Centralized Model Research Articles

Вплив технології блокчейн на фінансовий сектор глобальної економіки

The aim of the article is to highlight the importance and relevance of blockchain technology for the financial sector of the global economy, as well as to find out how blockchain technology affects security and reliability in the financial sector, simplifying the processes of exchanging assets and making transactions; emphasizing the importance of blockchain technology in improving the settlement system, reducing time, and increasing market liquidity. With rapid technological development, the role of blockchain technology is growing as a key factor determining new directions for the financial sector. Since its emergence as the foundation for cryptocurrencies, blockchain has quickly evolved from an alternative concept to a powerful tool that has the potential to change the paradigm of how financial services work. Outdated methods of providing financial services have long been in need of revision. Challenges such as data entry into various systems and manual processing of information are becoming increasingly common in the financial sector. The centralized model is becoming increasingly vulnerable to cyberattacks, as all data is concentrated in one place. Consequently, to date, outdated methods of information systems and the workload of paper documents in the financial sector have proven to be not only ineffective, but also vulnerable to security risks. These problems arise from deficiencies in centralized systems that store and process sensitive information. Lack of transparency, the possibility of errors, and suspicion about security are obstacles to the effective functioning of financial institutions. To solve these problems and improve processes, it is necessary to move towards digital innovations that promote collaboration, increase trust, independence and ensure a high level of security. One of the most promising technologies that meets these requirements is blockchain, which is based on a distributed data storage system and cryptographic principles, which can provide a high level of security, transparency, and authenticity of information. It avoids intermediaries by allowing direct collaboration between participants, and it also makes it possible to store and transmit data in a secure environment without the risk of their modification.

Point-of-care testing for COVID-19: a simple two-step molecular diagnostic development and validation during the SARS-CoV-2 pandemic.

During the coronavirus disease 19 (COVID-19) pandemic, diagnostic testing of the general population proved challenging due to limitations of the gold-standard diagnostic procedure using reverse transcription real-time polymerase chain reaction (RT-qPCR) for large-scale testing on the centralised model, especially in low-resource areas. To address this, a point-of-care (PoC) diagnostic protocol for COVID-19 was developed, providing fast, reliable, and affordable testing, particularly for low-mid develop areas. The PoC diagnostic process combines a simple paper-based RNA extraction method housed within a 3D-printed plastic device with a colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay. Nasopharyngeal/oropharyngeal swabs (NOS) and saliva samples were tested between 2020 and 2021, with the assistance of Santa Catarina's State Health Secretary, Brazil. The developed diagnostic protocol showed a limit of detection of 9,900 copies and an overall diagnostic specificity of 98% for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 1,348 clinical analysed samples. The diagnostic sensitivity was 95% for NOS samples, 85% for early morning saliva, and 69% for indiscriminate saliva. In conclusion, the developed device successfully extracted SARS-CoV-2 viral RNA from swabs and saliva clinical samples. When combined with colorimetric RT-LAMP, it provides results within 45 min using minimal resources, thus delivering a diagnostic kit protocol that is applicable in large-scale sampling.

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation.

Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of medical image segmentation, the growing imperative to curtail annotation costs has amplified the importance of weakly-supervised techniques which utilize sparse annotations such as points, scribbles, etc. A pragmatic FL paradigm shall accommodate diverse annotation formats across different sites, which research topic remains under-investigated. In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation. In FedLPPA, a learnable universal knowledge prompt is maintained, complemented by multiple learnable personalized data distribution prompts and prompts representing the supervision sparsity. Integrated with sample features through a dual-attention mechanism, those prompts empower each local task decoder to adeptly adjust to both the local distribution and the supervision form. Concurrently, a dual-decoder strategy, predicated on prompt similarity, is introduced for enhancing the generation of pseudo-labels in weakly-supervised learning, alleviating overfitting and noise accumulation inherent to local data, while an adaptable aggregation method is employed to customize the task decoder on a parameter-wise basis. Extensive experiments on four distinct medical image segmentation tasks involving different modalities underscore the superiority of FedLPPA, with its efficacy closely parallels that of fully supervised centralized training. Our code and data will be available at https://github.com/llmir/FedLPPA.

