Ecohydrological Modeling in Agroecosystems: Examples and Challenges

Human societies are increasingly altering the water and biogeochemical cycles to both improve ecosystem productivity and reduce risks associated with the unpredictable variability of climatic drivers. These alterations, however, often cause large negative environmental consequences, raising the question as to how societies can ensure a sustainable use of natural resources for the future. Here we discuss how ecohydrological modeling may address these broad questions with special attention to agroecosystems. The challenges related to modeling the two‐way interaction between society and environment are illustrated by means of a dynamical model in which soil and water quality supports the growth of human society but is also degraded by excessive pressure, leading to critical transitions and sustained societal growth‐collapse cycles. We then focus on the coupled dynamics of soil water and solutes (nutrients or contaminants), emphasizing the modeling challenges, presented by the strong nonlinearities in the soil and plant system and the unpredictable hydroclimatic forcing, that need to be overcome to quantitatively analyze problems of soil water sustainability in both natural and agricultural ecosystems. We discuss applications of this framework to problems of irrigation, soil salinization, and fertilization and emphasize how optimal solutions for large‐scale, long‐term planning of soil and water resources in agroecosystems under uncertainty could be provided by methods from stochastic control, informed by physically and mathematically sound descriptions of ecohydrological and biogeochemical interactions

Quantum dynamics of propagating photons with strong interactions: a generalized input-output formalism

There has been rapid development of systems that yield strong interactions between freely propagating photons in one dimension via controlled coupling to quantum emitters. This raises interesting possibilities such as quantum information processing with photons or quantum many-body states of light, but treating such systems generally remains a difficult task theoretically. Here, we describe a novel technique in which the dynamics and correlations of a few photons can be exactly calculated, based upon knowledge of the initial photonic state and the solution of the reduced effective dynamics of the quantum emitters alone. We show that this generalized "input-output" formalism allows for a straightforward numerical implementation regardless of system details, such as emitter positions, external driving, and level structure. As a specific example, we apply our technique to show how atomic systems with infinite-range interactions and under conditions of electromagnetically induced transparency enable the selective transmission of correlated multi-photon states

Psychedelics and the Human Receptorome

We currently understand the mental effects of psychedelics to be caused by agonism or partial agonism of 5-HT2A (and possibly 5-HT2C) receptors, and we understand that psychedelic drugs, especially phenylalkylamines, are fairly selective for these two receptors. This manuscript is a reference work on the receptor affinity pharmacology of psychedelic drugs. New data is presented on the affinity of twenty-five psychedelic drugs at fifty-one receptors, transporters, and ion channels, assayed by the National Institute of Mental Health – Psychoactive Drug Screening Program (NIMH-PDSP). In addition, comparable data gathered from the literature on ten additional drugs is also presented (mostly assayed by the NIMH-PDSP). A new method is introduced for normalizing affinity (Ki) data that factors out potency so that the multi-receptor affinity profiles of different drugs can be directly compared and contrasted. The method is then used to compare the thirty-five drugs in graphical and tabular form. It is shown that psychedelic drugs, especially phenylalkylamines, are not as selective as generally believed, interacting with forty-two of forty-nine broadly assayed sites. The thirty-five drugs of the study have very diverse patterns of interaction with different classes of receptors, emphasizing eighteen different receptors. This diversity of receptor interaction may underlie the qualitative diversity of these drugs. It should be possible to use this diverse set of drugs as probes into the roles played by the various receptor systems in the human mind

A Multi-Fidelity Deep Neural Network Approach to Structural Health Monitoring

The structural health monitoring (SHM) of civil structures and infrastructures is becoming a crucial issue in our smart and hyper-connected age. Due to structural aging and to unexpected loading conditions, partially linked to extreme events caused by the climate change, reliable and real-time SHM schemes are currently facing a burst in development and applications. In this work, we propose a procedure that relies upon a surrogate modeling scheme based on a multi-fidelity (MF) deep neural network (DNN), which has been conceived to sense and identify a structural damage under operational (and possibly environmental) variability. By exploiting the sensor recordings from a densely deployed network within a fully stochastic framework, the MF-DNN model is adopted to feed a Markov chain Monte Carlo (MCMC) sampling procedure and update the probability distribution of the structural state, conditioned on noisy observations. As information regarding the health of real structures is usually rather limited, the datasets to train the MF-DNN are generated with physical (e.g., finite element) models: high-fidelity (HF) and low-fidelity (LF) models are adopted to simulate the structural response under the mentioned varying conditions, respectively, in the presence or absence of a structural damage. As far as the architecture of the DNN is concerned, the MF approach is obtained by merging a fully connected LF-DNN and a long short-term memory HF-DNN. The LF-DNN mimics the output of the sensor network in the undamaged condition, while the HF-DNN is exploited to improve the LF model and appropriately catch the structural response in the presence of a pre-defined set of damaged patterns. Thanks to the adaptive enrichment of the LF signals carried out by the MF-DNN, the proposed model updating strategy is reported capable of locating (and possibly quantifying) a damage event

