Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation,analysis and validation

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the inter-comparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.     

Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets

Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST by adding noise to the MNIST dataset, and three labeled datasets formed by adding noise to the offline Bangla numeral database. Then we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST, n-MNIST and noisy Bangla datasets, our framework shows promising results and outperforms traditional Deep Belief Networks.     

Mechanistic origins of multi-scale reinforcements in segmented polyurethane-clay nanocomposites

The objective of the present work is to get insights into the mechanistic origin of the reinforcement effects of nanoclay on a segmented polybutadiene polyurethane-urea system. To this end, a convergent analysis of the hard domain morphology and conformational state of soft segment in the nanocomposites was carried out by using a combination of complementary characterization techniques, namely, Fourier transform infrared spectroscopy, small angle neutron scattering, transmission electron microscopy, modulated differential scanning calorimetry and dynamic mechanical analysis. Analysis of small angle neutron scattering data by a combination of Percus–Yevick hard sphere and Zernike-Ornstein model coupled with direct visualization of the dispersed hard domain morphology from transmission electron microscopy provided insight on clay induced changes in the hard domain morphology. A monotonic decrease in the domain size as well as the average interdomain distance was observed with increasing nanoclay content in the polymer matrix. Analysis of the carbonyl stretching region from FTIR showed increased degree of hydrogen bonding for the urethane carbonyl groups of the nanocomposites compared to the neat matrix. A combination of calorimetric and dynamic mechanical analysis revealed the existence of a constrained amorphous region; quantified to be ≈ 16% at the highest clay content experimented. The manifestation of these morphological and conformational changes on the nano-, micro- and macro scale reinforcements in the nanocomposites was investigated by mechanical properties at these length scales using nanoindentation, DMA and tensile testing, respectively.     

Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRR-mode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.     

Mathematical Morphology-Based Artificial Technique for Renewable Power Application

This paper suggests a combined novel control strategy for DFIG based wind power systems (WPS) under both nonlinear and unbalanced load conditions. The combined control approach is designed by coordinating the machine side converter (MSC) and the load side converter (LSC) control approaches. The proposed MSC control approach is designed by using a model predictive control (MPC) approach to generate appropriate real and reactive power. The MSC controller selects an appropriate rotor voltage vector by using a minimized optimization cost function for the converter operation. It shows its superiority by eliminating the requirement of transformation, switching table, and the PWM techniques. The proposed MSC reduces the cost, complexity, and computational burden of the WPS. On the other hand, the LSC control approach is designed by using a mathematical morphological technique (MMT) for appropriate DC component extraction. Due to the appropriate DC-component extraction, the WPS can compensate the harmonics during both steady and dynamic states. Further, the LSC controller also provides active power filter operation even under the shutdown of WPS condition. To verify the applicability of coordinated control operation, the WPS-based microgrid system is tested under various test conditions. The proposed WPS is designed by using a MATLAB/Simulink software.     

Neural Network and Fuzzy Control Based 11-Level Cascaded Inverter Operation

This paper presents a combined control and modulation technique to enhance the power quality (PQ) and power reliability (PR) of a hybrid energy system (HES) through a single-phase 11-level cascaded H-bridge inverter (11-CHBI). The controller and inverter specifically regulate the HES and meet the load demand. To track optimum power, a Modified Perturb and Observe (MP&O) technique is used for HES. Ultra-capacitor (UCAP) based energy storage device and a novel current control strategy are proposed to provide additional active power support during both voltage sag and swell conditions. For an improved PQ and PR, a two-way current control strategy such as the main controller (MC) and auxiliary controller (AC) is suggested for the 11-CHBI operation. MC is used to regulate the active current component through the fuzzy controller (FC), and AC is used to regulate the dc-link voltage of CHBI through a neural network-based PI controller (ANN-PI). By tracking the reference signals from MC and AC, a novel hybrid pulse width modulation (HPWM) technique is proposed for the 11-CHBI operation. To justify and analyze the MATLAB/Simulink software-based designed model, the robust controller performance is tested through numerous steady-state and dynamic state case studies.     

