Deep Process Research Articles

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

Hydrochemical Characteristics and Formation Mechanism of Geothermal Fluids in Zuogong County, Southeastern Tibet

Zuogong County is located in the southeast of Tibet, which is rich in hot spring geothermal resources, but its development and utilization degree are low, and the genetic mechanism of the geothermal system is not clear. Hydrogeochemical characteristics of geothermal water are of great significance in elucidating the genesis and evolution of geothermal systems, as well as the sustainable development and utilization of geothermal resources. The hydrogeochemical characteristics and genesis of the geothermal water in Zuogong County were investigated using hydrogeochemical analysis, a stable isotope (δD, δ18O) approach, and an inverse simulation model for water–rock reactions using the PHREEQC. The results indicated that the Zuogong geothermal system is a deep circulation heating type without a magmatic heat source. The chemical types present in the geothermal water from the Zuogong area are HCO3 and HCO3·SO4, and the main cations are Na+ and Ca2+. The groundwater is replenished by atmospheric precipitation and glacier meltwater. The salt content of geothermal water mainly comes from the interaction between water and surrounding rocks during the deep circulation process. The reservoir temperature of geothermal water in Zuogong is 120–176 °C before mixing with non-geothermal water and drops to 62–98 °C after mixing with 58 to 79% of non-geothermal water. According to the proposed conceptual model, geothermal water mainly produces water–rock interaction with aluminosilicate minerals in the deep formation, while in shallow areas it interacts mainly with sulfate minerals. These findings contribute to a better understanding of the geothermal system in Zuogong County, Tibet.

Learning With Board Games: Interplay of Context and Students’ Prior Knowledge

Board games can support engagement, deep information processing and learning outcomes for university students. The instructional context, notably the way an activity is introduced to students, can influence students’ behavior and learning outcomes. Yet, despite its potential important influence on in-game behavior and learning, the impact of the instructional context used to introduce a game-based learning activity has not received much attention. In two studies, we examined how framing a game-based learning activity with a serious or a non-serious context can impact university students’ behavior and learning outcomes. In the first study, 44 graduate students played the same word association game that was either introduced as a game or as a study activity. Results showed that the serious context increased recall as compared to the gaming context. In the second study, involving 87 participants playing the same game, results showed that the gaming context and prior gaming experience also reduced students’ recall. Overall, grouped analysis showed that the gaming context led to poorer learning than the serious context for recognition and recall measures, even when considering prior knowledge, in-game behavior, and prior gaming experience. Theoretical and practical implications for educators are discussed.

Generating Artistic Images Through Neural Style Transfer Using Machine Learning and Image Manipulation Techniques

Neural Style Transfer (NST) is a computational technique that merges the content of one image with the stylistic characteristics of another, creating visually appealing compositions. Leveraging deep learning and image processing, this method separates andcombines the content and style of input images using convolutional neural networks (CNNs). The content representation of an image is captured by the high-level features extracted from a pre-trained CNN, while its style is characterized by the statistical correlations of features at multiple network layers. It explores the fusion of machine learning and image processing techniques in NST. Initially introduced by Gatys et al., NST has gained popularity due to its ability to generate artistic renditions and novel visualizations. The process involves extracting content and style features from separate images and optimizing a generated image to minimize differences in content and style representations. In this work, explored into the underlying concepts and technical aspectsof NST. This proceedings discussed the utilization of pre-trained CNN models like VGG19 or ResNet for feature extraction and how optimization algorithms, such as gradient descent, iteratively refine the generated image to achieve a fusion of content and style. Additionally, This development has been examined the impact of hyper parameters and layer choices on the quality of stylized outputs.Mainly this technology focuses on reduction of total variation loss, which we incur during the style transfer.These proceedings has carried on work with VGG19, XCEPTION, EfficientnetB7. These findings show how Convolutional Neural Networks can learn deep image representations and show how they may be used for sophisticated image synthesis andmodification.

