Demand Imbalance Research Articles

Price incentive strategy for the E-scooter sharing service using deep reinforcement learning

The electric-scooter (e-scooter) has become a popular mode of transportation with the proliferation of shared mobility services. As with other shared mobility services, the operation of the e-scooter sharing service has a recurring problem of imbalance in supply and demand. Various strategies have been studied to resolve the imbalance problems, including demand prediction and relocation strategies. However, the difficulty of accurately predicting the fluctuating demand and the excessive cost-labor consumption of relocation are major limitations of these strategies. As a remedy, we propose a deep reinforcement learning algorithm that suggests price incentives and an alternative rental location for users who find it difficult to acquire e-scooters at their desired boarding locations. A proximal policy optimization algorithm considering temporal dependencies is applied to develop a reinforcement learning agent that allocates the given initial budget to provide price incentives in a cost-efficient manner. We allow the proposed algorithm to re-use a portion of the operating profit as price incentives, which brings higher efficiency compared to the same initial budget. Our proposed algorithm is capable of reducing as much as 56% of the unmet demands by efficiently distributing price incentives. The result of the geographical analysis shows that the proposed algorithm can provide benefits to both users and service providers by promoting the use of idle e-scooters with a price incentive. Through experimental analysis, optimal budget, i.e., the most efficient initial budget, is suggested, which can contribute to e-scooter operators developing efficient e-scooter sharing services.

Application of market-based control with thermal energy storage system for demand limiting and real-time pricing control

Imbalances in energy demand and supply related to increased use of renewable energy sources will eventually cause problems with the reliability of the power grid. The reliability of the grid requires ancillary services for power generation, as well as flexible consumption via demand response. In this paper, a multi agent-based distributed control strategy is proposed for commercial buildings to coordinate the flexible building loads with thermal energy storage systems (TESS). The distributed control uses the market mechanisms and price/incentive signals to engage self-interested responsive loads to provide services to the electrical power grid and managing buildings’ electricity consumption. The proposed strategy was validated in a simulation environment to provide two grid service use cases. One is to limit the peak demand of building in which a market clearing strategy with respect to the demand limit is proposed. The other one is to perform price responsive control, in which the peak demand of building responds to the time of use price signals. The peak load reduction is calculated as a percentage of baseline peak load measured in the baseline for the same day. Energy consumption cost saving is defined as the percentage change from the baseline over the entire 5-day simulation. The results show that: (1) the demand limit control can reduce by up to 14% of building energy cost and 13% of peak demand and (2) the price response control can reduce by up to 16% of building energy cost. Overall, the results demonstrate that the distributed control with TESS proposed in this study is effective in reducing electricity use and peak building electric demand.

COVID-19 and the heart.

An outbreak of coronavirus disease 2019 (COVID-19) occurred in December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is a strain of SARS-CoV. Patients infected with the virus present a wide spectrum of manifestations ranging from mild flu-like symptoms, cough, fever and fatigue to severe lung injury, appearing as bilateral interstitial pneumonia or acute respiratory failure. Although SARS-CoV-2 infection predominantly offends the respiratory system, it has been associated with several cardiovascular complications as well. For example, patients with COVID-19 may either develop type 2 myocardial infarction due to myocardial oxygen demand and supply imbalance or acute coronary syndrome resulting from excessive inflammatory response to the primary infection. The incidence of COVID-19 related myocarditis is estimated to be accountable for an average of 7% of all COVID-19 related fatal cases, whereas heart failure (HF) may develop due to infiltration of the heart by inflammatory cells, destructive action of pro-inflammatory cytokines, micro-thrombosis and new onset or aggravated endothelial and respiratory failure. Lastly, SARS-CoV-2 can engender arrhythmias through direct myocardial damage causing acute myocarditis or through HF decompensation or secondary, through respiratory failure or severe respiratory distress syndrome. In this comprehensive review we summarize the COVID-19 related cardiovascular complications (acute coronary syndromes, myocarditis, HF, arrhythmias) and discuss the main underlying pathophysiological mechanisms.

