High Quality Class Research Articles

High-quality recycled concrete aggregates developed through an integrated thermomechanical approach

This study demonstrates an integrated thermomechanical treatment process to produce high quality recycled concrete aggregate (RCA) for structural concrete. RCA is mechanically treated after 90 min of heating at 250 °C in this process. The material properties of RCA produced through 15 combinations of charges and drum revolutions are compared using a statistical method. The highest ranking of an RCA identifies it as a high-quality class. Concrete composed of 100% RCA (recycled aggregate concrete, RAC) has 1.7% higher compressive strength than natural aggregate concrete (NAC). RAC has tensile and flexural strengths of 0.95 and 1.03 in fraction of NAC. RAC meets durability criteria such as UPV greater than 4.4 km/s, rapid chloride ion penetration test (RCPT) less than 1000 C, electrical resistivity greater than 208 Ohm-m, and sorptivity closer to NAC. Water absorption and abrasion resistance are similar to or superior to those of NAC. Coherent and uniform surface morphology of RCA enhances the ITZ microstructure in RAC.

An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands

Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R2) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO3, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints.

Exploring Physical Education Teachers' Intention and Perceived Constraints in Offering Online Lessons Using the Theory of Planned Behavior: A Multi-Country Analysis.

Guided by the theory of planned behavior, this study aimed to determine the influence of Physical Education (PE) teachers' attitudes, their perceived behavioral control, and the influence of subjective norms on their intention and constraints (intrapersonal, interpersonal, and structural) to offer a high-quality class based on best practices to deliver PE lessons online during the COVID-19 pandemic. This cross-sectional, multi-country survey study recruited PE teachers from five countries (China, Malaysia, the Philippines, Turkey, and the United States). A total of 928 online questionnaires were used in the analysis. In terms of the overall intention to teach online, our findings showed that American and Filipino teachers had higher levels of intention to continue teaching online. In contrast, Turkish, Malaysian, and Chinese teachers showed a lower interest. Moreover, Malaysian teachers had more intrapersonal constraints while the teachers in the other four countries were not as restrained intrapersonally. The results highlight the significant influence of perceived behavioral control and attitudes on PE teachers' intention to deliver online courses. Constraints to online teaching had a considerably large negative impact on attitudes, subjective norms, and perceived behavioral control. Based on the results, the proposed extension to the theory of planned behavior was an appropriate framework for understanding the behavioral intent of PE teachers.

Different Quality Classes of Decomposing Plant Residues Influence Dissolved Organic Matter Stoichiometry Which Results in Different Soil Microbial Processing

The influence of the quantities and ratios of dissolved organic carbon (DOC) and dissolved nitrogen (DN) generated by different chemical quality classes of organic residues on soil microbial processes in the decomposition process is not well understood. If the DOC-to-DN ratio (hereafter, ratio) of the substrate is close to that of the microbial C-to-N ratio, then the DOC-and-DN stoichiometry of the substrate is balanced, resulting in enhanced microbial processing, i.e., carbon use efficiency (CUE). Uncertainty exists about the influence of DN and the DOC-to-DN ratio on CUE, particularly in high-quality class (high nitrogen) residue-treated soils. A long-term field experiment was used to explore the effect of the annual application of residues of different quality classes on decomposition processes, focusing on the effects of DOC, DN, and the ratio on the microbial metabolic quotient (qCO2), which is the inverse of CUE. DOC and DN were extracted from soils during the 13th year of the experiment. Soils treated with high-quality class groundnut residue (high-nitrogen) had higher DN (5.4 ± 2.6 mg N kg−1) and a lower ratio (6.8 ± 2.6) than those treated with medium-quality (medium-nitrogen) tamarind (3.0 ± 0.6 and 10.7 ± 2.2, respectively). The positive influence of DN on qCO2 (R2 = 0.49 *) in groundnut-treated soil suggested that the high bioavailability of DN reduced CUE due to imbalanced DOC-and-DN stoichiometry. This contradicted earlier published findings on high-nitrogen residues which had balanced DOC-and-DN stoichiometry. The positive influence of the ratio on qCO2 under the tamarind-treated soil (R2 = 0.60 *) indicated that its balanced DOC-and-DN stoichiometry enhanced CUE. High-quality class organic residues can result in either higher or lower CUE than their lower-quality class counterparts depending on whether the resulting DOC-and-DN stoichiometry is balanced or imbalanced.

