Direct conversion of carbon dioxide and steam into hydrocarbons and oxygenates using solid acid electrolysis cells.

Electrolysis at intermediate temperatures (100-600°C) is promising because high reaction rates and high product selectivity can be achieved simultaneously during CO2 reduction. However, intermediate temperature electrolysis has rarely been reported owing to electrolyte limitations. Here, solid acid electrolysis cells (SAECs) were adopted for electrochemically reducing CO2. Carbon monoxide, methane, methanol, ethane, ethylene, ethanol, acetaldehyde and propylene were produced from CO2 and steam, using Cu-containing composite cathodes at 220°C and atmospheric pressure. The results demonstrate the potential of SAECs for producing valuable chemical feedstocks. At the SAEC cathode, CO2 was electrochemically reduced by protons and electrons. The product selectivity and reaction rate were considerably different from those of thermochemical reactions with gaseous hydrogen. Based on the differences, plausible reaction pathways were proposed.

Influence of trip distance and population density on intra-city mobility patterns in Tokyo during COVID-19 pandemic.

This study investigates the influence of infection cases of COVID-19 and two non-compulsory lockdowns on human mobility within the Tokyo metropolitan area. Using the data of hourly staying population in each 500m×500m cell and their city-level residency, we show that long-distance trips or trips to crowded places decrease significantly when infection cases increase. The same result holds for the two lockdowns, although the second lockdown was less effective. Hence, Japanese non-compulsory lockdowns influence mobility in a similar way to the increase in infection cases. This means that they are accepted as alarm triggers for people who are at risk of contracting COVID-19.

Urban spatial structures from human flow by Hodge-Kodaira decomposition.

Human flow in cities indicates social activity and can reveal urban spatial structures based on human behaviours for relevant applications. Scalar potential is a mathematical concept that, when properly applied, can provide an intuitive view of human flow. However, the definition of such a potential in terms of the origin-destination flow matrix and its feasibility remain unresolved. In this case, we use Hodge-Kodaira decomposition, which uniquely decomposes a matrix into a potential-driven (gradient) flow and a curl flow. We depict the potential landscapes in cities resulting from commuting flow and reveal how the landscapes have either changed or remained unchanged by years or methods of transportation. We then determine how well the commuting flow is described by the potential, by evaluating the percentage of the gradient component for metropolitan areas in the USA and show that the gradient component is almost 100% in several areas; in other areas, however, the curl component is dominant, indicating the importance of circular flow along with triangles of places. The potential landscape provides an easy-to-use visualisation tool for showing the attractive places of human flow and will help in a variety of applications such as commerce, urban design, and epidemic spreading.

Analytical estimation of maximum fraction of infected individuals with one-shot non-pharmaceutical intervention in a hybrid epidemic model.

BACKGROUND: Facing a global epidemic of new infectious diseases such as COVID-19, non-pharmaceutical interventions (NPIs), which reduce transmission rates without medical actions, are being implemented around the world to mitigate spreads. One of the problems in assessing the effects of NPIs is that different NPIs have been implemented at different times based on the situation of each country; therefore, few assumptions can be shared about how the introduction of policies affects the patient population. Mathematical models can contribute to further understanding these phenomena by obtaining analytical solutions as well as numerical simulations. METHODS AND RESULTS: In this study, an NPI was introduced into the SIR model for a conceptual study of infectious diseases under the condition that the transmission rate was reduced to a fixed value only once within a finite time duration, and its effect was analyzed numerically and theoretically. It was analytically shown that the maximum fraction of infected individuals and the final size could be larger if the intervention starts too early. The analytical results also suggested that more individuals may be infected at the peak of the second wave with a stronger intervention. CONCLUSIONS: This study provides quantitative relationship between the strength of a one-shot intervention and the reduction in the number of patients with no approximation. This suggests the importance of the strength and time of NPIs, although detailed studies are necessary for the implementation of NPIs in complicated real-world environments as the model used in this study is based on various simplifications.

A model for simulating emergent patterns of cities and roads on real-world landscapes.