101 Differentiating Type 1 from Type 2 Acute Myocardial Infarction in the Emergency Department Using the N-terminal Pro B-type Natriuretic Peptide/High-sensitivity Cardiac Troponin T Ratio

Acute myocardial infarction (AMI) is currently further diagnosed as being a type 1 (T1), caused by coronary artery atherosclerosis with thrombosis or a type 2 (T2), resulting from cardiac oxygen supply and demand imbalance. It is hypothesized that patients with T2 might develop earlier and increased cardiac wall stressing before the development of AMI as compared to those with T1, resulting in higher N- terminal pro B-type natriuretic peptide (NT-proBNP)/high sensitivity cardiac troponin T (hs-cTnT) ratios.

Ride-hail to ride rail: Learning to balance supply and demand in ride-hailing services with intermodal mobility options

Growing spatiotemporal imbalance in supply and demand in ride-hailing services has been arousing concern. A variety of measures in the perspective of ride-hailing services per se is studied. However, approaching the imbalance problem in the broader perspective of intermodal mobility, which integrates different modes of passenger transportation in a single trip and aims to overcome limitations of any unimodal mobility, remains to be explored. This paper aims to investigate the potential of introducing intermodal mobility options to balance ride-hailing services. We first identify the importance of the availability decision on intermodal mobility options. Then we formulate the availability decision problem as a Markov decision process (MDP). Due to its convoluted system dynamics and large state space, we cast the intractable MDP into a reinforcement learning (RL) problem to approximately learn the availability policy. To stabilize the learning processes, we model the intermodal ride-hailing services as a stochastic queueing network and tailor a family of state-of-the-art RL algorithms to iteratively evaluate and improve the availability policy. Lastly, we test this optimization framework in a large-scale intermodal mobility scenario calibrated with real-world trip data. Results show that the learned availability policy can significantly dissipate riders’ queue and improve the service rates towards more balanced supply and demand.

Toward the integration of European gas futures market under COVID-19 shock: A quantile connectedness approach

Natural gas is an essential resource to achieve energy transition and carbon neutrality, and the allocation of natural gas resources depends on the integrated development of the natural gas market. This paper presents a quantile spillover approach to measure changes in the integration of the European gas futures market when major events occur. The empirical results show that extreme events exacerbate the transmission of market risk, and COVID-19 has weakened market integration. The arrival of severe cold weather led to a supply and demand imbalance in the market, and the level of market resource integration was hit hard. In addition, when active and effective reform policies are implemented, they help restore market connectivity. From the perspective of risk transmission within the system, this paper also finds that liberalized or highly liquid hubs can better cope with extreme risks. The findings have implications for policymakers to improve market integration and provide important insights for investors to trade during turbulent times such as COVID-19.

Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters.

An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand, this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem. Guided by the idea of intuitive reasoning, we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions. First, Selective Attention Mechanism (SAM) is introduced to reduce the search space by adaptively focusing on the important information in the features. Then, Quantum Annealing (QA) algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution. Experiments on the parking lot location dataset in Luohu District, Shenzhen, show that the proposed method has improved the accuracy and running speed of the solution, and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.

