Routine Tasks Research Articles

<b>The Future of Gynecology and Artificial Intelligence at BKMC/MMC Mardan</b>

automating routine tasks, and supporting clinical decision-making through predictive analytics. For example, machine learning algorithms are capable of identifying early signs of cervical cancer from imaging data with remarkable accuracy, allowing for earlier intervention and improved patient prognosis. Additionally, AI can personalize treatment plans based on patient-specific data, improving outcomes and optimizing resource allocation in our hospitals

Differentiating Essential and Dystonic Head Tremor: Exploring Arm Position Effects.

Head tremor poses diagnostic problems, especially when present as an isolated or predominant symptom. To assess how maneuvers activating upper limb postural tremor can help differentiate head tremor in essential tremor (ET) from dystonic tremor (DT) in cervical dystonia. 48 patients with head tremor (25 ET, 23 DT), underwent clinical examination and accelerometric evaluation of head and upper limb tremor during routine tremor-inducing tasks. While accelerometric power and clinical scores of head tremor did not significantly differ between patient groups, task-induced variations revealed distinctions. ET patients exhibited increased head tremor power and clinical scores during forward outstretched and lateral wing-beating arm positions, unlike DT patients. Coherence between head and upper limb tremor remained consistent. Tremor stability index showed no significant differences. Task-induced changes in head tremor could aid in distinguishing between ET and DT. Further research is needed to refine diagnostic approaches for head tremor.

Exploiting a non-mainstream financial scheme to innovate: SMEs in the developing world

PurposeThe study aims to explore the role of non-mainstream financial schemes in supporting innovation within SMEs in developing countries, particularly in sub-Saharan Africa. It investigates how informal credit, business group affiliation and foreign and state ownership arrangements influence SMEs’ innovative activities in environments with limited access to formal financial resources.Design/methodology/approachThe research utilizes data from the World Bank’s Enterprise Surveys, focusing on 8,466 firms across 11 sub-Saharan African countries from 2011 to 2020. A logistic regression analysis was conducted to assess the impact of various financial sources on SMEs’ innovation outputs, particularly incremental innovations, due to data constraints on radical innovations.FindingsThe findings reveal that informal credit significantly supports SME innovation, while business group resources can hinder innovative activities by restricting firms to routine tasks. State ownership positively influences innovation, whereas the impact of foreign ownership is inconclusive. These results highlight the critical role of alternative financial mechanisms in the innovation activities of SMEs in resource-limited settings.Originality/valueThis study contributes to the literature by providing empirical evidence on the effects of non-mainstream financial schemes on SME innovation in developing countries. It offers new theoretical insights into how SMEs navigate financial constraints to foster innovation and suggests policy implications for improving financial support systems for SMEs in such contexts. The research underscores the importance of contextualizing entrepreneurship studies to better understand the unique challenges and opportunities faced by SMEs in developing regions.

Disciplinary differences in undergraduate students' engagement with generative artificial intelligence

AbstractThe rapid development of generative artificial intelligence (GenAI) technologies has sparked widespread discussions about their potential applications in higher education. However, little is known about how students from various disciplines engage with GenAI tools. This study explores undergraduate students' GenAI knowledge, usage intentions, and task-specific engagement across academic disciplines. Using a disciplinary categorization framework, we examine how the hard/soft and pure/applied dimensions relate to students' interactions with GenAI. We surveyed 193 undergraduates from diverse disciplines at a university in Singapore. The questionnaire assessed students' GenAI knowledge, usage intentions, and engagement with GenAI for cognitive and routine tasks against their disciplinary background. The results indicate substantial disciplinary disparities in the level of engagement of students with GenAI. Compared to pure fields, applied fields (both hard and soft) consistently exhibit higher levels of GenAI knowledge and utilization intentions. Furthermore, the engagement of GenAI in routine tasks is relatively consistent across disciplines; however, there are substantial disparities in cognitive tasks, with applied fields exhibiting higher engagement. These results suggest that the practical orientation of applied fields drives GenAI adoption and utilization in academic settings. The study emphasizes considering disciplinary differences to better integrate GenAI into higher education and calls for tailored approaches that align with each field's unique epistemological and methodological traditions to balance GenAI's practical benefits with the preservation of core disciplinary knowledge and skills.

