Changes In Temperature Patterns Research Articles

Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data

Accurate aerosol optical depth (AOD) with high temporal resolution is required for dynamic monitoring changes of aerosol properties. The Himawari-8 Advanced Himawari imager (AHI) data with 10-min temporal resolution has been widely used for AOD retrieval but usually involves radiative transfer models and assumptions on surface reflection and aerosol properties that are hard to satisfy. This paper introduces a data-driven algorithm without any priori assumptions on surface or aerosol properties called Time series retrieval of Multi-wavelength AOD by adapting Transformer (TMAT). TMAT directly retrieves AOD at eight-wavelengths every 10-min throughout the day, using AHI top of atmosphere (TOA) reflectances in 6 wavebands and ancillary data. TMAT explores diurnal temporal change patterns of AOD and surface reflection using time series data. TMAT was trained and evaluated using samples derived by matching a year (2019) of AHI full-disk data and AERONET/SONET observations with 12,768 diurnal time series, each of which includes up to 81 10-min 6-band AHI TOA observations and 81 (time) × 8 (wavelength) AOD values per day. There are up to 273,413 (<12,768 × 81) TOA observations in 6 wavebands and up to 1,420,716 (<12,768 × 81 × 8) AOD values in all the time series samples, with filled values (−9999) to represent no data due to cloud contamination and incomplete wavelength configurations of AOD stations. The masking mechanism in Transformer is leveraged to exclude the contribution of the AHI filled values in time series AOD retrieval and exclude the contribution of AOD filled values in training. TMAT was trained with a physical constraint that the retrieved AOD decreases toward longer wavelengths. TMAT AOD was evaluated by separating training and validation samples using site-specific leave-one-station-out validation. TMAT AOD shows extremely high accuracy at all wavelengths, with RMSE = 0.101, R = 0.949 and 81.69% of retrievals within ±0.05+15%∗AODsites for the AOD at 550 nm. We also validated the accuracy of the NASA multiangle implementation of atmospheric correction (MAIAC) AHI AOD over these sites with RMSE = 0.186, R = 0.832 and 52.10% of retrievals within ±0.05+15%∗AODsites. The Ångström exponent (AE) derived from the multi-wavelength TMAT AOD also shows good agreement with AERONET/SONET AE, with a correlation coefficient of 0.620 and an RMSE of 0.291. The TMAT effectiveness to capture diurnal temporal change patterns of surface reflection and AOD was evidenced by the significant reduction of AOD accuracy when using a non-time series deep learning method. The effectiveness of multi-wavelength joint retrieval was evidenced by the significant reduction of AE accuracy when using single wavelength retrieval models. Evidence shows the training and evaluation split methods have a large impact on evaluation accuracy and this study further used ground network independent training and evaluation split to confirm the leave-one-station-out validation accuracy. Despite this, future study will focus on more spatially independent validation and more comprehensive comparison between the MAIAC and TMAT results.

Spatial–temporal patterns of ecological changes in the Dongting Lake region and their responses to climate factors and human activities

ABSTRACT The Dongting Lake region is an important ecological and environmental protection zone in the Yangtze River Economic Belt of China. Long-term monitoring and evaluation of its ecological environment is of great significance. In this study, the spatio-temporal pattern of ecological condition change and its response to climate factors and human activities in the Dongting Lake region was quantitatively analyzed using RSEI (Remote Sensing Ecological Index) model. The primary conclusions are as follows: (1) The temporal pattern of ecological changes has a small fluctuation period (1990–2007) and a continuous improvement period (2007–2020) with an ecological improvement rate of 1.17% per year. (2) The spatial pattern of ecological changes is directly tied to the land use types in this area. 17.36% of the regions with an excellent ecological level are primarily woodland, whereas 4.45% of the areas with a low ecological level are mostly urban. (3) The deterioration in ecological conditions in urban areas is primarily attributable to human activities, whereas the decline in ecological conditions in surrounding mountainous regions and central agricultural regions is primarily attributable to climate factors. Our research provides valuable support for ecological environment monitoring and management strategy formulation in the Dongting Lake region.

