Representative Categories Research Articles

Beyond the requirement: A novel patient follow‐up report

AbstractBackgroundResidents are required to participate in practice‐based learning and improvement. Most commonly, a resident‐initiated patient follow‐up log is used to meet the requirement. We sought to provide residents with follow‐up information in an efficient, value‐added manner via a patient follow‐up report (PFUR).MethodsThe PFUR is an automated monthly report sent to individual residents via email. It was generated from the electronic medical record and included five categories of cases: patients who were discharged and returned for admission within 72 h, diagnosis‐based criteria, patients who expired during the hospital stay, patients who were admitted to or upgraded to the intensive care unit (ICU) within 24 h of admission, and patients independently flagged by the care team for follow‐up. The PFURs were analyzed for 6 months after implementation for number and categorization of cases included as well as via survey of the residents.ResultsIn 6 months, 1078 patients, generating 1155 follow‐ups, were included on the PFUR. ICU upgrades were the most represented category (41%), followed by diagnosis‐based criteria (30%). Patients who were discharged and admitted within 72 h were least commonly represented on the PFUR (2%). Seventy‐eight percent of residents felt that patient follow‐ups were valuable to their education and 82% felt that the PFUR impacted the clinical care they provided. The PFUR was preferred by 90% of resident respondents and had an average value rating of 4.38 out of 5.ConclusionsOverall numbers of patient follow‐ups significantly increased after implementation of the PFUR. Certain categories were more represented overall and within each class, which has implications for future educational initiatives. After a pilot period, the novel PFUR appears to be more efficient, accessible, and highly valued than the log used previously. Programs looking to maximize the educational benefits of patient follow‐ups may consider a similar initiative.

Assessing trends for developed INBDE questions: A dental school experience.

The aim of this study was to assess trends related to integrated questions created by fourth-year dental students as a part of the Basic Science and Clinical Science Spiral Seminar Series (BaSiCSsss) at Tufts University School of Dental Medicine (TUSDM). Deidentified BaSiCSsss presentations from 2019 to 2023 were obtained from the Office of Academic Affairs at TUSDM. The integrated questions created by fourth-year dental students were extracted and compiled into an Excel sheet. Following the Joint Commission on National Dental Examinations (JCNDE) guidelines for the Integrated National Board Dental Examination (INBDE) question construction, each question was evaluated on 13 distinct categories. The categories consisted of 10 foundational knowledge (FK) and three clinical content (CC) areas. Calibration of the two investigators (Kyle E. Wright and Samar Shaikh) was performed. The chi-square test and Fisher's exact test were used to evaluate trends of FK and CC category representation across the 4 years. Twelve hundred fifty-six questions were extracted from the BaSiCSsss presentations between academic years 2019 and 2023. CC1 (diagnosis and treatment planning) emerged as the most represented category at 44.2% of the questions while FK2 (physics and chemistry to explain normal biology and pathobiology) had the least representation at 1.1%. Statistically significant year-to-year differences (p<0.05) were identified in FK1, FK5, FK6, FK8, CC1, and CC2 with FK5, FK6, and CC2 exhibiting a notable downward trend over the 4 years. The current study highlights the trends in student-created integrated questions. Understanding these trends may aid in curriculum development, focusing on areas with decreasing representation and ensuring a balanced coverage of topics optimizing learning outcomes.

Benthic litter in fishing grounds of the Northern Adriatic: Role of the trawling fleet as cleaners of the seafloor

This study represents the baseline of estimation of the potential service provided by fishermen as “cleaners of the sea”. The amount, composition and depth distribution of marine litter in fishing grounds of the Northern Adriatic seafloor has been investigated through the fishing for litter (FFL) scheme. Passive FFL campaigns were carried out by trawlers from two of the most important fishing ports in the northern Adriatic, Chioggia and Goro, from May 2020 to May 2021. Over the course of 256 days of fishing, over 6 tons of litter were removed from 265 km2 of seafloor. Abandoned, lost and derelict fishing gears (ALDFG) were the most represented litter category (48 % of the total litter), and of these 67 % were plastic ALDFG (mostly mussel socks and fishing nets). Fouling on plastic waste was analyzed to determine the fraction of collected litter items that could be destinated to recycling. Only a small percentage of the plastic litter analyzed was “clean” from adherent and/or encrusting organisms. Approximately 2.4 tons of plastic were recovered, but, due to the biological colonization of surfaces, they cannot be recycled by using the technologies present in the area.

