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Interactive picturebook reading is an easily accessible and enjoyable activity that provides preschoolers with opportunities to practice problem-solving skills. Increasingly, preschool age is considered the ideal time to foster problem-solving skills. This paper examines the effect of interactive picturebook reading on the problem-solving skills of preschool children. A total of 130 preschool children between 4 and 6 years old participated in a pretest-posttest quasi-experiment with a non-equivalent control group. During five interactive reading sessions in small groups, preschoolers were invited to discuss possible solutions to the problems the book characters encounter. Problem-solving skills were measured with two Pictorial Multiple-Solutions Tasks. Multilevel analysis indicates that interactive picturebook reading can improve children's flexibility, fluency, and originality in coming up with possible solutions to a problem. In addition, we found that the picture that was used and the emotional reactions of the children to the test situation influenced the test scores. The results show that giving children the opportunity to discuss possible solutions to problems presented in picturebooks is an effective way to work on problem-solving skills in preschool. Further research is needed to explore the optimal conditions for effective interactive picturebook reading to develop preschoolers' problem-solving skills and the extent to which the skills demonstrated during interactive picturebook reading can be transferred to real-life problem solving.

Writing a synthesis text involves interacting reading and writing processes, serving the comprehension of source information, and its integration into a reader-friendly and accurate synthesis text. Mastering these processes requires insight into process' orchestrations. A way of achieving this is via process feedback in which students compare their process orchestration with examples. Access to such examples of enacted process orchestration models might have an additional learning effect. In the present study we replicated and extended the study of Vandermeulen et al. ("Written Communication," 40(1), 90-144, 2023) on the effect of keystroke logging data-based process feedback with feed-forward exemplars when compared to national baseline performances. In addition, we report the effect of a brief extension in which learners had the opportunity to observe an enacted model of their choice, showing one of three orchestrations of the initial stage of writing a synthesis task. A total of 173 10th-grade students were randomly assigned to a process feedback condition with or without added models. A baseline, consisting of a nationally representative sample of upper-secondary students' texts and processes, served as an alternative control group. Results showed that the process feedback, both with and without observation, had a significant effect on text quality. Regarding the process data, students in the feedback condition had a more prominent focus on the sources as they spent more time in them and switched more often between text and sources, compared to the baseline. The observation task magnified this effect.

To writing anything on a keyboard at all requires us to know first what to type, then to activate motor programmes for finger movements, and execute these. An interruption in the information flow at any of these stages leads to disfluencies. To capture this combination of fluent typing and typing hesitations, researchers calculate different measures from keystroke-latency data--such as mean inter-keystroke interval and pause frequencies. There are two fundamental problems with this: first, summary statistics ignore important information in the data and frequently result in biased estimates; second, pauses and pause-related measures are defined using threshold values which are, in principle, arbitrary. We implemented a series of Bayesian models that aimed to address both issues while providing reliable estimates for individual typing speed and statistically inferred process disfluencies. We tested these models on a random sample of 250 copy-task recordings. Our results illustrate that we can model copy typing as a mixture process of fluent and disfluent key transitions. We conclude that mixture models (1) map onto the information cascade that generate keystrokes, and (2) provide a principled approach to detect disfluencies in keyboard typing.

Science education is increasingly integrating a focus on socio-scientific issues. Policy makers and scholars also suggest education for sustainable development (ESD) is important for tackling current and future sustainability issues. ESD may foster students' action competence in sustainable development (ACiSD; knowledgeability, willingness, capacity expectations and outcome expectancy) regarding contributing to sustainability action. However, reports on effectiveness research relating to ACiSD as a learning outcome of action-oriented ESD are scarce. The current study explores (1) how early adolescents perceive their teachers' ESD implementation and (2) whether perceived ESD (holism, pluralism, and action-orientedness) affects students' self-reported ACiSD. We administered two questionnaires to 12-16 year-old secondary school students, tapping into (1) their self-reported ACiSD and (2) their perceptions of teachers' ESD implementation efforts. Students' ESD perceptions were measured through descriptive statistics. Next, multilevel linear models were estimated to explore whether ESD and its components affect students' ACiSD. Our results suggest students did not distinctly perceive ESD implementation (esp. a participative approach). Holism and action-orientedness were neither perceived to be completely present nor completely missing. However, action-orientedness significantly affected students' reported ACiSD. While confirming the challenge an ESD implementation poses, our results may support efforts made. Implications for education and teacher development are discussed.

