Origins of structural and electronic transitions in disordered silicon.

Structurally disordered materials pose fundamental questions1-4, including how different disordered phases ('polyamorphs') can coexist and transform from one phase to another5-9. Amorphous silicon has been extensively studied; it forms a fourfold-coordinated, covalent network at ambient conditions and much-higher-coordinated, metallic phases under pressure10-12. However, a detailed mechanistic understanding of the structural transitions in disordered silicon has been lacking, owing to the intrinsic limitations of even the most advanced experimental and computational techniques, for example, in terms of the system sizes accessible via simulation. Here we show how atomistic machine learning models trained on accurate quantum mechanical computations can help to describe liquid-amorphous and amorphous-amorphous transitions for a system of 100,000 atoms (ten-nanometre length scale), predicting structure, stability and electronic properties. Our simulations reveal a three-step transformation sequence for amorphous silicon under increasing external pressure. First, polyamorphic low- and high-density amorphous regions are found to coexist, rather than appearing sequentially. Then, we observe a structural collapse into a distinct very-high-density amorphous (VHDA) phase. Finally, our simulations indicate the transient nature of this VHDA phase: it rapidly nucleates crystallites, ultimately leading to the formation of a polycrystalline structure, consistent with experiments13-15 but not seen in earlier simulations11,16-18. A machine learning model for the electronic density of states confirms the onset of metallicity during VHDA formation and the subsequent crystallization. These results shed light on the liquid and amorphous states of silicon, and, in a wider context, they exemplify a machine learning-driven approach to predictive materials modelling.

Iron Supplementation in Management of Neurodevelopmental Disorders: Systematic Review, Meta-Analysis, and Qualitative Synthesis.

OBJECTIVE: The authors sought to explore the role of iron supplementation in the management of neurodevelopmental disorders among children and youths. METHODS: A systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was undertaken. A subset of results was suitable for meta-analysis. The quality of the evidence and strength of the clinical recommendations were assessed by using the Grading of Recommendations, Assessment, Development, and Evaluation method, and critical appraisal was conducted with the Joanna Briggs Institute critical appraisal tools. RESULTS: Nine articles met inclusion criteria. These articles included studies of attention-deficit hyperactivity disorder (ADHD) (N=7), autism spectrum disorder (N=1), and Tourette's syndrome (N=1). Three randomized controlled trials evaluating iron supplementation for ADHD hyperactivity symptom severity (124 participants: placebo, N=56; supplement, N=68) met inclusion criteria for a meta-analysis. Effect sizes for the placebo and supplement groups were moderate (Cohen's d=0.76) and large (Cohen's d=1.70), respectively, although these differences were not significant. The impact of iron supplementation on inattentive ADHD symptom severity was examined in two trials (75 participants: placebo, N=31; supplement, N=44). Large, nonsignificant effect sizes were demonstrated for the placebo (Cohen's d=1.66) and supplementation (Cohen's d=3.19) groups. The quality of the evidence and strength of the clinical recommendations were considered very low. CONCLUSIONS: Further research is needed to examine the role of iron supplementation in the management of ADHD and neurodevelopmental disorders more generally. Additionally, iron supplementation comes with risks, including death in the case of overdose.

Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning.

The structure of amorphous silicon (a-Si) is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a-Si. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned atomic energies to develop a classification of the different types of defects in a-Si. The results suggest a revision of the established floating-bond model by showing that fivefold-bonded atoms in a-Si exhibit a wide range of local environments-analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone.

Structure and Bonding in Amorphous Red Phosphorus.

Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bonding in a-P from first principles, combining machine-learning and density-functional theory (DFT) methods. We show that a-P structures exist with a range of energies slightly higher than those of phosphorus nanorods, to which they are closely related, and that the stability of a-P is linked to the degree of structural relaxation and medium-range order. We thus complete the stability range of phosphorus allotropes [Angew. Chem. Int. Ed. 2014, 53, 11629] by now including the previously poorly understood amorphous phase, and we quantify the covalent and van der Waals interactions in all main phases of phosphorus. We also study the electronic densities of states, including those of hydrogenated a-P. Beyond the present study, our structural models are expected to enable wider-ranging first-principles investigations-for example, of a-P-based battery materials.

Robust Design of High-Performance Optoelectronic Chalcogenide Crystals from High-Throughput Computation.

