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Nowadays, many second/foreign language (L2) academic writing instruction programs place a high premium on pre-writing strategies. The current study examined the effect of software-supported concept mapping on lexical diversity (LD) of English learners’ argumentative essays within a process writing framework. Additionally, the relationship between the learners’ LD and their overall writing quality was investigated. To this end, 53 university English as a foreign language (EFL) undergraduates were assigned to a computer-aided concept mapping (CACM) and a traditional outlining condition over a span of seven weeks. The CACM group was instructed through the graphic organizer software Inspiration ® , whereas the comparison group underwent outlining instruction for planning their writing tasks. Measure of textual lexical diversity (MTLD) was used to assess the so-called D values of the assignments. The results revealed that the CACM group outperformed the outlining group in terms of LD scores. Also, no relationship was found between LD and overall quality of the essays. The findings provide L2 researchers and teachers with insights into understanding the use of CACM strategy in process writing. Moreover, exploiting MTLD afforded our experiment the opportunity to counteract potential pitfalls associated with text size. Further implications for the L2 teacher are also discussed.

This paper aims to examine the effects of Computer-Aided Argument Mapping (CAAM) on Saudi EFL learners’ argumentative writing performance across the development of writing content and coherence and their self-regulated learning skills. A total of 40 second-year university EFL learners were purposively selected as a one-group of pre- and post-test design. Using a mixed-method approach, three research tools were utilized: pre- and post-writing tests, a Self-regulated Learning Scale (SRLS), and semi-structured interviews. Quantitative results demonstrated that EFL learners’ argumentative writing performance made noteworthy gains, as manifested by the statistically significant differences between their pre- and post-test scores. Significant positive correlations were also found between the EFL learners’ overall argumentative writing performance and the SRL factor subscales, indicating an increase in the self-regulation mechanism relative to planning, self-monitoring, evaluation, effort, and self-efficacy. Qualitative results indicate that the participants have positively embraced the integration of CAAM to improve their writing skills and self-regulation processes. Recommendations for implementing digital mapping to revolutionize EFL learning classrooms in this digital era are provided.

GIS-based mineral prospectivity mapping (MPM) is a computer-aided methodology for delineating and better constraining target areas deemed prospective for mineral deposits of a particular type. The underlying algorithms are well-established and well-understood, but on the whole, MPM that is a multi-faceted and multi-criteria approach, is faced with a high degree of uncertainty. We distinguish three principal types of uncertainties: (1) data-related (e.g., the sometimes erroneous, inadequate, incomplete, unevenly distributed or poorly resolved nature of the input data); (2) model-related (e.g., the diversity and inherent natural variability of mineral deposits, our lack of complete knowledge of the targeted mineral deposit type, and our imperfect ability to interpret geoscience datasets); and (3) judgment-related (e.g., the influence of cognitive heuristics and biases). In this contribution, we review and characterize the key uncertainties listed above and provide possible solutions as to how they may be recognized and mitigated in the context of MPM. This review also clearly illustrates the need for future studies designed to carefully monitor each step of the MPM process and aims at reducing uncertainty by, for example, (1) using carefully vetted, high-quality input data, (2) developing targeting models based on the best possible understanding of the underlying mineral deposit models and backed by machine learning-based simulations of likely ore-forming processes, and (3) adopting advanced methods such as deep learning algorithms for effective integrating of predictor maps.

Isogeometric analysis has brought a paradigm shift in integrating computational simulations with geometric designs across engineering disciplines. This technique necessitates analysis-suitable parameterization of physical domains to fully harness the synergy between Computer-Aided Design and Computer-Aided Engineering analyses. Existing methods often fix boundary parameters, leading to challenges in elongated geometries such as fluid channels and tubular reactors. This paper presents an innovative solution for the boundary parameter matching problem, specifically designed for analysis-suitable parameterizations. We employ a sophisticated Schwarz–Christoffel mapping technique, which is instrumental in computing boundary correspondences. A refined boundary curve reparameterization process complements this. Our dual-strategy approach maintains the geometric exactness and continuity of input physical domains, overcoming limitations often encountered with the existing reparameterization techniques. By employing our proposed boundary parameter matching method, we show that even a simple linear interpolation approach can effectively construct a satisfactory analysis-suitable parameterization. Our methodology offers significant improvements over traditional practices, enabling the generation of analysis-suitable and geometrically precise models, which is crucial for ensuring accurate simulation results. Numerical experiments show the capacity of the proposed method to enhance the quality and reliability of isogeometric analysis workflows.