Abstract Resolving inflammation is thought to return the affected tissue back to homoeostasis but recent evidence supports a non-linear model of resolution involving a phase of prolonged immune activity. Here we show that within days following resolution of Streptococcus pneumoniae-triggered lung inflammation, there is an influx of antigen specific lymphocytes with a memory and tissue-resident phenotype as well as macrophages bearing alveolar or interstitial phenotype. The transcriptome of these macrophages shows enrichment of genes associated with prostaglandin biosynthesis and genes that drive T cell chemotaxis and differentiation. Therapeutic depletion of post-resolution macrophages, inhibition of prostaglandin E2 (PGE2) synthesis or treatment with an EP4 antagonist, MF498, reduce numbers of lung CD4+/CD44+/CD62L+ and CD4+/CD44+/CD62L-/CD27+ T cells as well as their expression of the α-integrin, CD103. The T cells fail to reappear and reactivate upon secondary challenge for up to six weeks following primary infection. Concomitantly, EP4 antagonism through MF498 causes accumulation of lung macrophages and marked tissue fibrosis. Our study thus shows that PGE2 signalling, predominantly via EP4, plays an important role during the second wave of immune activity following resolution of inflammation. This secondary immune activation drives local tissue-resident T cell development while limiting tissue injury

Abstract Wind is the most important external force in shaping aeolian landforms. Yet, it remains unclear what role the strong-wind events will play in the development of aeolian landforms compared with the effect of regular winds. A fundamental question is, what are the contributions of different wind speed levels to the deformation of aeolian landforms. Here, through in situ measurements of high-sampling-rate wind data and high-resolution topographic data, we analyzed short-term strong-wind events at different levels and monitored the rapid migration of barchan dunes, enabling us to provide a first report on the contribution rate of short-term strong winds to dune migration. Leveraging the linear relationship between sand flux and the migration distance of barchans, we found that the ratio of sand flux generated by short-term strong winds to the total sand flux is equal to the ratio of barchan migration distance caused by strong winds to the total migration distance in the same period. Moreover, a global analysis of three typical barchan fields confirmed the relationship. This study suggests that the development of aeolian landforms is dominantly controlled by the short-term strong-wind events rather than the previously reported time-averaging wind.

Thin cloud interference presents a significant challenge for the semantic segmentation of optical satellite imagery, which directly degrades the model accuracy and causes difficulties in sample selection. This paper generated a dataset named Populus euphratica and Tamarix chinensis discrimination (PTD), containing both cloudless and thin cloud scenarios. Based on this PTD dataset, an enhanced Atmospheric Scattering Model with Nonlinear Optimization (ASM_NL) was proposed to simulate high-fidelity thin clouds by incorporating two vital nonlinear terms: the point spread function and the Perlin noise. Additionally, we adopt a strategy of mixing simulated thin cloud-covered images (STCI) into the training set at a certain proportion to improve the semantic segmentation accuracy in thin cloud-covered scenarios. The conclusions are as follows: 1) ASM_NL can simulate high-fidelity clouds at an average Jensen-Shannon distance of 0.0699. 2) When dealing with medium- and high-cloud density datasets, mixing STCI proved to be more effective than cloud removal in mitigating thin cloud interference, resulting in average macro F1 score improvements of 0.164 and 0.094, respectively. 3) The semantic segmentation accuracy improved significantly by mixing STCI with a minimal proportion of 1/60, demonstrating the activation of model transfer capabilities. This study provides a concise and efficient methodology for effectively mitigating thin cloud interference in deep learning-based optical satellite imagery analysis.

