Simultaneous Failure Research Articles

Association between lactate to hematocrit ratio and 30-day all-cause mortality in patients with sepsis: a retrospective analysis of the Medical Information Mart for Intensive Care IV database.

The lactate to hematocrit ratio (LHR) has not been assessed for predicting all-cause death in sepsis patients. This study aims to evaluate the relationship between LHR and 30-day all-cause mortality in sepsis patients. This retrospective study used the data from Medical information mart for intensive care IV (MIMIC-IV, version 2.0). Our study focused on adult sepsis patients who were initially hospitalized in the Intensive care unit (ICU). The prognostic significance of admission LHR for 30-day all-cause mortality was evaluated using a multivariate Cox regression model, ROC curve analysis, Kaplan-Meier curves, and subgroup analyses. A total of 3,829 sepsis patients participated in this study. Among the cohort, 8.5% of individuals died within of 30 days (p < 0.001). The area under the curve (AUC) for LHR was 74.50% (95% CI: 71.6-77.50%), higher than arterial blood lactate (AUC = 71.30%), hematocrit (AUC = 64.80%), and shows no significant disadvantage compared to qSOFA, SOFA, and SAPS II. We further evaluated combining LHR with qSOFA score to predict mortality in sepsis patients, which shows more clinical significance. ROC curve analysis showed that 6.538 was the optimal cutoff value for survival and non-survival groups. With LHR ≥6.538 vs. LHR <6.538 (p < 0.001). Subgroup analysis showed significant interactions between LHR, age, sex, and simultaneous acute respiratory failure (p = 0.001-0.005). LHR is an independent predictor of all-cause mortality in sepsis patients after admission, with superior predictive ability compared to blood lactate or hematocrit alone.

Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs with time series and discriminative features

Sensor fault diagnosis is essential to guaranteeing the safety of lithium-ion batteries. To address the general drawbacks of the existing diagnosis methods, including the difficulty in determining the threshold, inability to handle multiple faulty sensors concurrently, and limited capacity in identifying fault modes, a multi-sensor multi-mode fault diagnosis method for lithium-ion battery packs is proposed. The proposed method utilizes time series and discriminative features to accomplish sensor-specific fault detection and fault mode identification. First, a total of 18 general time series features are extracted to characterize the measurements of each sensor during each charge–discharge cycle. Principal component analysis is then used to reduce the high-dimensional feature space to a two-dimensional space, such that fault detection can be carried out with the α-hull algorithm. For the detected faulty samples, a two-layer identification algorithm is designed based on three discriminative features, namely, correlation coefficient, impulse factor, and Hurst coefficient, to identify the specific fault modes. The diagnostics can decouple the information from different types of sensors so that the proposed method can effortlessly isolate current, voltage, and temperature sensors that are concurrently experiencing faults. Ultimately, experimental results from three scenarios, including simultaneous failure of multiple sensors, substantiate the effectiveness and feasibility of the proposed method.

A resilient battery electric bus transit system configuration

Electric mobility is fundamental to combat climate change and attaining the United Nations Sustainable Development Goals (SDG-11). However, electric mobility necessitates a seamless integration between power and transportation systems, as the resiliency of both systems is becoming far more interdependent. Here, we focus on disruption to Battery Electric Bus (BEB) transit system charging infrastructure and offer a resilient BEB transit system planning model. The proposed model optimizes the BEB system costs while ensuring the system’s robustness against simultaneous charging station failures. In our case study, a single charging station failure would lead to up to 34.03% service reduction, and two simultaneous failures would reduce the service by up to 58.18%. Our proposed two-stage robust model addresses this issue with a relatively small added cost (3.26% and 8.12% higher than the base model). This cost enables uninterrupted BEB system operation during disruption, ensuring personal mobility, social interaction, and economic productivity.

