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

Towards a better understanding of the bidirectional relationship between decision-making and addiction vulnerability

Impaired decision-making is recognized as a diagnostic criterion for many different psychiatric disorders including gambling disorder and substance use disorders. Habit formation and cue responsivity are known to play a role in driving these disorders, but less is known as to if and how decision-making is differentially altered by different classes of abused drugs. The impact of biological sex is also vastly understudied in such contexts. To gain further understanding on the bi-directional relationship between decision-making impairments and addiction, we combined the well-established rat gambling task with volitional drug self-administration in both male and female rats. In one aim, I sought to pharmacologically manipulate habit formation via the systemic administration of a histone deacetylation inhibitor. I found that inhibiting histone deacetylation resulted in increased risky choice during acquisition of our rat gambling task, while also altering how rats were learning from task outcomes. In another aim, I sought to further characterize the effect of cues on decision-making by allowing rats to choose between the cued and uncued rat gambling task on a trial-by-trial basis. I found that cues had negative effects on cognition, despite being preferred to their absence. I then paired this task with cocaine self-administration to investigate the bi-directional effects of gambling and cocaine-taking. I found evidence for cocaine-induced motivational deficits on subsequent gambling sessions. In my final aim, I sought to determine whether fentanyl could induce the same decision-making impairments that we had previously observed in response to cocaine. I found that while acute fentanyl self-administration did not impact cognition, fentanyl withdrawal did have long-lasting negative effects on decision-making.Medicine, Faculty ofGraduat

Particle swarm optimization approach for modelling a turning process,

This paper proposes the modelling of a turning process using particle swarm optimization (PSO). The independent input machining parameters for the modelling were cutting speed, feed rate, and cutting depth. The input parameters affected three dependent output parameters that were the main cutting force, surface roughness, and tool life. The values of the independent and dependent parameters were acquired by experimental work and served as knowledge base for the PSO process. By utilizing the knowledge base and the PSO approach, various models could be acquired for describing the cutting process. In our case, three different polynomial models were obtained: models a) for the main cutting force, b) for surface roughness, and c) for tool life. All the models had exactly the same basic polynomial form which was chosen similarly to that in the conventional regression analysis method. The PSO approach was used for optimization of the polynomials' coefficients. Several different randomly-selected data sets were used for the learning and testing phases. The accuracies of the developed models were analysed. It was discovered that the accuracies of the models for different learning and testing data sets were very good, having almost the same deviations. The least deviation was noted for the cutting force, whilst the most deviation, as expected was for tool life. The obtained models could then be used for later optimization of the turning process

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