Gender Equality in the Division of Housework and Fertility Intentions in Canada: The Moderating Effect of Employment and Education

Drawing on the Gender Revolution Framework, this research uses data from Canada General Social Survey and binary logistic regression to examine how the gender division of housework influences fertility intentions among partnered women aged 18–39 ( N = 1589), across various employment and education levels. Unadjusted results showed that women who shared household routine tasks (cooking, cleaning, dishes, and laundry) and intermittent tasks (grocery shopping, organizing social life, finance, and bill paying) with their partner had higher intentions to have children. The adjusted findings revealed that women’s employment status moderated the relationship between gender division of household routine tasks and fertility intentions, while education had no significant moderating effect. Employed women who did not share equally household routine tasks with their spouse had a lower probability of intention to have children (0.22), compared to their unemployed counterparts (0.48). The findings have significant implications for social and family policies that are discussed in detail.

COMPARATIVE ASSESSMENT OF VISUAL COLOR MATCHING FOR ARTIFICIAL AND NATURAL TEETH BY STUDENT OF DENTAL FACULTY AT KHARKIV NATIONAL MEDICAL UNIVERSITY

The purpose of this study was to conduct a comparative assessment of dental students’ ability to match the color of patients’ teeth with aesthetic restorations. Materials and Methods. The study was conducted from 2021 to 2024 with participation of 2nd- and 3rd-year students at the Faculty of Dentistry, Kharkiv National Medical University, Kharkiv, Ukraine. Results. Accurate determination and reproduction of natural tooth color is a challenging task in aesthetic dentistry, as color plays a crucial aesthetic role. This process depends heavily on the experience and perception of dentists and can be approached subjectively (visual analysis) or objectively (special devices). Although shade determination is a routine task for dentists, errors are still common. The study revealed that 2nd-year students struggled significantly with color matching, especially for natural teeth, with error rates of 66.6% for artificial teeth and 80.8% for natural teeth, indicating a need for more training. Students performed better using the VITA Classic shade guide, which is simplified, but matching the color of natural teeth requires additional practice. The results confirm that clinical training improves students’ color-matching skills. After training, 3rd-year students showed a marked decrease in error rates: 35.8% for artificial teeth and 53.3% for natural teeth. The Vita 3D Master scale proved more accurate due to its broader color-matching capabilities. Overall, the accuracy levels improved significantly, highlighting the effectiveness of the training program. Conclusion. The results of the study demonstrate that training significantly improves students' ability to accurately determine tooth color. The 3rd year students who underwent clinical training showed better results in matching the color of both artificial and natural teeth, reducing the number of errors by 35.8% and 53.3%, respectively. The Vita 3D Master scale proved to be more accurate due to a wider selection of colors and more evaluation parameters. The 81.5% increase in the overall score confirms the effectiveness of the training in improving the accuracy of tooth color determination in the educational process.

Exploring the Role of AI in UX Research

Presently, artificial intelligence (AI) is primarily utilized in user experience (UX) research for handling routine tasks like text conversion (e.g., transcription, survey preparation) to manage large volumes of data efficiently. However, its potential as a collaborative partner in generating ideas and insights remains largely untapped, possibly due to the limited AI education among UX researchers. There is a recognized need to enhance AI literacy among Human-Computer Interaction (HCI) students to effectively utilize AI tools in UX research for an AI-driven future. Despite its growing importance, the integration of AI literacy into HCI curricula remains insufficient. This study aims to explore AI’s potential in addressing current challenges in UX research and assess UX researchers’ readiness to adopt AI tools. Through in-depth interviews with five early-career UX researchers, it examined their experiences, challenges, and perspectives on integrating AI. Thematic analysis of the interview data uncovers insights, revealing that AI has significant potential to overcome challenges in collecting, interpreting, and synthesizing qualitative data, as well as in translating user needs into design concepts. The findings are intended to inform strategies for incorporating AI literacy into HCI education, aligning with industry demands for AI-enabled expertise in UX design.

