Updated Model Research Articles

Federated inception-multi-head attention models for cyber-attacks detection

<span lang="EN-US">With the proliferation of internet of things (IoT) devices, ensuring the security of these interconnected systems has become a critical concern. Cyberattacks targeting IoT devices pose significant threats to individuals and organizations due to the generation of vast amounts of data across many connected devices, which traditional centralized methods cannot solve. Federated learning (FL) could be a promising solution to mitigate privacy concerns associated with centralized approaches and address cybersecurity concerns. This paper uses FL and deep learning (DL) approaches to cybersecurity in IoT applications. The goal of cyber security is achieved by forming a federation of acquired and shared models at the head of the various participants. We use inception time and multi-head attention (CNN) algorithm based on FL to detect cyber-attacks and avoid data privacy leaks under two distribution modes, namely IID and Non-IID. In contrast, the FedAvg and FedMA algorithms aggregate local model updates. A global model is produced after several communication rounds between the IoT devices and the model parameter server. Cyber threats are simulated using edge-IIoT datasets. Experiment results show that the federated inception model's best global accuracy was 93, 91%, and 93, 49% using multi-head attention.</span>

Efficiency scaling of inductively coupled plasmas for radio frequency propulsion systems

AbstractA generalized inductively coupled plasma (ICP) efficiency model is derived, which may be used to predict plasma generation efficiency with many types of radio frequency (RF) electric propulsion system. The analytical ICP discharge model is used to examine device scaling, and investigate trends in ICP performance as a function of geometry and system inputs. The ICP efficiency model is applied to an analytical RF cathode model to predict device behavior. Results from the updated RF cathode model show improvements in agreement with experimental data.

Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning

Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together to train detection models without jeopardizing patient privacy. This paper presents an approach called federated learning that may be used to improve brain tumor identification while protecting patient privacy. Brain tumors are dangerous medical disorders that need to be accurately diagnosed in order to be effectively treated. However, sharing private patient information is a common practice in traditional medical data analysis methodologies, which raises privacy issues. Federated learning helps with this by enabling cooperative training of a common model amongst several hospitals or institutions without requiring the exchange of raw data. This method protects patient privacy by having each institution train the model using its own local data and only sharing model updates. We show through trials that our method is efficient in reliably identifying brain tumors while upholding privacy norms, presenting a viable option for improving medical diagnosis without jeopardizing patient privacy.

Early continuous glucose monitoring-derived glycemic patterns are associated with subsequent insulin resistance and gestational diabetes mellitus development during pregnancy.

Gestational diabetes mellitus (GDM) and insulin resistance (IR) increase the risk of adverse pregnancy outcomes. We aimed to examine the relationship of interstitial glucose assessed by continuous glucose monitoring (CGM) at early gestation, and the subsequent development of IR and GDM, and to determine 24-h interstitial glucose centile distributions in women with normal (non-IR and non-GDM) and suboptimal glycemic status (IR and/or GDM). CGM measurements were taken for 3-10days at 18-24weeks' gestation, followed by fasting serum insulin and oral glucose tolerance testing at 24-28weeks' gestation. IR and GDM were determined by the updated Homeostasis Model Assessment of IR score of ≥ 1.22 and 2013 World Health Organization criteria, respectively. Risks of IR and GDM were estimated using modified Poisson models, and hourly interstitial glucose centiles determined using Generalized Additive Models for Location, Scale and Shape. This prospective cohort study involved 167 pregnant women in Singapore, with a mean age of 31.7years, body mass index of 22.9kg/m2, and gestation of 20.3weeks. 25% of women exhibited IR and 18% developed GDM. After confounders adjustment, women with suboptimal glycemic control, indicated by higher mean daily glucose (risk ratio 1.42; 95% confidence interval 1.16, 1.73), glucose management indicator (1.08; 1.03, 1.12), and J-index (1.04; 1.02, 1.06), as well as those with greater glycemic variability, indicated by higher standard deviation (1.69; 1.37, 2.09), coefficient of variation (1.03; 1.00, 1.06), and mean amplitude of glycemic excursions (1.4; 1.14, 1.35) derived from CGM in early gestation were associated with higher risks of developing IR in later gestation. These associations were similarly observed for the development of GDM. Centile curves showed that, compared to those with normal glycemic status, women with suboptimal glycemic status had higher glucose levels, with greater fluctuations throughout 24h. In pregnant women who subsequently developed IR and GDM, interstitial glucose levels assessed by CGM were elevated and varied greatly. This supports the potential use of CGM to screen for glycemic changes early in pregnancy.

