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Technological-based early warning systems (EWS) in rural Indonesia have shown limited long-term adoption because centralised, top-down mechanisms fail to incorporate the contextual triggers trusted by local communities, ...

This study aims to examine the effect of the Early Warning System (EWS), solvency, and profitability on the value of Islamic insurance companies listed on the Indonesia Stock Exchange (IDX) during the period 2020–2024....

Abstract BackgroundAcute respiratory infections (ARIs) remain a significant global health challenge and are the second leading cause of disease burden and mortality. Early warning systems (EWS) play a key rol...

Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always remaining in the groove of rigorous scientific in-depth analysis. This sec...

ABSTRACT Background & Aims Dengue is a significant vector‐borne disease that has severely impacted public health in Bangladesh, underscoring the growing importance of digital health in enhancing surveillance and preven...

Modern agricultural systems face increasing uncertainties, rare risks, and complex dynamics that make traditional decision-making ineffective. This research presents an innovative digital twin–based framework for proac...

Mathematics readiness at entry remains critical for success in first-year engineering programs, yet many institutions lack transparent, reusable tools to diagnose risk and design levelling policies. This study develops a...

This work presents a hybrid deep learning approach for identifying students who are likely to experience academic difficulties in virtual learning environments. The proposed framework is evaluated on the Open University ...

This article examines how generative AI (GenAI) is reshaping strategic crisis decision-making through the emergence of ‘synthetic foresight’ – the algorithmic simulation of adversary intentions, escalation pathways...

This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historica...

Climate change and human impact are increasing the frequency and severity of flooding, making real-time, accurate simulation of mountainous flood inundation an urgent necessity. In this study, we developed a proxy model ...

Abstract Background Dengue fever is hyperendemic in the Lao People’s Democratic Republic (PDR), where transmission is driven by Aedes mosquitoes and influenced by large-scale climatic phenomena, including the El Niño-...

Abstract Climate change is driving increased incidence and severity of Phytophthora diseases in Tanzanian vegetable production systems, threatening food security and smallholder livelihoods. Field assessments were conduc...

Abstract Early and reliable smoke detection is critical for preventing fire-related disasters, safeguarding human lives, and ensuring infrastructure safety. Conventional sensor-based systems, however, are often constrain...

Abstract Objective China has achieved remarkable progress in tuberculosis (TB) control, with the incidence rates declining continuously; however, the disease burden among females remained substantial with pronounced r...

Maintenance of sewage systems requires workers to enter confined manholes, where hazardous gases can build up. These toxic, flammable gases create a serious safety risk. To address this problem, a portable gas monitoring...

Coral reef systems on the southeastern Cuban shelf are exposed to rapid warming and increasingly frequent marine heatwaves (MHWs). However, the physical drivers of local sea-surface temperature (SST) variability remain...

Paper ini memperkenalkan sebuah sistem manajemen Trafo Distribusi jaringan tegangan rendah (JTR) milik PT. PLN (Persero) yang disebut dengan Distributed Transformer Management System (DTMS). Trafo Distribusi merupakan as...

Abstract Using an idealized model of the Atlantic meridional overturning circulation (AMOC), we test whether changes in the statistical properties of an AMOC time series can reveal Critical Slowing Down (CSD) and serve a...

Tesis de Maestría External Early Warning Systems (EWSs) have traditionally been designed to warn policymakers of potential balance of payments crises. This study compares the performance of three different EWSs Machine ...

"This thesis investigates the application of machine learning methods to the task of deforestation monitoring using time-series satellite data. The objective is to assess how different algorithmic approaches perform in d...