The growing impact of pandemics and infectious disease outbreaks has highlighted the need for vaccine allocation strategies that balance risk-profile protection, equity, and operational feasibility under limited healthcare resources. However, many existing approaches rely on predefined population groups and do not sufficiently integrate data-driven risk-profile information into constrained allocation planning. To address this issue, this study proposes an equity-aware decision-support framework that combines machine learning-based risk-profile stratification with multi-objective vaccine allocation. Publicly available French COVID-19 hospital-surveillance data are reorganized into analytical records to construct operational risk-profile classes for allocation-scenario analysis. A Light Gradient Boosting Machine model classifies these records into ordered risk-profile groups, which are then incorporated into a constrained allocation model. The model aims to maximize protection of higher-priority risk profiles, promote equity across predicted risk-profile groups, and minimize vaccination delays under supply and capacity constraints. The resulting optimization problem is solved using a binary Particle Swarm Optimization algorithm with constraint-handling mechanisms. Computational experiments assess algorithmic performance under a common objective-evaluation budget and examine the repair strategy, policy-weight configurations, Pareto-based compromises, classification uncertainty, scalability, and resource-capacity sensitivity. Overall, the framework supports the exploration of risk-profile-based vaccine allocation policies under constrained pandemic-response settings.

In this paper, we introduce a global framework integrating predictive analytics and multi-objective optimization for the purpose of home healthcare logistics optimization. First, several machine learning approaches such as Multinomial Logistic Regression, Support Vector Machines, Random Forest, AdaBoost, and Gradient Boosting are implemented to predict and classify patients' care requirements. This categorization not only separates professional-grade nurses from primary-grade nurses but also decides whether one caregiver or two caregivers are to be deployed depending on the condition of the patient (bedridden or semi-dependent). Secondly, we create a Multi-Objective Vehicle Routing Problem with Time Windows (MOVRPTW) to schedule the caregivers efficiently and reduce transport costs. Since the corresponding optimization problem is NP-hard, we take two advanced genetic algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2) to find good-quality solutions. To solve the problems of bedridden patient care with multiple visits per day, our model incorporates synchronization constraints to ensure continuity of care and coordination among single-caregiver teams in case dedicated double teams are not possible. By combining predictive analytics with strong optimization techniques, our framework not only improves resource allocation effectiveness and facilitates timely service delivery but also decreases operating expenses, thus providing a holistic solution to the changing needs of home healthcare logistics.

As the need for emergency care services increases, healthcare facilities are recognizing the importance of tailored layout designs to improve patient care efficiency. Strategic layout planning is vital for managing variable productivity and meeting fluctuating demand effectively. The primary aim of tackling the emergency department layout (EDL) problem is to identify a facility configuration that satisfies both internal organizational needs and global healthcare certification standards. A novel mathematical model presented in the article offers a fresh approach to Emergency Department Layout (EDL) optimization, considering patient movement and process flow simultaneously. Our contribution lies in the development of a tailored solution to enhance the efficiency of healthcare facility layouts, setting our work apart from existing methods. The Particle Swarm Optimization (PSO) technique is proposed as a solution to the EDL problem, using a constructive heuristic to provide practical options. The technique's practical applicability is demonstrated through a real-world case study at Roanne Hospital in France, offering insights for improved healthcare delivery.

Twitter is one of the most popular microblog service providers, in this microblogging platform users use hashtags to categorize their tweets and to join communities around particular topics. However, the percentage of messages incorporating hashtags is small and the hashtags usage is very heterogeneous as users may spend a lot of time searching the appropriate hashtags for their messages. In this paper, the authors present an approach for hashtag recommendations in microblogging platforms by leveraging semantic features. Moreover, they conduct a detailed study on how the semantic-based model influences the final recommended hashtags using different ranking strategies. Also, users are interested by fresh and specific hashtags due to the rapid growth of microblogs, thus, the authors propose a time popularity ranking strategy. Furthermore, they study the combination of these ranking strategies. The experiment results conducted on a large dataset; show that their approach improves respectively lexical and semantic based recommendation by more than 11% and 7% on recommending 5 hashtags.

Service Oriented Architectures (SOA) assure better flexibility and increase efficiency by reusing the services to utilize distributed capabilities that may be located in independent trust domains and improve interoperability by providing new opportunities to connect heterogeneous platforms. But it also increases the complexity in the Cross-Organizational Boundaries due to increasing and outsourcing need for e-government organizations to work together and to meet their various customer needs, thus, there is a need for interoperability across organizational boundaries of e-government heterogeneous platforms. A broad range of SOA-platforms is available focusing on Web Service enabling and orchestration that is a key control mechanism that invokes Services to work and to provide control within an organization. This paper addresses a new Cross-Organizational Orchestrator Model that consists of three main components: business services, Cross-Organizational Meta service, and an orchestrator. Then, we are found that our architecture remains cross-organizational e-government interoperability while increases quickness and scalability, bring down the difficulty, improve the utility of advanced extensibility, reliability, flexibility and agility of whole e-government platforms.

