Hybrid Approach Research Articles

PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5.

An Accurate and Efficient Approach to Knowledge Extraction from Scientific Publications Using Structured Ontology Models, Graph Neural Networks, and Large Language Models.

The rapid growth of biomedical literature makes it challenging for researchers to stay current. Integrating knowledge from various sources is crucial for studying complex biological systems. Traditional text-mining methods often have limited accuracy because they don't capture semantic and contextual nuances. Deep-learning models can be computationally expensive and typically have low interpretability, though efforts in explainable AI aim to mitigate this. Furthermore, transformer-based models have a tendency to produce false or made-up information-a problem known as hallucination-which is especially prevalent in large language models (LLMs). This study proposes a hybrid approach combining text-mining techniques with graph neural networks (GNNs) and fine-tuned large language models (LLMs) to extend biomedical knowledge graphs and interpret predicted edges based on published literature. An LLM is used to validate predictions and provide explanations. Evaluated on a corpus of experimentally confirmed protein interactions, the approach achieved a Matthews correlation coefficient (MCC) of 0.772. Applied to insomnia, the approach identified 25 interactions between 32 human proteins absent in known knowledge bases, including regulatory interactions between MAOA and 5-HT2C, binding between ADAM22 and 14-3-3 proteins, which is implicated in neurological diseases, and a circadian regulatory loop involving RORB and NR1D1. The hybrid GNN-LLM method analyzes biomedical literature efficiency to uncover potential molecular interactions for complex disorders. It can accelerate therapeutic target discovery by focusing expert verification on the most relevant automatically extracted information.

Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling

Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer’s advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash–Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%.

Innovative hybrid approach: digital and analog fabrication of orbital prosthesis for post-COVID-19 mucormycosis defects using photogrammetry technique

In addition to anatomical loss, removal of the full or portion of the orbit results in a facial deformity and psychological distress for the patient. This article details a practical case of prosthetic rehabilitation using digital workflow for an orbital deformity caused by post-COVID-19 Mucor mycosis. The main goal of this case study was to create a maxillofacial orbital prosthesis that is well-retained, simple to use, and accurate in terms of appearance. The study addresses the problems involved in fabricating the orbital prosthesis, particularly the unique dimensions and form of the defect, replicating the natural skin tone, and accomplishing retention by the most prudent and patient-friendly approach. Through this article, a digitised algorithm, using photogrammetry technique for facial scan, is suggested for fabricating the prosthesis.

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events.

Detecting and Localizing Wireless Spoofing Attacks on the Internet of Medical Things

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based on spatial correlation calculated from received signal strength (RSS). To detect and mitigate wireless spoofing attacks in IoMT environments effectively, the hybrid approach combines spatial correlation analysis, Deep CNN classification, Elliptic Curve Cryptography (ECC) encryption, and DSRM-powered attack detection enhanced (DADE) detection and localization (DAL) frameworks. A deep neural network (Deep CNN) was used to classify trusted transmitters based on Python Spyder3 V5 and ECC encrypted Hack RF Quadrature Signals (IQ). For localizing targets, this paper also presents DADE and DAL frameworks implemented on Eclipse Java platforms. The hybrid approach relies on spatial correlation based on signal strength. Using the training methods of Deep CNN1, Deep CNN2, and Long Short-Term Memory (LSTM), it was possible to achieve accuracies of 98.88%, 95.05%, and 96.60% respectively.

Diagnostic performance of CCTA and CTP imaging for clinically suspected in-stent restenosis: A meta-analysis

