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Article

Published: 26 Sep 2024

Towards Low Complexity VLC Systems: A Multi-Task Learning Approach

Volume 1

Abstract

In the rapidly evolving landscape of wireless communication, visible light communication (VLC) stands out for its potential to redefine high-speed data exchange. Recently, VLC has utilized waveforms that combine multiple bitstreams in a unified physical layer, allowing for high-speed data exchange, precise localization, and robust control simultaneously. Particularly, the demodulation tasks of beacon position modulation (BPM) and beacon phase shift keying (BePSK) are central to decoding of such waveforms and..

https://doi.org/10.69709/COConnect.2024.097213
Article

Published: 26 Sep 2024

In Silico Investigation of the Constituents of Aroeira Honey (Astronium urundeuva) and the Binding Affinity with Important Proteins of M. leprae and M. tuberculosis

Volume 1

Abstract

The use of natural products has been gaining a lot of attention in recent years, among them organic honey, with its high phenolic content, has great potential in fighting diseases. The Aroeira-do-Sertão (Astronium urundeuva) is a tree that, when it blooms, is pollinated by bees and produces a honey rich in nutrients that is widely used by the population to treat flu and other symptoms. Leprosy and tuberculosis are two..

https://doi.org/10.69709/MolModC.2024.113103
Article

Published: 23 Sep 2024

Identification of prognostic biomarkers and tumor immune microenvironment about Uveal melanoma

Volume 1

Abstract

Uveal melanoma (UM) is a malignant eye cancer that has a high mortality rate and is notoriously difficult to diagnose clinically. Identifying prognostic biomarkers and evaluating the tumor immune microenvironment for UM in order to improve diagnosis, treatment decisions and even overall survival for patients. Our study involved a thorough analysis of the transcriptome profiling in the TCGA-UVM project with the aim of identifying biomarkers as well as exploring the..

https://doi.org/10.69709/GenomC.2024.196011
Editorial

Published: 09 Sep 2024

3D Printing of Biomaterials: Is It Disruptive or Destructive?

Volume 1

Abstract

No Abstract Available

https://doi.org/10.69709/BIOMATC.2024.191180
Article

Published: 10 Sep 2024

A Cloud Based IoT Electricity Consumption Monitoring Platform for a Residential Household

Volume 1

Abstract

Energy costs are a concern, given the rate of increase in energy costs. The unprecedented switch to electricity as a source of energy is reflected in the growth of the electric vehicle, solar pv panel and heat pump markets as consumers compete with reducing fossil energy consumption. This paper evaluates electricity usage in two households, namely Household A and Household B using energy kilowatt hour, power wattage and the outside..

https://doi.org/10.69709/COConnect.2024.195151
Article

Published: 10 Sep 2024

Investigation of TLR4, TLR6, TLR7, and CD36 expression on T lymphocytes in coronary artery disease

Volume 1

Abstract

Background: Coronary artery disease (CAD) is among the significant causes of death globally, caused by fatty deposits in blood vessel walls. Increasing evidence indicates that toll-like receptors (TLRs) are pivotal to atherosclerosis progression. The function of CD36 as a glycoprotein in atherosclerosis was also suggested. This study aimed to investigate the levels of TLR4, TLR6, and TLR7, as well as CD36 cell surface markers in CAD. Methods: This study included..

https://doi.org/10.69709/GenomC.2024.197851
Article

Published: 09 Sep 2024

Diversity of HLA Class I and II Genes in the North Indian Population

Volume 1

Abstract

Introduction: Numerous studies have concentrated on specific populations to explore the extensive polymorphism of class I and II HLA genes. This genetic diversity is crucial for various applications, such as advancing transplantation immunology, understanding genetic population patterns, and uncovering the pathways of different diseases. Objective: The objective of the present study was to determine and analyse the frequencies of class I (HLA-A, HLA-B and, HLA-C), and class II (HLA-DRB1, and..

