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This expansive photographic volume serves as a visual chronicle of the Emirate of Abu Dhabi, capturing a period of monumental architectural and social transformation. Through a curated \"tour\" that spans the urban heart of the capital to the serene depths of the desert, Chris Berry and a team of eleven contributing photographers document the striking contrast between the emirate’s ancient Bedouin heritage and its status as a futuristic global hub.

Developing countries have depleted their natural resources in economic interest to achieve high economic growth. Current urbanization patterns and energy consumption and natural resource extraction are largely unsustainable. In this background, this paper investigates the impact of natural resources rent, energy resources consumption, and tax revenue on carbon emissions for developing countries. The study employed data for 48 developing countries from 1990 to 2020. We used second-generation methods for empirical analysis that control heterogeneity and cross-sectional dependence in the data. The advanced panel data estimates of CS-ARDL provide reliable outcomes by addressing these panel data econometric issues. The study results revealed that natural resources or natural resources rent in their exploitation accelerates carbon emission. Similarly, energy resources excessive consumption and economic growth are highly carbon-intensive for these countries and lead to environmental degradation. In contrast, tax revenue and education stabilized the environmental quality of the study interest. Besides this, to analyze the directional association among variables, the study applied DH causality test, which indicates a bidirectional link between tax revenues and emissions, energy resources and emissions, and income and CO 2 emissions. Based on the finding, the study suggests some policy implications to limit the extraction of natural resources and abate carbon emissions by establishing appropriate strategies and imposing environmental charges.

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.

Now a day’s variety of nanomaterials is available, among these Aluminum Alloys AA7072 and AA705 are significant due to their thermal, physical and mechanical characteristics. These extensively used in manufacturing of spacecraft, aircraft parts and building testing. Keeping in view the significance of nanoliquids, the analysis of methanol suspended by AA7072 and AA7075 alloys under the multiple physical flow conditions is reported. The model is successfully treated by coupling of RK and shooting algorithm and examined the results for the flow regimes by altering the ingrained physical parameters. Then physical interpretation of the results discussed comprehensively. To validate the analysis, a comparison between the presented and existing is reported under certain assumptions on the flow parameters. It is found that the results are reliable inline with existing once.

Cellulose and chitin are the most abundant naturally occurring biopolymers synthesized in plants and animals and are used for synthesis of different organic compounds and acids in the industry. Therefore, cellulases and chitinases are important for their multiple uses in industry and biotechnology. Moreover, chitinases have a role in the biological control of phytopathogens. A bacterial strain Bacillus subtilis TD11 was previously isolated and characterized as a putative biocontrol agent owing to its significant antifungal potential. In this study, cellulase and chitinase produced by the strain B . subtilis TD11 were purified and characterized. The activity of the cellulases and chitinases were optimized at different pH (2 to 10) and temperatures (20 to 90°C). The substrate specificity of cellulases was evaluated using different substances including carboxymethyl cellulose (CMC), hydroxyethyl cellulose (HEC), and crystalline substrates. The cellulase produced by B . subtilis TD11 had a molecular mass of 45 kDa while that of chitinase was 55 kDa. The optimal activities of the enzymes were found at neutral pH (6.0 to 7.0). The optimum temperature for the purified cellulases was in the range of 50 to 70°C while, purified chitinases were optimally active at 50°C. The highest substrate specificity of the purified cellulase was found for CMC (100%) followed by HEC (>50% activity) while no hydrolysis was observed against the crystalline substrates. Moreover, it was observed that the purified chitinase was inhibitory against the fungi containing chitin in their hyphal walls i.e., Rhizoctonia , Colletotrichum , Aspergillus and Fusarium having a dose-effect relationship.

In this technologically developed scenario, many organizations in developing countries including Pakistan have expanded the enthusiasm for understanding and creating an encouraging administrative and managerial environment. Numerous organizations are struggling for structural changes by deserting the old-fashioned organizational management style and implementing an empowering leadership where leaders give more authority to subordinates in decision making and responsibilities with the aim to increase organizational productivity. Therefore, the study examined the leadership empowering behaviour as a predictor of employees’ psychological well-being of the educational institutions at secondary level in Kohat Division, Pakistan. A total sample of 564 secondary school teachers (male n = 379; female n = 185) was carefully chosen through a stratified random sampling technique. In this study, a non-experimental predictive correlational design was adopted. In order to collect data from the participants, two different standardized research tools i.e., the Leader Empowering Behaviour Questionnaire and Ryff’s Psychological Well-being Scale were used. After the collection of data, it was analyzed on the basis of mean, standard deviation, Pearson’s product-moment correlation, and multiple linear regression model. In conclusion, the study confirmed a significant positive correlation between leadership empowering behaviour and employees’ psychological well-being. Leadership empowering behaviours predict employees’ psychological well-being positively. Therefore, it was recommended that empowering behaviour might be adopted by the school leaders to improve the employees’ psychological well-being for better organizational productivity.

