Search Results (39)

Spatio-temporal features are crucial for maritime trajectory forecasting, especially in scenarios involving curved waterways or abrupt changes in ship motion patterns. Although Automatic Identification System (AIS) data, which are widely used for trajectory prediction, inherently include temporal and spatial information, effectively strengthening these features and integrating them into prediction models remains challenging. To address this challenge, we propose a Convolutional Neural Network (CNN)-Series-cOre Fused Time Series forecaster (SOFTS)-based framework that explicitly couples spatial and temporal features to achieve high-fidelity maritime trajectory forecasting, especially in scenarios with complex spatial patterns. We first employ a CNN-based spatial encoder to hierarchically abstract spatial density distributions through convolution and pooling operations, thereby learning global spatial structure patterns of ship movements. This encoder emphasizes overall spatial morphology rather than precise individual trajectory points. Second, we employ the SOFTS model to incorporate angular velocity, acceleration, and angular acceleration as input features to characterize ship motion states, which can capture the temporal dependencies of ship motion states from multivariate time series. Finally, the spatial embedding features extracted by the CNN are concatenated with the temporal feature representations learned by SOFTS along the feature dimension to form a joint spatiotemporal representation. This representation is then fed into a fusion regression module composed of fully connected layers to predict future ship trajectories. Experimental results on the validation dataset show that the proposed method achieves an MSE of 0.020 and an MAE of 0.060, outperforming several advanced time series forecasting models in prediction accuracy and computational efficiency. The introduction of angular velocity, acceleration, and angular acceleration features reduces the MSE and MAE by approximately 10.22% and 9.49%, respectively, validating the effectiveness of the introduced dynamic features in improving trajectory prediction performance. These results underscore the proposed method’s potential for intelligent navigation and traffic management systems by effectively enhancing inland river navigation safety and strengthening waterborne traffic monitoring capabilities. Full article

High-entropy alloys (HEAs) present a fundamental design paradox: their exceptional properties arise from complex, high-dimensional composition–process–microstructure–property (CPMP) relationships, yet the knowledge needed to navigate this space is fragmented across a vast and unstructured literature. Large language models (LLMs) offer a transformative interface to this complexity. By extracting structured facts from text, they can convert dispersed and heterogeneous evidence (i.e., findings scattered across many studies and reported with inconsistent test protocols or characterization standards) into queryable knowledge graphs. Through code generation and tool composition, they can automate simulation pipelines, surrogate model construction, and inverse design workflows. This review analyzes how LLMs can augment key stages of HEA research—from intelligent literature mining and multimodal data integration (using LLMs to automatically extract and structure data from texts and to combine information across text, images, and other data sources) to model-driven design and closed-loop experimentation—illustrated by emerging case studies. We propose concrete evaluation protocols that measure direct scientific utility, including knowledge-graph completeness, workflow setup efficiency, and experimental validation hit rates. We also confront practical limitations: data sparsity and noise, model hallucination, domain bias (where models may exhibit superior predictive performance for specific, well-represented alloy systems over others due to imbalances in training data), and the imperative for reproducible infrastructure. We argue that domain-specialized LLMs, embedded within grounded, verifiable research systems, can not only accelerate HEA discovery but also standardize the representation, sharing, and reuse of community knowledge. Full article

In intelligent unmanned agricultural machinery research, navigation line extraction in natural field/orchard environments is critical for autonomous operation. Existing methods still face two prominent challenges: (1) Dynamic shooting perspective shifts caused by natural environmental interference lead to geometric distortion of image features, making it difficult to acquire high-precision navigation features; (2) Symmetric distribution of crop row boundaries hinders traditional algorithms from accurately extracting effective navigation trajectories, resulting in insufficient accuracy and reliability. To address these issues, this paper proposes an environment-adaptive navigation path extraction method for multi-type agricultural scenarios, consisting of two core components: an Attention-Feature-Enhanced U-Net (AFU-Net) for semantic segmentation of navigation feature regions, and a key-point constraint-based adaptive navigation line extraction algorithm. AFU-Net improves the U-Net framework by embedding Efficient Channel Attention (ECA) modules at the ends of Encoders 1–3 to enhance feature expression, and replacing Encoder 4 with a cascaded Semantic Aware Multi-scale Enhancement (SAME) module. Trained and tested on both our KVW dataset and Yu’s field dataset, our method achieves outstanding performance: On the KVW dataset, AFU-Net attains a Mean Intersection over Union (MIoU) of 97.55% and a real-time inference speed of 32.60 FPS with only 3.95 M Params, outperforming state-of-the-art models. On Yu’s field dataset, it maintains an MIoU of 95.20% and 16.30 FPS. Additionally, compared with traditional navigation line extraction algorithms, the proposed adaptive algorithm reduces the mean absolute yaw angle error (mAYAE) to 2.06° in complex scenarios. This research exhibits strong adaptability and robustness, providing reliable technical support for the precise navigation of intelligent agricultural machinery across multiple agricultural scenarios. Full article