The efficacy and safety of bilateral synchronous transcutaneous auricular vagus nerve stimulation for prolonged disorders of consciousness: a multicenter, double-blind, stratified, randomized controlled trial protocol.

Treatment of disorders of consciousness (DOC) poses a huge challenge for clinical medicine. Transcutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive neuromodulation method, which shows potential in improving recovery of DOC. However, the evidence came from single-center, small-sample randomized controlled trial, which is insufficient to form a conclusion. Thereby, we propose a prospective, multicenter, double-blind, stratified, two-arm randomized controlled trial protocol to investigate the efficacy and safety of bilateral synchronous taVNS for treatment of DOC. We aim to recruit 382 patients with prolonged DOC, and divide them into an active stimulation group and a sham stimulation group. The patients in the active stimulation group will receive bilateral synchronous taVNS with a 200 μs pulse width, 20 Hz frequency, and personal adjusted intensity. The sham stimulation group will wear the same stimulator but without current output. Both groups will receive treatment for 30 min per session, twice per day, 6 days per week lasting for 4 weeks. The clinical assessment including Coma Recovery Scale-Revised (CRS-R), Full Outline of Unresponsiveness (FOUR), Glasgow Coma Scale (GCS), and Extended Glasgow Outcome Scale (GOS-E) will be conducted to evaluate its efficacy. Heart rate variability (HRV), blood pressure, and adverse events will be recorded to evaluate its safety. These results will enable us to investigate the efficacy and safety of taVNS for DOC. This protocol will provide multicenter, large-sample, high-quality Class II evidence to support bilateral synchronous taVNS for DOC, and will advance the field of treatment options for DOC.Clinical trial registration:https://www.chictr.org.cn/showproj.html?proj=221851, ChiCTR2400081978.

헤어전공 대학생이 지각하는 비대면수업의 학습실재감이 학습몰입과 학습성과에 미치는 영향

In rapidly changing social environments, the COVID-19 pandemic has brought dramatic changes in people's daily lives. They have worked to improve learning commitment and learning outcome by enhancing learning presence through smooth communications with students after going through trials and errors in non-face-to-face classes in order to adapt themselves to such new circumstances. This study attempted to investigate the influence of the learning presence of such online classes on learning commitment and learning outcome against college students majoring in hairstyling and develop a plan to deliver high-quality education with a goal of providing basic data for the growth and development of cosmetology education. For this, a self-administered questionnaire survey was performed against college students majoring in hairstyling in Seoul and Gyeonggi-do. A total of 301 copies were used for final analysis, and the results found the followings: It is required to develop high-quality class environments to make students recognize learning presence on their own and attain a smooth communication between teachers and students and among students. It is anticipated that there would be further studies with more specific academic majors.

Detection, instance segmentation, and classification for astronomical surveys with deep learning (deepdisc): detectron2 implementation and demonstration with Hyper Suprime-Cam data

ABSTRACT The next generation of wide-field deep astronomical surveys will deliver unprecedented amounts of images through the 2020s and beyond. As both the sensitivity and depth of observations increase, more blended sources will be detected. This reality can lead to measurement biases that contaminate key astronomical inferences. We implement new deep learning models available through Facebook AI Research’s detectron2 repository to perform the simultaneous tasks of object identification, deblending, and classification on large multiband co-adds from the Hyper Suprime-Cam (HSC). We use existing detection/deblending codes and classification methods to train a suite of deep neural networks, including state-of-the-art transformers. Once trained, we find that transformers outperform traditional convolutional neural networks and are more robust to different contrast scalings. Transformers are able to detect and deblend objects closely matching the ground truth, achieving a median bounding box Intersection over Union of 0.99. Using high-quality class labels from the Hubble Space Telescope, we find that when classifying objects as either stars or galaxies, the best-performing networks can classify galaxies with near 100 per cent completeness and purity across the whole test sample and classify stars above 60 per cent completeness and 80 per cent purity out to HSC i-band magnitudes of 25 mag. This framework can be extended to other upcoming deep surveys such as the Legacy Survey of Space and Time and those with the Roman Space Telescope to enable fast source detection and measurement. Our code, deepdisc, is publicly available at https://github.com/grantmerz/deepdisc.

