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Teachers' active role in school development has been recognized as important in school culture transformation. Leadership practices, such as distributed leadership and organizational support, aim to engage teachers and foster their participation and contribution opportunities. However, studies have shown that teachers' earlier experiences and beliefs shape their participation activities. To facilitate school culture transformation and the development of pedagogical practices, it is important to understand how teachers position themselves as school developers. This interview study aims to explore what kinds of views teachers express regarding school development work and teacher collaboration, along with how these views influence their perceived opportunity to contribute to school culture transformation. Altogether, 35 teachers from three schools in Helsinki, Finland, were interviewed. The analysis revealed five teacher profiles and, thus, five different ways of approaching school culture transformation: (1) "Visioner," (2) "Responsibility Bearer," (3) "Participating Observer," (4) "Traditionalist," and (5) "Stressed Withdrawer." Teachers' orientation to school development work and received organizational support influenced teachers' perceived contribution opportunities. Furthermore, the identified profiles experienced the needed organizational support for school development work differently; for some, it was mainly common time for collaboration, while for others, it meant reorganized structures. The results indicate that diverse support is needed to engage the whole teacher community in school culture transformation and that school leaders need to pay attention to how the distributed leadership model benefits all teachers, not just the visionary ones.

Background & aimsPathologists quantify liver steatosis as the fraction of lipid droplet-containing hepatocytes out of all hepatocytes, whereas the magnetic resonance-determined proton density fat fraction (PDFF) reflects the tissue triacylglycerol concentration. We investigated the linearity, agreement, and correspondence thresholds between histological steatosis and PDFF across the full clinical spectrum of liver fat content associated with non-alcoholic fatty liver disease.MethodsUsing individual patient-level measurements, we conducted a systematic review and meta-analysis of studies comparing histological steatosis with PDFF determined by magnetic resonance spectroscopy or imaging in adults with suspected non-alcoholic fatty liver disease. Linearity was assessed by meta-analysis of correlation coefficients and by linear mixed modelling of pooled data, agreement by Bland-Altman analysis, and thresholds by receiver operating characteristic analysis. To explain observed differences between the methods, we used RNA-seq to determine the fraction of hepatocytes in human liver biopsies.ResultsEligible studies numbered 9 (N = 597). The relationship between PDFF and histology was predominantly linear (r = 0.85 [95% CI, 0.80-0.89]), and their values approximately coincided at 5% steatosis. Above 5% and towards higher levels of steatosis, absolute values of the methods diverged markedly, with histology exceeding PDFF by up to 3.4-fold. On average, 100% histological steatosis corresponded to a PDFF of 33.0% (29.5-36.7%). Targeting at a specificity of 90%, optimal PDFF thresholds to predict histological steatosis grades were ≥5.75% for ≥S1, ≥15.50% for ≥S2, and ≥21.35% for S3. Hepatocytes comprised 58 ± 5% of liver cells, which may partly explain the lower values of PDFF vs. histology.ConclusionsHistological steatosis and PDFF have non-perfect linearity and fundamentally different scales of measurement. Liver fat values obtained using these methods may be rendered comparable by conversion equations or threshold values.Impact and implicationsMagnetic resonance-proton density fat fraction (PDFF) is increasingly being used to measure liver fat in place of the invasive liver biopsy. Understanding the relationship between PDFF and histological steatosis fraction is important for preventing misjudgement of clinical status or treatment effects in patient care. Our analysis revealed that histological steatosis fraction is often significantly higher than PDFF, and their association varies across the spectrum of fatty liver severity. These findings are particularly important for physicians and clinical researchers, who may use these data to interpret PDFF measurements in the context of histologically evaluated liver fat content.

Chemical ionisation inlets are fundamental instrument components in chemical ionisation mass spectrometry (CIMS). However, the sample gas and reagent ion trajectories are often understood only in a general and qualitative manner. Here, we evaluate two atmospheric pressure interface chemical ionisation inlets (MION2 and Eisele type inlet) with 3D computational fluid dynamics physicochemical models regarding the reagent ion and sample gas trajectories and estimate their efficiencies of reagent ion production, reagent ion delivery from the ion source volume into the ion–molecule mixing region, and the interaction between reagent ions and target molecules. The models are validated by laboratory measurements and quantitatively reproduce observed sensitivities to tuning parameters, including ion currents and changes in mass spectra. The study elucidates how the different transport and chemical reactions proceed within the studied inlets, where space charge can already be relevant at ion concentrations as low as 107 cm−3, and compares the two investigated inlet models. The models provide insights into how to operate the inlets and will help in the development of future inlets that further enhance the capability of CIMS.

