Search Results (30)

Inflation across the globe after the COVID-19 pandemic has shown some persistence and followed an upward trend well above inflation targets and beyond normal historical movements. The Saudi inflation rate followed similar patterns of global trends, surging significantly and persisting well above the pre-pandemic levels. This paper examines determinants of inflation in Saudi Arabia, considering internal and external factors, and evaluates whether inflation responds to common global shocks or is largely influenced by macroeconomic variabilities within the economy. Findings and analyses in this paper are based on both conventional Auto Regressive Distributive Lag (ARDL) and non-linear ARDL (NARDL) models using quarterly level data to capture short-run dynamics and long-run relationships between inflation rate and examined macroeconomics variables, namely oil prices real effective exchange rate, money supply, and government spending. Reported results reveal an asymmetrical relationship between oil price fluctuations and inflation rate volatilities in Saudi Arabia. Inclines in oil prices lead to higher inflation, while the decline in oil prices does not alleviate inflationary pressures, and these results are consistent both in the short-run and the long run. The influence of pass-through real effective exchange rate is also evident in transmitting global shocks to local consumer prices in the long run, where a depreciation in real effective exchange rate results in a higher cost of imported goods, exerting additional stresses on local inflation. For factors within the economy, findings indicate a substantial long-term inflationary effect of money supply on the inflation rate in Saudi Arabia, where a one percent increase in the money supply led to more than one-third increase in inflation in the long run. On the other hand, while the influence of government spending on inflation was statistically significant, its impact is less pronounced in explaining the inflation rate’s variations. The analysis reveals that the evaluated variables exert a stronger influence on inflation in the long run. This underscores the critical need for policymakers to consider the cumulative effects of these determinants when formulating effective long-term inflation stabilization policies. Full article

Two enigmatic gamma-ray features in the galactic central region, known as Fermi Bubbles (FBs), were found from Fermi-LAT data. An energy release, (e.g., by tidal disruption events in the Galactic Center, GC), generates a cavity with a shock that expands into the local ambient medium of the galactic halo. A decade or so ago, a phenomenological model of the FBs was suggested as a result of routine star disruptions by the supermassive black hole in the GC which might provide enough energy for large-scale structures, like the FBs. In 2020, analytical and numerical models of the FBs as a process of routine tidal disruption of stars near the GC were developed; these disruption events can provide enough cumulative energy to form and maintain large-scale structures like the FBs. The disruption events are expected to be ${10}^{-4}\sim {10}^{-5}{\mathrm{yr}}^{-1}$, providing an average power of energy release from the GC into the halo of $\dot{\mathcal{E}}\sim 3\times {10}^{41}$ erg ${\mathrm{s}}^{-1}$, which is needed to support the FBs. Analysis of the evolution of superbubbles in exponentially stratified disks concluded that the FB envelope would be destroyed by the Rayleigh–Taylor (RT) instabilities at late stages. The shell is composed of swept-up gas of the bubble, whose thickness is much thinner in comparison to the size of the envelope. We assume that hydrodynamic turbulence is excited in the FB envelope by the RT instability. In this case, the universal energy spectrum of turbulence may be developed in the inertial range of wavenumbers of fluctuations (the Kolmogorov–Obukhov spectrum). From our model we suppose the power of the FBs is transformed partly into the energy of hydrodynamic turbulence in the envelope. If so, hydrodynamic turbulence may generate MHD fluctuations, which accelerate cosmic rays there and generate gamma-ray and radio emission from the FBs. We hope that this model may interpret the observed nonthermal emission from the bubbles. Full article

Failure of a safety-critical system during mission execution can result in significant financial losses. Implementing mission abort policies is an effective strategy to mitigate the system failure risk. This research delves into systems that are subject to cumulative shock degradation, considering uncertainties in shock damage. To account for the varied degradation parameters, we employ a dynamic Bayesian learning method using real-time sensor data for accurate degradation estimation. Our primary focus is on modeling the mission abort policy with an integrated parameter learning approach within the framework of a finite-horizon Markov decision process. The key objective is to minimize the expected costs related to routine inspections, system failures, and mission disruptions. Through an examination of the structural aspects of the value function, we establish the presence and monotonicity of optimal mission abort thresholds, thereby shaping the optimal policy into a controlled limit strategy. Additionally, we delve into the relationship between optimal thresholds and cost parameters to discern their behavior patterns. Through a series of numerical experiments, we showcase the superior performance of the optimal policy in mitigating losses compared with traditional heuristic methods. Full article

