ANALISI DEI FLUSSI DI ATTIVITA NEI PROCESSI AZIENDALI: UN APPROCCIO BASATO SU AGENTI E STIGMERGIA

Il contesto economico attuale è caratterizzato da incertezza ed instabilità che comportano sempre più problemi per le aziende. La strada della competizione viene talvolta abbandonata per intraprendere percorsi collaborativi, in cui gruppi di aziende cooperano verso il raggiungimento di obiettivi comuni e condivisi in modo da ottenere vantaggi competitivi sui concorrenti. Considerando che talvolta è già difficile analizzare l'andamento dei processi di una singola azienda, avere una visione globale della situazione in un contesto collaborativo è tutt'altro che semplice in quanto la condivisione di informazioni diventa un problema molto delicato: ogni singola azienda ha dei dati propri che possono comportare un vantaggio nei confronti degli altri; condividere alcuni di questi dati con altre aziende consente di migliorare i risultati nel contesto di gruppo collaborativo facendo però perdere il vantaggio del singolo. La tesi intende affrontare il problema dell'analisi dei flussi di attività nei processi aziendali utilizzando un approccio basato sul concetto di agente e su meccanismi di scambio di informazioni indiretti come la stigmergia. Il principale vantaggio dell'utilizzo di agenti consiste nell'avere la capacità di potersi adattare a nuovi contesti senza dover effettuare modifiche strutturali del sistema informatico; la flessibilità di questo approccio attenua le difficoltà prodotte dall'incertezza dei mercati e del contesto economico. La stigmergia è una forma di comunicazione indiretta usata da alcune specie d'insetti e permette d'inserire un'alternativa al contatto diretto tra agenti rendendo possibile alla struttura di diventare flessibile e scalabile alle modifiche che avvengono nel tempo. La combinazione di questi due elementi permette di sviluppare un sistema che attraverso le azioni messe in atto dalle singole aziende, azioni che saranno comunque indirizzate al beneficio individuale, possa far emergere un comportamento di gruppo collaborativo

Simulating the Cost of Cooperation: A Recipe for Collaborative Problem-Solving

Collective problem-solving and decision-making, along with other forms of collaboration online, are central phenomena within ICT. There had been several attempts to create a system able to go beyond the passive accumulation of data. However, those systems often neglect important variables such as group size, the difficulty of the tasks, the tendency to cooperate, and the presence of selfish individuals (free riders). Given the complex relations among those variables, numerical simulations could be the ideal tool to explore such relationships. We take into account the cost of cooperation in collaborative problem solving by employing several simulated scenarios. The role of two parameters was explored: the capacity, the group’s capability to solve increasingly challenging tasks coupled with the collective knowledge of a group, and the payoff, an individual’s own benefit in terms of new knowledge acquired. The final cooperation rate is only affected by the cost of cooperation in the case of simple tasks and small communities. In contrast, the fitness of the community, the difficulty of the task, and the groups sizes interact in a non-trivial way, hence shedding some light on how to improve crowdsourcing when the cost of cooperation is high

Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search

Due to growing endurance, safety and non-invasivity, small drones can be increasingly experimented in unstructured environments. Their moderate computing power can be assimilated into swarm coordination algorithms, performing tasks in a scalable manner. For this purpose, it is challenging to investigate the use of biologically-inspired mechanisms. In this paper the focus is on the coordination aspects between small drones required to perform target search. We show how this objective can be better achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone senses a potential target, by releasing digital pheromone on its location. Multiple pheromone deposits are aggregated, increasing in intensity, but also diffused, to be propagated to neighborhood, and lastly evaporated, decreasing intensity in time. As a consequence, pheromone intensity creates a spatiotemporal attractive potential field coordinating a swarm of drones to visit a potential target. Flocking occurs when drones are spatially organized into groups, whose members have approximately the same heading, and attempt to remain in range between them, for each group. It is an emergent effect of individual rules based on alignment, separation and cohesion. In this paper, we present a novel and fully decentralized model for target search, and experiment it empirically using a multi-agent simulation platform. The different combination strategies are reviewed, describing their performance on a number of synthetic and real-world scenarios

