Industrial Circles Research Articles

Condition‐based maintenance policy for a multi‐component system considering stochastic dependence and quality loss

AbstractThe reliability evaluation of multi‐component systems has attracted increasing concern in academic and industrial circles. However, the research maintenance optimization for multi‐component systems is still underexplored due to the curse of dimensionality. This paper makes a novel contribution to the existing literature by proposing a condition‐based maintenance (CBM) policy for multi‐component systems with joint consideration of stochastic dependence and quality loss. The system is composed of critical and auxiliary components. The deterioration process of the critical component can be accelerated when the auxiliary one degrades to a certain state. Based on the states of the components, the operation cost of the system owing to quality loss is analyzed, and the operation cost is used as the threshold for the preventive maintenance (PM) decision. Semi‐regenerative technology is used to model the evolution process of the system, and the long‐run average cost is calculated. The optimal inspection period and PM threshold are derived by minimizing the long‐run average cost, and the stationary distribution of the system state under the optimal long‐run average cost is derived with a proposed algorithm. Sensitivity analysis is conducted to reveal the most sensitive parameter. Finally, an illustrative example is presented, and a comparison experiment with other maintenance policies is conducted to demonstrate the effectiveness of the proposed policy.

“Exploring Sustainable Investment Frontiers: A Bibliometric Analysis on current trends and hurdles in incorporating ESG into Investment decision-making”

Integrating environmental, social, and governance (ESG) factors into investment decision-making has emerged as a critical area of interest in both academic and industry circles. This literature review aims to comprehensively explore and synthesize existing research on this topic by providing valuable insights into current trends, challenges, and prospects in sustainable investing. Articles, reports, and case studies published in relevant databases from 2019 to 2023 are reviewed for this research. A comprehensive search strategy encompassing keywords related to ESG integration in investment decisions has been used in the review process. The integration of ESG factors in investment decisions has evolved significantly in recent years. However, the knowledge landscape is scattered, and it is imperative to consolidate this body of research to gain a holistic understanding thus this bibliometric analysis addresses the research agenda on the impact of the integration of ESG factors on investment decision-making and the challenges and opportunities associated with this evolving paradigm. Our review highlighted that ESG integration has been shown to mitigate risk and enhance long-term returns, supporting the argument that sustainability considerations are not only ethical but also financially prudent. As per the analysis, hurdles range from the lack of awareness regarding standardized ESG metrics and reporting to concerns about greenwashing. The complexity of ESG factors and the need for robust data and analytics pose significant hurdles to investors and practitioners. The synthesis of findings from the study highlights the need for standardized ESG reporting, enhanced data quality, and increased stakeholder engagement. Future research should explore the integration of ESG factors in specific asset classes and regions, as well as investigate the effectiveness of various ESG integration strategies.

Methods of maral (Caspian red deer) dried blood production by sublimation and evaluation of its quality

The presented article represents the results of a thorough investigation aimed at a deep understanding of the process of producing dried maral blood using the sublimation method. The main focus is on analyzing the technological aspects of this method and evaluating the chemical and biological properties of the resulting product. The results of the conducted research led to the conclusion that the sublimation method is an effective and promising means of preserving the biologically active components in dried maral blood. This assessment is based on a careful analysis of various product characteristics, including protein content, trace elements, and other important constituents. Scientific measurements and comparisons with standards, including physicochemical analysis, and other methods, allowed establishing that dried maral blood obtained by the sublimation method exhibits a high degree of preservation of biologically active substances. This confirms its potential for use in various fields such as medicine, the food industry, and cosmetology. Such results are of significant importance for the scientific community and industry as they confirm the prospects and effectiveness of this production method, which can contribute to the development of new technologies and products in the future. Ultimately, the conclusions drawn confirm the prospects for the widespread use of dried maral blood in various fields, including medicine, the food industry, and cosmetology. These findings are of great significance for the scientific community and industrial circles, as they emphasize not only the importance of this production method but also open up new horizons for further research and development in the field of biologically active products.

