Frequent Updates Research Articles

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

A practical guide to living evidence: Reducing the knowledge-to-practice gap.

Living evidence involves continuous evidence surveillance to incorporate new relevant evidence into systematic reviews and clinical practice guideline recommendations as soon as it becomes available. Thus, living evidence may improve the timeliness of recommendation updates and reduce the knowledge-to-practice gap. When considering a living evidence model, several processes and practical aspects need to be explored. Some of these include identifying the need for a living evidence model, funding, governance structure, time, team skills and capabilities, frequency of updates, approval and endorsement and publication and dissemination.

Event-triggered adaptive control for quadrotor unmanned aerial vehicle with prescribed performance

This paper proposes an event-triggered adaptive control algorithm with prescribed performance for quadrotor unmanned aerial vehicles (UAVs). The dynamic uncertainty and unknown external interference of the system are compensated by the property of approximation in a radial basis function (RBF) neural network. Based on this, introduce a performance constraint function to ensure that the trajectory tracking error of quadrotor UAV converges to a preset area, thereby guaranteeing transient and steady-state performance. In addition, design an event-triggered adaptive controller with the combination of the event-triggered mechanism, reducing the controller’s update frequency. Simulation results demonstrate that this algorithm exhibits strong anti-interference properties and effectively resolves the flight control problem of quadrotor UAV.

A novel fast hybrid capture sequencing method for high-efficiency common human coronavirus whole-genome acquisition.

Rapid and accurate sequencing of the entire viral genome, coupled with continuous monitoring of genetic changes, is crucial for understanding the epidemiology of coronaviruses. We designed a novel method called micro target hybrid capture system (MT-Capture) to enable whole-genome sequencing in a timely manner. The novel design of probes used in target binding exhibits a unique and synergistic "hand-in-hand" conjugation effect. The entire hybrid capture process is within 2.5 hours, overcoming the time-consuming and complex operation characteristics of the traditional liquid-phase hybrid capture (T-Capture) system. By designing specific probes for these coronaviruses, MT-Capture effectively enriched isolated strains and 112 clinical samples of coronaviruses with cycle threshold values below 37. Compared to multiplex PCR sequencing, it does not require frequent primer updates and has higher compatibility. MT-Capture is highly sensitive and capable of tracking variants.IMPORTANCEMT-Capture is meticulously designed to enable the efficient acquisition of the target genome of the common human coronavirus. Coronavirus is a kind of virus that people are generally susceptible to and is epidemic and infectious, and it is the virus with the longest genome among known RNA viruses. Therefore, common human coronavirus samples are selected to evaluate the accuracy and sensitivity of MT-Capture. This method utilizes innovative probe designs optimized through probe conjugation techniques, greatly shortening the time and simplifying the handwork compared with traditional hybridization capture processes. Our results demonstrate that MT-Capture surpasses multiplex PCR in terms of sensitivity, exhibiting a thousandfold increase. Moreover, MT-Capture excels in the identification of mutation sites. This method not only is used to target the coronaviruses but also may be used to diagnose other diseases, including various infectious diseases, genetic diseases, or tumors.

RUNNING A WORKFLOW WITHOUT WORKFLOWS: A BASIC ALGORITHM FOR DYNAMICALLY CONSTRUCTING AND TRAVERSING AN IMPLIED DIRECTED ACYCLIC GRAPH IN A NON-DETERMINISTIC ENVIRONMENT

This paper introduces a novel algorithm for dynamically constructing and traversing Directed Acyclic Graphs (DAGs) in workflow systems, particularly targeting distributed computation and data processing domains. Traditional workflow management systems rely on explicitly defined, rigid DAGs, which can be cumbersome to maintain, especially in response to frequent changes or updates in the system. Our proposed algorithm circumvents the need for explicit DAG construction, instead opting for a dynamic approach that iteratively builds and executes the workflow based on available data and operations, through a combination of entities like Data Kinds, Operators, and Data Units, the algorithm implicitly forms a DAG, thereby simplifying the process of workflow management. We demonstrate the algorithm’s functionality and assess its performance through a series of tests in a simulated environment. The paper discusses the implications of this approach, especially focusing on cycle avoidance and computational complexity, and suggests future enhancements and potential applications.

