bluuebunny/crossref_metadata_2025 huggingface dataset:
Yoruba Oral Traditions and Communal Aesthetics in Olu Obafemi’s Selected Drama
Authors: Abe Olanipekun Emmanuel
Published: January 2025

The history of African oral literature is located in oral tradition which is tied to communal living. African oral literature is a conglomerate of communal traditions that reflect African culture, experiences, and societies. Studies affirmed that Olu Obafemi’s ideological stance and investigation of the society is revolutionary and is geared towards seminal social change. This paper examines the aesthetics of oral literature, and explores the society through the prism of Olu Obafemi’s Naira Has No Gender and Scapegoats and Sacredcows by highlighting the different explication of oral forms portrayed by the dramatist. The paper reveals the writer's ideological disposition and vision as informed by his discursive engagement of larger issues through various oratory means. The placement of Nigerian songs and dance, proverbs, role-play technique, and audience participation are identified as signposts of Nigerian oral literature. The paper adopts Cultural Studies theory as its theoretical paradigm for its capacity to analyse and provides an assessment of the contributions that cultural practices and artifacts make to the society. This study reveals how African oral literature combines traditional and modern theatrical techniques to sustain communal living and cultural practices such as veneration, education, and moral instruction.
Early Detection and Intervention for Children's Mental Health Issues Using Machine Learning
Authors:   Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India., Safdar B Mr. Mohamed , Mr. Pandiarajan S  ,   Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.
Published: January 2025

The rise of mental health problems in children has created a need for early detection and intervention strategies. The routine method of diagnosing mental illness in child ren often relies on testing, which can lead to delays in treatment. Machine learning (ML) has become a powerful tool for analyzing complex data with the ability to identify subtle patterns associated with mental health. This article explores the potential of machine learning models for early detect ion of mental health problems in children, focusing on accuracy of facts, timeliness of intervention, and ethical considerations r elated to data privacy and algorithmic bias.
Developing A Hybrid Machine Learning Algorithm for Anemia Diagnosis
Authors:   Computer Science Department/ College of Science for Women University of Baghdad, Baghdad, Iraq, Safa Safa ,   Computer Science Department, University of Technology, Baghdad, Iraq,   Institute of Medical Technology, Jinnah Sindh Medical University, Karachi, Pakistan, Farhan Alaa k. , Kandhro Abdul Hafeez
Published: January 2025

The utilization of artificial intelligence (AI) algorithms has significantly transformed the field of blood disease diagnosis, enabling enhanced capabilities in prediction, categorization, and optimization. However, there is still a lack of research exploring the advancement of hybrid machine learning models that combine qualitative and quantitative datasets to address issues associated with blood diseases. To tackle this gap, we evaluate algorithmic combinations using datasets that include key characteristics from complete blood count (CBC) examinations. This manuscript presents an evaluation of prominent deep learning models, such as CNN, RNN, and RCNN, as part of our methodology. The assessment identified XGBoost as the optimal machine learning algorithm, and RCNN as the best deep learning model. Consequently, we propose a hybrid model named ‘RCNNX,’ which integrates Robust Scaler, SelectKBest feature selection, RCNN, and the XGBoost algorithm. The hybrid model, ‘RCNNX,’ achieves exceptional testing accuracy levels of 100% and 95.12% on the Anemia Diagnosis Dataset and a second dataset, respectively. Additionally, it demonstrates recall rates of 100% and 94.64% for the same datasets. These findings highlight the superiority of the proposed model, as it effectively utilizes feature selection to reduce the number of input variables, minimizing the risk of overfitting. Moreover, XGBoost enhances the predictive accuracy of RCNN.
K-Nearest Neighbors Approach to Analyze and Predict Air Quality in Delhi
Authors:   Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA., Ahmed Ahmed ,   School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China., Mahmood Shahid
Published: January 2025

The study considers the community of ”urban air quality improvement in modern cities” using an extensive dataset obtained from ”Air quality data of Delhi, India” for the period between 25 November 2020 and 24 January 2023. Research aims to significantly reduce air pollutants, including particulate matter, including PM2.5 and PM10, NO2, SO2, CO2, O3, and others. Different machine learning models are being used for airquality level forecasts. Among the models assessed, the Nearest Neighbors algorithm comes out on top and exhibits a very low Mean Squared Error (MSE) of 0.0002. The model’s superb precision is further supported by very low statistics in other key metrics, which confirm the Nearest Neighbors approach to forecasting the quality of air in urban zones. The Nearest Neighbors algorithm is shown to have its place in the application as a tool in the hands of researchers and decision-makers pursuing the fight against air pollution is also a signal of its efficiency and broad applicability. This modeling approach has thus the potential to first identify and later pinpoint localized empirical patterns and statistical dependencies from the data set. Its high predictive precision makes it a very valuable assistant to public health and environmental management, especially so in regions like Delhi.
Learning Innovation to Enhance Creative Young Entrepreneurial Skills in Schools of Kanchanaburi Province
Authors: Tiacharoen Saisuda , Boonkoum Wannawee , Modmoltin Sirikamol
Published: January 2025

