5,266 Results Found

Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study,...

Accurate estimation of flow discharge, Q, through hydraulic structures such as spillways and gates is of great importance in water resources engineering. Each hydraulic structure, due to its unique characteristics, requires a specific and comprehensi...

The FDA plans to gradually replace animal testing with organoid and organ-on-a-chip technologies for drug safety assessment, driving surging demand for gut-on-a-chip in food and drug safety evaluation and highlighting the need for efficient, precise...

Urban happiness prediction presents a complex challenge, due to the nonlinear and multifaceted relationships among socio-economic, environmental, and infrastructural factors. This study introduces an advanced hybrid model combining a gradient boostin...

This paper proposes a Stein Variational Gradient Descent (SVGD) solution framework for Model Predictive Control (MPC) to enhance robustness in bipedal locomotion. While conventional simplified rigid-body dynamics (SRBD) based MPC with deterministic s...

Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in fea...

Notch characteristics significantly affect the fatigue performance of engineered components, for which the stress gradient effect is worth careful consideration. The traditional stress gradient analysis method based on the Coffin–Manson equatio...

Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is p...

Accurate and reliable runoff prediction is critical for solving problems related to water resource planning and management. Deterministic runoff prediction methods cannot meet the needs of risk analysis and decision making. In this study, a runoff pr...

As one of the physical quantities concerned in agricultural production, soil moisture can effectively guide field irrigation and evaluate the distribution of water resources for crop growth in various regions. However, the spatial variability of soil...

Punchouts distress represents a major structural deficiency in Continuously Reinforced Concrete Pavements (CRCPs), contributing to premature deterioration, reduced ride quality, and increased maintenance demands. To improve the prediction of punchout...

The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machin...

Bus operation scheduling is closely related to passenger flow. Accurate bus passenger flow prediction can help improve urban bus planning and service quality and reduce the cost of bus operation. Using machine learning algorithms to find the rules of...

Predicting urban morphology based on local attributes is an important issue in urban science research. The deep generative models represented by generative adversarial network (GAN) models have achieved impressive results in this area. However, in su...

Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor...

The multi-parameter characteristics of the physical model pose a challenge to the fatigue life prediction of 2024-T3 aluminum (Al) alloy. In response to this issue, a parameter-solving method that integrates particle swarm optimization (PSO) with ext...

The auto-regressive integrated moving average (ARIMA) model has shown promise in predicting vehicle velocity and road gradient (V–G) for the purpose of constructing power demands in predictive energy management strategies (PEMS) for electric ve...

With the development of intelligent transportation systems (ITS), individual travel prediction has become a key technology for optimizing urban transportation. However, deep learning models are limited in decision-sensitive scenarios due to their lac...

The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict...

The model-free predictive control (MFPC) scheme is an effective scheme to enhance the parameter robustness of model predictive control. However, the MFPC scheme can be affected by the current gradient updating frequency. This paper proposes an improv...

Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity mod...

This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in cam...

The prediction of springback angle for ultra-thin metallic sheets becomes extremely difficult with the existence of size effects. In this study, size effects on the springback behavior of CuZn20 foils are investigated by experiments and analytical me...

Accurate prediction of key environmental parameters is crucial for intelligent control and optimization, yet it remains challenging due to gradient instability in deep learning models, like Long Short-Term Memory (LSTM), during time series forecastin...

Wind power is an essential component of renewable energy. It enables the conservation of conventional energy sources such as coal and oil while reducing greenhouse gas emissions. To address the stochastic and intermittent nature of ultra-short-term w...

Accurate prediction and reasonable warning for dam displacement are important contents of dam safety monitoring. However, it is difficult to identify abnormal displacement based on deterministic point prediction results. In response, this paper propo...

Loan default prediction is a critical task for financial institutions, directly influencing risk management, loan approval decisions, and profitability. This study evaluates the effectiveness of machine learning models, specifically XGBoost, Gradient...

Various offset-free economic model predictive control schemes that include a disturbance model and the modifier-adaptation principle have been proposed in recent years. These schemes are able to reach plant optimality asymptotically even in the prese...

Debris flows are highly unpredictable and destructive natural hazards that present significant risks to both human life and infrastructure. Despite advances in machine learning techniques, current predictive models often fall short due to the insuffi...

Atmospheric corrosion significantly impacts infrastructure worldwide, with traditional assessment methods being time-intensive and costly. This study developed a comprehensive machine learning framework for predicting atmospheric corrosion rates usin...

The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predi...

The improvement of the partially integrated reinforcement learning-model predictive control (RL-MPC) system is developed in the paper by introducing the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD...

With the development of bridge engineering, U-shaped girder bridges have been applied in numerous bridge designs due to their structural characteristics. However, the U-shaped girder bridge is sensitive to solar radiation effects, leading to uneven t...

The growing environmental impact of copper production necessitates innovative approaches for optimizing metallurgical processes and minimizing waste. This study addresses this challenge by leveraging advanced machine learning (ML) techniques to enhan...

Model predictive control, without strict constraints on the control model, effectively overcomes problems, such as poor system dynamic quality caused by time delay, can improve control accuracy to a certain extent, and can directly process input and...

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpirati...

This paper introduces an enhanced variant of the Adam optimizer—the BGE-Adam optimization algorithm—that integrates three innovative technologies to augment the adaptability, convergence, and robustness of the original algorithm under var...

We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influence...

The evaporation duct is a special atmospheric stratification that significantly influences the propagation path of electromagnetic waves at sea, and hence, it is crucial for the stability of the radio communication systems. Affected by physical param...

In deep engineering, rockburst hazards frequently result in injuries, fatalities, and the destruction of contiguous structures. Due to the complex nature of rockbursts, predicting the severity of rockburst damage (intensity) without the aid of comput...

Background: Traumatic brain injury (TBI) represents a significant global health issue; the traditional tools such as the Glasgow Coma Scale (GCS) and Abbreviated Injury Scale (AIS) which have been used for injury severity grading, struggle to capture...

Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared. A focus h...

The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effe...

Thermal processes with prolonged and variable delays pose considerable difficulties due to unpredictable system dynamics and external disturbances, often resulting in diminished control effectiveness. This work presents a hybrid control strategy that...

In order to conveniently and efficiently determine the Permissible Exposure Limits (PELs) of organic chemicals in the workplace, this study employed Quantitative Structure–Activity Relationship (QSAR) modeling to predict properties related to o...

This study investigates the efficacy of various genomic prediction models—Genomic Best Linear Unbiased Prediction (GBLUP), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP)—...

In this study, a machine learning method using gradient boost regression tree (GBRT) model was presented to predict the ultimate bearing capacity of stirrup-confined rectangular CFST stub columns (SCFST) by using a comprehensive data set and by adjus...

Despite widespread adoption, Electronic Medical Record (EMR) systems remain limited in providing intelligent clinical decision support, particularly for early detection of patient deterioration. We present MedROAD V2 (Medical Records Organization, An...

The noise prediction using machine learning is a special study that has recently received increased attention. This is particularly true in workplaces with noise pollution, which increases noise exposure for general laborers. This study attempts to a...

The Northwest Indian Ocean is a key fishing ground for China’s pelagic fisheries, with the purpleback flying squid being a significant target. This study uses commercial fishing logs of the Indian Ocean between 2015 and 2021, alongside pelagic...