Search Results (3,263)

Artificial intelligence (AI) techniques have propelled biomedical sensors beyond measuring physiological markers to interpreting subjective states like stress, pain, or emotions. Despite these technological advances, user trust is not guaranteed and is inadequately addressed in extant research. This review proposes the Cognition–Context–Contrast (CCC) conceptual framework to explain the trust and acceptance of AI-enabled sensors. First, we map cognition, comprising the expectations and stereotypes that humans have about machines. Second, we integrate task context by situating sensor applications along an intellective-to-judgmental continuum and showing how demonstrability predicts tolerance for sensor uncertainty and/or errors. Third, we analyze contrast effects that arise when automated sensing displaces familiar human routines, heightening scrutiny and accelerating rejection if roll-out is abrupt. We then derive practical implications such as enhancing interpretability, tailoring data presentations to task demonstrability, and implementing transitional introduction phases. The framework offers researchers, engineers, and clinicians a structured conceptual framework for designing and implementing the next generation of AI biosensors. Full article

Seafood contamination by heavy metals is a growing public health concern, particularly in regions like Tunisia where seafood is a major dietary component. This study assessed concentrations of cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) in the muscle tissue of the red shrimp Parapenaeus longirostris, collected in 2023 from four coastal regions: Bizerte, Monastir, Kerkennah, and Gabes. Metal analysis was conducted using flame atomic absorption spectroscopy. This species was chosen due to its ecological and economic importance. The study sites were chosen based on their differing levels of industrial, urban, and agricultural influence, providing a representative overview of regional contamination patterns. Mean concentrations were 1.04 µg/g for Zn, 0.59 µg/g for Cu, 1.56 µg/g for Pb, and 0.21 µg/g for Cd (dry weight). Pb was the most prevalent metal across sites. Statistically significant variation was observed only for Cu (p = 0.0334). All metal concentrations were below international safety limits set by FAO/WHO and the European Union. Compared to similar studies, the levels reported were similar or slightly lower. Human health risk was evaluated using target hazard quotient (THQ), hazard index (HI), and cancer risk (CR) values. For adults, THQ ranged from 5.44 × 10⁻⁶ to 8.43 × 10⁻⁴, while for children it ranged from 2.40 × 10⁻⁵ to 3.72 × 10⁻³. HI values were also well below 1, indicating negligible non-carcinogenic risk. CR values for Cd and Pb in both adults and children fell within the acceptable risk range (10⁻⁶ to <10⁻⁴), suggesting no significant carcinogenic concern. This study provides the first field-based dataset on metal contamination in P. longirostris from Tunisia, contributing valuable insights for seafood safety monitoring and public health protection. Full article

In the digital era, understanding the intention to use generative AI is critical, as it enhances productivity, transforms workflows, and enables humans to focus on higher-value tasks. Drawing upon the unified theory of acceptance and use of technology (UTAUT) and the technology threat avoidance theory (TTAT), this research integrates perceived AI literacy into the AI acceptance–avoidance framework as a central variable. This study gathered 583 valid survey responses from China and validated its model using a dual-phase, combined method that integrates structural equation modeling and artificial neural networks. Research findings indicate that the model explains 51.6% of the variance in generative AI usage intention. Except for social influence, all variables within the extended framework significantly impact the usage intention, with perceived AI literacy being the strongest predictor (β = 0.33, p < 0.001). Additionally, perceived AI literacy mitigates the adverse effect of perceived threats on the intention to use AI. Practical implications suggest that enterprises adopt a tiered strategy, as follows: maximize perceived benefits by integrating AI skills into reward systems and providing task-automation training; minimize perceived costs through dedicated technical support and transparent risk mitigation plans; and cultivate AI literacy via progressive learning paths, advancing from data analysis to innovation. Full article