Study of Knowledge, Attitudes and Practices Related to Smoking Among Health Professionals in Public Hospitals in the Department of Dakar in 2021

Introduction: In Senegal, previous studies showed a prevalence of smoking among health professionals of between 11.6 and 12.8%. The general objective was to study smoking among health professionals in public hospitals in the department of Dakar. This was a cross-sectional study with a descriptive and analytical aim, the data collection phase of which took place from October 11 to November 23, 2021. The study population consisted of medical and paramedical staff. We collected 262 cases, i.e. a non-participation rate of 12.67%. Results: The average age of the health workers surveyed was 28.19 %. There were 148 men and women with a sex ratio of 1.30. Thus, 68.3% were single. During our study, the health workers at Abass NDAO Hospital were 110 (42%), 80 (30.5%) at the Principal Hospital in Dakar and 72 (27.5%) at HOGGY. The majority of the workers surveyed (69 cases, 26.3%) were DES or 7th year medical students. The least represented professional category was that of nursing assistants. Thus, 3.4% of the health workers surveyed were current tobacco users compared to 10.2% (26 health workers) who were former users. The average age at which these workers first used tobacco was 16.5 ± 2.5 years. Within the study population, 86% (225 workers) had never used tobacco. Smoked tobacco was the only form used by the subjects surveyed. The average consumption of smoked tobacco was 3.5 ± 0.51 times per day. However, the last attempt to quit was more than a year ago for the majority (5/7) of consumer health workers. The main reason for this attempt (failed) was awareness of the dangers of tobacco consumption (5/7). The main reason (05/07) that justified their tobacco consumption was stress management. All the workers surveyed were unanimous on the dangers of tobacco consumption on health. The consequences of tobacco most cited by the health workers surveyed were lung cancer (224 responses or 85.5% of respondents), COPD (205 or 78.24%) and strokes (165 or 62.98%). Male health workers were more likely to have good knowledge about smoking than female health workers. The agents thus surveyed had for 73.7% (i.e. 193 people) of them an insufficient or weak attitude towards smoking. Conclusion: At the end of our study and in light of the conclusions reached, we make the following suggestions to the health authorities: To organize communication-education sessions during World Tobacco Day.

Deep Learning for Automated Classification of Hip Hardware on Radiographs.

To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports. Pelvic and hip radiographs from 4279 studies in 1073 patients were retrospectively obtained and reviewed by musculoskeletal radiologists. Two convolutional neural networks, EfficientNet-B4 and NFNet-F3, were trained to perform the image classification task into the following most represented categories: no hardware, total hip arthroplasty(THA), hemiarthroplasty, intramedullary nail, femoral neck cannulated screws, dynamic hip screw, lateral blade/plate, THA with additional femoral fixation, and post-infectious hip. Model performance was assessed on an independent test set of 851 studies from 262 patients and compared to individual performance of five subspecialty-trained radiologists using leave-one-out analysis against an aggregate gold standard label. For multiclass classification, the area under the receiver operating characteristic curve (AUC) for NFNet-F3 was 0.99 or greater for all classes, and EfficientNet-B4 0.99 or greater for all classes except post-infectious hip, with an AUC of 0.97. When compared with human observers, models achieved an accuracy of 97%, which is non-inferior to four out of five radiologists and outperformed one radiologist. Cohen's kappa coefficient for both models ranged from 0.96 to 0.97, indicating excellent inter-reader agreement. A deep learning model can be used to classify a range of orthopedic hip hardware with high accuracy and comparable performance to subspecialty-trained radiologists.

