A people-centred perspective on climate change, environmental stress, and livelihood resilience in Bangladesh

The Ganges–Brahmaputra delta enables Bangladesh to sustain a dense population, but it also exposes people to natural hazards. This article presents findings from the Gibika project, which researches livelihood resilience in seven study sites across Bangladesh. This study aims to understand how people in the study sites build resilience against environmental stresses, such as cyclones, floods, riverbank erosion, and drought, and in what ways their strategies sometimes fail. The article applies a new methodology for studying people’s decision making in risk-prone environments: the personal Livelihood History interviews (N = 28). The findings show how environmental stress, shocks, and disturbances affect people’s livelihood resilience and why adaptation measures can be unsuccessful. Floods, riverbank erosion, and droughts cause damage to agricultural lands, crops, houses, and properties. People manage to adapt by modifying their agricultural practices, switching to alternative livelihoods, or using migration as an adaptive strategy. In the coastal study sites, cyclones are a severe hazard. The study reveals that when a cyclone approaches, people sometimes choose not to evacuate: they put their lives at risk to protect their livelihoods and properties. Future policy and adaptation planning must use lessons learned from people currently facing environmental stress and shocks

Service Lifetime Prediction of PV Modules and Systems: Progress of the SOLAR-TRAIN Project

The project SOLAR TRAIN aims to develop novel and validated models for the service life time and energy yield prediction of PV modules and systems. PV modules’ and systems’ performances are being investigated along the entire modeling chain: climatic degradation factors, analysis of degradation and failure modes and evaluation of polymeric materials. This paper presents an overview of the current start of the art and some preliminary results from a work package on development of service lifetime prediction models for PV modules and systems

Handling user needs: methods for knowledge creation in Swedish transport planning

Transport planning faces new demands for a dialogue with users. Transport planners no longer just build roads; nowadays they also must listen to users, whose wishes are meant to have an impact on the design and maintenance of the road transport system. Yet how can we know what users really want? This article sets out to analyze the methods with which transport planners gather information about users and their needs; to do so, it uses a case-study of how transport planners at the National Swedish Road Authority handle these questions on a day-to-day basis. The results show that the planners’ practices can be analytically understood as something that produces knowledge, representativity, and the identities and needs of the users. The planners base their analyses of user need largely on personal experience. The descriptive, interpretative, and evaluating elements in their knowledge production tend to be hidden in central policy documents and theworkings of operational planning systems. If the goals with respect to user influence are to be attained, transport planning must be pursued with a greater understanding of how it conceives of its users as specific categories with particular needs and identities

Modelling and optimization of energy consumption for feature based milling

Energy consumption is increasing along with the world's population and industrialization level; thus, energy and resource efficiency in manufacturing is of vital importance. In order to increase energy and resource efficiency, the amount of consumed energy must first be accurately quantified for each manufacturing process. Milling is one of the most common machining operations. In this study, a prediction model for estimating theoretical energy consumption involved in milling of prismatic parts is presented. The prediction model relies on the STEP Application Protocol 224 features for volumetric information and material properties of prismatic parts. Verification tests exemplify how engineers can utilize the presented prediction model and approach to measuring machine tool energy consumption. Test results show that the prediction model runs with 5 % accuracy. Also, effect of cutter path for prismatic milling is investigated for certain features. Furthermore, response surface methodology is utilized in order to determine optimal milling parameters of slot feature in order to minimize energy consumption when machining AISI 304 stainless steel