem to obtain the optimal design problem. In adaptive salp swarm optimization (ASSO), this change's proposed model producer swarm behavior is regarded as an adaptive process that keeps the algorithm from prematurely converging during exploration. The proposed algorithm's performance was confirmed using benchmark test functions, and the results were compared with those of the salp swarm optimization (SSO) and other efficient optimization algorithms. LCOE condition as far as “land‐related cost” and “module‐related cost” demonstrates that the optimal design of bifacial farms is determined by the interaction of these parameters. This proposed model can be used to evaluate visibility on building surfaces that are suitable for mining applications like crushing. Experimentation results show Minimum LCOE AS 0.05 (€/Kw)minimum irradiance and collection light as 336.23(w/m2) and 83.02%n proposed framework model. The swarm optimization method is contrasted with the optimal parameters derived from a conventional solver.

Fusion with Joint Distribution and Adversarial Networks: A New Transfer Learning Approach for Intelligent Fault Diagnosis

Bearings and gears are important components in rotating machinery, and the diagnosis of faults in bearings and gears has always been an important topic. Currently, data-driven fault diagnosis is a better method. However, under actual working conditions, domain shift can easily occur due to different operating conditions, leading to difficulties in transfer learning and significantly reducing the diagnostic performance of the model. Re-labeling the fault types of the model is time-consuming and costly. To overcome these difficulties, a new unsupervised transfer learning framework based on the fusion of joint distribution and adversarial networks has been introduced for the fault diagnosis of bearings and gears in rotating machinery. The joint adaptation network learns the transfer network by aligning the joint distribution of multiple specific domain layers across domains, based on Joint Maximum Mean Discrepancy (JMMD) to achieve domain alignment. At the same time, the domain classifier in the adversarial network is used to minimize the domain classification loss as domain distribution difference to minimize domain shift. The fusion of these two methods achieves domain alignment, reduces model training time, and improves the accuracy and stability of the model. The experimental results demonstrate that the proposed model framework exhibits excellent performance in detecting and classifying different types of faults. The new model framework also demonstrates outstanding performance across various fault detection and classification tasks.

A novel approach to predicting customer lifetime value in B2B SaaS companies

AbstractIn this paper, we propose a flexible machine learning framework to predict customer lifetime value (CLV) in the Business-to-Business (B2B) Software-as-a-Service (SaaS) setting. The substantive and modeling challenges that surface in this context relate to more nuanced customer relationships, highly heterogeneous populations, multiple product offerings, and temporal data constraints. To tackle these issues, we treat the CLV estimation as a lump sum prediction problem across multiple products and develop a hierarchical ensembled CLV model. Lump sum prediction enables the use of a wide range of supervised machine learning techniques, which provide additional flexibility, richer features and exhibit an improvement over more conventional forecasting methods. The hierarchical approach is well suited to constrained temporal data and a customer segment model ensembling strategy is introduced as a hyperparameter model-tuning step. The proposed model framework is implemented on data from a B2B SaaS company and empirical results demonstrate its advantages in tackling a practical CLV prediction problem over simpler heuristics and traditional CLV approaches. Finally, several business applications are described where CLV predictions are employed to optimize marketing spend, ROI, and drive critical managerial insights in this context.

Artificial Intelligence Aided Crowd Analytics in Rail Transit Station

Crowd analysis and management is a key area of study for transit agencies seeking to optimize their operations and to facilitate safety risk management activities. Key features of crowd analytics include passenger flow volume, crowd density, and walking speed. This study proposes a generalized artificial intelligence (AI)-based crowd analytics model framework for rail transit stations, by analyzing and visualizing crowd analysis data from video records of high-density crowds. Specifically, we propose a generalized AI-aided methodological framework (AI-Crowd) for calculating flow volume, crowd density, and walking speed. You Only Look Once (YOLO) and Deep SORT are integrated into the model framework to detect and track each individual’s dynamic position. Camera calibration is utilized to transform detected trajectories into a real-world coordinate system. Methods for calculating crowd dynamic metrics are formulated based on the data. To validate the model framework, several video records from a platform scenario at a major rail transit station are used. The model’s pedestrian counting accuracy can reach 95% and the fundamental diagrams of density–speed are shown to be consistent with empirical studies. Further crowd analysis of a stair scenario and a transferring passage scenario using the proposed model framework shows some differentiations in walking behavior. The methodology has further practical applications, such as monitoring social distancing.

