Search Results (523)

Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education. Methods: This simulation-based study explored the application of an AI chatbot in an emergency planning course. The AIMS framework was developed and applied, consisting of six prompt-level domains (Phase 1) and eight performance criteria (Phase 2). These domains were selected based on current best practices in instructional design, simulation fidelity, and emerging AI evaluation literature. To assess the chatbots educational utility, the study employed a scoring rubric for each phase and incorporated a structured feedback loop to refine both prompt design and chatbox interaction. To demonstrate the framework’s practical application, the researchers configured an AI tool referred to in this study as “Eval-Bot v1”, built using OpenAI’s GPT-4.0, to apply Phase 1 scoring criteria to a real simulation prompt. Insights from this analysis were then used to anticipate Phase 2 performance and identify areas for improvement. Participants (three individuals)—all experienced healthcare educators and advanced practice nurses with expertise in clinical decision-making and simulation-based teaching—reviewed the prompt and Eval-Bot’s score to triangulate findings. Results: Simulated evaluations revealed clear strengths in the prompt alignment with course objectives and its capacity to foster interactive learning. Participants noted that the AI chatbot supported engagement and maintained appropriate pacing, particularly in scenarios involving emergency planning decision-making. However, challenges emerged in areas related to personalization and inclusivity. While the chatbot responded consistently to general queries, it struggled to adapt tone, complexity and content to reflect diverse learner needs or cultural nuances. To support replication and refinement, a sample scoring rubric and simulation prompt template are provided. When evaluated using the Eval-Bot tool, moderate concerns were flagged regarding safety prompts and inclusive language, particularly in how the chatbot navigated sensitive decision points. These gaps were linked to predicted performance issues in Phase 2 domains such as dialog control, equity, and user reassurance. Based on these findings, revised prompt strategies were developed to improve contextual sensitivity, promote inclusivity, and strengthen ethical guidance within chatbot-led simulations. Conclusions: The AIMS evaluation framework provides a practical and replicable approach for evaluating the use of AI chatbots in simulation-based education. By offering structured criteria for both prompt design and chatbot performance, the model supports instructional designers, simulation specialists, and developers in identifying areas of strength and improvement. The findings underscore the importance of intentional design, safety monitoring, and inclusive language when integrating AI into nursing and health education. As AI tools become more embedded in learning environments, this framework offers a thoughtful starting point for ensuring they are applied ethically, effectively, and with learner diversity in mind. Full article

Our temptation to personify machine intelligence is not unexpected. As a child we named our dolls and took our Teddy Bear to bed with us. Today we ask death bots to comfort us with post-mortem conversation. All the while we know this to be pretend. Yet we must ask: if Artificial General Intelligence (AGI) or even Artificial Super-Intelligence (ASI) become available, will our game of pretend continue? Or will intelligent robots actually become selves deserving of dignity that hitherto could be ascribed only to human persons? If government-imposed guardrails shut the door on development of AGI and ASI in order to preserve human safety and even dignity, we might never learn whether AGI or ASI could develop selfhood, personhood, virtue, or religious sensibilities. As we approach the future, can we live without knowing whether AGI or ASI would be capable of developing selfhood and commanding dignity? Full article

The robotic exploration of space began only five decades ago, and yet in the intervening years, a wide and diverse ecosystem of robotic explorers has been developed for this purpose. Such devices have greatly benefited from miniaturization trends and the increased availability of high-quality commercial off-the-shelf (COTS) components. This review outlines the specific taxonomic distinction between planetary surface rovers and other robotic space exploration vehicles, such as orbiters and landers. Additionally, arguments are made to standardize the classification of planetary rovers by mass into categories similar to those used for orbital satellites. Discussions about recent noteworthy trends toward the miniaturization of planetary rovers are also included, as well as a compilation of previous planetary rovers. This analysis compiles relevant metrics such as the mass, the distance traveled, and the locomotion or actuation technique for previous planetary rovers. Additional details are also examined about archetypal rovers that were chosen as representatives of specific small-scale rover classes. Finally, potential future trends for miniature planetary surface rovers are examined by way of comparison to similar miniaturized orbital robotic explorers known as CubeSats. Based on the existing relationship between CubeSats and their Earth-based simulation equivalents, CanSats, the importance of a potential Earth-based analog for miniature rovers is identified. This research establishes such a device, coining the new term ‘CanBot’ to refer to pathfinding systems that are deployed terrestrially to help develop future planetary surface exploration robots. Establishing this explicit genre of robotic vehicle is intended to provide a unified means for categorizing and encouraging the development of future small-scale rovers. Full article

