Model Of Region Research Articles

Analisis Perbandingan Moodle dan Google Classroom Menggunakan Technology Acceptance Model

The study aims to analyze the comparison of student acceptance levels of the Learning Management System (LMS) which are Moodle and Google Classroom using the Technology Acceptance Model (TAM) in the West Java region, Indonesia. The results of this study show that there is a difference in the level of acceptance of the two LMSs but the difference is not very significant. Moodle appears to be slightly more effective in increasing student productivity and is more commonly used in West Java due to the sample of respondents who are mostly from the Faculty of Information Technology (FIT) at Maranatha Christian University. FIT students have largely adopted the Moodle LMS implemented into the Morning Platform (Maranatha Online Learning). With more complex features, Moodle meets the needs of more experienced users. On the other hand, Google Classroom is considered easier to use, especially for students who are new to the LMS. However, over a longer period of time, Moodle maintained a slight edge in terms of usability and user satisfaction. This research makes an important contribution in understanding the preferences and needs of university students in terms of LMS usage, especially in the West Java region.

Empirical Models of the Hβ Broad Emission Line Gas Density Field

Abstract We present the second iteration of the caramel-gas code, an empirical model of the broad-line region (BLR) gas density field. Building on the initial development and testing of caramel-gas, we expand the meaning of the model parameter α, which initially represented only the power-law index of the dependency of emissivity on radial distance. In this work, we test a more generalized radial power-law index, α, that also includes a description of the effective emitting size(s) of the BLR structure as a function of radial distance. We select a sample of 10 active galactic nuclei (AGN) from three different Lick AGN Monitoring Project campaigns to further validate the caramel-gas code and test the generalized radial power-law index, α. Our results confirm that the caramel-gas results are in general agreement with the published results determined using the original caramel code, further demonstrating that our forward modeling method is robust. We find that a positive radial power-law index is generally favored and propose three possible scenarios: (i) the BLR structure has increasing effective emitting size(s) at larger radial distances from the central source, (ii) emission is concentrated at the outer edges of the BLR, and (iii) stronger theoretical assumptions are needed to break the degeneracies inherent to the interpretation of reverberation mapping data in terms of underlying gas properties.

Analysis of reconstructed modified [formula omitted] gravity

This paper studies the reconstruction criterion in the framework of f(Q,T) gravity using a ghost dark energy model, where Q represents the non-metricity and T is the trace of energy–momentum tensor. In this regard, the correspondence scenario for a non-interacting scheme is used to construct the ghost dark energy f(Q,T) model. We consider the flat Friedmann–Robertson–Walker universe with a power-law scale factor and pressure-less matter. We investigate the behavior of the equation of state parameter and examine the stability of the ghost dark energy model via the squared speed of sound parameter. The equation of state parameter depicts the phantom era, while the squared speed of sound reveals a stable ghost dark energy model for the entire cosmic evolutionary paradigm. Finally, we study the cosmography of the ωGDE−ωGDE′ and r−s planes that correspond to a freezing region and Chaplygin gas model, respectively. It is concluded that the reconstructed ghost dark energy model represents the evolution of the cosmos for suitable choices of the parametric values.

A coupling model based on spatial characteristics and evolution of terrestrial ecosystem carbon storage: a case study of Hanzhong.

Ecosystem carbon storage (ECS) is a critical consideration in reducing the impact of global warming and tackling environmental challenges, positioning it at the forefront of contemporary research. Due to the significant differences in the influence of land usage patterns on ECS in various policy contexts and China's commitment to attaining a carbon-neutral status, a model simulating different scenarios is needed to analyze the spatiotemporal characteristics and evolutionary process of carbon storage in terrestrial ecosystems accurately. To address this challenge, this study established a coupling model of "Geographical analysis -Evolution analysis -Predicting (GEP)" for assessing ecosystem ECS and analyzing its spatial characteristics and evolutionary patterns and projecting the spatial distribution of ECS under various developmental scenarios, which analyzed variations in ECS across different levels of magnitude and delineated the changing areas across a range of varying scenarios in the future additionally. The outcomes suggested that the ECS decreased by 1.17 × 106 t from 1990 to 2020, which pertaining to the utilization transfer of land in the area, whose change in ECS levels with a positive trend. It is predicted that the ECS will grow by 1.15 × 106 t and 1.44 × 106 t, in 2030 and 2060 compared with 2020 within the framework of natural development scenario (NDS), while within the framework of ecological protection scene (EPS), ECS will increase significantly, increasing by 3.06 × 106 t and 4.44 × 106 t. There will be more areas where ECS increases within the framework of EPS, by comparing with the NDS. This study offers a comprehensive analysis of Hanzhong City's carbon storage trends, demonstrating its significant impact on climate change mitigation and serving as a predictive model for similar regions, which underscores the importance of localized carbon management strategies, offering valuable insights for local governments in formulating effective climate adaptation and mitigation policies.