Transportation is often cited as a significant barrier to healthcare access by Veterans, particularly those from minority groups, who have disabilities, or live in rural areas. Autonomous shuttles (AS) offer a potential solution, yet limited research has explored Veterans’ experiences with this technology. This study qualitatively investigated Veterans’ lived experiences with AS through focus groups, enrolling participants aged 18+ from Gainesville, The Villages, and Lake Nona, Florida. Via a directed content analysis, six key themes were identified: Perceived Benefits, Safety, Experience with Autonomous Vehicles (AV), AS Experience, AV Adoption, and Perception Change. Among 26 participants (aged 30–85; 77% men; 88% urban residents), prominent themes included Safety (n = 161), Perceived Benefits (n = 153), and AS Experience (n = 118), with predominantly positive counts in all themes except AS Experience. Participants acknowledged safety advantages and multitasking potential of AS over human-operated vehicles while recommending improvements to the shuttle’s slow speed, availability and convenience. While the AS ride was positively received overall, some participants noted issues with comfort and braking, emphasizing the need for further technological enhancements. Real-world exposure to AS appeared to influence acceptance positively, offering insights for policymakers and industry stakeholders aiming to optimize AS deployment for mobility-vulnerable Veterans. Full article

Background/Objectives: This study aimed to assess real-world toxicity and efficacy data of patients with early and advanced breast cancer (BC) who received treatment with abemaciclib. Methods: This was a prospective/retrospective multi-institutional collection of clinicopathological, toxicity, and outcome data from patients with early or metastatic hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative BC who received treatment with abemaciclib in combination with endocrine therapy in departments of oncology in Greece. Treatment combinations of abemaciclib with any endocrine therapy were accepted. The primary end point was toxicity rate in all patients of the study. Results: From June/2021 to May/2024, 245 women received abemaciclib/endocrine combination therapy; the median age was 57 years. Of these, 169 (69%) received abemaciclib as adjuvant therapy for early-stage disease, while 76 (31%) were treated for advanced BC. At the time of the data cutoff, 133 (84.7%) patients remained in the 2-year treatment period. The most common adverse event (AE) was diarrhea (51%), primarily Grade ≤ 2. Dose modifications due to AEs were required in 19.2% of cases, while treatment discontinuation occurred in 5.1%. There was no difference in dose modification/discontinuation rates between older patients (>65 years) and the remaining patients. For early-stage BC patients, the 2-year DFS and OS rates were 90.8% and 100%, respectively. In patients with advanced cancer (70, 30.8%), 1-year PFS and OS rates were 78% and 96.3%, respectively. Conclusions: This study confirms the safety and effectiveness of abemaciclib in alignment with registrational trials offering valuable insights into toxicity management and clinical outcomes in routine practice without identifying new safety concerns. Clinical Trial Registration: ClinicalTrials.gov NCT04985058. Full article

New theologies developed in tandem with evolutionary biology during the nineteenth century, which have been called metaphysical evolutionisms and evolutionary theologies. A subset of these theologies analyzed here were developed by thinkers who accepted biological science but rejected both biblical creationism and materialist science. Tools from the cognitive science of religion, including conceptual metaphor theory (CMT) and blending theory, also known as conceptual integration theory (CIT), can help to explain the development of these systems and their transformation between the nineteenth and the twentieth centuries. The analysis focuses on several stable and popular blends of ideas, which have continued with some alteration into the twenty-first century. The three blends evaluated here are Progressive Soul Evolution, Salvation is Evolution, and Evolution is Therapy. Major contributors to these blends are the theosophist and theologian Helena P. Blavatsky and psychologist Frederic W. H. Myers, both influenced by the spiritualist movement, particularly the ideas of the spiritualist and biologist Alfred Russel Wallace. The influence of these blends can be seen in the twentieth-century “Aquarian Frontier,” a group of 145 thinkers and organizations identified in 1975 by counterculture historian Theodore Roszak. Part of the appeal of these blends may be seen in their use of metaphors, including the Great Chain of Being and A Purposeful Life is a Journey. The application of the polysemic term evolution in a sense that does much of the theological work of salvation in Christianity can in part be explained by applying the principles of blending theory, including the vital relation “achieve a human scale,” as well as compressions of time and identity. These blends have been successful because they meet the needs of a population who are friendly towards science but disenchanted with traditional religions. The blends provide a satisfying new theology that extends beyond death for a subset of adherents, particularly in the New Age and spiritual but not religious (SBNR) movements, who combine the agency of self-directed “evolution” with the religious concepts of grace and transcendence. Full article