Satellite-Derived Indicators of Drought Severity and Water Storage in Estuarine Reservoirs: A Case Study of Qingcaosha Reservoir, China

Estuarine reservoirs are critical for freshwater supply and security, especially for regions facing water scarcity challenges due to climate change and population growth. Conventional methods for assessing drought severity or monitoring reservoir water level and storage are often limited by data availability, accessibility and quality. We present an approach for monitoring estuarine reservoir water levels, storage and extreme drought via satellite remote sensing and waterline detection. Based on the CoastSat algorithm, Landsat-8 and Sentinel-2 images from 2013 to 2022 were adopted to extract the waterline of Qingcaosha Reservoir, the largest estuarine reservoir in the world and a key source of freshwater for Shanghai, China. This study confirmed the accuracy of the satellite-extracted results through two main methods: (1) calculating the angle of the central shoal slope in the reservoir using the extracted waterline data and measured water levels and (2) inverting the time series of water levels for comparison with measured data. The correlation coefficient of the estimated water level reached ~0.86, and the root mean square error (RMSE) of the estimated shoal slope was ~0.2°, indicating that the approach had high accuracy and reliability. We analyzed the temporal and spatial patterns of waterline changes and identified two dates (21 February 2014 and 15 October 2022) when the reservoir reached the lowest water levels, coinciding with periods of severe saltwater intrusions in the estuary. The extreme drought occurrences in the Qingcaosha Reservoir were firstly documented through the utilization of remote sensing data. The results also indicate a strong resilience of the Qingcaosha Reservoir and demonstrate that the feasibility and utility of using satellite remote sensing and waterline detection for estuarine reservoir storage can provide timely and accurate information for water resource assessment, management and planning.

Cultural differences in the ICE-Group experience: Diaries of French and British antarctic winteroverers

Cultural differences may influence the psychosocial adaptation to prolonged isolation and confinement in extreme environments. Recent results showed that the temporal pattern of mood changes may also be affected by cultural differences. The aim of the study was to gain deeper insight into the structure and temporal patterns of group-related themes mentioned in the diaries of Antarctic winteroverers in two different stations with different national and organizational backgrounds. The diaries of the corresponding 8-8 French and British crew members of the Concordia and Halley VI research stations were subjected to content analysis. Method. A total of 18324 sentences were coded. In the first phase of the analysis, we identified 5 major thematic groups (Self focus, Group focus, Antarctica focus, Outworld focus, and Castoff sentences). One-third of these sentences were Group Focus, which were classified into 4 code groups, depending on whether the sentences referred to Experienced or Observed Comfort or Discomfort in the ingroup, respectively. These 4 code groups were subject to further analyses to test whether each code cluster showed a significant difference between British and French winteroverers during the 4 quarters of the mission. The Q3 phenomenon was also investigated. Results. Throughout all four quarters, the British crewmembers used significantly more Experienced Comfort sentences, while the French expressed significantly more Observed Discomfort sentences. Regarding the temporal patterns, the amount of Experienced Comfort sentences was significantly lower in the French diaries compared to the British diaries in the third quarter of the mission. Conclusions. Content analysis detected the impact of cultural differences on the composition and temporal pattern of social experiences of winterovering crews. It thus can diagnose and predict the psychodynamics of groups in isolated, confined, and extreme (ICE) environments, such as polar stations or long-duration missions in space.

Spatial patterns of Holocene temperature changes over mid-latitude Eurasia

The Holocene temperature conundrum, the discrepancy between proxy-based Holocene global cooling and simulated global annual warming trends, remains controversial. Meanwhile, reconstructions and simulations show inconsistent spatial patterns of terrestrial temperature changes. Here we report Holocene alkenone records to address spatial patterns over mid-latitude Eurasia. In contrast with long-term cooling trends in warm season temperatures in northeastern China, records from southwestern Siberia are characterized by colder conditions before ~6,000 years ago, thus long-term warming trends. Together with existing records from surrounding regions, we infer that colder airmass might have prevailed in the interior of mid-latitude Eurasian continent during the early to mid-Holocene, perhaps associated with atmospheric response to remnant ice sheets. Our results challenge the proposed seasonality bias in proxies and modeled spatial patterns in study region, highlighting that spatial patterns of Holocene temperature changes should be re-considered in record integrations and model simulations, with important implications for terrestrial hydroclimate changes.