Artificial intelligence (AI), a transformative force, has revolutionised various aspects of human life and business operations. This has led to a drastic mutation of the career landscape, embedded with vast opportunities as well as challenges, particularly concerning career sustainability (CS). Despite myriad studies on CS, the paradoxical interplay of AI and CS remains underexplored, particularly for expatriates (expats). To address the aforementioned gap, our study incorporates an affordance perspective (AFP), positioning AI as an object and CS as a user context. Specifically, this study investigates whether AI facilitates the orchestration of an enhanced sustainable career within the boundary conditions of knowledge sharing (KS), encompassing both tacit and explicit knowledge pertinent to AI, cultivated through managerial initiatives and employee-driven activities. The study conducted a quantitative survey among 490 expats working in AI-integrated environments in China. The results reveal a curvilinear (U-shaped) relationship between AI and CS, where AI affordance at a moderate level enhances career adaptability and skill development. However, digital affordances become complex beyond a certain threshold, creating several career concerns, such as job insecurity and role ambiguity. Furthermore, the moderating effect of tacit and explicit KS mitigates numerous career disruptions while fostering long-term career growth. The study framed AI as both a tool and a collaborator that illuminates the importance of AI–human intelligence (AI–HI) synergy and knowledge augmentation in navigating digital transitions. Moreover, implications for international career development and human-oriented digital transformation are also discussed. Full article

Advances in AI are accelerating intelligent ship autonomy, yet robust trajectory tracking remains challenging under nonlinear dynamics and persistent environmental disturbances. Traditional model-based guidance becomes tuning-sensitive and loses robustness under strong disturbances, while data-driven approaches like reinforcement learning often suffer from poor generalization to unseen dynamics and brittleness in out-of-distribution conditions. To address these limitations, we propose a guidance architecture embedding a Large Language Model (LLM) directly within the closed-loop control system. Using in-context prompting with a structured Chain-of-Thought (CoT) template, the LLM generates adaptive k-step heading reference sequences conditioned on recent navigation history, without model parameter updates. A latency-aware temporal inference mechanism synchronizes the asynchronous LLM predictions with a downstream Model Predictive Control (MPC) module, ensuring dynamic feasibility and strict actuation constraints. In MMG-based simulations of the KVLCC2, our framework consistently outperforms conventional model-based baselines. Specifically, it demonstrates superior path-keeping accuracy, higher corridor compliance, and faster disturbance recovery, achieving these performance gains while maintaining comparable or reduced rudder usage. These results validate the feasibility of integrating LLMs as predictive components within physical control loops, establishing a foundation for knowledge-driven, context-aware maritime autonomy. Full article

This Editorial presents an integrative overview of the Special Issue “Advanced Autonomous Systems and Artificial Intelligence Stage”, which assembles fifteen peer-reviewed articles dedicated to the recent evolution of AI-enabled and autonomous systems. The contributions span a broad spectrum of domains, including renewable energy and power systems, intelligent transportation, agricultural robotics, clinical and assistive technologies, mobile robotic platforms, and space robotics. Across these diverse applications, the collection highlights core research themes such as robust perception and navigation, semantic and multi modal sensing, resource-efficient embedded inference, human–machine interaction, sustainable infrastructures, and validation frameworks for safety-critical systems. Several articles demonstrate how physical modeling, hybrid control architectures, deep learning, and data-driven methods can be combined to enhance operational robustness, reliability, and autonomy in real-world environments. Other works address challenges related to fall detection, predictive maintenance, teleoperation safety, and the deployment of intelligent systems in large-scale or mission-critical contexts. Overall, this Special Issue offers a consolidated and rigorous academic synthesis of current advances in Autonomous Systems and Artificial Intelligence, providing researchers and practitioners with a valuable reference for understanding emerging trends, practical implementations, and future research directions. Full article

This study presents the design and implementation of a real-time flight-path tracking control system for a quadrotor unmanned aerial vehicle (UAV) capable of accurately following a mobile ground target under dynamic and uncertain environmental conditions. The proposed framework integrates classical fixed-gain PID regulation executed on Pixhawk with its built-in adaptive mechanisms, namely autotuning, hover-throttle learning, and dynamic harmonic notch filtering, to enhance robustness under communication latency and disturbances. No machine learning PID tuning is used on Pixhawk; adaptive features are estimator based rather than ML based. The proposed system addresses critical challenges in trajectory tracking, including real-time delay compensation between the UAV and rover, external perturbations, and the requirement to maintain stable six-degree-of-freedom (DOF) control of altitude, yaw, pitch, and roll. A dynamic mathematical model, formulated using ordinary differential equations with embedded delay elements, is developed to emulate real-world flight behavior and validate control performance. Experimental evaluation demonstrates robust path-tracking accuracy, attitude stability, and responsiveness across diverse terrains and weather conditions, achieving a mean positional error below one meter and effective resilience against an 8.2 ms communication delay. Overall, this work establishes a scalable, computationally efficient, and high-precision control framework for UAV guidance and cooperative ground-target tracking, with potential applications in autonomous navigation, search-and-rescue operations, infrastructure inspection, and intelligent surveillance. The term “delay-aware” in this work refers to the explicit modeling of the measured 8.2 ms end-to-end delay and the use of Pixhawk’s estimator-based adaptive mechanisms, without any machine learning-based PID tuning. Full article

