Update Method Research Articles

Toward the Question of Writing a History of Cinema in the Context of Time

The article is devoted to the updated methods of writing on film history. The author’s conception is based on his personal experience of writing the text-book “The Recent History of Russian Cinema” as well as on the debate in the “Film Art” (Iskusstvo kino) magazine in the early 1970s, which revealed a number of issues that are still relevant nowadays: methods of cinema studies, the principles of writing both scholarly papers and teaching aids for students, certain expectations from a historic research, the approach to the subject matter, etc.The author reflects on the advantages and disadvantages of two approaches: individual, subjective, striving for the polemical sharpness of the personal historical assessments, on the one hand, and collective, claiming certain impartialness, on the other.All these reflections led him to a model that he still sees as a multiauthored monograph, as long as it takes on the task of a detailed fundamental presentation of extensive material that requires profound knowledge in various areas. However, he does not think it appropriate to ignore the subjective component. An individual viewpoint makes it possible to create a specific semantic high field that initiates various theoretical views on filmmaking.Therefore, the functions of the team of authors can be distributed as follows: each contributor writes a fragment of the description of the historical process, perhaps even in its synchronous understanding, and together they create something that cannot be done by one researcher: an initial empirical picture. After that, one researcher, the supervisor, combines the isolated fragments into a coherent whole, something that is completely beyond the power of a team of authors. This supervisor eventually writes an integral history in his own way (but in a dialogue with the other experts) thus supplementing the original empirical picture with a single essentially diachronic view.

CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century

The Qinghai-Tibetan Plateau (QTP), the source of many major Asian rivers, is sensitive to climate change, affecting billions of people’s livelihoods across Asia. Here, we developed high-resolution projections of precipitation and daily maximum/minimum temperatures at 0.1° spatial resolution over the QTP. The projections are based on the output from seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for historical (1979–2013) and projected (2015–2100) climates across four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). An updated nonstationary cumulative distribution function matching method (called CNCDFm) was used to remove model systemic bias. We verify the necessity of taking into account altitude in downscaling processes and the validity of nonstationary bias correction. Compared to the historical period, the climate in the QTP in the 21st century is warmer (1.2–5.1 °C, for maximum surface temperature) and wetter (3.9–26.8%) according to the corrected GCM projection. For precipitation, the Indus River (IDR), Tarim River (TMR), Inner of Qiangtang Basin (IQTB), Yarlung Zangbo (YLZBR), and Qaidam Basin (QDB) showed growth well above the global average across high radiative forcing scenarios, which could have a profound impact on the regional hydrological cycle. However, there is great uncertainty in precipitation prediction, which is demonstrated by a very low signal-to-noise ratio (SNR) and a large difference between Bayesian model averaging (BMA) and multi-model averages (MMAs). This bias-corrected dataset is available for climate change impact research in the QTP at the subregion scale.

Improved identification of fracture total factor characteristics and cross-scale flow channels by interpreting initial production data of horizontal wells

Fracture identification after hydraulic fracturing of tight gas reservoirs is crucial to fracturing evaluation, productivity analysis, and production plan. Owing to fracture branching, bending, extending, and reversing in reservoirs, a simplified straight fracture cannot fully describe fracture characteristics. The novelty of this study is that the coupling mechanism between the complex fracture network characteristics and well production performances is figured out, thus achieving the identification of fracture morphology in fractured reservoirs. In this study, a combined model including Laplace transform, boundary element method, Stehfest numerical inversion, and fractal theory presents transient well performances. The key contributions to productivities at different production stages are dynamically identified via a sensitivity analysis. The results clearly indicate that the fracture total length is the key factor determining productivities due to no fracture interference at initial production stage. Furthermore, the logarithm of productivity linearly depends on the logarithm of production time and the productivity contribution rate equals the ratio of fracture length to total length. Thus, an updated inversion method is proposed by interpreting initial production data based on a tree-shaped fracture network associated with fracture total factor and cross-scale characteristics. The inversion results well match with their targets, with an error of no more than 5 %, ensuring the accuracy and validity of the proposed method. This innovative approach with low requirements on field data is convenient to yield an accurate prediction of fracture morphology, thereby providing a feasible strategy for promoting sustainable production of clean energy in unconventional gas reservoirs.