ECC-CoAP: Elliptic Curve Cryptography Based Constraint Application Protocol for Internet of Things

Constraint Application Protocol (CoAP), an application layer based protocol, is a compressed version of HTTP protocol that is used for communication between lightweight resource constraint devices in Internet of Things (IoT) network. The CoAP protocol is generally associated with connectionless User Datagram Protocol (UDP) and works based on Representational State Transfer architecture. The CoAP is associated with Datagram Transport Layer Security (DTLS) protocol for establishing a secure session using the existing algorithms like Lightweight Establishment of Secure Session for communication between various IoT devices and remote server. However, several limitations regarding the key management, session establishment and multi-cast message communication within the DTLS layer are present in CoAP. Hence, development of an efficient protocol for secure session establishment of CoAP is required for IoT communication. Thus, to overcome the existing limitations related to key management and multicast security in CoAP, we have proposed an efficient and secure communication scheme to establish secure session key between IoT devices and remote server using lightweight elliptic curve cryptography (ECC). The proposed ECC-based CoAP is referred to as ECC-CoAP that provides a CoAP implementation for authentication in IoT network. A number of well-known cryptographic attacks are analyzed for validating the security strength of the ECC-CoAP and found that all these attacks are well defended. The performance analysis of the ECC-CoAP shows that our scheme is lightweight and secure.

     

Polar Hinges as Functionalized Conformational Constraints in (Bi)cyclic Peptides

Two polar hinges for cyclization of peptides have been developed, leading to bicyclic peptides and cyclized peptides with improved solubility and biological activity. Increasingly, we note that a good aqueous solubility of peptides is an absolute prerequisite, not only to allow handling and purification of our target peptides but also being crucial for biological activity characteristics. Compared to earlier hinges, the 1,1′,1“‐(1,3,5‐triazinane‐1,3,5‐triyl)tris(2‐bromoethanone) (TATB) and 2,4,6‐tris(bromomethyl)‐s‐triazine (TBMT), each containing three nitrogen atoms are structurally similar but chemically very different. Both were accessible in a one‐step fashion from bromoacetonitrile. TATB and TBMT are very suitable for the preparation of more soluble bicyclic peptides. Azide‐modified TATB and TBMT derivatives provide hinges for the preparation of cyclized peptides for incorporation on scaffolds to afford protein mimics.     

Development and validation of a simple analytical method for the determination of 2,4,6-trichloroanisole in wine by GC–MS

A novel method for the residue analysis of wine spoilage compound 2,4,6-trichloroanisole is reported. Wine (60 ml) was extracted with 2 ml toluene in presence of 24 g MgSO₄ and 6 g NaCl. Cleanup of the toluene phase by dispersive solid phase extraction with mixture of 100 mg CaCl₂, 25 mg primary secondary amine and 50 mg MgSO₄ was effective in minimising co-extractives and matrix effects. Time-of-flight and tandem mass spectrometric parameters were optimised to achieve linearity over 0.25–500 ng ml⁻¹ and method detection limit 0.0083 ng ml⁻¹ which is well below the odour threshold of 0.04 ng ml⁻¹. Recoveries at 0.04, 0.2 and 0.8 ng ml⁻¹ were within 80–110% (±8%). The method was reproducible when tested for Argentinean wines with intra-laboratory Horwitz ratios being <0.20 in white and red wines at both the laboratories of India and Argentina. The method could be successfully applied for incurred wine samples.     

Rate of degradation of λ-cyhalothrin and methomyl in grapes (Vitis vinifera L.)

Rates of degradation of λ-cyhalothrin and methomyl residues in grape are reported. The dissipation behavior of both insecticides followed first-order rate kinetics with similar patterns at standard and double-dose applications. Residues of λ-cyhalothrin were lost with pre-harvest intervals (PHI) of 12.0-12.5 and 15.0-15.5 days, corresponding to the applications at 25 and 50 g a.i. ha^-1, respectively. In the case of methomyl, residues were lost with PHI of 55.0 and 61.0 days, following applications at 1 and 2 kg a.i. ha^-1, respectively. The PHI, recommended on the basis of the experimental results, was shown to be effective in minimizing residue load of these insecticides below their maximum residue limits (MRLs) in vineyard samples.