Common Cycles Research Articles

Analysis and prediction of mesoscale eddy kinetic energy variations in the Kuroshio extension

The Kuroshio Extension (KE) region, a crucial area in the Northwest Pacific Ocean, exhibits eddy kinetic energy with various scales of periodicity. Understanding how to extract its characteristic features and analyze and predict their periodic correlations has become vital for studying the regulatory mechanisms of eddy kinetic energy in the KE region. This paper first introduces a mesoscale eddy hybrid identification algorithm based on the flow field vector and the closed flow field. Using this algorithm, we gather mesoscale eddy identification data from the KE region to extract the monthly average series of five typical features of the KE region. Subsequently, wavelet theory is applied to analyze the cycles of these main features, identifying the common cycles of the KE region as the primary focus for analyzing the vorticity kinetic energy. This analysis includes cycle correlations with globally recognized indices, and it predicts these correlations. Further analysis of the main characteristic cycles through wavelet theory reveals that the KE region's eddy kinetic energy is significantly influenced by solar activity over long periods and by the North Pacific ocean-atmosphere interaction over shorter, interannual periods. Finally, this paper introduces a W-LSTM (Wavelet Decomposition based Long Short-term Memory Networks) prediction model based on wavelet decomposition for the KE region, covering January 2023–December 2023. The model demonstrates its effectiveness, achieving a Root Mean Square Error (RMSE) of 0.2530 and a correlation coefficient of 0.8259 between the predicted data and the actual observations.

Comparing Influences of Solar Wind, ULF Waves, and Substorms on 20 eV–2 MeV Electron Flux (RBSP) Using ARMAX Models

AbstractElectron fluxes (20 eV–2 MeV, RBSP‐A satellite) show reasonable simple correlation with a variety of parameters (solar wind, IMF, substorms, ultralow frequency (ULF) waves, geomagnetic indices) over L‐shells 2–6. Removing correlation‐inflating common cycles and trends (using autoregressive and moving average terms in an ARMAX analysis) results in a 10 times reduction in apparent association between drivers and electron flux, although many are still statistically significant (p < 0.05). Corrected influences are highest in the 20 eV–1 keV and 1–2 MeV electrons, more modest in the midrange (2–40 keV). Solar wind velocity and pressure (but not number density), IMF magnitude (with lower influence of Bz), SME (a substorm measure), a ULF wave index, and geomagnetic indices Kp and SymH all show statistically significant associations with electron flux in the corrected individual ARMAX analyses. We postulate that only pressure, ULF waves, and substorms are direct drivers of electron flux and compare their influences in a combined analysis. SME is the strongest influence of these three, mainly in the eV and MeV electrons. ULF is most influential on the MeV electrons. Pressure shows a smaller positive influence and some indication of either magnetopause shadowing or simply compression on the eV electrons. While strictly predictive models may improve forecasting ability by including indirect driver and proxy parameters, and while these models may be made more parsimonious by choosing not to explicitly model time series behavior, our present analyses include time series variables in order to draw valid conclusions about the physical influences of exogenous parameters.

Multivariate Trend‐Cycle‐Seasonal Decompositions with Correlated Innovations*

AbstractMultivariate analysis can help to focus on important phenomena, including trend and cyclical movements, but any economic information in seasonality is typically ignored. The present paper aims to more fully exploit time series information through a multivariate unobserved component model for quarterly data that exhibits seasonality together with cross‐variable component correlations. We show that economic restrictions, including common trends, common cycles and common seasonals can aid identification. The approach is illustrated using Italian GDP and consumption data.

A comprehensive and systematic study of geothermal energy-based desalination systems for sustainable development

ContextGeothermal energy is one of the most promising renewable energy sources as it is a power source that produces electricity with minimal environmental impact. It has been proven that among renewable energy sources, it is the most reliable, safe, and clean, and therefore it is used for power production, heating, and cooling. The importance of geothermal desalination is beneficial in areas lacking natural groundwater or surface water supplies. Over time, water reuse desalination can solve water scarcity and, in some countries, provides over 90 % of the total water supply. The novelty of the workThis research presents an in-depth analysis of the desalination of seawater utilizing geothermal energy as the primary resource. It will also evaluate the risks and market possibilities of the system and the constraints on further development. MethodologyThe method of geothermal desalination has also been briefly covered, along with the most common cycles of thermal desalination. These cycles include multi-effect distillation, multistage flash distillation, and vapor compression. Main results and related interpretationsGeothermal desalination technologies address worldwide water scarcity sustainably and effectively. Geothermal energy can make desalination more sustainable and environmentally friendly. Compared to conventional fossil fuel-based systems, desalination using geothermal energy can result in lower overall energy usage and CO2 emissions.

