Index Data Research Articles

An improved framework for quantifying the contribution of climatic and anthropogenic factors to vegetation dynamics - A case study of China

Accurately quantifying the impact of climatic and anthropogenic factors on vegetation change is critical for developing and evaluating ecological management strategies. However, most presented studies typically ignore the climate temporal effects and assume that all pixels are affected by both climate change (CC) and human activities (HA), which often introduce uncertainties. In this study, Leaf area index (LAI), temperature, precipitation, solar radiation, and land cover data from 1982 to 2020 were used to detect and attribute vegetation dynamics in China. We used partial correlation analysis, generalize linear model, trend analysis, and improved RESTREND to analyze the spatiotemporal variation characteristics of vegetation LAI from 1982 to 2020 and to quantify the effects of CC and HA on vegetation dynamics since the implementation of the Grain for Green Project (GGP). The results indicate that significant vegetation greening appeared in most areas (66.2%) between 2000 and 2020. Both CC and HA have positive effects on vegetation greening. In arid and semi-arid regions, precipitation was the primary driver of vegetation change, while in high-latitude areas of southern and southwestern China, temperature was the primary determinant. After considering the temporal effects, the explanatory power of climate variables for vegetation dynamics increased by 4.0% compared to ignoring the temporal effects, accounting for 72.81%. After dividing the pixels into those affected by CC and those affected by both CC and HA, the contribution of HA was decreased from 31.19% to 25.96%. Although the contribution of HA is lower than that of CC, ecological engineering has effectively promoted vegetation greening. These research findings provide scientific data and theoretical basis for ecological environment protection and natural resource management.

Financial Technology, wealth Threshold, and Wealth Inequality in China

This study developed a theoretical model to examine the impact of financial technology (fintech) on wealth inequality from a wealth threshold perspective. The model's numerical simulation results suggest that fintech exerted an inverted U-shaped impact on wealth inequality. Empirical test results based on China Household Finance Survey panel data and fintech index data confirmed this finding. The impact varied across regions and among groups with differing education levels and financial literacy.

Temporal-spatial characteristics of climate drought and its effects on grain yield in Hunan Province, China

The examination of the spatiotemporal characteristics and developmental trends of drought is crucial for enhancing water resource management, bolstering drought resistance, and improving disaster prevention capabilities. This study employs the Standardized Precipitation Evapotranspiration Index (SPEI) and grain yield data across various time scales, in conjunction with methodologies such as Run Theory, Mann-Kendall, and Standardized Yield Residual Series, to conduct an in-depth investigation into the spatiotemporal variation characteristics of meteorological drought in Hunan Province and its impact on grain yield. The findings suggest that: (1) Since 1960, the likelihood of seasonal drought occurrence in Hunan Province has been ranked as autumn > winter > spring > summer, with mild drought occurring most frequently, followed by moderate drought, while the frequency of severe and extreme drought remains low. (2) Meteorological drought in Hunan Province exhibits spatial differences at the seasonal scale, with the overall drought changes in spring and summer displaying a non-significant upward trend; the western and southern regions exhibit a trend of aridification in autumn; and in winter, the Zhangjiajie and Xiangxi regions show an insignificant downward trend. (3) From 1960 to 2022, grain production in Hunan Province has demonstrated a pattern of fluctuation and increase. The meteorological yield of grain crops displays a high-low-high spatial distribution from south to north. Concurrently, there is a positive correlation between short-term climate change and meteorological output, while long-term climate change is not evident. (4) El Niño Southern Oscillation (ENSO) is a significant circulation factor affecting meteorological drought in Hunan Province, and the meteorological drought in autumn and winter in Hunan Province is significantly influenced by ENSO. The research findings can provide reference significance and a scientific basis for drought research and comprehensive management in Hunan Province, and offer data and theoretical support for promoting economic development.

