V. A. Korshunov, N. I. Briko, R. V. Polibin
et al.
Relevance. Pneumococcal infection remains one of the most significant health problems worldwide. Vaccination of adults against it has been carried out in the Russian Federation for over 10 years. During this time, more than 9 million people have been vaccinated. However, data on the level of coverage among adults of certain risk categories are not routinely collected. Our study in 2019 showed that it was low in most groups. Given the significant increase in the volume of vaccination over the past five years, it seems appropriate to conduct a study to assess changes in the level of coverage.Aim. To study the level of vaccination coverage against pneumococcal infection in adult risk groups in the Russian Federation.Materials and methods. An observational descriptive retrospective epidemiological study was conducted. Information on the number and contingents of people vaccinated against pneumococcal infection was collected by sending a request to the executive authorities of the constituent entities of the Russian Federation in the field of healthcare. The depth of data collection was 8 years (from 2015 to 2023 inclusive), information was received from 74 out of 89 regions. In addition, federal statistical observation forms were used: No. 5 «Information on preventive vaccinations» and No. 6 «Information on the contingents of children and adults vaccinated against infectious diseases», data from the Unified Interdepartmental Information and Statistical System. The results obtained were compared with the indicators obtained in the 2019 study, which was conducted according to a similar design. The analysis was carried out using descriptive statistics methods.Results. The coverage rate of vaccination against pneumococcal infection among the adult population in the Russian Federation increased from 1.5% in 2018 to 7.7% in 2023. The most significant coverage rates were achieved among persons subject to conscription for military service (78.5%) and persons over 60 years old, living in residential care facilities (87.7%). By 2023, vaccination coverage has increased among the following risk categories: individuals with chronic bronchopulmonary diseases (from 15.1% in 2018 to 47.9% in 2023), chronic heart diseases (from 3.8% to 17.0%), patients with endocrine diseases (from 1.1% to 17.6%), liver diseases (from 4.0% to 12.0%), healthcare workers (from 4.9% to 19.7%), school and preschool employees (from 3.1% to 12.9%), employees of residential care facilities (homes for elderly, nursing homes, ect.) (from 0.1% to 26.9%), the elderly population as a whole (from 1.4% to 12.7%), and working-age men (from 1.4% to 7.0%). There was virtually no increase in coverage among all groups of immunocompromised patients (1.0% in 2018, 6.2% in 2023), the working population with risk factors harmful to the respiratory system (0.9% in 2018, 5.0% in 2023), workers in the oil and gas and chemical industries (1.3% in 2018, 1.8% in 2023).Conclusion. The obtained results indicate the need to develop a strategy of measures to promote increased vaccination coverage in risk groups that are insufficiently covered by vaccination against pneumococcal infection.
Ocupando o duplo papel de veículo disseminador de males e de virtuoso ingrediente para a cura de doenças, as águas foram amplamente tematizadas pela bibliografia médica, cirúrgica e farmacêutica através do tempo. Em tratados, observações e avisos, as referências sobre os benefícios das águas ou os cuidados a se ter em sua identificação e uso se fizeram recorrentes. No Portugal do século XVIII, o escrutínio das propriedades de um “tipo” específico de água – aquele definido como termal, medicinal ou mineral – passa a ser feito, também, a partir das lentes da emergente ciência química, de modo que interpretações “mágicas” sobre sua composição são cada vez mais postas em xeque. Nessa esteira, um espaço específico do reino luso, nomeado justamente a partir de suas fontes e do benefício régio, as Caldas da Rainha, recebe especial atenção de uma série de letrados, dando corpo a uma significativa literatura sobre o tópico. Aqui, de modo a tornar esse rico conjunto sistematizado, apresento, além de um breve mapeamento e discussão dos principais contornos da temática, a edição anotada e ortograficamente atualizada de capítulos selecionados de um livreto, anônimo e pouco conhecido, que apesar de ter sido escrito por um empírico – ou, como se intitula, “um curioso” –, dialoga e confronta doutores, boticários e enfermeiros da época, o Observaçoens das agoas das Caldas da Rainha, de 1752.
