Estimations of Weibull-Geometric Distribution under Progressive Type II Censoring Samples

This paper deals with the Bayesian inferences of unknown parameters of the progressively Type II censored Weibull-geometric (WG) distribution. The Bayes estimators cannot be obtained in explicit forms of the unknown parameters under a squared error loss function. The approximate Bayes estimators will be computed using the idea of Markov Chain Monte Carlo (MCMC) method to generate from the posterior distributions. Also the point estimation and confidence intervals based on maximum likelihood and bootstrap technique are also proposed. The approximate Bayes estimators will be obtained under the assumptions of informative and non-informative priors are compared with the maximum likelihood estimators. A numerical example is provided to illustrate the proposed estimation methods here. Maximum likelihood, bootstrap and the different Bayes estimates are compared via a Monte Carlo Simulation

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

Coseismic deformation and fault slip distribution of the 2023 M_(W)7.8 and M_(W)7.6 earthquakes in Türkiye

On February 6,2023,a devastating earthquake with a moment magnitude of M_(W)7.8 struck the town of Pazarcik in south-central Türkiye,followed by another powerful earthquake with a moment magnitude of M_(W)7.6 that struck the nearby city of Elbistan 9 h later.To study the characteristics of surface deformation caused by this event and the influence of fault rupture,this study calculated the static coseismic deformation of 56 stations and dynamic displacement waveforms of 15 stations using data from the Turkish national fixed global navigation satellite system(GNSS)network.A maximum static coseismic displacement of 0.38 m for the M_(W)7.8 Kahramanmaras earthquake was observed at station ANTE,36 km from the epicenter,and a maximum dynamic coseismic displacement of 4.4 m for the M_(W)7.6 Elbistan earthquake was observed at station EKZ1,5 km from the epicenter.The rupture-slip distributions of the two earthquakes were inverted using GNSS coseismic deformation as a constraint.The results showed that the Kahramanmaras earthquake rupture segment was distinct and exposed on the ground,resulting in significant rupture slip along the Amanos and Pazarcik fault segments of the East Anatolian Fault.The maximum slip in the Pazarcik fault segment was 10.7 m,and rupture occurred at depths of 0–15 km.In the Cardak fault region,the Elbistan earthquake caused significant ruptures at depths of 0–12 km,with the largest amount of slip reaching 11.6 m.The Coulomb stress change caused by the Kahramanmaras earthquake rupture along the Cardak fault segment was approximately 2 bars,and the area of increased Coulomb stress corresponded to the subsequent rupture region of the M_(W)7.6 earthquake.Thus,it is likely that the M_(W)7.8 earthquake triggered or promoted the M_(W)7.6 earthquake.Based on the cumulative stress impact of the M_(W)7.8 and M_(W)7.6 events,the southwestern segment of the East Anatolian Fault,specifically the Amanos fault segment,experienced a Coulomb rupture stress change exceeding 2 bars,warranting further attention to assess its future seismic hazard risk.

Extended wet sieving method for determination of complete particle size distribution of general soils

The traditional standard wet sieving method uses steel sieves with aperture?0.063 mm and can only determine the particle size distribution(PSD)of gravel and sand in general soil.This paper extends the traditional method and presents an extended wet sieving method.The extended method uses both the steel sieves and the nylon filter cloth sieves.The apertures of the cloth sieves are smaller than 0.063 mm and equal 0.048 mm,0.038 mm,0.014 mm,0.012 mm,0.0063 mm,0.004 mm,0.003 mm,0.002 mm,and 0.001 mm,respectively.The extended method uses five steps to separate the general soil into many material sub-groups of gravel,sand,silt and clay with known particle size ranges.The complete PSD of the general soil is then calculated from the dry masses of the individual material sub-groups.The extended method is demonstrated with a general soil of completely decomposed granite(CDG)in Hong Kong,China.The silt and clay materials with different particle size ranges are further examined,checked and verified using stereomicroscopic observation,physical and chemical property tests.The results further confirm the correctness of the extended wet sieving method.

