Two-stage Methodology Research Articles

Can a Black-Box AI Replace Costly DMA Testing?-A Case Study on Prediction and Optimization of Dynamic Mechanical Properties of 3D Printed Acrylonitrile Butadiene Styrene.

The complex and non-linear nature of material properties evolution during 3D printing continues to make experimental optimization of Fused Deposition Modeling (FDM) costly, thus entailing the development of mathematical predictive models. This paper proposes a two-stage methodology based on coupling limited data experiments with black-box AI modeling and then performing heuristic optimization, to enhance the viscoelastic properties of FDM processed acrylonitrile butadiene styrene (ABS). The effect of selected process parameters (including nozzle temperature, layer height, raster orientation and deposition speed) as well as their combinative effects are also studied. Specifically, in the first step, a Taguchi orthogonal array was employed to design the Dynamic Mechanical Analysis (DMA) experiments with a minimal number of runs, while considering different working conditions (temperatures) of the final prints. The significance of process parameters was measured using Lenth’s statistical method. Combinative effects of FDM parameters were noted to be highly nonlinear and complex. Next, artificial neural networks were trained to predict both the storage and loss moduli of the 3D printed samples, and consequently, the process parameters were optimized via Particle Swarm Optimization (PSO). The optimized process of the prints showed overall a closer behavior to that of the parent (unprocessed) ABS, when compared to the unoptimized set-up.

What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology

Social media users share a variety of information and experiences and create Electronic Word of Mouth (eWOM) in the form of positive or negative opinions to communicate with others. In the context of the COVID-19 outbreak, eWOM has been an effective tool for knowledge sharing and decision making. This research aims to reveal what factors of eWOM can influence travelers’ trust in their decision-making to travel during the COVID-19 outbreak. In addition, we aim to find the relationships between trust in eWOM and perceived risk, and perceived risk and the decision to travel. These relationships are investigated based on online customers’ reviews in TripAdvisor’s COVID-19 forums. We use a two-stage data analysis which includes cluster analysis and structural equation modeling. In the first stage, a questionnaire survey was designed and the data was collected from 1546 respondents by referring to the COVID-19 forums on TripAdvisor. Specifically, we use k-means to segment the users’ data into different groups. In the second stage, Structural Equation Modeling (SEM) was performed to inspect the relations between the variables in the hypothesized research model using a subsample of 679 respondents. The results of the first stage of the analysis showed that three segments could be discovered from the collected data for trust based on eWOM source and eWOM message attributes. These segments clearly showed that there are significant relationships between trust and perceived risk, and between perceived risk and the decision to travel. The results in all segments showed that users with a low level of trust have a high level of perceived risk and a low level of intention to travel during the COVID-19 outbreak. In addition, it was found that users with a high level of e-trust have a low level of perceived risk and a high level of intention to travel. These results were confirmed in all segments and these relationships were confirmed by SEM. The results of SEM revealed that visual and external information moderated the relationship between eWOM length and trust, and experience moderated the relationship between trust and perceived risk. For the moderating role of gender, it was found that the perceived risk has a higher impact on the decision to travel in the female sample.

Electric Vehicle Charging Load Allocation at Residential Locations Utilizing the Energy Savings Gained by Optimal Network Reconductoring

In this study, a two-stage methodology based on the energy savings gained by optimal network reconductoring was developed for the sizing and allocation of electric vehicle (EV) charging load at the residential locations in urban distribution systems. During the first stage, the Flower Pollination Algorithm (FPA) was applied to minimize the annual energy losses of the radial distribution system through optimum network reconductoring. A multi-objective function was formulated to minimize investment, peak loss, and annual energy loss costs at different load factors. The results obtained with the flower pollination algorithm were compared with the particle swarm optimization algorithm. In the second stage, a simple heuristic procedure was developed for the sizing and allocation of EV charging load at every node of the distribution system utilizing part of the annual energy savings obtained by optimal network reconductoring. The number of electric cars, electric bikes, and electric scooters that can be charged at every node was computed while maintaining the voltage and branch current constraints. The simulation results were demonstrated on 123 bus and 51 bus radial distribution networks to validate the effectiveness of the proposed methodology.