High-performance functional materials are the cornerstones of the continuous advance of modern science and technology, but the development of new materials is still challenging. Here, we propose a robust design strategy for novel crystalline solids based on group-theory classification and high-throughput computation, as demonstrated by the successful identification of new optoelectronic semiconductors. First, by means of theoretical group analysis and composition engineering, we obtained 78 prototypical crystal structures and built a computational materials database containing 21,060 ternary chalcogenide compounds. Our high-throughput screening of the coordination characteristics, phase stability, and electronic structures provided 97 candidate semiconductors, including 93 completely new compounds. Among them, 22 crystals with excellent dynamical and thermal stability are predicted to show high photovoltaic conversion efficiency (>30%), comparable to the currently most efficient single-junction GaAs solar cell, owing to their optimal electronic properties and outstanding optical absorption. This discovery of new chalcogenide crystals offers excellent candidates for optoelectronic applications and suggests that our design strategy is a promising way to search for unknown high-performance functional materials.

Tropical and subtropical Asia's valued tree species under threat.

Tree diversity in Asia's tropical and subtropical forests is central to nature-based solutions. Species vulnerability to multiple threats, which affect provision of ecosystem services, is poorly understood. We conducted a region-wide, spatially explicit assessment of the vulnerability of 63 socioeconomically important tree species to overexploitation, fire, overgrazing, habitat conversion, and climate change. Trees were selected for assessment from national priority lists, and selections were validated by an expert network representing 20 countries. We used Maxent suitability modeling to predict species distribution ranges, freely accessible spatial data sets to map threat exposures, and functional traits to estimate threat sensitivities. Species-specific vulnerability maps were created as the product of exposure maps and sensitivity estimates. Based on vulnerability to current threats and climate change, we identified priority areas for conservation and restoration. Overall, 74% of the most important areas for conservation of these trees fell outside protected areas, and all species were severely threatened across an average of 47% of their native ranges. The most imminent threats were overexploitation and habitat conversion; populations were severely threatened by these factors in an average of 24% and 16% of their ranges, respectively. Our model predicted limited overall climate change impacts, although some study species were likely to lose over 15% of their habitat by 2050 due to climate change. We pinpointed specific natural areas in Borneo rain forests as hotspots for in situ conservation of forest genetic resources, more than 82% of which fell outside designated protected areas. We also identified degraded areas in Western Ghats, Indochina dry forests, and Sumatran rain forests as hotspots for restoration, where planting or assisted natural regeneration will help conserve these species, and croplands in southern India and Thailand as potentially important agroforestry options. Our results highlight the need for regionally coordinated action for effective conservation and restoration.

Especies de Árboles Valoradas y Amenazadas de Asia Tropical y Subtropical Resumen La diversidad de árboles en los bosques tropicales y subtropicales de Asia es un eje central para las soluciones basadas en la naturaleza. La vulnerabilidad de las especies ante las múltiples amenazas, las cuales afectan el suministro de servicios ambientales, es un tema poco comprendido. Realizamos una evaluación regional espacialmente explícita de la vulnerabilidad de 63 especies de árboles de importancia socioeconómica ante la sobreexplotación, incendios, sobrepastoreo, conversión del hábitat y cambio climático. Los árboles se seleccionaron para su evaluación a partir de listas nacionales de prioridades, y las selecciones fueron validadas por una red de expertos de 20 países. Usamos el modelado de idoneidad Maxent para predecir el rango de distribución de las especies, conjuntos de datos espaciales de libre acceso para mapear la exposición a las amenazas y rasgos funcionales para estimar la susceptibilidad a las amenazas. Con base en la vulnerabilidad a las amenazas actuales y al cambio climático, identificamos las áreas prioritarias para su conservación y restauración. En general, el 74% de las áreas más importantes para la conservación de estos árboles quedó fuera de las áreas protegidas y todas las especies estaban seriamente amenazadas en promedio en el 47% de su distribución nativa. Las amenazas más inminentes fueron la sobreexplotación y la conversión del hábitat; las poblaciones estuvieron seriamente amenazadas por estos factores en promedio en el 24% y 16% de su distribución, respectivamente. Nuestro modelo predijo un impacto general limitado del cambio climático, aunque algunas especies estudiadas tuvieron la probabilidad de perder más del 15% de su hábitat para el 2050 debido a este factor. Identificamos áreas naturales específicas en las selvas de Borneo como puntos calientes para la conservación in situ de los recursos genéticos forestales, más del 82% de los cuales estaban fuera de las áreas protegidas designadas. También identificamos áreas degradadas en los Ghats Occidentales, los bosques secos de Indochina y las selvas de Sumatra como puntos calientes para la restauración, en donde la siembra o la regeneración natural asistida ayudarán a conservar estas especies. Además, identificamos campos de cultivo al sur de India y Tailandia como potenciales opciones importantes de agrosilvicultura. Nuestros resultados resaltan la necesidad de acciones regionales coordinadas para la conservación y restauración efectivas.