Increasing automation and the rapidly growing use of robots in industrial as well as social areas result in a greater need for research regarding collaboration between humans and robots. Key factors for a safe and successful combination of human and robot abilities include acceptance and trust in the robot. In order to prevent physical and psychological harm to humans, reducing these negative emotions and increasing trust and acceptance are essential. One way to achieve this is through the use of virtual training methods and environments. However, current research scarcely covers this approach. Therefore, this research focusses on an experimental approach to investigate emotional and psychophysiological (ECG, EDA) reactions while performing a collaborative assembly task (screwing) with an industrial robot in a real and a virtual setting, respectively. The study sample consisted of 46 participants (23 female) with an age range from 20 to 58 years. The results of the analyzed subjective and objective psychophysiological (cardiovascular and electrodermal responses) measures provide more information regarding the suitability of virtual trainings for human–robot collaboration. Differences in task complexity were measurable in both virtual and real environments. Furthermore, gender differences were also shown. Full article

Human induced pluripotent stem cells (hiPSCs) are widely used in basic research because of their versatility and ability to differentiate into multiple cell types. In particular, differentiating hiPSCs into cardiac cells (hiPSC-CMs) has been an important milestone in cardiac pathophysiology studies. Although hiPSC-CMs offer a model for human cardiomyocytes, they still exhibit characteristics linked to the fetal cardiac cell phenotype. One important feature that prevents hiPSC-CMs from being identified as adult cells relates to their metabolism, which is a key factor in defining a mature phenotype capable of sustaining the workload requirements characteristic of fully differentiated cardiomyocytes. This review aims to present the most relevant strategies in terms of culture medium composition, culture times, and 3D culture methods that have been developed to promote the metabolic maturation of hiPSC-CMs, which are now widely used. Defining a standardized and universally accepted protocol would enable the creation of a cellular model for studies of cardiac pathophysiology from a patient-specific perspective and for drug screening. Full article

As autonomous driving technologies advance, the need for human-in-the-loop systems becomes increasingly critical to ensure safety, adaptability, and public confidence. This paper presents the design and evaluation of a context-aware immersive teleoperation interface that integrates real-time simulation, virtual reality, and multimodal feedback to support remote interventions in emergency scenarios. Built on a modular ROS2 architecture, the system allows seamless transition between simulated and physical platforms, enabling safe and reproducible testing. The experimental results show a high task success rate and user satisfaction, highlighting the importance of intuitive controls, gesture recognition accuracy, and low-latency feedback. Our findings contribute to the understanding of human-robot interaction (HRI) in immersive teleoperation contexts and provide insights into the role of multisensory feedback and control modalities in building trust and situational awareness for remote operators. Ultimately, this approach is intended to support the broader acceptability of autonomous driving technologies by enhancing human supervision, control, and confidence. Full article

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem. Full article

Ammonia (NH₃) emissions affect the environment, climate and human health and originate mainly from agricultural sources like synthetic nitrogen fertilizers. Accurate and replicable measurements of NH₃ emissions are crucial for research, inventories and evaluation of mitigation measures. There exist specific application limitations of NH₃ emission measurement techniques and a high variability in method performance between studies, in particular from small plots. Therefore, the aim of this study was the assessment of measurement methods for ammonia emissions from replicated small plots. Methods were evaluated in 18 trials on six sites in Germany (2021–2022). Urea was applied to winter wheat as an emission source. Two small-plot methods were employed: inverse dispersion modelling (IDM) with atmospheric concentrations obtained from Alpha samplers and the dynamic chamber Dräger tube method (DTM). Cumulative NH₃ losses assessed by each method were compared to the results of the integrated horizontal flux (IHF) method using Alpha samplers (Alpha IHF) as a micrometeorological reference method applied in parallel large-plot trials. For validation, Alpha IHF was also compared to IHF/ZINST with Leuning passive samplers. Cumulative NH₃ emissions assessed using Alpha IHF and DTM showed good agreement, with a relative root mean square error (rRMSE) of 11%. Cumulative emissions assessed by Leuning IHF/ZINST deviated from Alpha IHF, with an rRMSE of 21%. For low-wind-speed and high-temperature conditions, NH₃ losses detected with Alpha IDM had to be corrected to give acceptable agreement (rRMSE 20%, MBE +2 kg N ha⁻¹). The study shows that quantification of NH₃ emissions from small plots is feasible. Since DTM is constrained to specific conditions, we recommend Alpha IDM, but the approach needs further development. Full article