Methodology and Application of Safety Evaluation of Reinforced Concrete Girder Bridges during Earthquakes

The actual earthquake resistance performance and the seismic damage state of bridges during future earthquakes are important issues that need to be resolved. Using an expressway reinforced concrete (RC) girder bridge in a high seismic intensity area of China as the research object, the damage correlation between different structural components of the bridge is analyzed, and the key components that determine the structural safety state of the bridge are determined. Then, the safety evaluation indexes of the bridge pier and bearing are researched, and a two-stage seismic safety evaluation methodology for RC girder bridges is proposed. The first stage is a rapid and general evaluation using empirical statistical methods, and the second stage is a precise evaluation obtained by calculating the damage index of the components. Subsequently, the seismic damage prediction matrix is presented. Considering the modification of the bridge span number, service life, and skew angle, a seismic safety evaluation from a typical single bridge to a group of bridges of the same type is implemented. Finally, an actual expressway bridge in China is presented as a numerical example to illustrate the application of the method. The research results show that damage to the key components, including bearings, piers, and abutments, is the deciding factor of the bridge damage state. The seismic damage states of piers and bearings can be conveniently assessed according to the pier top displacement angle and bearing shear deformation during earthquakes. According to the suggested standard of RC girder bridge seismic damage, the seismic safety evaluation of the whole bridge structure can be obtained using the seismic safety evaluation of individual key components of the bridge structure. According to the evaluation results of individual bridges and considering the modification of influencing factors, an earthquake performance evaluation of a group of bridges of the same type can be obtained. The two-stage seismic safety evaluation methodology proposed in this study is effective and efficient.

Environmental assessment of land as a basis for improving the efficiency and sustainability of agricultural land use

Today, in agricultural land use, there is an increase in environmental problems, deterioration of environmental indicators, which negatively affect the economy of the agro-industrial complex. Elimination of negative environmental consequences often requires huge financial costs, which significantly exceed the costs of maintaining and protecting the environment, as a result of which production yield and the profitability of individual enterprises decreases, as well as the efficacy of the industry as a whole. The only way to preserve soil quality is to introduce economic mechanisms for greening land use, the most important of which is the environmental assessment of land. The aim of the research was to develop a concept for improving the efficiency and sustainability of agricultural land use based on environmental land assessment. The presented two-stage methodology for assessing the ecological state of arable land made it possible to reflect the nature and degree of manifestation of negative processes, as well as to establish the degree of anthropogenic impact on agricultural landscapes. The materials of the conducted environmental assessment served as an objective basis for the development of the concept of rational and environmentally sustainable land use within the boundaries of the municipal district and made it possible to formulate measures to improve the efficiency and sustainability of the use of arable land by agricultural enterprises.

Intra-operative OCT (iOCT) Super Resolution: a Two-Stage Methodology Leveraging High Quality Pre-operative OCT Scans.

Regenerative therapies have recently shown potential in restoring sight lost due to degenerative diseases. Their efficacy requires precise intra-retinal delivery, which can be achieved by robotic systems accompanied by high quality visualization of retinal layers. Intra-operative Optical Coherence Tomography (iOCT) captures cross-sectional retinal images in real-time but with image quality that is inadequate for intra-retinal therapy delivery. This paper proposes a two-stage super-resolution methodology that enhances the image quality of the low resolution (LR) iOCT images leveraging information from pre-operatively acquired high-resolution (HR) OCT (preOCT) images. First, we learn the degradation process from HR to LR domain through CycleGAN and use it to generate pseudo iOCT (LR) images from the HR preOCT ones. Then, we train a Pix2Pix model on the pairs of pseudo iOCT and preOCT to learn the super-resolution mapping. Quantitative analysis using both full-reference and no-reference image quality metrics demonstrates that our approach clearly outperforms the learning-based state-of-the art techniques with statistical significance. Achieving iOCT image quality comparable to pre-OCT quality can help this medical imaging modality be established in vitreoretinal surgery, without requiring expensive hardware-related system updates.