A categorical approach for defining digital twins in the AECO industry

Operations and Maintenance (O&M) costs account for 60-80% of a facility’s lifecycle costs. Using Digital Twins (DTs) can aid in making O&M more effective and efficient, leading to time and cost savings. The concept of DT started in the Aerospace domain, and other industries eventually adopted it. DTs are a new concept to the Architecture, Engineering, Construction, and Operations (AECO) Industry, and there is a lot of confusion around this concept. The purpose of this paper is to provide a DT definition along with a classification structure to create a common ground for understanding DTs in the AECO industry, which leads to easier adoption of DTs. A systematic literature review was completed to identify the existing DT definitions and classification approaches. Then, through a content analysis, the core components of definitions were extracted. The identified components were used to develop a comprehensive and inclusive DT definition for the AECO industry, using the domain language. In a similar fashion, existing DT classification structures were studied, and their components were identified through content analysis. Using the identified components, a DT classification structure was proposed for the AECO industry using domain concepts and terms. The results were validated and refined through a series of semi-structured expert interviews and surveys. Interviewees and survey participants comprised DT experts from academia and industry with diverse backgrounds. The components of the proposed DT definition include virtual representation, data connection between physical and digital entities, analysis, actuation, and frequency of updates. The classification structure consisted of three DT categories, namely Digital Twin Prototype (DTP), Digital Shadow (DS), and Cyber-Physical System (CPS).

Report of the Task Force on Food Security

The Report on Food Security was prepared by the Task Force set up in the Planning Commission by the Prime Minister of Pakistan in May 2008 to: (i) review the production, availability, storage, and consumption of essential food commodities in Pakistan; (ii) examine the food procurement system and status of storage facilities; (iii) analyse price trends of essential food commodities; (iv) assess the impact of (food) price hike (observed in 2007-08) on vulnerable groups; and (v) devise an action plan for promoting food security for the vulnerable groups. It produced two Interim Reports in July 2008 and September 2008 before the submission of the Final Report in February 2009. The setting up of the Task Force by the newly elected government soon after it had taken over was timely in that it preceded the start of the ‘Rabi sowing season for the next (winter 2008-09) wheat crop, the staple food in Pakistan. It must also be seen in the context of the unprecedented increase in global food prices in 2007-08 and rising threat of hunger and food insecurity, especially in developing countries. The need for evolving a comprehensive national strategy for food security was therefore pressing and urgent. The main elements of the national strategy for food security identified by the Task Force are: (i) achieving a growth rate of at least 4 percent during the next decade (2010 2020) for agricultural sector with special focus on livestock sub-sector; (ii) setting up an efficient food procurement and distribution system for ensuring around the year food security in all regions of the country; (iii) adopting a pro-poor growth strategy to enhance access of poor households to food; (iv) establishing transparent and efficient safety nets for the vulnerable groups. An important contribution of the Report was the construction of a food security index (FSI) incorporating (with suitable weights) major aspects of food security. It also recommended the adoption and frequent updating of the proposed FSI to monitor food security situation in the country.

Observer‐based event‐triggered consensus control of nonlinear cyber‐physical systems under backlash‐like hysteresis and denial‐of‐service attacks

AbstractIn this article, we investigate the problem of event‐triggered distributed consensus control for a class of complex cyber‐physical systems (CPSs), where unknown backlash‐like hysteresis inputs and denial‐of‐service (DoS) attacks are considered in actuators and communication channels, respectively. In the first place, when communication networks are subjected to malicious DoS attacks, the connection weights of network communication topologies experience disruptions. To solve this issue, this work proposes a solution by constructing topological models for communication networks employing switching topologies and utilizing observers to estimate the combined measurement errors during the time interval of DoS attacks. Secondly, to address the repercussions of DoS attacks on control units and to optimize the utilization of communication resources, a distributed control protocol based on an event‐triggered scheme and employing locally deployed estimators is designed to significantly decrease the update frequency of control signals. Meanwhile, utilizing Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is validated, thereby accomplishing the goal of consensus control. At last, the efficacy of the proposed control scheme is demonstrated through two simulation examples.

