This research aims to determine the method of learning Arabic in the Madrasah Aliyah Labolatorium UINSU Medan by using a type of field research with qualitative research method, which is the object ...of research for Arabic subject teachers and the methods used in learning Arabic. The method used at the Madrasah Aliyah Labolatorium UINSU is tu use various method and adapt tu the material to be tought and also the methods that are commonly used, namerly the group discussion method, demonstration method, field trip method and translation method. The method used was implemented well an made it easier for students at the Madrasah Aliyah Labolatorium UINSU to understand the lessons conveyed by the teacher. The importence of using methods and choosing metgots that are appropriate to the material ti be taught in learning Arabic will make it easier for students to understand learning and create affective an efficient learning.
Aggregated aerosol particles released from high temperature processes and combustion processes are often described as quasi-fractal aggregates, where the shape of these particles is represented by ...the scaling law. A detailed understanding of the morphology is quite important as various properties are strongly dependent on the particle shape. Electron microcopy based image analysis is the most commonly used technique to visualize and study the morphological features. In this study, we propose a machine learning (ML)-assisted retrieval method where ML techniques are combined with optimization algorithms to predict the morphological features and the corresponding 3-dimensional structures from microscopic images. The proposed algorithm is comprehensively tested with “synthetic” images as well as Transmission Electron Microcopy images. Various ML models, including Linear regression, Artificial Neural Network, K-nearest neighbours, Random Forest regression, and XGBoost are used for preliminary prediction of the morphological features (Number of monomers (N), fractal prefactor (kf) and fractal Dimension (Df)). These are used to narrow down the search space in the optimization algorithms. Random Forest and XGBoost methods achieved approximately 0.96 R2 score for N, 0.85 for Df and 0.73 for kf. Multiple optimization methods, including PSO, JAYA, and JAYA-SA, were tested in the study. The method was tested across a wide range of parameters, including N (up to 500), Df (1.1–2.7), and kf (0.6–2.1), and the results are quite promising while comparing various 3-dimensional properties of the retrieved structures. The retrieved fractal parameters, N and Df, exhibited errors under 10%, and the predicted kf values were found within approximately 15% using the proposed method. Results also show that the 3-dimensional properties of the predicted structure are quite close to the structures used for testing the algorithm. The algorithm was also parallelized to improve the computational time. The results show that the predicted fractal parameters and the retrieved 3-dimensional structures are quite similar to the structures used for testing across a wide range of particle morphologies. The incorporation of ML models has significantly improved the accuracy and computational speed, compared to the existing retrieval techniques.
•ML-assisted optimization to predict 3D fractal morphology from microscopic images.•Linear Regression, ANN, Random Forest Regressor, XGBoost, and kNN are tested.•FracVAL is employed for tuning, generating aggregate structures.•Particle Swarm Optimization, JAYA and self-adaptive JAYA are used for Optimization.•Accurate and fast prediction of 3-dimensional properties from microscopic images.
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•The contents and spatial distributions of soil heavy metals were well predicted.•Thirteen environmental factors related to heavy metal pollution were considered.•Soil pollution was ...mainly affected by soil pH, PM2.5 and polluting enterprises.•Potential risk areas were classified and identified by cluster analysis.•The cooperative use of RF and FKM supported efficient soil management.
From the perspective of the mechanism of soil pollution, it is difficult to explain the process of predicting the spatial distributions of soil heavy metal pollution using traditional geostatistical methods at a regional scale. Furthermore, few methods are available to proactively identify potential risk areas for preventing soil contamination. In this study, we selected 13 environmental factors related to the accumulation of soil heavy metals based on the source-sink theory. Then, the fuzzy k-means method in combination with the random forest (RF) method was used to classify potential risk areas. The concentrations and spatial distributions of the heavy metals were well predicted by RF, and the average values of the root mean square error of the prediction and R2 were 4.84 mg kg−1 and 0.57, respectively. The results indicated that the soil pH, fine particulate matter, and proximity to polluting enterprises significantly influenced the heavy metal pollution in soils, and the environmental variables varied significantly across the identified subregions. This study provides a theoretical basis for the sustainable management and control of soil pollution at the regional scale.