Weighted pressure matching with windowed targets for personal sound zones.

Personal sound zones (PSZ) systems use an array of loudspeakers to render independent audio signals to multiple listeners within a room. The performance of a PSZ system, designed using weighted pressure matching, depends on the selected target responses for the bright zone. In reverberant environments, the target responses are generally chosen to be the room impulse responses from one of the loudspeakers to the control points in the selected bright zone. This approach synthesizes the direct propagation component and all the reverberant components in the bright zone, while minimizing the energy in the dark zone. We present a theoretical analysis to show that high energy differences cannot be achieved for the diffuse reverberant components in the bright and dark zones, and so trying to synthesize these components in the bright zone does not lead to the best performance. It is then shown that the performance can be improved by using windowed versions of these measured impulse responses as target signals, in order to control which reverberant components are synthesized in the bright zone and which are not. This observation is supported by experimental measurements in two scenarios with different levels of reverberation.

Cerebral Microdialysate Metabolite Monitoring using Mid-infrared Spectroscopy.

The brains of patients suffering from traumatic brain-injury (TBI) undergo dynamic chemical changes in the days following the initial trauma. Accurate and timely monitoring of these changes is of paramount importance for improved patient outcome. Conventional brain-chemistry monitoring is performed off-line by collecting and manually transferring microdialysis samples to an enzymatic colorimetric bedside analyzer every hour, which detects and quantifies the molecules of interest. However, off-line, hourly monitoring means that any subhourly neurochemical changes, which may be detrimental to patients, go unseen and thus untreated. Mid-infrared (mid-IR) spectroscopy allows rapid, reagent-free, molecular fingerprinting of liquid samples, and can be easily integrated with microfluidics. We used mid-IR transmission spectroscopy to analyze glucose, lactate, and pyruvate, three relevant brain metabolites, in the extracellular brain fluid of two TBI patients, sampled via microdialysis. Detection limits of 0.5, 0.2, and 0.1 mM were achieved for pure glucose, lactate, and pyruvate, respectively, in perfusion fluid using an external cavity-quantum cascade laser (EC-QCL) system with an integrated transmission flow-cell. Microdialysates were collected hourly, then pooled (3-4 h), and measured consecutively using the standard ISCUSflex analyzer and the EC-QCL system. There was a strong correlation between the compound concentrations obtained using the conventional bedside analyzer and the acquired mid-IR absorbance spectra, where a partial-least-squares regression model was implemented to compute concentrations. This study demonstrates the potential utility of mid-IR spectroscopy for continuous, automated, reagent-free, and online monitoring of the dynamic chemical changes in TBI patients, allowing a more timely response to adverse brain metabolism and consequently improving patient outcomes.

Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits.

Urgent solutions to global climate change are needed. Ambitious tree-planting initiatives, many already underway, aim to sequester enormous quantities of carbon to partly compensate for anthropogenic CO2 emissions, which are a major cause of rising global temperatures. However, tree planting that is poorly planned and executed could actually increase CO2 emissions and have long-term, deleterious impacts on biodiversity, landscapes and livelihoods. Here, we highlight the main environmental risks of large-scale tree planting and propose 10 golden rules, based on some of the most recent ecological research, to implement forest ecosystem restoration that maximizes rates of both carbon sequestration and biodiversity recovery while improving livelihoods. These are as follows: (1) Protect existing forest first; (2) Work together (involving all stakeholders); (3) Aim to maximize biodiversity recovery to meet multiple goals; (4) Select appropriate areas for restoration; (5) Use natural regeneration wherever possible; (6) Select species to maximize biodiversity; (7) Use resilient plant material (with appropriate genetic variability and provenance); (8) Plan ahead for infrastructure, capacity and seed supply; (9) Learn by doing (using an adaptive management approach); and (10) Make it pay (ensuring the economic sustainability of the project). We focus on the design of long-term strategies to tackle the climate and biodiversity crises and support livelihood needs. We emphasize the role of local communities as sources of indigenous knowledge, and the benefits they could derive from successful reforestation that restores ecosystem functioning and delivers a diverse range of forest products and services. While there is no simple and universal recipe for forest restoration, it is crucial to build upon the currently growing public and private interest in this topic, to ensure interventions provide effective, long-term carbon sinks and maximize benefits for biodiversity and people.