The objectives of this research were to 1) study the basic data of learning innovation to enhance creative young entrepreneurial skills in schools of Kanchanaburi Province, 2) develop the learning innovation to enhance creative young entrepreneurial skills in schools of Kanchanaburi Province, 3) implement the learning innovation to enhance creative young entrepreneurial skills in schools of Kanchanaburi Province, and 4) evaluate the learning innovation to enhance creative young entrepreneurial skills in schools of Kanchanaburi Province. The Research and Development design (R&D) and Appreciation Influence Control technique (AIC) were used for developing the learning innovation to enhance creative young entrepreneurial skills in schools of Kanchanaburi Province. The target group was 59 secondary students in Kanchanaburi Province. The research instruments were interview questions, a questionnaire to confirm the innovation quality, an evaluated form of creative young entrepreneurial skills, and a satisfaction questionnaire for learning innovation. The data were analyzed by frequency, percentage, mean, standard deviation, and content analysis. The finding revealed that: 1) Basic data of learning innovation to enhance creative young entrepreneurial skills in schools found that learning model to enhance entrepreneurial skills comprised professional-based education, skill-based learning, work-based education, work-based learning, project-based learning, and practice-based learning etc.; 2) Learning innovation to enhance creative young entrepreneurial skills in schools, Kanchanaburi Province was composed of input, process and output aspects; 3) Learning innovation to enhance creative young entrepreneurial skills in schools, Kanchanaburi Province trial showed that creative young entrepreneurial skills were at high level in overall and in each aspect; and 4) the evaluation of learning innovation to enhance creative young entrepreneurial skills in schools of Kanchanaburi Province found out the opinion for learning innovation was at high level in overall and each aspect.
Dinamika Kompensasi dan Dampaknya terhadap Kepuasan serta Kinerja Karyawan: Sebuah Studi Literatur
Authors: Yakin Ipa Hafsiah
Published: January 2025

Penelitian ini bertujuan untuk mengeksplorasi dinamika kompensasi dan dampaknya terhadap kepuasan serta kinerja karyawan melalui pendekatan studi literatur. Fokus kajian meliputi konsep kompensasi modern, baik finansial maupun non-finansial, serta tantangan dan peluang dalam implementasinya di era digital. Hasil kajian menunjukkan bahwa kompensasi memainkan peran krusial dalam meningkatkan kepuasan kerja, yang pada akhirnya berdampak positif terhadap kinerja karyawan. Skema kompensasi yang efektif, seperti total rewards dan integrasi teknologi dalam manajemen penggajian, dapat meningkatkan loyalitas serta produktivitas tenaga kerja. Implikasi dari penelitian ini menekankan pentingnya fleksibilitas dalam kebijakan kompensasi untuk menyesuaikan dengan kebutuhan tenaga kerja modern dan perubahan lingkungan kerja. Kontribusi penelitian ini terletak pada pemetaan konseptual hubungan antara kompensasi, kepuasan kerja, dan kinerja karyawan serta rekomendasi strategis bagi organisasi. Penelitian lanjutan diharapkan dapat menguji temuan ini secara empiris di berbagai sektor industri untuk validasi lebih lanjut.
Importance of nutrological management and gut microbiota in alzheimer's disease: a systematic review
Authors: Alves Márcia Cavalheiro , Nogueira Mateus Antunes , Lopes Hugo Menezes , Carvalho Ricardo de Oliveira , Cano Ana Claudia Santana , Soares Vittor Cândido , Izidorio Frederico Teixeira , Pereira Juliana da Silva , Salvá Marília de Andrade , Alves Thamyres Veras
Published: January 2025

Introduction: In the context of neurodegenerative diseases, Alzheimer's disease (AD) is the most common form of dementia. It is estimated that more than 46 million people are affected worldwide. Several factors contribute to the risk of developing late-onset Alzheimer's disease, including advanced age, genetic factors, family history, history of head trauma, hypertension in midlife, obesity, diabetes, and hypercholesterolemia. Studies have shown the important role of the gut microbiota in controlling this condition, together with adequate nutrition. Objective: It was to analyze the relationship between dietary patterns, gut microbiota, micro and macronutrients, and cognitive disorders in Alzheimer's disease. Methods: The systematic review rules of the PRISMA Platform were followed. The search was conducted from May to August 2024 in the Scopus, PubMed, Science Direct, Scielo, and Google Scholar databases. The quality of the studies was based on the GRADE instrument and the risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: A total of 140 articles were found. 75 articles were fully assessed and 30 were included and developed in the present systematic review study. Considering the Cochrane tool for risk of bias, the overall assessment resulted in 15 studies with a high risk of bias and 25 studies that did not meet GRADE and AMSTAR-2. Most studies showed homogeneity in their results, with X2=89.5%>50%. It was concluded that several antioxidants found in a healthy natural diet can efficiently attenuate free radical attacks and neutralize dysregulated pro-oxidants, reducing oxidative stress and the induction of degenerative diseases such as Alzheimer's disease. Studies have found that plasma lipopolysaccharide levels in patients with Alzheimer's disease were three times higher than in healthy controls. Thus, increased concentrations of plasma lipopolysaccharide and fecal calprotectin indicate an altered intestinal barrier function and increased inflammation and intestinal permeability in patients with Alzheimer's disease.
Environmental Law and Corporate Social Responsibility: Assessing the Impact of Legal Frameworks on Circular Economy Practices
Authors: Daramola Oluwabukola Modupe , Apeh Charles Elachi , Basiru Joseph Ozigi , Onukwulu Ekene Cynthia , Paul Patience Okpeke
Published: January 2025