The functions of the intellect - which is one of the topics raised in the field of epistemology - are the activities and effects that the intellect performs in acquiring knowledge and recognizing objects and facts. The purpose of this research is to know the various functions of philosophical intellect and their application in the births and innovations of Islamic philosophy. This article has been made using analytical and argumentative strategies and document methods, logical and syllogistic analysis. The functions of intellect in philosophy should not be considered exclusive to its argumentative function. Philosophical intellect has various functions, and in order to know the differences of Islamic philosophy from other philosophical schools and to continue the growth of Islamic philosophy, we must pay attention to all these functions. The functions of philosophical intellect include imaginations perception, finding universal concepts, dividing concepts; combining concepts; making new concepts; building coherence between related objects, imaginations or affirmations; to affirm; questioning; description; explanation; interpretation; analysis; reasoning; ideation; theorizing; And the criticism that the application of the mentioned functions in Islamic philosophy has led to many births, such as the presentation of some divisions of existence by Islamic philosophers, the conceptualization of the perpetual origination by Mir Damad, the presentation of the problem of mental existence byFakhr al-Din al-Razi, a new reading of Plato's political philosophy by Farabi, the analysis of how existence Generalities by Avicenna, evidences proving the originality of existence, the supreme spacious of energy by Allameh Tabataba'i, Suhrawardi's illuminated metaphysical theory, etc.
Philosophy of religion. Psychology of religion. Religion in relation to other subjects
E. M. Bogorodskaya, I. V. Nozdrevatykh, E. L. Khristoforova
et al.
Relevance. Studying the experience of working under the conditions of COVID-19 allows us to more effectively assess the functioning of anti-epidemic measures in the event of the occurrence or threat of new pandemics, including those caused by new pathogens. The risk of contracting COVID-19 among employees of medical organizations is higher compared to the rest of the population of the metropolis, which determines the need to develop additional measures in medical organizations to protect workers. Timely assessment of risks and preventive measures during a pandemic allow building a system to protect medical institutions from various biological threats when providing medical care. Aim. Assessment of the risks of contracting COVID-19 among employees of a large anti-tuberculosis medical organization in a metropolis. Materials & Methods. A survey of employees of a large anti-tuberculosis medical organization in Moscow was conducted. The survey was conducted on 46 key questions of epidemiological history, compliance with the requirements of the sanitary and anti-epidemic regime, alleged sources of infection, prognostic risks of the occurrence and course of COVID-19. During the period from August 1 to October 1, 2022, 1225 employees of the institution were surveyed (43.8% of the total number). The survey was conducted on a voluntary basis and was anonymous. When answering questions, participants provided information about demographic factors (age, gender), place of actual residence, the fact of contracting a new coronavirus infection, frequency of laboratory testing for SARS-CoV-2, immunization against COVID-19, possible places of contact with patients with COVID-19, the use of personal protective equipment (PPE) at home and at work, the regular use of skin antiseptics, as well as other information that allowed us to establish the risks of infection of workers at home and at work. To assess the risks of infection, respondents were divided into two groups depending on the presence of a positive test for SARS-CoV-2 (group I) or its absence (group II) as of October 1, 2022. The material for testing was nasal/oropharyngeal swabs. The research of employees was carried out on the basis of the laboratory of the anti-tuberculosis medical organization, as well as in other accredited laboratories of the metropolis using the molecular genetic method, and since February 2022 also using the express method that determines the SARS-CoV-2 antigen. Results. Among the survey participants and according to the questionnaire data, 797 employees suffered from the new coronavirus infection COVID-19, and 138 (17.3%) of them had repeated cases of the disease. Factors that increase the risk of COVID-19 among personnel of anti-tuberculosis medical organizations directly involved in the fight against the new coronavirus infection have been identified. The results of the study demonstrated that important factors influencing the incidence of workers were the frequency of examination for COVID-19, the regular use of skin antiseptics, contact with sources of infection, the use of glasses for vision correction, the use of public transport and eating at work. Conclusion. The need to study the risks of contracting COVID-19 among employees of medical organization and, based on them, develop a set of additional anti-epidemic protection measures was confirmed.
How to leverage large language model's superior capability in e-commerce recommendation has been a hot topic. In this paper, we propose LLM-PKG, an efficient approach that distills the knowledge of LLMs into product knowledge graph (PKG) and then applies PKG to provide explainable recommendations. Specifically, we first build PKG by feeding curated prompts to LLM, and then map LLM response to real enterprise products. To mitigate the risks associated with LLM hallucination, we employ rigorous evaluation and pruning methods to ensure the reliability and availability of the KG. Through an A/B test conducted on an e-commerce website, we demonstrate the effectiveness of LLM-PKG in driving user engagements and transactions significantly.
Pat Pataranutaporn, Chayapatr Archiwaranguprok, Phoomparin Mano
et al.
This paper introduces Text2Tradition, a system designed to bridge the epistemological gap between modern language processing and traditional dance knowledge by translating user-generated prompts into Thai classical dance sequences. Our approach focuses on six traditional choreographic elements from No. 60 in Mae Bot Yai, a revered Thai dance repertoire, which embodies culturally specific knowledge passed down through generations. In contrast, large language models (LLMs) represent a different form of knowledge--data-driven, statistically derived, and often Western-centric. This research explores the potential of AI-mediated systems to connect traditional and contemporary art forms, highlighting the epistemological tensions and opportunities in cross-cultural translation.
