Fine-Tuning Models for Histopathological Classification of Colorectal Cancer.

This study aims to design and evaluate transfer learning strategies that fine-tune multiple pre-trained convolutional neural network architectures based on their characteristics to improve the accuracy and generalizability of colorectal cancer histopathological image classification. The application of transfer learning with pre-trained models on specialized and multiple datasets is proposed, where the proposed models, CRCHistoDense, CRCHistoIncep, and CRCHistoXcep, are algorithmically fine-tuned at varying depths to improve the performance of colorectal cancer classification. These models were applied to datasets of 10,613 images from public and private repositories, external sources, and unseen data.

An improved mathematical model for hypothetical oil reservoir for optimum oil recovery using magnetic nanomaterials.

In the oil and gas industry, enhanced oil recovery (EOR) strategies for unconventional reservoirs, characterized by complex geometries, differ significantly from those used in conventional reservoirs. This research focuses on the impact of 3D hexagonal prism geometries on EOR in hypothetical oil reservoirs using silicon dioxide (SiO₂) magnetic nanoparticles under liquid-phase flow conditions, a topic not extensively explored in existing literature. We developed an improved magnetohydrodynamic (MHD) mathematical models to simulate oil recovery processes in these geometries, using ANSYS Fluent for finite volume analysis.

A novel data-driven NLMPC strategy for techno-economic microgrid management with battery energy storage under uncertainty.

As renewable energy sources become more widespread and energy consumption continues to grow, there is an urgent requirement for smarter, more flexible control methods to manage microgrids (MGs) effectively. This study proposes a data-driven nonlinear model predictive control (NLMPC) framework for optimized MG operation, emphasizing energy storage system (ESS) integration. Effective MG management is crucial given increasing renewable penetration and energy demands.

Optimal multi-objective energy management of decentralized demand response incorporating uncertainties.

This paper presents a decentralized demand response (DR) framework that, incorporating optimal multi-objective energy management strategies, addresses uncertainties in power networks. The power industry faces challenges in operational optimization due to uncertainties in generation and consumption while evaluating environmental impacts and long-term economic implications. This research introduces an innovative approach by combining advanced DR techniques with a robust energy management strategy designed for uncertain conditions, enhanced by sensitivity analysis to key system parameters.

Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis.

It is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception, and MobileNet) with a spatial attention mechanism to enhance feature extraction and focus on critical image regions. A key innovation is the attention-driven fusion strategy at the decision level, where model predictions are weighted by relevance and confidence to improve classification performance.

An examination of the role of Big Five personality traits on employee creativity in Sudanese public universities: a gender-based approach.

Introduction: Sudanese higher education institutions must recognize the influence of Big Five personality traits on employee creativity to foster a workforce that is both innovative and adaptable. These traits play a key role in shaping how employees approach their work and generate new ideas. While studies have explored the link between each of the Big Five personality traits and creativity, the findings have been varied.

Synergistic Multi-Granularity Rough Attention UNet for Polyp Segmentation.

Automatic polyp segmentation in colonoscopic images is crucial for the early detection and treatment of colorectal cancer. However, complex backgrounds, diverse polyp morphologies, and ambiguous boundaries make this task difficult. To address these issues, we propose the Synergistic Multi-Granularity Rough Attention U-Net (S-MGRAUNet), which integrates three key modules: the Multi-Granularity Hybrid Filtering (MGHF) module for extracting multi-scale contextual information, the Dynamic Granularity Partition Synergy (DGPS) module for enhancing polyp-background differentiation through adaptive feature interaction, and the Multi-Granularity Rough Attention (MGRA) mechanism for further optimizing boundary recognition.

Development and validation of a digital community-based mental health protocol (RELATE-ME) in Malaysia.

To bridge the gaps of mental health service in Malaysia, we developed a digital intervention protocol (RELATE-ME) aiming to restore social connectedness and well-being during the COVID-19 pandemic lockdown. Co-designed with a cross-disciplinary panel, we integrate principles of digital mental health, support group modality, psychoeducation elements, and community engagement into a 2-week online group program. To validate the protocol, we trained three community health workers (CHWs) to facilitate a briefer version of RELATE-ME with two groups of participants (six elders and six youths) in suburban Malaysia.

Comparative analysis of direct coupling and MPPT control in standalone PV systems for solar energy optimization to meet sustainable building energy demands.

Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India.

Enhancement of the quality and preservation of frozen burgers by active coating containing L. extract nanoemulsions.