Simple and sustainable synthesis of loosely stacked nano-H-ZSM-5 aggregates from kaolin and catalytic studies

The conversion of natural clay to crystalline zeolites has been the subject of considerable interest from both academic and industrial circles. We present an effective strategy for converting kaolin to ZSM-5 zeolite, addressing the issue of reduced mesopore formation in conventional nano-ZSM-5 aggregates due to close-packing. This strategy utilizes kaolin as the sole source of silicon and aluminum, and by decoupling the nucleation and growth of ZSM-5 crystals, loosely stacked nano-H-ZSM-5 aggregates with high crystallinity crystals can be synthesized in a solid-like system by employing a tiny amount of TPAOH (TPAOH/SiO2 = 0.064). The resulting nano-H-ZSM-5 aggregates exhibited high specific surface area (405.66 m2/g), high mesopore volume (0.64 cm3/g), and superior catalytic activity. This strategy offers a novel approach to the cost-effective synthesis of nano-H-ZSM-5 aggregates suitable for industrial applications.

Research on CO concentration detection based on deep learning and TDLAS technology

In order to improve the accuracy of detecting CO and other gases in the industrial field, a CO concentration detection scheme based on tunable diode laser absorption spectroscopy (TDLAS) and the CO absorption spectral line at 1566 nm was proposed. At the outset, after removing the outliers from the original data, the harmonic signal is filtered using a moving average filtering method to enhance the signal-to-noise ratio. Subsequently, a one-dimensional convolutional neural network (1D-CNN) is employed to extract the peak and valley values, as well as the mean values of each segment of harmonic data. In the end, the Long short term memory network (LSTM) model establishes a nonlinear fitting relationship between gas characteristics and concentration, and the attention layer is added to further optimize the network weight parameters. The experimental results indicate that the R2 for the test set is 0.9884, and the RMSE is 156.869. Through Allan deviation analysis, the detection limit reaches 1.9605 ppm at an integration time of 60 s. Compared to the traditional regression model, the accuracy and stability are significantly improved, making it highly suitable for gas detection in the industrial circle.

Channel estimation for backscatter communication systems with retrodirective arrays

AbstractBackscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies.

Tomato Leaf Disease Detection using Flask Frame Work

In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

Nitrogen Effects Endowed by Doping Electron-Withdrawing Nitrogen Atoms into Polycyclic Aromatic Hydrocarbon Fluorescence Emitters.

Unveiling innovative mechanisms to design new highly efficient fluorescent materials and, thereby, fabricate high-performance organic light-emitting diodes (OLEDs) is a concerted endeavor in both academic and industrial circles. Polycyclic aromatic hydrocarbons (PAHs) have been widely used as fluorescent emitters in blue OLEDs, but device performances are far from satisfactory. In response, we propose the concept of "nitrogen effects" endowed by doping electron-withdrawing nitrogen atoms into PAH fluorescence emitters. The presence of the n orbital on the imine nitrogen is conducive to promoting electron coupling, which leads to increased molar absorptivity and an accelerated radiative decay rate of emitters, thereby facilitating the Förster energy transfer (FET) process in the OLEDs. Additionally, electronically withdrawing nitrogen atoms enhances host-guest interactions, thereby positively affecting the FET process and the horizontal orientation factor of the emitting layer. To validate the "nitrogen effects" concept, cobalt-catalyzed multiple C-H annulation has been utilized to incorporate alkynes into the imine-based frameworks, which enables various imine-embedded PAH (IE-PAH) fluorescence emitters. The cyclization demonstrates notable regioselectivity, thereby offering a practical tool to precisely introduce peripheral groups at desired positions with bulky alkyl units positioned adjacent to the nitrogen atoms, which were previously beyond reach through the Friedel-Crafts reaction. Blue OLEDs fabricated with IE-PAHs exhibit outstanding performance with a maximum external quantum efficiency (EQEmax) of 32.7%. This achievement sets a groundbreaking record for conventional blue PAH-based fluorescent emitters, which have an EQEmax of 24.0%.