Environmental law and corporate social responsibility (CSR) are increasingly recognized as pivotal in advancing the principles of a circular economy, which emphasizes waste reduction, resource efficiency, and sustainability. This study examines the interplay between legal frameworks and CSR initiatives in fostering circular economy practices. It explores how robust environmental regulations, coupled with corporate accountability, can drive the adoption of sustainable production and consumption patterns. The analysis begins by highlighting the evolution of environmental laws aimed at mitigating the adverse impacts of industrial activities on ecosystems. These laws establish regulatory obligations for businesses to reduce emissions, manage waste, and conserve natural resources. Concurrently, CSR has emerged as a voluntary yet impactful approach that aligns corporate objectives with societal and environmental priorities. When integrated, these mechanisms create a synergistic effect, compelling organizations to innovate and adopt circular economy strategies. This paper investigates case studies across sectors, demonstrating how legal incentives, such as tax benefits and extended producer responsibility (EPR) schemes, encourage businesses to design products with end-of-life considerations. Furthermore, it evaluates the role of international agreements, such as the European Union’s Circular Economy Action Plan, in harmonizing policies and fostering cross-border collaboration. The study also addresses challenges, including regulatory inconsistencies and the financial burden of compliance, which may deter businesses, especially small and medium enterprises (SMEs), from fully embracing circular practices. Recommendations are proposed to strengthen legal frameworks, including the adoption of clear metrics for measuring circularity, fostering public-private partnerships, and enhancing stakeholder engagement in policy formulation. By assessing the impact of environmental laws and CSR on circular economy practices, this study underscores the necessity of an integrated approach. The findings contribute to the discourse on sustainable development, emphasizing the critical role of legal and corporate governance in achieving a resilient and resource-efficient global economy.
Human Systems Integration and Design in Port Terminal Concessions: A Bibliometric Study of End – of – Life Management and Decommissioning Guidelines
Authors: Jugovic Alen , Poletan Tanja , Kolaric Goran , Sirotic Miljen
Published: January 2025

This paper addresses the complexities in managing port terminal concessions, focusing on the final asset compensation sub – phase during the pre – biding phase, a critical yet under – researched area in maritime academic literature. The employment of bibliometric analysis revealed four key clusters: (1) Sustainable Concession Strategies in Port Management; (2) Evolving Governance Models in Port Concessions; (3) Sensor – Driven End – of – Life Management in Supply Chains; and (4) End – of – Life Recovery Strategies in Manufacturing Systems. Sixteen decommissioning guidelines are developed: (1) The first eight focus on organizational structures and human factors to establish a structured approach; and (2) The second eight centre on technical integration for enhancing end – of – life management. These guidelines align with Human Systems Integration and Design (HSID) principles to clarify decommissioning and asset recovery processes. The paper highlights the need for early planning and consistent compensation approaches in terminal concession agreements and emphasizes future research to explore the application of HSID in different regional and economic contexts.
Human Intelligence and Artificial Intelligence Interaction in Start-Up Enterprise
Authors: Salminen Vesa , Pyykkönen Matti , Salminen Krista , Tian Oliver
Published: January 2025

Innovation capability is significant in the business environment, especially in start-up enterprises. This article emphasizes the growing complexity of technology and the need to manage the innovation process effectively. Integrating generative artificial intelligence tools using agent technology is essential for enhancing the efficiency of building start-up companies and innovation processes more broadly. Artificial intelligence (AI) supports team operations throughout all phases of the innovation process in start-up enterprises. The co-evolution of personal competence identification and team cohesion building is a crucial aspect of new entrepreneurship and start-up development. Data is recognized as a valuable currency in the innovation ecosystem, driving the data-driven innovation process. Human-oriented approach is necessary for capturing data from various sources and executing it in businesses. The strategic challenge is to apply a systematic approach using generative AI agents across all innovation phases. Overall, the abstract outlines the importance of human factors, team cohesion, artificial intelligence, and data-driven approaches in the innovation process, proposing a co-evolution framework for human and artificial intelligence interaction. The case study company analyzed is Husqtec Corp., a start-up company concentrating on situation and operational management. Use case is nature disaster management.
Does Military Experience Influence Strategic Decision-Making with Respect to Geographical Headquarter Placement
Authors: Nelson Justin , Heggedahl Timothy , Morgan Justin , Johnston Samuel , Cotter Jenna
Published: January 2025

