Large-strain thermo-mechanical behavior of cyclic olefin copolymers: Application to hot embossing and thermal bonding for the fabrication of microfluidic devices

Amorphous cyclic olefin copolymers (COCs) are beginning to be used for making microfluidic devices for life science applications. Typically, both micro-scale and nano-scale channels are imprinted onto the copolymer by hot embossing. However, optimal manufacturing process conditions will only be possible if the COCs thermo-mechanical behavior is experimentally well characterized, mathematically modeled, and implemented in a numerical simulation. We have conducted large-strain compression experiments on two commercial grades of COCs: TOPAS-8007, and TOPAS-6015 in a wide temperature, and strain rate range. A constitutive theory and numerical implementation developed by Srivastava et al. [1] was applied to model the behavior of TOPAS. We have employed that numerical implementation, together with the material parameters for TOPAS determined here, to predict the response of TOPAS in the following microfluidic fabrication operations: (i) micro-scale hot embossing on TOPAS-8007 to replicate a micro-chip; and (ii) for sealing the channels in the micro-chip: (a) thermal bonding of an embossed chip of TOPAS-8007 with a cover plate of TOPAS-8007; and (b) thermal bonding of an embossed chip of TOPAS-6015 with a cover plate of TOPAS-8007. We show that the model can provide a simulation capability for estimation of the processing parameters for hot embossing and thermal bonding.     

Fabrication of long-term hydrophilic surfaces of poly(dimethyl siloxane) using 2-hydroxy ethyl methacrylate

In the present work, 2-hydroxy ethyl methacrylate (HEMA) was used to modify surface of poly(dimethyl siloxane) (PDMS) elastomer. Fourier transform infrared spectroscopy (FTIR) and wetting angle measurements were used for the analysis of modified surface and hydrophilic stability of PDMS. Results of the surface reconstruction reveal that long-term hydrophilic surfaces of PDMS can be achieved by use of HEMA.     

Assessment and improvement of a robust satellite technique (RST) for thermal monitoring of volcanoes

The RST approach (robust satellite technique) is a multi-temporal scheme of satellite data analysis already successfully used to monitor volcanoes at different geographic locations. In this work, the results of a long-term validation analysis of RST-based hot spot products are presented. This study was performed processing fourteen years of NOAA-AVHRR (National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer) records acquired over Mt. Etna area between 1 January 1995 and 31 December 2008, at different overpass times (day/night), and analyzing hundreds of volcano bulletins reporting information on Mt. Etna eruptive activity, provided by visual observations and ground-based measurements. An optimized configuration of the RST approach, named RST_VOLC, is also, for the first time, presented and discussed here. This method, thanks to a better tradeoff between reliability and sensitivity, may be particularly suitable to support operational systems for volcano monitoring and hazard mitigation.     

Investigating the effects of soil moisture on thermal infrared land surface temperature and emissivity using satellite retrievals and laboratory measurements

This study investigates the effects of soil moisture (SM) on thermal infrared (TIR) land surface emissivity (LSE) using field- and satellite-measurements. Laboratory measurements were used to simulate the effects of rainfall and subsequent surface evaporation on the LSE for two different sand types. The results showed that the LSE returned to the dry equilibrium state within an hour after initial wetting, and during the drying process the SM changes were uncorrelated with changes in LSE. Satellite retrievals of LSE from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) were examined for an anomalous rainfall event over the Namib Desert in Namibia during April, 2006. The results showed that increases in Advanced Microwave Scanning Radiometer (AMSR-E) derived soil moisture and Tropical Rainfall Measuring Mission (TRMM) rainfall estimates corresponded closely with LSE increases of between 0.08-0.3 at 8.6 µm and up to 0.03 at 11 µm for MODIS v4 and AIRS products. This dependence was lost in the more recent MODIS v5 product which artificially removed the correlation due to a stronger coupling with the split-window algorithm, and is lost in any algorithms that force the LSE to a pre-determined constant as in split-window type algorithms like those planned for use with the NPOESS Visible Infrared Imager Radiometer Suite (VIIRS). Good agreement was found between MODIS land surface temperatures (LSTs) derived from the Temperature Emissivity Separation (TES) and day/night v4 algorithm (MOD11B1 v4), while the split-window dependent products (MOD11B1 v5 and MOD11A1) had cooler mean temperatures on the order of 1-2 K over the Namib Desert for the month of April 2006.     