Soil Quality Assessment in Response to Water Erosion and Mining Activity

Erosion significantly decreases the depth of a soil, the nutrients available for plants, the organic matter and, consequently, the productivity of the edaphic environment. Due to the above considerations, the objective of this study was to evaluate, through various properties, the quality of two eroded soils, one eroded by water and the other by mining activity, amended with biosolids. The quality for both soils was estimated through the selection of a minimum set of data by means of principal component analysis (PCA) and the subsequent realization of correlations, multiple regressions and finally calculations of normalized values (Vn) of those properties considered as indicators of soil quality. According to the results, inorganic nitrogen (NI) and respiratory activity (RA) were the properties selected as indicators to assess quality. For soil eroded by water and by mining activity, NI presented a low and very low quality, respectively (class 4 and 5 of quality according to the calculation of Vn). The quality of RA in soil eroded by mining extraction was very high (quality class 1 according to Vn), and thus it can be considered an ideal indicator for the evaluation of soil quality due to its sensitivity to anthropogenic changes (mining) in soil.

Risk factors for lumbar disc herniation recurrence after percutaneous endoscopic lumbar discectomy: a meta-analysis of 58 cohort studies.

Recurrent lumbar disc herniation (rLDH) is one of the most serious complications and major causes of surgical failure and paralysis following percutaneous endoscopic lumbar discectomy (PELD). There are reports in the literature on the identification of risk factors associated with rLDH; however, the results are controversial. Therefore, we conducted a meta-analysis to identify risk factors for rLDH among patients following spinal surgery. PubMed, EMBASE, and the Cochrane Library were searched without language restrictions from inception to April 2018 for studies reporting risk factors for LDH recurrence after PELD. MOOSE guidelines were followed in this meta-analysis. We used a random effects model to aggregate odds ratios (ORs) with 95% confidence intervals (CIs). The evidence of observational studies was classified into high quality (class I), medium quality (class II/III), and low quality (class IV) based on the P value of the total sample size and heterogeneity between studies. Fifty-eight studies were identified with a mean follow-up of 38.8months. Studies with high-quality (class I) evidence showed that postoperative LDH recurrence after PELD was significantly correlated with diabetes (OR, 1.64; 95% CI, 1.14 to 2.31), the protrusion type LDH (OR, 1.62; 95% CI, 1.02 to 2.61), and less experienced surgeons (OR, 1.54; 95% CI, 1.10 to 2.16). Studies with medium-quality (class II or III) evidence showed that postoperative LDH recurrence was significantly correlated with advanced age (OR, 1.11; 95% CI, 1.05 to 1.19), Modic changes (OR, 2.23; 95% CI, 1.53 to 2.29), smoking (OR, 1.31; 95% CI, 1.00 to 1.71), no college education (OR, 1.56; 95% CI, 1.05 to 2.31), obesity (BMI ≥ 25kg/m2) (OR, 1.66; 95% CI, 1.11 to 2.47), and inappropriate manual labor (OR, 2.18; 95% CI, 1.33 to 3.59). Based on the current literature, eight patient-related and one surgery-related risk factor are predictors of postoperative LDH recurrence after PELD. These findings may help clinicians raise awareness of early intervention for patients at high risk of LDH recurrence after PELD.

Master-CAM: Multi-scale fusion guided by Master map for high-quality class activation maps

Class Activation Map (CAM) is one of the most popular approaches to visually explain the convolutional neural networks (CNNs). To obtain fine-grained saliency maps, some works fuse saliency signals of the same image at larger scales. However, existing methods based on multi-scale fusion cannot effectively remove the noise from larger-scale images. In this paper, we propose Master-CAM, which uses Master map to guide multi-scale fusion process to obtain a high-quality class activation map. Master-CAM utilizes the general localization ability of the Master map to reduce the noise of the maps. We call the one with the general localization ability among the saliency maps from the same image as Master map, which is the saliency map of the original-scale input in the multi-scale scenario. In addition, we also present a simple yet effective fusion strategy, Master-Fusion, which is derived from the fusion operation in Master-CAM. Master-Fusion strategy can be easily attached to some saliency methods to improve the performance of these methods. We show through qualitative and quantitative experiments that the proposed Master-CAM outperforms the state-of-the-art methods in different CNN frameworks and datasets.

