Model Validation Results Research Articles

Comparison of travel-time and geostatistical inversion approaches for hydraulic tomography: Synthetic modeling study on data density and well configuration issues

Hydraulic tomography (HT) has been proven to be an effective approach in mapping the heterogeneity of hydraulic parameters. The travel-time based inversion (TTI) and geostatistical inversion (GI) approaches are two of several interpretation methods for HT data. In particular, GI is used to estimate heterogeneous hydraulic conductivity (K) and specific storage (Ss) tomograms, while TTI yields the diffusivity (D = K/Ss) tomogram. The main objective of this paper is to compare the performances of these two methods through a synthetic study. Two cases are designed based on different monitoring configurations. In each case, two independent scenarios are designed as: (1) providing the same dataset, and (2) providing datasets based on recommended strategies for each inversion approach. Then, the estimated tomograms are evaluated by direct comparison of estimated parameter distributions and assessment of model validation results. Comparison results show that the advantages of TTI are: (1) imaging of structural features representing high-D zones; and (2) requirement of less data for inverse modeling. In contrast, the advantages of GI are: (1) estimation of parameters throughout the simulation domain; (2) better characterization of low-D zones; and (3) generating the best estimated tomogram leading to best drawdown predictions. Collectively, our study suggests that TTI is suitable for rapid, coarse characterization of heterogeneity that could be utilized for providing hydrogeological structures for GI as an initial model. GI is more robust and preferable to applications that require higher accuracy in parameter estimation over a larger volume.

High-Resolution Model of Clew Bay—Model Set-Up and Validation Results

Clew Bay is an important aquaculture production area in Ireland. In this study, we focused on a high-resolution simulation of the Clew Bay region based on a regional ocean modeling system (ROMS). Freshwater discharges from eight rivers are included in the model and a wetting–drying scheme has been implemented. The Clew Bay model simulation was validated and calibrated with available observations (e.g., acoustic Doppler current profiler (ADCP), vertical salinity and temperature profiles, and a tide gauge) in the geographic area of the model domain. High correlations were found between the model data and observed temperature, salinity and water levels, along with small root mean square errors. This indicates that the model is able to reproduce the oceanographic phenomena in the study area. The Taylor diagram analysis showed a high correlation coefficient (R = 0.99) between the observed bottom temperature in the Inner Bay and Clew Bay model, along with a small centered root mean square error (RMSD = 0.5 °C). High correlation coefficients (R > 0.80) were found between the model and the two ADCPs for the zonal current component. There was a resemblance in structure between the model and the observed salinity profiles, indicating that freshwater was correctly implemented in the model. Moreover, the correlation coefficient between the model and the tidal sea surface height (SSH) was 0.99, with an RMSD of 0.09 m. We discovered that wind direction and speed had a significant impact on the bay’s water inflow rate. The model outputs can be used to provide scientists, fishermen, and decision-makers with hydrodynamic information on ocean conditions in the bay.

A nonlinear viscoelastic constitutive model with damage and experimental validation for composite solid propellant

The development of a nonlinear viscoelastic constitutive model of composite solid propellant (CSP) coupled with effects of strain rate and confining pressure is essential to assess the reliability of solid propellant grains during ignition operation process. In the present work, a nonlinear viscoelastic constitutive model with novel energy-based damage initiation criterion and evolution model was firstly proposed to describe the coupled effects of confining pressure and strain rate on mechanical responses of CSP. In the developed damage initiation criterion and evolution model, the linear viscoelastic strain energy density was introduced as the damage driving force, and the coupled effects of strain rate, damage history and confining pressure on damage growth were taken into account. Then, uniaxial tensile tests from low strain rates to medium strain rates and various confining pressures, and stress relaxation tests were conducted using a self-made active confining pressure device. Finally, the identification procedures of model parameters and validation results of the constitutive model were presented. Moreover, the master curve of damage initiation parameter was constructed through the time-pressure superposition principle (TPSP). The results show that the developed nonlinear constitutive model is capable of predicting the stress–strain responses of CSP under different strain rates and confining pressures.