The preparation and performances of Co-Zn 18H hexagonal ferrite with an ultra-high magnetic resonance frequency and low-loss characteristics

Modern 5G communication technologies require evolution of antennas towards high frequency and miniaturization. The search for magneto-dielectric materials with simultaneous high permeability, matched dielectric constant, and low loss properties for sub-6 GHz band applications has received considerable attention from both academic and industrial circles. Due to the low magnetic resonance frequency and high magnetic loss, traditional spinel and hexagonal ferrites hardly meet these requirements. In this study, polycrystalline Co-Zn 18H ferrites (Ba5Co2-xZnxTi3Fe12O31) were synthesized by co-precipitation method with only one-step sintering process. The polycrystalline Co-Zn 18H hexagonal ferrite possesses an ultra-high natural resonance frequency (14.5 GHz) and low magnetic loss (tanδμ≤0⊡10), which provides a better performance factor (PF= 68.6 GHz) at a higher operating frequency (4.97 GHz) compared to traditional microwave ferrites. The miniaturization rate of the planar inverted F antenna (PIFA) with Co-Zn 18H ferrite working at 4.0 GHz reaches 13.3 %. These results demonstrate the high potential of Co-Zn 18H ferrites for high frequency and low loss applications.

Enhancing aspect-based sentiment analysis with BERT-driven context generation and quality filtering

Fine-grained sentiment analysis, commonly referred to as aspect-based sentiment analysis (ABSA), has garnered substantial attention in both academic and industrial circles. ABSA focuses on unveiling the sentiment orientation associated with specific entities or attributes within textual data, resulting in a more precise depiction of intricate emotional nuances. However, due to the extensive range of applications for ABSA, certain domains face challenges such as constrained dataset sizes and the absence of exhaustive, high-quality corpora, leading to issues like few-shot learning and resource scarcity scenarios. To address the issue of limited training dataset sizes, one viable approach involves the utilization of text-based context generation to expand the dataset. In this study, we amalgamate Bert-based text generation with text filtering algorithms to formulate our model. Our model fully leverages contextual information using the Bert model, with a particular emphasis on the interrelationships between sentences. This approach effectively integrates the relationships between sentences and labels, resulting in the creation of an initial data augmentation corpus. Subsequently, filtering algorithms have been devised to enhance the quality of the initial augmentation corpus by eliminating low-quality generated data, ultimately yielding the final text-enhanced dataset. Experimental findings on the Semeval-2014 Laptop and Restaurant datasets demonstrate that the enhanced dataset enhances text quality and markedly boosts the performance of models for aspect-level sentiment classification.

Quality By Plan Approach-To Explanatory Strategy Approval

The analyst points to consider Quality By Plan Approach-To Explanatory Strategy Approval first Increment the proficiency of planning different steps. That includes creating strategies utilizing quality rules. Private transport trade organization and to clarify the different steps. Concerning the advancement of strategies employing a quality approach to explanatory strategies for examining private transport commerce organizations .Cargo sending could be a benefit trade that plays an imperative part in disseminating items to both household and universal markets. Cargo forwarders are dependable for moving products at each arrange of the supply chain. (from crude materials/primary items Middle of the road items and wrapped up products). The condition of the trade subsequently depends on the amount of merchandise which varies agreeing to the financial circumstance of the nation. Transportation trade is another commerce that's picking up ubiquity. Numerous business visionaries are turning to contribute in this trade. And it is likely to go well and grow even more within the future when numerous transportation businesses emerge. The way to bring your claim commerce to outlive and gotten to be one of the best choices among buyers is to get it the market outline of this sort of commerce. Counting taking after news within the important industry circles. To be able to apply it to commerce techniques within the transportation trade. Patterns and significance worth keeping an eye on: The transportation trade has begun to play a part and has been broadly known for some time presently. Within the past five a long time The transportation commerce slant has gotten a parcel of consideration. Since it not as it were makes a difference drive expansive mechanical businesses. but too makes a difference transport products and administrations of little business people. Broaden the course of organizations from B2B (Business-to-Business) commerce to more B2C (Business-to-Customer) trade The slant of transportation commerce in Thailand within the past period. It must be conceded that the transport commerce slant in Thailand is presently exceptionally well known. Counting the inclination to continue to develop within the future. The investigate strategy chosen by the analyst is quantitative investigate. Utilizing interviews as a inquire about device. administrators and work force of private transportation commerce organizations by analyzing quality plan approaches to review examination strategies, counting 1) benefit quality, counting capacity, get to administrations Communication Unwavering quality, security 2) Generally quality administration incorporates cleanliness, comfort, cleanliness, cleanliness, and building propensities. The results is methodologies and frequencies relating to checking and control. Concurring to the definition given by ICH Q10, a control strategy can "be a organized set of controls gathered from current thing and handle understanding that ensures plan execution and thing quality.