Soil quality estimation using environmental covariates and predictive models: an example from tropical soils of Nigeria

BackgroundInformation addressing soil quality in developing countries often depends on results from small experimental plots, which are later extrapolated to vast areas of agricultural land. This approach often results in misinformation to end-users of land for sustainable soil nutrient management. The objective of this study was to estimate the spatial variability of soil quality index (SQI) at regional scale with predictive models using soil–environmental covariates.MethodsA total of 110 composite soil samples (0–30 cm depth) were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State, Nigeria, and selected soil physical and chemical properties were determined. We employed environmental covariates derived from a digital elevation model (DEM) and Sentinel-2 imageries for our modelling regime. We measured soil quality using two approaches [total data set (TDS) and minimum data set (MDS)]. Two scoring functions were also applied, linear (L) and non-linear (NL), yielding four indices (MDS_L, MDS_NL, TDS_L, and TDS_NL). Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis (PCA). Random forest (RF), support vector regression (SVR), regression kriging (RK), Cubist regression, and geographically weighted regression (GWR) were applied to predict SQI in unsampled locations.ResultsThe computed SQI via MDS_L was classified into five classes: le 0.38, 0.38–0.48, 0.48–0.58, 0.58–0.68, and ge 0.68, representing very low (class V), low (class IV), moderate (class III), high (class II) and very high (class I) soil quality, respectively. GWR model was robust in predicting soil quality (R2 = 0.21, CCC = 0.39, RMSE = 0.15), while RF was a model with inferior performance (R2 = 0.02, CCC = 0.32, RMSE = 0.15). Soil quality was high in the southern region and low in the northern region. High soil quality class (> 49%) and moderate soil quality class (> 14%) dominate the study area in all predicted models used.ConclusionsStructural stability index, sand content, soil oganic carbon content, and mean weight diameter of aggregates were the parameters used in establishing regional soil quality indices, while land surface water index, Sentinel-2 near-infrared band, plane curvature, and clay index were the most important variables affecting soil quality variability. The MDS_L and GWR are effective and useful models to identify the key soil properties for assessing soil quality, which can provide guidance for site-specific management of soils developed on diverse parent materials.

A Deep Learning-Based Decision Support System for Mobile Performance Marketing

In Mobile Performance Marketing (MPM), monetary compensation only occurs when an advertisement results in a conversion (e.g., sale of a product or service). In this work, we propose an intelligent decision support system (IDSS) to automatically select mobile marketing campaigns for users. The IDSS is based on a computationally efficient mobile user conversion prediction model that assumes a novel Percentage Categorical Pruning (PCP) categorical preprocessing and an online deep multilayer perceptron (MLP) reuse model (MLPr). Using private (nonpublicly available) business MPM data provided by a marketing company, the MLPr model outperformed an offline multilayer perceptron and a logistic regression, obtaining a high quality class discrimination when applied to sampled (85% to 92%) and complete (90% to 94%) data. In addition, the MLPr compared favorably with other machine learning (ML) models (e.g., Random Forest, XGBoost), as well as with other deep neural networks (e.g., diamond shaped). Moreover, we designed two strategies (A — best campaign selection; and B — random selection among the top candidate campaigns) to build the IDSS, in which the predictive deep learning model is used to perform a real-time selection of advertisement campaigns for mobile users. Using recently collected big data (with millions of redirect events) from a worldwide MPM company, we performed a realistic IDSS evaluation that considered three criteria: response time, potential profit and advertiser diversity. Overall, competitive results were achieved by the IDSS B strategy when compared with the current marketing company ad assignment method.

Uncertainty-Aware Deep Learning With Cross-Task Supervision for PHE Segmentation on CT Images.

