Search Results (13)

This work presents a hybrid model that integrates a mechanistic multicomponent transport scheme in hollow-fiber membranes with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The physical model incorporates pressure drops on the feed and permeate sides (Hagen–Poiseuille), non-ideal gas behavior (Peng–Robinson equation of state), and temperature-dependent viscosity; species permeances are treated as constant for model validation. After validation, a post-validation parametric exploration of permeance variability is carried out by perturbing the methane (CH₄) permeance by one decade up and down. From an initial set of 18 variables, 4 key parameters were selected through rigorous statistical analysis (Pearson correlation, variance inflation factor (VIF), and mean absolute error (MAE)); likewise, other physical criteria have been considered: permeance, retentate volume, retentate pressure, and retentate viscosity. Trained with 70% of the simulated data and validated with the remaining 30%, the model achieves a coefficient of determination (R²) close to 0.999 and a root mean square error (RMSE) below 8 × 10⁻⁸ m³/h in predicting the methane volume in the retentate, effectively responding to both steady and dynamic fluctuations. The combination of first-principles modeling and adaptive learning captures both steady-state and dynamic behavior, positioning the approach as a viable tool for real-time analysis and supervisory control in petrochemical membrane operations. Full article

Evaluating the approach of the apple’s maturity is a crucial aspect of enhancing agricultural efficiency, especially in the context of harvesting. Traditional approaches depend on fixed criteria that fail to account for the natural growth conditions of the fruit. To address this limitation, a fuzzy logic-based system was introduced to evaluate apple ripeness. This model highlights a notable disparity between these factors and maturity. It incorporates the essential elements anticipated to correlate with ripeness, while maintaining the integrity of the inputs to create a holistic framework for assessing maturity. This system ensures that apples are harvested at the optimal time, thereby improving their overall quality. Full article

Background: Polycystic ovary syndrome (PCOS) is the most prevalent endocrinopathy in women of reproductive age, characterized by a broad spectrum of clinical, metabolic, and ultrasound findings. Over time, ultrasound has evolved into a cornerstone for diagnosing polycystic ovarian morphology (PCOM), thanks to advances in probe technology, 3D imaging, and novel stromal markers. The recent incorporation of artificial intelligence (AI) further enhances diagnostic precision by reducing operator-related variability. Methods: We conducted a narrative review of English-language articles in PubMed and Embase using the keywords “PCOS”, “polycystic ovary syndrome”, “ultrasound”, “3D ultrasound”, and “ovarian stroma”. Studies on diagnostic criteria, imaging modalities, stromal assessment, and machine-learning algorithms were prioritized. Additional references were identified via citation screening. Results: Conventional 2D ultrasound remains essential in clinical practice, with follicle number per ovary (FNPO) and ovarian volume (OV) functioning as primary diagnostic criteria. However, sensitivity and specificity values vary significantly depending on probe frequency, cut-off thresholds (≥12, ≥20, or ≥25 follicles), and patient characteristics (e.g., adolescence, obesity). Three-dimensional (3D) ultrasound and Doppler techniques refine PCOS diagnosis by enabling automated follicle measurements, stromal/ovarian area ratio assessments, and evaluation of vascular indices correlating strongly with hyperandrogenism. Meanwhile, AI-driven ultrasound analysis has emerged as a promising tool for minimizing observer bias and validating advanced metrics (e.g., SA/OA ratio) that may overcome traditional limitations of stroma-based criteria. Conclusions: The continual evolution of ultrasound, encompassing higher probe frequencies, 3D enhancements, and now AI-assisted algorithms, has expanded our ability to characterize PCOM accurately. Nevertheless, challenges such as operator dependency and inter-observer variability persist despite standardized protocols; the integration of AI holds promise in further enhancing diagnostic accuracy. Future directions should focus on robust AI training datasets, multicenter validation, and age-/BMI-specific cut-offs to optimize the balance between sensitivity and specificity, ultimately facilitating earlier and more precise PCOS diagnoses. Full article

