Similar Assessment Methods Research Articles

Lake regime evaluation based on similarity evaluation method, taking Poyang Lake as an example

Lake regime shift theory provides effective support for managing lake eutrophication. However, there are limited studies on identifying lake regime status using mathematical methods. Based on the similarities in characteristics and evolutionary patterns of lake ecosystems, and utilizing long-term lake water ecological monitoring data, this study introduces a method for evaluating lake regime status using total nitrogen (TN), total phosphorus (TP), and chlorophyll-a (Chl-a) as evaluation indices. Taking Poyang Lake as an example, the lake regime status was evaluated. The results showed that: The weights of total nitrogen (TN), total phosphorus (TP), and chlorophyll a (Chl-a) in Poyang Lake were 0.207, 0.234 and 0.559, respectively, with Chl-a being the main controlling factor for evaluating the lake regime. The lake regime of Poyang Lake is in the Algae-Macrophytes Coexist state. The similarity assessment method has been proven effective for determining a lake regime using sparse water eco-environmental data.

The LEADING Guideline: Reporting Standards for Expert Panel, Best-Estimate Diagnosis, and Longitudinal Expert All Data (LEAD) Studies.

Accurate assessments of symptoms and illnesses are essential for health research and clinical practice but face many challenges. The absence of a single error-free measure is currently addressed by assessment methods involving experts reviewing several sources of information to achieve a more accurate or best-estimate assessment. Three bodies of work spanning medicine, psychiatry, and psychology propose similar assessment methods: The Expert Panel, the Best-Estimate Diagnosis, and the Longitudinal Expert All Data (LEAD) method. However, the quality of such best-estimate assessments is typically very difficult to evaluate due to poor reporting of the assessment methods and when they are reported, the reporting quality varies substantially. Here, we tackle this gap by developing reporting guidelines for such best-estimate assessment studies. The development of the reporting guidelines followed a four-stage approach: 1) drafting reporting standards accompanied by rationales and empirical evidence, which were further developed with a patient organization for depression, 2) incorporating expert feedback through a two-round Delphi procedure, 3) refining the guideline based on an expert consensus meeting, and 4) testing the guideline by i) having two researchers test it and ii) using it to examine the extent previously published studies report the standards. The last step also provides evidence for the need for the guideline: 10 to 63% (Mean = 33%) of the standards were not reported across thirty randomly selected studies. The LEADING guideline comprises 20 reporting standards related to four groups: The Longitudinal design (four standards); the Appropriate data (four standards); the Evaluation - experts, materials, and procedures (ten standards); and the Validity group (two standards). We hope that the LEADING guideline will be useful in assisting researchers in planning, conducting, reporting, and evaluating research aiming to achieve best-estimate assessments.

Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery

Noises widely exist in the actual working environment of rotating machinery, which makes it difficult to extract high-quality fault features. Recently, the superiority of graph neural network (GNN) in the aspect of fault feature representation has been confirmed. By converting one-dimensional signals into graph structural data, hidden structural information inside the signals can be revealed. In this study, a novel graph-level fault diagnosis method based on spatial–temporal graph feature learning is developed. It mainly contains the following steps: First, Short-Time Fourier Transform (STFT) is used to construct time–frequency energy heatmaps, which can reflect the distribution of frequencies over time windows and the temporal variation characteristics. Second, a time–frequency similarity assessment method based on dynamic frequency warping is presented. On this basis, a time–frequency similarity graph (TFSG) is constructed to describe the frequency relationship over different time windows, which can bring extra structural information for the domain of time–frequency to support the subsequent classification task. Then, with the input of TFSG, a graph-level fault diagnosis model based on an improved GraphSAGE network (IGN) is developed. Finally, the fault labels for all TFSGs are recognized with a SoftMax classifier. Two experimental cases based on axial flow pumps and a public gearbox dataset verified the effectiveness of the proposed fault diagnosis method.

Predicting behavior in extinct reptiles from quantitative analysis of trigeminal osteological correlates

