University of Agder

Socioeconomic vulnerability analysis evaluates the extent to which a community or region is susceptible to the adverse impacts of natural disasters, considering the specific social and economic characteristics of the community. This study examines the socioeconomic vulnerability of the City of Oslo, Norway, to natural hazards using two distinct indexes: the Social Vulnerability Index (SoVI), which distinguishes the population considered as socially vulnerable (due to variables such as age and gender), and the Socio-Economic Status Index (SESI), which incorporates the social and economic status of the total population. The findings revealed that, while the least vulnerable subdistricts varied only slightly between the two indexes, the distribution of the most vulnerable subdistricts remained largely consistent. The measurement of socioeconomic status, rather than the extent of social vulnerability in the SESI, highlighted additional social aspects of vulnerability. Additionally, some variables contributed to the indexes in ways that contradicted expectations, likely due to theoretical inconsistencies in the index methodologies. Both indexes play a critical role in disaster risk management by capturing certain epistemic uncertainties arising from limitations in data and knowledge. However, it is important to note that any index provides only a static picture of vulnerability, offering an initial estimate of the conditions that may increase susceptibility to hazards. It does not fully reflect the dynamic nature of risk and resilience, which should be considered when engaging with disaster management authorities.

• Neither microfinance alone nor bundled with agricultural extension reduces food insecurity. • Dietary diversity fell during harvest and lean seasons in the bundled group. • Rigid loan repayment misaligns with agricultural cash flows, limiting program synergies. We study the impact of bundling microfinance with farming extension services on seasonal food security in rural Uganda. Using an experimental design, we monitor seasonal changes through a monthly panel spanning two years. We find that neither the combined approach nor standalone microfinance reduced food insecurity across seasons. Conversely, households in both treatment groups experienced declines in dietary diversity during harvest and land preparation seasons. Exploratory analysis suggests that the bundled approach offered sporadic benefits only to households with better market access, while standalone credit appeared to constrain dietary choices for the poorest. We attribute these results to a structural mismatch: the inflexible repayment schedule of the microfinance product likely discouraged agricultural investment, preventing the intended synergies. Our findings imply that for bundled interventions to succeed, financial products must be flexible enough to match the cash flows of the complementary agricultural activities.

Computer vision can transform wildlife monitoring by automating phenotyping and individual identification. Achieving this, however, depends on access to large, well-curated image datasets that capture natural variation across individuals and years. Here, we present Melops, a longitudinal dataset comprising 24 578 images of 9 861 individual corkwing wrasse, Symphodus melops, collected over seven years through a capture-mark-recapture survey. Each fish was PIT-tagged for re-identification and photographed from both sides against a standardized white background with a colour reference. Alongside the images, we provide metadata including body length, sex, and reproductive state. To support deep learning applications, the dataset includes both the original photographs and automatically cropped images focusing on the whole fish or specific body regions. Together, these resources provide a foundation for developing computer vision methods for individual re-identification, colour pattern analysis, sex classification and other visual phenotyping tasks. Beyond this species, Melops can serve as a model for similar datasets in other taxa. Because it contains thousands of individuals with repeated observations, it provides a rare opportunity to explore temporally aware re-identification and phenotypic change in wild fish.

