Poultry farms have played a significant role throughout human history, in feeding the growing population. A good environment is a perfect condition for the growth of poultry, preventing disease, and effective production. The temperature higher and the humidity favor the growth of bacteria and hence the production of ammonia (NH3) by the decomposition of organic matter. Ammonia (NH3), carbon monoxide (CO), and carbon dioxide (CO2), Methane (CH4), hydrogen sulfide (H2S) are poisonous gases that can cause poultry diseases and mortality. The combination of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing offer efficient and intelligent stand-alone systems of monitoring in real-time, predicting, and advanced automation. The paper aims to monitor in real-time and predict poultry barns' environmental conditions using an artificial intelligence algorithm. An intelligent system called Poultry-Edge-AI-IoT has been developed to gather, hash, store, pretreat, filter, knowledge extract, and transmit information from a heterogeneous wireless sensor network. The Poultry-Edge-AI-IoT system is based on IoT, AI, and Edge Computing for the detection of potential stress, the harmful gas concentration, and the prediction of poultry barns' environmental conditions. The system is modular and upgradeable.

This paper seeks to develop a generic framework for the hospital supply chain in order to describe this complex environment through a holistic and inductive approach. Developed using the grounded theory concept, the framework aims to describe both patient and material flow in the hospital. A three-level structured model is presented in this article where we identified first the most valued activities in the chain from both patient and hospital’s staff perspectives. Second, we proposed some best practices to overcome the most common bottlenecks in the chain. Finally, interactions between the processes are identified and the sequence is defined to help both professionals and researchers conduct further studies in the healthcare sector.

This study investigated the correlation between the levels of chromium (Cr), lead (Pb), cadmium (Cd), zinc (Zn), and copper (Cu) in surface sediment samples and those in the total soft tissues of the mussel Mytilus .galloprovincialis, collected off the northern Moroccan coast in the...

The assessment of the hospital supply chain management represents a key challenge by virtue of the complexity of the healthcare sector. The purpose of this study is to introduce a hybrid approach that helps hospital administrators to clearly identify, evaluate, and narrow the key performance criteria for their supply chain. The methodology attempts to minimise information loss, reduce the fuzziness and subjectivity of the collected data and describes the interdependence among criteria. The proposed generic framework can be valuable for hospitals organisations aiming for a sustainable performance decision-making process. The combination of the Fuzzy Delphi method and Structural Equation Modelling proved to be effective in determining the pillars driving the sustainable performance of the hospital supply chain.

The objective of this study is to comprehensively investigate the printability characteristics of a selective laser melting (SLM) system, which will be achieved through the development of a benchmark test part. In addition, the effect of the build location on the dimensional accuracy and precision of 316 L stainless steel parts produced by SLM was thoroughly evaluated.