Identification Of Boundaries Research Articles

Atomistic Probing and Identification of Point Defects and Grain Boundaries of 1H and 1T′ Molybdenum Telluride Monolayer

Atomistic Probing and Identification of Point Defects and Grain Boundaries of 1H and 1T′ Molybdenum Telluride Monolayer

Comprehensive Monitoring of Construction Spoil Disposal Areas in High-Speed Railways Utilizing Integrated 3S Techniques

High-speed railways are critical infrastructure in many countries, but their construction generates substantial spoil, particularly in mountainous regions dominated by tunnels and slopes, necessitating the establishment and monitoring of spoil disposal areas. Inadequate monitoring of spoil disposal areas can lead to significant environmental issues, including soil erosion and geological hazards such as landslides and debris flows, while also hindering the recycling and reuse of construction spoil, thereby impeding the achievement of circular economy and sustainable development goals for high-speed railways. Although the potential of geographic information systems, remote sensing, and global positioning systems in waste monitoring is increasingly recognized, there remains a critical research gap in their application to spoil disposal areas monitoring within high-speed railway projects. This study proposes an innovative framework integrating geographic information systems, remote sensing, and global positioning systems for monitoring spoil disposal areas during high-speed railway construction across three key scenarios: identification of disturbance boundaries (scenario 1), extraction of soil and water conservation measures (scenario 2), and estimation of spoil volume changes (scenario 3). In scenario 1, disturbance boundaries were identified using Gaofen-1 satellite data through processes such as imagery fusion, unsupervised classification, and spatial analysis. In scenario 2, unmanned aerial vehicle data were employed to extract soil and water conservation measures via visual interpretation and overlay analysis. In scenario 3, Sentinel-1 data were used to analyze elevation changes through the differential interferometric synthetic aperture radar method, followed by the estimation of spoil volume changes. The effectiveness of this integrated framework was validated through a case study. The results demonstrate that the framework can accurately delineate disturbance boundaries, efficiently extract soil and water conservation measures, and estimate dynamic changes in spoil volume with an acceptable error margin (15.5%). These findings highlight the framework’s capability to enhance monitoring accuracy and efficiency. By integrating multi-source data, this framework provides robust support for sustainable resource management, reduces the environmental impact, and advances circular economy practices. This study contributes to the efficient utilization of construction spoil and the sustainable development of high-speed railway projects.

Efficacy and safety of indocyanine green-fluorescence imaging guided liver resection: a single-arm prospective cohort study

PurposeThis study aimed to evaluate the efficacy of indocyanine green (ICG)-fluorescence imaging for the identification of hepatic boundaries during liver resection and its advantages in surgical outcomes over conventional methods.MethodsThis prospective, exploratory, single-arm clinical trial included 47 patients with liver tumors who underwent liver resection using ICG-fluorescence imaging (ICG-LR) between 2019 and 2020. The primary outcome measure was the successful identification of hepatic boundaries during liver resection, from the perspective of both the hepatic surface and intrahepatic boundary, using ICG-fluorescence imaging. The secondary outcomes comprised surgical outcomes. Using propensity score matching (PSM), the surgical outcomes were subsequently compared between the ICG-LR group and patients who underwent conventional liver resection (C-LR, n = 100) between 2017 and 2018.ResultsHepatic boundaries were successfully identified in 28 patients (60%; 95% confidence interval, 45–72%), including 21 and 7 who underwent anatomical and non-anatomical liver resection, respectively. After PSM, 40 patients were included in each of the ICG-LR and C-LR groups. The surgical outcomes were similar between the groups. Subsequently, surgical outcomes were compared between the groups focusing on anatomical liver resection. After PSM, 21 patients were included in each group. The ICG-LR group had a lower rate of Clavien–Dindo grade ≥ IIIa complications (0% vs. 24%; P = 0.017), including ascites and bile leak, and a shorter hospital stay (12 vs. 14 days, P = 0.041) than the C-LR group did.ConclusionICG-fluorescence imaging could be used to recognize hepatic boundaries during liver transection. Additionally, ICG-LR may be useful in preventing severe liver-associated complications.Trial registration numberThis study is registered at the UMIN Clinical Trials Registry: UMIN0000180139 and Japan Registry of Clinical Trials: jRCT1051180070. The Registration Data Set is available at https://jrct.niph.go.jp/.

