Median Slope Research Articles

Pegunigalsidase alfa: a novel, pegylated recombinant alpha-galactosidase enzyme for the treatment of Fabry disease.

Fabry disease, a rare X-linked genetic disorder, results from pathogenic variants in GLA, leading to deficient lysosomal α-galactosidase A enzyme activity and multi-organ manifestations. Since 2001, enzyme replacement therapy (ERT), using agalsidase alfa or agalsidase beta, has been the mainstay treatment, albeit with limitations such as rapid clearance and immunogenicity. Pegunigalsidase alfa, a novel PEGylated recombinant alpha-galactosidase, offers promise as an alternative. Produced in plant cells, pegunigalsidase alfa exhibits enhanced stability, prolonged half-life, and reduced immunogenicity due to pegylation. A phase 1/2 clinical trial demonstrated Gb3 clearance from renal capillary endothelial cells and its 48-month extension study revealed notable outcomes in renal function preservation. Three phase 3 clinical trials (BRIDGE, BRIGHT, and BALANCE) have shown favorable efficacy and safety profile, although caution is warranted in interpreting the results of BRIDGE and BRIGHT which lacked control groups. In BALANCE, the pivotal phase 3 trial comparing pegunigalsidase alfa with agalsidase beta, an intention-to-treat analysis of the eGFR decline over 2years showed that the intergroup difference [95%confidence interval] in the median slope was -0.36mL/min/1.73 m2/year [-2.44; 1.73]. The confidence interval had a lower limit above the prespecified value of -3mL/min/1.73 m2/year and included zero. Despite challenges such as occasional hypersensitivity reactions and immune-complex-mediated glomerulonephritis, pegunigalsidase alfa approval by the European Medicines Agency and the Food and Drug Administration represents a significant addition to Fabry disease therapeutic landscape providing an option for patients in whom enzyme replacement therapy with current formulations is poorly tolerated or poorly effective.

Relationships Between Plankton Size Spectra, Net Primary Production, and the Biological Carbon Pump

AbstractPhotosynthesis in the surface ocean and subsequent export of a fraction of this fixed carbon leads to carbon dioxide sequestration in the deep ocean. Ecological relationships among plankton functional groups and theoretical relationships between particle size and sinking rate suggest that carbon export from the euphotic zone is more efficient when communities are dominated by large organisms. However, this hypothesis has never been tested against measured size spectra spanning the >5 orders of magnitude found in plankton communities. Using data from five ocean regions (California Current Ecosystem, North Pacific subtropical gyre, Costa Rica Dome, Gulf of Mexico, and Southern Ocean subtropical front), we quantified carbon‐based plankton size spectra from heterotrophic bacteria to metazoan zooplankton (size class cutoffs varied slightly between regions) and their relationship to net primary production and sinking particle flux. Slopes of the normalized biomass size spectra (NBSS) varied from −1.6 to −1.2 (median slope of −1.4 equates to large 1–10 mm organisms having a biomass equal to only 7.6% of the biomass in small 1–10 μm organisms). Net primary production was positively correlated with the NBSS slope, with a particularly strong relationship in the microbial portion of the size spectra. While organic carbon export co‐varied with NBSS slope, we found only weak evidence that export efficiency is related to plankton community size spectra. Multi‐variate statistical analysis suggested that properties of the NBSS added no explanatory power over chlorophyll, primary production, and temperature. Rather, the results suggest that both plankton size spectra and carbon export increase with increasing system productivity.