СОЗДАНИЕ ГЕОИНФОРМАЦИОННОЙ СИСТЕМЫ СОПРОВОЖДЕНИЯ УСТОЙЧИВОСТИ ЭКОЛОГИЧЕСКОГО ЛАНДШАФТА СЕЛЬСКИХ ПОСЕЛЕНИЙ СУБЪЕКТА РФ (НА ПРИМЕРЕ РОСТОВСКОЙ ОБЛАСТИ)

work is devoted to the issues of integrated development of rural areas. The advantages of a systematic approach to the main policies of the sustainable development paradigm goals implementation and the possibility of using geoinformation technologies as a tool for multidimensional analysis of rural areas are considered. The assessment of the landscape ecological potential is complicated due to the variety of criteria for analyzing the territory and the lack of a common unit of measurement. The methodology of a comprehensive assessment of the territory allows integrating diverse information about the of rural settlements territories ecological potential. Special attention is paid to the state of ecology as one of the most important criteria for environmental quality. A two-stage methodology for monitoring the development of rural settlements is proposed, which allows assessing the territory at the federal and local levels according to a set of indicators reflecting the main aspects of socio-economic processes. For places where the population lives compactly, special attention is paid to assessing the quality of the environment according to three groups of factors that take into account environmental comfort, comfort of movement and other indicators that characterize the quality of the environment in terms of the individual needs of residents. The experimental part of the article presents electronic maps built in the ESRI ArcGIS and based on first-level evaluation factors for the Rostov region.

Tree-Partitioning as an Emergency Measure to Contain Cascading Line Failures

This paper proposes to replace controlled islanding, which is a defense mechanism against cascading failures, by tree partitioning whereby some of the tie-lines connecting the clusters are still connected in such a way that the cluster-level graph forms a tree. Tree-partitioning prevents line failures from spreading between clusters, similarly as for islanding, but keeps the clusters connected. That results in three main advantages. Power transfers between the clusters can still take place, helping to balance each cluster and limiting any necessary load shedding. Fewer lines are cut, which reduces the shock to the system. There is no need to re-synchronize the clusters after the emergency. This paper offers a simple graph-theoretic justification for tree-partitioning, rather than one based on the spectral analysis of network Laplacian proposed in the literature. It also proposes a two-stage methodology, which utilizes spectral clustering for splitting a network into tree-connected clusters. Test results performed on the 118 node IEEE test network have confirmed the usefulness of the methodology.

A 3D Voronoi diagram based form error estimation method for fast and accurate inspection of free-form surfaces

Coordinate Measuring Machines (CMM) are widely used in form inspection of free-form surfaces. Generally, the form error at each measured point is estimated using the widely known and accurate point-inversion method. This method has relatively high time complexity and cannot be preferred for fast inspection. Hence in this work, an alternative two-stage methodology based on the concept of the Voronoi diagram is proposed. In the first stage, the poles data is extracted from the Voronoi diagram of the discretized surface. In the second stage, the form-error-estimate algorithm executing in O(m log n) time estimates the errors using the poles data and the discretized surface. Numerical and experimental implementations are executed using NURBS surfaces. The proposed method’s accuracy is on par with the point-inversion method and is 94.97% faster than the latter. Hence this method can be used for fast and accurate CMM and CNC based (in-situ) free-form surface inspection.