Integrative biogeography: Validating hypotheses of species distribution

DEAR EDITOR,Biogeography is a scientific field dedicated to the investigation of the origins and distribution patterns of organisms,as well as predicting future alterations in their geographical distributions(Cox&Moore,2005).However,the majority of conclusions drawn within the field of biogeography are hypothetical.Rigorous testing of these biogeographic hypotheses remains a considerable challenge.This paper presents the concept of“integrative biogeography”,which emphasizes the experimental testing of biogeographic hypotheses through studies on geological history,as well as biotic and abiotic factors(Figure 1).

Influence of Topography on the Distribution and Structure of Woody Plants in the Senegalese Sahel (Sandy Ferlo)

This study describes the floristic composition and structure of a woody stand in the Senegalese Sahel, paying particular attention to the edaphic factors of its floristic composition. A stratified inventory considering the different relief units was adopted. Woody vegetation was surveyed using a dendrometric approach. The results obtained show that the flora is dominated by a few species adapted to drought, such as Balanites aegyptiaca (L.) Del., Calotropis procera Ait. and Boscia senegalensis (Pers.). The distribution of this flora and the structure of the ligneous plants are linked to the topography. In the lowlands, the flora is more diversified and the ligneous plants reach their optimum level of development compared with the higher relief areas. In the lowlands, there are a few woody species which, in the past, were indicative of better climatic conditions. These are Anogeissus leiocarpus (DC.), Commiphora africana (A. Rich.), Feretia apodanthera Del., Loeseneriella africana (A. Smith), Mitragyna inermis (Willd.) and Sclerocarya birrea (A. Rich). It is important that their reintroduction into reforestation projects takes account of their edaphic preference.

Neural stem cell-derived exosomes promote mitochondrial biogenesis and restore abnormal protein distribution in a mouse model of Alzheimer's disease

Mitochondrial dysfunction is a hallmark of Alzheimer’s disease.We previously showed that neural stem cell-derived extracellular vesicles improved mitochondrial function in the cortex of AP P/PS1 mice.Because Alzheimer’s disease affects the entire brain,further research is needed to elucidate alterations in mitochondrial metabolism in the brain as a whole.Here,we investigated the expression of several important mitochondrial biogenesis-related cytokines in multiple brain regions after treatment with neural stem cell-derived exosomes and used a combination of whole brain clearing,immunostaining,and lightsheet imaging to clarify their spatial distribution.Additionally,to clarify whether the sirtuin 1(SIRT1)-related pathway plays a regulatory role in neural stem cell-de rived exosomes interfering with mitochondrial functional changes,we generated a novel nervous system-SIRT1 conditional knoc kout AP P/PS1mouse model.Our findings demonstrate that neural stem cell-de rived exosomes significantly increase SIRT1 levels,enhance the production of mitochondrial biogenesis-related fa ctors,and inhibit astrocyte activation,but do not suppress amyloid-βproduction.Thus,neural stem cell-derived exosomes may be a useful therapeutic strategy for Alzheimer’s disease that activates the SIRT1-PGC1αsignaling pathway and increases NRF1 and COXIV synthesis to improve mitochondrial biogenesis.In addition,we showed that the spatial distribution of mitochondrial biogenesis-related factors is disrupted in Alzheimer’s disease,and that neural stem cell-derived exosome treatment can reverse this effect,indicating that neural stem cell-derived exosomes promote mitochondrial biogenesis.

Diversity of Orchids in Terms of Their Distribution, Uses and Conservation in Annapurna Conservation Area of Nepal

The Annapurna Conservation Area (ACA), the first conservation area and the largest protected area (PA) in Nepal, is incredibly rich in biodiversity. Notwithstanding this, orchids in the ACA have not been explored enough yet thus making the need for ambitious research to be carried out. Previous study only included 81 species of orchids within ACA. This study aims to update the record of species and genera richness in the ACA. In total 198 species of orchids, belonging to 67 genera (40% and 62% of the total recorded orchid species and genera in Nepal) has been recorded in ACA. This represents an increase of 144% in species and 56% in genera over the previous data. Out of the 198 species, 99 were epiphytes, 6 were holomycotrophic and 93 were terrestrial. Among the 67 genera, Bulbophyllum (17) species were dominant, followed by Dendrobium (16), Herminium (10), Coelogyne, Plantanthera (9 each), Eria, Habenaria, Oberonia (8 each), Calanthe (7), and Liparis (6). Fifty-six species were found to be ornamentally significant and 85 species medicinally significant.