The UV continuum slopes of early star-forming galaxies in JADES

ABSTRACT The power-law slope of the rest-ultraviolet (UV) continuum (fλ ∝ λβ) is a key metric of early star-forming galaxies, providing one of our only windows into the stellar populations and physical conditions of z ≳ 10 galaxies. Expanding upon previous studies with limited sample sizes, we leverage deep imaging from the JWST Advanced Deep Extragalactic Survey (JADES) to investigate the UV slopes of 179 z ≳ 9 galaxies with apparent magnitudes of mF200W ≃ 26–31, which display a median UV slope of β = −2.4. We compare to a statistical sample of z ≃ 5–9 galaxies, finding a shift towards bluer rest-UV colours at all $M_{\rm UV}$. The most UV-luminous z ≳ 9 galaxies are significantly bluer than their lower redshift counterparts, representing a dearth of moderately red galaxies within the first 500 Myr. At yet earlier times, the z ≳ 11 galaxy population exhibits very blue UV slopes, implying very low impact from dust attenuation. We identify a robust sample of 44 galaxies with β ≲ −2.8, which have spectral energy distributions requiring models of density-bounded H ii regions and median ionizing photon escape fractions of 0.51 to reproduce. Their rest-optical colours imply that this sample has weaker emission lines (median mF356W − mF444W = 0.19 mag) than typical galaxies (median mF356W − mF444W = 0.39 mag), consistent with the inferred escape fractions. This sample consists of relatively low stellar masses (median $\log (M/{\rm M}_{\odot })=7.5\pm 0.2$), and specific star formation rates (sSFRs; median $=79 \, \rm Gyr^{-1}$) nearly twice that of our full galaxy sample (median sSFRs $=44 \, \rm Gyr^{-1}$), suggesting these objects are more common among systems experiencing a recent upturn in star formation. We demonstrate that the shutoff of star formation provides an alternative solution for modelling of extremely blue UV colours, making distinct predictions for the rest-optical emission of these galaxies. Future spectroscopy will be required to distinguish between these physical pictures.

Dynamic monitoring of aboveground biomass in inner Mongolia grasslands over the past 23 Years using GEE and analysis of its driving forces

Aboveground biomass (AGB) in grasslands directly reflects the net primary productivity, making it a sensitive indicator of grassland resource quality and ecological degradation. Accurately estimating AGB over large regions to reveal long-term AGB evolution trends remains a formidable challenge. In this study, we divided Inner Mongolia Autonomous Region (IMAR) grasslands into three study regions based on their spatial distribution of grassland types. We combined remote sensing data with ground-based sample data collected over the past 19 years from 6114 field plots using the Google Earth Engine platform. We constructed random forest (RF) and traditional regression AGB inversion models for each region and selected the best-performing model through accuracy assessment to estimate IMAR grassland AGB for the period 2000–2022. We also examined the trends in AGB changes and identified the driving forces affecting IMAR grasslands through the application of Theil-Sen estimation, Mann-Kendall trend analysis, and the Geodetector model. The main findings are as follows: (1) Compared with the univariate parametric traditional regression model, the AGB monitoring accuracy of the multivariate non-parametric RF model in the three study regions increased by 5.94%, 5.08% and 19.14%, respectively. (2) The average AGB per unit area of IMAR grasslands from 2000 to 2022 was 731.41 kg/hm2, with alpine meadow having the highest average AGB (1271.70 kg/hm2) and temperate grassland desertification having the lowest (469.06 kg/hm2). IMAR grasslands exhibited an overall increasing trend in AGB over the past 23 years (6.01 kg/hm2•yr), with the increasing trend covering 83.52% of the grassland area and the decreasing trend covering 16.48%. (3) Spatially, IMAR grassland AGB showed a gradual decline from northeast to southwest and exhibited an increasing trend with increasing longitude (45.423 kg/hm2 per degree) and latitude (71.9 kg/hm2 per degree). (4) Meteorological factors were the most significant factors affecting IMAR grassland AGB, with precipitation (five-year average q value of 0.61) being the most prominent. In the western part of IMAR, where precipitation is consistently limited throughout the year, the primary drivers of influence were human activities, with particular emphasis on the number of livestock (with a five-year average q value of 0.44). It is evident that reducing human activity disturbance and pressure in fragile grassland areas or implementing near-natural restoration measures will be beneficial for the sustainable development of grassland ecosystems. The results of this research hold substantial reference importance for the protection and restoration of grasslands, the supervision and administration of grassland resources, as well as the development of policies related to grassland management.