The Role of Teleconnection Patterns on Wind Speed of Iraqi-Kurdistan Region

Changes in the wind speed of the Iraqi-Kurdistan region are associated with many types of teleconnection patterns. Daily wind speed datasets for eight meteorological stations in the Iraqi-Kurdistan region were gathered from Sulaymaniya, Penjwen, Hallabja, Kalar, Erbil, Sallahadin, Zakho, and Duhok weather stations. spectral comparability between the daily wind speed of the eight sites and prominent atmospheric indices was implemented for the North Atlantic Oscillation (NAO), Southern Oscillation (SO), Mediterranean Oscillation (MO), and Arctic Oscillation (AO), using the multi-taper method (MTM) for spectral analysis to find the source of the wind speed cycle of the cites for the study area. It was found that the common cycles for the eight stations are 2.22, 2.5, 3.33, 3.7, 4.35, 5.56, 7.14, 10, and 14.29 cycles. It is obvious that there are common cycles between each of the NAO, SO, MO, and AO indices and the cycles of daily wind speed of the eight stations. The results of this study will allow us to understand the relation between the regional daily wind speeds patterns fluctuations and the global teleconnection patterns and also to forecast the behavior of daily regional wind speed.

Financial cycles across G7 economies: A view from wavelet analysis

We analyse the cross-country dimension of financial cycles by examining cyclical co-movements in credit, house prices, equity prices and interest rates across the G7 economies. We use wavelet-based statistics to assess at which frequencies cyclical fluctuations and their cross-country co-movements are important and how these change over time. We show that cycles in interest rates and equity prices are at least as synchronised as cycles in real GDP, while cycles in credit and house prices are less synchronised. Furthermore, we propose a measure of the importance of common cycles for developments in individual countries. It shows that common cycles across countries in equity prices and long-term interest rates account for a larger share of the volatility of these variables at the country level than is the case for common cycles in credit aggregates. The importance of common cycles for fluctuations at the country level is roughly similar for house price growth and long-term interest rates.

Financial Cycles in Euro Area Economies: A Cross‐Country Perspective Using Wavelet Analysis*

AbstractWe study the cross‐country dimension of financial cycles for six euro area countries using wavelet analysis. Estimated wavelet cohesions show that cycles in equity prices and interest rates display stronger synchronization across countries than real output cycles, whereas credit variables and house prices show lower cross‐country synchronization. We propose a wavelet‐based extension to the spectral envelope that is similar to a frequency‐based time‐varying principal component analysis. The country loadings show that, contrary to all other variables, cycles in loans to households and house prices in Germany and the Netherlands are negatively or less strongly correlated with the common cycles.

Do crop prices share common trends and common cycles?*

Global crop prices decidedly co‐move: research supporting this view abounds. What is unclear is how strong the co‐movement is between them. This paper tests for a strong form of co‐movement amongst global crop prices before and after the global financial crisis (GFC) using a multivariate decomposition framework based on a serial‐correlation common feature. More specifically, we analyze common trends (i.e., long‐run co‐movement) and common cycles (i.e., short‐run co‐movement) amongst the global prices of five major crops: corn, palm oil, rice, soybean, and wheat. We show that corn and soybean prices are closely associated in the long and the short run—they respond similarly to persistent and transitory shocks. Furthermore, their associations have strengthened since the GFC. In contrast, the co‐movement of rice prices with the other crop prices has weakened during the 2010s. Overall, the cycles are relatively muted after the GFC, indicating that the five crop prices are trend‐dominated during this period; the observed prices adhere closely to their long‐run trends.

빈도영역 분석법을 통한 주택가격과 거래량의 공통 사이클 분석

The movements of housing prices and trading volume have long-term cycles. Thus, it can be presumed that there would be a close relationship between the cyclic components embedded in these two variables. However, it has been challenging to find studies that analyzed the cyclical characteristics and the relationship between the common cycles embedded in these two variables. This study, in contrast to previous studies that used methodologies in the time domain analysis to confirm the correlation and lead-lag relationship between housing prices and trading volume, analyzed, from different perspectives, the cyclical characteristics embedded in these two variables, using spectral analysis in the frequency domain. When forecasting cycles in housing prices and trading volume, for comparison, we added the results of the VAR(p) model analysis in the time domain to the results of the spectral analysis in the frequency domain. We could confirm that there were common cycles between the movements of housing prices and trading volume. Further, we confirmed that the movement of trading volume slightly precedes that of housing prices. According to our results, when the housing price has an upward trend, the housing market was expected to face a slow speed of an increase in housing prices while a decrease in trading volume for the time being after 2019. However, after 2020, both the trading volume and housing prices were expected to witness a relatively rapid increase. Ultimately, we improved the rationality of the forecasting results of future cycles by confirming the results from the time and frequency domain analyses.