In a European context, facing pressure to digitalize public administration, the integration of artificial intelligence (AI) at the local level remains a deeply uneven and empirically poorly understood process. This study investigates the degree of adoption of artificial intelligence (AI) in local public administrations in Germany, exploring territorial disparities and institutional factors influencing this transition. Based on a national sample of 347 municipalities, this research proposes a composite AI adoption index, built by integrating six relevant indicators (including the use of conversational bots and the automation of internal and decision-making processes). In the simulations, local administration profiles were differentiated according to factors such as IT staff (with a weight of 30%), the degree of urbanization (25%), and participation in digital networks (20%), reflecting significant structural variations between regions. The analysis model used is a multilevel one, which highlights the combined influences of local and regional factors. The results indicate a clear stratification of digital innovation capacity, with significant differences between eastern and western Germany, as well as between urban and rural environments. The study contributes to the specialized literature by developing a replicable analytical tool and provides public policy recommendations for reducing interregional digital divides. Full article

The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in a manner that is both environmentally sustainable and economically advantageous. Weed classification for autonomous agricultural robots is a challenging task for a single-camera-based system due to noise, vibration, and occlusion. To address this issue, we present a multi-camera-based system with decision-level sensor fusion to improve the limitations of a single-camera-based system in this paper. This study involves the utilization of a convolutional neural network (CNN) that was pre-trained on the ImageNet dataset. The CNN subsequently underwent re-training using a limited weed dataset to facilitate the classification of three distinct weed species: Xanthium strumarium (Common Cocklebur), Amaranthus retroflexus (Redroot Pigweed), and Ambrosia trifida (Giant Ragweed). These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. But the accuracy of CNN-based classification from video feed from a single camera was found to deteriorate due to noise, vibration, and partial occlusion of weeds. Test results from a single-camera video feed show that weed classification accuracy is not always accurate for the spray system of an agricultural robot (AgBot). To improve the accuracy of the weed classification system and to overcome the shortcomings of single-sensor-based classification from CNN, an improved Dempster–Shafer (DS)-based decision-level multi-sensor fusion algorithm was developed and implemented. The proposed algorithm offers improvement on the CNN-based weed classification when the weed is partially occluded. This algorithm can also detect if a sensor is faulty within an array of sensors and improves the overall classification accuracy by penalizing the evidence from a faulty sensor. Overall, the proposed fusion algorithm showed robust results in challenging scenarios, overcoming the limitations of a single-sensor-based system. Full article

Cobboldia elephantis (Oestridae: Gasterophilinae) is an obligate parasite of the alimentary tract of the Asian elephant, causing gastric myiasis. Current knowledge of its first instar larval morphology has been limited to observations under light microscopy, significantly constraining our understanding of morphological evolution within the genus Cobboldia and the broader subfamily Gasterophilinae. In this study, we provided ultrastructural and three-dimensional characterizations of C. elephantis using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) based on newly hatched first instar larvae. Features of the first instar larva of C. elephantis, which are either unique or shared with C. loxodontis Brauer, 1896, include (i) three pairs of mouthhooks, (ii) posterior spiracles bearing peristigmatic tufts, (iii) anal division with two spine-clusters and a row of elongated spines, and (iv) a planed, button-like sensilla and a sensillum placodeum. We also compared the first instar larval morphology of C. elephantis with that of Cobboldia loxodontis, Gyrostigma rhinocerontis (Owen, 1830), Gasterophilus pecorum (Fabricius, 1794), Portschinskia magnifica Pleske, 1926, and Oestrus ovis Linnaeus, 1758. Species of Gasterophilinae share several unique features of the first instar larva, including (i) an anal division composed of three subdivisions, (ii) spiracular slits on the posterior spiracles, and (iii) conserved positioning of thoracic sensilla. These findings fill a key gap in our knowledge of C. elephantis larval morphology and suggest that these distinctive structures play a role in adaptation to its parasitic lifestyle. Full article

