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Conventional oxidation processes for alkyl aromatics to ketones employ oxidants that tend to generate harmful byproducts and cause severe equipment corrosion, ultimately creating critical environmental problems. Thus, in this study, a practical, efficient, and green method was developed for the synthesis of aromatic ketones by applying a bis(2-butoxyethyl) ether/O2 system under external catalyst-, additive-, and base-free conditions. This O2-mediated oxidation system can tolerate various functional groups and is suitable for large-scale synthesis. Diverse target ketones were prepared under clean conditions in moderate-to-high yields. The late-stage functionalization of drug derivatives with the corresponding ketones and one-pot sequential chemical conversions to ketone downstream products further broaden the application prospects of this approach. [ABSTRACT FROM AUTHOR]

We present an environment-friendly and highly efficient method for the oxidation of aromatic alcohols to carboxylic acids or ketones in air via light irradiation under external catalyst-, additive-, and base-free conditions. The photoreaction system exhibits a wide substrate scope and the potential for large-scale applications. Most of the desired products are easily obtained via recrystallization and separation from low-boiling reaction medium acetone in good yields, and the products can be subsequent directly transformed without further purification. [ABSTRACT FROM AUTHOR]

Data sharing between different organizations is critical in supporting decision making in civil infrastructure engineering projects (e.g., transportation projects). Understanding the characteristics of data-sharing-related factors in civil infrastructure engineering is crucial for the civil engineering community to identify the priorities in promoting data sharing. The ASCE Data Sensing and Analysis (DSA) Committee initiated an investigation on data-sharing barriers in civil infrastructure engineering. This work produced (1) a classification of data-collection and data-use contexts in the domain of civil infrastructure engineering; (2) a comprehensive classification of data-sharing barriers and data-sharing promotion strategies in this domain; and (3) a synthesis of the opinions about the current practices, future vision, barriers, and data-sharing promotion strategies from civil infrastructure professionals and researchers using an online survey. The survey results revealed that (1) more data-sharing activities are occurring within the organizations, such as academia, government agencies, and for-profit corporations, compared with data exchanges among these communities; (2) "if there is a cost sharing in the data sharing," is the condition that makes data providers more willing to share data with others; and (3) compared with technology improvements, the protocol of data sharing in the business domain has more potential in promoting data sharing. Meanwhile, this research identified the top five priorities among 20 data-sharing promotion strategies, which are protocol in industry and organization, data-standard regulation, data validity, data security protection, and large-scale data-sharing technology. These five priorities play critical roles in a roadmap showing how policy, business models, and technical solutions should cooperate to promote data sharing in civil infrastructure engineering. [ABSTRACT FROM AUTHOR]

The structural deterioration knowledge in existing mathematical physics models offers a unique opportunity to develop data-driven, physics-informed machine learning (ML) for enhanced bridge deterioration prediction. However, existing physics models typically represent deterioration knowledge using mathematical symbols and are limited in capturing the semantic meanings and relationships of the symbols, making the models not understandable and operable by machines. As a result, it is challenging to automatically instantiate mathematical physics models to deduce knowledge-driven deterioration facts to inform the learning. To address this challenge, this paper proposes a new structural deterioration knowledge ontology (DT-KL-Onto). The ontology represents structural deterioration knowledge in mathematical physics models as related to structure deficiency, deficiency property, and deficiency contributing factor in a formal, explicit, and semantic way to allow for automated model instantiation. To evaluate the performance of the ontology, the DT-KL-Onto was implemented in automatically instantiating structural deterioration-related mathematical physics models using bridge design, construction, and inspection data. The evaluation results show that the ontology was able to accurately instantiate the models in an automated way: the coefficient of determination and mean squared error between the deterioration facts deduced from the automated instantiation and those from the manual instantiation are 1.0000 and 0.0000, respectively. Two bridge deterioration prediction models were also developed to further demonstrate the benefits of using the deterioration knowledge captured by the ontology to inform the learning. Compared to the purely ML-based prediction model, the physics-informed ML-based prediction model improved the performance of predicting the condition ratings of concrete decks by 10.0%. This research contributes to the body of knowledge by offering an ontology that can semantically represent structural deterioration knowledge in mathematical physics models to enable automated model instantiation. This research thus has the potential to fully unleash the power of the large amount of bridge data and deterioration knowledge that exist to develop next-generation learning machines for data-driven, physics-informed ML-based deterioration prediction and maintenance decision making. [ABSTRACT FROM AUTHOR]

