This study systematically investigates the hot deformation behavior of TC18 alloy under the conditions of deformation temperatures of 720–840 °C and strain rates of 0.001–1 s−1. Based on the stress–strain data obtained und...
The production of solar energy at altitudes higher than 3800 m above sea level is not constant because the relevant factors are highly varied and complex due to extreme solar radiation, climatic variations, and hostile environments. Therefore, it is...
Obstructive sleep apnea (OSA) is a common sleep disorder that impacts patient health and imposes a burden on families and healthcare systems. The diagnosis of OSA is usually performed through overnight polysomnography (PSG) in a hospital setting. In...
Accurate range prediction is crucial for reducing range anxiety and optimizing the energy management of electric two-wheelers (E2Ws). In this study, we propose a stacked ensemble model to predict the remaining driving range (RDR) of E2Ws with enhance...
Excessive use of animal manure as fertilizers can lead to pollution through the introduction of nitrogen, phosphorus, and other mineral compounds to the environment. Wet chemical analytical methods are traditionally used to determine the precise chem...
Transformers are essential for voltage regulation and power distribution in electrical systems, and monitoring their top-oil temperature is crucial for detecting potential faults. High oil temperatures are directly linked to insulation degradation, a...
Partial least square regression (PLSR) is a reference statistical model in chemometrics. In agronomy, it is used to predict components (response variables y) of chemical composition of vegetal materials from spectral near infrared (NIR) data X collec...
In today’s world, where sustainable energy is essential for the planet’s survival, accurate solar energy forecasting is crucial. This study focused on predicting short-term Global Horizontal Irradiance (GHI) using minute-averaged data fro...
Accurate prediction of natural gas purchase volumes is crucial for both the economy and the environment. It not only facilitates the rational allocation of resources for companies but also helps to reduce operational costs. Although existing predicti...
This work proposed an integrated model combining bagging and stacking considering the weight coefficient for short-time traffic-flow prediction, which incorporates vacation and peak time features, as well as occupancy and speed information, in order...
Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and...
Predicting the performance of multicomponent corrosion inhibitors requires models that relate composition to electrochemical response and provide interpretable formulation guidance. This study combined Latin hypercube sampling and Bayesian-optimized...
In this paper, we study the usage of machine-learning models for sales predictive analytics. The main goal of this paper is to consider main approaches and case studies of using machine learning for sales forecasting. The effect of machine-learning g...
Accurate non-invasive blood glucose level (BGL) prediction remains challenging, motivating the development of machine learning approaches that can account for heterogeneity in physiological measurements. Electronic nose (E-Nose) systems provide a pro...
Gender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, inte...
Semantic segmentation network-based methods can detect concrete damage at the pixel level. However, the performance of a single semantic segmentation network is often limited. To improve the concrete damage detection performance of a semantic segment...
This study investigates the fatigue performance of 3D-printed metal shafts fabricated via Wire Arc Additive Manufacturing (WAAM) with stacked steel ring substrates under rotating bending (ISO 1143:2021). A Taguchi L25 orthogonal array was used to ana...
Inaccurate prediction of shrinkage in self-compacting concrete (SCC) may result in underestimated cracking risk, increased permeability, serviceability deterioration, and reduced long-term durability of concrete structures. Although conventional empi...
To address the limited data availability and low predictive accuracy of nickel matte grade models in the early stages of facility operation, this study introduces a unique stepwise prediction methodology that integrates data augmentation and ensemble...
In this paper, we investigate the methods used to evaluate the admission chances of higher education institutions’ (HEI) entrants as a crucial factor that directly influences the admission efficiency, quality of education results, and future st...
Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a cl...
While past research has emphasized the importance of late blight infection detection and classification, anticipating the potato late blight infection is crucial from the economic point of view as it helps to significantly reduce the production cost....
With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with ti...
Brain tumor classification from magnetic resonance imaging (MRI) can support computer-aided diagnosis by enabling automated differentiation of tumor and non-tumor categories. This study presents DualXBrNet, a hybrid dual-level fusion framework for mu...
Since 2003, Brazil has striven to provide energy access to all, in rural areas, in an effort to economically empower the communities. Unpacking fuel stacking behaviour can shed light onto the speed of transition toward the exclusive use of advanced f...