Secondary organic aerosol (SOA) constitutes an important component of atmospheric particulate matter, with a substantial influence on air quality, human health and the global climate. The volatility basis set (VBS) framework has provided a valuable tool for better simulating the formation and evolution of SOA where SOA precursors are grouped by their volatility. This is done in order to avoid the computational cost of simulating possibly hundreds of atmospheric organic species involved in SOA formation. The accuracy of this framework relies upon the accuracy of the volatility distribution of the oxidation products of volatile organic compounds (VOCs) used to represent SOA formation. However, the volatility distribution of SOA-forming vapours remains inadequately constrained within global climate models, leading to uncertainties in the predicted aerosol mass loads and climate impacts. This study presents the results from simulations using a process-scale particle growth model and a global climate model, illustrating how uncertainties in the volatility distribution of biogenic SOA precursor gases affect the simulated cloud condensation nuclei (CCN). We primarily focused on the volatility of oxidation products derived from monoterpenes as they represent the dominant class of VOCs emitted by boreal trees. Our findings reveal that the particle growth rate and their survival to CCN sizes, as simulated by the process-scale model, are highly sensitive to uncertainties in the volatilities of condensing organic vapours. Interestingly, we note that this high sensitivity is less pronounce in global-scale model simulations as the CCN concentration and cloud droplet number concentration (CDNC) simulated in the global model remain insensitive to a 1-order-of-magnitude shift in the volatility distribution of organics. However, a notable difference arises in the SOA mass concentration as a result of volatility shifts in the global model. Specifically, a 1-order-of-magnitude decrease in volatility corresponds to an approximate 13 % increase in SOA mass concentration, while a 1-order-of-magnitude increase results in a 9 % decrease in SOA mass concentration over the boreal region. SOA mass and CCN concentrations are found to be more sensitive to the uncertainties associated with the volatility of semi-volatile compounds, with saturation concentrations of 10−1 µg m−3 or higher, than the low-volatility compounds. This finding underscores the importance of having a higher resolution in the semi-volatile bins, especially in global models, to accurately capture SOA formation. Furthermore, the study highlights the importance of a better representation of saturation concentration values for volatility bins when employing a reduced number of bins in a global-scale model. A comparative analysis between a finely resolved nine-bin VBS setup and a simpler three-bin VBS setup highlights the significance of these choices. The study also indicates that radiative forcing attributed to changes in SOA over the boreal forest region is notably more sensitive to the volatility distribution of semi-volatile compounds than low-volatility compounds. In the three-bin VBS setup, a 10-fold decrease in the volatility of the highest-volatility bin results in a shortwave instantaneous radiative forcing (IRFari) of −0.2 ± 0.10 W m−2 and an effective radiative forcing (ERF) of +0.8 ± 2.24 W m−2, while a 10-fold increase in volatility leads to an IRFari of +0.05 ± 0.04 W m−2 and an ERF of +0.45 ± 2.3 W m−2 over the boreal forest region. These findings underscore the critical need for a more accurate representation of semi-volatile compounds within global-scale models to effectively capture the aerosol loads and the subsequent climate effects.

Lately, new materialism has been proposed as a theoretical framework to better understand material-dialogic relationships in learning, and concurrently network analysis has emerged as a method in science education research. This paper explores how to include materiality in network analysis and reports the development of a method to construct network data from video. The approaches, (1) information flow, (2) material semantic and (3) material engagement, were identified based on the literature on network analysis and new materialism in science education. The method was applied and further improved with a video segment from an upper secondary school physics lesson. The example networks from the video segment show that network analysis is a potential research method within the materialist framework and that the method allows studies into the material and dialogic relationships that emerge when students are engaged in investigations in school.

This paper discusses the significance of school curricula in reflecting societal priorities and needs, focusing on the incorporation of computational thinking (CT) in Nordic national curricula. Our point of departure is that the preparedness of future generations for a digitally driven society can be determined by analysing how CT is either explicitly or implicitly framed in school curricula. Accordingly, this study examined the school curricula of Denmark, Finland, and Norway in terms of their similarities and differences in how they framed CT, as these countries have different approaches to the inclusion of CT. A framework for analysis that was grounded in influential works on CT in education was developed, focusing on problem-solving, algorithmic and transversal practices. National-level curricula were examined using a content analysis. Despite the differences in the approaches used in these countries, our findings indicate similarities across all three curricula, with an emphasis on how CT was framed.

This study contributes to the understanding of the relationship between emotions and development of scientific understanding by examining (1) how students perform in scientific sensemaking in the context of a three-cycle predict-observe-explain (POE) activity, (2) what kind of trajectories of situational epistemic emotions students show when making sense of the phenomenon, and (3) how students' performance in sensemaking is related to their emotional trajectories. Data from 109 participant students were collected in six upper secondary physics classes. Students' performance in sensemaking was evaluated based on their answers on POE items and categorised through qualitative content analysis. Situational epistemic emotions (surprise, curiosity, confusion, and boredom) were measured using a four-point Likert scale after each POE cycle. Latent class growth analysis was used to identify groups of students with distinctive emotional trajectories. The relationship between the performance in POE activity and emotional trajectories was explored by a chi-square test. The results indicate that students' inability to make relevant observations is significantly related to experienced boredom. Furthermore, students who perform better in making sense of the phenomenon are more likely to experience surprise, curiosity, and confusion. This implies that engaging students to be curious when they observe and test predictions is an important mission for curriculum designers and teachers in practice. The findings underline the importance of epistemic emotions in educational settings and the complexity of the interplay between cognitive and affective factors in learning situations.