Momentum exchange modeling for coarsely resolved interfaces in a multifield two‐fluid model

AbstractMorphology‐adaptive multiphase models are becoming more established for the numerical description of complex gas‐liquid flows adapting dynamically to the local flow morphology. In the present study, two different numerical methods originally designed for distinct flow morphologies are combined, namely the volume‐of‐fluid and the Euler–Euler method. Both edge cases have been proven to be capable of delivering reliable predictions in the respective use cases. The long‐term goal is to improve the prediction of gas‐liquid flows, regardless of the flow regime in a specific application. To capture the system dynamics with a given grid resolution, the flow fields need to be predicted as precise as possible, while the shape of structures such as gas bubbles need to be recovered adequately in topology and shape. The goal is to obtain reliable predictions on intermediate mesh resolutions rather than relying on fine meshes requiring more computational resources. Therefore, a procedure is proposed to locally measure the degree of resolution. With this information, the hydrodynamics in the interface region can be controlled by means of a dedicated interfacial drag formulation in order to improve simulation results across several levels of spatial resolution. A modified formulation of buoyancy is proposed to prevent unphysical oscillations of vertical velocity near a horizontal interface. The functionality is demonstrated in a three‐dimensional case of a gas bubble rising in stagnant liquid and in a co‐current stratified air‐water channel flow in two‐dimensional space. The choice of these different applications demonstrates the general applicability of the proposed model framework.

Simulation of the solute transport and microstructure evolution during the selective laser melting process

Selective laser melting is of great expectation to be used in additive manufacturing of aerospace components with complex geometry. However, there are still defects in the built parts, such as solutal segregation and unexpected microstructure, which contribute to cracks and lead to failure. At present, most of the simulations focus on the macroscopic grain structure, and the solute transport process has not been well demonstrated yet. In the present work, we develop a two-way fully coupled model based on cellular automaton and finite volume method to simulate the solute transport and dendritic structure evolution during the melting and solidification of the SLM process. The results reveal the microstructural evolution and solute transport during the melting, spreading, and smearing of the powder. The proposed model framework shows good potential to be applied to further numerical investigation on the solidification behaviours of the SLM process.

Longitudinal Data Analysis Based on Bayesian Semiparametric Method

A Bayesian semiparametric model framework is proposed to analyze multivariate longitudinal data. The new framework leads to simple explicit posterior distributions of model parameters. It results in easy implementation of the MCMC algorithm for estimation of model parameters and demonstrates fast convergence. The proposed model framework associated with the MCMC algorithm is validated by four covariance structures and a real-life dataset. A simple Monte Carlo study of the model under four covariance structures and an analysis of the real dataset show that the new model framework and its associated Bayesian posterior inferential method through the MCMC algorithm perform fairly well in the sense of easy implementation, fast convergence, and smaller root mean square errors compared with the same model without the specified autoregression structure.

Tourists’ travel mode and length of stay: Application of a fully nested Archimedean copula structure

Tourism decisions are interdependent of one another. This study aims to explore the interrelations among three pertinent tourism choices, i.e., travel season, transport mode and length of stay, from a data-driven perspective. To achieve a joint model of three travel behavioural decisions, this paper adopts a Frank-type fully nested Archimedean copula function, which is capable of efficiently incorporating a logistic regression model, a multinomial logit model and a lognormal accelerated failure time model. Then, we empirically evaluate the proposed model framework using single-destination Sydney-based trip-level data collected in 2017–2018. Gumbel, Clayton and Frank copula results are compared. To illustrate its application, the modelling results are used to build a simulated COVID-19 pandemic scenario according to the social distancing restrictions within New South Wales, Australia, and a three-dimensional elasticity analysis for trip destinations is performed. The findings suggest the model provides nuanced insights into simulating tourist behaviours and transport policy appraisal aimed at tourism recovery or/and development.