Volatility, Open Interest, and Trading Volume in Indian Futures Markets

Purpose : The paper’s objective was to scrutinize the impact of trading volume and open interest on the volatility of selected futures contracts of Indian markets. The change in notional behavior and size of such behavior was examined, and the contribution of trading volume and open interest in the behavior of the futures contract prices were investigated. Methodology and Approach : This study included a sample of daily prices for the period from 2011 – 2019 for 33 futures contracts from Indian futures markets, which included stock and commodity indices, commodities, government securities, and currencies. This study involved a two-stage methodology. In the first part, the E-GARCH model was used to investigate the disproportionate volatility response to various types of shocks. The second part of the methodology focused on investigating the coexistent relations between open interest, trading volume, and volatility using multiple regression analysis. Findings : The results showed that trading volume and open interest affected volatility, but the direction and quantum of impact depended on various variables. It was also found that trading volume and volatility had positive coexistent relations for most futures contracts. Practical Implications : These findings have substantial inferences and repercussions for portfolio managers, analysts, and investors for investment assessments and decisions regarding asset allocations in futures markets. Higher volatility will lead to a higher level of fretfulness among market participants and investors, which will push them to be more risk-averse. The results of the study also have pertinent effects for policymakers with respect to the Indian stock market and global countries. Originality/Value : The author believes that these results would magnify the volatility relations among different futures contracts.

Scoring to estimate the financial statement fraud risk during the audit

Subject. This article assesses the propensity for material misstatement risk due to unfair actions of persons charged with the financial statements preparation, based on their behavioral traits. Objectives. The article aims to develop a scoring type methodology for identifying the propensity for material misstatement risk due to unfair actions of persons charged with the financial statements preparation. Methods. For the study, we used a multidimensional statistical method of discriminant analysis based on empirical data from an author-conducted survey of 515 employees charged with the financial statements preparation in companies. Results. The article presents a two-stage methodology that helps estimate whether a person has traits associated with a hyperpropensity for financial statements fraud risk. Conclusions and Relevance. The developed methodology for detecting the fraud risk is easy to use. It gives the result in binary form and does not violate the principles of audit ethics. The estimated material misstatement risk due to unfair actions makes it possible to justify the need for appropriate audit procedures when developing a strategy and audit plan.

Modelling driver's reactive strategies in e-hailing platforms: an agent-based simulation model and an approximate analytical model

For an e-hailing taxi operation, we analyse a driver's profit-maximising reactive strategy (to either accept or refuse a ride request) in response to the ride request broadcast by the platform. We analyse four operating modes, each of which is a combination of either of two reactive strategies: no refusal and refusal based on proximity, and either of two broadcasting methods. In an operating mode, our objective is to evaluate the expected total profit in a shift. We adopt a two-stage methodology to answer the research questions. In the first stage, we develop an agent-based simulation model to capture the effect of multiple taxis on driver's reactive strategy. Using real trip data, we find that a driver could follow a strategy of refusal based on proximity and earn approximately 25% more than the baseline no refusal strategy. In the second stage, we develop an approximate analytical model for a single taxi operation and compare the performance against the agent-based simulation model. We develop closed-form expressions of the expected total profit for each operating mode and topology of the service region. We find that our approximate analytical model provides an upper bound, and the profit deviation lies within 20% of the agent-based simulation model.

Social media attachment: Conceptualization and formative index construction

There is little consensus on the dimensions of social media attachment, conceptualized as a unidimensional construct by some scholars and multidimensional by others. The conceptual labels given to those dimensions also differ across studies, posing challenges to researchers. This study addresses some of those conceptual issues by developing a formative index of social media attachment. It employs a two-staged methodology involving interviews that are used to develop the items and elicit the dimensions of attachment and a survey to test the index empirically. The results provide support for an eight-item parsimonious index that captures the multifaceted nature of social media attachment: connecting/socializing; keeping up to date; dependence; dysfunctional use; expressing feelings; self-presentation; seeking self-esteem; and seeking knowledge. The index displays sound psychometric properties and is nomologically valid. Theoretically, it reflects the various conceptualizations of social media attachment in a single short measure. The index is also practically useful to researchers.

The Impact of Perceived Social Support During Pregnancy on Postpartum Infant-Focused Anxieties: A Prospective Cohort Study of Mothers in Northern Jordan.