Emergence of cities and road networks have characterised human activity and movement over millennia. However, this anthropogenic infrastructure does not develop in isolation, but is deeply embedded in the natural landscape, which strongly influences the resultant spatial patterns. Nevertheless, the precise impact that landscape has on the location, size and connectivity of cities is a long-standing, unresolved problem. To address this issue, we incorporate high-resolution topographic maps into a Turing-like pattern forming system, in which local reinforcement rules result in co-evolving centres of population and transport networks. Using Italy as a case study, we show that the model constrained solely by topography results in an emergent spatial pattern that is consistent with Zipf's Law and comparable to the census data. Thus, we infer the natural landscape may play a dominant role in establishing the baseline macro-scale population pattern, that is then modified by higher-level historical, socio-economic or cultural factors.

An in situ DRIFTS study on nitrogen electrochemical reduction over an Fe/BaZr0.8Y0.2O3-δ -Ru catalyst at 220 °C in an electrolysis cell using a CsH2PO4/SiP2O7 electrolyte.

In situ DRIFTS measurements of an Fe/BZY-Ru cathode catalyst in an electrolysis cell using a CsH2PO4/SiP2O7 electrolyte were carried out in a mixed N2-H2 gas flow under polarization. The formation of N2H x species was confirmed under polarization, and an associative mechanism in the electrochemical NRR process was verified.

Chaos may enhance expressivity in cerebellar granular layer.

Recent evidence suggests that Golgi cells in the cerebellar granular layer are densely connected to each other with massive gap junctions. Here, we propose that the massive gap junctions between the Golgi cells contribute to the representational complexity of the granular layer of the cerebellum by inducing chaotic dynamics. We construct a model of cerebellar granular layer with diffusion coupling through gap junctions between the Golgi cells, and evaluate the representational capability of the network with the reservoir computing framework. First, we show that the chaotic dynamics induced by diffusion coupling results in complex output patterns containing a wide range of frequency components. Second, the long non-recursive time series of the reservoir represents the passage of time from an external input. These properties of the reservoir enable mapping different spatial inputs into different temporal patterns.

Estimation of socioeconomic attributes from location information.

Timely estimation of the distribution of socioeconomic attributes and their movement is crucial for academic as well as administrative and marketing purposes. In this study, assuming personal attributes affect human behavior and movement, we predict these attributes from location information. First, we predict the socioeconomic characteristics of individuals by supervised learning methods, i.e., logistic Lasso regression, Gaussian Naive Bayes, random forest, XGBoost, LightGBM, and support vector machine, using survey data we collected of personal attributes and frequency of visits to specific facilities, to test our conjecture. We find that gender, a crucial attribute, is as highly predictable from locations as from other sources such as social networking services, as done by existing studies. Second, we apply the model trained with the survey data to actual GPS log data to check the performance of our approach in a real-world setting. Though our approach does not perform as well as for the survey data, the results suggest that we can infer gender from a GPS log.

Hydrogen Production by Steam Electrolysis in Solid Acid Electrolysis Cells.

Hydrogen production by steam electrolysis at intermediate temperatures has potential for both the high energy conversion efficiency and the flexible operability suitable for the utilization of renewable energy resources. Employment of proton-conducting solid acid electrolytes at around 200 °C is considered promising but has rarely been investigated. Here, steam electrolysis was performed at 160-220 °C using a solid acid electrolysis cell (SAEC) composed of a CsH2 PO4 /SiP2 O7 composite electrolyte and Pt/C electrodes. Hydrogen production was successfully demonstrated with Faraday efficiencies around 80 %. Key factors affecting the SAEC stability were investigated in detail for the first time. It was revealed that a certain part of the electrolyte migrated into the porous anode structure during the operation. The migrated electrolyte prevented the gas diffusion and flooded the Pt/C catalyst layer. It was also found that carbonaceous materials in the anode was oxidized, leading to the decrease in the number of electrochemically active sites. Based on the findings, Pt mesh was employed as an alternative anode. The SAEC with the Pt mesh anode showed superior stability, demonstrating the importance of the anode design. The present work provides a comprehensive view of the stability issues, which is essential for the development of durable and practical SAECs.

