bluuebunny/crossref_metadata_2025 huggingface dataset:
Embedding Ethics Into Artificial Intelligence: Understanding What Can Be Done, What Can't, and What Is Done
Authors: Peterson Clayton
Published: July 2024

Embedding ethical considerations within the development of AI driven technologies becomes more and more pressing as new technologies are developed. Given the impact of autonomous technologies on individuals and society, it is worth taking the time to assess and manage the ethical aspects and possible consequences of our technological endeavors. While the growing rapidity of autonomous decision processes makes it hard to keep individuals in the decision loops, people are turning their attention to the ways in which ethics could be integrated to machines and algorithms, as well as to the possibility of defining autonomous ethical machines that would be able to solve ethical dilemmas and act ethically (e.g. autonomous vehicles). Notwithstanding theoretical and practical difficulties surrounding the possibility of defining such ethical machines, important elements should be considered when reflecting on the embedding of ethics into AI technologies. The present paper aims to critically analyze the limitations of such endeavors by exposing common misconceptions relating to AI ethics.
Intrinsic Prioritization in Answer Set Programming Based on an Adapted Notion of Tolerance
Authors: Wilhelm Marco , Spiegel Lars-Phillip , Kern-Isberner Gabriele
Published: July 2024

Answer set programming (ASP) is a declarative programming language suited to solve complex combinatorial search problems. Prioritized ASP is the subdiscipline of ASP which aims at prioritizing the models (answer sets) of ASP programs. Common approaches to prioritized ASP require an additional input, sometimes expressed as special literals within the ASP program, that guides the prioritization of the answer sets. In this paper, to the best of our knowledge, we propose the first approach that is able to prioritize answer sets solely based on the original ASP program. For this, we adapt the notion of tolerance from conditional reasoning according to System Z and establish tolerance partitions of ASP programs which can be used for prioritization.
Lattice-Based Generation of Euclidean Geometry Figures
Authors: Henning Jonathan , King Hanna , Ngo Sophie , Shore Jake , Gardner Alex , Alvin Chris , Stadnyk Grace
Published: July 2024

We present a user-guided method to generate geometry figures appropriate for high school Euclidean geometry courses: a useful starting point for an intelligent tutoring system to provide meaningful, realistic figures for study. We first establish that a two-dimensional geometry figure can be represented abstractly using a complete, lattice we call a geometry figure lattice (GFL). As input, we take a user-defined vector of primitive geometry shapes and convert each into a GFL. We then exhaustively combine each these ‘primitive’ GFLs into a set of complex GFLs using a process we call gluing. We mitigate redundancy in GFLs by introducing a polynomial-time algorithm for determining if two GFLs are isomorphic. These lattices act as a template for the second step: instantiating GFLs into a sequence of concrete geometry figures. To identify figures that are structurally similar to textbook problems, we use a discriminator model trained on a corpus of textbook geometry figures.
An Interpretable Transformer Model for Operational Flare Forecasting
Authors: Abduallah Yasser , Gazula Vinay Ram , Wang Jason T. L.
Published: July 2024

Interpretable machine learning tools including LIME (Local Interpretable Model-agnostic Explanations) and ALE (Accumulated Local Effects) are incorporated into a transformer-based deep learning model, named SolarFlareNet, to interpret the predictions made by the model.  SolarFlareNet is implemented into an operational flare forecasting system to predict whether an active region on the surface of the Sun would produce a >=M class flare within the next 24 hours. LIME determines the ranking of the features used by SolarFlareNet. 2D ALE plots identify the interaction effects of two features on the predictions. Together, these tools help scientists better understand which features are crucial for SolarFlareNet to make its predictions. Experiments show that the tools can explain the internal workings of SolarFlareNet while maintaining its accuracy.
Transformer Models for Brazilian Portuguese Question Generation: An Experimental Study
Authors: Da Rocha Junqueira Julia , Brisolara Corrêa Ulisses , Freitas Larissa
Published: July 2024