The remote learning period that took place due to the outbreak of the COVID-19 pandemic in the spring of 2020 was a novel experience for many students, teachers and guardians in Finland and globally. To be prepared for similar occasions in the future and to support all students appropriately, it is important to be aware of students' experiences. In this study, instant video blogging (IVB) was used to collect primary school students' first-hand reports of their emotions in remote learning situations. Through an experience sampling method, 23 Finnish fifth-grade students (aged 11-12 years) took part in IVB during the remote learning period 18 March 2020-13 May 2020. Students' expressions related to negative emotional experiences were more diverse than those related to positive ones. "Nice" was the most often reported positive evaluation related to studying. The most often reported negative feelings were "bored" and "irritated," and the most often reported negative aspects related to learning were "difficult tasks" or "not having learned anything." Towards the end of the research period, positive mentions about returning to school increased. The IVB method offered direct insight into how primary students experienced the remote learning period, which can support preparation for exceptional periods in the future and the development of digital learning solutions.

Abstract Natural aerosol feedbacks are expected to become more important in the future, as anthropogenic aerosol emissions decrease due to air quality policy. One such feedback is initiated by the increase in biogenic volatile organic compound (BVOC) emissions with higher temperatures, leading to higher secondary organic aerosol (SOA) production and a cooling of the surface via impacts on cloud radiative properties. Motivated by the considerable spread in feedback strength in Earth System Models (ESMs), we here use two long-term observational datasets from boreal and tropical forests, together with satellite data, for a process-based evaluation of the BVOC-aerosol-cloud feedback in four ESMs. The model evaluation shows that the weakest modelled feedback estimates can likely be excluded, but highlights compensating errors making it difficult to draw conclusions of the strongest estimates. Overall, the method of evaluating along process chains shows promise in pin-pointing sources of uncertainty and constraining modelled aerosol feedbacks.

The apparent volatility of atmospheric organic aerosol (OA) particles is determined by their chemical composition and environmental conditions (e.g., ambient temperature). A quantitative, experimental assessment of volatility and the respective importance of these two factors remains challenging, especially in ambient measurements. We present molecular composition and volatility of oxygenated OA (OOA) particles in different rural, urban, and mountain environments (including Chacaltaya, Bolivia; Alabama, US; Hyytiälä, Finland; Stuttgart and Karlsruhe, Germany; and Delhi, India) based on deployments of a filter inlet for gases and aerosols coupled to a high-resolution time-of-flight chemical ionization mass spectrometer (FIGAERO-CIMS). We find on average larger carbon numbers (nC​​​​​​​) and lower oxygen-to-carbon (O : C) ratios at the urban sites (nC: 9.8 ± 0.7; O : C: 0.76 ± 0.03; average ±1 standard deviation) compared to the rural (nC: 8.8 ± 0.6; O : C: 0.80 ± 0.05) and mountain stations (nC: 8.1 ± 0.8; O : C: 0.91 ± 0.07), indicative of different emission sources and chemistry. Compounds containing only carbon, hydrogen, and oxygen atoms (CHO) contribute the most to the total OOA mass at the rural sites (79.9 ± 5.2 %), in accordance with their proximity to forested areas (66.2 ± 5.5 % at the mountain sites and 72.6 ± 4.3 % at the urban sites). The largest contribution of nitrogen-containing compounds (CHON) is found at the urban stations (27.1 ± 4.3 %), consistent with their higher NOx levels. Moreover, we parametrize OOA volatility (saturation mass concentrations, Csat) using molecular composition information and compare it with the bulk apparent volatility derived from thermal desorption of the OOA particles within the FIGAERO. We find differences in Csat values of up to ∼ 3 orders of magnitude and variation in thermal desorption profiles (thermograms) across different locations and systems. From our study, we draw the general conclusion that environmental conditions (e.g., ambient temperature) do not directly affect OOA apparent volatility but rather indirectly by influencing the sources and chemistry of the environment and thus the chemical composition. The comprehensive dataset provides results that show the complex thermodynamics and chemistry of OOA and their changes during its lifetime in the atmosphere. We conclude that generally the chemical description of OOA suffices to predict its apparent volatility, at least qualitatively. Our study thus provides new insights that will help guide choices of, e.g., descriptions of OOA volatility in different model frameworks such as air quality models and cloud parcel models.

