State Dependence Research Articles

Poverty and Temporary Employment in Italy

AbstractThis paper explores the relationship between temporary employment and poverty in Italy by using 2016–2019 longitudinal EU-SILC data. We model both poverty and labour market outcomes and account for possible endogeneity of temporary employment in the poverty equation. We find that temporary employment reduces the risk of poverty whether compared with inactivity and unemployment. Temporary workers are more likely to stay in poverty with respect to their permanent counterpart. The different effect of permanent/temporary employment on the risk of poverty is more pronounced for householders than for partners, thereby stressing the leading role of the former in income formation. There is evidence of feedback effects from past poverty to current temporary employment. We also find significant genuine state dependence in both the processes of poverty and temporary employment. For the latter, we note the prevalence of a trapping effect into unstable jobs relationships, especially for householders. Finally, we stress the relatively weak role of social transfers in integrating labour income from temporary employment and therefore in breaking the detrimental effect of temporary work on poverty.

GRU-IKF scene taxiing trajectory prediction model based on attention mechanism

Aircraft taxi trajectory prediction helps solve operational problems such as airport taxiing conflicts and long waiting times, ensuring airport safety while improving service levels and increasing airport throughput. In view of the fact that the performance of machine learning models depends on good data sets, a method for predicting taxi trajectories of aircraft on the ground based on attention mechanism, fusion of gated recurrent unit (GRU) and improved Kalman filter algorithm (IKF) is proposed. Firstly, three independent gated recurrent unit networks are used to capture the motion state and temporal dependency of aircraft at future moments, and the attention mechanism is introduced to enhance the ability to extract data difference features and learn the mapping relationship from input to output; then, it is fused with the improved extended Kalman filter to integrate the output results of the neural network into the state prediction and update process to improve the accuracy of the predicted trajectory sequence. Finally, the effectiveness of the model was verified using the actual taxiing trajectory of aircraft at Lukou Airport. The simulation results show that the model can effectively and accurately predict the taxiing trajectory of aircraft on the surface, with an overall mean square error of about 0.00128. Compared with the single recurrent neural network (RNN), long short-term memory network (LSTM) and GRU model, the RMSE is reduced by 72.9%and respectively 39.9%, 54.7%and the prediction time is 40 ms. It can accurately and quickly predict the taxiing trajectory, which helps to reduce the operating load of the airport surface management system.

Traffic speed prediction of regional complex road network based on CapsNet and D-BiLSTM

Accurate and efficient short-term traffic prediction is of great significance in the study of regional traffic network. However, the complex and dynamic spatiotemporal correlation of traffic patterns makes the existing methods insufficient in learning traffic evolution in terms of structural depth and prediction scale. Therefore, this paper proposes a deep learning model combining capsule network (CapsNet) and deep bidirectional LSTM (D-BiLSTM). CapsNet is used to identify the spatial topological structure of the road network and extract spatial features, and D-BiLSTM network is integrated. The forward and backward dependencies of traffic states are considered at the same time, and the bidirectional temporal correlation of different historical periods is captured to predict the traffic of large-scale complex road networks in the target area. Experiments on real traffic network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10% on average , which is better than other methods. It has high prediction accuracy and good robustness in traffic prediction of regional complex road networks.

Does the direct effect of friction increase continuously with absolute temperature?

Constitutive models of fault friction form the basis of physics-based simulations of seismic activity. A generally accepted framework for the slip-rate and state dependence of friction involves a thermally activated process, whereby the probability of slip along microasperities adheres to an Arrhenius law. This model, which has become widely adopted among experimentalists and theoreticians, predicts a continuous increase of the direct effect with absolute temperature, but is it observed experimentally? Leveraging comprehensive laboratory data across diverse hydrothermal, barometric, and lithological conditions, we demonstrate that, contrary to the classical view, the direct effect for a given deformation mechanism remains largely temperature-independent. Instead, the incremental shifts in the direct effect often coincide with the brittle to semi-brittle transition, across which distinct deformation mechanisms operate. These considerations challenge the validity of the classical model. Realistic constitutive laws for rock failure within the lithosphere must incorporate the contributions of multiple deformation mechanisms within active fault zones.

