Dynamic Shift Research Articles

A temporal dynamics framework and methodology for computationally intensive social media research

The growing availability of expansive social media trace data (SMTD) offers researchers promising opportunities to create rich depictions of societal and social phenomena. Despite this potential, research analysing such data often struggles to construct novel theoretical insight. This paper argues that holistically incorporating temporality enhances data collection and data analysis, subsequently facilitating process theory construction from SMTD. Recommendations to integrate temporality are outlined in the proposed Temporal Dynamics Framework and Methodology (TDFM). We apply the TDFM to investigate the temporal dynamics of mental health discourse on Twitter (now X) across different phases of the COVID-19 pandemic, theoretically framed in the context of innate psychological needs satisfaction. The findings reveal dynamic shifts in social media use, indicating that different phases of the pandemic triggered changes in the needs motivating, and being motivated by, social media use. This illustrative case reflectively evaluates the TDFM's usefulness in contextualising SMTD collection, analytical strategies, and process theory construction by incorporating a dynamic perspective on time.

Religious, ethnic and regional identities in Nigerian politics: a shared interest theory

ABSTRACT This essay examines the complex interplay between religious, ethnic, and regional identities in Nigerian politics, challenging the idea that religion solely dictates political behavior. It delves into the manipulation of identities for political advantage, with an emphasis on religion. The Social Identity Theory (SIT) is discussed, highlighting the cognitive processes in forming social identities and their role in promoting in-group bias and out-group prejudice. The novel contribution that this essay makes is the introduction of what I termed the ‘Shared Interest Theory’ or the ‘Common Interest Theory,’ arguing that changes in interests, both tangible and intangible, can trigger shifts in social identity and group dynamics. This theory offers a rational framework for understanding social identity dynamics, providing insights for promoting social cohesion in diverse societies. This framework on identity and politics underscores the importance of recognizing and addressing the divergent interests of different groups to mitigate conflicts and foster social harmony.

The Impact of COVID-19 Pandemic on the Jordanian Stock Market Returns Volatility: Evidence from ASE20

This research examines the impact of the COVID-19 pandemic on the volatility behavior of Amman Stock Exchange (ASE) returns using ARMA–GARCH-type models for three sub-periods: pre-COVID-19, during COVID-19, and post-COVID-19. The research finds that volatility persistence is significant across all periods, with the pandemic period showing the highest impact of shocks. Bad news has no statistically significant impact on volatility in the pre-COVID-19 period or during the pandemic, while in the post-pandemic period, good news significantly influences volatility. Additionally, there exist notable changes in the autocorrelation and the shock structure of the AR and MA components. Considering these alterations in the asymmetric effects, the AR and MA components suggest significant shifts in market dynamics, investor sentiments, and economic policies in response to pandemic experiences.

A general unit hydrograph theory for water level and tidal range distributions in the Modaomen Estuary, China

Understanding the spatial distributions of river-tide dynamics in estuaries and their response to intensive human interventions is critical. However, the studies on the characteristics of the spatial distributions of water level and tidal range are insufficient, with inadequate direct established empirical formulas. In this study, we propose a general and analytical water level and tidal range distribution model based on the recently proposed General Unit Hydrograph (GUH) theory and apply it to the Modaomen Estuary in China. Due to the intensive human interventions, the evolution of river-tide dynamics in the Modaomen Estuary was divided into three distinct periods: Pre-human, Transitional and Post-human Periods. The results show that the water level increased at the SZ station, yet decreased at other stations, thereby reducing the maximum water level gradient by approximately 9.08 × 10−6 on average from the Pre-human to the Post-human Periods. The tidal range generally increased along the channel and the absolute value of the maximum tidal range gradient decreased by approximately 54 % on average. Correspondingly, the positions of both maximum water level gradient and minimum tidal range gradient generally move seaward by approximately 27.25 km and 0.34 km, respectively. Additionally, the spatial distributions of river-tide dynamics tended more linear after human interventions. The satisfactory correspondence of the model outputs with observations at the six gauging stations along the Modaomen Estuary (with maximum RMSE values being 0.05 m for the water level and 0.03 m for the tidal range, respectively) indicate that the newly proposed GUH model could serve as a simple tool to describe the spatial distributions of water level and tidal range in a specific channel together with their response to anthropogenic modifications, which is also particularly useful for the identification of regime shifts in river-tide dynamics and sustainable water resources management in other highly human-modified estuaries worldwide.

