(N-Isocyanimino)triphenylphosphorane-Mediated,One-Pot,Efficient Synthesis of Sterically Congested 1,1,1-Trifluoro-2-(5-aryl-1,3,4-oxadiazol-2-yl)-2-propanol Derivatives via Intramolecular Aza-Wittig Reaction

Reactions of (N-isocyanimino)triphenylphosphorane with 1,1,1-trifluoroacetone in the presence of aromatic (or heteroaromatic) carboxylic acids (3-methylbenzoic acid, 1-naphthalenecarboxylic acid, 2-naphthalenecarboxylic acid, 2-furancarboxylic acid, and 2-thiophenecarboxylic acid) proceed smoothly at room temperature and in neutral conditions to afford sterically congested 1,1,1-trifluoro-2-(5-aryl-1,3,4-oxadiazol-2-yl)-2-propanol derivatives in high yields. The reaction proceeds smoothly and cleanly under mild conditions, and no side reactions were observed.

Supplemental materials are available for this article. Go to the publisher's online edition of Phosphorus, Sulfur, and Silicon and the Related Elements to view the free supplemental file.     

In situ crystallization of ZnAl2O4/ZnO nanocomposite on alumina granule for photocatalytic purification of wastewater

Research on Chemical Intermediates - ZnAl2O4/ZnO nanocomposites with different ZnO (20, 30, and 40 mol%) concentrations and coated samples on supports were successfully prepared through...     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

A calibrated model of local food system of Hawaii: What are the economic implications of the state's food goals and policies?

We develop a fully calibrated positive mathematical programming model for Hawaii's local food systems—which captures the production and the consumer sides of the market. Then we use the model to assess two proposed policies—a general excise tax (GET) exemption on locally produced foods, and an investment in agricultural infrastructure. For the GET exemption case, our results indicate an economic gain of $118 per $100 cost. On the other hand, an investment in 1,200 acres of land injected to support local production may generate an economic gain of up to $357 per $100 annual cost of the investment. However, these estimates should be considered preliminary, and thus viewed with caution. Although the model is used to capture Hawaii's local food systems, we believe that our model is generalizable and can be adopted by other economies to assess their respective food localization policies. Recommendations for Resource Managers

• Local food policies need to be based on quantitative terms instead of mere armchair speculation because often their potential outcomes may vary significantly.

• The current modeling framework demonstrates the potential of using positive mathematical programming (PMP) in capturing the intricacies of local food systems. However, this exploratory exercise should be viewed as preliminary in nature and the ensuing results were taken with caution because many important factors such as labor availability may have been left out.

• Thus, further model refinements are necessary to better capture the complexities of local food systems such as farm heterogeneity, availability of farm labor, water availability, and interisland transportation of farm products in the case of Hawaii.

     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Tuning Chemical and Physical Properties of Phosphorus Corroles for Advanced Applications

Although the affinity of metallocorroles to axial ligands is quite low, this is not the case when the chelated element is phosphorus. This work is hence focused on the mechanism of ligand exchange of six-coordinate phosphorus corroles as a tool for affecting their chemical and physical properties. These fundamental investigations allowed for the development of facile methodologies for the synthesis of a large series of complexes and the establishment of several new structure/activity profiles that may be used to understand and predict spectroscopic features and for tailor-made modification of photophysical and electrochemical properties. This is exemplified by the facile access to complexes with terminal groups that are of large potential for practical applications based on click chemistry, optical imaging, and surface science.     

Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features

Extraction of relevant lip features is of continuing interest in the visual speech domain. Using end-to-end feature extraction can produce good results, but at the cost of the results being difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction approach, motivated by human-centric glimpse-based psychological research into facial barcodes, and demonstrate that these simple, easy to extract 3D geometric features (produced using Gabor-based image patches), can successfully be used for speech recognition with LSTM-based machine learning. This approach can successfully extract low dimensionality lip parameters with a minimum of processing. One key difference between using these Gabor-based features and using other features such as traditional DCT, or the current fashion for CNN features is that these are human-centric features that can be visualised and analysed by humans. This means that it is easier to explain and visualise the results. They can also be used for reliable speech recognition, as demonstrated using the Grid corpus. Results for overlapping speakers using our lightweight system gave a recognition rate of over 82%, which compares well to less explainable features in the literature.     

Binder-less and free-standing Co–Fe metal nanoparticles-decorated PVdF-HFP nanofiber membrane as an electrochemical probe for enzyme-less glucose sensors

Co–Fe bimetallic nanoparticles-affixed polyvinylidene fluoride-co-hexafluoropropylene (PVdF-HFP) nanofiber membrane is fabricated using the electrospinning and chemical reduction techniques. The semicrystalline polymeric backbone decorated with the highly crystalline Co–Fe bimetallic nanoparticles enunciates the mechanical integrity, while the incessant and swift electron mobility is articulated with the consistent dissemination of bimetallic nanoparticles on the intersected and multi-layered polymeric nanofibers. The diffusion and adsorption of glucose are expedited in the extended cavities and porosities of as-formulated polymeric nanofibers, maximizing the glucose utilization efficacy, while the uniformly implanted Co⁴⁺/Fe³⁺ active centers on PVdF-HFP nanofibers maximize the electrocatalytic activity toward glucose oxidation under alkaline regimes. Thus, the combinative sorts including nanofiber and nanocomposite strategies of PVdF-HFP/Co–Fe membrane assimilate the enzyme-less electrochemical glucose detection concerts of high sensitivity (375.01 μA mM^?1 cm^?2), low limit of detection (0.65 μm), and wide linear range (0.001 to 8 mM), outfitting the erstwhile enzyme-less glucose detection reports. Additionally, the endowments of high selectivity and real sample glucose-sensing analyses of PVdF-HFP/Co–Fe along with the binder-less and free-standing characteristics construct the state-of-the-art paradigm for the evolution of affordable enzyme-less electrochemical glucose sensors.