Assessment of mean precipitation and precipitation extremes in Iran as simulated by dynamically downscaled RegCM4

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991–2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000 s compared to the 1990 s

Robust approach to earnings forecast: A comparison

AbstractThis paper applies three robust approaches, namely, the MM estimation, the Theil–Sen estimation, and the quantile regression, to generate earnings forecasts in Chinese financial market and evaluates the forecast accuracy of these three methods based on three forecasting criteria. We examine six forecasting models where the predicted variables include earnings per share, net income, and three profitability measures. We show that the three robust methods significantly outperform the OLS method. Moreover, the MM estimation and the quantile regression have better forecast accuracy than the Theil–Sen approach.

Precipitation-dependent sensitivity of suspended sediment concentration to turbidity in a mountainous river in southwestern China

Suspended sediment concentration (SSC) has been used as a key indicator in various environmental studies concerning, for example, ecological integrity, river morphology, aquatic and riverine biota, and the management of river and reservoir systems. River turbidity has been widely used as a proxy indicator of SSC. However, the relationship between the two parameters varies considerably under different conditions. The present study investigated the relationships between SSC and turbidity under different rainfall conditions in the Baisha River, a mountainous river located in southwestern China. Data were continuously collected on site from 2015 to 2021. The results showed a significant positive correlation between SSC and turbidity, with the median slope model exhibiting the best linear fit. In addition, the linear regression slopes varied significantly between different seasons and rainfall intensities in the following descending orders: dry season (1.05) > rainy season (0.97), no rainfall (1.14) > non-erosive rainfall (0.99) > erosive rainfall (0.95), and mild rainfall (1.05) > moderate rainfall (0.97) > heavy rainfall (0.93) > rainstorm (0.89). Overall, during the rainy season and under higher rainfall intensities, the regression slope was gentler. This study discussed the mechanisms through which rainfall conditions affected the SSC-turbidity relationship. It was shown that such a relationship was generally influenced by sediment transport in the runoff and river water flow flux, and the number of suspended particles as well as their size were considered as main factors. The results of this study revealed the impact of rainfall conditions on the SSC-turbidity relationship and provided a reference for the rapid assessment of the suspended sediment load in mountain river basins at high altitudes. In addition, based on the results obtained, it is expected that a new method for predicting event-based soil erosion can be applied in similar high-altitude mountain valleys.

Spatio-temporal Variation in Net Primary Productivity of Different Vegetation Types and Its Influencing Factors Exploration in Southwest China

Studying the spatiotemporal variation in vegetation net primary productivity (NPP) and exploring its influencing factors are of considerable practical significance for understanding the spatiotemporal variation in vegetation and for guiding ecological restoration and management projects based on local conditions. Based on MODIS NPP data, combined with in situ meteorological data, land use data, and vegetation type data, this study explores the spatiotemporal variation in different types of vegetation NPP in southwest China via the Mann-Kendall significance test and Theil-Sen Median slope estimator. It reveals the influencing factors of spatial differentiation of different types of vegetation NPP and the interaction between influencing factors in combination with stability analysis and Geo Detectors. The results revealed that on the temporal scale, from 2000 to 2021, vegetation NPP, NPPPre (vegetation NPP exclusively under the influence of climate change), and NPPRes (vegetation NPP exclusively under the influence of human activities) in southwest China showed a fluctuating upward trend. Among different vegetation types, NPP, NPPPre, and NPPRes exhibited an upward trend, except for a minor decline in NPPRes of tree vegetation at a rate of -0.183 g·(m2·a)-1. Among them, NPP, NPPPre, and NPPRes of economic vegetation showed the most significant upward rates, 5.96, 3.09, and 2.94 g·(m2·a)-1, respectively. On the spatial scale, the tree vegetation NPP with the most significant downward trend was mainly distributed in Tibet and southern Yunnan, while the economic vegetation NPP with the highest upward trend was primarily distributed in eastern Sichuan Province. The stability of vegetation NPP in southwest China presented a spatial distribution pattern of "low in the south and high in the north," and the average value of the correlation coefficient increased in the ascending order of arbor vegetation (0.101), shrub vegetation (0.105), herb vegetation (0.110), and economic vegetation (0.114). The interaction between surface temperature and relative humidity was the main influencing factor for spatial differentiation of vegetation NPP, while the interaction between sunshine duration and warmth index had the most significant impact on vegetation in southwest China, with an increasing percentage of 30.91%. Different types of vegetation had different requirements for different climatic factors, but their requirements for surface temperature and warmth index were significantly consistent. When the surface temperature was 21.03-28.49℃, and the warmth index was 106.46-167.2, the NPP of different vegetation types peaked. Under natural succession, the impact of climate change on vegetation was inversely proportional to the stability of the vegetation community. The arbor vegetation community with high stability was less affected, while the herb vegetation community with low stability was highly affected by climate. In contrast, the stability of economic vegetation was directly proportional to the impact of climate due to the influence of human activities. This study establishes a theoretical foundation for evaluating the impact of regional climate on the growth of different vegetation types and can be crucial for formulating ecological restoration and management strategies in southwest China that are adapted to the local conditions.