High-resolution imagery is limited by sensor technology, atmospheric effects, and acquisition costs. This is a well-known challenge in satellite remote sensing, but it also applies to ground-level imaging with handheld devices such as smartphones. Super-resolution seeks to overcome these limitations by enhancing image resolution algorithmically. Single-image super-resolution, however, is an ill-posed inverse problem and therefore depends on strong priors, typically learned from high-resolution training data or imposed through auxiliary information such as high-resolution guidance from another modality. While these methods often produce visually appealing results, they are prone to hallucinating structures that do not reflect the true scene content.Multi-image super-resolution (MISR) addresses this issue by exploiting multiple low-resolution views of the same scene that are captured with sub-pixel shifts. In this work, we introduce SuperF, a test-time optimization approach for MISR based on coordinate-based neural networks, also known as neural fields. By representing images as continuous signals using implicit neural representations (INRs), neural fields are well suited for reconstructing high-resolution images from multiple aligned observations. The central idea of SuperF is to share a single INR across all low-resolution frames while jointly optimizing the image representation and the sub-pixel alignment between frames.Compared to prior INR-based approaches adapted from burst fusion and layer separation, SuperF directly parameterizes the sub-pixel alignment using optimizable affine transformation parameters and performs the optimization on a super-sampled coordinate grid corresponding to the target output resolution. We evaluate the proposed method on simulated bursts of satellite imagery as well as on ground-level images captured with handheld cameras, and observe consistent improvements for upsampling factors of up to 8. A key advantage of SuperF is that it operates entirely at test time and does not rely on any high-resolution training data.

The reference genome of Pyrrhula murina's reference genome will substantially enhance the current monitoring of genetic diversity and population viability and will allow us to understand the effective population size trends throughout time and recent bottlenecks and population expansions. A total of 42 contiguous chromosomal pseudomolecules were assembled from the genome sequence, using ONT, HI-C and Illumina data. This chromosome-level assembly encompasses 1.2 Gb, composed of 65 contigs and 60 scaffolds, with contig and scaffold N50 values of 65 Mb and 76 Mb, respectively.

Det overordnede målet med denne studien var å sammenfatte funnene fra tidligere studier om favorisering av idrettsflinke elever i kroppsøvingsfaget for å øke vår forståelse av problemet. Studien er gjennomført som en scoping review hvor 10 studier ble analysert i lys av Foucault sin forståelse av diskurs. Funnene viser at det er verdier og logikker fra ballidretten som i størst grad fører til favorisering, og at elever som ikke har erfaringer formet av deltakelse i ballidrett, blir marginalisert i faget. Idrettsdiskursen ser derfor ut til å handle om ferdigheter innen ballidretter.

Research on procrastination mostly focuses on person-related antecedents and neglects situational and social factors, such as group work. Prior research indicates that conjunctive and additive group work may increase individual effort and performance as compared to individual work. Based on these findings, we investigate whether conjunctive and additive group work may also help reduce procrastination as compared to individual work. In a registered field experiment, N = 218 students with high levels of trait procrastination worked on an academic task over the course of 10 days in one of three conditions (individual work vs. conjunctive group work vs. additive group work). Dependent variables comprised task procrastination, task performance, and positive and negative task-related affect. Regarding conjunctive group work, results are mixed, with some evidence that conjunctive group work leads to lower procrastination as compared to individual work. Both types of group work resulted in higher negative task-related affect when assessed prospectively. No other effects were found. The findings contribute to the idea that targeted changes in the learning environment, such as the implementation of group work, may help reduce procrastination.

Nonlinear friction has long been, and continues to be, one of the major challenges for precision motion control systems. A linear asymptotic observer of the motion state variables with nonlinear friction uses a dedicated state-space representation of the dynamic friction force (including pre-sliding) (Ruderman, 2023b), which is robust to the noisy input signals. The observer implements the reduced-order Luenberger observation law, while assuming the output displacement is the only available measurement. The latter is relevant for multiple motion control applications with use of encoder-type sensors. The uniform asymptotic stability and convergence analysis of the proposed observer are elaborated in the present work by using the Lyapunov function-based stability criterion by Ignatyev and imposing parametric constraints on the time-dependent eigenvalues of the system matrix to be always negative real. A design procedure for assigning a dominant, and thus slowest, real pole of the observer is proposed. Explanative numerical examples accompany the developed analysis. In addition, a thorough experimental evaluation is given for the proposed observer-based friction compensation which is performed for positioning and tracking tasks. The observer-based compensation, which can serve as a plug-in to a standard feedback controller, extends a PID feedback control that is optimally tuned for disturbance suppression. The experimental results are compared with and without the plugged-in observer-based compensator.