Categorical Nature of Major Factor Selection via Information Theoretic Measurements

Without assuming any functional or distributional structure, we select collections of major factors embedded within response-versus-covariate (Re-Co) dynamics via selection criteria [C1: confirmable] and [C2: irrepaceable], which are based on information theoretic measurements. The two criteria are constructed based on the computing paradigm called Categorical Exploratory Data Analysis (CEDA) and linked to Wiener–Granger causality. All the information theoretical measurements, including conditional mutual information and entropy, are evaluated through the contingency table platform, which primarily rests on the categorical nature within all involved features of any data types: quantitative or qualitative. Our selection task identifies one chief collection, together with several secondary collections of major factors of various orders underlying the targeted Re-Co dynamics. Each selected collection is checked with algorithmically computed reliability against the finite sample phenomenon, and so is each member’s major factor individually. The developments of our selection protocol are illustrated in detail through two experimental examples: a simple one and a complex one. We then apply this protocol on two data sets pertaining to two somewhat related but distinct pitching dynamics of two pitch types: slider and fastball. In particular, we refer to a specific Major League Baseball (MLB) pitcher and we consider data of multiple seasons.

Optimized routing with efficient energy transmission using Seline Trustworthy optimization for waste management in the smart cities

Rapid development in technology provides an emerging growth based on innovation, invention, and diffusion, where the diffusion of resources stands with the proper disposal of wastes, due to the over-utilization of resources, growing population growth, and migration increases the accumulation of wastes especially, in Indian cities. Therefore, managing the wastes effectively is a raising challenge in the metropolitan cities of India, where the continuous monitoring of the wastes and disposal needs to be initiated. In this research, an internet-of-things-based smart waste management system in smart cities (IoT-SWMS) is focused on proposing an optimal path selection protocol that facilitates the continuous monitoring and disposal of wastes. The proposed optimal path selection protocol named Seline trustworthy optimization developed to determine the optimal routing path in IoT network renders the faster communication of the collected data regarding the level of the dustbins, which is disposed properly at the right time. The analysis of the proposed Seline trustworthy optimization-based IoT network for SWMS is performed based on the performance measures, such as delay, throughput, energy, and Packet Delivery Ratio (PDR) in comparison with the traditional methods. The proposed methodology yields the maximal PDR of 99%, a minimum delay of 0.11 s, and a maximal throughput of 38,400 kbps.

The overlapping effect and fusion protocols of data augmentation techniques in iris PAD

Iris Presentation Attack Detection (PAD) algorithms address the vulnerability of iris recognition systems to presentation attacks. With the great success of deep learning methods in various computer vision fields, neural network-based iris PAD algorithms emerged. However, most PAD networks suffer from overfitting due to insufficient iris data variability. Therefore, we explore the impact of various data augmentation techniques on performance and the generalizability of iris PAD. We apply several data augmentation methods to generate variability, such as shift, rotation, and brightness. We provide in-depth analyses of the overlapping effect of these methods on performance. In addition to these widely used augmentation techniques, we also propose an augmentation selection protocol based on the assumption that various augmentation techniques contribute differently to the PAD performance. Moreover, two fusion methods are performed for more comparisons: the strategy-level and the score-level combination. We demonstrate experiments on two fine-tuned models and one trained from the scratch network and perform on the datasets in the Iris-LivDet-2017 competition designed for generalizability evaluation. Our experimental results show that augmentation methods improve iris PAD performance in many cases. Our least overlap-based augmentation selection protocol achieves the lower error rates for two networks. Besides, the shift augmentation strategy also exceeds state-of-the-art (SoTA) algorithms on the Clarkson and IIITD-WVU datasets.

