Search Results (1,577)

Background/Objectives: Immune checkpoint blockade (ICB) has emerged as a promising therapeutic option for hepatocellular carcinoma (HCC), yet reliable biomarkers to predict clinical outcomes remain limited. Tumor-associated macrophages (TAMs) are increasingly recognized as key regulators of the tumor immune microenvironment. Methods: We interrogated a publicly available HCC single-cell RNA sequencing (scRNA-seq) dataset to characterize intratumoral immune cell subpopulations. Through unsupervised clustering and gene signature analysis, we identified a distinct subset of SPP1 (secreted phosphoprotein 1, also known as osteopontin) and BCL2A1 (Bcl-2-related protein A1) double-positive TAMs. Their abundance was quantified and associated with patient outcomes. Further independent HCC transcriptomic datasets with annotated PD1-based ICB response status were used for examination. Results: Across the discovery (GSE149614; n = 10) cohort, elevated expression of intratumoral SPP1⁺BCL2A1⁺ TAMs was identified in HCC. In the ICB datasets (GSE151530; n = 4), patients with high SPP1⁺BCL2A1⁺ TAM expression further exhibited significantly poorer responses to ICB therapy. Further, the validation cohort (GSE206325; n = 18) confirmed these findings accordingly. Notably, these TAMs were expressed thoroughly within the immunosuppressive T-cell microenvironment in non-responders but were distinctly expressed among the cytotoxic T-cell responses in responders. Conclusions: Our findings identify SPP1⁺BCL2A1⁺ TAMs as a poor prognostic biomarker in HCC patients undergoing ICB therapy. By promoting an immunosuppressive microenvironment, SPP1⁺BCL2A1⁺ TAMs, which are survival-advantaged, may represent both a predictive marker and a potential therapeutic target to enhance the efficacy of immunotherapy. Full article

As the global automobile industry undergoes rapid intelligent transformation, understanding public acceptance of autonomous driving emerges as a critical research challenge. This study adopts the Technology Acceptance Model (TAM) as its theoretical framework to conduct a comparative analysis between China and Europe, two major automotive markets and central arenas for the development of autonomous driving. It investigates how contextual factors—including policy support, infrastructure, social trust, and cultural values—influence acceptance patterns. The findings show that in China, strong policy guidance, rapid infrastructure deployment, and large-scale demonstration projects have substantially increased willingness to adopt, while the widespread use of L2-level systems has enhanced public familiarity with the technology. Nonetheless, high-profile accidents have also exposed vulnerabilities in public trust. In contrast, European consumers demonstrate a more cautious stance, emphasizing legal liability, data privacy, and ethical compliance, while simultaneously regarding autonomous driving as a means of achieving carbon reduction, traffic safety, and sustainable mobility. The results further indicate that in the European context, institutional guarantees and prior experience are decisive, with accident memory and institutional trust serving as critical moderators within TAM pathways. Full article

As immersive technologies reshape how people experience identity, emotion, and loss, virtual memorialization is emerging as an important application of virtual reality. This study examines the psychological mechanisms influencing user intentions to engage in virtual memorialization by extending the Technology Acceptance Model (TAM) to incorporate Avatar Attachment and Social Identity theories. A survey of 437 participants with diverse experiences in virtual worlds and memorial practices was analyzed using structural equation modeling. The results show that Avatar Attachment (AA) and Social Identity (SI) significantly predict perceived usefulness (PU), Perceived Role Importance (PRI), and behavioral intention (BI), with PU and PRI mediating these effects. Perceived ease of use (PEOU) directly influences both PU and BI. Furthermore, perceived human-likeness (PHL) moderates the effect of AA on PU, indicating that anthropomorphic avatars enhance the perceived emotional value of memorialization. However, PHL does not moderate the AA–PRI pathway, suggesting that the salience of avatars in mourning contexts relies more on narrative identity than visual realism. This research advances the application of TAM in immersive environments and contributes to digital thanatology by highlighting the interplay between identity, emotion, and technology. The findings provide design implications for creating user-friendly and emotionally meaningful virtual memorial platforms within emerging VR ecosystems. Full article