In the growing season of 2021, a severe drought occurred in Central Asia impacting various local ecosystems and socio-economic conditions. However, there is limited research on the spatiotemporal characteristics of this drought and its effects on local vegetation. To address this gap, this study utilizes the standardized precipitation evapotranspiration index (SPEI) to quantitatively evaluate the spatiotemporal drought characteristics, and standardized anomalies to assess drought impacts on vegetation greenness and productivity. The drought-induced reduction in vegetation greenness and production was also decomposed into environmental drivers based on a random forest model. Our findings can be summarized as follows: During the 2021 growing season, Central Asia experienced one of the most severe drought events in the past 40 years, with approximately 42.57 % of the region facing record-breaking drought conditions. Abnormally low precipitation (PRE), prolonged high temperatures (TMP) and evapotranspiration also occurred during the drought event. The drought had a significant detrimental effect on vegetation, leading to an approximately 10 % reduction in vegetation greenness and around 13 % in productivity. Soil moisture (SM) was found to be the most critical factor influencing vegetation greenness loss during drought conditions. Leaf Area Index (LAI) and SM emerged as the primary drivers for the reduction of Gross Primary Productivity (GPP) and Solar-Induced Fluorescence (SIF). The antecedent environmental conditions had a significant impact on the decline in vegetation greenness and productivity during the 2021 drought event, accounting for approximately 30 %–35 % and 52–69 % of the respective losses. The findings of this study highlight the importance of taking into account antecedent climate factors when studying the impacts of drought on vegetation.

IntroductionPersons with substance use disorders (SUD) make up a considerable proportion of mental health care service users worldwide. Since 2010, Belgian mental health care has undergone a nationwide reform (‘Title 107’) aiming to realize a mental health care system that fosters more intensive collaboration, strengthens the cohesion and integration across and between different services, and is more responsive to the support needs of all service users. Although persons with SUD were named as a prioritized target group, how this reform impacted the lives and recovery journeys of persons with SUD remains understudied. This study aims to investigate how persons with SUD, regardless of whether they have co-occurring mental health issues, experience the accessibility of mental health care in light of the ‘Title 107’ reform.MethodsData were collected by means of in-depth interviews with a heterogeneous sample of persons with SUD (n=52), recruited from five regional mental health networks in Belgium. In-depth interviews focused on experiences regarding (history of) substance use, accessibility of services and support needs, and were analyzed thematically.ResultsFive dynamic themes came to the fore: fragmentation of care and support, the importance of “really listening”, balancing between treatment-driven and person-centered support, the ambivalent role of peers, and the impact of stigma.DiscussionDespite the ‘Title 107’ reform, persons with SUD still experience mental health care services as ‘islands in the stream’, pointing to several pressing priorities for future policy and practice development: breaking the vicious cycles of waiting times, organizing relational case management, tackling stigma and centralizing lived experiences, and fostering recovery-promoting collaboration.

In the global precipitation measurement (GPM) era, the integrated multisatellite retrievals for GPM (IMERG) stands as a pivotal precipitation algorithm. This study aims to evaluate the accuracy of IMERG in capturing precipitation frequency and volume in the Yellow River Basin, China. Four satellite-based precipitation estimates (SPEs) from three algorithmic versions of IMERG were analyzed on an hourly scale using gauge observations. An improved error component procedure was employed to identify error sources. Results showed that all four IMERG products effectively captured the spatial distribution patterns and seasonal changes of precipitation, IMERG_F demonstrated the best overall performance, followed by IMERG_L, while IMERG_E performed the worst. However, they tended to overestimate precipitation. IMERG_F_Cal performed the best for precipitation frequency detection, with the highest probability of detection (POD = 52.7%) and the lowest missed events (MIS = 47.3%). Error component analysis highlights false bias as the main source of error, followed by missed bias. In winter, missed bias was the primary error source. Notably, a significant overestimation of precipitation was observed along the Yellow River. In detail, false bias dominated IMERG_E, IMERG_L, and IMERG_F_UnCal below 800 m in spring, summer, and autumn. However, in winter, missed bias became the primary error source for these three products at elevations above 200m and for IMERG_F_Cal above 500 m. IMERG_F_Cal exhibited false bias as the primary error source in all seasons. Suggested algorithm developers focus on improving IMERG SPEs' identification capabilities for light precipitation events and rainstorms. Findings can provide a reference for improving the IMERG product algorithms and enhancing users’ understanding of the error characteristics and sources of IMERG products.