AimsThe objective of this study is to conduct a meta-analysis to assess the diagnostic performance of Coronary Computed Tomography Angiography (CCTA) and a hybrid approach that incorporates Computed Tomography Perfusion (CTP) in addition to CCTA (CCTA ​+ ​CTP) for the detection of in-stent restenosis (ISR), as defined by angiography. MethodsA comprehensive search of articles identified 18,513 studies. After removing duplicates, title/abstract screening, and full-text review, 17 CCTA and 3 CCTA ​+ ​CTP studies were included. Only studies using ≥64-slices multidetector computed tomography (CT) were considered eligible. ResultsThe per-patient ISR prevalence was 43 ​%, with 92 ​% of stents fully interpretable with CCTA. Meta-analysis exhibited a per-stent CCTA (n ​= ​2674) sensitivity of 90 ​% (95 ​% CI; 84–94 ​%), specificity of 89 ​% (95 ​% CI; 86–92 ​%), positive likelihood ratio of 7.17 (95 ​% CI; 5.24–9.61), negative likelihood ratio of 0.17 (95 ​% CI; 0.10–0.25), and diagnostic odds ratio of 45.7 (95 ​% CI; 22.71–82.43). Additional sensitivity analyses revealed no influence of stent diameter or strut thickness on the diagnostic yield of CCTA. The per-stent diagnostic performance of CCTA ​+ ​CTP (n ​= ​752) did not show differences compared to CCTA. ConclusionsWith currently utilized scanners, CCTA and CCTA ​+ ​CTP demonstrated high diagnostic performance for in-stent restenosis evaluation. Consequently, a history of previous stent implantation should not be an argument to preclude using these methods in clinically suspected patients.

Interval-valued q-rung orthopair fuzzy complex proportional assessment-based approach and its application for evaluating the factors of Blockchain Technology in various domains

Blockchain technology (BT) is a digitally decentralized, distributed and public ledger, which considers a secure and viable solution for storing and accessing the record transactions in a public or private peer-to-peer network and assures a smart world of automation of complex services. This paper aims to evaluate the factors persuading the BT adoption and also to assess the possible application areas for the adoption of BT. Implementation of the BT process considers multiple criteria and alternatives with uncertain information; therefore, it can be considered as an uncertain multi-criteria decision-making problem. In this context, a hybrid approach is proposed based on the criteria interaction through inter-criteria correlation (CRITIC) and the complex proportional assessment (COPRAS) methods with interval-valued q-rung orthopair fuzzy set (IVq-ROFS) named as the “IVq-ROF-CRITIC-COPRAS” framework. In this approach, the significance values of the factors are computed through novel score function-based CRITIC model, whereas the CRITIC-based COPRAS model is employed to rank different domains/application areas for the adoption of BT under IVq-ROFSs environment. In this regard, a novel score function is introduced with its desirable characteristics. Further, the developed model is executed on a case study of BT adoption considering 14 factors, which confirms the efficiency and applicability of introduced approach. Based on the results, the healthcare BT alternative is most appropriate among the others. To evaluate its permanence, sensitivity analysis with regard to various ratings of decision strategic coefficient is presented under IVq-ROFS context. The strength of the developed approach is emphasized by comparing it with some of the former models under the context of IVq-ROFSs. The proposed extension of the COPRAS approach can be utilized to solve other complex group decision-making problems with uncertainties.

Optimization of Hybrid Microwave Curing Approach Based On the Performance of Metakaolin-Based Geopolymer Mortars

Geopolymer binders have been highlighted due to their low carbon emission during production and processing. While metakaolin and F-type fly ash are commonly used as raw materials for aluminosilicate-based geopolymers, the long heat-curing requirements for hardening and strength development still pose challenges. This paper investigates the possible use of a hybrid microwave curing technique to design a set-on-demand approach to reduce the duration of heat curing in metakaolin-based geopolymer. The experimental design was established for samples with three different molar ratios (MR; 1.3,1.5, and 1.7) containing metakaolin, fly ash, and silica fume. Samples were subjected to 3 different curing regimes: oven curing, microwave (MW) curing, and hybrid curing (a combination of optimized microwave and oven curing). The performance evaluation was based on compressive strength, dimensional stability, and alkali leaching (efflorescence). Implementing only MW curing resulted in a significant decrease in compressive strength compared to their counterpart oven-cured samples. The reduction of compressive strength was more pronounced at lower molar ratios. The design of a hybrid curing approach where a portion of oven curing was replaced by MW resulted in a higher strength development than those only cured with MW. Similarly, the efficiency of hybrid curing was more pronounced in samples having MR of 1.5 and 1.7. Using MW curing in the geopolymer binders did not affect the alkali leaching; however, it increased the material’s drying shrinkage. Results showed that replacing a portion of oven curing with microwave curing in a hybrid approach can increase the operation speed and the hardening rate without significantly decreasing compressive strength.

