MIX: An Image Generation System Using Image Prompts for Industrial Design

The advent of Generative Artificial Intelligence (Generative AI or GAI) marks a significant inflection point in AI development. Long viewed as the epitome of reasoning and logic, Generative AI incorporates programming rules that are normative. However, it also has a descriptive component based on its programmers' subjective preferences and any discrepancies in the underlying data. Generative AI generates both truth and falsehood, supports both ethical and unethical decisions, and is neither transparent nor accountable. These factors pose clear risks to optimal decision-making in complex health services such as health policy and health regulation. It is important to examine how Generative AI makes decisions both from a rational, normative perspective and from a descriptive point of view to ensure an ethical approach to Generative AI design, engineering, and use. The objective is to provide a rapid review that identifies and maps attributes reported in the literature that influence Generative AI decision-making in complex health services. This review provides a clear, reproducible methodology that is reported in accordance with a recognised framework and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 standards adapted for a rapid review. Inclusion and exclusion criteria were developed, and a database search was undertaken within four search systems: ProQuest, Scopus, Web of Science, and Google Scholar. The results include articles published in 2023 and early 2024. A total of 1,550articles were identified. After removing duplicates, 1,532articles remained. Of these, 1,511 articles were excluded based on the selection criteria and a total of 21 articles were selected for analysis. Learning, understanding, and bias were the most frequently mentioned Generative AI attributes. Generative AI brings the promise of advanced automation, but carries significant risk. Learning and pattern recognition are helpful, but the lack of a moral compass, empathy, consideration for privacy, and a propensity for bias and hallucination are detrimental to good decision-making. The results suggest that there is, perhaps, more work to be done before Generative AI can be applied to complex health services.

The paramount challenge in precision oncology lies in further improving quality of life and response rates for individual patients. Efforts toward these goals are steadily expanding the scope of clinical implementation, despite ongoing challenges such as standardization, cost-effectiveness, and data harmonization. Building upon this maturing foundation, generative AI—which has evolved dramatically in recent years—is particularly valuable at this stage of advancing efficiency and adoption as an auxiliary technology linking literature, guidelines, trial protocols, and patient data. Specifically, through mutation interpretation, trial eligibility matching, and tumor board support, it is expected to contribute to advancing standardization, improving cost-effectiveness, accelerating data harmonization, and further accelerating human-centered decision-making. Accordingly, this review surveys the development history of generative AI and its current healthcare applications, organizing its implementation potential for precision oncology along three axes: (1) generative AI–based interpretation of genetic mutations and estimation of their pathological significance; (2) generative AI–driven verification of clinical trial eligibility; and (3) multimodal foundation models for imaging and pathology that compute “tumor phenotypes” using real-world data, contributing to report drafting and molecular surrogate estimation. In response, we propose a strategy centered on retrieval-augmented generation (RAG) and human-in-the-loop (HITL) workflows, encompassing data preparation based on OMOP, mCODE, and FHIR; multicenter prospective evaluation; auditable logs and governance aligned with Good Manufacturing Practice (GMP) and the EU AI Act; and a synthetic data strategy including differential privacy. Ultimately, this approach validates value through real-world outcomes and charts a path toward “learning oncology,” accelerating patient-centered decision-making and clinical trial development.

Heating, Ventilation, and Air Conditioning (HVAC) systems are major energy consumers in buildings, challenging the balance between efficiency and occupant comfort. While prior research explored generative AI for HVAC control in simulations, real-world validation remained scarce. This study addresses this gap by designing, deploying, and evaluating “Office-in-the-Loop,” a novel cyber-physical system leveraging generative AI within an operational office setting. Capitalizing on multimodal foundation models and Agentic AI, our system integrates real-time environmental sensor data (temperature, occupancy, etc.), occupants’ subjective thermal comfort feedback, and historical context as input prompts for the generative AI to dynamically predict optimal HVAC temperature setpoints. Extensive real-world experiments demonstrate significant energy savings (up to 47.92%) while simultaneously improving comfort (up to 26.36%) compared to baseline operation. Regression analysis confirmed the robustness of our approach against confounding variables like outdoor conditions and occupancy levels. Furthermore, we introduce Data-Driven Reasoning using Agentic AI, finding that prompting the AI for data-grounded rationales significantly enhances prediction stability and enables the inference of system dynamics and cost functions, bypassing the need for traditional reinforcement learning paradigms. This work bridges simulation and reality, showcasing generative AI’s potential for efficient, comfortable building environments and indicating future scalability to large systems like data centers.

