Future Structural Changes Research Articles

The social network perspective on power system co-evolution: responses to "double carbon target" realization in China.

The introduction of dual carbon targets will significantly impact power system development. Despite this, there is currently limited research on achieving system evolution and transition while ensuring safety, low-carbon output, and efficiency, as well as quantitatively analyzing the resulting changes dual carbon targets will have on the power system. Co-evolution of the power system offers a solution to balance the impact of dual carbon goals and enhance interaction among system entities, thereby facilitating the achievement of these goals. Our study focuses on constructing an evolutionary topological network by analyzing the dynamic evolution rule of power systems. We investigate the co-evolution pattern of power systems by analyzing the relationship between the role of power system agents and their dynamic structures. Furthermore, we analyze the future structural changes of power systems, which can provide theoretical support for achieving dual carbon goals in the power system. Our findings highlight key measures to promote synergistic evolution, including increasing energy storage capabilities, stabilizing renewable energy supply, breaking inter-provincial barriers in electricity transmission, and developing a multi-level intelligent power system. Through link analysis, we discover that future power systems will maintain a mild coordination among agents rather than implementing large-scale realignment and reconfiguration. We posit that overcoming obstacles can be achieved by fostering cohesion between the network and agents through technological innovation and widespread market diffusion to drive co-evolution.

Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model

Computable general equilibrium (CGE) models provide valuable insights into economy-wide impacts of anticipated future structural changes in the transportation sector, yet few CGE models offer detailed transportation representations. We use an enhanced Applied Dynamic Analysis of the Global Economy (ADAGE) CGE model to incorporate disaggregated transportation modes and technologies in on-road passenger and freight transportation. We assess the impacts of these inclusions on U.S. transportation patterns, energy consumption and greenhouse gas emissions. Simulating illustrative global oil price cases with and without transportation detail, we find subsector mode disaggregation and technology additions in a CGE model significantly alter the impacts of oil prices on global trade and freight patterns, energy consumption, and greenhouse gas (GHG) emissions. We find that: (1) alternative technologies are essential for capturing transportation sector impacts, (2) electrification may reduce emissions with electricity decarbonization, and (3) higher oil prices may hasten electrification.

Future Simulated Changes in Central U.S. Mesoscale Convective System Rainfall Caused by Changes in Convective and Stratiform Structure

AbstractMesoscale convective systems (MCSs) are responsible for a majority of warm season flash flood events in the central U.S. Given their high impact, it is critical to understand how MCSs will change in a future climate. This study identifies eight flood‐producing MCS cases of different archetypes and runs ensembles of 192 high‐resolution historical and future simulations in order to analyze the range in future MCS rainfall and structural changes. Future area‐average rainfall increases by 98% on average, with an 89%–111% range among ensemble members, while maximum rainfall increases by 31%, with a 24%–43% range. MCSs components are classified into deep convective cores (DCCs), wide convective cores (WCCs), and broad stratiform regions (BSRs) to understand how changes in these structures contribute to future rainfall changes. In a warmer climate, the number of DCCs increase on average by +30 (+5,545%), the average WCC area increases by 75%, and the average BSR area increases by 40%. The future rainfall increases are mainly due to increases in convective rainfall with large variability in rainfall from DCCs and less variability from WCCs. Despite their increase in size, BSR rainfall shows little increase. These rainfall and structural changes vary among MCS types, with back‐building MCSs exhibiting the largest increases in future rainfall mainly due to increases in the number of DCCs and WCC area. Such information is critical to better understand the drivers of change in future MCS rainfall and improve prediction of future flood impacts.

Statistical Mechanics of Long Walks in Dynamic Complex Networks: Statistical Arguments for Diversifying Selection

We study the thermodynamic limit of very long walks on finite, connected, non-random graphs subject to possible random modifications and transportation capacity noise. As walks might represent the chains of interactions between system units, statistical mechanics of very long walks may be used to quantify the structural properties important for the dynamics of processes defined in networks. Networks open to random structural modifications are characterized by a Fermi–Dirac distribution of node’s fugacity in the framework of grand canonical ensemble of walks. The same distribution appears as the unique stationary solution of a discrete Fokker–Planck equation describing the time evolution of probability distribution of stochastic processes in networks. Nodes of inferior centrality are the most likely candidates for the future structural changes in the network.