In the context of global warming, an increase in atmospheric aridity and global dryland expansion under the future climate has been expected in previous studies. However, this conflicts with observed greening over drylands and the insignificant increase in hydrological and ecological aridity from the ecohydrology perspective. Combining climatic, hydrological, and vegetation data, this study evaluated global dryland aridity changes at meteorological stations from 2003 to 2019. A decoupling between atmospheric, hydrological, and vegetation aridity was found. Atmospheric aridity represented by the vapor pressure deficit (VPD) increased, hydrological aridity indicated by machine-learning-based precipitation minus evapotranspiration (P − ET) data did not change significantly, and ecological aridity represented by the leaf area index (LAI) decreased. P − ET showed nonsignificant changes in most of the dominant combinations of the VPD, LAI, and P − ET. This study highlights the added value of using station-scale data to assess dryland change as a complement to results based on coarse-resolution reanalysis data and land surface models.

With the emergence of multisource data and the development of cloud computing platforms, accurate prediction of event-scale dust source regions based on machine learning (ML) methods should be considered, especially accounting for the temporal variability in sample and predictor variables. Arid Central Asia (ACA) is recognized as one of the world’s primary potential sand and dust storm (SDS) sources. In this study, based on the Google Earth Engine (GEE) platform, four ML methods were used for SDS source prediction in ACA. Fourteen meteorological and terrestrial factors were selected as influencing factors controlling SDS source susceptibility and applied in the modeling process. Generally, the results revealed that the random forest (RF) algorithm performed best, followed by the gradient boosting tree (GBT), maximum entropy (MaxEnt) model and support vector machine (SVM). The Gini impurity index results of the RF model indicated that the wind speed played the most important role in SDS source prediction, followed by the normalized difference vegetation index (NDVI). This study could facilitate the development of programs to reduce SDS risks in arid and semiarid regions, particularly in ACA.

This intervention study aimed to test the effect of writing process feedback. Sixty-five Grade 10 students received a personal report based on keystroke logging data, including information on several writing process aspects. Participants compared their writing process to exemplar processes of equally scoring (position-setting condition) or higher-scoring students (feed-forward condition). The effect of the feedback on writing performance and process was compared to a national baseline study. Results showed that feed-forward process feedback had an effect on text quality comparable to one grade of regular schooling. The feedback had an effect on production, pausing, revision, and source use, which indicates that it supported participants in self-regulating their writing process. Additionally, we explored the students' perception of the feedback to get an insight into its strengths and weaknesses. This study shows the potential of writing process feedback and discusses pedagogical implications and options for future research.

In order to support research on school effectiveness, there is a need for valid and reliable instruments to assess policymaking capacities of schools. Increasingly, policymaking is seen as a shared responsibility of the entire pedagogical team of a school. In this article, data were analysed from a sample of 1,696 (care) teachers coordinators and principals from 77 Flemish primary schools to assess critical aspects concerning validity and reliability of the Policy Making Capacities Questionnaire (PMC-Q). Confirmatory factor analysis indicates that the fit for the model with eight factors is limited. The subscales were found to be strongly correlated and difficult to distinguish, and one of the subscales turned out to be not well captured by the questionnaire. The results indicate sufficient interrater reliability and within-group agreement to aggregate responses to school level. Suggestions are made for further development of the PMC-Q and its use for research and practice.

Completing questionnaires is a complex task in which multiple cognitive processes play an important role. Despite critiques of the reliability of questionnaires, our research strived to gain more insight into the process of completing questionnaires by using eye-tracking. We investigated how both the question and the categories of answers were being processed and how processing the question influenced the processing of the categories of answers. We also considered the effects of participants' personal characteristics, such as effort and ability. Results showed there were individual differences involved when respondents complete a questionnaire. The respondents spent different amounts of time reading and answering the items, and there were important differences in the quality of processing.

This study investigates the differential effects of a task-based inside-school intervention and an outside-school project intervention on the development of speaking skills of 56 beginner second language (L2)-learners. The study extends previous research by looking at development across multiple occasions instead of two (i.e., a pretest and posttest), and by assessing speaking development by means of a non-intrusive task eliciting an informal conversation, rather than a formal test. To measure students' development of Dutch speaking skills, syntactic complexity, lexical diversity, accuracy, fluency, and communicative effectiveness were assessed. Multilevel analyses revealed differential effects for both interventions. The task-based intervention inside school led to significant positive development of communicative effectiveness and syntactic complexity measured by length and subordination, but also to more breakdowns in fluency. The outside-school project intervention, however, did not result in any significant speaking development. The findings demonstrate the learning potential of a task-based inside-school intervention for developing various aspects of informal speaking skills, except for fluency. From a methodological perspective, this study illustrates the complexity of test and measure selection when assessing L2 speaking development.

