Stochastic resonance (SR) is universal phenomenon, where noise amplifies a weak periodic signal in bistable nonlinear systems, with wide applications in biology, climate science, engineering etc., although in fluid dynamics it remains underexplored. Recently, we unexpectedly found SR above non-modal elastic instability onset in an inertialess viscoelastic channel flow, where it emerges on the top of a chaotic streamwise velocity power spectrum $E_u$ due to its interaction with white-noise spanwise velocity power spectrum $E_w$ and weak elastic waves. These three conditions necessary for SR emergence differ from those required for the classical SR emergence mentioned above. Here, we consider SR in an inertialess viscoelastic channel flow with a smoothed inlet causing order of magnitude lower noise intensity than in our former studies. Our observations reveal that SR appears at the same conditions mentioned above, where SR is found just upon the instability onset in a lower subrange of a transition regime, in contrast, here, SR persists across all flow regimes – transition, elastic turbulence and drag reduction. Furthermore, we provide experimental evidence that SR, presented by a sharp peak in $E_u$, manifests as either a standing or propagating wave in the $x$-direction, with a rather uniform amplitude of streamwise velocity fluctuations and zero propagation velocity in the $z$-direction. These findings reveal a new mechanism underpinning the transition to a chaotic channel flow of viscoelastic fluids and establish SR as a robust framework for understanding complex flow dynamics. This work opens new avenues for exploring SR in other nonlinear systems and practical applications such as mixing enhancement and flow control in industrial and biological contexts.

Recent studies utilizing AI-driven speech-based Alzheimer’s disease (AD) detection have achieved remarkable success in detecting AD dementia through the analysis of audio and text data. However, detecting AD at an early stage of mild cognitive impairment (MCI), remains a challenging task, due to the lack of sufficient training data and imbalanced diagnostic labels. Motivated by recent advanced developments in Generative AI (GAI) and Large Language Models (LLMs), we propose an LLM-based data generation framework, leveraging prior knowledge encoded in LLMs to generate new data samples. Our novel LLM generation framework introduces two novel data generation strategies, namely, the cross-lingual and the counterfactual data generation, facilitating out-of-distribution learning over new data samples to reduce biases in MCI label prediction due to the systematic underrepresentation of MCI subjects in the AD speech dataset. The results have demonstrated that our proposed framework significantly improves MCI Detection Sensitivity and F1-score on average by a maximum of 38% and 31%, respectively. Furthermore, key speech markers in predicting MCI before and after LLM-based data generation have been identified to enhance our understanding of how the novel data generation approach contributes to the reduction of MCI label prediction biases, shedding new light on speech-based MCI detection under low data resource constraint. Our proposed methodology offers a generalized data generation framework for improving downstream prediction tasks in cases where limited and/or imbalanced data have presented significant challenges to AI-driven health decision-making. Future study can focus on incorporating more datasets and exploiting more acoustic features for speech-based MCI detection.

Glowacki offers many new directions for understanding and even eliminating the problem of war, especially creating positive interdependencies with out-group members. We develop Glowacki's intriguing proposition that in-group dynamics provide a route to peace by describing a prosocial motivational system, the caregiving system, that aligns individual interests and eliminates the need to use coercion to achieve mutually beneficial outcomes.

Telecommunications data are being explored by many countries as a new source of data that can be incorporated into their national statistical systems. In particular, “mobile positioning data” are increasingly being used to study population movements and population distributions. However, the legal, ethical, and technical complexities of working with this type of data often pose many barriers, which can prevent the data from being used at the times when it is most urgently needed. We demonstrate how having a robust public–private partnership framework, a privacy-preserving technical setup, and a communications strategy already in place, prior to an emergency, can enable governments to harness the advantages of telecommunications data at the times when it is most valuable. However, even once these foundations are in place, the challenges of competing priorities, managing expectations, and maintaining communication with data consumers during a pandemic mean that the potential of the data is not automatically translated into direct impact. This highlights the importance of sensitisation exercises, targeted at potential data users, to make clear the potential and limitations of the data, as well as the importance of being able to maintain direct communication with data users. The views expressed in this work belong solely to the authors and should not be interpreted as the views of their institutions.

In China, one percent of the richest population holds more than one-third of the wealth, while the poorest 25% shares no more than two percent of the total. The country’s rapid economic development has resulted in increasing socio-economic disparities, and a rapidly deteriorating environment. This puts the Chinese citizens, especially the most vulnerable and deprived socio-economic status (SES) groups, at high risks of environmental inequality (EI). In most SES-based EI studies conducted in China, household wealth has often been overlooked, though it potentially serves a good economic indicator to capture the socio-economic effect of environmental change in China. Nevertheless, existing SES databases in China are of low spatial resolution and are insufficient to support fine-grained EI studies at the intra-city level in China. The core research challenge is to develop a representative household wealth proxy in high-spatial resolution for China. This study highlights the research gaps and proposes a new household wealth proxy, which integrates both fine-grained data/features such as daytime satellite imagery and easily accessible wealth indicators such as house prices. We also capitalize on everyday economic activity data retrieved from personal mobile phones and online transaction/social platforms in the composition of our wealth proxy to achieve a higher accuracy in estimating household wealth at fine-grained resolution via machine learning. Finally, we summarize the challenges in improving both the quality and the availability of Chinese socio-economic datasets, while protecting personal privacy and information security during the data collection process for household wealth proxy development in China.

