Airborne particulate matter (PM) poses significant health risks, necessitating accurate toxicity threshold determination for effective risk assessment. This study introduces a novel machine-learning (ML) approach to predict PM toxicity thresholds and identify the key physico-chemical and exposure characteristics. Five machine learning algorithms — logistic regression, support vector classifier , decision tree, random forest, and extreme gradient boosting — were employed to develop predictive models using a comprehensive dataset from existing studies. We developed models using the initial dataset and a class weight approach to address data imbalance. For the imbalanced data, the Random Forest classifier outperformed others with 87% accuracy, 81% recall, and the fewest false negatives (23). In the class weight approach, the Support Vector Classifier minimized false negatives (21), while the Random Forest model achieved superior overall performance with 86% accuracy, 80% recall, and an F1-score of 82%. Furthermore, eXplainable Artificial Intelligence (XAI) techniques, specifically SHAP (SHapley Additive exPlanations) values, were utilized to quantify feature contributions to predictions, offering insights beyond traditional laboratory approaches. This study represents the first application of machine learning for predicting PM toxicity thresholds, providing a robust tool for health risk assessment. The proposed methodology offers a time- and cost-effective alternative to classical laboratory tests, potentially revolutionizing PM toxicity threshold determination in scientific and epidemiological research. This innovative approach has significant implications for shaping regulatory policies and designing targeted interventions to mitigate health risks associated with airborne PM.

In distribution logistics, the planning of vehicles’ routes and vehicles’ loads are traditionally managed separately, despite these activities are correlated. This often leads to various re-designs to make the routes and load plans compatible and applicable in practice. Moreover, the planned routes, which are static by definition, cannot always cope with unexpected events. Traffic congestion, vehicle failures, adverse meteorological conditions, and further undesired events can make the planned routes inapplicable and requirevehicles’ re-routing. This results in lower service levels, undesired delays, and higher costs for logistics companies. With the aim of overcoming the above limitations, this work proposes a novel approach based on a matheuristic algorithm that jointly solves the problem ofdelivery planninganddynamic vehicle routingto automate the delivery process in a logistics 4.0 perspective. The presented algorithm includes two different phases: the static phase, which is executed offline and in advance with respect to the delivery day, and the dynamic phase, which is executed in real-time to cope with unexpected events during the delivery. For the first phase, a matheuristic approach is defined to efficiently solve the combined vehicle routing and loading problems. Differently, for the second phase, a genetic algorithm is proposed to re-route vehicles in real-time, considering both the redefinition in real-time of the nominal trip and/or of the sequence of the customers to be visited. The algorithm is tested both on a literature benchmark and on a real dataset provided by an Italian logistics company. The obtained results show that, on the one hand, the proposed algorithm can automatically provide feasible solutions that minimise travel costs, total travelled distance, and empty space on the vehicles; on the other hand, it can ensure in real-time effective re-routing solutions in case of unexpected events occurring during delivery.Note to Practitioners—This work is motivated by the need for facilitating the operations of planning and routing deliveries in the external logistics sector. We propose an algorithm that automatically generates feasible routing and loading plans for a set of Transport Units (TUs) (i.e., the static phase), and then updates in real-time the nominal route in case of unexpected events (i.e., the dynamic phase). More specifically, the first phase of the algorithm takes as input the set of different clients, the list of products packed into bins (i.e., standard packing units) to be delivered to each client, and the set of transport units available for the deliveries, and provides as output the number and type of TUs to be used, the composition of the bins in each transport unit, and the corresponding route, while optimising the space occupation in each TU and the travel costs. The second phase, instead, takes as input the nominal routes computed in the first phase and, in case of unexpected events (e.g., accidents, slowdowns, etc.) affecting one or more routes, it re-routes the involved trucks guaranteeing the maximum efficiency in regards to travel cost, travel time, and quality of service. The adoption of this algorithm by logistic companies supports the automation of the delivery process and drastically improves the efficiency of logistic operations, with particular regard to the number of used TUs, costs, safety of goods, and customers’ satisfaction.

Medication iatrogeny is a significant patient safety challenge in the healthcare field. This issue pertains to the undesirable effects resulting from the use of drugs, including errors in prescribing, dosing, or administration. In this context, the use of Machine Learning (ML) techniques to predict clinical outcomes is becoming increasingly common. The objective of this work is to develop a decision-support system designed to provide recommendations and assist pharmacists in analyzing prescriptions to reduce the risks associated with iatrogenic medication use for patients. ML algorithms are applied to classify prescriptions as valid or invalid using a MIMIC database containing patient medical data. We followed strict guidelines to process the data to improve model performance and then evaluated the model's performance using cross-validation, referring to standard metrics. The system integrates with existing hospital software, allowing pharmacists to receive recommendations and alerts for potential medication errors. We obtained an average accuracy of 96% for predicting the validity of medical prescriptions. Our study demonstrates that the use of ML algorithms for predicting the validity of medical prescriptions is an effective method. The results also suggest that diversifying the data could improve the model's performance. The findings of this study have valuable implications for clinical practice by providing a useful tool for the early detection of medication errors and could contribute to the enhancement of decision support systems in medicine.

