Condition Index Research Articles

Quantification of interactions among agricultural drought indices within Köppen–Geiger climate zones in Bangladesh

Agricultural productivity is highly correlated with climatic variations, including drought events. This study is aimed at performing a comprehensive assessment of agricultural drought conditions in the Köppen–Geiger climate zones in Bangladesh, based on the temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI). The zones are classified as follows: temperate dry winter with a hot summer zone, tropical savannah zone, and tropical monsoon zone. The range of VHI is optimized based on its correlation with TCI and VCI. The correlation among the stratified drought indices is also examined to quantify and measure the strength and direction of the linear relationships between them. Moreover, Mann–Kendall test is conducted to assess the statistical trends in the spatiotemporal propagation of droughts across different climate zones. The balanced correlation approach for VHI reveals that vegetation health is governed by the temperature conditions. Certain variations in the drought intensity, frequency, and duration are observed across climatic zones in earlier years while the recent years are noted with less droughts. Normal or no drought conditions are noticed mostly in the tropical monsoon zone through VCI and VHI. The correlations among the drought classes indicate that VHI is more strongly correlated with TCI than with VCI, while NDVI exhibits stronger correlations with VCI than with either VHI or TCI. The Mann-Kendall test revealed that VCI has significant downward trends in drought categories and an upward trend in normal conditions, whereas VHI and TCI displayed inconsistency in statistical trends. By extensively exploring the agricultural drought conditions within specific climate zones, this study offers valuable insights for agronomists and stakeholders involved in climate resilience planning and agricultural development in Bangladesh.

Utilizing the Google Earth Engine for Agricultural Drought Conditions and Hazard Assessment Using Drought Indices in the Najd Region, Sultanate of Oman

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities.

Body mass changes of dabbling and diving ducks wintering in California

AbstractBird body mass is often used as an index of body condition and fluctuates throughout the year in response to environmental conditions and avian life‐history events. We examined the body mass of 59,572 ducks representing 13 species (7 dabbling duck species and 6 diving duck species) harvested within the 3 regions of the Central Valley in California, USA (Sacramento Valley, Suisun Marsh, San Joaquin Valley). Data collection occurred in winter during 5 hunting seasons (2014–2015, 2015–2016, 2017–2018, 2018–2019, 2019–2020). For all species, age, and sex classes, the body mass of dabbling ducks varied temporally and was lowest at the end of the hunting season in late January, declining from the beginning of the hunting season in mid‐October (northern pintail [Anas acuta]: −11.4% to −20.4%; northern shoveler [Spatula clypeata]: −3.5% to −17.3%; cinnamon teal [Anas cyanoptera]: −5.0% to −17.1%; American wigeon [Mareca americana]: −6.9% to −12.2%; American green‐winged teal [Anas carolinensis]: −5.9% to −11.9%; mallard [Anas platyrhynchos]: −1.8% to −9.6%; gadwall [Mareca strepera]: −2.2% to −8.4%). As expected, adults (after hatch‐year) were heavier than immature (hatch‐year) birds (within males: 0.6–8.6%; within females: 0.1–6.6%) and males were heavier than females (within adults: 3.1–38.6%; within immatures: 5.4–33.5%) at the end of the hunting season in all species. Within dabbling duck species, body masses did not differ among regions at the beginning of the hunting season but were heavier in the Sacramento Valley (0.5–14.4%) than other areas by the end of the hunting season. In contrast, body masses of diving ducks did not vary substantially during the hunting season or among regions. Diving ducks demonstrated inconsistent changes in mass from the beginning to the end of the season (lesser scaup [Aythya affinis]: 3.4% to 12.3%; canvasback [Aythya valisineria]: 5.1% to 6.1%; bufflehead [Bucephala albeola]: −3.2% to 1.6%; ring‐necked duck [Aythya collaris]: −4.9% to 2.6%; common goldeneye [Bucephala clangula]: −3.2% to −2.4%; ruddy duck [Oxyura jamaicensis]: −16.5% to 5.5%). The substantial temporal and spatial differences in dabbling duck body masses suggest that habitat quality (as measured by caloric value of the available food) or quantity may decline during winter and varies regionally within California's Central Valley.

Association between Outlying Values in Body Condition Indices in Small Mammals and Their Habitats

Habitat type and habitat change are very important factors in the body condition of small mammals that inhabit them. The response can be positive, increasing, or the opposite, decreasing body condition. We analyzed outliers of the body condition indices (BCIs) of 12 species trapped in nine different habitats during 1980–2023 in Lithuania, a mid-latitude country. Mixed and fragmented habitats, as well as commensal habitats, could be considered the least suitable for small mammals, based on the highest proportions of underfit and low proportions of best-fit individuals. On the contrary, meadows and disturbed habitats (landfills and cormorant colonies) had the highest proportions of best-fit individuals, while the proportion of under-fit individuals was much lower than expected. We found outliers in the BCI in all species, except for the under-fit harvest mice (Micromys minutus), and in all habitats, though not numerous. The presence of the highest BCI in yellow-necked mice (Apodemus flavicollis) and bank voles (Clethrionomys glareolus) in the disturbed habitats studied and in house mice (Mus musculus) in commensal habitats may be related to the resources provided by these habitats. Our results demonstrate the feasibility of using retrospective small mammal morphometric data to analyze their relationship with habitat.

Cost-Effective Pavement Condition Survey for Municipal Road Networks

One of the most challenging issues for municipalities in the U.S. is to secure federal funding, state funding, or both, for local roadway improvement. Existing frameworks such as manual data collection, light detection and ranging have proven to be expensive and cumbersome. In this paper, a low-cost pavement management framework is proposed using artificial intelligence (AI). AI has solidified itself across industries as a revolutionary advancement that can automate many tasks that were performed by humans. AI has the potential to make roadway assessment easier and more cost-effective than ever, but this application has been hindered by dataset quality and quantity. Roadway datasets are often imbalanced, containing many more images of certain deformations than others. This decreases the performance of AI models. In this paper, different methods of pavement dataset labeling are tested to gain an understanding of which is best for pavement distress detection using a classification neural network. An AI-friendly pavement condition index is designed to give a clear indicator of the current pavement condition and provide a metric by which to rank the roads based on the need to repair them. The best-performing AI model is incorporated into the low-cost pavement management framework.

Temperature-dependent responses and trophic interaction strengths of a predatory marine gastropod and rock oyster under ocean warming

Predator-prey interactions are important in shaping ecosystem structure. Consequently, impacts of accelerating global warming on predators will have notable implications. Effects are likely to be particularly marked for tropical organisms which are anticipated to be sensitive to further thermal stress. Here, we investigated effects of future ocean warming on the predatory dogwhelk Reishia clavigera and its predation of Saccostrea cucullata. Mortality of the predators rapidly increased under the extreme elevated temperature, while those exposed to moderate elevated temperature displayed similar mortality as the ambient. Predators that survived moderate temperature increases altered their oxygen consumption patterns, increased average feeding rates, and functional responses, although condition index and energy reserves were unchanged. Overall, we show extreme ocean warming scenarios can remove predators and their consumption of prey from an ecosystem, whereas moderate warming can intensify predator-prey interactions. Such temperature-dependent alterations to predator-prey interactions would lead to fundamental changes of ecosystem structure as the ocean warms.

Temporal dynamics of condition index and glochidia larval occurrence in freshwater mussel, Lamellidens corrianus (Lea, 1834) collected from a wetland of Mymensingh, Bangladesh

We report the temporal pattern of condition index (CI), and glochidia larval presence and discharge in freshwater mussel, Lamellidens corrianus, obtained from the Darikathal Beel at Trishal upazila of Mymensingh district in Bangladesh. Samples were drawn monthly over a year from July 2021 to June 2022. The mean shell length, shell height, shell breadth, tissue wet weight, and shell dry weight were 86.29±8.36 mm, 19.31±1.42 mm, 40.31±4.90 mm, 12.52±4.77 g, and 12.05±2.85 g, respectively. The male-female sex ratio was 1.00:2.00 with no sexually undifferentiated animals in the sample.