Research on the Impact of Financial Technology on Traditional Accounting Models

In the context of the rapid development of financial technology, its impact on traditional accounting models has aroused widespread concern in the academic and industry circles. This study systematically explores the specific impact offintech on traditional accounting models. The research results show that financial technology has promoted the transformation of accounting work methods, facilitated the digital transformation of the accounting industry, and spawned new accounting business models. At the same time, this change also brings a series of challenges, including technology acceptance, data security and ethical issues. The desired findings point to the need for future research to delve into how to effectively address these challenges while driving innovation. In addition, policymakers need to fully consider the protection of data security and user privacy while encouraging innovation.

Trade Secrets and the Data Act

Automated systems collect vast amounts of data, which could be utilized across various business sectors. The EU has recognized their potential for the data economy. To exploit such potential, there is a need to provide access to data. For example, the objective of the EU’s Data Act is to provide new possibilities for accessing and reusing information. Some information exchanged may be subject to trade secret protection. However, specific characteristics of trade secret protection create difficulties for information sharing. In essence, trade secret protection requires that information should be kept secret, or at least relatively secret. This requirement contributes to the reluctance of trade secret holders to share their information. They would rather lock it down. That is because, if the information becomes generally known in industry circles, it will lose its trade secret status along with any related competitive advantage. At the same time, trade secret protection leaves considerable room for competition, which arguably makes it suited to the data economy. A trade secret is not an exclusive right, which means that competitors may independently create similar information. Access to information through reverse engineering is also allowed. All these specific characteristics make trade secret protection an uncertain means of protection. This article will elaborate on how the uncertainties and specifics related to trade secret protection create a conflict between demand for access to information and the protection of trade secrets. It will analyze how the provisions of the Data Act aim to accommodate the protection of trade secrets while simultaneously mandating access.

A Review on Decentralized Finance Ecosystems

The future of the internet is moving toward decentralization, with decentralized networks and blockchain technology playing essential roles in different sectors. Decentralized networks offer equality, accessibility, and security at a societal level, while blockchain technology guarantees security, authentication, and openness. Integrating blockchain technology with decentralized characteristics has become increasingly significant in finance; we call this “decentralized finance” (DeFi). As of January 2023, the DeFi crypto market capitalized USD 46.21 billion and served over 6.6 million users. As DeFi continues to outperform traditional finance (TradFi), it provides reduced fees, increased inclusivity, faster transactions, enhanced security, and improved accessibility, transparency, and programmability; it also eliminates intermediaries. For end users, DeFi presents asset custody options, peer-to-peer transactions, programmable control features, and innovative financial solutions. Despite its rapid growth in recent years, there is limited comprehensive research on mapping DeFi’s benefits and risks alongside its role as an enabling technology within the financial services sector. This research addresses these gaps by developing a DeFi classification system, organizing information, and clarifying connections among its various aspects. The research goal is to improve the understanding of DeFi in both academic and industrial circles to promote comprehension of DeFi taxonomy. This well-organized DeFi taxonomy aids experts, regulators, and decision-makers in making informed and strategic decisions, thereby fostering responsible integration into TradFi for effective risk management. This study enhances DeFi security by providing users with clear guidance on existing mechanisms and risks in DeFi, reducing susceptibility to misinformation, and promoting secure participation. Additionally, it offers an overview of DeFi’s role in shaping the future of the internet.

Research on the construction path of large-scale industrial agglomeration circle in Foshan based on computer big data fusion

This paper innovatively proposes the goal of research on the decision support system of the large-scale industrial agglomeration circle in Foshan. Based on the computer big data combined with the process of urban planning and management in Foshan, this paper proposes the construction content of the current planning decision support system for the new industrial agglomeration circle in Foshan. This paper proposes the relevant models for the analysis and construction of the development and evolution model and the construction of the auxiliary decision-making management system. The method of large-scale industrial agglomeration area in Foshan proposed in this paper provides services for the government to make rational and scientific decision-making. The computer big data industry circle planning model proposed in this paper has certain practical significance.