A novel quasi-smooth tetrahedral numerical manifold method and its application in topology optimization based on parameterized level-set method

In this paper, a novel quasi-smooth tetrahedral numerical manifold method (NMM) and its two-dimensional (2D) counterpart are proposed. A new topology optimization method is established by combining the quasi-smooth manifold element (QSME) with the parameterized level set method (PLSM). The QSME introduces an innovative displacement function characterized by high accuracy and high-order continuity, effectively addressing the “linear dependence” (LD) issue inherent in traditional high-order NMM. To integrate QSME and PLSM, the corresponding optimization formulations and sensitivity analyses are provided. In order to fully utilize advantages of this novel quasi-smooth NMM and the PLSM, an element subdivision technique based on model recognition is proposed to accurately capture the physical boundaries. Additionally, a volume fraction update method based on element refinement is proposed. Taking advantage of the characteristics of the PLSM, a structure visualization method based on the sign distance function is developed to accurately describe curve boundary. This method allows for precise visualization of optimized structures. This study verifies high efficiency of the QSME-based PLSM for minimum compliance topology optimization problems in both 2D and 3D structures. Some representative structural optimization examples are tested to demonstrate effectiveness of the proposed method in both 2D and 3D problems, especially in complex design domain.

Studies on acceleration algorithms for numerical simulations of hypersonic conjugate heat transfer problems

This research proposes acceleration algorithms for the coupled numerical simulation of the long-time hypersonic conjugate heat transfer problem from two perspectives. First, the newly developed disturbance region update method (DRUM) is introduced to enhance the convergence speed of the steady flow field. The numerical investigations reveal that, in the context of hypersonic CHT issues, the disturbance prompted by variations in wall temperature remains confined post the detached shock wave. The implementation of DRUM has been shown to substantially diminish the computational demand per iteration and the total iterations required for convergence. DRUM could reduce the time needed to calculate the flow field by over 60 % for a typical CHT case. Second, a new aerodynamic heat flux estimate method is introduced based on the local wall temperature within a coupled time step to increase the coupled time step. Combining this method with the adaptive coupled time step method, a long-time CHT problem with a total time of 1100s requires only 25 coupled time steps, much less than the 220 steps needed for the existing method. Meanwhile, the average error of the obtained solid temperature field, when compared to the temperature obtained from an accurate simulation with a fixed small coupled time step, is maintained below 0.22 %.

Seismic numerical analysis of an Inca stone wall in Sacsayhuaman using rigid body dynamics within a finite element framework

The study and conservation of the stone heritage is a global concern, mainly when the buildings are in seismic zones. Stone structures can be very simple or considerably complex, depending on their style and form of construction. A clear example is the Inca architecture in Peru, which has different and complex typologies due to its cultural diversity. Inca structures of remarkable heritage value are in Cusco, an area of active cortical faults that might produce a seismic event at any time. The main objective of this research is to contribute to the knowledge of the seismic vulnerability analysis of Inca's stone walls by using the dynamics of rigid bodies (within the finite element method) to evaluate the possible out-of-plane failure mechanisms. This methodology allows for reducing the high number of degrees of freedom usually considered when modelling an entire irregular large stone wall by other approaches, such as continuum models. A wall section from the archaeological site of Sacsayhuaman (Cusco, Peru) is analysed as a case study. Each stone is discretised and modelled as a rigid body, and the interaction among blocks is evaluated within a finite element numerical framework. The structure's predominant frequencies are evaluated using updated methods such as white noise and sinusoidal signals. Then, a non-linear dynamic analysis of the studied wall is performed considering three Peruvian seismic records. The analyses show that the structure could suffer significant damage and endanger its structural stability for cortical earthquakes with a Peak Ground Acceleration (PGA) greater than 0.2 g. Considering the historical significance of these structures, the analysis of Inca stone walls' seismic safety accounts for potential residual movements between individual stones.