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science.

The ever-growing abundance of data found in heterogeneous sources, such as scientific publications, has forced the development of automated techniques for data extraction. While in the past, in the physical sciences domain, the focus has been on the precise extraction of individual properties, attention has recently been devoted to the extraction of higher-level relationships. Here, we present a framework for an automated population of ontologies. That is, the direct extraction of a larger group of properties linked by a semantic network. We exploit data-rich sources, such as tables within documents, and present a new model concept that enables data extraction for chemical and physical properties with the ability to organize hierarchical data as nested information. Combining these capabilities with automatically generated parsers for data extraction and forward-looking interdependency resolution, we illustrate the power of our approach via the automatic extraction of a crystallographic hierarchy of information. This includes 18 interrelated submodels of nested data, extracted from an evaluation set of scientific articles, yielding an overall precision of 92.2%, across 26 different journals. Our method and associated toolkit, ChemDataExtractor 2.0, offers a key step toward the seamless integration of primary literature sources into a data-driven scientific framework.

Origin of radiation tolerance in amorphous Ge2Sb2Te5 phase-change random-access memory material.

The radiation hardness of amorphous Ge2Sb2Te5 phase-change random-access memory material has been elucidated by ab initio molecular-dynamics simulations. Ionizing radiation events have been modeled to investigate their effect on the atomic and electronic structure of the glass. Investigation of the short- and medium-range order highlights a structural recovery of the amorphous network after exposure to the high-energy events modeled in this study. Analysis of the modeled glasses reveals specific structural rearrangements in the local atomic geometry of the glass, as well as an increase in the formation of large shortest-path rings. The electronic structure of the modeled system is not significantly affected by the ionizing radiation events, since negligible differences have been observed before and after irradiation. These results provide a detailed insight into the atomistic structure of amorphous Ge2Sb2Te5 after irradiation and demonstrate the radiation hardness of the glass matrix.

Active control of the sound power scattered by a locally-reacting sphere.

Active control of the sound power scattered by a sphere is theoretically investigated using spherical harmonic expansions of the primary and secondary fields. The sphere has a surface impedance that is uniform, real, and locally reacting while being subjected to an incident monochromatic plane wave. The scattered power is controlled with a number of monopole sources, initially on the surface of the sphere, and is expressed as the sum of squared amplitudes of the spherical harmonics due to both the scattered and control fields. This quadratic function is minimized to identify the optimal strengths for different numbers of control sources. At low frequencies, the scattered field is dominated by the first few spherical harmonic terms, and its power can be significantly reduced with a single controlling monopole for a soft or absorbent sphere and with two monopoles for a hard sphere. The number of secondary sources required to significantly attenuate the scattered field at higher frequencies is found to be proportional to the square of the frequency, and the attenuation also falls off rapidly if the secondary sources are moved away from the surface of the sphere, no matter what its surface impedance.

Superposition of the uncertainties in acoustic responses and the robust design of active control systems.

The use of virtual sensing allows the frequency range of a local active noise control system located close to a listener's ears to be extended beyond what is possible when only controlling from remote physical sensors, particularly if head tracking is also used to determine the position of the virtual sensors. As the frequency range is extended, however, the uncertainties in the acoustic responses become more significant, and the design of multichannel adaptive controllers that are robustly stable to these uncertainties becomes more important. In order to fully characterise the uncertainties, a very large number of measurements would, in principle, need to be taken, due to the combination of all the possible changes in the acoustic environment. For uncertainties due to the simultaneous change in position of several objects within the acoustic environment, however, it is shown that the uncertainties can be accurately predicted by the superposition of these uncertainties, due to the change in position of the objects individually. This allows the uncertainty that is due to the change in position of a number of objects to be rapidly evaluated from a limited number of experiments and considerably simplifies the controller design process, which is illustrated here for an active headrest system using two different virtual sensing techniques.

