Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled in...
Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the process...
Condition-based maintenance of a machine tool spindle unit is known to reduce the total cost of operation in a manufacturing environment. However, standard vibration monitoring thresholds, which are based only on average vibration energy levels, do n...
In large heavy-duty machine tool applications, the parametrization of the controller that is used for the positioning of the machine can affect the machine tool dynamics. The aim of this paper is to build a Multiple-Input and Multiple-Output model th...
In order to ensure high productivity capabilities of machine tools at a low cost but at increased geometric accuracy, modeling of their static and dynamic behavior is a crucial task in structure optimization. The drive control and the frictional forc...
This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable o...
Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, a...
A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis...
The study of soil–plant–machine interaction (SPMI) examines the system dynamics at the interface of soil, machine, and plant materials, primarily consisting of soil–machine, soil–plant, and plant–machine interactions. A...
G protein-coupled receptor kinase 5 (GRK5) is an important therapeutic target involving cardiovascular diseases, cancer, and inflammatory disorders. However, the features of its activation as an essential function regulation process have been poorly...
The accurate description of nonadiabatic quantum molecular dynamics represents one of the most significant challenges in modern computational chemistry, serving as a gateway to understanding complex phenomena ranging from photochemistry and electron...
The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumpt...
Molecular dynamics (MD) can dynamically reveal the structural evolution and mechanical response of Zirconium (Zr) at the atomic scale under complex service conditions such as high temperature, stress, and irradiation. However, traditional empirical p...
Background: Anaplastic lymphoma kinase (ALK) is a validated oncogenic driver in non-small cell lung cancer and other malignancies, making it a clinically relevant target for small-molecule inhibition. Methods: Here, we report a computational discover...
This paper characterizes the static, dynamic, and controlled behavior of a high-performance electro-hydraulic actuator to assess its suitability for use in evaluating machine tool behavior. The actuator consists of a double-acting piston and cylinder...
Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still moni...
Two-dimensional ultraviolet (2DUV) spectroscopy is an emerging spectroscopic technique that offers high resolution and detailed insights into protein structures. However, traditional theoretical calculations of 2DUV spectra for proteins are computati...
The improved design of a vibratory lapping machine is developed in the SolidWorks software on the basis of a suspended double-mass oscillatory system. The system is set into motion by three pairs of electromagnets generating periodic excitation force...
This study presents a comprehensive three-dimensional computational fluid dynamics (CFD) analysis of abrasive fluid flow and its erosive effects on the active components of the WUXI YCWJ-380-1520 water jet cutting machine. The research investigates t...
Wind turbine blade structural dynamics are crucial in the turbine structural design phase. Blade deflections and loads can affect the weight of the rotor as well as the power performance of a wind turbine if the deflections are extremely high. Predic...
Obtaining a dynamic model of the robotic manipulator is a complex task. With the growing application of machine learning (ML) approaches in modern robotics, a question arises of using ML for dynamic modeling. Still, due to the large amounts of data n...
Agriculture is extremely vulnerable to climate change. Greenhouse farming is recognized as a promising measure against climate change. Nevertheless, greenhouse farming frequently encounters environmental adversity, especially greenhouses built to pro...
This study evaluated the generalization and reliability of machine learning models for multiclass classification of retinal pathologies using a diverse set of images representing eight disease categories. Images were aggregated from two public datase...
Although heavy oil remains a crucial energy source, its high viscosity makes its utilization challenging. We have performed an interpretable analysis of the relationship between the molecular structure of digital oil and its viscosity using molecular...
Compared with traditional alloys, high-entropy alloys have better mechanical properties and corrosion resistance. However, their mechanical properties and microstructural evolution behavior are unclear due to their complex composition. Machine learni...
The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learnin...
Conserved land represents an important mechanism for protecting ecologically sensitive lands while maintaining working landscapes. Despite their significance, nationwide tools for continuous hydrological monitoring of conservation easement lands rema...
