A new technological approach proposed for distillate production using immobilized cells

A new technological approach to distillate production using immobilized cells was investigated. The effect of temperature on the main volatile by-products in distillates was determined. Wines produced by delignified cellulose-, gluten- and kissiris-supported biocatalysis were used as starting materials. The produced distillates were analyzed for ethanol, methanol, acetaldehyde, ethyl acetate, propanol-1, isobutanol and amyl alcohol content. The results showed that distillates from delignified cellulosic material (DCM) at 16 degrees C contained smaller amounts of amyl alcohols, 57% of that produced by gluten and 32% of that produced by kissiris. The ethyl acetate content of distillates from DCM improved the aroma of distillates. These results agree with those of sensory evaluation. Subsequently, the scale-up for low-temperature distillate production at 16 degrees C using DCM was further investigated. A new version of an industrial multi-stage fixed bed tower (MFBT) bioreactor with a capacity of 11,000 l proved to be suitable for continuous fermentation by DCM-supported biocatalysis. Economic analysis showed a reduction in the cost of almost 30% for distillate production and 78% for wine production.     

Automated building image extraction from 360° panoramas for postdisaster evaluation

After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain new knowledge and extract lessons from the event. However, in many cases, the images collected are captured without sufficient spatial context. When damage is severe, it may be quite difficult to even recognize the building. Accessing images of the predisaster condition of those buildings is required to accurately identify the cause of the failure or the actual loss in the building. Here, to address this issue, we develop a method to automatically extract pre‐event building images from 360° panorama images (panoramas). By providing a geotagged image collected near the target building as the input, panoramas close to the input image location are automatically downloaded through street view services (e.g., Google or Bing in the United States). By computing the geometric relationship between the panoramas and the target building, the most suitable projection direction for each panorama is identified to generate high‐quality 2D images of the building. Region‐based convolutional neural networks are exploited to recognize the building within those 2D images. Several panoramas are used so that the detected building images provide various viewpoints of the building. To demonstrate the capability of the technique, we consider residential buildings in Holiday Beach in Rockport, Texas, United States, that experienced significant devastation in Hurricane Harvey in 2017. Using geotagged images gathered during actual postdisaster building reconnaissance missions, we verify the method by successfully extracting residential building images from Google Street View images, which were captured before the event.     

Simulation of thermal shock cracking in ceramics using bond-based peridynamics and FEM

The effects of moderate intensity ‘hot’ or ‘cold’ shock in brittle solids have been extensively studied, while much less is known about thermal shock response during large temperature variations. In this study, a combined finite element – peridynamics numerical procedure is proposed for the simulation of cracking in ceramic materials, undergoing severe thermal shock. Initially, Finite Element nonlinear heat transfer analysis is conducted. The effects of surface convection and radiation heat exchange are also included. Subsequently, the interpolated temperature field is used to formulate a varying temperature induced action for a bond-based peridynamics model. The present model, which is weakly coupled, is found to reproduce accurately previous numerical and experimental results regarding the case of a ‘cold’ shock. Through several numerical experiments it is established that ‘cold’ and ‘hot’ shock conditions give rise to different failure modes and that large temperature variations lead to intensified damage evolution.     

Multiwall carbon nanotubes filled polypropylene nanocomposites: Rheological and electrical properties

Two types of commercial multiwall carbon nanotubes (MWCNTs), Baytubes® C70P and C150P were incorporated into polypropylene (PP) using melt blending technique that employs a twin screw extruder (TSE) to prepare the nanocomposites of two different concentrations (1 and 3 wt%). Subsequently, American Society for Testing and Materials (ASTM) standard samples were prepared with an injection molding machine. The prepared nanocomposites were characterized by their rheological and electrical properties using a rheometer and a picoammeter/voltage source, respectively. The effect of different types of MWCNTs and loading percentage in rheological and electrical properties was investigated in detail. The rheological analysis demonstrated a considerable dependence of the melt rheological properties of the PP/MWCNTs nanocomposites on the MWCNTs loading. The storage modulus (G′), loss modulus (G″) and complex viscosity increased with increasing MWCNTs loading. In addition, type C70P exhibited superior rheological properties compared to C150P. In terms of electrical properties, the addition of MWCNTs in the PP matrix decreased the volume resistivity of the matrix in a manner, proportional to the MWCNTs loading. No significant difference in volume resistivity was observed between the MWCNTs types. POLYM. ENG. SCI., 54:1134–1143, 2014. © 2013 Society of Plastics Engineers     

