Search Results (1,549)

Vaccine quality control has long relied on animal testing, which involves time, cost, and ethical constraints. This study introduces a ganglioside binding (GB) assay as a complementary in vitro screening tool for tetanus toxoid quality control, which was validated in a single-laboratory environment as a foundational proof-of-concept. The assay reproduces tetanus toxin binding to gangliosides on microplates using simplified procedures. Validation with samples at different inactivation stages showed excellent linearity (0.0002–0.0156 Lf/mL), reproducibility, and a strong correlation with Ramon’s flocculation (R² = 0.999). The assay clearly distinguished between toxins and toxoids, with the toxoid results remaining at control levels. The time-course inactivation samples were consistent with the animal tests: partially inactivated samples (days 1–3) showed significant GB activity (p < 0.001) and caused 100% mortality, whereas samples from day 4 onward showed no activity and zero mortality. These findings demonstrate that the GB assay reliably differentiates active toxins from toxoids, which aligns with in vivo outcomes. The practical advantages include a simplified protocol, reduced complexity, and improved efficiency for routine testing of samples. As a complementary screening approach, this single-laboratory validation supports the 3Rs principle by demonstrating the potential for reducing animal use while ensuring quality assurance. Broader applicability requires multicenter validation and cross-reactivity, and multicenter validation is ongoing. Full article

Urban public spaces function as complex social and spatial systems, serving as fundamental elements in preserving cultural heritage and fostering democratic participation and urban stability. The dominant valuation methods depend on economic principles that prioritize financial returns and property values over social, ecological, and cultural aspects. This study examines the distinction between social value, which is based on personal experiences and social connections, and fair value, which relies on market operations and financial regulations. The research investigates how valuation practices affect spatial justice and urban governance through a conceptual framework and four empirical case studies: Syntagma Square in Athens, the metro station areas, the city of Barcelona, and waterfront redevelopment projects in Thessaloniki, London, and Mumbai. The official valuation systems we studied conceal the political elements of space while preserving social inequalities and forcing out the requirements of local communities. The proposed solution establishes an integrative, pluralist valuation system that incorporates qualitative, temporal, and ethical elements, supporting the Sustainable Development Goals (SDGs) and the New Urban Agenda. The framework transforms public space discussions from commercialized urban resources into communal civic systems, creating sustainable cities for all. Full article

Background: currently, there is a growing trend toward multifunctional cosmetics, which combine several active ingredients in a single product to enhance efficacy and user convenience. As ingredients may influence one another, it is important to study the behavior of mixing multiple compounds in complex formulations, especially regarding their interaction with the skin. Piceatannol, for instance, is a naturally occurring stilbene recognized for its in vitro potent antioxidant, anti-inflammatory, and anti-aging activities, making it a promising candidate for dermocosmetic use in suncare. But despite its beneficial biological activities, its cutaneous permeation remains poorly understood, particularly when delivered from complex formulations containing multiple ingredients. Objectives: in this sense, this study aimed to evaluate the in vitro skin diffusion profile of piceatannol from a passion fruit seed extract (P_ext) incorporated into a topical base (B_em) or an organic sunscreen emulsion (O_em), with or without a spilanthol-rich Acmella oleracea extract (J_ext) used as a natural permeation enhancer. Methods: due to ethical and variability issues with human and animal skins, the Strat-M™ synthetic membrane was chosen as a standardized model for the in vitro skin permeation assays. Piceatannol localization within membrane layers was examined by confocal Raman microscopy (CRM), while compound identification in donor and receptor compartments was performed via UHPLC-DAD. Results: piceatannol from B_em was detected up to 140 µm from the Strat-M™ surface and exceeded 180 µm in depth when J_ext and organic sunscreens were included in the formulation. Notably, formulations containing J_ext and those based on O_em promoted enhanced accumulation in both the stratum corneum and deeper skin layers, suggesting an improved delivery potential in lipid-rich vehicles. Conclusions: even though some instability issues were observed, piceatannol penetration into Strat-M™ from the proposed formulations was confirmed, and the results provide a foundation for further research on its topical delivery, supporting the rational development of formulations capable of harnessing its demonstrated biological properties. Full article

