Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.

Breast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion of sophisticated computational methodologies with extensive biological datasets has emerged as an effective strategy for unravelling complex patterns in cancer oncology. This research delves into breast cancer staging, classification, and diagnosis by leveraging the comprehensive dataset provided by the The Cancer Genome Atlas (TCGA).

Agrarian transformation and rural community food security in the lower Gangetic basin: A household survey dataset.

This data article describes a dataset derived from a comprehensive household survey aimed at understanding the dynamics of agrarian transformation and its impacts on rural community food security within the Lower Gangetic Basin, specifically along the Arial Khan River, Bangladesh. The survey was conducted as part of a doctoral research project and encompasses data collected from 250 households across a defined Mouza within the basin. The dataset provides detailed insights into various dimensions such as household demographics, land ownership and use, agricultural practices and transformations, socio-cultural and environmental factors, power dynamics, food security status, and the strategies employed to enhance food security.

Assessing the impact of disease incidence and immunization on the resilience of complex networks during epidemics.

Disease severity through an immunized population ensconced on a physical network topology is a key technique for preventing epidemic spreading. Its influence can be quantified by adjusting the common (basic) methodology for analyzing the percolation and connectivity of contact networks. Stochastic spreading properties are difficult to express, and physical networks significantly influence them.

Potential data for feasibility assessment and deployment of a 6.7 MW floating solar PV plant in Hatirjheel Lake, Dhaka, Bangladesh.

Floating solar photovoltaic has emerged as a highly sustainable and environmentally friendly solution worldwide from the various clean energy generation technologies. However, the installation of floating solar differs from rooftop or ground-mounted solar due to the significant consideration of the availability of water bodies and suitable climatic conditions. Therefore, conducting a feasibility analysis of the suitable climate is essential for installing a floating solar plant on water bodies.

Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach.

This article introduces a novel mathematical model analyzing the dynamics of Dengue in the recent past, specifically focusing on the 2023 outbreak of this disease. The model explores the patterns and behaviors of dengue fever in Bangladesh. Incorporating a sinusoidal function reveals significant mid-May to Late October outbreak predictions, aligning with the government's exposed data in our simulation.

Potentiality of phosphide-based nanotubes for breast cancer detection: A DFT investigation.

Breast cancer is one of the most basilisk cancers for women due to its high mortality rate which can be prevented drastically with early-stage detection. In this work, the adsorption mechanism of two volatile organic compounds that are present in the breath of breast cancer patients, 2-Methyloctane and 3, 3-Dimethylpentane, has been investigated on aluminum phosphide nanotubes (AlPNT) and gallium phosphide nanotubes (GaPNT) in order to understand their feasibility as sensor materials to diagnosis breast cancer at early stage. We have used the quantum mechanical approach by employing density functional theory using B3LYP-D3 hybrid potential for noncovalent interaction along with the LanL2DZ basis in the Gaussian 09 software package.

Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning.

Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This research study investigates the utilization of machine learning techniques to diagnose prostate cancer.

Optimizing photovoltaic arrays: A tested dataset of newly manufactured PV modules for data-driven analysis and algorithm development.

This data article presents a comprehensive dataset comprising experimentally tested characteristics of newly manufactured photovoltaic (PV) modules, which have been collected by using a commercial PV testing system from a solar panel manufacturer company. The PV testing system includes an artificial sunlight simulator to generate input light for the PV and the outputs of the PV are tested by a professional IV tracer in a darkroom environment maintaining IEC60904-9 standard. The dataset encompasses modules with power ratings of 10 W, 85 W, and 247 W, each represented by 40 individual module records.

SNS citizenship behavior based on D&M information system success model and social capital theory: Mediating role of subjective well-being.

Social media has become integral to contemporary society, with online behaviors impacting individual experiences and the wider community. In Bangladesh, a developing country, SNS have played a pivotal role in the nation's digitalization efforts. This study explores the relationship between social capital theory, D&M Information System Model, subjective well-being, and SNS Citizenship Behavior (SCB) among active social media users in Bangladesh.

Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state.

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare.

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities.

Micro-energy harvesting (MEH) is a technology of renewable power generation which is a key technology for hosting the future low-powered electronic devices for wireless sensor networks (WSNs) and, the Internet of Things (IoT). Recent technological advancements have given rise to several resources and technologies that are boosting particular facets of society. Many researchers are now interested in studying MEH systems for ultra-low power IoT sensors and WSNs.

Integrating PV-based energy production utilizing the existing infrastructure of MRT-6 at Dhaka, Bangladesh.

In a very dense urban landscape, incorporating renewables becomes challenging due to a lack of space, planning, and mindset. Utilization of already existing large infrastructures in combination with existing technology and necessary adaptation can create the right synergy for harnessing renewables like solar. This paper proposes the installation of a solar power plant in Dhaka, Bangladesh, using available space on Metro Rail Line 6 to meet the increasing demand for clean and renewable energy.

Evaluation of physical and mechanical characteristics of three-thread fleece knit fabric for their structural changes.