An Approach to Implementing High-Performance Computing for Problem Solving in Workflow-Based Energy Infrastructure Resilience Studies

Implementing high-performance computing (HPC) to solve problems in energy infrastructure resilience research in a heterogeneous environment based on an in-memory data grid (IMDG) presents a challenge to workflow management systems. Large-scale energy infrastructure research needs multi-variant planning and tools to allocate and dispatch distributed computing resources that pool together to let applications share data, taking into account the subject domain specificity, resource characteristics, and quotas for resource use. To that end, we propose an approach to implement HPC-based resilience analysis using our Orlando Tools (OT) framework. To dynamically scale computing resources, we provide their integration with the relevant software, identifying key application parameters that can have a significant impact on the amount of data processed and the amount of resources required. We automate the startup of the IMDG cluster to execute workflows. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our solution allows us to investigate large infrastructures by modeling multiple simultaneous failures of different types of elements down to the number of network elements. In terms of task and resource utilization efficiency, we achieve almost linear speedup as the number of nodes of each resource increases.

Backup Resource Allocation of Virtual Machines With Two-Stage Probabilistic Protection

In a cloud, protection through backup of virtual machines contained in physical machines (PMs) reduces damage to users due to failure of PMs, such as hardware malfunctions. However, from a resource cost perspective, it is necessary to reduce the amount of capacity for backup while limiting the probability of unsuccessful protection. Existing studies suggest the method of sharing backup capacity among primary resources, but the amount of capacity reduction required to protect is limited. This paper proposes a backup resource allocation model with two-stage probabilistic protection to minimize the total required backup capacity for multiple simultaneous failures of PMs. In probabilistic protection, backup resources are allocated so that the probability of backup failure does not exceed a given survivability parameter which represents the acceptable probability of backup failure. Probabilistic protection which achieves efficient sharing of backup capacity enables flexible allocation and reduces the required backup capacity. In order to increase the flexibility of backup capacity allocation, the proposed model extends the probabilistic protection to two stages. By dividing the protection into two stages, the weight of probability between stages can be adjusted, enabling more effective capacity sharing. Since it is uncertain which primary PMs fail, we apply robust optimization to the probabilistic protection. By using a table that takes into account the survivability parameter and the failure probability of PMs, the proposed model is formulated as a mixed integer linear programming problem. We prove the NP-hardness of considered problem. A heuristic is introduced to solve the optimization problem. The proposed model can reduce the total required backup capacity compared to the models with dedicated protection and one-stage probabilistic protection. The model can also provide protection in a range of survivability parameters that the model with one-stage probabilistic protection cannot satisfy.

Practical Design Considerations for Wide Locally Recoverable Codes (LRCs)

Most of the data in large-scale storage clusters is erasure coded. At exascale, optimizing erasure codes for low storage overhead, efficient reconstruction, and easy deployment is of critical importance. Locally recoverable codes (LRCs) have deservedly gained central importance in this field, because they can balance many of these requirements. In our work, we study wide LRCs; LRCs with large number of blocks per stripe and low storage overhead. These codes are a natural next step for practitioners to unlock higher storage savings, but they come with their own challenges. Of particular interest is their reliability , since wider stripes are prone to more simultaneous failures. We conduct a practically minded analysis of several popular and novel LRCs. We find that wide LRC reliability is a subtle phenomenon that is sensitive to several design choices, some of which are overlooked by theoreticians, and others by practitioners. Based on these insights, we construct novel LRCs called Uniform Cauchy LRCs , which show excellent performance in simulations and a 33% improvement in reliability on unavailability events observed by a wide LRC deployed in a Google storage cluster. We also show that these codes are easy to deploy in a manner that improves their robustness to common maintenance events. Along the way, we also give a remarkably simple and novel construction of distance-optimal LRCs (other constructions are also known), which may be of interest to theory-minded readers.