In this study we examine process configurations in synthesis tasks. We study whether these configurations are students traits or vary within students per task. In a national survey with a representative sample of 658 Dutch upper-secondary school students, we collected writing tasks, registered students' writing behaviors (via keylogging) and their task perceptions and assessed the quality of their texts. Each participant completed two informative and two argumentative synthesis tasks. Writing process configurations were based on a preselected set of writing behaviors that proved to be related to text quality: time spent on sources and production activities, switching between sources and between sources and text production, and speed of production; with reference to the phase in the process (first, mid, final part). Latent profile analyses distilled four process configurations, some of which were more likely to occur with the informative genre. One process configuration, that is, "Fast text production," was related to qualitatively higher text quality scores than the others. Additionally, at the age of 16-18 a writing process configuration is not a student trait: in most instances, we observed two or more task configurations within students. Writers' task experiences such as topic knowledge and topic interest predicted the occurrence of certain process configurations which could indicate adaptivity. The finding that writing configurations of writers vary even between similar tasks has important implications for the generalizability of (synthesis) writing research on the basis of a single writing task and process per student.

The Taitema Lake wetland in the lower reaches of the Tarim River (northwest China) is sensitive to hydrological changes and provides necessary ecosystem functions for biodiversity conservation. Monitoring and evaluating the long-term dynamics of the Taitema Lake wetland is essential for conserving and restoring regional wetland ecosystems. This paper used a dense time series of Landsat images from 1986 to 2021 to map the Taitema Lake wetlands. It analyzed the annual and seasonal variations of the wetlands by ten ecological indicators, which include area extent, vegetation, hydrology, and landscape features. A systematic assessment of the ecological quality of the wetlands over the past 36 years, along with their influencing factors, was conducted from the perspective of wetland health. The results showed that (1) the recovered water areas and the wetlands showed a clear trend of ecological restoration since 2000, accompanied by an increased fragmentation. The total wetland area increased from 69.95 km2 in 1986 to 1164.47 km2 in 2021, with a 15.6-fold increase, and the vegetation area increased twentyfold to 639.84 km2. (2) The wetness in the core zone increased consistently across seasons, coupled with a simultaneous vegetation expansion surrounding the core zone. The areas with increasing wetness levels account for 48 % during winter and spring, and the areas with vegetation expansion account for 81 % and 72 % in summer and autumn, respectively. (3) The wetland ecological health status recovered to a “Good” level in 2021 and has not reached the “Excellent” status. The expansion of the water body is the primary driver behind the promotion of wetland ecological recovery. Small water bodies within the area range of 0.05 to 0.1 km2 notably exhibited the most stimulating effects on the wetlands. Thus, increasing the count of smaller water bodies helps rehabilitate vegetation growth and mitigating the water supply’s stress to sustain large lacustrine bodies.

Abstract Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Abstract The eddy covariance (EC) flux stations have great limitations in the evaluation of the global net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty. In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the R square (R2), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest R2 was selected from the models with a prediction accuracy of R2 > 0.5 for the specific meteorological stations to estimate its NEE. 4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of R2 > 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9%) screened meteorological stations around the world are negative (carbon sink) and most (65.3%) of those showed an increasing trend in the mean annual NEE (carbon sink). The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with https://doi.org/10.6084/m9.figshare.20485563.v1.

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG.

Using statistical methods that do not directly represent the causality between variables to attribute climate and plant traits as controlling ecosystem functions may lead to biased perceptions. We revisited this issue using a causal graphical model, the Bayesian network (BN), capable of quantifying causality by conditional probability tables. Based on expert knowledge and climate, vegetation, and ecosystem function data from the FLUXNET flux stations, we constructed a BN representing the causal relationship of climate–plant-trait–ecosystem functions. Based on the sensitivity analysis function of the BN, we attributed the control of climate and plant traits over ecosystem functions and compared the results with those based on random forests and correlation analysis. The main conclusions of this study include the following: BN can be used for the quantification of causal relationships between complex ecosystems in response to climate change and enables the analysis of indirect effects among variables. The causality reflected in the BN is as good as the expert knowledge of the causal links. Compared to BN, the feature importance difference between “mean vapor pressure deficit and cumulative soil water index” and “maximum leaf area index and maximum vegetation height” reported by random forests is higher and can be overestimated. With the causality relation between correlated variables constructed, a BN-based sensitivity analysis can reduce the uncertainty in quantifying the importance of correlated variables. The understanding of the mechanism of indirect effects of climate variables on ecosystem functions through plant traits can be deepened by the chain casuality quantification in BNs.

