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Association Between PAI-1 4G/5G Genetic Polymorphism and Uncontrolled Allergic Asthma
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
Airway remodeling is a major challenge in the management of uncontrolled allergic asthma, despite standard therapy with a combination of inhaled corticosteroids (ICS) and long-acting bronchodilators (LABA). Increased levels of Plasminogen Activator Inhibitor-1 (PAI-1) are thought to play a role in this process, and the 4G/5G polymorphism in the PAI-1 gene is one of the genetic factors that affect it. This study aimed to analyze the association between the 4G/5G PAI-1 genetic polymorphism and unc...
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Fuzzy Logic-Based Clustering of Teacher Digital Pedagogy Using Cybergogy Framework for Sustainable Educational Innovation
Waryanto, Nur Hadi
; Retnawati, Heri
; Setyaningrum, Wahyu
; Insani, Nur
; Hery Murtianto, Yanuar
; Caturiyati, Caturiyati
; RR Rianto, Vivi
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
Rapid changes in educational technology necessitate innovative approaches to sustainable teacher development. However, implementing learning technologies like Cybergogy faces significant challenges due to imbalances in digital pedagogy competencies and motivation among secondary school mathematics teachers. This study aims to cluster mathematics teachers' profiles based on the Cybergogy model's application using the Fuzzy C-Means (FCM) algorithm. The study involved 88 mathematics teachers from v...
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Scenario-Based Dynamic Modeling for Urban Settlement Management
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
The growth of residential areas in peri-urban regions of metropolitan areas such as Jabodetabek demonstrates high complexity due to the dynamic interaction between population growth, land use, and environmental degradation. This study aims to develop a dynamic system-based simulation model using a scenario approach to analyze sustainable residential area management policies. The scenarios were developed consists of no intervention, pessimistic, moderate, and optimistic based on parameters such a...
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Multi-Horizon Short-Term Residential Load Forecasting Using Decomposition-Based Linear Neural Network
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
Short-Term Load Forecasting is crucial for grid stability and real-time energy management, particularly in residential settings where consumption is highly volatile and influenced by behavioral and external factors. Traditional models struggle to capture complex, non-linear patterns. This study proposes a forecasting framework based on the DLinear model, which decomposes time series data into trend and seasonal components using a simple linear neural network architecture. Designed for multi-hori...
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Comparative Performance of GLMM and GEE for Longitudinal Beta Regression in Economic Inequality Modelling
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
Due to the shortcomings of conventional Gaussian methods, specialized models are frequently needed for longitudinal data analysis with bounded outcomes, such as the Gini ratio. In order to model economic inequality in Indonesia, this study compares the effectiveness of Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) for beta-distributed longitudinal data. Root Mean Square Error (RMSE) and pseudo R-squared values are used to assess model performance using panel d...
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Evaluating the Role of Extractives in Biomass Pyrolysis for Enhanced Hydrogen Syngas Production
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
This study explores how extractive content in lignocellulosic biomass affects syngas quality during fixed-bed pyrolysis-gasification, specifically focusing on hydrogen (H₂) concentration. While woody biomass is a known energy source, the link between its non-structural organic compounds (extractives) and H₂ in syngas is often overlooked. We investigated teak, coconut, and jackfruit wood to understand this influence and optimize temperature for better biomass-to-hydrogen conversion. An MQ-8 senso...
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An Artificial Neural Network Approach for Predicting Pavement Distress: A Case Study Toward Sustainable Road Maintenance
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
The Surface Distress Index (SDI) is a crucial parameter to consider when determining road conditions as part of an effective maintenance strategy. This study aims to develop an SDI prediction model using road surface distress data to enhance maintenance planning. The developed Artificial Neural Network (ANN) model resulted in an optimal structure with two hidden layers comprising 6 neurons and 4 neurons, respectively. The model was trained using two years of surface distress data collected from...
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Comparative Analysis of Observed and Empirical Rainfall Distribution for Flood Hydrograph Modeling
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
Flood disasters in Indonesia are persistent challenges during the rainy season, primarily due to intense rainfall and inadequate flood control. This study evaluates hourly rainfall to characterize hydrology and predict flood discharge more accurately, benefiting water infrastructure planning. The research used modified Mononobe methods, observational data, and rainfall-runoff modeling, including HEC-HMS simulations with the SCS-CN unit hydrograph. Observed rainfall simulated a flood discharge of...
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Predicting Habitat Suitability of Mahseer Fish (Tor spp.) in Tropical River Systems Using MaxEnt and Google Earth Engine: A Geospatial Modeling Approach
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 3
(2025)
Rivers are vital freshwater habitats that face threats of degradation and climate change. Mahseer fish, a key species, is in decline. This study predicted Mahseer fish habitats in Central Java using the Google Earth Engine and the MaxEnt machine learning algorithm. Environmental predictors, including NDVI, elevation, slope, river order, temperature, and rainfall, were extracted from Sentinel, SRTM, MODIS, and CHIRPS data. The model identified river order as the most influential variable (73%), f...
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Hubungan Transformasi Digital SDM dengan Resiliensi Organisasi Melalui Moderasi Kepemimpinan Digital dan Budaya Inovasi
Jurnal Riset sosial humaniora, dan Pendidikan (Soshumdik)
Vol 4
, No 3
(2025)
This study examined the relationship between digital transformation factors (innovation culture, digital leadership, and digital transformation of human resources) and organizational resilience in Indonesian higher education institutions. The research method employed Structural Equation Modeling (SEM) with a Partial Least Squares (PLS) approach using SmartPLS 4, involving validation of the measurement and structural models through bootstrapping procedures. The findings indicated that all seven h...
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