A Framework for an adaptive grid scheduling: an organizational perspective

Grid systems are complex computational organizations made of several interacting components evolving in an unpredictable and dynamic environment. In such context, scheduling is a key component and should be adaptive to face the numerous disturbances of the grid while guaranteeing its robustness and efficiency. In this context, much work remains at low-level focusing on the scheduling component taken individually. However, thinking the scheduling adaptiveness at a macro level with an organizational view, through its interactions with the other components, is also important. Following this view, in this paper we model a grid system as an agent-based organization and scheduling as a cooperative activity. Indeed, agent technology provides high level organizational concepts (groups, roles, commitments, interaction protocols) to structure, coordinate and ease the adaptation of distributed systems efficiently. More precisely, we make the following contributions. We provide a grid conceptual model that identifies the concepts and entities involved in the cooperative scheduling activity. This model is then used to define a typology of adaptation including perturbing events and actions to undertake in order to adapt. Then, we provide an organizational model, based on the Agent Group Role (AGR) meta-model of Freber, to support an adaptive scheduling at the organizational level. Finally, a simulator and an experimental evaluation have been realized to demonstrate the feasibility of our approach

Evolutionary multiobjective optimization of the multi-location transshipment problem

We consider a multi-location inventory system where inventory choices at each location are centrally coordinated. Lateral transshipments are allowed as recourse actions within the same echelon in the inventory system to reduce costs and improve service level. However, this transshipment process usually causes undesirable lead times. In this paper, we propose a multiobjective model of the multi-location transshipment problem which addresses optimizing three conflicting objectives: (1) minimizing the aggregate expected cost, (2) maximizing the expected fill rate, and (3) minimizing the expected transshipment lead times. We apply an evolutionary multiobjective optimization approach using the strength Pareto evolutionary algorithm (SPEA2), to approximate the optimal Pareto front. Simulation with a wide choice of model parameters shows the different trades-off between the conflicting objectives

Analyse et annotation des images de transactions du Musée de Musique (Paris)

Le sujet général de cette étude consiste à réaliser une étude de faisabilité sur la reconnaissance de mots clés dans des images numérisées représentant des transactions de vente de violons, appartenant au Musée de la Musique à la Villette (Paris). Dans ce stage, nous nous sommes focalisées sur l’annotation du contenu des images ainsi que sur quelques prétraitements d’images afin de permettre l’extraction des mots.Dans la suite, nous allons évoquer le cahier des charges du Musée, puis nous détaillerons le travail réalisé au cours de ce stage

A Distributed Hybrid Algorithm for the Graph Coloring Problem

We propose a multi-agent based Distributed Hybrid algorithm for the Graph Coloring Problem (DH-GCP). DH-GCP applies a tabu search procedure with two different neighborhood structures for its intensification. To diversify the search into unexplored promising regions, two crossover operators and two types of perturbation moves are performed. All these search components are managed by a multi-agent model which uses reinforcement learning for decision making. The performance of the proposed algorithm is evaluated on well-known DIMACS benchmark instances

A multi-agent based optimization method applied to the quadratic assignment problem

Inspired by the idea of interacting intelligent agents of a multi-agent system, we introduce a multi-agent based optimization method applied to the quadratic assignment problem (MAOM-QAP). MAOM-QAP is composed of several agents (decision-maker agent, local search agents, crossover agents and perturbation agent) which are designed for the purpose of intensified and diversified search activities. With the help of a reinforcement learning mechanism, MAOM-QAP dynamically decides the most suitable agent to activate according to the state of search process. Under the coordination of the decision-maker agent, the other agents fulfill dedicated search tasks. The performance of the proposed approach is assessed on the set of well-known QAP benchmark instances, and compared with the most advanced QAP methods of the literature. The ideas proposed in this work are rather general and could be adapted to other optimization tasks. This work opens the way for designing new distributed intelligent systems for tackling other complex search problems

Job Shop Planning and Scheduling for Manufacturers with Manual Operations

Job shop scheduling systems are widely employed to optimise the efficiency of machine utilisation in the manufacturing industry, by searching the most cost-effective permutation of job operations based on the cost of each operation on each compatible machine and the relations between job operations. Such systems are paralysed when the cost of operations are not predictable led by the involvement of complex manual operations. This paper proposes a new genetic algorithm-based job shop scheduling system by integrating a fuzzy learning and inference sub-system in an effort to address this limitation. In particular, the fuzzy sub-system adaptively estimates the completion time and thus cost of each manual task under different conditions based on a knowledge base which is initialised by domain experts and then constantly updated based on its built-in learning ability and adaptability. The manufacturer of Point of Sale and Point of Purchase products is taken in this paper as an example case for both theoretical discussion and experimental study. The experimental results demonstrate the promising of the proposed system in improving the efficiency of manual manufacturing operations

Treatment of Acute Promyelocytic Leukemia with AIDA Based Regimen. Update of a Tunisian Single Center Study

In Tunisia, the ATRA era began in 1998 with the use, consecutively, of two regimens combining ATRA and an anthracycline with cytarabine (APL93), and without cytarabine (LPA99). From 2004, 51 patients with confirmed APL either by t(15;17) or PML/RARA were treated according to the PETHEMA LPA 99 trial. Forty three patients achieved CR (86%). The remaining seven patients had early death (one died before treatment onset): four caused by differentiation syndrome (DS) and three died from central nervous system hemorrhage. Multivariate analysis revealed that female gender (P=0.045), baseline WBC> 10 G/L (P=0.041) and serum creatinine > 1.4mg/dl (P=0.021) were predictive of mortality during induction. DS was observed in 16 patients (32%) after a median onset time of 15 days from treatment onset (range, 2–29). Body mass index ≥ 30 (P=0.01) remained independent predictor of DS. Occurrence of hypertensive peaks significantly predicted occurrence of DS (P=0.011) and was significantly associated with high BMI (p=0.003). With a median follow-up of 50 months, 5 year cumulative incidence of relapse, event free and overall survival were 4.7%, 74% and 78%, respectively

COVID-19: Is There Evidence for the Use of Herbal Medicines as Adjuvant Symptomatic Therapy?

