arXiv subjects

Meesoon Ha

Publications and source records attributed to Meesoon Ha.

At least 19 recordsLinked to original sources

arXiv2026

Impact of capacity volatility and input substitutability on supply chain resilience

Supply chains are intrinsically vulnerable to stochastic shocks due to their sequential production dependencies. Building on the Feld-Barthelemy framework, we investigate how capacity volatility and input substitutability determine critical demands in stochastic supply chains. By modeling production capacity with a truncated normal distribution, we show that in long supply chains, reducing capacity volatility is often more effective than increasing average capacity, emphasizing the need for firm-level synchronization. Furthermore, introducing a modified Leontief-type production function reveals that input substitutability effectively disperses stochastic shocks. Supplier diversification inherently raises critical demands, even under fixed maximum capacities, by introducing the effect of network topology that independently enhances the resilience of physical stock. Our findings demonstrate that mitigating capacity volatility and structurally diversifying supply routes are just as crucial to supply chain resilience as traditional inventory expansion.

cond-mat.stat-mech↗

arXiv2026

Role of volatility mixing in wealth condensation transition

We study the role of heterogeneous volatility in a networked wealth dynamics model and its impact on the wealth condensation transition. Extending the Bouchaud-M\'ezard framework, we introduce binary volatility in networks and investigate how its configuration affects the effective power-law tail exponent of the wealth distribution. Using a stochastic block model, we control the mixing between volatility groups and show that the effective exponent is governed not only by the global parameter $\Lambda=2J/\beta^2$ but also by the volatility configuration in the network. We find that local interactions between nodes with different volatility induce the neutralization of group-wise exponents, which lowers the aggregate tail exponent and yields a condensation transition across $\gamma_{\rm c}=2$. Our results identify volatility mixing as another control mechanism for wealth condensation and highlight the importance of noise heterogeneity in nonequilibrium systems on networks.

cond-mat.stat-mech↗

arXiv2025

Semantic Network Analysis of Achievement Standards in Physics of 2022 Revised Curriculum

We investigate semantic networks of achievement standards for physics subjects in the 2022 revised curriculum to derive information embedded in the curriculum. We extract each subject's keywords with node strength and random-walk betweenness, detect communities of physics terms by the optimized greedy algorithm, and find the connectivity of physics subjects using bipartite networks. The network analysis reveals three remarkable results: First, keywords are about scientific thinking and practices, evolving to a higher level as the grades increase. Second, there is a lack of connection to learning content in physics. Lastly, achievement standards for 'Integrated Science' are inadequate to fulfill the intended purpose of the curriculum. This is attributed to the reduced learning volume in the 2022 revised curriculum. Our study implies that the curriculum and achievement standards should be improved for the better connectivity of subjects.

physics.ed-ph↗

arXiv2025

Anomaly, class division, and decoupling in income dynamics

Economic inequality emerges from the interplay between regional growth-rate differences and the interaction network that couples regions. We propose a minimal income-dynamics model, where heterogeneity is governed by growth-rate assortativity $\mathcal{A}$ and regional concentration $\mathcal{R}$, allowing us to quantify the spatiotemporal patterns of empirically observed log-income distributions. To systematically analyze these patterns, we derive closed-form approximations for the Hellinger distance and the Gini index in limiting configurations. Our findings highlight the spatial segregation of growth rates as a key driver of economic class division and demonstrate how small-world shortcuts in the underlying network can disrupt this segregation. Finally, our framework provides a robust explanation for the bimodality and strong regional correlations found in global income distributions.

