Profit-driven planning and analysis of a WEEE recycling facility with a multi-period MILP model

Electronic waste is one of the fastest-growing waste streams in the world. The challenges associated with the recycling of Waste Electrical and Electronic Equipment (WEEE) represent both threats, as the improper disposal of this waste can harm the environment and human health, and opportunities, as this category of waste contains valuable and rare resources that can be recovered and repurposed, contributing to the circular economy. The EU is leading the way in improving the collection and treatment of WEEE, but this has not been sufficient to meet the targets set in its WEEE directive. Therefore, additional efforts must be made to ensure the costeffective and environmentally sound recycling of WEEE, both in the public and private sectors. In this thesis, we propose a multi-period MILP model for the planning of a WEEE recycling facility in Belgium and conduct various analyses to provide insights on what elements are the most crucial to the profitability of such a facility. The originality of our approach lies in the multi-period aspect of the model, and the addition of a limited amount of labour to be allocated to various labour-intensive tasks of WEEE recycling. Our main findings are that labour is the most critical resource, both in cost and utilization, such that the optimal quantity of WEEE to process is the one that results in complete utilization of labour, with little to no overtime. As such, the flexibility of labour, both in possible task allocation and overtime capabilities, is crucial to the proper functioning of the facility, especially when taking into account possible deviations from the optimal plan, caused by the heterogeneity of WEEE and other variations such as the timing of deliveries.nhhma

Expression, purification and X-ray crystallographic analysis of the Helicobacter pylori blood group antigen-binding adhesin BabA

Helicobacter pylori is a human pathogen that colonizes about 50% of the world's population, causing chronic gastritis, duodenal ulcers and even gastric cancer. A steady emergence of multiple antibiotic resistant strains poses an important public health threat and there is an urgent requirement for alternative therapeutics. The blood group antigen-binding adhesin BabA mediates the intimate attachment to the host mucosa and forms a major candidate for novel vaccine and drug development. Here, the recombinant expression and crystallization of a soluble BabA truncation (BabA25-460) corresponding to the predicted extracellular adhesin domain of the protein are reported. X-ray diffraction data for nanobody-stabilized BabA25-460 were collected to 2.25Å resolution from a crystal that belonged to space group P21, with unit-cell parameters a = 50.96, b = 131.41, c = 123.40Å, α = 90.0, β = 94.8, γ = 90.0°, and which was predicted to contain two BabA25-460-nanobody complexes per asymmetric unit.SCOPUS: ar.jFLWNAinfo:eu-repo/semantics/publishe

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases. 120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests. The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4 +/- 5.9% of the cases (range 56-88%). The interrater variability of kappa=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6 +/- 8.7% of the cases (range 24-62%) with a large interrater variability (kappa=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56-88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24-62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures).The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice.status: publishe