From hygrothermal adaptation of endemic plants to meteorosensitive biomimetic architecture: case of Mediterranean biodiversity hotspot in Northeastern Algeria.

This research consists in diagnosing the hygrothermal imbalance problem inside tourism buildings located at the edge of the Mediterranean Sea. In particular, we study the case of Ben M'Hidi tourism development area in Skikda coastline in Algeria. The southern room of "Royal Tulip" hotel was chosen as object of this study in order to investigate its internal hygrothermal behavior.

Deep Learning-Based Detection of Articulatory Features in Arabic and English Speech.

This study proposes using object detection techniques to recognize sequences of articulatory features (AFs) from speech utterances by treating AFs of phonemes as multi-label objects in speech spectrogram. The proposed system, called AFD-Obj, recognizes sequence of multi-label AFs in speech signal and localizes them. AFD-Obj consists of two main stages: firstly, we formulate the problem of AFs detection as an object detection problem and prepare the data to fulfill requirement of object detectors by generating a spectral three-channel image from the speech signal and creating the corresponding annotation for each utterance.

Effect of Spatial Ambiances on the Patient Satisfaction and Well-Being in Hospitals: The Case of UHC Ibn Sina Annaba and UHC Benbadis Constantine-Algeria.

Objectives: In this work, we aim to have a better understanding of the impact of the architectural ambiance on patient well-being, by exploring patient's perception of physical ambiantal factors in their rooms.

Background: Perception of ambiantal factors is beginning to be relatively documented in space psychology, including health. There is no research in this field in Algeria; therefore, we would like to fill this gap and draw attention to the importance of the ambiantal physical factors in our hospitals.

Date fruit dataset for intelligent harvesting.

The date palm is one of the most valuable fruit trees in the world. Most methods used for date fruit inspection, harvesting, grading, and classification are manual, which makes them ineffective in terms of both time and economy. Research on automated date fruit harvesting is limited as there is no public dataset for date fruits to aid in this.