Search Results (41)

Load balancing is a widely adopted strategy in modern distributed systems because it distributes workloads across servers, mitigating overload and improving overall performance. However, the rapid growth of such systems has created a need for more adaptive strategies to ensure optimal utilization and responsiveness of resources. Traditional algorithms such as Round Robin (RR) and Weighted Round Robin (WRR) assign requests without considering server states or request characteristics. We implement a machine learning (ML)–based predictive load balancer, forecasting the latency of a request based on the request itself and container parameters, specifically the average latency of the last 50 requests and the count of active requests, and evaluate it against RR and WRR. For the experiment, synthetic data were generated to replicate real-world requests by creating random URL and method combinations, attaching a task size in Million Instructions (MI), and distributing them among three containers with varying resources according to the load balancing strategies described above. Under the conditions tested, the ML approach achieved the worst performance, trailing both RR and WRR in terms of throughput and average latency, although the model accuracy was sufficiently high (R² = 0.8+). Post hoc analysis indicates that limited and occasionally stale runtime features caused the load balancer to direct all requests to a single container until the next statistics update, since that container was considered the ‘best’ during that interval. Full article

Composite membranes are a hot topic in the field of membrane research. With the continuous progress of technology, its development has advanced from the application of simple copolymers to diversified material combinations. This Perspective examines why many composite membranes that excel in the lab struggle to deliver credible, durable performance at scale. Our aim is to connect four issues that are often treated in isolation—interfacial stability, manufacturability, data quality, and circular design—and to translate them into practical reporting and testing habits for the community. The novelty lies in treating “credibility” as the target function: we propose discipline-first guidelines that couple dynamic interfacial measurements with standardized long-run fouling and cleaning protocols, techno-economic and life-cycle reporting, and process-aware chemistry that fits existing hardware. We outline near-term applications in water treatment and resource recovery where drop-in formats and safer solvents already enable pilot-level operation. The future scope includes round-robin builds, FAIR data deposits, and durability metrics aligned with widely used standards for fouling potential and system benchmarking. Progress, we argue, will be measured less by first-day flux and more by what survives months of operation with uncertainty and costs on the page. Full article

The adoption of artificial intelligence (AI) in industrial manufacturing lags behind research progress, partly due to smaller, imbalanced datasets derived from real processes. In non-destructive aerospace testing, this challenge is amplified by the low defect rates of high-quality manufacturing. This study evaluates the use of synthetic data, generated via multiresolution stochastic texture synthesis, to mitigate class imbalance in material defect classification for the superalloy Inconel 718. Multiple datasets with increasing imbalance were sampled, and an image classification model was tested under three conditions: native data, data augmentation, and synthetic data inclusion. Additionally, round robin tests with experts assessed the realism and quality of synthetic samples. Results show that synthetic data significantly improved model performance on highly imbalanced datasets. Expert evaluations provided insights into identifiable artificial properties and class-specific accuracy. Finally, a quality assessment model was implemented to filter low-quality synthetic samples, further boosting classification performance to near the balanced reference level. These findings demonstrate that synthetic data generation, combined with quality control, is an effective strategy for addressing class imbalance in industrial AI applications. Full article

Given the potential significance of the motivation to lead in answering the question of who will most highly emerge for leadership positions, the motivation to lead has garnered considerable attention. Nevertheless, we put forward a distinct perspective on the influence of the motivation to lead on leadership emergence. Based on the theory of leadership identity construction, we developed and tested a potential curvilinear relationship between individual motivation to lead and leadership emergence while also examining the moderating role of shared team vision. This study involved 639 employees across 159 work teams, with data collected using a multi-wave, round-robin approach. The results of social relations analyses indicated that individual motivation to lead has an inverted U-shaped relationship with leadership emergence. Meanwhile, shared team vision positively moderates the curvilinear relationship, such that those teams with weak shared vision experience foreshortened and weakened positive effect from motivation to lead. These results underscore the importance of comprehending the level of leadership motivation that can promote or prevent leadership emergence within work teams. Full article