Assessing 1D Hydrodynamic Modeling of Júcar River Behavior in Mancha Oriental Aquifer Domain (SE Spain)

In times of population growth, climate change, and increasing water scarcity around the world, it is important to take an objective look at water, a fundamental resource for life. Hydrodynamic modeling makes possible the research of different aspects of the water cycle and the evaluation of different hydrological and hydrogeological forecasting scenarios in the short and medium terms. The present research offers a more detailed scope at the hydrodynamic processes and their space-time distributions on a UE pilot in the Júcar River Basin, providing a calibrated and validated hydrodynamic model of 121 km river reach for 45 years period (1974–2019) on a daily scale. The obtained information is about discharge and water depths along the Júcar River reach within the hydrogeological boundaries of the Mancha Oriental Aquifer (MOA). The river–aquifer interactions have been represented as dynamic boundary conditions expressed as a difference between observed discharges measured in 3 gauging stations. The obtained calibration error performance evaluations of observed and simulated values cover two periods, according to observed data availability from gauging station 08036 with resulting R2 for both discharges and water depths over 0.96. The model validation results were obtained for a different gauge 08132 and the determination coefficients R2 also perform very well with value of 0.90. The model developed might be useful for decision making in water resources management and can be used to generate simulated time series of water depths, levels, discharges, and velocities in reaches where gauging measurements are not available with a desired space-time resolution (from meter/second to kilometer/month). Estimation of critical discharge value (1.973 m3s−1) for system equilibrium, based on the balance between losing and gaining sub-reaches of the river, is also made with a statistical significance at 95% for hydrologic years 2007–2010, period influenced by restrictions in groundwater withdrawals. The results of the present research are important for the proper and objective management of the scarce water resources on a watershed scale in Júcar River Basin, a complex case study representing semiarid climate, growing anthropogenic pressures, and complex river–aquifer interactions. The used approach of dynamic representation of the river–aquifer interactions as distributed source boundary condition in the one-dimensional hydrodynamic model might be applied in another study case on similar scale.

Development of a Basic Manipulative Movement Learning Model (Object control) Play Activity Based for Students Grade V Elementary School

The purpose of the study is to produce a learning model with valid, practical, and effective criteria. This type of research is development research using the Borg And Gall model. The population is all grade V students of State Elementary School 18 West Freshwater, Padang Utara District, Padang City. The sample was 30 class V students. The sampling technique is total sampling. The model validation design is validated by 2 experts: motor and language and model practicality by 2 sports teachers, The data collection instrument consists of tests of validity, practicality and effectiveness. As well as data analysis techniques using descriptive qualitative and quantitative techniques. The validation results of the learning model were obtained on average by 81.2.% with valid categories. In practicality, the results were obtained that this learning model was practical or good for use with learning implementation results of 91.7% The results of the effectiveness test using the Manipulative Basic Motion learning model based on play activities showed that there was a difference between obtaining manipulative motion ability results before using the model and after using the learning model. It was concluded that the model is effective and can improve the results of manipulative motion abilities (control objects) in the learning of sports and health physical education in grade V of primary school.

Establishment and validation of ambient temperature prediction model vehicle platform compartments

Purpose: The purpose of this study is to accurately predict the temperature induced by the vehicle platform in the process of driving, which can provide a more accurate input for proposing the environmental adaptability requirements of onboard equipment and carrying out environmental adaptability design and test validation. Approach: A dynamic temperature prediction algorithm of the vehicle platform based on the measured data is proposed by starting from the thermal formation mechanism of the vehicle platform compartment, taking into consideration the comprehensive influence of various factors on the thermal environment of the compartment, and introducing the position relationship, According to the established model, taking the normal driving of the vehicle as the starting point, the sinusoidal function is used to fit and regress the ambient temperature, and the nonlinear minimum function to fit the model parameters. Result:The result of model validation shows that the established dynamic temperature prediction model can accurately predict the curve of the temperature in the manned compartment of the vehicle platform that changes with the condition of the vehicle in travel. The error analysis results show that the maximum median error of the dynamic temperature model is not more than 2% in relation to the existing position of the passenger compartment of the vehicle platform.