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of Things (AIoT). We illustrate the operating procedures of IoHT systems in detail. We highlight their applications in disease management, health promotion, and active ageing. Key enabling technologies, including cloud computing, edge computing architectures, machine learning, and smart sensors, are examined in relation to continuous health monitoring, personalized interventions, and predictive decision support. This paper also indicates potential challenges that IoHT systems face, including data privacy, ethical concerns, and technology transition and aversion, and it reviews corresponding defense mechanisms from perception, policy, and technology levels. Future research directions are discussed, including explainable AI, digital twins, metaverse applications, and multimodal sensor fusion. By integrating IoT and AI, these systems offer the potential to support more adaptive and human-centered healthcare delivery, ultimately improving treatment outcomes and supporting healthy ageing. Full article

The evolution of decentralized clinical trials, driven by advanced digital technologies, is transforming traditional clinical research. It introduces innovative methods for informed consent, remote patient monitoring, and data analysis, enhancing study efficiency, validity, and participation while reducing patient burden. Some clinical procedures can be conducted remotely, increasing trial accessibility and reducing population selection biases, particularly for cardiovascular patients. However, this also presents complex regulatory and ethical challenges. The article explores how digital platforms and emerging technologies like block chain, AI, and advanced cryptography can promote traceability, security, and transparency throughout the trial process, ensuring participant identification and documentation of each procedural step. Clear, legally compliant informed consent, often managed through electronic systems, both for research participation and data management in line with GDPR, is essential. Ethical considerations include ensuring participants understand trial information, with adaptations such as simplified language, visual aids, and multilingual support. The transnational nature of decentralized trials highlights the need for coordinated regulatory standards to overcome jurisdictional barriers and reinforce accountability. This framework promotes trust, shared responsibility, and the protection of participants rights while upholding high ethical standards in scientific research. Full article

Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A dataset of 129 homes in Spain and Austria was analyzed, with variables including PM_2.5, PM₁, CO₂, temperature, humidity, and total VOCs. The final model, based on the XGBoost algorithm, achieved near-perfect household-level classification (100% accuracy in the test set and AUC = 0.96 in external validation). Analysis of PM_2.5 temporal profiles in representative households helped interpret model performance and highlighted cases where model predictions revealed inconsistencies in self-reported smoking status. These findings support the use of sensor-based approaches for behavioral inference and exposure assessment in residential settings. The proposed method could be extended to other indoor pollution sources and may contribute to risk communication, health-oriented interventions, and policy development, provided that ethical principles such as transparency and informed consent are upheld. Full article

Background/Objectives: Testicular germ cell tumours (GCT) have high cure rates, especially in early stages. MicroRNA-371a-3p (M371) has recently emerged as a highly sensitive biomarker for malignant GCTs, except teratoma. This study aimed to evaluate the diagnostic performance of M371-test in a real-life clinical setting, compared to conventional markers alpha-fetoprotein (AFP), lactate-dehydrogenase (LDH), and beta-human chorionic gonadotropin (β-HCG) in patients with suspected GCT. Methods: The study, approved by the Ethic-Committee of the Provincial Hospital of Bolzano (N.97-2021), included 91 M371-tests, performed from March 2021 to May 2025. A total of 75 patients had suspected GCT; 19 healthy males served as control. Serum levels of M371, AFP, LDH, and β-HCG were compared with final histopathological diagnosis. M371 was also assessed in controls to evaluate test performance. Secondary analyses investigated correlations between preoperative M371 levels and tumour size in non-metastatic patients, and between M371-levels and clinical stage in the entire GCT cohort. A cut-off of RQ > 5 (relative quantification) was used to calculate sensitivity, specificity, and predictive values. Results: M371 showed a sensitivity of 90.9% and specificity of 89.3%, outperforming in terms of sensitivity AFP (20.4%/96.4%), LDH (40.9%/96.4%), and β-HCG (43.1%/100%). Positive predictive value (PPV) and negative predictive value (NPV) were 93.0% and 86.2%, respectively. Sensitivity was 95% for non-seminomas and 87.5% for seminomas. In non-metastatic patients, M371 levels correlated with tumour size and were significantly higher in advanced stages (median RQ 1128.35 vs. 98.36; p = 0.015). Conclusions: M371 showed excellent diagnostic performance, even for small tumours, supporting its clinical use. Further studies are needed to define its role in treatment planning and follow-up. Full article

