Development of an Abstract Model for a Non-volatile Static Random Access Memory

The capability to protect against power fluctuations, which eventually prevents the corruption of the memory contents makes non-volatile static random access memory a very good choice for use in highly reliability applications. These random access memories are protected against data writing in addition to preserving the desired contents. Energy source and control circuitries are embedded into it for achieving the same. The control circuitry constantly monitors supply voltage level, inhibits data corruption, and switches on the energy source once it falls beyond a threshold level. In this paper, development of an abstract model for such a non-volatile static random access memory chip has been presented. Test sequences based on this model have been generated for this memory chip. These test sequences have been implemented in VLSI tester and exercised on the chips

Dynamic covalent crosslinked hyaluronic acid hydrogels and nanomaterials for biomedical applications

Hyaluronic acid (HA), one of the main components of the extracellular matrix (ECM), is extensively used in the design of hydrogels and nanoparticles for different biomedical applications due to its critical role in vivo, degradability by endogenous enzymes, and absence of immunogenicity. HA-based hydrogels and nanoparticles have been developed by utilizing different crosslinking chemistries. The development of such crosslinking chemistries indicates that even subtle differences in the structure of reactive groups or the procedure of crosslinking may have a profound impact on the intended mechanical, physical and biological outcomes. There are widespread examples of modified HA polymers that can form either covalently or physically crosslinked biomaterials. More recently, studies have been focused on dynamic covalent crosslinked HA-based biomaterials since these types of crosslinking allow the preparation of dynamic structures with the ability to form in situ, be injectable, and have self-healing properties. In this review, HA-based hydrogels and nanomaterials that are crosslinked by dynamic-covalent coupling (DCC) chemistry have been critically assessed.publishedVersionPeer reviewe

Geomechanical rock properties of a basaltic volcano

In volcanic regions, reliable estimates of mechanical properties for specific volcanic events such as cyclic inflation-deflation cycles by magmatic intrusions, thermal stressing, and high temperatures are crucial for building accurate models of volcanic phenomena. This study focuses on the challenge of characterizing volcanic materials for the numerical analyses of such events. To do this, we evaluated the physical (porosity, permeability) and mechanical (strength) properties of basaltic rocks at Pacaya Volcano (Guatemala) through a variety of laboratory experiments, including: room temperature, high temperature (935 °C), and cyclically-loaded uniaxial compressive strength tests on as-collected and thermally-treated rock samples. Knowledge of the material response to such varied stressing conditions is necessary to analyze potential hazards at Pacaya, whose persistent activity has led to 13 evacuations of towns near the volcano since 1987. The rocks show a non-linear relationship between permeability and porosity, which relates to the importance of the crack network connecting the vesicles in these rocks. Here we show that strength not only decreases with porosity and permeability, but also with prolonged stressing (i.e., at lower strain rates) and upon cooling. Complimentary tests in which cyclic episodes of thermal or load stressing showed no systematic weakening of the material on the scale of our experiments. Most importantly, we show the extremely heterogeneous nature of volcanic edifices that arise from differences in porosity and permeability of the local lithologies, the limited lateral extent of lava flows, and the scars of previous collapse events. Input of these process-specific rock behaviors into slope stability and deformation models can change the resultant hazard analysis. We anticipate that an increased parameterization of rock properties will improve mitigation power

Landslides near Enguri dam (Caucasus, Georgia) and possible seismotectonic effects

The Enguri dam and water reservoir, nested in the southwestern Caucasus (Republic of Georgia), are surrounded by steep mountain slopes. At a distance of 2.5&thinsp;km from the dam, a mountain ridge along the reservoir is affected by active deformations with a double vergence. The western slope, directly facing the reservoir, has deformations that affect a subaerial area of 1.2&thinsp;km2. The head scarp affects the Jvari–Khaishi–Mestia main road with offsets of man-made features that indicate slip rates of 2–9&thinsp;cm&thinsp;yr−1. Static, pseudostatic and Newmark analyses, based on field and seismological data, suggest different unstable rock volumes based on the environmental conditions. An important effect of variation of the water table is shown, as well as the possible destabilization of the slope following seismic shaking, compatible with the expected local peak ground acceleration. This worst-case scenario corresponds to an unstable volume on the order of up to 48±12×106&thinsp;m3. The opposite, eastern slope of the same mountain ridge is also affected by wide deformation affecting an area of 0.37&thinsp;km2. Here, field data indicate 2–5&thinsp;cm&thinsp;yr−1 of slip rates. All this evidence is interpreted as resulting from two similar landslides, whose possible causes are discussed, comprising seismic triggering, mountain rapid uplift, river erosion and lake variations.</p

