Synthesis of Oxazolidinones by a Hypervalent Iodine Mediated Cyclization of N‐Allylcarbamates

The preparation of oxazolidinones by the hypervalent iodine mediated cyclization of allylcarbamates is described. A versatile range of substrates can be converted into substituted oxazolidinones primed for further transformations. Derivatization of the products at both ends is demonstrated. A preliminary attempt at the enantioselective formation of an oxazolidinone using a chiral iodane is also presented

Freeze-thaw durability of recycled concrete from construction and demolition wastes

Road engineering is one of the most accepted applications for concrete including recycled aggregates from construction and demolition wastes as a partial replacement of the natural coarse aggregates. Amongst the durability concerns of such application, the deterioration due to freeze-thaw cycles is one of the most important causes decreasing the life span of concrete in countries with a continental climate. Moreover, the use of de-icing salts, which is a common practice to prevent ice formation on roadways and walkways, increases the superficial degradation of concrete due to frost-salt scaling. Thus, this paper aims to assess the resistance to frost salt with de-icing salts of two recycled concrete mixtures containing a 50% replacement of the conventional gravel by recycled aggregates both of mixed and ceramic nature, i.e. containing ceramic percentages of 34% and 100%, in comparison to a conventional concrete made with siliceous gravel. Therefore, the surface scaling was evaluated based on EN 1339 (2004) on 28 days cured cylinders, exposed to 7, 14, 21 and 28 freeze-thaw cycles in the presence of sodium chloride solution. Given that no airentraining admixture was used in any of the mixtures, the scaling of both conventional and recycled concretes exceeded the 1 kg/m2 limit established by the European standard. Nonetheless, for the casting surface, the recycled concrete with low ceramic content exhibited a similar behaviour to the conventional concrete, whereas the performance of the recycled concrete with high ceramic content was better. However, as expected, trowelled surfaces showed a worse performance and both recycled concretes had a lower freeze-thaw durability than the conventional mixture. In any case, the results suggested that the composition of the recycled aggregates could be used as a factor to limit the differences in performance between recycled and conventional mixtures

Controllable direction of liquid jets generated by thermocavitation within a droplet.

A high-velocity fluid stream ejected from an orifice or nozzle is a common mechanism to produce liquid jets in inkjet printers or to produce sprays among other applications. In the present research, we show the generation of liquid jets of controllable direction produced within a sessile water droplet by thermocavitation. The jets are driven by an acoustic shock wave emitted by the collapse of a hemispherical vapor bubble at the liquid-solid/substrate interface. The generated shock wave is reflected at the liquid-air interface due to acoustic impedance mismatch generating multiple reflections inside the droplet. During each reflection, a force is exerted on the interface driving the jets. Depending on the position of the generation of the bubble within the droplet, the mechanical energy of the shock wave is focused on different regions at the liquid-air interface, ejecting cylindrical liquid jets at different angles. The ejected jet angle dependence is explained by a simple ray tracing model of the propagation of the acoustic shock wave inside the droplet

Acculturation is associated with left ventricular mass in a multiethnic sample: the Multi-Ethnic Study of Atherosclerosis.

BackgroundAcculturation involves stress-related processes and health behavioral changes, which may have an effect on left ventricular (LV) mass, a risk factor for cardiovascular disease (CVD). We examined the relationship between acculturation and LV mass in a multiethnic cohort of White, African-American, Hispanic and Chinese subjects.MethodsCardiac magnetic resonance assessment was available for 5004 men and women, free of clinical CVD at baseline. Left ventricular mass index was evaluated as LV mass indexed by body surface area. Acculturation was characterized based on language spoken at home, place of birth and length of stay in the United States (U.S.), and a summary acculturation score ranging from 0 = least acculturated to 5 = most acculturated. Mean LV mass index adjusted for traditional CVD risk factors was compared across acculturation levels.ResultsUnadjusted mean LV mass index was 78.0 ± 16.3 g/m(2). In adjusted analyses, speaking exclusively English at home compared to non-English language was associated with higher LV mass index (81.3 ± 0.4 g/m(2) vs 79.9 ± 0.5 g/m(2), p = 0.02). Among foreign-born participants, having lived in the U.S. for ≥ 20 years compared to < 10 years was associated with greater LV mass index (81.6 ± 0.7 g/m(2) vs 79.5 ± 1.1 g/m(2), p = 0.02). Compared to those with the lowest acculturation score, those with the highest score had greater LV mass index (78.9 ± 1.1 g/m(2) vs 81.1 ± 0.4 g/m(2), p = 0.002). There was heterogeneity in which measure of acculturation was associated with LV mass index across ethnic groups.ConclusionsGreater acculturation is associated with increased LV mass index in this multiethnic cohort. Acculturation may involve stress-related processes as well as behavioral changes with a negative effect on cardiovascular health

Strong oviposition preference for Bt over non-Bt maize in Spodoptera frugiperda and its implications for the evolution of resistance