Utilizing Technology and Artificial Intelligence in Educational Administration to Enhance School Performance at Junior High School

This study aims to explore the use of technology and artificial intelligence (AI) in education administration to improve school performance. In the growing digital era, demands for efficiency and effectiveness in education administration are increasing, especially with the development of school information management systems (SIMS) and AI-based technologies. This research examines how these technologies can automate routine tasks such as class scheduling, student data management, as well as improve data-driven decision-making that is more accurate and efficient. The research method used in this study is descriptive qualitative, with the process of collecting data through in-depth interviews, observation, and document analysis directly from several schools that have adopted this technology. In analyzing the data, I used Miles and Hubberman's data triangulation technique in the form of data reduction, data analysis, and conclusion drawing. The results show that the implementation of technology and AI has improved operational efficiency, accelerated administrative processes, and facilitated the monitoring of student and staff performance. However, the study also found challenges in technology adoption, including limited infrastructure, resistance to change, and concerns regarding data privacy. To overcome these obstacles, a holistic strategy is needed, including the development of supportive policies, digital skills training for staff, and improvement of technology infrastructure in schools. This research concludes that although the application of technology and AI in education administration faces various barriers, the potential benefits are significant, especially in terms of improving the efficiency, transparency and overall quality of school performance.

Integrating AI into Agile Workflows: Opportunities and Challenges

Abstract: Over the past few years, Agile development approaches have become an increasingly popular methodology for software engineering, focused on iterative progress, continuous feedback, and teamwork cooperation. However, the raising difficulties of projects and more demand for faster and more efficient workflows have challenged the situation further. The introduction of Artificial Intelligence into the Agile processes paves the way to optimize decision-making, automate routine tasks, and boost team productivity. This review summarizes the innovations and challenges created by the integration of AI into Agile development practices. Using AI technologies like machine learning, predictive analysis, and natural language processing, Agile teams can enhance sprint planning, resource coordination, and risk management. Also, it presents some risks such as data privacy, workforce skills need, and possible over-dependence of AI. The paper emphasizes on providing a full overview of the innovations and challenges to the application of AI in Agile workflows, providing thoughts for future research and practices.

DIGITAL TRANSFORMATION IN SUPPLY CHAINS: BENEFITS, CHALLENGES AND TECHNOLOGICAL INNOVATIONS

Digitalization is revolutionizing supply chains, resulting in significant gains in efficiency and competitiveness. This study investigates the role of digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), big data, and blockchain, in transforming supply chain management. The research presents a review of the current literature and analyzes case studies of companies that have adopted these innovations. The results show that the integration of these technologies allows for better visibility into processes, facilitating informed and agile decision-making. The automation of routine tasks and demand prediction through AI algorithms contribute to cost reduction and waste minimization. In addition, the use of digital platforms improves collaboration between the various actors in the chain, optimizing the flow of information. However, the study also highlights the challenges associated with digitalization, such as resistance to change, the need for significant investments, and the shortage of technical skills. The analysis concludes that, despite the barriers, the benefits of digitalization outweigh the challenges, making it essential for the sustainability and growth of supply chains.

A review of the role of artificial intelligence in Journalism

Artificial intelligence (AI) technologies have revolutionized journalism in the digital era. This study is constructed on a general literature review revealing the role of AI in journalism and emphasizes the following key facets: (i) automated reporting, (ii) automated content creation, (iii) automated transcription and translation, (iv) data mining and analysis, (v) fact-checking and verification, and (vi) content personalization. The role of AI is observed in creating news reports like financial digests, sports outcomes, and weather updates, gearing up the automated content creation, transcribing the interviews, providing multilingual support for content translation, data mining and analysis, detecting fake news, personalizing the content in line with audience's preferences. The wide application of AI in journalism automates routine journalistic tasks, thereby improving efficacy and productivity and saving time and effort. Though AI is transforming journalism, there are several challenges facing journalism using AI, including biased algorithms, data availability and quality, data privacy and security, the need for training and education, transparency, and cost concerns. Journalists must be trained to identify and address issues such as data privacy, algorithmic bias, and the ethical implications of adopting AI in news reporting. News agencies should also implement strong data protection measures and transparent AI algorithms to overcome these challenges. They must attain a balance between considering user privacy and offering personalized content. It is paramount to have robust regulatory frameworks to oversee the utility of AI in journalism. Also, warranting ethical standards in AI implementation is crucial to preserving journalistic integrity.