Forest and Natural Vegetation Cover Loss Over 2000 to 2020 in Sri Lanka; A Canopy Density Base Analysis

As one of the most important biodiversity hotspot, forest and natural vegetation areas play an essential role in the global ecosystem. It provides an uncountable number of ecosystem services while controlling the adverse impacts of climatic changes. With the expansion of civilization and fulfilling the infinite needs of man, forests and natural vegetation areas were destroyed, especially for agricultural purposes, development projects and timber. Due to this, the accelerated growth of deforestation and loss of natural vegetation can be observed in Sri Lanka in the past few decades. This study aimed to identify the forest and natural vegetation area loss from 2000 to 2020 with a particular focus on the Canopy Density (CD) of tree cover (&gt;25%, &gt;50% and &gt;75%) in Sri Lanka. The satellite estimated data (30 mx30 m resolution) was downloaded from the 2000 to 2020 time period from the Global Forest Watch (GFW) online platform conducted by the World Resource Institute were used. Spatial and temporal changing patterns of the forest and natural vegetation areas have been identified using descriptive analysis methods (percentage) using MS Excel 2019 software. Results of the study found that in 2000, 61.38 percent of the country was covered with above 25% tree cover CD, while 53.86 percent and 39.76 percent of areas have covered with the tree cover CD above 50% and 75%, respectively. However, when it has come to 2020, these areas have decreased by 52.78 percent (&gt;25% CD), 44.27 percent (&gt;50% CD) and 28.65 percent (&gt;75% CD). During this period, 8.6 percent (&gt;25% CD), 9.59 percent (&gt;50% CD) and 11.11 percent (&gt;75% CD) of the forest and natural vegetation areas of Sri Lanka have lost the country. Throughout this period, 2016 can be identified as a year at risk for deforestation due to its annual deforestation rate (&gt;8.8 percent). The total area of forest cover lost in 2016 is 17799 ha (&gt;25% CD), 15583 ha (&gt;50% CD) and 9865 ha (&gt;75% CD). This is nearly 13 percent of forest cover loss for all three types of CD tree cover areas. According to the calculations of the forest areas that have &gt;75% of CD, Anuradhapura (12.06%), Kurunegala (9.82%), and Monaragala (8.49%) districts are the areas that contributed to forest cover loss in the country during 2000 to 2020. According to the study, the annual deforestation rate is still very high. Hence should be needed to monitor and take action to stop deforestation and achieve sustainability in natural resource management in Sri Lanka. &#x0D; Keywords: Deforestation, Canopy density, Tree cover, Natural resource management, Sri Lanka

BrGDGT-based seasonal paleotemperature reconstruction for the last 15 000 years from a shallow lake on the eastern Tibetan Plateau

Abstract. Understanding Holocene temperature changes is vital for resolving discrepancies between proxy reconstructions and climate models. The intricate temperature variations across the Tibetan Plateau (TP) add complexity to studying continental climate change during this period. Discrepancies between model-based and proxy-based reconstructions might stem from seasonal biases and environmental uncertainties in the proxies. Employing multiple proxies from a single sediment core for quantitative temperature reconstructions offers an effective method for cross-validation in terrestrial environments. Here, we present an ice-free-season temperature record for the past 15 kyr from a shallow, freshwater lake on the eastern TP, based on brGDGTs (branched glycerol dialkyl glycerol tetraethers). This record shows that the Holocene Thermal Maximum lags the pollen-based July temperature recorded in the same sediment core. We conclude that the mismatch between the brGDGT-based and pollen-based temperatures is primarily the result of seasonal variations in solar irradiance. The overall pattern of temperature changes is supported by other summer temperature records, and the Younger Dryas cold event and the Bølling–Allerød warm period are also detected. A generally warm period occurred during 8–3.5 ka, followed by a cooling trend in the late Holocene. Our findings have implications for understanding the seasonal signal of brGDGTs in shallow lakes and provide critical data for confirming the occurrence of seasonal biases in different proxies from high-elevation lakes. To further investigate the significance of the brGDGTs and temperature patterns on the TP, we examined existing brGDGT-based Holocene temperature records, which interpret these compounds as indicators of mean annual or growing season temperatures. The existing and available temperature records show complicated patterns of variation, some with general warming trends throughout the Holocene, some with cooling trends, and some with a warm middle Holocene. We analyzed the possible reasons for the diverse brGDGTs records on the TP and emphasize the importance of considering lake conditions and modern investigations of brGDGTs in lacustrine systems when using brGDGTs to reconstruct paleoenvironmental conditions.