Development of a mobile application for rapid detection of meat freshness using deep learning

The freshness or spoilage of meat is critical in terms of meat color and quality criteria. Detecting the condition of the meat is important not only for consumers but also for the processing of the meat itself. Meat quality is influenced by various pre-slaughter factors including housing conditions, diet, age, genetic background, environmental temperature, and stress factors. Additionally, spoilage can occur due to the slaughtering process, though post-slaughter spoilage is more frequent and has a stronger correlation with postslaughter factors. The primary indicator of meat quality is the pH value, which can be high or low. Variations in pH values can lead to adverse effects in the final product such as color defects, microbial issues, short shelf life, reduced quality, and consumer complaints. Many of these characteristics are visible components of quality. This study aimed to develop a mobile application using deep learning-based image processing techniques for the rapid detection of freshness. The attributes of the source and the targeted predictions were found satisfactory, indicating that further advancements could be made in developing future versions of the application.

Computer vision and deep transfer learning for automatic gauge reading detection

This manuscript proposes an automatic reading detection system for an analogue gauge using a combination of deep learning, machine learning, and image processing. The study suggests image-processing techniques in manual analogue gauge reading that include generating readings for the image to provide supervised data to address difficulties in unsupervised data in gauges and to achieve better accuracy using DenseNet 169 compared to other approaches. The model uses artificial intelligence to automate reading detection using deep transfer learning models like DenseNet 169, InceptionNet V3, and VGG19. The models were trained using 1011 labeled pictures, 9 classes, and readings from 0 to 8. The VGG19 model exhibits a high training precision of 97.00% but a comparatively lower testing precision of 75.00%, indicating the possibility of overfitting. On the other hand, InceptionNet V3 demonstrates consistent precision across both datasets, but DenseNet 169 surpasses other models in terms of precision and generalization capabilities.

Gradient-enhanced deep Gaussian processes for multifidelity modeling

Multifidelity models integrate data from multiple sources to produce a single approximator for the underlying process. Dense low-fidelity samples are used to reduce interpolation error, while sparse high-fidelity samples are used to compensate for bias or noise in the low-fidelity samples. Deep Gaussian processes (GPs) are attractive for multifidelity modeling as they are non-parametric, robust to overfitting, perform well for small datasets, and, critically, can capture nonlinear and input-dependent relationships between data of different fidelities. Many datasets naturally contain gradient data, most commonly when they are generated by computational models that have adjoint solutions or are built in automatic differentiation frameworks.Principally, this work extends deep GPs to incorporate gradient data. We demonstrate this method on an analytical test problem and two realistic aerospace problems: one focusing on a hypersonic waverider with an inviscid gas dynamics truth model and another focusing on the canonical ONERA M6 wing with a viscous Reynolds-averaged Navier-Stokes truth model.In both examples, the gradient-enhanced deep GP outperforms a gradient-enhanced linear GP model and their non-gradient-enhanced counterparts.

Polymer-Metal film induced Ultrahigh Output TVNG and Self-Powered Intelligent Sensor System Assisted by EEMD

Tribovoltaic nanogenerators (TVNGs) based on tribovoltaic effect can output DC current directly, and has the advantages of high output current density and low internal resistance. While TVNGs have been restricted by its low output voltage and power. Herein, we have developed a metal-polymer film with a micro-uneven structure on the surface, much higher corrosion resistance and wear resistance than metal. On this basis, a centimeter-level sliding structure metal-EP/GaN-TVNG has been fabricated, which shows a high and stable long-term DC electrical output (a maximized power output of 4.19W/m2 when matched with a 0.07 MΩ resistor, a stable output current of 2.25A/m2 and a peak Voc of 27V). Further, a TVNG vehicle travelling intelligent sensor and system which can integrate electrical signals collected by TVNG into digital signal processing algorithms and then feed backing to the driving system, has been designed and manufactured. As an effective method for deep analysis of signals, Ensemble Empirical Mode Decomposition (EEMD) has been used for the first time for deep processing of nanogenerator signals in this work. This work provides a new model of further development of high output TVNG and a new deep signal processing methods of TVNG/TENG signal.

