Customized physical and structural features of phosphate-based glass-ceramics: role of ag nanoparticles and ho3+ impurities

The effects of silver nanoparticles (Ag NPs) embedment on the physical and structural characteristics of the holmium ions (Ho3+) activated phosphate-based glass-ceramics were assessed. Two series of such glass-ceramics were prepared using the melt-quenching and characterized. In the first series, the Ag NPs were nucleated from the incorporated AgCl via the redox process. In the second series, the pure Ag nanopowder was directly added. The overall properties of these glass-ceramics were strongly sensitive to the cooling procedure and NPs addition strategies, leading to different density and refractive index modifications in the two series. The recorded O1s XPS peaks were exploited to determine the bridging to non-bridging oxygen ratios in the studied glass-ceramics network that enabled to unfold the differences in the observed inferences. A compelling correlation among various attributes in the achieved glass-ceramics was established. Briefly, the overall traits of the proposed glass-ceramics were tailored by regulating the preparation conditions

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.</jats:p

Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data

Terrestrial features extraction, such as roads and buildings from aerial images using an automatic system, has many usages in an extensive range of fields, including disaster management, change detection, land cover assessment, and urban planning. This task is commonly tough because of complex scenes, such as urban scenes, where buildings and road objects are surrounded by shadows, vehicles, trees, etc., which appear in heterogeneous forms with lower inter-class and higher intra-class contrasts. Moreover, such extraction is time-consuming and expensive to perform by human specialists manually. Deep convolutional models have displayed considerable performance for feature segmentation from remote sensing data in the recent years. However, for the large and continuous area of obstructions, most of these techniques still cannot detect road and building well. Hence, this work’s principal goal is to introduce two novel deep convolutional models based on UNet family for multi-object segmentation, such as roads and buildings from aerial imagery. We focused on buildings and road networks because these objects constitute a huge part of the urban areas. The presented models are called multi-level context gating UNet (MCG-UNet) and bi-directional ConvLSTM UNet model (BCL-UNet). The proposed methods have the same advantages as the UNet model, the mechanism of densely connected convolutions, bi-directional ConvLSTM, and squeeze and excitation module to produce the segmentation maps with a high resolution and maintain the boundary information even under complicated backgrounds. Additionally, we implemented a basic efficient loss function called boundary-aware loss (BAL) that allowed a network to concentrate on hard semantic segmentation regions, such as overlapping areas, small objects, sophisticated objects, and boundaries of objects, and produce high-quality segmentation maps. The presented networks were tested on the Massachusetts building and road datasets. The MCG-UNet improved the average F1 accuracy by 1.85%, and 1.19% and 6.67% and 5.11% compared with UNet and BCL-UNet for road and building extraction, respectively. Additionally, the presented MCG-UNet and BCL-UNet networks were compared with other state-of-the-art deep learning-based networks, and the results proved the superiority of the networks in multi-object segmentation tasks

Validation of the Antiproliferative Effects of Organic Extracts from the Green Husk of Juglans regia L. on PC-3 Human Prostate Cancer Cells by Assessment of Apoptosis-Related Genes

With the increased use of plant-based cancer chemotherapy, exploring the antiproliferative effects of phytochemicals for anticancer drug design has gained considerable attention worldwide. This study was undertaken to investigate the effect of walnut green husk extracts on cell proliferation and to determine the possible molecular mechanism of extract-induced cell death by quantifying the expression of Bcl-2, Bax, caspases-3, and Tp53. PC-3 human prostate cancer cells. In this study, we found that green husk extracts suppressed proliferation and induced apoptosis in a dose- and time-dependent manner by modulating expression of apoptosis-related genes. This involved DNA fragmentation (determined by TUNEL assay) and significant changes in levels of mRNA and the expression of corresponding proteins. An increase in expressions of Bax, caspase-3, and tp53 genes and their corresponding proteins was detected using real-time PCR and western blot analysis in PC-3 cells treated with the green husk organic extracts. In contrast, Bcl2 expression was downregulated after exposure to the extracts. Our data suggest the presence of bioactive compound(s) in walnut green husks that are capable of killing prostate carcinoma cells by inducing apoptosis and that the husks are a candidate source of anticancer drugs

Folk Knowledge and Perceptions about the Use of Wild Fruits and Vegetables–Cross-Cultural Knowledge in the Pipli Pahar Reserved Forest of Okara, Pakistan