P606 A Retrospective Observational Study of Patterns of Biologic Drug Change in Inflammatory Bowel Disease

Abstract Background The treatment of inflammatory bowel disease (IBD) has evolved over the past two decades. Due to the recent introduction of multiple new biologics, it is crucial to understand the temporal patterns of drug change. This study aimed to examine the patterns of biological drug change over time and identify predictors of change in a cohort of patients with IBD. Methods We performed a retrospective study of patients diagnosed with IBD who were initiated on biologics and followed up at King Abdulaziz University (KAU) hospital, Jeddah, Saudi Arabia between June 2017, and October 2022. The KAUH inflammatory bowel disease information system (IBDIS) database was used to identify eligible patients. The study's primary objective was to describe biologics drug change patterns. Secondary outcomes included identifying predictors of drug change and time to discontinue biologics. Uni- and multi-variable odds ratios (ORs) were used to determine the predictors of drug change. Results A total of 910 patients who were registered in the IBDIS database were screened; 475 patients fulfilled the study criteria. 67% had CD, and 53.3% were males. The mean age at diagnosis was 22.6 (11.0) years. The most selected first and second choice of biologic was adalimumab (58.2% and 39.1%, p&amp;lt;0.001) and infliximab (37.6% and 48.9%, p=0.004) for both CD and UC, respectively. Ustekinumab was the primary third choice for CD (65.4%), while vedolizumab was the third choice for UC (83.3%). The proportions of patients who switched from first- and second-line adalimumab, infliximab, and vedolizumab were 52.3% and 20.9%, 37.6% and 39.9%, and 10.1% and 18.3%, respectively. On multiple regression analysis, a history of venous thromboembolism (VTE) (OR=3.60, 95% CI=1.20-11.71, p=0.025) and active smoking (OR=0.34, 95% CI=0.12-0.82, p=0.026) were associated with drug change for all patients. When stratified by disease subtype, drug change was associated with a diagnosis made between age 17 and 40 years (OR=0.46, 95% CI=0.23-0.90, p=0.024) and EIMs (OR=2.07, 95% CI=1.16-3.76, p=0.015) in CD while selecting vedolizumab as first biologic (OR=0.30, 95% CI=0.09-0.92, p=0.041), male gender (OR=2.40, 95% CI=1.04-5.73, p=0.043), and history of VTE (OR=7.32, 95% CI=0.23-49.97, p=0.031) were associated with drug change in UC. Conclusion Despite introducing several new biologics, anti-TNF therapies remain the preferred first and second choice of biologics for patients with IBD. The use of adalimumab is associated with the highest risk of switch. Multiple predictors of drug change over time exist for both diseases. The selection of vedolizumab as the first biologic for UC is associated with a lower risk of drug change.

Climate Response to Vegetation Removal on Different Continents

AbstractUnderstanding the climatic effect of vegetation in different regions is important for understanding the climate impact of vegetation change in both the past and future. Here we quantify the climate response to vegetation removal on each continent, except Antarctica, using CESM1.2.2 under pre‐industrial climate condition, and the associated mechanisms are analyzed. Results show that removing the global vegetation lowers the global mean surface temperature (GMST) by 3.65°C. Removing vegetation over Eurasia and North America lowers GMST by 1.83°C and 0.88°C, respectively. They also reduce the global precipitation, but at a much slower rate than that would be caused in the CO2 perturbation experiments. Removing vegetation on all other continents has negligible influence on global climate, but has significant local warming effect due to weakening of evapotranspiration. The removal of low‐latitude vegetation tends to reduce the local precipitation, but increase the precipitation over nearby oceans, especially to the west. The feedbacks of thermodynamic sea ice and oceans amplify the initial direct cooling due to vegetation removal by a factor of &gt;5. The response of ocean circulation has a negligible impact on GMST, but has a significant influence on the pattern of temperature changes by redistributing heat. Without the ocean‐circulation feedback, the Northern Hemisphere would be 1.30°C colder while the Southern Hemisphere 1.17°C warmer when the global vegetation is removed.