Estimation of Height at Withers Based on Long Bone Measurements of Living Cats

In this study, coefficients enabling estimation of shoulder height were obtained by taking morphometric measurements from radiographic images of live cats using the radiogrammetric method. For this purpose, a total of 37 adult cats, including 17 females and 20 males, were involved. While the effect of sexual dimorphism was observed on the morphometric data, the presence of sexual dimorphism was only evident in the coefficients (factors) of the humerus and talus. No significant effect of gender was found on the shoulder height estimation coefficients (factors) for other thoracic and pelvic limb bones. Gender had an impact on the slenderness index in all thoracic limbs, but only on the tibia slenderness index among the pelvic limb bones. Generally, female individuals were found to have more slender limbs. It is believed that the obtained coefficients (factors) and index data will contribute significantly to the prediction of archaeological cat morphology.

Long-term changes in frailty and incident atrial fibrillation: multicenter prospective cohort study

Abstract Background Atrial fibrillation is the most common sustained cardiac arrhythmia and is prevalent among the elderly. Previous findings indicated a higher prevalence of AF in frail adults and frailty prevalent in AF patients. However, the association between dynamic changes of frailty and risk of new-onset AF is unclear. Purpose We aimed to investigate the associations of long-term changes in frailty with incident AF. Its associations with heart failure (HF), coronary heart disease (CHD), and stroke were also evaluated as a secondary aim. Methods Over 50,000 participants from UK Biobank cohort were included, with available frailty index (FI) data and free of AF, HF, CHD or stroke in both baseline and final follow-up assessments. Participants were categorized into nonfrailty, prefrailty and frailty based on their FI scores. Changes of frailty status from baseline to final follow-up assessment included alleviation, maintenance, and aggravation. Thus, transitions of frailty status included 7 categories: nonfrailty maintenance, nonfrailty aggravation (nonfrailty to prefrailty or frailty), prefrailty alleviation (prefrailty to nonfrailty), prefrailty maintenance, prefrailty aggravation (prefrailty to frailty), frailty alleviation (frailty to prefrailty or nonfrailty), and frailty maintenance. FI in baseline and follow-ups are used to calculate the trajectories of frailty (ΔFI). Cox proportional hazard models were used. Results Compared with nonfrailty at baseline, the HRs (95% CI) of AF for prefrailty and frailty were 1.32 (1.29, 1.36) and 1.89 (1.83, 1.96) in the multivariable-adjusted model. During a median of 5.1 years of follow-up from the final assessment, 1729 cases of AF were recorded. Frailty trajectory analysis showed that even a 0.01 point/year increase in ΔFI was associated with 14% (95% CI 1.08-1.20) higher risk of AF, independent of baseline FI and the risk rose to 1.89 (1.45, 2.46) with per 0.05 point/year increase in ΔFI. This association was strengthened in low genetic risk of AF, but sex, age, smoking status, alcohol intake, obesity, hypertension, diabetes and cancer did not materially modify the associations. Moreover, compared with nonfrailty maintenance, sustained frailty had the highest risk of AF (HR 1.95, 1.61-2.36). Alleviation from frailty reduced the risk of AF by 30% (95%CI 0.53-0.94), compared to sustained frailty. These associations were similar in HF and CHD, however, not significant in stroke. Conclusions Middle aged and elderly people, including those with diabetes, obesity and hypertension, may gain substantial benefits in risk of AF, HF and CHD by becoming less frail or maintaining nonfrailty, irrespective of past frailty status and established risk factors, delivering public health implications for the necessity of early identification and timely interventions of frailty in heart health management.

Sleep Efficiency Mediates the Association Between Neighborhood Disadvantage and Knee Osteoarthritis Pain: Findings from the Understanding Pain and Limitations in Osteoarthritic Disease Study 2.

