Publikationer från Uppsala Universitet
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Sustainability Reporting in the Dutch Construction Industry : A Study of Small and Medium-Sized Businesses
No full textThis study investigates the factors affecting sustainability reporting practices among small and medium-sized enterprises (SMEs) in the Dutch construction sector amid evolving European regulations, specifically the Omnibus updates to the Corporate Sustainability Reporting Directive. Drawing on institutional theory and a resource-based view, it explores why SMEs do or do not engage in sustainability reporting, and identifies key hindering and enabling factors. The study employs a qualitative approach, using semi-structured interviews with professionals from nine Dutch construction SMEs, complemented by a systematic literature review. Findings reveal that legislative uncertainty and complexity, resource constraints, and lack of industry-wide movement hinder reporting, while leadership commitment, intrinsic motivation, peer support networks, stakeholder demands, and in-house expertise facilitate it. The analysis highlights the interplay of external pressures and internal capabilities, emphasising the need for clearer legislation, more support for SMEs, and early reporting education to dismantle the obstacles that SMEs face with sustainability reporting
GIS Model for Forest Monitoring
No full textSveriges skogar utgör en viktig naturresurs med både ekologiskt och ekonomiskt värde. För att kunnabedriva ett hållbart och naturnära skogsbruk krävs kontinuerlig uppföljning och dokumentation avskogens tillstånd. Traditionella metoder som fältinverteringar och satellitbilder är dock tidskrävandeeller bristfälliga när det gäller detaljnivå. I detta arbete har en GIS-baserad modell utvecklas med syfteatt utgöra ett verktyg för uppföljning av ett företags skogsbruksplan med fokus på att kunna dokumenterainformation om enskilda träd såsom krondiameter, art, granbarkborreangrepp samt förekomst avevighetsträd. Modellen bygger på insamlade drönarbilder från fyra provytor tagna med en RGB kamera samten yta fotograferad med en infraröd kamera. I arbetet har två metoder jämförts: en traditionellsegmentering och en färdigtränad djupinlärningsmodell från ESRI. Jämförelsen har utgått ifrån fyrakriterier: tidsåtgång, antal korrekt identifierade träd, presterade i olika skogsvegetationer samtmetodernas förmåga att hitta granbarkborreangripna träd. Djupinlärningsmetoden presterade bättre än segmenteringsmetoden med avseende påovannämnda kriterier i alla provytor som fotograferades med RBG-bilder. Djupinlärningsmetodenvisade brister vid analys av provytan med de infraröda bilderna. För den slutliga modellen användesdärför djupinlärningsmetoden för provytorna fotograferade med RGB-bilderna och en kombination avdjupinlärning och segmentering i den provyta som fotograferades med infraröda kameran. Den slutliga modellen visualiserar trädens storlek, position och egenskaper i en interaktiv kartamed tillhörande attributtabell för respektive provyta. Modellen kan användas för vidare analys ochfiltrering av träddata och utgör ett effektivt och skalbart verktyg för modern skogsövervakning. Studienavslutas med rekommendationer och förslag hur modellen bäst kan skalas upp till större områden samtförslag på framtida utveckling med fokus på artbestämning och granbarkborreangrepp. Forests are one of Sweden’s most important natural resources, both economically and ecologically. Inorder to conduct sustainable forestry continuous monitoring and documentation of the forest’s conditionis required. However, traditional methods such as field surveys and satellite imagery are ither timeconsuming or lack sufficient details. In this study a GIS-based model was developed with the aim of serving as a tool for monitoringa company’s forest management plan with support for documentation of information for individual treesincluding tree species, the tree canopy diameter, potential infestation from the spruce bark beetle andwhether the tree is classified as a retention tree. The model is based on four sample areas captured with a drone equipped with an RGB cameraalong with one sampling area photographed using an infrared camera. Two methods were compared inthe study: traditional segmentation and pretrained deep leaning model from ESRI. The comparison wasbased of four criteria: time efficiency, number of identified trees, performance in different types of forestvegetation, and the ability to ability to detect damage from the spruce bark beetle. The deep learning method outperformed the segmentation method with respect to the criteriaearlier mentioned in all sample areas photographed with RBG images. The deep learning methodshowed shortcomings in the analysis of the sample area captured with the infrared images. Therefore,for the final model the results from the deep learning and the segmentation were combined in thesampling area photographed with the infrared camera while the final model of the sampling areasphotographed with the RBG camera were solely based on the results from the deep learning. The final model visualizes the tree crown size, position and characters in an interactive mapwith associated attribute table for each of the sampling areas. The model can be used for further analysisand filtering of tree data and serve as an efficient tool for modern forest monitoring. The study concludeswith recommendations based on scaling the model to fit larger areas as well as propose futuredevelopment focusing on species identification and detection of infestation from the spruce bark beetle.