Aquatic Condition Index: optimization of a rapid wetland assessment tool for evaluating urban wetland health

Urbanization poses significant threats to wetland ecosystems, leading to habitat loss, hydrological alterations, and the introduction of invasive species that adversely affect essential ecosystem services. This widespread threat underscores the need to develop a robust management tool for gauging urban wetland health. The Aquatic Condition Index (ACI) was developed as a diagnostic tool for monitoring urban wetland health in Calgary, Alberta, Canada. The ACI evaluates wetland health by incorporating functional indicators (i.e., hydrological, ecological, and water quality functions) chosen by scientific experts to provide municipal wetlands with relative condition scores that can inform citywide habitat management budgeting and prioritization. Gathering the data necessary to generate wetland indicators for the ACI requires substantial financial resources, time, and a high degree of analytical expertise for data collection (e.g., field surveys). This investigation aimed to enhance the widespread applicability and cost-efficiency of wetland monitoring by optimizing the ACI. This optimization entailed a sensitivity-driven indicator reduction, which strategically minimizes the number of indicators essential for ACI calculations. Our findings demonstrate that the refined selection of indicators produces comparable results to the original ACI. This highlights the potential of transitioning to more rapid and cost-efficient monitoring methods, creating a streamlined approach to enhance the efficiency of monitoring and assessment processes. Ultimately, this approach can facilitate long-term urban wetland assessments and promote the sustainability and management of these vital urban features .

Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran

Monitoring and predicting desertification in arid regions are crucial for addressing environmental and societal challenges. Remote sensing is vital for tracking land surfaces and ecosystems changes. The study aims to use remote sensing-based data to monitor and predict desertification in the Sistan Plain through a data screening approach. The study's satellite data consisted of Landsat 5 and 8 images taken in June each year over 10 years (1990–2020). Remote sensing-based indices, including land use and land cover (LULC) map, normalized differential vegetation index (NDVI), improved vegetation index (EVI), vegetation condition index (VCI), surface temperature condition index (TCI), modified normalized differential water level index (MNDWI) and salinity index (SI) were used in the study. In addition to satellite data, environmental indices, including standardized precipitation index (SPI) and streamflow drought index (SDI), were used. The study employed the random forest (RF) method and the mixed model of automated cells and Markov chain (CA-Markov) to monitor desertification and quantitatively predict its condition in 2030. Root-mean-square error (RMSE) and mean-square error (MSE) indicators were used to evaluate the error. Based on the findings, the RF correlation coefficient (R2) and RMSE were obtained about 0.97 and 0.08, respectively. High coefficient values and low RMSE values indicate that the random forest model is highly efficient in assessing desertification for the study period from 1990 to 2020. The change detection method revealed that desertification increased from 1990 to 2010 but decreased from 2010 to 2020. The decreasing trend is expected to continue until 2030. The Kappa coefficient for the prediction of desertification in 2030 was found to be 0.94, which indicates a correct classification based on the collected samples. In addition, the study identified the SI and SDI as effective indices in the desertification process in the study area. Overall, this study provides valuable insights into monitoring and predicting desertification, which could help develop appropriate strategies for managing and controlling desertification in the Sistan Plain through remote sensing and machine learning techniques.

Integrated Multi-Biomarker Responses of Juvenile Zebra Seabream (Diplodus cervinus) to Warming and Acidification Conditions

The impacts of climate change-related stressors are becoming more noticeable in the ocean, particularly in coastal marine ecosystems. Yet limited information still exists on the physiological state and ecological resilience of marine fish species, especially during their early life stages (i.e., larvae and juveniles). The present study investigated the effects of chronic exposure to seawater warming (OW; ΔT = +4 °C) and acidification (OA; ΔpH = −0.3 pH units, equivalent to pCO2~1000 µatm), acting alone or combined (OWA), on juvenile zebra seabream (Diplodus cervinus) physiological resilience, considering distinct levels of biological organization (i.e., biochemical, cell, organ and individual levels). After 60 days of exposure, both stressors, in isolation or combination, significantly decreased specific growth rate (−11% in OW, −42% in OA and −49% in OWA) and leukocyte counts (from −29% in OA and OWA up to −37% in OW) in relation to the control treatment. In addition, a decreased Fulton’s condition index (K) was observed under warming and acidification in combination (−35% in OWA). At the cell level, OW, OA and OWA triggered different biomarker responses in D. cervinus (i.e., up-regulation, down-regulation, or absence of significant effect). In general, the results are suggestive of an antagonistic effect when warming and acidification are combined. OWA yielded the highest integrated biomarker response (IBR) index value in the whole organism, muscle, brain and gills of D. cervinus juveniles, therefore suggesting that the effects of these stressors are more severe when they act together. The distinct patterns observed in each stress scenario highlight the importance of carrying out further studies adjusted to the specificities of different regions, i.e., accounting not only for the type and degree of severity of environmental stressors already felt and/or projected for that specific area, but also the physiological plasticity of species that inhabit a particular ecosystem. The gathered knowledge will allow one to determine the vulnerability of particular marine species and geographic areas and, most importantly, to draw up effective and tailor-made conservation strategies to overcome climate change impacts.

