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 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.

Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing.

Sodium alginate/titanium dioxide solid polymer composite membranes were prepared by solution casting method. The electrical, optical and thermal properties of these composite membranes are in great demand for advanced electronic device applications. The present study is aimed at the preparation of dioxide membranes to enhance the conductivity of sodium alginate. The diffraction pattern confirmed the change of sodium alginate polymer matrix structure from amorphous to polycrystalline upon dispersing TiO₂ powder particles into sodium alginate solution. The surface morphology also evidenced the non-crystalline and polycrystalline (containing grains and grain boundaries) nature of formed films. The infrared spectra showed the presence of various functional groups such as –OH, C=O, O–H, C–H and Na–O. The differential scanning calorimeter (DSC) investigation revealed the existence of exothermic/endothermic peaks, melting and crystallization temperatures. Furthermore, the weight loss of all the films was described as a function of temperature. The dielectric properties highlighted the 5 wt% of TiO₂ dispersed in the sodium alginate solution due to its high dielectric constant (ε′), dielectric loss (ε″) and ac-electrical conductivity (σ_ac).

Activities on the Sun’s surface can produce dynamic conditions in the Earth’s outer space environment, which can affect the Earth, space-borne and ground-based technologies, including Global Navigation Satellite Systems (GNSS). Delay of GNSS signal can occur during its propagation through the upper Earth’s atmosphere—the ionosphere, representing the major limitation in GNSS positioning applications. In this paper, high level of solar activity and intense bursts of radiation from the release of magnetic energy on the Sun, known as solar flares, are studied. The investigation covers the detection of events on the Sun’s surface, conditions in near-Earth’s space environment, geomagnetic field, ionosphere and GNSS positioning estimates. In October 2014, more than 200 solar flares were detected and about a quarter of total amount belonged to solar flares of M and X class. Impact on ionospheric layers is studied: D layer with SuperSID (sudden ionospheric disturbances) monitor and electron density to F2 layer with GNSS-derived total electron content. Used GNSS stations belong to EUREF Permanent Network (EPN) in Bosnia and Herzegovina and Croatia. Precise Point Positioning is performed in the Bernese GNSS Software. Solar radio emissions were high in the second half of the month, when more M and X solar flares occurred. Ionospheric electron density was enhanced, reaching its peak during the high level of solar activity and the period of strongest solar flares occurrence, while position estimates show higher deviations from the EPN weekly solution in Up component (at least for two times). Higher-order ionospheric terms remained after applying the L3 ionosphere-free solution, which should be taken into account in precise positioning during increased level of solar activity.