A conceptual model for multivariate data integration in reservoir modeling: exploring the role of neural networks in petrophysical analysis

Reservoir modeling is crucial for optimizing oil and gas production, as it helps characterize subsurface properties like porosity, permeability, and fluid saturation. Integrating diverse data sources such as well logs, seismic data, and core analyses presents a significant challenge due to their differing scales, resolutions, and data types. Traditional methods often struggle to accurately integrate these data sources, resulting in suboptimal predictive accuracy. This review proposes a conceptual model that leverages neural networks to integrate multivariate data for enhanced reservoir characterization. Neural networks, with their ability to handle nonlinear relationships and large, complex datasets, offer a transformative approach to unify diverse data inputs, resulting in more accurate predictions of reservoir properties. The model focuses on using feedforward neural networks (FNNs), convolutional neural networks (CNNs), and autoencoders to merge well logs, seismic interpretations, and core sample data. This integration aims to improve the identification of productive zones, reservoir boundaries, and fluid distributions, thereby enhancing reservoir models. Additionally, the review explores how neural networks can manage data gaps, predict missing values, and quantify uncertainty, which are common challenges in reservoir studies. By automating data processing and facilitating real-time analysis, neural networks can accelerate decision-making in reservoir management and field development. This framework highlights the potential of AI-driven techniques to revolutionize reservoir modeling, offering a pathway to more efficient, accurate, and adaptive resource management. The conceptual model aims to advance predictive capabilities in reservoir analysis, addressing the complexities of multivariate data integration and opening doors for future innovations in the energy industry. Keywords: Conceptual Model, Multivariate Data, Reservoir Modeling, Petrophysical Analysis.

Complex boundary identification based on Bayesian inference and tension force uncertainty prediction

Complex boundary identification based on Bayesian inference and tension force uncertainty prediction

Experimental research on pulsation shedding characteristics of tail cavities attached to underwater vehicles based on the U-net cavity boundary identification method

Tail cavities are separate bubbles of initially non-condensable gas attached to the bottom of the vehicle, affecting the dynamics characteristics of the vehicle. The evolution of the tail cavity under various launch pressures is experimentally simulated, and the pulsation shedding characteristics of the tail cavity are investigated with the U-net method. Additionally, the influence of the tail shape on the bubble-carrying capability of tail cavity is discussed. Research shows that gas break-off and environmental pressure reduction induce a quasi-periodic pulsation process in the tail cavity, maintaining an approximately fixed length. Due to the asymmetric flow disturbances and the formation of an end closure by bubble cluster, the tail cavity shedding is transformed from regular vortex ring to hairpin vortex. The cavity volume pulsation frequency is consistent with cavity length. The continuous expansion of the cavity induced by environmental depressurization compensates for the loss of cavity volume due to hairpin vortex shedding. Increasing the launch pressure leads to a change in the tail vortex morphology, but does not significantly increase the size of tail cavity. The bottom extension can improve the bubble-carrying capacity and effectively solve the exit angle and air column caused by the increase of launch pressure.

Robotic Real Anatomical Right Hepatectomy Preserving the Caudate Lobe: Separate Dissection of the Right Anterior and Posterior Glissonean Pedicles, Combined with the Use of ICG Fluorescent Imaging (with Video).

Conventional right hepatectomy typically involves resection of the right hemiliver, often including partial removal of the caudate lobe. However, recent advancements, particularly in indocyanine green (ICG) fluorescence imaging, have allowed for more accurate identification of anatomical boundaries between liver segments. In this context, we present a refined technique for real anatomical right hepatectomy that preserves the caudate lobe, offering enhanced surgical precision and several distinct advantages over traditional methods. This study involved a right hepatectomy using the Da Vinci Xi robotic system, employing ICG fluorescence imaging for parenchymal transection. Key steps included the dissection of the right anterior and posterior Glissonean pedicles and the preservation of the caudate lobe. ICG was administered to visualize the boundary between the caudate lobe and the right liver, guiding a precise resection. The procedure lasted 320min with 150mL blood loss. Pathology showed a 6.7cm × 5.5cm hepatocellular carcinoma with a 2.1cm margin. No bile leakage or significant complications occurred, and the patient was discharged on day 6 without issues. This technique, underpinned by a deep understanding of liver anatomy, the Glissonean approach, and advanced ICG fluorescence imaging, allows for greater precision in defining anatomical boundaries and preserving the caudate lobe. We propose that real anatomical right hepatectomy offers a promising alternative for surgical practice, with the potential for improved outcomes in patients with liver malignancies.

