Search Results (106)

The interplay between fiscal and monetary policy is critical for small open economies exposed to global volatility, yet the regime-dependent nature of this transmission often remains underexplored. This study investigates whether the Hungarian economy operated under fiscal or monetary dominance from 2010 to 2024, a period marked by significant external shocks. Adopting a Markov Regime-Switching VAR (MS-VAR) framework tailored to an open-economy context, the research estimates state-dependent reaction functions and Impulse Response Functions (IRFs) for both the central bank and the fiscal authority. The model explicitly controls for exogenous geopolitical and economic crises and is validated through rigorous stationarity and regime-selection tests. Empirical results reveal that Hungary predominantly operated under fiscal dominance, with the fiscal authority exhibiting non-Ricardian behavior and no significant response to debt accumulation across the sample. Conversely, the Magyar Nemzeti Bank demonstrated regime-switching behavior: a “Passive” stance accommodating fiscal expansion from 2013 to 2019, followed by a forced shift to an “Active” regime in 2022 characterized by aggressive responses to inflation and high-interest rate volatility. These findings suggest that in small open economies, policy dominance is frequently dictated by external constraints, with the burden of macroeconomic stabilization falling disproportionately on monetary policy during crisis episodes. Full article

IPM (Integrated Pest Management) strategies present a good theoretical framework for sustainably controlling pest populations. In this paper, we propose a switched IPM model with predation-induced fear and seasonally birth in a pest population. Employing theories of impulsive differential equations, we gain evidence showing that the pest-eradication solution $(0,\overline{y\left(t\right)})$ of the investigated system is GAS. The investigated system is also proven to be persistent. Our results provide new methods for IPM strategies. Full article

As distribution networks become increasingly intelligent, primary–secondary integrated distribution switches are replacing the traditional electromagnetic type. However, the high degree of integration intensifies inherent electromagnetic compatibility (EMC) challenges. This paper presents a field-circuit hybrid full-wave model to investigate switch characteristics during lightning strikes. A 3D full-wave model of the switch and a distributed parameter circuit model of the connecting lines are coupled via a network parameter matrix. This approach comprehensively accounts for the impacts of transmission lines and structural components on electromagnetic disturbances. Simulation and experimental results reveal that lightning strikes induce high-frequency damped oscillatory waves, primarily caused by traveling wave reflections along overhead lines. The characteristic frequency of disturbance is inversely proportional to the transmission line length. Additionally, internal components significantly influence this frequency; specifically, a larger voltage dividing capacitance in the voltage transformer results in a lower frequency. Model validation was performed using a 20 m transmission line setup. A 75 kV standard lightning impulse was injected into Phase B. At a distance of 500 mm from the voltage transformer, the measured radiated electric field amplitude was 14.12 kV/m (deviation < 5%), and the characteristic frequency was 1.11 MHz (deviation < 20%). These findings offer vital guidance for the lightning protection and EMC design of primary–secondary integrated distribution switches. Full article

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

Background: Aripiprazole is an atypical antipsychotic that acts as a partial agonist on the dopamine receptor D2 while also displaying agonistic activity on the 5-HT1A and antagonistic activity on the 5-HT2A receptors. As a partial agonist, aripiprazole stabilizes the activity of the D2 receptor, preventing overactivation. Case presentation: Within our deprescribing activity, we came across the case of a 30-year-old antipsychotic-naïve patient treated with the depot formulation of aripiprazole for bipolar disorder and acute mania, possibly developing hypersexuality due to an overdose that impacted negatively and heavily on his personal life. Results: The patient developed a peculiar subset of hypersexuality, changing his sexual orientation. Of interest, one month after discontinuing aripiprazole and switching to paliperidone, all the sexual-related symptoms and impulse control disorders resolved. Conclusions: We suggest stronger communication among the clinical teams involved in the patient’s care and screening patients for impulse control disorder prior to the administration of aripiprazole and monitoring them during treatment. Full article

