Structural, textural, and chemical controls on the OH stretching vibrations in serpentine-group minerals

The OH stretching vibrational properties of eight serpentine samples from veins of the New Caledonian ophiolite have been investigated by Fourier-transform infrared spectroscopy (FTIR) in the mid-infrared and near-infrared ranges and by Raman spectroscopy. The samples were selected for their monophasic composition (Lz: lizardite; Ctl: chrysotile; and Atg: antigorite) making them representative of the three serpentine species. Comparison of fundamental and overtone spectra allowed us to interpret most of the observed bands and to propose consistent spectral decomposition in individual components. The OH stretching bands related to intrinsic vibrational properties of the minerals are distinguished from those associated with chemical substitutions in octahedral sites (mainly Fe and Ni for Mg substitutions). Observations made on the most symmetric Lz are consistent with previous interpretations and underline the effect of macroscopic parameters on OH stretching bands in the FTIR spectra. The major importance of the distribution of OH bond lengths in the broadening of the vibrational signals of the less symmetric and more distorted Atg is confirmed. The combination of the three spectroscopic methods makes it possible to unravel the occurrence of two different types of interlayer OH environments in Ctl nanotubes. One corresponds to the features observed at 3684 and 7171 cm−1 in the fundamental and overtone spectra, respectively, and is similar to the local OH environment observed in Lz. The other corresponds to broader signals observed at 3693 and 7200 cm−1 in the fundamental and overtone spectra, respectively. It reflects a distribution of OH bond lengths likely related to local structural misfits between adjacent layers in the tubular structure of Ctl.

Multilane Microscopic Modeling to Measure Mobility and Safety Consequences of Mixed Traffic in Freeway Weaving Sections

Weaving sections are components of highway networks that introduce a heightened likelihood for bottlenecks and collisions. Automated vehicle technology could address this as it holds considerable promise for transportation mobility and safety improvements. However, the implications of combining automated vehicles (AuVs) with traditional human-driven vehicles (HuVs) in weaving freeway sections have not been quantitatively measured. To address this gap, this paper objectively experimented with bidirectional (i.e., longitudinal and lateral) motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. Our research begins by establishing a multilane microscopic model for studied vehicle types (i.e., AuV and HuV) from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the Highway Capacity Manual. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share. At the same time, steadiness in the average speed of traffic was experienced with increasing AuV share. The results also revealed that a 95% AuV share could reduce potential conflicts by 94.28%. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state and the optimal AuV share for the current inflow rate in weaving sections.

Competition for fluctuating resources reproduces statistics of species abundance over time across wide-ranging microbiotas

Across diverse microbiotas, species abundances vary in time with distinctive statistical behaviors that appear to generalize across hosts, but the origins and implications of these patterns remain unclear. Here, we show that many of these patterns can be quantitatively recapitulated by a simple class of resource-competition models, in which the metabolic capabilities of different species are randomly drawn from a common statistical ensemble. Our coarse-grained model parametrizes the intrinsic consumer-resource properties of a community using a small number of macroscopic parameters, including the total number of resources, typical resource fluctuations over time, and the average overlap in resource-consumption profiles across species. We elucidate how variation in these parameters affects various time series statistics, enabling macroscopic parameter estimation and comparison across wide-ranging microbiotas, including the human gut, saliva, and vagina, as well as mouse gut and rice. The successful recapitulation of time series statistics across microbiotas suggests that resource competition generally acts as a dominant driver of community dynamics. Our work unifies numerous time series patterns under one model, clarifies their origins, and provides a framework to infer macroscopic parameters of resource competition from longitudinal studies of microbial communities.

Machine learning active-nematic hydrodynamics

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.

Water Reuse Study from Urban WWTPs via c-Ultrafiltration and Ozonation Technologies: Basis for Resilient Cities and Agriculture

The water–development nexus is essential for the advancement and progress of cities in the face of problems such as climate change, water security and increasing environmental stress in the agricultural sector. Aiming for a circular economy and, at the same time, improving the resilience of water supply alternatives and achieving a goal of zero waste, this work presents a technical–economic study of a novel continuous ultrafiltration (c-UF) system with self-cleaning capacity coupled to an ozonation process, for the treatment of urban WWTP effluent. The removal efficiencies achieved were analysed both through macroscopic parameters (suspended solids, turbidity) and for the most frequently occurring contaminants of emerging concern (CECs). Consequently, an effluent suitable for irrigation was obtained, with a total recovery factor of 97.92%, a concentration of suspended solids (SS) below 1 mg L−1, 0.06 NTU turbidity and toxicity free, complying with the new European Regulation on Water Reuse (EU 2020/741). A comparative analysis of the proposed process with regard to conventional tertiary treatment revealed that the proposed process was 39.1% more economic, with a cost of 0.0325 € m−3. This alternative treatment will be of great interest because of its favourable technical–economic characteristics, being postulated as a basic process for implementation in modern water reuse plants.

Silver Sulfadiazine Cream Treatment Results in More Wound Contraction and More Itch in a Standardized Porcine Scald Model

A variety of dressings is available for the treatment of partial-thickness wounds, but none has strong evidence supporting their beneficial effect on healing. This may be due to variation in the type and depth of wounds in clinical studies. The aim of this study was to use a standardized porcine wound model to compare three dressings commonly used in burn centers for partial-thickness burns. Partial-thickness scalds were made on the flanks of pigs. Wounds were treated with silver sulfadiazine (SSD, flammazine), a hydrofiber dressing, or glycerol-preserved allogeneic (pig) skin. The healing process was monitored for 8 weeks. Macroscopic parameters were the itching behavior, the cosmetic appearance of the scars, and contraction. Microscopic parameters were the inflammatory response, myofibroblast influx, and the numbers of nerves. All wounds were closed on day 14 and wound infection did not occur. Treatment with SSD resulted in significantly more wound contraction compared to treatment with glycerol-preserved pig skin. Animals treated with SSD suffered more from itching (scratching) during the first 2 weeks after wounding. The number of nerves in healing wounds of these animals was significantly higher compared to wounds treated with hydrofiber dressing or allogeneic skin. In our standardized porcine partial-thickness wound model, treatment with SSD resulted in less favorable wound healing. Compared to treatment with glycerol-preserved allogeneic skin, SSD resulted in more contraction.

Связь макроскопических характеристик твердого тела с энергией связи иона в решетке металлов

The paper examines the relationship between the macroscopic parameters, such as the Young's modulus in the Hooke's law, the sound speed and the Debye characteristic temperature, with the binding energy of an individual atom. A formula for calculating the elastic deformation modulus is proposed. A simple formula is obtained to calculate the sound speed in a metal rod. It is suggested that the Debay characteristic temperature is connected with the binding energy of the ion in the solid lattice.