Search Results (271)

Background and Objectives: Kidney disease (KD) is a common complication of diabetes mellitus (DM) associated with adverse outcomes of renal failure, cardiovascular disease, and mortality. The aim of this study was to determine the prevalence and awareness of the KD among the DM type 2 (T2DM) patients. Materials and Methods: This cross-sectional study was conducted at the University Hospital of Split between November and December of 2023 during an open call for DM patients. For each participant, blood and urine samples, along with relevant medical information, were collected, and adherence to the Mediterranean diet (MeDi) was assessed using the Mediterranean Diet Service Score (MDSS). Furthermore, blood pressure was measured, along with body composition and anthropometric parameters. Results: Of 252 T2DM patients with a median age of 67 years (IQR: 60–73), 130 (51.6%) were women. The median duration of T2DM was 10 years (IQR: 6–20). Despite the fact that 80.95% of total participants reported receiving dietary guidelines from any source, only 53.2% reported adhering to the suggested instructions, while according to the MDSS, only 7.2% adhered to the MeDi. The median body mass index was 27.6 kg/m² (24.2–31), with 70.1% of participants overweight or obese. Only 6% of participants believed they had KD, but after blood and urine sample analysis, 31% were found to have KD. Conclusions: This study highlights a significant gap in awareness of KD, low adherence to MeDi, and a high prevalence of obesity among T2DM patients. Due to the increasing number of T2DM patients, it is crucial to improve healthy lifestyle education and make modifications within this group, as well as perform regular screening for KD and medical check-ups. Full article

Online social networks (OSNs) are inundated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers—whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models. Full article

This present article examines the frequency control of a dual-area interconnected hybrid power system that integrates conventional as well as non-conventional sources with additional support from electric vehicles. The complicated, non-linear behavior of the system adds to the grid’s already high level of complexity. To navigate this complex environment, it becomes essential to develop a resilient controller. In this respect, a robust secondary controller is developed to handle the problem. The controller is developed while taking into account the intricate design of the contemporary power system. An extensive comparison between well-established controllers is presented to verify the efficacy of the proposed controller. An AI-based optimization technique, namely, COVID-19, is employed to obtain optimal values for different parameters of the controller. This work also investigates the effect of the FACTS device as a static synchronous series compensator (SSSC) on the dynamics of the system. Moreover, it also investigates the role of electric vehicles (EVs) and an SSSC on system stability. Further, the developed system is subjected to significant load variations and intermittent solar and wind disturbances to check the response of the optimal controller under dynamic conditions. The results demonstrate that the proposed controller reactions successfully handle system disturbances, highlighting the strength of the proposed controller design. Lastly, a case study on an IEEE-39 bus system is carried out to check the optimality of the proposed secondary controller. Full article

According to European Union guidelines, the assessment of the ecological status of Transitional Water Systems (TWSs) should be based on the monitoring of biological communities rather than physico-chemical parameters and pollutants. Macrophytes, including aquatic angiosperms and macroalgae, are organisms that respond more quickly to environmental changes by varying the structure and biomass of their assemblages. There are several ecological indices based on macrophytes, among them the Macrophyte Quality Index (MaQI), which has been intercalibrated with water and sediment parameters, nutrient concentrations, and pollutants and is used to determine the ecological status of Italian TWSs. In the Venice Lagoon, it was applied to 87 stations, showing a significant score increase over the last ten years of monitoring (2011–2021) due to progressive lagoon environmental recovery. The dominant taxa assemblages, previously dominated by Ulvaceae, were replaced by species of higher ecological value, with an increase in the number and distribution of sensitive species, as well as the spread and cover of aquatic angiosperms. The rise in the Ecological Quality Ratio (EQR) determined by the MaQI confirms the key role of macrophyte monitoring in detecting environmental changes in TWSs. In fact, a simple check of the presence or absence of aquatic angiosperms and sensitive species is sufficient for an initial rapid assessment of the ecological status of these environments. Full article