The objective was to examine potential pathways linking neighborhood disadvantage to pain severity in individuals with knee pain consistent with or at risk for Knee Osteoarthritis (KOA). The current investigation is a cross-sectional analysis. Data were collected from 140 mid-to older non-Hispanic White and non-Hispanic Black adults from the Understanding Pain and Limitations in Osteoarthritic Disease Study 2 (UPLOAD 2). Relationships among neighborhood disadvantage, sleep efficiency, pain catastrophizing, and pain severity were assessed. Neighborhood disadvantage was quantified using the Area Deprivation Index (ADI) and actigraphy data were used to assess sleep efficiency. The Coping Strategies Questionnaire-Revised catastrophizing subscale and WOMAC (Western Ontario and McMaster Universities Arthritis Index) pain severity scale were used to assess pain catastrophizing, and pain severity, respectively. A serial mediation model assessed the neighborhood➔sleep➔catastrophizing➔pain pathway, as well as neighborhood➔sleep➔pain, and neighborhood➔catastrophizing➔pain pathways. Greater neighborhood disadvantage was associated with worse sleep efficiency, ultimately contributing to greater pain severity. While neither neighborhood disadvantage nor sleep efficiency were associated with pain catastrophizing, pain catastrophizing itself was associated with greater KOA pain. Neighborhood disadvantage impacts KOA pain outcomes through sleep efficiency, but not pain catastrophizing, thereby highlighting environmental aspects that impact sleep as potential targets for intervention.

Drivers of Spatiotemporal Patterns of Riparian Forest NDVI Along a Hydroclimatic Gradient

ABSTRACTIn the context of rising global temperatures and their impact on weather patterns and water cycles, understanding the relationship between vegetation and hydroclimatic forcing is critical. Riparian forests are highly vulnerable to hydroclimatic variability, which can significantly affect water availability in the soil on which they primarily depend. Along large rivers, hydroclimatic forcings can vary, resulting in different vegetative responses depending on the local climatic context and site conditions. To explore this, we studied riparian forest greenness along a 512‐km river corridor with a 3° latitudinal gradient, analysing the relative contributions of climate (latitude, season, temperature, precipitation) and local hydrological conditions (groundwater). Here, we show that riparian forests along a latitudinal gradient respond differently to hydroclimatic controls, with vegetative dynamics that can be attenuated or accentuated by local site conditions. We combined Sentinel‐2 satellite Normalised Difference Vegetation Index (NDVI) data over seven years (2016–2022) with hydroclimatic data to examine riparian forest greenness responses to latitudinal, seasonal and interannual hydroclimatic variability. We found contrasting hydroclimatic controls across the latitudinal gradient, with the northernmost sites predominantly controlled by temperature, while those further south are limited by water availability. In addition, we identified temperature as the primary driver of NDVI throughout the growing season, either positively or negatively. Late season precipitation and high phreatic water availability positively influenced NDVI, emphasising the role of local conditions in governing riparian forest resilience. This study enhances understanding of climate controls on riparian tree greenness, which is critical for designing effective landscape‐scale riparian ecosystem management and restoration strategies.

METODE MEAN-SEMIVARIANCE DALAM PEMBENTUKAN PORTOFOLIO REKSA DANA SAHAM TERBAIK BAROMETER BAREKSA

The most popular investment product today is mutual funds. One of the factors in selecting mutual funds is the low investment risk. This risk can be minimized again by combining several assets and then compiling them into a portfolio. The portfolio formation method used in this study is the Mean-Semivariance method and the Sharpe Index to calculate portfolio performance. The data used is the daily net asset value per unit of Mandiri Investa Atraktif Syariah, Rencana Cerdas, Manulife Saham SMC Plus and BNP Paribas Solaris for the period January 3 2022 to March 9 2023, Jakarta Composite Index (IHSG) data as a benchmark and BI-7 Day Reverse Repo Rate as a riskfree interest rate. The minimum risk of the portfolio will be achieved by compiling a portfolio of two mutual funds, namely Mandiri Investa Atraktif Syariah with a weight of 92,50% and the Smart Plan with a weight of 7,50% with an expected return rate of 0,00019 and a risk of 0,000000663. The Sharpe Index is 0.08971 indicating that the portfolio formed has good performance and can be used as a choice of investment assets by investors.

Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach

This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics.