Vessel trajectory prediction (VTP) plays a critical role in maritime safety and intelligent navigation. Existing methods struggle to simultaneously capture long-term dependencies and nonlinear dynamic patterns in vessel movements. To address this challenge, we propose MKAIS, a novel trajectory prediction model that integrates the selective state space modeling capability of Mamba with the strong nonlinear representation power of Kolmogorov–Arnold Networks (KAN). Specifically, we design a feature-separated embedding strategy for AIS inputs (longitude, latitude, speed over ground, course over ground), followed by an MKAN module that jointly models global temporal dependencies and nonlinear dynamics. Experiments on the public ct_dma dataset demonstrate that MKAIS outperforms state-of-the-art baselines (LSTM, Transformer, TrAISformer, Mamba), achieving up to 16.65% improvement in the Haversine distance over 3 h prediction horizons. These results highlight the effectiveness and robustness of MKAIS for both short-term and long-term vessel trajectory prediction. Full article

Total knee replacement (TKR) is a reliable treatment for end-stage degenerative conditions of the knee. Patient-reported outcome measures (PROMs) are central to assessing TKR outcomes, but they have limitations. Activities of daily living (ADLs) in the early post-operative period complement PROMs for holistic patient assessment. This study presents a method for capturing ADL parameters from data generated by inertial measurement unit (IMU) devices embedded in TKR prosthesis. A conventional posterior stabilized TKR was modified to create chambers in the femoral and tibial components. The prosthesis was implanted into a cadaver knee and movement was simulated using a hydraulic actuated knee simulator (AMTI, VIVO, MA, USA). A powered IMU device was placed in each of the chambers. The simulator was activated for various ADLs and the generated data was collected wirelessly. The pre-processed data was fed into a novel multimodal deep learning artificial intelligence model created to recognize specific ADL (trained on 70% of the data, with 30% reserved for validation and testing). The model achieved 95.68% overall accuracy, with 100% for sitting, standing, stance, and knee bending. Walking, stair navigation, and jogging showed F1 scores of 0.98, 0.92, 0.91, and 0.89, respectively. This technology enables seamless knee activity recognition and reporting with positive implications for patient-specific rehabilitation protocols. Full article

Higher technical education plays a strategic role in equipping the workforce to navigate rapid technological advancements and evolving labor market demands. Within the Rome Technopole framework, Spoke 4 targets ITS Academies, promoting the development of flexible, modular programs that integrate advanced technical skills with ethical, legal, and societal perspectives. This study reports on a pilot training initiative on Artificial Intelligence (AI) co-designed by the Istituto Superiore di Sanità (ISS), aimed at exploring the ethical, practical, and educational relevance of AI in higher technical education. The module was developed and tested through a multi-stakeholder collaboration involving educators, institutional actors, and learners. A four-phase approach was adopted: (1) initial stakeholder consultation to identify needs and content directions, (2) collaborative design of the training module, (3) online delivery and engagement using a CAWI-based focus group, and (4) mixed-method evaluation, combining quantitative assessments and open-ended qualitative feedback. This design facilitated asynchronous participation and encouraged critical reflection on the real-world implications of AI. Through the four-phase approach, the pilot module was developed, delivered, and assessed with 37 participants. Quantitative analysis revealed high ratings for clarity, relevance, and perceived utility in terms of employability. Qualitative feedback highlighted the interdisciplinary design, the integration of ethical reasoning, and the module’s broad applicability across sectors—particularly Healthcare and Industry. Participants suggested including more real-world case studies and collaborative learning activities to enhance engagement. The findings support the feasibility and added value of embedding ethically informed, interdisciplinary AI education in professional technical training pathways. Developed within the Rome Technopole ecosystem, the pilot module offers a promising approach to fostering critical digital literacy and preparing learners for responsible engagement with emerging technologies. Full article