РАЗВИТИЕ СИСТЕМЫ ПРОГНОЗИРОВАНИЯ СТРУКТУРНЫХ ИЗМЕНЕНИЙ В ОБЛАСТИ ЦИФРОВОГО ПРЕДПРИНИМАТЕЛЬСТВА

The article is devoted to the analysis of forecasts for the development of digital entrepreneurship in Russia in the post-normal crisis period. The purpose of the article is to identify the main barriers to the digital transformation of domestic business and to study the general digital trends of future structural changes in the field of digital entrepreneurship in Russia. The analytical material is addressed to system analysts, business leaders and anyone interested in the development of digital entrepreneurship.

Long-Term Energy Demand Forecasting Based on a Systems Analysis

—Energy demand forecasting plays a key role in solving the majority of problems connected with determining the economic and energy development prospects. In view of high inertia and capital intensity of energy generation facilities, the changes in the energy consumption structure and rates should be considered for a sufficiently long-term future of no less than 15 years. This adds much difficulty to the development of such forecasts, because it is necessary to take into account the possible results of technological progress in different sectors of the economy and changes in its structure. The suggested approach to energy demand forecasting is based on the systems analysis methods. Energy consumers are disaggregated by economic sectors, country regions, energy carriers, and their utilization areas. As a result, it becomes possible to take into account the possible future technological and structural changes in the economy, its differences in different regions, mutual replacements of energy carriers, and energy saving. The most important feature of the approach is that it involves the separation of economic and energy variables. Economic variables determine the development scales of economic sectors, and energy variables determine the energy consumption intensities in them. The separation of variables helps obtain essentially more accurate forecasts owing to the possibility of taking into account the differences in the variation trends of these variables in the forecast period. For forecasting the economic variables, a nonlinear conditionally dynamic model of interrelations between the economy and energy is used. The energy variables are forecasted on the basis of their links with economic factors, e.g., with cumulative investments in the fixed capital of the considered economic sectors. The discussed approach was implemented as the totality of adaptive simulation models united into the EDFS computing system. The approach was repeatedly and quite successfully used to forecast the energy demand for an up to 10–20-year horizon in solving various problems. The gained experience has demonstrated the correctness of applying the approach for solving long-horizon forecast problems. To illustrate the capacities of the approach, the electric energy demand forecast for the period of up to 2040 for the basic version of the social and economic development of Russia’s economy prepared on the basis of this approach is presented.

Global cargo gravitation model: airports matter for forecasts

The reliability of forecast models in the aviation sector is an important factor for industry and policy makers likewise. Expanding airports and fleets usually is a cost and time intensive process, and in order to maintain efficient market behavior, accurate anticipation of future demand and structural changes is attempted. We present a new quantitative approach to air cargo forecasts utilizing global airport-dyadic ICAO CASS data in general linearized airport fixed effects gravity models. While the strong explanatory power of our time invariant constant model has its natural difficulties predicting a variety of smaller indicators from previous models found in literature, we achieve very good results for selected time variant variables as gross domestic product per capita or kerosene prices. This makes our model a perfect tool for forecast simulations: extrapolating general economic forecast data provided by IHS Markit yield similar results to Boeing cargo forecasts (2020), with a slight decrease in the long run. Additionally, we do not need to split or control our sample in regional groups due to airport fixed effects, which makes the model on the other hand suitable for country- and airport level forecasts as well. The utilization of a large unique bilateral freight data set also helps answering classical gravity model questions in aviation: we track the distance effect to a matter of sample selection, finding no significant interaction following state of the art gravity econometrics.

