Trends In Indicators Research Articles

Assessing high-resolution precipitation extremes in Central Asia: evaluation and future projections

The sustainability of ecosystems in Central Asia’s semi-arid and arid regions is increasingly threatened by anthropogenic climate change, with shifts in extreme precipitation events playing a pivotal role. Effective adaptation strategies depend on precise forecasting of these changes. This study investigates projected trends in mean and extreme precipitation indices across Central Asia (CA) from 1985 to 2100. Utilizing datasets from the fifth generation of ECMWF Reanalysis (ERA5), the Climate Prediction Center (CPC), and high-resolution National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) models, we analyzed four Shared Socioeconomic Pathway (SSP) scenarios across three distinct time periods. The CMIP6 Multi-Model Ensemble (MME) accurately simulates mean annual precipitation (ANP) for much of the region, though it underperforms in mountainous areas. Specifically, it underestimates days with precipitation exceeding 10 mm (PD10MM) and the Simple Daily Intensity Index (SDII) while overestimating Consecutive Dry Days (CDD) in regions with higher altitudes and more precipitation. Projections indicate a potential increase in mean ANP by up to 50% across most of Central Asia, becoming especially prominent from the mid-century onward. Extremes in precipitation, such as the SDII, the maximum 1-day precipitation amount (RX1DAY), and days with over 10 mm of rainfall, are expected to rise in frequency and intensity across the region. In contrast, while CDD may decrease in eastern CA, it will likely increase in the west by the century’s end. These anticipated changes suggest increased wetness under warming scenarios, with more frequent heavy precipitation events and a reduction in prolonged dry periods, particularly under high-emission pathways. The data provides a foundation for developing effective adaptation strategies to enhance resilience against the impacts of climate change in Central Asia.

Planetary Health Diet Index Trends and Associations with Dietary Greenhouse Gas Emissions, Disease Biomarkers, Obesity, and Mortality in the United States (2005-2018).

Diet plays a vital role in human health and environmental effects. Monitoring diet quality and its relationship to both health and environment are essential for policy making. This study aimed to analyze trends in the Planetary Health Diet Index (PHDI) and its associations with daily greenhouse gas emissions from food (GHG), disease-related biomarkers, anthropometric measurements, obesity, and all-cause mortality in the US population. We analyzed 27,181 adults in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 except for the mortality analysis. 23,599 adults were analyzed as the 2017-2018 NHANES dietary data were removed due to the potential for reverse causation. We calculated PHDI scores by using two 24-hour dietary recalls and GHG by linking the consumption of individual foods to dataFRIENDS, a food-environmental impact database. To assess associations with the PHDI, we used generalized linear regression models for GHG, disease-related biomarkers, and obesity, and used the Cox proportional hazards model for all-cause mortality. The energy-adjusted mean of the PHDI (140 possible points) increased from 68.6 in 2005-2006 to 71.7 in 2017-2018 (p for trend < 0.001). When compared to the lowest quintile (Q1), the highest PHDI quintile (Q5) was associated with 25% lower GHG emissions, a better cardiometabolic profile, lower prevalence ratios of obesity (0.59, 95% CI: 0.50-0.69) and abdominal obesity (0.74, 95% CI: 0.66-0.82) and a lower risk of all-cause death (HR: 0.65, 95% CI: 0.54-0.78). These results underscore the potential health and greenhouse gas emission benefits aligned with the planetary health diet.