The system of methodological tools of forming the readiness of future teachers for the integrated teaching of primary schoolchildren

Relevance. Globalization highlights the need for quality integrated education in Kazakhstan's secondary schools. Developing effective methods to prepare future teachers for an integrated approach is crucial for improving education and ensuring students' future competitiveness. Purpose. The purpose of the article is to justify the need to implement a system of methodical means of forming the readiness of future teachers in universities of Kazakhstan as a guarantee of their ability to use integrative education of younger schoolchildren. Methodology. The methodological approach of the research is based on: empirical methods: survey, experiment and methods of mathematical statistics; theoretical methods, processing of final indicators of formation of readiness of education seekers for integrated training. Results. The content of the concepts �integration in education� and �readiness of future teachers for integrated education of younger schoolchildren� was defined; its features and peculiarities are revealed, the methodology of forming the readiness of future teachers for integrated learning in the context of their professional training is developed and implemented. During an experimental study at Pavlodar Pedagogical University named after A. Margulan, a methodological toolkit was developed to increase the readiness of future teachers for integrated education of junior high school students during professional training, namely: implementation of components and criteria of readiness with selected methods of their formation. Prospective directions for improving the methodology of forming the readiness of future teachers for the integrated education of younger schoolchildren through the formation of the components of the readiness of education seekers to carry out such activities have been determined. Conclusions. The study demonstrated that implementing a system of methodical tools in teacher training programs significantly improved future teachers' readiness to use integrated learning approaches in primary education, highlighting the need for updated training methods in Kazakhstan's educational institutions. Keywords: integrated education in primary school; training of future primary school teacher; integration of scientific knowledge; primary school; professional training

An adaptive differential evolution algorithm based on archive reuse

In recent years, differential evolution algorithms based on archives have achieved significant success because archives can increase population diversity and balance the exploration and exploitation of algorithms. However, insufficient utilisation of archives has led to an imbalance between exploration and exploitation. Herein, a new archive-reuse-based adaptive differential evolution (AR-aDE) algorithm framework is proposed that can be applied to (L)SHADE and its variants. It comprises three main strategies. First, a new external archive update method based on a cache mechanism is proposed, in which the archive size is the same as the population size, eliminating the need to adjust its size. Second, influenced by knowledge transfer in multitasking optimisation, we designed a new method of reusing the archive to better utilise the information in it. Finally, the classic parameter adaptation method was improved. The experimental results for the CEC2020 and CEC2021 competition problem sets show that the KR-aDE has a strong competitive advantage.

Research on Damage Identification of Truss Structures based on Bayesian Updating and Rejection Sampling Method

The research on the early damage detection method of truss structure has important practical value for taking remedial measures in time and reducing the hidden danger of safety. In this study, a specific structural truss beam was designed and the seismic action was simulated. Subsequently, specific nodes are analyzed to assess the resulting structural damage. Bayesian updating method solves the uncertainty problem effectively by introducing the prior distribution and updating the posterior distribution with new observation data. This allows the model to infer global patterns from local information and represent the predicted outcomes in probabilistic form. Compared with the traditional parameter estimation methods, Bayesian updating can make full use of the existing observational data and obtain more reliable and accurate model prediction results in the case of limited data. The Bayesian method of updating the truss structure is flexible and adaptable, allowing updates and adjustments to be made at any given time based on new observational data. As a result, it has significant advantages in analyzing and predicting real-time data and is suitable for scenarios where models need to be dynamically adjusted.

IoT service composition based on improved Shuffled Frog Leaping Algorithm

In the complex Internet of Things (IoT) environment, a plethora of IoT services with akin functions but varying qualities of service exist. To meet diverse customer needs and drive widespread application, service composition optimization becomes crucial. In the current era of rapid development in artificial intelligence, intelligent algorithms play a significant role in optimizing service composition. However, algorithms applied to IoT service composition optimization face common challenges of low search efficiency and insufficient optimization precision, including the Shuffled Frog Leaping Algorithm (SFLA) and Genetic Algorithm (GA). Therefore, this study seeks to enhance the perception of service quality in IoT service composition. It proposes an improved SFLA (ISFLA) based on the original SFLA. The algorithm integrates chaos theory and reverse learning theory for the acquisition of the initial population. It utilizes Euclidean distance to partition the population into groups and employs Gaussian mutation to optimize the optimal individual of each group. Finally, the entire population undergoes evolution through a local update method based on two strategies. Simulated experiments were conducted to search for optimal IoT service composition solutions of different scales. The results indicate that, compared to the SFLA, GA, ISFLA*, IGSFLA and SFLAGA, ISFLA achieves superior fitness values, better composition solutions, and exhibits faster convergence, higher stability, and greater overall operational efficiency.

