Toward an Empowerment Model of Community Education in China

We define community education as organized lifelong learning through voluntary participation in collective efforts to critically address both individual and community needs. Community education has roots in European folk schools, United States participatory democracy, and Latin American “popular education.” Community education developed more recently in China in response to Learning Society and Lifelong Education policy. We present a new framework of community education that includes a theoretical component, emphasizing learning and participation principles. The organizational component includes traditional and nontraditional schools and other local organizations engaged in community education. The program component includes community service, empowerment, and combined models. We also apply the framework to an ecological-psychopolitical model of community education, which considers multilevel (individual, organizational, community/societal) processes of liberation or empowerment across four environmental domains or forms of capital: sociocultural, physical, economic and political. We conclude by examining two brief ethnographic case studies of community education in Shanghai, China.

Low-dissipation model of three-terminal refrigerator: Performance bounds and comparative analyses

In the present paper, a general non-combined model of three-terminal refrigerator is established based on the low-dissipation assumption. The relation between the optimized cooling power and the corresponding coefficient of performance (COP) is analytically derived, according to which the COP at maximum cooling power (CMP) can be further determined. At two dissipation asymmetry limits, upper and lower bounds of CMP are obtained and found to be in good agreement with experimental and simulated results. Additionally, comparison of the obtained bounds with previous combined model is presented. In particular it is found that the upper bounds are the same, whereas the lower bounds are quite different. This feature indicates that the claimed universal equivalence for the combined and non-combined models under endoreversible assumption is invalid within the frame of low-dissipation assumption. Then, the equivalence between various finite-time thermodynamic models needs to be reevaluated regarding multi-terminal systems. Moreover, the correlation between the combined and non-combined models is further revealed by the derivation of the equivalent condition according to which the identical upper bounds and distinct lower bounds are theoretically shown. Finally, the proposed non-combined model is proved to be the appropriate model for describing various types of thermally driven refrigerator. This work may provide some instructive information for the further establishments and performance analyses of multi-terminal low-dissipation models.

Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

Location-Specific Radiomics Score: Novel Imaging Marker for Predicting Poor Outcome of Deep and Lobar Spontaneous Intracerebral Hemorrhage

Objective: To derive and validate a location-specific radiomics score (Rad-score) based on noncontrast computed tomography for predicting poor deep and lobar spontaneous intracerebral hemorrhage (SICH) outcome.Methods: In total, 494 SICH patients from multiple centers were retrospectively reviewed. Poor outcome was considered mRS 3–6 at 6 months. The Rad-score was derived using optimal radiomics features. The optimal location-specific Rad-score cut-offs for poor deep and lobar SICH outcomes were identified using receiver operating characteristic curve analysis. Univariable and multivariable analyses were used to determine independent poor outcome predictors. The combined models for deep and lobar SICH were constructed using independent predictors of poor outcomes, including dichotomized Rad-score in the derivation cohort, which was validated in the validation cohort.Results: Of 494 SICH patients, 392 (79%) had deep SICH, and 373 (76%) had poor outcomes. The Glasgow Coma Scale score, haematoma enlargement, haematoma location, haematoma volume and Rad-score were independent predictors of poor outcomes (all P < 0.05). Cut-offs of Rad-score, 82.90 (AUC = 0.794) in deep SICH and 80.77 (AUC = 0.823) in lobar SICH, were identified for predicting poor outcomes. For deep SICH, the AUCs of the combined model were 0.856 and 0.831 in the derivation and validation cohorts, respectively. For lobar SICH, the combined model AUCs were 0.866 and 0.843 in the derivation and validation cohorts, respectively.Conclusion: Location-specific Rad-scores and combined models can identify subjects at high risk of poor deep and lobar SICH outcomes, which could improve clinical trial design by screening target patients.

Serum Neurofilament Light Chain as a Marker of Progression in Parkinson’s Disease: Long-Term Observation and Implications of Clinical Subtypes

Background: Biochemical and clinical biomarkers correlate with progression rate and disease severity in Parkinson’s disease (PD) but are not sufficiently studied in late PD. Objective: To examine how serum neurofilament light chain (S-NfL) alone or combined with clinical classifications predicts PD outcome in later disease stages. Methods: Eighty-five patients with 7.9±5.1 years of PD duration were included in an observational cohort. Clinical scores were obtained at two separate examinations 8.2±2.0 years apart. S-NfL levels were determined with single molecule array (SiMoA). Five predefined disease progression milestones were assessed. After affirming combination potential of S-NfL and either of two clinical classifications, three combined models were constructed based on these factors and age at onset in different combinations. Results: S-NfL levels showed significant hazard ratios for four out of five disease progression milestones: walking-aid usage (HR 3.5; 95% CI 1.4–8.5), nursing home living (5.1; 2.1–12.5), motor end-stage (6.2; 2.1–17.8), and death (4.1; 1.7–9.7). Higher S-NfL levels were associated with lower ability in activities of daily living and poorer cognition at baseline and/or at follow-up. Combined models showed significantly improved area under receiver operating characteristic curves (0.77–0.91) compared to S-NfL levels alone (0.68–0.71) for predicting the five disease milestones. Conclusion: S-NfL levels stratified patients according to their likelihood to reach clinically relevant progression milestones during this long-term observational study. S-NfL alone reflected motor and social outcomes in later stages of PD. Combining S-NfL with clinical factors was possible and exploratory combined models improved prognostic accuracy.

VARIASI MODEL SUPERVISI PEMBELAJARAN (STUDI KASUS : SD MADINA ISLAMIC SCHOOL, TEBET, JAKARTA SELATAN)

(English) Variations of learning supervision models can improve the quality of education. This research aims to describe the supervision models developed in SD Madina Islamic School. The research used qualitative descriptive-naturalistic approach. Through triangulation of data collection and processing techniques, the findings of research that SD Madina Islamic School combined models of supervision of learning, secretive and spontaneous principles, and has the benefit for habits teachers in learning. Through the mix of curriculum namely DIKNAS, Al-Azhar Cairo, and Cambridge, variations of learning supervision are applied using special assessment criteria, to produce an optimal, directed, and authentic assessment and guidance. Abstrak (Bahasa Indonesia) Supervisi pembelajaran yang variatif dapat meningkatkan kualitas pendidikan. Penelitian ini bertujuan untuk mendeskripsikan model supervisi yang dikembangkan oleh SD Madina Islamic School. Penelitian menggunakan pendekatan kualitatif deskriptif-naturalistik. Melalui triangulasi teknik pengumpulan dan pengolahan data, didapatkan temuan penelitian bahwa SD Madina Islamic School memadukan model supervisi pembelajaran secara variatif, berprinsip rahasia dan spontanitas, serta bermanfaat habitualiasasi guru dalam pembelajaran. Melalui perpaduan kurikulumnya yakni DIKNAS, Al-Azhar Cairo, dan Cambridge, maka variasi supervisi pembelajaran diterapkan dengan berbagai kriteria penilaian khusus, guna menghasilkan penilaian dan bimbingan yang optimal, terarah, dan otentik.

VARIASI MODEL SUPERVISI PEMBELAJARAN (STUDI KASUS : SD MADINA ISLAMIC SCHOOL, TEBET, JAKARTA SELATAN)

(English) Variations of learning supervision models can improve the quality of education. This research aims to describe the supervision models developed in SD Madina Islamic School. The research used qualitative descriptive-naturalistic approach. Through triangulation of data collection and processing techniques, the findings of research that SD Madina Islamic School combined models of supervision of learning, secretive and spontaneous principles, and has the benefit for habits teachers in learning. Through the mix of curriculum namely DIKNAS, Al-Azhar Cairo, and Cambridge, variations of learning supervision are applied using special assessment criteria, to produce an optimal, directed, and authentic assessment and guidance. Abstrak (Bahasa Indonesia) Supervisi pembelajaran yang variatif dapat meningkatkan kualitas pendidikan. Penelitian ini bertujuan untuk mendeskripsikan model supervisi yang dikembangkan oleh SD Madina Islamic School. Penelitian menggunakan pendekatan kualitatif deskriptif-naturalistik. Melalui triangulasi teknik pengumpulan dan pengolahan data, didapatkan temuan penelitian bahwa SD Madina Islamic School memadukan model supervisi pembelajaran secara variatif, berprinsip rahasia dan spontanitas, serta bermanfaat habitualiasasi guru dalam pembelajaran. Melalui perpaduan kurikulumnya yakni DIKNAS, Al-Azhar Cairo, dan Cambridge, maka variasi supervisi pembelajaran diterapkan dengan berbagai kriteria penilaian khusus, guna menghasilkan penilaian dan bimbingan yang optimal, terarah, dan otentik.

Prediction of land degradation by Machine Learning Methods

In this study, three models were used to monitor and predict the GWL and the land degradation index via the IMDPA method. In all models, 70% of the data was applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, TWI index, the distance of the river, Geographic location was the inputs and the level of groundwater was the output of each method. we found that ANN has the highest efficiency, which agrees with other findings. We combined the results of ANN with Ordinary Kriging and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Using ANN, we predicted that around 99% of the area was severely degraded for 2017. We also used latitude and longitude as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in Ordinary Kriging and decreased the total error of two combined models.

A CT-Based Radiomics Nomogram To Predict The Biological Activity of Hepatic Alveolar Echinococcosis

BackgroundThis study aims to establish a computed tomography (CT) - based radiomics nomogram to predict the biological activity of hepatic alveolar echinococcosis (HAE).MethodsA total of 174 HAE patients (139 for training, 35 for test) were enrolled whose CT and positron emission tomography-computed tomography (PET/CT) examinations were performed before surgery, and the biological activity was evaluated according to the PET/CT. Radiomic features were extracted from CT images, based on which radiomic scores (Rad-score) were calculated with the least absolute shrinkage and selection operator logistic regression. Three radiomics models (K-Nearest Neighbors, Logical regression, and Multilayer Perceptron), including only radiomic features and a radiomics nomogram, comprised of demographics, clinical indexes, and radiomic features were constructed respectively to predict the biological activity of HAE. The model performance was evaluated by area under curve (AUC), decision curve, and calibration curve.Results30 features in total were selected as optimal radiomic features and considered as input to calculate the Rad-score. There were no significant differences in the predictive efficacy between the combined models and the radiomics models from the perspective of the decision curve. The radiomics models was unparalleled, with an AUC of 0.952 (95%CI=0.902~0.981, P<0.0001) and 0.800 (95%CI=0.631~0.916, P<0.0020) in the training and testing cohort, respectively.ConclusionThe radiomics nomogram model showed great potential in identifying HAE biological activity.

Evaluation of GGMs Based on the Terrestrial Gravity Data of Gravity Disturbance and Moho Depthin Afar, Ethiopia

To estimate Moho depth, geoid, gravity anomaly, and other geopotential functionals, gravity data is needed. But, gravity survey was not collected in equal distribution in Ethiopia, as the data forming part of the survey were mainly collected on accessible roads. To determine accurate Moho depth using Global Gravity Models (GGMs) for the study area, evaluation of GGMs is needed based on the available terrestrial gravity data. Moho depth lies between 28 km and 32 km in Afar. Gravity disturbances (GDs) were calculated for the terrestrial gravity data and the recent GGMs for the study area. The model-based GDs were compared with the corresponding GD obtained from the terrestrial gravity data and their differences in terms of statistical comparison parameters for determining the best fit GGM at a local scale in Afar. The largest standard deviation (SD) (36.10 mGal) and root mean square error (RMSE) (39.00 mGal) for residual GD and the lowest correlation with the terrestrial gravity (0.61 mGal) were obtained by the satellite-only model (GO_CONS_GCF_2_DIR_R6). The next largest SD (21.27 mGal) and RMSE (25.65 mGal) for residual GD were obtained by the combined gravity model (XGM2019e_2159), which indicates that it is not the best fit model for the study area as compared with the other two GGMs. In general, the result showed that the combined models are more useful tools for modeling the gravity field in Afar than the satellite-only GGMs. But, the study clearly revealed that for the study area, the best model in comparison with the others is the EGM2008, while the second best model is the EIGEN6C4.