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The objective of this paper is to investigate the effects of digital competence and technology expectancy and how they interact with cultural differences on digital informal learning (DIL) of university students have different cultural backgrounds. Empirical data collected from a survey conducted both in China and Belgium, the total sample consists of 335 Chinese and 197 Belgians. Partial least square with formative assessment was employed to test the proposed model and multi-groups comparison analysis was conducted to examine the moderation effects of cultural difference on influential factors related to students' DIL behaviors. The results highlighted the essential role of digital competence and technology expectancy in DIL. Cultural difference revealed a strong influence on the motive pattern of DIL behaviors. This study raised some major implications for educational researchers and practitioners alike. Specifically, the paper introduced and stressed the role of digital competence in informal learning context which was rarely considered in prior studies in terms of learning ecology. The study expanded current theoretical understanding of technology adoption in informal learning environments across different cultural contexts. The finding has shown both a homogeneous informal learning behavior and motive variance between different two cultural backgrounds. The study also provided a set of practical implications, including the need for digital competence training in today's digital era, and the need for a satisfactory user interface design globally.

Growing evidence indicates that non‐neuronal oligodendrocyte plays an important role in Amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases. In patient's brain, the impaired myelin structure is a pathological feature with the observation of TDP‐43 in cytoplasm of oligodendrocyte. However, the mechanism underlying the gain of function by TDP‐43 in oligodendrocytes, which are vital for the axonal integrity, remains unclear. Recently, we found that the primate‐specific cleavage of truncated TDP‐43 fragments occurred in cytoplasm of monkey neural cells. This finding opened up the avenue to investigate the myelin integrity affected by pathogenic TDP‐43 in oligodendrocytes. In current study, we demonstrated that the truncated TDP‐35 in oligodendrocytes specifically, could lead to the dysfunctional demyelination in corpus callosum of monkey. As a consequence of the interaction of myelin regulatory factor with the accumulated TDP‐35 in cytoplasm, the downstream myelin‐associated genes expression was downregulated at the transcriptional level. Our study aims to investigate the potential effect on myelin structure injury, affected by the truncated TDP‐43 in oligodendrocyte, which provided the additional clues on the gain of function during the progressive pathogenesis and symptoms in TDP‐43 related diseases. [ABSTRACT FROM AUTHOR]

Huntington's disease (HD) is a hereditary neurodegenerative disorder caused by a CAG tract expansion in the huntingtin gene (HTT). HD is characterized by involuntary movements, cognitive decline, and behavioral changes. Pathologically, patients with HD show selective striatal neuronal vulnerability at the early disease stage, although the mutant protein is ubiquitously expressed. Activation of the immune system and glial cell-mediated neuroinflammatory responses are early pathological features and have been found in all neurodegenerative diseases (NDDs), including HD. However, the role of inflammation in HD, as well as its therapeutic significance, has been less extensively studied compared to other NDDs. This review highlights the significantly elevated levels of inflammatory proteins and cellular markers observed in various HD animal models and HD patient tissues, emphasizing the critical roles of microglia, astrocytes, and oligodendrocytes in mediating neuroinflammation in HD. Moreover, it expands on recent discoveries related to the peripheral immune system's involvement in HD. Although current immunomodulatory treatments and inflammatory biomarkers for adjunctive diagnosis in HD are limited, targeting inflammation in combination with other therapies, along with comprehensive personalized treatment approaches, shows promising therapeutic potential. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fuzzy functional observer‐based sliding mode control for T‐S fuzzy cyber‐physical systems subject to disturbances and deception attacks. First, the deception attack is modeled by an exogenous system with state dependent nonlinearity. Second, a fuzzy learning functional observer with fuzzy logic systems learning unknown nonlinear function, whose parameters can be directly found, is designed to estimate the partly unavailable states, deception attacks and lumped disturbances simultaneously. Then, a fuzzy sliding mode control is designed to compensate the effect of deception attack and reject the disturbances. Furthermore, some sufficient conditions are given to guarantee that the closed‐loop fuzzy system is exponential convergent. Finally, a numerical example is given to illustrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Based on the analysis of the influencing factors of the current planning and utilization of river and lake shorelines, an index system of the current planning and utilization of river and lake shorelines is constructed. Based on the triangular fuzzy number method and the improved CRITIC method, the evaluation indicators are weighted and analyzed and then integrated to obtain the combined weight. An improved matter-element extension model was constructed to evaluate the current situation of shoreline planning and utilization in typical regions. At the same time, the TOPSIS evaluation model and the cloud theory evaluation model were compared and analyzed to verify the accuracy of the model. The improved matter-element model accounts for indicator incompatibility and the uncertainty of evaluation boundaries, offering a clearer reflection of the planning and utilization status. [ABSTRACT FROM AUTHOR]