Early growth of atmospheric particles is essential for their survival and ability to participate in cloud formation. Many different atmospheric vapors contribute to the growth, but even the main contributors still remain poorly identified in many environments, such as high-altitude sites. Based on measured organic vapor and sulfuric acid concentrations under ambient conditions, particle growth during new particle formation events was simulated and compared with the measured particle size distribution at the Chacaltaya Global Atmosphere Watch station in Bolivia (5240 m a.s.l.) during April and May 2018, as a part of the SALTENA (Southern Hemisphere high-ALTitude Experiment on particle Nucleation and growth) campaign. Despite the challenging topography and ambient conditions around the station, the simple particle growth model used in the study was able to show that the detected vapors were sufficient to explain the observed particle growth, although some discrepancies were found between modeled and measured particle growth rates. This study, one of the first of such studies conducted on high altitude, gives insight on the key factors affecting the particle growth on the site and helps to improve the understanding of important factors on high-altitude sites and the atmosphere in general. Low-volatility organic compounds originating from multiple surrounding sources such as the Amazonia and La Paz metropolitan area were found to be the main contributor to the particle growth, covering on average 65 % of the simulated particle mass in particles with a diameter of 30 nm. In addition, sulfuric acid made a major contribution to the particle growth, covering at maximum 37 % of the simulated particle mass in 30 nm particles during periods when volcanic activity was detected on the area, compared to around 1 % contribution on days without volcanic activity. This suggests that volcanic emissions can greatly enhance the particle growth.

Recognizing students’ learning-related emotions and taking them into consideration are important pedagogical tools for teachers. Taking students’ views into account promotes their participation. In this study, instant video blogging (IVB) is used to collect primary school students’ first-hand accounts of their emotions in learning situations. Through an experience sampling method (ESM), 22 Finnish sixth-grade students (12–13 years old) took part in IVB nine times during this study. The verbal expressions of students were analyzed to investigate students’ emotional experiences throughout the teaching sequence. The IVB method offered direct insight into how all students experienced teaching sequences. Compared to teacher observations, IVB offers a more diverse view into students’ experiences. This information can be employed when educating future teachers, and developing instructional methods. Further, offering students the opportunity to express themselves is the first step in supporting their participation.

Abstract Solution‐processable organic conductor–supramolecular elastomer blends are emerging materials for intrinsically stretchable and autonomously self‐healing organic electronics. Herein, the feasibility of a heterogenous multiphase polymer blend is demonstrated for soft, ultraflexible, self‐adhesive, and self‐healing multilayered film structures for electromagnetic interference (EMI) shielding and microwave absorber (MWA) purposes. The developed soft multilayered films achieve a 5–40 fold improvement in the thickness of the MWA compared to the current state‐of‐the‐art. The thickness normalized reflection loss (RL) is up to 65.26 dB mm−1 with 8.5 GHz bandwidth at 18–26.5 GHz. The maximum thickness normalized EMI shielding effectiveness peaks at up to 175 dB mm−1. The EMI shielding and MWA properties are maintainable up to 150% tensile strain with only a small decrease in the overall attenuation and RL. Furthermore, the developed films are capable of fully autonomously self‐healing and achieve a tough adhesion in temperatures of −30–145 °C, and underwater with maximum single‐lap shear adhesion strength of ≈481.5 kPa to soft thermoplastic polyurethane films. Thus, the developed multilayered films can be utilized for absorption‐dominant EMI shielding or MWA purposes as stretchable coatings. The developed materials also show considerable potential for emerging damage and puncture‐resistant organic soft electronics with autonomous material‐level self‐healing.