The endogenous money creation paradigm posits that banks generate money through lending, with deposits serving as a byproduct. This study investigates the mechanism driving the “credit–deposit paradox” during Poland’s high-interest-rate environment, introducing innovative methodological approaches to quantify systemic monetary impairment. Using comprehensive monthly data from 2006 to 2024, we employ a mixed-methods framework featuring: (1) Bayesian vector autoregression with Minnesota priors to test dynamic interdependencies; (2) a novel money shortage indicator (MSI) that operationalizes credit–deposit decoupling through three theoretically grounded components; (3) Markov regime-switching analysis to identify persistent monetary stress regimes. Key findings reveal a structural decoupling between deposit growth and credit creation, with robust evidence that exogenous money inflows accumulate as idle deposits rather than stimulating lending. The economy experienced significant periods of money shortage conditions, with the most severe impairment occurring during recent high-stress periods. The analysis confirms the dominance of cost-push inflation from energy and food prices, while monetary factors played a limited role. High interest rates amplified credit demand suppression, creating conditions consistent with endogenous money creation disruption. Methodologically, this study enables three key advances: (1) systematic measurement of monetary transmission breakdowns; (2) empirical identification of structural factors disrupting credit–deposit dynamics; (3) temporal characterization of monetary stress persistence patterns. These contributions advance the endogenous money framework by demonstrating its vulnerability to behavioral, policy-induced, and exogenous disruptions during high-stress periods. Practically, the MSI offers policymakers a real-time diagnostic tool for identifying monetary transmission breakdowns, while the regime analysis informs targeted countercyclical measures. Specific policy recommendations include developing sector-specific liquidity facilities, coordinating fiscal transfers with monetary policy to prevent deposit–loan decoupling, and prioritizing supply-side interventions during cost-push inflation episodes. By integrating post-Keynesian theory with empirical evidence from Poland, this study contributes to understanding money creation mechanisms in highly stressed economic environments. Full article

Background: Emotional dysregulation and impulsivity represent key risk factors for adverse trajectories in adults with ADHD and are frequently observed among patients with opioid use disorder (OUD). Levomethadone, the R-enantiomer of methadone, provides more stable dopaminergic modulation than the racemic formulation and may improve emotional control. The primary objective was to examine emotional, clinical, and substance use changes after the switch to levomethadone and to determine whether these trajectories differed according to ADHD screening status. This study evaluated emotional, clinical, and behavioral outcomes—including substance use—after transitioning from racemic methadone to levomethadone maintenance therapy, focusing on the moderating role of ADHD symptoms and dose escalation. Methods: Eighty-three OUD patients in methadone maintenance were assessed at baseline, T1 (mean = 2.13 months, SD = 0.65), and T2 (mean = 6.20 months, SD = 0.91). Emotional dysregulation (RIPOST), clinical severity (Clinical Global Impression), and days of substance use were analyzed using Linear Mixed Models (participants with ≥1 valid follow-up). ADHD symptoms (Adult ADHD Self-Report Scale DSM-5) were evaluated with Wilcoxon signed-rank tests. Dose escalation (↑levomethadone) was defined as ≥1 increase during follow-up and was only included in the mixed models. Substance use analyses were restricted to baseline active users. Results: Emotional impulsivity significantly decreased over time only in participants screening positive for ADHD symptoms (ASRS ≥ 14), independent of dose escalation. Emotional instability also declined but across the full cohort. CGI scores improved in all participants. Substance use patterns showed a modest overall improvement, with reductions most evident for sedatives and alcohol. The findings indicate a specific effect of levomethadone on affective regulation and clinical stabilization, particularly in individuals with impulsivity traits. Conclusions: Levomethadone maintenance appears to improve emotional regulation and global functioning beyond dose-related effects, supporting its potential value in complex OUD patients with clinically relevant ADHD symptomatology. Combined treatment with levomethadone and methylphenidate may further enhance executive control and craving regulation in this population. Full article

The indoor low-voltage power line network is characterized by highly irregular interferences, where background noise coexists with bursty impulsive noise originating from household appliances and switching events. Traditional noise models, which are considered monofractal models, often fail to reproduce the clustering, intermittency, and long-range dependence seen in measurement data. In this paper, a Multifractal Random Walk (MRW) framework tailored for Power Line Communication (PLC) noise modelling is developed. MRW is a continuous time limit process based on discrete-time random walks with stochastic log-normal variance. As such, the formulated MRW framework introduces a stochastic volatility component that modulates Gaussian increments, thus generating heavy-tailed statistics and multifractal scaling laws which are consistent with the measured PLC noise data. Empirical validation is carried out through structure function analysis and covariance of log-amplitudes, both of which reveal scaling characteristics that align well with MRW-simulated predictions. This proposed model captures both the bursty nature and correlation structure of impulsive PLC noise more effectively as compared to the conventional monofractal approaches, thereby providing a mathematically grounded framework for accurate noise generation and the robust system-level performance evaluation of PLC networks. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article

