Time Series Analysis Research Articles

The relationship of short-term exposure to meteorological factors on diabetes mellitus mortality risk in Hefei, China: a time series analysis.

The study aims to explore whether short-term exposure to meteorological factors has a potential association with the risk of diabetes mellitus (DM) mortality. During the period 2015-2018, we collected daily data on meteorological factors and deaths of diabetic patients in Hefei. A total of 1101 diabetic deaths were recorded. We used structural equation modeling to initially explore the relationships among air pollutants, meteorological variables, and mortality, and generalized additive modeling (GAM) and distributional lag nonlinear modeling (DLNM) to explore the relationship between meteorological factors and the mortality risk of DM patients. We also stratified by age and gender. The mortality risk in diabetic patients was expressed by relative risks (RR) and 95% confidence intervals (CI) for both single and cumulative days. Single-day lagged results showed a high relative humidity (RH) (75th percentile, 83.71%), a fairly high average temperature (T mean) (95th percentile, 30.32°C), and an extremely low diurnal temperature range (DTR) (5th percentile, 3.13°C) were positively related to the mortality risk of DM. Stratified results showed that high and very high levels of T mean were significantly positively linked to the mortality risk of DM among females and the elderly, while very high levels of DTR were linked to the mortality risk in men and younger populations. In conclusion, this study found that short-duration exposure to quite high T mean, high RH, and very low DTR were significantly positively related to the mortality risk of DM patients. For women and older individuals, exposure to high and very high T mean environments should be minimized. Men and young adults should be aware of daily temperature changes.

Application of time series analysis in predicting postpartum depression: integrating data from the hospitalization period and early postpartum weeks.

This study aimed to explore the dynamic changes in postpartum depressive symptoms from the hospitalization period to 4-8 weeks postpartum using time series analysis techniques. By integrating depressive scores from the hospital stay and the early postpartum weeks, we sought to develop a predictive model to enhance early identification and intervention strategies for Postpartum Depression (PPD). A longitudinal design was employed, analyzing Edinburgh Postnatal Depression Scale (EPDS) scores from 1,287 postpartum women during hospitalization and at 4, 6, and 8 weeks postpartum. Descriptive statistics summarized demographic characteristics. Time Series Analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model explored temporal trends and seasonal variations in EPDS scores. Correlation analysis examined the relationships between EPDS scores and demographic characteristics. Model validation was conducted using a separate dataset. EPDS scores significantly increased from the hospitalization period to 4-8 weeks postpartum (p < .001). The ARIMA model revealed seasonal and trend variations, with higher depressive scores in the winter months. The model's fit indices (AIC = 765.47; BIC = 774.58) indicated a good fit. The Moving Average (MA) coefficient was - 0.69 (p < .001), suggesting significant negative impacts from previous periods' errors. Monitoring postpartum depressive symptoms dynamically was crucial, particularly during the 4-8 weeks postpartum. The seasonal trend of higher depressive scores in winter underscored the need for tailored interventions. Further research using longitudinal and multi-center designs was warranted to validate and extend these findings. Our predictive model aimed to enhance early identification and intervention strategies, contributing to better maternal and infant health outcomes.

Oxytocin differentially modulates reward system responses to social and non-social incentives.