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.

There is a growing need for spatially continuous and quantitative soil information for environmental modeling and management, especially at the national scale. This study was aimed at predicting soil particle‐size fractions (PSF) for Nigeria using random forest model (RFM). Equal‐area quadratic splines were fitted to Nigerian legacy soil profile data to estimate PSFs at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) using the GlobalSoilMap project specification. We applied an additive log‐ratio (ALR) transformation of the PSFs. There was a better prediction performance (based on 33% model validation) in the upper depth intervals than the lower depth intervals (e.g., R2 of 0.53; RMSE of 13.59 g kg−1 for clay at 0–5 cm and R2 of 0.16; RMSE of 15.60 g kg−1 at 100–200 cm). Overall, the PSFs show marked variations across the entire Nigeria region with a higher sand content compared with silt and clay contents and increasing clay content with soil depth. The variation in soil texture (ST) shows a progressive transition from a coarse texture (sand) along the fringes of northern Nigeria (e.g., upper part of Maiduguri and Sokoto), to finer texture (loam to clay loam) toward the western part of the Niger Delta region in the south. The inclusion of depth as a predictor variable significantly improved the prediction accuracy of RFM especially at lower depth intervals. These results could be used for producing soil function maps for national agricultural planning and in assessments of environmental sustainability.

The benefits brought by the integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) are being proved by more and more research. The integration of the two systems is difficult for many reasons. Among them, data incompatibility is the most significant, as BIM and GIS data are created, managed, analyzed, stored, and visualized in different ways in terms of coordinate systems, scope of interest, and data structures. The objective of this paper is to review the relevant research papers to (1) identify the most relevant data models used in BIM/GIS integration and understand their advantages and disadvantages; (2) consider the possibility of other data models that are available for data level integration; and (3) provide direction on the future of BIM/GIS data integration.

Germination, the process whereby a dry, quiescent seed springs to life, has been a focus of plant biologist for many years, yet the early events following water uptake, during which metabolism of the embryo is restarted, remain enigmatic. Here, the nature of the cues required for this restarting in oilseed rape (Brassica napus) seed has been investigated. A holistic in vivo approach was designed to display the link between the entry and allocation of water, metabolic events and structural changes occurring during germination. For this, we combined functional magnetic resonance imaging with Fourier transform infrared microscopy, fluorescence-based respiration mapping, computer-aided seed modeling and biochemical tools. We uncovered an endospermal lipid gap, which channels water to the radicle tip, from whence it is distributed via embryonic vasculature toward cotyledon tissues. The resumption of respiration is initiated first in the endosperm, only later spreading to the embryo. Sugar metabolism and lipid utilization are linked to the spatiotemporal sequence of tissue rehydration. Together, this imaging study provides insights into the spatial aspects of key events in oilseed rape seeds leading to germination. It demonstrates how seed architecture predetermines the pattern of water intake, which sets the stage for the orchestrated restart of life.

Practical landslide inventory maps covering large-scale areas are essential in emergency response and geohazard analysis. Recently proposed techniques in landslide detection generally focused on landslides in pure vegetation backgrounds and image radiometric correction. There are still challenges in regard to robust methods that automatically detect landslides from images with multiple platforms and without radiometric correction. It is a significant issue in practical application. In order to detect landslides from images over different large-scale areas with different spatial resolutions, this paper proposes a two-branch Matrix SegNet to semantically segment input images by change detection. The Matrix SegNet learns landslide features in multiple scales and aspect ratios. The pre- and post- event images are captured directly from Google Earth, without radiometric correction. To evaluate the proposed framework, we conducted landslide detection in four study areas with two different spatial resolutions. Moreover, two other widely used frameworks: U-Net and SegNet, were adapted to detect landslides via the same data by change detection. The experiments show that our model improves the performance largely in terms of recall, precision, F1-score, and IOU. It is a good starting point to develop a practical, deep learning landslide detection framework for large scale application, using images from different areas, with different spatial resolutions.