The Metamorphosis of the Novel <i>Moment in Peking</i> in Japan, Taiwan, and China

This article focuses on the migration of Lin Yutang’s most-read novel, Moment in Peking (1939), across linguistic, geographic, and media boundaries in East Asia from the 1940s to recent times. It aims to reveal how this novel, originally composed in English and published in the US during the War of Resistance against Japanese Aggression, is translated, adapted, presented, and situated in different literary, cultural and ideological contexts in Japan, Taiwan and Mainland China. Drawing on the principles of Linda Hutcheon’s theory of adaptation and the Manipulation School’s literary translation studies, my analysis highlights the agency of translators, adaptors and publishers in literary production conditioned by the evolving social, historical and political circumstances. The research contends that the metamorphosis of Moment in Peking embodies a literary power game directly linked to shifts in power dynamics between the three entities. At the cultural level, it provides insights into the intricate interplay of three distinct cultural imageries—competing, conflicting or assimilating within a relational framework.

Dynamic Peripheral T-Cell Analysis Identifies On-Treatment Prognostic Biomarkers of Atezolizumab plus Bevacizumab in Hepatocellular Carcinoma

Introduction: Variability in response to atezolizumab plus bevacizumab (AB) treatment of hepatocellular carcinoma (HCC) underscores the critical need for the development of effective biomarkers. We sought to identify peripheral blood biomarkers reflecting response to AB treatment. Methods: We analyzed dynamic changes in peripheral blood mononuclear cells from a prospective, multicenter cohort of 65 patients with HCC, using flow cytometry to evaluate the T-cell population before and 3 weeks after the first AB treatment. Results: We found a unique response of the CD8+ T cells in terms of both frequency and phenotype, in contrast to CD4+ T cells and regulatory T cells. Notably, CD8+ T cells showed significant changes in expression of Ki-67 and T-cell immunoreceptors with Ig and ITIM domains (TIGIT). These distinct responses were observed particularly in the programmed cell death receptor-1 (PD-1)+ subpopulation of CD8+ T cells. Interestingly, the baseline differentiation status of PD-1+CD8+ T cells, particularly the central memory T-cell subset, correlated positively with greater proliferation (higher Ki-67 expression) of PD-1+CD8+ T cells after treatment. Moreover, effector memory cells expressing CD45RA correlated negatively with the increase in TIGIT+/PD-1+CD8+ T cells. The increase in TIGIT+/CD8+ T cells was associated with the development of immune-related adverse events, whereas increase in Ki-67+/PD-1+CD8+ T cells was associated with the better objective response rate. Importantly, dynamic shifts of Ki-67+/PD-1+CD8+ T cells and TIGIT+/CD8+ T cells significantly predicted progression-free survival and overall survival, as confirmed by multivariate analysis. Conclusion: These findings highlight the potential of dynamic changes in CD8+ T cells as an on-treatment prognostic biomarker. Our study underscores the value of peripheral blood profiling as a noninvasive and practical method for predicting the clinical outcomes of AB treatment in patients with HCC.

Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: A comprehensive review

As artificial intelligence (AI) technology evolves, data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life (RUL). Indeed, accurate RUL prediction is challenging, primarily because of the complex nature of the work and dynamic shifts in model parameters. To address these challenges, this article comprehensively explores five significant publicly accessible lithium-ion battery datasets, encompassing diverse usage conditions and battery types, offering researchers a rich repository of experimental data. In particular, we not only provide detailed information and access addresses for each dataset, but also present, for the first time, four innovative methods for battery aging health factor extraction. These methods, based on advanced AI techniques, are able to effectively identify and quantify key indicators of battery performance degradation, thereby enhancing the precision and dependability of RUL prediction. Additionally, the article identifies major challenges faced by current predictive techniques, including data quality, model generalization capabilities, and computational cost, highlighting the need for research focused on dataset diversity, multiple algorithm fusion, and hybrid physical-data-driven models to enhance prediction accuracy. We believe that this review will help researchers gain a comprehensive understanding of RUL estimation methods and promote the development of AI in battery.

