This paper examines whether standard price-discovery measures can reliably identify directional predictive precedence in a highly correlated commodity-equity system. Using 21 years of daily data for Petrobras and the Ibovespa (2005–2026), the s...
This study introduces a novel predictive model for atomization droplet size, developed using comprehensive data collected under elevated temperature and pressure conditions using a twin swirl airblast nozzle. The model, grounded in flow instability t...
This study investigates the effects of vehicle air-conditioning parameters on cabin thermal environment and occupant comfort. Computational fluid dynamics and discrete particle simulations involving different inlet-vent angles, inlet relative humidit...
A simplified algorithm using an artificial neural network (ANN, a feed-forward neural network) for the assessment of the predicted mean vote (PMV) index in summertime was developed, using solely three input variables (namely the indoor air temperatur...
This study provides comprehensive details of evaporative cooling options for building air-conditioning (AC) in Multan (Pakistan). Standalone evaporative cooling and standalone vapor compression AC (VCAC) systems are commonly used in Pakistan. Therefo...
The mean age of air (MAA) is one of the most useful parameters in evaluating indoor air quality in natural ventilated buildings. Its evaluation is generally based on the CO2 monitoring within the environment; however, other methods can be found in th...
Numerous studies have examined the responses of various structures to the mainshock–aftershock (MS–AS) ground motion, and the MS–AS ground motions are very important as the input. Therefore, in the absence of aftershock information,...
The purpose of this paper is to discuss the optimal operation of ice-storage air-conditioning systems by considering thermal comfort and demand response (DR) in order to obtain the maximum benefit. This paper first collects the indoor environment par...
In Kuwait, where the government subsidizes approximately 95% of residential electricity bills, most of the country’s energy consumption is for residential use. In particular, air-conditioning (AC) systems for cooling, which are used throughout...
Conventional heating ventilation and air-conditioning (HVAC) controllers have been designed to mainly control the temperature of a confined compartment, such as a room or a cabin of a vehicle. Other important parameters related to the thermal comfort...
The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation...
Achieving adequate thermal comfort in classrooms in hot cities in southern Mexico is challenging. A heterogeneous distribution of air conditioning flow leads to thermal discomfort, affecting occupants’ academic performance and increasing energy...
The predicted mean vote (PMV) is the most widely used model around the world to assess thermal comfort in indoor environments. The year 2020 marks the 50th anniversary of the PMV model and also the year in which the World Health Organization (WHO) de...
Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy cons...
Researchers have increasingly focused on thermal comfort, examining both individuals’ thermal sensations and the percentage of people dissatisfied with the thermal environment. Most studies rely on the widely used PMV (Predicted Mean Vote) mode...
Clustered proportion responses often exhibit bounded support, skewness, heterogeneous dispersion, and within-cluster dependence. We propose a correlated mean–precision random-effects beta regression model that jointly represents cluster-level h...
Weak lane discipline complicates lane-based assessment of motorcycle-dominated traffic. We combined effective road width, motorcycle proportion, spatiotemporal porosity, lateral velocity variance and spectral indicators to diagnose lateral operating...
The mean-field model (MFM) is the workhorse of statistical mechanics: one normally accepts that it yields results which, despite differing numerically from correct ones, are not “very wrong”, in that they resemble the actual behavior of t...
Human thermoregulation models can predict human thermal responses to evaluate thermal comfort and help create a healthy environment, while their applicability to older people has not been sufficiently validated. This study aimed to evaluate the perfo...
Accurate wake prediction under realistic atmospheric conditions is essential for wind-farm energy assessment. This study investigated the influence of wind-direction variability on single-turbine wake prediction using SCADA and vertical-LiDAR measure...
This paper presents a case study and a model to predict maintenance interventions based on condition monitoring of diesel engine oil in urban buses by accompanying the evolution of its degradation. Many times, under normal functioning conditions, the...
A method for prediction of properties of rubber materials utilizing electron microscope images of internal structures taken under multiple conditions is presented in this paper. Electron microscope images of rubber materials are taken under several c...
Accurate vehicle velocity prediction is of great significance in vehicle energy distribution and road traffic management. In light of the high time variability of vehicle velocity itself and the limitation of single model prediction, a velocity predi...
With the vigorous development of contemporary clean energy, the participation rate of photovoltaic (PV) power generation in the whole power system is increasing day by day, and accurate PV power prediction technology is crucial for the optimal schedu...
In order to realize early warning prediction of the distribution characteristics of atmospheric bioaerosol content, this paper proposes using fluorescence lidar as a technical means to establish a prediction model of atmospheric bioaerosol concentrat...