Unlike tasks such as translation or summarization, generating meaningful questions necessitates a profound understanding of context, semantics, and syntax. This complexity arises from the need to not only comprehend the given text comprehensively but also infer information gaps, identify relevant entities, and construct syntactically and semantically correct interrogative sentences. We address this challenge by proposing an experimental fine-tuning approach for encoder-decoder models (T5, FLAN-T5, and BART-PT) tailored explicitly for Brazilian Portuguese question generation. Our study involves fine-tuning these models on the SQUAD-v1.1 dataset and subsequent evaluation, also on SQUAD-v1.1. Through our experimental endeavors, BART returned a higher result in all the ROUGE metrics, as ROUGE-1 0.46, ROUGE-2 0.24, and ROUGE-L 0.43, suggesting a higher lexical similarity in the questions generated, and it is comparable to the results of the question generation task for the English language. We explored how these advancements can significantly enhance the precision and quality of the question generation task in Brazilian Portuguese, bridging the gap between training data and the intricacies of interrogative sentence construction.
Beyond Binary: Revealing Variations in Islamophobic Content with Hierarchical Multi-Class Classification
Authors: Aldreabi Esraa , Harahsheh Khawlah , Chhangani Mukul Dev , Chen Chung-Hao , Blackburn Jeremy
Published: July 2024

In the digital age, the rise of Islamophobia-marked by an irrational fear or discrimination against Islam and Muslims-has emerged as a pressing issue, especially on social media platforms. In this paper we employs a multi-class classification system, moving beyond traditional binary models. We categorize Islamophobic content into three main classes and various subclasses, covering a range from subtle biases to explicit incitement. Comparative analysis of data from Reddit and Twitter illuminates the distinct prevalence and types of Islamophobic content specific to each platform. This paper deepens our understanding of digital Islamophobia and provides insights for crafting targeted online counter strategies. Additionally, it highlights the role of machineand deep learning in detecting and addressing Islamophobic content, emphasizing their significance in resolving complex social issues in the digital sphere.
Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling Approach
Authors: Moore-Pizon Thomas , DePaula Nic , Hagen Loni
Published: July 2024

Different levels of government agencies have exerted great effort to communicate with the public during the Covid-19 pandemic on multiple social media platforms. This study uses BERT topic modeling, an artificial intelligence model, to extract topics from various public health agencies of cities, states and the federal government on Twitter and Facebook for the years 2020 and 2021. We contrast and compare major topics addressed by these agencies related to Covid-19 and the pandemic across the two major social media platforms. The findings show how we can employ BERT topic modeling to extract social media topics during a health emergency and evaluate the extent to which topics covered by these agencies address the major social and health concerns of the pandemic.
Model for Knowledge Transfer in Agent Organizations: a Case Study on Moise+
Authors: Farias Giovani , Rodrigues Paulo , Adamatti Diana , Gonçalves Eder
Published: July 2024

Knowledge transfer enables the development of complex multi-agent systems featuring agents that share knowledge to execute tasks. Environments shaped by knowledge transfer involve agents assuming specific roles, reasoning about the environment and other agents, and forming organizations that transfer knowledge through well-defined plans and strategies. This paper presents an organizational model for knowledge transfer, introducing a centralizing role, named organizer, responsible for managing relations between the system, agents, and their roles. An interaction protocol is established to guide the step-by-step communication between the organizer and other agents in the system during knowledge transfer. To model knowledge transfer within agent organizations, we employ a dynamic implementation of the Moise+ organizational model, called MoiseLight, enabling the creation of organizations at runtime. We validate the proposal by adapting the model to MoiseLight, demonstrating the organizer's ability to facilitate knowledge transfer among agents following the specified interaction protocol.
Hands-On Introduction to Quantum Machine Learning
Authors: Ismail Muhammad , Shaban Mohamed , Chen Samuel Yen-Chi
Published: July 2024