Field-Induced Quantum Criticality in Two-Coupled Two-Leg Heisenberg Ladders

We use the bond mean field theory, which is based on the Jordan–Wigner fermionization, to investigate the quantum critical behavior of a system composed of two-coupled Heisenberg Spin-[Formula: see text] two-leg ladders in the presence of an applied magnetic field. By examining the dependence of the ground state on the magnetic field for different coupling constants, we investigate the field-induced quantum criticality. The magnetization exhibits distinct plateaus: a zero magnetization plateau for fields below a threshold value of the field [Formula: see text] and a saturation plateau for fields exceeding a second critical value [Formula: see text]. The spin bond parameters are calculated and found to show a critical behavior at [Formula: see text].

Inflationary dynamics of labour market activity: Evidence from the Czech Republic

This paper provides an in-depth investigation into the intricacy of inflation dynamics in the Czech Republic, with particular emphasis on the influence exerted by labor market activity. The research employed principal component analysis and the local projections methodology proposed by Jorda (2005) in the context of the Phillips curve framework, followed by the extension of the analysis via the introduction of a nuanced state dependence dimension that recognizes that the impact of labor market activity on inflation may be contingent upon the broader economic situation. Our findings revealed the statistically significant impact of labor market activity on inflation. Specifically, a one standard deviation increase in the labor market activity component leads to an accumulated increase in inflation of approximately 3 percentage points over a three-year horizon. Our non-linear model uncovered the pivotal interaction between state dependence and the impact on inflation. The findings revealed that during expansionary periods, labor market pressures can be effectively managed for a limited period, albeit with a slight escalation in prices. However, over an extended period, the labor market is susceptible to encountering constraints that lead to markedly higher inflation rates - almost twice as high as those observed during recessionary periods. We subjected our benchmark results to extensive robustness verification.

Rotor dynamic response prediction using physics-informed multi-LSTM networks

The deep learning method provides an effective alternative to numerical simulations for establishing the nonlinear input-output relationship and calculating dynamic responses of rotor systems. To overcome the low generalization capability of pure data-driven long short-term memory (LSTM) networks when predicting dynamic responses to out-of-distribution inputs, a dynamic response prediction method using physics-informed multi-LSTM networks is proposed. This approach incorporates required physical constraints into the deep LSTM network, allowing the model training process to optimize the network parameters within the feasible solution space that adheres to physical laws. Consequently, this enhances the physical interpretability of the deep learning model. Specifically, two physics-informed multi-LSTM network architectures are introduced, and physical laws of equation of motion, state dependency and hysteretic constitutive relationship are considered to construct the physics loss. The feasibility of the proposed method is verified by a Bouc-Wen hysteresis model and a simulated gas generator rotor. The response prediction performance of the two networks is validated on a constructed fault rotor dataset with significant sample differences, along with cross-speed and cross-node prediction validation for the rotor system. The results demonstrate that the trained networks exhibit strong robustness and generalization capabilities, making them suitable as surrogate models for rotor systems.

Changing magnetic ground state by an electric field in a single-layer FeCl2

Abstract Employing the electric field has been performed to investigate the magnetic ground state in a single-layer iron dichloride (FeCl2). We provided some lattice parameters to see the ground state dependency on the electric field. To minimize the computational time, we applied the generalized Bloch theorem via density functional theory to avoid the supercell approach. As we varied the electric field, we saw the magnetic transition for some lattice parameters. In addition, the magnetic moments were nearly stable for all those states, indicating the high spin state. As a concluding remark, employing the electric field is very useful to manipulate the ground state that can be applied to magnetism-based nanotechnology.

A Multi-Phase Mission Success Evaluation Approach for Maritime Autonomous Surface Ships considering Equipment Performance Degradation and System Composition Changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Power-Law Scaling Relating the Average Charge State and Kinetic Energy in Expanding Laser-Driven Plasmas.

A universal power-law scaling z[over ¯]∝E^{0.4} in the correlation between the average ion charge state z[over ¯] and kinetic energy E in expanding laser-driven tin plasmas is identified. Universality here refers to an insensitivity to all experimental conditions: target geometry, expansion direction, laser wavelength, and power density. The power law is accurately captured in an analytical consideration of the dependence of the charge state on temperature and the subsequent transfer of internal to kinetic energy in the expansion. These analytical steps are individually, and collectively, validated by a two-dimensional radiation-hydrodynamic simulation of an expanding laser-driven plasma. This power-law behavior is expected to hold also for dense plasma containing heavier, complex ions such as those relevant to current and future laser-driven plasma light sources.