Purpose: Aberrant dopaminergic function is linked with motor, psychotic, and affective symptoms, but studies have typically compared a single patient group with healthy controls. Methods: Here, we investigated the variation in striatal (caudate nucleus, nucleus accumbens, and putamen) and thalamic type 2 dopamine receptor (D2R) availability using [11C]raclopride positron emission tomography (PET) data from a large sample of 437 humans including healthy controls, and subjects with Parkinson’s disease (PD), antipsychotic-naïve schizophrenia, severe violent behavior, pathological gambling, depression, and overweight. We analyzed regional group differences in D2R availability. We also analyzed the interregional correlation in D2R availability within each group. Results: Subjects with PD showed the clearest decline in D2R availability. Overall, the groups showed high interregional correlation in D2R availability, while this pattern was weaker in violent offenders. Subjects with schizophrenia, pathological gambling, depression, or overweight did not show clear changes in either the regional receptor availability or the interregional correlation. Conclusion: We conclude that the dopaminergic changes in neuropsychiatric conditions might not only affect the overall receptor availability but also how coupled regions are across people. The region-specific receptor availability more profoundly links to the motor symptoms, while the between-region coupling might be disrupted in violence.

The dynamics of Gross Primary Productivity (GPP) is key to understand the global carbon cycle. Multiple GPP products are currently available based on remote sensing, Light Use Efficiency model (LUE) or diagnostic biophysical model. However, little knowledge is available on the spatial patterns of the uncertainty of different GPP products and their potential drivers over the Central Asia (CA), a fragile environment for accurate GPP estimation. This study investigates the sensitivity of the 8-day, monthly and yearly GPP uncertainties based on the three-cornered hat (TCH) method and Shapley additive explanation (SHAP) model in terms of vegetation, energy, water, climate and terrain factors in the dryland ecosystem during the 2003–2015 period. Ten GPP products were examined, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the (LUE), two products (GOSIF and NIRv) from satellite-based direct proxies (Proxies) and one product (PML) from the diagnostic biophysical model. The results indicate that the spatial distribution of the ten GPP products in CA showed similar patterns at different time scales, but with values varied at different products and time scales. According to the eddy covariance (EC) observations and the TCH-based uncertainties, the FLUXCOM product showed smaller relative uncertainties than other products. The attribution analysis denotes that the sources of uncertainty of the GPP varied for each product, and thus the improvement strategies for different products should be implemented separately The FLUXCOM should adapt the vegetation- related module to the dryland environment of CA. The LUE model should optimize the LUE parameters for the dryland ecosystem and incorporate the water related variables in the model. The Proxies model should incorporate the water and energy variables (such as soil moisture and radiation) as input data to improve their performance in CA. The diagnostic model should consider the elevation variable as input data, which may improve the performance of the PML in CA. Our results do not only provide an important basis for the selection of GPP products in the study of the carbon cycle in CA, but also offer a new insight into the GPP model development and improvement for the dryland ecosystem.

Background While patients with COPD often cite weather conditions as a reason for inactivity, little is known about the relationship between physical activity (PA) and weather conditions. The present study investigated the association of day-to-day weather changes on PA in patients with COPD and investigated patient characteristics related to being more or less influenced by weather conditions. Methods In this longitudinal analysis, device-based day-by-day step counts were objectively measured in COPD patients for up to 12 months. Daily meteorological data (temperature, precipitation, wind speed, hours of sunlight and daylight) were linked to the daily step count and individual and multivariable relationships were investigated using mixed-model effects. Individual R2 was calculated for every subject to investigate the estimated influence of weather conditions on a patient level and its relationship with patient characteristics. Results We included 50 patients with a mean±sd follow-up time of 282±93 days, totalling 14 117 patient-days. Daily temperature showed a positive linear pattern up until an inflexion point, after which a negative association with increasing temperature was observed (p

In this study, we investigate the relation between the school organization and outcomes of education for sustainable development, namely students' action competence in sustainable development. Data were collected among 629 students. At the organizational level, we collected data among 207 school staff concerning organizational characteristics including school resources, sustainable leadership, pluralistic communication, supportive relations, collective efficacy, adaptability, democratic decision-making, and shared vision. Results indicate that the school organization is primarily connected to students' knowledge and willingness. They also show that organizational characteristics such as sustainable leadership, pluralistic communication, adaptability, and democratic decision-making have a significant correlation with student level outcomes.