Intelligence, Surveillance, and Reconnaissance (ISR) operations leverage five key disciplines to facilitate and support the detection of our adversaries defensive posture, battle rhythm pattern, and headquarter location. These disciplines include geospatial intelligence (GEOINT), measurement and signature intelligence (MASINT), signals intelligence (SIGINT), imagery intelligence (IMINT), and human intelligence (HUMINT). However, understanding when to utilize the appropriate discipline for intel collections can be extremely challenging. To combat this issue, the 711th Human Performance Wing at Wright-Patterson AFB developed Intrage. Intrage is a strategic decision-making game developed to augment and enhance intel analysts understanding of ISR operations. The objective is to identify the location of the opposition’s headquarters on a fictional map. Moreover, previous literature has discovered that behavioral characteristics learned through experience can significantly influence decision-making outcomes. Therefore, a study was conducted to determine if there is a correlation with respect to headquarter placement based on military experience when provided a fictional map. The findings provided underlying evidence that participants with military experience centralized their headquarter location compared to participants with no military experience (p=0.02). This discovery will support the maturity and development of Intrage and provide behavioral characteristics which can be used to predict future military actions.
Avatar-based emotion representation with varying degrees of realism
Authors: Wunsch Valentin , Möller Knut , Wagner-Hartl Verena
Published: January 2025

In the age of digitalization with its progressive visualization of a wide variety of content, avatars are becoming increasingly important. Consequently, virtual human representations are also being used in healthcare, for example in therapeutic applications. The use of technologies such as virtual reality or virtual characters like avatars can support people who find social interactions challenging. This is often the case for people with autism spectrum disorder (ASD). The aim of the presented study was to evaluate the suitability of different avatar types for the development of a future digital application for people with ASD. Based on the results of a previous study, two avatar types that differed in their degree of realism were analysed: a cartoonish, simple avatar and a stylized, moderately detailed avatar. First, it was investigated how participants set different basic emotions on these avatars and whether these settings are comparable between the different types of realism as well as with previously defined values for the basic emotions. Afterwards, a threshold test was used to determine at what point the emotions displayed by the avatars became recognizable and at what point they were rated as pronounced. Overall, ten men and ten women (M = 24.9 years old, SD = 3.64) participated in the study. The results show that the average values set by the participants correspond to the previously defined values for the basic emotions, with a relatively high degree of variation in some cases. Overall, the insights gained within this experiment provide a basis for future research on this topic.
The Challenges of Integrating AI in Aviation Incident-Accident Investigations: A Human-Centric Approach
Authors: Ziakkas Dimitrios , Henneberry Debra
Published: January 2025

Integrating Artificial Intelligence (AI) into aviation incident-accident investigations presents unique opportunities and significant challenges. This paper explores the complexities of incorporating AI into the aviation investigation process, emphasizing the importance of a human-centric approach to ensure the technology's reliability, transparency, and accountability. The application of AI in investigations necessitates thorough adherence to existing international frameworks, including ICAO Annex 13 and regulatory guidelines from the Federal Aviation Administration (FAA), the European Union Aviation Safety Agency (EASA), and the National Transportation Safety Board (NTSB). However, AI provides improved data analysis, predictive modeling, and pattern recognition capabilities. Through the examination of crucial case studies, such as the investigation into the Lion Air Flight 610 and Ethiopian Airlines Flight 302 (Boeing 737 MAX) accidents, we illustrate how AI-driven data analytics helped investigators to analyze large quantities of flight data recorder (FDR) and cockpit voice recorder (CVR) information (FAA, 2024). AI-based systems contributed to investigating the Air France Flight 447 accident (Airbus A-330), where advanced data analysis techniques provided insights into pilot responses under adverse conditions (Stewarts, 2017). These case studies highlight AI's strengths and limitations in understanding complex system failures and human-machine interactions.Moreover, these examples underscore the necessity of human oversight in interpreting AI outputs and ensuring accurate, context-driven conclusions. Considering regulatory differences, the research findings address the intricate challenges of harmonizing AI systems with established human-led investigative methodologies. Specifically, the research focuses on how AI can be effectively integrated without compromising the critical decision-making processes traditionally managed by human investigators.Furthermore, the presented research examines how human factors must be prioritized to prevent over-reliance on AI outputs, maintain investigative integrity, and foster cross-disciplinary collaboration between AI experts and aviation safety professionals. By analyzing these case studies and providing a comprehensive review of AI's role in modern aviation safety, the research team aims to illuminate the path toward developing AI frameworks that complement human expertise rather than replace it. Ultimately, this paper calls for a balanced approach that leverages AI's strengths while addressing its limitations, ensuring that future aviation incident-accident investigations remain human-centered and safety-focused.
A graph theory approach for data lineage in complex information systems using a technology, organization,and people integrated model in nuclear plants
Authors: Salazar Luigui , Malhomme Olivier , Benmiloud-bechet Lies , Plana Robert
Published: January 2025