Robust Model Watermarking for Image Processing Networks via Structure Consistency.

The intellectual property of deep networks can be easily "stolen" by surrogate model attack. There has been significant progress in protecting the model IP in classification tasks. However, little attention has been devoted to the protection of image processing models. By utilizing consistent invisible spatial watermarks, the work (Zhang et al. 2020) first considered model watermarking for deep image processing networks and demonstrated its efficacy in many downstream tasks. Its success depends on the hypothesis that if a consistent watermark exists in all prediction outputs, that watermark will be learned into the attacker's surrogate model. However, when the attacker uses common data augmentation attacks (e.g., rotate, crop, and resize) during surrogate model training, it will fail because the underlying watermark consistency is destroyed. To mitigate this issue, we propose a new watermarking methodology, "structure consistency", based on which a new deep structure-aligned model watermarking algorithm is designed. Specifically, the embedded watermarks are designed to be aligned with physically consistent image structures, such as edges or semantic regions. Experiments demonstrate that our method is more robust than the baseline in resisting data augmentation attacks. Besides that, we test the generalization ability and robustness of our method to a broader range of adaptive attacks.

Research on Knowledge Discovery and Sharing in AIGC Virtual Teaching and Research Room Empowered by Big Data Analysis and Natural Language Processing Algorithms

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today’s big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies.

Automated Identification of HeartFailureWith Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing.

The lack of automated tools for measuring care quality limits the implementation of a national program to assess guideline-directed care in heart failure with reduced ejection fraction (HFrEF). The authors aimed to automate the identification of patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care. The authors developed a novel deep-learning language model for identifying patients with HFrEF from discharge summaries of hospitalizations with heart failure at Yale New Haven Hospital during 2015 to 2019. HFrEF was defined by left ventricular ejection fraction<40% on antecedent echocardiography. The authors externally validated the model at Northwestern Medicine, community hospitals of Yale, and the MIMIC-III (Medical Information Mart for Intensive Care III) database. A total of 13,251 notes from 5,392 unique individuals (age 73 ± 14 years, 48% women), including 2,487 patients with HFrEF (46.1%), were used for model development (train/held-out: 70%/30%). The model achieved an area under receiver-operating characteristic curve (AUROC) of 0.97 and area under precision recall curve (AUPRC) of 0.97 in detecting HFrEF on the held-out set. The model had high performance in identifying HFrEF with AUROC=0.94 and AUPRC=0.91 on 19,242 notes from Northwestern Medicine, AUROC=0.95 and AUPRC=0.96 on 139 manually abstracted notes from Yale community hospitals, and AUROC=0.91 and AUPRC=0.92 on 146 manually reviewed notes from MIMIC-III. Model-based predictions of HFrEF corresponded to a net reclassification improvement of 60.2 ± 1.9% compared with diagnosis codes (P< 0.001). The authors developed a language model that identifies HFrEF from clinical notes with high precision and accuracy, representing a key element in automating quality assessment for individuals with HFrEF.

Rating pome fruit quality traits using deep learning and image processing.

Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit-quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individual raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse-grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine-learning and image-processing to address rater bias and improve resolution. Additionally, Granny supports backward compatibility by providing ratings compatible with long-established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny's improved resolution and reduced bias, such as linking fruit outcomes to global scale -omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open-source and freely available.