This study evaluates leadership uniformity—the degree to which the proposer role is evenly distributed among validator nodes over time—in Quorum-based Byzantine Fault Tolerance (QBFT), a Byzantine Fault-Tolerant (BFT) consensus algorithm used in permissioned blockchain networks. By introducing simulated follower timeouts derived from uniform, normal, lognormal, and Weibull distributions, it models a range of network conditions and latency patterns across nodes. This approach integrates Raft-inspired timeout mechanisms into the QBFT framework, enabling a more detailed analysis of leader selection under different network conditions. Three leader selection strategies are tested: Direct selection of the node with the shortest timeout, and two quorum-based approaches selecting from the top 20% and 30% of nodes with the shortest timeouts. Simulations were conducted over 200 rounds in a 10-node network. Results show that leader selection was most equitable under the Weibull distribution with shape $k=0.5$, which captures delay behavior observed in real-world networks. In contrast, the uniform distribution did not consistently yield the most balanced outcomes. The findings also highlight the effectiveness of quorum-based selection: While choosing the node with the lowest timeout ensures responsiveness in each round, it does not guarantee uniform leadership over time. In low-variability distributions, certain nodes may be repeatedly selected by chance, as similar timeout values increase the likelihood of the same nodes appearing among the fastest. Incorporating controlled randomness through quorum-based voting improves rotation consistency and promotes fairer leader distribution, especially under heavy-tailed latency conditions. However, expanding the candidate pool beyond 30% (e.g., to 40% or 50%) introduced vote fragmentation, which complicated quorum formation in small networks and led to consensus failure. Overall, the study demonstrates the potential of timeout-aware, quorum-based leader selection as a more adaptive and equitable alternative to round-robin approaches, and provides a foundation for developing more sophisticated QBFT variants tailored to latency-sensitive networks. Full article

The Geopyörä breakage test uses two counter-rotating wheels to nip and crush rock specimens with a tightly controlled gap between rollers. This paper presents the detailed measures conducted to evaluate the accuracy and precision of energy measurements across various ore types using the Geopyörä. Force measurement was assessed just for its precision. The outputs were compared directly to the drop weight test (DWT) measures of fragmentation at the same energy and fitted A and b parameters. Test reproducibility was evaluated using a Round-Robin methodology, testing several samples in multiple laboratories. The results confirmed that the new test has sufficient accuracy to match DWT results and excellent precision to assure reproducibility. Full article

Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux’s completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper. Full article

The limits of chip technology are constantly being pushed with the continuous development of integrated circuit manufacturing processes and equipment. Currently, chips contain several billion, and even tens of billions, of transistors, making chip testing increasingly challenging. The verification of very large-scale integrated circuits (VLSI) requires testing on specialized automatic test equipment (ATE), but their cost and size significantly limit their applicability. The current FPGA-based ATE is limited in its scalability and support for few test channels and short test vector lengths. As a result, it is only suitable for testing specific chips in small-scale circuits and cannot be used to test VLSI. This paper proposes a low-cost hardware and software solution for testing digital integrated circuits based on design for testability (DFT) on chips, which enables the functional and performance test of the chip. The solution proposed can effectively use the resources within the FPGA to provide additional test channels. Furthermore, the round-robin data transmission mode can also support test vectors of any length and it can satisfy different types of chip test projects through the dynamic configuration of each test channel. The experiment successfully tested a digital signal processor (DSP) chip with 72 scan test pins (theoretically supporting 160 test pins). Compared to our previous work, the work in this paper increases the number of test channels by four times while reducing resource utilization per channel by 37.5%, demonstrating good scalability and versatility. Full article

The contact wire wear is an important parameter to ensure the safety operation of electric railways. The contact wire may break if the wear is serious, which leads to transportation interruptions. This study proposes an optical measurement method of contact wire wear, using stereovision technology. The matching method of stereovision based on line-scan cameras is proposed. A lookup-table method is developed to exactly determine the image resolution caused by the contact wire being in different spatial positions. The wear width of the contact wire is extracted from catenaries’ images, and the residual thickness of the contact wire is calculated. The method was verified by field tests. The round-robin tests of the residual thickness at the same location present excellent measurement repetitiveness. The maximum difference value between dynamic test results and ground measurement results is 0.13 mm. This research represents a potential way to implement condition-based maintenance for contact wire wear in the future in order to improve the maintenance efficiency and ensure the safety of catenary infrastructure. Full article