Voice Quality of Choir Singers and the Effect of a Performance on the Voice

The voice use of choir singers is understudied despite the imbalance of high vocal demands versus low vocal education, and consequently increased risk for voice problems. Also, there is a lack of information on the effects of a performance on choristers' voices. Available studies included performances of at least one hour. To date, no studies investigated the effects of a choir performance with a duration resembling vocal warm-ups. The first purpose of this study was to determine the voice quality, capacities, symptoms and voice-related quality of life of choir singers. Secondly, the effect of a short choir performance, resembling warm-up duration (15 minutes), on the choristers' voices was investigated. A randomized controlled trial was used. Thirty adult choir singers (25 women, 5 men; mean age: 32 years) were assigned randomly to an experimental group or a control group. Participants in the experimental group sung in choir for 15 minutes immediately after their pre voice assessment, whereas the control group was instructed to have standard voice use (one-on-one conversation with the investigator, no singing) across that time span. A second voice assessment was repeated afterwards. The choir singers showed excellent voice quality and capacities with mean scores on the Dysphonia Severity Index and Acoustic Voice Quality Index of 7.5 and 2.0, respectively. Auditory-perceptually, the mean grade score was 5/100 corresponding with a normal to mildly deviant voice quality. Patient-reported outcome measures showed mean deviant scores, indicating a considerable singing voice handicap. The choir singers seem vulnerable for stress with a high occurrence rate of 76.7% (23/30). Compared with the control group, the Dysphonia Severity Index significantly improved, whereas the self-perceived presence of vocal fatigue and complaints increased after 15 minutes of choir singing. Fundamental frequency increased in both groups, being more outspoken in the experimental group. Choir singers show excellent voice quality and capacities, that further improve after a short choir performance of 15 minutes. Vocal fatigue and complaints, on the other hand, already increased after that short time span. Realizing that vocal load is much higher in real-life rehearsals, competitions and performances, choristers deserve and need a qualitative voice training and a strict follow-up. Future research should focus on effective vocal warm-up and cool-down programs for this population.

Collaboration-Enabled Intelligent Internet Architecture: Opportunities and Challenges

Since intelligent algorithms such as deep learning (DL) have strong expressiveness, flexibility, and scalability for complex problems, some DL-based methods have been applied to Internet architecture-related scenarios, such as congestion control and malicious traffic detection. However, DL-based models have relatively high requirements for resources such as data and computing, and the existing Internet architecture requires further evolution to break through these limitations. In this article, we propose the collaboration-enabled intelligent Internet architecture, which can leverage intelligence to facilitate the evolution of Internet architecture in more complex scenarios. Specifically, we first discuss the inherent opportunities and challenges of enabling the Internet architecture to be intelligent through collaboration, which are brought by the imbalance of Internet supply and demand, distributed organizational structure, and the lack of built-in security. Immediately after, we present the newly proposed collaboration-enabled intelligent Internet architecture, which consists of heterogeneous hardware infrastructure and a collaboration-oriented software service platform. Through the complementarity of these two components (i.e., providing hierarchical computing and full exploitation of hierarchical capabilities), it promotes the collaboration of intelligent algorithms built into the Internet architecture. Moreover, some flexible algorithmic modules for the proprietary requirements of the Internet architecture are built into the software service platform. Finally, we take multi-classification malicious traffic detection as a case study, and demonstrate the advantages of enabling Internet architecture to be intelligent through collaboration.

No Association of Early Postoperative Heart Rate With Outcomes After Coronary Artery Bypass Grafting.

Elevated perioperative heart rate potentially causes perioperative myocardial injury because of imbalance in oxygen supply and demand. However, large multicenter studies evaluating early postoperative heart rate and major adverse cardiac and cerebrovascular events (MACCEs) are lacking. To assess the associations of 4 postoperative heart rate assessment methods with in-hospital MACCEs after elective coronary artery bypass grafting (CABG). Using data from the eICU Collaborative Research Database in the United States from 2014 to 2015, the study evaluated postoperative heart rate measured during hospitalization within 24 hours after intensive care unit admission. Four heart rate assessment methods were evaluated: maximum heart rate, duration above heart rate 100/min, area above heart rate 100/min, and time-weighted average heart rate. The outcome was in-hospital MACCEs, defined as a composite of in-hospital death, myocardial infarction, angina, arrhythmia, heart failure, stroke, cardiac arrest, or repeat revascularization. Among 2585 patients, the crude rate of in-hospital MACCEs was 6.2%. In multivariable logistic regression analysis, the adjusted odds ratios (95% CI) for in-hospital MAC-CEs assessed by maximum heart rate in each heart rate category (beats per minute: >100-110, >110-120, >120-130, and >130) were 1.43 (0.95-2.15), 0.98 (0.56-1.64), 1.47 (0.76-2.69), and 1.71 (0.80-3.35), respectively. Similarly, none of the other 3 methods were associated with MACCEs. More research is needed to assess the usefulness of heart rate measurement in patients after CABG.

