Authenticity green coffee bean species and geographical origin using near-infrared spectroscopy combined with chemometrics

To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.     

Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model

Engineering with Computers - Over the past few decades, it has been observed a remarkable progression in the development of computer aid models in the field of civil engineering. Machine learning...     

Two Methods,One Goal: Structural Differences between Cocrystallization and Crystal Soaking to Discover Ligand Binding Poses

In lead optimization, protein crystallography is an indispensable tool to analyze drug binding. Binding modes and non-covalent interaction inventories are essential to design follow-up synthesis candidates. Two protocols are commonly applied to produce protein–ligand complexes: cocrystallization and soaking. Because of its time and cost effectiveness, soaking is the more popular method. Taking eight ligand hinge binders of protein kinase A, we demonstrate that cocrystallization is superior. Particularly for flexible proteins, such as kinases, and larger ligands cocrystallization captures more reliable the correct binding pose and induced protein adaptations. The geometrical discrepancies between soaking and cocrystallization appear smaller for fragment-sized ligands. For larger flexible ligands that trigger conformational changes of the protein, soaking can be misleading and underestimates the number of possible polar interactions due to inadequate, highly impaired positions of protein amino-acid side and main chain atoms. Thus, if applicable cocrystallization should be the gold standard to study protein–ligand complexes.     

Cyanogenic,carotenoids and protein composition in leaves and roots across seven diverse population found in the world cassava germplasm collection at CIAT,Colombia

The objective of this study was to characterise the nutritional potential of leaves and identify a diversity centre with low cyanide and high nutrient content among 178 Latin American cassava genotypes. This field-based collection represents the seven diversity centres, held at The International Center for Tropical Agriculture (CIAT Palmira, Colombia) by the Cassava Program. The cyanide, all-trans-β-carotene and lutein concentrations in cassava leaves ranged from 346 to 7484 ppm dry basis (db), from 174–547 μg g⁻¹ db and 15–181 μg g⁻¹ db, respectively. Cassava leaves also showed significant levels of essential amino acids leucine, lysine, phenylalanine, valine and threonine, and average total protein content of 26.24 g 100 g⁻¹ db. Among seven diversity centres, South American rainforest group showed low cyanide and high carotene content in leaves. In addition, VEN77 and PAN51 genotypes stood out for having low cyanide in leaves and roots and high carotene in leaves. This genetic diversity can be used to select high potential progenitors for breeding purposes.     

Ultra-Robust Flexible Electronics by Laser-Driven Polymer-Nanomaterials Integration

Polyethylene terephthalate (PET) is the most widely used polymer in the world. For the first time, the laser-driven integration of aluminum nanoparticles (Al NPs) into PET to realize a laser-induced graphene/Al NPs/polymer composite, which demonstrates excellent toughness and high electrical conductivity with the formation of aluminum carbide into the polymer is shown. The conductive structures show an impressive mechanical resistance against >10000 bending cycles, projectile impact, hammering, abrasion, and structural and chemical stability when in contact with different solvents (ethanol, water, and aqueous electrolytes). Devices including thermal heaters, carbon electrodes for energy storage, electrochemical and bending sensors show this technology's practical application for ultra-robust polymer electronics. This laser-based technology can be extended to integrating other nanomaterials and create hybrid graphene-based structures with excellent properties in a wide range of flexible electronics’ applications.     

Simulation is essential for embedded control systems with task jitter

Sampling or task jitter affects the performance of digital control systems but realistic simulation of this effect has not been possible to date. Our previous work has developed a novel method to simulate sampling jitter in MATLAB/Simulink simulation software where the jitter is generated randomly. What has been missing is a way to capture sampling jitter from a target platform and then feed this timing information into the simulation. This paper presents a low-cost and novel solution to these problems. The method uses an Arduino board to capture task jitter from two different hardware platforms with multiple stressing conditions. Then the recorded performance data is used to drive realistic simulations of a control system. Measurement shows that the task jitter data does not follow any specific random distribution such as Gaussian or Uniform. Furthermore, very occasional timing patterns, which may not be picked up while testing a real system, can result in extreme controller responses. This novel method allows comparisons of different platforms and reduces the effort required to choose the most appropriate platform for full implementation.

     

Recommender systems in the healthcare domain: state-of-the-art and research issues

Journal of Intelligent Information Systems - Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their...     

Hydrogen production enhancement using hot gas cleaning system combined with prepared Ni-based catalyst in biomass gasification

This paper investigates the hot gas temperature effect on enhancing hydrogen generation and minimizing tar yield using zeolite and prepared Ni-based catalysts in rice straw gasification. Results obtained from this work have shown that increasing hot gas temperature and applying catalysts can enhance energy yield efficiency. When zeolite catalyst and hot gas temperature were adjusted from 250 °C to 400 °C, H₂ and CO increased slightly from 7.31% to 14.57%–8.03% and 17.34%, respectively. The tar removal efficiency varies in the 70%–90% range. When the zeolite was replaced with prepared Ni-based catalysts and hot gas cleaning (HGC) operated at 250 °C, H₂ contents were significantly increased from 6.63% to 12.24% resulting in decreasing the hydrocarbon (tar), and methane content. This implied that NiO could promote the water-gas shift reaction and CH₄ reforming reaction. Under other conditions in which the hot gas temperature was 400 °C, deactivated effects on prepared Ni-based catalyst were observed for inhibiting syngas and tar reduction in the HGC system. The prepared Ni-based catalyst worked at 250 °C demonstrate higher stability, catalyst activity, and less coke decomposition in dry reforming. In summary, the optimum catalytic performance in syngas production and tar elimination was achieved when the catalytic temperature was 250 °C in the presence of prepared Ni-based catalysts, producing 5.92 MJ/kg of lower heating value (LHV) and 73.9% tar removal efficiency.     

Geosynthetic reinforced piled embankment modeling using discrete and continuum approaches

Understanding the load transfer mechanism can support engineers having more economical design of geosynthetic reinforced piled embankments. This study aims to investigate the load transfer mechanisms by two different numerical methods including the Discrete Element Method (DEM) and the Finite Difference Method (FDM). The DEM model adopts (a) discrete particles to simulate the micro-structure of the granular materials and (b) coupled discrete element – finite element method (DEM-FEM) to capture the interaction between granular materials and geotextiles. On the other hand, the FDM model uses an advanced constitutive soil model considering the hardening and softening behaviour of the granular materials. The numerical results show that the geotextiles can only contribute to the vertical loading resistance in cases where the soils between piles are soft enough. In terms of design, an optimum value of the geotextile tensile stiffness can be found considering the load, the soft soil stiffness and the thickness of the embankment. Both the DEM and the FDM show that a high geotextile tensile stiffness is not required since an extra stiffness will slightly contribute to the efficiency of the geosynthetic reinforced piled embankments. Nevertheless, both models are useful to optimize the design of geosynthetic reinforced piled embankments.