Switching Model Research Articles

Does private financial development present a trade-off for CO2 emissions reduction in Oman? Evidence from Markov-switching model

ABSTRACT The expansion of the financial sector is often promoted to facilitate economic growth. Nevertheless, the potential implications of financial development on environmental indicators, specifically carbon dioxide (CO2) emissions, have become a subject of concern. This research investigates the impact of financial development on CO2 emissions in Oman, along with other control variables including energy consumption, trade openness, and economic growth, using a Markov switching model. The analysis is conducted for the period from 1989 to 2020 and focuses on two regimes of high and low carbon emissions. The results of the Markov switching model show that energy consumption, gross domestic product (GDP), and private financial development have a significant positive effect on CO2 emissions. Although the study confirms the Kuznets curve, Oman is still in its initial phase. Moreover, our results suggest a higher likelihood of remaining in the high CO2 emissions regime. This underscores the importance for policymakers to carefully weigh the trade-offs associated with fostering financial development, particularly within climate change mitigation endeavors. These findings are highly relevant for policymakers striving to balance economic growth with the reduction of carbon emissions in Oman.

On the superior performance of SRI funds

We compare the performance of 1126 US mutual funds deemed socially responsible to that of a matching sample of conventional funds over the three-decade period January 1980–December 2019. The comparisons are made over the entire period as well as the descending (bear) and ascending (bull) market subperiods, classified either according to the NBER method or the regime switching model. Our results indicate that SRI funds outperform their peers over the entire sample period. Additionally, regardless of the method used to the identify market condition or the asset pricing model employed, SRI funds as a group significantly outperform their conventional counterparts during bull markets but insignificantly so during bear markets. However, all-equity SRI funds and those that includes Equity and Fixed Income funds, significantly outperform their peers under either regime, with both methods and for all asset pricing specifications. Further examination indicates that the main driver of differences in the performance of SRI funds and their conventional peers can be attributed to their superior selection ability.

Filament-based memristor switching model

The filaments rupture and recovery in oxide-type memristors have been theoretically studied. The model is based on the kinetics of oxygen vacancies and includes Joule heating of the oxide medium, which enhances the diffusion and drift of oxygen vacancies in an external electric field. The current-voltage characteristic of the model structure was obtained. Comparison with experimental results allowed for the determination of the constants used in the modeling. The peculiarity of the current-voltage characteristic observed in experimental works is explained.

A single point mutation in the Listeria monocytogenes ribosomal gene rpsU enables SigB activation independently of the stressosome and the anti-sigma factor antagonist RsbV.

Microbial population heterogeneity leads to different stress responses and growth behavior of individual cells in a population. Previously, a point mutation in the rpsU gene (rpsUG50C) encoding ribosomal protein S21 was identified in a Listeria monocytogenes LO28 variant, which leads to increased multi-stress resistance and a reduced maximum specific growth rate. However, the underlying mechanisms of these phenotypic changes remain unknown. In L. monocytogenes, the alternative sigma factor SigB regulates the general stress response, with its activation controlled by a series of Rsb proteins, including RsbR1 and anti-sigma factor RsbW and its antagonist RsbV. We combined a phenotype and proteomics approach to investigate the acid and heat stress resistance, growth rate, and SigB activation of L. monocytogenes EGDe wild type and the ΔsigB, ΔrsbV, and ΔrsbR1 mutant strains. While the introduction of rpsUG50C in the ΔsigB mutant did not induce a SigB-mediated increase in robustness, the presence of rpsUG50C in the ΔrsbV and the ΔrsbR1 mutants led to SigB activation and concomitant increased robustness, indicating an alternative signaling pathway for the SigB activation in rpsUG50C mutants. Interestingly, all these rpsUG50C mutants exhibited reduced maximum specific growth rates, independent of SigB activation, possibly attributed to compromised ribosomal functioning. In summary, the increased stress resistance in the L. monocytogenes EGDe rpsUG50C mutant results from SigB activation through an unknown mechanism distinct from the classical stressosome and RsbV/RsbW partner switching model. Moreover, the reduced maximum specific growth rate of the EGDe rpsUG50C mutant is likely unrelated to SigB activation and potentially linked to impaired ribosomal function.