Oxytocin has been shown to modulate behavior related to processing of monetary incentives and to regulate social and reproductive behavior, yet little is known about how oxytocin differentially influences neural responses to social and non-social incentives. We aimed to evaluate the effects of oxytocin administration on behavioral and neural responses to social and monetary incentives. Twenty-eight healthy adults (age 18-45 years) performed both monetary and social incentive tasks during blood oxygenation level dependent (BOLD) imaging. Intranasal oxytocin or placebo was administered before each scan using a double blind, randomized, cross-over design. Task performance and self-reported motivation and mood states were collected. Time-series analysis was conducted to assess the influence of oxytocin on the hemodynamic response in the ventral tegmental area and substantia nigra (VTA/SN) and nucleus accumbens (NAc). Oxytocin demonstrated a multifaceted effect on VTA/SN and NAc when processing reward incentives, with it increasing BOLD response in VTA/SN and decreasing BOLD response in NAc during social incentive anticipation. A reversal of this was shown with decreased BOLD responses in the VTA/SN and increased BOLD response in the NAc during monetary incentive anticipation. Our findings suggest a more nuanced purpose of oxytocin when evaluating reward incentive decision making. It is possible that while oxytocin does increase salience to rewards, that it is more important for cognitive control when determining short-term versus long-term benefits in rewards. Future studies should more closely examine the relationship between oxytocin and delay discounting.

Impact of COVID-19 on referrals to paediatric liaison psychiatry at Children's Health Ireland at Crumlin as the pandemic moved to endemic status.

Rates of acute mental health presentations in youth were increasing pre-pandemic internationally. Longitudinal studies following COVID-19 attest to ongoing deterioration in youth mental health, recognising adverse unintended consequences following public health restrictions. To examine whether the initial post-COVID-19 increase in mental health presentations persisted following the reclassification of COVID-19 to endemic status, accompanied by the removal of most restrictions. All referrals to paediatric liaison psychiatry (PLP) between January 2018 and December 2022 in a Dublin tertiary children's hospital were included in the study. An interrupted time series analysis with autoregressive integrated moving average models was conducted, examining referrals with respect to different phases of COVID-19 and application of public health restrictions. Some 1385 referrals to PLP were received over the 5-year study. There was a significant decrease in PLP referrals immediately post-COVID-19, followed by a significant and sustained increase as the pandemic progressed and moved to endemic status. Public health restriction phases had a unique effect on those presenting with suicidal ideation, with a significant increase in the number of referrals. There was no effect of restrictions on other clinical profiles. Increased referrals for youth with mental health difficulties, reported during the COVID-19 pandemic, persisted into the early endemic stage, after COVID-19 public health restrictions ceased. Specific impacts of restrictions on suicidal ideation referrals require further study. Investment in child and adolescent mental health services remains a priority, and future pandemic response strategies need to examine unintended consequences of any enforced public health measure.

Changes in Mental Health Among Adolescents in South Korea Before and After COVID-19: An Interrupted Time Series Analysis From 2015 to 2022

PurposeThis study aimed to examine changes in mental health among adolescents by comparing data from the period following the onset of the COVID-19 pandemic with the period before the pandemic. MethodsWe estimated the annual prevalence of stress perception, depressive symptoms, and suicidal ideation among middle and high school students using data from the Korean Youth Health Behavior Survey spanning from 2015 to 2022. We then compared mental health status across 2 periods—pre-COVID-19 (2015–2019) and during COVID-19 (2020–2022)—employing an interrupted time series analysis. We adjusted for covariates, such as household economic status, residence type, self-rated health, and history of hospitalization, due to violence. ResultsWe analyzed data from 472,385 adolescents (242,819 boys and 230,016 girls). Stress perception, depressive symptoms, and suicidal ideation showed an increasing trend during the pre-COVID-19 period, followed by a decrease in the first year of the pandemic and an increasing trend in the second and third years. Boys experienced a faster increase in stress and depressive symptoms during the second and third years of the pandemic compared with the pre-COVID-19 period, whereas girls showed trends similar to those observed before the pandemic. Middle school students experienced a more rapid increase in these indicators than high school students during the second and third years. DiscussionAdolescents' mental health initially improved in the first year of COVID-19 but worsened during the second and third years of the pandemic. This suggests a need for intervention policies and programs to support adolescent mental health.

Rising trend of dentist-prescribed antibiotics in Brazil: 2011-2021.