This tutorial covers a hands-on introduction to quantum machine learning. Foundational concepts of quantum information science (QIS) are presented (qubits, single and multiple qubit gates, measurements, and entanglement). Building on that, foundational concepts of quantum machine learning (QML) are introduced (parametrized circuits, data encoding, and feature mapping). Then, QML models are discussed (quantum support vector machine, quantum feedforward neural network, and quantum convolutional neural network). All the aforementioned topics and concepts are examined using codes run on a quantum computer simulator. All the covered materials assume a novice audience interested in learning about QML. Further reading and software packages and frameworks are shared with the audience.
Evaluating Vision-Language Models on the TriangleCOPA Benchmark
Authors: Chemburkar Ankur , Gordon Andrew , Feng Andrew
Published: July 2024

The TriangleCOPA benchmark consists of 100 textual questions with videos depicting the movements of simple shapes in the style of the classic social-psychology film created by Fritz Heider and Marianne Simmel in 1944. In our experiments, we investigate the performance of current vision-language models on this challenging benchmark, assessing the capability of these models for visual anthropomorphism and abstract interpretation.
On Soft Sets and Formal Concept Analysis (FCA) as mathematical categorization systems - An Engineering Application
Authors: Pascu Anca , Nana Laurent , Vareille Jean , Monin François
Published: July 2024

This paper deals with an analysis of the reasoning for the cognitive problem of categorization in systems. Such reasoning requires the construction of a formal model of the system that is performed in 3 steps: cognitive description of the system, extraction of system’s meaningful concepts, and design of the mathematical model based on the concepts. Our goal is to explore the cognitive features and their predominance in some old models of categorization well known in literature. We highlight the relation between Formal Concept Analysis (FCA) model and soft sets and we analyze the notion of "parameter" from the cognitive point of view. Based on this analysis, we propose the notion of double soft sets as a mathematical notion more adequate in the engineering applications. All our study is conducted in the framework of material selection in mechanical engineering.
Fluid Path Detection Model for Lab on a Chip Images Using Deep Learning-Based Segmentation Approach
Authors: Khalghollah Mahmood , Shakeri Esmaeil , Zare Azam , H. Far Behrouz , Sanati-Nezhad Amir
Published: July 2024

This paper explores the potential of Lab on a Chip (LOC) technologies in transforming diagnostic, biotechnology, and chemical/mechanical analysis fields. The proposed solution integrates advanced image processing into an automated tool, providing a robust and efficient method for precise data extraction from microfluidic chip images. In this study, we identify the fluid path in each frame, thereby improving the platform for tracking valuable fluid parameters over time, such as the viscosity of biofluids. Different patterns of LOC were developed then captured and related masks were established to create the 150 images dataset.[1]Using the DeepLabv3+ deep learning model on the dataset, this study achieves remarkable validation accuracy of 98.95% and a low loss value of 0.012 for chip analysis path segmentation. The successful integration of DeepLabv3+ and meticulous preprocessing enhances understanding of fluid behavior within microfluidic chips, paving the way for advancements in chip design, diagnostics, and fluid feature-based analyses.
Lung and Colon Cancer Histopathological Image Classification Using 1D Convolutional Channel-based Attention Networks
Authors: Shahadat Nazmul
Published: July 2024

Lung and Colon cancer are the leading diseases of death and disability in humans caused by a combination of genetic diseases and biochemical abnormalities. If these are diagnosed in their early stages, they can not be spread in organs and negatively impact human life. Many deep-learning networks have recently been proposed to detect and classify these malignancies. However, incorrect detection or misclassification of these fatal diseases can significantly affect an individual's health and well-being. This paper introduces a novel, cost-effective, and mobile-embedded architecture to diagnose and classify Lung squamous cell carcinomas and adenocarcinomas of the lung and colon from digital pathology images. Extensive experiment shows that our proposed modifications achieve 100% testing results for lung, colon, and lung-and-colon cancer detection. Our novel architecture takes around 0.65 million trainable parameters and around 6.4 million flops to achieve the best lung and colon cancer detection performance. Compared with the other results, our proposed architecture shows state-of-the-art performance.
Leveraging Organizational Hierarchy to Simplify Reward Design in Cooperative Multi-agent Reinforcement Learning
Authors: Liu Lixing , Ustun Volkan , Kumar Rajay
Published: July 2024