This paper investigated reliability modeling for systems subject to dependent competing risks considering that variation of the failure threshold is not considered in most studies on competing failure reliability. Firstly, the variation of degradation quantity under shocks was analyzed, and the variation of the threshold was considered on this basis. Secondly, the cumulative degradation under the influence of the random shock process was analyzed. The attractive property of Copula functions is symmetry. Then, a linear Wiener process model was applied to model performance degradation failure, and a multi-performance degradation correlated-competition model based on a Copula function was constructed, which considered the correlated competition between multi-performance degradation failures. Lastly, a micromotor system was used to analyze the applicability of the proposed model for bivariate instances, demonstrating the rationality and effectiveness of the proposed model. Full article

To investigate the machining effect of ultrasonic honing microjets on a titanium–tantalum alloy surface, a cavitation microjet flow impingement model was established using the smoothed particle hydrodynamics–finite element method (SPH–FEM) coupling method including the effects of wall elastic–plastic deformation, the ultrasonic field and the honing pressure field. Simulation analysis was conducted on a single impact with different initial speeds and a continuous impact at a constant initial speed. The results showed that the initial speed of the microjet needed to reach at least 580 to 610 m/s in order to obtain an obvious effect of the single impact. The single impact had almost no effect at low speeds. However, when the microjet continuously impacted the same position, obvious pits were produced via a cumulative effect. These pits were similar to that obtained by the single impact, and they had the maximum depth at the edge rather than the center. With the increase in the microjet’s initial speed, the total number of shocks required to reach the same depth gradually decreases. When the number of impacts is large, with the increase in the number of impacts, the growth rate of the maximum pit depth gradually slows down, and even shows no growth or negative growth at some times. Using the continuous impacts of the microjet by prolonging the processing time can enhance titanium–tantalum alloy machining with ultrasonic honing for material removal. Full article

It is a well-known fact that air approaches the eye region of the rotating impeller of a centrifugal fan with shock-less entry conditions in an ideal scenario. The flow in this region is associated with induced swirl losses, leading to cumulative performance losses. Proper flow guidance in the vicinity of the eye region is essential to minimize possible flow losses. The flow guiding structure may be in the form of a projection or extrusion connected to the rotating impeller of the turbo machines and is generally named a hub. These attachments enhance the overall flow augmentation of the turbo machines in terms of static pressure improvement by reducing a significant amount of inlet turning losses. This article attempts to highlight the efficacy of hubs of various shapes and sizes on the pressure rise of the centrifugal fan using Computational Fluid Dynamics (CFD). Simulation results revealed that the optimized hub configuration yields about an 8.4% higher head coefficient and 8.6% higher relative theoretical efficiency than that obtained for the hub-less base configuration. This improvement in these paraments therefore also commemorates the global progress in energy efficiency as per the UN’s Sustainable Development Goals, SDG 7 in particular. Simultaneously, in the Artificial Neural Network (ANN), a Multi-Layer Perceptron (MLP) model is used to forecast the performance of a centrifugal fan with an optimized hub design. The results predicted by the ANN model are found to be in close agreement with the optimized hub shape’s numerical results. Full article

The oil and gas sector remains pivotal in supplying energy globally. The COVID-19 pandemic and the Russia–Ukraine crisis intertwined the energy supply and demand, incurred the volatility of energy prices and disrupted the world economic order with profound effects on global political and economic paths in the long run. To investigate the impact of global COVID-19 on the energy prices of oil and natural gas for the period 2020–2022, a type of vector autoregressive (VAR) model, the vector error correction (VEC) model and the ordinary least squared (OLS) method were used for empirical analysis, producing the following main results. (i) COVID-19 significantly Granger caused both oil prices and natural gas prices to fluctuate at the 5% level. (ii) Oil prices significantly Granger caused natural gas prices to fluctuate at the 1% level because of the relations of substitutes for each other. (iii) OLS estimation validated that the cumulative number of COVID-19 confirmed cases was positively correlated with both oil prices and natural gas prices. However, the effect diminished in the long term as the pandemic was eventually brought under effective control. Exploring the effects of global issues including the pandemic and the war in Ukraine on the energy market is crucial to understanding the relationship between the supply shock and the energy sector green transitions and the global economy recovery. Full article

Recently developed Machine Learning (ML) interpretability techniques have the potential to explain how predictors influence the dependent variable in high-dimensional and non-linear problems. This study investigates the application of the above methods to damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), permutation and impurity-based techniques. Following previous investigations that examine the interdependence between predictors and the cumulative damage caused by a seismic sequence using classic statistical methods, the present study deploy ML interpretation techniques to deal with this multi-parametric and complex problem. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution with respect to the range of final damage is evaluated. Non-linear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by the Park and Ang damage index ($\mathrm{D}{\mathrm{I}}_{\mathrm{P}\mathrm{A}}$) and Intensity Measures (IMs), respectively. In addition to the interpretability analysis, the study offers also a comprehensive review of ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined. Among the 17 examined predictors, the initial damage, caused by the first shock, and the IMs of the subsequent shock—${\mathrm{I}}_{\mathrm{F}\mathrm{V}\mathrm{F}}$ and $\mathrm{S}{\mathrm{I}}_{\mathrm{H}}$—emerged as the most important ones. The novel results of this study provide useful insights in seismic design and assessment taking into account the structural performance under multiple moderate to strong earthquake events. Full article