Spatiotemporal patterns in precipitation whiplash over China during 1901–2100 based on CMIP6 simulations

Abstract The increasing risk of precipitation whiplash, characterized by rapid transitions between extreme drought and wet conditions, is largely related to atmospheric circulation changes due to anthropogenic greenhouse gas emissions. This study aims to examine the changing patterns of precipitation whiplash events for future projections (2021–2100) with regard to the current climate (1981–2020), and to unveil the relations between intensity, duration, and frequency of these events for various return periods across different regions of China. Multi-source observational datasets were also used to analyze the trend of precipitation whiplash indices for the current climate and to assess the ability of the CMIP6 ensemble model for reproducing the characteristics of precipitation whiplash events. The Intensity-Duration-Frequency (IDF) curves were estimated by using the nonstationary Generalized Extreme Value (GEV) model combined with Bayesian inference. The results show an increasing trend in frequency, intensity, and severity of precipitation whiplash events, particularly for the long-term future, while the duration of these transitions shows a decreasing trend. The peak occurrence months during the long-term future period exhibit notable changes in most parts of China. Specifically, the dry-to-wet whiplash events occur approximately two months earlier than in the current period, while the wet-to-dry whiplash occurs approximately two months later. The results also indicated that IDF curves shifted upward, particularly in the long-term future projections, suggesting an increased likelihood of more severe events. The findings of this study will serve as an essential reference for local authorities to develop more effective water resource management and disaster mitigation policies to tackle the impact of future climate change.

Pregnancy and Postpartum Trends in Self-Measured Blood Pressure and Derived Indices: The BOSHI Study.

In this study, we aimed to reveal the trends of self-measured blood pressure (SMBP) and SMBP-derived indices during pregnancy and the postpartum period. The Babies and Their Parents Longitudinal Observation in Suzuki Memorial Hospital in the Intrauterine Period (BOSHI) Study is a prospective cohort study in Japan. Participants were instructed to measure SMBP daily during pregnancy and for 1 month after delivery. Among 237 participants with normotensive blood pressure (BP) during pregnancy and the postpartum period who were analyzed using mixed-effects models for repeated measures, the SMBP was measured, on average, 14.3 times from the day before delivery to 28 days postpartum. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) on the day before delivery were 110.6±1.0 and 68.1±0.8mmHg (estimate ± standard error). Postpartum BP increased from postpartum Days 3-8 in SBP and from Days 3-22 in DBP, compared to that on the day before delivery. The SBP and DBP were 4.9 and 4.7mmHg higher on postpartum Days 8 and 7 than the day before pregnancy, respectively. During pregnancy, the pulse rate (PR) showed an inverted U-shaped trend and then sharply increased rapidly until the first postpartum day after delivery. The Shock Index showed a similar trend to that of the PR, decreased from labor until postpartum Day 8, and plateaued thereafter. The double product peaked during labor, remained higher than the prelabor levels for approximately 10 days, and then decreased in the postpartum period.

Assessment of Teleconnections of Extreme Precipitation with Large-Scale Climate Indices: A Case Study of the Zishui River Basin, China

With global climate change, the frequency of extreme precipitation events in the Zishui River Basin (ZRB) is increasing, presenting significant challenges for water resource management. This study focuses on analyzing the evolution of extreme precipitation trends during the flood season from 1979 to 2018 and investigating their remote correlations with 18 large-scale climate indicators (LCIs) using three-dimensional (3D) Vine Copula. The results indicate a significant downward trend in the sustained wetness index (CWD) during the flood season, while trends in other extreme precipitation indices (EPIs) are not significant. Notably, a significant correlation exists between Maximum Precipitation for One Day (RX1day) and the Pacific Decadal Oscillation (PDO), Pacific North American pattern (PNO), and Sustained Drought Index (CDD), as well as between Atlantic Multi-decadal Oscillation (AMO) and PDO. Excluding the optimal marginal distribution of PDO, which follows a Laplace distribution, the optimal marginal distributions of the other indices conform to a Beta distribution. The C-Vine Copula function was employed to establish the functional relationships among RX1day, PDO, PNO, CDD, and AMO, allowing for an analysis of the impact of model fitting on EPIs under different LCI scenarios. The findings of this study are significant for the ZRB and other inland monsoon climate zones, providing a scientific foundation for addressing climate extremes and enhancing flood monitoring and prediction capabilities in the region.