Laser-induced breakdown spectroscopy (LIBS) has played a critical role in Mars exploration missions, substantially contributing to the geochemical analysis of Martian surface substances. However, the complex nonlinearity of LIBS processes can considerably limit the quantification accuracy of conventional LIBS chemometric methods. Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. The hidden layer activation functions are key to ANN model performance, yet common activation functions usually suffer from problems such as gradient vanishing (e.g., Sigmoid and Tanh) and dying neurons (e.g., ReLU). In this study, we propose a novel LIBS quantification method, named the Bayesian optimization-based tunable Softplus backpropagation neural network (BOTS-BPNN). Based on a dataset comprising 1800 LIBS spectra collected by a laboratory duplicate of the MarSCoDe instrument onboard the Zhurong Mars rover, we have revealed that a BPNN model adopting a tunable Softplus activation function can achieve higher prediction accuracy than BPNN models adopting other common activation functions if the tunable Softplus parameter β is properly selected. Moreover, the way to find the proper β value has also been investigated. We demonstrate that the Bayesian optimization method surpasses the traditional grid search method regarding both performance and efficiency. The BOTS-BPNN model also shows superior performance over other common machine learning models like random forest (RF). This work indicates the potential of BOTS-BPNN as an effective chemometric method for analyzing Mars in situ LIBS data and sheds light on the use of chemometrics for data analysis in future planetary explorations. Full article

Smart cities necessitate ultra-secure and scalable communication frameworks to manage billions of interconnected IoT devices, particularly in the face of the emerging quantum computing threats. This paper proposes the QESIF, a novel Quantum-Enhanced Secure IoT Framework that integrates Quantum Key Distribution (QKD) with classical IoT infrastructures via a hybrid protocol stack and a quantum-aware intrusion detection system (Q-IDS). The QESIF achieves high resilience against eavesdropping by monitoring quantum bit error rate (QBER) and leveraging entropy-weighted key generation. The simulation results, conducted using datasets TON IoT, Edge-IIoTset, and Bot-IoT, demonstrate the effectiveness of the QESIF. The framework records an average QBER of 0.0103 under clean channels and discards over 95% of the compromised keys in adversarial settings. It achieves Attack Detection Rates (ADRs) of 98.1%, 98.7%, and 98.3% across the three datasets, outperforming the baselines by 4–9%. Moreover, the QESIF delivers the lowest average latency of 20.3 ms and the highest throughput of 868 kbit/s in clean scenarios while maintaining energy efficiency with 13.4 mJ per session. Full article

Construction projects still face persistent barriers to adopting whole life costing (WLC), such as fragmented data, a lack of standardization, and inadequate tools. This study addresses these limitations by proposing a core ontology for WLC, developed using an ontology design science research methodology. The ontology formalizes WLC knowledge based on ISO 15686-5 and incorporates professional insights from surveys and expert focus groups. Implemented in web ontology language (OWL), it models cost categories, temporal aspects, and discounting logic in a machine-interpretable format. The ontology’s interoperability and extensibility are validated through its integration with the building topology ontology (BOT). Results show that the ontology effectively supports cost breakdown, time-based projections, and calculation of discounted values, offering a reusable structure for different project contexts. Practical validation was conducted using SQWRL queries and Python scripts for cost computation. The solution enables structured data integration and can support decision-making throughout the building life cycle. This work lays the foundation for future semantic web applications such as knowledge graphs, bridging the current technological gap and facilitating more informed and collaborative use of WLC in construction. Full article