Rising residential land prices push up housing prices and worsen credit misallocation. These patterns emerge amid cyclical real estate fluctuations and heavy land-based public finance. Such pressures undermine macroeconomic stability and sustainable land-use. The land price circuit breaker is widely applied with a price cap and state dependence, yet its trigger mechanism and interaction with inflation targeting remain underexplored. This study addresses three core questions. First, how does the circuit breaker’s discrete trigger and rule-switching logic differ from traditional static price ceilings? Second, can the mechanism, via the collateral channel, restrain excessive land price hikes, improve credit allocation, and, thereby, stabilize land price dynamics and long-run macroeconomic performance? Third, how does the circuit breaker interact with inflation targeting, and through which endogenous channels does a strict target dampen housing prices and raise activation probability? This study develops a multi-sector DSGE model with an embedded land price circuit breaker. The price cap is modeled as an occasionally binding constraint. A dynamic price band and trigger indicator capture the policy’s switch between slack and binding states. The framework incorporates interactions among local governments, the central bank, developers, and households. It also links firms and the secondary housing market. Under different inflation-targeting rules, this study uses impulse responses, an event study, and welfare analysis to assess trigger conditions and macroeconomic effects. The findings are threefold. First, a strict inflation target increases the probability of a circuit breaker being triggered. It channels housing-demand shocks toward land prices and creates a “nominal anchor–relative price constraint” linkage. Second, once activated, the circuit breaker narrows the gap between land price and house-price growth. It weakens the procyclicality of collateral values. It also restrains credit expansion by impatient households. These effects redirect credit toward firms, improve corporate financing, reduce the decline in investment, and accelerate output recovery. Third, the circuit breaker limits new land supply and shifts demand toward the secondary housing market. This generates a supply-side effect that releases existing stock and stabilizes prices, thereby weakening the amplification mechanism of housing cycles. This study identifies the endogenous trigger logic and cross-market transmission of the land price circuit breaker under a strict inflation target. It shows that the mechanism is not merely a price-management tool in the land market but a systemic policy variable that links the real estate, finance, and fiscal sectors. By dampening real estate procyclicality, improving credit allocation, and stabilizing macroeconomic fluctuations, the mechanism offers new insights for sustainable land-use policy and macroeconomic stabilization. Full article

Major events and external uncertainty shocks have made energy risk connectedness increasingly complex. This paper applies a LASSO-regularized VAR combined with the Diebold-Yilmaz connectedness framework (LASSO-VAR-DY) to trace how China’s energy risk spillover effects evolve under major event shocks and to quantify sectoral risk spillover inflows. We then employ a TVP-SV-VAR model to further examine the impulse responses of energy sectors to external uncertainties. The results show that the energy system exhibits a high overall level of risk connectedness with pronounced stage-wise variation and is sensitive to different external uncertainty shocks. Major-event shocks intensify sector-level risk connectedness—the clean-energy sector consistently acts as a net risk receiver. In contrast, other sectors switch between net transmitters and net receivers across shocks. Different major events operate through heterogeneous mechanisms—the COVID-19 pandemic and the official launch of the national carbon market primarily strengthen node-to-node connectedness. In contrast, the Russia-Ukraine conflict chiefly amplifies spillover intensity between nodes. The effects of uncertainty index shocks differ markedly: economic policy uncertainty (EPU) has the most substantial impact, followed by climate policy uncertainty (CPU), while geopolitical risk (GPR) is the weakest. Full article