Novel Technique for Liver Segmentation by Fluorescence Intensity Gradient Using Two Different Doses of Indocyanine Green.

Techniques involving dye injection or regional ischemia are commonly used for the precise identification of liver regions during hepatectomy.1,2 The visualization of regions with indocyanine green (ICG) has been widely used for liver segmentation.3 ICG is typically administered only once during each hepatectomy. We developed a threshold-adjustable Medical Imaging Projection System (MIPS) that projects ICG fluorescent images directly onto a patient's organ, which shows potential for real-time navigation.4,5 We report a case in which a fluorescence intensity gradient using two different doses of ICG was effective during anatomical right anterior sectionectomy. A 73-year-old man underwent one radiation treatment and three radiofrequency ablations for hepatocellular carcinoma over the past 4 years. A follow-up computed tomography scan revealed a low-density lesion in segment 8 with a portal vein tumor thrombus (PVTT), and we planned a right anterior sectionectomy. A direct approach to the pedicle with the PVTT was not recommended as portal patency in the anterior section was unknown; therefore, we identified the three liver regions using a fluorescence intensity gradient. The ICG fluorescence intensity gradient was established using two doses of ICG, which effectively identified the right posterior and anterior sections and the left lobe. MIPS clearly projected each section boundary onto the liver surface. Right anterior sectionectomy was successfully performed with no postoperative complications. Pathological examination revealed a negative surgical margin. Utilizing multiple ICG dosages for boundary identification of liver regions has potential use for anatomic hepatectomy.

The Comprehensive Anxiety and Parkinson’s Scale (CAPS): co-development and initial validation of the long (CAPS-54) and short (CAPS-24) versions

Purpose Anxiety is a prevalent symptom of Parkinson’s disease, but is often under-recognised and challenging to characterise. The present study aimed to develop a comprehensive new scale that characterised the specific and nuanced experience of anxiety in people living with Parkinson’s disease. A shortened version of the scale was also developed. The psychometric properties of both versions of the scale were assessed for reliability and validity. Methods Secondary analyses were conducted on data from 254 people with Parkinson’s disease and anxiety collected in a modified Nominal Group Technique ranking survey. Secondary analyses included exploratory factor analysis, reliability and validity analyses, and confirmatory factor analysis. Results A standardised scale of anxiety and Parkinson’s disease, in its long Comprehensive Anxiety and Parkinson’s Scale − 54 (CAPS-54) and short versions (CAPS-24), was developed. Reliability and validity analyses of the scales demonstrated excellent factorial and internal consistency, as well as good convergent validity. Conclusions The CAP Scales offer researchers and clinicians a more comprehensive means of assessing the experience of anxiety in the context of Parkinson’s disease than is currently available. Initial validation of the scales is promising. Future validation and identification of clinical boundaries with an independent sample is recommended.

Conceptual framework for data-driven reservoir characterization: Integrating machine learning in petrophysical analysis

Reservoir characterization plays a critical role in optimizing oil and gas production by accurately determining subsurface properties such as porosity, permeability, and fluid saturation. Traditionally, this process has relied on the interpretation of well logs, seismic data, and core samples using manual or physics-based methods. However, these approaches often face limitations when dealing with complex datasets and heterogeneous reservoir properties. This review develops a conceptual framework for integrating machine learning (ML) into petrophysical analysis to revolutionize reservoir characterization. By utilizing advanced ML algorithms such as supervised learning, unsupervised learning, and deep learning, this model demonstrates the potential of data-driven methods to improve the accuracy and efficiency of reservoir analysis. The framework focuses on how ML techniques can automate the interpretation of well logs, enhance seismic data processing, and predict rock properties from core samples, leading to more accurate identification of reservoir boundaries and productive zones. This approach not only reduces interpretation errors but also accelerates decision-making in reservoir management. Moreover, machine learning can handle the vast amounts of data generated in reservoir studies, enabling real-time analysis and adaptive decision support. The study also addresses challenges such as data quality, model interpretability, and computational scalability, offering insights into future applications of ML in reservoir characterization. By combining petrophysical expertise with machine learning capabilities, this framework aims to significantly advance the field of reservoir characterization and contribute to more efficient resource management in the oil and gas industry.

