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Glasses form the basis of many modern applications and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. Here, we explore the application of an open-source pre-trained NN model, GlassNet, that can predict the characteristic temperatures necessary to compute glass stability (GS) and assess the feasibility of using these physics-informed ML (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation - from the original ML prediction errors, to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also breakdown the ML performance on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

An infinite family of graphs ${\cal F}$ is called feasible if for any pair of integers $(n,m)$, $n \geq 1$, $0 \leq m \leq \binom{n}{2}$, there is a member $G \in {\cal F}$ such that $G$ has $n$ vertices and $m$ edges. We prove that given a graph $G$, the family ${\cal F}$$(G)$ of all induced $G$-free graphs is feasible if and only if $G$ is not $K_k$, $K_k\backslash K_2$, $\overline{K_k}$, $\overline{K_k\backslash K_2}$, for $k \geq 2$.

We conducted a search for narrowband radio signals over four observing sessions in 2020-2023 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,680 stars and planetary systems in the ~9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. In this work, we also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense RFI are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ~15 times smaller at ~6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake Figure of Merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is detectable in our search (EIRP > 1e13 W). For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5e16 W) is at most 3e-4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al. (2023)., Comment: 21 pages, 9 figures, in press at AJ

A multitask deep neural network model was trained on more than 218k different glass compositions. This model, called GlassNet, can predict 85 different properties (such as optical, electrical, dielectric, mechanical, and thermal properties, as well as density, viscosity/relaxation, crystallization, surface tension, and liquidus temperature) of glasses and glass-forming liquids of different chemistries (such as oxides, chalcogenides, halides, and others). The model and the data used to train it are available in the GlassPy Python module as free and open source software for the community to use and build upon. As a proof of concept, GlassNet was used with the MYEGA viscosity equation to predict the temperature dependence of viscosity and outperformed another general purpose viscosity model available in the literature (ViscNet) on unseen data. An explainable AI algorithm (SHAP) was used to extract knowledge correlating the input (physicochemical information) and output (glass properties) of the model, providing valuable insights for glass manufacturing and design. It is hoped that GlassNet, with its free and open source nature, can be used to enable faster and better computer-aided design of new technological glasses., Comment: Supplementary material is included. Corrected version after peer review and published information added to first page

Glass hardness varies in a non-linear fashion with the chemical composition and applied load, a phenomenon known as the indentation size effect (ISE), which is challenging to predict quantitatively. Here, using a curated dataset of over approx. 3000 inorganic glasses from the literature comprising the composition, indentation load, and hardness, we develop machine learning (ML) models to predict the composition and load dependence of Vickers hardness. Interestingly, when tested on new glass compositions unseen during the training, the standard data-driven ML model failed to capture the ISE. To address this gap, we combined an empirical expression (Bernhardt law) to describe the ISE with ML to develop a framework that incorporates the symbolic law representing the domain reasoning in ML, namely Symbolic Reasoning-Informed ML Procedure (SRIMP). We show that the resulting SRIMP outperforms the data-driven ML model in predicting the ISE. Finally, we interpret the SRIMP model to understand the contribution of the glass network formers and modifiers toward composition and load-dependent (ISE) and load-independent hardness. The deconvolution of the hardness into load-dependent and load-independent terms paves the way toward a holistic understanding of composition and ISE in glasses, enabling the accelerated discovery of new glass compositions with targeted hardness.

Runtime enforcement is a dynamic analysis technique that instruments a monitor with a system in order to ensure its correctness as specified by some property. This paper explores bidirectional enforcement strategies for properties describing the input and output behaviour of a system. We develop an operational framework for bidirectional enforcement and use it to study the enforceability of the safety fragment of Hennessy-Milner logic with recursion (sHML). We provide an automated synthesis function that generates correct monitors from sHML formulas, and show that this logic is enforceable via a specific type of bidirectional enforcement monitors called action disabling monitors.