A Novel Public Sentiment Analysis Method Based on an Isomerism Learning Model via Multiphase Processing.

The dissemination of public opinion in the social media network is driven by public sentiment, which can be used to promote the effective resolution of social incidents. However, public sentiments for incidents are often affected by environmental factors such as geography, politics, and ideology, which increases the complexity of the sentiment acquisition task. Therefore, a hierarchical mechanism is designed to reduce complexity and utilize processing at multiple phases to improve practicality. Through serial processing between different phases, the task of public sentiment acquisition can be decomposed into two subtasks, which are the classification of report text to locate incidents and sentiment analysis of individuals' reviews. Performance has been improved through improvements to the model structure, such as embedding tables and gating mechanisms. That being said, the traditional centralized structure model is not only easy to form model silos in the process of performing tasks but also faces security risks. In this article, a novel distributed deep learning model called isomerism learning based on blockchain is proposed to address these challenges, the trusted collaboration between models can be realized through parallel training. In addition, for the problem of text heterogeneity, we also designed a method to measure the objectivity of events to dynamically assign the weights of models to improve aggregation efficiency. Extensive experiments demonstrate that the proposed method can effectively improve performance and outperform the state-of-the-art methods significantly.

Современные модели конституционного правосудия и их отличительные черты

The object of study in the article is the current models of constitutional justice used in various jurisdictions. The author examines the models of constitutional justice that are currently used by constitutional justice in various regions of the world. The relevance of the article is determined by the fact that the study of this issue makes it possible to choose the optimal model that allows effectively solving issues of national rule-making. For the Republic of Kazakhstan, this problem is very important in light of the creation in the country of a new model of constitutional justice, which requires scientific understanding taking into account the accumulated world experience in organizing constitutional justice. The purpose of the article is to study modern models of constitutional justice from the point of view of their main differences, advantages and disadvantages. Methods. The peculiarity of the methodology used is determined by the goal of a comprehensive study of existing models of constitutional justice. In this regard, the diversity of approaches, methods and means of understanding this form of legal process required reliance on comprehensive and systematic approaches. Conducting a study of the constitutional jurisdictional process required, firstly, the use of general scientific and proper legal methods, including methods of analysis and synthesis, induction and deduction, abstraction and generalization. Also in this work, a comparative legal method was used, which made it possible to carry out a comparative analysis of the models under study, identify their main characteristics, on the basis of which to formulate appropriate conclusions. In addition, in the process of working on the article, a country-specific legal approach was used, which made it possible to consider the features of national models of constitutional justice. Results. It has been determined that in the world practice of constitutional control, various models of constitutional justice are known. Based on the results of the study, it was determined that currently the most promising is the centralized (European) model. The conducted research allows us to say that the existing models of constitutional justice have both their advantages and disadvantages, however, in our opinion, the presence of a separate body that carries out constitutional justice is more preferable, since it ensures its autonomy, independence and objectivity in making decisions. decisions by professional judges who have the appropriate qualifications, authority and experience in considering complex issues related to rulemaking.

Exploring Symmetry-Induced Divergence in Decentralized Electric Vehicle Scheduling

Due to the low computational efficiency and high communication pressure caused by centralized control, decentralized coordination of electric vehicles (EVs) has become a research hotspot in recent years. However, the integration of numerous EVs with homogeneous parameters brings unprecedented challenges for the optimal scheduling of the power system. To explore symmetry-induced divergence in decentralized electric vehicle scheduling, the established centralized optimization scheduling model of electric vehicles is decomposed into a distributed optimization model by Lagrange relaxation, achieving parallel control of each EV aggregator (EVA). To solving the oscillation caused by parameter symmetry in the process of optimization, we propose a convergence acceleration algorithm based on a perturbation function, which is a random distribution function used to break parameter symmetry within an acceptable calculation error range. On the premise of sensitivity analysis, a closed form of the perturbation limit is theoretically derived. Case studies based on different scales of users validate the effectiveness and computational efficiency of the proposed method.