The aim of this study is to examine the impact of personality traits on students’ intention to accept the SAP S/4HANA business software. Grounded in the Big Five Factor (BFF) model of personality and the Technology Acceptance Model (TAM), the research analyzed the role of individual differences in students’ learning performance using this ERP system. The study was conducted on a sample of N = 418 first-year students who underwent a quasi-experimental treatment based on realistic business scenarios. The results indicate that conscientiousness emerged as a positive predictor, while agreeableness demonstrated negative predictive value in learning SAP S/4HANA, whereas neuroticism did not exhibit a significant effect. Moderation analysis revealed that both Perceived Usefulness and Actual Usage of technology moderated the relationship between conscientiousness and SAP learning performance, enhancing its predictive strength. These findings underscore the importance of individual differences in the process of SAP S/4HANA acceptance within an educational context and suggest that instructional strategies should be tailored to students’ personality traits in order to optimize learning outcomes. Full article

The integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, into higher education has introduced new opportunities and challenges for students and lecturers alike. This study investigates the psychological, ethical, and institutional factors that shape the adoption of GenAI tools in Saudi Arabian universities, drawing on an extended Technology Acceptance Model (TAM) that incorporates constructs from Self-Determination Theory (SDT) and ethical decision-making. A cross-sectional survey was administered to 578 undergraduate students and 309 university lecturers across three major institutions in Southern Saudi Arabia. Quantitative analysis using Structural Equation Modelling (SmartPLS 4) revealed that perceived usefulness, intrinsic motivation, and ethical trust significantly predicted students’ intention to use GenAI. Perceived ease of use influenced intention both directly and indirectly through usefulness, while institutional support positively shaped perceptions of GenAI’s value. Academic integrity and trust-related concerns emerged as key mediators of motivation, highlighting the ethical tensions in AI-assisted learning. Lecturer data revealed a parallel set of concerns, including fear of overreliance, diminished student effort, and erosion of assessment credibility. Although many faculty members had adapted their assessments in response to GenAI, institutional guidance was often perceived as lacking. Overall, the study offers a validated, context-sensitive model for understanding GenAI adoption in education and emphasises the importance of ethical frameworks, motivation-building, and institutional readiness. These findings offer actionable insights for policy-makers, curriculum designers, and academic leaders seeking to responsibly integrate GenAI into teaching and learning environments. Full article

This study explores a conceptual framework for integrating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems, emphasising its transformative potential in highly automated industrial environments, often referred to as ‘dark factories’, where operations are carried out with minimal human intervention using robotics, AI, and IoT. These lights-out manufacturing environments demand intelligent, autonomous systems that go beyond traditional ERP functionalities to deliver sustainable enterprise operations and supply chain management. Drawing from secondary data and a comprehensive review of existing literature, the study identifies significant gaps in current AI-ERP research and practice, namely, the absence of a unified adoption framework, limited focus on AI-specific implementation challenges, and a lack of structured post-adoption evaluation metrics. In response, this paper proposes a novel integrated conceptual framework that combines the Technology–Organisation–Environment (TOE) framework, the Technology Acceptance Model (TAM), and the Information Systems (IS) Success Model. The model incorporates industry-specific dark factors, such as AI autonomy, human–machine collaboration, operational agility, and sustainability, by optimising resource efficiency, enabling predictive maintenance, enhancing supply chain resilience, and supporting circular economy practices. The primary research aim of the current study is to provide a theoretical foundation for further empirical research on the input of AI-ERP systems into autonomous industry settings. The framework provides a robust theoretical foundation and actionable guidance for researchers, technology leaders, and policy-makers navigating the integration of AI and ERP in sustainable enterprise operations and supply chain management. Full article

The use of generative artificial intelligence (GenAI) chatbots in brands is growing exponentially, and higher education institutions are not unaware of how such tools effectively shape the attitudes and behavioral intentions of students. These chatbots are able to synthesize an enormous amount of data input and can create contextually aware, human-like conversational content that is not limited to simple scripted responses. This study examines the factors that determine chatbot brand trust in the adoption of GenAI in higher education. By extending the Technology Acceptance Model (TAM) with the construct of brand trust, the study introduces a novel contribution to the literature, offering fresh insights into how trust in GenAI chatbots is developed within the academic context. Using the convenience sampling technique, a sample of 609 students from public universities in North Central and Southwestern Nigeria was selected. The collected data were analyzed via partial least squares structural equation modelling. The results indicated that attitudes toward chatbots determine behavioral intentions and GenAI chatbot brand trust. Surprisingly, behavioral intentions do not affect GenAI chatbot brand trust. Similarly, the perceived ease of use of chatbots does not determine behavioral intention or attitudes toward GenAI chatbot adoption but rather determines perceived usefulness. Additionally, the perceived usefulness of chatbots affects behavioral intention and attitudes toward GenAI chatbot adoption. Moreover, social influence affects behavioral intention, perceived ease of use, perceived usefulness and attitudes toward GenAI chatbot adoption. The implications of the findings for higher education institutions are that homegrown GenAI chatbots that align with the principles of the institution should be developed, creating an environment that promotes a positive attitude toward these technologies. Specifically, the study recommends that policymakers and university administrators establish clear institutional guidelines for the design, deployment, and ethical use of homegrown GenAI chatbots, ensuring alignment with educational goals and safeguarding student trust. Full article