To describe the trends in antibiotic prescribing by dental practitioners and to investigate the relationship between these trends and some factors of public oral health services in Minas Gerais (MG), Brazil. This was a time-series analysis of antibiotics prescribed by dental practitioners between January 2011 and December 2021. The outcome variables were number of defined daily doses (DDD) and DDD/1000 population/year in a sample of cities in MG. Covariates were public oral healthcare factors, such as coverage, estimates of dental procedures, and frequency of dental pain. Linear time-series regression models were used to examine trends and the influence of covariates on antibiotic prescribing. Overall, the number of prescriptions increased by 334.69% between 2011 and 2021, with amoxicillin being the most commonly prescribed drug (78.53%). The number of DDD for all antibiotics increased from 17,147.13 to 77,346.67 and the average DDD/1000 inhabitants/year was 126.66 (SD: 130.28). The linear time-series regression model showed that for each one-year increase, the average log DDD/1000 inhabitants increased by 0.35 (standard error = 0.07, p < 0.001). No covariates were found to be associated with the outcome. In Minas Gerais, Brazil, a significant upward trend was observed in the number of prescriptions and the number of DDD of antibiotics prescribed by dental practitioners. No influence of factors related to public oral healthcare services on the outcome was observed, thereby emphasizing the need for further research on factors influencing medication use in dental practice.

Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study

BackgroundEpidemiological evidence regarding the association between air pollution and resting heart rate (RHR), a predictor of cardiovascular disease and mortality, is limited and inconsistent. ObjectivesWe used wearable devices and time-series analysis to assess the exposure-response relationship over an extended lag period. MethodsNinety-seven elderly individuals (>65 years) from the Taipei Basin participated from May to November 2020 and wore Garmin® smartwatches continuously until the end of 2021 for heart rate monitoring. RHR was defined as the daily average of the lowest 30-min heart rate. Air pollution exposure data, covering lag periods from 0 to 60 days, were obtained from nearby monitoring stations. We used distributed lag non-linear models and linear mixed-effect models to assess cumulative effects of air pollution. Principal component analysis was utilized to explore underlying patterns in air pollution exposure, and subgroup analyses with interaction terms were conducted to explore the modification effects of individual factors. ResultsAfter adjusting for co-pollutants in the models, an interquartile range increase of 0.18 ppm in carbon monoxide (CO) was consistently associated with increased RHR across lag periods of 0–1 day (0.31, 95 % confidence interval [CI]: 0.24–0.38), 0–7 days (0.68, 95 % CI: 0.57–0.79), and 0–50 days (1.02, 95 % CI: 0.82–1.21). Principal component analysis identified two factors, one primarily influenced by CO and nitrogen dioxide (NO2), indicative of traffic sources. Increases in the varimax-rotated traffic-related score were correlated with higher RHR over 0–1 day (0.36, 95 % CI: 0.25–0.47), 0–7 days (0.62, 95 % CI: 0.46–0.77), and 0–50 days (1.27, 95 % CI: 0.87–1.67) lag periods. Over a 0–7 day lag, RHR responses to traffic pollution were intensified by higher temperatures (β = 0.80 vs. 0.29; interaction p-value [P_int] = 0.011). Males (β = 0.66 vs. 0.60; P_int < 0.0001), hypertensive individuals (β = 0.85 vs. 0.45; P_int = 0.028), diabetics (β = 0.96 vs. 0.52; P_int = 0.042), and those with lower physical activity (β = 0.70 vs. 0.54; P_int < 0.0001) also exhibited stronger responses. Over a 0–50 day lag, males (β = 0.99 vs. 0.96; P_int < 0.0001), diabetics (β = 1.66 vs. 0.69; P_int < 0.0001), individuals with lower physical activity (β = 1.49 vs. 0.47; P_int = 0.0006), and those with fewer steps on lag day 1 (β = 1.17 vs. 0.71; P_int = 0.029) showed amplified responses. ConclusionsProlonged exposure to traffic-related air pollution results in cumulative cardiovascular risks, persisting for up to 50 days. These effects are more pronounced on warmer days and in individuals with chronic conditions or inactive lifestyles.