Perihematomal edema (PHE) volume, surrounding spontaneous intracerebral hemorrhage (SICH), is an important biomarker for the presence of SICH-associated diseases. However, due to irregular shapes and extremely low contrast of PHE on CT images, manually annotating PHE in pixel-wise is time-consuming and labour intensive even for experienced experts, which makes it almost infeasible to deploy current supervised deep learning approaches for automated PHE segmentation. How to develop annotation-efficient deep learning to achieve accurate PHE segmentation is an open problem. In this paper, we, for the first time, propose a cross-task supervised framework by introducing slice-level PHE labels and pixel-wise SICH annotations, which are more accessible in clinical scenarios compared to pixel-wise PHE annotations. Specifically, we first train a multi-level classifier based on slice-level PHE labels to produce high-quality class activation maps (CAMs) as pseudo PHE annotations. Then, we train a deep learning model to produce accurate PHE segmentation by iteratively refining the pseudo annotations via an uncertainty-aware corrective training strategy for noise removal and a distance-aware loss for background compression. Experimental results demonstrate that, the proposed framework achieves a comparative performance with the fully supervised methods on PHE segmentation, and largely improves the baseline performance where only pseudo PHE labels are used for training. We believe the findings from this study of using cross-task supervision for annotation-efficient deep learning can be applied to other medical imaging applications.

Systematic review and meta-analysis of the risk factors of surgical site infection in patients with colorectal cancer.

BackgroundSurgical site infection (SSI) influenced the result of surgical treatment, which was known as the second most prevalent hospital-based infection. But, the factors of SSI are not uniform. The purpose of this study was to identify the risk factors of SSI in patients with colorectal cancer. We conducted a meta-analysis of epidemiological research to provide a scientific basis for the prevention of SSI.MethodsThe PubMed, Medline, Embase, China National Knowledge Infrastructure (CNKI), and Wanfang databases were independently searched by 2 researchers to identify all relevant studies. Studies were selected if they met the selection criteria, which was defined according to the PICOS principles. The quality of the evidence was assessed using Egger’s P value, study heterogeneity, and sample size. Studies were categorized into 3 groups as follows: low quality (Class 4), moderate quality (Class 2/3), and high quality (Class 1). The meta-analysis was performed using RevMan 5.3 software.ResultsA total of 17 studies involving 61,611 patients were included in the meta-analysis. The results identified 7 patient-related risk factors of SSI, including male gender, obesity, diabetes mellitus, American Society of Anesthesiologists (ASA) score, cigarette smoking, tumor location, and serum albumin level, and 5 treatment-related risk factors, including laparoscopic surgery, operation time, blood loss, blood transfusion, and abdominal surgical history. Age was not directly related to SSI in colorectal cancer.ConclusionsIt is possible that patients can be treated effectively by identifying these factors of SSI.

HQ2CL: A High-Quality Class Center Learning System for Deep Face Recognition.

Benefited from the proposals of function losses margin-based, face recognition has achieved significant improvements in recent years. Those losses aim to increase the margin between the different identities to enhance the discriminability. Ideally, the class center of different identities is far from each other, and face samples are compact around the corresponding class center. Hence, it's very vital to produce a high-quality class center. However, the distribution of training sets determines the class center. With low-quality samples being in the majority, the class center would be close to the samples with little identity information. As a result, it would impair the discriminability of the learned model for those unseen samples. In this work, we propose a High-Quality Class Center Learning system (HQ2CL). This is an effective system and guides the class center to approach the high-quality samples to keep the discriminability. Specifically, HQ2CL introduces a quality-aware scale and margin layer for the identification loss and constructs a new high-quality center loss. We implement the proposed system without additional burden. And we present the experimental evaluation over different face benchmarks. The experimental results show the superiority of our proposed HQ2CL over the state-of-the-arts.

A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.

Research on IT English Flipped Classroom Teaching Model Based on SPOC

In order to improve the existing problems in the teaching process of IT English courses and improve the quality of IT English, this paper conducts a research on the flipped classroom teaching mode of IT English based on SPOC. The present situation of IT English teaching is analyzed, and the problems existing in the teaching process are also analyzed. On the basis of the above thought, with the support of SPOC platform build IT turn English classroom teaching mode, namely, by setting the course target, learning for class in advance, choose high-quality class, learning information collection, upload the related resources, and do a good job in teaching design complete teacher preparation, and design the specific teaching unit of teaching process, teaching quality evaluation model was constructed. In this way, the teaching quality evaluation results of IT English flipped classroom are obtained, in order to further improve the teaching quality. The experimental results show that the flipped English teaching mode based on SPOC can effectively improve students’ performance and increase students’ average daily learning time and course satisfaction, and the practical application effect is good.