https://doi.org/10.69709/GenomC.2024.193514
Review

Published: 29 Aug 2024

Ethical Approaches in Secondary Findings Report from Exome Sequencing Analysis

Volume 1

Abstract

In Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS) approaches, incidental findings (IFs) or more precisely secondary findings (SFs) have indicated controversial reports. To address SFs issues, well-known guidelines have been released such as the versions of the American College of Medical Genetics and Genomics (ACMG); however, when, to whom, why, and how these SFs should be reported are the key questions that need to be addressed ethically. The..

https://doi.org/10.69709/GenomC.2024.197183
Review

Published: 23 Aug 2024

The Secret Life of Microbes: The Expanding Role of Microbes in Shaping Endocrine Health

Volume 1

Abstract

This review was intended to attempt to establish the complex association between the endocrine system of the host and the gut microbiota. Microbial endocrinology is a relatively new field of study, which examines how microbes regulate hormonal signaling and affect metabolisms, immune system, and behavior responses. This is because the gut microbiome can be viewed as a virtual endocrine organ as it is involved in the synthesis of neuroactive substances..

https://doi.org/10.69709/GenomC.2024.193113
Review

Published: 28 Aug 2024

An Overview of Secure Network Segmentation in Connected IIoT Environments

Volume 1

Abstract

Network segmentation is a very important approach in enhancing network security. The approach involves breaking down the network into smaller, more manageable segments, each with its own specific security requirements. This strategy supports maintaining stable perimeters and effective access control while safeguarding critical resources, such as database servers, from unauthorized access. The relevance of network segmentation in IIoT comes right with the state-of-the-art and interconnected nature of many devices that..

https://doi.org/10.69709/CAIC.2024.193182
Article

Published: 02 Aug 2024

Challenging Conventions Towards Reliable Robot Navigation Using Deep Reinforcement Learning

Volume 1

Abstract

Effective indoor navigation in the presence of dynamic obstacles is crucial for mobile robots. Previous research on deep reinforcement learning (DRL) for robot navigation has primarily focused on expanding neural network (NN) architectures and optimizing hardware setups. However, the impact of other critical factors, such as backward motion enablement, frame stacking buffer size, and the design of the behavioral reward function, on DRL-based navigation remains relatively unexplored. To address this..

https://doi.org/10.69709/CAIC.2024.194188
Article

Published: 29 Jul 2024

Translucency and Polymerization Ability of Contemporary Resin Composites

Volume 1

Abstract

Objective: The present study aimed to characterize the translucency and polymerization ability of commercially available contemporary resin composites. Methods: The resin composites considered in this study were Forma (Enamel, Body, and Dentin; Ultradent), Empress Direct (Enamel and Dentin; Ivoclar/Vivadent), Sirius-Z (Enamel and Dentin; DFL), and Orion (Enamel and Dentin; DFL). All photoactivation procedures were performed using the same light source (Valo Cordless, Ul-tradent, 20 s). The translucency parameter (TP) was..

https://doi.org/10.69709/BIOMATC.2024.193183
Article

Published: 29 Jul 2024

Mirror, Mirror on the Wall: Automating Dental Smile Analysis with AI in Smart Mirrors

Volume 1

Abstract

This paper presents a smart diagnostic framework for dental smile analysis. To accurately and efficiently identify esthetic issues from a single image of a smile, a convolutional neural network (CNN) was trained. To overcome the limitations of scarce data, a diffusion model was employed to generate dental smile images in addition to manually curated data. The CNN was trained and evaluated on three datasets: all real images, all generated images,..

https://doi.org/10.69709/CAIC.2024.194138
Article

Published: 13 Jun 2024

Hierarchical Autoencoder-Based Lossy Compression for Large-Scale High-Resolution Scientific Data

Volume 1

Abstract

Lossy compression has become essential an important technique to reduce data size in many domains. This type of compression is especially valuable for large-scale scientific data, whose size ranges up to several petabytes. Although Autoencoder-based models have been successfully leveraged to compress images and videos, such neural networks have not widely gained attention in the scientific data domain. Our work presents a neural network that not only significantly compresses large-scale..

https://doi.org/10.69709/CAIC.2024.193132
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