This article analyzes music industry discourses about generative AI to understand competing and conflicting views across the industrial field. Our analysis mobilizes primary data from ethnographic fieldwork collected at music trade conferences between 2023 and 2024 and secondary data from trade press, corporate statements, reports published by governments, unions, and trade bodies. Our analysis illuminates tensions and contradictions among protectionist, liberalizing, and conciliatory views toward generative AI. Some corporate actors and public stakeholders advocate for protectionist business policies and “responsible” AI development that foregrounds potential harms of AI. Other corporate actors offer more liberalizing views, encouraging investment, experimentation, and adoption of generative AI systems to cut costs and increase profits. We also note conciliatory positions, mainly from musicians’ unions and trade bodies, trying to find compromises between these two poles. We argue that these contradictions reveal a fundamentally misunderstood notion of universal AI ethics in the music industry.

Generative AI (Gen-AI) revolutionizes design by leveraging machine learning to generate innovative solutions. It analyzes data to identify patterns, creates tailored designs, enhances creativity, and allows designers to explore complex possibilities for diverse industries. This study uses a Gen-AI design generation process to develop an urban landscape fumigation service robot. This study proposes a machine-learned multimodal and feedback-based variational autoencoder (MMF-VAE) model that incorporates a readily available spraying robot dataset and includes design considerations from various research efforts to ensure real-time deployability. The objective is to demonstrate the effectiveness of data-driven and feedback-based approaches in generating design specifications for a fumigation robot with the targeted requirements of autonomous navigation, precision spraying, and an extended runtime. The design generation process comprises three stages: (1) parameter fixation, emphasizing functionality-based and aesthetic-based specifications; (2) design specification generation using the proposed MMF-VAE model with and without a spraying robot dataset; and (3) robot development based on the generated specifications. A comparative analysis evaluated the impact of the dataset-driven design generation. The design generated with the dataset proved more feasible and optimized for real-world deployment with the integration of multimodal inputs and iterative feedback refinement. A real-time prototype was then constructed using the model’s parametric constraints and tested in actual fumigation scenarios to validate operational viability. This study highlights the transformative potential of Gen-AI in robotic design workflows.

Background Handwritten home-based vaccination records (HBRs) are a vital source of immunization data, yet manual transcription in household surveys is time-consuming, error-prone, and resource-intensive. Recent advances in multimodal generative AI models offer a potential pathway to partially automate this task under certain conditions, improving efficiency while maintaining data quality. Methods We evaluated the performance of generative multimodal AI models, specifically OpenAI’s GPT-4o, in transcribing handwritten vaccination cards from a 2022 survey targeting children aged 12 - 35 months in The Gambia. Using a curated dataset of 335 cards (6,700 vaccination entries), we developed a gold-standard benchmark from three human transcribers (all of them in The Gambia) and assessed AI model performance across transcription accuracy, vaccination coverage estimates, timeliness, and missed opportunities for simultaneous vaccination (MOSV). We also tested a confidence-based segmentation approach to identify high-confidence transcriptions suitable for automation versus low-confidence entries requiring human review. Results The fine-tuned GPT-4o model achieved 79% accuracy for exact date transcription and reached human-level performance (94% accuracy) on 69% of entries classified by the AI as high-confidence. Coverage and timeliness estimates from high-confidence transcriptions were 98.0% and 91.6% accurate, respectively, compared to 98.8% and 95.6% from human transcribers. Date errors by AI and humans differ systematically, with AI showing fewer year-shift errors. Conclusion Multimodal AI models show strong potential for automating HBR transcription in immunization coverage surveys, at least in a setting like The Gambia. When paired with confidence-based filtering, these models achieve human-level performance on coverage and timeliness estimates—the key metrics used in programmatic decision-making—across a large subset of records. This enables substantial gains in efficiency while preserving data quality. Further research should evaluate generalizability across diverse card formats, languages, and contexts to support integration into real-world immunization programs and health monitoring activities.

Embedded media systems have developed to sophisticated cyber-physical nexuses that comprise heterogeneous hardware, software subsystems, real-time data-processing pipeline, network communications, and artificial intelligence capabilities. Such architectural complexity raises the risk of program management especially in the environment of co-design of hardware-software, where a time constraint is present, supply-chain dependencies exist, and the firmware is linked in an iterative cycle of integration. The nonlinear and multimodal risk indicators in such systems are difficult to capture traditional qualitative risk matrices and the static probabilistic models. The paper analyses AI-driven risk prediction models such as probabilistic graphical models, supervised learning, ensemble, deep neural network, graph-based, and transformer-based generative approaches. A quantitative meta-analysis suggests that transformer-based and multimodal models are better than classical statistical models with the pooled AUC values around 0.89 in predicting defects and schedules. Nonetheless, the empirical validation under embedded media program settings is under-validated and structured metrics, unstructured documentation and dependency graphs are seldom employed within a single framework. A risk prediction framework of embedded media programs based on GenAI is suggested that integrates transformer-based contextual modelling, graph neural network dependency propagation, ensemble measures, reinforcement-learning-based mitigation optimization, and explainable AI attribution. optimization of mitigation by reinforcement learning and explainable AI attribution. Research gaps comprise building of domain specific benchmark data, limited combination of explainable and causal models, susceptibility to distributional changes and lack of standard validation procedures. The results indicate that generative AI has a potential to transform embedded program management to proactive and not reactive risk governance in line with industry reliability and compliance need.

