Search Results (539)

As one of the primary fatal diseases globally, cancer represents a severe threat to human health because of its high incidence and fatality rates. While traditional treatments including surgery, radiation, and conventional pharmacotherapy demonstrate therapeutic effects, they commonly suffer from issues like severe side effects, high rates of relapse, and immunosuppression. The advent of immune checkpoint inhibitors and targeted drugs has undoubtedly revolutionized cancer management and improved survival; however, a significant proportion of patients still encounter obstacles such as acquired resistance, an immunosuppressive tumor microenvironment, and poor drug delivery to avascular tumor regions. Recent integration of engineered bacteria with nanomaterials has offered novel strategies for cancer immunotherapy. Engineered bacteria feature natural tumor tropism, immune-stimulating properties, and programmability, while nanomaterials are characterized by high drug payload, tunable release profiles, and versatile functionality. This article reviews the application of hybrid systems integrating engineered bacteria and nanomaterials in cancer immunotherapy, exploring their potential for drug delivery, immunomodulation, targeted treatment, and smart responsiveness. The construction of an “intelligent drug factory” through the merger of bacterial biological traits and sophisticated nanomaterial functions enables precise manipulation of the tumor microenvironment and potent immune activation, thereby establishing a novel paradigm for the precise treatment of solid tumors. However, its clinical translation faces challenges such as long-term biosafety, genetic stability, and precise spatiotemporal control. Synergistic integration with therapies such as radiotherapy, chemotherapy, and immunotherapy represents a promising direction worthy of exploration. 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

Electronic skins (E-skins) are the integration of intelligent wearable sensors that can collect human physiological, motion, or environmental parameters in real-time through flexible, sensitive materials. The performance of E-skins depends on the selection of materials to a large extent. Hydrogel materials are an excellent candidate for E-skin preparation due to their tissue-like softness and biocompatibility. However, their low electrical conductivity, weak mechanical strength, and environmental instability seriously hinder high-fidelity signal acquisition and reliable operation in practical applications. To overcome these bottlenecks, conductive composite hydrogels have emerged as a promising alternative material. The unique properties of conductive composite hydrogels, such as high stretchability, self-healing ability, and adjustable electrical conductivity, address the relevant issues of traditional hydrogels in wearable applications. This review focuses on conductive composite hydrogels for wearable E-skins. Firstly, the types, characteristics, and preparation strategies of hydrogel matrix materials are introduced. Subsequently, the performance regulation mechanisms of key conductive fillers on composite hydrogels are discussed. Then, the application progress in electrophysiological signal monitoring, human–machine interaction, and human motion monitoring is reviewed. Finally, the current challenges and future development directions of hydrogel-based E-skins are prospected, aiming to provide comprehensive material and fabrication references for the practical application of composite hydrogel in electronic skins. Full article

The advent of large-scale AI models (LAMs) marks a pivotal shift in technological innovation with profound societal implications. While demonstrating unprecedented potential to enhance human well-being by fostering efficiency and accessibility in critical domains like medicine, agriculture, and education, their rapid deployment presents a double-edged sword. This progress is accompanied by significant, often under-examined, sustainability costs, including large environmental footprints, the risk of exacerbating social inequities via algorithmic bias, and challenges to economic fairness. This paper provides a balanced and critical review of LAMs’ applications across five key livelihood domains, viewed through the lens of sustainability science. We systematically analyze the inherent trade-offs between their socio-economic benefits and their environmental and social costs. We conclude by arguing for a paradigm shift towards ‘Sustainable AI’ and provide actionable, multi-stakeholder recommendations for aligning artificial intelligence with the long-term goals of a more equitable, resilient, and environmentally responsible world. Full article

