“Jo wunderbar so im apriu kann man nicht mehr erwarten, capisci?” Multilingualism in a German language class for retired Italian migrants in Berne

Our proposal presents some aspects and results of a project of the University of Bern dealing with the consequences of retirement on multilingual competences. Referring to De Bot (2007), who defined "language related major life events" as moments in life relevant for changes in multilingual competences, we assume that retirement can be a turning point in a language biography. Firstly, there are phenomena, such as the cessation of the use of a foreign language, which was formerly related to work. Secondly, retirement might elicit the improvement of foreign language skills as a way to spend excess time after retirement or as a “cognitive exercise”. Many language schools have identified the people of advanced age as a group of major interest and increasingly offer so-called 50+ (fifty plus) courses in their curriculum. Furthermore, the concept of lifelong learning is increasingly gaining importance, as the reference by the European commission (LLP) indicates. However, most of the programs are intended for educated middle-class people and there are considerably fewer offers for people who are less familiar with learning environments in general. The present paper aims at investigating the multilingual setting of an offer of the second kind: a German language course designed for retired, established Italian workforce migrants living in the city of Berne, Switzerland. The multilingual setting is given by the facts that migrants living in Berne are confronted with diglossia (Standard German and Swissgerman dialects), that the Canton of Berne is bilingual (German and French) and that the migrants' mother tongue, Italian, is one of the Swiss national languages. As previous studies have shown, most of the Italian migrants have difficulties with the acquisition of Standard German due to the diglossic situation (Werlen, 2007) or never even learnt any of the German varieties. Another outcome of the linguistic situation the migrants are confronted with in Berne, is the usage of a continuum of varieties between Swissgerman dialect and Standard German (Zanovello-Müller, 1998). Therefore, in the classroom we find several varieties of German, as well as the Italian language and its varieties. In the present paper we will investigate the use of multilingual competences within the classroom and the dynamics of second language acquisition in a setting of older adults (>60 years old), learning their host country’s language after 40 years or more of living in it. The methods applied are an ethnographic observation of the language class, combined with qualitative interviews to gain in-depth information of the subjects’ life stories and language biographies.

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

Towards Self-Learning Radio-Based Localization Systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

Social Learning Processes in Swiss Soil Protection? The 'From Farmer - To Farmer' Project

Social learning approaches have become a prominent focus in studies related to sustainable agriculture. In order to better understand the potential of social learning for more sustainable development, the present study assessed the processes, effects and facilitating elements of interaction related to social learning in the context of Swiss soil protection and the innovative ‘From Farmer - To Farmer’ project. The study reveals that social learning contributes to fundamental transformations of patterns of interactions. However, the study also demonstrates that a learning-oriented understanding of sustainable development implies including analysis of the institutional environments in which the organizations of the individual representatives of face-to-face-based social learning processes are operating. This has shown to be a decisive element when face-to-face-based learning processes of the organisations’ representatives are translated into organisational learning. Moreover, the study revealed that this was achieved not directly through formalisation of new lines of institutionalised cooperation but by establishing links in a ‘boundary space’ trying out new forms of collaboration, aiming at social learning and co-production of knowledge. It is argued that further research on social learning processes should give greater emphasis to this intermediary level of ‘boundary spaces’.

Root Foraging Increases Performance of the Clonal Plant Potentilla reptans in Heterogeneous Nutrient Environments

Background Plastic root-foraging responses have been widely recognized as an important strategy for plants to explore heterogeneously distributed resources. However, the benefits and costs of root foraging have received little attention. Methodology/Principal Findings In a greenhouse experiment, we grew pairs of connected ramets of 22 genotypes of the stoloniferous plant Potentilla reptans in paired pots, between which the contrast in nutrient availability was set as null, medium and high, but with the total nutrient amount kept the same. We calculated root-foraging intensity of each individual ramet pair as the difference in root mass between paired ramets divided by the total root mass. For each genotype, we then calculated root-foraging ability as the slope of the regression of root-foraging intensity against patch contrast. For all genotypes, root-foraging intensity increased with patch contrast and the total biomass and number of offspring ramets were lowest at high patch contrast. Among genotypes, root-foraging intensity was positively related to production of offspring ramets and biomass in the high patch-contrast treatment, which indicates an evolutionary benefit of root foraging in heterogeneous environments. However, we found no significant evidence that the ability of plastic foraging imposes costs under homogeneous conditions (i.e. when foraging is not needed). Conclusions/Significance Our results show that plants of P. reptans adjust their root-foraging intensity according to patch contrast. Moreover, the results show that the root foraging has an evolutionary advantage in heterogeneous environments, while costs of having the ability of plastic root foraging were absent or very small.

DAT1 polymorphism determines L-DOPA effects on learning about others' prosociality.

Despite that a wealth of evidence links striatal dopamine to individualś reward learning performance in non-social environments, the neurochemical underpinnings of such learning during social interaction are unknown. Here, we show that the administration of 300 mg of the dopamine precursor L-DOPA to 200 healthy male subjects influences learning about a partners' prosocial preferences in a novel social interaction task, which is akin to a repeated trust game. We found learning to be modulated by a well-established genetic marker of striatal dopamine levels, the 40-bp variable number tandem repeats polymorphism of the dopamine transporter (DAT1 polymorphism). In particular, we found that L-DOPA improves learning in 10/10R genoype subjects, who are assumed to have lower endogenous striatal dopamine levels and impairs learning in 9/10R genotype subjects, who are assumed to have higher endogenous dopamine levels. These findings provide first evidence for a critical role of dopamine in learning whether an interaction partner has a prosocial or a selfish personality. The applied pharmacogenetic approach may open doors to new ways of studying psychiatric disorders such as psychosis, which is characterized by distorted perceptions of others' prosocial attitudes.

Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications

Cloud Computing has evolved to become an enabler for delivering access to large scale distributed applications running on managed network-connected computing systems. This makes possible hosting Distributed Enterprise Information Systems (dEISs) in cloud environments, while enforcing strict performance and quality of service requirements, defined using Service Level Agreements (SLAs). {SLAs} define the performance boundaries of distributed applications, and are enforced by a cloud management system (CMS) dynamically allocating the available computing resources to the cloud services. We present two novel VM-scaling algorithms focused on dEIS systems, which optimally detect most appropriate scaling conditions using performance-models of distributed applications derived from constant-workload benchmarks, together with SLA-specified performance constraints. We simulate the VM-scaling algorithms in a cloud simulator and compare against trace-based performance models of dEISs. We compare a total of three SLA-based VM-scaling algorithms (one using prediction mechanisms) based on a real-world application scenario involving a large variable number of users. Our results show that it is beneficial to use autoregressive predictive SLA-driven scaling algorithms in cloud management systems for guaranteeing performance invariants of distributed cloud applications, as opposed to using only reactive SLA-based VM-scaling algorithms.

Object-Based Customization of Learning Content: Potentials and Pitfalls

Specification consortia and standardization bodies concentrate on e-Learning objects to en-sure reusability of content. Learning objects may be collected in a library and used for deriv-ing course offerings that are customized to the needs of different learning communities. How-ever, customization of courses is possible only if the logical dependencies between the learn-ing objects are known. Metadata for describing object relationships have been proposed in several e-Learning specifications. This paper discusses the customization potential of e-Learning objects but also the pitfalls that exist if content is customized inappropriately.