The multi-scheme chemical ionisation inlet 1 (MION1) enables rapid switching between the measurement of atmospheric ions without chemical ionisation and neutral molecules using various atmospheric pressure chemical ionisation methods. In this study, we introduce the upgraded version, the multi-scheme chemical ionisation inlet 2 (MION2). The new design incorporates enhanced ion optics, resulting in increased reagent ion concentration, ensuring a robust operation, and enabling the use of multiple chemical ionisation methods with the same ionisation time. In order to simplify the regular calibration of MION2, we developed an open-source flow reactor chemistry model called MARFORCE. This model enables quantification of the chemical production of sulfuric acid (H2SO4), hypoiodous acid (HOI), and hydroperoxyl radical (HO2). MARFORCE simulates the convection–diffusion–reaction processes occurring within typical cylindrical flow reactors with uniform inner diameters. The model also includes options to simulate chemical processes in the following two scenarios: (1) when two flow reactors with different inner diameters are connected and (2) when two flows are merged into one using a Y-shaped tee, although with reduced accuracy. Furthermore, the chemical mechanism files in the model are compatible with the widely used Master Chemical Mechanism (MCM), allowing for future adaptation to simulate other chemical processes in flow reactors. Furthermore, we conducted a comprehensive characterisation of the bromide (Br−) and nitrate (NO3-) chemical ionisation methods with different ionisation times. We performed calibration experiments for H2SO4, HOI, and HO2 by combining gas kinetic experiments with the MARFORCE model. The evaluation of sulfur dioxide (SO2), water (H2O), and molecular iodine (I2) involved dilution experiments from a gas cylinder (SO2), dew point mirror measurements (H2O), and a derivatisation approach combined with a high-performance liquid chromatography quantification (I2), respectively. Our findings indicate that the detection limit is inversely correlated with the fragmentation enthalpy of the analyte–reagent ion (Br−) cluster. In other words, stronger binding (resulting in a larger fragmentation enthalpy) leads to a lower detection limit. Additionally, a moderately longer ionisation time enhances the detection sensitivity, thereby reducing the detection limit. For instance, when using the Br− chemical ionisation method with a 300 ms ionisation time, the estimated detection limit for H2SO4 is 2.9×104 molec. cm−3. Notably, this detection limit is even superior to that achieved by the widely used Eisele-type chemical ionisation inlet (7.6×104 molec. cm−3), as revealed by direct comparisons. While the NO3- chemical ionisation method remains stable in the presence of high humidity, we have observed that the Br− chemical ionisation method (Br−–MION2) is significantly affected by the air water content. Higher levels of air water lead to reduced sensitivity for HO2 and SO2 under the examined conditions. However, we have found that a sharp decline in sensitivity for H2SO4, HOI, and I2 occurs only when the dew point exceeds 0.5–10.5 ∘C (equivalent to 20 %–40 % RH; calculated at 25 ∘C throughout this paper). For future studies utilising the atmospheric pressure Br− chemical ionisation method, including Br−–MION2, it is crucial to carefully consider the molecular-level effects of humidity. By combining approaches such as the water-insensitive NO3-–MION2 with Br−–MION2, MION2 can offer more comprehensive insights into atmospheric composition than what can be achieved by either method alone. By employing instrument voltage scanning, chemical kinetic experiments, and quantum chemical calculations, we have conclusively established that the presence of iodine oxides does not interfere with the detection of HIO3. Our comprehensive analysis reveals that the ions IO3-, HIO3⚫NO3-, and HIO3⚫Br−, which are detected using the Br− and NO3- chemical ionisation methods, are primarily, if not exclusively, generated from gaseous HIO3 molecules within atmospherically relevant conditions.

This paper examines how pupils with learning difficulties (LDs) used smartphones as supportive learning tools in an inclusive science class and how the usage developed over a two-year period. The case study was conducted in a Finnish primary school, where nine LD pupils' smartphone usage was followed in three science learning practices that supported LDs. The data consisted of repeated smartphone questionnaires, interviews, learning outcomes, and teachers' memoranda. The content and co-occurrence network analysis revealed that the smartphone usage varied in different practices, and its benefits developed gradually during the research period. Research highlights that teachers' and pupils' engagement with a dedicated, collaborative, and long-lasting process of smartphone usage in teaching and learning enables the achievement of change.

Saturation vapor pressure (psat) is an important thermodynamic property regulating the gas-to-particle partitioning of organic compounds in the atmosphere. Low-volatility organic compounds (LVOCs), with sufficiently low psat values, primarily stay in the particle phase and contribute to aerosol formation. Obtaining accurate information on the psat of LVOCs requires volatility measurements performed at temperatures relevant to atmospheric aerosol formation. Here, we present an isothermal evaporation method using a residence time chamber to measure psat for dry single-compound nanoparticles at 295 K. Our method is able to characterize organic compounds with psat spanning from 10−8 to 10−4 Pa at 295 K. The compounds included four polyethylene glycols (PEGs: PEG6, PEG7, PEG8, and PEG9), two monocarboxylic acids (palmitic acid and stearic acid), two dicarboxylic acids (azelaic acid and sebacic acid), two alcohols (meso-erythritol and xylitol), and one ester (di-2-ethylhexyl sebacate). There was a good agreement between our measured psat values and those reported by previous volatility studies using different measurement techniques, mostly within 1 order of magnitude. Additionally, quantum-chemistry-based COSMOtherm calculations were performed to estimate the psat values of the studied compounds. COSMOtherm predicted the psat values for most of the studied compounds within 1 order of magnitude difference between the experimental and computational estimates.

Background & Aims: Pathologists quantify liver steatosis as the fraction of lipid droplet-containing hepatocytes out of all hepatocytes, whereas the magnetic resonance-determined proton density fat fraction (PDFF) reflects the tissue triacylglycerol concentration. We investigated the linearity, agreement, and correspondence thresholds between histological steatosis and PDFF across the full clinical spectrum of liver fat content associated with non-alcoholic fatty liver disease. Methods: Using individual patient-level measurements, we conducted a systematic review and meta-analysis of studies comparing histological steatosis with PDFF determined by magnetic resonance spectroscopy or imaging in adults with suspected non-alcoholic fatty liver disease. Linearity was assessed by meta-analysis of correlation coefficients and by linear mixed modelling of pooled data, agreement by Bland–Altman analysis, and thresholds by receiver operating characteristic analysis. To explain observed differences between the methods, we used RNA-seq to determine the fraction of hepatocytes in human liver biopsies. Results: Eligible studies numbered 9 (N = 597). The relationship between PDFF and histology was predominantly linear (r = 0.85 [95% CI, 0.80–0.89]), and their values approximately coincided at 5% steatosis. Above 5% and towards higher levels of steatosis, absolute values of the methods diverged markedly, with histology exceeding PDFF by up to 3.4-fold. On average, 100% histological steatosis corresponded to a PDFF of 33.0% (29.5–36.7%). Targeting at a specificity of 90%, optimal PDFF thresholds to predict histological steatosis grades were ≥5.75% for ≥S1, ≥15.50% for ≥S2, and ≥21.35% for S3. Hepatocytes comprised 58 ± 5% of liver cells, which may partly explain the lower values of PDFF vs. histology. Conclusions: Histological steatosis and PDFF have non-perfect linearity and fundamentally different scales of measurement. Liver fat values obtained using these methods may be rendered comparable by conversion equations or threshold values. Impact and implications: Magnetic resonance-proton density fat fraction (PDFF) is increasingly being used to measure liver fat in place of the invasive liver biopsy. Understanding the relationship between PDFF and histological steatosis fraction is important for preventing misjudgement of clinical status or treatment effects in patient care. Our analysis revealed that histological steatosis fraction is often significantly higher than PDFF, and their association varies across the spectrum of fatty liver severity. These findings are particularly important for physicians and clinical researchers, who may use these data to interpret PDFF measurements in the context of histologically evaluated liver fat content.