Plasmonic gold helices for the visible range fabricated by oxygen plasma purification of electron beam induced deposits

Electron beam induced deposition (EBID) currently provides the only direct writing technique for truly three-dimensional nanostructures with geometrical features below 50 nm. Unfortunately, the depositions from metal-organic precursors suffer from a substantial carbon content. This hinders many applications, especially in plasmonics where the metallic nature of the geometric surfaces is mandatory. To overcome this problem a post-deposition treatment with oxygen plasma at room temperature was investigated for the purification of gold containing EBID structures. Upon plasma treatment, the structures experience a shrinkage in diameter of about 18 nm but entirely keep their initial shape. The proposed purification step results in a core-shell structure with the core consisting of mainly unaffected EBID material and a gold shell of about 20 nm in thickness. These purified structures are plasmonically active in the visible wavelength range as shown by dark field optical microscopy on helical nanostructures. Most notably, electromagnetic modeling of the corresponding scattering spectra verified that the thickness and quality of the resulting gold shell ensures an optical response equal to that of pure gold nanostructures

Casimir force on a piston

We consider a massless scalar field obeying Dirichlet boundary conditions on the walls of a two-dimensional L x b rectangular box, divided by a movable partition (piston) into two compartments of dimensions a x b and (L-a) x b. We compute the Casimir force on the piston in the limit L -> infinity. Regardless of the value of a/b, the piston is attracted to the nearest end of the box. Asymptotic expressions for the Casimir force on the piston are derived for a << b and a >> b.Comment: 10 pages, 1 figure. Final version, accepted for publication in Phys. Rev.

An ecohydrological model of malaria outbreaks

Abstract. Malaria is a geographically widespread infectious disease that is well known to be affected by climate variability at both seasonal and interannual timescales. In an effort to identify climatic factors that impact malaria dynamics, there has been considerable research focused on the development of appropriate disease models for malaria transmission driven by climatic time series. These analyses have focused largely on variation in temperature and rainfall as direct climatic drivers of malaria dynamics. Here, we further these efforts by considering additionally the role that soil water content may play in driving malaria incidence. Specifically, we hypothesize that hydro-climatic variability should be an important factor in controlling the availability of mosquito habitats, thereby governing mosquito growth rates. To test this hypothesis, we reduce a nonlinear ecohydrological model to a simple linear model through a series of consecutive assumptions and apply this model to malaria incidence data from three South African provinces. Despite the assumptions made in the reduction of the model, we show that soil water content can account for a significant portion of malaria's case variability beyond its seasonal patterns, whereas neither temperature nor rainfall alone can do so. Future work should therefore consider soil water content as a simple and computable variable for incorporation into climate-driven disease models of malaria and other vector-borne infectious diseases

Primordial Black Holes as Near Infrared Background sources

The near infrared background (NIRB) is the collective light from unresolved sources observed in the band 1-10 $\mu$m. The measured NIRB angular power spectrum on angular scales $\theta \gtrsim 1$ arcmin exceeds by roughly two order of magnitudes predictions from known galaxy populations. The nature of the sources producing these fluctuations is still unknown. Here we test primordial black holes (PBHs) as sources of the NIRB excess. Considering PBHs as a cold dark matter (DM) component, we model the emission of gas accreting onto PBHs in a cosmological framework. We account for both accretion in the intergalactic medium (IGM) and in DM haloes. We self consistently derive the IGM temperature evolution, considering ionization and heating due to X-ray emission from PBHs. Besides $\Lambda$CDM, we consider a model that accounts for the modification of the linear matter power spectrum due to the presence of PBHs; we also explore two PBH mass distributions, i.e. a $\delta$-function and a lognormal distribution. For each model, we compute the mean intensity and the angular power spectrum of the NIRB produced by PBHs with mass 1-$10^3~\mathrm{M}_{\odot}$. In the limiting case in which the entirety of DM is made of PBHs, the PBH emission contributes $<1$ per cent to the observed NIRB fluctuations. This value decreases to $<0.1$ per cent if current constraints on the abundance of PBHs are taken into account. We conclude that PBHs are ruled out as substantial contributors to the NIRB.Comment: Accepted for publication in MNRA

A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model

Axially loaded beam-like structures represent a challenging case study for unsupervised learning vibration-based damage detection. Under real environmental and operational conditions, changes in axial load cause changes in the characteristics of the dynamic response that are significantly greater than those due to damage at an early stage. In previous works, the authors proposed the adoption of a multivariate damage feature composed of eigenfrequencies of multiple vibration modes. Successful results were obtained by framing the problem of damage detection as that of unsupervised outlier detection, adopting the well-known Mahalanobis squared distance (MSD) to define an effective damage index. Starting from these promising results, a novel approach based on unsupervised learning data clustering is proposed in this work, which increases the sensitivity to damage and significantly reduces the uncertainty associated with the results, allowing for earlier damage detection. The novel approach, which is based on Gaussian mixture model, is compared with the benchmark one based on the MSD, under the effects of an uncontrolled environment and, most importantly, in the presence of real damage due to corrosion