In this paper, we propose an innovative meta-heuristic characterized by a multi-dimensional chromosome where each dimension is driven by a rational agent. These agents have to communicate in order to implement evolving and adaptive genetic operators to accelerate the convergence towards the optimal solution. This cooperative approach is applied to solve the patient scheduling problem in emergency department (ED). This problem is NP-difficult due to the permanent interference between three types of arrival: already programmed patients, non-programmed patients and urgent non-programmed patients. Our scheduling problem has to integrate several dimensions such as medical dimensional, patient dimensional, temporal dimensional. The multi-dimensional aspect of the chromosome is crucial to model the different dimensions of the ED. The main goal of the simulation results is to assess the performance of the proposed agent driven multidimensional chromosome. The simulation results confirm that the intra and inter chromosomal interactions allow to avoid the blind aspect of the genetic operators and impacts the quality of solutions. The agents’ cooperation and its ability to improve efficiently the quality of the solutions by exploring intelligently the research space are confirmed by the drop in average total patient waiting time by 15.09%

The early identification of the patients’ hospitalization at triage level within the Emergency Department (ED) presents a potential solution to reduce the risk of overcrowding and improve the quality of care. Thus, predicting patient out-come on arrival assists medical staff in the make of the appropriate patient pathway decision and so reduces the risk of medical error and complication of the patient’s condition. Previous works don’t consider the uncertainty of medical data while the management of this uncertainty is one of the most important and crucial tasks of medical information systems. Thus, we present in this paper an improved version of the classical prediction model by taking into account the uncertainty and by managing properly the missing information. In this context, we propose a new approach based on Dempster-Shafer theory and Dynamic Multi-Layer Perceptron algorithm. Our proposed approach deploys the correspondent neural network as follows: 1) computes for each input parameter the Basic Belief Assignment (BBA) that provides an assessment of the uncertainty pattern using the Dempster’s rule; 2) deduces the correspondent weights based on the computed BBA, and 3) uses an appropriate transfer function to activate the next layer neurons. In this paper, we demonstrate the effectiveness of our proposed method by using a real ED database. We prove that our proposed approach manages efficiently the uncertainty of the medical data sources and missing information, so improves the decision making and reduces errors and complexity.

This paper focuses on scheduling patients in emergency department (ED) according to the priority of patients’ treatments, determined by the triage process. This multi-skills patient scheduling problem is modeled through four dimensional (hypercube) solutions search space whose axes are: Medical staff, Patients, ED structure and Time and it can be formulated as a flexible job shop scheduling problem. We have then to solve a NP-hard combinatorial optimization problem (COP) in the emergency department (ED). The objective is to minimize a score integrating the total waiting time of patients in the (ED) with emphasis on patients with severe conditions. The Friends and Enemies collaboration protocol between agents is developed for solving the problem where each agent integrate a complete metaheuristic scheme in its behavior. Each agent act autonomously in the solution environment and interacts cooperatively with it and with the other agents. The interaction between agents allows the metaheuristic hybridization including the tuning of its parameters. The simulation results show that the scenarios with 2 or more agents were significantly higher in performance than the scenarios with 1 single agent. Thus, it is confirmed that the collaboration protocol between agents influences the quality of the solutions and the scalability of our approach, with the addition of new agents, there is an improvement in the results. Our approach is tested on a set of real (ED) data and the simulation results show that the proposed friends end enemies collaboration protocol can significantly improve the efficiency of the (ED) by reducing the score and especially the total waiting time of multi-skills patient scheduling problem.

The aim of this paper is to describe our proposed ABOS framework (Agent-Based Optimization Systems) by demonstrating the interest in using the multi-agent approach while operating hybrid metaheuristics to solve Combinatorial Optimization Problems (COP). Two main contributions are highlighted in this work: 1) to show that the alliance of the multi-agent systems (MAS) and the metaheuristics, based on the interaction and the parallelisms concepts, facilitates the hybrid metaheuristics development and allows the simultaneous exploration of different regions of the search space and 2) to demonstrate that the use the multi-agent approach, in the context of optimization, is a crucial option in the process of hybridization allowing the development of generic structures. These later promote the interaction between metaheuristics independent of the problem to be addressed. Our challenge in this ABOS framework is to endow the participant agents, with a set of rational behaviours allowing them to change in real time their strategies, according to the optimization process evolution. The simulation results show that the collaborative optimization can be effective in some cases, hence the need to set effectively the parameters of the optimization algorithms behaviours and the collaborative protocols. We also demonstrate that the use of ABOS framework with MAS allows a more robust and generic structure, capable with minimal changes handling different COP.

People with motor disabilities require assistance for navigating form one location to another. In order to improve the integration of wheelchair users into their daily life and work, we propose a real time adaptive planning algorithm for routing the user through an obstacle free optimal path. Our application is based on an augmented reality system for the assistance of wheelchair people (ARSAWP) and uses augmented reality (AR) smart glasses. The main goal is to support the development of indoor and outdoor navigation systems devoted to wheelchair users. In this paper we detail the design, the implementation and the evaluation of the proposed application, which was implemented in java for the Android operational system. Two types of database are used (local database and remote database). The information about navigation is displayed on AR glasses which give the user the possibility to interact with the system according to the external environment. The prototype is designed for use within the University of Lille campus.