We develop an efficient, sensitive, accurate, cost effective, and greener liquid chromatography method for the determination of a quantification method concerning Captopril in pharmaceutical dosage forms by using ultra high performance liquid chromatography (UHPLC) equipment. Satisfactory resolution was able to be achieved using a Water Aquity UHPLC BEH C18 1.7 $\mu$m $\times$ 2.1 mm $\times$ 50 mm column and an isocratic mobile phase composition of Methanol, Milli-Q water and Trifluoroacetic acid in the ratio of 55:45:0.05 v/v/v respectively at a flow rate of 0.1mL/min and a wavelength detection of 220nm. The retention time for Captopril was found to be 1.744 minutes while for Captopril disulfide it was found to be 2.657 minutes. The method was validated based on the ICH and USP guidelines., Comment: 8 pages, 7 figures, 6 tables

Due to their unique optical and electronic functionalities, chalcogenide glasses are materials of choice for numerous microelectronic and photonic devices. However, to extend the range of compositions and applications, profound knowledge about composition-property relationships is necessary. To this end, we collected a large quantity of composition-property data on chalcogenide glasses from SciGlass database regarding glass transition temperature ($T_g$), Young's modulus ($E$), coefficient of thermal expansion (CTE), and refractive index ($n_D$). With these data, we induced predictive models using three machine learning algorithms: Random Forest, K-nearest Neighbors, and Classification and Regression Trees. Finally, the induced models were interpreted by computing the SHAP (SHapley Additive exPlanations) values of the chemical features, which revealed the key elements that significantly impacted the tested properties and quantified their impact. For instance, Ge and Ga increase $T_g$ and $E$ and decrease CTE (three properties that depend on bond strength), whereas Se has the opposite effect. Te, As, Tl, and Sb increase $n_D$ (which strongly depends on polarizability), whereas S, Ge, and P diminish it. Knowledge about the effect of each element on the glass properties is precious for semi-empirical compositional development trials or simulation-driven formulations. The induced models can be used to design novel chalcogenide glasses with required combinations of properties., Comment: 19 pages, 11 figures

Vision and language tasks such as Visual Relation Detection and Visual Question Answering benefit from semantic features that afford proper grounding of language. The 3D depth of objects depicted in 2D images is one such feature. However it is very difficult to obtain accurate depth information without learning the appropriate features, which are scene dependent. The state of the art in this area are complex Neural Network models trained on stereo image data to predict depth per pixel. Fortunately, in some tasks, its only the relative depth between objects that is required. In this paper the extent to which semantic features can predict course relative depth is investigated. The problem is casted as a classification one and geometrical features based on object bounding boxes, object labels and scene attributes are computed and used as inputs to pattern recognition models to predict relative depth. i.e behind, in-front and neutral. The results are compared to those obtained from averaging the output of the monodepth neural network model, which represents the state-of-the art. An overall increase of 14% in relative depth accuracy over relative depth computed from the monodepth model derived results is achieved., Comment: 9 pages, 2 figures

Knowledge of relaxation processes is fundamental in glass science and technology because relaxation is intrinsically related to vitrification, tempering as well as to annealing and sev-eral applications of glasses. However, there are conflicting reports -- summarized here for different glasses -- on whether the structural relaxation time of glass can be calculated using the Maxwell equation, which relates relaxation time with shear viscosity and shear modulus. Hence, this study aimed to verify whether these two relaxation times are comparable. The structural relaxation kinetics of a lead metasilicate glass were studied by measuring the re-fractive index variation over time at temperatures between 5 and 25 K below the fictive temperature, which was initially set 5 K below the glass transition temperature. Equilibrium shear viscosity was measured above and below the glass transition range, expanding the current knowledge by one order of magnitude. The Kohlrausch equation described very well the experimental structural relaxation kinetics throughout the investigated temperature range and the Kohlrausch exponent increased with temperature, in agreement with studies on other glasses. The experimental average structural relaxation times were much longer than the values computed from isostructural viscosity, as expected. Still, they were less than one order of magnitude higher than the average relaxation time computed through the Maxwell equation, which relies on equilibrium shear viscosity. Thus, these results demon-strate that the structural relaxation process is not controlled by isostructural viscosity, and that equilibrium shear viscosity only provides a lower boundary for structural relaxation kinetics., Comment: 19 pages, 8 figures. Changed title. Minor updates to the text