The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jammi...
Remaining Useful Life (RUL) prediction for aircraft engines is a critical task in prognostics and health management, aiming to extract degradation information from multi-sensor operational data to support maintenance scheduling and operational risk m...
The advancement of technology has led humanity into the era of the information society, where information drives progress and knowledge is the most valuable resource. This era involves vast amounts of data, from which stored knowledge should be effec...
The existing range of the centrosymmetric, triclinic RE(OAc)3 · 2AcOH structure type has been extended for RE = Eu and Gd while the structure data of the Nd- and Sm-compounds have been revised and corrected, respectively, using low temperature (T = 1...
Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimatio...
Nowadays, various machine learning-based approaches using sequence information alone have been proposed for identifying DNA-binding proteins, which are crucial to many cellular processes, such as DNA replication, DNA repair and DNA modification. Amon...
Soft sensor technology has become an effective tool to enable real-time estimations of key quality variables in industrial rubber-mixing processes, which facilitates efficient monitoring and a control of rubber manufacturing. However, it remains a ch...
Recently, financial institutions and investors have placed an increasing emphasis on ESG (environmental, social, and governance) as a principal indicator for the evaluation of companies. However, the current ESG scoring systems lack uniformity and ar...
Market-capitalization concentration in the Standard & Poor’s 500 (S&P 500) rose sharply between 2016 and 2025, and the explanation usually offered is the economy of bits: negligible marginal cost, network effects, and intangible capital...
This study focuses on the problem of corn yield prediction, and a novel prediction model based on a dynamic ensemble stacking regression algorithm is proposed. The model aims to achieve more accurate corn yield prediction based on the in-depth explor...
The accurate estimation of anthropogenic carbon emissions is of great significance for understanding the global carbon cycle and guides the setting and implementation of global climate policy and CO2 emission-reduction goals. This study built a data-...
Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, qu...
Today, variable flow pattern, which uses static rule curves, is considered one of the challenges of reservoir operation. One way to overcome this problem is to develop forecast-based rule curves. However, managers must have an estimate of the influen...
Heart disease is the leading cause of death globally. The most common type of heart disease is coronary heart disease, which occurs when there is a build-up of plaque inside the arteries that supply blood to the heart, making blood circulation diffic...
Modelling and prediction of mean monthly flow are of particular importance for long-term planning in hydrology and water resources management. Therefore, a simplified and robust modelling procedure, derived from clearly and concisely established meth...
Predicting crop prices is a complex challenge that farmers must navigate each year, but machine learning algorithms can provide valuable insights to support more informed decision making. In recent years, agricultural price prediction models have mad...
Complex systems often face significant challenges in both efficiency and performance when making long-term degradation predictions. To address these issues, this paper proposes a predictive architecture based on multi-output sparse probabilistic mode...
Accurate quality prediction can find and eliminate quality hazards. It is difficult to construct an accurate quality mathematical model for the production of small samples with high dimensionality due to the influence of quality characteristics and t...
The structural parameters of the rare-earth diacetate halide trihydrates, RE(OAc)2Hal·3H2O with RE = Ce − (Pm) − Lu and Hal = Cl, Br, have been determined by low temperature, high-resolution SCXRD in order to examine the effect of...
The rate of people suffering from sleep disorders has been continuously increasing in recent years, such that interest in healthy sleep is also naturally increasing. Although there are many health-care industries and services related to sleep, specif...
Furan tests provide a non-intrusive and cost-effective method of estimating the degradation of paper insulation, which is critical for ensuring the reliability of power grids. However, conducting routine furan tests can be expensive and challenging,...
Soil salinization is an essential risk factor for agricultural development and food security, and obtaining regional soil salinity information more reliably remains a priority problem to be solved. To improve the accuracy of soil salinity inversion,...
Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low...
Electricity demand forecasting plays a crucial role in energy planning and power system operation. However, it is affected by numerous factors and complex relationships, making accurate prediction challenging. Therefore, from the perspective of sampl...
Background: Lung cancer is a leading cause of cancer-related mortality worldwide, often diagnosed in advanced stages, making early detection critical. This study aimed to evaluate the performance of various machine learning models in predicting lung...
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