Background: Current recommendations for the self-management of SARS-Cov-2 disease (COVID-19) include self-isolation, rest, hydration, and the use of NSAID in case of high fever only. It is expected that many patients will add other symptomatic/adjuvant treatments, such as herbal medicines. Aims: To provide a benefits/risks assessment of selected herbal medicines traditionally indicated for “respiratory diseases” within the current frame of the COVID-19 pandemic as an adjuvant treatment. Method: The plant selection was primarily based on species listed by the WHO and EMA, but some other herbal remedies were considered due to their widespread use in respiratory conditions. Preclinical and clinical data on their efficacy and safety were collected from authoritative sources. The target population were adults with early and mild flu symptoms without underlying conditions. These were evaluated according to a modified PrOACT-URL method with paracetamol, ibuprofen, and codeine as reference drugs. The benefits/risks balance of the treatments was classified as positive, promising, negative, and unknown. Results: A total of 39 herbal medicines were identified as very likely to appeal to the COVID-19 patient. According to our method, the benefits/risks assessment of the herbal medicines was found to be positive in 5 cases (Althaea officinalis, Commiphora molmol, Glycyrrhiza glabra, Hedera helix, and Sambucus nigra), promising in 12 cases (Allium sativum, Andrographis paniculata, Echinacea angustifolia, Echinacea purpurea, Eucalyptus globulus essential oil, Justicia pectoralis, Magnolia officinalis, Mikania glomerata, Pelargonium sidoides, Pimpinella anisum, Salix sp, Zingiber officinale), and unknown for the rest. On the same grounds, only ibuprofen resulted promising, but we could not find compelling evidence to endorse the use of paracetamol and/or codeine. Conclusions: Our work suggests that several herbal medicines have safety margins superior to those of reference drugs and enough levels of evidence to start a clinical discussion about their potential use as adjuvants in the treatment of early/mild common flu in otherwise healthy adults within the context of COVID-19. While these herbal medicines will not cure or prevent the flu, they may both improve general patient well-being and offer them an opportunity to personalize the therapeutic approaches

Urban planning and sustainable development in Tunisia : towards a new conception of the public policies in land use and transportation systems

La finalité de notre thèse est d'interroger la relation entre la planification urbaine et le développement durable en regard des politiques publiques urbaines de développement et plus particulièrement du management territorial. Dans notre conception de la démarche, nous mettons l'accent sur les différentes dynamiques (déplacements et mobilité) et mutations morphologiques et structurelles (occupations du sol) à l'échelle de l'agglomération afin de singulariser la décision publique en matière du développement urbain durable. Nos choix se sont inscrits volontairement dans une logique transdisciplinaire qui s'est révélée particulièrement adaptée à nos différents recours théoriques, méthodologiques et empiriques.Traitant le contexte tunisien en pleine transition, ce travail propose un modèle de décision publique hybride permettant de déterminer les traits d'une planification urbaine adaptée aux différents contextes actuels et aux exigences de la durabilité. Le recours à la fois aux techniques de la prospective, aux modèles intégrés de déplacements et d'occupation du sol, à la simulation et aux techniques d'analyse multicritère nous a permis une construction intégrée et itérative de plusieurs niveaux d'évaluation partielle et d'un niveau d'évaluation globale. Le modèle conçu et testé pour la ville de Sousse permettra aux décideurs publics de disposer d'une grille synthétique d'informations issues d'une prise en compte aussi complète que possible de la réalité urbaine. Il offre différents niveaux d'évaluation thématique et un niveau global intégrant l'ensemble. La démarche pourra servir de référent à d'autres villes tunisiennes et aura par conséquent un impact réel sur la qualité de leur développement.The purpose of this thesis is to investigate the relationship between urban planning and sustainable development in relation to urban development policy and in particular the territorial management. In our design approach, we focus on the different dynamics (travel and mobility) moreover, morphological and structural changes (land uses) on the scale of the urban area in order to single public decision in urban development long lasting. Our choices were enrolled voluntarily in a trans-disciplinary logic has proved to be particularly suited to our different theoretical, methodological and empirical appeal.Treat the Tunisian context in transition; this research proposes a hybrid public decision model to determine the features of an adapted urban planning to different contexts and current requirements of sustainability. Using both foresight technics, integrated land use-transportation models, simulation and multi-criteria analysis technics allowed us an integrated and iterative construction of several levels of partial evaluation and a level of overall evaluation. The model designed and tested for the town of Sousse will allow policy makers to have a synthetic grid information from a decision as complete as possible account of urban reality. It offers different levels of thematic evaluation and a global level integrating all. The approach can be used as reference to other Tunisian cities and will therefore have a real impact on the quality of their development