This study aimed to assess the impact of an edible coating holding within chia seed gum (CSG) and L. extract (RCE) nanoemulsions (10%, 20%, and 40% /w) on the oxidation, microbial load, and sensory characteristics of burgers in a 90-day frozen storage period. Based on the findings, the active CSG coatings showed remarkable antioxidant and antimicrobial activities.

Thermal image dataset for okra maturity analysis.

Okra, renowned for its abundance of essential nutrients, emerges as a promising solution in addressing malnutrition, advocating for sustainable agriculture, and showcasing versatile untapped potentials. Our objective is to enhance the quality, market attractiveness, and culinary adaptability of okra harvests by classifying them into over-matured and adequately matured groups through a non-invasive approach. This dataset is centered on thermal images capturing different maturity levels of okra, categorized into two distinct groups.

Navigating new product development: Uncovering factors and overcoming challenges for success.

New product development (NPD) frequently encounters failures, whether during the development phase or in the subsequent commercialization stage. These failures can often be attributed to root causes originating in the early stages of NPD, known as the front end. This study aims to investigate the factors that contribute to NPD success and the challenges faced by firms throughout the development process.

Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm.

Cognitive impairment and dementia-related diseases develop several years before moderate or severe deterioration in cognitive function occurs. Nevertheless, most dementia cases, especially in low- and middle-income countries, remain undiagnosed because of limited access to affordable diagnostic tools. Additionally, the development of accessible tools for diagnosing and predicting cognitive impairment has not been extensively discussed in the literature.

Cyberbullying, Social Media Addiction and Associations with Depression, Anxiety, and Stress among Medical Students in Malaysia.

This study aims to determine the prevalence and factors associated with cyberbullying and social media addiction. This cross-sectional study was conducted among 270 medical students from a public university in Kuching, Malaysia. The instruments used in this study included the cyberbullying questionnaire survey, Bergen Social Media Addiction Scale (BSMAS), and the Depression Anxiety Stress Scale 21-item (DASS-21).

Parental Factors Associated with Child or Adolescent Medication Adherence: A Systematic Review.

Medication adherence, especially among children and adolescents with psychiatric disorders, is often seen as a major treatment challenge. The purpose of this study is to systematically review studies addressing specific aspects of parental factors that are positively or negatively associated with medication adherence among children and adolescents with psychiatric disorders. A systematic literature search of English language publications, from inception through December 2021, was conducted from PubMed, Scopus, and MEDLINE databases.

Performance evaluation of phosphonium based deep eutectic solvents coated cerium oxide nanoparticles for CO capture.

The critical challenge being faced by our current modern society on a global scale is to reduce the surging effects of climate change and global warming, being caused by anthropogenic emissions of CO in the environment. Present study reports the surface driven adsorption potential of deep eutectic solvents (DESs) surface functionalized cerium oxide nanoparticles (CeNPs) for low pressure CO separation. The phosphonium based DESs were prepared using tetra butyl phosphoniumbromide as hydrogen bond acceptor (HBA) and 6 acids as hydrogen bond donors (HBDs).

Implementation of the energy efficiency existing ship index and carbon intensity indicator on domestic ship for marine environmental protection.

The International Maritime Organization has set a goal to achieve a 50% reduction of total annual greenhouse gas emission related to the international shipping by 2050 compared to the 2008 baseline emissions. Malaysia government has taken an initiative to investigate the assessment (cost-effectiveness) of this International Maritime Organization's short-term measure on Malaysian-registered domestic ships although this measure is only for international merchant ship. To achieve this, this paper collected the ship's data from the shipowners from 25 sample ships.

Synthesis of bioinspired sorbent and their exploitation for methylene blue remediation.

In this research article, novel starch phosphate grafted polyvinyl imidazole (StP-g-PIMDZs) was synthesized. Firstly, a phosphate group was attached to starch polymer via a phosphorylation reaction. Next, 1-vinyl imidazole (VIMDZ) was grafted on the backbone of starch phosphate (StP) through a free radical polymerization reaction.

Comparative study of ZIF-8-materials for removal of hazardous compounds using physio-chemical remediation techniques.

The inherent toxicity, mutagenicity and carcinogenicity of dyes that are discharged into aquatic ecosystems, harming the health of humans and animals. ZIF-8 based composites are regarded as good adsorbents for the breakdown of dyes in order to remove or degrade them. In the course of this research, metal-organic framework materials known as ZIF-8 and its two stable composites, ZIF-8/BiCoO (MZBC) and ZIF-8/BiYO (MZBY), were produced via a hydrothermal process and solvothermal process, respectively, for the dangerous Congo red (CR) dye removal from the solution in water using adsorption method.