The effectiveness of multi-agent reinforcement learning (MARL) hinges largely on the meticulous arrangement of objectives. Yet, conventional MARL methods might not completely harness the inherent structures present in environmental states and agent relationships for goal organization. This study is conducted within the domain of military training simulations, which are typically characterized by complexity, heterogeneity, non-stationary and doctrine-driven environments with a clear organizational hierarchy and a top-down chain of command. This research investigates the approximation and integration of the organizational hierarchy into MARL for cooperative training scenarios, with the goal of streamlining the processes of reward engineering and enhancing team coordination. In the preliminary experiments, we employed two-tiered commander-subordinate feudal hierarchical (CSFH) networks to separate the prioritized team goal and individual goals. The empirical results demonstrate that the proposed framework enhances learning efficiency. It guarantees the learning of a prioritized policy for the commander agent and encourages subordinate agents to explore areas of interest more frequently, guided by appropriate soft constraints imposed by the commander.
Decoding Complexity: A Mathematical Framework for Enhanced Translation Comprehension
Authors: Poirier Eric , Nirina Avo Ansta Nasandratra
Published: July 2024

Machine translation tools have demonstrated substantial progress in enhancing translation accuracy since the emergence of artificial intelligence. However, challenges persist in reasoning (or the lack thereof), considering contexts, addressing specific word games, and interpreting very long or very short sentences—those exceeding 50 and falling below 7 words (Bowker, 2023 : 893). Additionally, accurately translating technical or specialized terms and their variations remains a hurdle. This research introduces a categorical mathematical formalization of the comprehension stages in translation, along with a model for calculating acceptances (specific meanings of words) during the verification of meaning hypotheses. The goal is to elucidate the comprehension process and integrate contextual considerations. The formalism delineates a series of fundamental cognitive operations involved in comprehension. Furthermore, it advocates for evaluating meaning hypotheses using logical modalities, particularly hypostases, described as phrases (groups of words)—a unit of discourse rather than language—signifying the structure of arguments conveying the speaker's knowledge. The strength of our proposed mathematical model lies in its independence from both source and target languages, as well as the subjectivity of text authors or translators. Additionally, the assessment of meaning hypotheses relies on verifiable logical modalities, ensuring a reliable, explicable, and controllable outcome.
Initial Goal Allocation for Multi-agent Systems
Authors: Nguyen Tran Khanh Tra , Young Jonathan , Kondrakunta Sravya
Published: July 2024

In the multi-agent environment, a human expert engages inthe allocation of objectives among individual agents. How-ever, autonomous agents need to determine and allocate ob-jectives without external intervention from humans. There-fore, in this research, we attempt to solve initial goal distri-bution challenges in multi-agent settings by developing twogoal allocation algorithms. The primary objectives are to findcost-effective goal solution sets and distribute them evenlyamong available agents. We introduce two algorithms whenthe goals are structured in a hierarchical goal tree structure,and then test their efficiency across a variety of baseline al-location methods. Both algorithms were able to increase theperformance of agents in multi-agent settings by finding themost optimal distributions of goals and allowing agents to actindependently from human intervention.
Determinants of Share Prices of Agriculture Listed Firms
Authors: Abel Sanderson , Henry Muleya , Mokumako Tebogo , Mukarati Julius , Le Roux Pierre
Published: July 2024

The stock market plays an important role in economic development by promoting capital formation and enhancing economic growth. The stock market is among the main sources of financing in the critical sector of the economy. Trading of securities on the Zimbabwe stock exchange facilitates savers and users of capital by fund pooling, risk sharing and transferring wealth. One of the important sectors of the Zimbabwean economy devoid of adequate financing is the agricultural sector. This sector has been experiencing several challenges over the period 2002 -2022 mostly from lack of sustainable finance especially from private investment. It is assumed that lack of funding is due to volatility of stock prices. The objective of the study is to analyze different factors affecting share price of agriculture sector listed firm on the Zimbabwe Stock Exchange using the panel regression model. Based on the random effect the study established that share prices are determined by earnings per share, price earnings ratio and return on equity positively impacts stock prices in Zimbabwe. The government should control inflation to ensure price stability to aid in easy financial planning.   Further the governments should promote agricultural growth initiatives and strategies which would stimulate demand for agricultural oriented stocks.
Oil Price Dynamics and Sectoral Indices in India – Pre, Post and during COVID Pandemic: A Comparative Evidence from Wavelet-based Causality and NARDL
Authors: Mandal Koushik , Datta Radhika Prosad
Published: July 2024