An accelerated modelling method of full-bridge modular multilevel converter considering dead-time

To avoid short-circuits of the same side switching of power converters, the dead time is of significance in the pulse trigger gating signal for full bridge modular multilevel converter (FB-MMC). However, the previous equivalent models use the binary resistance switching model to equivalent the switching state which is impossible to simulate the switching with dead time, and the detailed model (DM) with higher levels is time-consuming and inefficient in electromagnetic transient (EMT) simulation. To address this problem, based on the state-space averaging method, a speed-up average value model (AVM) of FB-MMC considering dead time is proposed. Firstly, the working state of FB-MMC is obtained through analysing the switching states with dead time and is classified into three cases according to the capacitor. Secondly, derived from state-space equation, the three cases are integrated into a unified model by introducing the duty cycle, and then the equivalent controlled source is used as the interface circuit between the external grid and capacitor. Finally, a 49-level two-terminal MMC-HVDC system consisting of FB-MMC is constructed in PSCAD / EMTDC simulations, showing that the proposed AVM compared with DM has high accuracy and operation efficiency.

Accessing the effect of smartphone agricultural applications on land transfer: evidence from Sichuan province in China

PurposeThis study aims to examine farmers' decision to use smartphone agricultural applications (SAAs) and how SAAs adoption impact their land transfer behaviors in terms of the current land transfer-in area (LTA) and the future willingness to renew land transfer-in after it expires (WTR).Design/methodology/approachThis study provides empirical evidence on the relationship between farmers' use of SAAs and land transfer choice, using a field survey data of 752 rural farm households in 2020 from Sichuan province of China. The endogenous switching models are employed to address potential self-selection bias associated with voluntary SAAs use and to quantitatively examine the impacts of SAAs use on land transfer choice.FindingsThe empirical results reveal that SAAs significantly improves the probability of transfer-in of more land by 39.10%. We find SAAs use has heterogeneous impacts on land transfer-in choice in the groups of agricultural technology, extension service, marketing and credit. Besides, we also find that SAAs use exerts highly positive and significant impact on farmers with less land area transfer-in. Moreover, SAAs can increase the probability of farmers' willingness to renew the land transfer-in by 30%.Originality/valueTo the best of our knowledge, this study is the first to explore the quantitative relationship between the use of SAAs and farm households' land transfer choice. The findings of this work can provide policy-related insights to help government promote the development of digital applications in the agricultural sector.

Agricultural commodities market reaction to COVID-19

This study examines the volatility transmission mechanism in agricultural futures returns during crisis periods, with a specific focus on the COVID-19 pandemic. The research employs the Markov Switching model to analyze the market dynamics of key agricultural commodities—cotton, sugar, rice, wheat, and corn. The study period is divided into three phases: Pre-COVID, COVID, and Post-COVID, allowing for a comprehensive analysis of market trends and shifts in response to the pandemic. Key findings reveal significant disruptions and heightened volatility in these markets during the COVID-19 period, with notable deviations from traditional market behaviors. The research reveals how various macroeconomic factors, such as the S&P 500, US 10-year Treasury bills, Brent crude oil prices, and the US Dollar Index, distinctly affect these commodity markets. Crucially, it sheds light on the dual nature of these markets, demonstrating both their resilience and susceptibility to worldwide disruptions, along with their recovery patterns following the pandemic. The policy implications of these findings are crucial for investors and policymakers. The study provides insights into managing and mitigating the impacts of global crises on essential commodities, emphasizing the need for robust strategies to navigate future market uncertainties. Additionally, the research contributes to the broader discourse on the interplay between macroeconomic conditions and agricultural market dynamics, offering valuable perspectives for informed decision-making in these sectors.

Maximum Likelihood Estimation of Multivariate Regime Switching Student‐t Copula Models

SummaryWe propose a multivariate regime switching model based on a Student‐ copula function with parameters controlling the strength of correlation between variables and that are governed by a latent Markov process. To estimate model parameters by maximum likelihood, we consider a two‐step procedure carried out through the Expectation–Maximisation algorithm. To address the main computational burden related to the estimation of the matrix of dependence parameters and the number of degrees of freedom of the Student‐ copula, we show a novel use of the Lagrange multipliers, which simplifies the estimation process. The simulation study shows that the estimators have good finite sample properties and the estimation procedure is computationally efficient. An application concerning log‐returns of five cryptocurrencies shows that the model permits identifying bull and bear market periods based on the intensity of the correlations between crypto assets.

Simulation of a physical reservoir made of a Ag2S islands network

Recently, a physical reservoir operation utilizing atomic switch technologies was demonstrated. Atomic switch operates by controlling the formation and annihilation of a metal filament between two electrodes using solid-state electrochemical reactions. In this study, we simulated the operation of an atomic switch-based reservoir by arranging modeled atomic switches in a network. The aim of this study is to confirm that nonlinear transformation and short-term memory in a reservoir operation observed in the experiment can be realized by the integration of atomic switches showing nonvolatile bipolar operation. We incorporated these characteristics by making a simple operating model of a single atomic switch, which successfully reproduced major characteristics of the experimental results of a reservoir operation.