Predicting disease risk areas through co-production of spatial models: the example of Kyasanur Forest Disease in India’s forest landscapes

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global “One Health” initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014–2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014–2018). Consistent with suggestions that KFD is an “ecotonal” disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings

Modeling the Instantaneous Pressure–Volume Relation of the Left Ventricle: A Comparison of Six Models

Simulations are useful to study the heart’s ability to generate flow and the interaction between contractility and loading conditions. The left ventricular pressure–volume (PV) relation has been shown to be nonlinear, but it is unknown whether a linear model is accurate enough for simulations. Six models were fitted to the PV-data measured in five sheep and the estimated parameters were used to simulate PV-loops. Simulated and measured PV-loops were compared with the Akaike information criterion (AIC) and the Hamming distance, a measure for geometric shape similarity. The compared models were: a time-varying elastance model with fixed volume intercept (LinFix); a time-varying elastance model with varying volume intercept (LinFree); a Langewouter’s pressure-dependent elasticity model (Langew); a sigmoidal model (Sigm); a time-varying elastance model with a systolic flow-dependent resistance (Shroff) and a model with a linear systolic and an exponential diastolic relation (Burkh). Overall, the best model is LinFree (lowest AIC), closely followed by Langew. The remaining models rank: Sigm, Shroff, LinFix and Burkh. If only the shape of the PV-loops is important, all models perform nearly identically (Hamming distance between 20 and 23%). For realistic simulation of the instantaneous PV-relation a linear model suffices

‘None of my ancestors ever discussed this disease before!’ How disease information shapes adaptive capacity of marginalised rural populations in India

Smallholder farmer and tribal communities are often characterised as marginalised and highly vulnerable to emerging zoonotic diseases due to their relatively poor access to healthcare, worse-off health outcomes, proximity to sources of disease risks, and their social and livelihood organisation. Yet, access to relevant and timely disease information that could strengthen their adaptive capacity remain challenging and poorly characterised in the empirical literature. This paper addresses this gap by exploring the role of disease information in shaping the adaptive capacity of smallholder farmer and tribal groups to Kyasanur Forest Disease (KFD), a tick-borne viral haemorrhagic fever. We carried out household surveys (n = 229) and in-depth interviews (n = 25) in two affected districts–Shimoga and Wayanad–in the Western Ghats region. Our findings suggest that, despite the generally limited awareness about KFD, access to disease information improved households’ propensity to implement adaptation strategies relative to households that had no access to it. Of the variety of adaptation strategies implemented, vaccination, avoiding forest visits, wearing of protective clothing and footwear, application of dimethyl phthalate (DMP) oil and income diversification were identified by respondents as important adaptive measures during the outbreak seasons. Even so, we identified significant differences between individuals in exposure to disease information and its contribution to substantive adaptive action. Households reported several barriers to implement adaptation strategies including, lack of disease information, low efficacy of existing vaccine, distrust, religio-cultural sentiments, and livelihood concerns. We also found that informal information sharing presented a promising avenue from a health extension perspective albeit with trade-offs with potential distortion of the messages through misinformation and/or reporting bias. Altogether, our findings stress the importance of contextualising disease information and implementing interventions in a participatory way that sufficiently addresses the social determinants of health in order to bolster households’ adaptive capacity to KFD and other neglected endemic zoonoses

A retinal code for motion along the gravitational and body axes

Self-motion triggers complementary visual and vestibular reflexes supporting image-stabilization and balance. Translation through space produces one global pattern of retinal image motion (optic flow), rotation another. We examined the direction preferences of direction-sensitive ganglion cells (DSGCs) in flattened mouse retinas in vitro. Here we show that for each subtype of DSGC, direction preference varies topographically so as to align with specific translatory optic flow fields, creating a neural ensemble tuned for a specific direction of motion through space. Four cardinal translatory directions are represented, aligned with two axes of high adaptive relevance: the body and gravitational axes. One subtype maximizes its output when the mouse advances, others when it retreats, rises or falls. Two classes of DSGCs, namely, ON-DSGCs and ON-OFF-DSGCs, share the same spatial geometry but weight the four channels differently. Each subtype ensemble is also tuned for rotation. The relative activation of DSGC channels uniquely encodes every translation and rotation. Although retinal and vestibular systems both encode translatory and rotatory self-motion, their coordinate systems differ