Analysis of Vegetation Coverage Changes and Influencing Factors in Aksu, Xinjiang, China (2000–2020): A Comparative Study of Climate Factors and Urban Development

The ecological environment is fundamental to human survival and development, and China has seen a historical shift from localized to widespread improvements in its ecological conditions. Aksu, a typical ecologically sensitive region in Xinjiang, China, is significant for the study of vegetation dynamics and their driving factors, which is crucial for ecological conservation. This study evaluates the spatiotemporal changes in vegetation coverage in Aksu from 2000 to 2020 using long-term Normalized Difference Vegetation Index (NDVI) data and trend analysis. Additionally, this study explores key factors influencing vegetation changes through correlation analysis with temperature, precipitation, and nighttime light data. The results indicate the following: (1) vegetation coverage in Aksu exhibits significant spatial heterogeneity, with annual NDVI increasing at a rate of 0.83% per year (p &lt; 0.05); (2) the influence of temperature and precipitation on NDVI was weakly correlated from 2000 to 2020; and (3) a strong positive correlation was found between nighttime light intensity and NDVI, suggesting that urban development plays a dominant role in vegetation change, while temperature and precipitation have comparatively minor impacts. The findings provide a scientific basis for ecological conservation and sustainable development in the region.

Psychological health in health care workers: features of the formation of professional burnout, prevention opportunities

Introduction. The working conditions in medical workers are characterized by high tension, which leads to a violation of the psycho-emotional state in health care workers, including professional burnout, the features of which depend on the neurophysiological type of personality, the severity of chronic stress, and the level of commitment to a healthy lifestyle. The purpose is to identify risk factors for professional burnout and evaluate the effectiveness of preventing professional burnout in medical workers in conditions of increased work intensity. Materials and methods. The study included medical workers with indices of the intensity of the labour process of class 3.1 and 3.2. The questionnaire method was used to determine: the features of the formation of professional burnout, characteristics of the type of nervous system, the index of adherence to a healthy lifestyle, stress level, subjective assessment of the psychological state. The analysis of the trend in indices of professional burnout and psychological state after group psychosomatic correction sessions was carried out. Results. The structure of professional burnout in medical workers was dominated by high rates of reduction of professional achievements (53.8%), the frequency of emotional exhaustion was 25.6%, depersonalization – 30.8%. The probability of developing emotional exhaustion in persons with an average strength of the nervous system was 3.8 times higher than in persons with a strong type of nervous system. A lack of commitment to a healthy lifestyle is associated with an increased frequency of emotional exhaustion. The positive trend in indices of professional burnout and subjective assessment of the psychological state after the preventive program of resonant acoustic vibrations has been achieved. Limitations. The subjects were employees of a medical institution working under conditions of exposure to the intensity of the labour process exceeding hygienic standards. The data is limited to voluntary study participants. Conclusions. The features of the formation of professional burnout in medical workers are determined. The effectiveness of the program for the prevention of professional burnout with the use of group psychosomatic correction has been confirmed.

Impact of Extreme Climate Indices on Vegetation Dynamics in the Qinghai–Tibet Plateau: A Comprehensive Analysis Utilizing Long-Term Dataset