Effect of angled abutments in the posterior maxillary region on tilted implants: a 3D finite element analysis.

The aim of this study was to evaluate the bone tissue effects under dynamic loading using finite element analysis (FEA) for four angled abutments with different deviated palatal lateral tilt angles. A three-dimensional model of the posterior maxillary region and an implant crown model were reconstructed and assembled with a three-dimensional model of the implant, angled abutment, and central screw to create a total of 10 three-dimensional finite element models tilted at , , , and in three groups, and the dynamic loads simulating oral mastication were loaded on the implant crown to analyze the equivalent stresses and strains in the peri-implant bone tissues. Under the dynamic loading, the cortical bone on the buccal side of the implant neck showed different degrees of stress concentration, and the cortical bone stress was much higher than the cancellous bone, and the strain concentration area of each model was located in the bone tissue around the implant neck and base. For the use of angular abutment, under the premise that the cortical bone stresses and strains of the 10 models meet the requirements for use, the peak stresses of 2.907 MPa, 3.018 MPa, and 2.164 MPa were achieved by using the angular abutment to achieve the tilt angles of , , and implantation, which is more advantageous compared with other models.

Effect of thermal annealing on the surface plasmon properties of gold and silver nanodisk structures fabricated by electron beam lithography

This study investigates the effect of thermal annealing on the surface plasmon resonance properties of gold and silver nanodisk structures fabricated by electron beam lithography. Despite no significant size change due to a nickel adhesion layer, thermal annealing at 500 °C notably altered surface plasmon resonance properties, especially in gold nanodisks, which showed peak blue-shifts and narrowing, indicating improved crystallinity. We fitted the peak shift by using an electromagnetic field analysis and discussed the changes in the dielectric function. The dielectric function of the simple Drude model was adopted, which was fitted to the values of the Lorentz Drude model in a limited wavelength region. While both silver and gold exhibited increased oscillation strength, only gold nanodisk structures showed a decrease in damping frequency. Increased oscillation strength indicated that these structures should be useful for selective enhancement of the light at specific wavelengths by a very simple heat treatment.

Validation of Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) chlorodifluoromethane (HCFC-22) in the upper troposphere and lower stratosphere

Abstract. The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) is currently providing the only measurements of vertically resolved chlorodifluoromethane (HCFC-22) from space. This study assesses the ACE-FTS HCFC-22 v5.2 product in the upper troposphere and lower stratosphere, as well as simulations of HCFC-22 from a 39-year specified dynamics run of the Canadian Middle Atmosphere Model (CMAM39) in the same region. In general, ACE-FTS HCFC-22 observations tend to agree with subsampled CMAM39 data to within ±5 %, except for between ∼ 15 and 25 km in the extratropical regions where ACE-FTS exhibits a negative bias of 5 %–30 % and near 6 km in the tropics where ACE-FTS exhibits a bias of −15 %. When comparing against correlative satellite, aircraft, and balloon data, ACE-FTS typically exhibits a low bias on the order of 0 %–10 % between ∼ 5 and 15 km and is within ±15 % between ∼ 15 and 25 km. ACE-FTS, CMAM39, and surface flask measurements from the NOAA Global Monitoring Laboratory's surface air-sampling network all exhibit consistent tropospheric HCFC-22 trends ranging between 6.8 and 7.8 ppt yr−1 (within 95 % confidence) for 2004–2012 and between 3.1 and 4.7 ppt yr−1 (within 95 % confidence) for 2012–2018. Interhemispheric differences (IHDs) of HCFC-22 were also derived using ACE-FTS, NOAA, and CMAM39 data, and all three yielded consistent and correlated (r≥0.42) IHD time series, with the results indicating that surface IHD values decreased at a rate of 2.2 ± 1.1 ppt per decade between 2004 and 2018.