A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning

Offline-to-online Reinforcement Learning (O2O RL) aims to improve the performance of offline pretrained policy using only a few online samples. Built on offline RL algorithms, most O2O methods focus on the balance between RL objective and pessimism, or the utilization of offline and online samples. In this paper, from a novel perspective, we systematically study the challenges that remain in O2O RL and identify that the reason behind the slow improvement of the performance and the instability of online finetuning lies in the inaccurate Q-value estimation inherited from offline pretraining. Specifically, we demonstrate that the estimation bias and the inaccurate rank of Q-value cause a misleading signal for the policy update, making the standard offline RL algorithms, such as CQL and TD3-BC, ineffective in the online finetuning. Based on this observation, we address the problem of Q-value estimation by two techniques: (1) perturbed value update and (2) increased frequency of Q-value updates. The first technique smooths out biased Q-value estimation with sharp peaks, preventing early-stage policy exploitation of sub-optimal actions. The second one alleviates the estimation bias inherited from offline pretraining by accelerating learning. Extensive experiments on the MuJoco and Adroit environments demonstrate that the proposed method, named SO2, significantly alleviates Q-value estimation issues, and consistently improves the performance against the state-of-the-art methods by up to 83.1%.

Knowledge Transfer via Compact Model in Federated Learning (Student Abstract)

Communication overhead remains a significant challenge in federated learning due to frequent global model updates. Essentially, the update of the global model can be viewed as knowledge transfer. We aim to transfer more knowledge through a compact model while reducing communication overhead. In our study, we introduce a federated learning framework where clients pre-train large models locally and the server initializes a compact model to communicate. This compact model should be light in size but still have enough knowledge to refine the global model effectively. We facilitate the knowledge transfer from local to global models based on pre-training outcomes. Our experiments show that our approach significantly reduce communication overhead without sacrificing accuracy.

Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence

Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit time-dependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named Temporal MultiPersistence (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time. We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times.

Event-Triggered Consensus Control in Euler–Lagrange Systems Subject to Communication Delays and Intermittent Information Exchange

In this paper, we investigate the consensus control problem of Euler–Lagrange systems which can be used to describe the motion of various mechanical systems such as manipulators and quadcopters. We focus on consensus control strategies, which are important for achieving coordinated behavior in multi-agent systems. The paper considers the key challenges posed by random communication delays and packet losses that are increasingly common in networked control systems. In addition, it is assumed that each system receives information from neighboring agents intermittently. Addressing these challenges is critical to ensure the reliability and efficiency of such systems in real-world applications. Communication delay is time-varying and can be very large, but should be smaller than some bounded constant. To decrease the frequency of control input updates, we implement an event-triggered scheme that regulates the controller’s updates for each agent. Specifically, it does not update control inputs at traditional fixed intervals, but responds to predefined conditions and introduces a dynamic consensus item to handle information irregularities caused by communication delays and intermittent information exchange. The consensus can be achieved if the communication graph of agents contains a spanning tree with the desired velocity as the root node. That is, all Euler–Lagrange systems need to obtain the desired velocity, directly or indirectly (via neighbors), to reach consensus. We establish that the Zeno behavior can be avoided, ensuring a positive minimum duration between successive event-triggered instances. Finally, we provide simulation results to show the performance of our proposed algorithm.

Online as-Built Building Information Model Update for Robotic Monitoring in Construction Sites

Today, automated techniques for the update of as-built Building Information Models (BIM) make use of offline algorithms restricting the update frequency to an extent where continuous monitoring becomes nearly impossible. To address this problem, we propose a new method for robotic monitoring that updates an as-built BIM in real-time by solving a Simultaneous Localization and Mapping (SLAM) problem where the map is represented as a collection of elements from the as-planned BIM. The suggested approach is based on the Rao-Blackwellized Particle Filter (RBPF) which enables explicit injection of prior knowledge from the building’s construction schedule, i.e., from a 4D BIM, or its elements’ spatial relations. In the methods section we describe the benefits of using an exact inverse sensor model that provides a measure for the existence probability of elements while considering the entire probabilistic existence belief map. We continue by outlining robustification techniques that include both geometrical and temporal dimensions and present how we account for common pose and shape mistakes in constructed elements. Additionally, we show that our method reduces to the standard Monte Carlo Localization (MCL) in known areas. We conclude by presenting simulation results of the proposed method and comparing it to adjacent alternatives.