A Comprehensive Analysis on Galantamine Based Hybrids for the Management of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive chronic age-related neurodegenerative brain disorder characterized by the loss of memory and other cognitive functions. The exact etiology of AD is still under investigation, however several factors such as low level of neurotransmitter acetylcholine (ACh), aggregation of amyloid beta (Aβ) in the form of Aβ plaques, hyperphosphorylation of tau protein into neurofibrillary tangles (NFTs), oxidative stress, and metal ion imbalance are the major hallmarks of this disease. Of the multiple hypotheses for AD, the amyloid-β (Aβ) and cholinergic hypothesis are the main targeting hypotheses for AD. Some researchers hypothesized that the primary event associated with the cholinergic neurotransmitter (acetylcholine) is memory loss and cognitive impairment. Due to the disease's complicated pathogenesis, long-term therapy with a single target candidate is futile. As a result, multitargeted and multifunctional therapies have emerged. Various research teams are concentrating on addressing multiple disease factors through hybridization techniques. Consequently, this hybridization approach has been applied to all core scaffolds, including galantamine. In this article, we tried to provide a thorough overview of the most recent developments on galantamine, a prospective AChE inhibitor, and its hybrid analogs as possible therapeutic agents for treating AD. Furthermore, we also provided the design, synthesis, and SAR analysis of the galantamine-based compounds used in the last decades for the management of AD.

Combining predictive models: Hybrid approaches in crop recommendation systems

Combining predictive models: Hybrid approaches in crop recommendation systems

InterLabelGO+: unraveling label correlations in protein function prediction.

Accurate protein function prediction is crucial for understanding biological processes and advancing biomedical research. However, the rapid growth of protein sequences far outpaces the experimental characterization of their functions, necessitating the development of automated computational methods. We present InterLabelGO+, a hybrid approach that integrates a deep learning-based method with an alignment-based method for improved protein function prediction. InterLabelGO+ incorporates a novel loss function that addresses label dependency and imbalance and further enhances performance through dynamic weighting of the alignment-based component. A preliminary version of InterLabelGO+ achieved a strong performance in the CAFA5 challenge, ranking sixth out of 1625 participating teams. Comprehensive evaluations on large-scale protein function prediction tasks demonstrate InterLabelGO+'s ability to accurately predict Gene Ontology terms across various functional categories and evaluation metrics. The source code and datasets for InterLabelGO+ are freely available on GitHub at https://github.com/QuanEvans/InterLabelGO. A web-server is available at https://seq2fun.dcmb.med.umich.edu/InterLabelGO/. The software is implemented in Python and PyTorch, and is supported on Linux and macOS.

Solving Viscous Burgers’ Equation: Hybrid Approach Combining Boundary Layer Theory and Physics-Informed Neural Networks

In this paper, we develop a hybrid approach to solve the viscous Burgers’ equation by combining classical boundary layer theory with modern Physics-Informed Neural Networks (PINNs). The boundary layer theory provides an approximate analytical solution to the equation, particularly in regimes where viscosity dominates. PINNs, on the other hand, offer a data-driven framework that can address complex boundary and initial conditions more flexibly. We demonstrate that PINNs capture the key dynamics of the Burgers’ equation, such as shock wave formation and the smoothing effects of viscosity, and show how the combination of these methods provides a powerful tool for solving nonlinear partial differential equations.

A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India

Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.

Micromechanics and microstructure based machine learning approach: Unveiling the role of porosity and hydrated phases on the tensile behaviour of cement pastes

The fracture process and the mechanical performance of the cement paste rely significantly on its tensile behaviour. Machine learning methods have been recently developed to predict this behaviour based on numerous experimental measurements. Due to multi-phase heterogeneous nature of cement paste microstructure, evaluation of the role of each chemical phase on the cracking of cement paste is necessary to develop optimized binder systems. Numerous experimental tests would be necessary to determine the best cement pastes in a considered environment. Therefore, in this study, a hybrid approach based on an ensemble machine learning model and a microstructure informed micromechanical model is proposed for predicting the tensile behaviour of hydrated cement pastes. The model can be used as a quick prediction tool for tensile strength of a given microstructure or for deep analysis on the optimization of binder system. A Deep Neural Networks (DNN) model was developed by training and validating a large dataset detailing the chemical phase composition, phase volume fractions and the tensile strength of the cement pastes. This dataset covers an extensive range of Portland cements, hydration ages and water-to-cement ratios. It was developed by simulating cement thermodynamics and tensile behaviour using a damage micromechanical model; which is otherwise almost impossible to obtain through experimental means. Additionally, two generative algorithms, tabular generative adversial networks (TGAN) and conditional generative adversial networks (CTGAN), were employed for dataset augmentation as a sampling method for the sensitivity analysis. This machine learning study provides an in-depth investigation to identify the role of volume fraction of solid phases (C-S-H, ettringite, portlandite and hydrogarnet) and micro porosity (dry and filled) in affecting the tensile behaviour of hydrated cement paste. Optimization assessment of the microstructure was then performed using micromechanical analysis and damage propagation in hydrated cement paste phases and porous network.

