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The recent years have witnessed an increasing importance of machine translation systems due to the prolific production on online texts in different disciplines and furthermore, the inability of traditional translation methods in addressing translation needs all over the world. It is even argued that training on translation tools should be integrated into translation pedagogies and ultimately, courses should be provided for students and professionals. In spite of the effectiveness of translation tools and systems in providing solutions in relation to different disciplines and text genres, the usability and reliability of such systems in terms of literary texts, however, is still highly controversial. Many critics and educators still underestimate the usefulness of the machine translation systems in literature, which could be partially attributed to the unique nature of the language of the literary texts. The issue has its pedagogical implications to translation instruction due to the needs to integrate emerging technologies in teaching and learning practices. For proper use of translation technologies in educational contexts, these need to be well evaluated. For this purpose, this study evaluates the usefulness of applying machine translation systems to literature with the purpose of identifying the challenges that may have negative impacts on the reliability of machine translation systems. In order to do this this, two translation systems are selected, namely, Google Translate and Q Translate. By way of illustration, the study is based on a corpus of two English short stories. The study is based on two prose fiction texts. The first is J. K. Rowling’s novel Harry Potter and the Philosopher’s Stone. The second is Edgar Allan Poe’s short story The Black Cat. Automatic translations generated by the two machine translation systems were compared to human made Arabic translations with the purpose of identifying the problems within these translations. Results indicate that different lexical, structural, and pragmatic errors are encountered by users which negatively impact the reliability of these translations. Educators and translation instructors need to reflect on the challenges of machine translation systems in relation to literature. Software developers need also to address the problems faced by users and students in the translation from and into the Arabic language.

By relying on meticulous and precise readings of the Glorious Qur'an and Al-Suyuti's (2020) Qur'anic interpretation, this study extracts the noble verses that describe the faces of humans on the Day of Judgment. It analyzes the semiotic signs that intricately depict human faces during this crucial event, which will suddenly befall humankind. The study identifies sixteen locations in the Glorious Qur'an that exhibit signs of faces on that inescapable day. These locations are equally divided into two categories, with eight indicating blissful faces and the remaining eight indicating tormented faces. The semiotic units signifying blessed faces display signs of felicity manifested on the faces of true believers. These signs include whiteness, radiance, clarity, laughter, and delight. On the other hand, the semiotic units signifying tormented faces reveal signs of fear, anxiety, sorrow, anger, regret, and loss that appear on the faces of disbelievers. These signs encompass darkness, frowning, distortion, humiliation, engagement in hard labor, and evident fatigue on their faces.

Repetition is a common feature of political discourse and is often used by politicians to reinforce their key messages and ideas. While politicians may strive to avoid repetition and maintain their speeches' fluidity and coherence, repetition is sometimes necessary to emphasize essential points and capture the audience's attention. It allows politicians to drive home their message and make it more memorable to their audience. Therefore, interpreters dealing with political discourse must consider any occurrence of repetition, as it is an essential stylistic feature of this genre. The study reported in this article focuses on the professional interpreters' management of repetition in spoken political discourse and their strategies to transfer it into the target language. It adopts a linguistic analysis methodology to identify errors in interpretation. The findings indicate that the disregard for repetition during interpretation results in a discrepancy in the intended meaning and content of the message conveyed between the source discourse and its interpretation.

Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system performance. Existing methods for quantifying cooling efficiency lack precision, hindering the optimization of PV system maintenance and renewable energy output. This research introduces a novel approach utilizing deep learning techniques to address these limitations. A U-Net architecture is employed to segment solar panels from background elements in thermal imaging videos, facilitating a comprehensive analysis of cooling system efficiency. Two predictive models—a 3-layer Feedforward Neural Network (FNN) and a proposed Convolutional Neural Network (CNN)—are developed and compared for estimating cooling percentages from individual images. The study aims to enhance the precision and reliability of heat mapping capabilities for non-invasive, vision-based monitoring of photovoltaic cooling dynamics. By leveraging deep regression techniques, the proposed CNN model demonstrates superior predictive capability compared to traditional methods, enabling accurate estimation of cooling efficiencies across diverse scenarios. Experimental evaluation illustrates the supremacy of the CNN model in predictive capability, yielding a mean square error (MSE) of just 0.001171821, as opposed to the FNN’s MSE of 0.016. Furthermore, the CNN demonstrates remarkable improvements in mean absolute error (MAE) and R-square, registering values of 1.2% and 0.95, respectively, whereas the FNN posts comparatively inferior numbers of 3.5% and 0.85. This research introduces labeled thermal imaging datasets and tailored deep learning architectures, accelerating advancements in renewable energy technology solutions. Moreover, the study provides insights into the practical implementation and cost-effectiveness of the proposed cooling efficiency monitoring system, highlighting hardware requirements, integration with existing infrastructure, and sensitivity analysis. The economic viability and scalability of the system are assessed through comprehensive cost-benefit analysis and scalability assessment, demonstrating significant potential for cost savings and revenue increases in large-scale PV installations. Furthermore, strategies for addressing limitations, enhancing predictive accuracy, and scaling to larger datasets are discussed, laying the groundwork for future research and industry collaboration in the field of photovoltaic thermal management optimization.

Background The use of restorations with antibacterial activity has become mandatory to control secondary caries, especially in atraumatic restorative treatment (ART). Therefore, this study was conducted to enhance the antibacterial activity of conventional glass ionomer cement (GIC) by adding nanoselenium (NSe) and to assess the impact on its cytocompatibility and compressive strength. Methods NSe was prepared and characterized, and its minimum inhibitory concentration (MIC) against Streptococcus mutans (S. mutans) was determined via the broth microdilution technique. Based on the MIC, grouping was performed. Group I included unmodified GIC samples mixed with water, and groups II–IV included GIC samples mixed with three different concentrations (75, 112.5, and 150 ppm, respectively) of the NSe suspension. Antibacterial activity against S. mutans was assessed via an agar disc diffusion test over four time intervals (24, 48, 72 h, and 7 days). The cytocompatibility of 100% and 10% concentrations of the sample extract was evaluated via a sulforhodamine B (SRB) assay against oral epithelial cells. Additionally, compressive strength testing was performed according to ISO 9917-1 using a universal testing machine. Results Regarding antibacterial activity, group IV presented significantly higher values than the other groups, followed by group III, at all time intervals. For cytocompatibility, group II had higher values of cell viability at both concentrations. For all groups, the 10% concentration had significantly higher values of cell viability than the 100% concentration. Moreover, none of the groups showed a statistically significant difference in compressive strength. Conclusions The addition of NSe at concentrations up to 150 ppm resulted in extended antibacterial activity against S. mutans for up to 7 days without affecting its compressive strength. Furthermore, the addition of NSe at a low concentration, such as 75 ppm, increased the viability of oral epithelial cells.

Because corrosive environments are created, agricultural equipment used in livestock farms and poultry houses that handle animal waste is vulnerable to corrosion. Therefore, the purpose of this work was to increase the corrosion resistance of AISI 1020 low-carbon steel by applying protective Ni@SiTiCNO nanocomposite coatings using the electrodeposition technique, where ‘@’ denotes the incorporation of nanoparticles into the matrix, not a core-shell structure. Fourier Transform Infrared Spectroscopy (FT-IR), Raman, Field Emission Scanning Electron Microscopy (FE-SEM, QUANTA EG) with an energy dispersive X-ray system (FE-SEM, EDS), and High-Resolution Transmission Electron Microscopy (HR-TEM) have all been used to study the structure and morphology of the SiTiCNO nanoparticle. Additionally, the surface area and optical characteristics were examined. During the deposition process, various SiTiCNO nanoparticle concentrations (0–2 g/L) and current densities (3–5 A/dm 2 ) were employed. The surface morphology, corrosion resistance (in urea 3.5%), and abrasion behavior have all been thoroughly examined. According to the corrosion resistance results, uncoated steel had a high corrosion rate (0.556 mm/a), whereas Ni@SiTiCNO deposited at 5/Adm 2 from the Watts electrolyte bath containing 2 g/L SiTiCNO nanoparticle had the lower corrosion rate (0.008 mm/a) means the highest corrosion resistance.