The aim of this research is to explore the variations that can arise when three-thread fleece (3- TFL) fabric is manufactured with the same yarn type, count, stitch length, knitting machine gauge, and diameter but in different structural configurations. The physical and mechanical properties of 3-TFL fabrics vary depending on their structural construction, which has a significant impact on their intended usage. For this study, four distinct types of three-thread fleece fabric structures were developed titled straight, three-butt diagonal, four-butt diagonal, and double tuck 3-TFL.

4mC-CGRU: Identification of N4-Methylcytosine (4mC) sites using convolution gated recurrent unit in Rosaceae genome.

An epigenetic modification is DNA N4-methylcytosine (4mC) that affects several biological functions without altering the DNA nucleotides, including DNA conformation, cell development, replication, stability, and DNA structural changes. To prevent restriction enzyme from damaging self-DNA, 4mC performs a critical role in restriction-modification functions. Existing studies mainly focused on finding hand-crafted features to identify 4mC locations, but these methods are inefficient due to high time consuming and high costs.

4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework.

Introduction: The usage of Quick Response (QR) Codes has become widely popular in recent years, primarily for immense electronic transactions and industry uses. The structural flexibility of QR Code architecture opens many more possibilities for researchers in the domain of the Industrial Internet of Things (IIoT). However, the limited storage capacity of the traditional QR Codes still fails to stretch the data capacity limits.

Understanding the Impact of Vaccination and Self-Defense Measures on Epidemic Dynamics Using an Embedded Optimization and Evolutionary Game Theory Methodology.

Explaining how individual choice and government policy can appear in the same context in real society is one of the most challenging scientific problems. Controlling infectious diseases requires effective prevention and control measures, including vaccination and self-defense measures. In this context, optimal control strategies incorporating vaccination and self-defense measures have been proposed using the framework of evolutionary game theory.

Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data.

Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features.

Assessment of antidiabetic activity of three Phenylspirodrimanes from fungus MUT 3308 by ADME, QSAR, molecular docking and molecular dynamics simulation studies against protein tyrosine phosphatase 1B (PTP1B).

Phenylspirodrimanes (PSD) are the sesquiterpene quinone type meroterpenoids found in nature. PSDs are found to exhibit inhibitory activity against immunocomplex diseases, and tyrosine kinase receptors. Three of the different PSDs C1, C2, and C3 that have been reported to be isolated from the sponge-associated fungus MUT 3308 are selected for studying their possible inhibitory effect against type 2 diabetes mellitus.

Exploring the performance of volatile mutations on evolutionary game dynamics in complex networks.

The typical framework of replicator dynamics in evolutionary game theory assumes that all mutations are equally likely, meaning that the mutation of an evolving inhabitant only contributes constantly. However, in natural systems in biological and social sciences, mutations can arise due to their repetitive regeneration. The phenomenon of changing strategies (updating), typically prolonged sequences repeated many times, is defined as a volatile mutation that has been overlooked in evolutionary game theory.

The symmetric and asymmetric effects of climate change on rice productivity in Malaysia.

The current study aims to examine the symmetric and asymmetric effects of climate change (CC) on rice productivity (RP) in Malaysia. The Autoregressive-Distributed Lag (ARDL) and Non-linear Autoregressive Distributed Lag (NARDL) models were employed in this study. Time series data from 1980 to 2019 were collected from the World Bank and the Department of Statistics, Malaysia.

Electrical data of 10W, 40W, 80W, and 250W photovoltaic modules under the aging condition: Tested by a Solar Manufacturer Company in Bangladesh.

The health monitoring system of photovoltaic modules throughout their lifespan is an important research topic. The dataset of aged PV modules is required to investigate the performance of the aged PV array for simulation work. Different aging factors are responsible for decreasing the output power of aged PV modules and increasing the degradation rate.

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches.

Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases.

Correlated-Weighted Statistically Modeled Contourlet and Curvelet Coefficient Image-Based Breast Tumor Classification Using Deep Learning.

Deep learning-based automatic classification of breast tumors using parametric imaging techniques from ultrasound (US) B-mode images is still an exciting research area. The Rician inverse Gaussian (RiIG) distribution is currently emerging as an appropriate example of statistical modeling. This study presents a new approach of correlated-weighted contourlet-transformed RiIG (CWCtr-RiIG) and curvelet-transformed RiIG (CWCrv-RiIG) image-based deep convolutional neural network (CNN) architecture for breast tumor classification from B-mode ultrasound images.

Bioinformatics and System Biology Techniques to Determine Biomolecular Signatures and Pathways of Prion Disorder.

Prion disorder (PD) is caused by misfolding and the formation of clumps of proteins in the brain, notably Prion proteins resulting in a steady decrease in brain function. Early detection of PD is difficult due to its unpredictable nature, and diagnosis is limited regarding specificity and sensitivity. Considering the uncertainties, the current study used network-based integrative system biology approaches to reveal promising molecular biomarkers and therapeutic targets for PD.