A decade of shaking in the Garden City: the dynamics of preparedness, perceptions, and beliefs in Canterbury, New Zealand, and implications for earthquake information

IntroductionThis study explored earthquake preparedness over time—before, during, and 10 years after the Canterbury Earthquake Sequence (CES) in New Zealand (NZ; known as Aotearoa in te ao Māori).MethodSurveys of Canterbury residents were conducted in 2009, 2013, and 2021, using variables derived from Community Engagement Theory (CET). The surveys measured earthquake perceptions and beliefs, participation and engagement, and preparedness actions. Results were compared across the three samples.ResultsFindings indicate that perceptions and beliefs (e.g., risk perception, outcome expectancy beliefs), and types of preparedness actions taken (e.g., collection of survival items, structural preparedness, community and/or agency relationships), differed over time, depending on people's experiences before, during, and after the CES. For example, during and after the CES, people were more likely to believe that preparing provided a benefit to daily life, but less likely to think it could reduce property damage, perhaps due to people's experiences of disruption and damage during the earthquakes.DiscussionAn understanding of such dynamics can assist with the provision and timing of risk and preparedness information. This study highlights the importance of providing applicable and actionable preparedness information, that is relevant to people's experiences, throughout an earthquake sequence. Such information might evolve and change in focus over time depending on risks and needs. Focus could also be given to information that builds peoples beliefs and capacities to undertake preparedness in evolving situations. Understanding preparedness in the context of different experiences and timeframes is useful in helping update models such as the CET, where the dynamics of time might be better incorporated.

Updated risk prediction model for pancreaticoduodenectomy using data from the National Clinical Database in Japan

AbstractAimRisk prediction models for mortality, severe postoperative complications, and postoperative pancreatic fistula in patients undergoing pancreaticoduodenectomy were established using data from the National Clinical Database more than a decade ago, and the surgical outcomes of pancreaticoduodenectomy have improved over the years. The aim of this study is to update the risk prediction model for pancreaticoduodenectomy using National Clinical Database data.MethodsBetween 2019 and 2021, the data of 35 365 patients who underwent pancreaticoduodenectomy and who were registered in the National Clinical Database were retrospectively analyzed. According to the registration year, the patients were divided into two cohorts: the development cohort (2019–2020; n = 23 654) and the validation cohort (2021; n = 11 711). Logistic regression analyses were performed to create risk models for surgical mortality, severe postoperative complications, and grade C postoperative pancreatic fistula.ResultsOverall, the rates of surgical mortality, severe postoperative complications, and grade C postoperative pancreatic fistula were 1.8%, 2.2%, and 1.3%, respectively. Logistic regression analyses revealed 28, 28, and 14 risk factors for surgical mortality, severe postoperative complications, and grade C postoperative pancreatic fistula, respectively. The area under the receiver operating characteristic curve of the risk model in the development cohort was 0.759 for surgical mortality, 0.712 for severe complications, and 0.699 for postoperative pancreatic fistula, comparable to the validation cohort. The calibration plots were favorable in both cohorts.ConclusionThe updated risk model for pancreaticoduodenectomy will be useful to predict surgical mortality, severe postoperative complications, and grade C postoperative pancreatic fistula.