AI models, particularly generative AI and large language models, reshape digital information ecosystems by curating and amplifying content through user engagement metrics. Despite their capacity to reduce bias and promote inclusivity, these models simultaneously amplify cognitive biases, entrench filter bubbles, and spread misinformation. The intensification of human–machine interaction and hyper-industrialization complicates this further, as large language models increasingly mediate how information is produced and consumed. Socio-technical agency describes how AI systems co-construct human behavior and societal norms through their design, yet their effects remain understudied in regions with limited technological infrastructure. This paper investigates AI’s influence on information dissemination, cognitive biases, and user agency across digital media environments in key regions of the Global South. Drawing on qualitative interviews and a survey of 580 media technology users in South Africa, Indonesia, India, the Philippines, and Brazil it examines how generative AI affects emotional engagement, exposure to content, and perceptions of digital truth. Framed by media ecology theory, the study evaluates AI as a cognitive extension that can both reinforce and challenge digital biases. The study proposes strategies for using generative AI to support information integrity while addressing the risks of polarization and exclusion. By centering perspectives from regions in the Global South, it contributes to more equitable discourse on AI governance, advocating regulatory and design solutions responsive to diverse media ecologies.

RACHAEL POFFENBERGER, CHAZ CORNEJO, Dept of Computer Science, Mathematics and Engineering, Shepherd University, Shepherdstown, WV, 25443, and WEIDONG LIAO (Faculty Advisor), OSMAN GUZIDE (Faculty Advisor), Dept of Computer Science, Mathematics and Engineering, Shepherd University, Shepherdstown, WV, 25443. Detecting Parking Space with Generative AI and Large Multimodal Models Generative AI and Large Multimodal Models have gained significant advancements in image recognition, detailed responses, and complex reasoning. In this project, we utilize these capabilities to monitor a parking lot and estimate the number of free parking spots with pictures captured with cameras placed above a parking lot. This system aims to provide real-time information to users, helping them determine if they should seek alternative parking. Additionally, with sufficient data and machine learning, the system can predict peak hours and busy days for the parking lot. Despite the potential, current models sometimes struggle to produce accurate and consistent vehicle counts. This study focuses on improving the reliability and accuracy of the parking space monitoring system with comparative studies over various LMM models, architectural designs, and prompt engineering techniques.

With the computer software and hardware technology development and CAD technology is widely used in industrial design, industrial design ways and means to achieve constantly updated development, industrial design field is undergoing a new revolution. Introduction: With the rapid development of the computer, so that industrial design computerized reached a considerable level. Computer data analysis, model building, into the production system, etc., in an important part of human life and production, resulting in a more wide-ranging impact, and thus lead to new thinking is gradually penetrate the industrial design subject areas.

The study of Consumer Affective Factor (CAF) for Kansei engineering has been an important issue in the industrial design field in recent years. CAF is usually presented in the form of adjectives. Based on the Kansei Engineering (KE) concept, this study conducted Factor Analysis (FA) and Procuresses Analysis (PA) to select the CAF from digital camera product's shape. First, in the initial stage of the study, 120 samples of digital camera were col- lected from the fashion market place in Taiwan. In the meanwhile, we deleted more similar appearance digital camera samples to obtain 50 samples for experiment. Twenty pairs of ad- jectives describing the digital cameras were used for a Semantic Differential (SD) experiment. The results of the factor loaded from the FA, then we could obtain representative pairs of adjectives. PA was also used to decide adjective priorities according to the sorting rule. Final results showed that the PA was an effective method for the CAF selection.