Research on Reactive Power Coordination Control Strategies of Multi-Infeed Line-Commutated Converter–High-Voltage Direct Current Systems

For a receiving-end power grid with multi-infeed LCC-HVDC systems, simultaneous commutation failures may seriously threaten the safe and stable operation of the system. To evaluate the impact of commutation failure and improve the voltage stability of the commutation buses in multi-infeed HVDC systems, this paper proposes a method for evaluating the voltage stability of commutation buses and a reactive power coordination control (RPCC) method for commutation failure of multiple HVDC systems. Firstly, three indicators and the entropy weight method are adopted to comprehensively evaluate the voltage stability of commutation buses. Then, an RPCC method is proposed to resist commutation failure. The proposed RPCC method uses the voltage interaction factor (VIF) to screen out DC systems that are strongly related to dynamic reactive power compensation devices and activates the devices to provide RPCC to the DC systems through an auxiliary controller. Finally, the effectiveness of the proposed method is verified through a practical example of the Jiangsu power grid.

Enhancing hydraulic fracturing for in-situ remediation in low-permeability soils: A comprehensive investigation of fracture propagation

Enhancing the complexity of the hydraulic fractures to provide a wide channel for the injection of the agent is crucial for remediating low-permeability contaminated sites. This study involved a physical simulation experiment of large-scale true triaxial hydraulic fracturing in undisturbed soil, as well as field fracturing tests, to investigate fracture initiation mechanisms and the influence of different factors on fracture propagation. The study revealed a unique failure mode for low-permeability soils characterized by impact splitting, involving simultaneous tensile and shear failure. Three typical fracture propagation patterns emerged: (1) horizontal fracture, (2) parallel fracture, and (3) complex fracture. Silty clay predominantly exhibited horizontal fractures, while mucky clay facilitated the formation of complex fractures dominated by multiple transverse fractures. As the vertical stress difference coefficient increased from 1.0 to 1.5, the pressure on the fracture surface enhanced the connection between hydraulic fractures and natural fractures. Hydraulic fracturing in low-permeability soils necessitated large displacements and high-viscosity fracturing fluids to sustain fracture propagation. The field fracturing test results underscored that soil type and in-situ stress were the primary factors governing hydraulic fracture initiation and propagation. Identifying the optimal fracturing location was critical for achieving the maximum stimulated formation volume.

Vertex-connectivity for node failure identification in Boolean Network Tomography

We study the node failure identification problem in undirected graphs by means of Boolean Network Tomography. We argue that vertex-connectivity plays a central role. We prove bounds on the maximum number of simultaneous node failures that can be identified in arbitrary networks. We argue that (augmented) grids are a class of networks with large failure identifiability, and provide very tight results in this context.

Modeling offshore wind farm disturbances and maintenance service responses within the scope of resilience

Offshore wind farms are becoming ever more important means of energy production. Accordingly, they are started to be considered critical infrastructures with heightened attention on their protection and resilience. This paper studies how the maintenance service can sustain or recover offshore wind farm operations under different stressors. We conduct this study by modeling the failures in an offshore wind farm and how maintenance service is able to correct them. Our model enhances the traditional cause-consequence trees by including dynamical aspects, and the modeling of the maintenance process. Special attention in the maintenance model is given to limited personnel and material resources, as well as limited access to wind turbines. This limit is a result of occasional harsh weather conditions that make conducting repairs unsafe. The model is applied for two representative disturbance scenarios: a cyber-attack leading to high failure rates and a high-impact incident causing numerous simultaneous failures. The second scenario integrates results from a physical power system simulation that are used for depicting the incident. The results show that our model can present scenarios where different stressors challenge the operations. These can be used for testing and defining requirements for future countermeasures to improve the resilience.

Promoting the Maneuverability and Fault-Tolerant Control Capabilities of Dual-System/Hybrid VTOL UAVs