\ua9 2024 by the authors.Wild fruits and vegetables (WFVs) have been vital to local communities for centuries and make an important contribution to daily life and income. However, traditional knowledge of the use of wild fruits is at risk of being lost due to inadequate documentation. This study aimed to secure this knowledge through intermittent field visits and a semi-structured questionnaire. Using various ethnobotanical data analysis tools and SPSS (IBM 25), this study identified 65 WFV species (52 genera and 29 families). These species, mostly consumed as vegetables (49%) or fruits (43%), were predominantly herbaceous (48%) in wild and semi-wild habitats (67%). 20 WFVs were known to local communities (highest RFC), Phoenix sylvestris stood out as the most utilized species (highest UV). Surprisingly, only 23% of the WFVs were sold at markets. The survey identified 21 unique WFVs that are rarely documented for human consumption in Pakistan (e.g., Ehretia obtusifolia, Euploca strigosa, Brassica juncea, Cleome brachycarpa, Gymnosporia royleana, Cucumis maderaspatanus, Croton bonplandianus, Euphorbia prostrata, Vachellia nilotica, Pongamia pinnata, Grewia asiatica, Malvastrum coromandelianum, Morus serrata, Argemone mexicana, Bambusa vulgaris, Echinochloa colonum, Solanum virginianum, Physalis angulata, Withania somnifera, Zygophyllum creticum, and Peganum harmala), as well as 14 novel uses and five novel edible parts. Despite their ecological importance, the use of WFVs has declined because local people are unaware of their cultural and economic value. Preservation of traditional knowledge through education on conservation and utilization could boost economies and livelihoods in this and similar areas worldwide

Integration of radiometric ground-based data and high-resolution quickbird imagery with multivariate modeling to estimate maize traits in the nile delta of Egypt

In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91–0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources

The effectiveness of different explicit vocabulary-teaching strategies on learners’ retention of technical and academic words

The effectiveness of explicit instruction, within the context of strategy development in learners, has been widely accepted for several years. However, the methods used within explicit vocabulary instruction are varied and comparatively few studies have directly compared methods. This study investigates the type of instruction (in a visual or written context) as well as the timing of the activity (before or after the main activity). Seventy Arabic learners of English were tested in a 2*2 design. Results showed a positive effect for pre-teaching vocabulary in a visual condition. We conclude that this pre-emptive multimodal approach heightens the learners’ ability to notice vocabulary items thus providing an effective strategy to increase vocabulary intake

A Single DC Source Nine-Level Switched-Capacitor Boost Inverter Topology with Reduced Switch Count

This paper presents a new boost inverter topology with nine level output voltage waveform using a single dc source and two switched capacitors. The capacitor voltages are self-balancing and thus is devoid of any sensors and auxiliary circuitry. The output voltage is twice higher than the input voltage, which eliminates the need for an input dc boost converter especially when the inverter is powered from a renewable source. The merits of the proposed topology in terms of the number of devices and cost are highlighted by comparing the recent and conventional inverter topologies. In addition to this, the total voltage stress of the proposed topology is lower and have a maximum efficiency of 98.25%. The operation and dynamic performance of the proposed topology have been simulated using PLECS software and are validated using an experimental setup considering a different dynamic operation.This work was supported in part by the Scientific Research Deanship, Taif University, Saudi Arabia, under Grant 1-439 - 6072.Scopu

Altered subgenomic RNA abundance provides unique insight into SARS-CoV-2 B.1.1.7/Alpha variant infections

B.1.1.7 lineage SARS-CoV-2 is more transmissible, leads to greater clinical severity, and results in modest reductions in antibody neutralization. Subgenomic RNA (sgRNA) is produced by discontinuous transcription of the SARS-CoV-2 genome. Applying our tool (periscope) to ARTIC Network Oxford Nanopore Technologies genomic sequencing data from 4400 SARS-CoV-2 positive clinical samples, we show that normalised sgRNA is significantly increased in B.1.1.7 (alpha) infections (n = 879). This increase is seen over the previous dominant lineage in the UK, B.1.177 (n = 943), which is independent of genomic reads, E cycle threshold and days since symptom onset at sampling. A noncanonical sgRNA which could represent ORF9b is found in 98.4% of B.1.1.7 SARS-CoV-2 infections compared with only 13.8% of other lineages, with a 16-fold increase in median sgRNA abundance. We demonstrate that ORF9b protein levels are increased 6-fold in B.1.1.7 compared to a B lineage virus in vitro. We hypothesise that increased ORF9b in B.1.1.7 is a direct consequence of a triple nucleotide mutation in nucleocapsid (28280:GAT > CAT, D3L) creating a transcription regulatory-like sequence complementary to a region 3’ of the genomic leader. These findings provide a unique insight into the biology of B.1.1.7 and support monitoring of sgRNA profiles to evaluate emerging potential variants of concern