BACKGROUND: Transgenic crops expressing Bt toxins have substantial benefits for growers in terms of reduced synthetic insecticide inputs, area-wide pest management and yield. This valuable technology depends upon delaying the evolution of resistance. The ‘high dose/refuge strategy’, in which a refuge of non-Bt plants is planted in close proximity to the Bt crop, is the foundation of most existing resistance management. Most theoretical analyses of the high dose/refuge strategy assume random oviposition across refugia and Bt crops. RESULTS: In this study we examined oviposition and survival of Spodoptera frugiperda across conventional and Bt maize and explored the impact of oviposition behavior on the evolution of resistance in simulation models. Over six growing seasons oviposition rates per plant were higher in Bt crops than in refugia. The Cry1F Bt maize variety retained largely undamaged leaves, and oviposition preference was correlated with the level of feeding damage in the refuge. In simulation models, damage-avoiding oviposition accelerated the evolution of resistance and either led to requirements for larger refugia or undermined resistance management altogether. Since larval densities affected oviposition preferences, pest population dynamics affected resistance evolution: larger refugia were weakly beneficial for resistance management if they increased pest population sizes and the concomitant degree of leaf damage. CONCLUSIONS: Damaged host plants have reduced attractiveness to many insect pests, and crops expressing Bt toxins are generally less damaged than conventional counterparts. Resistance management strategies should take account of this behavior, as it has the potential to undermine the effectiveness of existing practice, especially in the tropics where many pests are polyvoltinous. Efforts to bring down total pest population sizes and/or increase the attractiveness of damaged conventional plants will have substantial benefits for slowing the evolution of resistance

Evaluating and Explaining Large Language Models for Code Using Syntactic Structures

Large Language Models (LLMs) for code are a family of high-parameter, transformer-based neural networks pre-trained on massive datasets of both natural and programming languages. These models are rapidly being employed in commercial AI-based developer tools, such as GitHub CoPilot. However, measuring and explaining their effectiveness on programming tasks is a challenging proposition, given their size and complexity. The methods for evaluating and explaining LLMs for code are inextricably linked. That is, in order to explain a model's predictions, they must be reliably mapped to fine-grained, understandable concepts. Once this mapping is achieved, new methods for detailed model evaluations are possible. However, most current explainability techniques and evaluation benchmarks focus on model robustness or individual task performance, as opposed to interpreting model predictions. To this end, this paper introduces ASTxplainer, an explainability method specific to LLMs for code that enables both new methods for LLM evaluation and visualizations of LLM predictions that aid end-users in understanding model predictions. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes, by extracting and aggregating normalized model logits within AST structures. To demonstrate the practical benefit of ASTxplainer, we illustrate the insights that our framework can provide by performing an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects. Additionally, we perform a user study examining the usefulness of an ASTxplainer-derived visualization of model predictions aimed at enabling model users to explain predictions. The results of these studies illustrate the potential for ASTxplainer to provide insights into LLM effectiveness, and aid end-users in understanding predictions

Long-range transport and microscopy analysis of Sangay volcanic ashes in Ecuador

This study aims to conduct a spatiotemporal analysis of the long-range transportation of volcanic ashes that originates from the eruption of the Sangay volcano and reached Guayaquil during the months of June 2020; September 2020; and April 2021. The particulate matter data (PM2.5) was obtained using a low-cost air quality sensor. During the wet season of 2020 (Jan–May), PM2.5 average concentrations were 6 ± 2 μg m−3 while during the dry season of 2020 (July–Nov), PM2.5 average concentrations were 16 ± 3 μg m−3 in Guayaquil. The most prominent plumes occurred on September 20th of 2020, a month with no rain but high wind speeds created by the Andes Mountain topography to the coast. During this event, PM2.5 concentrations started at 12:00 UTC-5 in a volcanic plume event that lasted 4 h with a maximum peak of 133 + 40 μg m−3. Electron microscopy of selected samples showed that the ashes of the three eruptions may differ in size and morphology. EDX analysis reveals that the ash contains certain elements—C, Si, Na, Mg, Al, Ca, S, and Fe—in similar proportions. In summary, this study remarks on the meteorological role and the long-range transport of Sangay volcanic ashes

Toward a Theory of Causation for Interpreting Neural Code Models

Neural Language Models of Code, or Neural Code Models (NCMs), are rapidly progressing from research prototypes to commercial developer tools. As such, understanding the capabilities and limitations of such models is becoming critical. However, the abilities of these models are typically measured using automated metrics that often only reveal a portion of their real-world performance. While, in general, the performance of NCMs appears promising, currently much is unknown about how such models arrive at decisions. To this end, this paper introduces $do_{code}$, a post hoc interpretability method specific to NCMs that is capable of explaining model predictions. $do_{code}$ is based upon causal inference to enable programming language-oriented explanations. While the theoretical underpinnings of $do_{code}$ are extensible to exploring different model properties, we provide a concrete instantiation that aims to mitigate the impact of spurious correlations by grounding explanations of model behavior in properties of programming languages. To demonstrate the practical benefit of $do_{code}$, we illustrate the insights that our framework can provide by performing a case study on two popular deep learning architectures and ten NCMs. The results of this case study illustrate that our studied NCMs are sensitive to changes in code syntax. All our NCMs, except for the BERT-like model, statistically learn to predict tokens related to blocks of code (\eg brackets, parenthesis, semicolon) with less confounding bias as compared to other programming language constructs. These insights demonstrate the potential of $do_{code}$ as a useful method to detect and facilitate the elimination of confounding bias in NCMs.Comment: Accepted to appear in IEEE Transactions on Software Engineerin