IMPLEMENTASI METODE SINGULAR SPECTRUM ANALYSIS (SSA) PADA PERAMALAN INDEKS LQ45

Stock market index forecasting is one of the research studies to obtain stock market index prediction using statistical and computational techniques, one of the techniques that can be used is Singular Spectrum Analysis (SSA). SSA aims to decompose a time series into a set of independent components which have nonstatic tendencies such as trends, oscillations, and noise. SSA can be applied without any prior assumptions such as stationarity, linearity, and normality which seem impossible to fulfill by the fluctuation of stock market index data. One of the stock indices listed in Indonesia Stock Exchange (IDX) is LQ45. The LQ45 index contains 45 selected stocks that have great performance in liquidity and fundamental aspects. LQ45 Index data has a nonstatic structure so it is suitable to be predicted using the SSA method. The forecasting uses the daily data of the LQ45 Index for the period of July 25, 2022 – November 14, 2022 with a window length (L) of 30. From the forecasting process, 4 groups were acquired and Mean Absolute Percentage Error (MAPE) value was 1.69% to forecast the next 20 days. These results indicate that the forecasting process using SSA for LQ45 Index of the period is accurate.

Unravelling the Influence of ENSO and SAM Patterns on Skate Growth: The Case of the Shorttail Yellownose Skate in Patagonia, Argentina

ABSTRACTIn recent decades, the growth, physiology and distribution of many elasmobranch species have been altered as a result of environmental changes that affect prey abundance, availability and composition. Consequently, variations in nutrient input during climate events could manifest in the growth of their hard tissues. This study focuses on assessing the impact of the El Niño‐Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) on the growth of Shorttail Yellownose skate (Zearaja brevicaudata) in Patagonia, Argentina. To achieve this, vertebrae from 115 Z. brevicaudata specimens were analysed, and growth rings were dated and measured. By using cubic splines with varying flexibility, we constructed three standard chronologies. Generalized additive models (GAMs), the chronology with the best the R‐bar () and expressed population signal (EPS) were employed and values obtained from them linked to annual time series data of the Multivariate ENSO Index (MEI) and SAM, considering lags in the biological response. Surprisingly, no significant association was found with the MEI time series. However, a noteworthy positive association emerged between the chronology and the SAM time series lagged by 1 year, suggesting that SAM‐related climatic conditions could delay their transfer into the Patagonian marine ecosystem, subsequently impacting the growth of this ectothermic predator.

Spatial–Temporal Analysis of Greenness and Its Relationship with Poverty in China

Ecological environmental protection and poverty alleviation are of great significance for the study of human–land relationship coordination and sustainable development, and they have also been a focus of attention in China in the past few decades. In this study, we chose 13 contiguous poverty-stricken areas in China as the study area. Using MODIS Leaf Area Index (LAI) data from 2000 to 2020, the spatial–temporal changes in greenness were obtained using the Bayesian spatial–temporal model (BYM). Spatial autocorrelation was used to identify the spatial distribution of poverty using socio-economic statistical data. Driving factors, including natural factors, poverty factors, and the Grain for Green Policy (GTGP), and their influence on greenness were analyzed by using the Geodetector model for detecting spatial differentiation and factors’ interactions. The results showed the following: (1) In 13 contiguous poverty-stricken areas (CPSAs) in China, 59% of the area presented an increasing trend of greenness. (2) In 2000, the high poverty levels with larger MPI values were widely distributed. After 20 years, the overall MPI value was lower, except in some northwest regions with increased MPI values. The spatial autocorrelation of poverty, which relates to the mutual influence of poverty in adjacent areas, also decreased. (3) In the study area, 65.24% of the regions showed strong synergistic effect between greening progress and poverty reduction in the interaction between poverty status and green development. With the improvement of greenness level, the positive correlation between poverty alleviation and ecological environment improvement has become increasingly close. (4) The impacts of interaction factors with the highest q values changed from temperature interacting with precision to regional division interacting with the Grain for Green Policy. The conclusions are that from 2000 to 2020, the impact of natural factors, geographical division, and poverty status on greenness has shown a decreasing trend; The effect of the Grain for Green Policy is gradually increasing; At the same time, the interaction and overlapping effects between the Grain for Green Policy and poverty were increasing. Taking into account the needs of ecological environment, poverty alleviation, and rural revitalization, this research provides valuable reference for formulating and implementing relevant policies based on the actual situation in different regions to promote harmonious coexistence between human-land relationship.