Review on the Application of Living PSA in Nuclear Power

With the increasing standards of safety management in nuclear power plants, Living Probabilistic Safety Assessment (Living PSA) technology has begin to play an increasingly important role in their operation. This paper aims to provide an overview of the application and development of Living Probabilistic Safety Assessment (Living PSA) technology in nuclear power plant safety monitoring and risk assessment, examining the key technologies and future challenges. Initially, we summarize the current safety needs in regard to nuclear power, examine the policy on configuration risk management technology for nuclear power plants, and outline its importance and development process in nuclear power plant safety management. Subsequently, we discuss the basic principle of Living PSAs and the working method of risk monitoring based on Living PSAs, including information monitoring data collection, online identification, real-time model updating, and risk calculation. Within the Living PSA framework, model development is not merely about creating a theoretical or static representation; it is a dynamic and ongoing process that involves a deep understanding and precise simulation of the behavior of nuclear power plant systems and components. This represents the main research efforts in Living PSAs at present. Additionally, this paper identifies the key technologies of Living PSAs in an in-depth manner, such as the reliability-model-updating technology and model building in dynamic reliability analyses, including the fault tree model, multi-layer flow model, GO-FLOW model etc. The paper lists the work of some scholars in this area in recent years, which helps readers and researchers to clearly understand the current progress of Living PSA technologies in terms of model establishment and updating. Finally, the paper summarizes the challenges and future development of Living PSA and emphasizes the possible problems in data quality, human factor engineering, and the development of Living PSA technologies in the future. In the future, Living PSAs will provide more solid support for the realization of safer and more economical methods of operating nuclear power plants.

Population pharmacokinetics and exposure-response relationships of dostarlimab in primary advanced or recurrent endometrial cancer in part 1 of RUBY.

Dostarlimab-gxly is a humanized monoclonal antibody of the IgG4 isotype that binds to the programmed cell death protein-1 (PD-1) receptor and blocks its ligands. RUBY (NCT03981796) is a two-part multicentre study in patients with recurrent or primary advanced endometrial cancer. The overall aims were to characterise the population pharmacokinetics (PopPK) from Part 1 of this study, identify relevant covariates of interest, and assess exposure-efficacy/safety (ER) relationships. A PopPK model developed using GARNET (NCT02715284) study data for dostarlimab monotherapy was externally validated with RUBY Part 1 study data. Subsequently, the model was updated with data across the two studies. Exposure-safety analyses for adverse events related to dostarlimab alone or in combination with standard of care (SOC) were modelled using logistic regression. Exposure-efficacy analysis included Cox proportional hazards analysis of the primary efficacy endpoint of progression-free survival (PFS). For the model update, 7957 pharmacokinetics observations from 868 patients pooled from both RUBY and GARNET studies were available. The model was consistent with the previous model. Dostarlimab clearance was estimated to be 7.79% lower when dostarlimab was given as SOC combination therapy. However, no significant covariates were clinically relevant. Hepatic or renal impairment did not affect pharmacokinetics. Among the safety endpoints, only rash showed a small yet statistically significant effect (P < .05) in all subjects; however, this was not not deemed clinically relevant. There were no other clinically significant exposure-safety or exposure-PFS relationships. The addition of chemotherapy to dostarlimab had limited effect on dostarlimab PopPK, with no clinically significant covariates or clinically relevant exposure-safety or exposure-PFS relationships.

Strategies for comparison of modern probabilistic seismic hazard models and insights from the Germany and France border region

Abstract. The latest generation of national and regional probabilistic seismic hazard assessments (PSHAs) in Europe presents stakeholders with multiple representations of the hazard in many regions. This raises the question of why and by how much seismic hazard estimates between two or more models differ, not only where models overlap geographically but also where new models update existing ones. As modern PSHA incorporates increasingly complex analysis of epistemic uncertainty, the resulting hazard is represented not as a single value or spectrum but rather as probability distribution. Focusing on recent PSHA models for France and Germany, alongside the 2020 European Seismic Hazard Model, we explore the differences in model components and highlight the challenges and strategy for harmonising the different models into a common PSHA calculation software. We then quantify the differences in the source model and seismic hazard probability distributions using metrics based on information theory, illustrating their application to the Upper Rhine Graben region. Our analyses reveal the spatial variation in and complexity of model differences when viewed as probability distributions and highlight the need for more detailed transparency and replicability of the models when used as a basis for decision-making and engineering design.