Enhancing scabies knowledge among Indonesian boarding school students through a <i>Wayang</i> video

Skin diseases, such as scabies, present a significant global health concern, particularly among Indonesian boarding school students, resulting in complications, disruptions to daily life, and academic hindrances. To effectively address this issue, a health education approach utilizing Wayang videos was implemented, leveraging cultural elements for enhanced outcomes. Our research aimed to assess the impact of a Wayang educational video on scabies knowledge among Indonesian boarding school students. This study, conducted from October 2019 to October 2020, involved 447 students. Pre- and post-tests were administered to measure knowledge levels. Our findings demonstrate a significant improvement in scabies knowledge among students following the Wayang video education intervention. The bivariate Wilcoxon test identified a significant difference (p<0.05) in average scores before and after the educational intervention. Knowledge scores increased from 76.75 in the pre-test to 83.09 in the post-test, marking an 8.27% improvement. This study underscores the efficacy of Wayang educational video in augmenting scabies knowledge among Indonesian boarding school students, with a particular emphasis on the impact of a video titled "Malin and the Mite". This innovative cultural approach holds promise as a potential model for analogous regions, contributing to scabies prevention and advancing public health objectives.

Hα Time Delays of Active Galactic Nuclei from the Zwicky Transient Facility Broadband Photometry

In our previous work on broadband photometric reverberation mapping (PRM), we proposed the interpolated cross-correlation function (ICCF)-Cut process to obtain the time lags of the Hα emission line from two broadband lightcurves via subtracting the continuum emission from the line band. Extending the work, we enlarge our sample to the Zwicky Transient Facility (ZTF) database. We adopt two criteria to select 123 type 1 active galactic nuclei (AGNs) with sufficient variability and smooth light curves from 3537 AGNs at z < 0.09 with more than 100 epoch observations in the g and r bands from the ZTF database. We calculate the Hα time lags for 23 of them that have previous spectroscopic reverberation mapping (SRM) results using the ICCF-Cut, Just Another Vehicle for Estimating Lags In Nuclei (JAVELIN), and χ 2 methods. Our obtained Hα time lags are slightly larger than the Hβ time lags, which is consistent with the previous SRM results and the theoretical model of the AGN broad-line region. The comparisons between the SRM and PRM lag distributions and between the subtracted emission line light curves indicate that after selecting AGNs with the two criteria, combining the ICCF-Cut, JAVELIN, and χ 2 methods provides an efficient way to get the reliable Hα lags from the broadband PRM. Such techniques can be used to estimate the black hole masses of a large sample of AGNs in large multiepoch photometric sky surveys such as the Legacy Survey of Space and Time and the survey from the Wide Field Survey Telescope in the near future.

Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States

The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studies have assessed the applicability of these LSTM models across different hydroclimate regions. This study investigated the single-basin trained local (one model for each basin), multi-basin trained regional (one model for one region), and grand (one model for several regions) models for predicting daily streamflow in water-limited Great Basin (18 basins) and energy-limited New England (27 basins) regions in the United States using the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) data set. The results show a general pattern of higher accuracy in daily streamflow predictions from the regional model when compared to local or grand models for most basins in the New England region. For the Great Basin region, local models provided smaller errors for most basins and substantially lower for those basins with relatively larger errors from the regional and grand models. The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. Findings from our study highlight the strengths and limitations of LSTM models across contrasting hydroclimate regions in the United States, which could be useful for local and regional scale decisions using standalone or potential integration of data-driven LSTM models with physics-based hydrological models.

Groundwater flow modelling in Neoproterozoic carbonate karst based on dye tracer pathways

The current study consisted of the development of a hydrogeological numerical model in the Lagoa Santa Karst Environmental Protection region, in order to represent the conduit-flow karst springs in the most well-studied karst system in Brazil. Once carbonate aquifers are characterized by a complex 3D pattern with a combination of a double porosity of matrix and conduits, two different modelling approaches were used: Equivalent Porous Medium (EPM), based on Darcy’s law, to represent laminar flow in the fractured massif and Conduit Network (CN), using Manning-Stricker Law and the discrete feature tool, to represent the karst conduits with turbulent flow. Modeling was calibrated in a steady-state and simulations of groundwater flow and an evaluation of water exchanges between the karstic aquifer and the other hydrogeological units, were conducted. The model was developed based on data available for many studies carried out in the area, including tracer-derived route data. The calibration results for spring discharges, water levels and mass balance were satisfactory and aligned with modeling good practices. In turn, the hydraulic conductivities and recharge rates obtained by back-analysis were in agreement with literature references. The regional groundwater flow was well represented and indicated how external water entrances can be relevant to explain the higher discharge rates observed in some karstic springs. In addition, the representation of conduits allowed for the simulation of tracer pathways, enabling the inference of potential flow rates to the karstic springs from the particle simulations.

Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep Learning

This study aimed to propose an automated prediction approach of the consolidation tumor ratios (CTRs) of part-solid tumors of patients treated with radiotherapy on treatment planning computed tomography images using deep learning segmentation (DLS) models. For training the DLS model for cancer regions, a total of 115 patients with non-small cell lung cancer (NSCLC) who underwent stereotactic body radiation therapy were selected as the training dataset, including solid, part-solid, and ground-glass opacity tumors. For testing the automated prediction approach of CTRs based on segmented tumor regions, 38 patients with part-solid tumors were selected as an internal test dataset A (IN) from a same institute as the training dataset, and 49 patients as an external test dataset (EX) from a public database. The CTRs for part-solid tumors were predicted as ratios of the maximum diameters of solid components to those of whole tumors. Pearson correlations between reference and predicted CTRs for the two test datasets were 0.953 (IN) and 0.926 (EX) for one of the DLS models (p &lt; 0.01). Intraclass correlation coefficients between reference and predicted CTRs for the two test datasets were 0.943 (IN) and 0.904 (EX) for the same DLS models. The findings suggest that the automated prediction approach could be robust in calculating the CTRs of part-solid tumors.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- = 0.51 (with LCS). We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Groundwater pollution vulnerability assessment using a modified DRASTIC model in Ho Chi Minh City, Vietnam

AbstractThe Pleistocene aquifer serves as a vital water source for various activities in Ho Chi Minh City, Vietnam, including concentrated and individual exploitation. In the present study, the DRASTIC (D, depth of water; R, net recharge; A, aquifer media; S, soil media; T, topography; I, impact of vadose zone; C, hydraulic conductivity) model was used to evaluate the groundwater sensitivity of the study area. To analyse Ho Chi Minh City's Upper‐Middle Pleistocene aquifer vulnerability, the analytic hierarchy process (AHP) method was used to optimize the DRASTIC score and include land use (LU) characteristics. Four distinct weights were used: DRASTIC, modified DRASTIC‐LU, AHP‐DRASTIC and modified AHP‐DRASTIC‐LU. This study identified low, moderate and high vulnerability for 12%, 55% and 33% of the DRASTIC‐LU index values, respectively. The AHP‐DRASTIC index classifies 61%, 26% and 13% of sites as low, moderate and highly vulnerable, respectively. The study reveals that 52%, 30% and 18% of the area are vulnerable to the modified AHP‐DRASTIC‐LU index classes. The most sensitive factors are shallow aquifer roofs, recharge and LU. The real‐world accuracy of the DRASTIC models was tested using 106 groundwater nitrate concentrations. The modified AHP‐DRASTIC‐LU is the most accurate and appropriate model for the current research region.

Risk Assessment and Categorization of Flood Risk Zones Using Geospatial Data and Multi-Criteria Decision Model in a Low-Lying Deltaic Region, Kuttanad, India

Risk Assessment and Categorization of Flood Risk Zones Using Geospatial Data and Multi-Criteria Decision Model in a Low-Lying Deltaic Region, Kuttanad, India

Habitat suitability model of future coral reef region in the Halmahera Waters under the projection of environmental pressures from future climate change and coastal pollution

The coral reef region of the Halmahera waters situated within the tropical area of the coral triangle, covers approximately 193,126 km2, with coral reef habitats occupying roughly 958.47 km2. The coral reefs in Halmahera waters are essential for ecosystem services and local fisheries. However, they are at risk from climate change and coastal pollution. This study aimed to develop habitat suitability models for coral reefs in the Halmahera waters under future climate change scenarios. MaxEnt modeling was employed to project suitability for the coral Acropora spp. using occurrence data. Suitability projections were made for 2050 under IPCC RCP 2.6, 6.0, and 8.5 scenarios, assuming no measures are taken to mitigate coastal water pollution. Environmental factors considered in the modeling included temperature, salinity, current velocity, pH, sunlight penetration, and chlorophyll levels. Results demonstrated a significant decline in habitat suitability, with an 85% loss of highly suitable areas under RCP 2.6 – 8.5 compared to current conditions (741 km2). Chlorophyll levels, a marker of coastal pollution, were predicted to be the most influential factor, followed by temperature and salinity. The findings highlight the need to reduce global carbon emissions and implement conservation measures to mitigate coastal water pollution in preserving coral reef habitats.