In the nuclear sector, the implementation of digital technologies plays a crucial role in optimizing the performance of installations throughout their entire lifecycle. It contributes to the timely and cost-effective delivery of all phases of the life cycle, including design, procurement, construction, commissioning, and operation, as well as facilitating the transition between these phases. The intricacy of nuclear projects and their digital transformation faces significant challenges related to the large and diverse supply chain lifecycle, which includes entities of varying sizes, durations, and maturity. This increases the complexity of information systems, which often results in fragmented data exchanges and the formation of silos, thereby creating loopholes in information exchanges and negatively impacting project delivery performance.The exponential growth of Complex Information Systems (CIS) has resulted in significant challenges in data management, data governance, and digitalized human-centered activities. To address this complexity and guarantee the lifecycle of these CIS, the emergence of the concept of data lineage—defined as the flowchart of all data manipulations—is a promising approach, for a large array of applications such as maintenance. However, CIS often consist of diverse elements communicating through various protocols and tools, which presents a considerable challenge for accurately modeling these interactions using traditional methods. The present study proposes a methodology based on graph theory for the analysis of data lineage. Moreover, the methodology incorporates the concept of Human System Integration (HSI), which is represented through the TOP (Technology, Organization, and People) model, with the objective of modeling the diverse interrelationships between CIS components. In this study, a maintenance chain of value was selected as a key use case of a real-world CIS system from the nuclear sector. The data flow begins at the stage of design of new components in nuclear power plants and continues through to the deployment of equipment that is active, operational and maintainable within the power plant units. This method permits the visual representation of data flow within the complex information system, thereby facilitating a more suitable data management and a comprehensive understanding of the interaction between its elements.
The Lack of Explainability in Automatic Speech Recognition Can Cause Faux Data Work
Authors: Vase Silja
Published: January 2025

Automatic Speech Recognition (ASR) is increasingly used in healthcare to reduce documentation workloads by transcribing spoken words into Electronic Health Records (EHRs). However, these systems, based on machine learning, require ongoing data annotation and validation by healthcare professionals to ensure accuracy. This paper, based on fieldwork at a public Danish hospital, investigates the challenges healthcare professionals face in detecting and addressing technical issues, such as glitches, within ASR systems. Using mixed methods, the study reveals that healthcare professionals spend significant time annotating and training the machine learning algorithms—time that could otherwise be dedicated to patient care. Without access to clear metrics, like recognition rates, healthcare professionals are unable to effectively evaluate their data annotating efforts, leading to "faux data work," where data tasks seem productive but fail to improve system performance. The paper proposes two strategies to mitigate this issue; 1) providing transparent system metrics to enhance user engagement; and 2) creating structured sites of collaboration between healthcare professionals and IT professionals for better reporting of technical issues. These solutions aim to reduce inefficiencies and improve ASR accuracy in clinical settings.
Bridging Creativity and Technology: Integrating Generative AI into Architectural Pedagogy Through Hybrid Frameworks
Authors: Longo Gianmarco , Albano Silvia
Published: January 2025

Integrating Generative AI into architectural pedagogy marks a transformative shift in how design is conceptualized and taught. However, the primary challenge for educators lies in understanding how students can effectively control AI tools to produce design outputs that align with their creative ideas and address the requirements of the modules, all while navigating the complexities introduced by these technologies. This study explores a hybrid, human-artificial system-centered design approach employed in a series of speculative design workshops, to enhance the educational experience by integrating AI tools with traditional design methodologies. Two workshops focused on developing the future of lighting were conducted at a transnational university in China, utilizing a speculative design methodology that combines future thinking with a series of interrelated experiences involving numerous dynamic phases (Burns,1999). These elements are introduced as forms of experiential learning during the workshops to enhance students' engagement in diverse and complex activity settings. (Kolb,1984). The hybrid framework allowed analog and digital inputs, encouraging students to maintain control over their creative processes while leveraging AI's capabilities to augment their design outcomes. Integrating generative AI within a structured pedagogical framework significantly enhanced students' cognitive processes and engagement with complex design tasks. This approach fostered an environment where students could experiment with unconventional ideas and rapidly iterate on their designs, leading to more innovative and cohesive outcomes. The experiential learning approach also helped bridge the gap between theoretical knowledge and practical application, enabling students to better understand the implications of AI in contemporary design practice. The findings underscore the need for a structured yet flexible approach to integrating AI in design education, which helps students navigate the complexities of new technologies while enhancing their creative potential and skills. This approach sets a precedent for future educational strategies in design disciplines, fostering the creation of powerful hybrid-human artificial systems fully centered on the development of high-quality design solutions.
Automating customer feedback in online marketplaces with retrieval augmented generation
Authors: Shalimov Gleb , Kuskov Mikhail , Askarbekuly Nursultan
Published: January 2025