Synoptic mapping of internal-wave motions and surface currents near the Lombok Strait using the Along-Track Stereo Sun Glitter technique

A multi-component satellite remote sensing program is required to track the response of the world's oceans, lakes and rivers to climate change. Central to this endeavor is the ability to detect the motions of internal waves, swell waves and currents and hence follow energy transport and exchange. However, the present methods of monitoring the motions of water bodies from space, such as those based on altimetry or gravity measurements, are geared mostly toward applications on large spatial scales, whereas the capacity to map the fine details of hydrospheric flows is limited. This paper describes a satellite-based method of detecting wave motion and surface currents at high (in principle metric) resolution that can be applied under specific circumstances in the confined environs of narrow sea straits, lakes and rivers and that compliments the use of other high-spatial resolution techniques such as those based on Synthetic Aperture Radar (SAR). The Along-Track Stereo Sun Glitter (ATSSG) technique makes use of images of water bodies that are separated in time by roughly 1 min and are gathered in the forward-, nadir- and backward-looking directions by space-borne optical sensors performing along-track observations. When sensor viewing geometries lead to the presence of Sun glitter in these images, surface slicks, internal waves, swell waves and other phenomena become highlighted through the surface roughness changes they induce, since these in turn modulate the reflected glitter radiance. Measurement of the differential displacements between congruent sections of the surface roughness signatures present within image pairs of the stereoscopic sequence then enables internal wave or swell wave motions to be determined, while surface currents can be deduced if “passive” tracers of the flow in the form of surface roughness structures (such as slicks) are present. The application of the ATSSG technique described herein makes use of data acquired at a spatial resolution of 2.5 m by the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) to provide the first along-track fine-scale synoptic mapping of the surface motions of individual components within a large group of internal waves and to generate supporting surface-current measurements. The PRISM data we employ were acquired to the south of the Lombok Strait (Indonesia), where highly energetic internal-wave growth takes place and where through-flow surface-current data, of the type derived by the ATSSG technique, can be of value in climate studies.     

Detection of geothermal anomalies using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA

Surface temperature anomalies associated with geothermal activity at Bradys Hot Springs, Churchill County, Nevada were mapped using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) image data. In order to highlight subsurface contributions of geothermal heat, the ASTER images were processed to minimize temperature variations caused by the diurnal heating effects of the sun. Surface temperature variations caused by changes in albedo were corrected with visible and near-infrared ASTER bands, and a 10-meter-smoothed Digital Elevation Model (DEM) was used to correct for topographic slope effects. Field measurements of ground surface temperatures made over 24-hour periods were used to design a thermal inertia correction incorporating day and night thermal infrared images.In the resulting processed image, background temperature variations were reduced 30-50% without reducing the intensity of geothermal anomalies, thus making it easier to distinguish geothermal activity from ‘false’ anomalies caused by non-thermal springs, topographic effects, and variable rock, soil, and vegetation compositions.     

Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP)

Symbolic Regression (SR) analysis, employing a genetic programming (GP) approach, was used to analyse laboratory strength and elasticity modulus data for some granitic rocks from selected regions in Turkey. Total porosity (n), sonic velocity (vp), point load index (Is) and Schmidt Hammer values (SH) for test specimens were used to develop relations between these index tests and uniaxial compressive strength (σ_c), tensile strength (σ_t) and elasticity modulus (E). Three GP models were developed. Each GP model was run more than 50 times to optimise the GP functions. Results from the GP functions were compared with the measured data set and it was found that simple functions may not be adequate in explaining strength relations with index properties. The results also indicated that GP is a potential tool for identifying the key and optimal variables (terminals) for building functions for predicting the elasticity modulus and the strength of granitic rocks.     

Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm)

Field emissivity measurements were made of leaves collected from nine deciduous tree and agricultural plant species. The data show, for the first time, that it is possible to discriminate subtle spectral emissivity features of leaves from the natural background emission. Under conditions of controlled measurement geometry (leaves arranged to cover a flat surface), the field emissivity spectra agreed fairly well with emissivity values calculated from laboratory directional hemispherical reflectance measurements. Spectral features associated with a variety of leaf chemical constituents, including cellulose, cutin, xylan, silica, and oleanolic acid could be identified in the field emissivity data. Structural aspects of leaf surfaces also influenced spectral behavior, notably the abundance of trichomes, as well as wax thickness and texture.Field spectral measurements made at increasing distances from natural plant canopies showed progressive attenuation of the spectral emissivity features. This attenuation is ascribed to increased multiple scattering that superimposes an opposite-in-sign reflected component on the emittance, and to the increasing number of canopy voids within the instrument field of view. Errors associated with the removal of atmospheric features and with the non-isotropic thermal characteristics of canopies also contribute to the loss of spectral information at greater measurement distances.In contrast to visible and short-wave infrared data, thermal infrared spectra of broad leaf plants show considerable spectral diversity, suggesting that such data eventually could be utilized to map vegetation composition. However, remotely measuring the subtle emissivity features of leaves still presents major challenges. To be successful, sensors operating in the 8-14 μm atmospheric window must have high signal-to-noise and a small enough instantaneous field of view to allow measurements of only a few leaf surfaces. Methods for atmospheric compensation, temperature-emissivity separation, and spectral feature analysis also will need to be refined to allow the recognition, and perhaps, exploitation of leaf thermal infrared spectral properties.     

An approach to recognize interacting features from B-Rep CAD models of prismatic machined parts using a hybrid (graph and rule based) technique

This paper presents a new hybrid (graph + rule based) approach for recognizing the interacting features from B-Rep CAD models of prismatic machined parts. The developed algorithm considers variable topology features and handles both adjacent and volumetric feature interactions to provide a single interpretation for the latter. The input CAD part model in B-Rep format is preprocessed to create the adjacency graphs for faces and features of associated topological entities and compute their attributes. The developed FR system initially recognizes all varieties of the simple and stepped holes with flat and conical bottoms from the feature graphs. A new concept of Base Explicit Feature Graphs and No-base Explicit Feature Graphs has been proposed which essentially delineates between features having planar base face like pockets, blind slots, etc. and those without planar base faces like passages, 3D features, conical bottom features, etc. Based on the structure of the explicit feature graphs, geometric reasoning rules are formulated to recognize the interacting feature types. Extracted data has been post-processed to compute the feature attributes and their parent-child relationships which are written into a STEP like native feature file format. The FR system was extensively tested with several standard benchmark components and was found to be robust and consistent. The extracted feature file can be used for integration with various downstream applications like CAPP.     

ECOCLIMAP-II: An ecosystem classification and land surface parameters database of Western Africa at 1 km resolution for the African Monsoon Multidisciplinary Analysis (AMMA) project

This work is devoted to a presentation of the ECOCLIMAP-II database for Western Africa, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, implemented at global scale. ECOCLIMAP-II is a dual database at 1-km resolution that comprises an ecosystem classification and a coherent set of land surface parameters. This new physiographic information (e.g. leaf area index, fractional vegetation cover, albedo and land cover classification), was especially developed in the framework of the African Monsoon Multidisciplinary Analysis (AMMA) programme in order to support the modelling of land-atmosphere interactions, which stresses the importance of the present study. Criteria for coherence between prevalent land cover classifications and the analysis of time series of the satellite leaf area index (LAI) between 2000 and 2007 constitute the analysis tools for setting up ECOCLIMAP-II. The LAI and inferred fraction of vegetation cover are spatially distributed per land cover unit. The fraction of vegetation cover is handled to split the land surface albedo into vegetation and bare soil albedo components, as is required for a large number of applications. The new ECOCLIMAP-II land cover product is improved with regard to the spatial coherence compared to former version. The reliability of the physiographic details is also confirmed through verification with land cover products at higher resolution.     

Electrochromic devices using Fe(II)‐based metallo‐supramolecular polymer: Introduction of ionic liquid as electrolyte to enhance the thermal stability

We fabricated electrochromic (EC) devices with Fe(II)‐based metallo‐supramolecular polymer (polyFe), 1‐butyl‐1‐methyl‐pyrrolidinium bis(trifluoromethanesulfonyl)imide (BMPyr(NTf)₂) ionic liquid ( IL ), and Prussian blue (PB) as electrochromic layer, electrolyte layer, and counter layer, respectively. Thermal stability and ionic conductivity of BMPyr(NTf)₂ IL was measured by thermogravimetric analysis (TGA) and impedance analysis to be 380°C and approximately 7.8 × 10^?4 S/cm, respectively. The fabricated device showed reversible electrochromism between bluish‐violet and colorless by applying +3.0/?1.5 V alternately. The 65% of the original EC contrast remained in the device after the repeated changes for 100 cycles at 100°C.     