ST-Elevation Myocardial Infarction in a Patient Having Dextrocardia with Situs Inversus.

BACKGROUND: Dextrocardia with situs inversus is a rare genetic condition in which the heart and internal organs are positioned on the opposite side of the body. Diagnosing and treating acute myocardial infarction correctly in a patient with dextrocardia is a difficult task. CASE REPORT: We present the case of an acute anterior wall ST elevation myocardial infarction (STEMI) in a patient with dextrocardia with situs inversus diagnosed after a lead reversal electrocardiogram (ECG). The patient then successfully underwent percutaneous coronary intervention and subsequent multivessel coronary artery bypass grafting. We discuss the original diagnosis and decision-making, clinical features, ECG characteristics, and disposition of the patient, as well as a review of the relevant literature. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Emergency physicians must identify and recognize the typical ECG of dextrocardia, especially when presenting with pathology, as its identification can lead to proper diagnosis and treatment.

Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon.

Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We combine a quantitative description of the nearest- and next-nearest-neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 1010  K s-1 . Our approach associates coordination defects in a-Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter.

Optical fibre-tip probes for SERS: numerical study for design considerations.

Enhancement of sub-wavelength optical fields using sub-micron plasmonic probes has found many applications in chemical, material, biological and medical sciences. The enhancement is via localised surface-plasmon resonance (LSPR) which enables the highly sensitive vibrational-spectroscopy technique of surface-enhanced Raman scattering (SERS). Combining SERS with optical fibres can allow the monitoring of biochemical reactions in situ with high resolution. Here, we study the electromagnetic-field enhancement of a tapered optical fibre-tip coated with gold nanoparticles (AuNPs) using finite-element simulations. We investigate the electric-field enhancement associated with metallic NPs and study the effect of parameters such as tip-aperture radius, cone angle, nanoparticle size and gaps between them. Our study provides an understanding of the design and application of metal-nanoparticle-coated optical-fibre-tip probes for SERS. The approach of using fibre-coupled delivery adds flexibility and simplifies the system requirements in SERS, making it suitable for cellular imaging and mapping bio-interfaces.

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures.

We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data. Rather than treating the full Li-C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li-Li interactions and to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials and in the longer run is promising for carrying out detailed atomistic studies of battery materials.

Estimation of the pressure at a listener's ears in an active headrest system using the remote microphone technique.

The remote microphone technique is considered in this paper as a way of estimating the error signals at a listener's ears in an active headrest system using remotely installed monitoring microphones. A least-squares formulation for the optimal observation filter is presented, including a regularization factor that is chosen to satisfy both the estimation accuracy and robustness to uncertainties. The accuracy of the nearfield estimation is first investigated for a diffuse field via simulations. Additionally, simulations of a free field are also used to investigate the effect of the spatial directivity of the primary field. Finally, experiments in an anechoic chamber are conducted with 24 monitoring microphones around a dummy head positioned in an active headrest system. When six loudspeakers driven by uncorrelated random disturbances are used to generate the primary field, the best arrangement of monitoring microphones is considered, taking into account both accuracy and robustness.

Application of the remote microphone method to active noise control in a mobile phone.

Mobile phones are used in a variety of situations where environmental noise may interfere with the ability of the near-end user to communicate with the far-end user. To overcome this problem, it might be possible to use active noise control technology to reduce the noise experienced by the near-end user. This paper initially demonstrates that when an active noise control system is used in a practical mobile phone configuration to minimise the noise measured by an error microphone mounted on the mobile phone, the attenuation achieved at the user's ear depends strongly on the position of the source generating the acoustic interference. To help overcome this problem, a remote microphone processing strategy is investigated that estimates the pressure at the user's ear from the pressure measured by the microphone on the mobile phone. Through an experimental implementation, it is demonstrated that this arrangement achieves a significant improvement in the attenuation measured at the ear of the user, compared to the standard active control strategy. The robustness of the active control system to changes in both the interfering sound field and the position of the mobile device relative to the ear of the user is also investigated experimentally.