Italy: 2021 Article IV Consultation-Press Release; Staff Report; and Statement by the Executive Director for Italy

The pandemic dealt a severe blow to the Italian people and their economy. GDP fell by nearly 9 percent in 2020, with much larger drops for contact-intensive services. Public and corporate debt increased strongly and preexisting vulnerabilities have likely worsened. The government is prioritizing resolving the health emergency and transforming the economy to lift productivity, improve social outcomes and strengthen resilience to future shocks and structural change. The large National Recovery and Resilience Plan—partly financed by sizable Next Generation EU resources—will be used to increase physical and social infrastructure. Outlook. GDP is expected to recover strongly in 2021–22 and to grow well-above trend over the medium term, supported by investment spending. Nonetheless, economic scarring could be sizable. The two-sided risks relate to how the pandemic progresses, the efficiency of investment spending and the extent of savings drawdown, with large costs associated with the downside.

Long-term repeatability of cartilage oligomeric matrix protein kinetics in response to walking stress

Purpose: The concentration of articular cartilage blood markers and their load induced changes can serve as surrogate parameter for investigating articular cartilage metabolism and may be useful for detecting early changes in cartilage homeostasis and ultimately predicting future structural change. One of these markers is serum cartilage oligomeric matrix protein (sCOMP), a cartilage constituent that is involved in collagen fibril organization and may represent cartilage turnover or degeneration.

Quantification of dual-energy CT-derived functional parameters as potential imaging markers for progression of idiopathic pulmonary fibrosis

ObjectivesThe individual course of disease in idiopathic pulmonary fibrosis (IPF) is highly variable. Assessment of disease activity and prospective estimation of disease progression might have the potential to improve therapy management and indicate the onset of treatment at an earlier stage. The aim of this study was to evaluate whether regional ventilation, lung perfusion, and late enhancement can serve as early imaging markers for disease progression in patients with IPF.MethodsIn this retrospective study, contrast-enhanced dual-energy CT scans of 32 patients in inspiration and delayed expiration were performed at two time points with a mean interval of 15.4 months. The pulmonary blood volume (PBV) images obtained in the arterial and delayed perfusion phase served as a surrogate for arterial lung perfusion and parenchymal late enhancement. The virtual non-contrast (VNC) images in inspiration and expiration were non-linearly registered to provide regional ventilation images. Image-derived parameters were correlated with longitudinal changes of lung function (FVC%, DLCO%), mean lung density in CT, and CT-derived lung volume.ResultsRegional ventilation and late enhancement at baseline preceded future change in lung volume (R - 0.474, p 0.006/R - 0.422, p 0.016, respectively) and mean lung density (R - 0.469, p 0.007/R - 0.402, p 0.022, respectively). Regional ventilation also correlated with a future change in FVC% (R - 0.398, p 0.024).ConclusionCT-derived functional parameters of regional ventilation and parenchymal late enhancement are potential early imaging markers for idiopathic pulmonary fibrosis progression.Key Points• Functional CT parameters at baseline (regional ventilation and late enhancement) correlate with future structural changes of the lung as measured with loss of lung volume and increase in lung density in serial CT scans of patients with idiopathic pulmonary fibrosis.• Functional CT parameter measurements in high-attenuation areas (- 600 to - 250 HU) are significantly different from normal-attenuation areas (- 950 to - 600 HU) of the lung.• Mean regional ventilation in functional CT correlates with a future change in forced vital capacity (FVC) in pulmonary function tests.

Trends, structural changes, and assessment of time series models for forecasting hospital discharge due to death at a Mexican tertiary care hospital.