Wavelet-based filtering and prediction of soil CO2 flux: Example from Etna volcano (Italy)

In this work, we propose a wavelet-based filtering for soil CO2 flux time series. The filter relies on the detection of the periodic components achieved by means of the long-term time-frequency characterization of the time series. For this purpose, we exploited the vast data set coming from the monitoring network installed at Mt. Etna volcano (Italy). The network provides hourly measure of CO2 flux together with the measure of the climatic variables. These data allow to investigate the relationships between CO2 time series and the potentially influencing meteorological factors. This has been assessed calculating the wavelet coherence between CO2 time series against air temperatures, atmospheric pressure, and relative humidity in all the sites where these information were available. Results highlight the occurrence of marked cycles at about ∼1 year for the most of the sites while shorter cycles occur only at some sites. From these cycles a periodic signal can be calculated, and therefore opportunely removed from the time CO2 series to enhance the volcano-related anomalies. We found also common cycles among CO2 and the climatic variables, which synchronicity is constant over time but it is site-specific. Starting from this consideration, we calculated a reference signal for CO2 combining analytically the temperature, the pressure, and the humidity cycles: this model of the climatic effect has been used to predict the seasonal trend of the CO2 output.

Measuring and Analyzing the Common and Idiosyncratic Cycles: An Application for Turkish Manufacturing Industry

This study uses the Butterworth filter to decompose cyclical signals at low and high frequencies in the production data of the manufacturing industry and its sub-sectors. At low frequencies, the production trend exhibits considerable differences among industrial activities while most of the sub-sectors are more sensitive to common cycle than their own dynamics at high frequencies. Moreover, it is predicted that changes in the manufacture of basic metals sub-sector production can be used as a leading indicator for the expansion and contraction periods of the common cycle estimated for the manufacturing industry.

Dollarization and economic interdependence: The case of Ecuador

Economic theory suggest that dollarization increases the degree of interdependence between the dollarized economy and the anchor country. We test this theory for the case of the U.S. and Ecuador. We evaluate the existence of common trends and common cycles amongst production variables. The analysis considers data at the aggregate and industry levels. At the aggregate level, the results show that the economies of the U.S. and Ecuador were interrelated prior to dollarization. This interrelation increased after Ecuador adopted the U.S. dollar. At the industry level, we find that synchronization existed between some Ecuadorian industries and the U.S. Gross Domestic Product prior to dollarization. The interdependence increased after dollarization and it is particularly evident for the financial industry. This result is especially important since it shows how the use of a common currency leads to the integration of financial markets in the dollarized economy and economic activity in the anchor country.

State-of-the-Art Water Treatment in Czech Power Sector: Industry-Proven Case Studies Showing Economic and Technical Benefits of Membrane and Other Novel Technologies for Each Particular Water Cycle.

The article first summarizes case studies on the three basic types of treated water used in power plants and heating stations. Its main focus is Czechia as the representative of Eastern European countries. Water as the working medium in the power industry presents the three most common cycles—the first is make-up water for boilers, the second is cooling water and the third is represented by a specific type of water (e.g., liquid waste mixtures, primary and secondary circuits in nuclear power plants, turbine condensate, etc.). The water treatment technologies can be summarized into four main groups—(1) filtration (coagulation) and dosing chemicals, (2) ion exchange technology, (3) membrane processes and (4) a combination of the last two. The article shows the ideal industry-proven technology for each water cycle. Case studies revealed the economic, technical and environmental advantages/disadvantages of each technology. The percentage of technologies operated in energetics in Eastern Europe is briefly described. Although the work is conceived as an overview of water treatment in real operation, its novelty lies in a technological model of the treatment of turbine condensate, recycling of the cooling tower blowdown plus other liquid waste mixtures, and the rejection of colloidal substances from the secondary circuit in nuclear power plants. This is followed by an evaluation of the potential novel technologies and novel materials.

Multivariate decompositions and seasonal gender employment

Multivariate analysis can help to focus on economic phenomena, including trend and cyclical movements. To allow for potential correlation with seasonality, the present paper studies a three component multivariate unobserved component model, focusing on the case of quarterly data and showing that economic restrictions, including common trends and common cycles, can ensure identification. Applied to seasonal aggregate gender employment in Australia, a bivariate male/female model with a common cycle is preferred to both univariate correlated component and bivariate uncorrelated component specifications. This model evidences distinct gender-based seasonal patterns with seasonality declining over time for females and increasing for males.