Occurrence of pharmaceutical residues in drinking water: a systematic review.

The aim of the present paper was to give a complete picture on the drinking water contamination by pharmaceutical residues all over the world. For this purpose, a systematic review was carried out for identifying all available research reporting original data resulting by sampling campaign and analysis of "real" drinking water samples to detect pharmaceutical residues. The investigated databases were PubMed, Scopus, and Web of Science. A total of 124 studies were included; among these, 33 did not find target analytes (all below the limit of detection), while the remaining 91 studies reported the presence for one or more compounds, in concentrations ranging from a few units to a few tens of nanograms. The majority of the studies were performed in Europe and the most represented categories were nonsteroidal anti-inflammatory drugs and analgesics. The most common analytical approach used is the preparation and analysis of the samples by solid-phase extraction and chromatography coupled to mass spectrometry. The main implications resulting from our review are the need for (a) further studies aimed to allow more accurate environmental, wildlife, and human health risk assessments and (b) developing integrated policies promoting less environmentally persistent drugs, the reduction of pharmaceuticals in livestock breeding, and the update of wastewater and drinking water treatment plants for a better removal of drugs and their metabolites.

Mapping actor networks in global multi-stakeholder initiatives for food system transformation

There is growing recognition that global food system transformation requires a fundamental shift in norms, perspectives and structural inclusion and exclusion of different actors in decision-making spaces. As multistakeholder governance approaches become increasingly common, significant concerns have been raised about their ability to deliver such change. Such concerns are based on case study findings repeatedly highlighting their susceptibility to corporate capture. This study goes beyond individual case studies, examining global multi-stakeholder initiatives (MSIs) whose stated aim is to drive a healthier and more sustainable food system. It identified and categorised actors within these MSIs, drawing on social network analysis to provide insights into actor centrality, power structures, and how this might impact MSIs’ potential to drive transformative change. Thirty global MSIs were included in our sample, including a total of 813 actors. Most actors were based in high-income countries (HIC) (n = 548, 67%). The private sector (n = 365, 45%) was the most represented actor category, comprising transnational corporations (TNCs) (n = 127) and numerous others representing their interests. NGOs, affected communities and low- and middle-income country actors remain underrepresented. The central involvement of TNCs which rely on the production and sale of unhealthy and unsustainable commodities represents a clear conflict of interest to the stated objectives of the MSIs. These findings lend weight to concerns that MSIs may reflect rather than challenge existing power structures, thus serving to maintain the status quo. This indicates a need to critically examine the use of multistakeholder governance approaches and their ability to drive global food system transformation.