ENERGY CRISIS IN EUROPE AND RUSSIA’S GAS SUPPLIES

The article examines the increasing tensions on world’s gas markets, especially in Europe where gas makes up a considerable share of energy balance. The subject of analysis is the nature and reasons behind supply and demand imbalances on European gas market. The author assesses risk factors that have caused the unsatisfactory situation in energy field, including strong volatility, stock levels, imbalances in supply, and political unpredictability. The conducted analysis of factors impacting the energy crisis reveals that the situation is largely dependent on supranational policy of the EU which is stimulating a surge in energy demand.

우리나라 항공운송산업의 포괄적 공익성 제고를 위한 슬롯 조정 및 배분 원칙 연구 : 우리나라 혼잡공항인 제3종 공항 중심으로

Until COVID-19 began in earnest, many airports around the world were forced to achieve a serious imbalance between airport slot availability and demand for requested slots due to increasing air traffic every year. This caused a number of problems at three airports in Korea (Incheon, Gimpo, and Jeju airports) as well as many congested airports around the world, including abnormal operation of regular flights, limitations on development of additional air demand, increase in airfare due to imbalance in supply and demand, and restrictions on development of new routes. These problems add to the demand for retaliatory travel, which has never been able to travel at the end of COVID-19, and congestion airports around the world are expected to experience more severe airport congestion than before COVID-19. Therefore, although fundamental measures were needed to solve these problems as soon as possible, in reality, satisfactory adjustment and allocation measures could not be clearly found for the requested slot desired by the air passenger transportation business operator. After all, the structural solution to this is to build a new airport, which is not a problem that can be solved in a short time due to the time it takes to build an airport (an average of seven to eight years to build one), related construction costs, conflicts of interest between stakeholders, and other environmental issues. Therefore, in this study, the priority-based slot allocation model (PSAM), which is the most useful and presentable model among existing scholars' models, was reorganized into a model applicable to Korea's three airports. Subsequently, to verify the significance of the optimization model of the available slot derived from the first research objective and the model that maximizes the benefits of airport operators and air carriers, the best slot adjustment and distribution combination optimized for profitability is derived when international, domestic, and various aircraft are introduced to Incheon, Gimpo, and Jeju airports.

Storage losses, market development and household maize-selling decisions in China

PurposeThe purpose of this paper is to analyse the impacts of storage losses and market development on the maize-selling behaviours of rural households in China.Design/methodology/approachBased on the survey data of 543 households from nine major maize production provinces in China, the authors introduce storage losses to a household's maize-selling decision-making model and use fractional logit model and ordered probit model to empirically analyse the impact of maize storage losses and market development on household maize-selling decisions in China. To overcome potential endogeneity problems, the authors select the weather at drying (whether bad weather occurs during the drying process) and harvest loss as instrumental variables and re-estimate the model.FindingsThe results show that increased storage losses prompt farmers to increase the proportion of maize sold within three months after harvest and sell maize in advance. Meanwhile, the degree of market development has a significant impact on farmers' maize-selling decisions. Other factors, such as the maize output, non-agricultural employment and awareness of loss control, also affect farmers' maize-selling behaviours.Research limitations/implicationsThe government should promote advanced storage facilities, reduce household storage losses, decrease the phenomenon of centralised sales after harvest and help farmers freely choose the suitable time for sales. The government also needs to strengthen market information releases and publicity, reduce transaction costs and help farmers make reasonable sales decisions.Originality/valueThe authors introduce storage losses as a separate variable in a farmer's grain-selling decision model to empirically analyse the impact of storage losses on farmers' grain-selling behaviours. Moreover, the authors analyse the impact of market development on household grain-selling behaviours in China. These findings can help avoid oversupply in the market during the harvest season and alleviate the pressure on the market from the supply and demand imbalance. These results are also beneficial for farmers waiting for a higher price and increasing their income.