Recent years have seen a significant amount of transportation data collected from multiple sources including road sensors, probe, GPS, CCTV and incident reports. Similar to many other industries, ...transportation has entered the generation of big data. With a rich volume of traffic data, it is challenging to build reliable prediction models based on traditional shallow machine learning methods. Deep learning is a new state-of-the-art machine learning approach which has been of great interest in both academic research and industrial applications. This study reviews recent studies of deep learning for popular topics in processing traffic data including transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, autonomous driving and driver behaviours. In general, the use of deep learning systems in transportation is still limited and there are potential limitations for utilising this advanced approach to improve prediction models.
An important component in learning is the method. The success or failure of a learning activity depends on the method used. Because it is the method that determines how the learning process is ...carried out in the classroom. The PAI learning method for ABK is a method used by PAI teachers in SLB so that the material presented is easy to understand and understand, so that SLB students can implement their understanding in everyday life. This study aims to identify and analyze the PAI learning method for children with special needs at SLB Negeri 1 Lombok Barat for the types of disabilities who are blind, deaf, mentally retarded and physically disabled. This research is a field research with a qualitative approach. Data collection was carried out using observation, interviews and documentation techniques. The research process lasted for almost 4 months with research subjects being PAI teachers, school principals, curriculum assistants, students with special needs and student guardians. The data analysis technique used is data collection, data condensation, data presentation and drawing conclusions. While the object of research is the learning method of Islamic religious education for Children with Special Needs in the types of disabilities who are Blind, Deaf, Mentally Disabled and Physically Disabled in SLB Negeri 1 West Lombok. The results showed that: the PAI learning method used by PAI teachers for the type of visual impairment is the lecture and question and answer method, the deaf class uses the lecture and demonstration method, the mentally retarded class uses the lecture method and the disabled class uses the lecture and question and answer method.
In this paper, we use AI to predict the ground information at unknown points from the ground information at known points in Urayasu City, which has a large amount of reclaimed land and a very high ...liquefaction potential. The paper also introduces a liquefaction hazard map and a method for indicating liquefaction hazard using the PL method based on the predicted ground information. Damage to structures due to liquefaction was widely recognized after the 1964 Niigata and Alaska earthquakes. Since then, analysis of the mechanism of liquefaction has been conducted based on field surveys and experiments, and research has been conducted on the occurrence of liquefaction and countermeasures against it. Given the concern about the occurrence of large-scale earthquakes such as the Nankai Trough Earthquake in the near future, the evaluation of liquefaction hazards will become even more important. In order to establish ground investigation and countermeasure methods against liquefaction and subsidence, it is essential to obtain detailed information in the ground. It is essential to develop and establish a method to predict unknown points or unknown areas in the ground with high accuracy based on the limited results of ground investigation.
Most human cancers develop from stem and progenitor cell populations through the sequential accumulation of various genetic and epigenetic alterations. Cancer stem cells have been identified from ...medulloblastoma (MB), but a comprehensive understanding of MB stemness, including the interactions between the tumor immune microenvironment and MB stemness, is lacking. Here, we employed a trained stemness index model based on an existent one‐class logistic regression (OCLR) machine‐learning method to score MB samples; we then obtained two stemness indices, a gene expression‐based stemness index (mRNAsi) and a DNA methylation‐based stemness index (mDNAsi), to perform an integrated analysis of MB stemness in a cohort of primary cancer samples (n = 763). We observed an inverse trend between mRNAsi and mDNAsi for MB subgroup and metastatic status. By applying the univariable Cox regression analysis, we found that mRNAsi significantly correlated with overall survival (OS) for all MB patients, whereas mDNAsi had no significant association with OS for all MB patients. In addition, by combining the Lasso‐penalized Cox regression machine‐learning approach with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature that accurately predicted survival in patients with Sonic hedgehog (SHH) MB. Furthermore, positive correlations between mRNAsi and prognostic copy number aberrations in SHH MB, including MYCN amplifications and GLI2 amplifications, were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature. Our findings based on stemness indices might advance the development of objective diagnostic tools for quantitating MB stemness and lead to novel biomarkers that predict the survival of patients with MB or the efficacy of strategies targeting MB stem cells.