Effects of collagen and chondroitin sulfate on relaxation at multiple magnetic field strengths
No full textPurpose To elucidate the connection between MRI relaxation properties of articular cartilage and tissue composition, in terms of collagen and chondroitin sulfate (CS). Additional aims were to determine the effect of different magnetic field strengths, as well as the effect of concentrations of the components on relaxation properties. Methods A series of MRI phantoms consisting of gels containing collagen and chondroitin sulfate were prepared with final concentrations of collagen in the range 20-60 mg/g and the CS concentration in the range 0-40 mg/g. R1 (= 1/T1) and R2 (= 1/T2) values of the phantoms were measured at three different MRI field strengths (1.5, 3.0 and 9.4 T), R1ρ (= 1/T1ρ) values were measured at 9.4 T. Results Relaxation rates generally increased with increasing concentration of either of the compounds. R1 values generally increased with CS, and at clinically used magnetic fields, namely 1.5 T and 3.0 T, with collagen concentration. At 9.4 T, R2 values also showed an increase with collagen concentration that could not be clearly identified at lower field strengths. R1ρ values increased with both collagen and CS concentration but the amplitude of the spin-lock pulse only had a limited effect on relaxation rates above 100 Hz. Conclusions Our results suggest that R1, R2, and R1ρ are modulated by collagen and CS concentrations, with collagen likely dominating at physiological concentrations
Att närvara där det händer : det pedagogiska ledarskapets betydelse för att leda lärares återkopplingsarbete
No full textThis article discusses instructional leadership and its implications for professional development of formative assessment conducted by teachers. Theories of instructional leadership claim that distributed leadership is seen as a strategy for principals to manage a complex mission. Therefore, distributed leadership has been studied in six upper secondary schools. Two methods have been used: a survey and semi-structured interviews. The results show that principals and assistant principals distribute the professional development of teachers to other teachers with leadership assignments. However, research on distributed leadership indicates that principals need to be present in contexts where the professional development of teachers take place
Psychological flexibility in everyday life during post-surgical recovery in youths undergoing spinal fusion surgery and the association with parental responses : a prospective daily diary study using a single-case approach
No full textBackground Chronic post-surgical pain (CPSP) affects approximate to 20% of children after major surgery, and the condition is associated with functional disability and ill-health. Psychological flexibility (PF) and parental factors have been shown to predict CPSP in youth following spinal fusion surgery. However, the daily dynamics of these processes throughout post-surgical recovery remain unknown. This study aimed at exploring how PF fluctuates in everyday life for youths undergoing spinal fusion surgery, and to investigate the associations between parental responses and adolescent PF. Methods Adolescents with Idiopathic Scoliosis (AIS), aged 12-18 years, undergoing spinal fusion surgery at four hospitals in Belgium, and their parents, completed diaries, measuring adolescent PF and parental responses (including instructions to avoid or engage in activities, parental protective behavior, and parental pain catastrophizing) for 7 days, at five phases: before surgery (T0), at 3 (T1) and 6 weeks (T2), and 6 (T3) and 12 (T4) months, post-surgery. A single-case approach with aggregated results was used, including Tau-U calculations and cross-lagged correlations. Results In total, data from 47 adolescents and seven parents were analyzed. Substantial within- and between-person variability characterized the patterns of adolescent PF. Cross-lagged correlations showed bidirectional relationships, demonstrating that parental responses predicted adolescent PF, and that adolescent PF, similarly, predicted parental responses. Discussion The results reveal the complex dynamics of PF among adolescents following surgery, and that parent-adolescent patterns after surgery may vary across both individuals and time. These findings also emphasize the need for idiographic pain research and individual-level assessments as well as person-centered treatments in clinical practice