Monitoring of Neotropical Streams Using Macroinvertebrate Communities: Evidence from Honduras

Assessing the water quality by using biological indicators is a reliable and economically feasible way to promote environmental conservation in developing tropical countries. Here, we report one of the few examples of river biomonitoring in Honduras. In June 2005, benthic macroinvertebrates were collected from six sites in the Río Cangrejal basin. An adapted version of the Biological Monitoring Working Party index (BMWP) was used to assess the water quality because it is simple, consolidated, relatively easy to use, and needs a family-level identification. Moreover, two other community metrics were calculated, namely the total taxon richness and local contribution to beta diversity (LCBD). Differences in the biomonitoring and diversity metrics among sites and their correlations were statistically tested. Thirty-nine macroinvertebrate taxa were collected and, despite significant differences in the BMWP score, all sampling sites were classified in the high environmental quality class. A very strong and positive correlation between the BMPW and taxon richness was found, while LCBD did not vary significantly and did not correlate with the other metrics. Our results suggest that taxon richness could be used as a surrogate indicator to assess the water quality when consolidate biomonitoring methods are not available.

Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions

Food security has become a global concern for humanity with rapid population growth, requiring a sustainable assessment of natural resources. Soil is one of the most important sources that can help to bridge the food demand gap to achieve food security if well assessed and managed. The aim of this study was to determine the soil quality index (SQI) for El Fayoum depression in the Western Egyptian Desert using spatial modeling for soil physical, chemical, and biological properties based on the MEDALUS methodology. For this purpose, a spatial model was developed to evaluate the soil quality of the El Fayoum depression in the Western Egyptian Desert. The integration between Digital Elevation Model (DEM) and Sentinel-2 satellite image was used to produce landforms and digital soil mapping for the study area. Results showed that the study area located under six classes of soil quality, e.g., very high-quality class represents an area of 387.12 km2 (22.7%), high-quality class occupies 441.72 km2 (25.87%), the moderate-quality class represents 208.57 km2 (12.21%), slightly moderate-quality class represents 231.10 km2 (13.5%), as well as, a low-quality class covering an area of 233 km2 (13.60%), and very low-quality class occupies about 206 km2 (12%). The Agricultural Land Evaluation System for arid and semi-arid regions (ALESarid) was used to estimate land capability. Land capability classes were non-agriculture class (C6), poor (C4), fair (C3), and good (C2) with an area 231.87 km2 (13.50%), 291.94 km2 (17%), 767.39 km2 (44.94%), and 416.07 km2 (24.4%), respectively. Land capability along with the normalized difference vegetation index (NDVI) used for validation of the proposed model of soil quality. The spatially-explicit soil quality index (SQI) shows a strong significant positive correlation with the land capability and a positive correlation with NDVI at R2 0.86 (p < 0.001) and 0.18 (p < 0.05), respectively. In arid regions, the strategy outlined here can easily be re-applied in similar environments, allowing decision-makers and regional governments to use the quantitative results achieved to ensure sustainable development.

Improving Learning Outcome for GSL (German as a Second Language) Students in a Blended Learning Cumulative Assessment Material Science Course

First year students especially with migration background and language deficiencies rate material science in mechanical engineering as one of the fundamental courses with high work load and necessity of language skills due to the descriptive nature of the course. Therefore a blended learning course structure using based on inverted classroom teaching scenarios was established. Heart of the self-study period are visualizing peer-to-peer lecture films supported by micro-lectures along with various online teaching materials. Although students with migration background generally scored lower in tests due to the lack of language skills improved learning outcomes are demonstrated in high quality class discussions and in overall understanding. This paper introduces the learning structure and graded activities, evaluates the course and compares results of native German-speaking students to those of students with migration background.