Search Results (263)

With the rapid evolution of Large Language Models (LLMs) in natural language understanding and generation, Relation Extraction (RE) has achieved substantial milestones in low-resource and open-domain scenarios. However, prevailing LLM-based RE methodologies predominantly rely on one-shot prompting or static workflows, which lack autonomous evaluation and iterative optimization mechanisms. Consequently, these approaches are prone to issues such as missing relations, type confusion, and factual hallucinations when navigating complex relational contexts. To address these limitations, this paper proposes DyFIRER (Dynamic Feedback Iterative Relation Extraction Framework), a multi-agent framework characterized by dynamic feedback. By constructing three functionally complementary agents—Extraction, Verification, and Optimization—the framework models the RE task as a closed-loop iterative process consisting of “extraction-verification-feedback-optimization,” thereby enabling dynamic adjustment and continuous refinement of extraction strategies. Experimental results on the DuIE 2.0 open relation extraction extension subset demonstrate that DyFIRER achieves an F1-score of 80.5%, modestly but statistically significantly outperforms GPT-4 (p = 0.014), a result that holds on both the augmented and non-augmented test sets and mainstream static methods (yielding a 10.3% improvement over Qwen-7B). Ablation studies further substantiate the critical role of the dynamic feedback iterative mechanism and the strategy retrieval module in mitigating complex relation omissions and factual hallucinations. The framework requires no additional annotated data or fine-tuning, suggesting potential applicability to low-resource settings, though this was not directly evaluated in the current study. Full article

Proto-AGI describes the intermediate stage of artificial intelligence between narrow task-specific tools and fully autonomous general intelligence. This paper presents the first formal operationalisation of Proto-AGI traits within a cross-domain digital workflow for structural engineering and demonstrates it through a four-domain computational ecosystem applied to seismic vulnerability assessment. Viktor.ai processes cone penetration test data to stratify a three-layer soil profile, identifying a compressible intermediate stratum at 5 to 12 m depth with amplification characteristics in the 0.3 to 0.7 second period range, based on the depth and stiffness contrast of the weak layer rather than a formal site response analysis. The Fayaz RotD script computes orientation-independent RotD50 and RotD100 response spectra for two contrasting ground motion records: the near-fault Northridge record (RSN 1086, Mw 6.69) delivers RotD50 = 2.00 g and RotD100 = 2.79 g at the structural natural period of 0.41 s, a 39.6% directional uplift; the moderate-distance Kobe record (RSN 1107, Mw 6.9) delivers RotD50 = 0.591 g and RotD100 = 0.795 g at the same period. OpenSeesPy nonlinear dynamic analysis of a five-storey reinforced concrete frame produces peak inter-storey drifts of 0.45% and 0.34% under the two records respectively, both within the FEMA 356 Immediate Occupancy threshold of 1.0%. A 3.4-fold spectral demand difference produces only a 1.32-fold drift difference, reflecting the combined effects of frequency content, pulse characteristics, duration and nonlinear structural response under the two contrasting records. The Viktor.ai RC Section Analyzer yields a curvature ductility factor of 2.3 under ACI 318-25, identifying deformation capacity as the governing constraint under more severe future demands. These four findings form a causal chain connecting site conditions, spectral demand, structural response and sectional capacity that no single domain produces independently—the emergent ecosystem intelligence that defines Proto-AGI in structural engineering practice. Full article

Instrument control remains a practical bottleneck in laboratory automation and self-driving laboratories. Although large language models (LLMs) have shown strong potential in document understanding, code generation, and scientific workflow automation, most existing systems assume that ready-to-use instrument interfaces are already available. However, converting heterogeneous device manuals and communication protocols into tested, reusable software drivers therefore still requires substantial manual effort and iterative hardware-level debugging. In this work, we present ChemAutoAgent, a multi-agent system that converts text-based instrument manuals into tested and reusable laboratory instrument drivers through a staged, evidence-traceable pipeline. We evaluate ChemAutoAgent tested and reusable drivers on three representative instruments—a magnetic stirrer, a peristaltic pump, and a Raman spectrometer—spanning three communication protocols: ASCII/NAMUR, MODBUS RTU, and a custom binary-frame protocol. Following iterative testing and repair, all evaluated driver functions passed the predefined tests for connection establishment, parameter configuration, command execution, and data acquisition. The evaluated drivers required between one and four repair cycles, with autonomous operation ratios ranging from 73% to 81%. A cross-device invocation experiment further demonstrates the feasibility of reusing published drivers in a multi-device workflow. Full article