20-year water quality analysis reveals spatial variability and long-term changes at North Carolina's Masonboro Island National Estuarine Research Reserve

Urbanization impacts, especially stormwater runoff and pollutant discharge, pose ecological threats to coastal ecosystems globally. Long-term environmental monitoring datasets and assessments are used to quantify anthropogenic impacts on ecosystem health. This study synthesized twenty years (2002–2021) of water quality data from the National Estuarine Research Reserve System-wide Monitoring Program at the understudied Masonboro Island Reserve (North Carolina, United States) and provided an analytical methodology that can be applied to other Reserves and monitoring programs world-wide. Time series and correlation analyses of biological, chemical, and physical water quality parameters were used to identify spatiotemporal trends of nutrient dynamics and phytoplankton biomass at three stations within a 5-km stretch of the Atlantic Intracoastal Waterway. Heterogenous water quality conditions were observed between stations, especially for chlorophyll a, and were influenced by the relative proximity to marine waters, human developments, and urban tidal creeks. Long-term trends indicated significant water quality changes (i.e., increased chlorophyll a, reduced pH and salinity) related to anthropogenic influences. An unexpected and rapid shift in nutrient dynamics occurred around 2007, marked by significantly decreased ammonium and orthophosphate concentrations, which considerably reduced DIN/DIP ratios. This positive change was potentially influenced by sewage infrastructure improvements and stormwater wetland construction, removing waste-derived nutrients that previously entered the ecosystem. Impacts of major storms were also observed with weeks of hypoxia and low salinity following Hurricane Florence. These findings emphasized the value of long-term trend analyses of monitoring program data at multiple sites and provided insights into the significant water quality impacts of urbanization and benefits from wetland protection. This study further encourages the evaluation of long-term water quality monitoring datasets in estuarine environments globally.

Traffic Speed Prediction Based on Dynamic Spatial-Temporal Attention Graph Convolutional Network

Abstract The prediction of traffic speed represents a crucial branch within the broader field of traffic forecasting. Its objective is to forecast the average speed or speed distribution on road segments or within a network for a designated future time period . The prediction results can provide scientific decision support for traffic management departments to effectively control and guide traffic flow. However, given the limitations of urban road network topology and its dynamic temporal shifts, traffic prediction has consistently been deemed a perpetual scientific challenge. To simultaneously encapsulate both spatial and temporal dependencies, we introduce an innovative Dynamic Spatio-Temporal Attention Network (DSTANet). This model integrates spatio-temporal attention with graph convolutional networks and temporal convolution to enhance its analytical capabilities. Specifically, the graph convolutional network is leveraged to learn the intricate topology for capturing spatial relations, while temporal convolution is employed to ascertain the dynamic fluctuations of traffic data, thereby acquiring temporal dependencies. Subsequently, we implement our model to forecast traffic patterns within the framework of road networks. Experimental results demonstrate that our model effectively captures the spatio-temporal dependencies among roadway junctions derived from traffic data, outperforming state-of-the-art benchmarks on real-world traffic datasets in terms of predictive accuracy.

The Salience of Less Important Product Attributes

ABSTRACTThe significance of product attributes, as reflected in online reviews during the initial purchase, may undergo changes following positive or negative product usage experiences. The purpose of this research was to investigate whether positive or negative product consumption experiences influence shifts in the perceived importance of product attributes and subsequent product choices, based on online quality ratings by attributes. A series of experiments demonstrated that the importance associated with an attribute's expectation in the pre‐purchase process shifted after a negative consumption experience with that attribute. The results showed that a change in both attribute importance and product choice following a negative attribute experience. A less important attribute that malfunctioned during the consumption experience became more crucial in the subsequent purchase (Study 1). However, this shift in importance may not be universal for all attributes; it was observed only when the attribute was moderately important (Study 2). This study contributes valuable empirical findings to the existing literature on online reviews by highlighting dynamic shifts in the importance weight of product attributes. E‐commerce practitioners are encouraged to allow formal purchasers to leave product reviews without time constraints, providing a platform for reflecting users' lifelong consumption experiences.

