AEROSAT - a space-borne sensor for continental aerosols: evaluation of the conceptual model

Even though satellite observations are the most effective means to gather global information in a short span of time, the challenges in this field still remain over continental landmass, despite most of the aerosol sources being land-based. This is a hurdle in global and regional aerosol climate forcing assessment. Retrieval of aerosol properties over land is complicated due to irregular terrain characteristics and the high and largely uncertain surface reflection which acts as `noise' to the much smaller amount of radiation scattered by aerosols, which is the `signal'. In this paper, we describe a satellite sensor the - `Aerosol Satellite (AEROSAT)', which is capable of retrieving aerosols over land with much more accuracy and reduced dependence on models. The sensor, utilizing a set of multi-spectral and multi-angle measurements of polarized components of radiation reflected from the Earth's surface, along with measurements of thermal infrared broadband radiance, results in a large reduction of the `noise' component (compared to the `signal). A conceptual engineering model of AEROSAT has been designed, developed and used to measure the land-surface features in the visible spectral band. Analysing the received signals using a polarization radiative transfer approach, we demonstrate the superiority of this method. It is expected that satellites carrying sensors following the AEROSAT concept would be `self-sufficient', to obtain all the relevant information required for aerosol retrieval from its own measurements.

Using long time series of agricultural-derived nitrates for estimating catchment transit times

The estimation of water and solute transit times in catchments is crucial for predicting the response of hydrosystems to external forcings (climatic or anthropogenic). The hydrogeochemical signatures of tracers (either natural or anthropogenic) in streams have been widely used to estimate transit times in catchments as they integrate the various processes at stake. However, most of these tracers are well suited for catchments with mean transit times lower than about 4-5 years. Since the second half of the 20th century, the intensification of agriculture led to a general increase of the nitrogen load in rivers. As nitrate is mainly transported by groundwater in agricultural catchments, this signal can be used to estimate transit times greater than several years, even if nitrate is not a conservative tracer. Conceptual hydrological models can be used to estimate catchment transit times provided their consistency is demonstrated, based on their ability to simulate the stream chemical signatures at various time scales and catchment internal processes such as N storage in groundwater. The objective of this study was to assess if a conceptual lumped model was able to simulate the observed patterns of nitrogen concentration, at various time scales, from seasonal to pluriannual and thus if it was relevant to estimate the nitrogen transit times in headwater catchments. A conceptual lumped model, representing shallow groundwater flow as two parallel linear stores with double porosity, and riparian processes by a constant nitrogen removal function, was applied on two paired agricultural catchments which belong to the Research Observatory ORE AgrHys. The Global Likelihood Uncertainty Estimation (GLUE) approach was used to estimate parameter values and uncertainties. The model performance was assessed on (i) its ability to simulate the contrasted patterns of stream flow and stream nitrate concentrations at seasonal and inter-annual time scales, (ii) its ability to simulate the patterns observed in groundwater at the same temporal scales, and (iii) the consistency of long-term simulations using the calibrated model and the general pattern of the nitrate concentration increase in the region since the beginning of the intensification of agriculture in the 1960s. The simulated nitrate transit times were found more sensitive to climate variability than to parameter uncertainty, and average values were found to be consistent with results from others studies in the same region involving modeling and groundwater dating. This study shows that a simple model can be used to simulate the main dynamics of nitrogen in an intensively polluted catchment and then be used to estimate the transit times of these pollutants in the system which is crucial to guide mitigation plans design and assessment. (C) 2015 Elsevier B.V. All rights reserved.

Absence of systemic toxicity in mouse model towards BaTiO3 nanoparticulate based eluate treatment

One of the existing issues in implant failure of orthopedic biomaterials is the toxicity induced by the fine particles released during long term use in vivo, leading to acute inflammatory response. In developing a new class of piezobiocomposite to mimic the integrated electrical and mechanical properties of bone, bone-mimicking physical properties as well as in vitro cytocompatibility properties have been achieved with spark plasma sintered hydroxyapatite (HA)-barium titanate (BaTiO3) composites. However, the presence of BaTiO3 remains a concern towards the potential toxicity effect. To address this issue, present work reports the first result to conclusively confirm the non-toxic effect of HA-BaTiO3 piezobiocomposite nanoparticulates, in vivo. Twenty BALB/c mice were intraarticularly injected at their right knee joints with different concentrations of HA-BaTiO3 composite of up to 25 mg/ml. The histopathological examination confirmed the absence of any trace of injected particles or any sign of inflammatory reaction in the vital organs, such as heart, spleen, kidney and liver at 7 days post-exposure period. Rather, the injected nanoparticulates were found to be agglomerated in the vicinity of the knee joint, surrounded by macrophages. Importantly, the absence of any systemic toxicity response in any of the vital organs in the treated mouse model, other than a mild local response at the site of delivery, was recorded. The serum biochemical analyses using proinflammatory cytokines (TNF-alpha and IL-1 beta) also complimented to the non-immunogenic response to injected particulates. Altogether, the absence of any inflammatory/ adverse reaction will open up myriad of opportunities for BaTiO3 based piezoelectric implantable devices in biomedical applications.