A RETROSPECTIVE OF PILGRIMAGE LITERATURE AS A COMMUNICATION GUIDE TO THE WORLD OF SACRED CULTURE

The purpose of the article is to examine the pilgrimage genre in literature as a social and communication guide in the world of contemporary sacred culture from a communication perspective. Different types of guidebooks are considered, i.e., interviews, travelogues, diaries, records, review texts, memoirs, notes, and pilgrimage travel notes are given as a necessary communication tool during a pilgrimage. The research methodology consists of descriptive (using observation and interpretation techniques), historical and literary, and biographical methods of analyzing pilgrimage literature. The author used the analytical-descriptive method, which consists in the selection, description and analysis of data; the system-structural method helped to arrange the works of different genres and apply them according to the subject in chronological order, reproducing the logical sequence of the material presented. The scientific novelty of the article involves expanding the concept of pilgrimage and the pilgrimage genre in literature as a social and communication guide to the world of contemporary sacred culture based on pilgrimage guides, diaries, memoirs, pilgrimage notes, and other sacred literature. Conclusions. In the article, the author examines the multi-genre works of pilgrimage literature as a communication guide to the sacred world in order to encourage the transformation from a book or a movie to a real pilgrimage for those who have such an opportunity. The article analyzes pilgrimage records, guidebooks, diaries, travelogues, novels, novellas, short stories, and pilgrimage notes to study pilgrimage from a communication perspective for different categories of pilgrims. As a rule, various categories of pilgrims and representatives of different professions go on sacred journeys, and then they write memoirs, pilgrimage texts, songs, scripts for future plays and films, and artists reproduce all details in artworks, and thus pilgrimage penetrates all spheres of an individual’s cultural life, which indicates the highly spiritual component of such journeys. Before going on a pilgrimage, you need to prepare thoroughly, both physically and spiritually, so that guides allow you to follow the trail of others and then plan your sacred journey relatively well. You will never return from a pilgrimage the same as you were before because such travels utterly change the vector of your life. Observing different groups of pilgrims, we can conclude that they all have one thing in common: spiritual enrichment, purification from sins and temptations, and the achievement of catharsis, which will be present in the everyday life of each of us for as long as possible. These works can keep living in ideas for film scripts about pilgrimage, thus expanding their socio-cultural horizons.

Characterization, hazard identification, and risk assessment of volatile organic compounds in Poly(butylene adipate-co-terephthalate)-based food contact articles

The chemical safety of poly (butylene adipate-co-terephthalate) (PBAT) based food contact articles (FCAs) has aroused increasing toxicological concerns in recent years, but the chemical characterization and associated risk assessment still remain inadequate as it fails to elucidate the distribution pattern and discern the potential genotoxic and carcinogenic hazards of the identified substances. Herein, the volatile organic compounds (VOCs) in 50 batches of PBAT-based FCAs of representative categories and 10 batches of PLA and PBAT pellets were characterized, by which 237 VOCs of 10 chemical categories were identified and exhibited characteristic distribution patterns in the chemical spaces derived from their molecular descriptors. Chemical hazards associated with the identified VOCs were discerned by a hazard-driven classification scheme integrating hazard-related knowledge from multiple publicly available sources, and 34 VOCs were found to bear genotoxic or carcinogenic hazards and to feature higher average molecular weight than the other VOCs. Finally, the Risk and hazard quotient (HQ) calculated as the metrics of risk suggested that all identified VOCs posed acceptable risks (Risk<10–4 or HQ < 1), whereas oxolane, butyrolactone, N,N-dimethylacetamide, 2-butoxyethanol, benzyl alcohol, and 1,2,3-trichloropropane posed non-negligible (Risk>10–6) genotoxic or carcinogenic risk and thus should be of prioritized concern to promote the chemical safety of PBAT-based FCAs.

Comprehensive Overview of Broadly Neutralizing Antibodies against SARS-CoV-2 Variants.

Currently, SARS-CoV-2 has evolved into various variants, including the numerous highly mutated Omicron sub-lineages, significantly increasing immune evasion ability. The development raises concerns about the possibly diminished effectiveness of available vaccines and antibody-based therapeutics. Here, we describe those representative categories of broadly neutralizing antibodies (bnAbs) that retain prominent effectiveness against emerging variants including Omicron sub-lineages. The molecular characteristics, epitope conservation, and resistance mechanisms of these antibodies are further detailed, aiming to offer suggestion or direction for the development of therapeutic antibodies, and facilitate the design of vaccines with broad-spectrum potential.