Conventional academic warning systems in higher education often rely on end-of-semester grades, which severely limits opportunities for timely intervention. To address this, our interdisciplinary study developed and validated a comprehensive socio-technical framework that integrates social-cognitive theory with learning analytics. The framework combines educational data mining with culturally responsive, personalized interventions tailored to a non-Western context. A two-phase mixed-methods design was employed: first, predictive models were built using Learning Management System (LMS) data from 2,856 students across 64 courses. Second, a quasi-experimental trial (n = 48) was conducted to evaluate intervention efficacy. Historical academic performance, attendance, and assignment submission patterns were the strongest predictors, achieving a Balanced Area Under the Curve (AUC) of 0.85. The intervention, specifically adapted to Confucian educational values, yielded remarkable results: 73% of at-risk students achieved passing grades, with a large effect size for academic improvement (Cohen’s d = 0.91). These findings empirically validate a complete prediction–intervention–evaluation cycle, demonstrating how algorithmic predictions can be effectively integrated with culturally informed human support networks. This study advances socio-technical systems theory in education by bridging computer science, psychology, and educational research. It offers an actionable model for designing ethical and effective early warning systems that balance technological innovation with human-centered pedagogical values. Full article

The adoption of artificial intelligence has risen, yet research on its impact on innovation processes between actual businesses remains sparse. This research fills the present gap by investigating ten workers from a tech startup who utilize artificial intelligence tools in operational and creative activities. The paper analyzes business-related AI functionality through a qualitative analysis of ten tech start-up employees. The examination reveals that AI produces significant enhancements in problem resolution by executing mundane actions while analyzing large datasets to deliver data-driven suggestions to users. The interview respondents mentioned that AI’s role in diminishing supply chains is 15%, while allowing AI to manage customer service without employee engagement in 80% of interactions. The implementation costs, along with data dependency and occasional contextual blindness in AI systems, represented some of the problems in this system. Analysis demonstrated that AI tools enable the development of innovative concepts and challenge established viewpoints, prompting participants to create a gamified loyalty system and dynamic content planning. Participants in the study emphasized the need for human involvement to refine AI-based insights, recognizing how human imagination complements AI capabilities effectively. The work enhances academic discussions about AI-related problem-solving and creativity while offering specific business-related recommendations for implementation. The recommendations begin with establishing initial experimental programs, while providing support for employee’s skills development, and fostering strong alliances between technical AI personnel and professional subject matter experts. Research topics focused on AI application fields and the anticipated impacts on company decision-making, as well as the ethical ramifications, need further exploration. This research confirms the revolutionary potential of artificial intelligence systems for problem-solving methods, but requires proper execution, along with human supervision, to fully realize their advantages. Full article

This paper charts AI’s transformative path toward advancing sustainability within art museums, introducing a Human–AI compass as a conceptual framework for navigating its integration. It advocates for human-centric AI that optimizes operations, modernizes collection management, and deepens visitor engagement—anchored in meaningful human–technology synergy and thoughtful human oversight. Drawing on extensive literature review and real-world museum case studies, the paper explores AI’s multifaceted impact across three domains. Firstly, it examines how AI improves operations, from audience forecasting and resource optimization to refining marketing, supporting conservation, and reshaping curatorial practices. Secondly, it investigates AI’s influence on digital collection management, highlighting its ability to improve organization, searchability, analysis, and interpretation through automated metadata and advanced pattern recognition. Thirdly, the study analyzes how AI elevates the visitor experience via chatbots, audio guides, and interactive applications, leveraging personalization, recommendation systems, and co-creation opportunities. Crucially, this exploration acknowledges AI’s complex challenges—technical-operational, ethical-governance, socioeconomic-cultural, and environmental—underscoring the indispensable role of human judgment in steering its implementation. The Human-AI compass offers a balanced, strategic approach for aligning innovation with human values, ethical principles, museum mission, and sustainability. The study provides valuable insights for researchers, practitioners and policymakers, enriching the broader discourse on AI’s growing role in the art and cultural sector. Full article