AI-Powered Search Systems : Integrating Machine Learning with Search Technology for High-Scalability Applications

This article comprehensively analyzes integrating Artificial Intelligence (AI) and machine learning techniques into high-scalability search systems. We explore AI-powered search's theoretical foundations and practical implementations, focusing on advanced ranking algorithms, natural language processing for query understanding, and optimized distributed architectures. We demonstrate significant improvements in search relevance and efficiency through experiments conducted on a large-scale dataset comprising 100 million web pages and 1 million real-world queries. Our AI-powered system showed a 15% increase in Normalized Discounted Cumulative Gain (NDCG) for complex queries and a 12% improvement in Mean Reciprocal Rank (MRR) for navigational queries compared to traditional keyword-based approaches. We also address critical challenges in maintaining system scalability and performance, including data synchronization, real-time model updates, and resource management in distributed environments. The article further discusses emerging trends, such as graph neural networks and multimodal search capabilities, alongside ethical considerations and data privacy concerns. Our findings provide valuable insights for researchers and practitioners aiming to develop next-generation search platforms capable of handling the increasing complexity and volume of digital information while ensuring responsible AI integration.

Prognosis of reflective pavement cracking development with Bayesian updating and surrogate models

ABSTRACT The main objective of this study is to establish an integrated prognostic method for predicting the development of thermal-induced reflective cracking in asphalt overlay on concrete pavement. The integrated prognosis process for crack growth is developed on the modified Paris’ law and includes three steps: (1) calculation of J-integral; (2) Bayesian updating of crack growth parameters and (3) prediction of remaining life. Finite element modelling (FEM) was conducted to calculate J-integrals at different crack depths and cold weather conditions. A surrogate model was built based on the original Kriging method and it shows high accuracy in predicting J-integrals from maximum air temperature, temperature drop and crack depth. The Bayesian updating process starts from the laboratory-measured distribution of fracture model parameters (A and n) in Paris’ Law and converges to the true values to match the measured crack depths at different loading cycles from field inspection. On the other hand, the consideration of material damage in asphalt overlay avoids over-estimation of remaining loading cycles for reflective cracking to reach the pavement surface. Further study is recommended to accurately assess in-situ pavement modulus and detect crack depth over concrete joints for implementation of the proposed prognostics method.

A novel maximum likelihood based probabilistic behavioral data fusion algorithm for modeling residential energy consumption.

The current research effort is focused on improving the effective use of the multiple disparate sources of data available by proposing a novel maximum likelihood based probabilistic data fusion approach for modeling residential energy consumption. To demonstrate our data fusion algorithm, we consider energy usage by fuel type variables (for electricity and natural gas) in residential dwellings as our dependent variable of interest, drawn from residential energy consumption survey (RECS) data. The national household travel survey (NHTS) dataset was considered to incorporate additional variables that are not available in the RECS data. With a focus on improving the model for the residential energy use by fuel type, our proposed research provides a probabilistic mechanism for appropriately fusing records from the NHTS data with the RECS data. Specifically, instead of strictly matching records with only common attributes, we propose a flexible differential weighting method (probabilistic) based on attribute similarity (or dissimilarity) across the common attributes for the two datasets. The fused dataset is employed to develop an updated model of residential energy use with additional independent variables contributed from the NHTS dataset. The newly estimated energy use model is compared with models estimated RECS data exclusively to see if there is any improvement offered by the newly fused variables. In our analysis, the model fit measures provide strong evidence for model improvement via fusion as well as weighted contribution estimation, thus highlighting the applicability of our proposed fusion algorithm. The analysis is further augmented through a validation exercise that provides evidence that the proposed algorithm offers enhanced explanatory power and predictive capability for the modeling energy use. Our proposed data fusion approach can be widely applied in various sectors including the use of location-based smartphone data to analyze mobility and ridehailing patterns that are likely to influence energy consumption with increasing electric vehicle (EV) adoption.