Global oil price and stock markets in oil exporting and European countries: Evidence during the Covid-19 and the Russia-Ukraine war

The relationship between oil price movements and stock markets during the COVID-19 pandemic and the geopolitical crisis like the ongoing Russian-Ukraine war is yet unexplored extensively. This study therefore examines the return-correlation effects of oil prices on stock markets and their spillover effects in oil-exporting and European countries using daily closing data. After estimating the GARCH process, we employ the static and dynamic Markov Switching model that allow the relationship between oil price and stock market to switch between two regimes coined the COVID-19 and the Russia-Ukraine war periods. The static model shows stock price returns to respond positively and significantly to oil price returns in Italy, Germany and the US during the Covid-19 period while the response is significantly positive only for US in the Russia-Ukraine war period. As regards the volatility spillover, significant spillover is found from stock to oil market for Nigeria, vice versa for Saudi Arabia and bi-directional volatility spillover found for the US, Italy and Germany during the COVID-19 period. The policy implication is that Nigeria and Saudi Arabia should prioritize financial policy and energy policy respectively while US, Italy and Germany should adopt policy coordination to stabilize oil-stock market volatility during low oil price period like the COVID-19 period.

Multitasking scheduling with job switching allowed between alternate periods

This paper explores the multitasking scheduling problem via alternate periods, which is common in manufacturing and service industries. The shift pattern comprises regular shifts interspersed with breaks, allowing job switching. We focus on two alternate periods consisting of odd periods and even periods. Each job is not only processed completely within odd periods or completely within even periods, but job switching is also allowed between alternate periods with additional overhead. For this problem, we propose two job switching models. The first model is alternate periods multitasking scheduling with switching time, where a fixed switching time is incurred whenever a job is switched from one period to its adjacent period. The second model is alternate periods multitasking scheduling with diverse speeds, where an urgent switching cost is produced if a job is processed through the alternate periods with different speeds. For the two models, we study three scheduling objectives including minimising the total completion time, minimising the maximum lateness, and minimising the number of tardy jobs, respectively. For each of the problems under consideration, we investigate the structural properties of the optimal schedule and develop the corresponding pseudo-polynomial time dynamic programming algorithm. Furthermore, we give examples to effectively illustrate the proposed algorithms.

Efficient Prototyping of a Field-Programmable Gate Array-Based Real-Time Model of a Modular Multilevel Converter

Field-programmable gate array (FPGA)-based real-time simulation plays a crucial role in testing power–electronic dominated systems with the formation of controller hardware-in-the-loop (CHIL) or power hardware-in-the-loop (PHIL). This work describes an efficient implementation of computation time and resource usage in the FPGA-based study of a modular multilevel converter (MMC) with detailed electromagnetic transients. The proposed modeling technique can be used in continuous control mode (CCM) and discontinuous control mode (DCM) for high-switching frequency semiconductor technologies. An FPGA-based designed solver structure is also presented to take advantage of the parallel features of FPGAs to achieve an ultra-fast calculation speed. In addition, two different switch modeling techniques are discussed with a five-level MMC case study. Experimental results on the NI PXIe platform show the feasibility of the proposed implementation, and a time step of 100 nanoseconds is achieved.

Predicting systemic financial risk with interpretable machine learning

Predicting systemic financial risk is essential for understanding the financial system's stability and early warning of financial crises. In this research, we use the financial stress index to measure systemic financial risk. We construct the stress index for five financial submarkets and composite stress index, employ the Markov regime switching model to identify the systemic financial risk stress state. On this basis, we use interpretable machine learning models to forecast systemic financial risk, analyze and compare the results of the intrinsic interpretable machine learning models and the post-hoc explainable methods. The results indicate that systemic financial risk can be effectively predicted using both the submarket stress index and the feature variables, with the submarket stress index as the independent variable providing relatively higher accuracy. There is a linearly positive relationship between the stress index of each submarket and systemic financial risk, with financial stress in the stock and money markets having the greatest impact on systemic financial risk. For each feature variable, stock–bond correlation coefficient, stock valuation risk, the maximum cumulative loss of the SSE Composite Index (SSE CMAX), and loan-deposit ratio have strong predictive power. Our research can provide reference for government to construct prediction model and indicator monitoring platform of systemic financial crisis.