The Qinghai–Tibet Plateau (QTP) is crucial for global climate regulation and ecological equilibrium. However, the phenomenon of global climate warming has increased the frequency of extreme weather events on the QTP, exerting substantial effects on both regional and global ecological systems. This study utilized long-term series NDVI and extreme climate indices to comprehensively evaluate the impact of extreme climatic changes on diverse vegetation types within the QTP. A variety of analytical methodologies, including trend analysis, a Mann–Kendall test, correlation analysis, and random forest importance ranking, were employed in this study. These methodologies were applied to investigate the distribution patterns and variation trends of diverse vegetation types and extreme climate indices. This comprehensive approach facilitated a detailed analysis of the responses of different vegetation types to interannual variability under extreme climatic conditions and enabled the assessment of the impact of extreme climate indices on these vegetation types. The findings have the following implications: (1) Except for forests, the annual NDVI for overall vegetation, meadows, steppes, deserts, and alpine vegetation in the QTP exhibits a significant upward trend (p &lt; 0.01). Notably, meadows and deserts demonstrate the highest growth rates at 0.007/10y, whereas the annual NDVI of forests is not statistically significant (p &gt; 0.05). Substantial increases in vegetation were predominantly detected in the central and northeastern regions of the QTP, while significant decreases were mostly observed in the southeastern and western regions. The area exhibiting significant vegetation increase (38.71%) considerably surpasses that of the area with a significant decrease (14.24%). (2) There was a statistically significant reduction (p &lt; 0.05) in the number of days associated with extreme cold temperature indices, including CSDI, DTR, FD, ID, TN10p, and TX10p. In contrast, indices related to extremely warm temperatures, such as GSL, WSDI, SU25, TN90p, TNn, TNx, TX90p, and TXx, exhibited a statistically significant increase (p &lt; 0.01). The pronounced rise in minimum temperatures, reflected by fewer cold days, has notably contributed to climate warming. Although extreme precipitation events have become less frequent, their intensity has increased. Notable spatial variations in extreme precipitation were observed, although no consistent changing pattern emerged. (3) The annual NDVI for non-forest vegetation types showed a significant negative correlation with most extreme cold temperature indices and a significant positive correlation with extreme warm temperature indices. A significant positive correlation (p &lt; 0.05) between annual NDVI and extreme precipitation indices is found only in steppe and desert ecosystems, with no such correlation observed in other vegetation types. Both correlation analysis and random forest methodologies underscore the impact of extreme climate indices on vegetation variations, with the random forest model exhibiting superior capability in capturing nonlinear relationships. In conclusion, global climate change is projected to result in a heightened frequency of extreme warm events. Although these conditions might temporarily enhance vegetation growth, they are also associated with numerous detrimental impacts. Therefore, it is imperative to enhance awareness and take proactive measures for early warning and prevention.

Beijing Stock Exchange: Results of Two Years Operation, Challenges and Prospects

The establishment of the Beijing Stock Exchange (BSE) in November 2021 was due to a number of factors. First, a specialized channel for financing small and medium-sized businesses (SME) was needed. Second, it was the result of increased regulatory requirements for Chinese companies on the US stock market and their massive transition to domestic exchanges. Trends of the BSE indices often sharply contrast with those of the 2 major Chinese stock exchanges, which attract millions of retail investors to the BSE. Institutional investors show more interest in the new exchange as well. The access of foreign investors is regulated through licenses and quotas. The BSE initial market is vivid: IPOs were held all temporary suspension periods long. The exchange infrastructure and trading system are successfully developing and expanding. The market makers facility was introduced, margin trading was allowed, the bond market was formed. The BSE development plan provides for in 5–10 years it transforms into a full-fledged financial center by key indicators.

Spatiotemporal characterization and hydrological impact of drought patterns in northwestern Morocco

Drought assessment and management, intensified by global warming, present critical challenges in semi-arid Mediterranean regions, impacting environmental sustainability and economic stability. This study evaluates spatiotemporal drought risk in the Bouregreg watershed in northwest Morocco by integrating remote sensing data with various drought indices. The Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Standardized Temperature Index (STI) were utilized to assess meteorological drought over a 12-month period. The Temperature Conditions Index (TCI) was used to evaluate temperature-related conditions for agricultural drought, while the GRACE Drought Severity Index (GRACEDSI) assessed hydrological drought on a monthly scale. Additionally, trend analysis was performed using Mann-Kendall and Sen’s slope methods, and Pearson correlations were conducted among the indices. The findings revealed an overall downward trend in drought indices, with evapotranspiration (SPEI) being the primary drought driver. Over the study period, there was a significant increase in total evaporation demand, largely attributed to rising temperatures (STI and TCI). Meanwhile, precipitation conditions (SPI) remained relatively stable, highlighting the impact of global warming on agricultural and hydrological drought severity patterns in recent years. The results further indicated that drought risk is more pronounced in the plateau and plain areas of the Bouregreg compared to the mountainous regions. In evaluating water reserves, total water storage (TWS) data obtained from the Gravity Recovery and Climate Experiment (GRACE) was utilized. Comparisons were made between in situ groundwater level (GWL) data and those from GRACE TWS at a resolution of 0.25°. Our results reveal concordant trends between the two datasets, despite the differences in resolution. The TWS appears to be strongly correlated with GWL measurements and precipitation data with a lag of 1–4 months. The findings underscored a significant decline in water reserves and worsening drought conditions in recent years. Correlation analyses also revealed a moderate relationship between this decline and the systematic temperature rise, suggesting shared trends influenced by other anthropogenic factors not accounted for in the analysis. In summary, these results underscore the vulnerability of the entire study area to various forms of drought, ranging from mild to extreme severity.