This paper proposes a solution for automating customer review responses in online marketplaces. The goal is to save time and resources for sellers. The proposed system combines traditional methods with Large Language Models, allowing the sellers to provide a personalized service and improve customer satisfaction. The system has been implemented and integrated with two main online marketplaces in Russia: Wildberries and Ozon. The study demonstrates promising results in terms of response quality and efficiency. In particular, the system was used to answer more than 3800 reviews for three sellers, which was estimated to be an equivalent of 120 working hours.
The Evaluation of AR-HUD Visual Augmentation Method Based on Situation Awareness
Authors: Rong Weiran , Wu Wenyu , Sun Xuan
Published: January 2025

Augmented Reality Head-Up Display (AR-HUD) enhances driving safety and experience by overlaying virtual information into the driver’s forward view. However, AR-HUD may lead to attention capture, causing the driver to focus excessively on the AR-HUD and neglect other critical environmental information. Therefore, it is crucial to study how to balance the driver’s attention allocation between AR-HUD information and environmental information to ensure effective situational awareness (SA). The study based on Situational Awareness Theory, evaluates the impact of different AR-HUD augmentation methods (boxes, arrows, and shadows) on drivers' SA through analyzing eye movement data and Situation Awareness Rating Technique (SART) data. The results show that the different augmentation methods significantly affect drivers' SA levels. The box-type augmentation method performs the best overall and effectively improves the driver's SA. The study provides a theoretical foundation and practical guidance for the optimization and application of AR-HUD design.
Designing Adaptive Immersive Therapeutic Spaces Using Convolutional Neural Networks for Community-Based Elderly Care
Authors: Zhang Han , Zhu Siqi , Fang Yue
Published: January 2025

As global aging accelerates, it is projected that by 2050, over 22% of the global population will be aged 60 or older. In this context, promoting healthy aging is crucial, with mental health challenges among the elderly receiving increasing attention. Art therapy has been recognized as an effective intervention to maintain physiological, cognitive, and social functions in older adults, significantly improving emotional well-being and social engagement. However, traditional art therapy faces limitations in scalability and assessment accuracy. Digital art therapy, with its real-time adaptability and sustainability, offers promising potential for expanding mental health interventions. This study focuses on Wuhan, China, surveyed and interviewed 2,000 elderly individuals to explore psychological health factors. Results indicate that individuals aged 60-69 exhibit significantly higher levels of depression and anxiety compared to those aged 50-59, with depression (M60-69=12.231>6.272,p<.001) and anxiety (M60-69=13.837>6.441,p<.001). High social participation was found to significantly enhance mental health (p<.001), with most respondents holding favorable views on community-based art therapy activities. To address these findings, this research proposes adaptive immersive therapeutic spaces for elderly care, integrating sensor and projection technologies. Using Convolutional Neural Networks (CNNs), the system analyzes real-time behavioral and emotional data, dynamically adjusting the visual and auditory elements to match the users' emotional states. A follow-up survey with 1,897 participants confirmed the feasibility of these spaces, with the majority anticipating improved emotional regulation. This study contributes to the advancement of mental health interventions for the elderly, offering novel perspectives for future research and practice.
Tourists Behavior Prediction through Online Reviews by Analyzing their Sentiments using Machine Learning Approach
Authors:   Associate Professor  BCIIT,New Delhi, Arora Dr. Harsh
Published: January 2025

Sentiment Analysis is that part of research area where different people opinions or sentiments are extracted in form of textual data from various websites. In this paper sentiment analysis has been described along with machine learning techniques on tourist’s reviews to see their behavior towards various tourist places, hotels and other services provided by tourism industry. Emotions in the form of tourist’s reviews extracted are interpreted and classified by preprocessing of data and further feature extraction is done through machine learning highly efficient technique called deep learning. In this paper, the proposed idea has been given to use deep learning methods like CNN, RNN and LSTM rather than using machine learning classical algorithms like SVM, Naive Bayes, KNN, RF etc. Also, comparison of various machine learning and deep learning techniques working on tourist sentiments has been done here in this paper to show that deep learning techniques analyze and classify emotions and polarity with deep layers efficiently where on the other hand classical algorithms of machine learning give results not better than deep learning techniques. In this way sentiment analysis has been done and the proposed idea of this research paper is change in the machine learning techniques or methods from classical algorithms to neural network deep learning methods which in future definitely will give better results to analyze deeply the sentiments of tourists to find out the liking and disliking of various tourist places, hotels and related tourism services that will help tourism business industry to work on the gap in existing services provided by them and system can become more efficient in future. Such improved tourism system will give benefits to tourists or users in terms of better services and undoubtedly it will help tourism industry to enhance business in future.