E-government presents a new innovative approach to solving traditional problems of government services using the IT technology. This paper presents the views of citizens, businesses and government to advance the transformation enabling e-government by using an Enterprise Architecture paradigm that will be able to attract citizens who are connected online; move people online who are not there; and enable the transformation to e-Government at three levels, citizen, Government and business.

E-learning is a promising research area, as they are expected to increase enrollment and improve the quality of education. Adaptive e-learning systems, traditionally focused on content personalization, are in need to cope with continuous changing requirements and changing environment. Indeed, the specification and the management quality attributes of such systems, supported throughout the whole lifecycle are still missing. In this paper, we propose continuous requirements monitoring that uses a constraint program to check the conformity of adaptive e-learning systems to their requirements and react properly when deviations occur at runtime. To this end, we specify system's requirements in the form of a dynamic software product line. A novel requirements engineering language that combines goal-driven requirements with features and claims is applied for the specification, from which the constraint program is automatically generated.

Twitter has became an invaluable source of information, due to his dynamic nature with more than 400 million tweets posted per day. Determining what an individual post is about can be a non trivial task because his high contextualization and his informal nature. Named Entity Linking (NEL) is a subtask of information extraction that aims to ground entity mentions to their corresponding node in a Knowledge Base (KB), which requires a disambiguation step, because many resources can be matched to the same entity that lead to synonymy and polysemy problems. To overcome these problems, especially in the context of short text, we present a novel system for tweet entity linking based on graph centrality and DBpedia as knowledge base. Our approach relies on the assumption that related entities tend to appear in the same tweet as tweets are topic specific. Also, we address the problem of irregular name mentions. Finally, to show the effectiveness of our system we evaluate it using a real twitter dataset and compare it to a well known state-of-the-art named entity linking system for short text.

Operating Theater Layout Problem (OTLP) has a great impact on the productivity and the efficiency of the health process. While solving OTLP, Real-life Operating Theater (OT) sizes are larger than exact methods capacity, this lead to explore other methods as heuristics, metaheuristics or parallel treatment looking for approximate solutions. In this paper we developed a novel approach using a Multi-Agent (MA) Decision Making System (DMS) based on Mixed Integer Linear Programming (MILP) for large-sized OTLP with objective of minimizing total traveling costs. The DMS generates exact solutions in reasonable time and gives the final OT layout in a graphic interface.

The focus of this paper is on facilities with multiple sections where the material transport between sections occurs through corridors. A Mixed Integer Linear Programming (MILP) formulation for the Operating Theater Layout problem is proposed. The formulation uses a multi-goal approach to optimize two objectives: the first quantitative objective minimizes the interdepartmental traveling costs, whereas the second qualitative objective maximizes the closeness of the facilities. The presented model determines the position and orientation of each activity according to the OT international standards. The applicability of the model is demonstrated on four illustrative examples using commercial optimization software.

In this paper, we present a state of the art on the principal components analysis (PCA) and the possibility of its use for horizontal and vertical fragmentation of data warehouses (DW), in order to reduce the time of query execution. We focus on the study of correlation matrices, the impact of the eigenvalues evolution on the determination of suitable situations to achieve the PCA, and a study of criteria for extracting principal components. Then, we proceed to the projection of individuals on the first principal plane, and the 3D vector space generated by the first three principal components. We try to determine graphically homogeneous groups of individuals and therefore, a horizontal fragmentation schema for the studied data table. The study of correlations between the original variables and the principal components allow us to draw the circle of correlations and define graphically, under some conditions, candidate variables to be collected in vertical fragments. To satisfy a maximum of decision queries OLAP, our approach is independent from any set of queries, and seeks to exploit the graphical representations provided by the PCA. We conclude our study by an experiment on a data warehouse which shows the interest and the originality of our approach.

The current economic crisis combined with a hyper-competitive environment has developed a real need for optimization practices within supply chains. Realizing the importance of knowledge management (KM) in improving the supply chain (SC), knowledge management is a major enabler of supply chain management, and is a critical element in information intensive and multi-cultured enterprise environments [1]. The growing number of articles on application knowledge management in supply chain in the literature is an indication of the importance of this area and of its role in improving the competitiveness of an organization [2]. This paper summarizes several theoretical and methodological characteristics that have been developed recently to highlight the way in which knowledge management applications are proposed in the supply chain context. In particular, the paper focuses on three areas of research: Knowledge transfer, knowledge sharing and knowledge creation and learning.