Hybrid dynamic event-triggered leader-following consensus for multi-agent systems with external disturbances

Herein, we address the leader-following consensus for multi-agent systems with a directed graph while considering external disturbances. To optimize resource utilization, we propose a hybrid dynamic event-triggered mechanism (HDETM) that minimizes the inter-agent communication and reduces the frequency of controller updates. Furthermore, regularization techniques are employed to ensure that the considered system maintains a Zeno-free behavior even in the presence of an external disturbance by guaranteeing a strictly positive inter-event time. Additionally, we propose a hybrid system framework incorporating auxiliary variables to depict both flow and jump dynamics. A Lyapunov consensus analysis is performed within this framework to verify finite-gain L2 stability. Finally, we validate the efficacy of the HDETM design method through numerical simulations.

The non-Cattell-Horn-Carroll (non-CHC) Model of Ancillary Broad and Narrow Abilities

The Cattell-Horn-Carroll (CHC) theory of abilities is a comprehensive model of human cognitive abilities denoted by the G-code. Developed by three prominent psychologists - Raymond Cattell, John Horn, and John Carroll - the CHC theory suggests that there are three strata of cognitive abilities. In this paper, the authors argued that the CHC model faces seven challenges: First, it is a complex model with numerous factors and subtests, posing difficulties in administration and interpretation. Second, the theory itself has limited coverage and may not address all cognitive abilities. Third, critics have been arguing that the CHC theory has failed to sufficiently address socio-cultural variations. Fourth, the CHC model of broad and narrow abilities lacks concrete operational definitions, relying on theoretical constructs. Fifth, it always fails to account for complex interactions between cognitive abilities. Sixth, the CHC model continually evolves, requiring frequent updates. Lastly, the CHC theory might not fully capture individual differences in cognitive abilities and potential, leaving room for improvement. There are alternative models (e.g., extended fluid-crystallized and verbal-perceptual-image rotation theories) to the CHC model. Despite these limitations, the CHC model remains a valuable tool for understanding cognitive abilities. In this paper, the authors have chosen to focus on the non-CHC categories of broad abilities with their respective narrow abilities. Termed as ancillary abilities denoted by the Q-code, this paper aims to provide another model of abilities (not necessarily cognitive), not to replace the current CHC model, but to complement or supplement it.

Leader-following mean square consensus of stochastic switched multi-agent systems with event-triggered control

ABSTRACT In this paper, a novel distributed event-triggered mechanism (ETM) is used to solve the problem of the leader-following mean square consensus of stochastic switched multi-agent systems (SSMASs) with switching dynamics of agents, disturbed by the multiplicative noise. Moreover, the distributed ETM utilises only the state information at the triggering instants of neighbouring agents, which eliminates continuous communication among agents while reducing the frequency of controller updates. A general agent-dependent switching law is developed to achieve leader-following mean square consensus of SSMASs. Subsequently, it is proved that under the distributed ETM, linear SSMASs can achieve leader-following mean square consensus and no Zeno behaviour occurs with the help of stochastic analysis and the average dwell time approach. Finally, the effectiveness of the study is illustrated by a numerical example.

An improved alternating direction method of multipliers for solving deterministic user equilibrium

An improved alternating direction method of multipliers (iADMM) algorithm is proposed for solving the deterministic user equilibrium (DUE) problem. The iADMM algorithm introduces a key improvement wherein the primal variables can be updated sequentially, possibly multiple times, before updating the dual variables. Subsequently, a sensitivity analysis method is applied to optimise the update frequency of primal variable, and two indices are introduced to evaluate the performance of various policies. Additionally, eliminating equality constraints through the augmented Lagrangian function makes it challenging to completely satisfy flow conservation conditions, resulting in the presence of primal residual. To address this issue, two schemes are proposed to handle the primal residual, involving modifications to the primal variables based on iterative messages. Numerical experiments highlight that the convergence of ADMM algorithm is significantly influenced by the update frequency of primal variable, and addressing the primal residual after each cycle enhances the convergence of ADMM algorithm.