Evaluating and Predicting Overall Equipment Effectiveness for Deep Water Disposal Pump using ANN- GA Analysis Approach

This study proposes the Artificial Neural Network with a Genetic Algorithm analysis approach to investigate the Overall Equipment Effectiveness of the deep-water disposal pump system. The ANN-GA model was developed based on six big losses over eighteen successive months of the operating period to evaluate the current and future performance of the DWD system. 70% of the data was used for training and 15% for each data validation and testing. The DWD system faces frequent failure issues, significantly impacting its performance, so it is important to reveal the main causes of these failures to manage them properly. ANN-GA is applied to make a linear trend prediction and assesses the confidence and accuracy of the results obtained. Analysis of ANOVA (variance) was adopted as an additional decision tool for detecting the variation of process parameters. ANN-GA results showed that the current OEE value ranges between 29% to 54%, whereas the predicted future system performance average is approximately 49%, which reflects the poor performance of the DWD pump system in the future compared to the world- class target (85%). ANN-GA analysis results indicated were very close and matched with the actual values. The model framework and analysis presented are used to develop a decision support tool for managers for early intervention to minimize system deterioration, reduce maintenance costs and increase productivity. Furthermore, it allows early identifying the potential area ofimprovement to support continuous improvement (CI) objectives by identifying and eliminating unnecessary maintenance activities. The proposed model framework uses the ANN approach to identify the current state and predict the future of the system performance to ensure confidence in the results. The contribution of the paper will be helpful for experts like managers, reliability engineers, and maintenance engineers to identify the state of the system's performance in advance.

Improving Intra-Urban Prediction of Atmospheric Fine Particles Using a Hybrid Deep Learning Approach

Growing evidence links intra-urban gradients in atmospheric fine particles (PM2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM2.5 mass concentration at the street level. By using a full-year monitoring data (including meteorological parameters, hourly concentrations of PM2.5, and gaseous precursors) from multiple stations in Shanghai, the largest city in China, as a training dataset, we first apply a convolutional neural network to obtain cross-domain and time-series features so that the inherent features of air quality and meteorological data associated with PM2.5 can be effectively extracted. Next, a Gaussian weight calculation layer is used to determine the potential interaction effects between different regions and neighboring stations. Finally, a long and short-term memory model layer is used to efficiently extract the temporal evolution characteristics of PM2.5 concentrations from the previous output layer. Further comparative analysis reveals that our proposed model framework significantly outperforms previous benchmark methods in terms of the stability and accuracy of PM2.5 prediction, which has important implications for the intra-urban health assessment of PM2.5-related pollution exposures.

LRT-CLUSTER: A New Clustering Algorithm Based on Likelihood Ratio Test to Identify Driving Genes.

Somatic mutations often occur at high relapse sites in protein sequences, which indicates that the location clustering of somatic missense mutations can be used to identify driving genes. However, the traditional clustering algorithm has such problems as the background signal over-fitting, the clustering algorithm is not suitable for mutation data, and the performance of identifying low-frequency mutation genes needs to be improved. In this paper, we propose a linear clustering algorithm based on likelihood ratio test knowledge to identify driver genes. In this experiment, firstly, the polynucleotide mutation rate is calculated based on the prior knowledge of likelihood ratio test. Then, the simulation data set is obtained through the background mutation rate model. Finally, the unsupervised peak clustering algorithm is used to, respectively, evaluate the somatic mutation data and the simulation data to identify the driver genes. The experimental results show that our method achieves a better balance of precision and sensitivity. It can also identify the driver genes missed by other methods, making it an effective supplement to other methods. We also discover some potential linkages between genes and between genes and mutation sites, which is of great value to target drug therapy research. Method framework: Our proposed model framework is as follows. a. Counting mutation sites and the number of mutations in tumor gene elements. b. The nucleotide context mutation frequency is counted based on the likelihood ratio test knowledge, and the background mutation rate model is obtained. c. Based on Monte Carlo simulation method, data sets with the same number of mutations as gene elements are randomly sampled to obtain simulated mutation data, and the sampling frequency of each mutation site is related to the mutation rate of polynucleotide. d. The original mutation data and the simulated mutation data after random reconstruction are clustered by peak density, respectively, and the corresponding clustering scores are obtained. e. We can obtain the clustering information statistics in each gene segment and score of each gene segment from the original single nucleotide mutation data through step d. f. According to the observed score and the simulated clustering score, the p-value of the corresponding gene fragment is calculated. g. We can obtain the clustering information statistics in each gene segment and score of each gene segment from the simulated single nucleotide mutation data through step d.