SUPPORTING ENVIRONMENTALLY-BENIGN DESIGN ELUCIDATING ENVIRONMENTAL IMPACT PROPAGATION IN CONCEPTUAL DESIGN PHASE BY SAPPHIRE MODEL OF CAUSALITY

Conceptual Design Phase is the most critical for design decisions and their impact on the Environment. It is also a phase of many `unknowns' making it flexible and allowing exploration of many solutions. Thus, it is a challenge to determine the most Environmentally-benign Solution or Concept to be translated in to a `good' product. The SAPPhIRE Model captures the various levels of abstractions present in Conceptual Design by Outcomes and defines a Solution-variant as a set of verifiable and quantifiable Outcomes. The Causality explains the propagation of Environmental Impact across Outcomes at varying levels of abstraction, suggesting that the Environmental Impact of an Outcome at a certain level can be represented as a collation of Environmental Impact information of all the Outcomes at each of its subsequent lower levels of abstraction. Thus a ball-park impact value can be associated with the higher-levels of abstraction, thereby supporting design decisions taken earlier on in Conceptual Design directing towards Environmentally-benign Design.

GEODERM: geometric shape design system using an entity-relationship model

GEODERM, a microcomputer-based solid modeller, which incorporates the parametric object model, is discussed. The entity-relationship model, which is used to describe the conceptual schema of the geometric database, is also presented. Three of the four modules of GEODERM, which have been implemented are described in some detail. They are the Solid Definition Language (SDL), the Solid Manipulation Language (SML) and the User-System Interface.

An Empirical Model of the Process of Synthesis of Multiple State Mechanical Devices

Automated synthesis of mechanical designs is an important step towards the development of an intelligent CAD system. Research into methods for supporting conceptual design using automated synthesis has attracted much attention in the past decades. The research work presented here is based on the processes of synthesizing multiple state mechanical devices carried out individually by ten engineering designers. The designers are asked to think aloud, while carrying out the synthesis. The ten design synthesis processes are video recorded, and the records are transcribed and coded for identifying activities occurring in the synthesis processes, as well as for identifying the inputs to and outputs from the activities. A mathematical representation for specifying multi-state design task is proposed. Further, a descriptive model capturing all the ten synthesis processes is developed and presented in this paper. This will be used to identify the outstanding issues to be resolved before a system for supporting design synthesis of multiple state mechanical devices that is capable of creating a comprehensive variety of solution alternatives could be developed.

Lysozyme: A model protein for amyloid research

Ever since lysozyme was discovered by Fleming in 1922, this protein has emerged as a model for investigations on protein structure and function. Over the years, several high-resolution structures have yielded a wealth of structural data on this protein. Extensive studies on folding of lysozyme have shown how different regions of this protein dynamically interact with one another. Data is also available from numerous biotechnological studies wherein lysozyme has been employed as a model protein for recovering active recombinant protein from inclusion bodies using small molecules like L-arginine. A variety of conditions have been developed in vitro to induce fibrillation in hen lysozyme. They include (a) acidic pH at elevated temperature, (b) concentrated solutions of ethanol, (c) moderate concentrations of guanidinium hydrochloride at moderate temperature, and (d) alkaline pH at room temperature. This review aims to bring together similarities and differences in aggregation mechanisms, morphology of aggregates, and related issues that arise using the different conditions mentioned above to improve our understanding. The alkaline pH condition (pH 12.2), discovered and studied extensively in our lab, shall receive special attention. More than a decade ago, it was revealed that mutations in human lysozyme can cause accumulation of large quantities of amyloid in liver, kidney, and other regions of gastrointestinal tract. Understanding the mechanism of lysozyme aggregation will probably have therapeutic implications for the treatment of systemic nonneuropathic amyloidosis. Numerous studies have begun to focus attention on inhibition of lysozyme aggregation using antibody or small molecules. The enzymatic activity of lysozyme presents a convenient handle to quantify the native population of lysozyme in a sample where aggregation has been inhibited. The rich information available on lysozyme coupled with the multiple conditions that have been successful in inducing/inhibiting its aggregation in vitro makes lysozyme an ideal model protein to investigate amyloidogenesis.