cond-mat.stat-mech↗

arXiv2024

Interplay of network structure and talent configuration on wealth dynamics

The economic success of individuals is often determined by a combination of talent, luck, and assistance from others. We introduce a new agent-based model that simultaneously considers talent, luck, and social interaction. This model allows us to explore how network structure (how agents interact) and talent distribution among agents affect the dynamics of capital accumulation through analytical and numerical methods. We identify a phenomenon as ``talent configuration effect", which refers to the influence of how talent is allocated to individuals (nodes) in the network. We analyze this effect through two key properties: talent assortativity (TA) and talent-degree correlation (TD). In particular, we focus on three economic indicators: growth rate ($n_{\rm rate}$), Gini coefficient (inequality: $n_{\rm Gini}$), and meritocratic fairness ($n_{LT}$). This investigation helps us understand the interplay between talent configuration and network structure on capital dynamics. We find that, in the short term, positive correlations exist between TA and TD for all three economic indicators. Furthermore, the dominant factor influencing capital dynamics depends on the network topology. In scale-free networks, TD has a stronger influence on the economic indices than TA. Conversely, in lattice-like networks, TA plays a more significant role. Our findings address that high socioeconomic homophily can create a dilemma between growth and equality, and that hub monopolization by few highly talented agents makes economic growth strongly dependent on their performances.

physics.soc-ph↗

arXiv2023

Hidden multiscale organization and robustness of real multiplex networks

Hidden geometry enables the investigation of complex networks at different scales. Extending this framework to multiplex networks, we uncover a novel kind of mesoscopic organization in real multiplex systems, named $\textit{clan}$, a group of nodes that preserve their local geometric arrangement across layers. Furthermore, we reveal the intimate relationship between the unfolding of clan structure and mutual percolation against targeted attacks, leading to an ambivalent role of clans: making a system fragile yet less prone to complete shattering. Finally, we confirm the correlation between the multiscale nature of geometric organization and the overall robustness. Our findings expand the significance of hidden geometry in network function, while also highlighting potential pitfalls in evaluating and controlling catastrophic failure of multiplex systems.

physics.soc-ph↗

arXiv2020

Quantumness and thermodynamic uncertainty relation of finite-time Otto cycle

To reveal the role of the quantumness in the Otto cycle and to discuss the validity of the thermodynamic uncertainty relation (TUR) in the cycle, we study the quantum Otto cycle and its classical counterpart. In particular, we calculate exactly the mean values and relative error of thermodynamic quantities. In the quasistatic limit, quantumness reduces the productivity and precision of the Otto cycle compared to that in the absence of quantumness, whereas in the finite-time mode, it can increase the cycle's productivity and precision. Interestingly, as the strength (heat conductance) between the system and the bath increases, the precision of the quantum Otto cycle overtakes that of the classical one. Testing the conventional TUR of the Otto cycle, in the region where the entropy production is large enough, we find a tighter bound than that of the conventional TUR. However, in the finite-time mode, both quantum and classical Otto cycles violate the conventional TUR in the region where the entropy production is small. This implies that another modified TUR is required to cover the finite-time Otto cycle. Finally, we discuss the possible origin of this violation in terms of the uncertainty products of the thermodynamic quantities and the relative error near resonance conditions.

quant-ph↗

arXiv2019

Finite-time quantum Otto engine: Surpassing the quasi-static efficiency due to friction

In finite-time quantum heat engines, some work is consumed to drive a working fluid accompanying coherence, which is called `friction'. To understand the role of friction in quantum thermodynamics, we present a couple of finite-time quantum Otto cycles with two different baths: Agarwal versus Lindbladian. We solve them exactly and compare the performance of the Agarwal engine with that of the Lindbladian engine. In particular, we find remarkable and counterintuitive results that the performance of the Agarwal engine due to friction can be much higher than that in the quasistatic limit with the Otto efficiency, and the power of the Lindbladian engine can be nonzero in the short-time limit. Based on additional numerical calculations of these outcomes, we discuss possible origins of such differences between two engines and reveal them. Our results imply that even with an equilibrium bath, a nonequilibrium working fluid brings on the higher performance than what an equilibrium working fluid does.