This study develops and validates a Reduced Order Model (ROM) integrated with Digital Twin technology for real-time temperature control in the Main Control Room (MCR) of a nuclear power plant. Utilizing Computational Fluid Dynamics (CFD) simulations, we obtained detailed three-dimensional thermal flow distributions under various operating conditions. A ROM was generated using machine learning techniques based on 94 CFD cases, achieving a mean temperature error of 0.35%. The ROM was further validated against two excluded CFD cases, demonstrating high correlation coefficients (R > 0.84) and low error metrics, confirming its accuracy and reliability. Integrating the ROM with the Heating, Ventilating, and Air Conditioning (HVAC) system, we conducted a two-month simulation, showing effective maintenance of MCR temperature within predefined criteria through adaptive HVAC control. This integration significantly enhances operational efficiency and safety by enabling real-time monitoring and control while reducing computational costs and time associated with full-scale CFD analyses. Despite promising results, the study acknowledges limitations related to ROM’s dependency on training data quality and the need for more comprehensive validation under diverse and unforeseen conditions. Future research will focus on expanding the ROM’s applicability, incorporating advanced machine learning methods, and conducting pilot tests in actual nuclear plant environments to further optimize the Digital Twin-based control system. Full article

Solar irradiation (Rs) is the electromagnetic radiation energy emitted by the Sun. It plays a crucial role in sustaining life on Earth by providing light, heat, and energy. Furthermore, it serves as a key driver of Earth’s climate and weather systems, influencing the distribution of heat across the planet, shaping global air and ocean currents, and determining weather patterns. Variations in Rs levels have significant implications for climate change and long-term climate trends. Moreover, Rs represents an abundant and renewable energy resource, offering a clean and sustainable alternative to fossil fuels. By harnessing solar energy, we can actively reduce greenhouse gas emissions. However, the utilization of Rs comes with its own challenges that must be addressed. One problem is its variability, which makes it difficult to predict and plan for consistent solar energy generation. Its intermittent nature also poses difficulties in meeting continuous energy demand unless appropriate energy storage or backup systems are in place. Integrating large-scale solar energy systems into existing power grids can present technical challenges. Rs levels are influenced by various factors; understanding these factors is crucial for various applications, such as renewable energy planning, climate modeling, and environmental studies. Overcoming the associated challenges requires advancements in technology and innovative solutions. Measuring and harnessing Rs for various applications can be achieved using various devices; however, the expense and scarcity of measuring equipment pose challenges in accurately assessing and monitoring Rs levels. In order to address this, alternative methods have been developed with which to estimate Rs, including artificial intelligence and machine learning (ML) models, like neural networks, kernel algorithms, tree-based models, and ensemble methods. To demonstrate the impact of feature selection methods on Rs predictions, we propose a Multivariate Time Series (MVTS) model using Recursive Feature Elimination (RFE) with a decision tree (DT), Pearson correlation (Pr), logistic regression (LR), Gradient Boosting Models (GBM), and a random forest (RF). Our article introduces a novel framework that integrates various models and incorporates overlooked factors. This framework offers a more comprehensive understanding of Recursive Feature Elimination and its integrations with different models in multivariate solar radiation forecasting. Our research delves into unexplored aspects and challenges existing theories related to solar radiation forecasting. Our results show reliable predictions based on essential criteria. The feature ranking may vary depending on the model used, with the RF Regressor algorithm selecting features such as maximum temperature, minimum temperature, precipitation, wind speed, and relative humidity for specific months. The DT algorithm may yield a slightly different set of selected features. Despite the variations, all of the models exhibit impressive performance, with the LR model demonstrating outstanding performance with low RMSE (0.003) and the highest R2 score (0.002). The other models also show promising results, with RMSE scores ranging from 0.006 to 0.007 and a consistent R2 score of 0.999. Full article

Modern management requires the highest level of analytics and the optimisation of business processes with a low risk of poor management decisions. These are essential since rapid changes in the financial world and the external environment can have critical effects. The direction of a company’s growth and the effectiveness of its management systems depend directly on the quality of intellectualisation. This study aims to develop a new methodology for studying the criteria and results of the intellectualisation processes to achieve the highest efficiency in company management systems. This study used sociological and empirical methods to find intellectualisation efficiency criteria. These criteria were then used to analyse the intellectualisation process in ten Russian companies. The correlation analysis method revealed a close relationship between the intellectualisation integral indicator and company performance over time. The results showed that the intellectualisation efficiency criteria are intellectualisation indicators in human resource management systems as well as computer-aided and automated management systems. In addition, it was found that company performance depends on the intellectualisation integral indicator, the human intelligence and artificial intelligence synergy, as well as on the efficiency of using artificial intelligence in business processes. Full article