We propose and demonstrate a new two-stage maximum likelihood estimator for parameters of a social relations structural equation model (SR-SEM) using estimated summary statistics ($\widehat{\mathsf{\Sigma }}$) as data, as well as uncertainty about $\widehat{\mathsf{\Sigma }}$ to obtain robust inferential statistics. The SR-SEM is a generalization of a traditional SEM for round-robin data, which have a dyadic network structure (i.e., each group member responds to or interacts with each other member). Our two-stage estimator is developed using similar logic as previous two-stage estimators for SEM, developed for application to multilevel data and multiple imputations of missing data. We demonstrate out estimator on a publicly available data set from a 2018 publication about social mimicry. We employ Markov chain Monte Carlo estimation of $\widehat{\mathsf{\Sigma }}$ in Stage 1, implemented using the R package rstan. In Stage 2, the posterior mean estimates of $\widehat{\mathsf{\Sigma }}$ are used as input data to estimate SEM parameters with the R package lavaan. The posterior covariance matrix of estimated $\widehat{\mathsf{\Sigma }}$ is also calculated so that lavaan can use it to calculate robust standard errors and test statistics. Results are compared to full-information maximum likelihood (FIML) estimation of SR-SEM parameters using the R package srm. We discuss how differences between estimators highlight the need for future research to establish best practices under realistic conditions (e.g., how to specify empirical Bayes priors in Stage 1), as well as extensions that would make 2-stage estimation particularly advantageous over single-stage FIML. Full article

Ice adhesion tests are widely used to assess the performance of potential icephobic surfaces and coatings. A great variety of test designs have been developed and used over the past decades due to the lack of formal standards for these types of tests. In many cases, the aim of the research was not only to determine ice adhesion values, but also to understand the key surface properties correlated to low ice adhesion surfaces. Data from different measurement techniques had low correspondence between the results: Values varied by orders of magnitude and showed different relative relationships to one another. This study sought to provide a broad comparison of ice adhesion testing approaches by conducting different ice adhesion tests with identical test surfaces. A total of 15 test facilities participated in this round-robin study, and the results of 13 partners are summarized in this paper. For the test series, ice types (impact and static) as well as test parameters were harmonized to minimize the deviations between the test setups. Our findings are presented in this paper, and the ice- and test-specific results are discussed. This study can improve our understanding of test results and support the standardization process for ice adhesion strength measurements. Full article

Furan tests provide a non-intrusive and cost-effective method of estimating the degradation of paper insulation, which is critical for ensuring the reliability of power grids. However, conducting routine furan tests can be expensive and challenging, highlighting the need for alternative methods, such as machine learning algorithms, to predict furan concentrations. To establish the generalizability and robustness of the furan prediction model, this study investigates two distinct datasets from different geographical locations, Utility A and Utility B. Three scenarios are proposed: in the first scenario, a round-robin cross-validation method was used, with 75% of the data for training and the remaining 25% for testing. The second scenario involved training the model entirely on Utility A and testing it on Utility B. In the third scenario, the datasets were merged, and round-robin cross-validation was applied, similar to the first scenario. The findings reveal the effectiveness of machine learning algorithms in predicting furan concentrations, and particularly the stacked generalized ensemble method, offering a non-intrusive and cost-effective alternative to traditional testing methods. The results could significantly impact the maintenance strategies of power and distribution transformers, particularly in regions where furan testing facilities are not readily available. Full article