A balance between support and independence in guardianship systems is of high concern, especially with those who need help in making decisions. The research presents a novel approach to evaluating voluntary models of guardianship, focusing on the preservation of individual autonomy and examining the underlying decision symmetry in assessing diverse guardianship options. The ultimate solution to the inherent uncertainty and lack of objectivity in expert evaluations is to apply the circular q-rung orthopair fuzzy (Cq-ROF) combined compromise for ideal solution (CoCoFISo) approach, an effective multi-criteria decision-making (MCDM) model that integrates ranking and sorting views using a Cq-ROF framework within a symmetry-oriented analytical perspective. These are five major assessment factors: how well autonomy is preserved, legal and ethical adherence, psychological health, social integration aid, and risk prevention. It explores ten alternative approaches to guardianship, ranging from complete legal guardianship to community-based self-management solutions, and the use of technology as an element of support. The suggested approach can facilitate more sophisticated modelling of expert opinions, rather than relying on simplistic and straightforward distinctions and diverse evaluations. The case study results indicate that the hybrid and supported forms of decision-making could offer opportunities to preserve a high degree of personal autonomy while ensuring safety and compliance. The research gives a coherent, adaptable, and explainable approach to managing ethical and policy-level judgment concerning voluntary guardianship systems. Full article

This study investigates hotel employees’ perceptions of AI-driven human resource (HR) management systems within the Accor Group’s properties across three major European cities: Paris, Berlin, and Amsterdam. These diverse urban contexts, spanning a broad portfolio of hotel brands from luxury to economy, provide a rich setting for exploring how AI integration affects employee attitudes and work–life balance. A total of 437 employees participated in the survey, offering a robust dataset for structural equation modeling (SEM) analysis. Exploratory factor analysis identified two primary factors shaping perceptions: AI Perceptions, which encompasses employee views on AI’s impact on job performance, communication, recognition, and retention, and balanced management, reflecting attitudes toward fairness, personal consideration, productivity, and skill development in AI-managed environments. The results reveal a complex but optimistic view, where employees acknowledge AI’s potential to enhance operational efficiency and career optimism but also express concerns about flexibility loss and the need for human oversight. The findings underscore the importance of transparent communication, contextual sensitivity, and continuous training in implementing AI systems that support both organizational goals and employee well-being. This study contributes valuable insights to hospitality management by highlighting the relational and ethical dimensions of algorithmic HR systems across varied organizational and cultural settings. Full article

The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article

The rapid development of generative artificial intelligence (GenAI) has revolutionized how individuals and organizations interact with technology. These systems, ranging from conversational agents to creative tools, are increasingly embedded in daily life. However, their effectiveness relies heavily on the usability of their graphical user interfaces (GUIs), which serve as the primary medium for user interaction. Moreover, the design of these interfaces must align with ethical principles such as transparency, fairness, and user autonomy to ensure responsible usage. This study evaluates the usability of GUIs for three widely-used GenAI applications, including ChatGPT (GPT-4), Gemini (1.5), and Claude (3.5 Sonnet) , using a heuristics-based and user-based testing approach (experimental-qualitative investigation). A total of 12 participants from a research organization in Australia, participated in structured usability evaluations, applying 14 usability heuristics to identify key issues and ethical concerns. The results indicate that Claude’s GUI is the most usable among the three, particularly due to its clean and minimalistic design. However, all applications demonstrated specific usability issues, such as insufficient error prevention, lack of shortcuts, and limited customization options, affecting the efficiency and effectiveness of user interactions. Despite these challenges, each application exhibited unique strengths, suggesting that while functional, significant enhancements are needed to fully support user satisfaction and ethical usage. The insights of this study can guide organizations in designing GenAI systems that are not only user-friendly but also ethically sound. Full article

Inclusive education is a central pillar of contemporary educational policy worldwide, aiming to ensure equity, participation, and success for all learners. Understanding how inclusive policies are implemented in everyday school contexts is essential to identify both enabling factors and persistent barriers. In Portugal, this process has been shaped by a progressive legislative framework, notably Decree-Law 54/2018 and its regional adaptation in the Autonomous Region of Madeira (RAM) through Regional Legislative Decree 11/2020/M. This case study combined qualitative documentary analysis with a questionnaire applied to coordinators of the Multidisciplinary Teams to Support Inclusive Education (EMAEI) from 11 of the 14 secondary schools in the RAM (79% of the total). The questionnaire covered five domains: inclusive principles, leadership, professional development, resources, and diagnosis of learning barriers. Descriptive statistics and content analysis were used to interpret the data. Results show that coordinators share a strong ethical commitment to inclusion and recognize EMAEI as a strategic organizational structure. However, they point to irregular and insufficient training, delays and mismatches in resource allocation, and limited institutionalization of co-teaching and joint planning. Leadership emerges as a decisive enabler of collaborative cultures. The study concludes that ambitious legislation must be complemented by agile resource management, sustained context-based training, and distributed leadership for inclusive schooling to become everyday practice. Full article