EF-net: Accurate edge segmentation for segmenting COVID-19 lung infections from CT images

Despite advances in modern medicine including the use of computed tomography for detecting COVID-19, precise identification and segmentation of lesions remain a significant challenge owing to indistinct boundaries and low degrees of contrast between infected and healthy lung tissues. This study introduces a novel model called the edge-based dual-parallel attention (EDA)-guided feature-filtering network (EF-Net), specifically designed to accurately segment the edges of COVID-19 lesions. The proposed model comprises two modules: an EDA module and a feature-filtering module (FFM). EDA efficiently extracts structural and textural features from low-level features, enabling the precise identification of lesion boundaries. FFM receives semantically rich features from a deep-level encoder and integrates features with abundant texture and contour information obtained from the EDA module. After filtering through a gating mechanism of the FFM, the EDA features are fused with deep-level features, yielding features rich in both semantic and textural information. Experiments demonstrate that our model outperforms existing models including Inf_Net, GFNet, and BSNet considering various metrics, offering better and clearer segmentation results, particularly for segmenting lesion edges. Moreover, superior performance on the three datasets is achieved, with dice coefficients of 98.1, 97.3, and 72.1 %.

Rapid analysis of drill core data for detection of geological boundaries

Regolith geochemical and geophysical patterns related to underlying mineralisation may be difficult to detect in regions where the regolith is thick, transported or has developed complex layering during its mineralogical and geochemical evolution. Detection and accurate logging of geochemical and mineralogical interfaces and horizons within drill samples obtained from such regolith profiles, including the boundary between regolith and underlying bedrock can be critical for detection and interpretation of geochemical patterns. In turn, this assists in selecting zones for systematic sampling and the optimum combination of geochemical, mineralogical and geophysical analysis and data processing to enhance signals or patterns associated with mineralisation.This study presents a multivariate data-driven approach to detecting boundaries within regolith and other profiles potentially incorporating near real-time, in-situ geochemical, mineralogical, and petrophysical data acquisition methods to aid decision-making during stratigraphic/exploration drilling campaigns. The approach is demonstrated using geochemical and mineralogical data from a drillhole at the McKinnons Au deposit in New South Wales (NSW), Australia. Embedding feature (variable) selection techniques to support vector machines and random forest approaches, followed by application of a wavelet tessellation technique encoded in Data Mosaic™ to the selected variables, delivered more detailed and refined identification of zonation and boundaries within the regolith compared with approaches using only visual core logging and geochemical assays. The method was subsequently applied to geochemical, petrophysical and spectral data acquired from two drillholes in the Delamerian Orogen of western NSW. Several subtle lithological boundaries were detected within the regolith and the interface between weathered profiles and basement rocks at different spatial scales. This included some zones displaying elevated Pb and Zn within the saprock part of the regolith profile. A critical zone above the saprock, highlighted by a high variance interval was also detected using the multivariate wavelet tessellation. This indicated shorter core sampling intervals may be needed to improve the likelihood of detecting mineralisation in this region. The methodology improves the identification of mineralised zones by enabling dynamic drilling adjustments through near real-time knowledge feedback, which also reduces costs and enhances field efficiency.

