An inter-temporal resource emergency management model

One of the most important ways for promoting the service level during emergency management in public sectors is through enhancing management of strategic resources. From the perspective of vertical integration of operational process in emergency management, emergency resources can be classified as those resources used during the response period as well as those resources that are consumed during the recovery period. An inter-temporal integrated single-period resource model for solving optimal order quantity is proposed that meets the characteristics during the demand for the recovery resources depending on the stock shortage of the response resources. In the light of the classification of the dependent relationship of the two kinds of resources, namely deterministic or stochastic, the research investigates the analytical properties of the model, based on which, a genetic algorithm-based simulation approach is proposed. Finally, a real case with numerical example is provided to assess and validate our model, as well as managerial insights are obtained through key parameters sensitivity analysis.     

Mechanical fatigue in aluminium at elevated temperature and remaining life prediction based on natural frequency evolution

Fatigue is one of the principle damage mechanisms in materials especially at elevated temperatures. It possesses a great influence on the material properties and the possible working life. In this work, a novel technique for the analysis of fatigue at elevated temperature is suggested. Material fundamental frequency and its drop are used as the key parameters to predict the remaining useful life of a selected shape specimen, which is operated under low‐cyclic fatigue loading at elevated temperature. Experiments are performed on aluminium 1050 with two different lengths of a non‐prismatic cantilever beam at room temperature, 100, 200 and 300 °C, respectively. The experimental data is further transformed in to an empirical correlation that can predict specimen useful life. The error in this life prediction is less than 5%, and it reduces more once the frequency drop is increased.     

A vector for double epitope tagging with a recyclable marker

Multimeric protein complexes play diverse and vital roles in the cell, but following the composition of these complexes under varying growth conditions can be challenging. Toward that goal, we have designed a vector that permits the double epitope tagging of a protein at its carboxy terminus. One 'universal' tag, a triple repeat of the HA1 epitope, is fused with every protein to be studied, allowing the composition and stoichiometry of the proteins in a complex to be detected with a single antibody. Each protein also can be tagged with a second epitope specific for that protein. This 'specific' tag can be used to immunoprecipitate complexes containing that protein of interest. Any epitope to which a specific antibody is available can be used for this second tag. Because there are a limited number of selection markers for cloning in yeast, the kanamycin cassette, flanked by loxP sites, was incorporated into the vector to permit marker recycling using Cre-lox recombinase. This vector was used to tag 4 proteins involved in ribosome biogenesis-Ytm1, Cic1, Brx1 and Drs1. An anti-HA1 antibody could detect all four proteins in crude lysates and yielded the relative abundance of these four proteins, of which Drs1 is reproducibly less abundant than any of the others, which may have implications for the control of ribosome biogenesis. The Ytm1 protein was also tagged with the VSV epitope and can be specifically detected using an anti-VSV antibody. This vector may prove useful for exploring other protein complexes.     

Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate more highlights from the medical images. Other than that, we have identified the loss function by utilizing several dice measurements approach and received Dice Result on top of a specific test case. The average mean score of dice coefficient and soft dice loss for three test cases was 0.0980. At the same time, for two test cases, the sensitivity and specification were recorded to be 0.0211 and 0.5867 using patch level predictions. On the other hand, a software integration pipeline was integrated to deploy the concentrated model into the webserver for accessing it from the software system using the Representational state transfer (REST) API. Eventually, the suggested models were validated through the Area Under the Curve–Receiver Characteristic Operator (AUC–ROC) curve and Confusion Matrix and compared with the existing research articles to understand the underlying problem. Through Comparative Analysis, we have extracted meaningful insights regarding brain tumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imaging modalities.     

