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For decades, self-report measures based on questionnaires have been widely used in educational research to study implicit and complex constructs such as motivation, emotion, cognitive and metacognitive learning strategies. However, the existence of potential biases in such self-report instruments might cast doubts on the validity of the measured constructs. The emergence of trace data from digital learning environments has sparked a controversial debate on how we measure learning. On the one hand, trace data might be perceived as \"objective\" measures that are independent of any biases. On the other hand, there is mixed evidence of how trace data are compatible with existing learning constructs, which have traditionally been measured with self-reports. This study investigates the strengths and weaknesses of different types of data when designing predictive models of academic performance based on computer-generated trace data and survey data. We investigate two types of bias in self-report surveys: response styles (i.e., a tendency to use the rating scale in a certain systematic way that is unrelated to the content of the items) and overconfidence (i.e., the differences in predicted performance based on surveys' responses and a prior knowledge test). We found that the response style bias accounts for a modest to a substantial amount of variation in the outcomes of the several self-report instruments, as well as in the course performance data. It is only the trace data, notably that of process type, that stand out in being independent of these response style patterns. The effect of overconfidence bias is limited. Given that empirical models in education typically aim to explain the outcomes of learning processes or the relationships between antecedents of these learning outcomes, our analyses suggest that the bias present in surveys adds predictive power in the explanation of performance data and other questionnaire data.

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues. The integration of spatial, imaging, and sequencing information enables the mapping of cellular dynamics within a tissue. Here, authors show three algorithms in stLearn software to accurately reveal spatial trajectory, detect cell-cell interactions, and impute missing data.

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.

Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred , a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/ .

Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipment and the lack of radar datasets for deep-learning model training. Moreover, conventional radar signal processing methods have the obstacles of poor resolution or complex computation. Therefore, this paper discusses an innovative approach in the integration of radar technology and machine learning for effective surveillance systems that can surpass the aforementioned limitations. This approach is detailed into three steps: signal acquisition, signal processing, and feature-based classification. A hardware prototype of the signal acquisition circuitry was designed for a Continuous Wave (CW) K-24 GHz frequency band radar sensor. The collected radar motion data was categorized into non-human motion, human walking, and human walking without arm swing. Three signal processing techniques, namely short-time Fourier transform (STFT), mel spectrogram, and mel frequency cepstral coefficients (MFCCs), were employed. The latter two are typically used for audio processing, but in this study, they were proposed to obtain micro-Doppler spectrograms for all motion data. The obtained micro-Doppler spectrograms were then fed to a simplified 2D convolutional neural networks (CNNs) architecture for feature extraction and classification. Additionally, artificial neural networks (ANNs) and 1D CNN models were implemented for comparative analysis on various aspects. The experimental results demonstrated that the 2D CNN model trained on the MFCC feature outperformed the other two methods. The accuracy rate of the object classification models trained on micro-Doppler features was 97.93%, indicating the effectiveness of the proposed approach.

Lithium-ion batteries (LIBs) have become a hot topic worldwide because they are not only the best alternative for energy storage systems but also have the potential for developing electric vehicles (EVs) that support greenhouse gas (GHG) emissions reduction and pollution prevention in the transport sector. However, the recent increase in EVs has brought about a rise in demand for LIBs, resulting in a substantial number of used LIBs. The end-of-life (EoL) of batteries is related to issues including, for example, direct disposal of toxic pollutants into the air, water, and soil, which threatens organisms in nature and human health. Currently, there is various research on spent LIB recycling and disposal, but there are no international or united standards for LIB waste management. Most countries have used a single or combination methodology of practices; for instance, pyrometallurgy, hydrometallurgy, direct recycling, full or partial combined recycling, and lastly, landfilling for unnecessary waste. However, EoL LIB recycling is not always easy for developing countries due to multiple limitations, which have been problems and challenges from the beginning and may reach into the future. Laos is one such country that might face those challenges and issues in the future due to the increasing trend of EVs. Therefore, this paper intends to provide a future perspective on EoL LIB management from EVs in Laos PDR, and to point out the best approaches for management mechanisms and sustainability without affecting the environment and human health. Significantly, this review compares the current EV LIB management between Laos, neighboring countries, and some developed countries, thereby suggesting appropriate solutions for the future sustainability of spent LIB management in the nation. The Laos government and domestic stakeholders should focus urgently on specific policies and regulations by including the extended producer responsibility (EPR) scheme in enforcement.