People with disabilities often face challenges like moving around independently and depending on personal caregivers for daily life activities. Traditional assistive devices are universally accepted by these communities, but they are designed with one-size-fits-all approaches that cannot adjust to individual human sizes, are not easily customized, and are made from rigid materials that do not adapt as a person’s condition changes over time. This systematic review examines the integration of smart materials, sensors, actuators, and 3D/4D printing technologies in advancing assistive devices, with a particular emphasis on mobility aids. In this work, the authors conducted a comparative analysis of traditional devices with commercially available innovative prototypes and research stage assistive devices by focusing on smart adaptable materials and sustainable additive manufacturing techniques. The results demonstrate how artificial intelligence drives smart assistive devices in hospital decentralized additive manufacturing, and policy frameworks agree with the Sustainable Development Goals, representing the future direction for adaptive assistive technology. Also, by combining 3D/4D printing and AI, it is possible to produce adaptive, affordable, and patient centered rehabilitation with feedback and can also provide predictive and preventive healthcare strategies. The successful commercialization of adaptive assistive devices relies on cost effective manufacturing techniques clinically aligned development supported by cross disciplinary collaboration to ensure scalable, sustainable, and universally accessible smart solutions. Ultimately, it paves the way for smart, sustainable, and clinically viable assistive devices that outperform conventional solutions and promote equitable access for all users. Full article

The integration of artificial intelligence (AI) into bioclimatic building design is reshaping the architecture, engineering, and construction (AEC) industry by addressing critical challenges in sustainability and efficiency. By aligning structures with local climates, bioclimatic design addresses global challenges such as energy consumption, urbanization, and climate change. Complementing these principles, AI technologies—including machine learning, digital twins, and generative algorithms—are revolutionizing the sector by optimizing processes across the entire building lifecycle, from design and construction to operation and maintenance. Amid the diverse array of AI-driven innovations, this research highlights digital twin (DT) technologies as a key to AI-driven transformation, enabling real-time monitoring, simulation, and optimization for sustainable design. Applications like façade optimization, energy flow analysis, and predictive maintenance showcase their role in adaptive architecture, while frameworks like Construction 4.0 and 5.0 promote human-centric, data-driven sustainability. By bridging AI with bioclimatic design, the findings contribute to a vision of a built environment that seamlessly aligns environmental sustainability with technological advancement and societal well-being, setting new standards for adaptive and resilient architecture. Despite the immense potential, AI and DTs face challenges like high computational demands, regulatory barriers, interoperability and skill gaps. Overcoming these challenges will be crucial for maximizing the impact on sustainable building, requiring ongoing research to ensure scalability, ethics, and accessibility. Full article

This study focuses on the evolution of the Internet Water Army on social media, identifying a novel form known as artificial-intelligence-generated-content-enhanced social bots (AESBs), and compares their structural influence with traditional social bots in the context of public opinion guidance. Based on 3 years of real-world data from Weibo, this study develops a comprehensive framework integrating bot account detection, AESB content identification, and quantitative assessments of opinion guidance. A large-scale opinion propagation network is constructed to examine the structural roles of traditional social bots and AESB across three analytical levels: the node, community, and overall network. The results reveal substantial differences between AESB and traditional social bots. Social bots play a limited guiding role but help maintain network connectivity. In contrast, AESBs produce highly consistent and human-like content that demonstrates a significant capacity to reinforce topic focus, amplify emotional homogeneity, and deepen diffusion pathways, indicating a shift toward strategic content manipulation. These results suggest that AESBs are not merely passive generators but active agents of structural opinion control, capable of combining human mimicry with machine-level efficiency. This study advances theoretical understanding of IWA manipulation mechanisms, provides a replicable methodological approach, and offers practical implications for platform governance. Full article

Background: Artificial intelligence tools are increasingly being used to assist literature reviews, but their effectiveness compared to traditional methods is not well established. This study compares Scopus AI with PubMed keyword searches on the topic of primary prepectoral breast reconstruction after radical mastectomy. Methods: On 28 May 2025, two literature searches were conducted on the topic of primary prepectoral breast reconstruction after radical mastectomy—one using Scopus AI and the other using manual keyword searches in PubMed. Both searches were limited to peer-reviewed clinical studies in English, excluding case reports and studies with fewer than 10 patients. Data extracted included study design, sample size, outcomes, and key findings. Results: The Scopus AI search retrieved 25 articles, while the traditional method identified 4. After removing duplicates, non-English texts, and non-relevant sources, 17 articles were included in the final analysis. Scopus AI provided automatic summaries, while manual review was required for the traditional method. No overlap was found between the two methods. Conclusions: AI tools like Scopus AI can enhance the speed and breadth of literature reviews, but human oversight remains essential to ensure relevance and quality. Combining AI with traditional methods may offer a more balanced and effective approach for clinical research. Full article