Evolving malware detection through instant dynamic graph inverse reinforcement learning

The rapid development and increasing evolution of malware necessitate novel defensive techniques with high accuracy and minimal false positives to safeguard information systems against potential threats. Unfortunately, current malware detection methods, primarily relying on deep learning to identify malicious fingerprints from extensive training sets, prove ineffective against few-shot and evolving malware variants. To address these challenges, this paper introduces MalIRL, which designs a model-free inverse reinforcement learning (IRL) mechanism to automatically capture the evolving attack intent of malware. Specifically, MalIRL explores six representative categories of malware actions and employs sliding windows to organically divide the massive malware execution event stream into multiple attack stages, achieving a reduced action space and state space. To model dynamic malicious environments, MalIRL proposes an instant dynamic heterogeneous graph representation learning technique. This technique learns state representations of different malware attack stages, enhancing detection accuracy and efficiency by incrementally capturing the newly added contextual semantics of diverse malware entities and relations. In experiments with three real-world malware datasets, MalIRL surpasses existing state-of-the-art methods, particularly in few-shot malware detection scenarios, MalIRL exhibits performance benefits of up to 18.9%.

Deep learning networks for olive cultivar identification: A comprehensive analysis of convolutional neural networks

Deep learning networks, more specifically convolutional neural networks, have shown a notable distinction when it comes to computer vision problems. Their versatility spans various domains, where they are applied for tasks such as classification and regression, contingent primarily on the availability of a representative dataset. This work explores the feasibility of employing this approach in the domain of agriculture, particularly within the context of olive growing. The objective is to enhance and facilitate cultivar identification techniques by using images of olive tree leaves. To achieve this, a comparative analysis involving ten distinct convolutional networks (VGG16, VGG19, ResNet50, ResNet152-V2, Inception V3, Inception ResNetV2, XCeption, MobileNet, MobileNetV2, EfficientNetB7) was conducted, all initiated with transfer learning as a common starting point. Also, the impact of adjusting network hyperparameters and structural elements was explored. For the training and evaluation of the networks, a dedicated dataset was created and made available, consisting of approximately 4200 images from the four most representative categories of the region. The findings of this study provide compelling evidence that the majority of the examined methods offer a robust foundation for cultivar identification, ensuring a high level of accuracy. Notably, the first nine methods consistently attain accuracy rates surpassing 95%, with the top three methods achieving an impressive 98% accuracy (ResNet50, EfficientNetB7). In practical terms, out of approximately 2016 images, 1976 were accurately classified. These results signify a substantial advancement in olive cultivar identification through computer vision techniques.

A simple rapid sample-based clustering for large-scale data

Large-scale data clustering is a crucial task in addressing big data challenges. However, existing approaches often struggle to efficiently and effectively identify different types of big data, making it a significant challenge. In this paper, we propose a novel sample-based clustering algorithm, which is very simple but extremely efficient, and runs in about O(n×r) expected time, where n is the size of the dataset and r is the category number. The method is based on two key assumptions: (1) The data of each sufficient sample should have similar data distribution, as well as category distribution, to the entire data set; (2) the representative of each category in all sufficient samples conform to Gaussian distribution. It processes data in two stages, one is to classify data in each local sample independently, and the other is to globally classify data by assigning each point to the category of its nearest representative category center. The experimental results show that the proposed algorithm is effective, which outperforms other current variants of clustering algorithm.

FoodEx2 Harmonization of the Food Consumption Database from the Italian IV SCAI Children's Survey.

Estimating the habitual food and nutrient intakes of a population is based on dietary assessment methods that collect detailed information on food consumption. Establishing the list of foods to be used for collecting data in dietary surveys is central to standardizing data collection. Comparing foods across different data sources is always challenging. Nomenclatures, detail, and classification into broad food groups and sub-groups can vary considerably. The use of a common system for classifying and describing foods is an important prerequisite for analyzing data from different sources. At the European level, EFSA has addressed this need through the development and maintenance of the FoodEx2 classification system. The aim of this work is to present the FoodEx2 harmonization of foods, beverages, and food supplements consumed in the IV SCAI children's survey carried out in Italy. Classifying foods into representative food categories predefined at European level for intake and exposure assessment may lead to a loss of information. On the other hand, a major advantage is the comparability of data from different national databases. The FoodEx2 classification of the national food consumption database represented a step forward in the standardization of the data collection and registration. The large use of FoodEx2 categories at a high level of detail (core and extended terms) combined with the use of descriptors (facets) has minimized information loss and made the reference food categories at country level comparable with different food databases at national and international level.