BackgroundData on hospital discharges can be used as a valuable instrument for hospital planning and management. The quantification of deaths can be considered a measure of the effectiveness of hospital intervention, and a high percentage of hospital discharges due to death can be associated with deficiencies in the quality of hospital care.ObjectiveTo determine the overall percentage of hospital discharges due to death in a Mexican tertiary care hospital from its opening, to describe the characteristics of the time series generated from the monthly percentage of hospital discharges due to death and to make and evaluate predictions.MethodsThis was a retrospective study involving the medical records of 81,083 patients who were discharged from a tertiary care hospital from April 2007 to December 2019 (first 153 months of operation). The records of the first 129 months (April 2007 to December 2017) were used for the analysis and construction of the models (training dataset). In addition, the records of the last 24 months (January 2018 to December 2019) were used to evaluate the predictions made (test dataset). Structural change was identified (Chow test), ARIMA models were adjusted, predictions were estimated with and without considering the structural change, and predictions were evaluated using error indices (MAE, RMSE, MAPE, and MASE).ResultsThe total percentage of discharges due to death was 3.41%. A structural change was observed in the time series (March 2009, p>0.001), and ARIMA(0,0,0)(1,1,2)12 with drift models were adjusted with and without consideration of the structural change. The error metrics favored the model that did not consider the structural change (MAE = 0.63, RMSE = 0.81, MAPE = 25.89%, and MASE = 0.65).ConclusionOur study suggests that the ARIMA models are an adequate tool for future monitoring of the monthly percentage of hospital discharges due to death, allowing us to detect observations that depart from the described trend and identify future structural changes.

Growing Population and Climate Challenges How To Effects the Future Social Structural Changes and World Conflicts In 21st Century

Increasing population generates a number of challenges for today and in the future world. According to the results of World Population Prospect 2015, world population reached 7.3 billion as of mid-2015 and continually increasing to 11.2 billion in 21001. on the one hand climate and global temperature changes effected to the growing sea level (expected giving average sec level between 0.64 m end of this century2) and decreasing the world land spaces, on the other hand world population effects raising the density problems, as a reason world have to face number of future problems such as density, food, human health, population demography, Law and law institutional problems, energy and etc.3 . Therefore in this paper try to illustrate and forecasting general views of growing population and climate challenges how to effects the future social structural changes and world conflicts in 21004( john paul,2005) if the world needs to save future, they have to trust technology under the number of innovations which effected makes a real radical change for the society.

Measuring heterogeneity in soil networks: a network analysis and simulation-based approach

Quantifying soil structural and ecological heterogeneity is crucial for understanding their interactions and their relationships to the resilience and health of the wider ecosystem. However, a clear understanding of how structural heterogeneity affects soil biodiversity is still emerging. Previous work has primarily used expensive, often laboratory-based methods to quantify soil pore network structure, and typically separated study of structural and biological dimensions. Here, we test whether standard network metrics can be used to quantify structural heterogeneity in soil pore networks, and how this network structure, along with characteristics of the consumer and resource populations, affects the heterogeneity of a population of consumers. Specifically, we extract simplified soil pore networks from digital photographs of soil profiles and apply established metrics from network science and transport geography to quantify and compare the networks. The networks are also used as the medium for an agent-based model of generalised consumers, to analyse the effects of consumer and resource parameterisations and network structure. Combining network analysis and simulation modelling in this way can provide insights on the structure, function, and diversity possible in the soil, as well as avenues for exploring the impact of future structural or environmental changes.