What drives economics students to use generative artificial intelligence?

The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used. An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical methods, and exploratory factor analysis. The study found that students’ main motivations for using AI are the automation of routine tasks (34.2%) and the need to save time (21.5%), while 18.7% use AI to compensate for lack of experience. Among Bachelor’s students, motivations such as automating routine tasks and saving time increased from 53% to 58% over the course of their studies, while lack of experience decreased from 22% to 15%. In contrast, Master’s students showed a decrease in routine automation (from 36% to 28%) but an increase in the need to compensate for lack of experience (from 15% to 28%) and to save time (from 18% to 25%). In terms of gender, men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work. More than 38% of respondents say they need to use at least 2 AIs to achieve their goals. Acknowledgment This publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024.’

THE IMPACT OF AI ON TEACHERS: SUPPORT OR REPLACEMENT?

The article explores the growing role of AI in education, analyzing whether it serves as a supportive tool for teachers or poses a threat to their jobs. AI can automate routine tasks such as grading and administrative work, freeing up teachers’ time for more meaningful activities like personalized student interaction and lesson planning. Additionally, AI-powered tools enhance data-driven teaching, providing teachers with valuable insights to improve student performance and offer individualized support. While AI offers significant advantages, the article emphasizes that it cannot replace the human qualities essential to teaching, such as emotional intelligence, empathy, and creativity. These are areas where AI falls short, making it unlikely to fully replace teachers. Instead, AI allows teachers to evolve their roles from traditional instruction to mentorship and guidance, focusing on fostering critical thinking and creativity in students. However, there are concerns that AI could reduce teaching jobs or fundamentally change the role of educators. Teachers may also resist AI due to fears of job displacement or lack of sufficient training to use these technologies effectively. The article concludes that AI, when used responsibly, acts as a valuable partner rather than a replacement. By taking over repetitive tasks, AI enables teachers to concentrate on more impactful educational activities, ensuring that the human element remains at the heart of learning.

Cooperation or Collaboration: Which Really Drives the Employee Experience?

Abstract: This article explores the key differences between cooperation and collaboration and their relative impacts on employee experience in today's workplace. Through a review of relevant academic literature and examples from management consulting experiences, cooperation is defined as the coordination of individual efforts without shared ownership, most effective for routine tasks. Collaboration is characterized as a collective process requiring interdependent work, shared goals and outcomes, and trust—critical for complex problem-solving. The article argues that true collaboration cultivates more meaningful work, autonomy, social support, continuous learning and organizational commitment among employees. It maintains fostering collaborative ways of working through intentional cultural changes can unlock higher engagement, performance, innovation and retention compared to relying solely on cooperation. Practical recommendations are provided for organizations seeking to embed collaboration as a core value and operating practice.

An analysis of Psychophysiological Responses in the Prefrontal Cortex Depending on the Use of Universal Tools and Traditional Methods During Agricultural Activities by Individuals With Physical Disabilities Using Mobility Aids

