An Efficient Kriging Modeling Method Based on Multidimensional Scaling for High-Dimensional Problems

Kriging-based modeling has been widely used in computationally intensive simulations. However, the Kriging modeling of high-dimensional problems not only takes more time, but also leads to the failure of model construction. To this end, a Kriging modeling method based on multidimensional scaling (KMDS) is presented to avoid the “dimensional disaster”. Under the condition of keeping the distance between the sample points before and after the dimensionality reduction unchanged, the KMDS method, which mainly calculates each element in the inner product matrix due to the mapping relationship between the distance matrix and the inner product matrix, completes the conversion of design data from high dimensional to low dimensional. For three benchmark functions with different dimensions and the aviation field problem of aircraft longitudinal flight control, the proposed method is compared with other dimensionality reduction methods. The KMDS method has better modeling efficiency while meeting certain accuracy requirements.

Assessing the dynamic tourism inter-industry linkages and economic structural changes in Cambodia’s economy

This study employs the SAM-based model combining with multiplier product matrix and field of influence approaches. Under three input–output transaction table matrices of the years 2005, 2010, and 2015, these approaches assess the dynamic tourism inter-industry linkages and structural economic changes in Cambodia. We find that the overall inter-industry connection is relatively low. The textile, other manufacturing, and transportation and communication are key sectors. They have the largest coefficient field of influence of changes in the economic system. Tourism has shifted to be a key sector in 2010 and 2015. However, its backward and forward linkages are still small. It is a relatively promising sector generating a large coefficient field of influence of changes, showing less strength of overall connection with other industries. This study may suggest that there would be a need for promoting, encouraging, and investing in key economic sectors. Policy intervention should focus on developing domestic tourism linkages and strengthening inter-industry ties to diversity tourism benefits the local economy.

Convolver Design and Convolve-Accumulate Unit Design for Low-Power Edge Computing

Convolution operations have a significant influence on the overall performance of a convolutional neural network, especially in edge-computing hardware design. In this paper, we propose a low-power signed convolver hardware architecture that is well suited for low-power edge computing. The basic idea of the proposed convolver design is to combine all multipliers’ final additions and their corresponding adder tree to form a partial product matrix (PPM) and then to use the reduction tree algorithm to reduce this PPM. As a result, compared with the state-of-the-art approach, our convolver design not only saves a lot of carry propagation adders but also saves one clock cycle per convolution operation. Moreover, the proposed convolver design can be adapted for different dataflows (including input stationary dataflow, weight stationary dataflow, and output stationary dataflow). According to dataflows, two types of convolve-accumulate units are proposed to perform the accumulation of convolution results. The results show that, compared with the state-of-the-art approach, the proposed convolver design can save 15.6% power consumption. Furthermore, compared with the state-of-the-art approach, on average, the proposed convolve-accumulate units can reduce 15.7% power consumption.

The Shortest Duration Constrained Hidden Markov Model: Data denoise and forecast optimization on the country-product matrix for the Fitness-Complexity Algorithm

The Economic Fitness Index describes industrial completeness and comprehensively reflects product diversification with competitiveness and product complexity in production globalization. The Fitness-Complexity Algorithm offers a scientific approach to predicting GDP and obtains fruitful results. As a recursion algorithm, the non-linear iteration processes give novel insights into product complexity and country fitness without noise data. However, the Country-Product Matrix and Revealed Comparative Advantage data have abnormal noises which contradict the relative stability of product diversity and the transformation of global production. The data noise entering the iteration algorithm, combined with positively related Fitness and Complexity, will be amplified in each recursion step. We introduce the Shortest Duration Constrained Hidden Markov Model (SDC-HMM) to denoise the Country-Product Matrix for the first time. After the country-product matrix test, the country case test, the noise estimation test and the panel regression test of national economic fitness indicators to predict GDP growth, we show that the SDC-HMM could reduce abnormal noise by about 25% and identify change points. This article provides intra-sample predictions that theoretically confirm that the SDC-HMM can improve the effectiveness of economic fitness indicators in interpreting economic growth.

Insights Gained from an Approximate Analytical Solution of the Evaporation Model Used by ConsExpo Web

Evaporation of chemicals is an important source of inhalative exposure. We analyzed here the ConsExpo evaporation model, which is characterized by a set of nonlinear differential equations only solvable by numerical means. It shows qualitatively different behavior for different parameters, but the exact conditions remain unclear. This article presents an approximate analytical solution of the ConsExpo evaporation model, derived by using a specific linearization of the nonlinear equations valid for small concentrations. From this solution, three different boundary cases or regimes are found: quick release, near equilibrium, and ventilation driven regime. Depending on the evaporation regime, different parameters influence peak substance air concentration: Quick release regime: total substance amount and room volume; near equilibrium regime: vapor pressure, substance concentration in the product, and molecular weight of the product matrix; ventilation driven regime: vapor pressure, substance concentration in the product, room volume, surface area, mass transfer coefficient, ventilation rate, and molecular weight of the product matrix. A graphical method is developed to display the position of a given scenario in relation to the three regimes. Thus, the approximate analytical solution allows for a given situation to prioritize research for reducing uncertainty of the most sensitive parameters and helps to identify promising risk management measures.

Light Microscopy as a Tool to Evaluate the Functionality of Starch in Food

Light microscopy (LM) is commonly used in the study of biological materials to determine the morphology of cells and tissues. The potential of this technique for studying the structure of food products is also recognized but less known. Especially rare are information regarding LM studies of the supramolecular structure of starch. The aim of the work was to fill this gap by providing data on the possibilities for application of LM in starch studies. It was shown that in spite of an enormous progress in the development of microscopic techniques, including both increase of resolution and improvement of image analysis methods, light microscopy still has a huge potential for starch studies. The advantage of LM over other microscopic techniques is the possibility of differentiating between amylose and amylopectin by iodine staining. That makes LM especially useful in the analysis of the process of gelatinization of starch, the extent of molecular dispersion of its macromolecules, and the changes in its structure caused by modification. Moreover, it can be particularly useful for studying the changes in the supramolecular structure of starch in a food product matrix, providing more information than scanning electron microscopy (SEM)–the most common technique used for these purposes.

Polyphenol Stability and Physical Characteristics of Sweetened Dried Cranberries

There is little research on how product matrix and processing affect phenolic compounds in sweetened dried cranberries over time. The objective of this research was to assess polyphenol content and stability in sweetened dried cranberries between product matrix types. This research assessed five commercially available sweetened dried cranberry matrices: (1) sliced apple juice infused, (2) whole apple juice infused, (3) sliced sucrose infused, (4) whole sucrose infused, and (5) sliced soluble corn fiber, glycerin, sucrose, and sucralose infused (three replicates/treatment). Proanthocyanidins, anthocyanins (HPLC), total phenolic content (Folin–Ciocalteu), water activity, moisture content, color, and texture were evaluated over 12 months at 21 °C. Data were analyzed by ANOVA (p < 0.05). Results demonstrate that sweetened dried cranberry polyphenols are unstable regardless of product matrix. More research is needed to determine optimal processing parameters for sweetened dried cranberries to maintain polyphenol stability as healthier food options for consumers.