Utilizing Baidu Index Data to Investigate the Spatiotemporal Characteristics of Public Concern towards Lifelong Education

Background and purpose: With the advancement of Industry 4.0 and the vigorous development of the Internet, search engines have become one of the important platforms for the public to obtain information. As the world's largest Chinese search engine, Baidu is widely popular with the Chinese public. Similar to Google Trends, the Baidu Index is one of the most important databases on the Internet and even in the entire data age. This study adopted the Baidu Index to investigate the public's attention to "Lifelong Education" and analyze its changing trend and geographical distribution, to grasp the public's attention to it in a more comprehensive way. Materials and methods: The Baidu Index was used to obtain the attention of the keyword “Lifelong Education" from July to November 2023. The characteristics of attention are analyzed from three aspects: trend research, demand graph, and user portrait. Finally, it focuses on the comparison between the keyword "Lifelong Education" and the related word "Lifelong Education Platform". Results: The term "Lifelong Education" showed two fluctuations during the period, both of which showed an initial rise and then a decline. For the demand graph, the public pays the most attention to "Lifelong Education Platform", "Lifelong Learning" and "Compulsory Education". The group that pays more attention to "Lifelong Education" is mainly distributed in East China, mostly female, 20~29 years old. Conclusion: Similar to Google Trends, the Baidu Index is a useful tool to study the trend of keyword searches, insight into the change in netizens' demand and monitoring the trend of media public opinion. The results show that the main reasons for the two obvious changes in search volume are that the National Open University of China opens freely lifelong education platform and provides free and comprehensive learning resources, and "Lifelong Education" appears as a translation in the National College English Test Band 4 (CET-4) in June 2023.

Drug overdose deaths among homeless veterans in the United States Department of Veterans Affairs healthcare system.

With an epidemic of drug overdoses, contemporary research is needed to examine drug overdose deaths among homeless populations. This study measured rates, types and correlates of drug overdose deaths occurring over a 5-year study period among veterans experiencing homelessness (VEH) and non-homeless veterans (NHV) in the US Department of Veterans Affairs (VA) healthcare system. Retrospective cohort study. USA. A total of 6 128 921 veterans. We followed 399 125 VEH and 5 729 796 NHV between 2017 and 2021 using linked administrative VA and National Death Index data. Multivariable Cox regression models were constructed to estimate hazard ratios (HRs) for homelessness as a predictor of time-to-occurrence of overdose deaths with 95% confidence interval (CIs), sequentially controlling for demographic, medical, substance use and mental health characteristics. Among overdose deaths, 8653 [93.7%, 95% confidence interval (CI) = 93.2-94.2%] were unintentional and 5378 (57.8%, 95% CI = 56.8-58.8%) involved opioids. The overdose-specific mortality rate (per 100 000 person-years) was 227.3 among VEH and 23.2 among NHV (HR = 9.76, 95% CI = 9.36, 10.16), with rates 7-14 times higher among VEH than NHV, depending on the drug involved. In fully adjusted models, homelessness was associated with greater risk of drug overdose death (HR = 3.33, 95% CI = 3.18, 3.49), with the greatest risk involving psychostimulants (HR = 4.19), followed by antiepileptic/sedative/hypnotic drugs (HR = 3.69), synthetic opioids (HR = 3.50) and natural and semi-synthetic opioids (HR = 2.79). US veterans experiencing homelessness appear to have three times the risk for drug overdose deaths than non-homeless veterans. There may be specific risks associated with psychostimulant, antiepileptic, sedative and hypnotic drugs in this population that deserve greater attention.

Colostrum conductivity, pH and Brix index as predictors of passive immunity transfer in foals.