SWIX: A Memory-efficient Sliding Window Learned Index

Data stream processing systems enable querying over sliding windows of streams of data. Efficient index structures for the streaming window are a crucial building block to enable querying the sliding window for operations such as aggregation and joins. This paper proposes SWIX, a novel memory-efficient learned index for sliding windows. Unlike conventional learned indexes that rely on tree structures to achieve logarithmic query cost, SWIX has a flat structure that uses substantially less memory and enables efficient query execution while having a low cost for index maintenance when inserting (and retraining). SWIX dynamically adapts itself to the real-time distribution shifts of data streams. SWIX outperforms existing indexes in terms of query execution time and memory footprint for workloads characterized by very frequent updates. Our results show that SWIX has a significantly smaller memory footprint than conventional, streaming, and learned indexes, using only 22% to 42% of the size compared to state-of-the-art approaches, yet outperforming them by up 1.2× to 1.6× on average (and up to 52×) in terms of query time, making it a space- and time-efficient method for indexing data streams. For concurrent learned indexes, Parallel SWIX can achieve up to 3.45× throughput with only 34% of memory consumption.

LST-Bench: Benchmarking Log-Structured Tables in the Cloud

Data processing engines increasingly leverage distributed file systems for scalable, cost-effective storage. While the Apache Parquet columnar format has become a popular choice for data storage and retrieval, the immutability of Parquet files renders it impractical to meet the demands of frequent updates in contemporary analytical workloads. Log-Structured Tables (LSTs), such as Delta Lake, Apache Iceberg, and Apache Hudi, offer an alternative for scenarios requiring data mutability, providing a balance between efficient updates and the benefits of columnar storage. They provide features like transactions, time-travel, and schema evolution, enhancing usability and enabling access from multiple engines. Moreover, engines like Apache Spark and Trino can be configured to leverage the optimizations and controls offered by LSTs to meet specific business needs. Conventional benchmarks and tools are inadequate for evaluating the transformative changes in the storage layer resulting from these advancements, as they do not allow us to measure the impact of design and optimization choices in this new setting. In this paper, we propose a novel benchmarking approach and metrics that build upon existing benchmarks, aiming to systematically assess LSTs. We develop a framework, LST-Bench, which facilitates effective exploration and evaluation of the collaborative functioning of LSTs and data processing engines through tailored benchmark packages. A package is a mix of use patterns reflecting a target workload; LST-Bench makes it easy to define a wide range of use patterns and combine them into a package, and we include a baseline package for completeness. Our assessment demonstrates the effectiveness of our framework and benchmark packages in extracting valuable insights across diverse environments. The code for LST-Bench is open source and is available at https://github.com/microsoft/lst-bench/.

Leaf Senescence Database v5.0: A Comprehensive Repository for Facilitating Plant Senescence Research

Through an extensive literature survey, we have upgraded the Leaf Senescence Database (LSD v5.0; https://ngdc.cncb.ac.cn/lsd/), a curated repository of comprehensive senescence-associated genes (SAGs) and their corresponding mutants. Since its inception in 2010, LSD undergoes frequent updates to encompass the latest advances in leaf senescence research and its current version comprises a high-quality collection of 31,740 SAGs and 1,209 mutants from 148 species, which were manually searched based on robust experimental evidence and further categorized according to their functions in leaf senescence. Furthermore, LSD was greatly enriched with comprehensive annotations for the SAGs through meticulous curation using both manual and computational methods. In addition, it was equipped with user-friendly web interfaces that facilitate text queries, BLAST searches, and convenient download of SAG sequences for localized analysis. Users can effortlessly navigate the database to access a plethora of information, including literature references, mutants, phenotypes, multi-omics data, miRNA interactions, homologs in other plants, and cross-links to various databases. Taken together, the upgraded version of LSD stands as the most comprehensive and informative plant senescence-related database to date, incorporating the largest collection of SAGs and thus bearing great utility for a wide range of studies related to plant senescence.