Boosting Adversarial Training with Learnable Distribution

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.However,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training.This paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture model.The distribution centroid is built to classify samples and constrain the distribution of the sample features.The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model.The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training.This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation.Finally,the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid.The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense.The effectiveness of the proposed method is demonstrated through comprehensive experiments.

Effect of distribution of fines on evolution of cooperation in spatial public goods game

In the realm of public goods game,punishment,as a potent tool,stands out for fostering cooperation.While it effectively addresses the first-order free-rider problem,the associated costs can be substantial.Punishers incur expenses in imposing sanctions,while defectors face fines.Unfortunately,these monetary elements seemingly vanish into thin air,representing a loss to the system itself.However,by virtue of the redistribution of fines to cooperators and punishers,not only can we mitigate this loss,but the rewards for these cooperative individuals can be enhanced.Based upon this premise,this paper introduces a fine distribution mechanism to the traditional pool punishment model.Under identical parameter settings,by conducting a comparative experiment with the conventional punishment model,the paper aims to investigate the impact of fine distribution on the evolution of cooperation in spatial public goods game.The experimental results clearly demonstrate that,in instances where the punishment cost is prohibitively high,the cooperative strategies of the traditional pool punishment model may completely collapse.However,the model enriched with fine distribution manages to sustain a considerable number of cooperative strategies,thus highlighting its effectiveness in promoting and preserving cooperation,even in the face of substantial punishment cost.

Simulation of Haloxylon Ammodendron Stand Basic Diameter Structure Based on Fuzzy Distribution Function

Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R2 values showed that Fuzzy-Γ1, Fuzzy-Γ2, Fuzzy-Γ3, Fuzzy-Γ4 had good fitting performance, among which Fuzzy-Γ1 had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ2 distribution function was the second, and Fuzzy-Γ4 distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ5 is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ1 and Fuzzy-Γ2.

Approximating the Radial Distribution Function of the Electron in a Hydrogen Atom by a Normal Distribution Suggests That Magnetic Confinement Fusion Would Be Less Energy Efficient than Inertial Confinement Fusion

Since the position of the electron in a hydrogen atom cannot be determined, the region in which it resides is said to be determined stochastically and forms an electron cloud. The probability density function of the single electron in 1s orbit is expressed as φ2, a function of distance from the nucleus. However, the probability of existence of the electron is expressed as a radial distribution function at an arbitrary distance from the nucleus, so it is estimated as the probability of the entire spherical shape of that radius. In this study, it has been found that the electron existence probability approximates the radial distribution function by assuming that the probability of existence of the electron being in the vicinity of the nucleus follows a normal distribution for arbitrary x-, y-, and z-axis directions. This implies that the probability of existence of the electron, which has been known only from the distance information, would follow a normal distribution independently in the three directions. When the electrons’ motion is extremely restricted in a certain direction by the magnetic field of both tokamak and helical fusion reactors, the probability of existence of the electron increases with proximity to the nucleus, and as a result, it is less likely to be liberated from the nucleus. Therefore, more and more energy is required to free the nucleus from the electron in order to generate plasma.