Habitat and Body Condition of Small Mammals in a Country at Mid-Latitude

The relationship between the body condition of different small mammal species and the habitat they occupy is poorly analyzed. We analyzed the body condition index, BCI, of 18 small mammal species trapped in forest, shrub, wetland, meadow, riparian, mixed and fragmented, disturbed, agricultural, and commensal habitats of Lithuania during the span of 1980–2023. The composition of small mammal communities was habitat-dependent, being richest in meadows, with eighteen species, and poorest in riparian habitats, with nine species. A significant variation in the BCI with respect to habitat was observed in eight small mammal species (Sorex araneus, S. minutus, Apodemus agrarius, A. flavicollis, Clethrionomys glareolus, Alexandromys oeconomus, Microtus agrestis, and M. arvalis). The highest average BCI for most of these species was found in disturbed habitats, with S. minutus and M. arvalis showing the highest BCI in agricultural habitats. The lowest average BCI for most species was found in mixed habitats, while C. glareolus and M. arvalis exhibited the lowest BCI in shrub habitats. In general, species dominating certain habitats did not have the highest BCI. This is the first multi-species, multi-habitat study of body condition in small mammals at mid-latitudes.

Optimization on self-assembled ultra-micro foam system for carbon dioxide flooding

Given the problem that the conventional foam injection and salt resistance are poor, the oil displacement effect is not obvious, and it is difficult to enhance oil recovery in the ultra-low permeability and high salinity reservoirs of Yanchang Oilfield, it is imperative to study a new foam flooding agent. Based on the surfactant’s self-assembled properties, foaming agent, stabilizer and complexing agent were screened in the absence of polymer and non-alkali. Three sets of self-assembly worm-like micelle systems were screened, and then a set of carbon dioxide foaming system was selected through comparative analysis: 0.50 wt% EAPMAC-12 + 0.20 wt% SDS + 0.09 wt% TEOA + 0.08 wt% EDTA. Under this condition, its viscosity, foaming volume, foam half-life and foam composite index are 18.8 mpa·s, 531 mL, 25 min and 9956 mL·min, respectively. Under the condition of the ratio of gas to liquid 3:1, the ultra-micro foam was obtained by gas–liquid co-injection in the sand pack, and the foam was round rather than the traditional pentagonal or hexagonal shape and the size was 5–31 um, indicating that the foam had good injection. Oil displacement experiment showed the average enhanced recovery rate was 12.84 % in ultra-low permeability cores, indicating that the foam had good oil displacement effect in ultra-low permeability reservoirs.

Spatiotemporal analysis of tropical vegetation ecosystems and their responses to multifaceted droughts in Mainland Southeast Asia using satellite-based time series

ABSTRACT Drought ranks among the costliest of all climate-related phenomena and manifests in various forms, posing significant challenges in understanding its influence on agriculture and natural ecosystems. Mainland Southeast Asia (MSEA), a significant region of tropical agriculture and vegetation ecosystems, has become increasingly susceptible to drought hazards. In this study, we characterized and assessed vegetation dynamics and their drought impacts using correlation analysis and explainable machine learning methods under different vegetation types and elevation zones during the dry growing seasons from 2000 to 2022. Specifically, we characterized the vegetation dynamics and their trend in space and time. Next, we assessed vegetation-drought responses in consideration of meteorological, hydrological, and agricultural droughts under different land cover types and elevation characteristics. Lastly, we used an explainable machine learning method to quantify the drivers and impacts of multifaceted droughts on natural and undisturbed vegetation ecosystems. Our findings revealed that nearly 70% of the MSEA region experienced a greening trend despite large areas of vegetative damage during the drought years. Vietnam witnessed increasing vegetation condition in most observed years while the declining trend was mainly found in Cambodia and southern Laos. Vegetation-drought responses showed that tropical vegetation had a high sensitivity to drought conditions, and stronger responses were observed in rainfed crop, mixed forest, and deciduous forest at lower altitude areas. In natural and undisturbed ecosystems, short-term meteorological and agricultural drought disturbances accounted for nearly 93% of variations in tropical vegetation. Among the different examined drought indices, the 3-month Standardized Precipitation Evapotranspiration (SPEI-3) and Temperature Condition Index (TCI) were identified as the factors having the largest influence, together explaining about 55% of the observed natural undisturbed vegetation variations. These findings deepen our understanding of tropical vegetation-drought responses and the underlying drivers of natural and undisturbed ecosystems. Such insights could provide valuable information to assist national and local governments in the MSEA region in developing effective drought management and adaptation programs to safeguard tropical agricultural production and natural ecosystems amidst growing climate challenges.