This study empirically investigates the asymmetric effects of spot (future) prices and storage on rig counts in the US natural gas and crude oil markets from January 1986 to May 2020. It adopts the Nonlinear Autoregressive Distributed Lag (NARDL) model and establishes a flexible and efficient framework that measures the effects of positive and negative shocks in each of these variables on rig counts while modeling possible asymmetries in both the short and long term. For the natural gas market, the results reveal significant long-term asymmetric effects of spot (future) gas prices and storage on gas rigs. The positive and statistically significant cumulative effect of changes in natural gas storage suggests that larger natural gas storage has caused changes in the use of natural gas drilling rigs. For the crude oil market, we find significant short-term asymmetric effects of spot (future) gas prices and oil stocks on oil rigs. Furthermore, in addition to the optimal price and level of storage, the cost, as proxied by the interest rate, is a crucial determinant in rig drilling decision-making in the energy sector. Full article

The analysis of the dynamic process of winding destabilization under sudden short-circuit conditions is of great importance to accurately assess the short-circuit resistance of power transformers. Based on magneto-solid coupling, an axisymmetric model of the transformer and a 3D multilayer model of the transformer considering the support components are established, respectively, and the short-circuit electromagnetic force (EF) is simulated by using the finite element method. It is concluded that the middle layer of the winding is subjected to the larger radial EF, while the axial EF has a greater effect on the layers at both ends. Moreover, the impression of the preload force, aging temperatures, and the area share of spacers on the vibration and deformation of windings are studied under short-circuit conditions. The overall distribution of plastic strain and residual stress in the winding is symmetrical, and the maximum values occur in the lower region of the middle of the winding. Finally, considering the material properties of disks and insulating components, the cumulative effect of plastic deformation under multiple successive short-circuit shocks is calculated. Compared with the traditional axisymmetric model of transformer, the three-dimensional multilayer model of the transformer established in this paper is more suitable for the actual winding structure and the obtained results are more accurate. Full article

This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 CPR episodes from out-of-hospital cardiac arrest (OHCA) interventions reviewed in a period of interest from 30 s before to 10 s after regular analysis of automated external defibrillators (AEDs). Three convolutional neural networks (CNNs) with raw ECG input (duration of 5, 10, and 15 s) were applied for the shock advisory decision during CPR in 26 sequential analyses shifted by 1 s. The start and stop of chest compressions (CC) can occur at arbitrary times in sequential slides; therefore, the sliding hands-off time (sHOT) quantifies the cumulative CC-free portion of the analyzed ECG. An independent test with CPR episodes in 393 ventricular fibrillations (VF), 177 normal sinus rhythms (NSR), 1848 other non-shockable rhythms (ONR), and 3979 asystoles (ASYS) showed a substantial improvement of VF sensitivity when increasing the analysis duration from 5 s to 10 s. Specificity was not dependent on the ECG analysis duration. The 10 s CNN model presented the best performance: 92–94.4% (VF), 92.2–94% (ASYS), 96–97% (ONR), and 98.2–99.5% (NSR) for sliding decision times during CPR; 98–99% (VF), 98.2–99.8% (ASYS), 98.8–99.1 (ONR), and 100% (NSR) for sliding decision times after end of CPR. We identified the importance of sHOT as a reliable predictor of performance, accounting for the minimal sHOT interval of 2–3 s that provides a reliable rhythm detection satisfying the American Heart Association (AHA) standards for AED rhythm analysis. The presented technology for sliding shock advisory decision during CPR achieved substantial performance improvement in short hands-off periods (>2 s), such as insufflations or pre-shock pauses. The performance was competitive despite 1–2.8% point lower ASYS detection during CPR than the standard requirement (95%) for non-noisy ECG signals. The presented deep learning strategy is a basis for improved CPR practices involving both continuous CC and CC with insufflations, associated with minimal CC interruptions for reconfirmation of non-shockable rhythms (minimum hands-off time) and early treatment of VF (minimal pre-shock pauses). Full article

