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Introduction: Body dysmorphic disorder (BDD) is an obsessive compulsive disorder that is marked by a distressing preoccupation with perceived physical flaws causing the individual to engage in compulsive behavior. The Appearance-related Repetitive Behavior Online self-report scale (ARBO) was developed to easily screen for appearance related behaviors believed to be common among BDD patients. Examining the psychometric properties of a newly developed scale is an essential part of its development. The aim of this study is to examine the factor structure, internal consistency and validity of the ARBO. Method: Two studies were conducted in order to examine the psychometric properties of the ARBO. The first analysis was an Exploratory Factor Analysis (EFA) with the purpose of establishing a primary factor structure and the second consisted of a Confirmatory Factor Analysis (CFA) aimed at confirming the factor structure. Internal consistency tests were conducted in both studies. The validity of the factor model was examined after the CFA. Result: The EFA revealed a two-factor structure that was later confirmed by the CFA after omitting items that were not consistent with the model. The internal consistency was exceptional in both samples. The confirmed factor model showed good convergent and divergent validity. Discussion: Three items were removed during the EFA & CFA analyses in order to improve the model fit and internal consistency. The two factors are thought to be repetitive behaviors and reassurance seeking in an online environment. Screening for appearance-related repetitive online behaviors could be helpful in identifying patients with potential appearance concerns.

A survey, aimed at Swedish researchers in the field of spatial data, was conducted in 2021. 113 individuals responded to the questionnaire, which gives us insight into how researchers in the field view; sharing of data, how they work with data, which metadata they think are central and what support they ask for in order to be able to handle spatial data. According to the survey a majority of the researchers share and use data shared by others in their research. Central metadata that is emphasized are coordinate systems, projections, time of the information, data capture, data content and author. The question of how to make data available is very varied among the respondents. The questionnaire responses contain a wide range of individual solutions to make the information available. A majority of researchers believe that the work with data management and accessibility has increased over the past 5 years. As an obstacle to working more with this is time, but there are also other obstacles. The researchers are seeking additional support that can be summarized as: support, technical guidance, storage, information, time and money., En enkätundersökning genomfördes under 2021. Den var riktad till svenska forskare inom fältet rumsliga data. 113 individer svarade på enkäten som ger oss insikt hur forskare inom fältet ser på tillgängliggörande av data, hur man arbetar med data, vilka metadata man tycker är centrala och vilket stöd man efterfrågar för att kunna hantera specifikt rumsliga data. Bland huvudresultaten från enkäten kan nämnas att en majoritet av forskarna delar och använder data som delats av andra i sin forskning. Centrala metadata som man framhåller är koordinatsystem, projektioner, tidpunkt för informationen, datafångst, datainnehåll och upphovsman. Frågan om hur man tillgängliggör data är mycket varierande bland respondenterna. I enkätsvaren återfinns en lång rad individuella lösningar för att kunna tillgängliggöra informationen. En majoritet av forskarna menar att arbetet med datahantering och tillgängliggörandet har ökat under de senaste 5 åren. Hinder för att arbeta mer med detta menar man är tid, men det finns också andra hinder. Forskarna efterlyser ytterligare stöd som kan sammanfattas som: stöd/support, teknisk vägledning, lagring, information, tid och pengar.

The performance of a new statistical framework, developed for the evaluation of simulated temperature responses to climate forcings against temperature reconstructions derived from climate proxy data for the last millennium, is evaluated in a so-called pseudo-proxy experiment, where the true unobservable temperature is replaced with output data from a selected simulation with a climate model. Being an extension of the statistical model used in many detection and attribution (D&A) studies, the framework under study involves two main types of statistical models, each of which is based on the concept of latent (unobservable) variables: confirmatory factor analysis (CFA) models and structural equation modelling (SEM) models. Within the present pseudo-proxy experiment, each statistical model was fitted to seven continental-scale regional data sets. In addition, their performance for each defined region was compared to the performance of the corresponding statistical model used in D&A studies. The results of this experiment indicated that the SEM specification is the most appropriate one for describing the underlying latent structure of the simulated temperature data in question. The conclusions of the experiment have been confirmed in a cross-validation study, presuming the availability of several simulation data sets within each studied region. Since the experiment is performed only for zero noise level in the pseudo-proxy data, all statistical models, chosen as final regional models, await further investigation to thoroughly test their performance for realistic levels of added noise, similar to what is found in real proxy data for past temperature variations.

Evaluation of climate model simulations is a crucial task in climate research. Here, a new statistical framework is proposed for evaluation of simulated temperature responses to climate forcings against temperature reconstructions derived from climate proxy data for the last millennium. The framework includes two types of statistical models, each of which is based on the concept of latent (unobservable) variables: confirmatory factor analysis (CFA) models and structural equation modelling (SEM) models. Each statistical model presented is developed for use with data from a single region, which can be of any size. The ideas behind the framework arose partly from a statistical model used in many detection and attribution (D&A) studies. Focusing on climatological characteristics of five specific forcings of natural and anthropogenic origin, the present work theoretically motivates an extension of the statistical model used in D&A studies to CFA and SEM models, which allow, for example, for non-climatic noise in observational data without assuming the additivity of the forcing effects. The application of the ideas of CFA is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. The result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.

Effects of hot-pressing on anisotropic sheets with less good formation was here investigated. The main objective was to study water absorption capacity in relation to the wet strength of hot-pressed paper. A pilot paper machine was used to produce papers from TMP and CTMP furnishes. The results indicate that it is not only the high dry content after wetting that contributes to the high wet strength of the paper hot-pressed at 200C. If it is required to have a paper with both low absorption of water and high wet strength, hot-pressing at 200C seems to be more desirable than using con-ventional drying and adding wet chemical agents to the furnish.

Systematic comparisons of proxy-based reconstructions and climate model simulations of past millennium temperature variability offer insights into climate sensitivity and feedback mechanisms, besides allowing model evaluation independently from the period covered by instrumental data. Such simulation-reconstruction comparisons can help to distinguish more skillful models from less skillful ones, which may subsequently help to develop more reliable future projections. This study evaluates the low-frequency simulation-reconstruction agreement within the past millennium through assessing the amplitude of temperature change between the Medieval Climate Anomaly (here, 950-1250 CE) and the Little Ice Age (here, 1450-1850 CE) in PMIP3 model simulations compared to proxy-based local and continental-scale reconstructions. The simulations consistently show a smaller temperature change than the reconstructions for most regions in the Northern Hemisphere, but not in the Southern Hemisphere, as well as a partly different spatial pattern. A cost function analysis assesses how well the various simulations agree with reconstructions. Disregarding spatial correlation, significant differences are seen in the agreement with the local temperature reconstructions between groups of models, but insignificant differences are noted when compared to continental-scale reconstructions. This result points toward a limited possibility to rank models by means of their low-frequency temperature variability alone. The systematically lower amplitude of simulated versus reconstructed temperature change indicates either too-small simulated internal variability or that the analyzed models lack some critical forcing or have missing or too-weak feedback mechanisms. We hypothesize that too-cold initial ocean conditions in the models-in combination with too-weak internal variability and slow feedbacks over longer time scales-could account for much of the simulation-reconstruction disagreement.

The abstract is included in the text.