Update Time Research Articles

Towards real-time optimal control of wind farms using large-eddy simulations

Abstract. Large-eddy simulations (LESs) are commonly considered too slow to serve as a practical wind farm control model. Using coarser grid resolutions, this study examines the feasibility of LES for real-time, receding-horizon control to optimize the overall energy extraction in wind farms. By varying the receding-horizon parameters (i.e. the optimization horizon and control update time) and spatiotemporal resolution of the LES control models, we investigate the trade-off between computational speed and controller performance. The methodology is validated on the TotalControl Reference Wind Power Plant using a fine-grid LES model as a wind farm emulator. Analysis of the resulting power gains reveals that the performance of the controllers is primarily determined by the receding-horizon parameters, whereas the grid resolution has minor impact on the overall power extraction. By leveraging these insights, we achieve near-parity between our LES-based controller and real-time computational speed, while still maintaining competitive power gains up to 40 %.

A broad learning model guided by global and local receptive causal features for online incremental machinery fault diagnosis

Intelligent fault diagnosis (IFD) plays a significant function in ensuring the reliable operation of mechanical equipment. However, most existing IFD methods are trained in batch learning way and the feature extraction process is unexplainable and it is challenging to satisfy the complex diagnosis requirements. Thus, this paper proposes a causal broad learning model (CBLM) guided by global and multi-scale local causal features for incremental machinery fault diagnosis. Firstly, rich global and multi-scale local causal features are extracted for training CBLM. Then, incremental learning capability is developed to update model by expanding or modifying the original weights, considerably reducing the time consumption and greatly improving the computational efficiency. When the initial model has inadequate nodes for feature learning, CBLM performs structural incremental learning by adding extra nodes to improve the diagnostic performance. As new samples with various fault degrees are entered, sample incremental learning is built to rapidly update based on previous model without retraining. Finally, two experimental results on bearings indicate that CBLM improves the testing accuracy of the initial model by 0.13% to 52.22% and 1.95% to 57.64%, respectively, and remains above 97.22% and 99.77% for the updated models. The training time consumption is reduced by 25.06 s to 133.0 s, 20.78 s and 141.9 s, respectively, and the subsequent models update time are only about 12 s and 10 s. Consequently, the proposed CBLM is an effective online IFD method because it is obviously superior to the existing IFD methods in regards to time consumption and diagnosis accuracy.

Civil Aeronautical Ad Hoc Network Zero-Overhead Clustering Algorithm Based on Realtime Position Information of the Aircraft

Clustering is an important means to solve the poor scalability of aeronautical ad hoc networks (AANET). To improve the stability and performance of AANET and avoid unnecessary waste of resources caused by civil aircraft in communication, we proposed a zero-overhead clustering algorithm according to the real-time position of the aircraft based on the known trajectory. Firstly, the route and trajectory models are used to obtain geographical coordinates by the aircraft positioning algorithm. On this basis, the geographical cluster and cluster head region are divided in order to complete the cluster setting. Considering the aircraft maintenance cluster generation time updates, we use the communication sub-cluster generation algorithm to control the size of the cluster, and also, the flexibility of cluster hops is guaranteed by the subsidiary cluster members. The continuity of communication and the scalability of the cluster are maintained by the gateway node, thereby forming a network structure and increasing the stability of clusters. Finally, the actual route data are used to simulate the performance of the algorithm. The experimental and analytical results show that clustering and maintenance of the algorithm have zero overhead. Additionally, compared with the traditional algorithm, our proposed method can maintain a reasonable number of clusters, reduce the frequency of cluster head replacement, reduce the number of cluster members entering and leaving the cluster and avoid the loss of control of cluster heads to cluster members. So, it has important application value in the field of civil aviation.