Heat hardening improves thermal tolerance in abalone, without the trade-offs associated with chronic heat exposure

Marine animals are challenged by chronically raised temperatures alongside an increased frequency of discrete, severe warming events. Exposure to repeated heat shocks could result in heat hardening, where sub-lethal exposure to thermal stress temporarily enhances thermotolerance, and may be an important mechanism by which marine species will cope with future thermal challenges. However, we have relatively little understanding of the effects of heat hardening in comparison to chronic exposure to elevated temperatures. Therefore, we compared the effects of heat hardening from repeated exposure to acute heat shocks and chronic exposure to elevated temperatures on thermal tolerance in the European abalone, Haliotis tuberculata. Adult abalones were exposed to either control temperature (15 °C), chronic warming (20 °C) or a regime of two events of repeated acute heat shock cycles (23–25 °C) during six months, and their thermal tolerance and performance, based upon cardiac activity, compared using a dynamic ramping assay. The cost associated with each treatment was also estimated via measurements of condition index (CI). Abalone exposed to both temperature treatments had higher upper thermal limits than the control, but heat-hardened individuals had significantly higher CI values, indicating an enhancement in condition status. Differences in the shape of the thermal performance curve suggest different mechanisms may be at play under different temperature exposure treatments. We conclude that heat hardening can boost thermal tolerance in this species, without performance trade-offs associated with chronic warming.

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Effect of aging on the toxicity of polyethylene microplastics on the estuarine bivalve Scrobicularia plana

Microplastics (MP) are now present in all ecosystems and undergo weathering processes, including physical or chemical degradation. Although most studies have been carried out on MP toxicity in the marine ecosystem, interest is growing for the terrestrial and entire aquatic compartments. However, the interface between both environments, also known as the soil/water continuum, is given little consideration in MP toxicity studies. Only a few studies considered the toxicity of artificially aged or soil field-collected MP on species living at this interface. The present study evaluates the impact of artificial and field aging polyethylene (PE) MP on the bivalve Scrobicularia plana, a key organism of the estuarine compartment, living at the soil/water interface. Clams were exposed for 21 days to environmental concentrations (0.008, 10 and 100 μg L−1) of unaged as well as artificially and field aged PE MP. Toxicity was assessed from individual to molecular levels including condition index, clearance rate, burrowing behavior, energy reserves, enzyme activities and DNA damage. Results showed differential effects at all biological levels depending on the type and the concentration of the MP tested. Indeed, a decrease in burrowing behavior was observed in S. plana exposed to aged and field PE at low concentration (0.008 μg L−1). In the gills of clams, exposures to aged PE (0.008 and 100 μg L−1), virgin PE (10 μg L−1) and field PE (all tested concentrations) decreased CAT activity while DNA damage increased after exposure to virgin PE (0.008 μg L−1 and 10 μg L−1) and field PE (0.008 μg L−1). Our findings suggest that aging modifies the toxicity profile of PE polymer on S. plana and considering plastic from field at environmental concentrations is important when performing ecotoxicological studies.

