Search Results (2,056)

Global warming is partly attributed to the off-gas from petrochemical plants. This can be utilized as a feed to produce hydrogen (H₂) as a promising measure for battling climate change. In this study, we evaluated the potential for converting waste off-gas to H₂ via steam-reforming technology accompanied by a carbon capture unit, through hybrid absorption/adsorption processes, to achieve a 99.99% hydrogen product. Also, the ReCiPe methodology was used for environmental analysis. The results showed that the equivalent greenhouse emissions when producing of 1 kg of H₂ were 3.53 kg CO₂-Eq, which were almost 2.5 times lower than those for H₂ production without carbon capture, i.e., grey H₂. Also, it was shown that using refinery gas as a reforming furnace fuel instead of conventional fuel oil led to about an 11% decrease in emissions. Full article

(1) Background: This paper presents the results of a study on developing a hybrid evaluation model for air cargo handling systems, combining fuzzy logic and reliability theory. (2) Methods: The research methodology consisted of two stages: the first used reliability analysis to calculate the performance of individual processes in the cargo handling system. In contrast, the second used fuzzy logic to integrate these metrics and generate an overall system evaluation. Statistical metrics, including mean and standard deviation, were used to construct adaptable membership functions for the fuzzy logic model. (3) Results: 27 test scenarios were built, in which the impact of individual compositions of operator teams (depending on their experience) implementing individual air cargo handling processes on the final assessment of the entire system was examined. Configurations with experienced operators consistently achieved the highest performance evaluations, although the strategic integration of less experienced personnel in noncritical roles was shown to maintain system functionality. (4) Conclusions: The results confirm that the proposed model is a practical decision-support tool for air cargo terminal management. It enables precise process evaluation, supports resource optimization and increases air cargo operations’ overall reliability and efficiency. Full article

The current study aims to model the South African crude oil prices using the hybrid of Box-Jenkins autoregressive integrated moving average (ARIMA) and Neural Networks (NNs). This study introduces a hybrid approach to forecasting methods aimed at resolving the issues of lack of precision in forecasting. The proposed methodology includes two models, namely, hybridisation of ARIMA with artificial neural network (ANN)-based Extreme Learning Machine (ELM) and ARIMA with general regression neural network (GRNN) to model both linear and nonlinear simultaneously. The models were compared with the base ARIMA model. The study utilised monthly time series data spanning from January 2021 to March 2023. The formal stationarity test confirmed that the crude oil price series is integrated of order one, I(1). For the linear process, the ARIMA (2,1,2) model was identified as the best fit for the series and successfully passed all diagnostic tests. The ARIMA-ANN-based ELM hybrid model outperformed both the individual ARIMA model and the ARIMA-GRNN hybrid. However, the ARIMA model also showed better performance than the ARIMA-GRNN hybrid, highlighting its strong competitiveness compared to the ARIMA-ANN-based ELM model. The hybrid models are recommended for use by policy makers and practitioners in general. Full article

Patient satisfaction and operational efficiency are critical in healthcare. Long waiting times negatively affect patient experience and hospital performance. Addressing these issues requires accurate system time predictions and actionable strategies. This paper presents a hybrid framework combining predictive modeling and optimization to reduce system times and enhance satisfaction, focusing on registration, vitals, and doctor consultation. We evaluated three predictive models: multiple linear regression (MLR), log-transformed regression (LTMLR), and artificial neural networks (ANN). The MLR model had the best performance, with an $R^2$ of 0.93, an MAE of 7.29 min, and an RMSE of 9.57 min. MLR was chosen for optimization due to its accuracy and efficiency, making it ideal for implementation. The hybrid framework combines the MLR model with a simulation-based optimization system to reduce waiting and processing times, considering resource constraints like staff and patient load. Simulating various scenarios, the framework identifies key bottlenecks and allocates resources effectively. Reducing registration and doctor consultation wait times were identified as primary areas for improvement. Efficiency factors were applied to optimize waiting and processing times. These factors include increasing staff during peak hours, improving workflows, and automating tasks. As a result, registration wait time decreased by 15%, vitals by 20%, and doctor consultation by 25%. Processing times improved by 10–15%, leading to an average reduction of 22.5 min in total system time. This paper introduces a hybrid decision support system that integrates predictive analytics with operational improvements. By combining the MLR model with simulation, healthcare managers can predict patient times and test strategies in a risk-free, simulated environment. This approach allows real-time decision-making and scenario exploration without disrupting operations. This methodology highlights how reducing waiting times has a direct impact on patient satisfaction and hospital operational efficiency, offering an applicable solution that does not require significant structural changes. The results are practical and implementable in resource-constrained healthcare environments, allowing for optimized staff management and patient flow. Full article