Abstract The study examines students’ disciplinary learning in physics and interdisciplinary science learning opportunities that students encounter during a collaborative invention project. Thirteen student teams (aged 11 to 12, N = 46) designed and constructed a prototype of a technology invention meant to solve one of the challenges students face in daily life. The data was collected from a physics achievement test taken both before and after the invention project and artifacts (student essays and process portfolios) that students constructed during the project. Seven inventions were categorized as physics-intensive and six as non-physics in nature. The change in students’ achievement prior to and after the invention project was rather modest, and the increase was related to the level of physics-intensity of the inventions made during the project. However, the process portfolios revealed various interdisciplinary science learning opportunities and physics learning that could not be identified with the achievement test. Further, the co-occurrence analysis revealed several interdisciplinary learning opportunities that connected physics contents to the interdisciplinary themes. Working with varied materials and technologies and experimenting with them enabled the students to ponder different science topics and perhaps deepen their understanding through creative problem-solving. We conclude that such collaborative invention projects challenge teachers to take an active role in designing invention challenges so as to more explicitly interlink students’ invention processes with science learning. In order to foster students’ science learning opportunities, teachers should intensively evaluate each student-team’s learning throughout the project and use portfolios to reflect on and scaffold their science learning systematically.

Research on student retention in higher education (HE) physics could benefit by studying emotions in the context of engagement and learning. However, popular retention theories include only a narrow selection of emotions, creating a need to look elsewhere. In this study, we borrow the lens of an affective engagement model, the framework of an optimal learning moment, which has rarely been used in HE research so far. It defines situational engagement and three categories of learning enhancing, detracting, or accelerating emotions via twelve singular situational emotions. These twelve emotions in learning and other situations form intensive longitudinal data collected from twenty students using the experience sampling method (ESM) during their first two months of studying physics in a Finnish university. A two-level hierarchical dataset consisting of ESM measures (N[subscript 1] = 440) and student records (N[subscript 2] = 20), with gender as a background factor, are analyzed in two steps: first with hierarchical linear modeling, followed by multinomial logistic regression, giving results on both levels of the hierarchy, which is quite uncommon still. The results show how situational engagement and learning situations are separately connected to situational emotions and, further, how especially the learning enhancing emotions connect to success in courses (passing, grades) and first year student retention, surpassing the effect of course success.

The purpose of this study was to analyze knowledge co-construction as a self-organization process and the role of technology as its catalyst. Novel AI-directed speech recognition technology and the artifacts it generates were deployed to scaffold the knowledge co-construction process in two groups of pre-service teachers in a science education context. Throughout the lesson, the focus of the learning tasks was on pedagogical content knowledge and students' preconceptions. Analysis was conducted through the key characteristics of the social system's self-organization theory. The process of self-organization refers to the system's capacity to diverge from familiar structures, perspectives, and operations. Through the lenses of system theories, the active role of artifacts in co-construction was grasped and the role of technology in the self-organization of knowledge was analyzed. The pedagogical design of knowledge co-construction followed the principles of student-engaging learning. The technology used in co-construction was novel speech recognition AI software, which produced visual and editable word cloud artifacts from oral discussions on the large-format screen to edit. The data included videos and audio recordings. In this qualitative study, a content analysis and interaction analysis were used with descriptive analysis. The results showed that when technology became visible, as an active component of the system, artifacts triggered key signs of the social system's self-organization in co-construction. Exchange of information, “entropy levels,” were rapidly increased, and different viewpoints were expressed. Also, “chaos zones,” far-from-equilibrium states, were reached in both groups. Editable artifacts on the screen represented bifurcation spaces where groups' discussions were crystallized for the first time. Information was further categorized and evaluated through artifacts and this demonstrated how the groups processed communication into learning insights. Based on the results, the role played by this kind of technology was significant in the self-organization of knowledge. Materialized artifacts pushed the groups from small group conversation phases, comfort zones, toward uncertainty and confusion, which are central in self-organization. Technology in the system is seen not only as an interactor but also as an active agent that can facilitate epistemic emotions and support the group in the self-organization of knowledge.