Due to the COVID pandemic, the stock market has been affected adversely around the globe and investment decisions are now more challenging and riskier. Hence, in this paper, we aim to investigate the impact of oil prices on the Indian stock market and eight sectoral indices for the period of pre, post, and during the COVID pandemic. The maximal overlap discrete wavelet transform (MODWT) is used to decompose and to denoise the original time series data as oil price and market return are found to be noisy. We employ the wavelet-based Granger causality (WGC) and non-linear, autoregressive distributed lag model (NARDL) to investigate the causality in the frequency domain as well as the short-run and long-run asymmetry of oil price impact. Our analysis shows a feedback relation between low frequency (higher investment horizon) and the long-run asymmetric impact of oil prices on all sectors during all three periods. We discuss the dynamic time-varying relationship between the oil price and sectoral return along with the investment implications in detail.
Қазақ паремиологиялық кеңістігіндегі «билік» субконцептісінің семантикалық өрісі
Authors: Амирова А. , Бейсенбай А. , Өмірбекова Ж.
Published: July 2024

Мақалада қазақ паремиологиялық кеңістігіндегі «билік» субконцептісінің семантикалық өрісі талданады. Билік – қоғамдық қатынастарды реттеуші. Сондықтан ол халықтық тәжірибе мен құндылықтарды, халықтық ойлау жүйесін, тұрмыс қағидаттарын, өмір сүру формасын қалыптастыруда да маңызды рөл атқарады. Сол себепті билік тақырыбы қай кезеңде де өзектілігін жоймайды. Осы тұста «билік» ұғымының қазақ танымындағы сипатынпаремиологиялық жүйе тұрғысынан сипаттау маңызды нәтижелерге қолжеткізуге мүмкіндік береді. Өйткені паремиологиялық кеңістік те белгілі бірхалыққа тән талғам, тәжірибе үлгілерін бойына жинақтайды.Зерттеу мақсаты – қазақ дәстүрлі қоғамындағы «билікке» қатыстықалыптасқан ұғым-түсініктердің паремиологиялық кеңістіктегі семантикалықөрісін зерделеу. «Билік» субконцептісін құрайтын семантикалық бірліктердіңмақал-мәтелдер құрамындағы сипаты және қоғамдық өмірдегі маңызы талданады.Қазақ халқының басқару жүйесін білдіретін тілдік бірліктердің паремиологиялықжүйедегі мәні ашылып, «билік» субконцептісінің семантикалық өрісін құрайтын лексемалар айқындалады. Сондай-ақ зерттеуде қазақ халқының ұжымдық санасында билік туралы ұғымдарды қалыптастыратын тарихи-этнографиялық және әлеуметтік-мәдени контекстке де назар аударылады.Алынған нәтижелер қазақ паремиологиясы аясындағы «билік» субконцептісіне тән мағыналарды тереңірек түсінуге және түсіндіруге, қазақ халқының мәдени ерекшеліктерінің өзіндік сипатын ашуға мүмкіндік береді.Зерттеу нәтижелері осы бағыттағы және гуманитарлық сала мамандары үшінқосымша материал болып саналады.
Түйін сөздер: Паремиология, концепт, лингвомәдениеттану, билік жүйесі,мақал-мәтел.
Evidências farmacológicas sobre plantas medicinais usadas para o tratamento de artrite reumatoide e osteoartrite (artrose)
Authors: Baratto Leopoldo Clemente , Monteiro Katharine Augusto da Silva , Pereira Beatriz Prata
Published: July 2024