The high-voltage system of high-speed trains is now in the form of combined electrical apparatus, which has a high probability of insulation breakdown due to frequent overvoltage during operation. To solve this issue, an electric field simulation model of the high-voltage combined electrical system was established, the electric field distribution of the high-voltage box electrode under overvoltage operating conditions was analyzed, and the air breakdown characteristics under field action were studied. The study shows that under overvoltage conditions, the electric field intensity near the small electrodes of the combined electrical unit is higher than the air breakdown field intensity, and the statistical time delay is approximately 5.94 μs when 150 kV voltage is applied. When the size of the connected electrode is doubled and 150 kV voltage is applied, the statistical delay is about 7.20 μs and the probability of discharge is reduced. Further installation of an insulating partition between the circuit breaker and the ground switch completely solved the problem of low electrical gap insulation capacity. Combined with impulse withstand tests, the effectiveness of the electrode size design was verified, and the research results provided theoretical support for the miniaturization and high-reliability design of vehicle-mounted high-voltage electrical appliances. Full article

High-voltage ceramic insulators are routinely exposed to short-duration overvoltages such as lightning impulses, switching surges, and partial discharges. These events occur on microsecond to millisecond timescales and can produce highly localized thermal spikes that are difficult to measure directly but may compromise long-term material integrity. This paper addresses the estimation of the internal temperature distribution immediately after a lightning impulse by solving a three-dimensional inverse heat conduction problem (IHCP). The forward problem is modeled by the transient heat diffusion equation with constant thermal diffusivity, discretized using the finite element method (FEM). Surface temperature measurements are assumed available from a 12 kV ceramic post insulator and are used to reconstruct the unknown initial condition. To address the ill-posedness of the IHCP, a spatio-temporal regularization framework is introduced and compared against spatial-only regularization. Numerical experiments investigate the effect of measurement time ($T=60$ s, 600 s, and 1800 s), mesh resolution (element sizes of 20 mm, 15 mm, and 10 mm), and measurement noise ($\sigma =1$ K and 5 K). The results show that spatio-temporal regularization significantly improves reconstruction accuracy and robustness to noise, particularly when early-time measurements are available. Moreover, it is observed that mesh refinement enhances accuracy but yields diminishing returns when measurements are delayed. These findings demonstrate the potential of spatio-temporal IHCP methods as a diagnostic tool for the condition monitoring of ceramic insulators subjected to transient electrical stresses. Full article

Under operating conditions, metallic particle contaminants inside Gas-Insulated Switchgears (GIS) represent a major threat that can initiate partial discharges (PD) and lead to insulation failure. To investigate the discharge patterns under combined AC and switching impulse voltages, this paper presents an experimental study conducted in SF6 gas on wire-shaped, spherical, and Mixed Metal Particles. By synchronously analyzing PD time-domain waveforms, Phase-Resolved Partial Discharge (PRPD) patterns, and high-speed motion camera recordings, the correlation between particle motion behavior and discharge signals was systematically examined. The results indicate that wire particles exhibit a significant discharge initiation delay under the combined voltage; however, intense, discrete discharges with large magnitudes occur during their vertical jumping phase. In contrast, spherical particles can be activated within the first power frequency cycle without delay, but the subsequent discharge magnitudes are limited. The characteristics of hybrid particles lie between these two types, demonstrating a staged evolution described as “spherical particles lead initiation, wire particles dominate discharge.” Furthermore, under the sustained AC voltage, hybrid particles trigger a more dispersed and violent discharge process. These findings reveal the complex motion-discharge mechanism of Mixed Metal Particles, providing critical insights for fault mechanism analysis and insulation protection related to particle contamination in practical GIS equipment. Full article

This study examines how monetary and fiscal policies affect economic growth in China under global economic uncertainty. We estimate a Markov Switching Regression (MSR) model using quarterly data from 1996: Q1 to 2024: Q4. We also apply Bayesian Model Averaging (BMA) to choose the relevant control variables. During expansions, higher policy rates, government revenue, moderate inflation, FDI inflows, and export growth support growth. Government expenditure can crowd out private investment. During recessions, higher policy rates reduce growth. Government expenditure has limited impact, but revenue collection remains growth-supportive. Global uncertainty steadily reduces growth. Government expenditure shows negative effects, which indicates possible crowding out. The findings support that monetary and fiscal policies coordination may sustain long-term growth in China and strengthen the resilience amid global uncertainty. The Impulse response functions (IRFs) from Bayesian Vector Autoregression (BVAR) confirm the persistence and dynamics of policy shocks under global uncertainty. This study adds to the empirical literature on the role of macroeconomic policies in shaping economic growth in the case of China. Full article