Staining is a crucial step in histopathology that prepares tissue sections for microscopic examination. Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models. These models have the potential to improve staining accuracy, reduce staining time, and minimize the use of hazardous chemicals, making histopathology a safer and more efficient field. In this study, we introduce a novel three-stage, dual contrastive learning-based, image-to-image generative (DCLGAN) model for virtually applying an \"H&E stain\" to unstained skin tissue images. The proposed model utilizes a unique learning setting comprising two pairs of generators and discriminators. By employing contrastive learning, our model maximizes the mutual information between traditional H&E-stained and virtually stained H&E patches. Our dataset consists of pairs of unstained and H&E-stained images, scanned with a brightfield microscope at 20 × magnification, providing a comprehensive set of training and testing images for evaluating the efficacy of our proposed model. Two metrics, Fréchet Inception Distance (FID) and Kernel Inception Distance (KID), were used to quantitatively evaluate virtual stained slides. Our analysis revealed that the average FID score between virtually stained and H&E-stained images (80.47) was considerably lower than that between unstained and virtually stained slides (342.01), and unstained and H&E stained (320.4) indicating a similarity virtual and H&E stains. Similarly, the mean KID score between H&E stained and virtually stained images (0.022) was significantly lower than the mean KID score between unstained and H&E stained (0.28) or unstained and virtually stained (0.31) images. In addition, a group of experienced dermatopathologists evaluated traditional and virtually stained images and demonstrated an average agreement of 78.8% and 90.2% for paired and single virtual stained image evaluations, respectively. Our study demonstrates that the proposed three-stage dual contrastive learning-based image-to-image generative model is effective in generating virtual stained images, as indicated by quantified parameters and grader evaluations. In addition, our findings suggest that GAN models have the potential to replace traditional H&E staining, which can reduce both time and environmental impact. This study highlights the promise of virtual staining as a viable alternative to traditional staining techniques in histopathology.

Khan and Lanktree discuss glucagon-like peptide-1 (GLP-1) receptor agonists for treating chronic kidney disease in patients with type 2 diabetes. GLP-1 receptor agonists (also called \"glutides\") are an effective treatment for patients with type 2 diabetes and chronic kidney disease. They are a second-line treatment for patients with type 2 diabetes and chronic kidney disease. GLP-1 receptor agonists should not be used in combination with dipeptidyl peptidase 4 inhibitors.

Our cells are powered by small internal compartments known as mitochondria, which host several copies of their own ‘mitochondrial’ genome. Defects in these semi-autonomous structures are associated with a range of severe, and sometimes fatal conditions: easily checking the health of mitochondria through cheap, quick and non-invasive methods can therefore help to improve human health. Measuring the concentration of mitochondrial DNA molecules in our blood cells can help to estimate the number of mitochondrial genome copies per cell, which in turn act as a proxy for the health of the compartment. In fact, having lower or higher concentration of mitochondrial DNA molecules is associated with diseases such as cancer, stroke, or cardiac conditions. However, current approaches to assess this biomarker are time and resource-intensive; they also do not work well across people with different ancestries, who have slightly different versions of mitochondrial genomes. In response, Chong et al. developed a new method for estimating mitochondrial DNA concentration in blood samples. Called AutoMitoC, the automated pipeline is fast, easy to use, and can be used across ethnicities. Applying this method to nearly 400,000 individuals highlighted 71 genetic regions for which slight sequence differences were associated with changes in mitochondrial DNA concentration. Further investigation revealed that these regions contained genes that help to build, maintain, and organize mitochondrial DNA. In addition, the analyses yield preliminary evidence showing that lower concentration of mitochondrial DNA may be linked to a higher risk of dementia. Overall, the work by Chong et al. demonstrates that AutoMitoC can be used to investigate how mitochondria are linked to health and disease in populations across the world, potentially paving the way for new therapeutic approaches.

The emergence and outbreak of the novel coronavirus (COVID-19) had a devasting effect on global health, the economy, and individuals’ daily lives. Timely diagnosis of COVID-19 is a crucial task, as it reduces the risk of pandemic spread, and early treatment will save patients’ life. Due to the time-consuming, complex nature, and high false-negative rate of the gold-standard RT-PCR test used for the diagnosis of COVID-19, the need for an additional diagnosis method has increased. Studies have proved the significance of X-ray images for the diagnosis of COVID-19. The dissemination of deep-learning techniques on X-ray images can automate the diagnosis process and serve as an assistive tool for radiologists. In this study, we used four deep-learning models—DenseNet121, ResNet50, VGG16, and VGG19—using the transfer-learning concept for the diagnosis of X-ray images as COVID-19 or normal. In the proposed study, VGG16 and VGG19 outperformed the other two deep-learning models. The study achieved an overall classification accuracy of 99.3%.