Background and objective: Physical disabilities are significant and permanent limitations in physical capabilities, which make routine tasks and participation in various activities increasingly difficult. This study aimed to investigate the effect of different agricultural activities on prefrontal cortex (PFC) activation in individuals with physical disabilities (IWPDs) using mobility aids, by comparing activities with and without the use of universal tools.Methods: The study involved 26 participants aged between the age of 20 and 65 years, with physical disabilities and utilizing mobility aids. It was conducted at a laboratory within the campus of Konkuk University. Participants engaged in six different agricultural activities: soaking (with/without universal tool), sowing (with/without universal tool), and planting (with/without universal tool), performed in a random order for 180 seconds each. Functional Near-Infrared Spectroscopy (fNIRS) was used to measure changes in oxyhemoglobin (oxy-HB) concentration in the PFC during each activity.Results: The results indicated that oxy-HB concentration in the overall PFC was significantly lower during planting performed without the use of universal tools (p < .001). During the soaking activity, the use of a universal soaking tool led to lower oxy-HB concentration across the entire PFC (p < .05). During sowing activity performed without universal tools, a lower oxy-HB concentration was observed across the entire PFC (p < .01). Similarly, during planting activity performed without universal tools, a lower concentration of oxy-HB was observed across the entire PFC (p < .01).Conclusion: Overall, engaging in planting without the use of universal tools and utilizing a universal soaking tool can lead to stability through a decrease in oxy-HB concentration. The study concluded that the use of universal tools in agricultural activities affects PFC activation in IWPDs using mobility aids. Specifically, planting without universal tools and soaking with a universal tool led to significantly lower oxy-HB concentrations, indicating potential stability and reduced cognitive load.

THE INNOVATIVE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON DIGITAL BUSINESS TRANSFORMATION

In the context of intensifying competition and evolving market dynamics, the deployment of cutting-edge technologies has become not merely a discretionary choice, but an indispensable imperative for any enterprise aspiring to achieve successful growth. Generative artificial intelligence, with its substantial potential for automation, personalisation and optimisation of business processes, is emerging as a highly promising avenue of digital transformation. This study is dedicated to investigating approaches and delineating strategies for aligning generative artificial intelligence with the requirements of digital business transformation. The research examines the development of artificial intelligence, with a focus on symbolic artificial intelligence, machine learning, deep learning and generative artificial intelligence. In addition, it considers the impact of these developments on business processes. The article identifies the potential benefits and challenges associated with the adaptation of generative artificial intelligence to the needs of modern business, in the areas of marketing, sales and data analysis. The utilisation of diverse methodologies and techniques, including prompts, fine-tuning, and the incorporation of interactive guidance systems, can enhance the efficacy and precision of generative AI in a business setting, thereby facilitating optimal outcomes in a multitude of tasks. The authors put forth the proposition of employing generative artificial intelligence technology in conjunction with Retrieval-Augmented Generation, with the objective of enhancing the quality and relevance of responses to user queries. Additionally, they advocate for the utilisation of agents or orchestration tools to provide guidance to models. The successful implementation of generative artificial intelligence hinges on three key factors: the clear definition of objectives, the selection of suitable tools and technologies, and the assurance of managerial and staff support. The implementation of generative artificial intelligence will contribute to increased efficiency through the automation of routine tasks, enhanced competitiveness through personalisation and innovation, optimised cost structures that increase profitability, and expanded opportunities for research and development.

Impact of AI on cybersecurity and security compliance

Artificial Intelligence (AI) is transforming the field of cybersecurity and reshaping security compliance practices. As cyber threats become increasingly complex, AI offers powerful tools for identifying, mitigating, and preventing attacks in real-time. AI-driven systems excel at processing vast amounts of data, detecting anomalies, and identifying patterns that traditional security systems might miss. Machine learning and deep learning algorithms enhance the ability to predict potential threats, reducing response time and improving the accuracy of threat detection. These capabilities make AI a valuable asset in addressing sophisticated threats such as zero-day vulnerabilities, advanced persistent threats (APTs), and ransomware attacks. In the realm of security compliance, AI plays a pivotal role by automating routine tasks such as monitoring, auditing, and reporting. This reduces the burden on organizations to manually enforce regulatory standards, leading to more efficient compliance with frameworks like GDPR, HIPAA, and PCI-DSS. AI can continuously assess systems to ensure that they meet compliance requirements, enhancing the ability to detect and respond to violations. Furthermore, AI contributes to governance by helping organizations develop robust security policies, track compliance metrics, and streamline incident response. However, the integration of AI in cybersecurity also presents challenges, including adversarial AI, data privacy concerns, and transparency in AI decision-making processes. Additionally, AI-driven attacks are an emerging threat that necessitates further research and regulation. Despite these challenges, the future of AI in cybersecurity and compliance looks promising, with advancements in predictive analytics, quantum computing, and autonomous security systems poised to further revolutionize the field. As AI technologies evolve, they will continue to play a critical role in fortifying cybersecurity defenses and ensuring regulatory compliance across industries.