A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease.

The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient's risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group (P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from -7.08 ml/min/1.73 m2/year to -4.27 ml/min/1.73 m2/year in high-risk patients (P = .0003), -2.65 to -1.04 in intermediate risk, and -3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk (P < .001). Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.

Precipitation Microphysics in Tropical Cyclones: A Global Perspective Using the NASA Global Precipitation Measurement Mission Dual‐Frequency Precipitation Radar

AbstractPrecipitation microphysics in tropical cyclones (TCs) are often poorly represented in numerical simulations, which ultimately affects TC structure, evolution, and prediction. This provides a large incentive to better observe and understand the underlying microphysical processes in TCs in order to improve precipitation forecasts and warning operations. 112 TCs from 2014 to 2020 were matched up with overpasses from the NASA Global Precipitation Measurement (GPM) mission Dual‐Frequency Precipitation Radar (DPR) on a global scale to identify cloud properties and associated precipitation processes by quantifying vertical slopes of reflectivity in the liquid and ice phase. Further, vertical profiles of reflectivity were partitioned into different 850–200 hPa shear‐relative quadrants of each storm, different annuli from the storm center, and 5 TC ocean basins globally. Preliminary results showed the highest echo top heights occurred in the Northwest Pacific and Indian Ocean basins, with all basins and quadrants exhibiting median negative slopes of KuPR in the liquid phase. Additionally, all shear‐relative quadrants revealed negative slopes of reflectivity in the ice phase implying ice hydrometeor growth. These findings can potentially be used to improve the representation of cloud properties and the accuracy of the DPR particle size distribution algorithm in TCs.

Understanding the Biering-Sørensen test: Contributors to extensor endurance in young adults with and without a history of low back pain

The Biering-Sørensen test is commonly used to assess paraspinal muscle endurance. Research using a single repetition of the test has provided conflicting evidence for the contribution of impaired paraspinal muscle endurance to low back pain (LBP). This study investigated how Sørensen test duration, muscle activation, and muscle fatigability are affected by multiple repetitions of the test and determined predictors of Sørensen test duration in young adults with and without a history of LBP. Sixty-four young individuals performed three repetitions of the Sørensen test. Amplitude of activation and median frequency slope (fatigability) were calculated for the lumbar and thoracic paraspinals and hamstrings. Duration of the test was significantly less for the 3rd repetition in individuals with LBP. In individuals without LBP, test duration was predicted by fatigability of the lumbar paraspinals. In individuals with LBP, Sørensen test duration was predicted by fatigability of the hamstrings and amplitude of activation of the thoracic and lumbar paraspinals. Our findings demonstrate that it is necessary to amplify the difficulty of the Sørensen test to reveal impairments in young, active adults with LBP. Training programs aiming to improve lumbar paraspinal performance should monitor performance of other synergist muscles during endurance exercise.