Introduction Deep generative models are reshaping de novo drug design by enabling creation of novel, property-optimized molecules beyond traditional chemical libraries. Advances in deep learning, molecular representation learning, and structure-aware modeling now enable algorithms to propose molecules that satisfy complex pharmacological constraints, accelerating hit identification. Areas covered This review outlines recent advances in generative molecular design, including neural network-based frameworks, reinforcement learning systems, diffusion models, and language model-based transformers. The authors outline how each class generates and optimizes molecular structures and review generative AI’s practical applications in drug discovery, illustrating translational progress. Current bottlenecks are critically analyzed alongside emerging solutions. This review is based on a systematic literature search conducted in Google Scholar and PubMed, covering studies published up to December 2025. Expert opinion Generative AI’s greatest promise lies not in generating more molecules, but in generating better hypotheses, structures that are synthetically accessible, biologically plausible, optimized across potency, selectivity, and pharmacokinetics. The next phase will be led by multimodal foundation models capable of reasoning jointly about chemistry, protein structure, and cellular response, supported by automated synthesis and high-throughput experimentation. As these components are integrated, generative molecular design will guide lead optimization and reshape how new therapies are discovered and developed.

The growing availability of IoT-enabled sensor networks has transformed how slope stability is monitored within Landslide Early Warning Systems (LEWS), producing vast datasets on pore pressures, groundwater levels, displacements, and external drivers such as rainfall. In the literature, there is an increased trend to apply advanced data analysis approaches and surrogate models to support slope stability assessment and early warning. However, the sheer volume and complexity of these data often limit users’ ability to interact with them in a flexible and intuitive way. Emerging advances in multi-modal generative AI models and agentic frameworks suggest a new paradigm: chat-with-your-data.In this approach, users interact directly with slope monitoring data through natural language, requesting tailored visualizations, summaries, analyses, or forecasts without the need for bespoke coding or rigid workflows. In the context of slope stability assessment and early warning, a practitioner could ask for recent pore pressure trends, rainfall-displacement correlations, threshold exceedances, or anticipated changes in stability conditions based on forecasted meteorological inputs for a specific site. The system identifies the relevant data sources, retrieves data, performs the required operations, and returns insights in user-friendly formats such as maps, diagrams, or downloadable datasets.The potential benefits include more direct access to relevant data and analyses, uncovering correlations, and enabling real-time decision support. However, challenges remain. These include ensuring that project-level access controls are respected, handling heterogeneous geospatial references, providing tailored data representations across spatial scales, and maintaining transparency and reliability in automatically generated outputs. Addressing these issues requires combining domain-specific knowledge in slope stability and landslide processes with expertise in generative AI and data governance.This work outlines a vision for how conversational interfaces could enhance slope-scale Landslide Early Warning Systems by supporting monitoring, modelling, and forecasting activities through intuitive human–data interaction. By allowing experts to query their data directly, we move toward systems that are more adaptable, interpretable, and insight-driven, promoting more effective use of monitoring data for targeted warning and risk mitigation.

Generative AI is emerging as a transformative tool in foreign language education because it can enable natural language interaction, adaptive feedback, and personalized learning. In China, where English as a Foreign Language (EFL) education is closely tied to national development and individual mobility, interest in AI-enhanced learning is growing. Yet adult EFL instruction remains shaped by teacher-centered traditions, exam pressures, and hierarchical norms, even as learner-centered and andragogical practices gain attention. This article analyzes the cultural and systemic context of adult EFL in China and reports survey findings on Chinese EFL teachers’ orientations toward teacher-centered versus andragogical approaches. Results suggest that while learner-centered elements appear, instruction is still predominantly teacher-directed. To address this gap, the article proposes the AI-Mediated Andragogical EFL framework, integrating andragogy, sociocultural theory, self-regulated learning, and formative feedback theory to guide coherent AI use. The framework positions teachers as learning orchestrators and identifies contextual constraints shaping implementation.

The growing scale and complexity of higher education have exposed limitations in traditional academic advising models, which are often manual, reactive, and constrained by limited human resources. As institutions serve increasingly diverse student populations across physical and digital learning environments, there is a critical need for intelligent, scalable, and personalized advising solutions. This paper presents a comprehensive framework for Generative Artificial Intelligence (GenAI)–driven digital academic advising and intelligent student support systems that integrates large language models, predictive analytics, and institutional academic data. The proposed framework enables natural language interaction, personalized academic planning, early risk identification, and career-oriented guidance by leveraging student records, learning management data, and policy knowledge bases. Generative AI enhances advising by providing context-aware, explainable recommendations and supporting scenario-based academic decision-making. To ensure responsible adoption, the framework incorporates ethical AI principles, including transparency, bias mitigation, privacy preservation, and human-in-the-loop governance. A modular, cloud-native, and API-driven architecture is proposed to support scalability and seamless integration with existing Student Information Systems and Learning Management Systems. Experimental insights and related studies indicate that AI-enabled advising can significantly improve student engagement, advising efficiency, and retention outcomes while reducing advisor workload. Overall, the framework demonstrates how generative AI can transform academic advising into a proactive, intelligent, and continuously learning support ecosystem that aligns institutional goals with student success and long-term educational outcomes.