Salinity stress is one of the major environmental constraints responsible for a reduction in agricultural productivity. This study investigated the effect of exogenously applied nitric oxide (NO) (50 μM and 100 μM) in protecting wheat plants from NaCl-induced oxidative damage by modulating protective mechanisms, including osmolyte accumulation and the antioxidant system. Exogenously sourced NO proved effective in ameliorating the deleterious effects of salinity on the growth parameters studied. NO was beneficial in improving the photosynthetic efficiency, stomatal conductance, and chlorophyll content in normal and NaCl-treated wheat plants. Moreover, NO-treated plants maintained a greater accumulation of proline and soluble sugars, leading to higher relative water content maintenance. Exogenous-sourced NO at both concentrations up-regulated the antioxidant system for averting the NaCl-mediated oxidative damage on membranes. The activity of antioxidant enzymes increased the protection of membrane structural and functional integrity and photosynthetic efficiency. NO application imparted a marked effect on uptake of key mineral elements such as nitrogen (N), potassium (K), and calcium (Ca) with a concomitant reduction in the deleterious ions such as Na+. Greater K and reduced Na uptake in NO-treated plants lead to a considerable decline in the Na/K ratio. Enhancing of salt tolerance by NO was concomitant with an obvious down-regulation in the relative expression of SOS1, NHX1, AQP, and OSM-34, while D2-protein was up-regulated.

The degradation performance of solar photovoltaic (SPV) panels, is a critical issue for its adoption. The current study introduces a novel dual-cooling technique to enhance the performance of the SPV panels under conditions of contamination from bird droppings. The front and backside temperatures, output power, and efficiency of the cooled SPV panels were evaluated and compared. Results showed that the cooling process reduced the front and backside temperatures by 24–47% and 34–48% respectively, compared to contaminated SPV panels. The cooled SPV module exhibited an output current increase of 8–9% and an output voltage increase of 7–9% compared to both contaminated and controlled modules. Consequently, output power for the cooled SPV module increased by 12–33% and 7–12% compared to bird droppings and controlled modules, respectively. Moreover, the overall efficiency of the SPV module dropped to 15% in the presence of bird droppings, compared to 20% with the cooling process was applied. These findings suggest significant potential benefits for large-scale SPV installations, enhancing performance and efficiency.

Cancer is one of the most dangerous diseases as a result of its characteristic features such as indefinite growth of cells, infringement of healthy tissues, and also metastasis. In recent years, a wide range of new ways to treat cancer has been used. Dendrimer is a category of highly branched 3D structures that has multiple surface functional groups that increase its functionality and biocompatibility. The extraordinary, unequaled structural characteristics of dendrimers allow for their numerous assured biomedical applications. Gold nanoparticles with controllable physical and chemical characteristics have received attention for versatile diagnostic and curative applications. In this work, the synthesis methods and applications of dendrimer/gold nanoparticles in cancer therapy are summarized.

Extensive use of engineered nanoparticles has led to their eventual release in the environment. The present work aims to study the removal of Polyvinylpyrrolidone-coated silver nanoparticles (PVP-Ag-NPs) using Aspergillus niger and depict the role of exopolysaccharides in the removal process. Our results show that the majority of PVP-Ag-NPs were attached to fungal pellets. About 74% and 88% of the PVP-Ag-NPs were removed when incubated with A. niger pellets and exopolysaccharide-induced A. niger pellets, respectively. Ionized Ag decreased by 553 and 1290-fold under the same conditions as compared to stock PVP-Ag-NP. PVP-Ag-PVP resulted in an increase in reactive oxygen species (ROS) in 24 h. Results show an increase in PVP-Ag-NPs size from 28.4 to 115.9 nm for A. niger pellets and 160.3 nm after removal by stress-induced A. niger pellets and further increased to 650.1 nm for in vitro EPS removal. The obtained findings show that EPS can be used for nanoparticle removal, by increasing the net size of nanoparticles in aqueous media. This will, in turn, facilitate its removal through conventional filtration techniques commonly used at wastewater treatment plants.