Marginal Distribution Fitting Method for Modelling Flood Extremes on a River Network

This study utilized a max-stable process (MSP) model with a dependence structure defined via a non-Euclidean distance metric, with the goal of modelling extreme flood data on a river network. The dataset was composed of mean daily discharge observations from 22 United States Geological Survey streamflow gaging stations for river basins in Missouri and Arkansas. The analysis included the application of the elastic-net penalty to automatically build spatially varying trend surfaces to model the marginal distributions. The dependence model accounted for the river distance between hydrologically connected gaging sites and the hydrologic distance, defined as the Euclidean distance between the centers of site’s associated drainage areas, for all stations. Modelling the marginal distributions and spatial dependence among the extremes are two key components for spatially modelling extremes. Among the 16 covariates evaluated for marginal fitting, 7 were selected to spatially model the generalized extreme value (GEV) location parameter (for each gaging station’s contributing drainage basin, its outlet elevation, centroid x coordinate, centroid elevation, area, average basin width, elevation range, and median land surface slope). The three covariates selected for the GEV scale parameter included the area, average basin width, and median land surface slope. The GEV shape parameter was assumed to be constant throughout the entire study area. Comparisons of estimates obtained from the spatial covariate model with their corresponding “at-site” estimates resulted in computed values of 0.95, 0.95, 0.94 and 0.85, 0.84, 0.90 for the coefficient of determination, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency for the GEV location and scale parameters, respectively. Brown–Resnick MSP models were fit to independent multivariate events extracted from a set of common discharge data, transformed to unit Fréchet margins while considering different permutations of the non-Euclidean dependence model. Each of the fitted model’s log-likelihood values indicated improved fits when using hydrologic distance rather than Euclidean distance. They also demonstrated that accounting for flow-connected dependence and anisotropy further improved model fit. In this study, the results from both parts were illustrative; however, further research with larger datasets and more heterogeneous systems is recommended.

Evolution and drivers of secondary suspended rivers in typical wandering sections of the lower Yellow River from 1960–2021

The secondary suspended river increases the probability of the occurrence of “Heng river”, “Xie river” and “Gun river” in the lower Yellow River, and is the main factor threatening the safety of human life and property in the Yellow River levee and beach area. Here, the Dongbatou–Gaocun section of the severe secondary suspended river in the lower Yellow River was taken as the research object. The trend and periodicity of the evolution characteristics of the secondary suspended river in the study area from 1960–2021 were systematically analyzed using the Theil-Sen estimator and wavelet analysis, and the factors influencing its development were quantitatively explored. Over the past 62 years, development of secondary suspended rivers can be divided into four stages: initial formation (1960–1973), slow development (1974–1986), rapid development (1987–1999) and stable (2000–2021) periods. The evolution period of the beach transverse gradient differed significantly before and after operation of Xiaolangdi Reservoir commenced, prior to which there was a first main period of 31 years followed by a second main period of 21 years. Development of secondary suspended rivers was primarily related to floodplain flooding and human activity. When the inflow sediment coefficient of a floodplain flood (ζ) was &amp;lt; 0.04 and the floodplain coefficient was &amp;lt; 1.29, the secondary suspended river was relieved; when ζ &amp;gt; 0.04 or ζ &amp;lt; 0.04 and the floodplain coefficient was &amp;gt; 1.29, the secondary suspended river was intensified. The production levees have exacerbated development of secondary suspended rivers to some extent.

Heat Island dynamics and heat stress implications on the population of El-Mansourah conurbation, Egypt: temporal and proposed solutions using MODIS imagery and ENVI-met simulation

In Egypt, El-Mansourah Conurbation (EMC) is the second largest urban complex after Cairo Conurbation. This study focuses on identifying seasonal trends in land surface temperature during the daytime/nighttime in both urban and rural areas (RA), as well as the corresponding urban heat island (UHI) intensity 2001 to 2021. A time series of the Moderate Resolution Imaging Spectroradiometer (Terra MODIS) images was analyzed using the statistical methods of Mann-Kendall test (MKT) and Theil-Sen estimator (TSE). The findings showed that the diurnal cooling in ranged from −0.66 to −0.58 °C (p < .01) across all seasons. In contrast, the nocturnal UHI exhibited a warming trend in all seasons (except for spring) ranging from 0.08 to 0.16 °C (p > .1). Calculations of the Temperature-Humidity Index showed a decline in the population comfort over time, especially in the hot months, while there was a noticeable amelioration in the cool ones. To explore potential mitigation strategies, the simulation capabilities of ENVI-met were utilized. Based on a simple scenario, the ambient temperature of Salah Salem Street (SST) decreased by more than 2 °C. One suggestion put forth by the research is to implement mitigation measures to address the adverse consequences that naturally arise from the unavoidable dynamics of urban areas.