P1000Aetiology and efficacy of atrial fibrillation ablation in young adults

Abstract Introduction Little is known regarding the aetiology or outcome of atrial fibrillation (AF) occurring in young adults. This retrospective analysis was performed to explore the demographics and efficacy of AF ablation in this population. Methods Patients were included who had undergone ≥1 AF ablation under the age of 40 between 2006-2018. Recurrence was defined as return of either documented AF or previous symptoms for >30s following a 3-month blanking period. Initial exploratory co-variates were included in a univariate analysis and those terms with P-value of <0.1 were then used to generate a Cox proportional-hazards multivariate model. Results 124 patients (33.6 ± 4.7 yrs, 77% men), initially presenting with paroxysmal AF (pAF; n = 97) or persistent AF (n = 27), underwent 175 AF ablation procedures. 22.6% (n = 28) also had atrial flutter. Time from symptom onset to first ablation was 50.7 ± 46.2 months. Relevant cardiovascular-related demographics were analysed: hypertension in 8.9% (n = 11); diabetes in 1.6% (n = 2); positive family history of AF in 12.9% (n = 16); and family history of sudden cardiac death in 2.4% (n = 3). Mean CHA2DS2-VASc score was 0.35. Of those patients with documented echocardiogram imaging (n = 91), 26.4% (n = 24) had LA dilatation and 6.6% (n = 6) had LV dysfunction. Patients with LA dilatation underwent more ablations (2.3 ± 0.3) compared to controls (1.5 ± 0.1; p < 0.001). Ablation strategy was pulmonary vein isolation (PVI) only in 67.2% (n = 119), with additional ablation in the remaining: roof line in 18.9% (n = 33); cavotricuspid isthmus line in 13.1% (n = 23); mitral isthmus line in 2.3% (n = 4); superior vena cava isolation in 2.3% (n = 4); complex fractionated atrial electrograms in 14.9% (n = 26). Mean procedure time was 155 ± 41 min, mean ablation time was 1657 ± 991 s and mean fluoroscopy time was 32.6 ± 23.4 min. General anaesthesia was used in 43.4% (n = 76). Complications included femoral haematoma (n = 2), tamponade (n = 1) and pulmonary vein stenosis (n = 2). 90 days of follow-up was available for 137 procedures performed for pAF (n = 105) and persistent AF (n = 32). For pAF, overall recurrence was 61.9% for first ablations and 62.9% overall. Recurrence was 56.3% for persistent AF. Factors significantly associated with increased AF recurrence in univariate analysis were male gender (hazard ratio (HR) 2.3, 95% confidence interval (CI): 1.2-4.4, p = 0.011), hypertension (HR 0.5, CI: 0.2-1.1, p = 0.067), family history of sudden cardiac death (HR 6.8, CI: 1.6-29.0 , p = 0.010) and enlarged LA size (HR 2.2, CI: 1.3-3.6, p = 0.003). In multivariate analysis, the only significant predictor of poor outcome was enlarged LA size (HR 2.0, 95% CI: 1.2-3.5, p = 0.011). Conclusions Young patients with AF may have structurally abnormal hearts, and therefore do not only present with lone AF. LA size may be used as a predictor for success. Surveillance imaging may be useful to detect future structural change, which will be the subject of future prospective studies. Abstract Figure. AF ablation recurrence in young adults

Spark-Based Parallel Method for Prediction of Events

Prediction of events is imperative in many areas of social network (SN) applications. These events influence the temporal evolutionary characteristic of social networks. A study of these events can give better insights to understand the evolutionary patterns (communities) in social networks. One of the major challenges in such implementation is the processing and structuring of large datasets to suit ML models. This paper proposes a Spark-based parallel method for detection, mining, and prediction of these events that influence the evolution of communities in a temporal SN. The proposed framework processes large temporal data (taken from the DBLP dataset), uses parallel algorithms to detect the structural changes, and applies ML techniques to predict the future structural changes (events). The proposed methodology uses ensemble ML methods in the Spark ML pipeline to achieve the desired performance and accuracy. The experimental results justify that the proposed framework can predict future events with an accuracy of 82% and saves 99% of computational time.

A Modern Rendition of a Pre-modern Scenario: Imperfect Institutions and Obscured Vulnerabilities

In recent decades, the West has appeared almost ‘invincible’ when faced with the threat of exogenous environmental or biological shocks. In accordance with traditional modernity narratives, infectious diseases particularly seemed to belong to either the premodern world or a contemporary ‘underdeveloped’ world. Now that the West is in the full grip of a pandemic, however, it has become increasingly difficult to uphold the same modern/non-modern dichotomy. Moreover, the arrival of COVID-19 in Western countries has been characterised as a consequence of institutional failure or at least an omen of future structural institutional change. These institutions, however, are known to have been designed for perpetuating the ‘status quo’ rather than protecting the societies they govern against environmental shocks. Accordingly, we argue that modern institutions should not be seen as smooth, hermetically sealed, protective systems, but rather as inherently uneven, imperfect structures whose imperfections come to the surface in times of crisis. That is to say that institutional systems may ultimately prove capable of withstanding environmental shocks, yet social groups and ecological systems may still remain vulnerable, raising questions with regard to theoretical frameworks and methodologies used by historians on this topic.