Towards multi-agent system for learning object recommendation

The rapid increase of online educational content has made it harder for students to find specific information. E-learning recommender systems help students easily find the learning objects they require, improving the learning experience. The effectiveness of these systems is further improved by integrating deep learning with multi-agent systems. Multi-agent systems facilitate adaptable interactions within the system's various parts, and deep learning processes extensive data to understand learners' preferences. This collaboration results in custom-made suggestions that cater to individual learners. Our research introduces a multi-agent system tailored for suggesting learning objects in line with learners' knowledge levels and learning styles. This system uniquely comprises four agents: the learner agent, the tutor agent, the learning object agent, and the recommendation agent. It applies the Felder and Silverman model to pinpoint various student learning styles and organizes educational content based on the newest IEEE Learning Object Metadata standard. The system uses advanced techniques, such as Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP), to propose learning objects. In terms of creating personalized learning experiences, this system is a considerable step forward. It effectively suggests learning objects that closely match each learner's personal profile, greatly enhancing student engagement and making the learning process more efficient.

A deep treatment process for chemical polishing wastewater towards resource recovery: Optimization and performance

The chemical polishing industry generates abundant wastewater that contains high concentrations of phosphoric acid and metal ions. In light of the rapid growth of the economy and the stringent demands for sustainability, developing cost-effective and energy-efficient technologies for wastewater treatment is crucial. Herein, an EPRC-ICT-ANT process integrating electrodialysis, iron contact, and alkali neutralization, was proposed to deeply treat chemical polishing wastewater and achieve optimal recovery of acid, phosphorus, and aluminum. The results indicated that with the electrochemical phosphoric acid recovery cell (EPRC), the maximum recovery efficiency and final phosphoric acid concentration were 34.6 % and 25.2 g/L. Following the iron contact tank and alkali neutralization tank (ICT-ANT) treatment, a remarkable 99.17 % removal of Al3+ and 49.5 kg/t wastewater yield of AlPO4 were achieved. Simultaneously, the released Fe2+ and remaining PO43- triggered the rapid formation of vivianite with a yield of 120.7 kg/t wastewater. This comprehensive treatment strategy led to the near-complete removal of Al3+ and PO43- (∼100 %) with an impressive recovery of Al3+ (∼100 %) and PO43- (75.7 %), emphasizing its immense potential for chemical polishing wastewater treatment.

Statistical Approaches Used in Studies Evaluating the Reliability of Autonomous Vehicles Based on Disengagements and Reaction Times

The rapid development of autonomous vehicle (AV) technology highlights the critical importance of enhancing the reliability of these vehicles. Due to the need to test the reliability of AVs, since 2014, the California Department of Motor Vehicles has permitted autonomous vehicle manufacturers to establish an AV Testing program, enabling them to test automated systems on the transportation network. With this, studies on the reliability of AVs have increased rapidly. The most emphasized issues regarding the reliability of AVs have been disengagements, accidents, and reaction times. In this study, disengagements and reaction times are categorized and explained in detail according to the data type, company, period, and statistical method. The data used in the studies cover the years 2014-2020. When examining studies on the reliability of AVs, until 2018, inferences were generally made using real data and descriptive statistics, particularly with methods such as correlation analysis and calculation of disengagements per mile, which investigates the relationship between distance traveled and disengagements. However, since 2018, machine learning has gained importance in evaluating AV reliability. It has been observed that regression, classification, and decision trees were frequently used during this period. Techniques such as deep transfer learning, text mining, and natural language processing also stand out. Furthermore, Software Reliability Growth Models are used to measure software reliability, playing an essential role in evaluating, analyzing, and improving the performance and reliability of AVs. This study aims to reveal the development and diversity of the statistical methods used to determine AV reliability. Additionally, this study aims to guide and provide insights to researchers in the field about the statistical approaches they can utilize.