Spatiotemporal change of cultivated land in China during 2000-2020.

Cultivated land is of great significance for ensuring food security. Since the 21st century, China's rapid development has led to urban construction occupying a lot of cultivated land. The understanding of stage characteristics and regional differences needs to be improved. And it is necessary to quantify the temporal and spatial pattern of cultivated land changes since the 21st century. We used the global land cover dataset (GlobeLand30) to investigate the quantity and spatial distribution of cultivated land change in China from 2000 to 2020. The results show that: 1) Over the last two decades, China's arable land has diminished by 54,996 square kilometers. Notably, the arable land loss between 2010 and 2020 was 1.74 times greater than that from 2000 to 2010. This decline has been exacerbated by factors such as the expansion of urban and rural areas, as well as initiatives to revert farmland to forests and grasslands; 2) The eastern coastal regions experienced the most severe net arable land loss, with a net reduction of 42,989 square kilometers from 2000 to 2020, primarily driven by urban expansion; 3) In contrast, the western regions demonstrated the most substantial net increase in arable land, expanding by 11,583 square kilometers from 2000 to 2020, primarily driven by the development of forests and grasslands. It is noteworthy that despite some areas successfully implementing policies to return farmland to forests and grasslands, the ecologically fragile western regions continue to experience accelerated conversion of these natural landscapes into arable land, partly to compensate for the farmland decrease driven by urbanization in the eastern regions. Looking ahead, China's cultivated protection policy must strike a balance between the ecological value of the western region and the economic value of the eastern region.

Synchronic shifts in phenolic compounds and fungal communities during litter decomposition in boreal forests

Phenolic compounds in plant litter influence decomposition process both directly by their own degree of decomposability and indirectly by affecting decomposer processes. While phenolic characteristics of aboveground plant biomass are well-known, the fate of litter phenolics and their regulatory effects on belowground decomposition remain unclear. We conducted a two-year decomposition experiment distributing litterbags of spruce, pine, and bilberry along a gradient from mesic spruce-dominated to xeric pine-dominated forests. Litter material was analysed for mass loss, low molecular weight phenolic compounds, condensed tannins, fungal biomass, and fungal community composition (using DNA metabarcoding). Bilberry litter initially decayed more rapidly than the conifer litter, but the remaining mass of bilberry litter was the same as for pine and higher than that of spruce litter after two years. The concentration and composition of phenolic compounds decreased with time, with mainly MeOH-insoluble condensed tannins remaining at the end of the experiment. Correspondingly, fungal biomass increased in the first year, while all litter types underwent rapid changes in fungal community composition. The species-specific pattern of temporal change in mass loss, as well as in the synchronic shift in composition of phenolic compounds and fungal community, give new insights in the importance of litter quality for the decomposition process.

Assessment of observed temperature trend patterns of Bhubaneswar city, India with special prominence on future projections using SimCLIM climate model and farmer’s perception

&lt;p&gt;Temperature dynamics is a widely recognized indicator of the global warming phenomenon. Changes in temperature patterns in Bhubaneswar, India, were assessed by examining the monthly minimum and maximum temperature data for 60 years (1956–2015) sourced from the Indian Meteorological Department. SimCLIM climate change risk assessment software was used for projecting the temperature regime for four different representative concentration pathways. Further, a survey of 112 farmers was conducted to understand their perceptions of temperature variations in and around Bhubaneswar city using a multi-stage sampling technique. Mann–Kendall statistics and linear regression were used to analyze the monthly temperature data and trend detection. The study reveals a change of +4%,-4.44%, and +1.09% in the mean monthly maximum, minimum, and annual temperature. The results of the future projection show a temperature change of 0.81°C for RCP 2.6, 1.12°C for RCP 4.5, 1.03°C for RCP 6.0, and 1.54°C for RCP 8.5 for the year 2050. Confirming the analysis findings, most of the interviewed farmers also perceived increasing temperatures and decreasing precipitation in and around the city. The study outcome of temperature trend analysis and future projections will be helpful for farmers and policymakers in formulating adaptation strategies to climate change.&lt;/p&gt;&#x0D;