This work is related to the setup of overflowing exponential fed-batch cultures (O-EFBC) derived from carbon limited EFBC dedicated to the production of mycosubtilin, an antifungal lipopeptide belonging to the iturin family. O-EFBC permits the continuous removal of the product from the bioreactor achieving a complete extraction of mycosubtilin. This paper also provides a dynamical Monod-based growth model of this process that is accurate enough to simulate the evolution of the specific growth rate and to correlate it to the mycosubtilin specific productivity. Two particular and dependant phenomena related to the foam overflow are taken into account by the model: the outgoing flow rate of a broth volume and the loss of biomass. Interestingly, the biomass concentration in the foam was found to be lower than the biomass concentration in the bioreactor relating this process to a recycling one. Parameters of this model are the growth yield on substrate and the maximal specific growth rate estimated from experiments led at feed rates of 0.062, 0.071 and 0.086 h --1. The model was extrapolated to five additional experiments carried out at feed rates of 0.008, 0.022, 0.040, 0.042 and 0.062 h --1 enabling the correlation of the mean specific growth rates with productivity results. Finally, a feed rate of 0.086 h --1 corresponding to a mean specific growth rate of 0.070 h --1 allowed a specific productivity of 1.27 mg of mycosubtilin g --1 of dried biomass h --1 .

With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. We investigated the predictive performance of three machine learning algorithms for six different glass properties. For such, we used an extensive dataset of about 150,000 oxide glasses, which was segmented into smaller datasets for each property investigated. Using the decision tree induction, k-nearest neighbors, and random forest algorithms, selected from a previous study of six algorithms, we induced predictive models for glass transition temperature, liquidus temperature, elastic modulus, thermal expansion coefficient, refractive index, and Abbe number. Moreover, each model was induced with default and tuned hyperparameter values. We demonstrate that, apart from the elastic modulus (which had the smallest training dataset), the induced predictive models for the other five properties yield a comparable uncertainty to the usual data spread. However, for glasses with extremely low or high values of these properties, the prediction uncertainty is significantly higher. Finally, as expected, glasses containing chemical elements that are poorly represented in the training set yielded higher prediction errors. The method developed here calls attention to the success and possible pitfalls of machine learning algorithms. The analysis of the SHAP values indicated the key elements that increase or decrease the value of the modeled properties. It also estimated the maximum possible increase or decrease. Insights gained by this analysis can help empirical compositional tuning and computer-aided inverse design of glass formulations., Comment: Added SHAP analysis and updated the text

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature ($T_g$) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index ($n_d$) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high $n_d$ (1.7 or more) and low $T_g$ (500 {\deg}C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses., Comment: 24 pages, 8 figures. Updated with journal reference

Viscosity ($\eta$) is one of the most important properties of disordered matter. The temperature-dependence of viscosity is used to adjust process variables for glass-making, from melting to annealing. The aim of this work was to develop a physics-informed machine learning model capable of predicting $\eta(T)$ of oxide liquids. Instead of predicting the viscosity itself, the NN predicts the parameters of the MYEGA viscosity equation: the liquid's fragility index, the glass transition temperature, and the asymptotic viscosity. With these parameters, $\eta$ can be computed at any temperature of interest, with the advantage of good extrapolation capabilities inherent to the MYEGA equation. The dataset was collected from the SciGlass database; only oxide liquids with enough data points in the high and low viscosity regions were selected, resulting in a final dataset with 17,584 data points containing 847 different liquids. About 600 features were engineered from the liquids' chemical composition and 35 of these features were selected using a feature selection protocol. The hyperparameter (HP) tuning of the NN was performed in a set of experiments using both random search and Bayesian strategies, where a total of 700 HP sets were tested. The most successful HP sets were further tested using 10-fold cross-validation, and the one with the lowest average validation loss was selected as the best set. The final trained NN was tested with a test dataset of 85 liquids with different compositions than those used for training and validating the NN. The $R^2$ for the test dataset's prediction was 0.97. This work introduces three advantages: the model can predict viscosity as well as the liquids' glass transition temperature and fragility index; the model is designed and trained with a focus on extrapolation; finally, the model is available as free and open-source software licensed under the GPL3., Comment: 31 pages, 27 figures. Layout change. Added journal reference

Glass forming ability (GFA) is a property of utmost importance in glass science and technology. In this paper, we used a statistical methodology---involving bootstrap sampling and the Wilcoxon test---to find out which glass stability parameters can better predict the glass forming ability. We collected or measured the necessary data for twelve stoichiometric oxide glasses that underwent predominant heterogeneous nucleation (the most common case). We found that some GS parameters could predict the GFA of these oxide glasses quite well, whereas others perform poorly. Parameter $K_w$ was the top ranked, closely followed by the $K_H$, $\gamma$, $H^{'}$, $\Delta T_{rg}$, and $K_{cr}$. Our results corroborate previous reports carried out using a smaller number of glasses, much less GS parameters, and less rigorous statistics. We also found that using $T_c$ instead of $T_x$ improved the predictive power of these parameters. Finally, the Jezica, the only parameter considered here that predicts the GFA without requiring the production of a glass piece (i.e., without relying on any crystallization information), ranked reasonably well in our analysis., Comment: 27 pages, 9 figures