ThermoNet: advanced deep neural network-based thermogram processing pipeline for automatic time series analysis of specific skin areas in moving legs

AbstractInfrared thermography is an emerging technique in biomedical research, potentially providing diagnostic insights into psychological stress, physical strain, muscle fatigue, inflammation, tissue damage, and diseases with thermogenic effects. However, manual analysis strategies are frequently applied causing incomparable, non-reproducible results and hampering standardization. Moreover, widely applied manual analysis cannot recognize blood vessel-related thermal radiation patterns during physical exercise. Therefore, an enhanced processing pipeline, “ThermoNet”, has been developed to automatically process thermograms captured during running. For acquisition, an automatic temperature calibration technique has been introduced to obtain reliable pixel-temperature mapping. The thermograms are semantically segmented in the processing pipeline to extract the anatomical regions of interest (ROIs) by a state-of-the-art deep neural network rather than considering both legs as a single area. A second neural network further examines the ROIs to identify different venous and arterial (perforator) patterns. Within the segments, advanced statistical features are computed to provide time series data. Separate analysis of venous and perforator vessel patterns is carried out on individual connected components, resulting in the extraction of 276 features for each thermogram. The enhanced ROI extraction achieved a high accuracy for the left and right calf on the manually annotated test set. Each step of the ThermoNet pipeline represents a significant improvement over previous analysis methods. Finally, ThermoNet is a transferable pipeline for automatic, reproducible, and objective analysis of ROIs in thermal image sequences of moving test individuals.

Deep Kinetic Energy Response to the Variability of the Kuroshio Extension

AbstractThe transfer of energy from the upper to deep oceans is a well‐known and challenging subject in physical oceanography. This research investigates the intricate relationship between surface and deep ocean currents. Utilizing nearly 2 years of observations from the Kuroshio Extension System Study (KESS), our study unveils the deep‐ocean kinetic energy response to the Kuroshio Extension (KE) variability. We introduce the Kuroshio Extension Jet Path Index (KEJPI), which identifies two distinct modes of the KE jet on an intra‐seasonal timescale. Our findings reveal a strong correlation (0.73) between KEJPI and the mean kinetic energy of deep‐ocean geostrophic circulation, suggesting that the KE jet's large amplitude meanders have a significant impact on deep‐ocean kinetic energy. Singular Value Decomposition (SVD) analysis further unveils a co‐evolving spatial pattern between upper and lower ocean kinetic energy. We investigate the dynamic vertical coupling (DVC) mechanism by examining the coherent variation among the sea surface height (SSH), 15°C isotherm (Z15), deep pressure anomaly, and abyssal flow. The KE jet migration can induce net divergence and convergence within the water column, which in turn generates deep‐ocean quasi‐geostrophic currents. These currents show a marked increase in kinetic energy, reaching levels three times higher than the background. This DVC‐driven kinetic energy can further cascade into near‐inertial and high‐frequency internal waves, contributing to abyssal mixing. Our study underscores the role of large current system instabilities in transferring energy to the deep ocean and facilitating deep mixing processes.

Application of ultrasound for leaching Vietnamese monazite concentrate with NaOH solutions to obtain chlorides of rare earth elements

Relevance. Despite the use of alkali stripping technology for processing monazite concentrate in industry, this method has a number of limitations, such as low efficiency of concentrate stripping, the need to use a large excess of alkali and a long process time. Aim. Improvement of existing technology to overcome these limitations. Methods. Fusion with NaOH under pressure and fusion with NaOH combined with ball milling, as well as fusion with NaOH using high intensity ultrasound. High-intensity ultrasound has long been studied and used in industry to intensify leaching/alloying in various applications to increase efficiency and speed in leaching for copper, vanadium, nickel, bauxite and uranium ores. This article is devoted to studying the influence of high-intensity ultrasound on the efficiency of opening monazite concentrate with NaOH solutions. Experimental data were obtained using mass spectrometric, X-ray diffraction and ICP-OES analysis methods. Results and conclusions. The optimal conditions for leaching batches of samples weighing from 100 g to 1 kg were determined. The authors have carried out the research on the separation of radioactive elements and impurities in a solution of rare earth chlorides, obtaining a solution of rare earth chlorides purified from radioactive components and impurities. It was shown that ultrasonic exposure eliminates the disadvantages of traditional fusion technology with NaOH, which can be used for deep processing of Vietnamese monazite concentrate. The conditions for obtaining REE chlorides from a previously opened monazite concentrate are given.