An analytical approach for identifying trend-seasonal components and detecting unexpected behaviour in psychological time-series.

The recent advances in technological capabilities have led to a massive production of time-series data and remarkable progress in longitudinal designs and analyses within psychological research. However, implementing time-series analysis can be challenging due to the various characteristics and complexities involved, as well as the need for statistical expertise. This paper introduces a statistical pipeline on time-series analysis for studying the changes in a single process over time at either a population or individual level, both retrospectively and prospectively. This is achieved through systemization and extension of existing modelling and inference techniques. This analytical approach enables practitioners not only to track but also to model and evaluate emerging trends and apparent seasonality. It also allows for the detection of unexpected events, quantifying their deviations from baseline and forecasting future values. Given that other discernible population- and individual-level changes in psychological and behavioural processes have not yet emerged, continued surveillance is warranted. A near real-time monitoring tool of time-series data could guide community psychological responses across multiple ecological levels, making it a valuable resource for field practitioners and psychologists. An empirical study is conducted to illustrate the implementation of the introduced analytical pipeline in practice and to demonstrate its capabilities.

Exploring Saudi Arabia’s 2060 net zero-emission paths via fractional frequency Fourier procedures. The imperatives of resource efficiency, energy efficiency, and digitalization

ABSTRACT By applying the novel fractional frequency Fourier ARDL technique, we unraveled the intricacies underpinning resource efficiency, energy efficiency, digitalization, and environmental sustainability in Saudi Arabia. Compelled by the conflicting narratives of prior investigations and the need for updated insights, the analyzed time series spanning 1990–2022 periods divulged interesting insights amenable to policy optimization. First, the fractional frequency Fourier ADL cointegration procedure reveals a long-term association among all the enlisted series. Second, resource and energy efficiencies, as well as digitalization, improved environmental quality by limiting carbon intensities and ecological footprint with some heterogeneous attributes across the timeframes. Notably, while resource efficiency is more effective toward ecological footprint mitigations, energy efficiency proves more efficient for carbon intensity reductions. Lastly, the fractional frequency Fourier Toda-Yamamoto causality test affirms the predictive attributes of the enlisted series on environmental performance. Hence, these factors are critical to KSA’s environmental sustainability paths. Based on these empirical discoveries, policymakers in KSA must pay adequate attention to the efficient utilization of resources and energy to complement other steps toward realizing Saudi Arabia’s 2060 net zero-emission. Likewise, policymakers must ensure the efficient allocation of income to environmentally sensitive ventures to curtail its negative repercussions on the environment. Other relevant steps to curtail carbon intensities and ecological footprint for optimal environmental performance in KSA have been highlighted.

Exploring the equity of distribution of general medical services funding allocations in Wales: a time-series analysis.

Good access to quality primary care in high-income countries can improve population health. Access to primary care is however often not equal among socioeconomic groups; our analysis sought to explore whether funding, a determinant of service supply, is equitably distributed among GP practices in Wales. We sought to explore the relationship between funding and deprivation among GP practices in Wales, to understand the equity of current funding policies. We obtained funding data for general practices in Wales between 2014 and 2022 and explored the equity of distribution using the percentage of practice patients living in the 20% most deprived small areas in Wales. We generated a linear regression model exploring the relationship between practice funding and deprivation, with an interaction term with time in years. Practice funding rose for all practices between 2014 and 2022. Practice deprivation and time in years were both associated with practice funding, with increases in practice deprivation associated with reduced funding allocations, and time being associated with a small increase in funding over the study period. Over the period of analysis of 2004-2022, for every 10% increase in patients living in the most deprived LSOAs, funding per patient decreases on average by 1%. Primary cares in Wales in more deprived areas receive discernibly less funding per patient than those in less deprived areas. Given the potential and likelihood primary care can affect population health outcomes, this underinvestment may be contributing to existing health inequalities and requires urgent further analysis and action.