An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

During faults in a distribution network,the output power of a distributed generation(DG)may be uncertain.Moreover,the output currents of distributed power sources are also affected by the output power,resulting in uncertainties in the calculation of the short-circuit current at the time of a fault.Additionally,the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources.Thus,it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network.In this study,an affine arithmetic algorithm for calculating short-circuit current intervals in distribution networks with distributed power sources while considering power fluctuations is presented.The proposed algorithm includes two stages.In the first stage,normal operations are considered to establish a conservative interval affine optimization model of injection currents in distributed power sources.Constrained by the fluctuation range of distributed generation power at the moment of fault occurrence,the model can then be used to solve for the fluctuation range of injected current amplitudes in distributed power sources.The second stage is implemented after a malfunction occurs.In this stage,an affine optimization model is first established.This model is developed to characterizes the short-circuit current interval of a transmission line,and is constrained by the fluctuation range of the injected current amplitude of DG during normal operations.Finally,the range of the short-circuit current amplitudes of distribution network lines after a short-circuit fault occurs is predicted.The algorithm proposed in this article obtains an interval range containing accurate results through interval operation.Compared with traditional point value calculation methods,interval calculation methods can provide more reliable analysis and calculation results.The range of short-circuit current amplitude obtained by this algorithm is slightly larger than those obtained using the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Therefore,the proposed algorithm has good suitability and does not require iterative calculations,resulting in a significant improvement in computational speed compared to the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Furthermore,the proposed algorithm can provide more reliable analysis and calculation results,improving the safety and stability of power systems.

Security analysis of satellite-to-ground reference-frame-independent quantum key distribution with beam wandering

The reference-frame-independent(RFI)quantum key distribution(QKD)is suitable for satellite-based links by removing the active alignment on the reference frames.However,how the beam wandering influences the performance of RFI-QKD remains a pending issue in satellite-to-ground links.In this paper,based on the mathematical model for characterizing beam wandering,we present the security analysis for satellite-to-ground RFI-QKD and analytically derive formulas for calculating the secret key rate with beam wandering.Our simulation results show that the performance of RFI-QKD is better than the Bennett–Brassard 1984(BB84)QKD with beam wandering in asymptotic case.Furthermore,the degree of influences of beam wandering is specifically presented for satellite-to-ground RFI-QKD when statistical fluctuations are taken into account.Our work can provide theoretical support for the realization of RFI-QKD using satellite-to-ground links and have implications for the construction of large-scale satellite-based quantum networks.

Evaluation of Multi-Temporal-Spatial Scale Adjustment Capability and Cluster Optimization Operation Method for Distribution Networks with Distributed Photovoltaics

Themassive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations.To accurately assess the flexible regulation capabilities of distribution stations,amulti-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics,considering different geographical locations,coverage areas,and response capabilities.Firstly,the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed,and a resource regulation capability model is established to quantify the adjustable range of different regulation resources.On this basis,considering the limitations of line transmission capacity,a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities.Secondly,considering different geographical locations and coverage areas,a comprehensive performance index based on electrical distance modularity and active power balance is established,and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters.Simultaneously,an economic optimization model with the objective of minimizing the economic cost of the distribution station is established,comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources.This model can provide scientific guidance for the economic dispatch of the distribution station area.Finally,case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations,facilitate the consumption of distributed photovoltaics,and enhance the economic efficiency of the distribution station area.

Heterogeneous population distribution enhances resistance to wheat lodging by optimizing the light environment

Lodging is still the key factor that limits continuous increases in wheat yields today,because the mechanical strength of culms is reduced due to low-light stress in populations under high-yield cultivation.The mechanical properties of the culm are mainly determined by lignin,which is affected by the light environment.However,little is known about whether the light environment can be sufficiently improved by changing the population distribution to inhibit culm lodging.Therefore,in this study,we used the wheat cultivar“Xinong 979”to establish a low-density homogeneous distribution treatment(LD),high-density homogeneous distribution treatment(HD),and high-density heterogeneous distribution treatment(HD-h)to study the regulatory effects and mechanism responsible for differences in the lodging resistance of wheat culms under different population distributions.Compared with LD,HD significantly reduced the light transmittance in the middle and basal layers of the canopy,the net photosynthetic rate in the middle and lower leaves of plants,the accumulation of lignin in the culm,and the breaking resistance of the culm,and thus the lodging index values increased significantly,with lodging rates of 67.5%in 2020–2021 and 59.3%in 2021–2022.Under HD-h,the light transmittance and other indicators in the middle and basal canopy layers were significantly higher than those under HD,and the lodging index decreased to the point that no lodging occurred.Compared with LD,the activities of phenylalanine ammonia-Lyase(PAL),4-coumarate:coenzyme A ligase(4CL),catechol-O-methyltransferase(COMT),and cinnamyl-alcohol dehydrogenase(CAD)in the lignin synthesis pathway were significantly reduced in the culms under HD during the critical period for culm formation,and the relative expression levels of TaPAL,Ta4CL,TaCOMT,and TaCAD were significantly downregulated.However,the activities of lignin synthesis-related enzymes and their gene expression levels were significantly increased under HD-h compared with HD.A partial least squares path modeling analysis found significant positive effects between the canopy light environment,the photosynthetic capacity of the middle and lower leaves of plants,lignin synthesis and accumulation,and lodging resistance in the culms.Thus,under conventional high-density planting,the risk of wheat lodging was significantly higher.Accordingly,the canopy light environment can be optimized by changing the heterogeneity of the population distribution to improve the photosynthetic capacity of the middle and lower leaves of plants,promote lignin accumulation in the culm,and enhance lodging resistance in wheat.These findings provide a basis for understanding the mechanism responsible for the lower mechanical strength of the culm under high-yield wheat cultivation,and a theoretical basis and for developing technical measures to enhance lodging resistance.