IntroductionVertebrate sensory systems are in close contact with surrounding tissues, often leaving bony signatures behind. These bony features are the keys to assessing variation in sensory systems in fossil taxa. The trigeminal sensory system (e.g., trigeminal ganglion, ophthalmic, maxillary, and mandibular divisions) has osteological correlates throughout the skull, including the braincase (e.g., trigeminal fossa, prootic notch, ophthalmic and maxillomandibular foramina) and rostrum (e.g., mandibular canal, neurovascular foramina).MethodsHere we measured and compared these features among a morphologically, phylogenetically, and ecologically diverse sample of sauropsids to determine strength of osteological correlates and to explore ecomorphological trends. We determined several suitable osteological correlates for trigeminal soft tissue features and discounted foramen count alone as a suitable osteological correlate. However, when size was accounted for, foramen count becomes a useful indicator of sensory ecology.Results and discussionAmong extant taxa, those engaging in tactile sensory behaviors with the face exhibit relatively larger trigeminal tissues and osteological correlates than those not engaging in tactile sensory behaviors. Though patterns are unclear among several clades, both relative feature sizes and models used to predict sensory capacity reveal a trend of increasing tactile sensitivity along the pseudosuchian lineage. Overall, a quantitative assessment of ecomorphological trends of trigeminal osteological correlates proves informative for the hypotheses of sensory behavior in extinct taxa and supports the use of similar assessment methods for other osteological correlates.

Amorphous calcium carbonate as a novel potential treatment for osteoarthritis in dogs: a pilot clinical study.

Amorphous calcium carbonate (ACC) is a potential new treatment for canine osteoarthritis (OA) with novel mechanisms based on local pH modulation and targeting bone remodeling, inflammation, and pain. The aim of this pilot exploratory clinical study was to obtain initial data on the potential efficacy and safety of ACC in OA dogs and to determine if further investigation was appropriate using similar assessment methods. In this prospective, randomized, double-blind, controlled pilot study, 41 client-owned dogs were allocated in a 2:1 ratio to ACC: placebo given orally for 56 days. Efficacy assessments included improvements in pain and mobility using owner questionnaires [Canine Brief Pain Inventory (CBPI), Client Specific Outcome Measure (CSOM), and Veterinary Orthopedic Scores (VOS)]. Safety in the study population was monitored by veterinary examinations, clinical pathology, and adverse events. Fifty-three dogs were screened, of which 41 enrolled and served for the safety assessment. Thirty-six dogs were found evaluable for initial efficacy assessment. Three dogs given placebo (21.4%) and one given ACC (4.5%) were removed before day 56 due to owner-perceived pain and were considered treatment failures. There were no serious adverse events or clinically significant treatment-related effects in the study. Overall, ACC was found safe in the small study population. On day 56, proportionally more ACC than placebo dogs were treatment successes based on CBPI (45.5% vs. 21.4%) and CSOM (63.6% vs. 30.8%, respectively); however, these differences were not statistically significant (p = 0.15 and 0.06, respectively). On day 56, within the ACC group but not the placebo group, the CBPI, CSOM, and VOS assessments were lower compared to day 0 and day 14 (p < 0.05). The relatively small number of dogs limited the statistical power of the pilot study in evaluating the efficacy and safety of ACC. Study results support the conduct of larger, appropriately powered studies using similar assessments to confirm whether ACC may be a safe and effective treatment for OA in dogs.

Machine Learning for Pedestrian-Level Wind Comfort Analysis

(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies.

Voice-Controlled Intelligent Personal Assistant for Call-Center Automation in the Uzbek Language

The demand for customer support call centers has surged across various sectors due to the pandemic. Yet, the constraints of round-the-clock human services and fluctuating wait times pose challenges in fully meeting customer needs. In response, there’s a growing need for automated customer service systems that can provide responses tailored to specific domains and in the native languages of customers, particularly in developing nations like Uzbekistan where call center usage is on the rise. Our system, “UzAssistant,” is designed to recognize user voices and accurately present customer issues in standardized Uzbek, as well as vocalize the responses to voice queries. It employs feature extraction and recurrent neural network (RNN)-based models for effective automatic speech recognition, achieving an impressive 96.4% accuracy in real-time tests with 56 participants. Additionally, the system incorporates a sentence similarity assessment method and a text-to-speech (TTS) synthesis feature specifically for the Uzbek language. The TTS component utilizes the WaveNet architecture to convert text into speech in Uzbek.

Similarity evaluation method of single flow point energy consumption mapping based on Fréchet distance

Abstract Aiming at the problem of inaccurate measurement results caused by factors such as gas leakage during the calibration of the standard meter gas flow standard device of the same type and small diameter, this paper proposes a similarity assessment method of single flow point energy consumption mapping based on Fréchet distance (FD). First, through the statistical analysis of the historical energy consumption data of gas flow standard device, a discrete standard energy consumption mapping is formed. Next, the energy consumption mapping to be tested is standardized and the similarity of the energy consumption mapping is determined by the binary matrix accelerated search of the FD between the energy consumption mapping to be tested and the standard energy consumption mapping. Finally, the similarity threshold is calculated by combining the standard deviation of the FD and the reliability of the gas flowmeter calibration process is evaluated accordingly. The test shows that the method in this paper can effectively determine the reliability of the gas flowmeters calibration results.