Improving organizational agility through ethical leadership, employee job satisfaction and Islamic OCB

In the contemporary business realm, characterized by dynamic shifts and technological progress, agility has emerged as a vital concept to enable businesses to navigate and thrive amidst uncertainty and intensified competition. Key to fostering Agility is garnering comprehensive support from all stakeholders, notably organizational leaders and employees. This research aims to explore several factors contributing to Organizational Agility, including Ethical Leadership, Islamic OCB, and Employee Job Satisfaction. Adopting a quantitative methodology, the research focuses on employees and managers in the café and restaurant businesses in Yogyakarta, renowned for its dynamic nature and competitive environment. With a total sample of 179 managers and employees, participants were selected through purposive sampling criteria, targeting establishments operational for at least one year, employing a minimum of ten staff, and respondents with tenure exceeding six months in supervisory, managerial, or ownership capacities. Primary data collection utilized questionnaires, and structural equation modeling via AMOS software facilitated analysis. Results reveal a positive correlation between Ethical Leadership and Employee Job Satisfaction, Islamic OCB, and Organizational Agility. Additionally, Employee Job Satisfaction and Islamic OCB were found to impact Organizational Agility positively. Thus, integrating Islamic OCB into business practices offers a promising avenue for enhancing Agility, particularly in Muslim-majority regions.

The Enigmatic Interplay of Interleukin-10 in the Synergy of HIV Infection Comorbid with Preeclampsia.

Cytokines coordinate the intricate choreography of the immune system, directing cellular activities that mediate inflammation, pathogen defense, pathology and tissue repair. Within this spectrum, the anti-inflammatory prowess of interleukin-10 (IL-10) predominates in immune homeostasis. In normal pregnancy, the dynamic shift of IL-10 across trimesters maintains maternal immune tolerance ensuring fetal development and pregnancy success. Unravelling the dysregulation of IL-10 in pregnancy complications is vital, particularly in the heightened inflammatory condition of preeclampsia. Of note, a reduction in IL-10 levels contributes to endothelial dysfunction. In human immunodeficiency virus (HIV) infection, a complex interplay of IL-10 occurs, displaying a paradoxical paradigm of being immune-protective yet aiding viral persistence. Genetic variations in the IL-10 gene further modulate susceptibility to HIV infection and preeclampsia, albeit with nuanced effects across populations. This review outlines the conceptual framework underlying the role of IL-10 in the duality of normal pregnancy and preeclampsia together with HIV infection, thus highlighting its regulatory mechanisms and genetic influences. Synthesizing these findings in immune modulation presents avenues for therapeutic interventions in pregnancy complications comorbid with HIV infection.

Examining human mobility changes during COVID-19 across socioeconomic groups: a comparative analysis of San Diego County and New York City

The COVID-19 pandemic brought unprecedented changes to various aspects of daily life, profoundly affecting human mobility. These changes in mobility patterns were not uniform, as numerous factors, including public health measures, socioeconomic status, and urban infrastructure, influenced them. This study examines human mobility changes during COVID-19 in San Diego County and New York City, employing Latent Profile Analysis (LPA) and various network measures to analyze connectivity and socioeconomic status (SES) within these regions. While many COVID-19 and mobility studies have revealed overall reductions in mobility or changes in mobility patterns, they often fail to specify ’where’ these changes occur and lack a detailed understanding of the relationship between SES and mobility changes. This creates a significant research gap in understanding the spatial and socioeconomic dimensions of mobility changes during the pandemic. This study aims to address this gap by providing a comprehensive analysis of how mobility patterns varied across different socioeconomic groups during the pandemic. By comparing mobility patterns before and during the pandemic, we aim to shed light on how this unprecedented event impacted different communities. Our research contributes to the literature by employing network science to examine COVID-19’s impact on human mobility, integrating SES variables into the analysis of mobility networks. This approach provides a detailed understanding of how social and economic factors influence movement patterns and urban connectivity, highlighting disparities in mobility and access across different socioeconomic groups. The results identify areas functioning as hubs or bridges and illustrate how these roles changed during COVID-19, revealing existing societal inequalities. Specifically, we observed that urban parks and rural areas with national parks became significant mobility hubs during the pandemic, while affluent areas with high educational attainment saw a decline in centrality measures, indicating a shift in urban mobility dynamics and exacerbating pre-existing socioeconomic disparities.