Assessing empathy among caregivers: a cross-sectional study

IntroductionEmpathy plays an important role in everyday human relationships. It is the ability to put oneself in the place of others, to represent what they think and feel. In healthcare settings, several studies have highlighted its positive effects on patients in terms of physical and psychological well-being.ObjectivesEvaluate empathy among caregivers.MethodsThis is a cross-sectional study, conducted over a 1- month -period and enrolling nursing staff working at Farhat Hached Academic hospital. Empathy was assessed using the Jefferson Scale of physician’s empathy (JSPE) scale.ResultsA total of 92 caregivers were enrolled in this study. The average age was 40.41 years with a sex ratio of 0.95. The most represented category was nurses (64.1%) with an average seniority of 17.2 years. The average empathy score was 98.4. Scores above half were reported in 69.5% of cases. The presence of empathy was significantly associated with female gender (p=0.002).ConclusionsEmpathy is a key point in the patient-caregiver relationship. Thus, the nursing staff must be aware of this concept in order to improve the quality of care.Disclosure of InterestNone Declared

Epidemiological profile of long-term leave for psychiatric illnesses

IntroductionLong-term leave for psychiatric illness is the most frequently prescribed reason for leave, and appears to be on the increase in recent years.ObjectivesTo draw up a sociodemographic, occupational and clinical profile of workers who have taken long-term sick leave for psychiatric illnessMethodsRetrospective descriptive study involving the medical files of workers from both the public and private sectors, having benefited from long-term sick leave over a period going from August 17, 2022 to September 12, 2023, referred to the occupational medicine and pathology department of Charles Nicolle Hospital in Tunis for medical fitness-for-work assessment. Data collection was based on a pre-established synoptic form.Results During the study period, we identified 639 long-term sick leave prescribed for psychiatric illnesses. Our study population was predominantly female, with a sex ratio of 0.29 and a mean age of 46.82 ± 25.06 years. Sixty percent of employees were married. The most represented occupational category was nurses (33%). Average job seniority was 17.21±10.41 years. Depressive syndrome was the most common psychiatric pathology in our population (80.3%), followed by bipolar disorder (6.4%) and anxiety disorder (5%). Long-term sick leave was prescribed by a psychiatrist working in the private sector in 90.3% of cases. The average duration of leave was 63.70±31.58 days. The triggering factor was work-related and social in 33.6% and 30.1% of cases respectively. The agents returned to work after the long-term sick leave in 92% of cases.ConclusionsLong-term sick leave for psychiatric reasons is a handicap to productivity in society. Non-occupational factors are thought to be responsible for these mental health disorders. Setting up and improving social structures in the workplace would reduce the number of cases of long-term sick leaveDisclosure of InterestNone Declared

Transcriptomic investigation of the interaction between a biocontrol yeast, Papiliotrema terrestris strain PT22AV, and the postharvest fungal pathogen Penicillium expansum on apple

Biocontrol strategies offer a promising alternative to control plant pathogens achieving food safety and security. In this study we apply a RNAseq analysis during interaction between the biocontrol agent (BCA) Papiliotrema terrestris, the pathogen Penicillium expansum, and the host Malus domestica. Analysis of the BCA finds overall 802 upregulated DEGs (differentially expressed genes) when grown in apple tissue, with the majority being involved in nutrients uptake and oxidative stress response. This suggests that these processes are crucial for the BCA to colonize the fruit wounds and outcompete the pathogen. As to P. expansum analysis, 1017 DEGs are upregulated when grown in apple tissue, with the most represented GO categories being transcription, oxidation reduction process, and transmembrane transport. Analysis of the host M. domestica finds a higher number of DEGs in response to the pathogen compared to the BCA, with overexpression of genes involved in host defense signaling pathways in the presence of both of them, and a prevalence of pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) only during interaction with P. expansum. This analysis contributes to advance the knowledge on the molecular mechanisms that underlie biocontrol activity and the tritrophic interaction of the BCA with the pathogen and the host.