An ab initio study of the magnetic and electronic properties of Fe, Co, and Ni nanowires on Cu(001) surface

Magnetism at the nanoscale has been a very active research area in the past decades, because of its novel fundamental physics and exciting potential applications. We have recently performed an ab initio study of the structural, electronic and magnetic properties of all 3d transition metal (TM) free-standing atomic chains and found that Fe and Ni nanowires have a giant magnetic anisotropy energy (MAE), indicating that these nanowires would have applications in high density magnetic data storages. In this paper, we perform density functional calculations for the Fe, Co and Ni linear atomic chains on Cu(001) surface within the generalized gradient approximation, in order to investigate how the substrates would affect the magnetic properties of the nanowires. We find that Fe, Co and Ni linear chains on Cu(001) surface still have a stable or metastable ferromagnetic state. When spin–orbit coupling (SOC) is included, the spin magnetic moments remain almost unchanged, due to the weakness of SOC in 3d TM chains, whilst significant orbital magnetic moments appear and also are direction-dependent. Finally, we find that the MAE for Fe, and Co remains large, i.e., being not much affected by the presence of Cu substrate.     

A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach

Knowledge inference systems are built to identify hidden and logical patterns in huge data. Decision trees play a vital role in knowledge discovery but crisp decision tree algorithms have a problem with sharp decision boundaries which may not be implicated to all knowledge inference systems. A fuzzy decision tree algorithm overcomes this drawback. Fuzzy decision trees are implemented through fuzzification of the decision boundaries without disturbing the attribute values. Data reduction also plays a crucial role in many classification problems. In this research article, it presents an approach using principal component analysis and modified Gini index based fuzzy SLIQ decision tree algorithm. The PCA is used for dimensionality reduction, and modified Gini index fuzzy SLIQ decision tree algorithm to construct decision rules. Finally, through PID data set, the method is validated in the simulation experiment in MATLAB.     

Retrieval of high-resolution sea surface temperature data for Sendai Bay, Japan, using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

We studied sea surface temperature (SST) retrieval algorithms for Sendai Bay, using output from the thermal-infrared channels of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board Terra. While the highest resolutions of other satellite SST products are about 1 km, the ASTER thermal-infrared channels provide 90-m spatial resolution. To develop the ASTER algorithm, we employed statistical methods in which SSTs retrieved from the thermal-infrared measurements were tuned against the Moderate Resolution Imaging Spectroradiometer (MODIS) SST product with a 1-km spatial resolution. Terra also carries a MODIS sensor, which observed the same area as the ASTER sensor at the same time. The MODIS SST was validated around Sendai Bay, revealing a bias of −0.15 °C and root mean-square difference (RMSD) of 0.67 °C against in situ SSTs. Taking into account the spatial-resolution difference between ASTER and MODIS, match-up was generated only if the variability of ASTER brightness temperatures (T₁₃) was small in a pixel of MODIS SST (MP). The T₁₃ within one MP was about 121 pixels. The standard deviation (σ₁₃) of T₁₃ was calculated for each cloud-free MP, and the threshold of σ₁₃ for choosing match-up MPs was decided by analyzing the σ₁₃ histogram of one ASTER image. The 15 synchronous pairs of ASTER/MODIS images are separated into two groups of 8 pairs called set (A) and 7 pairs called set (B). Using the common procedure, the match-ups are generated for set (A) and set (B). The former is used for developing the ASTER Multi-Channel SST (MCSST) algorithm, and the latter for validation of the developed ASTER SST. Analysis of the whole 15 pairs indicated that ASTER SST does not depend on the satellite zenith angle. We concluded that, using Akaike's information criterion with set (A) match-ups, the multiple regression formula with all five thermal-infrared channels was adequate for the ASTER SST retrieval. Validation of ASTER SST using match-up set (B) indicated a bias of 0.101 °C and RMSD of 0.455 °C.     

Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage.