Ethical Considerations Research Articles

A novel quadrotor carrying payload concept via PID with Feedforward terms

PurposeThis paper introduces control strategy to enhance the performance of a novel quadrotor unmanned aerial vehicle designed for medical payload delivery. The aim is to achieve precise control and stability when carrying and releasing payloads, which alter the quadrotor’s mass and inertia characteristics.Design/methodology/approachThe equations of motion specific to the payload-carrying quadrotor are derived. A feedforward-proportional-integral-derivative (FF-PID) control strategy is then proposed to address the dynamic changes during payload release. The PID components use propeller speed/orientation information for stability. FF terms based on derivatives of desired position/orientation variables enable adaptation to real-time mass fluctuations.FindingsExtensive simulations, encompassing various fault scenarios, substantiate the effectiveness of the FF-PID approach. Notably, our findings demonstrate superior performance in maintaining altitude precision and stability during critical phases such as takeoff, payload release and landing. Graphical representations of thrust and mass dynamics distinctly illustrate the payload release event. In contrast to the linear quadratic regulator (LQR) and conventional PID control, which encountered difficulties during the payload release process, our approach proves its robustness and reliability.Research limitations/implicationsThis study, primarily based on simulations, demands validation through real-world testing in diverse conditions. Uncertainties in dynamic parameters, external factors and the applicability of the proposed approach to other quadrotor configurations require further investigation. Additionally, this research focuses on controlled payload release, leaving unexplored the challenges posed by unforeseen scenarios or disturbances. Hence, adaptability and fault tolerance necessitate further exploration. While our work presents a promising approach, practical implementation, adaptability and resilience to unexpected events are vital considerations for future research in the field of autonomous aerial medical deliveries.Practical implicationsThe proposed control strategy promises enhanced efficiency, reliability and adaptability for autonomous aerial medical deliveries in critical scenarios.Social implicationsThe innovative control strategy introduced in this study holds the potential to significantly impact society by enhancing the reliability and adaptability of autonomous aerial medical deliveries. This could lead to faster and more efficient delivery of life-saving supplies to remote or disaster-affected areas, ultimately saving lives and reducing suffering. Moreover, the technology’s adaptability may have broader applications in fields like disaster relief, search and rescue missions, and industrial cargo transport. However, its successful integration into society will require careful regulation, privacy safeguards and ethical considerations to ensure responsible and safe deployment while addressing potential concerns related to noise pollution and privacy intrusion.Originality/valueWhile PID control of quadrotors is extensively studied, payload release dynamics have been overlooked. This research studies integration of FF control to enable PID adaptation for a novel payload delivery application.

Ethical and practical considerations of pediatric refusal in clinical anesthesia: An educational review.

Children commonly refuse induction of anesthesia. Anesthesia providers must then decide whether to honor the child's dissent or to proceed over objection. In some circumstances, a forced induction involves restraining the child, incurring both practical and ethical harms to the patient-provider encounter. This educational review explores the practical dilemma encountered when a child dissents to induction of anesthesia. In the course of exploring this dilemma, dissent and associated terms are defined and compared, and the prominent ethical underpinnings regarding pediatric decision-making are described to clarify dissent as an ethical and practical concept. Important legal and professional standards are summarized, and practice trends are discussed to depict the current state of practice, including novel approaches to honoring pediatric dissent for elective surgeries. This information is then used to invite providers to consider where they ethically situate themselves within a legally and professionally defined space of acceptable practice. Finally, these considerations are synthesized to discuss important nuances regarding pediatric refusal, and some key questions are presented for clinicians to ponder as they consider their practice of choosing whether to honor pediatric dissent at induction.

The Role of AI in Drug Discovery.

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Defining “data” in conversations with students about the ethical use of learning analytics

In any conversation about the development of ethical standards for practice, it is vital that all stakeholders have a shared understanding of the main concepts in order to reach agreement. In the context of higher education and learning analytics, while many conversations are underway, it is less clear that such a shared understanding exists around the concept of “data”. In order to understand this situation more fully we conducted a study to investigate students’ perceptions of the ethical and privacy considerations related to the data that universities collect and use about them for the purposes of learning analytics. In this paper, we focus specifically on the understandings students have of the types of data that can be collected about them within the educational environment. The outcomes showed that there was a diversity of understandings, but that five main data types emerged. In developing a better understanding of the ways students understand data, it can assist institutions to have more effective conversations with students about the ethical use of learning analytics.

Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers’ perspectives

BackgroundThe integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers’ perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them.MethodsA structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives.ResultsThree hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions.ConclusionsThis study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.

Rise of the Algorithm: A Journey into the World of AI

In the era of technological ascendancy, "Rise of the Algorithm: A Journey into the World of AI" embarks on a compelling exploration of the dynamic and ever-evolving domain of artificial intelligence. This journey unfolds through the lens of algorithms, the bedrock of AI, guiding readers through the intricate landscapes of machine learning, neural networks, and the convergence of data-driven intelligence. The abstract navigates the transformative impact of AI on diverse sectors, spanning from healthcare to finance, and delves into the ethical considerations inherent in the rise of intelligent algorithms. As we traverse this captivating odyssey, we discover the profound implications, challenges, and potential of AI, illuminating the path forward into a future shaped by the relentless rise of the algorithm.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists.

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

Book Recommendation System

In the digital age, the vast availability of books poses a significant challenge for readers to discover titles that match their interests and preferences. This report explores the use of machine learning for a Book Recommendation System (BRS) to help readers discover books suited to their interests in the digital age. It will utilize advanced algorithms and techniques, including collaborative filtering, content-based filtering, and hybrid models, to offer personalized recommendations based on users' reading history and behavior. The project aims to tackle challenges like data sparsity and the cold-start problem while ensuring scalability. Evaluation will be based on standard metrics like precision, recall, accuracy, and novelty, with a focus on ethical considerations such as fairness and user privacy. Ultimately, the goal is to develop a robust and user-centric system that enhances the book discovery process across digital platforms. A Graphical User Interface is also developed to discover information and display the recommended books. Keywords—Machine Learning, Book Recommendation System, K-Nearest Neighbor Algorithm

Unlocking Tomorrow’s Landscape: How AI Will Reshape Customer Journeys (B2B & B2C)

Businesses must ask themselves this question as we approach a revolutionary era: how will artificial intelligence (AI) change the B2B and B2C customer journey landscapes? This capstone study explores and covers the journey of artificial intelligence (AI), it’s impact on the business, the new opportunities along with challenges and the future predictions following the literature reviews, case studies, and expert insights. The research unveils a wide variety of possibilities waiting to be unlocked. This research focuses on the transformative potential of artificial intelligence (AI) for businesses and customers alike. It serves as a valuable resource for business leaders, marketing and sales professionals, customer service teams to managing artificial intelligence (AI) adoption plans and optimizing customer journey opportunities. Using AI-powered tools for personalized engagement and data-driven decisions. Using AI to improve productivity and customize assistance. Includes key Findings like Personalized Experiences, Data-Driven Decisions Automation & Efficiency. AI-powered chatbots offer seamless B2B customer service, while B2C brands use recommendation engines to provide hyper-personalized product suggestions. AI streamlines B2B workflows, boosting productivity. Beyond these, the research identifies: Opportunities: How AI increases customer engagement to a greater extent, unlocking brand loyalty and advocacy. Areas to integration of AI in business regular process. Challenges: What could be the Ethical considerations and potential job displacement carefully.

Web-based Dashboard on ECMO Utilization in Germany: An Interactive Visualization, Analyses, and Prediction Based on Real-life Data

In Germany, a comprehensive reimbursement policy for extracorporeal membrane oxygenation (ECMO) results in the highest per capita use worldwide, although benefits remain controversial. Public ECMO data is unstructured and poorly accessible to healthcare professionals, researchers, and policymakers. In addition, there are no uniform policies for ECMO allocation which confronts medical personnel with ethical considerations during health crises such as respiratory virus outbreaks.Retrospective information on adult and pediatric ECMO support performed in German hospitals was extracted from publicly available reimbursement data and hospital quality reports and processed to create the web-based ECMO Dashboard built on Open-Source software. Patient-level and hospital-level data were merged resulting in a solid base for ECMO use analysis and ECMO demand forecasting with high spatial granularity at the level of 413 county and city districts in Germany.The ECMO Dashboard (https://www.ecmo-dash.de/), an innovative visual platform, presents the retrospective utilization patterns of ECMO support in Germany. It features interactive maps, comprehensive charts, and tables, providing insights at the hospital, district, and national levels. This tool also highlights the high prevalence of ECMO support in Germany and emphasizes districts with ECMO surplus – where patients from other regions are treated, or deficit – origins from which ECMO patients are transferred to other regions. The dashboard will evolve iteratively to provide stakeholders with vital information for informed and transparent resource allocation and decision-making.Accessible public routine data could support evidence-informed, forward-looking resource management policies, which are urgently needed to increase the quality and prepare the critical care infrastructure for future pandemics.

Advancements in CRISPR-Based Therapies for Genetic Modulation in Neurodegenerative Disorders.

: Neurodegenerative disorders pose significant challenges in the realm of healthcare, as these conditions manifest in complex, multifaceted ways, often attributed to genetic anomalies. With the emergence of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology, a new frontier has been unveiled in the quest for targeted, precise genetic manipulation. This abstract explores the recent advancements and potential applications of CRISPR-based therapies in addressing genetic components contributing to various neurodegenerative disorders. The review delves into the foundational principles of CRISPR technology, highlighting its unparalleled ability to edit genetic sequences with unprecedented precision. In addition, it talks about the latest progress in using CRISPR to target specific genetic mutations linked to neurodegenerative diseases like Huntington's disease, Alzheimer's disease, amyotrophic lateral sclerosis (ALS), and Parkinson's disease. It talks about the most important studies and trials that show how well and safely CRISPR-based therapies work. This shows how this technology can change genetic variants that cause diseases. Notably, the discussion emphasizes the challenges and ethical considerations associated with the implementation of CRISPR in clinical settings, including off-target effects, delivery methods, and long-term implications. Furthermore, the article explores the prospects and potential hurdles in the widespread application of CRISPR technology for treating neurodegenerative disorders. It touches upon the need for continued research, improved delivery mechanisms, and ethical frameworks to ensure responsible and equitable access to these groundbreaking therapies.

Human Trafficking from the Islamic Perspective

This study examines human trafficking from the Islamic perspective, exploring its fundamental principles, historical background, ethical considerations, and contemporary challenges. This study utilizes a qualitative approach which employs both doctrinal and socio-legal approaches. The doctrinal approach allows researchers to understand the Islamic principles of exploitation, while the socio-legal approach scrutinizes the application of Islamic rulings in society. The findings reveal the prohibition of seven types of exploitation, as emphasized in Islamic jurisprudence, which includes exploitation in slavery, transactions, labour, sexual activities, human organs, adoption and acts of injustice. Furthermore, this study explores the principle of non-punishment for victims and the types of punishment for the offender. This study emphasizes the need for collective action to combat exploitation, promote justice, and protect the rights and dignity of all individuals. It strongly encourages individuals, communities, religious leaders, policymakers, and international organizations to work together to eradicate exploitation, uphold justice, and safeguard every individual’s dignity and rights in alignment with Islamic principles and universal human values.

Advancements in Text-to-Image Generation through Generative AI

Text-to-image generation, a fascinating intersection of natural language processing and computer vision, has witnessed remarkable progress in recent years. This research paper provides a comprehensive review of the state-of-the-art techniques, challenges, and applications in the field of text-to-image generation. The paper aims to analyze various approaches, discuss their strengths and limitations, and highlight potential directions for future research. Generative Artificial Intelligence (Generative AI) has revolutionized the fusion of textual information and visual content, giving rise to sophisticated Text-to-Image generators. In this context we will discuss dynamic landscape of Generative AI-driven text-to-image synthesis, exploring the state-of-the-art models, underlying architectures, and the impact of training strategies on the quality of generated images. The paper provides a comprehensive overview of Training strategies, encompassing dataset selection and fine-tuning approaches, are scrutinized for their impact on model performance. Common challenges, such as handling ambiguous textual descriptions and ensuring the avoidance of mode collapse, are addressed, offering insights into potential avenues for improvement. Training strategies, encompassing dataset selection and fine-tuning approaches, are scrutinized for their impact on model performance. Common challenges, such as handling ambiguous textual descriptions and ensuring the avoidance of mode collapse, are addressed, offering insights into potential avenues for improvement. Applications of Generative AI text-to-image generators are explored, ranging from content creation to virtual environment design, highlighting their versatility and real-world utility. Ethical considerations surrounding potential misuse, including the creation of deepfakes, are examined to foster a balanced understanding of the technology's societal implications. The research paper concludes with a forward-looking exploration of future directions in Generative AI text-to-image generation. Emphasizing the transformative potential of this technology, the paper envisions advancements in model architectures, training methodologies, and emerging applications, inviting researchers and practitioners to contribute to the ongoing evolution of this exciting field.

A Review on Bridging Molecular Biology and Ecological Dynamics through Integrative Approaches in Zoology

The integration of molecular biology with ecological dynamics has emerged as a transformative approach in zoology, enhancing our understanding of biodiversity, ecosystem health, and the adaptive responses of species to environmental changes. This review synthesizes key developments and methodological innovations at the intersection of molecular biology and ecological dynamics, highlighting the application of DNA barcoding, environmental DNA (eDNA) analyses, molecular phylogenetics, and advanced computational models in elucidating complex biological interactions and evolutionary patterns. Significant advancements include the use of high-throughput sequencing technologies and CRISPR-Cas systems that have expanded our ability to explore genetic diversity and manipulate genetic material for conservation purposes. The review discusses the predictive capabilities of integrative models that combine genetic with ecological data, offering insights into species resilience and ecosystem stability under varying environmental scenarios. Challenges in data integration, such as issues of scale, complexity, and the necessity for interdisciplinary cooperation, are critically examined. Technical limitations related to data management and ethical considerations in the use of genetic information are also explored. Looking forward, the review identifies emerging technologies and their potential impacts on ecological and conservation biology, emphasizing the need for policies that support sustainable management and conservation strategies. This review underscores the profound impact of integrating molecular biology with ecological dynamics, which not only enhances our scientific understanding but also provides practical frameworks for addressing global environmental challenges.

Strategi Mobile Journalism dalam Meningkatkan Kinerja Peliputan Wartawan (Studi pada Media TribunBengkulu.com)

Mobile Journalism (MOJO) has become a significant innovation in the field of journalism. MOJO has numerous effects on the performance of journalists in news coverage. This research investigates the use of MOJO as an effectibve tool to enchance responsiveness, engagement, and flexibility in carrying out journalistic tasks. Through a qualitative approach and case studies, this study explores the experiences of Tribun Bengkulu journalists who have adopted mobile journalism in converiing news in various locations. The research results indicat that the use of MOJO devices enables journalists to respond to news quickly and efficiently. With the ability to record videos, take photos, and engage in live broadcasts, journalists can cover events more dynamically. Futhermore, audience engagement is also enchanced through social media, particularly through live broadcasts. Financial affordability has proven to be an advantage, allowing media organizations with limited budgets to produce quality content without the need for expensive equipment. However, challenges related to production quality and ethical considerations need to remain a special focus. This can be addressed through specialized training and robust ethical guidelines for journalists adopting mobile journalism to ensure that high journalistic standards are maintained. In conclusion, MOJO is not just a practical tool for efficient news coverage but also an innovative approach that can enhance the engagement and flexibility of journalists in delivering information to the public.

"Incorporating large language models into academic neurosurgery: embracing the new era".

This correspondence examines how LLMs, such as ChatGPT, have an effect on academic neurosurgery. It emphasises the potential of LLMs in enhancing clinical decision-making, medical education, and surgical practice by providing real-time access to extensive medical literature and data analysis. Although this correspondence acknowledges the opportunities that come with the incorporation of LLMs, it also discusses challenges, such as data privacy, ethical considerations, and regulatory compliance. Additionally, recent studies have assessed the effectiveness of LLMs in perioperative patient communication and medical education, and stressed the need for cooperation between neurosurgeons, data scientists, and AI experts to address these challenges and fully exploit the potential of LLMs in improving patient care and outcomes in neurosurgery.

Ethical Considerations of Declining Surgical Intervention: Balancing Patient Wishes with Fiduciary Responsibility.

Orthopaedic surgeons face increasing pressure to meet quality metrics due to regulatory changes and payment policies. Poor outcomes, including patient mortality, can result in financial penalties and negative ratings. Importantly, adverse outcomes often increase surgeon stress level and lead to job dissatisfaction and burnout. Despite optimization efforts, some orthopaedic patients remain at high risk for complications. In this article, we explore the ethical considerations when surgeons are presented with high-risk surgical candidates. We examine how the ethical tenets of patient interests, namely beneficence, nonmaleficence, autonomy, and justice, apply to such patients. We discuss external forces such as the malpractice environment, financial challenges in health-care delivery, and quality rankings. Informed consent and the challenges of communicating risks to patients are discussed, as well as the role of modifiable and nonmodifiable risk factors. Case examples with varied outcomes highlight the complexities of decision-making with high-risk patients and the potential role of palliative care. We provide recommendations for surgeons and care teams, including the importance of justifiable reasons for not operating, the utilization of institutional resources to help make care decisions, and the robust communication of risks to patients.

Like Water: Feeling and Negotiating Relational Complexity in School-Based Ethnographic Childhood Research in China Through the Lens of Emotional Reflexivity

Ethnography is often considered as a suitable methodological approach to explore childhoods, nevertheless, there is limited reflection on ethics in the field in the Global South educational settings, such as China. Previous China-based fieldwork studies suggest complex field relationships as a major ethical challenge. Although the issue is especially prominent in research that contests “normative” Chinese childhoods, it hasn’t been sufficiently discussed due to a culture of silence surrounding researchers’ emotions and positionality. This paper uses emotional reflexivity as a lens to revisit two Chinese female researchers’ experiences of fieldwork, respectively, with left-behind children and disabled children in Chinese primary schools. The analysis examines how we identified ourselves and negotiated complex relationships with school members and unpacks experiences of “sameness” and “otherness” and the vulnerability and creditability impacted by our fluid identities. The paper adds insights into ethical considerations of school-based ethnographic research with diverse children in the Global South.

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

This study introduces a novel framework that leverages artificial intelligence (AI), specifically deep learning and reinforcement learning, to enhance energy efficiency in architectural design. The goal is to identify architectural arrangements that maximize energy efficiency. The complexity of these models is acknowledged, and an in-depth analysis of model selection, their inherent complexity, and the hyperparameters that govern their operation is conducted. This study validates the scalability of these models by comparing them with traditional optimization techniques like genetic algorithms and simulated annealing. The proposed system exhibits superior scalability, adaptability, and computational efficiency. This research study also explores the ethical and societal implications of integrating AI with architectural design, including potential impacts on human creativity, public welfare, and personal privacy. This study acknowledges it is in its preliminary stage and identifies its potential limitations, setting the stage for future research to enhance and expand the effectiveness of the proposed methodology. The findings indicate that the model can steer the architectural field towards sustainability, with a demonstrated reduction in energy usage of up to 20%. This study also conducts a thorough analysis of the ethical implications of AI in architecture, emphasizing the balance between technological advancement and human creativity. In summary, this research study presents a groundbreaking approach to energy-efficient architectural design using AI, with promising results and wide-ranging applicability. It also thoughtfully addresses the ethical considerations and potential societal impacts of this technological integration.

En] Exploring safety and agency in social media: a case study of online crossdressing

Introduction. Are there safe spaces for the construction of online communities with diverse gender identities and sexual desires? What makes some online spaces safer than others? And for whom? Does the architecture of these spaces influence the ways in which users navigate the Internet? Methodology. To address these questions, we conducted a digital ethnography on a social media platform oriented towards the Spanish-speaking crossdresser community. Findings and analysis. Our analysis suggests that this platform acts as a digital counterpublic, as it allows users to inhabit a safe environment for self-expression, building support networks, organizing as a collective, and articulating their sexual intimacies. Based on these findings, we infer that online safety and agency are closely interrelated, the latter being a result of the first. In addition, we found that the concept of online safety should be evaluated on a case-by-case basis, in accordance with the feminist perspective, where online safety is always construed in a situated manner and conceives the subjects of study as active agents involved in the definition of the concept itself. Ethical considerations. The research process raised ethical questions of great relevance to the conclusions of this study, suggesting that the same factors to be considered when conducting research in digital platforms should be contemplated when designing and navigating online safe spaces. We thus propose that, both in carrying out online qualitative research and in the construction of online safe spaces, the following aspects should be taken into consideration: the implications of the privacy settings offered by the platform, the vulnerability of the users that populate it, the sensitivity of the topics covered by the platform’s community and, last but not least, an ongoing negotiation and reaffirmation of consent in the utilization of the users’ personal data.