A Study on Functional Requirements and Inspection Items for AI System Change Management and Model Improvement on the Web Platform

The rapid adoption of artificial intelligence (AI) on the web platform across multiple sectors has highlighted not only its inherent technical hurdles, such as unpredictability and lack of transparency, but also significant societal concerns. These include the misuse of AI technology, invasions of privacy, discrimination fueled by biased data, and infringements of copyright. Such challenges jeopardize the sustainable growth of AI and risk the erosion of societal trust, industry adoption and financial investment. This analysis explores the AI system’s lifecycle, emphasizing the essential continuous monitoring and the need for creating trustworthy AI technologies. It advocates for an ethically oriented development process to mitigate adverse effects and support sustainable progress. The dynamic and unpredictable nature of AI, compounded by variable data inputs and evolving distributions, requires consistent model updates and retraining to preserve the integrity of services. Addressing the ethical aspects, this paper outlines specific guidelines and evaluation criteria for AI development, proposing an adaptable feedback loop for model improvement. This method aims to detect and rectify performance declines through prompt retraining, thereby cultivating robust, ethically sound AI systems. Such systems are expected to maintain performance while ensuring user trust and adhering to data science and web technology standards. Ultimately, the study seeks to balance AI’s technological advancements with societal ethics and values, ensuring its role as a positive, reliable force across different industries. This balance is crucial for harmonizing innovation with the ethical use of data and science, thereby facilitating a future where AI contributes significantly and responsibly to societal well-being.

The Use of Image‐Based Data and Abundance Modelling Approaches for Predicting the Location of Vulnerable Marine Ecosystems in the South Pacific Ocean

ABSTRACTVulnerable marine ecosystems (VMEs) are typically fragile and slow to recover, thereby making them susceptible to disturbance, including fishing. In the high seas, the United Nations General Assembly (UNGA) requested regional fishery management organisations (RFMOs) to implement measures to prevent significant adverse impacts on VMEs. Here, we predict spatial abundances of 15 taxa, 13 VME indicator taxa, in the South Pacific RFMO (SPRFMO) area. Models used seafloor imagery data, an important advance on previously developed presence‐only predictions, to provide information on spatial variation in taxa abundance that is crucial for better inferring likely location of VMEs, rather than just distribution of VME indicator taxa. Abundance models varied in predictive power (mean R2 ranged 0.02–0.40). Uncertainty estimates of model predictions were developed to inform future spatial planning processes for conservation and management of VMEs. Using the VME index concept, abundance model outputs and previously published presence‐only model predictions were weighted using vulnerability scores, to explore how modelled outputs could provide spatial estimates of likely VME distribution. Spatial predictions of abundance improved on previous modelling to provide an almost complete suite of abundance models for VME indicator taxa in the western portion of the SPRFMO Convention area. Nevertheless, to improve utility of modelled outputs, we recommend more high‐quality seafloor imagery data be gathered within the SPRFMO Convention area to (1) validate abundance models developed here with independent data from the model area, (2) update models, if necessary, (3) link abundance information to ecosystem function and (4) explore validity of the adapted VME index approach used here.