The development of high‐efficiency and low‐cost heterogeneous catalysts with superior selectivity for the reduction of nitro compounds is desirable. Herein, we prepared Co‐based nitrogen‐doped meso‐microporous carbon (Co/N−C) derived from polyvinylpyrrolidone (PVP)‐coated zeolitic imidazolate framework (ZIF)‐67 as a heterogeneous catalyst for the synthesis of aromatic amines via in‐situ restricted pyrolysis. The structure, morphology, and catalytic activity of Co/N−C were investigated, and the results indicated that Co/N−C‐800 has the combined advantage of the excellent catalytic activity of cobalt nanoparticles and the high mass transfer rate of the N‐doped meso‐microporous carbon framework, achieving superior catalytic ability for the selective hydrogenation of 20 representative nitroaromatic compounds. Co/N−C‐800 was recyclable by simple separation due to the strong magnetism of the Co nanoparticles and exhibited excellent reusability, with 100 % yield of aniline even after six cycles. The catalytic reduction may be attributed to the dissociation of hydrazine to H+/H− via the action of Co/N−C. [ABSTRACT FROM AUTHOR]

Existing data-driven bridge deterioration prediction methods mostly learn from abstract inventory data from a single source to predict the future conditions of bridges. Bridge inventory data [e.g., the National Bridge Inventory (NBI) data] are undoubtedly important but are not enough. They mainly describe bridge conditions using abstract, aggregated condition ratings that do not contain detailed information about bridge deficiencies and maintenance actions, thus limiting the performance of data-driven deterioration prediction and its usefulness in supporting bridge maintenance decision making. Learning from the wealth of heterogeneous (i.e., structured and unstructured) bridge data from multiple sources opens an unprecedented opportunity for enhanced data-driven bridge deterioration prediction. Such data include structured NBI and National Bridge Elements (NBE) data, structured traffic and weather data, and unstructured textual bridge inspection reports. To capitalize on this opportunity, this paper proposes a novel bridge data analytics framework, which allows for the extraction, integration, and analysis of structured and unstructured bridge data from different sources. At the cornerstone of this framework is a proposed deep learning–based bridge deterioration prediction method for analyzing and learning from the integrated bridge data to predict bridge deterioration. The proposed method includes three primary components: manifold learning for embedding the integrated bridge data into a low-dimensional dense space, cost-sensitive learning for modulating the misclassification costs to address the class imbalance in the data, and recurrent neural networks for learning from the embedded and balanced data from past years to predict the conditions of the primary bridge components (decks, superstructures, and substructures) in the next year. The method was evaluated in predicting the condition ratings of the decks, superstructures, and substructures of 2,646 bridges in the state of Washington. It achieved an average macroprecision and macrorecall of 89.9% and 85.8%, which are 15.0% and 22.4% higher than those achieved by learning from only NBI data. [ABSTRACT FROM AUTHOR]

Bridge owners possess important data sources, such as bridge construction records and inspection and maintenance reports, which hold great promise for improving understanding of bridge deterioration and informing maintenance decision making. However, the valuable data buried in these documents are not being fully exploited due to their unstructured nature. Domain-specific semantics are needed to facilitate analysis of the data based on content and domain-specific meaning and to bridge terminology gaps across different sources. To address this need, this paper proposes a bridge ontology (BridgeOnto) that captures deterioration knowledge and semantics related to bridge elements, deficiencies, deficiency causes, and maintenance actions. The proposed ontology was evaluated through answering competency questions, automated consistency and redundancy checking, expert interviews, and application-oriented validation. The BridgeOnto was implemented in supporting automated extraction of information describing bridge conditions and maintenance actions from bridge inspection reports. The experimental results show that the ontology was able to improve information extraction precision, recall, and F-1 measure by 11.7%, 12.4%, and 12.0%, on average. This research contributes to the body of knowledge by offering an ontology that can capture the key deterioration knowledge areas with sufficient coverage and in-depth classification for achieving adequate support for bridge document analytics. By allowing better access to and use of crucial textual data, this research has the potential to improve the understanding of bridge deterioration and enhance maintenance decisions. [ABSTRACT FROM AUTHOR]

