In recent years, ‘transcreation’ has emerged in translation, integrating linguistic, cultural, and creative reinterpretation. With the rise of bibliometric analysis tools, mapping and assessing scientific activities across various fields have become possible, yet few studies apply these methods to translation. This study explores the developments and trends in transcreation within literary texts using quantitative and qualitative bibliometric analysis, particularly through VOSviewer. By analyzing 558 documents from the Scopus database focused on Arabic, rhetoric, transcreation, and translation, the study identifies the United States, United Kingdom, Jordan, Saudi Arabia, and Spain as the most active countries. The USA leads in total citations, followed by Saudi Arabia and the UK. King Saud University is highlighted as a leading institution. These findings provide a roadmap for future research and policy in applying Arabic rhetoric to the translation and transcreation of literary texts

Introduction: Parkinson’s disease (PD) is the most common motor neurodegenerative disease worldwide. Given the complexity of PD etiology and the different metabolic derangements correlated to the disease, metabolomics profiling of patients is a helpful tool to identify patho-mechanistic pathways for the disease development. Dopamine metabolism has been the target of several previous studies, of which some have reported lower phenylalanine and tyrosine levels in PD patients compared to controls.Methods: In this study, we have collected plasma from 27 PD patients, 18 reference controls, and 8 high-risk controls to perform a metabolomic study using liquid chromatography-electrospray ionization–tandem mass spectrometry (LC-ESI-MS/MS).Results: Our findings revealed higher intensities of trans-cinnamate, a phenylalanine metabolite, in patients compared to reference controls. Thus, we hypothesize that phenylalanine metabolism has been shifted to produce trans-cinnamate via L-phenylalanine ammonia lyase (PAL), instead of producing tyrosine, a dopamine precursor, via phenylalanine hydroxylase (PAH).Discussion: Given that these metabolites are precursors to several other metabolic pathways, the intensities of many metabolites such as dopamine, norepinephrine, and 3-hydroxyanthranilic acid, which connects phenylalanine metabolism to that of tryptophan, have been altered. Consequently, and in respect to Metabolic Control Analysis (MCA) theory, the levels of tryptophan metabolites have also been altered. Some of these metabolites are tryptamine, melatonin, and nicotinamide. Thus, we assume that these alterations could contribute to the dopaminergic, adrenergic, and serotonergic neurodegeneration that happen in the disease.

A limited number of electroencephalography (EEG) channels are useful for neonatal sleep classification, particularly in the Internet of Medical Things (IoMT) field, where compact and lightweight devices are essential to monitoring health effectively. A streamlined and cost-effective IoMT solution can be achieved by utilizing fewer EEG channels, thereby reducing data transmission and device processing requirements. Using only two channels of an EEG device, this study presents a binary and multistage classification of neonatal sleep. The binary classification (sleep vs awake) achieved an accuracy of 87.56%, and a Cohen’s kappa of 74.13%. The quiet sleep ( QS ) detection accuracy was 95.63%, with a Cohen’s kappa of 83.87%. For the three-stage classification, accuracy was 83.72%, and Cohen’s kappa was 69.73%. With only two channels, these are the highest performance parameters. The focus is on the fusion of features extracted through flexible analytical wavelet transform (FAWT) & discrete wavelet transform (DWT), ensemble-based voting models, and fewer channels. To feed crucial features into the ensemble-based voting model, feature importance, feature selection, and validation mechanisms were used. To design the voting classifier, several machine learning models were used, compared, and optimized. With SelectKBest feature selection, the proposed methodology was found to be the most effective. By using only two channels, this study shows the practicality of classifying neonatal sleep stages.

Facial expressions demonstrate the important information about our emotions and show the real intentions. In this study, a novel texture transformation method using graph structures is presented for facial expression recognition. Our proposed method consists of five steps. First the face image is segmented and resized. Then the proposed graph-based texture transformation is used as feature extractor. The exemplar feature extraction is performed using the proposed deep graph texture transformation. The extracted features are concatenated to obtain one dimensional feature set. This feature set is subjected to maximum pooling and principle component analysis methods to reduce the number of features. These reduced features are fed to classifiers and we have obtained the highest classification accuracy of 97.09% and 99.25% for JAFFE and TFEID datasets respectively Moreover, we have used CK + dataset to obtain comparison results and our textural transformation based model yielded 100% classification accuracy on the CK + dataset. The proposed method has the potential to be employed for security applications like counter terrorism, day care, residential security, ATM machine and voter verification.