The Classical Nucleation Theory allegedly fails to describe the temperature dependence of the homogeneous crystal nucleation rates below the temperature of maximum nucleation, $T_{\mathrm{max}}$. Possible explanations for this suspected breakdown have been advanced in the literature. However, the simplest hypothesis has never been tested, that it is a byproduct of nucleation datasets that have not reached the steady-state regime. In this work, we tested this possibility by analyzing published nucleation data for oxide supercooled liquids, using only nucleation and viscosity data measured in samples of the same glass batch that also have satisfied a steady-state regime test. Furthermore, all the uncertainty and regression confidence bands were computed and considered. Having this rigorous protocol, among the 6 datasets analyzed, we only found weak evidence supporting the existence of the nucleation break in 2 datasets. Our collective results thus indicate that the break at $T_{\mathrm{max}}$ is not a common feature of all glass-formers., Comment: 38 pages, 21 figures

An essential parameter of the Classical Nucleation Theory (CNT) is the surface energy between a critical-size nucleus and the ambient phase, $\sigma$. In condensed matter, this parameter cannot be experimentally determined independently of CNT. A common practice to obtain $\sigma$ is to assume a model for its temperature-dependence and perform a regression of the CNT equation against experimental nucleation data. The drawback of this practice is that assuming the temperature-dependence of $\sigma$ adds a bias to the analysis. Nonetheless, this practice is common because an analytical solution of the Classical Nucleation Theory with respect to $\sigma$ is not possible considering common expressions of this theory. In this article, a general numerical solution to this problem using the Lambert $W$ function is proposed, tested, and compared with typical regression methods. The major advantage of the proposed method is that there is no need to assume a model for the temperature-dependence of $\sigma$., Comment: Revised text and new Section 4 added, results unchanged. 4 pages, 1 figure

Runtime enforcement is a dynamic analysis technique that uses monitors to enforce the behaviour specified by some correctness property on an executing system. The enforceability of a logic captures the extent to which the properties expressible via the logic can be enforced at runtime. We study the enforceability of Hennessy-Milner Logic with Recursion (muHML) with respect to suppression enforcement. We develop an operational framework for enforcement which we then use to formalise when a monitor enforces a muHML property. We also show that the safety syntactic fragment of the logic, sHML, is enforceable by providing an automated synthesis function that generates correct suppression monitors from sHML formulas., Comment: 38 pages

The ubiquitous reliance on software systems increases the need for ensuring that systems behave correctly and are well protected against security risks. Runtime enforcement is a dynamic analysis technique that utilizes software monitors to check the runtime behaviour of a software system with respect to a correctness specification. Whenever the runtime behaviour of the monitored system is about to deviate from the specification (either due to a programming bug or a security hijack attack), the monitors apply enforcement techniques to prevent this deviation. Current Runtime Enforcement techniques require that the correctness specification defines the behaviour of the enforcement monitor itself. This burdens the specifier with not only having to define property that needs to be enforced, but also with having to specify how this should be enforced at runtime; we thus relieve the specifier from this burden by resorting to a highly expressive logic. Using a logic we allow the specifier to define the correctness specification as a logic formula from which we can automatically synthesise the appropriate enforcement monitor. Highly expressive logics can, however, permit for defining a wide variety of formulae, some of which cannot actually be enforced correctly at runtime. We thus study the enforceability of Hennessy Milner Logic with Recursion for which we identify a subset that allows for defining enforceable formulae. This allows us to define a synthesis function that translates enforceable formulae into enforcement monitors. As our monitors are meant to ensure the correct behaviour of the monitored system, it is imperative that they work correctly themselves. We thus study formal definitions that allow us to ensure that our enforcement monitors behave correctly.