Coevolution of Stars and Gas: Using an Analysis of Synthetic Observations to Investigate the Star–Gas Correlation in STARFORGE

We explore the relation between stellar surface density and gas surface density (the star–gas, or S-G, correlation) in a 20,000 M ⊙ simulation from the STAR FORmation in Gaseous Environments (starforge) project. We create synthetic observations based on the Spitzer and Herschel telescopes by modeling contamination by active galactic nuclei, smoothing based on angular resolution, cropping the field of view, and removing close neighbors and low-mass sources. We extract S-G properties such as the dense gas-mass fraction, the Class II:I ratio, and the S-G correlation (ΣYSO/Σgas) from the simulation and compare them to observations of giant molecular clouds, young clusters, and star-forming regions, as well as to analytical models. We find that the simulation reproduces trends in the counts of young stellar objects and the median slope of the S-G correlation. This implies that the S-G correlation is not simply the result of observational biases, but is in fact a real effect. However, other statistics, such as the Class II:I ratio and dense gas-mass fraction, do not always match observed equivalents in nearby clouds. This motivates further observations covering the full simulation age range and more realistic modeling of cloud formation.

Detection and quantification of wave trends in the Mediterranean basin

A study of a 42-year (1979–2020) long wave time series was performed for the Mediterranean Sea to detect and quantify trends in two relevant wave parameters adopted for coastal and offshore engineering purposes: significant wave height, Hs and peak period, Tp. The high resolution MeteOcean Re-Analysis database by the Department of Civil, Chemical and Environmental Engineering (DICCA) was used. At yearly and seasonal scales, the trends of the mean and maximum values were detected by the Mann–Kendall test, choosing a significance level equal to 90%. The slope or increase/decrease trends in Hs and Tp were assessed by the Theil–Sen estimator. For the mean values of Hs and Tp, increasing trends were detected for the Libyan, Levantine and Aegean seas, while for the maximum values this increasing trend was observed in a large part of the Mediterranean Sea. Finally, for the different marginal seas of the Mediterranean basin, a running trend analysis was applied in order to quantify the effect of the time window in the trend detection. The obtained results can be significant for flooding and erosion control strategies, ship and port operations, design and verification of structures, installations of Wave Energy Converters, in a hot spot for climate change such as the Mediterranean basin.

Long-term variability analysis of wave energy resources and its impact on wave energy converters along the Chinese coastline

The long-term variability of wave energy resources will affect the practical decadal application of wave energy converters. This paper has evaluated the distributions and long-term trend of wave energy resources in the sea regions along the Chinese coastline for 83 years based on reliable ERA5 wave reanalysis data from 1940 to 2022 after verifying by 39 local buoys, and found that it is of great potential to develop and apply wave energy resources projects. The long-term trend analysis of the wave energy density and its major contributing wave parameters, significant wave height, and wave energy period, is carried out using Theil-Sen estimation showing the general positive tendency. And the impacts of the decadal variation on four wave energy converters are evaluated via computing the decadal mean power production by combining the wave energy density and the wave energy characteristic. It is verified that the long-term wave energy density in this region increases along with the decrease in power production and the capture width. This study shows that long-term variability of wave energy resources and wave energy characteristics should also be considered in the designing of WECs and the identification of energy hotspots.