Background/Objectives: Conducting advanced resuscitation requires medical personnel to carry out appropriately coordinated actions. Certain difficulties arise when it becomes necessary to conduct cardiopulmonary resuscitation (CPR) on two patients at the same time. The aim of this paper was to assess the actions of teams participating in emergency medicine championships in tasks related to simultaneous cardiac arrests in two patients. Methods: The study was conducted on the basis of an analysis of assessment cards for tasks carried out during the ‘International Winter Championships in Emergency Medicine’. Three-person medical response teams (MRTs), with the support of two people, had the task of conducting advanced resuscitation on an adult and child simultaneously. The tasks were prepared and developed by European Resuscitation Council (ERC) instructors. Results: The study showed that teams used four methods of checking heart rhythm and performing defibrillation during CPR—using paddles only, using paddles and self-adhesive electrodes, using paddles and a three-lead ECG and using two pairs of self-adhesive electrodes. Teams performing cardiopulmonary resuscitation using paddles and a three-lead ECG performed significantly more actions incorrectly than other teams—in part due to the fact that they incorrectly interpreted which patient’s heart rhythm was displayed on the defibrillator screen. The effectiveness of the remaining methods was similar for most of the actions. The CPR method using two pairs of electrodes enabled personal safety to be maintained to the significantly highest percentage during defibrillation. Conclusions: The study demonstrated that the need to conduct CPR on two patients at the same time, irrespective of the method used, caused MRT members considerable difficulties in correctly conducting some of the actions. The method of assessing heart rhythm using paddles and a three-lead ECG should not be used. The study showed that the optimal method of CPR in use appears to be the method using two pairs of adhesive electrodes—it provided, among other things, the significantly highest percentage of safely conducted defibrillation. Full article

This paper continues the series of publications of our interdisciplinary research findings at the crossroads of higher education sustainability (SDG 4.3), smart education, and artificial intelligence (AI) tools. AI has begun to be used by universities to increase the quality of higher educational services. AI tools are expected to help university teachers in the teaching process. Students also use AI to help them complete their tasks. At the same time, AI may threaten Sustainable Development Goal 4 (SDG 4). In particular, this is a “blank spot” in the study of AI and non-violent learning environments (SDG 4.3). The aim of the study was to verify competing statistical hypotheses. To achieve this aim, the authors used modern, economically sound methods. The authors processed the responses of 1102 students from eight Eastern European universities using a special electronic questionnaire. The authors statistically processed the student survey results and then tested a pair of conflicting statistical hypotheses. The authors adopted a standard level (α = 0.05) of hypothesis checking. Testing statistical hypotheses led to obtaining two statistically substantiated new scientific facts: (1) The requirement for “non-violent” learning environments does not meet some students’ needs. (2) The number of these students can be up to 31.94%. Summary: The new scientific facts are helpful for further developing world pedagogical theory and practice. They are the basis for forecasting and preparing for managerial actions aimed at SDG 4.3. Full article

(1) Background: News deserts are communities without a local news outlet, or communities where residents face significantly reduced access to the news of the local public sphere. The demise of a local news outlet can have negative effects on community engagement and on the discussion of solutions to community problems. In Portugal, for example, 25% of municipalities do not have their own media outlets. When there are no journalists reporting on reality, studies show that much of the local information in these territories is obtained through social media, such as Facebook pages and groups, which can be a source of disinformation and manipulation that communities become vulnerable to. (2) Methods: Through focus groups in the municipality of Manteigas, we researched perspectives and behaviours, as well as the factors that influence people’s choices in the consumption of information. (3) Results: We found that citizens used a wide range of informational sources, with a strong dependence on social media and institutional channels to access local information. (4) Conclusions: Proximity relationships are the basis of fact-checking processes, and citizens showed less concern about disinformation and more trust in the information they accessed through official institutions’ pages and through word-of-mouth in their communities. Full article

The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checking, have been employed to combat disinformation, but these methods often fall short in the face of increasingly sophisticated fake content. This review article explores the emerging role of Large Language Models (LLMs) in enhancing the detection of fake news and fake profiles. We provide a comprehensive overview of the nature and spread of disinformation, followed by an examination of existing detection methodologies. The article delves into the capabilities of LLMs in generating both fake news and fake profiles, highlighting their dual role as both a tool for disinformation and a powerful means of detection. We discuss the various applications of LLMs in text classification, fact-checking, verification, and contextual analysis, demonstrating how these models surpass traditional methods in accuracy and efficiency. Additionally, the article covers LLM-based detection of fake profiles through profile attribute analysis, network analysis, and behavior pattern recognition. Through comparative analysis, we showcase the advantages of LLMs over conventional techniques and present case studies that illustrate practical applications. Despite their potential, LLMs face challenges such as computational demands and ethical concerns, which we discuss in more detail. The review concludes with future directions for research and development in LLM-based fake news and fake profile detection, underscoring the importance of continued innovation to safeguard the authenticity of online information. Full article