Unraveling the variability and geoclimatic drivers of daily precipitation concentration over a Western Himalayan Province of India.

A sound understanding of daily precipitation concentration provides critical insights about extreme precipitation and associated hazards such as rainstorms, flash floods, cloud bursts, and soil erosion. Therefore, this study examines the spatiotemporal variability and geoclimatic drivers of daily precipitation concentration using the concentration index (CI), precipitation concentration degree (PCD), Q Index (QI), and Theil index (TI) over a Western Himalayan Province, namely Himachal Pradesh, India. For this, the daily precipitation data pertaining to 111 grids (0.25° × 0.25° resolution), encompassing entire province, have been downloaded from India Meteorological Department (IMD), Pune, for the period 1951 to 2020. The trends in concentration indices have been detected using Mann-Kendall (MK) and Sen's slope methods, whereas their relationship with geoclimatic drivers have been assessed using Pearson's correlation coefficient (physiographic) and random forest regression model (climatic) at annual and monsoon season time scales. The results have revealed a high correlation between CI-TI (> 0.95), followed by QI-TI (> 0.84), QI-PCD (> 0.80), TI-PCD (> 0.78), QI-CI (> 0.65), and CI-PCD (> 0.60), representing nearly similar precipitation concentration distribution. Among the concentration indices, QI has shown maximum sensitivity to long-term changes followed by TI, CI, and PCD at both time scales. Similarly, physiographic factors have maximum effect on QI, showing its greatest sensitivity to latitude, longitude and elevation. While considering the impact of climatic drivers, Atlantic Meridian Oscillation (AMO) and Sunspot activity have exerted maximum influence on annual variability of CI, PCD, QI, and TI, whereas monsoon variability of these indices has been modulated by Pacific Decadal Oscillation (PDO), especially over northern parts of Himachal Pradesh. These findings will assist soil and water resource managers, disaster executives, agricultural stakeholders, and urban planners in understanding the unexplored aspects of daily precipitation concentration and making informed decisions over the mountainous regions of western Himalaya.

Body mass index trends over four years in persons with multiple sclerosis.