Uncertainty quantification (UQ) is essential for reliable predictions in artificial neural networks when latent sources of randomness produce non-Gaussian outputs for a given deterministic input. We propose UQ-ANN, a data-driven framework composed of...
Several models for predicting dry matter intake (DMI) of replacement dairy heifers have been developed; however, only a few have been evaluated using data from heifers of different breeds raised under tropical conditions. Thus, the objective of this...
Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield predict...
Accurate trend prediction of valve internal leakage is crucial for the safe and economical operation of thermal power units. To address the issues of prediction lag and insufficient accuracy in existing methods when dealing with the dynamic changes i...
The stability of navigation in waves is crucial for ships, and the effect of the waves on navigation stability is complicated. Hence, the LSTM neural network technique is applied to predict the course changing of a ship in different wave conditions,...
This study introduces a simplified method for predicting the optimal cutting conditions to maximize excavation efficiency based on tool forces. A laboratory-scale linear rock-cutting test was conducted using a conical pick on Finike limestone. The to...
A critical review on the improvement of penetration theory is presented in this work. The volumetric liquid-phase mass transfer coefficients kLa in seven different liquids (1-butanol, 2-propanol, anilin, decalin, nitrobenzene, tetralin, and ethylene...
Ventilatory pump failure is a common cause of death for patients with neuromuscular diseases. The vital capacity plateau value (VCPLAT) is an important indicator to judge the status of ventilatory pump failure for patients with congenital myopathy, D...
Data-driven models utilizing machine learning algorithms provide an effective approach for predicting power in heavy-duty gas turbines, extracting valuable insights from large-scale operational datasets. However, global unified models often struggle...
The fertilizer suction performance of Venturi injectors is jointly governed by fertilizer-solution properties and hydraulic boundary conditions, resulting in multivariable and potentially nonlinear responses that may be difficult to characterize usin...
The mean slope flow velocity is critical in soil erosion models but the mechanism of its variation under rigid vegetation cover remains unclear. On natural slopes, vegetation grows predominantly perpendicular to the horizontal plane (BH), with some g...
The SOURCE prediction model predicts individualised survival conditional on various treatments for patients with metastatic oesophageal or gastric cancer. The aim of this study was to validate SOURCE in an external cohort from the Belgian Cancer Regi...
Coastal regions are increasingly vulnerable to sea-level rise and extreme storm events, making the accurate prediction of wave run-up on seawalls crucial for effective flood and erosion protection. This study presents a novel hybrid approach combinin...
Traffic prediction techniques are classified as having parametric, non-parametric, and a combination of parametric and non-parametric characteristics. The extreme learning machine (ELM) is a non-parametric technique that is commonly used to enhance t...
When a subject’s visual condition is restricted (e.g., blindfolding), it is more difficult to walk while performing an additional task (e.g., drinking, a phone call, etc.) at the same time. However, previous research indicates that it remains u...
Electricity systems are increasingly exposed to demand variability driven by extreme weather conditions, creating significant challenges for maintaining grid reliability and operational stability. Conventional forecasting approaches focus primarily o...
In practical scenarios, rolling bearing vibration signals suffer from detail loss, and information loss occurs during feature dimensionality reduction and fusion, leading to inaccurate life prediction results. To address these issues, this paper firs...
In order to improve the reliability and maintainability of rod control power cabinets in nuclear power plants, this paper uses insulated gate bipolar transistors (IGBTs), the key power device of rod control power cabinets, as the object of research o...
We investigate equilibrium-consistent reservoir-computing (RC) prediction and adaptively weighted model predictive control (MPC) for a nonlinear pitch–plunge flutter benchmark at
Rock excavation is essentially an unloading behavior, and its mechanical properties are significantly different from those under loading conditions. In response to the current deficiencies in the peak strength prediction of rocks under unloading cond...
Mean-field models have the ability to predict the evolution of grain size distribution that occurs through thermomechanical solicitations. This article focuses on a comparison of mean-field models under grain-growth conditions. Different microstructu...
Electric vehicle headlamp illuminance directly affects the driver’s visibility. Accurately predicting electric vehicle headlamp illuminance is crucial to enhancing driving safety. Existing deep learning models are trained using data collected f...
Data assimilation (DA) is often used for merging observations to improve the predictions of the current and future states of characteristics of interest. In forest inventory, DA has so far found limited use, although dense time series of remotely sen...
Affected by factors such as complex production operation data, high dimensions, and weak regularity, the existing ultra-short-term working condition prediction method struggles to guarantee the prediction accuracy and operation speed. Therefore, we p...
This study presents PAGER-Life, an adaptive hybrid framework for the early prediction of lithium-ion battery service life under heterogeneous operating conditions. The system integrates physically interpretable degradation features and standardized c...
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