Perceived Value and Behavioral Intention of Chengde homestay tourists based on Cognition-Affection-Conation

Against the backdrop of the new era, changes in tourism consumption demand have led tourists to prefer diversified, autonomous, and personalized tourism products and services in the process of tourism experience. They long for in-depth experiences as well as emotional identification and communication during travel. As a new industrial form emerging in China in recent years, the homestay industry has become a major attraction for tourists because it integrates beautiful scenery, creativity, and comfort, and each establishment has its own unique characteristics. Perceived value, as a comprehensive evaluation made by tourists of the perceived benefits and losses of tourism products or services in a specific context, is a source of competitive advantage. To achieve sustainable development of tourist destinations, exploring the formation mechanism of tourists' positive behavioral intentions has become an important research topic in both academic and industrial circles. Based on the "cognition-emotion-intention" relationship theory, this article explores the relationship between perceived value, satisfaction, place attachment, and behavioral intention among Chengde homestay tourists. Using quantitative research methods, questionnaire surveys, random sampling, descriptive statistical analysis, reliability and validity analysis, variance analysis, factor analysis, correlation analysis, and regression analysis, 450 questionnaires collected from tourists who have experienced Chengde star-rated homestays were analyzed. The results show that the behavioral intentions of homestay tourists are directly affected by place dependence, place identity, and perceived value, while indirectly influenced by tourist satisfaction. These findings provide reference value and significance for homestay owners, management departments, investors, local residents, and homestays in other regions.

Analysis of the Impact of Community Institutional Strengthening on the Sustainability of the "Spirit of Independence" Farmer Group and the "Industrial Circle" Livestock Group in Uso Village, Banggai Regency

The purpose of this research was to analyse the impact of strengthening community institutions on the sustainability of farmer and livestock groups. This included the process of establishing and strengthening community institutions, how the company's strategy in developing governance systems, decision-making mechanisms, system evaluation including community institutional benefits, contributions to SDGs 16 and their relation to ISO 26000. The method of this research used quantitative methods sourced from primary data and secondary data to determine the impact of strengthening community institutions on the sustainability of the Semangat Mandiri farmer group and the Lingkar Industri livestock group located in Uso village, Batui sub-district, Banggai regency, Central Sulawesi province. Data analysis was carried out using triangulation to validate data linkages from field research. Furthermore, an in-depth study of the relationship between the subject and object under study was carried out to explain the formulation of the problem and the purpose of the study and draw conclusions from the research results. The results showed that PT. PAU has a complete commitment in implementing CSR programs. This commitment could be seen from the implementation of community institutional strengthening, aspects of governance systems, decision-making mechanisms, and evaluation of governance systems in achieving ISO 26000 and SDGs 16 in farmer and livestock groups in Uso village, Banggai Regency which have a good impact on the community and the company.