The role of obesity in persons with multiple sclerosis (PwMS) is incompletely understood. Obesity predisposes individuals to a pro-inflammatory state and cardiovascular comorbidities, both of which can negatively impact MS disease course. A better understanding of weight trends in PwMS will inform optimal management of those who are overweight or obese. To determine body mass index (BMI) trends in newly diagnosed PwMS and individual characteristics associated with weight gain after diagnosis. Also, to determine the impact of physical disability on BMI changes in PwMS. We conducted two longitudinal retrospective cohort analyses using the Multiple Sclerosis Partners Advancing Technology Health Solutions (MS PATHS) database. The first characterized BMI trends in newly diagnosed PwMS. Multivariable linear regression was used to determine characteristics associated with baseline BMI. Multivariable logistic regression was used to determine characteristics associated with annualized BMI changes of ≥ 0.5 kg/m2/year and ≥ 2.0 kg/m2/year in PwMS who were overweight or obese at baseline. The second analysis evaluated BMI trends in individuals with more longstanding MS to determine the impact of disability on weight changes. Multivariable logistic regression was used with consolidated Patient Determined Disease Steps (PDDS) categories as a disability measure. There were 942 PwMS in the newly diagnosed cohort, of whom 265 (27.9 %) were overweight and 403 (42.4 %) were obese. The average follow-up time was 2.1 years, and the average annualized BMI change was 0.19 kg/m2/year (SD=1.5). Greater education was associated with lower baseline BMI (β=-0.31, p = 0.003) but no other characteristics were significantly associated. Greater education was also associated with lower odds of rapid weight gain at ≥ 2.0 kg/m2/year (OR=0.88, p = 0.001), while female sex was associated with higher odds of rapid weight gain (OR=1.84, p = 0.047) at the same threshold. The disability analysis included 10,394 PwMS, of whom 5,699 (54.8 %) had low disability (PDDS 0-1), 3,221 (31.0 %) had moderate disability (PDDS 2-4), and 1,474 (14.2 %) had high disability. The average annualized BMI change was 0.08 kg/m2/year for all subjects (SD=1.7), 0.10 kg/m2/year (SD=1.6) for those with low baseline disability, 0.06 kg/m2/year (SD=1.9) for those with moderate baseline disability, and 0.04 kg/m2/year (SD=1.9) for those with high baseline disability. Relative to those with low disability, moderate disability (β=1.09, p < 0.001) and severe disability (β=1.274, p < 0.001) were both associated with higher baseline BMI. Greater disability was not associated with odds of meeting thresholds for annualized BMI increases. However, women had 22 % higher odds of a ≥ 0.5 kg/m2/year increase (p = 0.002) and 31.6 % higher odds of a ≥ 2.0 kg/m2/year increase (p = 0.01). Being overweight or obese is common at MS diagnosis. Women with MS are especially susceptible to rapid weight gain, both at diagnosis and later in the disease course. Greater education is protective against rapid weight gain in newly diagnosed MS patients. Baseline disability did not have a significant relationship with annualized BMI change, but the relationship appears to vary according to the patient's age. The frequency of overweight and obese PwMS suggests that weight management is an important part of MS care.

Trends of temperature and precipitation extreme indices in north Maharashtra

Climate change has intensified extreme weather events, posing major challenges to agriculture-dependent regions like Northern Maharashtra. This study analyzed temperature and precipitation extremes across five districts—Nashik, Dhule, Nandurbar, Jalgaon and Ahmednagar using data from 1982 to 2022 with the help of RClimDex model. Key temperature indices, including tropical nights (TR25), warm days (TX90p), and frost days (FD13) showed an increase in warm events and a decline in cool nights and frost days. Reduced diurnal temperature range (DTR) indicated less nighttime cooling, consistent with global warming. For precipitation, extreme rainfall events are rising as indicated by the maximum 1-day precipitation (Rx1day), while consecutive dry days (CDD) are shortening. These shifts heighten risks such as crop heat stress, altered growing seasons, soil erosion, and water management challenges. The study underscores the urgent need for adaptive agricultural strategies, improved irrigation, and early warning systems to mitigate the impacts of climate change and enhance resilience in Northern Maharashtra.

Trends in the epidemiology of anxiety disorders from 1990 to 2021: A global, regional, and national analysis with a focus on the sociodemographic index.