Keywords—sentiment analysis, machine learning, deep learning, tourist reviews.
Demarcation Problem: Karl Popper’s Solution in the Contemporary Retrospective
Authors: Sepetyi Dmytro
Published: January 2025

The article analyses post-Popperian debates about the problem of demarcation. The case is made that Karl Popper’s proposition to demarcate empirical science from the rest («metaphysics») on the basis of falsifiability as the criterion of empirical character of theories remains a tenable way of seeing the most valuable aspect of the character of the scientific enterprise. It is explained that for the falsifiability criterion to be adequate for the purpose, we should consider it, as Popper did, as two-aspect — involving the logical content of theories and the critical attitude of researchers. Although falsifiability, in this sense, is not sufficient to neatly demarcate science from non-science, it can be considered as the most important necessary condition, which can be supplemented by some further requirements, for an empirical hypothesis to count as part of the body of science. In particular, it is argued that Lee McIntyre’s proposition to see empirical science as a matter of what he calls «scientific attitude» perfectly fits with this falsifiability-based perspective. William Bartley’s and Harry Laudan’s deflationary approaches to the demarcation problem are criticised as failing to recognise the practical significance of distinguishing what is and what is not empirical science, and the necessity of some theoretical grounding (however imperfect) for practicing this distinction. The suggestions of Joseph Agassi, Imre Lakatos, and Thomas Kuhn purported at the improval of Popper’s demarcational proposition are discussed, and the author’s own proposition is advanced and explained. It is also pointed out that Popper’s remark that the distinctive feature of scientific theories is openness to criticism and modification in its light serves as the basis for a wider conception of science, without the qualification «empirical».
La migración y las implicaciones en la vida de las personas adultas mayores en situación de abandono
Authors: Miranda Martínez Paco Vinicio , Gamboa Ugalde Alex Bayardo
Published: January 2025

Los adultos mayores en el Ecuador son grupos de atención prioritaria, inclusive en doble vulnerabilidad, según los artículos 35, 36, 37 y 38 de la Constitución de la República, en este sentido, el objetivo es analizar de manera crítica el fenómeno de la migración para determinar sus implicaciones en la vida de las personas adultas mayores en situación de abandono. Para el estudio del problema jurídico se aplicará el método inductivo, analítico y descriptivo, con un enfoque mixto, de tipo pura, documental, dogmática descriptiva. Para ello, se trabajó con una muestra probabilística de 139 personas pertenecientes a PEA de la ciudad de Riobamba, cuyos datos fueron recolectados mediante encuestas. Los resultados obtenidos indican que la mayoría de ellos enfrenta problemas como la soledad, la falta de atención médica y un escaso respaldo institucional. La investigación subraya la necesidad urgente de implementar políticas públicas que aseguren la inclusión social y económica de los adultos mayores, facilitando su acceso a servicios y protecciones. Se concluye que la migración no solo afecta a quienes se van, sino también a aquellos que se quedan, sobre todo en términos de aislamiento y deterioro de su calidad de vida.
El Rol de la Familia frente al Embarazo Adolescente: Un Análisis Bibliométrico
Authors: Jácome Vera Angélica María , Barragán Erazo Virginia , Gavilanes Capelo Dolores Berthila , Guffantte Salazar Dayana Lysset
Published: January 2025

El embarazo adolescente constituye un desafío global con implicaciones profundas en la salud física, emocional y socioeconómica de las jóvenes y sus familias. El objetivo de esta investigación es explorar la relación entre la dinámica familiar y el embarazo adolescente, identificando tendencias, vacíos de conocimiento y áreas prioritarias para futuras investigaciones. Se emplearon herramientas como Bibliometrix y Biblioshiny para analizar 140 artículos publicados entre 1976 y 2024. Los resultados muestran una evolución fluctuante en la producción científica, con picos significativos en los años 1996, 2005 y 2024, lo que sugiere un interés creciente en el tema. Términos como "embarazo adolescente" y "apoyo familiar" destacan como elementos clave en la literatura. Asimismo, se identificaron redes de colaboración entre autores e instituciones, siendo Estados Unidos y Brasil los principales contribuyentes. La investigación se agrupa en cuatro áreas temáticas: (1) embarazo adolescente, (2) desarrollo adolescente, (3) influencia familiar, y (4) salud integral en la adolescencia. Este estudio concluye que la familia desempeña un papel crucial en la prevención y gestión del embarazo adolescente, destacando la importancia del apoyo social, la comunicación efectiva y la educación sexual integral.
La biomasse forestière pour la bioénergie comme outil de lutte contre les changements climatiques : quelles implications en aménagement forestier durable dans l’est du Canada?
Authors: Canuel Claudie-Maude , Thiffault Evelyne , Labelle Eric R. , Thiffault Nelson
Published: January 2025