Unwinding quantitative easing: State dependency and household heterogeneity

This paper studies the asymmetry in the macroeconomic effects of central bank asset market operations induced by state dependency and the associated role of household heterogeneity. We build a New Keynesian model with borrowers and savers in which quantitative easing and tightening operate through portfolio rebalancing between short-term and long-term government bonds. We highlight the significance of an occasionally binding zero lower bound in explaining a weaker aggregate impact of asset sales relative to asset purchases. In this context, when close to the lower bound, raising the nominal interest rate prior to unwinding quantitative easing mitigates the economic costs of monetary policy normalization. Furthermore, our results imply that household heterogeneity in combination with state dependency amplifies the revealed asymmetry, while household heterogeneity alone does not enhance the aggregate effects of asset market operations.

Physical model learning based false data injection attack on power system state estimation

The cyber security of power system state estimation (PSSE) is crucial, and its robustness against evolving false data injection attacks (FDIA) is being rigorously assessed to develop advanced countermeasures. Existing FDIA methods have achieved satisfactory success rates but often fail to align with practical constraints such as the assumption of partial or complete knowledge of the power system network by the attacker, modifications in generator output measurements, and the sparsity of the attacks. This work proposes a near practical, stealthy approach using a deep generative adversarial network-long short-term memory autoencoder (GAN-LSTMAE) learning based sparse FDIA method against AC PSSE, leveraging only measurement data. To evade the bad data detection (BDD) mechanism effectively, an LSTMAE-based PSSE mimic is proposed, further optimizing the GAN-based attack generator to embed the physical laws of the system along with measurement residuals and temporal dependencies of states to the generated false data. The proposed modified training data preparation algorithm, coupled with the attack sub-graph method, defines the optimal attack region while keeping generator output measurements intact. The generated attack is validated extensively using IEEE 14 and 118 bus test benchmarks against various defense techniques, demonstrating high success rates.

Smallholder access to purchased seeds in the presence of pervasive market imperfections and rainfall shocks: panel data evidence from Malawi and Ethiopia

Seed purchasing enables farmers to respond to adverse events that may cause chronic and temporary seed insecurity by allowing them to exploit opportunities associated with accessing new seeds. However, as with other inputs, seed purchasing is complicated by pervasive market imperfections and climate risk common in Sub-Sahara Africa. This study uses balanced household panel data for Malawi (2010–2018) and Ethiopia (2012–2016) and applies dynamic random effects Probit and Tobit models to assess how seed purchase decisions are affected by earlier participation in the market, lagged rainfall shocks, and historical climate variables. Our findings show that there are nonlinear effects of lagged seed purchase decisions on subsequent decisions with strong initial effects (weakening over time). For instance, initial maize seed purchase decisions are associated with about 11 and 22% higher probability of purchase and 1 and 2% higher shares of seed volumes purchased in later rounds in Malawi and Ethiopia, respectively. Seed purchase decisions also respond to climate variability and shocks. For instance, lagged drought shocks enhance subsequent maize purchase decisions in both countries. Historical average rainfall and temperature enhance maize seed purchase decisions in both countries. Overall, results point to state dependency on the demand side of the seed market, leading to selective access to purchased seeds. Also, seed purchase in smallholder farming is a liquidity and risk-dependent input choice. Policy efforts need to continue targeting reducing transaction costs and other barriers to entry into seed markets to enhance access to off-farm seed and support adaptation to rainfall shocks.

Applicability of energy-theta mapping in resonant soft X-ray reflectometry to probe depth dependence of oxidation state and crystallographic environment in iron oxide multilayers

In the present paper we discuss applicability of the synchrotron method of resonant x-ray reflectometry to non-destructive depth profiling of multilayer heterostructures with low optical contrast between the sublayers. The two-dimensional mapping approach comprising evaluation and measurement of reflectance as a function of photon energy and grazing angle is shown to provide a convenient way to effectively utilize the enhancement of optical contrast between the sublayers that exists at the absorption edges of chemical elements due to particular spectral shape peculiarities related to oxidation state, crystallographic environment and magnetization. In the present study the evaluation of the resonant X-ray reflectivity maps is performed using a specially developed modeling and fitting software. Estimation of the photon energy / grazing angle combinations at which the reflectance is most sensitive to the minor changes in the physical properties of the sublayers is illustrated by the example of ferroic-on-semiconductor nanoscale heterostructures composed of nanoscale film of metallic iron oxidized from either top or bottom side. The subtle differences in resonant soft x-ray reflectance caused by variation of the oxide film thickness, oxidation state and crystallographic environment are discussed. The presented approach is applicable to a large class of heterostructures composed of sublayers that can be distinguished only by the differences in their near edge X-ray absorption fine structure.

