Effects of High Hydrostatic Pressure Combined with Vacuum-Freeze Drying on the Aroma-Active Compounds in Blended Pumpkin, Mango, and Jujube Juice

A combination process of completely non-thermal processing methods involving high hydrostatic pressure (HHP) and vacuum-freeze drying (VFD) for producing a new snack from fruit and vegetable blends was developed, and the effect of the process on flavor quality was investigated. The HHP–VFD treatment did not significantly reduce volatile compound contents compared to single HHP or VFD. Gas chromatography–olfactometry showed that HHP–VFD raised the contents of floral-like volatile compounds (e.g., β-ionone) compared to the untreated sample. Sensory evaluation analysis confirmed that the overall liking was unchanged after the HHP–VFD treatment. The HHP–VFD combined treatment is effective in maintaining the flavor and extending shelf life, and is convenient for the portability and transportation of ready-to-drink juice.

Evolutionary Computation for Feature Manipulation in Salient Object Detection

<p>The human visual system can efficiently cope with complex natural scenes containing various objects at different scales using the visual attention mechanism. Salient object detection (SOD) aims to simulate the capability of the human visual system in prioritizing objects for high-level processing. SOD is a process of identifying and localizing the most attention grabbing object(s) of a scene and separating the whole extent of the object(s) from the scene. In SOD, significant research has been dedicated to design and introduce new features to the domain. The existing saliency feature space suffers from some difficulties such as having high dimensionality, features are not equally important, some features are irrelevant, and the original features are not informative enough. These difficulties can lead to various performance limitations. Feature manipulation is the process which improves the input feature space to enhance the learning quality and performance. Evolutionary computation (EC) techniques have been employed in a wide range of tasks due to their powerful search abilities. Genetic programming (GP) and particle swarm optimization (PSO) are well-known EC techniques which have been used for feature manipulation. The overall goal of this thesis is to develop feature manipulation methods including feature weighting, feature selection, and feature construction using EC techniques to improve the input feature set for SOD. This thesis proposes a feature weighting method utilizing PSO to explore the relative contribution of each saliency feature in the feature combination process. Saliency features are referred to the features which are extracted from different levels (e.g., pixel, segmentation) of an image to compute the saliency values over the entire image. The experimental results show that different datasets favour different weights for the employed features. The results also reveal that by considering the importance of each feature in the combination process, the proposed method has achieved better performance than that of the competitive methods. This thesis proposes a new bottom-up SOD method to detect salient objects by constructing two new informative saliency features and designing a new feature combination framework. The proposed method aims at developing features which target to identify different regions of the image. The proposed method makes a good balance between computational time and performance. This thesis proposes a GP-based method to automatically construct foreground and background saliency features. The automatically constructed features do not require domain-knowledge and they are more informative compared to the manually constructed features. The results show that GP is robust towards the changes in the input feature set (e.g., adding more features to the input feature set) and improves the performance by introducing more informative features to the SOD domain. This thesis proposes a GP-based SOD method which automatically produces saliency maps (a 2-D map containing saliency values) for different types of images. This GP-based SOD method applies feature selection and feature combination during the learning process for SOD. GP with built-in feature selection process which selects informative features from the original set and combines the selected features to produce the final saliency map. The results show that GP can potentially explore a large search space and find a good way to combine different input features. This thesis introduces GP for the first time to construct high-level saliency features from the low-level features for SOD, which aims to improve the performance of SOD, particularly on challenging and complex SOD tasks. The proposed method constructs fewer features that achieve better saliency performance than the original full feature set.</p>

Evolutionary Computation for Feature Manipulation in Salient Object Detection

<p>The human visual system can efficiently cope with complex natural scenes containing various objects at different scales using the visual attention mechanism. Salient object detection (SOD) aims to simulate the capability of the human visual system in prioritizing objects for high-level processing. SOD is a process of identifying and localizing the most attention grabbing object(s) of a scene and separating the whole extent of the object(s) from the scene. In SOD, significant research has been dedicated to design and introduce new features to the domain. The existing saliency feature space suffers from some difficulties such as having high dimensionality, features are not equally important, some features are irrelevant, and the original features are not informative enough. These difficulties can lead to various performance limitations. Feature manipulation is the process which improves the input feature space to enhance the learning quality and performance. Evolutionary computation (EC) techniques have been employed in a wide range of tasks due to their powerful search abilities. Genetic programming (GP) and particle swarm optimization (PSO) are well-known EC techniques which have been used for feature manipulation. The overall goal of this thesis is to develop feature manipulation methods including feature weighting, feature selection, and feature construction using EC techniques to improve the input feature set for SOD. This thesis proposes a feature weighting method utilizing PSO to explore the relative contribution of each saliency feature in the feature combination process. Saliency features are referred to the features which are extracted from different levels (e.g., pixel, segmentation) of an image to compute the saliency values over the entire image. The experimental results show that different datasets favour different weights for the employed features. The results also reveal that by considering the importance of each feature in the combination process, the proposed method has achieved better performance than that of the competitive methods. This thesis proposes a new bottom-up SOD method to detect salient objects by constructing two new informative saliency features and designing a new feature combination framework. The proposed method aims at developing features which target to identify different regions of the image. The proposed method makes a good balance between computational time and performance. This thesis proposes a GP-based method to automatically construct foreground and background saliency features. The automatically constructed features do not require domain-knowledge and they are more informative compared to the manually constructed features. The results show that GP is robust towards the changes in the input feature set (e.g., adding more features to the input feature set) and improves the performance by introducing more informative features to the SOD domain. This thesis proposes a GP-based SOD method which automatically produces saliency maps (a 2-D map containing saliency values) for different types of images. This GP-based SOD method applies feature selection and feature combination during the learning process for SOD. GP with built-in feature selection process which selects informative features from the original set and combines the selected features to produce the final saliency map. The results show that GP can potentially explore a large search space and find a good way to combine different input features. This thesis introduces GP for the first time to construct high-level saliency features from the low-level features for SOD, which aims to improve the performance of SOD, particularly on challenging and complex SOD tasks. The proposed method constructs fewer features that achieve better saliency performance than the original full feature set.</p>