B-352 Effects of COVID-19 on Breast Cancer Positivity: A Retrospective and Predictive Study

Abstract Background The COVID-19 pandemic has had a significant impact on the healthcare system, resulting in overall reduction in access to healthcare services, including the suspension of screening programs and postponement of surgeries and medical treatments. The aim of this study was to evaluate the effects of the pandemic on breast cancer positivity from cases received at a laboratory in São Paulo, Brazil. Methods A retrospective analysis of a database from a pathology laboratory in São Paulo, Brazil, was performed during the 2017-2023 period and classified as following: pre-pandemic (January 2017-February 2020), pandemic (March 2020 - April-2022), and post-pandemic (May 2022-December 2023). Cases were classified as negative or positive for malignancies. Positive anatomopathological examinations (breast biopsies and surgical specimens) considered primary, metastatic mesenchymal or endothelial malignancies. The Mann-Kendall test was used to evaluate the trend, and the PACF (Partial Autocorrelation Function) correlogram was used to analyze historical memory for definition of the autoregressive model. The augmented Dickey-Fuller hypothesis test was applied to analyze whether the series is stationary or not, and the first differences test was applied. The ARIMA model was used for the estimates (1,1,1). Statistics were performed using the gretl software and pymannkendall library in the Python language. Results A total of 74,973 samples were evaluated during the 2017-2023 period. In the three-year pre-pandemic period, 32,644 tests were recorded, with an average of 29.34% positive cases. From the analysis of the pre-pandemic time series, there was a positive trend towards an increase in cases (p &amp;lt; 0.0001, z=6.1360, Tau=0.6955). In a non-pandemic scenario, from March 2020 to February 2021, the number of new tests expected would be 11,535; however, in the pandemic period, only 8,904 were observed, a 22.81% reduction on expected cases. According to the historical average positivity rate in the pre-pandemic period, the expected and observed total were, respectively, 3,384 and 2,566 positive cases for malignancy. Conclusions This study reveals a significant decrease of approximately 22.81% in new breast anatomopathological diagnoses during the COVID-19 pandemic in a laboratory in São Paulo, Brazil. For comparison purposes, we identified a 24.17% of missed opportunities on diagnoses of new malignancy-positive cases, compared to the pre-pandemic period. These findings highlight the need for strategies to mitigate the negative effects of the pandemic on the early detection and treatment of breast cancer.

Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain

Evolutionarily relevant networks have been previously described in several mammalian species using time-averaged analyses of fMRI time-series. However, fMRI network activity is highly dynamic and continually evolves over timescales of seconds. Whether the dynamic organization of resting-state fMRI network activity is conserved across mammalian species remains unclear. Using frame-wise clustering of fMRI time-series, we find that intrinsic fMRI network dynamics in awake male macaques and humans is characterized by recurrent transitions between a set of 4 dominant, neuroanatomically homologous fMRI coactivation modes (C-modes), three of which are also plausibly represented in the male rodent brain. Importantly, in all species C-modes exhibit species-invariant dynamic features, including preferred occurrence at specific phases of fMRI global signal fluctuations, and a state transition structure compatible with infraslow coupled oscillator dynamics. Moreover, dominant C-mode occurrence reconstitutes the static organization of the fMRI connectome in all species, and is predictive of ranking of corresponding fMRI connectivity gradients. These results reveal a set of species-invariant principles underlying the dynamic organization of fMRI networks in mammalian species, and offer novel opportunities to relate fMRI network findings across the phylogenetic tree.