Endoluminal Approaches for Colorectal Neoplasia in Inflammatory Bowel Disease, A Viable Alternative for Colectomy?

IntroductionEndoscopic resection of visible dysplastic lesions in patients with inflammatory bowel disease (IBD) provides an alternative to colectomy for management. Endoscopic techniques that can be used include endoscopic mucosal resection (EMR), endoscopic submucosal dissection (ESD), and a combined hybrid approach of ESD and EMR. These endoscopic treatments may allow organ preservation in patients with IBD. The aim of this study was to evaluate outcomes of endoscopic resection, including EMR, ESD and hybrid ESD/EMR, for the treatment of colorectal dysplasia in patients with IBD. MethodsWe performed a retrospective review of IBD patients who underwent endoscopic resection for colorectal dysplasia at our tertiary care center between 2014 to 2023. Patients were identified through a search of our endoscopy database. Medical records were reviewed to collect data on patient demographics, IBD history, details of endoscopic technique, procedural factors, final pathology results, and outcomes including recurrence of dysplasia. ResultsA total of 50 IBD patients underwent endoscopic resection, including 38 ESD, 11 hybrid ESD/EMR, and 1 EMR. Median age was 62 (IQR: 54-68) years and 34 (68%) were male. Median BMI was 27.6 (IQR: 24.8-31.7). Underlying diagnosis was ulcerative colitis in 33 (66%) and Crohn's disease in 17 (33%). Median disease duration was 27.5 years (IQR 8-30). Polyp locations were distributed as follows: right colon, rectum, left colon, transverse colon, and cecum, with 9 (18%) in each area. Median procedure time was 47.5minutes (IQR: 31.2-73.7). En bloc resection was achieved in 34 (68%) and piecemeal resection in 16 (32%). On pathology, median lesion size was 23.5 (IQR: 20.2-40) mm. High-grade dysplasia was found in 7 (14%) and low-grade dysplasia in 15 (30%). During a median follow-up period of 7.2 years, 18% of patients experienced recurrence. No significant difference in recurrence rates was observed between the ESD and ESD/EMR groups. ConclusionEndoscopic resection, including EMR, ESD and hybrid techniques, enables effective treatment of visible dysplastic lesions as an alternative to colectomy in patients with IBD. In this cohort, favorable short-term outcomes were demonstrated after endoscopic resection.

A hybrid model for accurate prediction of composite longitudinal elastic modulus

This research presents a novel hybrid model that integrates a physical-based empirical model with an Artificial Neural Network (ANN) to accurately predict the longitudinal modulus of elasticity for composites under compression. The study focuses on a composite material with a pore inclusion within an ABS plastic matrix, exploring various pore volumes, orientations, and shapes. As part of the proposed hybrid model, a regression-type neural network was trained in MATLAB® to predict and correct discrepancies between the Generalized Stiffness Formulation (GSF) homogenization-based modeling method and the collected compression experimental test results. Using MATLAB® neural network, random error datasets were used to train the feed-forward neural network, and the remaining error datasets were used for validating the performance of the proposed hybrid modeling scheme.The hybrid model demonstrated superior performance, achieving the lowest Mean Error (ME) of 0.1684864, Mean Absolute Error (MAE) of 1.051846, Mean Squared Error (MSE) of 3.500952, and highest R-squared of 0.998797. The proposed hybrid model outperformed both the Generalized Stiffness Formulation (GSF) and standalone ANN models. The significant improvement in prediction accuracy underscores the novelty and robustness of the hybrid approach in composite material modeling. Furthermore, this method can be used to refine any existing physical model by focusing on improving these established models to match experimental results and reducing the discrepancies, which offers a more efficient and attractive strategy for accurate predictions.

