Dear Authors and Readers,The closing issue of “Zeszyty Teoretyczne Rachunkowości” (ZTR, “The Theoretical Journal of Accounting”) for 2025, vol. 49, number 4, once again provides an engaging and multidimensional review of contemporary research trends in accounting. This Special Issue, titled Contemporary challenges, conditions and directions of development of accounting, gathers 13 studies that explore the ongoing transformation of the accounting discipline driven by technological advancements, sustainability demands, and evolving expectations from professionals and educators. The featured articles reflect a diverse range of approaches, from theoretical modelling and comparative analysis to bibliometric synthesis and empirical evaluation, offering a comprehensive perspective on the accounting field as it advances into a new digital and regulatory era.At the intersection of behavioural finance and accounting communication, Adeel Ali Qureshi and Mateusz Lemańczyk present a comprehensive literature review in their paper Attention metrics and stock market reactions to accounting events: A literature review. By combining bibliometric analysis with the TCCM frame- work, they investigate how investor attention, measured by media coverage, online search activity, and textual complexity, influences market reactions to accounting disclosures. Their findings highlight the increasing significance of behavioural insights and data analytics in understanding how financial information is perceived, processed, and priced.The paper by Mateja Brozović, Sanja Sever Mališ, and Dominik Piršić, titled Financial accounting analysis of leverage and profitability: Evidence from Croatian SMEs, expands the discussion to corporate financial performance. Using key financial ratios from small and medium-sized enterprises in Croatia, the authors analyse the relationship between leverage and profitability, providing empirical evidence that enhances understanding of the financial resilience and risk structures of SMEs, a vital yet often overlooked segment of the European economy.Renáta Hornická and Renáta Pakšiová examine the development of non-financial disclosure in their paper Scope of sustainability reporting in the largest companies in Slovakia in 2017 and 2022. By analysing textual data from the annual and sustainability reports of major Slovak firms, they document a noticeable growth in the scope and depth of ESG reporting following the introduction of the Non-Financial Reporting Directive. Their findings offer timely insight into how regulatory pressure drives increased corporate accountability and the institutionalisation of sustainability reporting in Central and Eastern Europe.A broader institutional and regulatory perspective on sustainability assurance is examined by Tanja Laković, Daniel Zdolšek, and Milica Vukčević in their paper Development of the regulatory framework for sustainability assurance: A comparative analysis of the transition from NFRD to CSRD in Slovenia and Montenegro. This comparative study highlights the challenges and opportunities of implementing the new EU Corporate Sustainability Reporting Directive in Montenegro, a non-EU member state. It highlights differences in readiness and institutional adaptation between EU member and candidate countries.From a theoretical perspective, Serhii Lehenchuk and Viktoriia Makarovych offer an innovative conceptual discussion in Theoretical foundations of accounting for intellectual investment property: Towards standard setting. Their paper develops a framework for recognising and measuring intellectual investment property, bridging gaps between traditional accounting and emerging forms of intangible capital. By proposing theoretical principles for potential standardisation, the study adds a significant perspective to debates on accounting for knowledge-based assets in the digital economy.The linguistic and communicative aspects of accountability are examined in Raili Lilo, Elina Paemurru, and Ülle Pärl’s paper, Accountability through linguistic features: A holistic theoretical framework for sustainability reports. Through a meta- -analysis of previous empirical studies, the authors incorporate insights from legitimacy, stakeholder, signalling, and institutional theories to illustrate how language can both promote and conceal accountability in sustainability reporting. Their comprehensive framework offers a valuable basis for analysing how textual choices such as tone, clarity, and structure can influence stakeholders’ perceptions of corporate responsibility and transparency.The public sector perspective is presented by Diana Papradanova and Ventsislav Vechev in their paper An evaluation of the accounting model for reporting public sector entities’ revenues in Bulgaria in the context of the International Public Sector Accounting Standards. The authors carry out a detailed comparative analysis of Bulgarian regulations and IPSAS provisions, highlighting conceptual differences and gaps that impede transparency and comparability. Their findings offer practical recommendations for aligning public-sector accounting practices with international standards and fiscal accountability principles.The human factor and digital transformation in accounting are central themes in Katarzyna Prędkiewicz and Krzysztof Biegun’s article, Factors that influence accountants’ acceptance of Artificial Intelligence: An extended Technology Acceptance Model, which incorporates technology anxiety and experience. The authors empirically expand the Technology Acceptance Model by including variables related to technological anxiety and professional experience, offering fresh insights into how accountants view, accept, and adopt AI tools in their work. Their findings emphasise both the opportunities and psychological barriers in the move towards automation and intelligent systems in accounting practice.The contribution by Ana Rep Romić, Marzena Remlein, and Sanja Sever Mališ, titled Information technology in accounting education: A bibliometric-systematic literature review (2006–2025), focuses on the intersection of pedagogy and digitalisation. Drawing on a bibliometric and systematic literature review spanning two decades of research, the authors map global trends in the integration of IT into accounting education. Their study identifies emerging competencies, evolving educational technologies, and the changing role of educators in developing digitally literate accounting professionals capable of responding to sustainability and AI-driven challenges.Kristina Rudžionienė, Aušrinė Tamulevičiūtė, and Aurelija Kustienė’s study, The relationship between CSR and earnings management in Lithuanian listed companies, explores how sustainability efforts relate to financial behaviour in a small, transitional economy. Contrary to prior expectations, their results indicate a positive link between corporate social responsibility and both accrual- and real-activity earnings management. This surprising outcome suggests that, in some cases, CSR initiatives might be strategically used to hide opportunistic actions. The study offers new empirical insights into ethical authenticity and transparency in financial reporting across Central and Eastern Europe.The intersection of family business and accounting research is explored in Amin Soheili’s paper Family business and accounting research: A structured literature review. Through a systematic review of seventy peer-reviewed papers published between 2000 and 2024, the author maps the theoretical and methodological development of accounting research within family business contexts. Using a SWOT framework, the study highlights the underrepresentation of socioemotional and qualitative dimensions. The review advocates a broader investigation into private and emerging-market family firms, emphasising the need for interdisciplinary approaches that account for the behavioural and relational dynamics of family-owned enterprises.Gintarė Špogienė, Daiva Tamulevičienė, and Kristina Rudžionienė analyse five leading Lithuanian retail chains in their paper Integrating corporate social responsibility into internal decision-making in leading retail chains in Lithuania: A responsibility accounting perspectiveThey highlight a gap between publicly disclosed CSR and the information that genuinely influences managerial decisions. To reduce “informational noise” and enhance accountability, they suggest adapting responsibility accounting and reporting (RAR) to incorporate stakeholder-impact assessment and to categorise decisions as financial, philanthropic, or socially responsible, aligning internal controls with public CSR commitments and fostering more transparent, ethics-based governance.Finally, considering preparedness for the EU’s sustainability regime, Aleksandra Sulik-Górecka, Marzena Strojek-Filus, and Daniel Iskra, in their article Assessment of Polish companies’ preparedness for ESG reporting in the context of its determinants as evaluated by report preparers, explore Polish companies’ readiness through a nationwide survey and non-parametric inference. Most respondents rated themselves as only moderately prepared, with preparedness significantly linked to firm size (but not industry), about 70% viewing ESG reporting as complex, and they highlight a need for investment in personnel and reporting technologies. The study places these findings in the context of the roll-out of CSRD/ESRS and presents them as a baseline for more in-depth quality analysis.Taken together, the articles in this Special Issue reflect the complexity of modern accounting as a discipline that is simultaneously technological, behavioural, regulatory, and ethical. The contributions show how accounting continues to broaden beyond its traditional financial scope, including data analytics, artificial intelligence, linguistic transparency, and sustainability assurance. Each paper not only advances academic discussion but also provides valuable insights for practitioners, educators, and policymakers, enhancing the quality, relevance, and integrity of accounting information.The Editorial Team extends its gratitude to all authors and reviewers for their valuable contributions and diligent work in preparing this issue. We also thank our readers for their continued interest and engagement with the journal. We hope that the studies presented here will inspire further discussion, research, and innovation in the ever-evolving field of accounting.Marzena Remlein* Ana Rep Romić**The Editorial Team of ZTR is pleased to announce that in ZTR’s 49th year of publication, its four quarterly issues contained 39 articles: 25 in English and 14 in Polish. Their authors come from eleven countries (Bulgaria, Estonia, Croatia, Montenegro, Lithuania, Poland, the Czech Republic, Slovakia, Slovenia, Sweden, and Ukraine). We thank all the authors for their cooperation with the Editorial Team and the reviewers of their articles. The manuscripts submitted to ZTR were reviewed in 2025 by 73 reviewers, including 52 from Poland and 21 from abroad. The Editorial Team would like to thank all specialists who provided anonymous reviews and insightful feedback. The list of Polish and foreign reviewers is included in this issue of ZTR and on our journal’s website at https://ztr.skwp.pl/ cms/reviewers. We encourage authors and readers to visit ZTR’s website at https://ztr.skwp.pl/, which contains extensive information about ZTR, including its presence in databases (including Scopus, Web of Science, BazEkon, EBSCO Business Source Ulti-mate, Erich Plus, CEEOL, Cejsh, CROSSREF, DOAJ, and ICI Journals Master List), as well as an invitation to a thematic issue of ZTR in 2026 titled Accounting’s Expanded Horizon: Redefining Internal Practices for Organizational Flourishing (for more, see Call for papers published in ZTR, Vol. 49, No. 2 and at https://ztr.skwp.pl/cms/CMS:647). On behalf of the entire ZTR Editorial Team, I wish all authors, reviewers, members of the Editorial Board, and readers of ZTR a lot of health, happi-ness, and peace, as well as numerous professional successes in 2026. Yours sincerely,Anna Szychta
Christopher D. Yang, Christine K. Kim, Melissa M. Chang
et al.
Objective To evaluate the current body of literature pertaining to the use of ocular point-of-care ultrasound (POCUS) in the emergency department (ED). Methods A comprehensive literature search was conducted on Scopus, Web of Science, MEDLINE, and Cochrane Central Register of Controlled Trials (CENTRAL) databases. Inclusion criteria were studies written in English and primary clinical studies involving ocular POCUS scans in an ED setting. Exclusion criteria were nonprimary studies (e.g., reviews or case reports), studies written in a non-English language, nonhuman studies, studies performed in a nonemergency setting, studies involving non-POCUS ocular ultrasound modalities, or studies published more than 10 years prior. Data extraction was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. Results The initial search yielded 391 results with 153 duplicates. Of the remaining 238 studies selected for retrieval and screening, 24 met the inclusion criteria. These 24 included studies encompassed 2,448 patients across prospective, retrospective, cross-sectional, and case series study designs. The majority of included studies focused on the use of POCUS in the ED to measure optic nerve sheath diameter as a proxy for papilledema and metabolic aberrations, while a minority of studies used ocular POCUS to assist in the diagnosis of orbital fractures or posterior segment pathology. Conclusion The vast majority of studies investigating the use of ocular POCUS in recent years emphasize its utility in measuring optic nerve sheath diameter and fluctuations in intracranial pressure, though additional outcomes of interest include pathology of the posterior segment, orbit, and globe.
Medical emergencies. Critical care. Intensive care. First aid
Anton Vijeevaraj Ann Sinthusha, Eugene Y. A. Charles, Ruvan Weerasinghe
Machine Reading Comprehension (MRC) is a challenging task in Natural Language Processing (NLP), crucial for automated customer support, enabling chatbots and virtual assistants to accurately understand and respond to queries. It also enhances question-answering systems, benefiting educational tools, search engines, and help desks. The introduction of attention-based transformer models has significantly boosted MRC performance, especially for well-resourced languages such as English. However, MRC for low-resourced languages (LRL) remains an ongoing research area. Although Large Language Models show exceptional NLP performance, they are less effective for LRL and are expensive to train and deploy. Consequently, simpler language models that are targeted at specific tasks remain viable for these languages. This research examines high-performing language models on the Tamil MRC task, detailing the preparation of a Tamil-translated and processed SQuAD dataset to establish a standard dataset for Tamil MRC. The study analyzes the performance of multilingual transformer models on the Tamil MRC task, including Multilingual DistilBERT, Multilingual BERT, XLM-RoBERTa, MuRIL, and RemBERT. Additionally, it explores improving these models’ performance by fine-tuning them with English SQuAD, Tamil SQuAD, and a newly developed Tamil Short Story (TSS) dataset for MRC. Tamil’s agglutinative nature, which expresses grammatical information through suffixation, results in a high degree of word inflexion. Given this characteristic, the BERT score was chosen as the evaluation metric for MRC performance. The analysis shows that the XLM-RoBERTa model outperformed the others for Tamil MRC, achieving a BERT score of 86.29% on the TSS MRC test set. This superior performance is attributed to the model’s cross-lingual learning capability and the increased number of data records used for fine-tuning. The research underscores the necessity of language-specific fine-tuning of multilingual models to enhance NLP task performance, for low-resourced languages.