Kaposi's sarcoma-associated herpesvirus terminal repeat regulates inducible lytic gene promoters.

The Kaposi's sarcoma-associated herpesvirus (KSHV) genome consists of an approximately 140-kb unique coding region flanked by 30-40 copies of a 0.8-kb terminal repeat (TR) sequence. A gene enhancer recruits transcription-related enzymes by having arrays of transcription factor binding sites. Here, we show that KSHV TR possesses transcription regulatory function with latency-associated nuclear antigen (LANA). Cleavage under targets and release using nuclease demonstrated that TR fragments were occupied by LANA-interacting histone-modifying enzymes in naturally infected cells. The TR was enriched with histone H3K27 acetylation (H3K27Ac) and H3K4 tri-methylation (H3K4me3) modifications and also expressed nascent RNAs. The sites of H3K27Ac and H3K4me3 modifications were also conserved in the KSHV unique region among naturally infected primary effusion lymphoma cells. KSHV origin of lytic replication (Ori-Lyt) showed similar protein and histone modification occupancies with that of TR. In the Ori-Lyt region, the LANA and LANA-interacting proteins colocalized with an H3K27Ac-modified nucleosome along with paused RNA polymerase II. The KSHV transactivator KSHV replication and transcription activator (K-Rta) recruitment sites franked the LANA-bound nucleosome, and reactivation evicted the LANA-bound nucleosome. Including TR fragments in reporter plasmid enhanced inducible viral gene promoter activities independent of the orientations. In the presence of TR in reporter plasmids, K-Rta transactivation was drastically increased, while LANA acquired the promoter repression function. KSHV TR, therefore, functions as an enhancer for KSHV inducible genes. However, in contrast to cellular enhancers bound by multiple transcription factors, perhaps the KSHV enhancer is predominantly regulated by the LANA nuclear body.IMPORTANCEEnhancers are a crucial regulator of differential gene expression programs. Enhancers are the cis-regulatory sequences determining target genes' spatiotemporal and quantitative expression. Here, we show that Kaposi's sarcoma-associated herpesvirus (KSHV) terminal repeats fulfill the enhancer definition for KSHV inducible gene promoters. The KSHV enhancer is occupied by latency-associated nuclear antigen (LANA) and its interacting proteins, such as CHD4. Neighboring terminal repeat (TR) fragments to lytic gene promoters drastically enhanced KSHV replication and transcription activator and LANA transcription regulatory functions. This study, thus, proposes a new latency-lytic switch model in which TR accessibility to the KSHV gene promoters regulates viral inducible gene expression.

Deduction and Application of the Average Switch Model in Power Electronic Devices for Simulation Time Reduction

Context: Time is a crucial issue in the simulation of power electronics (PE) devices, even more when these elements are integrated into microgrids.Method: This paper deals with the deduction of the average switch model for PE devices with the purpose of reducing simulation times. For doing this, the average model is only applied over the power switches of PE devices, not being applied over the complete topology as traditionally done. The proposed average model switch permits eliminating the ripple of voltage and currents but keeping the transient of the signals. The average model switch is derived for Boost and Buck converter switches and then generalized to power inverter switches. The proposed approach is validated using OpenModelica software.Results: A system featuring a battery, a DC/DC converter, and an inverter connected to the power grid was simulated. A comparison was performed between a simulation that considers the power switches and a simulation that uses the proposed average model switch, the time simulation was reduced up to 99.788 %, which validates the proposed approach.Conclusions: The proposed average switch model significantly reduces simulation times. This method offers a promising way to streamline power electronics device simulations, particularly in the context of microgrids and other applications where time efficiency is critical.

Analysis of Current-Bidirectional Buck-Boost Based Switch-Mode Audio Amplifier

The following study was carried out in order to assess quantitatively the performance of the buck-boost converter when used as switch-mode audio amplifier. It comprises of, to begin with, the delimitation of design criteria based on the state- of-the-art solution, which is based in a differential mode buck- based amplifier with a boost converter as power supply. The averaged switch modelling of the differential mode current bidirectional topology is also used, in order to analyze the steady state and frequency-wise behaviour of this parameterize it to meet the design criteria converter and Next, several piecewise-linear simulation results are shown with detail enough to emphasize the features of the converter. A simple prototype is implemented to verify the main predicted features. Presently no previous publication could be found containing a thorough analysis of this topology in such configuration when applied for audio

Examining the business cycle of the tourism industry in Mexico at the state level using the Markov Switching Model (1992 to 2017)

Despite being a popular tourist destination, state-level research on the business cycle in Mexico's tourism industry is lacking. This study aims to answer three research questions: Are the growth stages of the international tourism market similar across states? Are the growth stages of the domestic tourism market similar among entities? Is there a relation between the economic cycle of the international tourism market and the domestic market? We use the Markov Switching Model to estimate the business cycle of the tourism industry in each Mexican state from 1992 to 2017. We identify the most attractive states for international and domestic tourists. Notably, eleven of the coefficients belong to the economic cycle of the foreign tourist market, indicating a greater economic impact compared to the domestic market. The results show that each state has its own business cycle, requiring individual projections for each market. This information is relevant for tourism planners and policymakers to address the challenges posed by the SDGs.