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations. Full article

The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these considerations, this study presents an innovative methodology for forecasting EV demand. This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. Experimental research has investigated four different categories of electric vehicles: battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and all electric vehicles (ALL). Performance measures were calculated after conducting experimental studies to assess the model’s ability to predict electric vehicle demand. When the performance measures (mean absolute error, root mean square error, mean squared error, R-Squared) of EVs-PredNet and machine learning regression methods are compared, the proposed model is more effective than the other forecasting methods. The experimental results demonstrate the effectiveness of the proposed approach in forecasting the electric vehicle demand. This model is considered to have significant application potential in assessing the adoption and demand of electric vehicles. This study aims to improve the reliability of forecasting future demand in the electric vehicle market and to develop relevant approaches. Full article

This paper presents a novel hybrid approach that integrates computational fluid dynamics (CFD), physics-informed neural networks (PINN), and fluid–structure interaction (FSI) methods to simulate fluid flow in stenotic coronary artery trees and predict fractional flow reserve (FFR) in areas of stenosis. The primary objective is to utilize a 1D PINN model to accurately predict outlet flow conditions, effectively addressing the challenges of measuring or estimating these conditions within complex arterial networks. Validation against traditional CFD methods demonstrates strong accuracy while embedding physics-based training to ensure compliance with fundamental fluid dynamics principles. The findings indicate that the hybrid CFD PINN FSI method generates realistic outflow boundary conditions crucial for diagnosing stenosis, requiring minimal input data. By seamlessly integrating initial conditions established by the 1D PINN into FSI simulations, this approach enables precise assessments of blood flow dynamics and FFR values in stenotic regions. This innovative application of 1D PINN not only distinguishes this methodology from conventional data-driven models that rely heavily on extensive datasets but also highlights its potential to enhance our understanding of hemodynamics in pathological states. Ultimately, this research paves the way for significant advancements in non-invasive diagnostic techniques in cardiology, improving clinical decision making and patient outcomes. Full article

“Smart” devices, such as contemporary smartphones and PDAs (Personal Digital Assistance), play a significant role in our daily live, be it for navigation or location-based services (LBSs). In this paper, the use of Ultra-Wide Band (UWB) and Wireless Fidelity (Wi-Fi) based on RTT (Round-Trip Time) measurements is investigated for pedestrian user localization. For this purpose, several scenarios are designed either using real observation or simulated data. In addition, the localization of user groups within a neighborhood based on collaborative navigation (CP) is investigated and analyzed. An analysis of the performance of these techniques for ranging the positioning estimation using different fusion algorithms is assessed. The methodology applied for CP leverages the hybrid nature of the range measurements obtained by UWB and Wi-Fi RTT systems. The proposed approach stands out due to its originality in two main aspects: (1) it focuses on developing and evaluating suitable models for correcting range errors in RF-based TWR (Two-Way Ranging) technologies, and (2) it emphasizes the development of a robust CP engine for groups of pedestrians. The results obtained demonstrate that a performance improvement with respect to position trueness for UWB and Wi-Fi RTT cases of the order of 74% and 54%, respectively, is achieved due to the integration of these techniques. The proposed localization algorithm based on a P2I/P2P (Peer-to-Infrastructure/Peer-to-Peer) configuration provides a potential improvement in position trueness up to 10% for continuous anchor availability, i.e., UWB known nodes or Wi-Fi access points (APs). Its full potential is evident for short-duration events of complete anchor loss (P2P-only), where an improvement of up to 53% in position trueness is achieved. Overall, the performance metrics estimated based on the extensive evaluation campaigns demonstrate the effectiveness of the proposed methodologies. Full article