This phase 3 trial showed that persons with noninfectious uveitis who received adalimumab were more likely to have serious adverse events and less likely to have ophthalmic inflammation, uveitic flare, or visual impairment than were those who received placebo. Noninfectious uveitis is a group of vision-threatening diseases that are characterized by intraocular inflammation; it can occur as a syndrome isolated to the eye or in association with a systemic condition. Uveitis has an estimated incidence of 17 to 52 cases per 100,000 person-years 1 and is estimated to cause 10 to 15% of cases of blindness in Western countries. 2 , 3 Glucocorticoids remain the mainstay of therapy despite their well-known ocular and systemic adverse effects. 4 – 6 Thus, there is a large unmet medical need for and a great interest in identifying more effective, glucocorticoid-sparing therapies, ideally targeting specific mediators of the . . .

Purpose The purpose of this study is to develop a building information modelling (BIM)-based mixed reality (MR) application to enhance and facilitate the process of managing bridge inspection and maintenance works remotely from office. It aims to address the ineffective decision-making process on maintenance tasks from the conventional method which relies on documents and 2D drawings on visual inspection. This study targets two key issues: creating a BIM-based model for bridge inspection and maintenance; and developing this model in a MR platform based on Microsoft Hololens. Design/methodology/approach Literature review is conducted to determine the limitation of MR technology in the construction industry and identify the gaps of integration of BIM and MR for bridge inspection works. A new framework for a greater adoption of integrated BIM and Hololens is proposed. It consists of a bridge information model for inspection and a newly-developed Hololens application named “HoloBridge”. This application contains the functional modules that allow users to check and update the progress of inspection and maintenance. The application has been implemented for an existing bridge in South Korea as the case study. Findings The results from pilot implementation show that the inspection information management can be enhanced because the inspection database can be systematically captured, stored and managed through BIM-based models. The inspection information in MR environment has been improved in interpretation, visualization and visual interpretation of 3D models because of intuitively interactive in real-time simulation. Originality/value The proposed framework through “HoloBridge” application explores the potential of integrating BIM and MR technology by using Hololens. It provides new possibilities for remote inspection of bridge conditions.

The fruits of Canarium tramdenum are commonly used as foods and cooking ingredients in Vietnam, Laos, and the southeast region of China, whilst the leaves are traditionally used for treating diarrhea and rheumatism. This study was conducted to investigate the potential use of this plant bark as antioxidants, and α-amylase and α-glucosidase inhibitors. Five different extracts of C. tramdenum bark (TDB) consisting of the extract (TDBS) and factional extracts hexane (TDBH), ethyl acetate (TDBE), butanol (TDBB), and water (TDBW) were evaluated. The TDBS extract contained the highest amount of total phenolic (112.14 mg gallic acid equivalent per g dry weight), while the TDBB extract had the most effective antioxidant capacity compared to other extracts. Its IC50 values were 12.33, 47.87, 33.25, and 103.74 µg/mL in 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis (ABTS), reducing power (RP), and nitric oxide (NO) assays, respectively. Meanwhile, the lipid peroxidation inhibition of the four above extracts was proximate to that of butylated hydroxytoluene (BHT) as a standard antioxidant. The result of porcine pancreatic α-amylase inhibition showed that TDB extracts have promising effects which are in line with the commercial diabetic inhibitor acarbose. Interestingly, the inhibitory ability on α-glucosidase of all the extracts was higher than that of acarbose. Among the extracts, the TDBB extract expressed the strongest activity on the enzymatic reaction (IC50 = 18.93 µg/mL) followed by the TDBW extract (IC50 = 25.27 µg/mL), TDBS (IC50 = 28.17 µg/mL), and TDBE extract (IC50 = 141.37 µg/mL). The phytochemical constituents of the TDB extract were identified by gas chromatography–mass spectrometry (GC-MS). The principal constituents included nine phenolics, eight terpenoids, two steroids, and five compounds belonging to other chemical classes, which were the first reported in this plant. Among them, the presence of α- and β-amyrins were identified by GC-MS and appeared as the most dominant constituents in TDB extracts (1.52 mg/g). The results of this study revealed that C. tramdenum bark possessed rich phenolics and terpenoids, which might confer on reducing risks from diabetes. A high quantity of α- and β-amyrins highlighted the potentials of anti-inflammatory, anti-ulcer, anti-hyperlipidemic, anti-tumor, and hepatoprotective properties of C. tramdenum bark.

Understanding intratumoral heterogeneity—the molecular variation among cells within a tumor—promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.This Review describes spatial omics and multiplexed imaging technologies and their current and future impact in studying tumor heterogeneity and cancer biology.