Опубликована: Июнь 28, 2024
Язык: Английский
Applied Sciences, Год журнала: 2024, Номер 14(11), С. 4506 - 4506
Опубликована: Май 24, 2024
An electronic nose, designed to replicate human olfaction, captures distinctive ‘fingerprint’ data from mixed gases or odors. Comprising a gas sensing system and an information processing unit, noses have evolved significantly since their inception in the 1980s. They transitioned bulky, costly, energy-intensive devices today’s streamlined, economical models with minimal power requirements. This paper presents comprehensive systematic review of nose technology domain, special focus on advancements over last five years. It highlights emerging applications, innovative methodologies, potential future directions that not been extensively covered previous reviews. The explores application across diverse fields such as food analysis, environmental monitoring, medical diagnostics, including new domains like veterinary pathology pest detection. work aims underline adaptability contribute continued development various industries, thereby addressing gaps current literature suggesting avenues for research.
Язык: Английский
Процитировано
27
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 11, 2025
Predicting the compressive strength of Compressed Earth Blocks (CEB) is a challenging task due to nonlinear relationships among their diverse components, including cement, clay, sand, silt, and fibers. This study employed PyCaret, an automated machine learning platform, address this complexity by developing evaluating predictive models. The analysis demonstrated that fiber content exhibited strong positive correlation with cement content, coefficient 0.9444, indicating significant influence on strength. Multiple algorithms were tested using metrics such as determination (R2), root mean square error (RMSE), absolute (MAE) assess model performance. Among these, Extra Trees Regressor showed best capability R2 = 0.9444 (highly accurate predictions), RMSE 0.4909 (low variability in prediction errors) MAE 0.1899 (minimal average error). results confirm PyCaret effectively automates workflow, enabling modeling complex material behavior. outperformed other its ability handle highly multivariate datasets, making it particularly well-suited for predicting CEB. approach offers advantage over traditional laboratory testing, which time-consuming resource-intensive. By incorporating techniques, especially PyCaret's streamlined processes, CEB becomes more efficient reliable, providing practical tool engineers researchers science.
Язык: Английский
Процитировано
1
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 4, 2025
Extended power outages are not only a nuisance but critical problem in the modern world, which demands continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within microgrid (MG) play an important role delivering to rural and off-grid areas avoiding potential outages. This research describes in-depth study three phases, design, optimization, performance analysis stand-alone hybrid for residential area remote province Adrar southern Algeria. The system is composed photovoltaic (PV) modules wind turbine, set batteries as storage unit, diesel generator backup source, inverter. paper investigates four recent methodologies based on Multi-objective Particle Swarm Optimization (MOPSO), Ant Lion Optimizer (MOALO), Dragonfly Algorithm (MODA), Evolutionary (MOGA) identify optimal sizing integrated with sources (RES). proposed methods carried out select size, multi-objective involving minimization annual cost electricity (COE), loss probability (LPSP) simultaneously. To achieve this, combined management strategy (EMS) rules that coordinate flows between various components. findings reveal MOPSO method has most efficient configuration generation (COE) 0.2520 $/kWh 9.164%, dominates MOALO (COE 0.1625$/kWh LPSP 8.4872%), MOGA 0.1577$/kWh 10%), MODA 0.02425$/kWh 7.8649%). Furthermore, sensitivity performed effect COE variants may have design variables.
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Окт. 29, 2024
Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving development goals. However, predicting lifetime remains challenging task complex interactions between internal factors such as material degradation, interface stability, morphological changes, external like environmental conditions, mechanical stress, encapsulation quality. In this study, we propose machine learning-based technique predict degradation over time OPVs. Specifically, employ multi-layer perceptron (MLP) long short-term memory (LSTM) neural networks power conversion efficiency (PCE) inverted organic solar (iOSCs) made from blend PTB7-Th:PC70BM, with PFN electron transport layer (ETL), fabricated under an N2 environment. We evaluate performance proposed using several statistical metrics, including mean squared error (MSE), root (rMSE), relative (RSE), absolute (RAE), correlation coefficient (R). The results demonstrate high accuracy our technique, evidenced by minimal predicted experimentally measured PCE values: 0.0325 RSE, 0.0729 RAE, 0.2223 rMSE, 0.0541 MSE LSTM model. findings highlight potential models in accurately OPVs, thus contributing advancement technologies.
Язык: Английский
Процитировано
2
Energy Conversion and Management, Год журнала: 2024, Номер 323, С. 119261 - 119261
Опубликована: Ноя. 14, 2024
Язык: Английский
Процитировано
2
Опубликована: Июнь 28, 2024
Язык: Английский
Процитировано
0