258 Promoting Academics and Community Engagement (PACE): A Protocol on the ScienCE of Community Engagement in Clinical and Translational Science

OBJECTIVES/GOALS: The Michigan Institute for Clinical &amp; Health Research Community Engagement (MICHR CE) has collaborated for 15+ years with underserved communities on public health issues. As Michigan’s sole CTSA institution, we aim to advance the science of community engagement in translational science statewide. METHODS/STUDY POPULATION: MICHR CE PACE (Promoting Academic-Community Engagement) will advance clinical and translational science. Key steps include: 1) form a PACE Steering Committee; 2) PACE Trainings with community-academic partner Detroit Urban Research Center; 3) PACE Training Manual; 4) community Grand Rounds to bridge academic-community insights; and 5) community resources (e.g., toolkits). PACE projects will spotlight community-engaged research (CEnR) via diverse media including social media and community-level communications. The PACE ScienCE initiative will embody cultural humility by addressing power imbalances, emphasizing trust. We will utilize assessments and root cause analyses to navigate CEnR barriers. RESULTS/ANTICIPATED RESULTS: Anticipated results from MICHR CE’s planned approach with PACE include strengthened academic-community relationships. Formation of the PACE Steering Committee and collaborations are expected to foster more structured and impactful CEnR trainings. The PACE Training Manual will become a foundational resource for CEnR best practices. Community Grand Rounds will reveal increased interdisciplinary insights and community trust. All PACE projects will utilize diverse media to enhance CEnR visibility and community-level access. Lastly, by addressing power disparities via PACE ScienCE, we foresee enhanced trust, reduced barriers, and more transparent community-academic collaborations. DISCUSSION/SIGNIFICANCE: This strategy, based on co-planned efforts and community consultations, aims to reshape Michigan’s CEnR landscape and offers a replicable model for other regions. The insights gained will guide community-academic collaborations promoting translational, inclusive, and efficient community engagement.

Influence of the Complex Interface between Transport and Catalyst Layer on Water Electrolysis Performance

The interface design between anode catalyst layer (ACL) and porous transport layer (PTL) significantly influences the performance of proton exchange membrane water electrolyzers. Lately, the influence of the ACL/PTL interface on performance is more intensively investigated, including modeling approaches. Contrary to other models that apply through-plane resolved modeling, in-plane models better characterize the ACL/PTL interface. These models separate the interface into three domains: in an open pore area (P), under a contacted solid of the PTL (S), and the interfacial point between the pore and solid (S│P). In our work, we focused on the behavior of the model in the kinetic region, in which no two-phase behavior is to be expected. Consequently, we apply a one-phase model as the main model and a simple two-phase model for comparison. We find that for most reference samples, the one-phase model well describes polarization behavior. However, for samples with larger interfacial contact area, a two-phase model might explain the found effect better even for very low current densities. Finally, we show that the one-phase model and the simple two-phase model can be used to study the general behavior of different solid to pore ratios to guide electrode development in the future.

Impact of Industrial Water Usage on Aquatic Biodiversity in the Yangtze River Delta and Its Economic Valuation

The Yangtze River Delta (YRD), a pivotal economic and ecological region in China, faces significant environmental challenges due to rapid industrialization and urbanization. This paper explores various strategies and technologies aimed at mitigating the adverse impacts of industrial activities on the region’s aquatic ecosystems and biodiversity. We examine the implementation of advanced water treatment technologies, the adoption of sustainable industrial practices, and the enforcement of stringent environmental regulations. Predictive models are discussed as essential tools for forecasting environmental changes and guiding conservation efforts. The paper highlights the importance of an integrated approach involving government, industries, and communities to ensure sustainable development. By adopting these strategies, the YRD can achieve a balance between economic growth and environmental sustainability, serving as a model for similar regions globally.

Data merging methods for S-wave velocity and azimuthal anisotropy from different regions

When inverting the S-wave velocity and azimuthal anisotropy from ambient noise data, it is always to obtain the partial overlapped inversion results in contiguous different regions. Merging different data to achieve a consistent model becomes an essential requirement. Based on the S-wave velocity and azimuthal anisotropy obtained from different contiguous regions, this paper introduces three kinds of methods for merging data. For data from different regions with partial overlapping areas, the merged results could be calculated by direct average weighting (DAW), linear dynamic weighting (LDW), and Gaussian function weighting (GFW), respectively. Data tests demonstrate that the LDW and GFW methods can effectively merge data by reasonably allocating data weights to capitalize on the data quality advantages in each zone. In particular, they can resolve the data smoothness at the boundaries of data areas, resulting in a consistent data model in larger regions. This paper presents the effective methods and valuable experiences that can be referred to as advancing data merging technology.