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting. Full article

We propose and discuss an effective tool for substance detection and identification using a broadband THz pulse that is based on frequency conversion near the substance absorption frequencies. With this aim, we analyze the evolution of spectral intensities at the doubled absorption frequencies in order to prove their similarity to those at which the absorption of THz pulse energy occurs. This analysis is provided for both artificial THz signals and the real signals reflected from the substances under consideration. We demonstrate the feasibility of the proposed approach in the detection and identification of substances with an inhomogeneous surface, which is the most difficult case for practice, by using the method of spectral dynamic analysis and integral correlation criteria. Full article

Modern-day flood management has evolved into a variety of flood management alternatives. The selection of appropriate flood measures is crucial under a variety of flood management practices, approaches, and assessment criteria. Many leading countries appraise the significance of risk-based flood management, but the fixed return period is still the de facto standard of flood management practices. Several measures, approaches, and design criteria have been developed over time. Understanding their role, significance, and correlation toward risk-based flood management is crucial for integrating them into a plan for a floodplain. The direct impacts of a flood are caused by direct contact with the flood, while indirect impacts occur as a result of the interruptions and disruptions of the socio-economical aspects. To proceed with a risk-based flood management approach, the fundamental requirement is to understand the risk dynamics of a floodplain and to identify the principal parameter that should primarily be addressed so as to reduce the risk. Risk is a potential loss that may arise from a hazard. On the one hand, exposure and susceptibility of the vulnerable system, and on the other, the intensity and probability of the hazard, are the parameters that can be used to quantitatively determine risk. The selection of suitable measures for a flood management scheme requires a firm apprehension of the risk mechanism. Under socio-economic and environmental constraints, several measures can be employed at the catchments, channels, and floodplains. The effectiveness of flood measures depends on the floodplain characteristics and supporting measures. Full article

The implementation of advertising for green housing usually involves consideration of individual differences among potential buyers, their desires for residential unit features as well as location impacts on a selected property. Much more rarely, there is consideration of the arousal and valence, affective behavior, emotional, and physiological states of possible buyers of green housing (AVABEPS) while they review the advertising. Yet, no integrated consideration of all these factors has been undertaken to date. The objective of this study was to consider, in an integrated manner, the AVABEPS, individual differences, and location impacts on property and desired residential unit features. During this research, the applications for the above data involved neuromarketing and multicriteria examination of video advertisements for diverse client segments by applying neuro decision tables. All of this can be performed by employing the method for planning and analyzing and by multiple criteria and customized video neuro-advertising green-housing variants (hereafter abbreviated as the ViNeRS Method), which the authors of this article have developed and present herein. The developed ViNeRS Method permits a compilation of as many as millions of alternative advertising variants. During the time of the ViNeRS project, we accumulated more than 350 million depersonalized AVABEPS data. The strong and average correlations determined in this research (over 35,000) and data examination by IBM SPSS tool support demonstrate the need to use AVABEPS in neuromarketing and neuro decision tables. The obtained dependencies constituted the basis for calculating and graphically submitting the ViNeRS circumplex model of affect, which the authors of this article developed. This model is similar to Russell’s well-known earlier circumplex model of affect. Real case studies with their related contextual conditions presented in this manuscript show a practical application of the ViNeRS Method. Full article

Low efficiency of the standard THz TDS method of the detection and identification of substances based on a comparison of the spectrum for the signal under investigation with a standard signal spectrum is demonstrated using the physical experiments conducted under real conditions with a thick paper bag as well as with Si-based semiconductors under laboratory conditions. In fact, standard THz spectroscopy leads to false detection of hazardous substances in neutral samples, which do not contain them. This disadvantage of the THz TDS method can be overcome by using time-dependent THz pulse spectrum analysis. For a quality assessment of the standard substance spectral features presence in the signal under analysis, one may use time-dependent integral correlation criteria. Full article

We propose an effective method for the detection and identification of dangerous substances by using the broadband THz pulse. This pulse excites, for example, many vibrational or rotational energy levels of molecules simultaneously. By analyzing the time-dependent spectrum of the THz pulse transmitted through or reflected from a substance, we follow the average response spectrum dynamics. Comparing the absorption and emission spectrum dynamics of a substance under analysis with the corresponding data for a standard substance, one can detect and identify the substance under real conditions taking into account the influence of packing material, water vapor and substance surface. For quality assessment of the standard substance detection in the signal under analysis, we propose time-dependent integral correlation criteria. Restrictions of usually used detection and identification methods, based on a comparison between the absorption frequencies of a substance under analysis and a standard substance, are demonstrated using a physical experiment with paper napkins. Full article

In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR). The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporatesthe mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark ExponentialWeighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability. Full article