Artificial intelligence (AI)-powered mental health chatbots have evolved quickly as scalable means for psychological support, bringing novel solutions through natural language processing (NLP), mobile accessibility, and generative AI. This systematic literature review (SLR), following PRISMA 2020 guidelines, collates evidence from 25 published, peer-reviewed studies between 2020 and 2025 and reviews therapeutic techniques, cultural adaptation, technical design, system assessment, and ethics. Studies were extracted from seven academic databases, screened against specific inclusion criteria, and thematically analyzed. Cognitive behavioral therapy (CBT) was the most common therapeutic model, featured in 15 systems, frequently being used jointly with journaling, mindfulness, and behavioral activation, followed by emotion-based approaches, which were featured in seven systems. Innovative techniques like GPT-based emotional processing, multimodal interaction (e.g., AR/VR), and LSTM-SVM classification models (greater than 94% accuracy) showed increased conversation flexibility but missed long-term clinical validation. Cultural adaptability was varied, and effective localization was seen in systems like XiaoE, okBot, and Luda Lee, while Western-oriented systems had restricted contextual adaptability. Accessibility and inclusivity are still major challenges, especially within low-resource settings, since digital literacy, support for multiple languages, and infrastructure deficits are still challenges. Ethical aspects—data privacy, explainability, and crisis plans—were under-evidenced for most deployments. This review is different from previous ones since it focuses on cultural adaptability, ethics, and hybrid public health incorporation and proposes a comprehensive approach for deploying AI mental health chatbots safely, effectively, and inclusively. Central to this review, symmetry is emphasized as a fundamental idea incorporated into frameworks for cultural adaptation, decision-making processes, and therapeutic structures. In particular, symmetry ensures equal cultural responsiveness, balanced user–chatbot interactions, and ethically aligned AI systems, all of which enhance the efficacy and dependability of mental health services. Recognizing these benefits, the review further underscores the necessity for more rigorous academic research into the development, deployment, and evaluation of mental health chatbots and apps, particularly to address cultural sensitivity, ethical accountability, and long-term clinical outcomes. Full article

The proliferation of malicious social bots poses severe threats to cybersecurity and social media information ecosystems. Existing detection methods often overlook the semantic value and emotional cues conveyed by emojis in user-generated tweets. To address this gap, we propose ESA-BotRGCN, an emoji-driven multi-modal detection framework that integrates semantic enhancement, sentiment analysis, and multi-dimensional feature modeling. Specifically, we first establish emoji–text mapping relationships using the Emoji Library, leverage GPT-4 to improve textual coherence, and generate tweet embeddings via RoBERTa. Subsequently, seven sentiment-based features are extracted to quantify statistical disparities in emotional expression patterns between bot and human accounts. An attention gating mechanism is further designed to dynamically fuse these sentiment features with user description, tweet content, numerical attributes, and categorical features. Finally, a Relational Graph Convolutional Network (RGCN) is employed to model heterogeneous social topology for robust bot detection. Experimental results on the TwiBot-20 benchmark dataset demonstrate that our method achieves a superior accuracy of 87.46%, significantly outperforming baseline models and validating the effectiveness of emoji-driven semantic and sentiment enhancement strategies. Full article