Evolution of a capstone exam for third-year doctor of pharmacy students

Background and purpose: Pharmacy capstone exams are an example of an assessment strategy to evaluate student competency and practice-readiness. Capstone exam processes have been discussed in the literature; however, description of format, evolution of the process, and implementation in the year prior to rotations is lacking. This manuscript discusses the development, implementation, and utilization of a third professional year pharmacy capstone exam to assess advanced pharmacy practice experience (APPE) readiness.Educational activity and setting: A pharmacy year three (PY3) capstone exam was initially developed prior to enrollment of the inaugural graduating class. Since the first iteration, utility has remained the same, but the overall development and implementation process has evolved due to changes in programmatic and student learning outcomes, curricular mapping, student and faculty feedback, and practice expectations.Findings: The entities responsible for exam implementation perceive faculty and students have a positive perception of the exam evolution and improvements implemented. Specifically, feedback from students and faculty mentioned clear expectations, comfort with the new capstone exam process, and an appreciation for the focus on interrater reliability. As assessment is an iterative process, quality improvement strategies continue to be implemented to address feedback regarding the PY3 capstone exam.Summary: Purposeful utilization of capstone exams in the third year is one method to assess APPE readiness. Sharing one college's experience with the evolution of such an exam, quality improvement methods, and experience in implementing reliability and validity measures may provide a model for institutions to implement similar assessment methods efficiently.

Airway dimensional changes following bone anchored maxillary protraction: a systematic review

BackgroundThe introduction of skeletal anchorage utilized for maxillary protraction with a face mask or class III elastics has been developed for the management of class III malocclusions with minimal dental effect. The objective of the present review was to evaluate the current evidence regarding airway dimensional changes following bone-anchored maxillary protraction. A search was conducted by two authors (S.A & B.A) in the following databases: MEDLINE via PubMed, Cochrane Library, Web of Science, Scopus, Google Scholar and Open Grey; besides a hand search in references of selected articles and developing a search alert in electronic databases. Selection criteria comprised randomized as well as prospective clinical trials evaluating airway dimensional changes following bone-anchored maxillary protraction. Relevant data were extracted after studies retrieval and selection. The risk of bias was thereafter evaluated using the revised RoB 2 tool for randomized clinical trials and the ROBINS-I tool was used for non-randomized clinical trials. The quality of studies was assessed using the modified Jadad score. After examining (eligibility) full-text articles, four clinical trials were ultimately included. These studies evaluated the airway dimensional changes, following bone-anchored maxillary protraction in comparison to different control study groups. Based on the available evidence, all the bone-anchored maxillary protraction devices used in the eligible studies in the present systematic review resulted in an improvement in the airway dimensions. However, due to the few numbers of studies available and the guarded evidence due to the low quality of evidence of three out of four included articles, there is no strong evidence to support a significant increase in the airway dimensions following bone-anchored maxillary protraction. Therefore, there is a need for more randomized controlled clinical trials with similar bone-anchored protraction devices and similar assessment methods for more valid comparisons, excluding any confounding factors, on airway dimensional changes.

MAIN PROVISIONS OF THE METHODOLOGY FOR ASSESSING THE POSSIBILITIES OF RESTORING WEAPONS AND MILITARY EQUIPMENT OF THE AIR FORCES OF UKRAINE DAMAGED DURING COMBAT OPERATIONS

Analysis of the performance of combat tasks by military units and individual units of the Air Forces of the Armed Forces of Ukraine in the zone of operation of the United Forces in the east of Ukraine (anti-terrorist operation on the territory of the Donetsk and Luhansk regions) and full-scale armed aggression by the russian federation indicates a significant change in the modern forms of use of troops (forces). This leads to the need to improve existing ones and to find new ways to carry out logistical tasks, in particular to restore damaged samples of weapons and military equipment. In the basic provisions of the well-known methodology for assessing the possibilities of restoring weapons and military equipment of the Air Force of the Armed Forces of Ukraine, factors are defined. The article offers basic indicators of the possibilities of restoration of armament and military equipment damaged during combat operations (military operations). The article develops and offers methods of assessment of restoration of armament and military equipment of the Air Force of the Armed Forces of Ukraine. The method is aimed at the use of logistical support authorities in the conduct of operational and tactical and logistical calculations at the stages of advance, direct planning and combat operations. The method is one of the components in the creation of a methodical apparatus for the functioning of the system for the restoration of armament and military equipment of the process of support for the decision on logistics of troops (forces) in the preparation and conduct of combat operations (military operations). This method can be used as a basis for the development of similar methods of assessment of possibilities of restoration of armament and military equipment of other branches of the Armed Forces of Ukraine.