Моделирование антенн методом моментов: аппроксимация поверхности проводами

This paper is devoted to antenna modeling using one of the most common approaches based on the method of moments (MoM): wire grid. An overview of this approach is given, including its history, limitations and applications. Its use in modeling and manufacturing of lens antennas and antenna arrays is discussed. Works devoted to its use not only with MoM but also with other numerical methods are reviewed. A number of works on the validation of modeling results using this approach are also presented. The conclusion reflects the relevance of the study of the approach and possible directions of its development.

Deciphering the Hypoxia-immune interface in esophageal squamous carcinoma: a prognostic network model.

The rapid progress and poor prognosis of the exercise of esophageal squamous cell carcinoma (ESCA) bring great challenges to the treatment. Hypoxia in the tumor microenvironment has become a key factor in the pathogenesis of tumors. However, due to the lack of clear therapeutic targets, hypoxia targeted therapy of ESCA is still in the exploratory stage. To bridge this critical gap, we mined a large number of gene expression profiles and clinical data on ESCA from public databases. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed. We next delved into the relationship between hypoxia and apoptotic cell interactions. Meanwhile, using LASAS-Cox regression, we designed a robust prognostic risk score, which was subsequently validated in the GSE53625 cohort. In addition, we performed a comprehensive analysis of immune cell infiltration and tumor microenvironment using cutting-edge computational tools. Hypoxia-related genes were identified and classified by WGCNA. Functional enrichment analysis further elucidated the mechanism by which hypoxia affected the ESCA landscape. The results of the interaction analysis of hypoxia and apoptotic cells revealed their important roles in driving tumor progression. The validation results of the prognostic risk score model in the GSE53625 cohort obtained a good area under the receiver operating characteristic (ROC) curve, and the risk score was independently verified as a significant predictor of ESCA outcome. The results of immune cell infiltration and tumor microenvironment analysis reveal the profound impact of immune cell dynamics on tumor evolution. Overall, our study presents a pioneering hypoxiacentered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice.

Segmentation of mycotoxin's contamination in maize: A deep learning approach

Maize is the main staple food and feed inSub-Saharan African countries and is highly susceptible to mycotoxin contamination under opportune environmental conditions. The presence of mycotoxins in maize affects the health of consumers and impacts global trade. According to the literature, the lack of mycotoxin awareness and the existence of strategies that are labor- and cost-prohibitive have led to the ongoing mycotoxin contamination in maize. Therefore, this study developed a cost-effective deep learning-based mobile application for segmentation of mycotoxin contamination in maize; using the RESNET152 model with performance rates of accuracy, test accuracy, epochs, time used, loss and image size results at 99.5%, 99.9%, 40, 07:30 min, and 0.051; and 460 respectively and performance evaluation metrics of F1-Score and sensitivity 0.62 and 0.997 respectively. During, the development processes, a total of 4800 images were collected and augmented. Then, the resulting 9600 data points were randomly shuffled and then split into the ratio of 70%:20:10% for training, validation, and testing datasets in order to avoid overfitting and biases in the resulting model. Lastly, the average result of model validation was 89% which was conducted among the farmers in the Maize area, Maize entrepreneurs, ICT experts, decision-makers from the Government, and policymakers. Therefore, the study recommends the collection of quality data which can be in the form of images, satellite, and biochemical properties of mycotoxin in order to enable researchers to analyze the contamination of mycotoxin and its linkages with environmental factors such as weather, soil characteristics, geographical position, and other unexpected events.