Use of Polyurethane Foam as the Impaction Substrate/Collection Medium in Conventional Inertial Impactors

Open pore polyurethane foam (PUF) can be used effectively as a substrate for conventional inertial impactors with both high particle collection efficiency and minimal vaporization of semi-volatile particle components. The collection characteristics of PUF as an impaction substrate were studied as a function of PUF density, Reynolds number, impaction substrate diameter, and nozzle-toplate distance. The conventional impaction substrate of the PM_2.5 Harvard Impactor sampler was replaced with the PUF substrate. The use of PUF resulted in significant changes in the collection efficiency curve, with the 50% cut-off size (     

Effect of Tooling Temperature on the Transient Lubricant Behavior in Hot Metal Forming Processes

In hot metal forming processes, the temperature of the forming tool progressively increases under serial production conditions. Water-based two-phase lubricants may be applied to cool the forming tool and moderate temperature, in which the liquid agent would evaporate or decompose rapidly with dry matter deposited on the tooling surface during the dwelling time before the forming process commences. Herein, an interactive friction model for a two-phase lubricant is developed to predict the transient lubricant behaviors, i.e., predicting the effects of tool temperature and dwelling time on the friction coefficient evolution and lubricant breakdown. Friction tests between a warm pin and hot aluminum workpiece are conducted using the advanced friction testing system, TriboMate, to validate the modeling results.     

Partially automated driving has higher workload than manual driving: An on-road comparison of three contemporary vehicles with SAE Level 2 features

Vehicles with SAE Level 2 automated features are already in active use on the road, and vehicles with Level 3 or 4 will be with us soon. Although the vehicles provide support for longitudinal and lateral control, partially automated driving experience is sometimes more demanding than manual driving. However, the effects of automated driving on workload in naturalistic conditions have not been extensively investigated, as most studies have been undertaken in driving simulators. This study aims to extend the current understanding about workload in partially automated driving on public roads. Drivers' perceived workload was assessed after conducting manual and automated driving activities using a small sample (N = 8). They performed driving tasks in three contemporary vehicles with SAE Level 2 features, in highway and urban environments. The comparative findings revealed that drivers' perceived workload was higher in partially automated driving than manual driving. Furthermore, perceived workload was higher in urban environments than highway environments and in less experienced drivers than more experienced drivers. Although the findings may need to be interpreted with caution due to the small sample size, they provide a future research agenda that can be built upon.     

Machine-learning modeling of hourly potential thermal refuge area: A case study from the Sainte-Marguerite River (Quebec,Canada)

To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub-daily time-step in two tributary confluences of the Sainte-Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon (Salmo salar), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine-learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree-based and kernel-based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold-water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data-driven techniques in order to understand stream temperature heterogeneity at tributary confluences.     

Internal control of brain networks via sparse feedback

The human brain is a complex system whose function depends on interactions between neurons and their ensembles across scales of organization. These interactions are restricted by anatomical and energetic constraints, and facilitate information processing and integration in response to cognitive demands. In this work, we considered the brain as a closed loop dynamic system under sparse feedback control. This controller design considered simultaneously control performance and feedback (communication) cost. As proof of principle, we applied this framework to structural and functional brain networks. Under high feedback cost only a small number of highly connected network nodes were controlled, which suggests that a small subset of brain regions may play a central role in the control of neural circuits, through a trade-off between performance and communication cost.