Standardizing No Net Loss Stream Mitigation Assessment Methods: Tradeoffs between Expediency and River Science

ABSTRACTEnvironmental regulators use No Net Loss policies to minimize the damaging impacts of development projects on water resources. In this context, regulators prefer standard environmental assessment protocols over ad hoc approaches. This article examines how the St. Louis District of the U.S. Army Corps of Engineers (the Corps) directed the development of statewide standard stream mitigation assessment protocols in Missouri and Illinois to comply with Section 404 of the Clean Water Act. Protocol development in each state included Corps district representatives along with river scientists and various stakeholders. A primary tension in these discussions centered on the tradeoff between the efficiency of using the protocol vs. its scientific rigor. While consensus in Missouri was achieved by leveraging statewide agreements, Illinois lacked similar agreements and included a wider range of scientific participants, which hindered consensus. Despite these differences, the two states produced remarkably similar stream assessment methods. The similarity largely reflected the authority that Corps districts had over the Section 404 process and their privileging of simple and flexible stream mitigation assessments that can be used across different Corps districts. This study provides insights into the social processes that shape no net loss assessment protocols and the practical challenges of protecting the environmental quality of streams.

Deep learning Mueller matrix feature retrieval from a snapshot Stokes image.

A Mueller matrix (MM) provides a comprehensive representation of the polarization properties of a complex medium and encodes very rich information on the macro- and microstructural features. Histopathological features can be characterized by polarization parameters derived from MM. However, a MM must be derived from at least four Stokes vectors corresponding to four different incident polarization states, which makes the qualities of MM very sensitive to small changes in the imaging system or the sample during the exposures, such as fluctuations in illumination light and co-registration of polarization component images. In this work, we use a deep learning approach to retrieve MM-based specific polarimetry basis parameters (PBPs) from a snapshot Stokes vector. This data post-processing method is capable of eliminating errors introduced by multi-exposure, as well as reducing the imaging time and hardware complexity. It shows the potential for accurate MM imaging on dynamic samples or in unstable environments. The translation model is designed based on generative adversarial network with customized loss functions. The effectiveness of the approach was demonstrated on liver and breast tissue slices and blood smears. Finally, we evaluated the performance by quantitative similarity assessment methods in both pixel and image levels.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin.

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision–making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann–Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the Tmin in the coldest month over the whole basin at a rate of 0.03–0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00477-022-02172-8.

Improved similarity assessment and spectral clustering for unsupervised linking of data extracted from bridge inspection reports

Textual bridge inspection reports are important data sources for supporting data-driven bridge deterioration prediction and maintenance decision making. Information extraction methods are available to extract data/information from these reports to support data-driven analytics. However, directly using the extracted data/information in data analytics is still challenging because, even within the same report, there exist multiple data records that describe the same entity, which increases the dimensionality of the data and adversely affects the performance of the analytics. The first step to address this problem is to link the multiple records that describe the same entity and same type of instances (e.g., all cracks on a specific bridge deck), so that they can be subsequently fused into a single unified representation for dimensionality reduction without information loss. To address this need, this paper proposes a spectral clustering-based method for unsupervised data linking. The method includes: (1) a concept similarity assessment method, which allows for assessing concept similarity even when corpus or semantic information is not available for the application at hand; (2) a record similarity assessment method, which captures and uses similarity assessment dependencies to reduce the number of falsely-linked records; and (3) an improved spectral clustering method, which uses iterative bi-partitioning to better link records in an unsupervised way and to address the transitive closure problem. The proposed data linking method was evaluated in linking records extracted from ten bridge inspection reports. It achieved an average precision, recall, and F-1 measure of 96.2%, 88.3%, and 92.1%, respectively.

Computational image translation from Mueller matrix polarimetry to bright-field microscopy.

Mueller matrix (MM) polarimetry can provide comprehensive information about the polarization properties that are closely related to the microstructural features and has demonstrated its potential in biomedical studies and clinical practices, and bright-field microscopy is widely used in pathological diagnosis as the golden standard. In this work, we improve the throughput of MM microscopy by learning a statistical transformation between these two imaging systems based on deep learning. Using this approach, the MM microscope can generate an image that is equivalent to a bright-field microscope image of the matching field of view. We add new transformative capability to the existing MM imaging system without requiring extra hardware. The translation model is based on conditional generative adversarial network with customized loss functions. We demonstrated the effectiveness of our approach on liver and breast tissues and evaluated the performance by four quantitative similarity assessment methods in pixel, image and distribution levels, respectively.