Changes in evaporation patterns and their impact on Climatic Water Balance and river discharges in central Poland, 1961–2020

This study investigates the changes in precipitation and evaporation patterns and their impact on Climatic Water Balance and river discharges in central Poland from 1961 to 2020. The analysis focuses on two “normal” periods, 1961–1990 and 1991–2020 (according to the World Meteorological Organization). Bartlett’s test and the Kruskal–Wallis rank sum test were used to assess the homogeneity of variances and compare distributions of analyzed variables over two “normal” periods. The probability density functions were estimated using a kernel density estimator with a Gaussian kernel function. Significant findings indicate alterations in evaporation rates and shifts in water balance dynamics. Mean evaporation increased from 530.8 to 637.9 mm, leading to a notable decrease in the mean Climatic Water Balance from 1.1 to − 107.5 mm (in the periods 1961–1990 and 1991–2020, respectively). Additionally, rivers showed reduced mean annual discharges (from 4.28 to 3.01 m3·s−1 and 1.25 to 0.87 m3·s−1, for the Zgłowiączka and Skrwa Lewa rivers, respectively). These climatic changes in central Poland have substantial implications for regional water resources, especially in spring and summer and particularly in agricultural areas, potentially exacerbating drought conditions and impacting agricultural productivity mainly in the warm half-year.

Modelling climate change-induced nonstationarity in rainfall extremes: A comprehensive approach for hydrological analysis

Climate change and global warming have induced a dynamic shift in extreme rainfall patterns, leading to nonstationary behaviour. This alteration in behaviour challenges conventional hydrologic design, which assumes stationarity and can yield misleading outcomes. This study aims to address nonstationarity by modelling distribution parameters with covariates. Utilizing a 70-year (1951–2020) high-resolution India Meteorological Department (IMD) gridded dataset, extreme annual rainfall across diverse Indian cities was extracted and modelled. Previous research and goodness-of-fit tests favour the Generalized Extreme Value (GEV) distribution for modelling extremes. This study incorporates indices like Nino3.4, dipole mode index, global and local temperature, CO2, and time to characterize nonstationarity in extreme annual rainfall, leveraging climate cycles and global warming. Performance assessment employs the Akaike information criterion, Bayesian information criterion and Likelihood ratio test, while quantile reliability is evaluated through confidence intervals (CIs). Findings reveal widespread nonstationary trends in most grid points, translating to wider CIs in estimated quantiles for chosen non-exceedance probability and covariates in fitted models. Generally, nonstationary conditions are associated with broader confidence bands in return levels, highlighting nonstationary model weaknesses compared to stationary models. However, the results showed that the rainfall extremes follow a nonstationary pattern. Hence, there is a strong need to develop nonstationary models of low uncertainty.

An output-null signature of inertial load in motor cortex

Coordinated movement requires the nervous system to continuously compensate for changes in mechanical load across different conditions. For voluntary movements like reaching, the motor cortex is a critical hub that generates commands to move the limbs and counteract loads. How does cortex contribute to load compensation when rhythmic movements are sequenced by a spinal pattern generator? Here, we address this question by manipulating the mass of the forelimb in unrestrained mice during locomotion. While load produces changes in motor output that are robust to inactivation of motor cortex, it also induces a profound shift in cortical dynamics. This shift is minimally affected by cerebellar perturbation and significantly larger than the load response in the spinal motoneuron population. This latent representation may enable motor cortex to generate appropriate commands when a voluntary movement must be integrated with an ongoing, spinally-generated rhythm.

Time-varying exchange rate pass-through over 2005–2021 using dynamic model averaging

This study reexamines the time-varying structure of exchange rate pass-through (ERPT) by employing the dynamic model averaging (DMA) method. DMA is a novel empirical methodology that allows both parameters and predictors of the model to change over time, thereby addressing model uncertainty in determining time-varying ERPT degrees. We apply DMA to quarterly time series data spanning from 1996:1–2021:3 for each of the 39 advanced and emerging economies in the sample. The findings reveal a shift in short run ERPT dynamics prompted by the global crisis in 2008-09. While ERPT levels are initially low, they have been rising steadily since the onset of the pandemic across all advanced countries. Conversely, the ERPT levels are approximately twice as high in emerging economies, exhibiting a U-shaped pattern over the sample period.