Validation of Rain Thickness Model Based on GPM Satellite and Surface Measurements

The limited acquisition of rainfall data from the Climatological Station of Deli Serdang has resulted in constraints on research in several fields. Global Precipitation Measurement (GPM) has spatial and temporal advantages as a solution to this problem. This study aims to provide information on the rain thickness equation model based on the Global Precipitation Measurement (GPM) Satellite as an alternative to calculating rain thickness at the Climatological Station of Deli Serdang. Data from the Global Precipitation Measurement (GPM) Satellite and Climatological Station of Deli Serdang were used, covering the period from January 1 to December 31, 2021. The Rescaled Adjusted Partial Sums (RAPS) method was used to verify the data quality, and the two data were consistent. The rain thickness equation model was obtained using a simple linear regression method. At the same time, the validation of the rain thickness equation model with surface measurements used the Root Mean Square Error (RMSE), Nash Sutcliffe Efficiency (NSE), and Correlation Coefficient (R) methods. The validation findings demonstrate that the rain thickness data from the Global Precipitation Measurement (GPM) Satellite was close to that of the Climatological Station of Deli Serdang, with an RMSE value of 5.85 mm, NSE of 0.99 mm, and R of 0.99. Hi = 5.21ti0.50 mm was the model used to calculate the equation for rain thickness. 6.84 mm, 0.69 mm, and 0.94 mm were the validation results of the equation model for RMSE, NSE, and R.Keywords: Rain Thickness, Global Precipitation Measurement (GPM), Climatological Station of Deli Serdang, Equation Model, Validation

Explicitly Modeling Stress Softening and Thermal Recovery for Rubber-like Materials

Rubber-like materials exhibit stress softening when subject to loading–unloading cycles, i.e., the Mullins effect. However, this phenomenon can be recovered after annealing the previously stretched sample under a stress-free state. The aim of this paper is to establish a constitutive model with thermodynamic consistency to account for the stress softening and thermal recovery. Towards this goal, (i) an explicit form of Helmholtz free energy can be found such that the restrictions from thermodynamic law can be satisfied; (ii) a compressible, multi-axial strain-energy function considering energy dissipation is proposed by introducing specific invariants; (iii) a unified shape function based on the symmetry property of the test data in a one-dimensional case with stress softening and thermal recovery is provided by introducing a weight variant; (iv) it is proven that the new potential can automatically reduce to the one-dimensional case, i.e., uniaxial tension, equal biaxial, or plane strain; (v) numerical results for model validation are exactly matched with classical experimental data.

Performance analysis of the water quality index model for predicting water state using machine learning techniques

Existing water quality index (WQI) models assess water quality using a range of classification schemes. Consequently, different methods provide a number of interpretations for the same water properties that contribute to a considerable amount of uncertainty in the correct classification of water quality. The aims of this study were to evaluate the performance of the water quality index (WQI) model in order to classify coastal water quality correctly using a completely new classification scheme. Cork Harbour water quality data was used in this study, which was collected by Ireland's environmental protection agency (EPA). In the present study, four machine-learning classifier algorithms, including support vector machines (SVM), Naïve Bayes (NB), random forest (RF), k-nearest neighbour (KNN), and gradient boosting (XGBoost), were utilized to identify the best classifier for predicting water quality classes using widely used seven WQI models, whereas three models are completely new and recently proposed by the authors. The KNN (100% correct and 0% wrong) and XGBoost (99.9% correct and 0.1% wrong) algorithms were outperformed in predicting the water quality accurately for seven WQI models. The model validation results indicate that the XGBoost classifier outperformed, including accuracy (1.0), precision (0.99), sensitivity (0.99), specificity (1.0), and F1 (0.99) score, in order to predict the correct classification of water quality. Moreover, compared to WQI models, higher prediction accuracy, precision, sensitivity, specificity, and F1 score were found for the weighted quadratic mean (WQM) and unweighted root mean square (RMS) WQI models, respectively, for each class. The findings of this study showed that the WQM and RMS models could be effective and reliable for assessing coastal water quality in terms of correct classification. Therefore, this study could be helpful in providing accurate water quality information to researchers, policymakers, and water research personnel for monitoring using the WQI model more effectively.