Analysis of differences in fossil fuel consumption in the world based on the fractal time series and complex network

Fossil fuels remain indispensable energy resources despite their non-renewable nature. Understanding the patterns of global fossil fuel consumption is essential for energy security and policy-making. This study employs complex network theory and fractal time series analysis to explore the underlying dynamics and patterns of fossil fuel consumption globally, with a focus on coal, oil, and gas consumption.The study applies the Hurst index to raw fossil fuel consumption data to identify fractal characteristics. Additionally, the visibility graph method is used to convert time series data into complex networks, allowing further analysis of consumption patterns. The study examines fossil fuel consumption in 38 countries to assess global trends and differences. The analysis reveals that global fossil fuel consumption follows a fractal time series pattern, with Hurst index values exceeding 0.9, indicating long-term memory characteristics. The application of the visibility graph method demonstrates variations in the Hurst index of degree distribution, enabling the differentiation of consumption patterns across regions. The method also uncovers distinct features of coal, oil, and gas consumption when viewed from a network perspective. The findings suggest that fossil fuel consumption has predictable long-term patterns, which are crucial for assessing future energy demands. The study highlights the importance of legislative measures to safeguard fossil fuel resources, especially for countries like China, where energy security and international competitiveness are paramount. Understanding these consumption patterns could guide future energy policies aimed at managing non-renewable resources more effectively.

Impact of the COVID-19 pandemic on health-care use among patients with cancer in England, UK: a comprehensive phase-by-phase time-series analysis across attendance types for 38 cancers

The COVID-19 pandemic resulted in the widespread disruption of cancer health provision services across the entirety of the cancer care pathway in the UK, from screening to treatment. The potential long-term health implications, including increased mortality for individuals who missed diagnoses or appointments, are concerning. However, the precise impact of lockdown policies on national cancer health service provision across diagnostic groups is understudied. We aimed to systematically evaluate changes in patterns of attendance for groups of individuals diagnosed with cancer, including the changes in attendance volume and consultation rates, stratified by both time-based exposures and by patient-based exposures and to better understand the impact of such changes on cancer-specific mortality. In this retrospective, cross-sectional, phase-by-phase time-series analysis, by using primary care records linked to hospitals and the death registry from Jan 1, 1998, to June 17, 2021, we conducted descriptive analyses to quantify attendance changes for groups stratified by patient-based exposures (Index of Multiple Deprivation, ethnicity, age, comorbidity count, practice region, diagnosis time, and cancer subtype) across different phases of the COVID-19 pandemic in England, UK. In this study, we defined the phases of the COVID-19 pandemic as: pre-pandemic period (Jan 1, 2018, to March 22, 2020), lockdown 1 (March 23 to June 21, 2020), minimal restrictions (June 22 to Sept 20, 2020), lockdown 2 (Sept 21, 2020, to Jan 3, 2021), lockdown 3 (Jan 4 to March 21, 2021), and lockdown restrictions lifted (March 22 to March 31, 2021). In the analyses we examined changes in both attendance volume and consultation rate. We further compared changes in attendance trends to cancer-specific mortality trends. Finally, we conducted an interrupted time-series analysis with the lockdown on March 23, 2020, as the intervention point using an autoregressive integrated moving average model. From 561 611 eligible individuals, 7 964 685 attendances were recorded. During the first lockdown, the median attendance volume decreased (-35·30% [IQR -36·10 to -34·25]) compared with the preceding pre-pandemic period, followed by a median change of 4·38% (2·66 to 5·15) during minimal restrictions. More drastic reductions in attendance volume were seen in the second (-48·71% [-49·54 to -48·26]) and third (-71·62% [-72·23 to -70·97]) lockdowns. These reductions were followed by a 4·48% (3·45 to 7·10) increase in attendance when lockdown restrictions were lifted. The median consultation rate change during the first lockdown was 31·32% (25·10 to 33·60), followed by a median change of -0·25% (-1·38 to 1·68) during minimal restrictions. The median consultation rate decreased in the second (-33·89% [-34·64 to -33·18]) and third (-4·98% [-5·71 to -4·00]) lockdowns, followed by a 416·16% increase (409·77 to 429·77) upon lifting of lockdown restrictions. Notably, across many weeks, a year-over-year decrease in weekly attendances corresponded with a year-over-year increase in cancer-specific mortality. Overall, the pandemic period revealed a statistically significant reduction in attendances for patients with cancer (lockdown 1 -24 070·19 attendances, p<0·0001; minimal restrictions -19 194·89 attendances, p<0·0001; lockdown 2 -31 311·28 attendances, p<0·0001; lockdown 3 -43 843·38 attendances, p<0·0001; and lockdown restrictions lifted -56 260·50 attendances, p<0·0001) compared with before the pandemic. The UK's COVID-19 pandemic lockdown affected cancer health service access negatively. Many groups of individuals with cancer had declines in attendance volume and consultation rate across the phases of the pandemic. A decrease in attendances might lead to delays in cancer diagnoses, treatment, and follow-up, putting such groups of individuals at higher risk of negative health outcomes, such as cancer-specific mortality. We discuss the factors potentially responsible for explaining changes in service provision trends and provide insight to help inform clinical follow-up for groups of individuals at risk, alongside potential future policy changes in the care of such patients. Wellcome Trust, National Institute for Health Research University College London Hospitals Biomedical Research Centre, National Institute for Health Research Great Ormond Street Hospital Biomedical Research Centre, Academy of Medical Sciences, and the University College London Overseas Research Scholarship.