Digital transformation (DT) is rapidly reshaping education at multiple levels, including curriculum, instructional practices, and institutional culture. Within this context, entrepreneurship education has become a key field for preparing individuals to navigate uncertainty and generate social and economic value in a digital society. Entrepreneurial competencies are increasingly conceptualized as a multidimensional construct that encompasses creativity, problem-solving, critical thinking, collaboration, and digital literacy. This study aims to identify core entrepreneurial competencies relevant to the digital era and examine how technology-integrated instructional strategies contribute to their development. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines, analyzing 72 peer-reviewed journal articles published between January 2021 and June 2025. The findings indicate that DT drives structural changes in education beyond tool adoption, with technologies such as artificial intelligence (AI), data analytics, and digital collaboration platforms serving as catalysts for innovative thinking and entrepreneurial behavior. These technologies are not merely supportive tools but are embedded in competency-based learning processes. This review provides a comprehensive competency framework integrating three domains, AI-collaborative pedagogy validation, and implementation strategies, enabling educators, curriculum developers, and policymakers to redesign entrepreneurship education that aligns with the realities of digital learning environments and fosters future-ready entrepreneurial capabilities. This conceptual framework theoretically systematizes the integration of innovative thinking and ethical execution capabilities required in the digital era, contributing to defining the future direction of entrepreneurship education. Full article

The increasing adoption of artificial intelligence across domains presents new opportunities to enhance access to justice. In this paper, we introduce a human-centric AI tool that utilises advances in Automatic Speech Recognition (ASR) and Large Language Models (LLMs) to facilitate semantic linking between written UK Supreme Court (SC) judgements and their corresponding hearing videos. The motivation stems from the critical role UK SC hearings play in shaping landmark legal decisions, which often span several hours and remain difficult to navigate manually. Our approach involves two key components: (1) a customised ASR system fine-tuned on 139 h of manually edited SC hearing transcripts and legal documents and (2) a semantic linking module powered by GPT-based text embeddings adapted to the legal domain. The ASR system addresses domain-specific transcription challenges by incorporating a custom language model and legal phrase extraction techniques. The semantic linking module uses fine-tuned embeddings to match judgement paragraphs with relevant spans in the hearing transcripts. Quantitative evaluation shows that our customised ASR system improves transcription accuracy by 9% compared to generic ASR baselines. Furthermore, our adapted GPT embeddings achieve an F1 score of 0.85 in classifying relevant links between judgement text and hearing transcript segments. These results demonstrate the effectiveness of our system in streamlining access to critical legal information and supporting legal professionals in interpreting complex judicial decisions. Full article

This theoretical study rigorously investigates how generative artificial intelligence reshapes decision-making in social entrepreneurship by modeling the opportunity search process through the lens of optimal stopping theory. Social entrepreneurs often face high uncertainty and resource constraints, requiring them to strategically balance the cost of continued searching with the chance of identifying socially impactful opportunities. This study develops a formal model that captures two core mechanisms of generative AI: reducing search costs and increasing the probability of mission-aligned opportunity success. The theoretical analysis yields three key findings. First, generative AI accelerates the optimal stopping point, allowing social entrepreneurs to act more quickly on high-potential opportunities by lowering cognitive and resource burdens. Second, the influence of increased success probability outweighs that of reduced search costs, underscoring the strategic importance of insight quality over efficiency in socially embedded contexts. Third, the benefits of generative AI are amplified in uncertain environments, where it helps navigate complexity and mitigate information asymmetry. These insights contribute to a deeper conceptual understanding of how intelligent technologies transform the cognitive and strategic dimensions of social entrepreneurship, and they offer empirically testable propositions for future research at the intersection of AI, innovation, and mission-driven opportunity pursuit. Full article

Objectives: Artificial intelligence (AI) symptom-checker apps are proliferating, yet their everyday usability and transparency remain under-examined. This study provides a triangulated evaluation of three widely used AI-powered mHealth apps: ADA, Mediktor, and WebMD. Methods: Five usability experts applied a 13-item AI-specific heuristic checklist. In parallel, thirty lay users (18–65 years) completed five health-scenario tasks on each app, while task success, errors, completion time, and System Usability Scale (SUS) ratings were recorded. A repeated-measures ANOVA followed by paired-sample t-tests was conducted to compare SUS scores across the three applications. Results: The analysis revealed statistically significant differences in usability across the apps. ADA achieved a significantly higher mean SUS score than both Mediktor (p = 0.0004) and WebMD (p < 0.001), while Mediktor also outperformed WebMD (p = 0.0009). Common issues across all apps included vague AI outputs, limited feedback for input errors, and inconsistent navigation. Each application also failed key explainability heuristics, offering no confidence scores or interpretable rationales for AI-generated recommendations. Conclusions: Even highly rated AI mHealth apps display critical gaps in explainability and error handling. Embedding explainable AI (XAI) cues such as confidence indicators, input validation, and transparent justifications can enhance user trust, safety, and overall adoption in real-world healthcare contexts. Full article

Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users. Full article