Bilingual parallel corpora is a very important basic resource in the research field of natural language processing based on statistics. There are cross alignment and empty alignment in Chinese-English bilingual text, it is easy to affect the effect of Chinese-English sentence alignment. Therefore, we propose a novel Chinese-English sentence alignment method based on multi-feature self-attention mechanism fusion. First, the long features of Chinese-English bilingual sentences are integrated into the Glove word vector. Then bidirectional gated recurrent unit is used to encode the feature word vector to obtain more fine-grained sentence local information. Second, the interactive attention mechanism is introduced to extract global information in bilingual sentences to ensure the effective use of contextual semantic features. Finally, the Kuhn-Munkres (KM) algorithm is introduced on the basis of multi-layer perceptron, which can deal with non-monotonic aligned text and improve the generalization ability of the model. Experiments show that, the F index with the proposed method exceeds 90%, the proposed method can effectively improve the correct rate and recall rate of sentence alignment, and improve the construction efficiency of Chinese-English parallel corpora.
Engineering (General). Civil engineering (General), Chemical engineering
Ishika Agarwal, Nimet Beyza Bozdag, Dilek Hakkani-Tür
Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a nuance in this training objective, and find that by changing the language of the input query, we can improve the question answering ability of language models. Our contributions are two-fold. First, we introduce the term Language Specific Knowledge (LSK) to denote queries that are best answered in an "expert language" for a given LLM, thereby enhancing its question-answering ability. We introduce the problem of language selection -- for some queries, language models can perform better when queried in languages other than English, sometimes even better in low-resource languages -- and the goal is to select the optimal language for the query. Second, we introduce simple to strong baselines to test this problem. Additionally, as a first-pass solution to this novel problem, we design LSKExtractor to benchmark the language-specific knowledge present in a language model and then exploit it during inference. To test our framework, we employ three datasets that contain knowledge about both cultural and social behavioral norms. Overall, LSKExtractor achieves up to 10% relative improvement across datasets, and is competitive against strong baselines, while being feasible in real-world settings. Broadly, our research contributes to the open-source development (https://github.com/agarwalishika/LSKExtractor/tree/main) of language models that are inclusive and more aligned with the cultural and linguistic contexts in which they are deployed.
Adisai Na-Thalang, Chanakan Wittayasakpan, Kritsadha Phatcharoen
et al.
This paper introduces the development of the first open conversational speech dataset for the Isan language, the most widely spoken regional dialect in Thailand. Unlike existing speech corpora that are primarily based on read or scripted speech, this dataset consists of natural speech, thereby capturing authentic linguistic phenomena such as colloquials, spontaneous prosody, disfluencies, and frequent code-switching with central Thai. A key challenge in building this resource lies in the lack of a standardized orthography for Isan. Current writing practices vary considerably, due to the different lexical tones between Thai and Isan. This variability complicates the design of transcription guidelines and poses questions regarding consistency, usability, and linguistic authenticity. To address these issues, we establish practical transcription protocols that balance the need for representational accuracy with the requirements of computational processing. By releasing this dataset as an open resource, we aim to contribute to inclusive AI development, support research on underrepresented languages, and provide a basis for addressing the linguistic and technical challenges inherent in modeling conversational speech.