Enabling swarm aggregation of position data via adaptive stigmergy: a case study in urban traffic flows

Urban road congestion estimation is a challenge in traffic management. City traffic state can vary temporally and spatially between road links, depending on crossroads and lanes. In addition, congestion estimation requires some sort of tuning to “what is around” to trigger appropriate reactions. An adaptive aggregation mechanism of position data is therefore crucial for traffic control. We present a biologically-inspired technique to aggregate position samples coming from on-vehicle devices. In essence, each vehicle position sample is spatially and temporally augmented with digital pheromone information, locally deposited and evaporated. As a consequence, an aggregated pheromone concentration appears and stays spontaneously while many stationary vehicles and high density roads occur. Pheromone concentration is then sharpened to achieve a better distinction of critical phenomena to be triggered as detected traffic events. The overall mechanism can be actually enabled if structural parameters are correctly tuned for the given application context. Determining such correct parameters is not a simple task since different urban areas have different traffic flux and density. Thus, an appropriate tuning to adapt parameters to the specific urban area is desirable to make the estimation effective. In this paper, we show how this objective can be achieved by using differential evolution

Thermoplastic Blends Based on Poly(Butylene Succinate- co -Adipate) and Different Collagen Hydrolysates from Tanning Industry: I—Processing and Thermo-mechanical Properties

AbstractIn this study, blends of a biodegradable thermoplastic polyester, poly (butylene succinate-co-adipate) (PBSA) with two different raw hydrolyzed collagens (HCs), derived from the tannery industry, were investigated in terms of processability, rheological, thermal and mechanical properties. HCs, obtained by alkaline (HCa) and enzymatic (HCe) hydrolysis of the solid wastes generated during the shaving of the tanned leather, were used in PBSA/HC blends, up to 20 wt% of HC, produced by melting extrusion and processed by injection molding. All the blends up to 20 wt% HCs resulted suitable for the injection molding obtaining flexible molded specimens with good tensile properties. The different secondary structure of the two HCs influenced the rheology, morphology and mechanical properties of the produced blends. In particular, HCa, due its higher content of oligopeptides and free amino-acids, showed a good compatibility with the polymeric matrix acting as a plasticizer with consequent reduction of melt viscosity with increasing its loading. The molded dog-bones specimens containing 20 wt% HCa showed a value of elongation at break of 810%. While, HCe, due its higher presence of b-sheet structures, behaved as organic filler, showing a poor interfacial interaction with PBSA with consequent decrease of the tensile properties with increasing its loading. The good processability and satisfactory mechanical properties obtained encourage the use of both investigated collagen hydrolysates in the production of thermoplastic blends and relative molded products for applications in agriculture and plant nurseries, such as pots or small containers with fertilizing properties, due the presence of HCs

Modeling the overalternating bias with an asymmetric entropy measure

Psychological research has found that human perception of randomness is biased. In particular, people consistently show the overalternating bias: they rate binary sequences of symbols (such as Heads and Tails in coin flipping) with an excess of alternation as more random than prescribed by the normative criteria of Shannon's entropy. Within data mining for medical applications, Marcellin proposed an asymmetric measure of entropy that can be ideal to account for such bias and to quantify subjective randomness. We fitted Marcellin's entropy and Renyi's entropy (a generalized form of uncertainty measure comprising many different kinds of entropies) to experimental data found in the literature with the Differential Evolution algorithm. We observed a better fit for Marcellin's entropy compared to Renyi's entropy. The fitted asymmetric entropy measure also showed good predictive properties when applied to different datasets of randomness-related tasks. We concluded that Marcellin's entropy can be a parsimonious and effective measure of subjective randomness that can be useful in psychological research about randomness perception

Managing female urinary incontinence: A regional prospective analysis of cost-utility ratios (curs) and effectiveness