Template switching between the leading and lagging strands at replication forks generates inverted copy number variants through hairpin-capped extrachromosomal DNA.

Inherited and germ-line de novo copy number variants (CNVs) are increasingly found to be correlated with human developmental and cancerous phenotypes. Several models for template switching during replication have been proposed to explain the generation of these gross chromosomal rearrangements. We proposed a model of template switching (ODIRA-origin dependent inverted repeat amplification) in which simultaneous ligation of the leading and lagging strands at diverging replication forks could generate segmental inverted triplications through an extrachromosomal inverted circular intermediate. Here, we created a genetic assay using split-ura3 cassettes to trap the proposed inverted intermediate. However, instead of recovering circular inverted intermediates, we found inverted linear chromosomal fragments ending in native telomeres-suggesting that a template switch had occurred at the centromere-proximal fork of a replication bubble. As telomeric inverted hairpin fragments can also be created through double strand breaks we tested whether replication errors or repair of double stranded DNA breaks were the most likely initiating event. The results from CRISPR/Cas9 cleavage experiments and growth in the replication inhibitor hydroxyurea indicate that it is a replication error, not a double stranded break that creates the inverted junctions. Since inverted amplicons of the SUL1 gene occur during long-term growth in sulfate-limited chemostats, we sequenced evolved populations to look for evidence of linear intermediates formed by an error in replication. All of the data are compatible with a two-step version of the ODIRA model in which sequential template switching at short inverted repeats between the leading and lagging strands at a replication fork, followed by integration via homologous recombination, generates inverted interstitial triplications.

From Old to New Technologies: Exploring the Role and Impact of Value Resonance

In the research of technology switch, theories such as Push-Pull-Mooring (PPM), Behavioral Reasoning (BRT), Innovation Resistance (IRT), and Status Quo Bias (SQB) have been employed to extend the Technology Acceptance Model (TAM), focusing on how old technology factors such as inertia as barriers to embracing new technological innovations. However, our research contends that the impact of these old technology factors on the technology switch is not unequivocal, as they may either hinder or facilitate the transition, depending on whether the new technology retains valued features of the old technology. To delve into this complexity, we introduce the concept of value resonance between old and new technologies. Utilizing Nintendo Switch for empirical analysis, two quantitative studies were conducted. Out of 630 distributed questionnaires, 553 valid responses were collected, yielding an 87.7% response rate. The first study used multi-groups analysis within TAM to differentiate between the technology acceptance group (TAG) and the technology switch group (TSG), contributing to the development of the Technology Switch Model (TSM). The second study applied Covariance-Based Structural Equation Modeling (CB-SEM) to examine value resonance’s mediating role in the TSM. The empirical results revealed discernible differences between TAG and TSG within the TAM framework, providing a robust theoretical basis for the TSM. Furthermore, the result indicated that factors associated with old technology could enhance users’ switching intentions through the mediating mechanism of value resonance. These findings aim to provide a more nuanced understanding of technology switch, highlighting that the dynamics between old and new technologies are more complex and variable than previously assumed.

Research of simulation and experiment on arc characteristics of gas mechanical switch

AbstractAn arc is the high‐temperature discharge plasma produced in the opening process of mechanical switches, which directly affects the breaking capability of a hybrid DC circuit breaker. According to the physical mechanism of an electric arc, the construction of an arc model for simulation analysis is an important technical means in the electrical field. In this study, based on the theory of magneto hydrodynamics (MHD), a gas mechanical switch model of a natural commutation DC circuit breaker with a compound gap is established. The arc motion process under different conditions is simulated and calculated. The influence of different initial pressures, different opening speeds, and different striking currents on the arc voltage characteristics is analysed. The results show that the larger the gas pressure, the smaller the arc volume and the higher the arc voltage. The faster the opening speed, the longer the arc and the higher the arc voltage; with the increase of the current, the arc voltage increases rapidly at a low current, while the arc voltage increases slowly at a high current.