Contribution of blowing-snow sublimation to the surface mass balance of Antarctica

Abstract. Blowing-snow sublimation is a key boundary layer process in polar regions and is the major ablation term in the surface mass balance (SMB) of the Antarctic ice sheet. This study updates the blowing-snow model in the Regional Atmospheric Climate Model (RACMO), version 2.3p3, incorporating blowing-snow sublimation into the prognostic equations for temperature and water vapour. These updates address numerical artefacts in the previous model version by replacing the uniformly discretised ice particle radius distribution, which limited the maximum ice particle radius to ≤ 50 µm, with a non-uniform distribution covering radii from 2 to 300 µm without additional computational overhead. The improved model is validated against meteorological observations from site D47 in Adélie Land, East Antarctica. The updates fix the numerical artefacts, successfully predicting the power-law variation in the blowing-snow flux with wind speed while improving the prediction of its magnitude. Additionally, a qualitative comparison with CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite data shows that RACMO accurately forecasts the spatial pattern of monthly blowing-snow frequencies. The model also yields an average blowing-snow layer depth of 230±116 m at D47, matching typical satellite observation values. Results reveal that, without blowing snow, sublimation in Antarctica mainly occurs in summer (October–March), with minimal surface sublimation in winter (April–September). Introducing the blowing-snow model creates an additional sublimation mechanism primarily contributing in winter. From 2000–2012, model-integrated blowing-snow sublimation averaged 175±7 Gt yr−1, a 52 % increase from the previous version. Total sublimation, summing blowing-snow and surface sublimation, reached 234±10 Gt yr−1, 47 % higher than in simulations without the blowing-snow model. This increase leads to a 1.2 % reduction in the Antarctic ice sheet's integrated SMB. Additionally, changes in sublimation in coastal and lower escarpment zones underscore the importance of the model updates for Antarctic climatology.

Validating and updating the OHTS-EGPS model predicting 5-year glaucoma risk among ocular hypertension patients using electronic records

ObjectiveTo validate and update the OHTS-EGPS model predicting risk of conversion from OHT to glaucoma using electronic medical records (EMR). DesignEvaluation and update of a risk prediction algorithm using EMRs and linked visual field (VF) tests. ParticipantsNewly diagnosed OHT patients attending hospital glaucoma services in England. Inclusion criteria: IOP 22-32 mmHg (either eye); normal baseline VF test, defined as Glaucoma Hemifield Test (GHT) ‘within normal range’ in a reliable VF test; at least two VF tests in total; no significant ocular co-morbidities. MethodsRisk factors: age, ethnicity, sex, IOP, vertical cup-to-disc ratio, central corneal thickness, VF pattern standard deviation, family history of glaucoma, systemic hypertension, diabetes mellitus, glaucoma treatment. Glaucoma conversion was defined as two consecutive and reliable VF tests with GHT ‘outside normal limits’ and/or need for glaucoma surgery. For validation, the OHTS-EGPS model was applied to predict a patient’s risk of developing glaucoma in 5 years. In the updating stage, the OHTS-model was re-fitted by re-estimating the baseline hazard and regression coefficients. The updated model was cross-validated and several variants were explored. Main Outcome MeasuresMeasures of discriminative ability (c-index) and calibration (calibration slope) were calculated and pooled across hospitals using random effects meta-analysis. ResultsFrom a total of 138,461 patients from ten hospital glaucoma services in England 9030 patients with OHT fitted the inclusion criteria. A total of 1530 (16.9%) patients converted to glaucoma during this follow-up period. The OHTS-EGPS model provided a pooled c-index of 0.61 (95% confidence interval: 0.60, 0.63), ranging from 0.55 to 0.67 between hospitals. The pooled calibration slope was 0.45 (0.38, 0.51), ranging from 0.25 to 0.64 among hospitals. The overall re-fitted model performed better than the OHTS-EGPS model, with a pooled c-index of 0.67 (0.65, 0.69), ranging from 0.65 to 0.75 between hospitals. ConclusionsWe performed an external validation of the OHTS-EGPS model in a large English population. Re-fitting the model achieved modest improvements in performance. Given the poor performance of the OHTS-EGPS model in our population, one should use caution in its application to populations that differ from those in the OHTS and EGPS.