Dynamic Connectivity in Disk Graphs

Let S subseteq mathbb {R}^2 be a set of nsites in the plane, so that every site s in S has an associated radiusr_s > 0. Let mathcal {D}(S) be the disk intersection graph defined by S, i.e., the graph with vertex set S and an edge between two distinct sites s, t in S if and only if the disks with centers s, t and radii r_s, r_t intersect. Our goal is to design data structures that maintain the connectivity structure of mathcal {D}(S) as sites are inserted and/or deleted in S. First, we consider unit disk graphs, i.e., we fix r_s = 1, for all sites s in S. For this case, we describe a data structure that has O(log ^2 n) amortized update time and O(log n/log log n) query time. Second, we look at disk graphs with bounded radius ratioPsi , i.e., for all s in S, we have 1 le r_s le Psi , for a parameter Psi that is known in advance. Here, we not only investigate the fully dynamic case, but also the incremental and the decremental scenario, where only insertions or only deletions of sites are allowed. In the fully dynamic case, we achieve amortized expected update time O(Psi log ^{4} n) and query time O(log n/log log n). This improves the currently best update time by a factor of Psi . In the incremental case, we achieve logarithmic dependency on Psi , with a data structure that has O(alpha (n)) amortized query time and O(log Psi log ^{4} n) amortized expected update time, where alpha (n) denotes the inverse Ackermann function. For the decremental setting, we first develop an efficient decremental disk revealing data structure: given two sets R and B of disks in the plane, we can delete disks from B, and upon each deletion, we receive a list of all disks in R that no longer intersect the union of B. Using this data structure, we get decremental data structures with a query time of O(log n/log log n) that supports deletions in O(nlog Psi log ^{4} n) overall expected time for disk graphs with bounded radius ratio Psi and O(nlog ^{5} n) overall expected time for disk graphs with arbitrary radii, assuming that the deletion sequence is oblivious of the internal random choices of the data structures.

Energy-efficient Online Continual Learning for Time Series Classification in Nanorobot-based Smart Health.

Nanorobots have been used in smart health to collect time series data such as electrocardiograms and electroencephalograms. Real-time classification of dynamic time series signals in nanorobots is a challenging task. Nanorobots in the nanoscale range require a classification algorithm with low computational complexity. First, the classification algorithm should be able to dynamically analyze time series signals and update itself to process the concept drifts (CD). Second, the classification algorithm should have the ability to handle catastrophic forgetting (CF) and classify historical data. Most importantly, the classification algorithm should be energy-efficient to use less computing power and memory to classify signals in real-time on a smart nanorobot. To solve these challenges, we design an algorithm that can Prevent Concept Drift in Online continual Learning for time series classification (PCDOL). The prototype suppression item in PCDOL can reduce the impact caused by CD. It also solves the CF problem through the replay feature. The computation per second and the memory consumed by PCDOL are only 3.572M and 1KB, respectively. The experimental results show that PCDOL is better than several state-of-the-art methods for dealing with CD and CF in energy-efficient nanorobots.

Knowledge management system as an efficient and sustainable strategy to minimize uncertainty in the process of risk assessment

Risk assessors could adopt qualitative, semi-quantitative, or quantitative approaches to analyze various risks; the combination of these approaches alleviates the shortcomings of risk assessment techniques, namely uncertainty, knowledge dimension, and time dynamics when techniques are used alone. The knowledge dimension plays a pivotal role in these shortcomings, as knowledge reduces uncertainty (United States Environmental Protection Agency [EPA], n.d.-a) and the timely knowledge update of global trends and emerging risks is expected to resolve the issue of time dynamics (another cause of uncertainty) by reassessing risks and characterizing risk data over a time interval (Wassénius & Crona, 2022). However, substantial research and development are required to generate adequate modeling and analytical methods to deal with different and complex systems. Based on the literature review and industry best practices, the study develops a risk assessment knowledge management system framework that focuses on the root of the shortcomings of risk assessment techniques, namely the knowledge dimension; this strategy is efficient and sustainable by indirectly addressing the unresolved issues of uncertainty and time dynamics through the knowledge dimension. The conceptual framework minimizes the uncertainty (the root of risk) in the decision-making process of selecting the appropriate risk assessment tools and effectively implementing them.