Patients' Experiences of Pain Have an Impact on Their Pain Management Attitudes and Strategies

Objectives: Postoperative pain remains undermanaged in orthopedic surgery. To identify areas of improvement for future structural changes in pain management, patients’ experiences of pain and pain management when undergoing elective lumbar spine surgery were explored, using a qualitative method with focus group interviews. Setting: The study setting was an orthopedic spine surgery department at a University Hospital in Sweden. Methods: This study consisted of two focus group interviews with patients (n = 6/group, a total of 12 patients) who had undergone lumbar spine surgery 4 days to 5 weeks prior to the focus group interviews. The interviews were semi-structured, and the analysis was performed using qualitative content analysis. Results: The main result of this study revealed that patients’ experiences of pain influenced their attitudes and strategies for pain management. Three main categories emerged from the focus group interviews: I. Coping with pain while waiting for surgery; II. Using different pain-relieving strategies after surgery; and III. How organizational structures influence the pain experiences. Conclusions: In conclusion the results from this study acknowledge that postoperative pain experiences and coping strategies after spine surgery are highly diverse and individual. This calls for staff having a more personalized approach to pain management in order to optimize pain relief, which was stressed as highly valued by the patients.

On Replication in Communication Science

Replications are an important part of the research process because they allow for greater confidence in the findings of communication research. However, engaging in replications is often undervalued, replication studies can be difficult to publish, and thus it is difficult for individual scholars to devote their resources toward replication. This essay outlines the importance of replications for communication science and provides a framework for this special issue on replications. The authors also issue a call for communication scholars to consider future projects and structural changes that would incentivize future replication studies.

Analysis and modelling regarding frequency regulation of power systems and power supply–demand‐control based on penetration of renewable energy sources

As a large amount of distributed generations power supply is introduced, there is concern about the problem of threatening stable power supply such as voltage rise, fluctuation of the distribution line locally, frequency fluctuation and power regulation shortage in a wide area. In additional, regarding the measures against the supply-demand imbalance and frequency problems, it is necessary to consider not only introducing further distributed generation sources but also new approaches such as utilization of storage batteries or load side control seen in demand response. Against this backdrop, in Japan, an analytical model for supply-demand control/frequency regulation simulation has been developed, which, named AGC30, can work on MATLAB/Simulink. It is possible to simulate the supply and demand balance control considering both economic dispatching control and load frequency control. In this research, with the purpose of contribution to the development of supply-demand control technology that responds to the future large structural change of electric power system, as the introduction and diffusion of renewable energy power generation and smart grid technology, as a common foundation for the supply-demand / frequency control of electric power system, it is aimed to construct a standard model for computer simulation that can be implemented efficiently.

Factors affecting farm growth intentions of family farms in mountain regions: Empirical evidence for Central Switzerland

Family farms in mountain regions are undergoing a progressive structural change and an ongoing shift in the allocation of production factors land, capital and labor. In Switzerland, various policy measures influence the re-allocation of these production factors. To understand the effectiveness of these schemes and to assess future farm structural change, it is useful to analyze the underlying drivers which support and hinder the emergence of individual farm growth strategies. We study the family farms’ growth intentions using a logistic regression model based on a combination of census and survey data on family characteristics from two mountain case study regions in Central Switzerland. Factors supporting farm growth intentions are the relative change in farm size in recent years, farm related sunk costs, farm diversification and farm size. We found no support for the hypothesis that farm growth intention is also influenced by the perceived personal situation represented by indicators for the perceived workload, psychological stress and financial problems. In addition, off-farm labor did not prevent farmers from stating growth intentions. Our empirical findings suggest that (i) the most important factors which support farm growth intentions correspond with factors driving observed patterns of structural change; (ii) limited availability of family labor may result in a new critical threshold for farm growth strategies; (iii) aims and non-pecuniary preferences of farmers will impede a rapid structural change in the near future.