Assessment and projections of sediment budget resilience in Marbella, Spain

This paper discusses how the beaches of Marbella, on the southwestern Mediterranean coast of Spain, may no longer stay in their natural resilience envelope due to a critically delicate natural sediment transport balance and a degree of artificialization that has entered the whole sedimentary system into a new resilience state. The combination of vigorous terrain and millenary human action and disruptions across and alongshore on the coastline have increased stress on sediment availability. Although sediment circulation in the coastal cell has often been studied, the investigation of the connection between soil loss and river sediment transport and retention at a major dam remains a challenge. In this article, a first-order sediment yield prediction was established by using a GIS-based model applied to the area’s main river basin, and validation of model results is provided by empirical measurements of sedimentation in the main reservoir lake of La Concepción using Differential Global Positioning System (D_GPS)/Echo-sounder combination and measurements from remotely piloted aircraft compared with preconstruction blueprint topography documenting spot heights where sediments accumulated or eroded over 50 years. The marine circulation is interpreted from previous research by the authors that established a source-sink pattern similar to the Atlantic platform-fed marine system that originated the significant Cabopino dunes. The significant erodibility that we have estimated seems matched by potentially high sediment accumulation rates along selected profiles and spot heights across the bottom of the reservoir lake, and, in combination with the marine circulation model, our results identify that sediment budget key elements in Mediterranean settings, such as soil loss, sediment entrapment in reservoirs, and the coastal marine circulation, are in a state of deficit that suggests that the resilience envelope is surpassed and the system as a whole is entering a new resilience state in which the engineering factor is key. Some ecosystem services, such as the protection offered by the natural resilience of the beach and dune system of Cabopino, are no longer recoverable in the current artificialization state of the system as a whole.

Kinetic model and kerosene surrogate for calculating gas turbine engine emission of carcinogenic hydrocarbons

To calculate the emission of carcinogenic polycyclic aromatic hydrocarbons by the combustion chambers of aircraft gas turbine engines, the A17 kinetic model has been developed, characterized by new blocks of elementary chemical reactions of hydrocarbon compounds oxidation and synthesis of polycyclic aromatic hydrocarbons. The results of model validation showed satisfactory agreement with the experimental data and the possibility of applying the model to describe combustion processes in gas turbine engine combustion chambers. A review and numerical study was carried out for 14 surrogates (model fuels) of aviation kerosene, the combustion of which can be described using the A17 model. Simulation of stabilized flame of a previously prepared mixture showed the effectiveness of Drexel, Liu, su4, UM1 surrogates, the predictions for which agree satisfactorily with the experimental data and provide the expected levels of concentration of polycyclic aromatic hydrocarbons. The calculations show the dependence of the concentration of the most carcinogenic polycyclic aromatic hydrocarbon benzo(a)pyrene, and the ratio of the main combustion products CO2/H2O on the molar mass of the fuel. For the experimentally determined value of the molar mass of kerosene TS-1, the smallest deviation (up to 0.25%) is demonstrated by the su4 and UM1 surrogates. Due to the best predictive capability for the ignition delay time, normal flame propagation speed, pyrolysis and combustion products, the su4 and UM1 surrogates can be chosen to calculate the emission of carcinogenic polycyclic aromatic hydrocarbons from aircraft gas turbine engine combustion chambers.

Macroscopic traffic flow modelling of large-scale freeway networks with field data verification: State-of-the-art review, benchmarking framework, and case studies using METANET

Macroscopic traffic flow models are of paramount importance to traffic surveillance and control. Before their employments in applications, the models need to be calibrated and validated against real traffic data. The model calibration determines an optimal set of model parameters that minimizes the discrepancy between the modeling results and real traffic data. The model validation is furthermore performed to corroborate the accuracy of a calibrated model using data other than used for calibration. The model calibration aims to reflect traffic reality, while model validation focuses on the prediction of future traffic using calibrated models. This paper delivers a comprehensive review of state-of-the-art works on macroscopic model calibration and validation, proposes a benchmarking framework on traffic flow modeling, and has conducted a large number of case studies based on the framework using macroscopic traffic flow model METANET with respect to the urban expressway network in Shanghai. In comparison to previous works, quite more comprehensive results on model calibration have been presented in this paper, in consideration of congestion tracking, traffic flow inhomogeneity, capacity drop, stop-and-go waves, scattering, adverse weather conditions, and accidents. The paper has also reported many results of model validation with respect to the same field examples. The results demonstrate that METANET is able to model complex traffic flow dynamics in large-scale freeway networks with sufficient accuracy. The paper is closed with discussion on limitations and future works.