Drivers of rising monthly water temperature in river estuaries

AbstractRiver estuaries are habitats for a variety of organisms, including many temperature‐sensitive species; water temperatures in estuaries are affected by several factors as they are influenced by both terrestrial and marine environments. Therefore, understanding the factors that influence river estuaries are essential for environmental management. However, no information exists on temperature change at high temporal resolution and over a wide area; moreover, the relationship with environmental factors has not yet been clarified. Here, we have described the actual status of water temperature change in riverine estuaries and its relationship with environmental factors for a wide area of the Japanese archipelago. Our results indicated that 217 of 294 rivers showed a significant monthly increase in water temperature. The average annual change in rates of water temperature increase was the highest in October at 0.090°C yr−1 and lowest in February at 0.068°C yr−1. Furthermore, snowfall, air temperature increase, sea water temperature, and land use were identified as factors increasing the rate of water temperature increase. Factors influencing the increase in river estuarine water temperature varied from month to month—with meteorological factors being the strongest influencers from spring to fall, and anthropogenic factors in winter. Our findings emphasize the importance of considering not only meteorological and oceanographic phenomena, but also anthropogenic influences and topographic features to understand the pattern of water temperature changes in river estuaries.

Patterns of air temperature changes in air ducts of non-contact air heating or cooling systems

The article is devoted to the study of the patterns of changes in air temperature during its movement in a sheet steel air duct during the operation of non-contact air heating systems, when the temperature of the moving air through the duct is higher than the room air temperature, as well as in the process of non-contact air cooling, when the temperature of the moving air in the duct below the room temperature. Based on the mathematical model of the ongoing physical processes developed in the article, an analytical formula is derived that allows, taking into account the presence of a heat-insulating layer or without it, to determine the patterns of changes in the temperature of moving air through the air duct at a given distance, depending on the thickness of the wall of the air duct and the speed of its movement. A dependence is given that determines the value of the heat transfer coefficient of air moving inside the duct to the inner surface of the duct on the speed, which at a constant flow rate is presented as a dependence of heat transfer on the open cross-sectional area of the duct. An analysis was carried out of the change in temperature of the moving volume of air inside the duct as a result of heat exchange with the surrounding air. A comparative calculation of the change in temperature of air moving in a thermally insulated air duct and in an air duct without thermal insulation was performed. The necessity of carrying out a calculation to determine the patterns of changes in the temperature of moving air inside the air duct when designing air heating systems or air conditioning systems is substantiated. It is concluded that it is necessary to conduct a technical and economic analysis on the feasibility of installing thermal insulation of air ducts made of thin sheet steel.

Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study.