Impacts of polyhydroxybutyrate (PHB) microplastic exposure on physiology and metabolic profiles of Litopenaeus vannamei

In light of increasing concerns about microplastic pollution, it is crucial to understand the biological impacts of biodegradable PHB microplastics on marine organisms. This study included a 96-h exposure experiment to assess acute toxicity at PHB concentrations of 0 mg/L, 100 mg/L, 500 mg/L and 1000 mg/L. Additionally, a 60-day feeding trial was conducted with PHB concentrations of 0, 0.5, 1.0 and 2.0 g/kg to evaluate the long-term effects on growth, physiological health and metabolic responses of Litopenaeus vannamei. Results from the exposure experiment indicated that PHB microplastics up to 100 mg/L were non-toxic to shrimp. However, the 60-day feeding trial revealed that higher concentrations led to slight reductions in survival rates and growth performance, indicating a concentration-dependent response. Analysis of antioxidant and immune enzymes showed minimal changes across most parameters. However, increases in malondialdehyde content and lysozyme activity at higher PHB levels suggested a stress response. Microbial analysis indicated higher species richness and greater community diversity in the PHB group compared to controls, as evidenced by Chao1, ACE, Shannon and Simpson indices. Linear discriminant analysis revealed that Enterobacteriales and related taxa were more prevalent in the PHB group, while Rhodobacteraceae and associated taxa dominated the control group. Pathway analysis highlighted enhanced signal transduction, cell mobility and metabolic resource reallocation in response to PHB-induced stress. Integrated transcriptomic and metabolomic analyses revealed significant regulatory changes, especially in lipid metabolism pathways. These findings suggest that while PHB microplastics trigger adaptive metabolic responses in shrimp, they do not cause acute toxicity. Significant variations in intestinal microbiome composition reflect potential shifts in gut health dynamics due to PHB ingestion. This study enhances our understanding of the ecological impacts of microplastics and underscores the necessity for further research into the environmental safety of biodegradable alternatives.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BackgroundDescribing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.MethodsThis study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.ResultsWhen the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9).ConclusionThis study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Dengue Hemorrhagic Fever in Quang Nam Province (Vietnam) from 2020 to 2022-A Study on Serotypes Distribution and Immunology Factors.

Background: Dengue hemorrhagic fever (DHF) is the most prevalent and fastest-growing vector-borne disease globally, with symptoms ranging from mild to severe and, in some cases, fatal. Quang Nam province in Vietnam can serve as a model for dengue epidemiological study, as it is an endemic region for DHF with a tropical climate, which significantly constrains the health system. However, there are very few epidemiological and microbiological reports on Dengue virus (DENV) serotypes in this region due to the limited availability of advanced surveillance infrastructure. Aims of the study: This study aims to (1) assess the PCR positivity rates among hospitalized patients with clinical Dengue presentation; (2) identify the circulating DENV serotypes; and (3) assess the impact of secondary DENV infections on outbreak severity by detecting the presence of DENV-specific IgG antibodies in the plasma of DENV-infected patients. Materials and methods: Blood samples from patients clinically diagnosed with DHF and admitted to Quang Nam General Hospital (2020-2022) were analyzed. RNA extraction was performed using the NKDNA/RNAprep MAGBEAD kit, followed by Multiplex Reverse Transcription real-time Polymerase Chain Reaction (MLP RT-rPCR) for DENV detection and serotype identification. Positive samples were further tested for DENV-specific IgG antibodies using an enzyme-linked immunosorbent assay (ELISA). Results: The PCR positivity rate among hospitalized patients was approximately 68% throughout the study period. A significant shift in DENV serotypes was observed, with DENV-2 initially dominant and later giving way to DENV-1. IgG was detected in nearly half of the MPL RT-rPCR-positive samples, indicating secondary DENV infections. Conclusions: Our study highlights persistent dengue prevalence and dynamic shifts in DENV serotypes in Quang Nam province, emphasizing the need for improved diagnostic strategies and timely sample collection. The significant serotype shifts and the presence of IgG in hospitalized patients suggest potential severe outcomes from recurrent DENV infections, possibly linked to antibody-dependent enhancement (ADE) effect, underscoring the importance of advanced surveillance, vector control, vaccination campaigns, and public education to predict and prevent future DHF epidemics.