A Gene Rank Based Approach for Single Cell Similarity Assessment and Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides quantitative gene expression profiles at single-cell resolution. As a result, researchers have established new ways to explore cell population heterogeneity and genetic variability of cells. One of the current research directions for scRNA-seq data is to identify different cell types accurately through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise level, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the biological difference between cell types has become the key to analyze scRNA-seq data. For these reasons, the unsupervised learning for cell population discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method plays a significant role in cell clustering. Here, we present BioRank, a new cell similarity assessment method based on annotated gene sets and gene ranks. To evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. In addition, BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to 12 public scRNA-seq datasets, we show that it is better than or at least as well as several popular similarity assessment methods for single cell clustering.

Reliable damage localization in cantilever beams using an image similarity assessment method applied to wavelet-enhanced modal analysis

Modal analysis is a powerful tool for the detection of damage in structures such as cantilever beams. The Continuous Wavelet Transform (CWT) is often used to locate damage, but may lead to unsatisfactory results when methods designed and tested successfully on simulated beams are applied to experimental tests. In the present work, a novel approach is presented that allows to reliably detect the correct damage locations along a beam in an experimental setup, using only finite-element simulated data as a reference. The novel method is based on two main innovations: (1) an image similarity assessment method making an efficient use of the full wavelet scalogram, and (2) an ensemble approach accounting for uncertainties in materials properties. The image processing method is shown to dramatically outperform three reference methods constructed for comparison, two of them based on signal processing approaches proposed in literature, combined with a neural network processing step. This superiority is demonstrated through a systematic assessment both of simulated data, where the noise level was varied for sensitivity analyses, and of experimental data. When combined with the ensemble approach the new methodology is shown to have a 100% damage recognition rate on the experimental data studied.

A Cloud Model‐Based Risk Assessment Methodology for Tunneling‐Induced Damage to Existing Tunnel

This study presents a cloud model‐based approach for risk assessment of existing tunnels in tunneling construction environments where the cloud model provides a basis for uncertainty transformation between its qualitative concepts and quantitative expressions. An evaluation index system is established for risk assessment of existing tunnels based on the tunnel‐induced failure mechanism analysis. The assessment result is obtained through the correlation with the cloud model of each risk level. Risk assessment for existing Guangzhou‐Shenzhen‐Hong Kong Railway Tunnel in the tunneling environment of Shenzhen Metro Line 6 is shown in a case study. Comparisons between Fuzzy Analytic Hierarchy Process (FAHP) methods are further discussed according to results. The proposed evaluation method is verified to be more competitive as the fuzziness and randomness of uncertainties in the risk assessment system can be considered comprehensively. This method can serve as a decision‐making tool for other similar project risk assessment methods to increase the likelihood of a successful project in an uncertain environment.

Analysis of “underdevelopment whirlpools” as a tool of managing the regional market of education in the conditions of Industry 4.0

Purpose The purpose of this paper is to determine the perspectives and to adapt the analysis of “underdevelopment whirlpools” to the current needs of state management of digital modernization of the regional market of educational services in the aspect of determination and assessment of its disproportions as a tool of managing the regional market of education and managing the process of reorganization of regional universities in the conditions of transition to Industry 4.0. Design/methodology/approach Analysis of “underdevelopment whirlpools”, adapted by the authors to the specifics of the regional market of educational services, is used. The statistical basis for this paper includes the information and analytical materials as a result of monitoring the effectiveness of activities of educational organizations of higher education and the materials of the Federal State Statistics Service of the Russian Federation. Findings “Underdevelopment whirlpools” of the Kirov branch of St. Petersburg University of the Humanities and Social Sciences are analyzed, and reorganization of this university is recommended. “Underdevelopment whirlpools” in Kirov Oblast are analyzed, and an increase of regulation of this market is recommended. Originality/value It is substantiated that the method of analysis of “underdevelopment whirlpools” could be a tool of managing the regional educational market and managing the process of reorganization of regional universities in the conditions of transition to Industry 4.0. The advantage of analysis of “underdevelopment whirlpools”, as compared to the existing similar methods of assessment of disproportions in the development of the regional market of educational services (e.g. comparative analysis and plan-fact analysis), is the possibility not only to evaluate the static underrun from the model/plan but also to study its dynamics (depth and speed of sucking into “underdevelopment whirlpool”), which allows achieving high precision of the results of assessment.