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Abstract Biofilm formation of Geobacillus thermodenitrificans, Geobacillus thermoglucosidans and Anoxybacillus flavithermus in milk on stainless steel were monitored at 55°C, 60°C, and 65°C for various incubation time...

Abstract Background Preterm low birth weight (PLBW) is a major global contributor to neonatal morbidity and mortality. This study aims to develop and evaluate a novel artificial neural network (ANN) based predictive mode...

This study addresses the limitations of deterministic constitutive models for polymer foams, which often fail to capture intrinsic material variability. It introduces a framework to quantify the predictive uncertainty of...

Abstract Physical sciences are essential to the predictive modeling of complex environmental systems, particularly environmental monitoring and sustainability applications, as they provide the deterministic foundation re...

Abstract This research proposes an innovative Arabic financial forecasting model that integrates linguistic relation extraction with advanced deep learning and machine learning techniques. The framework combines Arabic O...

Predictive modeling of natural sediment-laden flows requires rheometric protocols that are both reproducible and time-aware of sediment dynamics. This study establishes a standard measurement protocol for homogeneous sed...

This research investigates the integration of Artificial Intelligence (AI) into the implementation of the United Nations Convention on the Law of the Sea (UNCLOS) to enhance the effectiveness of maritime dispute resoluti...

Global food supply chains continue to expand in scale and complexity, heightening risks of fraud, mislabeling, and cross-contamination across certification systems. These vulnerabilities are particularly consequential fo...

Introduction Conducting surveys on domestic violence across diverse countries, particularly in lower-middle-income nations like Nepal, poses significant challenges in understanding and addressing the multifaceted dynamic...

BackgroundClimate change is increasingly recognized as a critical driver affecting the transmission and distribution of parasitic diseases. However, a comprehensive synthesis of the global research landscape in this inte...

Colorimetric assays offer a low-cost, accessible means of diagnostic testing but often suffer from subjective interpretation and variability caused by inconsistent imaging conditions. To address these challenges, we pres...

Abstract Artificial intelligence (AI) is emerging as a transformative tool for stormwater management with the potential to enhance resilience, efficiency, and equity across urban water systems. This paper examines the op...

Abstract This paper investigates the extent to which dishonest behavior can be predicted using behavioral personality traits and physiological information. An interpretable machine learning approach, SHapley Additive exP...

Abstract Background Terpenes and phenolic compounds are multifunctional plant metabolites that contribute to defense, signaling, and ecological interactions, while supporting industrial applications. Here, we implemented...

Prediction of building occupancy is a very important element of structural health monitoring (SHM), smart building systems, and energy management. In this study, a number of visualization tools are used to analyze the pe...

Abstract We developed SPARFlow, an open-source KNIME workflow for structure–activity or structure–property relationship (SAR/SPR) analyses. The workflow integrates data preprocessing, chemical structure curation, sim...

In recent years, fuzzy soft topology has emerged as a powerful approach that contributes significantly to advancing practical applications in uncertain environments, especially in information systems design, predictive m...

Reliable prognostics in predictive maintenance requires not only accurate remaining useful life (RUL) estimation but also robustness under distribution shift and support for decision-making under uncertainty. However, mo...

Abstract Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, improved characterization of tumor heterogeneity, and the accel...

BackgroundInfluenza poses a significant global public health threat, with its pandemic potential and seasonal variability presenting formidable challenges to prediction accuracy. This study leverages high-quality weekly ...

Abstract Wearable sensor–based human activity recognition (HAR) is central to smart sport and health-monitoring applications, yet existing deep learning models often rely on ad hoc multimodal fusion and offer limited i...

The analysis and modeling of educational data are of great significance to the evaluation of teaching quality and personalized learning guidance. However, the acquisition of academic data is often limited by the costs of...

The sustainable utilization of post-consumer waste glass in concrete has emerged as a promising approach to reduce cement consumption, mitigate landfill disposal, and enhance material performance. However, most previous ...

Objectives: Survival rates in patients with oral squamous cell carcinoma (OSCC) have failed to improve greatly over time, and novel biomarkers are urgently needed. Therefore, the aim was to evaluate the predictive abilit...