Modelling and experiment of buildings thermo-aeraulic behavior according to the level-compactness in Saharan climate conditions

A large number of studies of building energy simulation neglect the humidity, or well represented, with a very simplified method. It is for this reason that we have developed a new approach to the description and modelling of multizone buildings in Saharan climate. The concept of the form factor and index compactness “quotient of external walls area and volume of the building” are two of the key elements for analyzing the building geometry. We can introduce it’s as validation tools in some cases. In this paper, governing equations of physical phenomena allow to build a model of the thermo-aeraulic behavior. The primary objective is the validation of numerical results able to determine the humidity and temperature in a multizone space. The calculated results were compared with firstly, experimental values, and secondly with simulated results using TRNSYS software. We check if the results change radically for an invariable compactness index. The comparison shows that the found results are to some extent satisfactory. For buildings of similar thermal properties, especially, the used construction materials, the thermal insulation and thermal inertia level, orientation, etc., the result proves that temperature and specific humidity varie slightly when the compactness index is constant

Management strategy in actinomycosis brain abscess

Key Clinical Message We reported herein a case of isolated cerebral actinomycosis in a 54‐year‐old immunocompetent man. Brain MRI showed a left frontal intra‐axial lesion and perilesional edema. We performed an open biopsy of the left frontal enhancing lesion. Intraoperative findings showed a yellowish, malleable, and capsulated lesion that was well defined with surrounding normal tissue within pus inside and lacked any necrotic content. MR spectroscopy showed a high level of choline, lactate, and lipid peaks with a choline/N‐Acetylaspartic acid ratio of 1.8. The diagnosis was confirmed histologically, and the patient was treated successfully for 3 months after surgical aspiration. Surgical management allowed to confirm the diagnosis with a shorten antibiotics, a rapid resolution of symptoms, and a complete recovery

Predviđanje globalnog Sunčeva zračenja po satu: usporedba neuronske mreže / bootstrap agregacija

This research work explores the use of single neural networks and bootstrap aggregated neural networks for predicting hourly global solar radiation. A database of 3606 data points were from the Renewable Energies Development Center, radiometric station ‘Shems’ of Bouzareah. The single neural networks and bootstrap aggregated neural networks were built together. The precision and durability of neural network models generated with an incomplete quantity of training datasets were improved using bootstrap aggregated neural networks. To produce numerous sets of training data points, the training data was re-sampled utilising bootstrap resampling by replacement. A neural network model was built for each of the data points. The individual neural network models were then combined to produce the bootstrap aggregated neural networks. The experimental and predicted values of global solar radiation were compared, and lower root mean squared errors (68.3968 and 62.4856 Wh m–2) were discovered during the testing phases for single neural networks and bootstrap aggregated neural networks, respectively. The results of these models show that the bootstrap aggregated neural networks model is more accurate and robust than single neural networks.U ovom radu istražena je primjena pojedinačnih i bootstrap agregiranih neuronskih mreža u predviđanju globalnog Sunčeva zračenja po satu. Baza od 3606 podatkovnih točaka dobivena je iz Centra za razvoj obnovljivih izvora energije, radiometrijske postaje ‘Shems’ u Bouzareahu. Pojedinačne neuronske mreže i bootstrap agregirane neuronske mreže izgrađene su zajedno. Preciznost i trajnost modela neuronskih mreža generiranih uz nepotpuni set podataka za treniranje poboljšani su primjenom bootstrap agregiranih neuronskih mreža. Da bi se proizveli brojni setovi podataka za treniranje, primijenjeno je ponovljeno uzorkovanje podataka primjenom metodologije slučajnog uzorkovanja sa zamjenom. Za svaku podatkovnu točku izgrađena je neuronska mreža. Pojedinačne neuronske mreže su potom kombinirane u bootstrap agregirane neuronske mreže. Uspoređene su eksperimentalne i predviđene vrijednosti globalnog Sunčeva zračenja te su tijekom faza testiranja dobivene niže vrijednosti srednje kvadratne pogreške za pojedinačne odnosno bootstrap agregirane neuronske mreže (68,3968 i 62,4856 Wh m–2). Rezultati su pokazali da je model bootstrap agregiranih neuronskih mreža precizniji i robusniji od pojedinačnih neuronskih mreža

Prediction of Hourly Global Solar Radiation: Comparison of Neural Networks / Bootstrap Aggregating

This research work explores the use of single neural networks and bootstrap aggregated neural networks for predicting hourly global solar radiation. A database of 3606 data points were from the Renewable Energies Development Center, radiometric station ‘Shems’ of Bouzareah. The single neural networks and bootstrap aggregated neural networks were built together. The precision and durability of neural network models generated with an incomplete quantity of training datasets were improved using bootstrap aggregated neural networks. To produce numerous sets of training data points, the training data was re-sampled utilising bootstrap resampling by replacement. A neural network model was built for each of the data points. The individual neural network models were then combined to produce the bootstrap aggregated neural networks. The experimental and predicted values of global solar radiation were compared, and lower root mean squared errors (68.3968 and 62.4856 Wh m−2) were discovered during the testing phases for single neural networks and bootstrap aggregated neural networks, respectively. The results of these models show that the bootstrap aggregated neural networks model is more accurate and robust than single neural networks

Temperature Effects on the Energy Yield of Perovskite Solar Cells

International audienceIn this work, we study the dynamic temperature-dependent performance of perovskite solar cells (PSCs) and propose a full model to predict its energy yield (EY) under realistic conditions. This model stands out for the inclusion of a robust thermal model which allows to estimate the cell temperature from given meteorological conditions. Linking the experimental electrical and optical dependence of PSCs and the thermal model we analyze the most sensible layers that increases the device temperature. Finally, we evaluate the EY of PSC working on different geographical locations and show that the impact of temperature on this prediction can be more than 10 %