Abstract. Secondary organic aerosol (SOA) is an important constituent of theatmosphere where SOA particles are formed chiefly by the condensation orreactive uptake of oxidation products of volatile organic compounds (VOCs).The mass yield in SOA particle formation, as well as the chemicalcomposition and volatility of the particles, is determined by the identityof the VOC precursor(s) and the oxidation conditions they experience. Inthis study, we used an oxidation flow reactor to generate biogenic SOA fromthe oxidation of Scots pine emissions. Mass yields, chemical compositionand volatility of the SOA particles were characterized and compared with SOAparticles formed from oxidation of α-pinene and from a mixture ofacyclic–monocyclic sesquiterpenes (farnesenes and bisabolenes), which aresignificant components of the Scots pine emissions. SOA mass yields forScots pine emissions dominated by farnesenes were lower than for α-pinene but higher than for the artificial mixture of farnesenes andbisabolenes. The reduction in the SOA yield in the farnesene- andbisabolene-dominated mixtures is due to exocyclic C=C bond scission inthese acyclic–monocyclic sesquiterpenes during ozonolysis leading to smallerand generally more volatile products. SOA particles from the oxidation ofScots pine emissions had similar or lower volatility than SOA particlesformed from either a single precursor or a simple mixture of VOCs. Applyingphysical stress to the Scots pine plants increased their monoterpene, especially monocyclic β-phellandrene, emissions, which further decreased SOA particle volatility and increased SOA mass yield. Our resultshighlight the need to account for the chemical complexity and structure ofreal-world biogenic VOC emissions and stress-induced changes to plantemissions when modelling SOA production and properties in the atmosphere.These results emphasize that a simple increase or decrease in relativemonoterpene and sesquiterpene emissions should not be used as an indicator ofSOA particle volatility.

Abstract. Secondary organic aerosol (SOA) is an important constituent of theatmosphere where SOA particles are formed chiefly by the condensation orreactive uptake of oxidation products of volatile organic compounds (VOCs).The mass yield in SOA particle formation, as well as the chemicalcomposition and volatility of the particles, is determined by the identityof the VOC precursor(s) and the oxidation conditions they experience. Inthis study, we used an oxidation flow reactor to generate biogenic SOA fromthe oxidation of Scots pine emissions. Mass yields, chemical compositionand volatility of the SOA particles were characterized and compared with SOAparticles formed from oxidation of α-pinene and from a mixture ofacyclic–monocyclic sesquiterpenes (farnesenes and bisabolenes), which aresignificant components of the Scots pine emissions. SOA mass yields forScots pine emissions dominated by farnesenes were lower than for α-pinene but higher than for the artificial mixture of farnesenes andbisabolenes. The reduction in the SOA yield in the farnesene- andbisabolene-dominated mixtures is due to exocyclic C=C bond scission inthese acyclic–monocyclic sesquiterpenes during ozonolysis leading to smallerand generally more volatile products. SOA particles from the oxidation ofScots pine emissions had similar or lower volatility than SOA particlesformed from either a single precursor or a simple mixture of VOCs. Applyingphysical stress to the Scots pine plants increased their monoterpene, especially monocyclic β-phellandrene, emissions, which further decreased SOA particle volatility and increased SOA mass yield. Our resultshighlight the need to account for the chemical complexity and structure ofreal-world biogenic VOC emissions and stress-induced changes to plantemissions when modelling SOA production and properties in the atmosphere.These results emphasize that a simple increase or decrease in relativemonoterpene and sesquiterpene emissions should not be used as an indicator ofSOA particle volatility.

The study focuses on examining elementary pupils' (N = 42, 11-13 years old) reflections on collaborative design processes, team collaboration and their co-inventions. Digital and traditional fabrication technologies were used in a 2-year co-invention project containing approximately 16 sessions during year 1 and 11 sessions in year 2. Between the two project periods, the pupils were asked to write a structured essay about their co-invention and design process, and in year 2 they kept journal notes in each team's design-folder. Each pupil's structured essay was analyzed with qualitative content analysis that focused on three main aspects: (1) description of the co-invention, (2) progress of the co-invention process and (3) quality of collaboration. Based on the essay analysis, we constructed a "Co-invention Table" with five categories: user, conceptual design, technical design, appearance, and construction. The nature of the 13 pupil-teams' co-inventions varied greatly, and these co-inventions were divided according to three main functions: (1) improving cleanliness, (2) providing reminders or (3) addressing well-being. The essays provided information on how creative ideas were generated and how critical thinking and evaluation of ideas were crucial in improving ideas for further development. The collaboration was dependent on everyone's active and equal participation into work and the importance of an adequate division of the labor was highlighted.