Rheumatoid arthritis (RA) and osteoarthritis (OA) are social impact disorders that lead to intense pain, deformities, and inability to perform daily and work-related activities. In Brazil, despite the National Policies for Integrative and Complementary Practices (PNPIC) and Medicinal Plants and Phytotherapies (PNPMF) that have expanded access to Phytotherapy, the incorrect dissemination of the use of medicinal plants remains common, often without any scientific evidence of efficacy. The aim of this study was to select some plants popularly recommended on the internet for treating RA and OA and to gather data on their efficacy and safety. These plants include Harpagophytum procumbens, Hypericum perforatum, Miconia albicans, Uncaria tomentosa, and Varronia curassavica. A literature review covering in vitro, preclinical, and clinical trials revealed promising results regarding the action of these plants on RA and OA. Most studies emphasize the anti-inflammatory, anti-arthritic, and analgesic effects of these species. However, as the mechanisms are not yet fully understood, further studies, particularly clinical trials, are necessary to reach a more definitive conclusion. This literature review can contribute to and alert healthcare professionals and patients seeking these products for the treatment of rheumatic diseases.
Media Sosial Instagram sebagai Pusat Informasi Kota Bandung
Authors: Fikri Miftahul
Published: July 2024

Currently, social media has become an information center which aims to create awareness among the public to position social media not only as entertainment, but also as a source of information. Social media, which is included in the new media section, is widely used by the public to obtain the information they need. Social media has the advantage of being a digital communication tool that is able to spread messages and events in the form of information to the public, such as Instagram which is chosen and used by the Indonesian people. Instagram users have the opportunity and freedom to absorb information in the city of Bandung. The Instagram account @humas_bandung is one of the accounts that fills in events, happenings and information in the city of Bandung social media with the account @humas_bandung actively uses Instagram to share information and educate the public regarding incidents in the city of Bandung. The purpose of this research is to determine the influence of social media Instagram @humas_bandung on social media as an information center for the city of Bandung. This research uses qualitative content analysis with the direction of finding out uploads from @humas_bandung. The results of this research show that the social media Instagram @humas_bandung is the information center for the city of Bandung.
CHALLENGES AND PROSPECTS OF THE DEVELOPMENT OF AGRICULTURAL ENTERPRISES IN THE CONDITIONS OF EUROPEAN INTEGRATION
Authors: Kryvetskyi Ihor ,   Eco Farm Edem LLC
Published: July 2024

This study analyzes the importance of the impact of the European integration process on the development of agricultural enterprises in Ukraine, highlighting both the opportunities and challenges faced by the agricultural sector of Ukraine in the context of globalization and integration into the European economic space. The study reveals how adaptation to European standards affects product quality assurance, the introduction of modern technologies, as well as strengthening the competitiveness of Ukrainian agricultural enterprises. The main focus is on strategic planning, the need for innovative development, environmental initiatives and the search for effective financial mechanisms for sustainable development. Currently, as part of preparations for integration into the European Union, Ukraine adopts and adapts legislation in order to comply with EU norms and standards. The identified main aspects that are regulated in this context include: 1. Product quality standards. 2. Land legislation. 3. Environmental norms. 4. Support to farmers. 5. Product safety. These changes are aimed at increasing the competitiveness of the Ukrainian agricultural sector, ensuring resistance to market challenges and protecting the interests of consumers and producers. The main challenges of the development of agricultural enterprises in the conditions of European integration are identified: 1. Compliance with EU standards. 2. Financing and investments. 3. Climate changes and their impact. 4. Technological integration. 5. Demining and security. The prospects for the development of agricultural enterprises in the conditions of European integration are also outlined: 1. Expansion of sales markets. 2. Innovations and technological development. 3. Financial support and subsidies. 4. Improvement of product quality. 5. Environmental sustainability. In general, the results of the study indicate a critical need for the modernization of internal management processes and the formation of new competencies that would allow agricultural enterprises to effectively adapt and use new opportunities to expand their markets and increase production efficiency.
ЗВУКОВИЙ ОБРАЗ АКОРДЕОНА В СУЧАСНОМУ ХУДОЖНЬОМУ ПРОСТОРІ
Authors: Жила Євген Миколайович
Published: July 2024