Sustainable Design Methods Translated from the Thermodynamic Theory of Vernacular Architecture: Atrium Prototypes

In the context of China’s sustainable development and dual carbon goals, research on thermodynamic architecture theory and vernacular architecture increasingly aligns with international trends, developing distinct characteristics. This research addresses the challenge of rapid changes in the built environment by focusing on climate adaptability and passive technologies. However, the development of thermodynamic theory in vernacular architecture faced technical limitations in the early 21st century and was later overshadowed by the industry’s reliance on active technologies to meet green building standards, resulting in a reduced role for architects in the green building field. This article traces the origins of passive architecture, rooted in vernacular architecture, and applies thermodynamic theory to explore architectural prototypes. It examines the theoretical feasibility of architectural design in achieving low-carbon and sustainable goals, aiming to fill a gap in thermodynamic theory within the broader context of sustainable architectural development. After demonstrating the various passive prototypes inherent in vernacular architecture, this paper proposes a courtyard prototype focused on residential comfort for design translation and analysis. The research methods employed include bioclimatic charting, balance point temperature analysis in time series, and extensive computer simulations. Through the process of prototype extraction, performance analysis, validation, and optimization, the paper systematically discusses sustainable design methods within the framework of thermodynamic architecture theory. It also provides practical demonstrations of these methods across four distinct climate regions in China. By translating vernacular architectural designs, this research systematically organizes the theoretical framework for architects’ early involvement in low-carbon and green building design, offering a theoretical foundation for initiating the design process through prototype translation while guiding the generation of green ecological buildings.

GCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

High-precision global navigation satellite system (GNSS) time series data contain rich tectonic and non-tectonic motion information. However, such data also have common-mode errors (CME) related to spatiotemporal characteristics. CME will produce bias in accurate and reliable GNSS station positions and velocities estimations, ultimately deciphering erroneous geophysical signals. Currently, CME extraction methods can be divided into three categories: regional stack filtering, reference frame filtering, and statistical signal decomposition. The third method is widely used in geodetic data processing, including GNSS time series analysis, global time-varying gravity signal extraction, interferometric synthetic aperture radar (InSAR) time series analysis, etc. The demand for GNSS time series analysis software with interactive, visualization, and multifunctional features has also increased with the increase in GNSS stations. Therefore, we developed gCMEbox based on MATLAB. The gCMEbox’s primary functions include GNSS time series preprocessing, multiple statistical signal decomposition algorithms for extracting CME, GNSS time series analysis, visualization, velocity field construction, and generation of multiple time series post-processing products. Based on GNSS time series data, this study utilized the gCMEbox for data preprocessing, including format conversion, outlier removal, missing data recovery, etc. By applying statistical signal decomposition methods, gCMEbox successfully extracted CME. The primary objective of this study is to share this toolbox with the scientific community, providing a comprehensive tool for GNSS time series analysis and applications.