A smart home is realized by setting up various services. Several methods have been proposed to create smart home services, which can be divided into knowledge-based and data-driven approaches. However, knowledge-based approaches usually require manual input from the inhabitant, which can be complicated if the physical phenomena of the concerned environment states are complex, and the inhabitant does not know how to adjust related actuators to achieve the target values of the states monitored by services. Moreover, machine learning-based data-driven approaches that we are interested in are like black boxes and cannot show the inhabitant in which situations certain services proposed certain actuators' states. To solve these problems, we propose a hybrid system called HKD-SHO (Hybrid Knowledge-based and Data-driven services based Smart HOme system), where knowledge-based and machine learning-based data-driven services are profitably integrated. The principal advantage is that it inherits the explicability of knowledge-based services and the dynamism of data-driven services. We compare HKD-SHO with several systems for creating dynamic smart home services, and the results show the better performance of HKD-SHO.
A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational knowledge graph embedding methods assume that the entities are discrete objects, some information should be represented using numeric values, e.g., (J.R.R., was born in, 1892). Also, a triplet (J.R.R., educated at, Oxford Univ.) can be associated with a qualifier such as (start time, 1911). In this paper, we propose a unified framework named HyNT that learns representations of a hyper-relational knowledge graph containing numeric literals in either triplets or qualifiers. We define a context transformer and a prediction transformer to learn the representations based not only on the correlations between a triplet and its qualifiers but also on the numeric information. By learning compact representations of triplets and qualifiers and feeding them into the transformers, we reduce the computation cost of using transformers. Using HyNT, we can predict missing numeric values in addition to missing entities or relations in a hyper-relational knowledge graph. Experimental results show that HyNT significantly outperforms state-of-the-art methods on real-world datasets.
Knowledge graph embedding (KGE) aims at learning powerful representations to benefit various artificial intelligence applications. Meanwhile, contrastive learning has been widely leveraged in graph learning as an effective mechanism to enhance the discriminative capacity of the learned representations. However, the complex structures of KG make it hard to construct appropriate contrastive pairs. Only a few attempts have integrated contrastive learning strategies with KGE. But, most of them rely on language models ( e.g., Bert) for contrastive pair construction instead of fully mining information underlying the graph structure, hindering expressive ability. Surprisingly, we find that the entities within a relational symmetrical structure are usually similar and correlated. To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models. Concretely, a plug-and-play approach is proposed by taking entities in the relation-symmetrical positions as positive pairs. Besides, a self-supervised alignment loss is designed to pull together positive pairs. Experimental results on link prediction and entity classification datasets demonstrate that our KGE-SymCL can be easily adopted to various KGE models for performance improvements. Moreover, extensive experiments show that our model could outperform other state-of-the-art baselines.
Стаття присвячена особливостям застосування маркетингових і менеджерських технологій у рамках узгодження природоохоронних заходів та економічного розвитку. Метою статті є з’ясування смислового контекст сталого розвитку як сукупності політичних меседжів, орієнтованих на широку аудиторію. Було підкреслено, що просування сталого розвитку як політичної доктрини та мотивації політичної поведінки становить значний інтерес з точки зору впровадження програмних цілей сучасних політичних партій, неурядових організації та груп інтересів. Проаналізовано механізми аналізу каналів публічного просування ідей сталого розвитку на глобальному рівні. У світлі подій, що відбуваються, інформаційна адженда публічної влади має бути спрямована на популяризацію ресурсозбереження не лише індивідуальними господарствами, але й потужними економічними гравцями. З’ясовано, що політичний контекст сталого розвитку визначає стимулювання політичних акторів до активної поведінки в контексті координації зусиль стосовно забезпечення балансу споживання та навколишнього природного середовища. Розкрито процеси сталого розвитку, які вимагають особливого комунікаційно-менеджерського підходу з урахуванням стану колективного та індивідуального світогляду цільових груп. Доведено, що оцінка перспектив корекції політичного контексту сталого розвитку, вимагає узгодження економічної діяльності з населенням відповідних територій. З’ясовано перспективи та ефективність пропагандистської трансляції ідеалів сталого розвитку в межах територіальної або ідеологічної системи. Припущено, що перелік соціально-політичних аспектів сталого розвитку формує очікування громадян від політики сталого розвитку та його впровадження у трансформаційних державах. Встановлено, що аналіз інтересів та потреб базових соціально-демографічних груп вимагає експертної та планувальної роботи щодо адресного представлення політичних цінностей сталого розвитку в комунікаційні сфері. Зроблено висновок, що сталий розвиток має політико-мобілізаційну спроможність, яка ґрунтується на прагматичних інтересах громадянина, на потребах місцевих громад у відстоюванні політичних інтересів, на тенденціях формування культури споживання тощо.