Мета статті полягає у виявленні особливостей звукообразу акордеона у творах В. Зубицького «Роcсініана» та П. Роффі “Hysteria”. Методологія статті ґрунтується на компаративних засадах, які уможливлюють застосування музикознавчого дослідницького інструментарію, пов’язаного з методами жанрового та стильового аналізу, а також історичного та культурологічного методів. Наукова новизна зумовлюється уточненням і розширенням уявлення щодо особливостей звукообразу акордеона в «Россініані» В. Зубицького й уведенні в музикознавчий обіг уявлення щодо специфіки звукообразу інструмента у творі П. Роффі “Histeria”. Висновки. У статті виявлено такі риси звукового образу акордеону в «Россініані» В. Зубицького, як дифузійність, полікультурність, зумовлені проєкцією художнього досвіду класицизму в естрадно-джазову царину та сферу полістилістики, резонансом із традиціями класицистського та романтичного віртуозного виконавства, зв’язками з оперою та жанрами концерту, сонати, жанрами-memory як знаками концентрації культурної пам’яті. Акцентовано «ризомний», «відкритий» характер твору внаслідок множинності його версій, значущість нетрадиційних технічних і перкусійно-сонорних прийомів, інтенцій візуалізації та театралізації виконавства, які формують «інтертекстуальний» постмодерний звуковий образ акордеона як синтезу маркерів різних історико-культурних царин. У творі П. Роффі “Histeria” чинниками полікультурного звукового образу акордеона є концептуально-образні зв’язки з бароко, з концептом perpetuum mobile, комплекс сонорних і перкусійних ефектів. Дихотомічний хронотоп твору, у якому у специфічній єдності постають рух і статика, зумовлює значущість технічно-віртуозної «іпостасі» інструмента – як відбиття зв’язків із віртуозним виконавством класицизму та романтизму, та мелодичної, кантиленної, яка резонує із сакральною сферою, що в комплексі демонструє крос-культурний потенціал звукового образу акордеона у просторі сучасної культури.
ЕКОНОМІЧНИЙ МЕХАНІЗМ ЗАБЕЗПЕЧЕННЯ ФУНКЦІОНУВАННЯ СІЛЬСЬКОГОСПОДАРСЬКИХ ПІДПРИЄМСТВ В УМОВАХ ЄВРОІНТЕГРАЦІЙНИХ ПРОЦЕСІВ
Authors: Кривецький Ігор
Published: July 2024

Стаття присвячена розробці та обґрунтуванню економічного механізму забезпечення функціонування сільськогосподарських підприємств України в умовах євроінтеграційних процесів та військових дій. Актуальність дослідження зумовлена необхідністю адаптації агропромислового комплексу до нових викликів, пов'язаних з інтеграцією до Європейського Союзу та військовими ризиками. Обгрунтовано, що агропромисловий комплекс відіграє важливу роль в економіці України, забезпечуючи продовольчу безпеку та значний експортний потенціал. Євроінтеграція передбачає реформування законодавчої бази, впровадження нових стандартів якості, технологій та підвищення конкурентоспроможності на міжнародних ринках. Визначено, що за тридцять років незалежності України сільське господарство зазнало значних змін, від переходу від соціалістичних до сучасних підприємницьких структур, використання новітніх технологій, що призвело до підвищення продуктивності. Доведено, що розробка економічного механізму є критично важливою для підтримки стабільності, розвитку та конкурентоспроможності аграрного сектора. Цей механізм має включати організаційні, фінансові, технологічні та правові аспекти, а також додаткові заходи для кризового управління. Розроблено концептуальну схему економічного механізму забезпечення функціонування сільськогосподарських підприємств в умовах євроінтеграційних процесів України. Такий механізм для сільськогосподарських підприємств є необхідною умовою для успішної інтеграції України до європейського економічного простору та забезпечення стійкого розвитку аграрного сектора, особливо в умовах війни.