In this dissertation we focus on providing effective adaptations that can be localised and applied to specific concurrent actors, thereby only causing a temporary disruption to the parts of the system requiring mitigation, while leaving the rest of the system intact. We make the application of localised adaptations efficient through incremental synchronisation, whereby the specifier can strategically suspend specific parts of the system, whenever this is strictly required for ensuring that adaptations are effectively applied. We also study static analysis techniques to determine whether the specified incremental synchronisation is in some sense adequate for local adaptations to be carried out. We thus identify a number of generic adaptations that can be applied to any actor system, regardless of its design and the code that it executes. We implement the identified adaptations as an extension of an existing Runtime Verification tool for actor-systems, thereby creating a RA framework for monitoring and mitigating actor systems. In parallel to our implementation we also develop a formal model of our RA framework that further serves to guide our implementation. This model also enables us to better understand the subtle errors that erroneously specified adaptation scripts may introduce. We thus develop a static type system for detecting and rejecting erroneous adaptation scripts prior to deployment, thereby providing the specifier with assistance for writing valid scripts. Although the static typesystem analyses scripts with respect to certain assumptions, we do not assume that the monitored system abides by these assumptions. We therefore augment our RA framework with dynamic checks for halting monitoring whenever the system deviates from our assumption. Based on this dynamically checked model of our RA framework, we prove type soundness for our static type system.

Distributed programs are hard to get right because they are required to be open, scalable, long-running, and tolerant to faults. In particular, the recent approaches to distributed software based on (micro-)services where different services are developed independently by disparate teams exacerbate the problem. In fact, services are meant to be composed together and run in open context where unpredictable behaviours can emerge. This makes it necessary to adopt suitable strategies for monitoring the execution and incorporate recovery and adaptation mechanisms so to make distributed programs more flexible and robust. The typical approach that is currently adopted is to embed such mechanisms in the program logic, which makes it hard to extract, compare and debug. We propose an approach that employs formal abstractions for specifying failure recovery and adaptation strategies. Although implementation agnostic, these abstractions would be amenable to algorithmic synthesis of code, monitoring and tests. We consider message-passing programs (a la Erlang, Go, or MPI) that are gaining momentum both in academia and industry. Our research agenda consists of (1) the definition of formal behavioural models encompassing failures, (2) the specification of the relevant properties of adaptation and recovery strategy, (3) the automatic generation of monitoring, recovery, and adaptation logic in target languages of interest., Comment: In Proceedings PrePost 2017, arXiv:1708.06889

Runtime Monitoring is a lightweight and dynamic verification technique that involves observing the internal operations of a software system and/or its interactions with other external entities, with the aim of determining whether the system satisfies or violates a correctness specification. Compilation techniques employed in Runtime Monitoring tools allow monitors to be automatically derived from high-level correctness specifications (aka. properties). This allows the same property to be converted into different types of monitors, which may apply different instrumentation techniques for checking whether the property was satisfied or not. In this paper we compare and contrast the various types of monitoring methodologies found in the current literature, and classify them into a spectrum of monitoring instrumentation techniques, ranging from completely asynchronous monitoring on the one end and completely synchronous monitoring on the other., Comment: In Proceedings PrePost 2017, arXiv:1708.06889

The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use cases that illustrate its applicability and benefits. We also present simple experimental results that support its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN)., Comment: 8 pages, 22 references, 6 figures and 1 table

We design monitor optimisations for detectEr, a runtime-verification tool synthesising systems of concurrent monitors from correctness properties for Erlang programs. We implement these optimisations as part of the existing tool and show that they yield considerably lower runtime overheads when compared to the unoptimised monitor synthesis., Comment: In Proceedings FESCA 2015, arXiv:1503.04378

We study the impact of synchronous and asynchronous monitoring instrumentation on runtime overheads in the context of a runtime verification framework for actor-based systems. We show that, in such a context, asynchronous monitoring incurs substantially lower overhead costs. We also show how, for certain properties that require synchronous monitoring, a hybrid approach can be used that ensures timely violation detections for the important events while, at the same time, incurring lower overhead costs that are closer to those of an asynchronous instrumentation., Comment: In Proceedings FOCLASA 2014, arXiv:1502.03157