This paper presents an active fault-tolerant flight control system (FCS) design for the dual-system/hybrid VTOL UAV configuration that maximally exploits its inherent over-actuation abilities to provide higher levels of fault-tolerance and reliability compared to that of the other VTOL UAVs configurations up to this point. The significance of this study is to convert the main drawback of having the vertical rotors as dead weights during the cruise to a key advantage where all the UAV's controls are kept active during all the flight phases raising the UAV's fault-tolerance and maneuverability abilities. The FCS proposed elaborates on a new architecture for the hierarchical automatic control loops using the dynamic inversion control to design the trajectory tracking virtual commands and employing optimal dynamic control allocation to optimally resolve the controls redundancy. Regarding fault-tolerance, the presented FCS handles simultaneous failures and jamming of all the control surfaces during flight where the control commands to the healthy controls (vertical rotors) were automatically adapted such that the trimming and trajectory tracking were successfully maintained till the mission end. Additionally, this paper addresses for the first time the employment of the controls redundancy to enhance the VTOL UAV's maneuverability in fault-free cases, where we present a study of reducing the minimum turn radius of the UAV when the vertical rotors are employed with the control surfaces during the maneuver. A reduction of 38% in the coordinated steady-level minimum turn radius turn at 35 m/sec flight speed was achieved.

Concentration-Dependent Attenuation of Pro-Fibrotic Responses after Cannabigerol Exposure in Primary Rat Hepatocytes Cultured in Palmitate and Fructose Media.

Hepatic fibrosis is a consequence of liver injuries, in which the overproduction and progressive accumulation of extracellular matrix (ECM) components with the simultaneous failure of matrix turnover mechanisms are observed. The aim of this study was to investigate the concentration-dependent influence of cannabigerol (CBG, Cannabis sativa L. component) on ECM composition with respect to transforming growth factor beta 1 (TGF-β1) changes in primary hepatocytes with fibrotic changes induced by palmitate and fructose media. Cells were isolated from male Wistar rats' livers in accordance with the two-step collagenase perfusion technique. This was followed by hepatocytes incubation with the presence or absence of palmitate with fructose and/or cannabigerol (at concentrations of 1, 5, 10, 15, 25, 30 µM) for 48 h. The expression of ECM mRNA genes and proteins was determined using PCR and Western blot, respectively, whereas media ECM level was evaluated using ELISA. Our results indicated that selected low concentrations of CBG caused a reduction in TGF-β1 mRNA expression and secretion into media. Hepatocyte exposure to cannabigerol at low concentrations attenuated collagen 1 and 3 deposition. The protein and/or mRNA expressions and MMP-2 and MMP-9 secretion were augmented using CBG. Considering the mentioned results, low concentrations of cannabigerol treatment might expedite fibrosis regression and promote regeneration.

A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance

In Contemporary automation systems, Fault detection and classification of electrical transmission lines in grid systems are given top priority. The broad application of Machine Learning (ML) methods has enabled the substitute of conventional methods of fault identification and classification. These methods are more effective ones that can identify faults early on using a significant quantity of sensory data. So detecting simultaneous failures is difficult in the context of distracting the noise and several faults in the transmission lines. This study contributes by offering a unique way for concurrently detecting and classifying several faults using a multilabel classification approach based on binary relevance classifiers. The proposed binary relevance multilabel detection and classification models’ performances are examined. Under both ideal and problematic circumstances, faults in the dataset are collected. A variety of multilabel fault types detection and classification determines the suggested method’s effectiveness.

Experimental study on anchorage performance and mechanism of hooked bars with a pre-yield anchorage failure

In RC exterior beam-column joints, reliable anchorage of beam bars using hooked bars is a common approach to ensure sufficient joint rotation capability. The anchorage failure criterion and the influence of axial load and side cover on anchorage strength are unclear with the limitings of the existing test setup and test approach. Twenty-two simulated beam-column joint tests with hooked bars anchorage were therefore conducted. A specially designed test setup was proposed to ensure the simultaneous failure of hooked bars within a specimen, while a novel approach was employed to separate the anchorage force of the hook portion into bearing and bond forces based on stress distribution measurement. The experimental results show that the anchorage strength of hooked bars and hook portion bearing strength increase as side cover thickness and axial load increase, which may potentially alleviate joint congestion. Additionally, the study identifies that the hook portion bearing strength dominates the anchorage failure mechanism of hooked bars, prompting the proposal of an anchorage failure criterion. Based on this criterion, models for anchorage strength and anchorage force-slip relationship were proposed, which are applicable to hooked bars with varying inner bend diameters in actual structures. To examine the safety and validity of the proposed equation for anchorage strength, it was compared against a hooked bar test database with 363 anchorage specimens, as well as several existing models. The database analysis showed potential safety risks with the GB50010-2010 model. Notably, the proposed model demonstrated the highest accuracy in predicting the anchorage strength of hooked bars.