Optimal Period Input Design in FIR System Identification With Binary-Valued Observations and Event-Triggered Communication

In the identification of networked systems, the sender uses the event-triggered mechanism to reduce the times of sending data to save communication resources, but the receiver needs as much data as possible to improve the estimation accuracy. This paper tries to mediate the contradiction between the two by means of input design. First, based on the communication rate and the convergence rate of the identification algorithm, the problem of optimal input design is became an optimization problem with constraints. Then, the algorithm for solving this optimization problem is given. Next, the two-time-scale implementation method of optimal input is proposed, where the updated time of the parameter estimation constitutes the identified time scale, and the input is updated based on the estimated value of the system parameter and lasts for a period of time that constitutes the duration time scale of the input. Finally, the validity of the algorithm is verified by numerical simulation.

First detection of X-ray pulsations and spectrum of the high Galactic latitude pulsar PSR J0837–2454 and direct Urca cooling implications

Abstract PSR J0837 $-$ 2454 is a young 629 ms radio pulsar whose uncertain distance has important implications. A large distance would place the pulsar far out of the Galactic plane and suggest it is the result of a runaway star, while a short distance would mean the pulsar is extraordinarily cold. Here we present further radio observations and the first deep X-ray observation of PSR J0837 $-$ 2454. Data from the Parkes Murriyang telescope show flux variations over short and long timescales and also yield an updated timing model, while the position and proper motion (and, less strongly, parallax) of the pulsar are constrained by a number of low-significance detections with the Very Long Baseline Array. XMM-Newton data enable detection of X-ray pulsations for the first time from this pulsar and yield a spectrum that is thermal and blackbody-like, with a cool blackbody temperature $\approx$ $70\ \mbox{eV}$ or atmosphere temperature $\approx$ $50\ \mbox{eV}$ , as well as a small hotspot. The spectrum also indicates the pulsar is at a small distance of $\lesssim$ $1\ \mbox{kpc}$ , which is compatible with the marginal VLBA parallax constraint that favours a distance of $\gtrsim$ 330 pc. The low implied luminosity ( $\sim7.6\times10^{31}\mbox{erg\, s}^{-1}$ at 0.9 kpc) suggests PSR J0837 $-$ 2454 has a mass high enough that fast neutrino emission from direct Urca reactions operates in this young star and points to a nuclear equation of state that allows for direct Urca reactions at the highest densities present in neutron star cores.

Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking

In order to increase a nonlinear system’s state estimate precision, an iterated orthogonal simplex cubature Kalman filter (IOSCKF) is presented in this study for target tracking. The Gaussian-weighted integral is decomposed into a spherical integral and a radial integral, which are approximated using the spherical simplex-radial rule and second-order Gauss–Laguerre quadrature rule, respectively, and result in the novel simplex cubature rule. To decrease the high-order error terms, cubature points with appropriate weights are taken from the cubature rule and processed using the provided orthogonal matrix. The structure supporting the nonlinear Kalman filter incorporates the altered points and weights and the calculation steps; from this, the updated time and measurement can be inferred. The Gauss–Newton iteration is employed repeatedly to adjust the measurement update until the termination condition is met and the IOSCKF is attained. The proposed algorithms are applied in target tracking, including CV target tracking and spacecraft orbit tracking, and the simulation results reveal that the IOSCKF can achieve higher accuracy compared to the CKF, SCKF, and OSCKF. In spacecraft orbit tracking simulation, compared with the SCKF, the position tracking accuracy and velocity tracking accuracy of the OSCKF are increased by 2.21% and 1.94%, respectively, which indicates that the orthogonal transformation can improve the tracking accuracy. Furthermore, compared with the OSCKF, the position tracking accuracy and velocity tracking accuracy of the IOSCKF are increased by 2.71% and 2.97%, respectively, which indicates that the tracking accuracy can be effectively improved by introducing iterative calculation into the measurement equation, thus verifying the effectiveness of the method presented in this paper.