ObjectiveThis study aimed to evaluate the association between perceived social support during pregnancy and levels of anxiety among postpartum women using an anxiety-specific screening instrument.MethodsUsing a prospective cohort design, a two-stage methodology was conducted to collect data from women seeking maternal care at the King Abdullah University Hospital in northern Jordan. In the first stage, perceived social support was assessed among pregnant women using the Medical Outcomes Study Social Support Survey. During the first six months after childbirth, postpartum women were contacted to complete the second stage, wherein their perceptions of infant-focused anxieties were assessed using the Postpartum Specific Anxiety Scale. In our study, two types of infant-focused anxieties were investigated among a final sample of 419 mothers: infant safety and welfare anxieties and practical infant care anxieties.ResultsThe results of multivariate linear regression analysis indicated that providing pregnant women with high levels of emotional support from close social networks (β= −0.08, p= 0.01) and perceiving informational support from health care providers (β= −0.71, p< 0.01) were protective factors for reducing the levels of postpartum anxiety concerning infant safety and welfare. Our findings also demonstrated that pregnant mothers who perceived high informational support from health care providers had a lower level of postpartum anxiety about practical infant care (β= −0.20, p< 0.01). In contrast, mothers who reported receiving high tangible support from close social networks during pregnancy had a significantly higher level of perceived anxiety concerning practical infant care after delivery (β= 0.13, p= 0.02).ConclusionOur study suggests that postpartum anxiety would be reduced if effective informational support were readily available for pregnant women. There is a clear need for building bridges between women, their families, and providers to distinguish the specific type and amount of support that should be provided to mothers during pregnancy.

New manufacturing challenges facing sustainability

We sought to identify opportunities for Industry 4.0 technologies in Sustainable Manufacturing using a two-stage methodology. In the first stage, we investigated Industry 4.0, sustainability, and Smart Factories and identified eleven technological trends linked to Sustainable Manufacturing. In the second stage, we graphically integrated the technological trends with Sustainable Manufacturing and a Smart Factory scheme in the context of Industry 4.0. We formally discussed the interconnection between these concepts and the relevant literature from which we derived these opportunities.

Formulation and Prioritization of Sustainable New Product Design in Smart Glasses Development

Due to fierce competition in the global market, success in product innovation has always been challenging for most enterprises to be able to stand out in business values and product novelty. Typically, available technological features in the market are taken into consideration in the innovation process for differentiation from existing products. In order to enhance the likelihood of innovation success, project portfolio management (PPM) has recently been advocated to examine the supply chain performance of new product development (NPD) projects in terms of economic, social, and sustainable aspects. In this study, a two-stage methodology is proposed to formulate and select the most appropriate NPD project portfolio by means of multi-criteria decision-making (MCDM) approaches in probabilistic and group decision-making processes. In stage one, the available product features on the market are searched for and ranked to indicate a number of potential NPD projects. In stage two, such projects are evaluated by the sustainable supply chain operation reference (SustainableSCOR) model to select the most sustainable NPD project for product development. Moreover, a case study of developing augmented reality (AR) smart glasses is conducted to demonstrate the above methodology, with the result indicating that the functions of voice commands, 3D visualization, and phone calls should be focused on for the next generation of smart glasses.

Determinants of Bank Efficiency in ASEAN5: Size as a Control Variable

Purpose – The purpose of this study was to analyze the effect of Bank Age, Loan to Assets Ratio (LAR), Net Interest Margin (NIM), and non-interest margin (Non NIM) on bank efficiency, especially in ASEAN-5 countries (Indonesia, Singapore, Thailand, Malaysia and Philippines). In this research, bank size was used as a control variable. Design/methodology/approach – The sample of the research was taken form general banking entities listed in each country over the period of 2014-2018. Purposive sampling method was used to select 58 banks as the sample population. We use two-stage methodology in this research by using Data Envelopment Analysis (DEA) to calculate bank efficiency and Multiple Regression Analysis (MRA). Findings- The result showed that capitalization and bank age had a negative significant effect on bank efficiency, while the variable of Loan to Asset Ratio (LAR), Net Interest Margin (NIM) and No-NIM had a positive significant effect on bank efficiency. Originality/value – The measurement of bank efficiency was conducted by utilizing DEA in this study to examine the bank efficiency in ASEAN 5.