A model for growth and engulfment of gas microporosity during aluminum alloy solidification process

A new coupled approach is presented for modeling the hydrogen bubble evolution and engulfment during an aluminum alloy solidification process in a micro-scale domain. An explicit enthalpy scheme is used to model the solidification process which is coupled with a level-set method for tracking the hydrogen bubble evolution. The volume averaging techniques are used to model mass, momentum, energy and species conservation equations in the chosen micro-scale domain. The interaction between the solid, liquid and gas interfaces in the system have been studied. Using an order-of-magnitude study on growth rates of bubble and solid interfaces, a criterion is developed to predict bubble elongation which can occur during the engulfment phase. Using this model, we provide further evidence in support of a conceptual thought experiment reported in literature, with regard to estimation of final pore shape as a function of typical casting cooling rates. The results from the proposed model are qualitatively compared with in situ experimental observations reported in literature. The ability of the model to predict growth and movement of a hydrogen bubble and its subsequent engulfment by a solidifying front has been demonstrated for varying average cooling rates encountered in typical sand, permanent mold, and various casting processes. (C) 2012 Elsevier B.V. All rights reserved.

Context aware M-commerce services: C-IOB model approach

Context-aware computing is useful in providing individualized services focusing mainly on acquiring surrounding context of user. By comparison, only very little research has been completed in integrating context from different environments, despite of its usefulness in diverse applications such as healthcare, M-commerce and tourist guide applications. In particular, one of the most important criteria in providing personalized service in a highly dynamic environment and constantly changing user environment, is to develop a context model which aggregates context from different domains to infer context of an entity at the more abstract level. Hence, the purpose of this paper is to propose a context model based on cognitive aspects to relate contextual information that better captures the observation of certain worlds of interest for a more sophisticated context-aware service. We developed a C-IOB (Context-Information, Observation, Belief) conceptual model to analyze the context data from physical, system, application, and social domains to infer context at the more abstract level. The beliefs developed about an entity (person, place, things) are primitive in most theories of decision making so that applications can use these beliefs in addition to history of transaction for providing intelligent service. We enhance our proposed context model by further classifying context information into three categories: a well-defined, a qualitative and credible context information to make the system more realistic towards real world implementation. The proposed model is deployed to assist a M-commerce application. The simulation results show that the service selection and service delivery of the system are high compared to traditional system.

SYNTHESIS OF CONCEPTUAL DESIGNS FOR SENSORS USING SAPPHIRE-LITE

The demand for variety of products and the shorter time to market is encouraging designers to adopt computer aided concept generation techniques. One such technique is being explored here. The present work makes an attempt towards synthesis of concepts for sensors using physical laws and effects as building blocks. A database of building blocks based upon the SAPPhIRE-lite model of causality is maintained. It uses composition to explore the solution space. The algorithm has been implemented in a web based tool. The tool generates two types of sensor designs: direct sensing designs and feedback sensing designs. According to the literature, synthesis using building blocks often lead to vague solutions principles. The current work tries to avoid uninteresting solutions by using some heuristics. A particularly novel outcome of the work described here is the generation of feedback based solutions, something not generated automatically before. A number of patent violations were observed with the set of generated concepts; thus emphasizing some amount of novelty in the designs.

Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk

Complex systems inspired analysis suggests a hypothesis that financial meltdowns are abrupt critical transitions that occur when the system reaches a tipping point. Theoretical and empirical studies on climatic and ecological dynamical systems have shown that approach to tipping points is preceded by a generic phenomenon called critical slowing down, i.e. an increasingly slow response of the system to perturbations. Therefore, it has been suggested that critical slowing down may be used as an early warning signal of imminent critical transitions. Whether financial markets exhibit critical slowing down prior to meltdowns remains unclear. Here, our analysis reveals that three major US (Dow Jones Index, S&P 500 and NASDAQ) and two European markets (DAX and FTSE) did not exhibit critical slowing down prior to major financial crashes over the last century. However, all markets showed strong trends of rising variability, quantified by time series variance and spectral function at low frequencies, prior to crashes. These results suggest that financial crashes are not critical transitions that occur in the vicinity of a tipping point. Using a simple model, we argue that financial crashes are likely to be stochastic transitions which can occur even when the system is far away from the tipping point. Specifically, we show that a gradually increasing strength of stochastic perturbations may have caused to abrupt transitions in the financial markets. Broadly, our results highlight the importance of stochastically driven abrupt transitions in real world scenarios. Our study offers rising variability as a precursor of financial meltdowns albeit with a limitation that they may signal false alarms.