Dynamic bicycle relocation problem with broken bicycles

In response to the demand imbalance across stations with broken bicycles in a bicycle sharing system (BSS), this study proposes a novel decision problem that aims to determine the size of the fleet of relocation vehicles, the bicycle stations they are assigned to serve, and efficient adaptive routing plans to ensure a good level of bicycle inventory at each station and on a timely basis, by considering the broken bicycles in each station, which is referred to as the dynamic bicycle relocation problem with broken bicycle consideration (a.k.a DBRPB). Assuming that the numbers of broken bicycles and bicycle relocation demand at each bicycle station are independent random variables and will only be revealed upon the arrival of the relocation vehicle, the objective of the DBRPB is to maximize the expected total satisfied demand, comprising both relocation demand and broken bicycle demand, using the adaptive routing strategy while incorporating the deployment cost of the relocation vehicles. The relocation vehicle will adjust its relocation route after the actual demand is revealed, every time it visits a station. A tailored branch-and-price (B&P) approach is proposed to find the exact optimal solution of the DBRPB. To solve the pricing problem, a tailored Markov decision process (MDP) is formulated in the pricing problem of the B&P approach, to determine both the optimal value of the expected satisfied demand and the next station to visit, given the available information, including time, current station, the unordered set of unvisited stations and the bicycle inventory of the relocation vehicle. A hybrid heuristic method incorporating variable neighbourhood search (VNS) and partial optimization is further proposed to solve the large-scale problem. Numerical experiments using a randomly generated BSS network and the Nanjing BSS respectively are conducted to validate the efficiency and effectiveness of the proposed methodology as well as to obtain insights into the impacts of key parameters on the solution.

Ecosystem services in Jiangsu province: Changes in the supply and demand patterns and its influencing factors

Revealing the spatial and temporal changing characteristics and key factors driving the relationship between the supply of and demand for ecosystem services (SDES) is paramount for the effective management of regional ecosystems and the rational allocation of natural resources. This study calculated the supply of ecosystem services (ESS) in Jiangsu Province, China, in 2000, 2010, and 2020, and quantitatively evaluated the ecosystem service demand (ESD) in those years by considering land development intensity, population density, and economic conditions. The ‘coupling coordination degree and relative development degree’ (CDRD) model is introduced to explore the degree of coordination and change in the relationship of supply versus demand of ecosystem services, and then to reveal the impact of various factors on the spatial differentiation characteristics of the SDES. The results show that: 1) the ESS is high in the Taihu Lake Plain, the southwest of Huaihai area and the eastern coastal zone of Jiangsu Province, yet low in the northwest and middle of Huaihai area, and along the Yangtze River Plain. The overall distribution pattern of ESD and the coordinating degree of the SDES is high in the south and north of Jiangsu Province but low in the province’s middle. 2) The degree of coordination for the SDES increased from 0.371 to 0.415, and the relationship between supply and demand changed from one of moderate imbalance to one of basic coordination. The imbalance of supply and demand is mainly concentrated in the Lixiahe area, coastal plain area, and middle of the Huaihe River Basin, for which the predominant type is a moderate imbalance–demand lag. Meanwhile, the analysis of influencing factors shows that there is an obvious synergistic effect between different factors. The results of this research could contribute to sustainable ecosystem management and decision-making for the construction of ecological civilization in Jiangsu Province.

Leaf water potential-dependent leaflet closure contributes to legume leaves cool down and drought avoidance under diurnal drought stress.

Efficient thermoregulation under diurnal drought stress protects leaves from photosystem damage and water supply-demand imbalance, yet the cool effect and drought avoidance by leaflet closure have not been well understood. We investigated the cool effect and the drought avoidance of leaflet closure in legume species that survived in the semi-arid region facing seasonal and diurnal drought stress. The results showed that leaflet closure effectively cooled down legume leaves through a reduction of projected leaflet area and the cosine of the angle of incidence (cos i). The leaflet closure was strongly dependent on leaf water potential (Ψleaf). In addition, by characterizing the sequence of key leaf drought response traits, we found leaflet closure occurred after stomatal closure and reduced transpiration rate but before hydraulic failure and turgor loss point (Ψtlp). The meta-analysis also showed that the leaflet closure and cos i decreased after the stomatal conductance declined but before midday. These results imply that Ψleaf-dependent leaflet closure as an alternative to transpiration for leaflet cooling down and as a protective drought avoidance strategy assisting sessile legume plants survival under drought stress.

