TLA与聚酯纤维复合改性热再生沥青混合料路用性能研究

为了改善热再生沥青混合料的路用性能及耐久性,首先采用TLA与聚酯纤维进行复配设计,然后基于马歇尔、高温车辙、低温弯曲、浸水马歇尔、冻融劈裂、两点弯曲与浸水APA等试验,针对TLA与聚酯纤维复合改性热再生沥青混合料的高温稳定性、低温抗裂性、水稳定性、抗疲劳耐久性能展开综合考察。结果表明:单掺TLA或聚酯纤维均能改善热再生沥青混合料的高温稳定性能、低温抗裂性能及水稳定性,且TLA的各项性能改善效果相对较为明显;经复掺TLA与聚酯纤维改性后热再生沥青混合料的路用性能均得到显著的提升,可适用于我国北方严寒或南方高温湿热多雨等复杂气候地区的沥青路面建设之中;TLA能够有效提高热再生沥青混合料的劲度模量与疲劳寿命,合理复掺聚酯纤维后提升效果更为明显,均符合高模量沥青混合料的规定及要求;采用合理比例的TLA与聚酯纤维进行复掺,可综合提升热再生沥青混合料的路用性能及耐久性。

Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

Layer chicken microbiota:a comprehensive analysis of spatial and temporal dynamics across all major gut sections

Background The gut microbiota influences chicken health,welfare,and productivity.A diverse and balanced microbiota has been associated with improved growth,efficient feed utilisation,a well-developed immune system,disease resistance,and stress tolerance in chickens.Previous studies on chicken gut microbiota have predominantly focused on broiler chickens and have usually been limited to one or two sections of the digestive system,under con-trolled research environments,and often sampled at a single time point.To extend these studies,this investigation examined the microbiota of commercially raised layer chickens across all major gut sections of the digestive system and with regular sampling from rearing to the end of production at 80 weeks.The aim was to build a detailed picture of microbiota development across the entire digestive system of layer chickens and study spatial and temporal dynamics.Results The taxonomic composition of gut microbiota differed significantly between birds in the rearing and pro-duction stages,indicating a shift after laying onset.Similar microbiota compositions were observed between proven-triculus and gizzard,as well as between jejunum and ileum,likely due to their anatomical proximity.Lactobacil-lus dominated the upper gut in pullets and the lower gut in older birds.The oesophagus had a high proportion of Proteobacteria,including opportunistic pathogens such as Gallibacterium.Relative abundance of Gallibacterium increased after peak production in multiple gut sections.Aeriscardovia was enriched in the late-lay phase compared to younger birds in multiple gut sections.Age influenced microbial richness and diversity in different organs.The upper gut showed decreased diversity over time,possibly influenced by dietary changes,while the lower gut,specifi-cally cecum and colon,displayed increased richness as birds matured.However,age-related changes were inconsist-ent across all organs,suggesting the influence of organ-specific factors in microbiota maturation.Conclusion Addressing a gap in previous research,this study explored the microbiota across all major gut sections and tracked their dynamics from rearing to the end of the production cycle in commercially raised layer chickens.This study provides a comprehensive understanding of microbiota structure and development which help to develop targeted strategies to optimise gut health and overall productivity in poultry production.

Enhancing quantum temporal steering via frequency modulation

Various strategies have been proposed to harness and protect space-like quantum correlations in different models under decoherence.However,little attention has been given to temporal-like correlations,such as quantum temporal steering(TS),in this context.In this work,we investigate TS in a frequency-modulated two-level system coupled to a zero-temperature reservoir in both the weak and strong coupling regimes.We analyze the impact of various frequency-modulated parameters on the behavior of TS and non-Markovian.The results demonstrate that appropriate frequency-modulated parameters can enhance the TS of the two-level system,regardless of whether the system is experiencing Markovian or non-Markovian dynamics.Furthermore,a suitable ratio between modulation strength and frequency(i.e.,all zeroes of the 0th Bessel function J_(0)(δ/?))can significantly enhance TS in the strong coupling regime.These findings indicate that efficient and effective manipulation of quantum TS can be achieved through a frequency-modulated approach.

Cognitive Disorders, Depression and Anxiety in Temporal Lobe Epilepsy: An Overview

Partial epilepsies, originating in a specific brain region, affect about 60% of adults with epilepsy. Temporal lobe epilepsy (TLE) is the most prevalent subtype within this category, often necessitating surgical intervention due to its refractoriness to antiepileptic drugs (AEDs). Hippocampal sclerosis, a common underlying pathology, often exacerbates the severity by introducing cognitive and emotional challenges. This review delves deeper into the cognitive profile of TLE, along with the risk factors for cognitive disorders, depression, and anxiety in this population.