La bioénergie produite à partir de biomasse forestière résiduelle peut substituer les énergies fossiles et son apport est essentiel à la transition énergétique. Cependant, les coûts d’approvisionnement et sa valeur sur le marché peinent à assurer sa viabilité, de sorte qu’elle rivalise difficilement avec d’autres sources d’énergies. De plus, des préoccupations persistent concernant les impacts écologiques de la bioénergie forestière, notamment sur le cycle du carbone, la biodiversité et la productivité des sites. Notre objectif est d’identifier des moyens pouvant faciliter le développement de la filière de la bioénergie forestière dans le cadre d’aménagement durable des forêts de l’est du Canada. Nous avons examiné la littérature afin d’aborder le rôle de la biomasse forestière pour la bioénergie d’un point de vue opérationnel, sylvicole et écologique. Il ressort que la bioénergie forestière représente une opportunité pour l’essor du secteur forestier. Il est toutefois nécessaire de préciser les spécifications de la biomasse forestière à récolter pour harmoniser sa mobilisation au sein de l’écosystème industriel en place tout en maintenant les fonctions écologiques des écosystèmes forestiers. L’intégration de la récolte de biomasse aux activités sylvicoles et d’aménagement forestier est un élément clé pour le développement de plans d’affaires profitables de bioénergie forestière.
THE ROLE OF INSURANCE COMPANIES IN IMPROVING THE FINANCIAL CONDITION OF AGRICULTURAL ENTERPRISES
Authors:   Associate Professor In Department Of " Finance", KEEI, Uzbekistan, Ugli Iskandarov Abdurasul Mirjalil
Published: January 2025

The аrticle highlights the risks аssociаted with the аctivities of аgriculturаl enterprises аnd their clаssificаtion, аs well аs the issues of аchieving the effectiveness of providing insurаnce services through stаte support, аnd conclusions аre drаwn.
PROBLEMS OF FINANCING AND DEVELOPMENT OF INNOVATIONS IN UZBEKISTAN
Authors:   Associate Professor of Karshi Engineering and Economics Institute, PhD, Uzbekistan, Mukhiddinovich Babakhonov Jafar
Published: January 2025

In the Republic of Uzbekistan, the issues of financing and development of innovative activities are key to ensuring sustainable economic growth and increasing the country's competitiveness. Uzbekistan, with significant potential for innovative development, faces a number of problems related to financing and implementation of innovative technologies. This article examines the main obstacles to the development of an innovative economy in the country, as well as possible solutions to these problems. The role of government institutions, the private sector and international cooperation in financing innovative projects is analyzed.
ASSESSMENT OF THE ADULT POPULATION'S NEED FOR DENTAL PROSTHETICS WITH SINGLE DEFECTS
Authors:   Samarkand State Medical University, Samarkand, Uzbekistan, Xusanovich Chaqqanov Faxriddin
Published: January 2025

Dental prosthetics play a crucial role in restoring function and aesthetics for individuals with missing teeth. This article assesses the need for dental prosthetics among the adult population with single defects, highlighting the prevalence, factors influencing the need, and implications for dental practice. Understanding these aspects is essential for healthcare providers to develop effective treatment plans and improve patient outcomes.
IMPLANTS IN THE PRESENCE OF MILLED METAL AND METAL-FREE FRAMES IN PROSTHETICS: AN EDUCATIONAL OVERVIEW
Authors:   Assistant of orthopedic dentistry, Samarkand State Medical University, Samarkand, Uzbekistan, Dilafruz Kosimova
Published: January 2025

The integration of dental implants with various prosthetic frameworks is essential for restoring function and aesthetics in dentistry. This article explores the use of milled metal and metal-free frameworks in dental prostheses, discussing their properties, advantages, and clinical applications. By examining the implications of these materials in implant-supported prosthetics, dental professionals can make informed decisions to optimize treatment outcomes for their patients.
THE IMPORTANCE OF THE SCIENCE OF "BALĀǦAT" IN UNDERSTANDING THE MEANING OF A SENTENCE
Authors:   Doctor of Philological Sciences, Professor, Oriental University, Department of Eastern Languages, Uzbekistan, Tashkent, qizi Rustamiy Salimakhon Aliybeg
Published: January 2025

The article substantiates the significance of the science of balāǧat, a branch of Arabic philology (literary sciences), in comprehending the meanings of texts in Eastern languages such as Arabic, Persian, and Turkic. This discipline equips learners with essential knowledge for understanding textual meanings accurately, expressing ideas clearly, and constructing impactful sentences that captivate the listener both in meaning and auditory appeal.
ANALYZING THE ROLES OF THE AUTHOR, NARRATOR, AND CHARACTERS IN NARRATIVE TEXTS
Authors:   Lecturer at Karshi State University, Uzbekistan, Ashirova Bonu
Published: January 2025

This study delves into the intricate roles of the author, narrator, and characters in narrative texts, highlighting their interdependent functions in shaping the overall narrative structure. By examining the perspectives and interactions of these elements, the research explores how authors create meaning, narrators mediate the story, and characters bring it to life. Particular emphasis is placed on the interplay between these roles and their impact on narrative style, reader engagement, and the delivery of thematic content. The findings provide valuable insights for literary analysts, educators, and students aiming to deepen their understanding of narrative techniques.