Background and Objectives: Dentofacial deformities require a multidisciplinary surgical–orthodontic protocol (SOP) in which orthodontic preparation, orthognathic surgery, postoperative finishing, and retention are closely coordinated to achieve stable functional and aesthetic outcomes. The increasing adoption of clear aligners, digital workflows, and artificial intelligence (AI) has profoundly modified this therapeutic pathway. This communication examines the role of clear-aligner therapy and AI throughout the contemporary surgical–orthodontic protocol, with particular emphasis on their integration into clinical practice. Discussion: From the surgical perspective, clear aligners provide treatment outcomes comparable to those achieved with conventional fixed appliances while offering greater predictability of treatment duration through digital treatment planning. However, their use requires careful management of specific clinical aspects, including surgical timing, intraoperative anchorage, postoperative dentoalveolar stabilisation, transverse dimension management, and retention, all of which demand close collaboration between the orthodontist and the maxillofacial surgeon. The communication also summarises current AI applications in orthodontics and orthognathic surgery, including automated cephalometric analysis, image segmentation, treatment-decision support, virtual patient construction, clear-aligner setup, soft-tissue prediction, refinement-risk assessment, and remote monitoring. Although AI has significantly improved the efficiency, reproducibility, and standardisation of diagnosis and treatment planning, current evidence supports its use as a clinician-supervised decision-support technology rather than as an autonomous system capable of managing the entire orthodontic–surgical pathway. Conclusions: The integration of clear aligners, digital planning, and AI represents an important step toward a more personalised and efficient workflow, while continued clinical validation and multidisciplinary expertise remain essential for achieving predictable long-term outcomes. Full article

Ambient Assisted Living (AAL) environments aim to enable older people to remain active and lead an autonomous and independent life for as long as possible. Among the technologies that can be incorporated into these settings, socially assistive robots (SAR) seek to establish a more natural and intuitive means of interaction with people, whilst helping them to carry out everyday tasks. One of the main challenges facing the design of these robots is how to enable them to undertake more complex tasks. Recent advances in Large Language Models (LLMs) have opened new avenues for flexible robot deliberation, yet their integration into real-time robotic systems remains challenging due to latency constraints, reasoning reliability, and the complexity of coordinating multi-step tasks. This paper proposes a hierarchical multi-agent architecture for robot deliberation that addresses these challenges by combining LLM-based planning with structured execution mechanisms within the ROS 2 ecosystem. The proposed architecture employs a supervisor agent that decomposes high-level natural language instructions into prioritised subtasks, enabling a priority-driven execution model that dynamically adapts to task relevance, temporal constraints, and environmental feedback. Subtasks are delegated to a set of Single-Purpose Agents (SPAs), orchestrated via LangGraph state machines and coordinated through a priority-aware scheduling mechanism. A key design principle is the use of Behaviour Trees (BTs) as high-level callable tools through the Model Context Protocol (MCP), encapsulating closed-loop control strategies while enabling preemptive and priority-consistent execution. This reduces the number of LLM inference steps required per task and improves robustness under dynamic conditions. A further contribution concerns the deployment of fine-tuned, lightweight LLMs—on the order of 0.6 billion parameters—specifically adapted for both the supervisor and the individual SPA roles through parameter-efficient low-rank adaptation (LoRA). These models are trained on role-specific tool-calling datasets to specialise in constrained reasoning patterns and task-specific decision-making, enabling efficient, low-latency inference directly on edge hardware. The combination of fine-tuning and hierarchical priority control enhances both the determinism and responsiveness of the system while mitigating error propagation across agent interactions. The paper presents the full software architecture, a formal characterisation of the system as a priority-aware hierarchical policy over a graph of agent workflows, and an experimental evaluation in an Ambient Assisted Living scenario assessing task success rate, inference efficiency, responsiveness under competing priorities, and overall user experience. Because SPA execution is decoupled from the supervisor’s own reasoning loop, the architecture is designed to keep accepting, processing, and queuing new user queries while previously dispatched SPAs are still executing their tasks. Full article