Federated Label-Noise Learning with Local Diversity Product Regularization

Training data in federated learning (FL) frameworks can have label noise, since they must be stored and annotated on clients' devices. If trained over such corrupted data, the models learn the wrong knowledge of label noise, which highly degrades their performance. Although several FL schemes are designed to combat label noise, they suffer performance degradation when the clients' devices only have limited local training samples. To this end, a new scheme called federated label-noise learning (FedLNL) is developed in this paper. The key problem of FedLNL is how to estimate a noise transition matrix (NTM) accurately in the case of limited local training samples. If a gradient-based update method is used to update the local NTM on each client's device, it can generate too large gradients for the local NTM, causing a high estimation error of the local NTM. To tackle this issue, an alternating update method for the local NTM and the local classifier is designed in FedLNL, where the local NTM is updated by a Bayesian inference-based update method. Such an alternating update method makes the loss function of existing NTM-based schemes not applicable to FedLNL. To enable federated optimization of FedLNL, a new regularizer on the parameters of the classifier called local diversity product regularizer is designed for the loss function of FedLNL. The results show that FedLNL improves the test accuracy of a trained model by up to 25.98%, compared with the state-of-the-art FL schemes that tackle label-noise issues.

Abstract 2337: Accelerated optical genome mapping analysis with Stratys Compute and Guided Assembly

Abstract Background Optical genome maps (OGM) from Bionano enable the detection of genomic structural and copy number variants that cannot be detected by next-generation sequencing (NGS) technologies and are often missed by conventional cytogenetic techniques. Bionano has developed bioinformatics pipelines for calling structural and copy number variants including the Bionano Solve de novo assembly pipeline for constitutional analysis and the Rare Variant Analysis (RVA) pipeline for low-allele-fraction cancer applications. Both pipelines are computationally intensive and currently take 5-10 hours on the latest generation of the Bionano Saphyr® Compute which is deployed as a four-node compute cluster and requires significant IT resources. Methods To increase throughput and simplify the deployment of the compute resources needed to support the Stratys™ optical genome mapping instrument, Bionano has developed Stratys™ Compute. Stratys Compute improves both compute and analytical performance by adapting compute-intensive stages in the Solve pipeline to run on GPUs and by developing the Guided Assembly pipeline. Stratys Compute is powered by state-of-the-art NVIDIA RTX 6000 Ada generation cards and CUDA-optimized refinement, alignment, and structural variation detection kernels to accelerate OGM analysis. Stratys Compute is a standalone workstation placed adjacent to Stratys instrument. This will minimize the IT footprint and bypass the integration with the customer site’s data center. We have incorporated these optimizations into the new Guided Assembly pipeline, which aims to combine the low-allele fraction detection capability of the RVA pipeline with the whole genome coverage and ability to detect smaller structural variants enabled by the de novo assembly pipeline. The Guided Assembly pipeline uses the reference genome as an initial seed followed by extension, refinement, and structural variant calling. This new analysis method has been evaluated through comparison to previous results from both existing pipelines and standard benchmarking datasets used to estimate structural variant calling performance and is deployed on Stratys Compute for both constitutional and low allele-fraction applications. Results Guided Assembly has been adapted to run on GPU hardware in a simplified compute tower that can be deployed to a lab along with the Stratys instrument without the need for a dedicated server room. We found concordance between the guided assembly results and our previous de novo and RVA pipeline results. We also found increased sensitivity at low allele fractions for detecting insertion variants smaller than 5 kb and larger than 200 kb while finding equivalent performance for other variant types with the updated methods. The accelerated Guided Assembly for constitutional and low-allele-fraction applications will be available to early access customers in Q4 2023 with full commercial release in Q2 2024. Citation Format: Damla Senol Cali, Thomas Anantharaman, Martin Muggli, Samer Al-Saffar, Charles Schoonover, Neil Miller. Accelerated optical genome mapping analysis with Stratys Compute and Guided Assembly [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2337.

