Boumediene Khaouani

Packet loss in Voice over Internet Protocol (VoIP) communications can significantly degrade speech quality, particularly affecting Conjugate-Structure Code-Excited Linear Prediction (CS-CELP) type speech coders like G.729. Traditional Packet Loss Concealment (PLC) methods often struggle with consecutive frame losses. Recent advancements in deep learning have introduced Deep Neural Networks (DNNs) as a promising solution for PLC. This article explores the integration of DNNs into PLC for CS-CELP-type speech coders, focusing on methodologies, experimental setups, results, and future directions.

The economic and social development that Algeria has experienced in recent years has generated large quantities of waste, including special waste. Despite the efforts and the laws promulgated, their management and treatment are difficult. This article analyzes the state of hospital waste management in Algeria and particularly in the communes of the Laghouat wilaya located in the Algerian south. The evaluation of the associated socio-environmental impacts shows that the quantity of hospital waste produced in the Laghouat wilaya amounts to nearly 163 tons/year. The results indicate 151 tons/year of healthcare waste, 0.57 tons/year of expired drugs, 10 tons/year, 2256 liters/year of radiology liquids. The production of these harmful materials has become a source of problems, at the same time of insalubrity and public health. Indeed, the inadequacies in the management of these special residues contribute globally to a degradation of the quality of the environment and, more precisely, to visual and olfactory pollution and potential impacts on human health. Faced with this situation and to minimize environmental and health risks, this work proposes recommendations and modalities for better and more rigorous management of hospital waste to all actors concerned and through the various health institutions of the Laghouat wilaya.