Water and wastewater services are invaluable for communities. The aim of this article is to understand and explain the overall long-term development of water services in Finland in the wider PESTEL framework with policy implications for the future. The original megastudy was based on an extensive literature review. The article first covers the birth and development of urban and rural water systems in Finland. This is followed by analysing selected decisions with long-term impacts, institutional issues, discussion and lessons learnt, and conclusions. The development of water services in Finland has largely been based on trust. In socio-institutional factors, institutional diversity is fundamental. Gradual expansion of water supply and wastewater systems has brought technical, economic and social benefits in a country with quite a dispersed population. Efficient water pollution control in communities was implemented within two decades through developing technology and applying proper legislation and control. Continuous and dynamic development has been a key principle. Nature-based solutions include raw water source selection, treatment processes such as managed aquifer recharge, sites for final effluents disposal, and water related land use planning. Aging infrastructure is the most pressing future challenge in Finland and worldwide and will need new requirements and innovations.

Despite a large number of studies, out of all drivers of radiative forcing, the effect of aerosols has the largest uncertainty in global climate model radiative forcing estimates. There have been studies of aerosol optical properties in climate models, but the effects of particle number size distribution need a more thorough inspection. We investigated the trends and seasonality of particle number concentrations in nucleation, Aitken, and accumulation modes at 21 measurement sites in Europe and the Arctic. For 13 of those sites, with longer measurement time series, we compared the field observations with the results from five climate models, namely EC-Earth3, ECHAM-M7, ECHAM-SALSA, NorESM1.2, and UKESM1. This is the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five earth system models (ESMs). We found that the trends of particle number concentrations were mostly consistent and decreasing in both measurements and models. However, for many sites, climate models showed weaker decreasing trends than the measurements. Seasonal variability in measured number concentrations, quantified by the ratio between maximum and minimum monthly number concentration, was typically stronger at northern measurement sites compared to other locations. Models had large differences in their seasonal representation, and they can be roughly divided into two categories: for EC-Earth and NorESM, the seasonal cycle was relatively similar for all sites, and for other models the pattern of seasonality varied between northern and southern sites. In addition, the variability in concentrations across sites varied between models, some having relatively similar concentrations for all sites, whereas others showed clear differences in concentrations between remote and urban sites. To conclude, although all of the model simulations had identical input data to describe anthropogenic mass emissions, trends in differently sized particles vary among the models due to assumptions in emission sizes and differences in how models treat size-dependent aerosol processes. The inter-model variability was largest in the accumulation mode, i.e. sizes which have implications for aerosol–cloud interactions. Our analysis also indicates that between models there is a large variation in efficiency of long-range transportation of aerosols to remote locations. The differences in model results are most likely due to the more complex effect of different processes instead of one specific feature (e.g. the representation of aerosol or emission size distributions). Hence, a more detailed characterization of microphysical processes and deposition processes affecting the long-range transport is needed to understand the model variability.

Atmospheric aerosol particle concentrations are strongly affected by various wet processes, including below and in-cloud wet scavenging and in-cloud aqueous-phase oxidation. We studied how wet scavenging and cloud processes affect particle concentrations and composition during transport to a rural boreal forest site in northern Europe. For this investigation, we employed air mass history analysis and observational data. Long-term particle number size distribution (∼15 years) and composition measurements (∼8 years) were combined with air mass trajectories with relevant variables from reanalysis data. Some such variables were rainfall rate, relative humidity, and mixing layer height. Additional observational datasets, such as temperature and trace gases, helped further evaluate wet processes along trajectories with mixed effects models. All chemical species investigated (sulfate, black carbon, and organics) exponentially decreased in particle mass concentration as a function of accumulated precipitation along the air mass route. In sulfate (SO4) aerosols, clear seasonal differences in wet removal emerged, whereas organics (Org) and equivalent black carbon (eBC) exhibited only minor differences. The removal efficiency varied slightly among the different reanalysis datasets (ERA-Interim and Global Data Assimilation System; GDAS) used for the trajectory calculations due to the difference in the average occurrence of precipitation events along the air mass trajectories between the reanalysis datasets. Aqueous-phase processes were investigated by using a proxy for air masses travelling inside clouds. We compared air masses with no experience of approximated in-cloud conditions or precipitation during the past 24 h to air masses recently inside non-precipitating clouds before they entered SMEAR II (Station for Measuring Ecosystem–Atmosphere Relations). Significant increases in SO4 mass concentration were observed for the latter air masses (recently experienced non-precipitating clouds). Our mixed effects model considered other contributing factors affecting particle mass concentrations in SMEAR II: examples were trace gases, local meteorology, and diurnal variation. This model also indicated in-cloud SO4 production. Despite the reanalysis dataset used in the trajectory calculations, aqueous-phase SO4 formation was observed. Particle number size distribution measurements revealed that most of the in-cloud SO4 formed can be attributed to particle sizes larger than 200 nm (electrical mobility diameter). Aqueous-phase secondary organic aerosol (aqSOA) formation was non-significant.