Uncovering the initial nucleation process during rapid heating of Fe-Co-Nb-B metallic glasses
No full textFe-based metal amorphous nanocomposites, consisting of dispersed nanocrystallites within an amorphous metal-lic glass matrix, are used as low-loss soft-magnetic components in energy conversion devices. The nanocrystallitesare formed by partial devitrification of the amorphous matrix and the properties of the composite are a resultof the devitrification process. Understanding the rate-dependent crystallisation kinetics is therefore essential fortailoring the properties of such nanocomposites. In this study, we monitor the devitrification process in situ duringrapid heating of metallic glasses with composition (Fe0.75 Co0.25 )95− Nb5 Bx, = 15, 20 at.%, by using high-energywide angle X-ray scattering. The results are compared to samples devitrified at low heating rates, analysed usingdifferential scanning calorimetry, X-ray diffraction, and magnetometry. Additionally, we present a model describ-ing the crystallisation kinetics based on classical nucleation and growth theory coupled with thermodynamic datafor a generalised Fe-B system. The model successfully reproduces the onset of devitrification as a function of time,temperature, and B-concentration, thereby providing valuable insights for the design of advanced soft-magneticmetal amorphous nanocomposites
The Context of COVID-19 at 18 Months in Relation to Depression, Anxiety, Insomnia : The Emerging Role of Post COVID-19 Symptoms
No full textBackground: The COVID-19 pandemic naturally raised concerns about mental health and wellbeing around the world. As time passed, persisting physical and mental symptoms of post COVID-19, referred to as Post COVID Condition (PCC), have become an increasing concern. The aim of this study was to investigate the stability of symptoms of mental ill health in Sweden in the late phase of the pandemic and the prevalence of persistent symptoms post COVID-19 and interrelations between them. Method: We measured depression, anxiety, and insomnia, through a one-time online survey in Sweden (n = 1,482, mean age 47.6 years; 89.5% women) and used correlation and regression analysis to study potential predictors and their interrelations with PCC symptoms. Results: Compared to our previous study during the pandemic (May-June 2020), a marginal decrease was found for depression (27% versus 30%), a larger decrease for anxiety (16% vs 24%), and an increase for insomnia (45% vs 38%). Persistent symptoms were frequently reported, with 84.5% reporting at least one symptom, and 49.7% attributing one or more of these to COVID-19 infection. A history of poor mental health and COVID-19 related worry appeared as the strongest risk factors for mental ill health. Persistent symptoms also predicted these outcomes. Conclusions: Based on comparison with pre-pandemic rates, it appears that the pandemic continued to exert a negative impact on mental health in Sweden. Persistent symptoms, associated with COVID-19 exposure, appear common and may represent a vulnerability factor for mental ill health, along with other factors, including history of a mental ill health and specific pandemic worries
On parameter estimation for N(μ,σ2I3) based on projected data into S2
No full textThe projected normal distribution, with isotropic variance, on the 2-sphere is considered using intrinsic statistics. It is shown that in this case, the expectation commutes with the projection, and that the covariance of the normal variable has a 1-1 correspondence with the intrinsic covariance of the projected normal distribution. This allows us to estimate, after the model identification, the parameters of the underlying normal distribution that generates the data