Web-based smart attendance monitoring system using QR code and GSM module for SMS notificatio

The study was designed and developed to address certain problems and concerns of the school administration and parents of students attending Ciriaco Mariano Elementary School. The system was used to provide the school an innovative way to record attendance of students, provide parents with real time updates regarding the entry and exit of their children in the school premises, and be able to monitor students by generating attendance reports. The Web-based Smart Attendance Monitoring System using QR code and GSM module for SMS notification was developed through the implementation of the Iterative System Development Life Cycle Model. This iterative model ensures that the web-based system conformed to the all of the requirements from the CMES and of the requirements set by the ISO 25010 criteria. To assess the quality of the developed web-based system, the researcher adapted the ISO 25010 Software Quality Standard as evaluation tool. Two sets of questionnaires were developed and were given to respondents who were selected using purposive sampling. The first set of questionnaires were given to IT experts to assess the technical qualities of the system which includes functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. The second set of questionnaires were given to the school principal, teachers, and parents to assess the web-based system quality on selected criteria which includes functional suitability, performance efficiency, and usability. The Web-based Smart Attendance Monitoring System using QR code and GSM module for SMS notification was successfully developed using the iterative SDLC model and found conformed to the ISO 25010 software quality standards as implied by the results from the evaluation of IT experts, school principal, teachers, and parents.

Online assessment of transient power angle & voltage in power system: Realizing fast prediction and fast update in any scenarios

Transient voltage and transient power angle are in the same time scale, both of which are the basis of safety operation of the power system. However, there are few studies on the integration of power angle and voltage at present. How to quickly realize the integrated assessment of voltage & power angle and how to shorten the update period when the model accuracy drops are the problems that need to be solved urgently. Therefore, this paper proposes a fast assessment scheme that considers both transient voltage and transient power angle. In this study, the stable boundary between the transient power angle and the transient voltage is first constructed from the time dimension by the variable step dichotomy. And then, a convolutional neural network with multiscale residual squeeze excitation (MRSE-CNN) is proposed, which can assess the voltage and power angle without post-fault-clearance data. It only takes three sampling points to accurately learn the mapping between the input feature and the stable boundary. At the same time, the results of whether the transient voltage and the transient power angle are stable, and the corresponding margin are output. By introducing the improved Huber loss function to dynamically adjust the cost of misjudgment and missing judgment, the reliability of the model is further enhanced. In the online application, a pool-based active transfer learning is proposed for the unlearned scenarios under load, topology, and renewable energy, which greatly reduces the adaptive update time of the model in unlearned scenarios. The model is verified in the improved IEEE 39 bus system and provincial interconnection system. It shows that the proposed method can quickly and accurately realize the integrated adaptive assessment of transient power angle and voltage in any scenarios.

Improved Period Variations of 32 Contact Binaries with Rapidly Decreasing Periods in the Galactic Bulge

We present detailed analyses of updated eclipse timing diagrams for 32 contact binary merger candidates in the Galactic bulge. The photometric data was obtained from 2016 to 2021 using the Korea Microlensing Telescope Network with the 1.6 m telescopes located at three southern sites (CTIO, SAAO, and SSO). The times of minimum lights were determined by applying the binary-star model to full light curves created at half-year intervals from the observations. The orbital period variations of the binary systems were analyzed using the O − C diagrams from our new timings with the others published in the literature, which are based on the OGLE observations from 2001 to 2015. As results, the orbital periods and period-decreasing rates of 32 binary systems were located to be in the ranges of 0.370 to 1.238 days and from −3.0 to −13.1 × 10−6 day yr−1, respectively. Out of these stars, 24 systems show a combination effect of a parabola and a light travel time caused by a third body, and their outer orbital periods are in the range of 9.1–26.5 yr. We propose that all of our merger candidates need additional monitoring observations to study a luminous-red nova progenitor.

Online advance respiration prediction model for percutaneous puncture robotics.