Extracellular vesicles released from irradiated neonatal mouse cheek tissue increased cell survival after radiation.

Alopecia is one of the common symptoms after high-dose radiation exposure. In our experiments, neonatal mice that received 7 Gy X-ray exhibited defects in overall hair growth, except for their cheeks. This phenomenon might suggest that some substances were secreted and prevented hair follicle loss in the infant tissues around their cheeks after radiation damage. In this study, we focused on exosome-like vesicles (ELV) secreted from cheek skin tissues and back skin tissues, as control, and examined their radiation protective effects on mouse fibroblast cell lines. We observed that ELV from irradiated cheek skin showed protective effects from radiation. Our results suggest that ELV from radiation-exposed cheek skin tissue is one of the secreted factors that prevent hair follicle loss after high-dose radiation.

Dynamical impacts of the coupling in a model of interactive infectious diseases.

Multiple models have been proposed to describe the epidemic spreading in the presence of interactions between two or more infectious diseases, but less is known about how dynamical aspects, such as time scales of diseases, affect the epidemic spreading. In this work, we evaluate the time shift produced in the number of people infected from one disease when interacting with another disease. Using a compartmental model, we produce different forms of relationship as competition, cooperation, and independence, assessing the effect of each one in the final result. We focus on the case of the unidirectional coupling between diseases, which enables us to study the impact of a perturbation to a driving disease on the driven one. We found that the prevalence of the driven disease is strongly affected if its time scale, defined by the time where the infection reaches the peak, is comparable to that of the driving disease. The secondary peak of the infection was observed under cooperative coupling if the time scale of the driving disease is much longer than that of the driven one.

Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic.

While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

Direct electrochemical synthesis of oxygenates from ethane using phosphate-based electrolysis cells.

Ethane was converted directly to acetaldehyde and ethanol by partial oxidation at 220 °C and ambient pressure using an electrolysis cell with a proton-conducting electrolyte, CsH2PO4/SiP2O7, and Pt/C electrodes. The ethane conversion and the selectivity to the products increased with the voltage applied to the cell. It was found that O species generated by water electrolysis functioned as a favorable oxidant for partial oxidation of ethane on the Pt/C anode at intermediate temperatures. The production rates of acetaldehyde and ethanol recorded in this study were significantly higher than those in preceding reports.

Understanding post-disaster population recovery patterns.

Despite the rising importance of enhancing community resilience to disasters, our understandings on when, how and why communities are able to recover from such extreme events are limited. Here, we study the macroscopic population recovery patterns in disaster affected regions, by observing human mobility trajectories of over 1.9 million mobile phone users across three countries before, during and after five major disasters. We find that, despite the diversity in socio-economic characteristics among the affected regions and the types of hazards, population recovery trends after significant displacement resemble similar patterns after all five disasters. Moreover, the heterogeneity in initial and long-term displacement rates across communities in the three countries were explained by a set of key common factors, including the community's median income level, population, housing damage rates and the connectedness to other cities. Such insights discovered from large-scale empirical data could assist policymaking in various disciplines for developing community resilience to disasters.

Smoothing effect for spatially distributed renewable resources and its impact on power grid robustness.

In this paper, we show that spatial correlation of renewable energy outputs greatly influences the robustness of the power grids against large fluctuations of the effective power. First, we evaluate the spatial correlation among renewable energy outputs. We find that the spatial correlation of renewable energy outputs depends on the locations, while the influence of the spatial correlation of renewable energy outputs on power grids is not well known. Thus, second, by employing the topology of the power grid in eastern Japan, we analyze the robustness of the power grid with spatial correlation of renewable energy outputs. The analysis is performed by using a realistic differential-algebraic equations model. The results show that the spatial correlation of the energy resources strongly degrades the robustness of the power grid. Our results suggest that we should consider the spatial correlation of the renewable energy outputs when estimating the stability of power grids.

A perturbation-theoretic approach to Lagrangian flow networks.

Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway, or airline infrastructures over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (as arising if the background flow is perturbed itself). Our results demonstrate that in all three cases, changes to the steady state solution can be analytically expressed in terms of the eigensystem of the unperturbed flow and the perturbation itself. These results are potentially relevant for developing more efficient strategies for coping with contaminations of fluid or gaseous media such as ocean and atmosphere by oil spills, radioactive substances, non-reactive chemicals, or volcanic aerosols.

Synchronization of mobile chaotic oscillator networks.

We study synchronization of systems in which agents holding chaotic oscillators move in a two-dimensional plane and interact with nearby ones forming a time dependent network. Due to the uncertainty in observing other agents' states, we assume that the interaction contains a certain amount of noise that turns out to be relevant for chaotic dynamics. We find that a synchronization transition takes place by changing a control parameter. But this transition depends on the relative dynamic scale of motion and interaction. When the topology change is slow, we observe an intermittent switching between laminar and burst states close to the transition due to small noise. This novel type of synchronization transition and intermittency can happen even when complete synchronization is linearly stable in the absence of noise. We show that the linear stability of the synchronized state is not a sufficient condition for its stability due to strong fluctuations of the transverse Lyapunov exponent associated with a slow network topology change. Since this effect can be observed within the linearized dynamics, we can expect such an effect in the temporal networks with noisy chaotic oscillators, irrespective of the details of the oscillator dynamics. When the topology change is fast, a linearized approximation describes well the dynamics towards synchrony. These results imply that the fluctuations of the finite-time transverse Lyapunov exponent should also be taken into account to estimate synchronization of the mobile contact networks.

The role of asymmetrical and repulsive coupling in the dynamics of two coupled van der Pol oscillators.

A system of two asymmetrically coupled van der Pol oscillators has been studied. We show that the introduction of a small asymmetry in coupling leads to the appearance of a "wideband synchronization channel" in the bifurcational structure of the parameter space. An increase of asymmetry and transition to repulsive interaction leads to the formation of multistability. As the result, the tip of the Arnold's tongue widens due to the formation of folds defined by saddle-node bifurcation curves for the limit cycles on the torus.

Development of a Numerical Method for Patient-Specific Cerebral Circulation Using 1D-0D Simulation of the Entire Cardiovascular System with SPECT Data.

The detailed flow information in the circle of Willis (CoW) can facilitate a better understanding of disease progression, and provide useful references for disease treatment. We have been developing a one-dimensional-zero-dimensional (1D-0D) simulation method for the entire cardiovascular system to obtain hemodynamics information in the CoW. This paper presents a new method for applying 1D-0D simulation to an individual patient using patient-specific data. The key issue is how to adjust the deviation of physiological parameters, such as peripheral resistance, from literature data when patient-specific geometry is used. In order to overcome this problem, we utilized flow information from single photon emission computed tomography (SPECT) data. A numerical method was developed to optimize physiological parameters by adjusting peripheral cerebral resistance to minimize the difference between the resulting flow rate and the SPECT data in the efferent arteries of the CoW. The method was applied to three cases using different sets of patient-specific data in order to investigate the hemodynamics of the CoW. The resulting flow rates in the afferent arteries were compared to those of the phase-contrast magnetic resonance angiography (PC-MRA) data. Utilization of the SPECT data combined with the PC-MRA data showed a good agreement in flow rates in the afferent arteries of the CoW with those of PC-MRA data for all three cases. The results also demonstrated that application of SPECT data alone could provide the information on the ratios of flow distributions among arteries in the CoW.

Towards dynamical network biomarkers in neuromodulation of episodic migraine.

Computational methods have complemented experimental and clinical neurosciences and led to improvements in our understanding of the nervous systems in health and disease. In parallel, neuromodulation in form of electric and magnetic stimulation is gaining increasing acceptance in chronic and intractable diseases. In this paper, we firstly explore the relevant state of the art in fusion of both developments towards translational computational neuroscience. Then, we propose a strategy to employ the new theoretical concept of dynamical network biomarkers (DNB) in episodic manifestations of chronic disorders. In particular, as a first example, we introduce the use of computational models in migraine and illustrate on the basis of this example the potential of DNB as early-warning signals for neuromodulation in episodic migraine.