cond-mat.stat-mech↗

arXiv2019

Impact of temporal connectivity patterns on epidemic process

To provide a comprehensive view for dynamics of and on many real-world temporal networks, we investigate the interplay of temporal connectivity patterns and spreading phenomena, in terms of the susceptible-infected-removed (SIR) model on the modified activity-driven temporal network (ADTN) with memory. In particular, we focus on how the epidemic threshold of the SIR model is affected by the heterogeneity of nodal activities and the memory strength in temporal and static regimes, respectively. While strong ties (memory) between nodes inhibit the spread of epidemic to be localized, the heterogeneity of nodal activities enhances it to be globalized initially. Since the epidemic threshold of the SIR model is very sensitive to the degree distribution of nodes in static networks, we test the SIR model on the modified ADTNs with the possible set of the activity exponents and the memory exponents that generates the same degree distributions in temporal networks. We also discuss the role of spatiotemporal scaling properties of the largest cluster and the maximum degree in the epidemic threshold. It is observed that the presence of highly active nodes enables to trigger the initial spread of epidemic in a short period of time, but it also limits its final spread to the entire network. This implies that there is the trade-off between the spreading time of epidemic and its outbreak size. Finally, we suggest the phase diagram of the SIR model on ADTNs and the optimal condition for the spread of epidemic under the circumstances.

physics.soc-ph↗

arXiv2019

Passive Tracer Dynamics in Slow-Bond Problem

Asymptotic Kardar-Parisi-Zhang (KPZ) properties are investigated in the totally asymmetric simple exclusion process (TASEP) with a localized geometric defect. In particular, we focus on the universal nature of nonequilibrium steady states of the modified TASEP. Since the original TASEP belongs to the KPZ universality class, it is mathematically and physically a quite interesting question whether the localized columnar defect, the slow bond (SB), is really always relevant to the KPZ universality or not. However, it is numerically controversial to address the possibility of the non-queued SB phase in the weak-strength SB limit. Based on the detailed statistical analysis of KPZ-type growing interfaces, we present a comprehensive view of the non-queue SB phase, compared to finite-size crossover effects that reported in our earlier work [Soh {\it et al.}, Phys. Rev. E {\bf 95}, 042123 (2017)]. Moreover, we employ two types of passive tracer dynamics as the probe of the SB dynamics. Finally, we provide intuitive arguments for additional clues to resolve the controversy of the SB problem.

cond-mat.stat-mech↗

arXiv2018

Role of hubs in the synergistic spread of behavior

The spread of behavior in a society has two major features: the synergy of multiple spreaders and the dominance of hubs. While strong synergy is known to induce mixed-order transitions (MOTs) at percolation, the effects of hubs on the phenomena are yet to be clarified. By analytically solving the generalized epidemic process on random scale-free networks with the power-law degree distribution $p_k \sim k^{-\alpha}$, we clarify how the dominance of hubs in social networks affects the conditions for MOTs. Our results show that, for $\alpha < 4$, an abundance of hubs drive MOTs, even if a synergistic spreading event requires an arbitrarily large number of adjacent spreaders. In particular, for $2 < \alpha < 3$, we find that a global cascade is possible even when only synergistic spreading events are allowed. These transition properties are substantially different from those of cooperative contagions, which are another class of synergistic cascading processes exhibiting MOTs.

physics.soc-ph↗

arXiv2017

Dynamic topologies of activity-driven temporal networks with memory

We propose dynamic scaling in temporal networks with heterogeneous activities and memory, and provide a comprehensive picture for the dynamic topologies of such networks, in terms of the modified activity-driven network model [H. Kim \textit{et al.}, Eur. Phys. J. B {\bf 88}, 315 (2015)]. Particularly, we focus on the interplay of the time resolution and memory in dynamic topologies. Through the random walk (RW) process, we investigate diffusion properties and topological changes as the time resolution increases. Our results with memory are compared to those of the memoryless case. Based on the temporal percolation concept, we derive scaling exponents in the dynamics of the largest cluster and the coverage of the RW process in time-varying networks. We find that the time resolution in the time-accumulated network determines the effective size of the network, while memory affects relevant scaling properties at the crossover from the dynamic regime to the static one. The origin of memory-dependent scaling behaviors is the dynamics of the largest cluster, which depends on temporal degree distributions. Finally, we conjecture of the extended finite-size scaling ansatz for dynamic topologies and the fundamental property of temporal networks, which are numerically confirmed.