Foal immunity relies heavily on the absorption of colostrum immunoglobulins; thus, colostrum evaluation is used to predict the transfer of passive immunity (TPI), and its conductivity is associated with TPI in cattle. Leading up to and at parturition, a reduction in colostrum pH and conductivity is thought to be necessary for TPI; however, this remains to be determined. To assess conductivity, pH, and Brix refractometer index of colostrum to predict the TPI in foals. Field study. The colostrum of 241 mares was assessed for conductivity, pH, and Brix index using handheld devices immediately after parturition. Twenty-hour postpartum foals had complete blood cell count and plasma IgG concentrations assessed. Foals were split in complete versus incomplete TPI (i.e., IgG ≥8 g/L vs. <8 g/L). Mare (breed, duration of pregnancy, parity, and age), foal (sex), and colostrum (conductivity, pH, and Brix index) data were computed to assess factors affecting TPI. Multivariate regression and receiver operating characteristic (ROC) curves were employed for analysis. Complete TPI was achieved in 89.4% of foals. The ROC TPI values were conductivity ≤5 mS/cm (AUC = 0.68), Brix ≥23.4% (AUC = 0.70), and pH ≤6.4 units (AUC = 0.73). The odds ratio for TPI in 24 h post-partum was 6.1 (2.1-17.1, 95% CI) for conductivity, 3.2 (1.2-9.3, 95% CI) for pH, and 4.5 (1.5-12.7, 95% CI) for Brix index. Sensitivity and specificity of 93% (88-96 95% CI), 56% (44-7, 95% CI), 92% (87-96, 95% CI), 28% (14-47, 95% CI), 94% (88-97, 95% CI) and 18% (14-47, 95% CI) for conductivity, Brix and pH, respectively. Low incidence of incomplete TPI. Conductivity, pH and Brix are strong predictors of foals that succeed in the transfer of passive immunity; however, all three presented low to moderate specificity. The established cutoff values obtained herein can be used on-field by other foaling programs. All three handheld devices used in the study are inexpensive and ready to use on-farm.

Evaluation of the influence of athlete neural activity patterns on the dynamic index of leaping ability using data mining techniques

There are many dynamic indicators of jumping ability, and the athlete’s neural activity pattern is an important factor in regulating limb activities. This article uses data mining technology to collect, preprocess, data modeling and analysis, and data visualization of dynamic index data of athletes’ neural activity patterns of jumping ability. The results show that the jumping ability of athletes with higher neural activity intensity increased significantly after training to around 40 cm–50 cm, while athletes with lower neural activity intensity did not change significantly and remained around 30 cm–35 cm. The overall learning ability of athletes with higher levels of neural activity improved by about 10 cm, and the base of neural activity also increased significantly. It shows that there is a significant correlation between the intensity of neural activity and dynamic indicators of jumping ability, which is the main driving force for athletes’ jumping explosive power. The research results can help formulate reasonable and scientific training methods to improve athletes’ jumping ability and overall sports level.

Sudden Cardiac Death Reporting in US Dialysis Patients: Comparison of USRDS and National Death Index Data.

Cause-specific mortality data from the United States Renal Data System (USRDS) form the basis for identifying cardiovascular disease (CVD), specifically sudden cardiac death (SCD), as the leading cause of death for patients on dialysis. Death certificate data from the National Death Index (NDI) is the epidemiological standard for assessing causes of death for the United States population. The cause of death has not been compared between the USRDS and the NDI. Among 39,507 adults starting dialysis in the US, we identified 6436 patients who died between 2003-2009. We classified the cause of death as SCD, non-SCD CVD, cancer, infection, and others; and compared the USRDS to the NDI. Median age at the time of death was 70 years, 44% were female, and 30% were non-Hispanic Black individuals. The median time from dialysis initiation to death was 1.2 years. Most deaths occurred in-hospital (N=4681, 73%). The overall concordance in cause of death between the two national registries was 42% (κ=0.23, 95% confidence interval 0.22 to 0.24). CVD, including SCD and non-SCD CVD, accounted for 67% of deaths per the USRDS but only 52% per the NDI; this difference was mainly driven by the larger proportion of SCD in the USRDS (42%) versus the NDI (22%). Of the 2962 deaths reported as SCD by the USRDS, only 35% were also classified as SCD by the NDI. Out-of-hospital deaths were more likely to be classified as SCD in the USRDS (60%) versus the NDI (29%), compared to in-hospital deaths (41% in the USRDS; 25% in the NDI). Significant discordance exists in the causes of death for patients on dialysis reported by the USRDS and the NDI. Our findings underscore the urgent need to integrate NDI data into the USRDS registry and enhance the accuracy of cause-of-death reporting.