The goal of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign is to develop a predictive understanding of the export, fate, and carbon cycle impacts of global ocean net primary production. To accomplish this goal, observations of export flux pathways, plankton community composition, food web processes, and optical, physical, and biogeochemical (BGC) properties are needed over a range of ecosystem states. Here we introduce the first EXPORTS field deployment to Ocean Station Papa in the Northeast Pacific Ocean during summer of 2018, providing context for other papers in this special collection. The experiment was conducted with two ships: a Process Ship, focused on ecological rates, BGC fluxes, temporal changes in food web, and BGC and optical properties, that followed an instrumented Lagrangian float; and a Survey Ship that sampled BGC and optical properties in spatial patterns around the Process Ship. An array of autonomous underwater assets provided measurements over a range of spatial and temporal scales, and partnering programs and remote sensing observations provided additional observational context. The oceanographic setting was typical of late-summer conditions at Ocean Station Papa: a shallow mixed layer, strong vertical and weak horizontal gradients in hydrographic properties, sluggish sub-inertial currents, elevated macronutrient concentrations and low phytoplankton abundances. Although nutrient concentrations were consistent with previous observations, mixed layer chlorophyll was lower than typically observed, resulting in a deeper euphotic zone. Analyses of surface layer temperature and salinity found three distinct surface water types, allowing for diagnosis of whether observed changes were spatial or temporal.The 2018 EXPORTS field deployment is among the most comprehensive biological pump studies ever conducted. A second deployment to the North Atlantic Ocean occurred in spring 2021, which will be followed by focused work on data synthesis and modeling using the entire EXPORTS data set.

EXport Processes in the Ocean from Remote Sensing (EXPORTS) is a large-scale NASA-led and NSF co-funded field campaign that will provide critical information for quantifying the export and fate of upper ocean net primary production (NPP) using satellite information and state of the art technology.

Examines young children's ability to use simple morphological relations among words as a source of information about the words' spelling. Found that children used morphological relations among words only to a small extent. Suggests that although phonology plays an important role in early spelling, young children can also use other sources of information, including certain morphological relationships among words. (AA)

We present an IR lunar occultation and direct imaging search for companions in the Ophiuchus star-forming region and update a similar search of the Taurus region. The search is sensitive to companions in the angular separation range 0.005-10 sec. In Ophiuchus, we surveyed 35 young star targets; this sample contains at least 10 binaries, two triples, and one quadruple. Ten of the companion stars are newly discovered. In Taurus, the survey now includes 47 systems among which there are at least 22 binaries and four triples. Only two companion stars are newly identified because there is strong overlap with prior work. All the triples and quadruple are hierarchical. The observed binary frequency in Ophiuchus, in the 3-1400 AU range of separations, is at least 1.1 +/- 0.3 that of the nearby solar-like stars. This value is a lower bound because we make no corrections for incompleteness. In Taurus, in the same range of separations, the observed binary frequency is at least 1.6 +/- 0.3 that of the nearby solar-like stars. This value extends Ghez et al.'s (1993) and Leinert's et al.'s (1993) determination of an excess binary frequency to 3 AU separation. We used the weak-line T Tauri star/T Tauri star (WT/TT) type and the K-L color index to distinguish between systems with and without inner disks. We find no convincing difference in the binary frequency or distribution of separations of the systems with and without inner disks. The 1.3 mm continuum emission of the single systems exceeds that of the multiples suggesting that their extensive outer disks are more massive. The specific angular momenta of the binaries overlap those of molecular cloud cores measured by Goodman et al. (1993).

A complete listing is given for objects in the IRAS Point Source Catalog which will be occulted by the moon over the course of 1986-1990. A total of 14,148 ASCII card images is encompassed by the complete listing of objects having geocentric events during this period. The results contained in this complete listing are illustrated in two of the present tables for the brightest objects at 12 and 100 micron wavelengths.

Far-infrared, near-infrared, and radio molecular-line observations of the regions of HFE 2, HFE 3, and FJM 6 are described. At positions of high molecular column density nearest to the reported positions of these sources, their infrared emission cannot be confirmed at upper bounds below those of the original detection. Near-infrared observations of the FJM 6 region (which includes the Bok globule Barnard 361) reveal a number of stellar sources, most of which are behind the molecular cloud and are reddened by it. Visual extinction through B 361 estimated by star counts yields A sub V/N(/C-13/O) = 3.7 + or - 1.6) x 10 to the -16th mag sq cm. The gas temperature and the upper bound on the dust temperature in the FJM 6 region are consistent with cosmic-ray heating of the cloud, while the values of these parameters for the clouds in the HFE 2 and HFE 3 regions do not appear consistent with either cosmic-ray or radiative heating.