Introduction: To evaluate the cost-utility of incontinence treatments, particularly anticholinergic therapy, by examining costs and quality-adjusted life years. Materials and methods: A prospective cohort study of women who were consecutively referred by general practitioners (GPs) to the Urology Department because of urinary incontinence. The primary outcome was evaluation of the cost-utility of incontinence treatments (surgery, medical therapy and physiotherapy) for stress and/or urgency incontinence by examining costs and quality-adjusted life years. Results: 137 consecutive female patients (mean age 60.6 ± 11.6; range 36-81) were enrolled and stratified according to pathologies: SUI and UUI. Group A: SUI grade II-III: 43 patients who underwent mid-urethral sling (MUS); Group B: SUI grade I-II 57 patients who underwent pelvic floor muscle exercise and Group C: UUI: 37 patients who underwent antimuscarinic treatment with 5 mg solifenacin daily. The cost utility ratio (CUR) was estimated as saving more than €1200 per QALY for surgery and physiotherapy and as costing under € 100 per QALY for drug therapy. Conclusions: This study shows that appropriate diagnosis and treatment of a patient with incontinence lowers National Health Service costs and improves the benefits of treatment and quality of life

Fostering Distributed Business Logic in Open Collaborative Networks: an integrated approach based on semantic and swarm coordination

Given the great opportunities provided by Open Collaborative Networks (OCNs), their success depends on the effective integration of composite business logic at all stages. However, a dilemma between cooperation and competition is often found in environments where the access to business knowledge can provide absolute advantages over the competition. Indeed, although it is apparent that business logic should be automated for an effective integration, chain participants at all segments are often highly protective of their own knowledge. In this paper, we propose a solution to this problem by outlining a novel approach with a supporting architectural view. In our approach, business rules are modeled via semantic web and their execution is coordinated by a workflow model. Each company’s rule can be kept as private, and the business rules can be combined together to achieve goals with defined interdependencies and responsibilities in the workflow. The use of a workflow model allows assembling business facts together while protecting data source. We propose a privacy-preserving perturbation technique which is based on digital stigmergy. Stigmergy is a processing schema based on the principle of self-aggregation of marks produced by data. Stigmergy allows protecting data privacy, because only marks are involved in aggregation, in place of actual data values, without explicit data modeling. This paper discusses the proposed approach and examines its characteristics through actual scenarios

mTOR-Dependent Cell Proliferation in the Brain

The mammalian Target of Rapamycin (mTOR) is a molecular complex equipped with kinase activity which controls cell viability being key in the PI3K/PTEN/Akt pathway. mTOR acts by integrating a number of environmental stimuli to regulate cell growth, proliferation, autophagy, and protein synthesis. These effects are based on the modulation of different metabolic pathways. Upregulation of mTOR associates with various pathological conditions, such as obesity, neurodegeneration, and brain tumors. This is the case of high-grade gliomas with a high propensity to proliferation and tissue invasion. Glioblastoma Multiforme (GBM) is a WHO grade IV malignant, aggressive, and lethal glioma. To date, a few treatments are available although the outcome of GBM patients remains poor. Experimental and pathological findings suggest that mTOR upregulation plays a major role in determining an aggressive phenotype, thus determining relapse and chemoresistance. Among several activities, mTOR-induced autophagy suppression is key in GBM malignancy. In this article, we discuss recent evidence about mTOR signaling and its role in normal brain development and pathological conditions, with a special emphasis on its role in GBM

Monitoring elderly behavior via indoor position-based stigmergy

In this paper we present a novel approach for monitoring elderly people living alone and independently in their own homes. The proposed system is able to detect behavioral deviations of the routine indoor activities on the basis of a generic indoor localization system and a swarm intelligence method. For this reason, an in-depth study on the error modeling of state-of-the-art indoor localization systems is presented in order to test the proposed system under different conditions in terms of localization error. More specifically, spatiotemporal tracks provided by the indoor localization system are augmented, via marker-based stigmergy, in order to enable their self-organization. This allows a marking structure appearing and staying spontaneously at runtime, when some local dynamism occurs. At a second level of processing, similarity evaluation is performed between stigmergic marks over different time periods in order to assess deviations. The purpose of this approach is to overcome an explicit modeling of user's activities and behaviors that is very inefficient to be managed, as it works only if the user does not stray too far from the conditions under which these explicit representations were formulated. The effectiveness of the proposed system has been experimented on real-world scenarios. The paper includes the problem statement and its characterization in the literature, as well as the proposed solving approach and experimental settings