Coupled Thorens and Soil Conservation Service Models for Soil Erosion Assessment in a Loess Plateau Watershed, China

Assessing soil erosion in China’s severely eroded Loess Plateau is urgently needed but is usually limited by suitable erosion models and long-term field measurements. In this study, we coupled the Thorens and Soil Conservation Service (SCS) models to evaluate runoff and sediment yield during the 1980s and 2010s in the Xiaolihe watershed on the Loess Plateau. Results showed the proposed model framework had a satisfactory performance in modelling spatially distributed runoff and sediment yield. The Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS) and the root mean square error-measured standard deviation ratio (RSR) were 0.93, 4.42% and 0.27 for monthly runoff; and 0.31, 62.31% and 0.82 for monthly sediment yield. The effects of land use changes on runoff and sediment yield were well captured by the SCS and Thorens models. The proposed modelling framework is distributed with a simple structure, requires relatively little data that can be obtained from public datasets, and can be used to predict runoff and sediment yield in other similar ungagged or poorly monitored watersheds. This work has important implications for runoff and erosion assessment in other arid and semi-arid regions, to derive runoff and erosion rates across large areas with scarce field measurements.

An optimal model for domain specific named entity linking with heterogeneous information networks

In this web era, entity linking plays a major role. In the web the information’s are associated with different kinds of data and objects. Heterogeneous information networks (HIN) involved multi composed interlinked interconnected objects with various types of connections which is more prominent in this real world. Most of the research work focused towards processing homogeneous networks as well as linking entities with Wikipedia as knowledge base. In this paper we proposed a probabilistic based domain specific entity linking system that will link named entity mentions detected from unstructured web text corpus with corresponding entity in the existing domain specific Heterogeneous information networks as knowledge base. This work is most challenging due to entity name ambiguity as well as knowledge in the network that are limited one. The proposed model framework presents a model that will link named entity from unstructured web text with domain specific Heterogeneous information network mainly focuses on to learn the weight of meta path. The experiments are done over real world dataset such as DBLP and IMDB dataset.

Automatic Clustering of Indoor Area Features in Shopping Malls

The comprehensive expression of indoor maps directly affects the visualization effect of the map and the user’s map reading experience. Currently, only the points, lines, and polygons of outdoor maps are used as objects of cartographic generalization. Therefore, this study considers indoor map area features as generalization objects and deems the automatic clustering of the indoor area features of shopping malls as the research goal. The approach is used to construct an encoder-decoder clustering model, where the encoder consists of a graph convolutional network and its variant models. The results show that the proposed model framework effectively extracts the area features suitable for the indoor space clustering of shopping malls and improves clustering efficacy. Specifically, the model with the Relational Graph Convolutional Network as the encoder demonstrated the best performance, time complexity, and accuracy of clustering results, with accuracy up to 95%. This study extends the research object of cartographic generalization to indoor maps, enabling the automatic clustering of indoor area features, and proposes a clustering model for the important indoor scene of shopping malls. This is valuable for scholars interested in the cartographic generalization of indoor maps.