Mean-Reverting 4/2 Principal Components Model. Financial Applications

In this paper, we propose a new multivariate mean-reverting model incorporating state-of-the art 4/2 stochastic volatility and a convenient principal component stochastic volatility (PCSV) decomposition for the stochastic covariance. We find a quasi closed-form characteristic function and propose analytic approximations, which aid in the pricing of derivatives and calculation of risk measures. Parameters are estimated on three bivariate series, using a two-stage methodology involving method of moments and least squares. Moreover, a scaling factor is added for extra degrees of freedom to match data features. As an application, we consider investment strategies for a portfolio with two risky assets and a risk-free cash account. We calculate value-at-risk (VaR) values at a 95% risk level using both simulation-based and distribution-based methods. A comparison of these VaR values supports the effectiveness of our approximations and the potential for higher dimensions.

Using Quantile Regression to Analyze the Relationship between Socioeconomic Indicators and Carbon Dioxide Emissions in G20 Countries

Numerous studies addressed the impacts of social development and economic growth on the environment. This paper presents a study about the inclusive impact of social and economic factors on the environment by analyzing the association between carbon dioxide (CO2) emissions and two socioeconomic indicators, namely, Human Development Index (HDI) and Legatum Prosperity Index (LPI), under the Environmental Kuznets Curve (EKC) framework. To this end, we developed a two-stage methodology. At first, a multivariate model was constructed that accurately explains CO2 emissions by selecting the appropriate set of control variables based on model quality statistics. The control variables include GDP per capita, urbanization, fossil fuel consumption, and trade openness. Then, quantile regression was used to empirically analyze the inclusive relationship between CO2 emissions and the socioeconomic indicators, which revealed many interesting results. First, decreasing CO2 emissions was coupled with inclusive socioeconomic development. Both LPI and HDI had a negative marginal relationship with CO2 emissions at quantiles from 0.2 to 1. Second, the EKC hypothesis was valid for G20 countries during the study period with an inflection point around quantile 0.15. Third, the fossil fuel consumption had a significant positive relation with CO2 emissions, whereas urbanization and trade openness had a negative relation during the study period. Finally, this study empirically indicates that effective policies and policy coordination on broad social, living, and economic dimensions can lead to reductions in CO2 emissions while preserving inclusive growth.

Machine learning-based forecasting of firemen ambulances’ turnaround time in hospitals, considering the COVID-19 impact

When ambulances’ turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g., the current coronavirus disease 2019 (COVID-19) pandemic. Taking this into consideration, this paper presents a first study on the COVID-19 impact on ambulances’ TT by analyzing historical data from the Departmental Fire and Rescue Service of the Doubs (SDIS 25), in France, for three hospitals. Because the TTs of SDIS 25 ambulances increased, this paper also calculated and analyzed the number of breakdowns in services, which augmented due to shortage of ambulances that return on service in time. It is, therefore, vital to have a decision-support tool to better reallocate resources by knowing the time EMSs ambulances and personnel will be in use. Thus, this paper proposes a novel two-stage methodology based on machine learning (ML) models to forecast the TT of each ambulance in a given time and hospital. The first stage uses a multivariate model of regularly spaced time series to predict the average TT (AvTT) per hour, which considers temporal variables and external ones (e.g., COVID-19 statistics, weather data). The second stage utilizes a multivariate irregularly spaced time series model, which considers temporal variables of each ambulance departure, type of intervention, external variables, and the previously predicted AvTT as inputs. Four state-of-the-art ML models were considered in this paper, namely, Light Gradient Boosted Machine, Multilayer Perceptron, Long Short-Term Memory, and Prophet. As shown in the results, the proposed methodology provided remarkable results for practical purposes. The AvTT accuracies obtained for the three hospitals were 90.16%, 97.02%, and 93.09%. And the TT accuracies were 74.42%, 86.63%, and 76.67%, all with an error margin of ±10 min.