Construction and Evaluation of a Risk Score Model for Lymph Node Metastasis-Associated Circadian Clock Genes in Esophageal Squamous Carcinoma.

Studies suggested that circadian clock genes (CCGs) in human esophageal squamous carcinoma (ESCC) samples are dysregulated. However, the relevance of CCGs to lymph node metastasis (LNM) and prognosis of ESCC remains unclear. The differentially expressed genes (DEGs) between normal and ESCC samples in The Cancer Genome Atlas database (TCGA) database were intersected with the genes associated with LNM (LNMGs) in ESCC samples and 300 CCGs to obtain the differentially expressed LNM-associated CCGs (DE-LNM-CCGs). The risk model was constructed by Cox regression analysis in the TCGA-ESCC training set, and the accuracy of the risk model was verified by risk profile and overall survival profile. Furthermore, differences of 23 immune cells, 13 immune functions, and immune checkpoint molecules between the high- and low-risk groups were assessed using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Gene set enrichment analysis (GSEA) was conducted to investigate the functional differences between low- and high-risk groups. Finally, we validated the mRNA expression levels of prognostic model genes by quantitative real-time polymerase chain reaction (qRT-PCR). A total of six DE-LNM-CCGs were identified in TCGA-ESCC. TP53 and NAGLU were selected by Cox regression analysis to construct the risk model. Risk profile plots, overall survival plots, and validation results of the risk model in the validation set indicated that the constructed risk model was reliable. The result of ssGSEA showed that the percentages of activated B cells, activated dendritic cells, effector memory CD8 T cells, immune function in neutrophils, plasmacytoid dendritic cells, T cell co-inhibition, and Type 17 T helper cells were different between the high- and low-risk groups. In addition, the expression of CD274, PDCD1, TNFRSF18, and TNFRSF9 was dysregulated between the high- and low-risk groups. GSEA revealed that the high-risk group was associated with cell differentiation, oxidative phosphorylation, and steroid biosynthesis pathways, while the low-risk group was associated with chromosome, ECM-receptor interaction, and other pathways. Finally, qRT-PCR results showed that the mRNA expression levels of two prognostic genes were consistent with TCGA. In conclusion, the risk model constructed based on TP53 and NAGLU could accurately predict the prognosis.

Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research.

In this paper, in-depth research analysis of anti-hepatocellular carcinoma molecular targets for hepatocellular carcinoma diagnosis was conducted using artificial intelligence. Because BRD4 plays an important role in gene transcription for cell cycle regulation and apoptosis, tumor-targeted therapy by inhibiting the expression or function of BRD4 has received increasing attention in the field of antitumor research. Study subjects in small samples were used as the validation set for validating each diagnostic model constructed based on the training set. The diagnostic effect of each model in the validation set is evaluated by calculating the sensitivity, specificity, and compliance rate, and the model with the best and most stable diagnostic value is selected by combining the results of model construction, validation, and evaluation. The total sample was divided into a training set and test set by using a stratified sampling method in the ratio of 7 : 3. Logistic regression, weighted k-nearest neighbor, decision tree, and BP artificial neural network were used in the training set to construct diagnostic models for early-stage liver cancer, respectively, and the optimal parameters of the corresponding models were obtained, and then, the constructed models were validated in the test set. To evaluate the diagnostic efficacy, stability, and generalization ability of the four classification methods more robustly, a 10-fold crossover test was performed for each classification method. BRD4 is an epigenetic regulator that is associated with the upregulation of expression of various oncogenic drivers in tumors. Targeting BRD4 with pharmacological inhibitors has emerged as a novel approach for tumor treatment. However, before we implemented this topic, there were no detailed studies on whether BRD4 could be used for the treatment of HCC, the role of BRD4 in HCC cell proliferation and apoptosis, and the ability of small molecule BRD4 inhibitors to induce apoptosis in hepatocellular carcinoma cells.