Effect of gas components on the postdischarge temporal behavior of OH and O of a non-equilibrium atmospheric pressure plasma driven by nanosecond voltage pulses

OH radicals and O atoms are two of the most important reactive species of non-equilibrium atmospheric pressure plasma(NAPP),which plays an important role in applications such as plasma medicine.However,experimental studies on how the gas content affects the postdischarge temporal evolutions of OH and O in the noble gas ns-NAPP are very limited.In this work,the effect of the percentages of O_(2),N_(2),and H_(2)O on the amounts of OH and O productions and their post-discharge temporal behaviors in ns-NAPP is investigated by laser-induced fluorescence(LIF)method.The results show that the productions of OH and O increase and then decrease with the increase of O_(2)percentage.Both OH and O densities reach their maximum when about 0.8%O_(2)is added.Further increase of the O_(2)concentration results in a decrease of the initial densities of both OH and O,and leads to their faster decay.The increase of N_(2)percentage also results in the increase and then decrease of the OH and O densities,but the change is smaller.Furthermore,when the H_(2)O concentration is increased from 100 to 3000 ppm,the initial OH density increases slightly,but the OH density decays much faster,while the initial density of O decreases with the increase of the H_(2)O concentration.After analysis,it is found that OH and O are mainly produced through electron collisional dissociation.O(^(1)D)is critical for OH generation.O_(3)accelerates the consumption processes of OH and O at high O_(2)percentage.The addition of H_(2)O in the NAPP considerably enhances the electronegativity,while it decreases the overall plasma reactivity,accelerates the decay of OH,and reduces the O atom density.

TSCND:Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.

Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions

Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation.

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.

Multi-Stream Temporally Enhanced Network for Video Salient Object Detection

Video salient object detection(VSOD)aims at locating the most attractive objects in a video by exploring the spatial and temporal features.VSOD poses a challenging task in computer vision,as it involves processing complex spatial data that is also influenced by temporal dynamics.Despite the progress made in existing VSOD models,they still struggle in scenes of great background diversity within and between frames.Additionally,they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term duration.We propose a multi-stream temporal enhanced network(MSTENet)to address these problems.It investigates saliency cues collaboration in the spatial domain with a multi-stream structure to deal with the great background diversity challenge.A straightforward,yet efficient approach for temporal feature extraction is developed to avoid the accumulative noises and reduce time consumption.The distinction between MSTENet and other VSOD methods stems from its incorporation of both foreground supervision and background supervision,facilitating enhanced extraction of collaborative saliency cues.Another notable differentiation is the innovative integration of spatial and temporal features,wherein the temporal module is integrated into the multi-stream structure,enabling comprehensive spatial-temporal interactions within an end-to-end framework.Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on five benchmark datasets while maintaining a real-time speed of 27 fps(Titan XP).Our code and models are available at https://github.com/RuJiaLe/MSTENet.

Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

Deep reinforcement learning using least-squares truncated temporal-difference

Policy evaluation(PE)is a critical sub-problem in reinforcement learning,which estimates the value function for a given policy and can be used for policy improvement.However,there still exist some limitations in current PE methods,such as low sample efficiency and local convergence,especially on complex tasks.In this study,a novel PE algorithm called Least-Squares Truncated Temporal-Difference learning(LST2D)is proposed.In LST2D,an adaptive truncation mechanism is designed,which effectively takes advantage of the fast convergence property of Least-Squares Temporal Difference learning and the asymptotic convergence property of Temporal Difference learning(TD).Then,two feature pre-training methods are utilised to improve the approximation ability of LST2D.Furthermore,an Actor-Critic algorithm based on LST2D and pre-trained feature representations(ACLPF)is proposed,where LST2D is integrated into the critic network to improve learning-prediction efficiency.Comprehensive simulation studies were conducted on four robotic tasks,and the corresponding results illustrate the effectiveness of LST2D.The proposed ACLPF algorithm outperformed DQN,ACER and PPO in terms of sample efficiency and stability,which demonstrated that LST2D can be applied to online learning control problems by incorporating it into the actor-critic architecture.

The Spatiotemporal Distribution Characteristics of Cloud Types and Phases in the Arctic Based on CloudSat and CALIPSO Cloud Classification Products

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.

Discrete Choice Analysis of Temporal Factors on Social Network Growth

Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.