Background: The rising demand for aesthetic dental treatments has spurred interest in peroxide-free color correctors as alternatives to traditional hydrogen peroxide formulations, which are associated with tooth sensitivity and potential enamel demineralization. This systematic review evaluates the whitening efficacy and safety profile of hydrogen peroxide-free color corrector (HPFCC) products, focusing on color change metrics, enamel and dentin integrity, and adverse effects. Methods: Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science throughout January 2025 for randomized controlled trials, observational studies, and in vitro experiments comparing HPFCC to placebo or peroxide-based agents. The data extraction covered study design, sample characteristics, intervention details, shade improvement (ΔE00 or CIE Lab), enamel/dentin mechanical properties (microhardness, roughness, elastic modulus), and incidence of sensitivity or tissue irritation. Risk of bias was assessed using the Cochrane tool for clinical studies and the QUIN tool for in vitro research. Results: Six studies (n = 20–80 samples or subjects) met the inclusion criteria. In vitro, HPFCC achieved mean ΔE00 values of 3.5 (bovine incisors; n = 80) and 2.8 (human molars; n = 20), versus up to 8.9 for carbamide peroxide (p < 0.01). Across studies, HPFCC achieved a mean ΔE00 of 2.8–3.5 surpassing the perceptibility threshold of 2.7 and approaching the clinical acceptability benchmark of 3.3. Surface microhardness increased by 12.9 ± 11.7 VHN with HPFCC (p < 0.001), and ultramicrohardness rose by 110 VHN over 56 days in prolonged use studies. No significant enamel erosion or dentin roughness changes were observed, and the sensitivity incidence remained below 3%. Conclusions: These findings derive from one clinical trial (n = 60) and five in vitro studies (n = 20–80), encompassing violet-pigment serums and gels with differing concentrations. Due to heterogeneity in designs, formulations, and outcome measures, we conducted a narrative synthesis rather than a meta-analysis. Although HPFCC ΔE00 values were lower than those of carbamide peroxide, they consistently exceeded perceptibility thresholds while maintaining enamel integrity and causing sensitivity in fewer than 3% of subjects, supporting HPFCCs as moderate but safe alternatives for young patients. Full article

Human induced pluripotent stem cell-derived cardiomyocytes (iCMs) provide a powerful platform for investigating cardiac biology. However, structural, metabolic, and electrophysiological immaturity of iCMs limits their capacity to model adult cardiomyocytes. Currently, no universally accepted criteria or protocols for effective iCMs maturation exist. This study aimed to identify practical culture conditions that promote iCMs maturation, thereby generating more physiologically relevant in vitro cardiac models. We evaluated the effects of short- and long-term culture in media supplemented with various stimulatory compounds under 2D conditions, focusing on intracellular content and localization of slow skeletal troponin I (ssTnI) and cardiac troponin I (cTnI) isoforms. Our findings demonstrate that the multicomponent metabolic maturation medium (MM-1) effectively enhances the transition toward a more mature iCM phenotype, as evidenced by increased cTnI expression and formation of cross-striated myofibrils. iCMs cultured in MM-1 more closely resemble adult cardiomyocytes and are compatible with high-resolution single-cell techniques such as electron microscopy and patch-clamp electrophysiology. This work provides a practical and scalable approach for advancing the maturation of iPSC-derived cardiac models, with applications in disease modeling and drug screening. Full article

This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry. Full article

The concept of sustainable development requires a more human-centered approach to management. Frederic Laloux’s organizational management models—green and teal organizations—offer a response to this challenge. Generational cohorts currently active in the labor market (Baby Boomers, Generation X, Generation Y, and Generation Z) differ in values, beliefs, and preferences, which may influence their acceptance of various organizational management models. This study aimed to examine how representatives of these generations perceive organizational management styles in the context of sustainable development. A qualitative study was conducted using a questionnaire completed by 263 respondents. The survey focused on teal, green, orange, amber, and red organizational models, and the results were analyzed statistically. The analysis showed that respondents’ work experiences with specific organizational management models are not dependent on generational affiliation. The highest levels of acceptance were observed for models aligned with sustainable development—green and teal organizations. Acceptance of these models is higher among younger generations, with the teal organizational model showing a statistically significant generational dependency. As Generation Z enters the labor market, some traditional management practices are becoming obsolete. The green organizational model demonstrates strong potential for current labor market conditions, while the teal organizational model shows high future implementation potential. The forecast suggests that acceptance of the teal organizational model among Generation Alpha may exceed 90%. Full article