Background: The widespread use of the human papillomavirus (HPV) vaccine and the rise in digital technologies like artificial intelligence (AI) are causing significant changes in the paradigms surrounding cervical cancer screening. Objective: This review addresses potential future paths toward risk-based, customized screening and prevention while summarizing the available data on how vaccination and digital innovation are changing cervical cancer screening methods. Results: A shift from cytology-based screening to HPV-based primary testing with longer intervals has been supported by the notable decrease in high-grade cervical lesions brought about by HPV vaccination. However, AI and digital health tools, such as digital colposcopy, automated cytology, telemedicine, and self-sampling, have the potential to increase diagnostic access, accuracy, and efficiency, especially in low-resource environments. Implementation hurdles, data security, and algorithmic bias are major obstacles. Conclusions: Digital platforms, molecular diagnostics, and vaccination histories must all be incorporated into screening methods in order to keep up with the decline in HPV-related illness. The elimination of cervical cancer could be accelerated and equality and efficiency increased with customized, risk-based algorithms. Full article

Multidimensional force sensors are key devices capable of simultaneously perceiving and analyzing force in multiple directions (normally triaxial forces). They are designed to provide intelligent systems with skin-like precision in environmental interaction, offering high sensitivity, spatial resolution, decoupling capability, and environmental adaptability. However, the inherent complexity of tactile information coupling, combined with stringent demands for miniaturization, robustness, and low cost in practical applications, makes high-performance and reliable multidimensional sensing and decoupling a major challenge. This drives ongoing innovation in sensor structural design and sensing mechanisms. Various structural strategies have demonstrated significant advantages in improving sensor performance, simplifying decoupling algorithms, and enhancing adaptability—attributes that are essential in scenarios requiring fine physical interactions. From this perspective, this article reviews recent advances in multidimensional force sensing technology, with a focus on the operating principles and performance characteristics of sensors with different structural designs. It also highlights emerging trends toward multimodal sensing and the growing integration with system architectures and artificial intelligence, which together enable higher-level intelligence. These developments support a wide range of applications, including intelligent robotic manipulation, natural human–computer interaction, wearable health monitoring, and precision automation in agriculture and industry. Finally, the article discusses remaining challenges and future opportunities in the development of multidimensional force sensors. Full article

Contemporary discussions of AI are often framed according to generally held assumptions which have largely escaped serious critical analysis. For instance, those who promote AI tout its predictive prowess: powerful algorithms fed massive amounts of data are able to discover knowable patterns that can accurately forecast the behaviors in anything from individual movie preferences to financial markets. Armed with this type of knowledge we can then use AI, the hope goes, to be more objective in our ethical practices. And most seriously, we must extend this to the way we develop AI, not only do we want AI to function ethically, but we caution ourselves that if Artificial General Intelligence (AGI), superintelligence, or anything like the “singularity” is ever developed, it should be positively aligned with human values. Reflecting on these positions from the perspective of classical Daoism gives us reason to pause. While Daoist texts also assume there are patterns in the world which one can successfully go along with, they are not enthusiastic about the rational or knowable nature of these patterns—rather, they encourage us to appreciate them as fundamentally complex and mysterious. In this article, some Daoist attitudes are also concretely applied to ethical considerations, which cannot easily be controlled or known, much less put into code. Inspired by Daoist texts, we might cultivate an attitude less filled with hubris than humility, where we are allowed more space from which we can reflect on how we think about AI. Many of the most pressing issues associated with AI could, in fact, be significantly alleviated simply by shifting the way we think about, use, and develop these technologies. Full article

Student trust plays a pivotal role in shaping the future integration of artificial intelligence (AI) in higher education. This study investigates how AI Facilitating Conditions (FCs), Performance Expectancy (PE), and task type influence students’ System-like Trust (AST) and Human-like Trust (AHT) in AI and further examines the mediating role of human-like trust in fostering the willingness to continue AI-assisted learning. Drawing on valid data collected from 466 Chinese university students, we employed partial least squares structural equation modeling (PLS-SEM) in combination with artificial neural networks (ANN) to test the hypothesized relationships, mediating mechanisms and the relative importance of influencing factors. The findings indicate that AI facilitating conditions significantly enhance both system-like trust and usage intention; performance expectancy exerts a positive effect on both forms of trust, with particularly strong effects observed in subjective tasks. Moreover, system-like trust positively promotes human-like trust, and together, these dimensions jointly strengthen students’ intention to engage in AI-assisted learning. Results from the ANN analysis further highlight that performance expectancy, system-like trust, and facilitating conditions are the primary determinants of system-like trust, human-like trust, and usage intention, respectively. This study extends the application of interpersonal trust theory to the AI domain and offers theoretical insights for fostering more positive and effective patterns of AI adoption in higher education. Full article