This research examines the spread of disinformation on social media platforms and its impact on state resilience through a systematic literature review of 150 peer-reviewed studies published between 2014 and 2024. The analysis revealed that disinformation spreads six times faster than accurate information, with emotions and platform algorithms playing a significant role in its spread. Factors such as low digital literacy, political polarization, and declining trust in institutions increase people’s vulnerability to disinformation. Impacts on national security include threats to the integrity of democratic processes, the erosion of social cohesion, and decreased public trust. The most effective coping strategies include improving digital literacy (78 percent effective), fact-checking (65 percent), and content regulation (59 percent). However, these efforts face ethical and legal challenges, especially regarding freedom of expression. This research highlights the need for a multidimensional approach in addressing the “information pandemic”, integrating technological, educational, and policy strategies while considering ethical implications. The findings provide a foundation for further policy development and research to protect the integrity of public information spaces and state resilience in the digital age. Full article

Researching the question of how the Roman Catholic Church (RCC), and by extension other institutional systems, respond or do not respond to the lived reality of abuse and its cover-up cannot be done without seeking to understand the underlying issue: What is the RCC responding (or not responding) to? One elucidating and perhaps surprising answer lies in the little and often misunderstood word vulnerability. Vulnerability, most probably counter-intuitively, has in fact the power to enhance violence or to reverse its destructive influence. This thought forms the basis for an exploration into what Professor Dr. Hildegund Keul has named the vulnerability and expenditure paradox. The logic in both of them seems understandable and straightforward. Yet, when genuinely understood, they are unsettling. They reveal an uncomfortable dilemma, a reality check and, ultimately, a choice as the paradox raises the rather earthly question: do we attempt to cheat death and therefore lose life, or do we opt for “creation through loss”? The first might, though linked to violence, lead to a feeling of security and invulnerability. The second exemplifies the passion of authentic suffering, humility and identity dependence. From a Christian perspective, it is the incarnation of love. Full article

The novelty of this article lies in the fact that we extend the use of a multistep method for developing a sequence whose limit solves a Banach space-valued equation. We suggest the error estimates, local convergence, and semi-local convergence, a radius of convergence, the uniqueness of the required solution that can be computed under ω-continuity, and conditions on the first derivative, which is on the method. But, earlier studies used high-order derivatives, even though those derivatives do not appear in the body structure of the proposed method. In addition to this, they did not propose computable estimates and semi-local convergence. We checked the applicability of our study to three real-life problems for semi-local convergence and two problems chosen for local convergence. Based on the obtained results, we conclude that our approach improves its applicability and makes it suitable for challenges in applied science. Full article

Contemporary discussions about the application of artificial intelligence in newsrooms are commonplace because of the unique opportunities it presents for news media. This study investigated the intricate relationship between journalism and AI with the broad research question: How are journalists adopting AI technologies and what challenges and opportunities do such technologies present to them? Eighteen journalists practising in Ghana and South Africa were interviewed through qualitative research techniques. Transcribed interview data were analysed thematically using the data analysis method proposed by Charmaz. The findings were that most newsrooms in the two countries have not formally incorporated AI tools into newsroom practices. However, journalists use AI tools at their discretion in a non-complex manner, such as transcription, research, generating story ideas, and fact-checking. Practical limitations to the formal integration of AI technology into newsroom operations include cost, language barrier, and aversion to change. Although participants recognised the advantages of employing AI for newsroom tasks, they were also concerned about the ethical quandaries of misinformation, improper attribution, and intellectual property. Participants also thought that fact-checking and mindfulness regarding ethical usage might increase ethical AI usage in newsrooms. This study adds an important perspective on AI’s role in African journalism, addressing the obstacles and ethics concerns. Full article