cond-mat.stat-mech↗

arXiv2017

Jamming and condensation in one-dimensional driven flow

We revisit the slow-bond (SB) problem of the one-dimensional (1D) totally asymmetric simple exclusion process (TASEP) with modified hopping rates. In the original SB problem, it turns out that a local defect is always relevant to the system as jamming, so that phase separation occurs in the 1D TASEP. However, crossover scaling behaviors are also observed as finite-size effects. In order to check if the SB can be irrelevant to the system with particle interaction, we employ the condensation concept in the zero-range process. The hopping rate in the modified TASEP depends on the interaction parameter and the distance up to the nearest particle in the moving direction, besides the SB factor. In particular, we focus on the interplay of jamming and condensation in the current-density relation of 1D driven flow. Based on mean-field calculations, we present the fundamental diagram and the phase diagram of the modified SB problem, which are numerically checked. Finally, we discuss how the condensation of holes suppresses the jamming of particles and vice versa, where the partially-condensed phase is the most interesting, compared to that in the original SB problem.

cond-mat.stat-mech↗

arXiv2016

Effects of a local defect on one-dimensional nonlinear surface growth

The slow-bond problem is a long-standing question about the minimal strength $\epsilon_\mathrm{c}$ of a local defect with global effects on the Kardar--Parisi--Zhang (KPZ) universality class. A consensus on the issue has been delayed due to the discrepancy between various analytical predictions claiming $\epsilon_\mathrm{c} = 0$ and numerical observations claiming $\epsilon_\mathrm{c} > 0$. We revisit the problem via finite-size scaling analyses of the slow-bond effects, which are tested for different boundary conditions through extensive Monte Carlo simulations. Our results provide evidence that the previously reported nonzero $\epsilon_\mathrm{c}$ is an artifact of a crossover phenomenon, which logarithmically converges to zero as the system size goes to infinity.

cond-mat.stat-mech↗

arXiv2015

Universality classes of generalized epidemic process on random networks

We present a self-contained discussion of the universality classes of the generalized epidemic process (GEP) on Poisson random networks, which is a simple model of social contagions with cooperative effects. These effects lead to rich phase transitional behaviors that include continuous and discontinuous transitions with tricriticality in between. With the help of a comprehensive finite-size scaling theory, we numerically confirm static and dynamic scaling behaviors of the GEP near continuous phase transitions and at tricriticality, which verifies the field-theoretical results of previous studies. We also propose a proper criterion for the discontinuous transition line, which is shown to coincide with the bond percolation threshold.

cond-mat.stat-mech↗

arXiv2015

Scaling Properties in Time-Varying Networks with Memory

The formation of network structure is mainly influenced by an individual node's activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between nodes. In our study, we define the activity through the appearance pattern in the time-aggregated network representation, and quantify the memory through the contact pattern of empirical temporal networks. To address the role of activity and memory in epidemics on time-varying networks, we propose temporal-pattern coarsening of activity-driven growing networks with memory. In particular, we focus on the relation between time-scale coarsening and spreading dynamics in the context of dynamic scaling and finite-size scaling. Finally, we discuss the universality issue of spreading dynamics on time-varying networks for various memory-causality tests.

physics.soc-ph↗

arXiv2014

Effects of junctional correlations in the totally asymmetric simple exclusion process on random regular networks

We investigate the totally asymmetric simple exclusion process on closed and directed random regular networks, which is a simple model of active transport in the one-dimensional segments coupled by junctions. By a pair mean-field theory and detailed numerical analyses, it is found that the correlations at junctions induce two notable deviations from the simple mean-field theory which neglects these correlations: (1) the narrower range of particle density for phase coexistence and (2) the algebraic decay of density profile with exponent $1/2$ even outside the maximal-current phase. We show that these anomalies are attributable to the effective slow bonds formed by the network junctions.

cond-mat.stat-mech↗

arXiv2014

Comment on "Fluctuation theorem for hidden entropy production"

Recently, Kawaguchi and Nakayama (KN) [Phys. Rev. E {\bf 88}, 022147 (2013)] showed that the hidden entropy production associated with a coarse-graining procedure obeys the integral fluctuation theorem (IFT) if the original process does not involve any odd-parity variable that changes its sign under time reversal. This was interpreted as the evidence that odd-parity variables play an important role in the derivation of irreversible stochastic dynamics from time-reversible deterministic dynamics. In this Comment, we show that KN's approach is inadequate for describing the origin of irreversible stochastic dynamics, which calls into question whether odd-parity variables are required for the emergence of macroscopic irreversibility.

cond-mat.stat-mech↗