Here, we employed a trained stemness index model to perform an integrated analysis of medulloblastoma (MB) stemness. By combining the Lasso‐penalized Cox regression with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature. Furthermore, positive correlations between gene expression‐based stemness index and prognostic copy number aberrations were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature.
In this paper, we do a critical review of statistical methods for landslide susceptibility modelling and associated terrain zonations. Landslide susceptibility is the likelihood of a landslide ...occurring in an area depending on local terrain conditions, estimating “where” landslides are likely to occur. Since the first attempts to assess landslide susceptibility in the mid-1970s, hundreds of papers have been published using a variety of approaches and methods in different geological and climatic settings. Here, we critically review the statistically-based landslide susceptibility assessment literature by systematically searching for and then compiling an extensive database of 565 peer-review articles from 1983 to 2016. For each article in the literature database, we noted 31 categories/sub-categories of information including study region/extent, landslide type/number, inventory type and period covered, statistical model used, including variable types, model fit/prediction performance evaluation method, and strategy used to assess the model uncertainty. We present graphical visualisations and discussions of commonalities and differences found as a function of region and time, revealing a significant heterogeneity of thematic data types and scales, modelling approaches, and model evaluation criteria. We found that the range of thematic data types used for susceptibility assessment has not changed significantly with time, and that for a number of studies the geomorphological significance of the thematic data used is poorly justified. We also found that the most common statistical methods for landslide susceptibility modelling include logistic regression, neural network analysis, data-overlay, index-based and weight of evidence analyses, with an increasing preference towards machine learning methods in the recent years. Although an increasing number of studies in recent years have assessed the model performance, in terms of model fit and prediction performance, only a handful of studies have evaluated the model uncertainty. Adopting a Susceptibility Quality Level index, we found that the quality of published models has improved over the years, but top-quality assessments remain rare. We identified a clear geographical bias in susceptibility study locations, with many studies in China, India, Italy and Turkey, and only a few in Africa, South America and Oceania. Based on previous literature reviews, the analysis of the information collected in the literature database, and our own experience on the subject, we provide recommendations for the preparation, evaluation, and use of landslide susceptibility models and associated terrain zonations.
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•PCA and t-SNE improved the model performance for predicting BaP concentration.•RF improved the model performance for predicting BaP concentration.•Raman spectroscopy is efficient in ...the detection of BaP concentration in peanut oil.
Benzo(a)pyrene (BaP) generated in the production process of oil is harmful to human severely as a kind of carcinogenic substance. In this study, the qualitative and quantitative detection of BaP concentration in peanut oil was investigated based on Raman spectroscopy combined with machine learning methods. The glass substrates and magnetron sputtered gold substrates for the Raman spectra were compared and the data preprocessing methods of principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were used to process Raman signal. Back propagation neural network (BPNN), partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) algorithms were developed to obtain the qualitative and quantitative detection model of BaP concentration in peanut oil. The results showed that the Raman spectra with the glass substrate was more suitable for the BaP detection than magnetron sputtered gold substrates. RF combined with t-SNE could achieve an accuracy of 97.5% in the qualitative detection of BaP concentration levels in model validation experiment, and the correlation coefficient of the prediction set (Rp) in the quantitative detection was 0.9932, the root mean square error (RMSEP) was 0.8323 μg/kg and the bias was 0.1316 μg/kg. It can be concluded that Raman spectroscopy combined with machine learning methods could provide an effective method for the rapid determination of BaP concentration in peanut oil.
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