Successfully separating wood and leaves in forest plots is a prerequisite for measuring structural parameters and reconstructing 3D forest models. Terrestrial laser scanning (TLS) can distinguish between the leaves and wood of trees through precise and dense point clouds. However, most existing wood–leaf separation methods face significant accuracy issues, especially in dense forests, due to the complications introduced by canopy shading. In this study, we propose a method to separate the wood and leaves in forest plots using the clustering features of TLS data. The method first filters a point cloud to remove the ground points, and then clusters the point cloud using a region-growing algorithm. Next, the clusters are processed based on their sizes and numbers of points for preliminary separation. Chaos Distance is introduced to characterize the observation that wood points are more orderly while leaf points are more chaotic and disorganized. Lastly, the clusters' Chaos Distance is used for the final separation. Three representative plots were used to validate this method, achieving an average accuracy of 0.938, a precision of 0.927, a recall of 0.892, and an F1 score of 0.907. The three sample plots were processed in 5.18, 3.75, and 14.52 min, demonstrating high efficiency. Comparing the results with the LeWoS and RF models showed that our method better addresses the accuracy issues of complex canopy structures. [ABSTRACT FROM AUTHOR]

The large uncertainties in estimating CH4 emissions from wetland ecosystems, the leading natural source to the atmosphere, substantially hinder the quantification of the global CH4 budget. This study used the IBIS‐CH4 (Integrated BIosphere Simulator‐Methane) model, a process‐based model integrating microbial mechanisms associated with CH4 production and oxidation processes, to simulate global wetland CH4 emissions from 2001 to 2020. Initially, we employed the IBIS‐CH4 model to evaluate its performance across 26 diverse wetland sites worldwide. The results showed that the magnitude and seasonality of observed CH4 fluxes over various wetland sites were well reproduced. We then used this model to estimate the annual global wetland CH4 emissions from 2001 to 2020, averaging 152.67 Tg CH4 yr−1, with a range of 135.72–167.57 Tg CH4 yr−1. The estimated global wetland CH4 emissions are generally in agreement with the current bottom‐up estimates (117–256 Tg CH4 yr−1) and closely overlap with independent top‐down estimates (139–183 Tg CH4 yr−1). During 2001–2020, the estimated global wetland CH4 emissions initially showed an increasing trend, followed by a decline. The peak of CH4 emissions reached in 2010, coinciding with the peak of wetland area. The majority of global wetland CH4 emissions were concentrated in tropical regions, which exhibited a clear seasonality and had a peak in July. The impact of meteorological factors on wetland CH4 emissions was greater than that of leaf area index, indicating the importance of soil hydrothermal conditions on wetland CH4 emissions. Plain Language Summary: Wetlands are crucial sources of methane (CH4), an important greenhouse gas, while accurately estimating wetland CH4 emissions has been challenging. This research simulated global wetland CH4 emissions from 2001 to 2020 using a process‐based model known as IBIS‐CH4. The model was tested at 26 wetland sites globally and accurately predicted observed CH4 emissions. Overall, the study estimated that global wetlands emitted about 152.67 Tg CH4 yr−1 annually, which agrees with alternative estimates. The estimated wetland CH4 emissions exhibited a tendency of initially rising and then decreasing, reaching a peak in 2010. Most CH4 emissions came from tropical regions and showed a clear seasonality. Additionally, the study revealed that soil hydrothermal conditions and wetland area were key factors influencing CH4 emissions from wetlands, emphasizing the relevance of climate change in controlling CH4 emissions. Key Points: We used a process‐based model to simulate global wetland CH4 emissions during 2001–2020, averaging 152.67 Tg CH4 yr−1Global wetland CH4 emissions increased at first and then decreased, peaking in 2010, consistent with the peak of wetland areasBased on the model experiments, soil hydrothermal conditions and wetland area were the main factors controlling wetland CH4 emissions [ABSTRACT FROM AUTHOR]