Thermal fatigue cracks occurring in pipes in nuclear power plants pose a high degree of risk. Thermal fatigue cracks are generated when the thermal fatigue load caused by local temperature gradients is repeatedly applied. The flaws are mainly found in welds, owing to the effects of stress concentration caused by the material properties and geometric shapes of welds. Thermal fatigue pipes are classified as targets of risk-informed in-service inspection, for which ultrasonic testing, a volumetric non-destructive testing method, is applied. With the advancement of ultrasonic testing techniques, various studies have been conducted recently to apply the phased array ultrasonic testing (PAUT) method to the inspection of thermal fatigue cracks occurring on pipes. A quantitative reliability analysis of the PAUT method must be performed to apply the PAUT method to on-site thermal fatigue crack inspection. In this study, to evaluate the quantitative reliability of the PAUT method for thermal fatigue cracks, we fabricated crack specimens with the thermal fatigue mechanism applied to the pipe welds. We performed a round-robin test to collect PAUT data and determine the validity of the detection performance (probability of detection; POD) and the error in the sizing accuracy (root-mean-square error; RMSE) evaluation. The analysis results of the POD and sizing performance of the length and depth of thermal fatigue cracks were comparatively evaluated with the acceptance criteria of the American Society of Mechanical Engineers Code to confirm the effectiveness of applying the PAUT method. Full article

The EN 1793-5 and EN 1793-6 standards have been in use for many years as a method for measuring the intrinsic characteristics of noise barriers installed along highways and railways. They require a sound source and a grid of microphones, to be placed near the barrier and in free field conditions, according to predetermined distances. In principle, small errors in positioning the sound source and microphone grid may affect the results obtained. An international round-robin test was carried out in 2012 to evaluate the repeatability and reproducibility of the method, but until now no studies have been carried out to evaluate and compare the repeatability of laboratory versus in-place measurements performed with the same equipment and its variance when an imperfect positioning of sound source and microphones is taken into account. In the present work, multiple series of sound reflection index and sound insulation index measurements performed on noise barriers of the same kind installed in the laboratory or along a highway are presented. The measurements were repeated in different ways: (1) in the laboratory, leaving the source and microphones unmoved to assess the repeatability of the results and of the measurement system under controlled conditions; (2) in the laboratory, repositioning for each measurement the source and microphone grid to assess the robustness of the method under real conditions but in a controlled environment; (3) in situ, along a highway open to traffic, repositioning for each measurement the source and microphone grid to assess the repeatability of the method under real conditions in a critical environment. In both reflection index and sound insulation index measurements, the standard deviation on single-number ratings in all cases examined is well below the value presented in EN 1793-5 and EN 1793-6, which was obtained from statistical analysis of the international round-robin test performed a dozen years ago, suggesting that expert operators with state-of-the art equipment can achieve much better results now. Full article

The insulation condition of HVDC grids consisting of cable systems, GIS, and converters should be monitored by partial discharge (PD) analysers using artificial intelligence (AI) tools for efficient insulation diagnosis. Although there are many experiences of PD monitoring solutions developed for the supervision of the insulation condition of HVAC grids using PD analysers, there are no standardised requirements for their qualification available yet. The international technical specification TS IEC 62478 provides general rules for PD measurements using electromagnetic methods but does not define performance requirements for qualification tests. HVDC and HVAC PD analysers must be tested by unambiguous test procedures. This paper compiles experiences of using PD analysers with HFCT sensors in HVAC grids (cable systems, GIS, and AIS) to define a qualification procedure for HVAC systems. This procedure is applicable to HVDC grids (cable systems, GIS, AIS, and converters) because the particularities related to the insulation behaviour under HVDC voltage are also considered. Representative PD sources are discussed in HVAC and HVDC positive and negative polarity. The PD pulse trend of representative insulation defects in HVDC cable systems is quite different from that of HVAC grids. Special attention should be paid to the acquisition of PD signals in HVDC grids since few pulses appear in solid insulations, mainly during voltage changes (polarity reversals or surges), but rarely in continuous operation with constant direct voltage. A synthetic PD simulator has been developed to reproduce trains of PD pulses or noise signals, similar to those that can appear in the power network. A set of three functionality tests has been developed for qualification of the diagnostic capabilities of PD analysers working up to 30 MHz addressed to HVDC or HVAC grids: (1) PD recognition test, (2) PD clustering test, and (3) PD location test. This qualification procedure has been validated by means of a round-robin test performed by five research institutes (RISE, FFII, TUDelft, TAU, and UPM) using commercial and in-development AI PD recognition and clustering tools to demonstrate its robustness and applicability. Applying this qualification procedure, two PD methods for electrical detection and prevention of insulation defects have been approved, one for HVAC and the other for HVDC grids. Full article