Salinity fronts shape spatial patterns in zooplankton distribution in Hangzhou Bay

Ocean fronts play important roles in nutrient transport and in the shaping ecological patterns.Frontal zones in small bays are typically small in scale,have a complex structure,and they are spatially and temporally variable,but there are limited data on how biological communities respond to this variation.Hangzhou Bay,a mediumsized estuary in China,is an ideal place in which to study the response of plankton to small-scale ocean fronts,because three water masses(Qiantang River Diluted Water,Changjiang River Diluted Water,and the East China Sea current) converge here and form dynamic salinity fronts throughout the year.We investigate zooplankton communities,and temperature,salinity and chlorophyll a(Chl a) in Hangzhou Bay in June(wet perio d) and December(dry period) of 2022 and examine the dominant environmental factors that affect zooplankton community spatial variability.We then match the spatial distributions of zooplankton communities with those of salinity fronts.S alinity is the most important explanatory variable to affect zooplankton community spatial variability during both wet and dry periods,in that it contributes>60% of the variability in community structure.Furthermore,the spatial distributions of zooplankton match well with salinity fronts.During December,with weaker Qiantang River Diluted Water and a stronger secondary Changjiang River Plume,zooplankton communities occur in moderate salinity(MS,salinity range 15.6±2.2) and high salinity(HS,22.4±1.7) regions,and their ecological boundaries closely match the Qiantang River Diluted Water front.In June,different zooplankton communities occur in low salinity(LS,3.9±1.0),MS(11.7±3.6) and HS(21.3±1.9) regions.Although the LS region occurs abnormally in the central bay rather than its apex because of the anomalous influence of rising and falling tides during the sampling perio d,the ecological boundaries still match salinity interfaces.Low-salinity or brackish-water zooplankter taxa are relatively more abundant in LS or MS regions,and the biomass and abundance of zooplankton is higher in the MS region.

Red cell distribution width/platelet ratio estimates the 3-year risk of decompensation in Metabolic Dysfunction-Associated Steatotic Liver Disease-induced cirrhosis