This study addresses solar energy applications in protected agriculture, focusing on greenhouses and related technologies. A bibliometric and technical analysis is developed, covering research published between 1976 and 2024, to identify the main trends and challenges in the use of solar energy in controlled environments. The methodology was based on the PRISMA approach, using the Scopus database to retrieve relevant documents. From an initial total of 221 documents, 216 were selected after a filtering and debugging process, ensuring the relevance of the final set. In the analytical phase, the results showed a moderate growth of 3.68% in the annual publication rate, highlighting the impact of research on solar energy’s application to air conditioning and energy efficiency in greenhouses. Most of the studies reviewed feature hybrid systems that combine solar energy with other resources, and we highlight both advances in climate control through artificial intelligence and the implementation of photovoltaic and thermal technologies to improve the energy efficiency of agricultural systems. The results also underline the importance of tomato cultivation in the selected studies, reflecting its global economic impact. The conclusions highlight the need for the further integration of energy storage and desalination technologies, especially in arid regions with high solar irradiation, to ensure the sustainability of greenhouses. It is proposed that future research should address the wider implementation of hybrid systems and advanced climate control technologies, optimizing both the use of energy resources and the performance of crops under cover. In addition, it is recommended that international collaboration be strengthened to address technical and climatic challenges in protected agriculture and to expand the adoption of innovative solutions in different geographical contexts. Full article

In response to the growing demand for potable water, this study presents a practical methodology for designing and fabricating a hybrid desalination system that integrates reverse electrodialysis and electrodialysis using 3D-printing technology. The hybrid system combines the energy generation potential of RED with the salt removal capabilities of ED, reducing energy consumption. Customized reactors were designed to enhance flow distribution and ion exchange, with computational fluid dynamics simulations validating the hydrodynamic performance. The reactors were fabricated using 3D printing, allowing rapid, cost-effective production, with functional reactors constructed in under 24 h. The system achieved a 15% reduction in salt concentration within one hour, with a specific energy consumption of 0.1388 Wh/m³ and a water recovery rate of 50%. These results demonstrate the functionality of the RED-ED hybrid system for achieving energy savings and performing water desalination. This methodology provides a scalable and replicable solution for water treatment applications, especially in regions with abundant salinity gradients and limited freshwater resources, while offering a multidisciplinary approach that integrates physicochemical and engineering principles for effective device development. Full article

Particulate matter smaller than 2.5 μm (PM2.5) in Madrid is a critical concern due to its impacts on public health. This study employs advanced methodologies, including the CRISP-DM model and hybrid Prophet–Long Short-Term Memory (LSTM), to analyze historical data from monitoring stations and predict future PM2.5 levels. The results reveal a decreasing trend in PM2.5 levels from 2019 to mid-2024, suggesting the effectiveness of policies implemented by the Madrid City Council. However, the observed interannual fluctuations and peaks indicate the need for continuous policy adjustments to address specific events and seasonal variations. The comparison of local policies and those of the European Union underscores the importance of greater coherence and alignment to optimize the outcomes. Predictions made with the Prophet–LSTM model provide a solid foundation for planning and decision making, enabling urban managers to design more effective strategies. This study not only provides a detailed understanding of pollution patterns, but also emphasizes the need for adaptive environmental policies and citizen participation to improve air quality. The findings of this work can be of great assistance to environmental policymakers, providing a basis for future research and actions to improve air quality in Madrid. The hybrid Prophet–LSTM model effectively captured both seasonal trends and pollution spikes in PM2.5 levels. The predictions indicated a general downward trend in PM2.5 concentrations across most districts in Madrid, with significant reductions observed in areas such as Chamartín and Arganzuela. This hybrid approach improves the accuracy of long-term PM2.5 predictions by effectively capturing both short-term and long-term dependencies, making it a robust solution for air quality management in complex urban environments, like Madrid. The results suggest that the environmental policies implemented by the Madrid City Council are having a positive impact on air quality. Full article

Background/Objectives: The accurate categorization of brain MRI images into tumor and non-tumor categories is essential for a prompt and effective diagnosis. This paper presents a novel methodology utilizing advanced Convolutional Neural Network (CNN) designs to tackle the complexity and unpredictability present in brain MRI data. Methods: The methodology commences with an extensive preparation phase that includes image resizing, grayscale conversion, Gaussian blurring, and the delineation of the brain region for preparing the MRI images for analysis. The Multi-verse Optimizer (MVO) is utilized to optimize data augmentation parameters and refine the configuration of trainable layers in VGG16 and ResNet50. The model’s generalization capabilities are significantly improved by the MVO’s ability to effectively balance computational cost and performance. Results: The amalgamation of VGG16 and ResNet50, further refined by the MVO, exhibits substantial enhancements in classification metrics. The MVO-optimized hybrid model demonstrates enhanced performance, exhibiting a well-calibrated balance between precision and recall, rendering it exceptionally trustworthy for medical diagnostic applications. Conclusions: The results highlight the effectiveness of MVO-optimized CNN models for classifying brain tumors in MRI data. Future investigations may examine the model’s applicability to multiclass issues and its validation in practical clinical environments. Full article