The centralised data envelopment analysis model integrated with cost information and utility theory for power price setting under carbon peak strategy at the firm-level

The post-pandemic economic rebound has accelerated power demand. However, in a power generation system dominated thermal power plants, implementing the carbon peak strategy induces a contradiction between power supply and demand. This gives rise to the issues of the rational power allocation. To address this issue, based on enterprise micro-data, this study develops a model that allows power allocation across industrial enterprises to maximise their output. The ladder price is used for power redistribution seeking to ensure efficiency and fairness of the power allocation. Then, the utility theory is used to explore the effects of changes in the power use under different ladder prices. The paper relies on the unique dataset for Chinese enterprises. The results show that the proposed model renders power re-allocation among the enterprises (31 % of enterprises should reduce their power use, 32 % whereas should increase their power use by more than 50 %). By including the power cost in the model, significant differences were discovered between the optimal power use of three ladder price levels. This study provides recommendations for power allocation and the use of the ladder prices.

A Q-learning-based smart clustering routing method in flying Ad Hoc networks

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly.

Development of a route setting model in the dispatching centralization system

These days, automation and remote control technologies provide the foundation for the organization of train movement across several categories. At railroad sections and stations, dispatcher centralization systems are utilized to guarantee the security of train movement. However, in the railroad portions where high-speed train movements are organized, time limitations based on the human factor are being implemented. Consequently, this results in longer wait times for technological processes at stations and longer train intervals on some railway segments. Developing an automated route preparation procedure at stations in dispatching centralization systems will allow for the resolution of current issues. A dispatching centralized system model for route establishment has been created in this scientific study. Using a two-layer neural network, a mathematical model of route setting in the dispatcher centralization system was determined. Neural networks are employed in rail dispatch rooms for intelligent route comparison and in dispatcher centralized systems for tracking train routes at stations.

Cost-effectiveness of a decentralized care model for managing multi-drug-resistant tuberculosis in low- and middle-income countries: a systematic review protocol.

The purpose of this systematic review is to assess the available economic evidence of a decentralized care model compared to a centralized model for treating multi-drug-resistant tuberculosis (MDR-TB) in low- and middle-income countries (LMICs). Diseases that affect physiological health create a burden on human livelihoods and the economy. There is a lack of studies examining the economic evaluation of MDR-TB across different countries. A preliminary search identified no published or ongoing reviews on MDR-TB in LMICs. Studies will be eligible if they include both patients receiving centralized care (ie, care provided by specialist centers through inpatient or outpatient services) and patients receiving decentralized care (ie, care provided by grassroots community workers in peripheral facilities or in the patients' residence) for MDR-TB in LMICs. Eligible studies will report economic evaluations of treatment for MDR-TB. A preliminary search of MEDLINE (PubMed) was undertaken using MeSH terms, such as MDR-TB, economic evaluation, therapeutics, LMICs . Two reviewers will independently screen the titles, abstracts, and full text against the inclusion criteria. Disagreements will be resolved through discussion or with a third reviewer. The JBI checklist for economic evaluations will be utilized to evaluate the methodological quality. Data will be extracted using a modified JBI data extraction form for economic evaluations. The Dominance Ranking Matrix, developed by JBI for economic evaluations, will be used to summarize and compare the results of different types of economic evaluations (cost-effectiveness, cost-benefit analysis, cost-utility analysis, or cost-minimization analysis). Cost per quality-adjusted life year gained and cost per disability-adjusted life year averted will be measures for economic evaluation. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach will be used to assess the certainty of economic evidence. PROSPERO CRD42022368696.

An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetwork.

The use of graph centrality measures applied to biological networks, such as protein interaction networks, underpins much research into identifying key players within biological processes. This approach however is restricted to dyadic interactions and it is well-known that in many instances interactions are polyadic. In this study we illustrate the merit of using hypergraph centrality applied to a hypernetwork as an alternative. Specifically, we review and propose an extension to a recently introduced node and edge nonlinear hypergraph centrality model which provides mutually dependent node and edge centralities. A Saccharomyces Cerevisiae protein complex hypernetwork is used as an example application with nodes representing proteins and hyperedges representing protein complexes. The resulting rankings of the nodes and edges are considered to see if they provide insight into the essentiality of the proteins and complexes. We find that certain variations of the model predict essentiality more accurately and that the degree-based variation illustrates that the centrality-lethality rule extends to a hypergraph setting. In particular, through exploitation of the models flexibility, we identify small sets of proteins densely populated with essential proteins. One of the key advantages of applying this model to a protein complex hypernetwork is that it also provides a classification method for protein complexes, unlike previous approaches which are only concerned with classifying proteins.