Surgical robots have significant research value and clinical significance in the field of percutaneous punctures. There have been numerous studies on ultrasound-guided percutaneous surgical robots; however, addressing the respiratory compensation problem of deep punctures remains a significant obstacle. Herein we propose a robotic system for percutaneous puncture with respiratory compensation. We proposed an online advance respiratory prediction model based on Bidirectional Gate Recurrent Unit (Bi-GRU) for the respiratory prediction requirements of surgical robot systems. By analyzing the main factors governing the accuracy of the respiratory motion prediction models, various parameters of the online advance prediction model were optimized. Subsequently, we integrated and developed ultrasound-guided percutaneous puncture robot software and a hardware platform to implement respiratory compensation, thus verifying the effectiveness and reliability of various key technologies in the system. The proposed respiratory prediction model has a significantly reduced update time, with an average root mean square error (RMSE) of less than 0.4mm. This represents a reduction of ~ 20% compared to the online training long short-term memory(LSTM). By conducting puncture experiments based on a respiratory phantom, the average puncture error was 2.71 ± 0.65mm and the average single-round puncture time was 65.00 ± 6.67s. Herein we proposed and optimized an online training respiratory prediction network model based on Bi-GRU. The stability and reliability of this system are verified by conducting puncture experiments on a respiratory phantom.

Power Plastics: A Hybrid Lagrangian/Eulerian Solver for Mesoscale Inelastic Flows

We present a novel hybrid Lagrangian/Eulerian method for simulating inelastic flows that generates high-quality particle distributions with adaptive volumes. At its core, our approach integrates an updated Lagrangian time discretization of continuum mechanics with the Power Particle-In-Cell geometric representation of deformable materials. As a result, we obtain material points described by optimized density kernels that precisely track the varying particle volumes both spatially and temporally. For efficient CFL-rate simulations, we also propose an implicit time integration for our system using a non-linear Gauss-Seidel solver inspired by X-PBD, viewing Eulerian nodal velocities as primal variables. We demonstrate the versatility of our method with simulations of mesoscale bubbles, sands, liquid, and foams.

Indoor fire emergency evacuation path planning based on improved NavMesh algorithm

With the acceleration of urbanization, the frequency of building fire incidents has been increasing year by year. Therefore, rapid, efficient, and safe evacuation from buildings has become an urgent and important task. A construction fire escape path planning method based on an improved NavMesh algorithm is proposed in this paper. Firstly, by using the method of local updates in the navigation grid, redundant computation is reduced, and the update time of the improved algorithm is about 6.8% of that of the original algorithm, immediate generation of navigation is achieved. Secondly, the heuristic function of the pathfinding algorithm is improved, and a multi-exit path planning mechanism is proposed to achieve more efficient, which can quickly plan a safe evacuation path away from the spreading fire and smoke in the event of a fire. Finally, a new evaluation index called Navigation Grid Complexity (NGC) is proposed and demonstrated to measure the quality of navigation grids. The feasibility and effectiveness of the proposed method are validated through simulation experiments on actual building models, which can provide real-time, efficient, intelligent, and safe path planning for rapid evacuation of evacuees in the fire scene.

Optimizing process and methods for a living systematic review: 30 search updates and three review updates later

ObjectiveTo describe the living systematic review (LSR) process and to share experience of planning, searches, screening, extraction, publishing and dissemination to inform and assist authors planning their own LSR. Many LSR do not publish more than one update, we hope this paper helps to increase this. Study Design and SettingA Cochrane LSR with an international author team that has been ‘living’ for two years, with monthly search updates and three full updates published in this time. LSRs are regularly updated systematic reviews that allow new evidence to be incorporated as it becomes available. LSR are ideally suited to policy-relevant topics where there is uncertainty and new evidence will likely impact the interpretation and/or certainty of outcomes. ResultsThe key features of the process that require consideration are: specifying the frequency of searches and triggers for full updates in the protocol; stakeholder input; publishing and disseminating monthly search findings. A strong team, incorporating methodological and topic expertise, with core members that meet regularly is essential. Regular search updates make it important to have a clear cyclical schedule of activity. To achieve timely updates this process should be streamlined, for example, using automated monthly searches, and systematic reviewing software for screening. LSR provide a unique opportunity to incorporate stakeholder feedback. ConclusionsWe recommend that LSRs should be: justified; carefully planned including the timing of search updates, triggers for publication and termination; published in a timely manner; have a clear dissemination plan; and a strong core team of authors.