BACKGROUND For compensated advanced chronic liver disease(cACLD)patients,the first decompensation represents a dramatically worsening prognostic event.Based on the first decompensation event(DE),the transition to decompensated advanced chronic liver disease(dACLD)can occur through two modalities referred to as acute decompensation(AD)and non-AD(NAD),respectively.Clinically Significant Portal Hypertension(CSPH)is considered the strongest predictor of decompensation in these patients.However,due to its invasiveness and costs,CSPH is almost never evaluated in clinical practice.Therefore,recognizing noninvasively predicting tools still have more appeal across healthcare systems.The red cell distribution width to platelet ratio(RPR)has been reported to be an indicator of hepatic fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease(MASLD).However,its predictive role for the decompensation has never been explored.AIM In this observational study,we investigated the clinical usage of RPR in predicting DEs in MASLD-related cACLD patients.METHODS Fourty controls and 150 MASLD-cACLD patients were consecutively enrolled and followed up(FUP)semiannually for 3 years.At baseline,biochemical,clinical,and Liver Stiffness Measurement(LSM),Child-Pugh(CP),Model for End-Stage Liver Disease(MELD),aspartate aminotransferase/platelet count ratio index(APRI),Fibrosis-4(FIB-4),Albumin-Bilirubin(ALBI),ALBI-FIB-4,and RPR were collected.During FUP,DEs(timing and modaities)were recorded.CSPH was assessed at the baseline and on DE occurrence according to the available Clinical Practice Guidelines.RESULTS Of 150 MASLD-related cACLD patients,43(28.6%)progressed to dACLD at a median time of 28.9 months(29 NAD and 14 AD).Baseline RPR values were significantly higher in cACLD in comparison to controls,as well as MELD,CP,APRI,FIB-4,ALBI,ALBI-FIB-4,and LSM in dACLD-progressing compared to cACLD individuals[all P<0.0001,except for FIB-4(P:0.007)and ALBI(P:0.011)].Receiving operator curve analysis revealed RPR>0.472 and>0.894 as the best cut-offs in the prediction respectively of 3-year first DE,as well as its superiority compared to the other non-invasive tools examined.RPR(P:0.02)and the presence of baseline-CSPH(P:0.04)were significantly and independently associated with the DE.Patients presenting baseline-CSPH and RPR>0.472 showed higher risk of decompensation(P:0.0023).CONCLUSION Altogether these findings suggest the RPR as a valid and potentially applicable non-invasive tool in the prediction of timing and modalities of decompensation in MASLD-related cACLD patients.

Prevalence and ecological factors affecting the distribution of plant-parasitic nematodes in Prunus groves in Spain

A wide survey was conducted to study plant-parasitic nematodes(PPNs)associated with Prunus groves in Spain.This research aimed to determine the prevalence and distribution of PPNs in Prunus groves,as well as the influence of explanatory variables describing soil,climate and agricultural management in structuring the variation of PPNs community composition.A total of 218 sampling sites were surveyed and 84 PPN species belonging to 32 genera were identified based of an integrative taxonomic approach.PPN species considered as potential limiting factors in Prunus production,such as Meloidogyne arenaria,M.incognita,M.javanica,Pratylenchus penetrans and P.vulnus,were identified in this survey.Seven soil physico-chemical(C,Mg,N,Na,OM,P,pH and clay,loamy sand and sandy loam texture classes),four climate(Bio04,Bio05,Bio13 and Bio14)and four agricultural management variables(grove-use history less than 10 years,irrigation,apricot seedling rootstock,and Montclar rootstock)were identified as the most influential variables driving spatial patterns of PPNs communities.In particular,younger plantations showed higher values for species richness and diversity indices than groves cultivated for more than 20 years with Prunus spp.Our study increases the knowledge of the distribution and prevalence of PPNs associated with Prunus rhizosphere,as well as on the influence of explanatory variables driving the spatial structure PPNs communities,which has important implications for the successful design of sustainable management strategies in the future in this agricultural system.

Tuning mechanical behaviors of highly entangled hydrogels with the random distribution of mobile entanglements

Highly entangled hydrogels exhibit excellent mechanical properties,including high toughness,high stretchability,and low hysteresis.By considering the evolution of randomly distributed entanglements within the polymer network upon mechanical stretches,we develop a constitutive theory to describe the large stretch behaviors of these hydrogels.In the theory,we utilize a representative volume element(RVE)in the shape of a cube,within which there exists an averaged chain segment along each edge and a mobile entanglement at each corner.By employing an explicit method,we decouple the elasticity of the hydrogels from the sliding motion of their entanglements,and derive the stress-stretch relations for these hydrogels.The present theoretical analysis is in agreement with experiment,and highlights the significant influence of the entanglement distribution within the hydrogels on their elasticity.We also implement the present developed constitutive theory into a commercial finite element software,and the subsequent simulations demonstrate that the exact distribution of entanglements strongly affects the mechanical behaviors of the structures of these hydrogels.Overall,the present theory provides valuable insights into the deformation mechanism of highly entangled hydrogels,and can aid in the design of these hydrogels with enhanced performance.