A Bayesian Federated Learning Framework With Online Laplace Approximation.

Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to share data. Most existing FL methods train local models separately on different clients, and then simply average their parameters to obtain a centralized model on the server side. However, these approaches generally suffer from large aggregation errors and severe local forgetting, which are particularly bad in heterogeneous data settings. To tackle these issues, in this paper, we propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. On the server side, a multivariate Gaussian product mechanism is employed to construct and maximize a global posterior, largely reducing the aggregation errors induced by large discrepancies between local models. On the client side, a prior loss that uses the global posterior probabilistic parameters delivered from the server is designed to guide the local training. Binding such learning constraints from other clients enables our method to mitigate local forgetting. Finally, we achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.

Granular fuzzy rule-based model construction under the collaboration of multiple organizations

In the real world, phenomena are often observed and recorded by multiple organizations which results in multiple sources of data. When dealing with such data, the centralized modeling approach aims to achieve collaborative modeling by fusing multiple sources of data into a single data set, which may pose challenges to data privacy. Unlike centralized modeling, the distributed modeling approach can effectively solve the privacy issue. However, modeling approaches based on this idea still suffer from either low prediction accuracy or high communication costs. In this study, we propose a collaborative modeling strategy for multi-source data based on fuzzy rule-based models (FRBMs) to balance the needs of both model prediction accuracy and efficiency. First, we adopt the concept and algorithm of collaborative fuzzy clustering (CFC) to improve prediction accuracy and reduce communication costs by improving the CFC algorithm. Then, we construct a granular FRBM for multi-source data based on the principle of justifiable granularity (PJG) by integrating local models into a more robust and perfect global model. Finally, we improve the performance evaluation index of the existing granular FRBM and propose two model optimization schemes to further improve the performance of the model. We conduct experiments on both synthetic and publicly available data sets to demonstrate the effectiveness of the proposed method.

Non-conservative diffusion and its application to social network analysis

Abstract Is the random walk appropriate for modelling and analysing social processes? We argue that many interesting social phenomena, including epidemics and information diffusion, cannot be modelled as a random walk, but instead must be modelled as broadcast-based or non-conservative diffusion. To produce meaningful results, social network analysis algorithms have to take into account differences between these diffusion processes. We formulate conservative (random walk-based) and non-conservative (broadcast-based) diffusion mathematically and show how these are related to well-known metrics: PageRank and Alpha-Centrality, respectively. This formulation allows us to unify two distinct areas of network analysis–centrality and epidemic models–and leads to insights into the relationship between diffusion and network structure, specifically, the existence of an epidemic threshold in non-conservative diffusion. We demonstrate, by ranking nodes in an online social network used for broadcasting news, that non-conservative Alpha-Centrality leads to a better agreement with empirical ranking schemes than conservative PageRank. In addition, we give a scalable approximate algorithm for computing the Alpha-Centrality in a massive graph. We hope that our investigation will inspire further exploration of the applications of non-conservative diffusion in social network analysis.

A Novel Smartphone App for Self-Monitoring of Neonatal Jaundice Among Postpartum Mothers: Qualitative Research Study.

