Application of Fickian and non-Fickian diffusion models to study moisture diffusion in asphalt mastics

The objective of this study was to investigate certain aspects of asphalt mastic moisture diffusion characteristics in order to better understand the moisture damage phenomenon in asphalt mixtures. Moisture sorption experiments were conducted on four asphalt mastics using an environmental chamber capable of automatically controlling both relative humidity (85 %) and temperature (23 °C). The four mastics tested were identical in terms of bitumen type (40/60 pen), bitumen amount (25 % by of wt% total mix), mineral filler amount (25 % by wt%) and fine aggregate amount (50 % by wt%). The materials differed in terms of mineral filler type (granite or limestone) and fine aggregate type (granite or limestone). Preliminary data obtained during the early part of the study showed certain anomalous behavior of the materials including geometry (thickness)-dependent diffusion coefficient. It was therefore decided to investigate some aspects related to moisture diffusion in mastics by applying the Fickian and two non-Fickian (anomalous) diffusion models to the moisture sorption data. The two non-Fickian models included a two-phase Langmuir-type model and a two-parameter time-variable model. All three models predicted moisture diffusion in mastics extremely well (R 2 > 0.95). The observed variation of diffusion coefficient with thickness was attributed in part to microstructural changes (settlement of the denser fine aggregates near the bottom of the material) during the rather long-duration diffusion testing. This assertion was supported by X-ray computed tomography imaging of the mastic that showed significant accumulation of aggregate particles near the bottom of the sample with time. The results from the Langmuir-type model support a two-phase (free and bound) model for moisture absorbed by asphalt mastic and suggests about 80 % of absorbed water in the free phase remain bound within the mastic. The results also suggest that moisture diffusion in asphalt mastic may be time-dependent with diffusion decreasing by about four times during a typical diffusion test lasting up to 500 h. The study concludes that both geometry and time-dependent physical characteristics of mastic are important factors to consider with respect to moisture diffusion in asphalt mastics

Synthesis of Janus compounds for the recognition of G-U mismatched nucleobase pairs

The design and synthesis of two Janus-type heterocycles with the capacity to simultaneously recognize guanine and uracyl in G-U mismatched pairs through complementary hydrogen bond pairing is described. Both compounds were conveniently functionalized with a carboxylic function and efficiently attached to a tripeptide sequence by using solid-phase methodologies. Ligands based on the derivatization of such Janus compounds with a small aminoglycoside, neamine, and its guanidinylated analogue have been synthesized, and their interaction with Tau RNA has been investigated by using several biophysical techniques, including UV-monitored melting curves, fluorescence titration experiments, and 1H NMR. The overall results indicated that Janus-neamine/guanidinoneamine showed some preference for the +3 mutated RNA sequence associated with the development of some tauopathies, although preliminary NMR studies have not confirmed binding to G-U pairs. Moreover, a good correlation has been found between the RNA binding affinity of such Janus-containing ligands and their ability to stabilize this secondary structure upon complexation

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

The anatomy of a pyroclastic density current: the 10 July 2015 event at Volcán de Colima (Mexico)

Pyroclastic density currents (PDCs) represent one of the most dangerous phenomena occurring in explosive volcanic eruptions, and any advance in the physical understanding of their transport and sedimentation processes can contribute to improving their hazard assessment. The 10–11 July 2015 eruption at Volcán de Colima provided a unique opportunity to better understand the internal behaviour of PDCs based on seismic monitoring data. On 10 July 2015, the summit dome collapsed, producing concentrated PDCs that filled the main channel of the Montegrande ravine. A lahar monitoring station installed 6 km from the volcano summit recorded a PDC before being completely destroyed. Real-time data acquisition from a camcorder and a geophone that were part of the station, along with field observations and grain-size data of the pyroclastic deposits, are used here to interpret the internal flow structure and time-variant transport dynamics of low-volume, valley-confined concentrated PDCs. The PDC that reached the monitoring station moved at a velocity of ~ 7 m/s and filled a 12-m-deep channel. The outcrops show massive, block-and-ash flow deposits with trains of coarse clasts in the middle and towards the top of the depositional units. The seismic record gathered with the geophone was analysed for the time window when the flow travelled past the sensor. The geophone record was also compared with the recordings of a broadband seismic station located nearby. Two main frequency ranges were recognised which could be correlated with the basal frictional forces exerted by the flow on the channel bed (10–20 Hz) and a collisional regime (20–40 Hz) interpreted to be associated with a clast segregation process (i.e. kinematic squeezing). This latter regime promoted the upward migration of large blocks, which subsequently deviated towards the margin of the flow where they interacted with the sidewall of the main channel. The energy calculated for both seismic components shows that the collisional regime represents 30% of the total energy including an important sidewall-stress component. These results, gathered directly from a moving flow, contribute to unravelling the internal behaviour of concentrated PDCs providing information on energy partitioning and particle-particle interactions. This confirms previous assumptions inferred from field observations, and tested by analogue or numerical modelling. The nature of the contact between grains is still poorly documented in natural PDCs, and there is still much uncertainty and discussion about dominant forces in such currents. Data reported here may thus be useful to better constrain the physics of low-volume, valley-confined concentrated PDCs and our findings need to be considered in theoretical models. In parallel, this study shows how geophones can provide a cheap alternative for PDC detection. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.This work was supported by the PAPIIT-DGAPA IN105116 project granted to L. Capra. V. Coviello is grateful for his DGAPA-UNAM postdoctoral fellowship, and D. Doronzo for his Juan de la Cierva contract (JdC 2015) – MINECO. The SPOT image was obtained through a collaborative agreement between the UNAM and the Agrifood-Fishery Mexican Service (SIAP) - ERMEX, under the license of BAirbus Defense & Space^.Peer reviewe

A simple ligand that selectively targets CUG trinucleotide repeats and inhibits MBNL protein binding

This work describes the rational design, synthesis, and study of a ligand that selectively complexes CUG repeats in RNA (and CTG repeats in DNA) with high nanomolar affinity. This sequence is considered a causative agent of myotonic dystrophy type 1 (DM1) because of its ability to sequester muscleblind-like (MBNL) proteins. Ligand 1 was synthesized in two steps from commercially available compounds, and its binding to CTG and CUG repeats in oligonucleotides studied. Isothermal titration calorimetry studies of 1 with various sequences showed a preference toward the T-T mismatch (Kd of 390 ± 80 nM) with a 13-, 169-, and 85-fold reduction in affinity toward single C-C, A-A, and G-G mismatches, respectively. Binding and Job analysis of 1 to multiple CTG step sequences revealed high affinity binding to every other T-T mismatch with negative cooperativity for proximal T-T mismatches. The affinity of 1 for a (CUG)4 step provided a Kd of 430 nM with a binding stoichiometry of 1:1. The preference for the U-U in RNA was maintained with a 6-, >143-, and >143-fold reduction in affinity toward single C-C, A-A, and G-G mismatches, respectively. Ligand 1 destabilized the complexes formed between MBNL1N and (CUG)4 and (CUG)12 with IC50 values of 52 ± 20 μM and 46 ± 7 μM, respectively, and Ki values of 6 ± 2 μM and 7 ± 1 μM, respectively. These values were only minimally altered by the addition of competitor tRNA. Ligand 1 does not destabilize the unrelated RNA-protein complexes the U1A-SL2 RNA complex and the Sex lethal-tra RNA complex. Thus, ligand 1 selectively destabilizes the MBNL1N-poly(CUG) complex

Regional patterns in the food habits of the Mexican spotted owl

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