Temporal lobe malacia as a rare cause of gelastic seizure:A case report

BACKGROUND Gelastic seizure(GS)is a rare type of epilepsy that most commonly appears in patients with hypothalamic hamartoma.It is rarely associated with other types of brain damage.This particular type of epilepsy is relatively rare and has few links to other brain lesions.Temporal lobe malacia is mostly caused by cerebral infarction or cerebral hemorrhage,which can lead to seizures.We report a case of GS in a woman with temporal lobe malacia which was reported for the first time in the literature.CASE SUMMARY A 73-year-old female,diagnosed case of GS,presented with repetitive stereotyped laughter a month prior to presentation,happening multiple times daily and with each time lasting for 5-15s.Electroencephalogram displayed a focal seizure seen in the right temporal region.Magnetic resonance imaging head with contrast showed a right temporal lobe malacia.The patient was started on levetiracetam daily.The patient indicated that they had fully recovered and were not experiencing any recurrent or stereotyped laughter during their daily routines.These results remained consistent even after a one-year follow-up period.CONCLUSION GS can be caused by temporal lobe malacia,which is an uncommon but potentially grave condition.The outcome of this present case exhibited the importance of the temporal lobe in the genesis of GS.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

Hemichorea in patients with temporal lobe infarcts: Two case reports

BACKGROUND Hemichorea and other hyperkinetic movement disorders are uncommon present-ations of stroke and are usually secondary to deep infarctions affecting the basal ganglia and thalamus.Therefore,temporal ischemic lesions causing hemichorea are rare.We report the cases of two patients with acute ischemic temporal lobe infarct strokes that presented as hemichorea.CASE SUMMARY Patient 1:An 82-year-old woman presented with a 1-mo history of involuntary movement of the left extremity,which was consistent with hemichorea.Her diffusion-weighted imaging(DWI)revealed an acute ischemic stroke that predominantly affected the right temporal cortex,and magnetic resonance angiography of the head showed significant stenosis of the right middle cerebral artery(MCA).Treatment with 2.5 mg of olanzapine per day was initiated.When she was discharged from the hospital,her symptoms appeared to have improved compared with those previously observed.Twenty-seven days after the first admission,she was readmitted due to acute ischemic stroke.Computed tomogra-phy perfusion showed marked hypoperfusion in the right MCA territory.An emergency transfemoral cerebral angiogram was performed and showed severe stenosis in the M1 segment of the right MCA.After percutaneous transluminal angioplasty was successfully performed,abnormal movements or other neuro-logic problems did not occur.Patient 2:A 76-year-old man was admitted to our hospital for a 7-d history of right-upper-sided involuntary movements.DWI showed an acute patchy ischemic stroke in the left temporal lobe without basal ganglia involvement.Subsequent diffusion tensor imaging confirmed fewer white matter fiber tracts on the left side than on the opposite side.Treatment with 2.5 mg of olanzapine per day improved his condition,and he was discharged.CONCLUSION When acute hemichorea suddenly appears,temporal cortical ischemic stroke should be considered a possible diagnosis.In addition,hemichorea may be a sign of impending cerebral infarction with MCA stenosis.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications worldwide.Timely detection is crucial as sepsis can rapidly escalate if left undetected.Recentadvancements in deep learning(DL)offer powerful tools to address this challenge.Aim:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis onset.Implementing the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier predictions.Method:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of sepsis.These time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis onset.All models were trained usingthe Medical Information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical environment.The confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate themodels.Result:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis onset.These results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one hour.Implication:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

Persistence of fertilization effects on soil organic carbon in degraded alpine wetlands in the Yellow River source region

In the restoration of degraded wetlands,fertilization can improve the vegetation-soil-microorganisms complex,thereby affecting the organic carbon content.However,it is currently unclear whether these effects are sustainable.This study employed Biolog-Eco surveys to investigate the changes in vegetation characteristics,soil physicochemical properties,and soil microbial functional diversity in degraded alpine wetlands of the source region of the Yellow River at 3 and 15 months after the application of nitrogen,phosphorus,and organic mixed fertilizer.The following results were obtained:The addition of nitrogen fertilizer and organic compost significantly affects the soil organic carbon content in degraded wetlands.Three months after fertilization,nitrogen addition increases soil organic carbon in both lightly and severely degraded wetlands,whereas after 15 months,organic compost enhanced the soil organic carbon level in severely degraded wetlands.Structural equation modeling indicates that fertilization decreases the soil pH and directly or indirectly influences the soil organic carbon levels through variations in the soil water content and the aboveground biomass of vegetation.Three months after fertilization,nitrogen fertilizer showed a direct positive effect on soil organic carbon.However,organic mixed fertilizer indirectly reduced soil organic carbon by increasing biomass and decreasing soil moisture.After 15 months,none of the fertilizers significantly affected the soil organic carbon level.In summary,it can be inferred that the addition of nitrogen fertilizer lacks sustainability in positively influencing the organic carbon content.