The temporal dynamics of forest-associated insects are closely associated with climatic variability, and identifying their temporal patterns and climatic associations is important for understanding insect population dynamics. In this study, automatic...
Membrane technologies play a role in water treatment, energy conversion, and industrial separation processes; however, their performance is limited by fouling, polarization phenomena, transport inefficiencies, and energy consumption. This study prese...
Agitated bioreactors are the subject of many studies regarding their design and scale-up to enhance the productivity in various chemical and biochemical industries. In this regard, accurately predicting their power consumption is very important, beca...
The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationa...
Cold chain temperature management is crucial for preserving product quality and safety across various industries. While Computational Fluid Dynamics (CFD) provides detailed insights into thermal analysis and fluid dynamics, its computational intensit...
Cryptic sites, which are transient binding sites that emerge through protein conformational changes upon ligand binding, are valuable targets for drug discovery, particularly for allosteric modulators. However, identifying these sites remains challen...
Beta Secretase (BACE1) is a well-validated target for Alzheimer’s therapies, but there has been attrition in drug development. Herein, we leveraged machine learning (ML), virtual screening and molecular dynamics (MD) to identify novel compounds...
This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) in forecasting tumor dynamics within three months following Gamma Knife radiosurgery (GKRS) in 77 brain metastasis (BM) patients...
This study presents a hybrid framework primarily designed to predict electrical energy consumption in tubular light pipe systems while also providing interpretability through wavelet-based analysis. Indoor and outdoor illuminance were continuously mo...
Coronavirus disease 2019 (COVID-19) produced devastating health and economic impacts worldwide. While progress has been made in vaccine development, effective antiviral treatments remain limited, particularly those targeting the papain-like protease...
Triclosan (TCS) exposure is linked to increased acute myocardial infarction (AMI) risk, but underlying mechanisms remain unclear. Here, we integrated network toxicology, machine learning, molecular simulations, and in vitro assays to delineate this p...
Accurate and efficient CFD simulations are essential for a wide range of engineering and scientific applications, from resilient structural design to environmental analysis. Traditional methods such as RANS simulations often face challenges in captur...
This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aim...
A better understanding of the microstructure–property relationship can be achieved by sampling and analyzing a microstructure leading to a desired material property. During the simulation of filled rubber, this approach includes extracting common agg...
The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this w...
Epoxy resin is an of the most widely used adhesives for various applications owing to its outstanding properties. The performance of epoxy systems varies significantly depending on the composition of the base resin and curing agent. However, there ar...
Large river floodplain systems (LRFS) are among the most diverse and dynamic ecosystems. Accurately monitoring the dynamics of LRFS over long time series is fundamental and essential for their sustainable development. However, challenges remain becau...
Understanding the atomic-scale mechanisms of coal pyrolysis is essential for efficient coal utilization and carbon-neutral energy strategies, yet conventional computational approaches often struggle to balance between the high accuracy of quantum-che...
Ulcerative colitis (UC) is a chronic inflammatory bowel disease associated with disrupted lipid metabolism. This study aimed to uncover novel molecular subtypes and biomarkers by integrating sphingolipid metabolism-related genes (SMGs) with machine l...
Background: Intracranial atherosclerotic stenosis (ICAS) is a leading cause of ischemic stroke, particularly in the anterior circulation. Understanding the underlying stroke mechanisms is essential for guiding personalized treatment strategies. This...
The utilization of machine learning (ML) has brought more opportunities for the discovery of high-performance materials with specific properties to replace traditional engineering materials. The glass transition temperature (Tg) is a crucial characte...
Molecular dynamics simulations of protein folding typically consider the polypeptide chain at equilibrium and in isolation from the cellular components. We argue that in order to understand protein folding as it occurs in vivo, it should be modeled a...
Low porosity and ultra-low permeability are common characteristics of shale reservoirs. Traditional imbibition theory is unable to adequately describe fluid transport behavior in nanopores or capture microscopic mechanisms. In this study, imbibition...
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