Over the past three years, the rapid advancement of Large Language Models (LLMs) has had a profound impact on multiple areas of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) across diverse languages, including Arabic. Although Arabic is considered one of the most widely spoken languages across 27 countries in the Arabic world and used as a second language in some other non-Arabic countries as well, there is still a scarcity of Arabic resources, datasets, and tools. Arabic NLP tasks face various challenges due to the complexities of the Arabic language, including its rich morphology, intricate structure, and diverse writing standards, among other factors. Researchers have been actively addressing these challenges, demonstrating that pre-trained Large Language Models (LLMs) trained on multilingual corpora achieve significant success in various Arabic NLP tasks. This study provides an overview of using large language models (LLMs) for the Arabic language, highlighting early pre-trained Arabic Language models across various NLP applications and their ability to handle diverse Arabic content tasks and dialects. It also provides an overview of how techniques like finetuning and prompt engineering can enhance the performance of these models. Additionally, the study summarizes common Arabic benchmarks and datasets while presenting our observations on the persistent upward trend in the adoption of LLMs.
Perceptions of vaccine safety, importance and effectiveness are at the core of vaccine hesitancy around the world, and Africa has had its own share of vaccine revolts. This study uses the 2018 Wellcome Global Monitor on public perceptions of vaccines in 40 African countries to examine the predictors of vaccine hesitancy. It examines levels of hesitancy from a language perspective, comparing French speakers with others, mostly English speakers. Results show that French speakers were significantly more hesitant about importance and safety, while English speakers and others were more hesitant about effectiveness. This reflects the continuing influence of colonial ties on African countries. Respondents with high levels of trust in social actors (such as national government, journalists, people neighborhood, doctors and nurses) were also more hesitant about the safety and effectiveness of vaccines, indicating the importance of non-scientists in influencing vaccine hesitancy. Those with high levels of education were more likely to be hesitant about vaccines in general, indicating that having more education may have an opposite effect. Perception of science as progress was significant for all three hesitancy types, indicating that Africans with more progressive attitudes were less likely to worry about the importance, safety and effectiveness of vaccines. At the country level, there was no overarching predictor, indicating the strong role of local social and cultural factors. These findings improve our understanding of the drivers of vaccine hesitancy in Africa and provide valuable input for future vaccine policy and health-awareness campaigns.
This study aimed at shedding more light on the roles of perceptual learning style preferences (PLSP) on EFL achievement. The study applied quantitative approach and correlational design. Selected using simple random sampling, 180 grade eleven students were participants of the study. The study employed Reid’s (1984) PLSPQ, and students’ semester final exam results to measure PSLP and FLA, respectively. Six units of grade eleven English textbook was as secondary data. Findings of the study revealed that the major PLSP of the students was group followed by visual, minor PLSP, and negligible others four. Moreover, the activities addressed by the teaching material were found incongruent with the perceived learning styles. Furthermore, results of One-way ANOVA confirmed that PLSP is significantly related with FLA. It was implied that the construct PLSP need to be considerably reflected in the instructional materials preparation towards the EFL students’ success. As a mismatch between activities of teaching materials would result in learners’ failure, teaching materials need to be geared towards PSLP dimensions. Teachers are expected to identify students’ ways of learning and apply accommodating strategies to address individual differences in the classroom in terms of PLSP dimensions.
Recently, much work has concerned itself with the enigma of what exactly pretrained language models~(PLMs) learn about different aspects of language, and how they learn it. One stream of this type of research investigates the knowledge that PLMs have about semantic relations. However, many aspects of semantic relations were left unexplored. Generally, only one relation has been considered, namely hypernymy. Furthermore, previous work did not measure humans' performance on the same task as that performed by the PLMs. This means that at this point in time, there is only an incomplete view of the extent of these models' semantic relation knowledge. To address this gap, we introduce a comprehensive evaluation framework covering five relations beyond hypernymy, namely hyponymy, holonymy, meronymy, antonymy, and synonymy. We use five metrics (two newly introduced here) for recently untreated aspects of semantic relation knowledge, namely soundness, completeness, symmetry, prototypicality, and distinguishability. Using these, we can fairly compare humans and models on the same task. Our extensive experiments involve six PLMs, four masked and two causal language models. The results reveal a significant knowledge gap between humans and models for all semantic relations. In general, causal language models, despite their wide use, do not always perform significantly better than masked language models. Antonymy is the outlier relation where all models perform reasonably well. The evaluation materials can be found at https://github.com/hancules/ProbeResponses.