Analysis of Changes in Rainfall Concentration over East Africa

Understanding the spatial and temporal distribution of precipitation is important in agriculture, water management resources, and flood disaster management. The present study analyzed the changes in rainfall concentration over East Africa (EA). Three matrices—the precipitation concentration index (PCI), the precipitation concentration degree (PCD), and the precipitation concentration period (PCP)—were used to examine the changes in rainfall during 1981–2021. The changes in spatial variance annually and during two seasons, namely, “long rains” (March to May [MAM]) and “short rain” (October to December [OND]), were estimated using an empirical orthogonal function (EOF). The study employed the robust statistical metrics of the Theil–Sen estimator to detect the magnitude of change and modified Mann–Kendall (MMK) to examine possible changes in rainfall concentration. The localized variation of the power series within the series for PCI, PCD, and PCP variability was performed using the continuous wavelet transform. The findings showed that the concentration of rainfall patterns of EA occurred in four months of the total months in a year over most parts, with the western sides experiencing uniform rainfall events throughout the year. The EOF analysis revealed a homogeneous negative pattern during the MAM season over the whole region for PCD, PCI, and PCP for the first mode, which signified reduced rainfall events. Moreover, the MMK analysis showed evidence of declining trends in the PCD annually and during the MAM season, while the opposite tendency was noted for the OND season where an upward trend in the PCD was observed. Interestingly, areas adjacent to Lake Victoria in Uganda and Lake Tanganyika in Tanzania showed increasing trends in the PCD for annual and seasonal time scales. The analysis to characterize the rainfall cycle and possible return period, considering the indices of PCD, PCI, and PCP, showed higher variability during the year 2000, while much variability was presented in the PCP for the annual period. During the MAM and OND seasons, a 1-year band as a dominant period of variability was observed in all the indices. Overall, the findings of the present study are crucial in detecting the observed changes in rainfall concentration for avoiding the loss of life and property, as well as for coping with potential changes in water resources.

Soil erosion due to rainfall and the impacts of climate change in an Andean highland in Colombia

Trends and median slope of daily rainfall that can affect rainfall aggressiveness and cause erosion in the Bogotá - Duitama corridor were studied. For this, the daily records of 26 stations (35 years, from 1980 to 2014) were evaluated, using the Sen's statistic and the Mann-Kendall test with confidence levels higher than 90%. The studied area covered about 8,100 km2, located between 2,100 and 3,300 m a.s.l. in the Colombian Andes. Four stations with positive trends in median annual rainfall were found (from 6.90 mm/year to 28.80 mm/year) and one station with a decrease in median rainfall of -6.86 mm/year. In order to analyze the pluvial aggressiveness as the main agent of soil erosion, the Modified Fournier Indices (MFI) were generated for periods of 10 days. With the maximum decadal Modified Fournier Indices (MFIdmax) of each year, it was possible to establish the median positive trend (Sen) of rainfall aggressiveness in five stations and three stations with negative trends. Through the correlation between the degree of erosion with the square of the decadal average maximum values of each year (MFIdmax2) and the negative annual precipitation, a coefficient of determination (R2) greater than 0.50 was found. The validation of MFIdmax2 to explain the degree of soil erosion is a new useful methodology for land use planning and monitoring. In this way, developing countries have the possibility of using a tool to face the processes of pluvial erosion, vulnerability and adaptation to climate change.

Two-stage prediction technique for rolling bearings based on adaptive prediction model

Due to the discrepancy of the components themselves, operating conditions and loads, the bearings show different degradation processes, and a single fixed model cannot describe different degradation processes accurately. In order to solve the adaptive prediction model for component degradation, a hybrid prediction technique is proposed in this paper, firstly, the Theil-sen estimator (TSE) is proposed as the health indicator of bearing operation, and it has been validated that the TSE has good trendability, monotonicity, and robustness. Based on the TSE, in order to identify the time to start prediction (TSP) accurately, the idea of deviation degree is proposed to reflect the difference between local and global degradation trend. Then, the RUL prediction is performed by matching the Wiener model suitable for the prediction task through the model adaptive algorithm. Finally, the effectiveness of the proposed method is verified by XJTU-SY bearing dataset and bearing accelerated degradation experiments.