The Feasibility of Establishing a Morphological Cell Profiling Assay to Assess the Bioactivity of Natural Products

<p><b>Morphological cell profiling (MCP) is an assay which quantifies the morphology of cells and cellular components. Changes in the morphology of cells following compound treatment has been shown to assess the bioactivity, and even propose a target of said compound. It is a powerful assay that can be used to assess novel compounds for drug candidature. However, it is currently not transferable between institutions. Thus, this project evaluated the feasibility of establishing an MCP assay at Victoria University of Wellington - Te Herenga Waka to assess the bioactivity of novel natural products from the natural products laboratory.</b></p> <p>First, a new individualistic approach to select compound concentration for MCP was assessed by flow cytometry with six exemplary drugs. Results concluded this flow cytometry approach can realise an ideal concentration for individual compounds in which the compound treatment was bioactive but not cytotoxic. This approach provides more biologically relevant and accurate information about a compound's bioactivity than previous MCP methods.</p> <p>Second, the feasibility of establishing the assay was assessed by testing the same six drugs through protocol common to MCP assays. Various stains and microscopes were tested for suitability for an MCP; image quantitative software CellProfiler was evaluated for MCP analysis ease; and quality control protocol was attempted. Ultimately, an MCP is currently not feasible at Te Herenga Waka as multiple components to establish the assay are too difficult and time intensive to complete.</p> <p>Third, discorhabdin E, a pyrroloiminoquinone alkaloid, was isolated from the New Zealand marine sponge, Latrunculia kaakaariki. The compound was tested as if it were a novel natural product compound through the new flow cytometry concentration selection protocol to test its effectivity, and was subsequently assessed by morphological analysis.</p>

The Feasibility of Establishing a Morphological Cell Profiling Assay to Assess the Bioactivity of Natural Products

<p><b>Morphological cell profiling (MCP) is an assay which quantifies the morphology of cells and cellular components. Changes in the morphology of cells following compound treatment has been shown to assess the bioactivity, and even propose a target of said compound. It is a powerful assay that can be used to assess novel compounds for drug candidature. However, it is currently not transferable between institutions. Thus, this project evaluated the feasibility of establishing an MCP assay at Victoria University of Wellington - Te Herenga Waka to assess the bioactivity of novel natural products from the natural products laboratory.</b></p> <p>First, a new individualistic approach to select compound concentration for MCP was assessed by flow cytometry with six exemplary drugs. Results concluded this flow cytometry approach can realise an ideal concentration for individual compounds in which the compound treatment was bioactive but not cytotoxic. This approach provides more biologically relevant and accurate information about a compound's bioactivity than previous MCP methods.</p> <p>Second, the feasibility of establishing the assay was assessed by testing the same six drugs through protocol common to MCP assays. Various stains and microscopes were tested for suitability for an MCP; image quantitative software CellProfiler was evaluated for MCP analysis ease; and quality control protocol was attempted. Ultimately, an MCP is currently not feasible at Te Herenga Waka as multiple components to establish the assay are too difficult and time intensive to complete.</p> <p>Third, discorhabdin E, a pyrroloiminoquinone alkaloid, was isolated from the New Zealand marine sponge, Latrunculia kaakaariki. The compound was tested as if it were a novel natural product compound through the new flow cytometry concentration selection protocol to test its effectivity, and was subsequently assessed by morphological analysis.</p>

An IRGA-MACS Based Cluster-Head Selection Protocol for Wireless Sensor Networks

In a volatile environment, a substantial number of sensor nodes are extensively dispatched to track and detect changes in physical environment. Although sensor nodes have limited energy resources, so energy-efficient routing is a major concern in Wireless Sensor Networks (WSN) to extend the network’s lifespan. Recent research shows that less throughput, increased delay, and high execution time have been provided with high energy usage. A new mechanism called the IRGA-MACS is proposed to overcome these inherent problems. Firstly, the Improved Resampling Genetic Algorithm (IRGA) is used for the best Cluster Head (CH) selection. Secondly, to assess the shortest path among CHs and nodes, the Modified Ant Colony Optimization based Simulated Annealing (MACS) has been speculated to minimize the time consumption during the transmission. The results show that the proposed approaches attain the supreme goal of increasing the network lifetime compared to existing methods.