Anxiety disorders are widespread across the world. Understanding the global burden of anxiety disorders in the past and future can provide important references for optimizing prevention and control strategies in healthcare systems. The estimation of anxiety disorders in the Global Burden of Disease Study 2021 using systematic review was conducted to describe incidence, prevalence and disability adjusted life years (DALYs) in 204 countries and regions from 1990 to 2021. Trends were examined using joinpoint regression analysis and correlation between Sociodemographic Index (SDI) and temporal trends was assessed. In 2021, the global age-standardised prevalence rate (ASPR), age-standardised incidence rate (ASIR), and age-standardised DALYs rates for anxiety disorders were 4421.9 (95 % uncertainty intervals [UIs] 3768.3 to 5182.1), 678.3(565.2 to 832.4), and 524.3(363.1 to 716.3) per 100,000 population, which were 18 %, 20.6 %, and 18.2 % higher than in 1990, respectively. In 2021, Brazil (9007.4), Portugal (9712.4), and Paraguay (8390) had the highest ASPR of anxiety disorders. Brazil (53.2 %), Peru (38.4 %), and Bolivia (42.2 %) showed the largest increases in ASPR across the study period. In women, the global DALY rate of anxiety disorders increased up to age 20-44 years and then decreased with advancing age, whereas for men the rate the same as in women. Regionally, an overall undulating pattern association was found between SDI and the age standardised DALYs rate of anxiety disorders. The global female ASIR slightly increased from 1990 to 2019 (Annual percent change [APC]: 0.02 %). However, from 2019 to 2021, it saw a substantial rise (APC: 10.78 %). With the development of society, the disease burden of anxiety disorder will become increasingly severe. It is very important to study the accurate epidemiological data on anxiety disorder for clinical diagnosis and treatment and health policy.

Variability of Temperature Extreme Observes in Kalimantan

This study aims to analyze the trend of extreme temperatures in Kalimantan over the past few decades. The data used are daily temperatures from meteorological stations in West Kalimantan, Central Kalimantan, and South Kalimantan, with an observation period of January 1985 to December 2022. The research methodology involves four main stages: data collection, extreme temperature index calculation, trend detection, and correlation analysis with ENSO and IOD. The results showed an increasing trend in extreme temperatures (warming). The increase in mean annual maximum temperature (TXmean) ranged from 0.03°C to 2°C per century, while the mean annual minimum temperature increased from 0.2°C to 0.5°C per century. Monthly maximum value of daily max temperature (TXx) the increasing trend ranged from 0.19°C to 1.7°C, monthly maximum value of daily min temperature (TNx) increased from 0.1°C to 0.5°C. The monthly mean difference between daily max and min temperature (DTR) also shows an increase of 0.5°C to 1.7°C. This trend indicates that daytime (TXmean, TXx) and nighttime (TNmean, TNx) conditions in Kalimantan, especially in West, Central, and South Kalimantan, are getting hotter, with the daytime experiencing a more significant increase in temperature. The correlation between the extreme temperature index and ENSO and IOD is negative and positive, indicating that ENSO and IOD do not fully influence the increase in extreme temperatures. These findings have important implications for disaster mitigation planning and adaptation to climate change in the Kalimantan region.

Enhancing Stock Market Prediction with Sentiment Analysis Using a BERT-based Model

This study explores the prediction of stock price trends in the financial market, emphasizing the impact of investor sentiment and macroeconomic policies. Traditional research often uses mathematical, statistical, or deep learning methods to predict stock prices but overlooks the emotional factors in vast unstructured text data, such as financial news. This paper proposes a Bidirectional Encoder Representations from Transformers (BERT)-Transformer model that integrates sentiment analysis to enhance stock market prediction. Using financial news text data from the Oriental Fortune Network, the BERT model performs sentiment analysis to extract emotional polarity features. These features, combined with stock transaction data, are then input into a Transformer model to predict the index trends. The experimental results demonstrate a 60% accuracy in predicting stock indices' rise and fall, indicating the model's effectiveness while highlighting areas for improvement. The methodology details dataset preprocessing, the BERT- Back Propagation (BP) model structure, and how sentiment classification is combined with stock trading data for predictions. Performance comparisons with other prediction models confirm the benefits of incorporating sentiment analysis. The BERT-Transformer model’s long-term memory capabilities make it a promising tool for financial time series predictions, offering new research ideas and methods for the financial field. Future research will aim to automate the sentiment feature labeling process to save time and improve efficiency.

Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

ABSTRACTThis paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year−1 for PRCPTOT, 0.7 day year−1 for R10mm, 0.2 day year−1 for R20mm, 0.01 mm year−1 for Rx1day, 0.03 mm year−1 for Rx5day, 0.4 day year−1 for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year−1. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.

The Impact of Drought on Vegetation at Basin Scale: A Case Study of the Wei River Basin, China

Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends in vegetation cover, use a copula function to analyze relationships between drought and vegetation cover, and assess the probability of vegetation loss in different drought scenarios. A vegetation index trends upwards from north to south in this basin; from 2001 to 2017, vegetation cover also trends upward in most areas, although it decreases in areas with high vegetation cover. An escalated susceptibility to drought has been observed in the southern and eastern sectors, where proximity to the riverbank correlates with heightened drought sensitivity, particularly in zones of intensified vegetation density. The probability of vegetation loss at the same vegetation loss preset point gradually increases with increased drought severity. These results will facilitate the formulation of countermeasures to prevent and combat the effects of drought on vegetation and land management.

Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA

Drought is a prolonged dry period that can have severe impacts on the environment, human health, economies, agriculture, and energy resources. It can lead to water shortages, ruin crops, dry out forests, and reduce the availability of food and water for wildlife and livestock. The primary objectives of this study are to: 1) quantify the variability and distributions of drought patterns in Arkansas, United States (US), 2) use remotely sensed indices to investigate the correlation between drought and vegetation cover in the area, and 3) develop a cloud-based framework (user-friendly app) to facilitate the assessment of drought impact in Arkansas over the past decades. A correlation analysis was also performed between the Vegetation Health Index (VHI) and meteorological indices to better understand the impact of meteorological drought on vegetation stress. In addition, Mann-Kendall trend analysis was used to assess trends in meteorological drought indices. The results indicate that drought is most prevalent during March and August months. The results of this study revealed that approximately 31% of the study area fell under the four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. In August, approximately 31% of the study area fell under the Four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. This study provides an essential foundation for policymakers, environmental scientists, and agricultural stakeholders aiming to mitigate drought impacts and safeguard against future climate uncertainties.

Impact of CFTR modulatory therapies on liver function and fibrosis indices in cystic fibrosis patients: a retrospective analysis from two Romanian medical centers

Background. Patients with cystic fibrosis (CF) frequently require modulatory therapies such as Lumacaftor/Ivacaftor (LI) and Elexacaftor/Tezacaftor/Ivacaftor (ETI) to manage their condition. Given the potential hepatic complications associated with CF, it is critical to understand the impact of these therapies on liver function and fibrosis indices. This study aimed to evaluate the changes in liver function markers and fibrosis indices in CF patients undergoing LI and ETI therapies, with a specific focus on the influence of underlying hepatic disease. Methods. In this retrospective analysis, liver function markers and fibrosis indices were assessed in CF patients receiving ETI (n=24), LI (n=4), or a combination of both (LI/ETI, n=8). Key liver function markers, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin, platelet count, and fibrosis indices (APRI and FIB-4), were measured at baseline and at various time points up to 12 months. Results. In patients receiving LI therapy, ALT and AST levels demonstrated a slight but non-significant decrease over six months, accompanied by significant fluctuations in total bilirubin levels. Among those receiving ETI therapy, ALT and AST levels initially increased but stabilized over time, while total bilirubin levels significantly increased from baseline to 12 months. No significant differences were observed in liver function markers between patients with and without hepatic disease under ETI therapy. Trends in fibrosis indices (APRI and FIB-4) were modest and largely non-significant across both therapies. Conclusions. ETI therapy appears to be safe for CF patients, including those with pre-existing hepatic disease, with no significant deterioration in liver function over a 12-month period. However, the observed fluctuations in bilirubin levels underscore the necessity for ongoing monitoring. Further research is warranted to investigate the long-term hepatic effects of LI and ETI therapies.