Adaptive Fragment-Based Parallel State Recovery for Stream Processing Systems

Today, large-scale cloud organizations are deploying datacenters and “edge” clusters globally to provide their users with low-latency access to their services. Running stream applications across these geo-distributed sites are emerging as a daily requirement, such as making business decisions from marketing streams, identifying spam campaigns from social network streams, and analyzing existing genomes in different labs and countries to track the sources of a potential epidemic. However, while the progress has been encouraging, the existing efforts have dominantly centered around <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stateless stream processing</i> , leaving another urgent trend- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stateful stream processing</i> -much less explored. A driving need is that next-generation stream processing systems need to store and update states during processing, and most importantly, successfully recover large distributed states when faults and failures happen. Existing studies exhibit major limitations including: (1) they mostly inherit MapReduce's “single master/many workers” architecture, where the central master is responsible for all scheduling activities and easily becomes a scalability bottleneck; (2) they offer state recovery mainly through the use of three approaches: replication recovery, checkpointing recovery, and DStream-based lineage recovery, which are either slow, resource-expensive or failing to handle multiple failures; and (3) they are not adaptive to heterogeneous hardware settings in the cloud. In this paper, we present A-FP4S, a novel adaptive fragments-based parallel state recovery mechanism for stream processing systems to manage and recover large distributed states for a massive number of stream applications. The novelty of A-FP4S is that we organize stream operators into a distributed hash table (DHT) based peer-to-peer (P2P) overlay. Then we divide each node's local state into many fragments and periodically store them in each node's multiple neighbors (the leaf set nodes of DHT), ensuring that different sets of available fragments can reconstruct failed states in parallel. By doing that, this failure recovery mechanism is extremely scalable to the size of the lost state, significantly reduces the failure recovery time, and can tolerate multiple node failures. A-FP4S is adaptive to heterogeneous hardware settings (e.g., CPU speed, disk/file-system speed, network bandwidth) by automatic parameter tuning over phases. Compared to Apache Storm, A-FP4S achieves a significant 31.8% to 50.5% reduction in recovery latency. It can scale to many simultaneous failures and successfully recover the state, even more than half of the operators fail or get lost. Large-scale experiments using real-world datasets demonstrate A-FP4S's attractive scalability and adaptivity properties.

Risks of synchronized low yields are underestimated in climate and crop model projections

Simultaneous harvest failures across major crop-producing regions are a threat to global food security. Concurrent weather extremes driven by a strongly meandering jet stream could trigger such events, but so far this has not been quantified. Specifically, the ability of state-of-the art crop and climate models to adequately reproduce such high impact events is a crucial component for estimating risks to global food security. Here we find an increased likelihood of concurrent low yields during summers featuring meandering jets in observations and models. While climate models accurately simulate atmospheric patterns, associated surface weather anomalies and negative effects on crop responses are mostly underestimated in bias-adjusted simulations. Given the identified model biases, future assessments of regional and concurrent crop losses from meandering jet states remain highly uncertain. Our results suggest that model-blind spots for such high-impact but deeply-uncertain hazards have to be anticipated and accounted for in meaningful climate risk assessments.

Fed-TSN: Joint Failure Probability-Based Federated Learning for Fault-Tolerant Time-Sensitive Networks