Numerical Solutions for Heat Transfer of An Unsteady Cavity with Viscous Heating

The mechanism of viscous heating of a Newtonian fluid filled inside a cavity under the effect of an external applied force on the top lid is evaluated numerically in this exploration. The investigation is carried out by assuming a two-dimensional laminar in-compressible fluid flow subject to Neumann boundary conditions throughout the numerical iterations in a transient analysis. All the walls of the square cavity are perfectly insulated and the top moving lid produces a constant finite heat flux even though the fluid flow attains the steady-state condition. The objective is to examine the effects of viscous heating in the fully insulated lid-driven cavity under no-slip and free-slip Neumann boundary conditions coupled with variations in Reynolds and Prandtl numbers. The partial differential equations of time-dependent vorticity-stream function and thermal energy are discretized and solved using a self-developed finite difference code in MATLAB^® environment. Time dependence of fluid thermodynamics is envisaged through contour and image plots. A commercial simulation software, Ansys Fluent^® utilizing a finite element code is employed to verify the finite difference results produced. Although the effect of viscous heating is very minimal, Neumann no-slip and free-slip boundary conditions are able to trap the heat inside the fully insulated cavity as the heat flux is constantly supplied at the top lid. A lower Reynolds number and a greater Prandtl number with free-slip effects reduce temperature distribution in the cavity with a faster velocity than in the no-slip condition as the free-slip behaves as a lubricant.     

An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

There are numerous application areas of computing similarity between process models. It includes finding similar models from a repository, controlling redundancy of process models, and finding corresponding activities between a pair of process models. The similarity between two process models is computed based on their similarity between labels, structures, and execution behaviors. Several attempts have been made to develop similarity techniques between activity labels, as well as their execution behavior. However, a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them. However, neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity. To that end, we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models. Furthermore, we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences. Finally, we have evaluated the proposed approach using our generated collection of process models.     

Heterogeneous Cubic Multidimensional Integrated Circuit for Water and Food Security in Fish Farming Ponds

Among major food production sectors, world aquaculture shows the highest growth rate, providing more than 50% of the global seafood market. However, water pollution in fish farming ponds is regarded as the leading cause of fish death and financial losses in the market. Here, an Internet of Things system based on a cubic multidimensional integration of circuit (MD‐IC) is demonstrated for water and food security applications in fish farming ponds. Both faces of the silicon substrate are used for thin‐film‐based device fabrication. The devices are interconnected via through‐silicon‐vias, resulting in a bifacial complementary metal‐oxide‐semiconductor‐compatible electronics system. The demonstrated cubic MD‐IC is a complete, small, and lightweight system that can be easily deployed by farmers with no need for specialists. The system integrates on its outer sides simultaneous air and water quality monitoring devices (temperature, electrical conductivity, ammonia, and pH sensors), solar cells for energy‐harvesting, and antenna for real‐time data‐transfer, while data‐management circuitry and a solid‐state battery are integrated on its internal faces. Microfluidic cooling technology is used for thermal management in the MD‐IC. Finally, a biofriendly polymeric encapsulation is used to waterproof the embedded electronics, improve the mechanical robustness, and allow the system to float on the surface of the water.     

A genetic algorithm approach to solving the anti-covering location problem

Abstract: In this paper we address the problem of locating a maximum weighted number of facilities such that no two are within a specified distance from each other. A natural process of evolution approach, more specifically a genetic algorithm, is proposed to solve this problem. It is shown that through the use of a commercially available spreadsheet-based genetic algorithm software package, the decision-maker with a fundamental knowledge of spreadsheets can easily set up and solve this optimization problem. Also, we report on our extensive computational experience using three different data sets.     

Machine learning and directional switching median-based filter for highly corrupted images

In this paper, two-stage machine learning-based noise detection scheme has been proposed for identification of salt-and- pepper impulse noise which gives excellent detection results for highly corrupted images. In the first stage, a window of size $3\times 3$ is taken from image and some other features of this window are used as input to neural network. This scheme has distinction of having very low missed detection (MD) and false positives rates. In the second stage, decision tree-based algorithm (J48) is applied on some well-known statistical parameters to generate rules for noise detection. These noise detection methods give promising results for identification of noise from highly corrupted images. A modified version of switching median filter (directional weighted switching median filter) is proposed for noise removal. Performance of noise detector is measured using MD and false alarm FA. Filtering results are compared with state-of-the-art noise removal techniques in terms of peak signal-to-noise ratio and structural similarity index measure. Extensive experiments are performed to show that the proposed technique gives better results than state-of-the-art noise detection and filtering methods.