GLOBAL PRACTICE OF DIVERSIFICATION OF THE OIL AND GAS SECTOR OF THE ECONOMY

The article considers global trends in the development of the oil and gas sector of the economy. World economic systems, developing due to the availability of large amounts of natural resources, are increasingly specialised in the extraction and export of energy products, which are mainly exported to industry markets in raw form. Countries that supply hydrocarbon commodities to world markets are subject to price volatility and pricing policies, with the result that the economies of oil and gas exporting countries are not always sustainable. Historically, such countries have historically been less able to develop manufacturing sectors that produce value-added goods, while supplementing government budgets with quick revenues from oil and gas exports. These countries have a chance to develop their economies by selling raw materials for a certain period of time, but in the long term they switch to economic diversification to ensure sustainability in the face of uncertainties (lower demand for raw materials, volatility of resource prices, environmental challenges, scientific and technological development, increased use of unconventional energy sources, the impact of the COVID-19 pandemic, etc.). Oil and gas producing and exporting countries have been carrying out reforms for the last two decades in order to ensure strong economic growth through the expansion of industrial sectors of the economy by producing deep processing products with high added value. The purpose of the research is to study the world experience of oil and gas industry diversification development in order to adapt it to the realities of Kazakhstan Methodology - The work uses statistical, monographic, abstract-logical and other methods of research. Originality/value of the study is to consider the experience of foreign countries in the development of diversification of the oil and gas sector of the economy. Results of the study - The main directions of development of diversification of the oil and gas sector of the country's economy are determined.

Development of Prospective Economic Specializations as a Condition for Regional Economic Growth

Introduction. In the new economic conditions caused by the sanctions pressure, the Russian economy needs new sources of growth. Economic specializations in Russian regions, which are understood as prospective sectoral priorities and types of economic activity, can act as a source of revitalizing economic dynamics revival. Geopolitical turbulence and the associated focus on the development of the domestic market have led to additional attention from researchers and experts to the study of the economic specializations of Russian subjects. The objective of this study is to substantiate the development of economic specializations in the regional economy, acting as a condition for its growth. Materials and Methods. Using the materials of the Vologda Region, the export-oriented region of the Northwest, the list of economic specialization, the development of which is promising from the point of view of long-term economic growth, was formulated. The main sources of information are Rosstat data and input-output tables for the Vologda Region. The economic specializations for the region are primarily based on studies that focus on identifying and developing economic specialization in Russian regions. The use of input-output modeling methodology has allowed to determine the impact on the regional economy of extending the value chain in the timber industry due to increased domestic demand for wood products. Results. Based on the analysis of the dynamics of industrial production in the Vologda Region, it was revealed that the Vologda Region is among the regions that have felt the impact of the sanctions. Вased on the approaches of the authors engaged in the search for promising economic specializations of the region, it is established that the timber industry and tourism can act as such. The results of the input-output modeling are shown that stimulating consumer demand for woodworking products has a significant impact on the growth of a region’s economy. This demand is driven by the needs of the tourism sector. The calculations were performed in determining the significance of promoting projects for the development of hotels utilizing CLT panel-based technologies in the region. Discussion and Conclusion. In the study, it was determined that stimulating demand for the production of deep processing of wood (houses from CLT panels) would have a significant impact on the growth of the regional economy, ensuring an increase in the production of goods and services, the wage fund and the number of employed. It was also shown that the development of proposed economic specializations in the Vologda Region is promising and allows for the concentration of added value within the region, which is particularly important for the development of the domestic market. For the regional authorities, the interest lies in the possibility of obtaining a quantitative assessment of the development of economic specializations on their territory, which can be used by them in the improvement of economic policy.