Cross-industry synergistic emission reduction has become a new strategy for achieving a carbon emissions peak and carbon neutrality. To explore the typical spatial distribution and cross-industry synergy effect of carbon emissions in key industries, this paper analyzes the carbon emissions of coal and power industries in Jiangsu Province from 2006 to 2020 using the empirical orthogonal function (EOF) and a panel vector autoregressive (PVAR) model. The results show that: (1) The distribution of coal resources determines the distribution of carbon emissions in the coal industry. Carbon emissions in the power industry have two typical distributions: consistent changes in cities and a “south-north” inverse phase, with a cumulative variance contribution rate of 86.74%. (2) The impulse response of carbon emissions from the coal industry to the power industry is >0 in the first period. There is a synergistic relationship of carbon emissions from the energy consumption side to the energy production side. (3) The shock effect of carbon emissions on economic development is >0. In resource-based cities, economic development explains about 2% of carbon emission fluctuations in the coal industry and 9.9% in the power industry, which is only 2% in non-resource-based cities. Carbon emissions would promote economic development. However, the impact of economic development on them varies significantly by industry and region. These findings can provide scientific support for developing differentiated measures to carbon emissions reduction and serve as an important reference role for other regions to promote collaborative carbon emission reduction in key industries. Full article

Sentiment and extrapolation are ubiquitous in the financial market, and they are not only the embodiment of human nature, but also the primary drivers of asset price bubbles. In this study, we first constructed a theoretical model that included fundamental traders and extrapolated investors, and we assessed the time series characteristics of asset prices under different types of information shocks. According to the research results, good news about the fundamentals can lead to positive asset price bubbles, and correspondingly, bad news can lead to negative asset price bubbles; however, the decrease in asset prices in the case of negative bubbles is not as substantial as the increase in prices in the case of positive bubbles, and the time for prices to reverse is also long, which can be explained by the short-selling constraints. According to the comparative static analysis, the scales of the positive and negative foams depend on the proportion of investors in the market and the extrapolation coefficient. We verified the conclusion of the theoretical model from two aspects: (1) we analyzed the relationship between investor sentiment and the prevalence of informed trading, and according to the results, the increase (decrease) in investor sentiment can reduce the information content of asset prices and increase price volatility; however, the impact of low sentiment is not substantial, which preliminarily tests the conclusion of the theoretical model; (2) we examined the relationship between the cumulative change in investor sentiment and future portfolio returns, and we found that the cumulative increase in investor sentiment can have a positive impact on future portfolio returns at the initial stage, and depress future portfolio returns in the long term, which forms positive asset price bubbles. The cumulative depression of investor sentiment can depress the future portfolio returns at the initial stage, and positively influence the future portfolio returns in the long term, which forms negative asset price bubbles. Moreover, these two nonlinear relationships exhibit cross-sectional differences in different types of asset portfolios, which further validates the key proposition of the theoretical model. Full article

The question of how to proactively respond to population aging has become a major global issue. As a country with the largest elderly population in the world, China suffers a stronger shock from population aging, which makes it more urgent to transform its industrial and economic development model. Concretely, in the context of the new macroeconomic environment that has undergone profound changes, the shock of population aging makes the traditional industrial structure upgrading model (driven by large-scale factor inputs, imitation innovation and low-cost technological progress, and strong external demand) more unsustainable, and China has an urgent need to transform it to a more sustainable one. Only with an in-depth analysis of the influence mechanism of population aging on the upgrading of industrial structure can we better promote industrial structure upgrading under the impact of population aging. Therefore, six MSVAR models were constructed from each environmental perspective based on data from 1987 to 2021. The probabilities of regime transition figures show that the influencing mechanisms have a clear two-regime feature from any view; specifically, the omnidirectional environmental transition occurs in 2019. A further impulse–response analysis shows that, comparatively speaking, under the new environment regime the acceleration of population aging (1) aggravates the labor shortage, thus narrowing the industrial structure upgrading ranges; (2) has a negative, rather than positive, impact on the capital stock, but leads to a cumulative increase in industrial structure upgrading; (3) forces weaker technological progress, but further leads to a stronger impact on the industrial structure upgrading; (4) forces greater consumption upgrading, which further weakens industrial structure upgrading; (5) narrows rather than expands the upgrading of investment and industrial structures; and (6) narrows the upgrading of export and industrial structures. Therefore, we should collaboratively promote industrial structure upgrading from the supply side relying heavily on independent innovation and talent, and the demand side relying heavily on the upgrading of domestic consumption and exports. Full article

Utilising a nonlinear (regime-switching) mixed-frequency panel vector autoregression model, we study the effects of government spending shocks in the United States (US) over the business cycle, while considering the role of partisan conflict. In particular, we investigate whether partisan conflict is relevant to the differences in fiscal spending multipliers in expansionary and recessionary business cycle phases upon the impact of annual government spending shocks, using quarterly state-level data covering 1950:Q1 to 2016:Q4. We find new evidence that fiscal multipliers can vary with economic and political conditions. The cumulated effects of government spending shocks are strong and persistent in recessions when the level of partisan conflict is low. Full article