Experimental and numerical investigation of damping in a hybrid automotive damper combining viscous and multiple-impact mechanisms

One of the least investigated approaches in passive vibration control is the possibility of combining different types of dampers that use different damping principles. Such a combination process, if wisely designed and implemented, has the potential to increase the damping performance and extend the damper’s application. The primary purpose of this work is to experimentally and numerically investigate the damping behavior of a novel Fluid-Impact Hybrid Damper. This damper combines a conventional Viscous Fluid Damper with a Particle-Impact Damper. The Fluid-Impact Hybrid Damper comprises a 3D-printed plastic box attached to the Viscous Fluid Damper’s moving rod and filled with stainless steel balls. An experimental setup was designed to drive the Viscous Fluid Damper’s rod into harmonic oscillations at different frequencies (1, 2, 4, 6, and 8 Hz). The number of balls was changed three times (5, 10, and 15) to assess the effect of this parameter on the damping performance of the Fluid-Impact Hybrid Damper. A finite element model of the Fluid-Impact Hybrid Damper was developed using LS-Dyna explicit simulation program. The objective of the FE model is to investigate the elastoplastic balls-box collisions using a piecewise-linear plasticity material model. For both the experimental and numerical results, the Frequency Response Function was considered as the main comparison component for a set of force-independent results. The measured Frequency Response Functions showed a noticeable reduction in amplitude at the system’s natural frequency (2 Hz), with an acceptable accuracy between the two approaches.

Business Combination Process And Its Audit Review Of Financial Statements

Enterprises are known to be important subjects of accounting, which is the main link in the economy of any individual society, respectively, as a means of management. Through enterprises, tangible and intangible goods are created, various works are performed, services are provided and they are delivered to consumers. Increasing the number of enterprises, merging them, enlarging them, developing joint activities is one of the important factors in increasing the country's GDP, employment and income, improving living standards and welfare. This article reveals the basic rules of accounting and financial reporting in the context of reorganization of enterprises on the basis of existing laws and regulations, including national standards. The essence, features and procedures of reorganization of the main branches of the economy of the republic, respectively, the main subjects of accounting are revealed. The article also outlines the general requirements for accounting and financial reporting in the context of reorganization of enterprises.

Investigation on the Thermoforming of Pmsq-Hdpe for the Manufacture of a NACA Profile of Small Dimensions

Unmanned aerial vehicles (UAVs) or drones are attracting increasing interest in the aviation industry, both for military and civilian applications. The materials used so far in the manufacture of UAVs are wood, plastic, aluminum and carbon fiber. In this regard, a new family of high-density polyethylene (HDPE) nanocomposites reinforced with polymethylsilsesquioxane nanoparticles (PMSQ), with mechanical performances significantly superior to those of pure HPDE, has been prepared by a fusion-combination process. Their viscoelastic properties were determined by oscillatory shear tests and their viscoelastic behavior characterized by the Lodge integral model. Then, the Lagrangian formulation and the membrane theory assumption were used in the explicit implementation of the dynamic finite element formulation. For the forming phase, we considered the thermodynamic approach to express the external work in terms of closed volume. In terms of von Mises stress distribution and thickness in the blade, the results indicate that HDPE-PMSQ behaves like virgin HDPE. Furthermore, its materials, for all intents and purposes, require the same amount of energy to form as HDPE.

Technological Risk in the Context of Process Approach and Risk Assessment

The processes effectiveness means their ability to achieve the planned aims. While planning the aims, one should currently take into consideration not only the quality parameters but also all of the criteria reflecting the expectations stated by the interested parties. Influence on the processes by the factors disturbing their realization requires such their monitoring and regulation to make the aims achievable. In return, it requires development and application of the processes assessment methods enabling pointing out their pivotal points and the possibilities of their monitoring. The study proposes the following combination: process approach and risk assessment enabling pointing out the “risky” processes as well as their “risky” aspects. The risk of the particular process has been evaluated by taking advantage of two methods considering various risk parameters as well as various individual criteria for assessing the mentioned parameters. Application of the developed methodologies of risk analysis and assessment of its acceptability allowed for formulating the conclusion that while risk assessment the following aspects are important: choice of the risk parameters, application of the individual assessment criteria of these parameters and taking advantage of the risk acceptability scale of the process, depending on the phase of its improvement. Properly prepared method of risk assessment gives the chance for the effective monitoring of the process risk refraining from its particular threats.

Development of a cost-effective wastewater treatment process: combination of different process

Caprolactam wastewater produced by the production process of caprolactam is characterized by a very high toxicity and chemical oxygen demand (COD) values, having potential harm to the environment if treated improperly. However, these characteristics make caprolactam wastewaters difficult to treat using traditional methods. So the aim of this work was to develop a cost-effective caprolactam wastewater treatment process. Fenton oxidation, sequencing batch reactor activated sludge process (SBR) and electro-catalytic oxidation were proposed to treat caprolactam wastewater in the laboratory scale, and the treatment effects were investigated. Compared with Fenton oxidation, SBR and electro-catalytic oxidation can treat caprolactam wastewater at a lower cost and more efficiently. The pilot test results indicate that the COD can be decreased to less than 1000 mg/L by the combination process, and when the COD removal rates maintain 90%, the cost of caprolactam wastewater treatment is below 6 yuan/m3. The combination process showed better economic benefit.