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods. The convergence of DKGE can be guaranteed theoretically. Extensive experiments demonstrate that DKGE achieves superior accuracy than classical hashing functions that map the effective continuous embeddings into discrete codes. Besides, DKGE reaches comparable accuracy with much lower computational complexity and storage compared to many continuous graph embedding methods.
Knowledge Discovery plays a very important role in analyzing data and getting insights from them to drive better business decisions. Entrepreneurship in a Knowledge based economy contributes greatly to the development of a country's economy. In this paper, we analyze surveys that were conducted on women in entrepreneurship in UAE. Relevant insights are extracted from the data that can help us to better understand the current landscape of women in entrepreneurship and predict the future as well. The features are analyzed using machine learning to drive better business decisions in the future.
We provide a universal characterization of the construction taking a scheme $X$ to its stable $\infty$-category $\text{Mot}(X)$ of noncommutative motives, patterned after the universal characterization of algebraic K-theory due to Blumberg--Gepner--Tabuada. As a consequence, we obtain a corepresentability theorem for secondary K-theory. We envision this as a fundamental tool for the construction of trace maps from secondary K-theory. Towards these main goals, we introduce a preliminary formalism of "stable $(\infty, 2)$-categories"; notable examples of these include (quasicoherent or constructible) sheaves of stable $\infty$-categories. We also develop the rudiments of a theory of presentable enriched $\infty$-categories -- and in particular, a theory of presentable $(\infty, n)$-categories -- which may be of intependent interest.
Recent years have seen a rapid growth of utilizing graph neural networks (GNNs) in the biomedical domain for tackling drug-related problems. However, like any other deep architectures, GNNs are data hungry. While requiring labels in real world is often expensive, pretraining GNNs in an unsupervised manner has been actively explored. Among them, graph contrastive learning, by maximizing the mutual information between paired graph augmentations, has been shown to be effective on various downstream tasks. However, the current graph contrastive learning framework has two limitations. First, the augmentations are designed for general graphs and thus may not be suitable or powerful enough for certain domains. Second, the contrastive scheme only learns representations that are invariant to local perturbations and thus does not consider the global structure of the dataset, which may also be useful for downstream tasks. Therefore, in this paper, we study graph contrastive learning in the context of biomedical domain, where molecular graphs are present. We propose a novel framework called MoCL, which utilizes domain knowledge at both local- and global-level to assist representation learning. The local-level domain knowledge guides the augmentation process such that variation is introduced without changing graph semantics. The global-level knowledge encodes the similarity information between graphs in the entire dataset and helps to learn representations with richer semantics. The entire model is learned through a double contrast objective. We evaluate MoCL on various molecular datasets under both linear and semi-supervised settings and results show that MoCL achieves state-of-the-art performance.
Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne
et al.
Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.
Md. Shazibul Islam Shamim, Farzana Ahamed Bhuiyan, Akond Rahman
Kubernetes is an open-source software for automating management of computerized services. Organizations, such as IBM, Capital One and Adidas use Kubernetes to deploy and manage their containers, and have reported benefits related to deployment frequency. Despite reported benefits, Kubernetes deployments are susceptible to security vulnerabilities, such as those that occurred at Tesla in 2018. A systematization of Kubernetes security practices can help practitioners mitigate vulnerabilities in their Kubernetes deployments. The goal of this paper is to help practitioners in securing their Kubernetes installations through a systematization of knowledge related to Kubernetes security practices. We systematize knowledge by applying qualitative analysis on 104 Internet artifacts. We identify 11 security practices that include (i) implementation of role-based access control (RBAC) authorization to provide least privilege, (ii) applying security patches to keep Kubernetes updated, and (iii) implementing pod and network specific security policies.
Multi-task learning (MTL) is to learn one single model that performs multiple tasks for achieving good performance on all tasks and lower cost on computation. Learning such a model requires to jointly optimize losses of a set of tasks with different difficulty levels, magnitudes, and characteristics (e.g. cross-entropy, Euclidean loss), leading to the imbalance problem in multi-task learning. To address the imbalance problem, we propose a knowledge distillation based method in this work. We first learn a task-specific model for each task. We then learn the multi-task model for minimizing task-specific loss and for producing the same feature with task-specific models. As the task-specific network encodes different features, we introduce small task-specific adaptors to project multi-task features to the task-specific features. In this way, the adaptors align the task-specific feature and the multi-task feature, which enables a balanced parameter sharing across tasks. Extensive experimental results demonstrate that our method can optimize a multi-task learning model in a more balanced way and achieve better overall performance.