The Privilege against Self-Incrimination in EU Competition Law: Time for a Case Law Update?

Abstract Since 1989, the Court of Justice of the European Union has recognised a privilege against self-incrimination for undertakings subject to public enforcement procedures on the basis of Articles 101 and 102 TFEU. That privilege forms part of the fundamental rights of the defence. Over time, the privilege has been read into Article 6 ECHR and has gained ground in other domains of EU law as well. Against that background, the question arises as to whether the CJEU’s original case law in the field of EU competition law needs to be updated. This paper revisits that case law by comparing it with developments in the context of the ECHR and in other domains of EU law. It argues that, in light of those developments, a case law update may indeed prove necessary. However, such an update alone would not sufficiently address the practical difficulties currently surrounding the application of the privilege in practice. For that, more coordinated legislative action would be warranted.

Estimation algorithm of pod installation angles based on cubature Kalman filter

To estimate the installation angles of the pod, a detailed derivation of the attitude transformation relationship between the pod, aircraft, and inertial navigation systems is performed. Based on this, a cubature Kalman filtering algorithm is proposed to estimate the pod’s installation angles. The algorithm architecture integrates the time update part of the Kalman filter and measurement update part of the cubature Kalman filter. This algorithm has been validated through a flight test, demonstrating its effectiveness in estimating the installation angles of the master inertial navigation system relative to the aircraft wing, as well as the slave inertial navigation system relative to the pod frame. After compensating for the installation angles, the standard deviations of roll, pitch, and azimuth between the two inertial systems have significantly improved: roll angle from 0.1414° to 0.0282°, pitch angle from 0.0803° to 0.0335°, and azimuth angle from 0.2629° to 0.0490°.

Twelve tips for medical educators to optimize their curricula vitae

A good curriculum vitae (CV) highlights medical educators’ academic achievements and supports their professional goals. Many faculty struggle with timely updates and strategic formatting. These twelve tips will help medical educators optimize their CV to best showcase their strengths and accomplishments. The first three tips outline a process: identify a system to collect potential entries and schedule regular time for updates. Tips four and five detail how to tailor traditional CV formatting to best describe the work of medical educators. The next few tips offer concrete strategies and examples of CV entries to consider for inclusion. The remaining tips remind faculty to ask for help from colleagues, who can share a sample CV and identify overlooked activities. Our intention is to transform a task that can be burdensome into a process that seamlessly captures the breadth of our work as medical educators and allows for introspection and growth.

Novel pharmacological developments in the management of paediatric inflammatory bowel disease: Time for guideline update - A narrative review.

The incidence of paediatric inflammatory bowel disease (IBD) continues to increase in both adults and children across the globe, with more than one third of the patients not responding to anti-tumour necrosis factor biologics and immune modulators. This narrative review provides an overview of novel pharmacological developments in the management of paediatric IBD, including new biological therapies. A PubMed Medline search was performed to include randomised controlled trials, retrospective and prospective observational studies, and relevant case reports of children with IBD published between 2018 and January 2023. Guidelines and protocols from relevant paediatric and adult gastroenterology societies, such as the European Society for Paediatric Gastroenterology, Hepatology and Nutrition and the European Crohn's and Colitis Organisation, were also included. Non-pharmacological treatments including therapeutic diets and faecal microbiota transplantation were outside the scope of this work. Early real-world evidence suggests that newer biologics and small molecules, such as anti-integrins, interleukin-12 and/or interleukin-23 inhibitors, Janus kinase and signal transducer and activator of transcription proteins inhibitors, are safe and effective in adult patients with IBD, with promising growing evidence for paediatric IBD. While many developments have been achieved with novel pharmacological treatments to manage IBD, ongoing research is required to confirm their effectiveness and safety in the paediatric age. Extending the licence of novel treatments to children will be crucial to tackle the increasing loss of response to conventional treatments. International guidelines will require timely updating to incorporate novel treatments within the existing protocols.