Content-based recommender system is a subclass of information systems that recommends an item to the user based on its description. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.
The introduction outlines the subject of the research. One of the most
relevant early medieval elite kinship groups of the Italian kingdom were
the Hucpoldings, named after that Hucpold who had held the office of
count palatine under Louis II. Key features of the research are the long
chronological range and the wide geographical area investigated. The chapter
then retraces the main historiographical steps taken in investigations of early
medieval kinship groups from the second half of the twentieth century until
the latest developments. A specific section is dedicated to the presentation
and analysis of the documentary and narrative sources used in this research.
The conclusion summarizes the key features of the Hucpoldings as a
wide kinship group. Beyond assessing once more the legitimacy of such
prosopographic effort by placing this research in the proper historiographical
context, it underlines that the specific attention given to the women
of the kindred and to their cognatic ties allows us to draw a varied and
striking picture of the Hucpoldings, and in general of early medieval elites
kinship groups, compared with previous studies.
The global urgency to uncover medical countermeasures to combat the COVID-19 pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has revealed an unmet need for robust tissue culture models that faithfully recapitulate key features of human tissues and disease. Infection of the nose is considered the dominant initial site for SARS-CoV-2 infection and models that replicate this entry portal offer the greatest potential for examining and demonstrating the effectiveness of countermeasures designed to prevent or manage this highly communicable disease. Here, we test an air–liquid-interface (ALI) differentiated human nasal epithelium (HNE) culture system as a model of authentic SARS-CoV-2 infection. Progenitor cells (basal cells) were isolated from nasal turbinate brushings, expanded under conditionally reprogrammed cell (CRC) culture conditions and differentiated at ALI. Differentiated cells were inoculated with different SARS-CoV-2 clinical isolates. Infectious virus release into apical washes was determined by TCID50, while infected cells were visualized by immunofluorescence and confocal microscopy. We demonstrate robust, reproducible SARS-CoV-2 infection of ALI-HNE established from different donors. Viral entry and release occurred from the apical surface, and infection was primarily observed in ciliated cells. In contrast to the ancestral clinical isolate, the Delta variant caused considerable cell damage. Successful establishment of ALI-HNE is donor dependent. ALI-HNE recapitulate key features of human SARS-CoV-2 infection of the nose and can serve as a pre-clinical model without the need for invasive collection of human respiratory tissue samples.
This article is devoted to the history of the tradition of translations of the Qur’an into Russian from the nineteenth century to the translation by I. Yu. Krachkovsky. The article examines the background to the creation of these translations, their key features and their importance for the development of the Russian tradition of translation and interpretation of the Qur’an. Particular attention is paid to the importance of studying these translations of the Qur’an into Russian in the context of the development of the Russian tradition of Qur’anic interpretation and the Russian school of Islamic studies. The purpose of this study is also to attract Russian and foreign Islamologists and Qur’anologists to a thorough study of the heritage of the Russian tradition of Qur’anic translation and to consider the prospects of its development in the twenty- fi rst century.
Background: It's critical to identify COVID-19 patients with a higher death risk at early stage to give them better hospitalization or intensive care. However, thus far, none of the machine learning models has been shown to be successful in an independent cohort. We aim to develop a machine learning model which could accurately predict death risk of COVID-19 patients at an early stage in other independent cohorts.
Methods: We used a cohort containing 4711 patients whose clinical features associated with patient physiological conditions or lab test data associated with inflammation, hepatorenal function, cardiovascular function and so on to identify key features. To do so, we first developed a novel data preprocessing approach to clean up clinical features and then developed an ensemble machine learning method to identify key features.
Results: Finally, we identified 14 key clinical features whose combination reached a good predictive performance of AUC 0.907. Most importantly, we successfully validated these key features in a large independent cohort containing 15,790 patients.
Conclusions: Our study shows that 14 key features are robust and useful in predicting the risk of death in patients confirmed SARS-CoV-2 infection at an early stage, and potentially useful in clinical settings to help in making clinical decisions.