The purpose of this study is to compare the accuracy and readability of Coronavirus Disease 2019 (COVID-19)-prevention and control knowledge texts generated by four current generative artificial intelligence (AI) models—two international models (ChatGPT and Gemini) and two domestic models (Kimi and Ernie Bot)—and to evaluate the other performance characteristics of texts generated by domestic and international models. This paper uses the questions and answers in the COVID-19 prevention guidelines issued by the U.S. Centers for Disease Control and Prevention (CDC) as the evaluation criteria. The accuracy, readability, and comprehensibility of the texts generated by each model are scored against the CDC standards. Then the neural network model in the intelligent algorithms is used to identify the factors that affect readability. Then the medical topics of the generated text are analyzed using text analysis technology. Finally, a questionnaire-based manual scoring approach was used to evaluate the AI-generated texts, which was then compared to automated machine scoring. Accuracy: domestic models have higher textual accuracy, while international models have higher reliability. Readability: domestic models produced more fluent and publicly accessible language; international models generated more standardized and formally structured texts with greater consistency. Comprehensibility: domestic models offered superior readability, while international models were more stable in output. Readability factors: the average words per sentence (AWPS) emerged as the most significant factor influencing readability across all models. Topic analysis: ChatGPT emphasized epidemiological knowledge; Gemini focused on general medical and health topics; Kimi provided more multidisciplinary content; and Ernie Bot concentrated on clinical medicine. From the empirical results, it can be found that the manual and machine scoring are highly consistent in the indicators SimHash and FKGL, which proves the effectiveness of the evaluation method proposed in this paper. Conclusion: Texts generated by domestic models are more accessible and better suited for public education, clinical communication, and health consultations. In contrast, the international model has a higher accuracy in generating expertise, especially in epidemiological studies and assessing knowledge literature on disease severity. The inclusion of manual evaluations confirms the reliability of the proposed assessment framework. It is therefore recommended that future AI-generated knowledge systems for infectious disease control balance professional rigor with public comprehensibility, in order to provide reliable and accessible reference materials during major infectious disease outbreaks. Full article

Advancements in Internet of Things (IoT) technologies have had a profound impact on interconnected devices, leading to exponentially growing networks of billions of intelligent devices. However, this growth has exposed Internet of Things (IoT) systems to cybersecurity vulnerabilities. These vulnerabilities are primarily caused by the inherent limitations of these devices, such as finite battery resources and the requirement for ubiquitous connectivity. The rapid evolution of deep learning (DL) technologies has led to their widespread use in critical application domains, thereby highlighting the need to integrate DL methodologies to improve IoT security systems beyond the basic secure communication protocols. This is essential for creating intelligent security frameworks that can effectively address the increasingly complex cybersecurity threats faced by IoT networks. This study proposes a hybrid methodology that combines fractional discrete Tchebichef moment analysis with deep learning for the prevention of IoT attacks. The effectiveness of our proposed technique for detecting IoT threats was evaluated using the UNSW-NB15 and Bot-IoT datasets, featuring illustrative cases of common IoT attack scenarios, such as DDoS, identity spoofing, network reconnaissance, and unauthorized data access. The empirical results validate the superior classification capabilities of the proposed methodology in IoT cybersecurity threat assessments compared with existing solutions. This study leveraged the synergistic integration of discrete Tchebichef moments and deep convolutional networks to facilitate comprehensive attack detection and prevention in IoT ecosystems. Full article

This study introduces a novel method that merges propagation-based transfer learning with word embeddings for rumor detection. This approach aims to use data from languages with abundant resources to enhance performance in languages with limited availability of annotated corpora in this task. Furthermore, we augment our rumor detection framework with two supplementary tasks—stance classification and bot detection—to reinforce the primary task of rumor detection. Utilizing our proposed multi-task system, which incorporates cascade learning models, we generate several pre-trained models that are subsequently fine-tuned for rumor detection in English and Spanish. The results show improvements over the baselines, thus empirically validating the efficacy of our proposed approach. A Macro-F1 of 0.783 is achieved for the Spanish language, and a Macro-F1 of 0.945 is achieved for the English language. Full article

This study presents a system for automatic cookie collection using bots that simulate user browsing behavior. Five bots were deployed, one for each of the most commonly used university browsers, enabling comprehensive data collection across multiple platforms. The infrastructure included an Ubuntu server with PiHole and Tshark services, facilitating cookie classification and association with third-party advertising and tracking networks. The BotSoul algorithm automated navigation, analyzing 440,000 URLs over 10.9 days with uninterrupted bot operation. The collected data established relationships between visited domains, generated cookies, and captured traffic, providing a solid foundation for security and privacy analysis. Machine learning models were developed to classify suspicious web domains and predict their vulnerability to XSS attacks. Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. The results highlight the system’s effectiveness in detecting security threats and improving navigation through adaptive recommendations. This research marks a significant advancement in web security and privacy, laying the groundwork for future improvements in protecting user information. Full article