Simulating change in surface runoff depth due to LULC change using soil and water assessment tool for flash floods prediction

Accurate documentation of land-use/land-cover (LULC) change and evaluating its hydrological impact are of great interest for catchment hydrological management. Jukskei River catchment has undergone a rapid infrastructural and residential development which had an influence on runoff depth. The objective of the study is to integrate Geographical Information System (GIS) and remote sensing (RS) techniques with Soil and Water Assessment Tool (SWAT) model to quantify the spatial and temporal changes in surface runoff depth resulting from LULC change. Landsat images of 1987 MSS, 2001 TM and 2015 OLI were pre-processed and classified using a supervised classification method with maximum likelihood. Results indicated that, there was a significant increase in built-up area from 28700.4ha in 1987 LULC to 36313.6ha in 2001 and 42713.1ha in 2015 at the expense of bare surface, intact vegetation and sparsed vegetation. However, during hydrological modelling, soil, DEM and climatic data were kept constant except LULC images which were interchanged during each simulation phase. Calibrated with observed hydrological data at the catchment outlets, SWAT model was used to evaluate the effect of LULC change on surface runoff depth. The analysis of SWAT model showed increases surface runoff depth from 70.5mm in 1987 LULC to 134.2mm in 2001 and 199.3mm in 2015 LULC. The SWAT model indicated satisfactorily results based on model calibration and validation results. Therefore, this study concluded that, integration of GIS and RS techniques with SWAT model can help in formulating policy guidelines for land-use practices thereby reducing hydrological impacts associated with LULC changes.

산림 생태계에 활용된 생지화학모형의 국내 적용성 분석

Identifying the carbon cycle in forest ecosystems is important when studying climate change. Since 2010, however, most of the suitable process-based biogeochemical models for forest sectors in South Korea have not been validated. For forest ecosystems, Korean and international research using a process-based biogeochemical model was collected and examined in this study. The applicability of models based on methodology, input/output parameters, and the availability of relevant spatio-temporal data was determined. After 2010, a total of 13 models, including one model commonly used by Koreans and foreign studies, were applied, with five originating domestically and nine originating internationally. Global, regional and temporal scales were used in each model, and a variety of targets, including agriculture, forestry and vegetation, were included. The input/output factors in most of the models were comparable. It was found that the CLM3.5-DGVM, MC1, VISIT and BIOME-BGC series models have been used most frequently in Korea and the models are highly applicable. Recent research, however, was limited by poor spatio-temporal resolution and lack of local characteristic data. Future research should include the creation of algorithms that represent forest management and the validation of modeling results using diverse models.

Coffee Shop New Retail Business Model

New retail is an innovative business model combining online and offline (O2O) shopping experiences (Alibaba Gorup, 2018) and is discovering how to adapt to the digital transformation. Several factors that could affect consumer experience when shopping online include ease of accessing and using the web, Hedonic and Utilitarian Features, Convenience, personalization, social interaction, and compatibility on various devices. The study aims to measure the relationship between the new retail business model dimension: customer, convenience, and communication to customer experience. This research identifies the factors that influence the customer experience in the new retail business model; three hypotheses were built in this study, then tested. The quantitative approach uses a partial least square (PLS) application to obtain the data and measure the impact of each dimension on the new retail concept. The population in this study is Starbucks consumers who have used all online-offline channels and are living in Bandung West Java. Based on the research objectives, the conclusion from the research results is the establishment of a model that represents the relationship between consumer variables, convenience, communication, and customer experience. Based on the model validation results, consumers and communication are the factors that influence the customer experience in coffee shops online and offline in the new retail concept. The communication variable is the most significant variable that directly affects the customer experience.