Multi-focal lipid membrane characterization by combination of DAS-deconvolution and anisotropy: Novel insight of fluorescence analysis

Three analogue solvatochromic probes, Laurdan, Prodan, and Acdan, are extensively used in the study of biological sciences. Their locations in lipid membranes vary greatly in depth, and their fluorescence responds to their surrounding environment based on their corresponding locations in the membrane. Utilizing the fluorescence lifetimes (τ) and emission peaks (λ) acquired from time-resolved emission spectrum, one can effectively determine the local lipid environment using the analytical approach refer to as τ and λ plots. Herein, τ and λ plot was created using the aforementioned probes to expand the analytical field according to their location. Furthermore, the solvent modeling method in τ and λ plot was upgraded to artificially emulate the complex environment in lipid membranes by utilizing liquid paraffin and glycerol to assess the contribution of viscosity to each fluorescence distribution. According to the results from a series of solvent mixtures, the effect of solvent viscosity on lifetime values was confirmed in the short lifetime region (∼ 3 ns). However, it was impossible to emulate the long lifetime values (4 ns ∼) observed in lipid membranes containing 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) in the range of viscosity applied in this study. From the insight of the limiting anisotropy (r∞), the τ and λ plot was divided into a solvent-like region with isotropic environment (r∞ < 0.15) and a highly ordered region enough to define as anisotropic environment (0.15 <r∞) at τ: 4 ns. Also, the membrane-specific distribution was illustrated as 4 ns <τ and λ < 460 nm from this work. Updated analytical model was created to visualize multiple fluorescence components of each probe in 6 types of lipid bilayers, confirming the different distributions between these probes. Our results well-illustrated the multiplicity of lipid environments modeled with solvent and ordered environments in each lipid bilayer systems.

A Catalog of Pulsar X-Ray Filaments

Abstract We present the first Chandra X-ray Observatory (CXO) catalog of “pulsar X-ray filaments,” or “misaligned outflows.” These are linear, synchrotron-radiating features powered by ultrarelativistic electrons and positrons that escape from bow shock pulsars. The filaments are misaligned with the (large) pulsar velocity, distinguishing them from the pulsar wind nebula (PWN) trail, which is also often visible in CXO ACIS images. Spectral fits and morphological properties are extracted for five secure filaments and three candidates using a uniform method. We present a search of archival CXO data for linear diffuse features; the known examples are recovered and a few additional weak candidates are identified. We also report on a snapshot CXO ACIS survey of pulsars with properties similar to the filament producers, finding no new filaments but some diffuse emission, including one PWN trail. Finally, we provide an updated model for the pulsar properties required to create filaments in light of these new observations.

Using prediction models to improve care and communicate risk: updated modeling for children with IgA nephropathy

Clinical risk prediction models are being generated at an increasing rate. One important component is the identification of groups for whom such models might require recalibration to retain their desired performance. To this end, an update of the postbiopsy International IgA Nephropathy Prediction Tool for children has been published in this issue of Kidney International. We review the methods used and generalizability in practice, along with broader concepts in model development and application.

Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning.

With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network instability and dynamic resource variations, which can lead to task interruptions or failures. To address these issues, developing a fault-tolerant scheduling mechanism is crucial to ensure that a system continues to operate efficiently even when some nodes experience failures. In this paper, we propose an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning. This model incorporates a deep reinforcement learning framework built upon a graph neural network, allowing it to accurately capture the complex communication relationships between computing nodes. The model generates fault-tolerant scheduling actions as output, ensuring robust performance in dynamic environments. Additionally, we introduce an asynchronous model update strategy, which enhances the model's capability of real-time dynamic scheduling through multi-threaded parallel interactions with the environment and frequent model updates via running threads. The experimental results demonstrate that the proposed method outperformed the baseline algorithms in terms of quality of service (QoS) assurance and fault-tolerant scheduling capabilities.