The discourse of quality has permeated everyday discussions about education, yet the concept as such remains complex. Moreover, during the constant struggle to improve educational quality, governments have increasingly devoted attention to the results of international large-scale assessments. As those results are used to inform policy decisions to improve countries' educational quality, it is important to understand how this concept of quality is appropriated. As a synthesis of how quality is conceptualised and operationalised in this body of research is lacking, the current paper seeks to fill that gap by presenting a systematic review. Our analysis identified definitional patterns and explored the corresponding methods for measuring quality. Based on this analysis of the reviewed studies, a framework of four approaches towards quality emerged to render the concept more accessible. In light of these results, benefits and limitations of the concept's complexity are discussed, and implications for research are forwarded.

Based on the concept of action competence, the current study introduces an integrated framework of Professional Action Competence in Education for Sustainable Development (PACesd) described by three defining features: willingness, knowledge of pedagogical approaches, and self-efficacy. To validate this framework, we developed a questionnaire measurement instrument: 'PACesd-Q'. This questionnaire was administered to a sample of 557 primary and secondary school in-service teachers in Flanders, Belgium. The instrument's psychometric quality was assessed in terms of its reliability, construct, and criterion-related validity. Results of reliability analysis and hierarchical (second-order) confirmatory factor analysis provided evidence of good measurement properties of the instrument in terms of reliability and construct and criterion-related validity. We suggest the instrument can be successfully used to provide educational practitioners with a tool to monitor and eventually identify professional development needs regarding education for sustainable development.

Wind erosion, a crucial disruptor of ecosystem health and stability, especially in arid regions, remains unincorporated in extensively utilized ecosystem services (ESs) quantification models such as InVEST. This neglect results in a gap: the profound influence of wind erosion on ESs remains largely underexplored and unquantified. Moreover, current models predominantly concentrate on individual ESs, thus bypassing the necessary comprehensive evaluation of various ESs' developmental status. To address this gap, we propose a systematic framework based on the Distance between Indices of Simulation and Observation (DISO), which integrates the Revised Wind Erosion Equation (RWEQ) and key modules of the InVEST model to derive a Comprehensive Ecosystem Services Assessment Index (CESAI) for arid regions. We evaluated five ESs, including water yield, soil retention, sand fixation (measured by wind erosion), carbon storage, and habitat quality. To further investigate the influence of each ES on the comprehensive assessment, we employed a suite of statistical methods, including independent samples t-tests and a generalized additive model (GAM), to elucidate the complex relationships and significant effects of wind erosion on the CESAI. The results of the t-tests and GAM highlighted the significant influence of wind erosion, which demonstrated the highest edf-value (2.000) and the highest F-value (1039.6, p

Quality of life (QOL) has gained increased interest as a critical pathway to better understanding the lives and circumstances of children and adolescents in both the general population and among specific populations. Yet, QOL assessment among youngsters in youth care services remains a highly under-researched topic. This study examines the suitability and psychometric properties of a new QOL self-report scale for adolescents between 12 and 18 years old in youth care: the Quality of Life in Youth Services Scale (QOLYSS). The provisional version of the QOLYSS was pre-tested in a sample of 28 adolescents in youth care to examine its applicability and feasibility. Next, a comprehensive evaluation of the psychometric properties of the field-test version was conducted in a sample of 271 adolescents in youth care in Flanders, Belgium (M = 15.43, SD = 1.73). Classical item and factor analyses were carried out per subscale, (test-retest) reliability and item-discriminant validity of the subscales were examined, convergent validity was explored, and confirmatory factor analysis was used to examine the goodness-of-fit of different measurement models. Reliability measures of the scale are satisfactory, results are indicative of convergent validity, and confirmatory factor analysis provides evidence for the eight correlated factors model. Future lines of research concerning the ongoing development and application of the QOLYSS are discussed.