Cardiac arrest (CA) is the leading cause of death in critically ill patients. Clinical research has shown that early identification of CA reduces mortality. Algorithms capable of predicting CA with high sensitivity have been developed using multivariate time series data. However, these algorithms suffer from a high rate of false alarms, and their results are not clinically interpretable. We propose an ensemble approach using multiresolution statistical features and cosine similarity-based features for the timely prediction of CA. Furthermore, this approach provides clinically interpretable results that can be adopted by clinicians. Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV database and the eICU Collaborative Research Database. Based on the multivariate vital signs of a 24-hour time window for adults diagnosed with heart failure, we extracted multiresolution statistical and cosine similarity-based features. These features were used to construct and develop gradient boosting decision trees. Therefore, we adopted cost-sensitive learning as a solution. Then, 10-fold cross-validation was performed to check the consistency of the model performance, and the Shapley additive explanation algorithm was used to capture the overall interpretability of the proposed model. Next, external validation using the eICU Collaborative Research Database was performed to check the generalization ability. The proposed method yielded an overall area under the receiver operating characteristic curve (AUROC) of 0.86 and area under the precision-recall curve (AUPRC) of 0.58. In terms of the timely prediction of CA, the proposed model achieved an AUROC above 0.80 for predicting CA events up to 6 hours in advance. The proposed method simultaneously improved precision and sensitivity to increase the AUPRC, which reduced the number of false alarms while maintaining high sensitivity. This result indicates that the predictive performance of the proposed model is superior to the performances of the models reported in previous studies. Next, we demonstrated the effect of feature importance on the clinical interpretability of the proposed method and inferred the effect between the non-CA and CA groups. Finally, external validation was performed using the eICU Collaborative Research Database, and an AUROC of 0.74 and AUPRC of 0.44 were obtained in a general intensive care unit population. The proposed framework can provide clinicians with more accurate CA prediction results and reduce false alarm rates through internal and external validation. In addition, clinically interpretable prediction results can facilitate clinician understanding. Furthermore, the similarity of vital sign changes can provide insights into temporal pattern changes in CA prediction in patients with heart failure-related diagnoses. Therefore, our system is sufficiently feasible for routine clinical use. In addition, regarding the proposed CA prediction system, a clinically mature application has been developed and verified in the future digital health field.

The numerical analysis to assess the second-law features of a solar water heater equipped with a dual-twisted tape turbulator; Developing a predictive model for useful thermal exergy based on the nonlinear calibration using the Chaos Control Method (CCM)

The solar thermal collectors are widely used for several applications nowadays. The improvement of their thermal performance helps to mitigate the initial costs and sizes of these components. In addition, the robust prediction of the solar thermal collectors’ performance is a promising technique to eliminate the future numerical simulations of these devices. To this end, the present numerical research is performed to investigate how placing two similar twisted tape turbulators side by side inside the absorber tube of a solar water heater affects the second-law aspects of the device. The pattern of changes in outlet water temperature, useful thermal exergy (Qu,ex), and rate of entropy production due to heat transfer (Sg,th) and fluid flow (Sg,fr) in different values of Reynolds number (Re) and twist pitch (H) were presented and analyzed. Validation of the numerical method was done with the experimental results available in the literature. The negative effect of boosting the Reynolds number and twist pitch on the entropy was reported. In addition, the downward (9.31–11.43 %) and upward (2.51–4.97 %) influence of Re and H on the Sg,th were reported. Moreover, it was found that the growth in Re and H is associated with the growth (654.97–661.27 %) and reduction (2.39–3.20 %) of Sg,fr. Finally, the nonlinear calibration using the Chaos Control Method (CCM) was employed to determine a predictive model for the Qu,ex in terms of Re and H. The results demonstrated that two predictive models can estimate Qu,ex with high degree of accuracy with the correlation coefficient of R2=0.999 and RMSE of 0.089.

Temporal Patterns of Change in Physical and Cognitive Performance.

This study examined the relation between declines in physical and cognitive performance in older people. A population-based cohort of 7483 adults (average age 72 years) were interviewed. Physical performance was assessed with 3 standardized tests and a combination of 4 cognitive tests was used to assess cognitive function. Rate of change in physical and cognitive performance was determined for each interval between interviews. In mixed effects linear regression models adjusted for age, sex, race, and study time, and change in each factor was used to predict change in the other factor. We examined time associations by using changes in the predictor measured at 1, 2, or 3 intervals before the outcome change. Decline in cognitive function was most strongly predicted by physical decline in the same 3-year interval. The decline in cognitive function was weaker in the 1-time interval after the decline in physical function and was not significant in later intervals. When a decline in cognitive function was used to predict a decline in physical function, the results were similar. The strongest association occurred in the same time interval so that declines in cognitive and physical performance tend to occur together. Decline in cognition and physical function seem to occur together in a short timeframe. It is important to investigate the reasons for these changes that are short-term to guide the development of interventions.