Patients with liver cirrhosis, due to their weakened innate and adaptive immunity, are more prone to frequent and severe vaccine-preventable infections. Moreover, impaired adaptive immunity results in a limited antibody response to vaccines. Despite this suboptimal antibody response, vaccines have proven to be very effective in reducing severe outcomes and deaths in these patients. In the Western world, regulatory authorities and scientific liver societies (e.g., AASLD and EASL) have recommended vaccinations for cirrhotic patients. However, despite these strong recommendations, vaccine coverage remains suboptimal. Improving vaccine effectiveness and safety information, providing comprehensive counseling to patients, fact-checking to combat fake news and disinformation and removing barriers to vaccination for disadvantaged individuals may help overcome the low coverage rate. In view of this, vaccines should be administered early in the course of chronic liver diseases, as their efficacy declines with the increasing severity of the disease. Full article

There is a pressing need for healthcare conversational agents with domain-specific expertise to ensure the provision of accurate and reliable information tailored to specific medical contexts. Moreover, there is a notable gap in research ensuring the credibility and trustworthiness of the information provided by these healthcare agents, particularly in critical scenarios such as medical emergencies. Pilgrims come from diverse cultural and linguistic backgrounds, often facing difficulties in accessing medical advice and information. Establishing an AI-powered multilingual chatbot can bridge this gap by providing readily available medical guidance and support, contributing to the well-being and safety of pilgrims. In this paper, we present a comprehensive methodology aimed at enhancing the reliability and efficacy of healthcare conversational agents, with a specific focus on addressing the needs of Hajj pilgrims. Our approach leverages domain-specific fine-tuning techniques on a large language model, alongside synthetic data augmentation strategies, to optimize performance in delivering contextually relevant healthcare information by introducing the HajjHealthQA dataset. Additionally, we employ a retrieval-augmented generation (RAG) module as a crucial component to validate uncertain generated responses, which improves model performance by 5%. Moreover, we train a secondary AI agent on a well-known health fact-checking dataset and use it to validate medical information in the generated responses. Our approach significantly elevates the chatbot’s accuracy, demonstrating its adaptability to a wide range of pilgrim queries. We evaluate the chatbot’s performance using quantitative and qualitative metrics, highlighting its proficiency in generating accurate responses and achieve competitive results compared to state-of-the-art models, in addition to mitigating the risk of misinformation and providing users with trustworthy health information. Full article

Based on the nucleotide sequences of the mitochondrial genome (mitogenome) of specimens taken from two mussel species (Arcuatula senhousia and Mytilus coruscus), an investigation was performed by means of the complex approaches of the genomics, molecular phylogenetics, and evolutionary genetics. The mitogenome structure of studied mussels, like in many other invertebrates, appears to be much more variable than in vertebrates and includes changing gene order, duplications, and deletions, which were most frequent for tRNA genes; the mussel species’ mitogenomes also have variable sizes. The results demonstrate some of the very important properties of protein polypeptides, such as hydrophobicity and its determination by the purine and pyrimidine nucleotide ratio. This fact might indirectly indicate the necessity of purifying natural selection for the support of polypeptide functionality. However, in accordance with the widely accepted and logical concept of natural cutoff selection for organisms living in nature, which explains its action against deleterious nucleotide substitutions in the nonsynonymous codons (mutations) and its holding of the active (effective) macromolecules of the polypeptides in a population, we were unable to get unambiguous evidence in favor of this concept in the current paper. Here, the phylogeny and systematics of mussel species from one of the largest taxons of bivalve mollusks are studied, the family known as Mytilidae. The phylogeny for Mytilidae (order Mytilida), which currently has no consensus in terms of systematics, is reconstructed using a data matrix of 26–27 mitogenomes. Initially, a set of 100 sequences from GenBank were downloaded and checked for their gender: whether they were female (F) or male (M) in origin. Our analysis of the new data confirms the known drastic differences between the F/M mitogenome lines in mussels. Phylogenetic reconstructions of the F-lines were performed using the combined set of genetic markers, reconstructing only protein-coding genes (PCGs), only rRNA + tRNA genes, and all genes. Additionally, the analysis includes the usage of nucleotide sequences composed of other data matrices, such as 20–68 mitogenome sequences. The time of divergence from MRCA, estimated via BEAST2, for Mytilidae is close to 293 Mya, suggesting that they originate in the Silurian Period. From all these data, a consensus for the phylogeny of the subfamily of Mytilinae and its systematics is suggested. In particular, the long-debated argument on mussel systematics was resolved as to whether Mytilidae, and the subfamily of Mytilinae, are monophyletic. The topology signal, which was strongly resolved in this paper and in the literature, has refuted the theory regarding the monophyly of Mytilinae. Full article