Neonatal jaundice (NNJ) or hyperbilirubinemia is a ubiquitous condition in newborn infants. Currently, the transcutaneous bilirubinometer is used to screen for NNJ in health care facilities, where neonates need to be physically present (ie, a centralized model of care for NNJ screening). Mobile health (mHealth) apps present a low-cost, home-based, and noninvasive system that could facilitate self-monitoring of NNJ and could allow mothers the convenience of screening for NNJ remotely. However, end users' acceptability of such mHealth apps is of fundamental importance before the incorporation of such apps into clinical practice. The study aimed to explore the perception of postpartum mothers toward self-monitoring of NNJ using a novel mHealth app. Mothers attending video consultations for early postpartum care at 2 Singapore primary care clinics watched an instructional video for a hyperbilirubinemia-screening mHealth app (HSMA). An independent researcher used a semistructured topic guide to conduct in-depth interviews with 25 mothers, assessing their views on HSMAs. All interviews were audio recorded, transcribed verbatim, and checked for accuracy before data analysis. Two researchers independently analyzed the transcripts via thematic analysis. Data were managed using NVivo qualitative data management software. The identified themes were grouped under perceived usability and utility. Mothers valued the convenience and utility of HSMAs for remote monitoring of NNJ. They appreciated the objectivity the app readings provided compared to visual inspection. However, they perceived that the app's applicability would be restricted to severe jaundice, were concerned about its accuracy and restriction to the English language, and lacked confidence in using it. Nevertheless, they were willing to use it once its accuracy was proven and when they received adequate guidance from health care professionals. They also suggested including an action plan for the measured readings and clinical signs within the app. Mothers proposed pairing teleconsultations with HSMAs to boost their confidence and enhance adoption. Mothers were receptive to using HSMAs but had concerns. Multiple languages, proof of accuracy, and resources to guide users should be incorporated into the app in the next phase to increase its successful adoption. Complementing such apps with a teleconsultation service presents a plausible and pragmatic NNJ care delivery model in general practice.

Adaptive Decentralized Payback Normalization for Socio-Economic Rebalancing of RE subsidies - A Case Study

Clean energy subsidies are designed to stimulate consumer participation and deliver greater benefits to society. India has a fixed centralised federal subsidy model for residential rooftop solar (RTS). Different states have different tariff schemes for electricity consumption and RTS feed-in. Thus, grid parity, payback period (PBP), and willingness to pay (WTP) differ for consumers of different states and income classes, resulting in social and geographical disparity in RTS implementation. A Socio-Economic Rebalancing of RE subsidies thus becomes necessary. This paper proposes a novel adaptive decentralised heterogeneous PBP equalisation-based subsidy policy to enhance the WTP for all prosumers and ensure uniformity of RTS implementation. A two-stage subsidy optimisation is conducted using a case study in diverse states to achieve Grid Parity initially and subsequently desired PBP for all states and income classes. The proposed subsidy ensures grid parity for all, sufficient to transit all the prosumers to 100% net electricity from RTS. The subsidy is further normalised by equalising the PBP to a target PBP thereby ensuring uniformity of RTS implementation in the country bridging the rural-urban WTP disparity, leading to a better social scenario. The strategy is adaptive to the future, as the subsidy can be revised with changes in techno-economic parameters.

Semi-physical simulation and coordinated control of SOFC-PV/T-HP system

Due to the differences in the dynamic characteristics of various energy processes, the operation and control of multi-energy coupled integrated energy systems (IES) pose significant challenges. This paper focuses on the semi-physical simulation and coordinated control of SOFC-PV/T-HP thermoelectric system, which includes solid oxide fuel cells, a solar photovoltaic/thermal panel and a heat pump. In order to enhance the experimental safety and reliability of the research findings, a semi-physical simulation platform is developed by integrating physical equipment and dynamic models. The operational characteristics of the cogeneration system are investigated and controlled using an extended state-space based model predictive algorithm. The experimental results demonstrate that the designed centralized model predictive controller effectively stabilizes the demand power of the battery. The standard deviation of the battery demand power is reduced from 2.39 W to 0.66 W within 500 s, which alleviates the regulation burden of the battery. At the same time, the hot water temperature is also well controlled. The fluctuation of hot water supply temperature does not exceed 0.11℃ after stability. The system can maintain stable operation under external disturbance, and maintain the balance between electricity/heat supply and demand.