COVID-19 represents a novel variant of the coronavirus disease, having rapidly disseminated across the globe. In recent studies pertaining to computer vision, image processing, and classification techniques, numerous methodologies have been introduced employing chest X-ray images and computerized tomography (CT) images. This investigation introduces novel fused deep features founded on CT and X-ray image classification, with the aim of detecting COVID-19 disease. The proposed approach encompasses three key phases: deep feature generation, iterative feature selection, and classification. During the feature generation phase, well-known pre-trained deep convolutional neural networks, namely DenseNet201, MobileNetV2, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were leveraged. Each network model generated 1000 features, which were subsequently fused, culminating in the acquisition of a final feature vector comprising 7000 dimensions. In order to distill the most pertinent information, the ReliefF and iterative maximum relevance minimum redundancy (RFImRMR) feature selection techniques were employed in the generation of the ultimate feature vector. To evaluate the performance of the proposed method, publicly available datasets of CT images and X-ray images were employed. Notably, the suggested deep learning approach attained accuracy rates of 99.33% and 93.10% for COVID-19 detection using CT and X-ray images, respectively. These achieved results serve as compelling evidence substantiating the efficacy of the proposed fused deep features and RFImRMR-based COVID-19 detection.

Metaverse is a new emerging platform that enables users to interact with each other and engage in many activities inside a collective virtual shared space. The metaverse is rapidly evolving, and it is essential to understand the perception and attitude of its potential users. Therefore, this research aims to study the users of the metaverse and obtain their opinions on some of the key variables of the metaverse. These variables include the core metaverse concept, readiness, ease of use, intention to belong to metaverse, intention to adopt, and value realization. We designed an online questionnaire that aims to measure these variables. Then, a deep analysis of the collected data was conducted using various exploratory descriptive statistics on all participants. Subsequently, we performed further descriptive and correlation analysis on the participants from Saudi Arabia, which resulted in clustering participants into three groups. The result of this work can help in understanding the current and potential users of the metaverse, especially the users from Saudi Arabia. This study will consequently help in developing the metaverse space, enhancing its features, and providing users with the best experiences. Our research contributes to the theory of the metaverse by justifying a bold value-based framework for metaverse adoption. This study also introduces three clusters, Skeptical, Unaware, and Optimists, of potential users of metaverse platforms, providing a clear description of each group.

Advances in “Precision Medicine” initiative, also known as “Personalized Medicine” is an emerging approach for disease treatment and prevention that has already led to innovative discoveries and has created “smart” applications and solutions tailored to a person's or a group of individuals' genetic profile, lifestyle, and environment interaction. Already many physicians as part of patient care routinely prescribe various molecular/genetic and other tests enabling them to select personalized treatments that improve the chances of survival and reduce exposure to adverse effects. This initiative should provide to medical and healthcare professionals with adequate resources and readily available solutions so that the target to specific treatments and care of the illnesses is achieved while at the same time protecting the privacy and safety of the individual is secured as well as the Electronic Health Records (EHRs) and whatever additional data is necessary within the context of Precision Medicine. This study, through a literature review mostly, and some case study analysis, examine whether personalized medicine is delivered to the patient in an “accurate” and “precise” way, as expected. This requires that Health and Human Services and other stakeholders and agencies collaborate to solicit the right input from patients while at the same time can identify and address any educational, practical, legal, and technical issues and providing smart solutions. The lack of proper medical education and advanced infrastructure are still major barriers to the adoption of Precision Medicine, therefore, the role of medical training in Precision Medicine is also examined and analyzed. Specific examples are discussed in an integrated, precise, and data-driven manner to provide “smart” solutions.