Improving evaluation of groundwater heavy metal(loid)s pollution efficiencies: Insights from novel Shannon entropy-weight and one-way ANOVA analysis

This study explores the hydrogeochemical behaviour of heavy metal(loid)s [HMs] in groundwater, their seasonal variations in enrichment and depletion, and adverse effects on human health in the Bahraich district of Uttar Pradesh, a part of the Ghaghara River basin, India. The Shannon's Entropy-Weighted Heavy Metal(loid)s Index (EW-HMI), a novel tool, has been applied to quantify cumulative groundwater pollution levels at each 42 monitoring sites, including critical evaluation of existing conventional pollution indices. Results from One-way ANOVA analysis indicate a significant seasonal difference in the mean concentrations of As, Fe, and Mn, demonstrating that monsoonal recharge is a governing factor for observed seasonal variation. The computation of EW-HMI reveals that >50% of sites are marked by ‘poor’ to ‘extremely poor’ water quality in Pre-monsoon 2022 (Pre-M). Distinctly, there is a notable improvement in 'excellent water quality', increasing from 9.5 to 57.1% in Post-monsoon 2022 (Post-M). This pattern of temporal change in EW-HMI, in contrast to HPI, is clearly depicted by the GIS-based spatial distribution maps. Thus, EW-HMI is a robust index and more effectively addresses apparent flaws in conventional indexing systems that could be used for wider applications. Health risk assessments (HRA) show that both children and adults in the Bahraich district are at risk of non-carcinogenic (NCR) and carcinogenic (CR) health effects from drinking contaminated groundwater. Children are anticipated to have a higher total CR mean value of 3.6 and 1.7 per 1000 children while adults estimated to have relatively low total CR mean values of 2.3 and 1.1 per 1000 adults in Pre-M and Post-M, respectively. This research offers valuable findings that can guide policymakers and stakeholders in identifying priority areas to facilitate the implementation of sustainable water resource management initiatives on both national and global scales.

Plausible impacts on crop production under climate change in Bangladesh: An analysis of the Denitrification-Decomposition (DNDC) model

This paper ffrst analyzes the changing pattern of temperature and precipitation and then constructs a Denitriffcation-Decomposition (DNDC) model to better understand the climate change impact on crop yields in Bangladesh. In the DNDC model, historical daily precipitation and temperature data used for baseline scenario and projected data have been taken from general circulation model (GCM). Different general circulation models (GCM) have been employed to analyze and estimate future temperatures and precipitations. The result of the general circulation model (GCM) study ffnds that the overall temperature in Bangladesh tends to increase by 1.5 0C and 2.8 0 C in the years 2030 and 2050. Precipitation patterns are also projected to increase in 2030 and 2050. The result from the Denitriffcation-Decomposition (DNDC) model ffnds that overall rice, corn, winter wheat, potato, vegetable, and pulses yields decrease both in 2030 and 2050, and decrease more rapidly in 2050. In the year 2050, the output of rice, potatoes, and pulses falls by -33%, -35%, and -54%, respectively, while the production of corn and wheat falls by -22% collectively. Since rice is the main food consumed in Bangladesh, a decline in rice output will pose a serious threat to the country's ability to feed itself.

Examining change in students’ self-regulated learning patterns after a formative assessment using process mining techniques

The growing popularity of e-learning platforms, such as learning management systems, has foregrounded the role of self-regulated learning (SRL) in student success. In many e-learning environments, students typically complete learning assignments outside of school hours with little or no instructor support, which requires students to be highly self-regulated. The current study used trace data from a Moodle platform to examine both the temporal pattern of students' SRL behaviors and changes in high- and low-performing students' SRL behaviors following a formative in-course exam. This study employed repeated-measures ANOVA, multivariate ANOVA, Fuzzy miner, and pMineR on 122,167 event logs from 527 undergraduate students. Findings revealed that students engaged in a loosely sequenced recursive SRL cycle. Following the formative assessment, each group made different adjustments to their SRL processes. High-performing students exhibited more SRL behaviors and developed more structured and interconnected SRL patterns. Low-performing students displayed a smaller increase in SRL behaviors while maintaining their established SRL patterns. Findings from this study could provide a more in-depth theoretical understanding of the nature of SRL cycles. Furthermore, students’ adjustment of SRL patterns in response to assessment may be informative for practitioners to assist students in enhancing their SRL through formative feedback.