PREDICTION OF UNIT VALUE INDEX OF EXPORTS OF SITC 897 JEWELRY AND PRECIOUS GOODS GROUP IN INDONESIA

Export is an international trade activity that plays an important role in the economic progress in Indonesia. One of Indonesia's leading commodities that dominate the export market is jewelry. In export activities, the export unit value index is an important component that serves to describe the development of export commodity prices. This unit value index always changes every time and fluctuates. This research conducts a comparative analysis of the performance of parametric method, non-parametric method, and machine learning, specifically, ARIMA, Fourier series estimator, and Support Vector Regression (SVR). This study aims to evaluate the effectiveness of various methods in improving prediction accuracy for the unit value index of the SITC code 897 in Indonesia. The research data used is secondary data including monthly export unit value index data with SITC code 897 in Indonesia obtained from the Central Bureau of Statistics. The data divided into 90% training data and 10% testing data. The methods used in this analysis are ARIMA, Fourier series estimator, and SVR. The best model obtained from each method is ARIMA (1,1,1) with MAPE of 10.92%, Fourier series estimator with MAPE of 8.47%, and an SVR RBF kernel function with MAPE of 3.73%. The results of this study obtained the best method for predicting the unit value index of SITC code 897 is SVR with an RMSE value of 8.288 and very good prediction accuracy.

Reconstruction of the Temperature Index Series of China in 1368–1911 based on REACHES database

This study reports the methodology for reconstructing anomalous temperature index series of China in 1368–1911 based on the REACHES database which digitizes the Chinese records quoted in the Compendium of Meteorological Records of China in the Last 3000 Years. The reconstruction adopts an ordinal scale index approach ranging from −2 (very cold) to 1 (warm). Based on the grading criteria, a total of 12,871 records were retrieved through a standard coding system established at REACHES. Sensitivity experiments were performed to test robustness of the index system and a reasonability test was conducted to develop an appropriate method for deriving areal mean temperature index. The reconstructed series were validated through comparison with early instrumental data from Global Historical Climatology Network which shows good correlations and reliability of the REACHES reconstructed index data. Annual and semi-annual (winter and summer) temperature index data series were released for the whole domain as well as the 3- and 15-subregion geographical domains in China.

Social Vulnerability Index and Dental Caries in Children: An Exploratory Study.

This retrospective cross-sectional study evaluated the association between caries outcomes in a pediatric population visiting a dental clinic and the social vulnerability index, an area-based measure capturing 4 main social determinants of health: socioeconomic status, household composition/disability, minority status/language, and housing/transportation. The Centers for Disease Control Social Vulnerability Index (SVI) and electronic dental record data of children (0 to 18 y) reporting a caries diagnosis at the Children's Hospital Colorado in 2020 were extracted for 9,201 children. Logistic regressions were used to test the association between SVI and the presence or absence of dental caries, adjusting for age, sex, ethnicity, and race. Children with a caries diagnosis had a greater mean overall SVI percentile (62.0, standard deviation [SD] = 29.1) compared with patients without a caries diagnosis (59.1, SD = 29.8; P < 0.001). With each 10-point increase in the overall SVI percentile, having a caries diagnosis visit was 2.7% more likely compared with having a visit without a caries diagnosis (odds ratio [OR] 1.027, 95% confidence interval [CI] 1.012, 1.042; P = 0.0004). Those with an overall SVI percentile between 51 and 75 were 23% more likely to have a caries diagnosis compared with those with a percentile ≤25 (OR 1.23, 95% CI 1.07, 1.42; P = 0.003), and those with a percentile >75 were 23.6% more likely to have a caries diagnosis compared with those with a percentile ≤25 (OR 1.236, 95% CI 1.09, 1.40; P = 0.001). Children (0 to 18 y) living in socially vulnerable environments or areas were more likely to have a caries diagnosis at their dental exam. This study showed an association between social determinants of health demonstrating social vulnerability and dental caries in children. Ultimately, understanding upstream factors for children living in socially vulnerable areas could support policymakers in creating more effective policies to support socially vulnerable populations.