Graphical Modeling for Multi-Source Domain Adaptation.

Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain, which is a more practical and challenging problem compared to the conventional single-source domain adaptation. In this problem, it is essential to model multiple source domains and target domain jointly, and an effective domain combination scheme is also highly required. The graphical structure among different domains is useful to tackle these challenges, in which the interdependency among various instances/categories can be effectively modeled. In this work, we propose two types of graphical models, i.e. Conditional Random Field for MSDA (CRF-MSDA) and Markov Random Field for MSDA (MRF-MSDA), for cross-domain joint modeling and learnable domain combination. In a nutshell, given an observation set composed of a query sample and the semantic prototypes (i.e. representative category embeddings) on various domains, the CRF-MSDA model seeks to learn the joint distribution of labels conditioned on the observations. We attain this goal by constructing a relational graph over all observations and conducting local message passing on it. By comparison, MRF-MSDA aims to model the joint distribution of observations over different Markov networks via an energy-based formulation, and it can naturally perform label prediction by summing the joint likelihoods over several specific networks. Compared to the CRF-MSDA counterpart, the MRF-MSDA model is more expressive and possesses lower computational cost. We evaluate these two models on four standard benchmark data sets of MSDA with distinct domain shift and data complexity, and both models achieve superior performance over existing methods on all benchmarks. In addition, the analytical studies illustrate the effect of different model components and provide insights about how the cross-domain joint modeling performs.

Current Developments and Perspectives Regarding Financial Reporting in Romania. Qualitative Requirements for Financial Audit

Through this paper, the authors have proposed to highlight the current topics that are of real interest for the economic environment, in general, and for the accounting profession, in particular, mainly aiming at the following: the dynamics of the evolution of regulations regarding financial reporting at the national level, in correlation with the overall requirements at European and international level; the novelty of approaches regarding financial reporting/corporate reporting and their complexity in the context of current European policies and strategies regarding sustainable development, achieving the green transition and building an economy in the service of citizens; the content analysis of the conceptual and substantive developments of recent European regulations, embodied in Directive (EU) 2022/2464 on sustainability reporting by certain categories of entities, in order to understand and properly transpose its requirements into domestic legislation and then in current practice, by: national entities, the accounting profession in general, and financial auditors, in particular. Last but not least, the authors aimed to identify the impact of these regulations in the changes that should be taken into account in university education, in the research programs that will be launched in the following periods, as well as in the continuous training programs of some representative professional categories, including of the accounting profession.

An Analysis of Illocutionary Act on Song Lyrics by Sia Furler in The Album This is Acting

This study examines the illocutionary acts present in the lyrics of the album "This Is Acting" by Sia Furler. Utilizing a qualitative research approach, the study categorizes and analyzes the various types of illocutionary acts found within the album's songs. The data collection process involves selecting song lyrics from the album and identifying instances of illocutionary acts, which are then classified into representative, directive, commissive, expressive, and declarative categories. The findings reveal that the representative type of illocutionary act is the most prevalent, with instances of describing situations, stating facts, and conveying truths forming a significant portion of the analyzed data. Following this, directive illocutionary acts, including commands and invitations, are also prominent within the lyrical content. Commissive acts, expressing hopes, rejections, threats, and offers, represent a smaller yet significant portion of the data. Additionally, the analysis identifies expressive acts, such as expressions of praise, thanks, and regret, as well as declarative acts, featuring expressions of confession. This research contributes to the understanding of linguistic expressions in song lyrics and sheds light on how illocutionary acts are employed to convey messages, emotions, and themes within the context of music. Furthermore, it suggests avenues for future research, including exploring alternative media forms and applying the analysis of speech acts to literary works. Overall, this study offers valuable insights into the intricate interplay between language and music in conveying meaning and emotion to listeners.