Thermodynamic Modeling of oxyhydrogen fueled combined cycle power plant

Renewable power generation can reduce the dependence on fossil fuels while minimizing greenhouse gas emissions from electric power generation. However, most renewable energy sources are naturally occurring which makes them seasonal and generally unpredictable over time. With more countries trending toward renewable power by 2050, it is imperative that technologies are developed which can utilize and optimize the storage and distribution of this type of power. Hydrogen energy storage is becoming increasingly popular due to its versatility. It is considered an energy carrier like electricity and can be generated and stored in large quantities and for long periods of time. Hydrogen can be derived from water, biomass, and other technologies and can generate electric power using fuel cells and through combustion. This study investigates a novel combined cycle configuration which is thermodynamically analyzed to identify its potential to adapt steam from a hydrogen oxygen steam generator. A thermodynamic analysis on the system is performed using Engineering Equation Solver from F Chart Software. Results show that the oxygen hydrogen fueled combined cycle excels in the specific power ratio, as this cycle was able to achieve the lowest pressure values at the highest points for both thermal loading and pressure loading. This is a major advantage since the thermal loading on some of the power cycles are much higher that what is currently in use, thus reducing it even by a smaller percentage is significant. The oxygen hydrogen fueled combine cycle reduced the specific power by 78%, pressure at the most thermal loaded point by 157%, and pressure at the most pressure loaded element by 10% when compared to other common cycles.

On Optimal Currency Areas and Common Cycles: Are the Acceding Countries Ready to Join the Euro?

The former EU president Jean-Claude Junker has proposed that all countries of the European Union should also adopt the euro as their currency and recent research has shown that countries currently pursuing this goal indeed fulfill the classical Optimal Currency Area (OCA) criterion of positively correlated shocks with the European Monetary Union (EMU). We illustrate, however, that not only the correlation of shocks but also a common impulse response pattern over time is needed for a currency area to be optimal. We test this additional OCA criterion using the concept of a common serial correlation test. The test clearly rejects the notion that the potentially acceding countries share a common cyclical response pattern with the EMU aggregate – except for Sweden. Instead, the business cycles in most of the other countries exhibit only a very weak form of codependence.

A critical review on self-healing composites

The idea of an unintentional self-mending material in which the commencement of fixes is a basic piece of the substance presently being considered for designing purposes. This thought propelled by essentialness empowers the architect to consolidate auxiliary practical materials fit for balancing debasement of administration while as yet satisfying fundamental, normally primary necessities. In nature, most substances are self-mending composites themselves. This paper analyzes various self-mending procedures for fiber-strengthened polymer composites as of now being made, a large portion of which are motivated by science; enlivened naturally perception. The latest self-recuperating examines endeavored to utilize a more exhaustive investigation of common cycles to mirror non-intrusive treatment. A perspective is given on current and potential ways to deal with self-mending. In this energizing region, the point is to invigorate conversation and fortify the significance of a multidisciplinary approach.

Prokaryotic Cells: Structure and its Significance

Prokaryotes join microorganisms and archaea, two of the three spaces of life. Prokaryotic cells were the essential kind of life on Earth, depicted by having basic common cycles including cell hailing. They are not so much troublesome but rather more unobtrusive than eukaryotic cells, and miss the mark on a center, and other layer bound organelles. The DNA of a prokaryotic cell involves a lone round chromosome that is in direct contact with the cytoplasm. The nuclear territory in the cytoplasm is known as the nucleoid. Most prokaryotes are the tiniest of all natural substances going from 0.5 to 2.0 mm in estimation.

The dynamics of energy prices and the Norwegian economy: A common trends and common cycles analysis

Considering the significance of the oil and gas sector to the Norwegian economy, this study investigates the short-term and long-term associations between crude oil and natural gas prices, and the real GDP of Norway. To this end, we employ a common-feature-based, multivariate trend-cycle decomposition approach to examine common trends and common cycles between them. Upon isolating the trend and cyclical components of the three series, this study finds a negative association between the real GDP and the two energy prices both in the short-run as well as in the long-run. This result contradicts the commonly-held view that the performance of energy-exporting economies is positively associated with energy prices. The test results also find that the cycles of natural gas prices follow the cycles of oil prices.

Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction

The objective of this work is to develop an optimal management strategy to improve the energetic efficiency of a hybrid electric vehicle. The strategy is built based on an extensive experimental study of mobility in order to allow trips recognition and prediction. For this experimental study, a dedicated autonomous acquisition system was developed. On working days, most trips are constrained and can be predicted with a high level of confidence. The database was built to assess the energy and power needed based on a static model for three types of cars. It was found that most trips could be covered by a 10 kWh battery. Regarding the optimization strategy, a novel real time capable energy management approach based on dynamic vehicle model was created using Energetic Macroscopic Representation. This real time capable energy management strategy is done by a combination of cycle prediction based on results obtained during the experimental study. The optimal control strategy for common cycles based on dynamic programming is available in the database. When a common cycle is detected, the pre-determined optimum strategy is applied to the similar upcoming cycle. If the real cycle differs from the reference cycle, the control strategy is adapted using quadratic programming. To assess the performance of the strategy, its resulting fuel consumption is compared to the global optimum calculated using dynamic programming and used as a reference; its optimality factor is above 98%.