Artificial Intelligence (AI) and Automation in Human Resources : Shifting the Focus from Routine Tasks to Strategic Initiatives for Improved Employee Engagement

This study examines how implementing artificial intelligence (AI) in human resource management (HR) impacts employee engagement and operational efficiency. By automating tasks like data management, scheduling, and payroll, AI enables HR teams to focus on strategic initiatives such as talent management and culture development. Using a descriptive qualitative approach and literature review, the research finds that AI adoption improves operational efficiency by 30% and enhances employee engagement through personalized experiences and real-time feedback. Additionally, AI supports better strategic decision-making with predictive employee data analysis. However, challenges include employee resistance and the need for HR retraining. For optimal results, companies should strengthen internal communication and establish supportive policies that enhance engagement and well-being through AI integration.

Exploring the Role of AI in Automating Content Marketing: Unlocking Opportunities and Navigating Challenges

As artificial intelligence (AI) continues to evolve, it has significantly influenced the field of content marketing, offering new opportunities for automation. This research explores how AI is transforming content marketing by automating processes such as content creation, curation, personalization, and distribution. AI-driven tools like Natural Language Processing (NLP) and machine learning algorithms enable marketers to generate large-scale content, analyze audience preferences, and deliver tailored messages with unprecedented efficiency. The study aims to examine the benefits of AI in content marketing, including improved productivity, cost-efficiency, and enhanced targeting capabilities. At the same time, it addresses critical challenges, such as balancing automation with creativity, maintaining content authenticity, and mitigating the risks of algorithmic bias. Additionally, the research delves into the ethical considerations surrounding AI in content marketing, particularly with respect to data privacy and transparency in AI-generated content. By analyzing case studies and industry trends, this research provides insights into how businesses can effectively leverage AI for content marketing while overcoming the inherent challenges. Ultimately, it offers a comprehensive understanding of how AI can be harnessed to enhance marketing efforts in the digital age while maintaining a human touch and ethical standards. In addition to examining the operational and ethical dimensions, this research also investigates how AI is reshaping the role of marketers. As AI automates routine tasks such as keyword optimization, content scheduling, and performance tracking, marketers are free to focus on more strategic, creative, and high-value activities. However, this shift raises questions about the evolving skill sets required for marketers in an AI-driven landscape. The study explores how AI can complement human creativity rather than replace it, emphasizing the need for marketers to adapt to new tools while maintaining a strong foundation in storytelling, branding, and customer engagement. By highlighting both the technological and human aspects of AI integration, this research provides a balanced perspective on the future of content marketing in an increasingly automated world.

A micro-beamstop with transmission detection by fluorescence for scanning-beam synchrotron scattering beamlines

Quantitative X-ray diffraction approaches require careful correction for sample transmission. Though this is a routine task at state-of-the-art small-angle X-ray scattering (SAXS), wide-angle X-ray scattering (WAXS) or diffraction beamlines at synchrotron facilities, the transmission signal cannot be recorded concurrently with SAXS/WAXS when using the small, sub-millimetre beamstops at many X-ray nanoprobes during SAXS/WAXS experiments due to the divergence-limited size of the beamstop and the generally tight geometry. This is detrimental to the data quality and often the only solution is to re-scan the sample with a PIN photodiode as a detector to obtain transmission values. In this manuscript, we present a simple yet effective solution to this problem in the form of a small beamstop with an inlaid metal target for optimal fluorescence yield. This fluorescence can be detected with a high-sensitivity avalanche photodiode and provides a linear counter to determine the sample transmission.