The rapid digital transformation of higher education creates opportunities and challenges, particularly in professional programmes where students must balance academic learning with preparation for operational duties, such as in medicine, engineering, and defence. While digital technologies are widely used in higher education, their sustainable integration into professional contexts, especially security and defence education, remains underexplored. This study investigates the determinants of perceived e-learning usefulness among undergraduates (cadets) at the Lithuanian Military Academy, applying an adapted technology acceptance model framework. A structured questionnaire was used to measure constructs related to distance learning effectiveness, classroom innovation, security, sustainability of digital systems, and individual learning preferences, with hypotheses tested through mediation and moderated mediation models. The results indicate that the effectiveness of distance learning is the strongest factor influencing intention to use it, supported by the roles of classroom innovation and system security. Perceived usefulness further emerges as both a direct predictor of adoption and a conditional factor shaping the impact of pedagogical and infrastructural design on acceptance. These findings extend traditional technology acceptance frameworks and provide new insights into how sustainable digital teaching can be fostered in higher professional education, where academic quality and operational readiness must be aligned. Full article

This study examines Chinese university students’ adoption of generative artificial intelligence (GenAI) tools by integrating the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and AI literacy dimensions into a hybrid framework. Survey data from 1006 students across various majors and regions are analyzed using partial least squares structural equation modeling. Notably, AI literacy (i.e., students’ AI ethics, evaluation, and awareness) positively affect their attitudes, subjective norms, and perceived behavioral control, although the influence patterns vary according to the literacy dimension. Perceived privacy risks reduce AI trust, which mediates adoption behavior. Overall, core TPB pathways are validated, with behavioral intentions significantly predicting students’ actual use. Gender and regional differences moderate the key relationships. The results of this study suggest that enhancing students’ ethical and evaluative competencies, building user trust, and addressing privacy concerns could promote generative AI integration in education. Full article

Detecting deception in emotionally grounded natural language remains a significant challenge due to the subtlety and context dependence of deceptive intent. In this work, we use a structured behavioral dataset in which participants produce truthful and deceptive statements under emotional and social constraints. To maintain label accuracy and semantic consistency, we propose a multilayer validation pipeline combining selfconsistency prompting with feedback-guided revision, implemented through the CoTAM (Chain-of-Thought Assisted Modification) method. Our results demonstrate that this framework enhances deception detection by leveraging a sentence decomposition strategy that highlights subtle emotional and strategic cues, improving interpretability for both models and human annotators. Full article

Grounded in a sociocultural theory, this study investigates how distinct interaction scenarios influence Chinese English as a Foreign Language (EFL) learners’ technology acceptance: perceived usefulness (PU) and perceived ease of use (PEU), and their willingness to communicate with AI (AI-WTC). A total of 367 university students completed a scenario-based questionnaire measuring PU, PEU, and AI-WTC across four empirically derived scenarios: advisory interaction, language skills support, academic knowledge inquiry, and factual information retrieval. Repeated-measures ANOVA with Bonferroni tests revealed significant scenario effects on all three constructs, though effect sizes were small to moderate. Factual Information Retrieval Scenario consistently received the highest ratings, whereas Academic Knowledge Inquiry and Language Skills Support Scenario scored lowest. A salient divergence emerged in complex scenarios: Advisory Interaction Scenario was rated more useful than Language Skills Support Scenario, yet both elicited equally low willingness to communicate, indicating that perceived usefulness alone may not sustain engagement under high interactional demands. These findings suggest that the effectiveness of AI as a communicative scaffold is not inherent but co-constructed through scenario-specific affordances and constraints. The study contributes a scenario-sensitive framework to TAM and WTC research, providing pedagogical guidance for designing differentiated AI-mediated language tasks. Full article