A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of Things

The Internet of Things (IoT) has garnered considerable attention from academic and industrial circles as a pivotal technology in recent years. The escalation of security risks is observed to be associated with the growing interest in IoT applications. Intrusion detection systems (IDS) have been devised as viable instruments for identifying and averting malicious actions in this context. Several techniques described in academic papers are thought to be very accurate, but they cannot be used in the real world because the datasets used to build and test the models do not accurately reflect and simulate the IoT network. Existing methods, on the other hand, deal with these issues, but they are not good enough for commercial use because of their lack of precision, low detection rate, receiver operating characteristic (ROC), and false acceptance rate (FAR). The effectiveness of these solutions is predominantly dependent on individual learners and is consequently influenced by the inherent limitations of each learning algorithm. This study introduces a new approach for detecting intrusion attacks in an IoT network, which involves the use of an ensemble learning technique based on gray wolf optimizer (GWO). The novelty of this study lies in the proposed voting gray wolf optimizer (GWO) ensemble model, which incorporates two crucial components: a traffic analyzer and a classification phase engine. The model employs a voting technique to combine the probability averages of the base learners. Secondly, the combination of feature selection and feature extraction techniques is to reduce dimensionality. Thirdly, the utilization of GWO is employed to optimize the parameters of ensemble models. Similarly, the approach employs the most authentic intrusion detection datasets that are accessible and amalgamates multiple learners to generate ensemble learners. The hybridization of information gain (IG) and principal component analysis (PCA) was employed to reduce dimensionality. The study utilized a novel GWO ensemble learning approach that incorporated a decision tree, random forest, K-nearest neighbor, and multilayer perceptron for classification. To evaluate the efficacy of the proposed model, two authentic datasets, namely, BoT-IoT and UNSW-NB15, were scrutinized. The GWO-optimized ensemble model demonstrates superior accuracy when compared to other machine learning-based and deep learning models. Specifically, the model achieves an accuracy rate of 99.98%, a DR of 99.97%, a precision rate of 99.94%, an ROC rate of 99.99%, and an FAR rate of 1.30 on the BoT-IoT dataset. According to the experimental results, the proposed ensemble model optimized by GWO achieved an accuracy of 100%, a DR of 99.9%, a precision of 99.59%, an ROC of 99.40%, and an FAR of 1.5 when tested on the UNSW-NB15 dataset.

Digital Twin as A New Approach to Data Management: A Review

The concept of digital twin has gained substantial attention in both academic and industrial circles in recent years due to its potential for facilitating smart manufacturing and contributing to Industry 4.0. The digital twin concept revolves around the idea of replicating the characteristics and behaviors of real-world objects to provide accurate representations in virtual environments. It has been widely used in various industries for different purposes, such as virtual experimentation, prediction and performance optimization, monitoring, and control. However, the existing studies have not investigated the potential application of digital twin for data management in a structured and methodological manner. Therefore, this paper aims to review articles that explored the use of digital twin as a data management method at different levels and dimensions. Moreover, this review critically analyses studies that explored relevant methods that can support digital twin applications in a data management context. This study follows PRISMA guidelines and reviewed 51 academic articles from various academic databases. The primary findings indicate that the digital twin can serve as a comprehensive platform for collecting, processing, analyzing, and managing data from various sources in a unified approach. Based on the critical review, future directions in this research context are outlined.

Перспективы развития экономических отношений России и Болгарии в начале ХХ в.: взгляд российских дипломатов

Introduction. The article is devoted to the history of relations between Bulgaria and Russia at the beginning of the 20th century. The two states were interested in joint trade and economic development. This issue received a lot of attention in Russian periodicals. The press pointed out great benefits from the economic relations of the two states. Bulgaria at this time was a country with a rapidly developing economy. This state aroused the interest of many European powers. Diplomatic missions occupied the main place in assessing the possibilities of Russian penetration into Bulgaria. Materials and methods. The sources of the article are related to diplomatic materials from the beginning of the 20th century: reports of secretaries of the diplomatic agency in Sofia, reports of the vice-consul in Bulgaria, diaries and memoirs. The main focus is on assessing trading opportunities. When analyzing sources, the author used traditional methods of objectivity and a critical approach, taking into account the subjective nature of these materials. Results. Diplomats believed that Russian commercial and industrial circles, given the rapid development of the domestic market, showed little interest in Bulgaria. Russian entrepreneurs did not have a clear idea of the needs of the Bulgarian consumer, of the Bulgarian companies with which they could conduct trade operations, or of their creditworthiness. Unlike representatives of other countries, especially Austria-Hungary, Russian business made no efforts to develop trade with the Bulgarians. Russian entrepreneurs did not have their own traveling salesmen who traveled around Bulgaria studying the demand of the local population. Interest in Bulgaria was weak because Russian business was associated with domestic economic development. All this had a very negative impact on the development of bilateral trade. Conclusion. In the current conditions, it was not Russia, but Austria-Hungary that played the main role in the development of the Bulgarian economy. This also determined the political sympathies of the Bulgarian authorities. St. Petersburg understood this problem. However, political complications on the peninsula and the outbreak of the First World War did not allow this creative work to be completed, which could have led to a significant increase in the volume of economic relations between Russia and Bulgaria.