Background: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options. Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images. Methods: The research team expanded the dataset, consisting of ultrasound images, and used it for training deep convolutional neural networks (CNNs) such as InceptionV3, ResNet50, DenseNet121, and EfficientNetB0. A hybrid approach, combining InceptionV3 for feature extraction with a Random Forest classifier, emerged as the most accurate and stable method. An approach of dual dichotomy classification was used to categorize images into two stages: healthy vs. sick, and then mild versus severe ballooning.. Features obtained from CNNs were integrated with conventional machine learning classifiers like Random Forest and Support Vector Machines (SVM). Results: The hybrid approach achieved an accuracy of 97.40%, an area under the curve (AUC) of 0.99, and a sensitivity of 99% for the ‘Many’ class during the third phase of evaluation. The dual dichotomy classification enhanced the sensitivity in identifying severe instances of HB. The cross-validation process confirmed the strength and reliability of the suggested models. Conclusions: These results indicate that this combination method can decrease the need for invasive liver biopsies by providing a non-invasive and precise alternative for early identification and monitoring of NAFLD and NASH. Subsequent research will prioritize the validation of these models using larger datasets from multiple centers to evaluate their generalizability and incorporation into clinical practice. Full article

The genus Vitis, known for its economically important fruit—grape—is divided into three geographical groups, American, East Asian, and Eurasian, along with a hybrid group. However, previous studies on grape phylogeny using chloroplast genomes have been hindered by limited sample sizes and inconsistent methodologies, resulting in inaccuracies. In this study, we employed the GetOrganelle software with consistent parameters to assemble the chloroplast genomes of 21 grape cultivars, ensuring comprehensive representation across four distinct groups. A comparative analysis of the 21 grape cultivars revealed structural variation, showing chloroplast genome sizes ranging from 160,813 bp to 161,275 bp. In 21 Vitis cultivars, genome annotation revealed 134 to 136 genes, comprising 89 to 91 protein-coding genes (PCGs), 37 tRNAs, and 8 rRNAs. Our observations have pinpointed specific occurrences of contraction and expansion phenomena at the interfaces between inverted repeat (IR) regions and single-copy (SC) regions, particularly in the vicinity of the rpl2, ycf1, ndhF, and trnN genes. Meanwhile, a total of 193 to 198 SSRs were identified in chloroplast genomes. The diversification pattern of chloroplast genomes exhibited strong concordance with the phylogenetic relationships of the Euvitis subgenera. Phylogenetic analysis based on conserved chloroplast genome strongly clustered the grape varieties according to their geographical origins. In conclusion, these findings enhance our understanding of chloroplast genome variation in Vitis populations and have important implications for cultivar selection, breeding, and conservation efforts. Full article

The state of charge (SoC) is a critical parameter in lithium-ion batteries and their alternatives. It determines the battery’s remaining energy capacity and influences its performance longevity. Accurate SoC estimation is essential for making informed charging and discharging decisions, mitigating the risks of overcharging or deep discharge, and ensuring safety. Battery management systems rely on SoC estimation, utilising both hardware and software components to maintain safe and efficient battery operation. Existing SoC estimation methods are broadly classified into direct and indirect approaches. Direct methods (e.g., Coulumb counting) rely on current measurements. In contrast, indirect methods (often based on a filter or observer) utilise a model of a battery to incorporate voltage measurements besides the current. While the latter is more accurate, it faces challenges related to sensor drift, computational complexity, and model inaccuracies. The need for more precise and robust SoC estimation without increasing complexity is critical, particularly for real-time applications. Recently, sliding mode observers (SMOs) have gained prominence in this field for their robustness against model uncertainties and external disturbances, offering fast convergence and superior accuracy. Due to increased interest, this review focuses on various SMO approaches for SoC estimation, including first-order, adaptive, high-order, terminal, fractional-order, and advanced SMOs, along with hybrid methods integrating intelligent techniques. By evaluating these methodologies, their strengths, weaknesses, and modelling frameworks in the literature, this paper highlights the ongoing challenges and future directions in SoC estimation research. Unlike common review papers, this work also compares the performance of various existing methods via a comprehensive simulation study in MATLAB 2024b to quantify the difference and guide the users in selecting a suitable version for the applications. Full article