Development of a time pressure-based model for the simulation of an evacuation in a fire emergency

The study of pedestrian behaviour in a fire emergency is essential for effectively reducing the number of casualties. However, most studies only consider the effect of fire on pedestrian behaviour and ignore the effect of a fire emergency on pedestrian psychology. In this study, we introduce a method of assessing the pedestrian time pressure within a cellular automaton model using the available safe egress time and required safe egress time in fire evacuations. Moreover, a more realistic reflection of the evacuation process in a fire emergency is obtained by introducing Pyrosim to compute the fire floor field and by proposing a sequential update method based on time pressure. To assess the model, we conduct sensitivity analyses on model parameters across various environmental configurations. Numerical simulation results show that fire accelerates the evacuation rate and radiated heat transfer and temperature have the greatest effects on the time pressure of pedestrians in a fire evacuation. The distribution of pedestrians is U-shaped owing to fire avoidance behaviour and in the later stage of evacuation, pedestrians keeping away from heated walls. The trend of time pressure over time has a bimodal distribution as pedestrians initially pass the fire and the environment then becomes untenable as the fire fully develops. In addition, the exit location affects the evacuation process, with a symmetrical positioning improving the evacuation efficiency. We verify the model in a series of physical experiments. The agreement between the numerical simulation results and experimental results demonstrates that the time pressure-based model is suitable for simulating the route choice behaviour and evacuation process in fire emergencies.

Lc‐Stream: An elastic scheduling strategy with latency constraints in geo‐distributed stream computing environments

SummaryAn effective scheduling strategy is critical for achieving better performance in real‐time stream processing systems. How to quickly and efficiently process real‐time data stream is always challenging, especially when clusters are collaborating in a Geo‐Distributed computing environment. To address these challenges, we propose an elastic scheduling strategy with Latency Constraints in Geo‐Distributed stream computing environments called Lc‐Stream. This article discusses our work from the following aspects: (1) An optimized data stream redirection method that is proposed based on queuing network algorithm, along with a computing resource model, a latency constrained scheduling model and a communication energy consumption model. (2) An updated node selection method based on the inter‐layer task correlation, to reduce the communication latency between groups at the executor granularity. (3) A network cluster distribution for Geo‐Distributed computing environment to ensure energy saving under low transmission latency. Experimental results show that compared to R‐Storm, Lc‐Stream reduces total latency by over 19% and increases throughput by over 37% in typical cross‐domain multi‐task topologies. Compared to Ts‐Stream, Lc‐Stream also reduces total latency by over 15% and increases throughput by over 21%. At the same time, it helps to balance the load among the systems and avoid overuse of compute nodes.

Dynamic response of piles embedded in granular soil to lateral impacts

Soil-embedded vehicle barriers, such as W-beam guardrail systems, play a pivotal role in transportation safety, mitigating the risks associated with vehicular collisions with roadside hazards. The efficacy of these barriers greatly depends on the pile-soil system's kinetic energy dissipation capability during vehicular impacts. However, a comprehensive understanding of how soil strength, embedment depth, and impact velocity collectively govern the dynamic behavior of the pile-soil system remains a gap in current research. This study explores the dynamics of lateral impacts on piles embedded in various granular soils. The process of dynamic lateral impact and interaction between the pile and the soil was modeled using the Updated Lagrangian Finite Element Method (UL-FEM). A damage-based element erosion algorithm was incorporated into the model to accommodate severe mesh distortions and element entanglements of the soil material brought by the pile impact. Validation against well-documented large-scale physical impact tests ascertained the model's fidelity. Our findings elucidate the significant differences in resistive forces between piles in strong versus weak granular soils – notably, the former exhibited resistive forces roughly double their weaker counterparts under equivalent embedment depths and varied impact velocities. Intriguingly, a stiff pile in weak soil necessitates nearly double the embedment depth to match the energy dissipation of its strong-soil counterpart. Furthermore, the study discerned consistent depth of rotation point ranges for piles embedded in distinct soil strengths, regardless of embedment depth and impact velocity.