REFINING THE EMPIRICAL MODEL OF THE HEAT TRANSFER BOUNDARY CONDITIONS OF THE PISTON OF A HIGH-SPEED DIESEL ENGINE

The level of temperature of ICE pistons is a factor that greatly affects their reliability. Therefore, the need to simulate the temperature state of pistons with sufficient accuracy and minimal allocation of resources and time exists in research and during design. Primary approaches for determining the boundary conditions of the problem of thermal conduction of the piston on the basis of criterion equations, high-level mathematical models, and empirical models are considered. The latter ones have become widespread by virtue of simplicity and ease of use in practice; their usability is limited to a certain set of operating modes of one engine or a few ones of a similar design. On the basis of data from four series of experimental tests for a well-studied diesel engine 4ChN12/14 the existing empirical models of boundary conditions, suitable for identifying the temperature state of the piston only within the respective separate series separately, are refined in the paper. Processing of the data set on the measured temperatures in the control zones of the piston was performed, and linear function approximations were found that satisfactorily describe the effect of diesel power on the specified temperatures. The model of boundary conditions for a set of eighteen separate regions on the surfaces of the piston was proposed, which takes into account the dependence of the piston temperature state on the brake power of the ICE, the crank angle of the start of fuel injection, and the conditions of cooling the piston with oil. The influence of the heat insulation layer on the piston crown surface on the heat transfer was accounted for by introducing an additional thermal resistance term into the boundary condition equations. The model made obtaining the temperature field of the piston possible in simulation that is consistent with the results of all series of experiments in the control zones of the piston periphery, the edge of the combustion chamber, the vicinity of the groove of the first piston ring, the cooled surface opposite to the combustion chamber, and the piston skirt. The importance of a gradual change in the temperature of the oil and coolant during the transition of the internal combustion engine from one mode of operation to another regarding the temperature state of the piston is demonstrated. Taking the temperatures of oil from previous modes of operation into account is recommended during the analysis of engine transient processes.

Phase diagram of strongly-coupled Rashba systems

Abstract Motivated by the recent discovery of a possible field-mediated parity switch within the superconducting state of CeRh2As2 [Khim et al., Science 373, 1012 (2021)], we thoroughly investigate the dependence of the superconducting state of a strongly-coupled Rashba mono- and bilayer on internal parameters and an applied magnetic field. The role of interlayer pairing, spin orbit coupling, doping rate and applied magnetic field and their interplay was examined numerically at low temperature within a t-J-like model, uncovering complex phase diagrams and transitions between superconducting states with different symmetry.

Inhibitory Actions of Potentiating Neuroactive Steroids in the Human α1β3γ2L γ-Aminobutyric Acid Type A Receptor.

The γ-aminobutyric acid type A (GABAA) receptor is modulated by a number of neuroactive steroids. Sulfated steroids and 3β-hydroxy steroids inhibit, while 3α-hydroxy steroids typically potentiate the receptor. Here, we have investigated inhibition of the α1β3γ2L GABAA receptor by the endogenous neurosteroid 3α-hydroxy-5β-pregnan-20-one (3α5βP) and the synthetic neuroactive steroid 3α-hydroxy-5α-androstane-17β-carbonitrile (ACN). The receptors were expressed in Xenopus oocytes. All experiments were done using two-electrode voltage-clamp electrophysiology. In the presence of low concentrations of GABA, 3α5βP and ACN potentiate the GABAA receptor. To reveal inhibition, we conducted the experiments on receptors activated by the combination of a saturating concentration of GABA and propofol to fully activate the receptors and mask potentiation, or on mutant receptors in which potentiation is ablated. Under these conditions, both steroids inhibited the receptor with IC50s of ∼13 μM and maximal inhibitory effects of 70-90%. Receptor inhibition by 3α5βP was sensitive to substitution of the α1 transmembrane domain (TM) 2-2' residue, previously shown to ablate inhibition by pregnenolone sulfate. However, results of coapplication studies and the apparent lack of state dependence suggest that pregnenolone sulfate and 3α5βP inhibit the GABAA receptor independently and through distinct mechanisms. Mutations to the neurosteroid binding sites in the α1 and β3 subunits statistically significantly, albeit weakly and incompletely, reduced inhibition by 3α5βP and ACN. SIGNIFICANCE STATEMENT: The heteromeric GABAA receptor is inhibited by sulfated steroids and 3β-hydroxy steroids, while 3α-hydroxy steroids are considered to potentiate the receptor. We show here that 3α-hydroxy steroids have inhibitory effects on the α1β3γ2L receptor, which are observed in specific experimental settings and are expected to manifest under different physiological conditions.