Bridge inspection reports are important sources of technically detailed data/information about bridge conditions and maintenance history, yet remain untapped for bridge deterioration prediction. To capitalize on these reports for improved bridge deterioration prediction, there is a need for dependency parsing methods, in order to extract dependency relations from the reports for linking the isolated words into concepts and representing the semantically low concepts in a semantically rich structured way. To address this need, this paper proposes a novel semantic neural network ensemble (NNE)–based dependency parsing methodology. It uses a similarity-based method to sample similarly distributed configurations into the same clusters, a set of constituent neural network (NN) classifiers to learn from both the syntactic and semantic text features of the similarly distributed and therefore more easily separable configurations, and a combiner classifier to capture the classification patterns of the NN classifiers to make final predictions on the transition types. The proposed dependency parsing methodology was evaluated in extracting dependency relations from bridge inspection reports for representing information—about bridge conditions and maintenance actions—in a semantically rich structured way. It achieved a precision, recall, and F-1 measure of 96.6%, 90.4%, and 93.3% with a margin of error of 3.8%, 4.4%, and 3.8% at the semantic information element level, and 88.2%, 81.5%, and 84.7% with a margin of error of 5.4%, 5.8%, and 5.4% at the semantic information set level, respectively. [ABSTRACT FROM AUTHOR]

Data buried in textual bridge inspection reports offer great promise for enhanced data-driven bridge deterioration prediction. However, learning from these reports is challenging because they typically use multiple concept names to refer to the same entity and typically describe multiple instances of the same type of deficiency. Such multiple names and instances increase the dimensionality and the sparsity of the feature space, which would cause overfitting to a particular feature, undermine the generalizability of the machine learning models, and compromise the performance of the data-driven prediction. To address this challenge, this paper proposes a new hybrid data fusion method. It combines an unsupervised named entity normalization method and an entropy-based numerical data fusion method to fuse concept names and numerical data, respectively. The proposed normalization method uses an n -gram model to generate candidate canonical identifier names and utilizes corpus statistics and lexical patterns to fuse the multiple concept names into a candidate name that balances abstraction and detailedness. The proposed fusion method uses data discretization and information entropy to fuse the multiple deficiency measures (of the instances) into a single representation. The hybrid fusion method was validated in fusing data extracted from textual bridge inspection reports for supporting the prediction of future bridge condition ratings. Learning from the fused data, compared to learning from the unfused data, improved the accuracies of predicting the ratings of decks, superstructures, and substructures by 8.0%, 8.5%, and 7.9%, respectively. [ABSTRACT FROM AUTHOR]

Rolling circle amplification is an isothermal method for amplifying DNA. The technique rapidly synthesizes a long (more than 1000 bases), linear, tandemly-repetitive single-stranded DNA chain by a short (less than 100 bases long) circular template. The reaction may proceed in either linear or exponential amplification modes and may provide copies of the circle up to 104-fold. The robustness, speed, and sensitivity of the rolling circle amplification strategies have gained widespread application in DNA and protein assays, single nucleotide polymorphism, in situ signal amplification, drug discovery, and nanotechnology. [ABSTRACT FROM PUBLISHER]

Teleseismic imaging techniques utilizing mode converted/scattered waves are gaining importance due to the deployment of increasingly dense broad-band seismograph arrays. Although common-conversion point (CCP) stacking is widely used to determine structure from Ps or Sp scattered wavefields isolated by receiver function (RF) processing, this method is limited due to its assumption of a layered medium: Dipping events and diffractions are not treated correctly. As an extension of previous 2-D generalized Radon transform (GRT) imaging methods, we present a 3-D Kirchhoff-approximate imaging technique to migrate scattered waves in 3-D. We first derive the 3-D migration formula for P-to-S conversions using the GRT solution to the linear inverse elastic wave scattering problem. Then we illustrate the Kirchhoff method using finite-difference synthetic seismograms from several 3-D models.Next, we apply the method to two portable broad-band array data sets in the western United States to image the Mendocino Triple Junction and the High Lava Plains (HLP) crust and uppermost mantle structures. From the HLP data, we construct the Ps transmission coefficient images with three-component Green's functions. The 1.0 and 0.5 Hz images show a continuous undulating Moho, as well as three negative upper-mantle events at 50–80 km depth. Compared to the CCP images, the Moho is more clearly imaged, particularly near 117.5°W–117.8°W at the western edge of the Owyhee Plateau. The three negative events in the upper mantle correlate well with the top of three low-Vs zones (−3 per cent contour) in the Rayleigh wave tomography model. The migrated Ps RF data from Mendocino clearly image the rapid decrease in depth of the lithosphere–asthenosphere boundary from ∼65 km beneath the subducting Gorda Plate to 30–50 km beneath the Coast Ranges slab window. The final image is consistent with, but has higher resolution than the Vs structure determined from joint receiver function/Rayleigh wave inversions and images from Ps CCP stacks. Although restricted by array aperture and spatial aliasing criteria to dense, large aperture arrays, the 3-D Kirchhoff technique provides a promising tool for imaging complicated lithospheric structures that are poorly mapped or unresolved by more traditional imaging methods. [ABSTRACT FROM AUTHOR]

