Additive manufacturing of aluminum alloys is considered a promising layer-wise manufacturing method which can produce lightweight critical automotive/aerospace/military components with enhanced physical and mechanical properties. This paper aims at assessing the correlation between microstructure and small-scale characteristics of an additive manufactured AlSi10Mg alloy in the as-printed and heat treated conditions. Depth-sensing nanoindentation testing, as a non-destructive, robust, and convenient testing approach, along with microstructural assessments, using optical microscopy and scanning electron microscopy, were employed to compare the nano-hardness of the printed (selective laser melting method) and the heat treated (age-hardening) materials. Considering the distance from the build plate, a gradation in the cooling rate, and therefore the microstructure, is expected which directly affect the nano-hardness gradient along the deposition direction. Results show a transition in the microstructure from cellular grains, with coral-like silicon fiber colonies, to fragmented/spheroidized eutectic silicon particles upon the heat treatment. Unlike conventionally manufactured AlSi10Mg alloys, upon aging heat treatment in the additive manufactured AlSi10Mg alloy, the nano-hardness is decreased which is mainly contributed to stress relief, elimination of solid solution strengthening, and silicon spheroidization phenomena. These are considered in detail in the current paper.

The organism Caenorhabditis elegans is a performant model system for studying human biological processes and diseases, but until now all phenome data are produced as population-averaged read-outs. Monitoring of individual responses to drug treatments would however be more informative. Here, a new strategy to track different phenotypic traits of individual C. elegans nematodes throughout their full life-cycle—i.e., embryonic and post-embryonic development, until adulthood onset, differently from life-span—is presented. In an automated fashion, single worms were synchronized, isolated, and cultured from egg to adulthood in a microfluidic device, where their identity was preserved during their whole development. Several phenotypes were monitored and quantified for each animal, resulting in high-content phenome data. Specifically, the method was validated by analyzing the response of C. elegans to doxycycline, an antibiotic fairly well-known to prolong the development and activate mitochondrial stress-response pathways in different species. Interestingly, the obtained extensive single-worm phenome not only confirmed the dramatic doxycycline effect on the worm developmental delay, but more importantly revealed subtle yet severe treatment-dependent phenotypes that are representative of minority subgroups and would have otherwise stayed hidden in an averaged dataset. Such heterogeneous response started during the embryonic development, which makes essential having a dedicated chip that allows including this early developmental stage in the drug assay. Our approach would therefore allow elucidating pharmaceutical or therapeutic responses that so far were still being overlooked.

The present report explores the discussion on 3D flow of dual stratified Oldroyd-B fluid in the direction of thermal radiation, viscous dissipation and heat source/sink. Mathematical modeling is formulated with an applied magnetic field through a stretching surface. The present flow governing system has been transformed as nonlinear ODE via suitable transformations and then concluded by using bvp4c. The graphs are described and illustrated for various non-dimensional parameters. Thermal stratification and Prandtl number parameters reduce the temperature, whereas thermal radiation and heat source parameters show reverse behavior. Numerical results are used to obtain the values of frictional drag, rate of heat and mass transfers. Finally, numerical results are compared with previous established work in a limiting case.

Reinforced epoxy composite adhesives of expanded graphite (EG) and graphene nanoplatelet (GNPs) were prepared by hand layup and mechanical mixing method. Disk-shaped epoxy composite samples with EG and GNP mixtures in varying ratios were prepared to measure the thermal conductivity (TC) enhancement. Thermal characterization testing data showed high thermal conductivity enhancement with three different hybrid filler concentrations of 10, 25, and 35 wt%, respectively. The highest thermal conductivity of 3.6 W/m K was obtained for an epoxy adhesive composite having 30 wt.% EG and 5 wt.% GNP which was tested by guarded heat flow meter technique. This significant improvement in thermal conductivity can be attributed to the lowering of overall thermal interface resistance due to small amounts of nanofiller (GNP) improving the thermal contact between the primary microfillers (graphite). The synergistic effect of this hybrid filler system is lost at higher loadings of the GNP relative to expanded graphite. The structure of the graphite flake, GNP/epoxy, EG/epoxy and hybrid EG/GNP/epoxy composites was investigated by XRD. The EG prepared by acid intercalation and abrupt thermal expansion showed good compatibility with GNP and the epoxy resin. From scanning electron microscopy photographs, the formation of conducting network observed through the expanded graphite and GNPs in a low conducting epoxy matrix. The thermal decomposition temperature of the composite increased to 450 and 615 °C with the addition of 10 and 35 wt% of hybrid EG/GNP inside epoxy matrix, respectively. LAP shear strength of single and hybrid filled epoxy adhesive decreased at 35 wt.% loading than neat epoxy.

If we are to realize the potential of self-driving cars, we need to recognize the limits of machine learning. We should not pretend self-driving cars are around the corner: it will still take substantial time and effort to integrate the technology safely and fairly into our societies.

The Himalayan Mountains and valley glaciers within it are coupled in such a way that they maintain the ecosystem along the draining stream from the young to old stage. They produce meltwater and nutrient-rich sediments, which serve as precious resources for low-lying areas. Meltwater is assumed to be an important agent of transportation and deposition of sediments in warm-based valley glaciers. Overall, the glacial environment acts as a good erosional system, and the sediments produced form a good-quality aquifer above the hard bedrock in the outwash plain. The glaciofluvial and glaciolacustrine sediments were collected from the Chandra Basin and analysed for the permeability, porosity, density and statistical parameters. Correlation among the different parameters was explained through the regression analysis. The correlation between permeability and mean grain size showed a good regression coefficient, R² = 0.86 and R² = 0.97, for glaciofluvial and glaciolacustrine sediments respectively. Correlation between density and porosity was also established against the mean grain size with a good regression coefficient. The physioempirical parameters, effective diameter (D₁₀) and coefficient of uniformity (C_u) were also examined for their dependability on permeability. Hence, this preliminary study attempts to use sediment grain size and texture as a tool to understand the permeability, porosity, density and movement and mobility of water through the glaciofluvial and glaciolacustrine sediments. Also, the correlation study showed that the mean grain size could be used as a factor for predicting the physioempirical model in that region.

The article examines the rules for performing basic arithmetic operations on fuzzy gradations. The tables for performing operations of addition, limited addition, multiplication, as well as operations of minimum and maximum are obtained. A generalization of Markov models of decision making in the case of ambiguity, when there is no information about the probabilities of states, is proposed. In this case, the components of the model are considered as fuzzy and the concept of fuzzy risk is additionally introduced. An example of application of Markov model to economic problem is considered. The models of optimal choice were studied for various decision-making strategies, namely, the threshold criterion method, the worst criterion method, additive convolution, and the distance method for different selection functions. Theoretical studies are accompanied by numerical calculations.