Electronic and magnetic phase diagram of Sr2FeO4 at high pressure: A synchrotron Mössbauer study

Transition metal (TM) oxides with high oxidation state TM ions exhibit a variety of unconventional electronic and magnetic states owing to electron correlations effects combined with highly covalent TM–O bonding. Here, we have studied the pressure dependence of electronic state and magnetism of the K2NiF4-type iron(IV) oxide Sr2FeO4 up to 89 GPa by temperature and magnetic field dependent energy-domain synchrotron Mössbauer spectroscopy and derived a (P,T) magnetic phase diagram. Considering also previous resistance studies [Rozenberg , ] several magnetic and electronic regimes with increasing pressure can be identified. Near 7 GPa, the insulating cycloidal antiferromagnetic low-P state is transformed into a semiconducting ferromagnetic state and the magnetic ordering temperature Tm increases from 55 K at ambient pressure to about 100 K at 13 GPa. Between 18 and about 50 GPa the system is ferromagnetic and metallic (FMM) with a strong rise of Tm to above room temperature (RT). Contrary to a recent theoretical study [Kazemi-Moridani , ], the FMM state is attributed to a high-spin t2g3eg1 electronic state with itinerant eg coupled to more localized t2g electrons. Between 50 and 89 GPa a doublet with large quadrupole splitting in the RT Mössbauer spectra indicates a partial high-spin to low-spin (t2g4) transition leading to a decrease in Tm again. The general features of the (P,T) phase diagram of Sr2FeO4 are comparable to those of other simple and A-site ordered iron(IV) perovskite-related oxides with the peculiarity that Sr2FeO4 adopts an insulating state without charge disproportionation of Fe4+ in the low-P region. The high-pressure behavior of Sr2FeO4 and other iron(IV) oxides may be relevant for exploring the role of Hund's physics in multiorbital systems and contributing to the understanding of the electronic situation in unconventional superconductors such as La3Ni2O7 and Sr2RuO4. Published by the American Physical Society 2024

US Dollar Exchange Rate Elasticity of Gold Returns at Different Federal Fund Rate Zones

We examine the relationship between gold prices and the U.S. dollar exchange rate, arguing that their interactions are state-dependent and asymmetric under different market conditions. State dependency hinges on different short-term interest rate zones. To prove this point, we determine three distinct levels or zones of the effective federal funds rate using SETAR(2,p) tests. Subsequently, we perform conditional least square estimations of log changes in gold prices as a function of log changes in the nominal broad U.S. dollar exchange rate index for each of the obtained zones. Their relationship is consistently inverse, suggesting that gold and the U.S. dollar are risk-hedging substitutes for normal market periods. This also implies that gold is a safe-haven asset against the U.S. dollar exchange rate risk against a broad range of currencies. The substitution is weaker in the low-interest rate zone, more robust in the intermediate zone, and very pronounced in the high zone. We also perform a Markov switching test on the double-log function of gold prices and the exchange rate. The tests show a pronounced inverse relationship, i.e., substitution between assets, at normal market conditions. The relationship becomes significantly positive during episodes of financial distress, indicating complementarity between gold and U.S. dollar assets.

Decomposing momentum: The forgotten component

We split up the standard momentum return over months t−12 to t−2 at the highest stock price within this formation period. Of the overall momentum profits in month t, 84% can be attributed to the return prior to this peak price although research has exclusively focused on the post-peak return so far. The return predictability of the forgotten component is consistent with investor underreaction as underlying mechanism. Contrary to standard momentum strategies, the corresponding long-short returns are positively skewed, avoid momentum crashes, show no market state dependence, and yield consistent return premiums in both the US and international stock markets.