Research on urban economic centrality in the perspectives of knowledge stocks and flows

In light of the profound advancements in information and communication technologies ushering in the knowledge economy, the urban space is undergoing a transformation from the traditional “space of places” to the emerging “space of flows.” This shift poses questions about the influence of knowledge flows on urban economic centrality. This paper seeks to address this knowledge gap by introducing a theoretical framework that elucidates how knowledge contributes to urban economic centrality. Our analysis focuses on both intra-city knowledge stocks and inter-city knowledge flows. Empirical findings in China highlight that knowledge stocks based on R&D and education levels, along with knowledge flows, significantly and positively influence all four dimensions of centrality. In contrast, knowledge spatial agglomeration within the knowledge stocks exhibits a significant positive correlation solely with power centrality. Additionally, although the knowledge stock structures don't yield significant results in the diffusion centrality model, for agglomeration and power centralities, a combination of specialized knowledge and telecommunications enhances urban economic centrality, while face-to-face communication strengthens the positive impact of diverse knowledge on urban economic centrality. The results suggest implementing knowledge-based policies tailored to different nodes in the city network is expected to promote sustainable competitiveness in the urban system.

Traditional social institutions in Romanian youth policy

Youth policy is an important area of the EU social policy. The national specificity of the EU member states allows different models for the implementation of youth policy, corresponding to their history and political culture. The study aims to explore a transition from the universalist model of implementing the Romanian youth policy of the socialist period to a centralised (or Mediterranean) one in which traditional social institutions play a pivotal role, such as family and the Church. The study applies the content analysis and the systemic method. Romania's accession to the EU made the government pay more attention to the youth. The funds for the youth policy were allocated from European integration funds. The government's strategy was to develop education and additional educational services and help children and the youth without parental care and large families. However, the quality of education deteriorated, aid did not reach its recipients, and tactically strategic goals were not achieved. The state youth policy turned out to be ineffective. The Romanian Orthodox Church (RuOC) became involved in solving issues of supporting children and the youth. Together with municipalities, sponsors, foreign philanthropists, the RuOC opens a network of social centers for children of permanent residence, day care, socio-cultural (educational, leisure, sports). The RuOC conducts actions in support of the development of secondary education to prevent dropouts from school, provide clothing and school supplies, study native history, etc. In general, the participation of the RuOC in solving youth problems convincingly indicates that a centralised model for the implementation of youth policy has been formed in Romania.

RESEARCH ON READINESS FOR BUSINESS ORGANIZATIONS FROM UZBEKISTAN FOR COOPERATION WITH LOCAL AUTHORITIES DURING THE FORMATION OF LOCAL ECONOMIC POLICY

Changes are taking place in Uzbekistan, which are characteristic of countries in transition from a centralized to a market economy. The country is entering a new era of development, which has an impact on the overall economic picture. After the presidential elections in 2016, a policy of reform and openness began to be implemented in Uzbekistan, which has a great impact on the political situation in the entire Central Asian region. According to many observers, the scale of political changes is unprecedented in the development of Central Asian countries since their independence. Unlike Tajikistan and Kyrgyzstan, reforms in Uzbekistan were not carried out under external pressure, but were voluntary and spontaneous with strong internal motivation. Due to the fact that the country realized that the centralized development model was unable to respond effectively to numerous internal and external challenges, it began to accelerate internal reforms, making successive changes to adapt to new realities. The positions of Uzbekistan in the international rating rankings are improving.Despite the successes achieved, the country’s highly centralized governance model does not allow local communities to be effectively involved in the planning and implementation of local policies for economic development. They are not active enough in identifying their infrastructure needs, in preparing community development plans, in implementing projects to modernize or build drinking water, gas and electricity systems, roads, schools and other basic infrastructure and public services. Underestimating the role of local communities in influencing the economic development of their regions is a restraining factor for economic development. Conversely, realizing the role of business organizations in shaping regional policy for local economic development is a great potential that has not yet been fully exploited.To prove this thesis, the authors conducted a survey of the attitude of business organizations in Uzbekistan to cooperation with local authorities. The results show that business representatives do not particularly rely on local authorities to stimulate the economic development of their cities and regions and, for this reason, do not show a particular desire to cooperate with them. This is a challenge, the overcoming of which can give the country a new impetus in terms of the economic development of the regions and settlements.