As the number falls,alternatives to the Hagberg–Perten falling number method: A review

This review examines the application, limitations, and potential alternatives to the Hagberg–Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.     

Synthesis of Ionic Ultramicroporous Polymers for Selective Separation of Acetylene from Ethylene

The design of highly stable and efficient porous materials is essential for developing breakthrough hydrocarbon separation methods based on physisorption to replace currently used energy-intensive distillation/absorption technologies. Efforts to develop advanced porous materials such as zeolites, coordination frameworks, and organic polymers have met with limited success. Here, a new class of ionic ultramicroporous polymers (IUPs) with high-density inorganic anions and narrowly distributed ultramicroporosity is reported, which are synthesized by a facile free-radical polymerization using branched and amphiphilic ionic compounds as reactive monomers. A covalent and ionic dual-crosslinking strategy is proposed to manipulate the pore structure of amorphous polymers at the ultramicroporous scale. The IUPs exhibit exceptional selectivity (286.1–474.4) for separating acetylene from ethylene along with high thermal and water stability, collaboratively demonstrated by gas adsorption isotherms and experimental breakthrough curves. Modeling studies unveil the specific binding sites for acetylene capture as well as the interconnected ultramicroporosity for size sieving. The porosity-engineering protocol used in this work can also be extended to the design of other ultramicroporous materials for the challenging separation of other key gas constituents.     

Oil windfalls and export diversification in oil-producing countries: Evidence from oil booms

This paper examines the factors behind export diversification in oil countries. Specifically, we investigate the impact of oil booms on export diversification through an empirical framework. The paper finds that economy's export structure before the oil boom determines whether oil windfalls might affect the diversification process. Thus, an oil boom negatively affects export diversification only if countries initially exhibit low levels of diversification. In countries with a high level of diversification before the boom, an oil boom has no impact on diversification. These results are based on a large sample of 134 countries, and are robust to various sensitivity analyses. They are corroborated with data from the manufacturing sector which show that oil booms only reduce diversification in countries with a small manufacturing sector prior to the boom.     

A model- and simulation-based approach for tolerancing and verifying the functional capability of micro/nano-structured workpieces

The evaluation of functional features of manufactured workpieces is based on GO- and NO-GO-test results, which are obtained by comparing measured geometric characteristics with nominal dimensions and tolerances specified by the designer. These geometrical specifications are based on a tolerancing system, which was originally defined for the function mating capability. Against the background of upcoming lots of other new functions (like reduction of flow resistance, light absorption, reduction of friction, diffraction of light, self-cleaning or mass transmission) are to be realized with our products – particularly by micro- and nano scaled features. If the verification process will deliver the prediction of the achievable degree of functionality, the usability of a part can be assessed more accurately and in consequence quality and economics can be improved. So, a new principle for tolerancing and verifying turns out to be necessary. In this paper the fundamental deficit of the actual tolerancing and specification systems GPS and ASME Y14.5 is derived and the path for enlarging the system by preposing a functional model is shown. To verify the functional capability of the workpieces an approach based on simulations done with the parameterized mathematical–physical model of the function is suggested. Advantages of this approach will be discussed and demonstrated by examples with microstructured inking rolls, crankshafts and injection valves.     

Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

Design optimization of a centrifugal compressor vaneless diffuser

Centrifugal compressors are used in many heat pumps and refrigeration systems. Radial vaneless diffuser is a principal component in these compressors. Therefore, the present research aims at improving the centrifugal compressor performance by optimizing the design of the radial vaneless diffuser. Two radial vaneless diffuser geometries were proposed, investigated and numerically optimized. The optimization aimed at minimizing the diffuser loss coefficient and maximizing the pressure coefficient. Simulations were performed by solving the Reynolds averaged Navier–Stokes equation under 2D axisymmetric condition. A genetic optimization algorithm was implemented in order to conclude the optimum diffuser geometry. 2D axisymmetric simulations with air and R134a as working fluids showed that the optimized geometry reduced the diffuser loss coefficient by up to 10% and increased the pressure coefficient by up to 3.8%. Additional 3D simulations with an impeller located before the diffuser were performed. These 3D simulations showed that the optimized diffuser geometry reduced the diffuser loss coefficient by up to 4.7% and increased the pressure coefficient by up to 6.6% under jet-wake and swirl flow conditions.     

Effect of nanosilica addition on bioactivity and in vivo properties of calcium aluminate cement

This study aims at assessing the influence of nanosilica on the bioactivity and mechanical properties of calcium aluminate cement. For this purpose, nanosilica was applied as a replacement for calcium aluminate cement at 0, 2, 5 and 8 wt%. The main components were analyzed by scanning electron microscope coupled with surface imaging and elemental analysis, fourier transform infrared spectroscopy and X-ray diffraction analysis. To estimate the bioactivity of specimens, hydroxyapatite formation on the surface of cement paste was investigated in the simulated body fluid solution. In addition, in vivo evaluation of calcium aluminate cement was performed in subcutaneous tissue of rats. To investigate the mechanical properties, both compressive and flexural strengths were also measured. The results revealed that by increasing nanosilica up to 8 wt%, the strength enhanced. Moreover all cement paste samples with various amounts of nanosilica represented good bioactivity because of formation of bonelike apatite layer on the surface of specimens within 28 days after soaking in simulated body fluid. In vivo experiments indicated that the cement sample was absorbed by the tissue and there was no infection at the implant site. Based on the in vitro and in vivo results, the specimen with 2 wt% nanosilica represented the highest bioactivity.