PromptCNER: A Segmentation-based Method for Few-shot Chinese NER with Prompt-tuning

Recognizing Chinese entities in low-resource settings is a challenging but promising task, which extracts structured pre-defined entities and corresponding types from unstructured text. Compared with the prosperous Named Entity Recognition (NER) methods for Indo-European languages, such as English, the research on Chinese NER is still in its infancy. The main obstacles to the development of Chinese NER methods include the ambiguity of Chinese entity boundary recognition and limited data resources. To address these issues, in this paper, a word-segmentation-based model is present for few-shot Chinese NER. First, we enumerate all possible candidate entity spans on the character level for accurate entity boundary identification with the proposed word segmentation and combination strategy. Then, one kind of question-answer-based prompt template loaded with the candidate entity spans is proposed to cast entity extraction into the masked token prediction task, for dealing with the low-data problem by taking full advantage of the generality and transferability of the pre-trained language model. The extensive experimental results show that our method outperforms the state-of-the-art baselines in low-data settings and also achieves comparable performance in full-data settings.

Ex Ante Construction of Flow Pattern Maps for Pulsating Heat Pipes

A novel methodology is proposed for the development of empirical flow pattern maps for pulsating heat pipes (PHPs), which relies on the concept of virtual superficial velocity of the liquid and vapour phases. The virtual superficial velocity of each phase is defined using solely the design and operational parameters of the pulsating heat pipe, allowing the resulting flow pattern map to serve as a predictive instrument. This contrasts with existing flow pattern maps that necessitate direct measurements of temperatures and/or velocities within one or more channels of the pulsating heat pipe. Specifically, the virtual superficial velocities are derived from the relative significance of the driving forces and the resistances encountered by each phase during flow. The proposed methodology is validated using flow visualisation datasets obtained from two separate experimental campaigns conducted on flat-plate polypropylene pulsating heat pipe prototypes featuring transparent walls and meandering channels with three turns, five turns, seven turns, and eleven turns, respectively. The PHP prototypes were tested for gravity levels ranging between 0 g and 1 g and heat inputs ranging from 5 W to 35 W. The proposed approach enables the identification of empirical boundaries for flow pattern transitions as well as the establishment of an empirical criterion for start-up.

SURG-23. ESTABLISHMENT OF AN APPROPRIATE GLIOMA SURGERY SYSTEM CONSIDERING MOLECULAR AND FUNCTIONAL BOUNDARIES

Abstract Gliomas are invasive tumors and cannot be cured by surgery. But surgical removal has also been proven to affect treatment outcomes. Recent advances in molecular diagnostics have identified IDH mutations, especially in lower grade gliomas, allowing the identification of so-called molecular boundaries. It is conceivable that expanded resection to the molecular border may improve treatment outcome. On the other hand, extended resection may be incompatible with functional preservation. In such cases, it is also important to consider that the impact of surgery on treatment outcome has also been proven to vary by glioma genotype. While total resection has a significant prognostic impact on survival in astrocytomas with IDH mutations, the impact on survival in oligodendrogliomas that are sensitive to chemoradiotherapy and, conversely, in high-grade tumors without IDH mutations is lower than in the former. Therefore, it is important to determine the extent of removal after considering the tumor type. In general, various surgical support devices such as surgical navigation systems, fluorescent diagnosis using 5ALA, intraoperative electrophysiological monitoring, awake functional brain mapping, and intraoperative MRI are applied during glioma surgery. In addition to these, we have developed a surgical system that also focuses on tumor subtypes by introducing preoperative tumor type prediction using AI imaging and intraoperative rapid genetic diagnosis. We have further developed intraoperative rapid genetic diagnosis to identify genetic mutations around the tumor, enabling the detection of molecular boundaries. It is now possible to remove as much as possible, considering the molecular boundary, while paying sufficient attention to preservation of brain functions. Gliomas are intractable diseases, but their treatment outcomes have steadily improved over time. We are now in an era of a tailor-made surgery based on a variety of data. Implementing “appropriate surgical procedures” we aim at further improvement of treatment outcomes.

An Analysis of the Future Trends and Challenges of Autonomous Driving Technology

Abstract. Autonomous driving technology is one of the most groundbreaking innovations in the automotive industry in recent years. This article will explore the latest trends in autonomous driving, analyzing its historical development, current status, and future prospects, while also assessing its potential across different application scenarios. With the support of data and illustrations, this article aims to provide a comprehensive perspective to understand the development and challenges of this technology. As technology progresses, autonomous driving will significantly enhance traffic safety, efficiency, and convenience, ultimately leading the transportation industry into a new era. Although current autonomous driving systems still face challenges such as boundary identification and safety concerns, ongoing research and innovation across various fields will drive continuous improvements. In the future, autonomous driving is expected to become part of mainstream transportation, not only boosting traffic efficiency but also reducing the workload of drivers and lowering the incidence of traffic accidents. Plans for its widespread global adoption are already underway.

Study on the redistribution mechanism and secondary purge strategy of proton exchange membrane fuel cells

Effective control of membrane water content is essential for increasing the space for ice formation during the cold start stage and enhancing the success rate of start-up. Shutdown purge can effectively lower the membrane water content following fuel cell operation. However, during the cooling and standing process after purge, the rapid change in saturated vapor pressure can result in the redistribution of membrane dissolved water, leading to an increase in its content and a reduction in the success rate of cold start. Therefore, this study establishes a multidimensional, multiphase simulation model to comprehensively and thoroughly analyze the redistribution mechanism after purging and investigates the relationship between membrane water content and cold start. This is achieved by identifying the maximum membrane water content boundary during the cold start process and ultimately improving the success rate of cold start through a secondary purge strategy. The research results indicate that the membrane water content of the fuel cell increases from 2.31 to 8.31 after redistribution. During the cold start stage, the cold start success of the fuel cell under different environmental temperatures exhibits relatively specific boundary conditions, with the cold start process being closely related to the load current density and initial membrane water content. After implementing the secondary purging strategy, the membrane water content of the fuel cell decreases again, displaying favorable cold start characteristics in the cold start stage and successfully starting at −10 °C. This study can provide a reliable basis for the development of purging strategies during shutdown and offer a theoretical foundation for the boundary identification process of cold start.

Использование вегетационных индексов для корректировки почвенных контуров в условиях пашни Калужской области

Within the scope of this research, the potential of utilizing multi-year vegetation indices obtained through remote sensing was evaluated for refining soil cover structure contours. It was determined that visualizing average multi-year NDVI, GNDVI, EVI, and CVI indices allows for the refinement of current information on the contours of productivity zones. This approach enables the refinement of boundaries between soil variations and the identification of current boundaries of lands with differing productivity. The findings indicate that high average multi-year values of these indices are associated with semi-hydromorphic areas exhibiting increased moisture levels. Keywords: VEGETATION INDICES, REMOTE SENSING, SOIL COVER STRUCTURE

Research on coal-rock boundary identification based on the morphological sobel algorithm

Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation; to a certain extent, subsequent intelligent mining is limited. A morphological Sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological Sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological Sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the Sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

The problem of ammonoid substantiation of the Artinskian–Kungurian boundary in Western Verkhoyanie

Kungurian ammonoids had high taxonomic diversity, but were distinguished by an extremely high level of endemism and sharp geographic differentiation. Therefore, outside the regions containing the most important conodont taxa, the Artinskian-Kungurian boundary based on ammonoids must be established through indirect evidence due to the lack of common species with potential global stratotypes of the lower boundary of the Kungurian. A long-term study of the reference sections of the Echian and Tumarian regional stages and their ammonoids made it possible to change the understanding of the bio- and lithostratigraphy of the Artinskian-Kungurian boundary deposits of the Kuranakh structural-facies zone of Western Verkhoyanie. Clarification of the upper boundary of the Khabakh Formation in the stratotype section led to the fact that the ammonoids previously belonging to this formation began to be considered in the lower part of the Orol formation. They characterize the Paratumaroceras? sp. nov. biostratigraphic beds of the lower part of the Tumarian Regional Stage. At the current level of knowledge, the ammonoid substantiation of the Artinskian–Kungurian boundary in Verkhoyanie is difficult due to the existing hiatus corresponding to the Khabakh formation and its age analogues. However, the appearance at the base of the Tumarian Regional Stage of the first paragastrioceratids with a ventral sinus in transverse sculpture (Paratumaroceras? sp. nov.) suggests that the boundary in question is located near the base of the indicated regional stage or even combined with it. For further clearer identification of the Kungurian lower boundary in Verkhoyanie, new studies of the Echian–Tumarian boundary deposits in typical sections, independent of paleontology, are necessary. Such research should primarily include isotope-stratigraphic subdivision and high-precision U-Pb dating.