Living-Skin Detection Based on Spatio-Temporal Analysis of Structured Light Pattern.

Living-skin detection is an important step for imaging photoplethysmography and biometric anti-spoofing. In this paper, we propose a new approach that exploits spatio-temporal characteristics of structured light patterns projected on the skin surface for living-skin detection. We observed that due to the interactions between laser photons and tissues inside a multi-layer skin structure, the frequency-domain sharpness feature of laser spots on skin and non-skin surfaces exhibits clear difference. Additionally, the subtle physiological motion of living-skin causes laser interference, leading to brightness fluctuations of laser spots projected on the skin surface. Based on these two observations, we designed a new living-skin detection algorithm to distinguish skin from non-skin using spatio-temporal features of structured laser spots. Experiments in the dark chamber and Neonatal Intensive Care Unit (NICU) demonstrated that the proposed setup and method performed well, achieving a precision of 85.32%, recall of 83.87%, and F1-score of 83.03% averaged over these two scenes. Compared to the approach that only leverages the property of multilayer skin structure, the hybrid approach obtains an averaged improvement of 8.18% in precision, 3.93% in recall, and 8.64% in F1-score. These results validate the efficacy of using frequency domain sharpness and brightness fluctuations to augment the features of living-skin tissues irradiated by structured light, providing a solid basis for structured light based physiological imaging.

Automated text classification of opinion vs. news French press articles. A comparison of transformer and feature-based approaches

This study explores Natural Language Processing (NLP) methods for distinguishing between press articles belonging to the journalistic genres of ‘objective’ news and ‘subjective’ opinion. Two classification models are compared: CamemBERT, a French transformer model fine-tuned for the task, and a machine learning model using 32 linguistic features. Trained on 8000 Belgian French articles, both models are evaluated on 1000 Canadian French articles. Results show CamemBERT’s superiority but highlight potential for hybrid approaches and emphasizes the need for robust and transparent methods in NLP. The research contributes to understanding NLP’s role in journalism by addressing challenges of point of view detection in press discourse.

Thermal Management Enhancement of Building-Integrated Photovoltaic Systems using Coupled Heat Pipe and Evaporative Porous Clay Cooler

The building-integrated photovoltaic (BIPV) panel systems often lead to a notable decline in PV panels efficiency and lifetime due to their temperature rise because of inadequate cooling. This study investigates the performance of innovative cooling strategy of using a coupled heat pipes and porous clay cooler and compare it with other related three BIPV cooling systems configurations including a conventional BIPV cooling with and without air gap and a pure evaporative porous clay cooling. The aim is to improve the PV panel cooling, ultimately reduce the PV temperature and enhance the overall performance of the BIPV system as well as reducing the building indoor temperature and boosting energy efficiency within the building. The system model, comprising sets of transient equations for the different system configurations, was solved using MATLAB and confirmed with previous experimental findings. The results indicate that comparing with the traditional BIPV system, using BIPV/Clay cooling systems and the hybrid BIPV/Clay-heat pipe cooling systems achieved peak PV temperature reductions of up to 14 °C and 14.7 °C, respectively. Additionally, these cooling methods led to a maximum interior room temperature reduction of approximately 14°C and an average decrease of 8°C. Moreover, the hybrid BIPV/Clay-heat pipe cooling system demonstrates superior performance compared to traditional BIPV system, achieving the highest improvements in PV electrical efficiency, output power, and exergy efficiency, with gains of 7.8%, 6.4%, and 8.4%, respectively. Further, when the hybrid BIPV/Clay-heat pipe cooling system was employed, the clay cooling efficiency and clay exergy efficiency values improved on average by 30.2% and 29.7%, respectively, compared to the BIPV/Clay cooling system in addition to the increase of the lifetime of the PV panels due to isolating it from the contact with the water/water vapor of the clay cooling system. However, this hybrid approach resulted in a rise in electricity production costs from 0.077 $/kWh to roughly 0.138 $/kWh and extended the payback time from 7.38 years to 13.6 years. But, despite its initial economic drawbacks, the hybrid BIPV/Clay-HP cooling system demonstrates considerable effectiveness and long lifetime compared to other cooling configurations.