This paper aims to review some of the available tunable devices with emphasis on the techniques employed, fabrications, merits, and demerits of each technique. In the era of fluidic microstrip communication devices, versatility and stability have become key features of microfluidic devices. These fluidic devices allow advanced fabrication techniques such as 3D printing, spraying, or injecting the conductive fluid on the flexible/rigid substrate. Fluidic techniques are used either in the form of loading components, switching, or as the radiating/conducting path of a microwave component such as liquid metals. The major benefits and drawbacks of each technology are also emphasized. In this review, there is a brief discussion of the most widely used microfluidic materials, their novel fabrication/patterning methods.
The article introduced the anti-value concept of cowardice in the humorous discourse of Anglo-Saxon linguistic culture. This concept is one of the main anti-values of modern Anglo-Saxon linguistic culture; however, it received very little scientific attention. Based on cognitive and axiological analyses, the author identified and analyzed the main characteristics of cowardice in humorous discourse. The analysis involved 50 episodes of stand-up specials and 500 episodes of various sitcoms. The key features of the anti-value concept of cowardice included fear, the level of danger, and the importance of overcoming danger. Other important characteristics of cowardice included immoral actions, avoiding danger, inaction, loss of control over one’s bodily functions, and unreasonable behavior. The fear of death / injury / social disapproval proved to be the main reasons for cowardice. The author also analyzed the gender aspect of the concept. Cowardice appeared to be a typical male feature because humor is often derived from breaking the gender stereotype "a man is stronger / braver than a woman".
AbstractMirror self-recognition (MSR), widely regarded as an indicator of self-awareness, has not been demonstrated consistently in gorillas. We aimed to examine this issue by setting out a method to evaluate gorilla self-recognition studies that is objective, quantifiable, and easy to replicate. Using Suarez and Gallup’s (J Hum Evol 10:175–183, 1981) study as a reference point, we drew up a list of 15 methodological criteria and assigned scores to all published studies of gorilla MSR for both methodology and outcomes. Key features of studies finding both mark-directed and spontaneous self-directed responses included visually inaccessible marks, controls for tactile and olfactory cues, subjects who were at least 5 years old, and clearly distinguishing between responses in front of versus away from the mirror. Additional important criteria include videotaping the tests, having more than one subject, subjects with adequate social rearing, reporting post-marking observations with mirror absent, and giving mirror exposure in a social versus individual setting. Our prediction that MSR studies would obtain progressively higher scores as procedures and behavioural coding practices improved over time was supported for methods, but not for outcomes. These findings illustrate that methodological rigour does not guarantee stronger evidence of self-recognition in gorillas; methodological differences alone do not explain the inconsistent evidence for MSR in gorillas. By implication, it might be suggested that, in general, gorillas do not show compelling evidence of MSR. We advocate that future MSR studies incorporate the same criteria to optimize the quality of attempts to clarify the self-recognition abilities of gorillas as well as other species.
Federated learning is a new framework of machine learning, it trains models locally on multiple clients and then uploads local models to the server for model aggregation iteratively until the model converges. In most cases, the local epochs of all clients are set to the same value in federated learning. In practice, the clients are usually heterogeneous, which leads to the inconsistent training speed of clients. The faster clients will remain idle for a long time to wait for the slower clients, which prolongs the model training time. As the time cost of clients’ local training can reflect the clients’ training speed, and it can be used to guide the dynamic setting of local epochs, we propose a method based on deep learning to predict the training time of models on heterogeneous clients. First, a neural network is designed to extract the influence of different model features on training time. Second, we propose a dimensionality reduction rule to extract the key features which have a great impact on training time based on the influence of model features. Finally, we use the key features extracted by the dimensionality reduction rule to train the time prediction model. Our experiments show that, compared with the current prediction method, our method reduces 30% of model features and 25% of training data for the convolutional layer, 20% of model features and 20% of training data for the dense layer, while maintaining the same level of prediction error.