Accurate sand and dust storm (SDS) detection is important for assessing SDS disaster risk. Machine learning (ML) based SDS detection approaches have been widely used in recent years due to their higher accuracy and better detection results. However, this approach usually requires manual annotation of numerous training samples that are, in practice, laborious and time-consuming. To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. Based on 8 SDS events captured by MODIS over Arid Central Asia (ACA), the effectiveness and accuracy of this method were assessed and compared to traditional approaches. The experimental results indicate that the proposed method can distinguish between mixed pixels (thin cloud and land surface) and SDS pixels and that it minimizes misdetection more effectively. This method achieved more than 98% training accuracy and validation accuracy in SDS detection.

Abstract Climate change will have an impact on not only flood magnitude but also on flood timing. This paper studies the shifting in flood timing at 6167 gauging stations from 1970 to 2010, globally. The shift in flood timing and its relationship with three influential factors (maximum 7-day precipitation, soil moisture excess, and snowmelt) are investigated. There is a clear global pattern in the mean flooding date: winter (Dec–Feb) across the western Coastal America, western Europe and the Mediterranean region, summer (Jun–Aug) in the north America, the Alps, Indian Peninsula, central Asia, Japan, and austral summer (Dec–Feb) in south Africa and north Australia area. The shift in flood timing has a trend from − 22 days per decade (earlier) to 28 days per decade (delayed). Earlier floods were found extensively in the north America, Europe and northeast Australia while delayed floods were prevailing in the Amazon, Cerrado, south Africa, India and Japan. Earlier flood timing in the north America and Europe was caused by earlier snowmelt while delayed extreme soil moisture excess and precipitation have jointly led to delayed floods around the monsoon zone, including south Africa, India and Japan. This study provides an insight on the shifting mechanism of flood timing, and supports decisions on the global flood mitigation and the impact from future climate change.

With the rapid accumulation of water flux observations from global eddy-covariance flux sites, many studies have used data-driven approaches to model water fluxes, with various predictors and machine learning algorithms used. However, it is unclear how various model features affect prediction accuracy. To fill this gap, we evaluated this issue based on records of 139 developed models collected from 32 such studies. Support vector machines (SVMs; average R-squared = 0.82) and RF (random forest; average R-squared = 0.81) outperformed other evaluated algorithms with sufficient sample size in both cross-study and intra-study (with the same data) comparisons. The average accuracy of the model applied to arid regions is higher than in other climate types. The average accuracy of the model was slightly lower for forest sites (average R-squared = 0.76) than for croplands and grasslands (average R-squared = 0.8 and 0.79) but higher than for shrubland sites (average R-squared = 0.67). Using Rn/Rs, precipitation, Ta, and the fraction of absorbed photosynthetically active radiation (FAPAR) improved the model accuracy. The combined use of Ta and Rn/Rs is very effective, especially in forests, while in grasslands the combination of Ws and Rn/Rs is also effective. Random cross-validation showed higher model accuracy than spatial cross-validation and temporal cross-validation, but spatial cross-validation is more important in spatial extrapolation. The findings of this study are promising to guide future research on such machine-learning-based modeling.

Net ecosystem exchange (NEE) is an important indicator of carbon cycling in terrestrial ecosystems. Many previous studies have combined flux observations and meteorological, biophysical, and ancillary predictors using machine learning to simulate the site-scale NEE. However, systematic evaluation of the performance of such models is limited. Therefore, we performed a meta-analysis of these NEE simulations. A total of 40 such studies and 178 model records were included. The impacts of various features throughout the modeling process on the accuracy of the model were evaluated. Random forests and support vector machines performed better than other algorithms. Models with larger timescales have lower average R2 values, especially when the timescale exceeds the monthly scale. Half-hourly models (average R2 = 0.73) were significantly more accurate than daily models (average R2 = 0.5). There are significant differences in the predictors used and their impacts on model accuracy for different plant functional types (PFTs). Studies at continental and global scales (average R2 = 0.37) with multiple PFTs, more sites, and a large span of years correspond to lower R2 values than studies at local (average R2 = 0.69) and regional (average R2 = 0.7) scales. Also, the site-scale NEE predictions need more focus on the internal heterogeneity of the NEE dataset and the matching of the training set and validation set.