Industrial Internet of Things (IIoT) applications have diverse network session requirements. Certain critical applications, such as emergency alert relays, as well as industrial floor evacuation and surveillance systems, require fresh updates that can maintain the most recently delivered packets. This requires high reconfigurability to an extent where the system can measure the impact of an event and adapt the network accordingly. Recent research has demonstrated that network failures can undermine the sustainability of Industry 4.0 or Industrial IoT in general. In this paper, we design an intelligent Federated learning based Time-Sensitive Networking (Fed-TSN) controller framework to optimize the failure recovery. In industrial IoT scenarios, such as emergency evacuations on factory floors due to natural disasters, there can be multiple link failures with no disjoint paths which require a sustainable recovery solution. Accordingly, we consider multiple simultaneous link failures, both for networks with and without disjoint paths. The typically probabilistic network failures on a factory floor call for designing a mechanism that can search for routes with minimum joint failure probability (JFP). We formulate the JFP minimization problem as a non-linear integer program. We design a Software Defined Networking (SDN) controller that runs an application to produce near-optimal solutions for providing enhanced sustainability in a wide range of Industry 4.0 scenarios. We employ this non-linear integer program solution as input to our intelligent Fed-TSN fault recovery strategy that predicts the migration location based on the changes in the TSN gate schedule. We conduct simulations to quantify the improvements achieved with Fed-TSN compared to state-of-the-art approaches.

Model for calculating local bearing capacity of concrete with spiral stirrups

This study evaluates the mechanical and failure modes of 30 concrete specimens under local compression. The experimental results indicate that the specimens undergo three types of local compressive failure modes. These modes primarily depend on the strengths of the tensile and compression regions. Based on the test results, a calculation model for the local bearing capacity of concrete with spiral stirrups Fl is formulated. In the model, the local compressive failure of concrete with spiral stirrups is classified into tension and compression. The yielding of spiral stirrups in the tensile region is regarded as tensile failure, and compressive failure is considered to have occurred when the strain value in the compression region reaches 0.002. To simplify the model calculation, the spacing s, diameter d, and yield strength fy of spiral stirrup as well as the local compression area ratio Ab/Al (ratio of the concrete bearing area Ab to the loading area Al) are comprehensively analyzed. Based on the analysis results, a formula for the bearing capacity of the reinforcement Ns is derived. The independent variables of the formula are yield force of spiral stirrup fyAssl, stirrup spacing s, and ratio of specimen side length to loading plate side length h/a. A simplified formula for calculating the tensile failure of the specimen is derived. To ensure simultaneous tensile and compressive failures, a critical reinforcement ratio of the spiral stirrup ρvu is proposed based on the calculation model. Test data from similar axial local compression tests are collected to verify the accuracy of the calculation model. The local bearing capacity of concrete is recalculated according to the calculation method proposed in this study. Results show that the calculation model is highly accurate. Accordingly, it may be used as reference for calculating the local bearing capacity.

Increase of Simultaneous Soybean Failures Due To Climate Change

AbstractWhile soybeans are among the most consumed crops in the world, most of its production lies in the US, Brazil, and Argentina. The concentration of soybean growing regions in the Americas renders the supply chain vulnerable to regional disruptions. In 2012, anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all‐time records. In this study, we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process‐based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long‐term mean trends. We find long‐term trends in mean climate increase the frequency of 2012 analogs by 11–16 times and the magnitude by 4–15% compared to changes in climate variability only depending on the global climate scenario. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long‐term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.

Efficient Sender-Based Message Logging Tolerating Simultaneous Failures with Always No Rollback Property

Most of the existing sender-based message logging protocols cannot commonly handle simultaneous failures because, if both the sender and the receiver(s) of each message fail together, the receiver(s) cannot obtain the recovery information of the message. This unfortunate situation may happen due to their asymmetric logging behavior. This paper presents a novel sender-based message logging protocol for broadcast network based distributed systems to overcome the critical constraint of the previous ones with the following three features. First, when more than one process crashes at the same time, the protocol enables the system to ensure the always no rollback property by symmetrically replicating the recovery information at each process or group member connected on a network. Second, it can make the first feature persist even if the general form of communication for the system is a combination of point-to-point and group ones. Third, the communication overhead resulting from the replication can be highly lessened by making full use of the capability of the standard broadcast network in both communication modes. Experimental outcomes verify that, no matter which communication patterns are applied, it can reduce about 4.23∼9.96% of the total application execution time against the latest enabling the traditional ones to cope with simultaneous failures.