Land Use Zoning Management to Coordinate the Supply–Demand Imbalance of Ecosystem Services: A Case Study in the City Belt Along the Yellow River in Ningxia, China

The imbalance between the supply and demand of ecosystem services (ESs) is one of the main reasons for ecological degradation, which significantly impacts human well-being and ecological safety. Spatial heterogeneity of ES supply–demand, ES tradeoffs, and the socioecological environment calls for zoning management, while few studies have combined the above three aspects in dividing management zones and proposed strategies. Using the City Belt along the Yellow River in Ningxia in northwestern China as a case study, this study quantified the supply and demand for five key ESs (crop production, carbon sequestration, nutrient retention, sand fixation, and recreational opportunity), analyzed ES tradeoffs/synergies and bundles through correlation analysis and the self-organizing map (SOM) method, and investigated their socioecological driving mechanisms through a random forest model and the SOM method. Management zones were proposed and differentiated suggestions were provided through overlaying ES bundles and driver clusters. The results suggested that crop production, carbon sequestration, and nutrient retention mostly correlated to the same intrinsic ecological process, resulting in consistent synergies among these three ESs at both supply and demand sides. On the contrary, the variance in interactions between the two ESs of sand fixation and recreational opportunity and the other three ESs is due to the low similarity of their intrinsic ecological processes and external driving mechanisms. Fourteen socioecological factors could effectively explain the spatial heterogeneity of ES supply, demand, and match degree. Fourteen management zones with similar ecological problems and socioecological environments were delineated, and differentiated suggestions were provided for each zone. Adopting both ES characteristics and the socioecological environment into zoning management could effectively detect ecological problems and help to promote management suggestions in different socioecological contexts. This framework could offer new insights for integrating ESs into actual decision-making and ecosystem management.

Primary logistics planning of oil products under the imbalance of supply and demand

This paper intends to complete the primary logistics planning of oil products under the imbalance of supply and demand. An integrated mathematical programming model is developed to simultaneously find the balance between supply and demand, and optimize the logistics scheme. The model takes minimum logistics cost and resource adjustment cost as the objective function, and takes supply and demand capacity, transportation capacity, mass balance, and resource adjustment rules as constraints. Three adjustment rules are considered in the model, including resource adjustment within oil suppliers, within oil consumers, and between oil consumers. The model is tested on a large-scale primary logistics of a state-owned petroleum enterprise, involving 37 affiliated refineries, 31 procurement departments, 286 market depots and dedicated consumers. After the unified optimization, the supply and demand imbalance is eased by 97% and the total cost is saved by 7%, which proves the effectiveness and applicability of the proposed model.

The Russian Economy under the Impact of the Pandemic Crisis.

The main channels of influence of the new coronavirus infection pandemic on the economy are analyzed. The key features of the economic crisis caused by the pandemic are emphasized, the scale and depth of the economic decline largely depending on the actions of the authorities to restrict economic activity. It has been stated that as the pandemic crisis developed, the governments of the largest countries of the world adapted to its negative consequences for the economy, which was reflected in a decrease in the dependence of economic activity on the incidence of COVID-19. The effectiveness of economic support measures implemented in Russia and key countries of the world during the crisis is assessed. The failures of market self-regulation during the crisis, which led to a significant imbalance of supply and demand in world markets, are considered. Along with the success of the Russian government in reducing the negative impact of the pandemic on the economy, attention is drawn to the serious social and demographic damage that our country has suffered as a result of the pandemic. The main trends for compensating for losses are formed, and the medium- and long-term potential for the development of the Russian economy after the pandemic crisis is assessed. This article is an amended and updated version of the author’s report at the Scientific Session of the RAS General Meeting on December 15, 2021.