Machine learning-based technology for asphalt concrete pavement performance decision-making in hot and humid climates

Highway state agencies incur significant budget savings through optimal allocation of pavement Maintenance, Rehabilitation, and Reconstruction (MR&R) activities. These activities require robust prediction models that can handle large-scale, real-world data and can forecast pavement performance in the long run. Unfortunately, the traditional performance prediction models have been questionable in terms of efficiency and accuracy, are based on a limited number of explanatory variables, and are designated to predict short-term (up to five years) pavement conditions. Therefore, the goal of this study was to propose a machine learning-based technology that can predict the field performance by up to 11 years of Asphalt Concrete (AC) overlays placed on asphalt pavements in Southern states in the US based on key project conditions. The proposed technology resulted from assessing the prediction accuracy of machine learning algorithms, including Decision-Tree (DT), eXtreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and ensemble-learning method, in forecasting the Pavement Condition Index (PCI) as the pavement performance indicator. For each algorithm, six models were developed sequentially based on historical pavement condition data collected from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. The six models learned from 892 log miles of randomly placed AC overlay sections in Louisiana. The output of these models was the future PCI of AC overlays at a biannual rate from one to 11 years. The findings showed that XGBoost and ensemble learning showed similar performance during model training and were further evaluated using the testing dataset. During model testing, the ensemble learning method yielded higher prediction accuracy than other algorithms, with R2 values decreasing from 0.77 at age 1 to 0.67 at age 11, Root Mean Square Error (RMSE) values increasing from ±1.65 to ±4.74, and Mean Absolute Error (MAE) increasing from 1.24 to 4.59. The resulting models can be used by state agencies to replace traditional prediction techniques.

Liver stiffness in hepatocellular carcinoma and chronic hepatitis patients: Hepatitis B virus infection and transaminases should be considered.

Liver condition is a crucial prognostic factor for patients with hepatocellular carcinoma (HCC), but a convenient and comprehensive method to assess liver condition is lacking. Liver stiffness (LS) measured by two-dimensional shear wave elastography may help in assessing liver fibrosis and liver condition. Chronic hepatitis B (CHB) is an important risk factor for HCC progression, but LS was found to be less reliable in assessing liver fibrosis following hepatitis viral eradication. We hypothesize that the status of hepatitis virus infection would affect the accuracy of LS in assessing the liver condition. To test the feasibility and impact factors of using LS to assess liver condition in patients with HCC and CHB. A total of 284 patients were retrospectively recruited and classified into two groups on the basis of serum CHB virus hepatitis B virus (HBV)-DNA levels [HBV-DNA ≥ 100.00 IU/mL as Pos group (n = 200) and < 100.00 IU/mL as Neg group (n = 84)]. Correlation analyses and receiver operating characteristic analyses were conducted to evaluate the relationship between LS and liver condition. A significant correlation was found between LS and most of the parameters considered to have the ability to evaluate liver condition (P < 0.05). When alanine aminotransferase (ALT) concentrations were normal (≤ 40 U/L), LS was correlated with liver condition indices (P < 0.05), but the optimal cutoff of LS to identify a Child-Pugh score of 5 was higher in the Neg group (9.30 kPa) than the Pos group (7.40 kPa). When ALT levels were elevated (> 40 U/L), the correlations between LS and liver condition indices were not significant (P > 0.05). LS was significantly correlated with most liver condition indices in patients with CHB and HCC. However, these correlations varied according to differences in HBV-DNA and transaminase concentrations.

Climatic teleconnection of the future trend of meteorological, GRACE-DSI, and vegetation-conditioned-based drought analysis in the Ganga Basin

ABSTRACT This study assesses drought in the Ganga River Basin using standard precipitation index (SPI), Gravity Recovery and Climate Experiment Drought Severity Index (GRACE-DSI), and vegetation condition index (VCI) via geographic information system (GIS) and non-parametric tests. Future SPI trends show increasing drought severity from 1986 to 2020. GRACE-DSI analysis (2002–2020) indicates a potential rise in drought conditions. VCI computations reveal vegetation health dynamics. Findings suggest an impending severe drought in the Ganga Basin, necessitating urgent water resource management. Drought causes are linked to precipitation changes and resource over-exploitation in changing climate conditions. The study emphasizes implementing measures like water conservation, sustainable land use, and groundwater recharge strategies to mitigate drought impacts.

Temporal changes in glochidia larval occurrence and condition index in freshwater mussels, Lamellidens marginalis (Lamarck, 1819) collected from a wetland of Bangladesh

Temporal changes in glochidia larval occurrence and condition index in freshwater mussels, Lamellidens marginalis (Lamarck, 1819) collected from a wetland of Bangladesh