The Tibetan Plateau (TP) lakes are sensitive to climate change due to ice-albedo feedback, but almost no study has paid attention to the ice albedo of TP lakes and its potential impacts. Here we present a recent field experiment for observing the lake ice albedo in the TP, and evaluate the applicability of the Moderate Resolution Imaging Spectroradiometer (MODIS) products as well as ice-albedo parameterizations. Most of the observed lake ice albedos on TP are <0.12, and the clear blue ice albedo is only 0.075, much lower than reported in the previous studies. Even that of ice covered with snow patches is only 0.212. MOD10A1 albedo product has the best agreement with observations, followed by those of MYD10A1. MCD43A3 product is consistently higher than the observations. Due to an error of snow flag and inconsistent time windows in MCD43A2 and MCD43A3, at certain times, the albedo of the ice without snow is even higher than that covered with snow. When the solar zenith angle is not considered, there is no significant correlation between the albedo and the ice surface temperature. None of the existing ice-albedo parameterizations can reproduce well the observed relationship of the albedo and surface temperature.

Observations show that glaciers around the world are in retreat and losing mass. Internationally coordinated for over a century, glacier monitoring activities provide an unprecedented dataset of glacier observations from ground, air and space. Glacier studies generally select specific parts of these datasets to obtain optimal assessments of the mass-balance data relating to the impact that glaciers exercise on global sea-level fluctuations or on regional runoff. In this study we provide an overview and analysis of the main observational datasets compiled by the World Glacier Monitoring Service (WGMS). The dataset on glacier front variations (∼42 000 since 1600) delivers clear evidence that centennial glacier retreat is a global phenomenon. Intermittent readvance periods at regional and decadal scale are normally restricted to a subsample of glaciers and have not come close to achieving the maximum positions of the Little Ice Age (or Holocene). Glaciological and geodetic observations (∼5200 since 1850) show that the rates of early 21st-century mass loss are without precedent on a global scale, at least for the time period observed and probably also for recorded history, as indicated also in reconstructions from written and illustrated documents. This strong imbalance implies that glaciers in many regions will very likely suffer further ice loss, even if climate remains stable.

In his Afterword to The Singapore Grip, J. G. Farrell thanks Giorgio and Ginevra Agamben for suggesting the phrase that became the title of his novel. What can we make of this surprising and unexpected connection between an Anglo-Irish author’s novel about colonial Singapore on the eve of its fall to the Japanese army during World War II and Agamben’s writings on biopolitics? Despite the serendipitous nature of the encounter between the two writers and the lack of any causal relation between their works, my paper argues that there is an unacknowledged affinity that allows us to open them up to what Agamben calls their Entwicklungsfähigkeit, “the locus and the moment wherein they are susceptible to development,” thereby bringing out the biopolitical elements in Farrell’s novel and turning Agamben’s insights into dispositifs or biopolitical apparatuses in the direction of the analysis of colonial rule.

Dielectric thin films of high- and low-refractive index are the essential components for optical coatings. To achieve high sputtering rates and superior film quality, the authors have developed novel conductive SiO2:Si and ZnO:Zn composites that become conductive once the doped silicon and metal Zn reach a critical ratio. The sputtering characteristics of the composite targets in direct current and radio-frequency (RF) plasma discharge are quite different from the corresponding element targets. The optical properties of the RF sputtered SiO2 and ZnO films from the composite targets is comparable with the films obtained from RF sputtering of pure oxide targets.

Concerns with global warming prompted many governments to mandate increased proportion of electricity generation from renewable sources. This, together with the desire to have more efficient and secure power generation and distribution, has driven research in the next-generation power grid, namely, the smart grid. Through integrating advanced information and communication technologies with power electronic and electric power technologies, smart grid will be highly reliable, efficient, and environmental-friendly. A key component of smart grid is the communication system. This paper explores the design goals and functions of the smart grid communication system, followed by an in-depth investigation on the communication requirements. Discussions on some of the recent developments related to smart grid communication systems are also introduced.

In Markov Chain Monte Carlo (MCMC) simulations, thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples. These samples are selected in accordance with the probability distribution function, known from the partition function of equilibrium state. As the stochastic error of the simulation results is significant, it is desirable to understand the variance of the estimation by ensemble average, which depends on the sample size (i.e., the total number of samples in the set) and the sampling interval (i.e., cycle number between two consecutive samples). Although large sample sizes reduce the variance, they increase the computational cost of the simulation. For a given CPU time, the sample size can be reduced greatly by increasing the sampling interval, while having the corresponding increase in variance be negligible if the original sampling interval is very small. In this work, we report a few general rules that relate the variance with the sample size and the sampling interval. These results are observed and confirmed numerically. These variance rules are derived for the MCMC method but are also valid for the correlated samples obtained using other Monte Carlo methods. The main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them.

The emergence of fiat money is studied in a multiple-matching environment in which exchange is organized around trading posts and prices are determined with a dynamic monopolistically competitive framework. Each household consumes a bundle of commodities and has a preference for consumption variety. We determine the endogenous organization of exchange between firms and shoppers, the means of factor payment (remuneration), and the prices at which these trades occur. We verify that the endogenous linkage of factor payments with the medium of exchange can lead to a monetary equilibrium outcome where only fiat money trades for goods, an ex ante feature of cash-in-advance models. We also examine the long-run effects of money growth on equilibrium exchange patterns. A key finding, consistent with documented hyperinflationary episodes, is that a sufficiently rapid expansion of the money supply leads to the gradual emergence of barter, where sellers accept both goods and cash payments and workers receive part of their remuneration in goods.