A Review of Crowdsourcing Update Methods for High-Definition Maps

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps.

Potential Output and Cyclically Adjusted Balance: An Application of the Updated Okun Method to Spain (1987–2022)

ABSTRACT The estimation of potential output is at the heart of the ongoing debate on the reformulation of fiscal rules in the European Union as it is an essential variable for their application. Spain constitutes a striking case, with a small and even positive output gap along with one of the highest unemployment rates in the European Union. To resolve this contradiction, in this article we estimate the potential output of the Spanish economy for the period 1987Q2–2022Q4 by means of the Updated Okun Method. Our results indicate that both the output gap and the cyclically adjusted balance are much higher than what European institutions estimate. Consequently, Spain has much more fiscal space to implement expansionary fiscal policies.

A protocol for distilling animal body water from biological samples and measuring oxygen and hydrogen stable isotopes via cavity ring-down spectroscopy

ABSTRACT The application of stable isotope analysis (SIA) to the fields of ecology and animal biology has rapidly expanded over the past three decades, particularly with regards to water analysis. SIA now provides the opportunity to monitor migration patterns, examine food webs, and assess habitat changes in current and past study systems. While carbon and nitrogen SIA of biological samples have become common, analyses of oxygen or hydrogen are used more sparingly despite their promising utility for tracing water sources and animal metabolism. Common ecological applications of oxygen or hydrogen SIA require injecting enriched isotope tracers. As such, methods for processing and analyzing biological samples are tailored for enriched tracer techniques, which require lower precision than other techniques given the large signal-to-noise ratio of the data. However, instrumentation advancements are creating new opportunities to expand the applications of high-throughput oxygen and hydrogen SIA. To support these applications, we update methods to distill and measure water derived from biological samples with consistent precision equal to, or better than, ± 0.1 ‰ for δ 17O, ± 0.3 ‰ for δ 18O, ± 1 ‰ for δ 2H, ± 2 ‰ for d-excess, and ± 15 per meg for Δ 17O.

Multistrategy Fusion Particle Swarm for Dynamic Economic Dispatch Optimization of Renewable Energy Sources

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching.

Shiga Toxin-Producing Escherichia coli Testing in New York 2011-2022 Reveals Increase in Non-O157 Identifications.

Shiga toxin-producing Escherichia coli (STEC) are an important cause of bacterial enteric infection. STEC strains cause serious human gastrointestinal disease, which may result in life-threatening complications such as hemolytic uremic syndrome. They have the potential to impact public health due to diagnostic challenges of identifying non-O157 strains in the clinical laboratory. The Wadsworth Center (WC), the public health laboratory of the New York State Department of Health, has isolated and identified non-O157 STEC for decades. A shift from initially available enzyme immunoassay testing to culture-independent diagnostic tests (CIDTs) has increased the uptake of testing at clinical microbiology laboratories. This testing change has resulted in an increased number of specimen submissions to WC. During a 12-year period between 2011 and 2022, WC received 5037 broths and/or stool specimens for STEC confirmation from clinical microbiology laboratories. Of these, 3992 were positive for Shiga toxin genes (stx1 and/or stx2) by real-time PCR. Furthermore, culture methods were utilized to isolate, identify, and characterize 2925 STEC from these primary specimens. Notably, WC observed a >200% increase in the number of STEC specimens received in 2021-2022 compared with 2011-2012 and an 18% increase in the number of non-O157 STEC identified using the same methodologies. During the past decade, the WC testing algorithm has been updated to manage the increase in specimens received, while also navigating the novel COVID-19 pandemic, which took priority over other testing for a period of time. This report summarizes updated methods for confirmation, surveillance, and outbreak detection of STEC and describes findings that may be related to our algorithm updates and the increased use of CIDTs, which is starting to elucidate the true incidence of non-O157 STEC.