The concepts and terms defining the thrust of this special issue, i.e. gender, entrepreneurship (Berger et al., 2021), the digital divide (Bowen and Morris, 2019; Millan et al., 2021; Satalkina et al., 2021), Global South (Simaan, 2020) and Global North, are very well established in the literature. Nevertheless, relatively little has been written about (1) the gendered dimension of the digital divide, (2) the digital divide and the gendered dimension of entrepreneurship (Elliott et al., 2021); and finally, (3) the specificity of these topics as they are in the Global South and Global North's peripheries (Ojediran and Anderson, 2020; Wood et al., 2021; Althalathini et al., 2020). Even if research on each of these individual domains exists, relatively little research on the intersection of these three areas exists (but cf. Visvizi et al., 2023, and earlier Kasusse, 2005; Alden, 2003). Notably, given the pace and the pervasive impact of digital transformation globally, and their diverse political, social and economic manifestations, it is necessary that the mechanisms underlying these interconnected issues and developments are identified and explored. This special issue sought to encourage this kind of conversation, always in context of the United Nation's (UN) Sustainable Development Goals (SDGs). Specifically, the key objective of this special issue was to examine, (1) how the onset of increasingly sophisticated information and communication technology (ICT) influences gender and entrepreneurship in the Global South and in the peripheries of the Global North; (2) what types of interpretive lens and explanatory potential are offered by the existing literature on the subject and (3) whether best practice-sharing and specific business and policy strategies might be helpful in alleviating negative implications of the global digital transformation.

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption ofmedical images is very important to secure patient information. Encrypting these images consumes a lot of time onedge computing; therefore, theuse of anauto-encoder for compressionbefore encodingwill solve such a problem. In this paper, we use an auto-encoder to compress amedical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted. Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder, which is utilized to compress and encrypt the images, and the other assesses the classification accuracy of the image after decryption and decoding. Different hyperparameters of the encoder were tested, followed by the classification of the image to verify that no critical information was lost, to test the encryption and encoding resolution. In this approach, sixteen hyperparameter permutations are utilized, but this research discusses three main cases in detail. The first case shows that the combination of Mean Square Logarithmic Error (MSLE), ADAgrad, two layers for the auto-encoder, and ReLU had the best auto-encoder results with a Mean Absolute Error (MAE) = 0.221 after 50 epochs and 75% classification with the best result for the classification algorithm. The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error (MSE), RMSprop, three layers for the auto-encoder, and ReLU, which had the best classification accuracy of 65%, the auto-encoder gives MAE = 0.31 after 50 epochs. The third case is the worst, which is the combination of the hinge, RMSprop, three layers for the auto-encoder, and ReLU, providing accuracy of 20% and MAE = 0.485.

Background Identifying the most important predictors of substance use is crucial for developing effective prevention policies. Traditional statistical methods have some limitations in this regard. To address these limitations, the researchers utilized artificial intelligence (AI) methods to identify the top 10 most important predictors of cannabis use in Finland. Objective The objective of this study was to apply AI techniques to identify the key predictors of cannabis use in Finland. Specifically, the researchers aimed to determine the top 10 most important features related to cannabis use from a dataset consisting of 3229 observations and 313 questionnaire items, with 48 selected for preprocessing. Methods The researchers employed the recursive feature elimination (RFE) method as part of their AI analysis. This technique was used on 60 processed variables, following the application of missing data imputation, resampling, and scaling techniques. The RFE method allowed the researchers to narrow down the 60 variables to the top 10 most important features associated with cannabis use. Results The AI models developed using the selected features were able to predict cannabis use with a remarkable accuracy of 96% for the previous 12 months. The results of the study revealed that the social settings of individuals played the most significant role in predicting cannabis use in the context of Finland. Conclusions In conclusion, this study demonstrated the effectiveness of AI-based approaches in identifying the most critical predictors of cannabis use in Finland. The research highlighted that social settings had the highest impact on cannabis use in this setting. Moreover, the study showcased the potential of AI methods not only for identifying key risk indicators among various factors but also for optimizing the utilization of limited public resources when devising prevention strategies. These findings can be valuable for shaping targeted and efficient prevention policies to address cannabis use in Finland.