The Internet of Things (IoT) has enabled a vast network of devices to communicate over the Internet. However, the fragmentation of IoT systems continues to hinder seamless data sharing and coordinated management across platforms.However, there is currently no actual search engine for IoT data. Existing IoT search engines are considered device discovery tools, providing only metadata about devices rather than enabling access to IoT application data. While efforts such as IoTCrawler have striven to support IoT application data, they have largely failed due to the fragmentation of IoT systems and the heterogeneity of IoT data.To address this, we recently introduced SensorsConnect—a unified framework designed to facilitate interoperable content and sensor data sharing among collaborative IoT systems, inspired by how the World Wide Web (WWW) enabled shared and accessible information spaces for humans. This paper presents the IoT Agentic Search Engine (IoTASE), a real-time semantic search engine tailored specifically for IoT environments. IoTASE leverages LLMs and Retrieval-Augmented Generation (RAG) techniques to address the challenges of navigating and searching vast, heterogeneous streams of real-time IoT data. This approach enables the system to process complex natural language queries and return accurate, contextually relevant results in real time. To evaluate its effectiveness, we implemented a hypothetical deployment in the Toronto region, simulating a realistic urban environment using a dataset composed of 500 services and over 37,000 IoT-like data entries. Our evaluation shows that IoT-ASE achieved 92% accuracy in retrieving intent-aligned services and consistently generated concise, relevant, and preference-aware responses, outperforming generalized outputs produced by systems such as Gemini. These results underscore the potential of IoT-ASE to make real-time IoT data both accessible and actionable, supporting intelligent decision-making across diverse application domains. Full article

Potholes threaten public safety and automated vehicles (AVs) safe navigation by increasing accident risks and maintenance costs. Traditional pavement inspection methods, which rely on human assessment, are inefficient for rapid pothole detection and reporting due to potholes’ random and sudden occurring. Advancements in Artificial Intelligence (AI) now enable automated pothole detection using image-based object recognition, providing innovative solutions to enhance road safety and assist agencies in prioritizing maintenance. This paper proposes a novel approach that evaluates the integration of 3 state-of-the-art AI models (YOLOv8n, YOLOv11n, and YOLOv12n) with an ADAS-like camera, GNSS receiver, and Robot Operating System (ROS) to detect potholes in uncontrolled real-life scenarios, including different weather/lighting conditions and different route types, and generate ready-to-use data in a real-time manner. Tested on real-world road data, the algorithm achieved an average precision of 84% and 84% in recall, demonstrating its effectiveness, stable, and high performance for real-life applications. The results highlight its potential to improve road safety, allow vehicles to detect potholes through ADAS, support infrastructure maintenance, and optimize resource allocation. Full article

The transition toward human-centered innovation models, as reflected in Industry 5.0 frameworks, calls for the integration of sustainability and digital ethics into higher education. Despite the growing international discourse, little is known about how systematically these dimensions are embedded in curricula in Central and Eastern Europe. This study addresses this gap by analyzing the extent to which Polish higher education institutions (HEIs) incorporate elements of sustainable development and digital ethics into their educational programs. Drawing on survey data from 187 Polish HEIs, we employed Cramér’s V and chi-square tests to explore bivariate associations, multiple correspondence analysis (MCA) to examine patterns among categorical variables, and ordinal logistic regression to identify key predictors of curricular integration. The results reveal that institutions offering Industry 5.0-oriented specializations and maintaining regular cooperation with enterprises are significantly more likely to achieve full integration of sustainability and ethics, whereas many others remain at a stage of only partial adoption. These findings underscore the uneven progress of curricular reforms and highlight the importance of institutional capacity and external partnerships. This study contributes to theory by extending institutional and resource-based perspectives to curriculum innovation, and it contributes to practice by recommending targeted accreditation standards, cross-sector partnerships, and interdisciplinary modules (e.g., “Artificial Intelligence and Society,” “Sustainable Technology Futures”) as concrete mechanisms for embedding ethical and sustainable innovation competencies in higher education. Implications for policy, institutional practice, and future research are discussed. Full article