Abstract: A methodology for the dimerization of substituted styrenes is reported using a PhP(O)HOR/Mn(II)/Co(II)O2 catalyst, in which the alkyl phenylphosphinate compound acted as a catalyst rather than a substrate as is generally observed. Mechanistic studies suggested the reaction most likely proceeded via addition–elimination. [Copyright &y& Elsevier]

Abstract: The migration of the Mendocino Triple Junction in northern California creates a complicated lithosphere–asthenosphere boundary system at shallow depths (<60km), following the progressive transition from Cascadia subduction to San Andreas transform motion. It provides a natural laboratory to examine lithospheric evolution associated with slab removal in an active tectonic setting. However, pathways of the asthenospheric sources that fill the slab-free region created by the triple junction migration remain unclear. Previous active source profiles and body-wave tomography are limited to either crustal or a deeper upper mantle scale, respectively. In this study, we present a three-dimensional shear velocity model from joint inversion of ambient noise (8–30s) and ballistic Rayleigh wave dispersion curves (22–100s), as well as Ps receiver functions, using 111 stations from the Flexible Array Mendocino Experiment, USArray Transportable Array and the regional Berkeley Digital Seismic Network. In the crust, we have observed the low-Vs Franciscan Complex in the Coast Ranges and the relatively high-Vs Great Valley ophiolite abutting the low-Vs Sierran basement. The low-Vs uppermost mantle imaged near the sudden steepening of the subducted oceanic slab extends seismic evidence for forearc mantle serpentinization further south along the Cascadia margin. In addition to the asthenosphere beneath the Gorda plate, the joint inversion Vs model further identifies three other young asthenospheres resulting from different partial melting mechanisms. Northward motion of the triple junction causes asthenospheric flow both from under the Gorda plate and from the cooling former mantle wedge left under the Great Valley and Sierra Nevada, imaged from the joint inversion as a relatively deep (>75km) low-Vs anomaly. These two mantle flows appear to begin mixing ~100km south of the southern edge of the Gorda plate in the slab window region. We speculate that the latter provides the wedge-type geochemical signature seen in the Coast Range volcanic rocks, reconciling slab window models and volcanic geochemistry. This ‘staggered’ upwelling model proposed here also explains the ~3Myr delay in onset of volcanism after triple junction migration. [Copyright &y& Elsevier]

Mafic volcanism gradually encroaching on the tectonically stable Colorado Plateau in the southwestern United States appears to originate from within Paleoproterozoic lithosphere. New Hf and Nd isotope data strengthen evidence for magma source heterogeneity; other geochemical signatures show these sources to consist dominantly of peridotite. Tomographic and receiver function analyses reveal that young volcanism occurs above or outboard of a pronounced shallowing in the lithosphere-asthenosphere boundary. Melt extraction extends to the base of lithosphere thinned to <75 km, with more shallowly derived melts characterized by higher degrees of partial melting. Accordingly, decompression melting of a reactivated chemical boundary layer ± asthenosphere, rather than in situ lithospheric melting or melting of lithospheric mantle drips/delaminations, appears to be responsible for recent Colorado Plateau magma generation. [ABSTRACT FROM AUTHOR]

Abstract: A model ternary heparin conjugate by direct covalent bond strategy has been developed, in which modified heparin using active mix anhydride as intermediate conjugates with model drug molecule and model specific ligand, respectively. Designed ester bonds between model drug and heparin facilitate hydrolysis kinetics research. The strategy can be extended to design and synthesize a targeted drug delivery system. The key point is to use mixed anhydride groups as activating intermediates to mediate the synthesis of the ternary heparin conjugate. Formation of mixed anhydride is detected by the conductimetry experiment. The ternary heparin conjugate is characterized by 13C NMR, FT-IR and GPC, respectively. The decreased trend on degree of substitution (DS) is consistent with that of introduced anticancer drug and specific ligand in drug delivery system. Moreover, their anticoagulant activity is evaluated by measuring activated partial thromboplastin time (APTT) and anti-factor Xa activity. The results show that model ternary heparin conjugate with reduced anticoagulant activity may avoid the risk of severe hemorrhagic complication during the administration and is potential to develop a safe and effective drug delivery system on anticancer research. [Copyright &y& Elsevier]