The Integration of Machine Learning in Central Banks: Implications and Innovations

The effectiveness of using artificial intelligence (AI) techniques to lessen the negative effects of economic cycles is examined in this study article. Economic cycles, which are defined by variations in the level of economic activity, present important difficulties for decision-makers in government, business, and society at large. This research investigates many approaches to reducing economic cycles, such as forecasting, policy creation, and adaptive decision-making, by utilizing AI, namely machine learning algorithms. The first section of the paper reviews the body of research on economic cycles and how artificial intelligence might be used to address them. After that, it explores certain AI methods including neural networks, time series analysis, and natural language processing, explaining how they might be used for risk management and economic forecasting. Additionally, the study investigates AI-driven policy interventions, examining how machine learning algorithms might be used to optimize monetary and fiscal policies in order to sustain growth and prevent economic downturns. The study also addresses the difficulties and constraints that come with using AI technologies in economic management, such as problems with algorithmic biases, data quality, and legal considerations. It highlights how crucial interdisciplinary cooperation and openness are to creating AI-driven solutions that are reliable, moral, and socially conscious. This research paper offers insights into the possible advantages and disadvantages of employing AI tools for damping economic cycles through empirical analysis and case studies. In order to fully realize AI's promise in fostering resilience and stability in the economy, it ends by presenting future research areas and policy suggestions. Keywords: artificial intelligence, trend, economic cycles, resilience

The Existence and Development of Variants in English Writing Teaching in International Corpus based on Time Series Data Analysis

This study investigates the evolution and prevalence of variant forms in English writing instruction within an international context, utilizing a time series analysis of corpus data. Recognizing the dynamic nature of language, especially in educational settings, this research aims to identify and analyze the changes in teaching methodologies and language usage over time. By examining a comprehensive corpus compiled from diverse international sources, the study focuses on how English writing instruction has adapted to linguistic variations and evolving pedagogical approaches.The methodology involves a quantitative analysis of corpus data spanning several decades, enabling a longitudinal view of trends in English writing teaching. The corpus includes a wide range of educational materials, academic publications, and teaching resources from different countries, ensuring a global perspective. Time series analysis is employed to track the frequency and context of various teaching practices and linguistic forms, offering insights into their development and dissemination.Key findings reveal a significant shift in teaching strategies, reflecting an increased emphasis on learner-centered approaches and the integration of digital technologies. Variants in language usage, influenced by cultural and regional differences, are also evident, highlighting the importance of context-specific teaching methods.

7496 Differences in seasonality's influence on individual lipid parameters: A time series analysis

7496 Differences in seasonality's influence on individual lipid parameters: A time series analysis

Granular computing-based time series anomaly pattern detection with semantic interpretation

Time series analysis may suffer from the “curse of dimensionality” due to its high-dimensionality characteristics. In terms of this issue, information granulation offers an effective vehicle to process time series at a higher level of abstraction. To take this advantage, this study conducts an interpretable time series anomaly pattern detection under the framework of granular computing, where each time series is granulated into a series of semantics according to its patterns and the anomaly ones are identified based on the obtained semantics. First, the time series is partitioned into a predefined number of segments and trend-based information granules are formed for the data points in each time interval. Guided by the principle of justifiable granularity, the granular results maintaining the main features of the original time series realize informative feature extraction and meaningful dimensionality reduction. Then, to realize semantic anomaly pattern detection, the Axiomatic Fuzzy Set (AFS) theory is generalized to construct and compute with semantic time series, and an AFS-based anomaly score is proposed to discover anomaly patterns. In the experiments, the proposed method is conducted on both UCR data sets and real-world time series, where the detected anomaly patterns are equipped with well-defined semantics.