Background and Objectives: TAM receptors—Tyro3, Axl, and Mer—and their ligand Growth Arrest-Specific 6 (Gas6) represent a pleiotropic system implicated in fibrosis. Increased Gas6 and Axl expression have previously been observed in lung samples and fibroblast cultures from Idiopathic Pulmonary Fibrosis (IPF) patients. The study explored the contribution of Gas6/TAM system in fibrosis development and the impact of its pharmacological inhibition in fibroblasts. Materials and Methods: IPF fibroblasts (IPF FBs) and control human pulmonary fibroblasts (HPFs) were treated with R428 (Axl-specific inhibitor), LDC1267 (TAM inhibitor), or Nintedanib (an IPF-approved drug) to evaluate the influence of these drugs on cell proliferation, migration, and the expression of pro-inflammatory and pro-fibrotic genes. Fibroblast-to-myofibroblast differentiation was induced by TGF-β. The impact of IPF FBs and HPF on macrophage polarization was investigated through a co-culture of fibroblasts with monocyte-derived macrophages, with the further gene expression analysis of markers of the M1 (pro-inflammatory) or M2 (pro-fibrotic) polarization forms. Results: Cell proliferation was monitored in fibroblasts treated with TGF-β, the drugs, and their combination. In the presence of LDC1267 and Nintedanib, minor differences in cell confluence were detected between IPF FBs and HPFs; R428 (1 μM) seemed to have a higher inhibitory impact on IPF FBs. Regarding cell migration, the fibroblasts treated with LDC1267 exhibited slower wound closure. R428 treatment led to a relative wound closure of 76% in HPFs but only 56% in IPF FBs (60 h). R428 (1 μM) significantly reduced the expression of the pro-fibrotic markers ACTA2, COL1A1, and FN1 in HPFs and IPF FBs compared to TGF-β treatment. HPFs and IPF FBs co-cultured with monocyte-derived macrophages demonstrated a significantly increased expression of MRC1 while the expression of FN1, TNFα, and CXCL10 was moderately increased. Conclusions: These findings suggest that R428 and LDC1267 modulate the proliferation, migration, and gene expression of activated fibroblasts via TAM signaling. Fibroblast-mediated effects on macrophage polarization underscore the relevance of intercellular crosstalk in fibrotic disease. Full article

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article

This study explores older passengers’ attitudes, behavior, and evaluations of digital air travel, as well as the impact of digital technologies on this demographic, using China as a case study. The findings of this study offer valuable insights for air transport companies to develop sustainable operational strategies, increase passenger satisfaction, and potentially achieve long-term viability. A structured questionnaire survey was conducted targeting this subgroup, applying the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) as the primary analytical frameworks. While the study’s sample is skewed towards digitally literate individuals, this subgroup remains highly relevant for analyzing digital impact trends, as they are the most likely to interact with and be influenced by digital air travel tools. This study suggests that older passengers, particularly young-old passengers, in China have a generally positive attitude towards the use of digital air travel tools, with time saving, convenience, and cost saving identified as the top three perceived benefits. Over 80% of participants indicated that digital technology influenced their decision to continue choosing air travel, highlighting a link between digital engagement and sustainable passenger behavior. However, as this study is limited to digitally literate “young-old” passengers in China, the findings should be interpreted as exploratory and context-specific rather than globally generalizable. Future studies are needed with broader age groups and mixed methods to verify these results. Full article

Hepatocellular carcinoma (HCC) ranks among the most prevalent malignancies globally. Although treatment strategies have improved, the prognosis for patients with advanced HCC remains unfavorable. Tumor-associated macrophages (TAMs) play a dual role, exhibiting both anti-tumor and pro-tumor functions. In this study, we analyzed single-cell RNA sequencing data from 10 HCC tumor cores and 8 adjacent non-tumor liver tissues available in the dataset GSE149614. Using dimensionality reduction and clustering approaches, we identified six major cell types and nine distinct TAM subtypes. We employed Monocle2 for cell trajectory analysis, hdWGCNA for co-expression network analysis, and CellChat to investigate functional communication between TAMs and other components of the tumor microenvironment. Furthermore, we estimated TAM abundance in TCGA-LIHC samples using CIBERSORT and observed that the relative proportions of specific TAM subtypes were significantly correlated with patient survival. To identify TAM-related genes influencing patient outcomes, we developed a high-dimensional, gene-based transformer survival model. This model achieved superior concordance index (C-index) values across multiple datasets, including TCGA-LIHC, OEP000321, and GSE14520, outperforming other methods. Our results emphasize the heterogeneity of tumor-associated macrophages in hepatocellular carcinoma and highlight the practicality of our deep learning framework in survival analysis. Full article