The global fermentation industry faces persistent bottlenecks in scaling laboratory innovations to industrial production, and the integration of synthetic biology (SynBio) and artificial intelligence (AI) within the Design–Build–Test–Learn (DBTL) loop has yielded inconsistent industrial outcomes. This review proposes that transformative impact requires a “scale-up-gated DBTL” framework, in which explicit decision gates constrain every iteration. At the Design phase, scale-down simulation data must inform genetic design choices. At the Test phase, downstream processing compatibility and industrial robustness metrics are enforced as non-negotiable evaluation criteria. At the Learn phase, techno-economic analysis (TEA) and life-cycle assessment (LCA) serve as the convergence criteria, replacing traditional titer plateaus. Through a qualitative cross-sectoral analysis of food, pharmaceutical, agricultural, and energy fermentation, the analysis reveals that workflows incorporating such constraints consistently bridge the valley of death, whereas unconstrained DBTL systematically converges on laboratory optima that are industrially unviable. Five strategic priorities are outlined—embedding TEA/LCA into DBTL, adopting scale-down simulation, building open fermentation data repositories, harmonizing regulatory frameworks, and fostering cross-disciplinary training—as prerequisites for progressing toward fully autonomous, scale-up-aware biomanufacturing. Full article

Background/Objectives: Pharmacogenetics (PGx) may improve drug safety and effectiveness, but its integration into routine care remains heterogeneous across the Spanish National Health System (NHS). This article presents BioFRAM PGx, a multicentre implementation framework that defines the methodological and operational basis for subsequent observational validation in cardiovascular and mental-health care settings. Methods: The framework was developed by healthcare centres from eight Spanish Autonomous Communities through a structured review of PharmGKB/ClinPGx clinical annotations, CPIC and DPWG guidelines, and national regulatory resources. Gene–drug pairs were prioritized according to clinical actionability, therapeutic relevance, applicability to the Spanish population, and analytical feasibility. BioFRAM PGx defines a panel of seven pharmacogenes and 35 drugs, standardized genotyping and phenotype-assignment procedures, pharmacovigilance and clinical variables, healthcare-resource measures, and centralized data management. The proposed non-interventional validation phase includes adults receiving at least one selected drug in hospital or primary-care settings, with a minimum six-month follow-up. PGx results are not returned to clinicians and do not modify treatment. Its primary objective is to assess the predictive value of PGx genotyping for adverse drug reactions (ADRs); secondary objectives include ADR incidence and implementation feasibility. Results: The main outputs are the proposed multicentre observational design, the prioritized gene–drug panel, harmonized analytical and pharmacovigilance workflows, standardized data domains, and a centralized data-management strategy. No patient-level clinical, implementation, or economic outcomes are presented. Conclusions: BioFRAM PGx provides a structured multicentre framework for harmonizing pharmacogenetic procedures across the Spanish NHS. Its clinical effectiveness, influence on prescribing, scalability, and economic outcomes remain to be assessed in subsequent studies. Full article

Objectives: To evaluate the clinical accuracy and patient-reported outcomes (PROMs) of robotic-assisted full-arch implant rehabilitation performed with an autonomous robotic implant system. Materials and Methods: Six consecutive edentulous patients requiring implant-supported full-arch rehabilitation were treated using the Yakebot task-autonomous robotic implant system following a fully digital workflow. A total of 36 Straumann Bone Level Tapered implants were placed using a flapless approach. Implant placement accuracy was assessed by comparing planned and postoperative CBCT datasets. Coronal, apical, depth, and angular deviations were automatically calculated using dedicated robotic planning software. Patient-reported outcomes were prospectively evaluated through a structured questionnaire assessing preoperative perceptions, postoperative morbidity, and overall treatment satisfaction. Results: All implants were successfully placed according to the robotic-assisted workflow without intraoperative complications or conversion to conventional surgery. Mean three-dimensional coronal and apical deviations were 0.45 ± 0.18 mm and 0.47 ± 0.19 mm, respectively, while mean total angular deviation was 1.30 ± 0.63°. Patients reported low preoperative anxiety (0.5 ± 0.8/10), limited postoperative pain (2.2 ± 3.5/10), swelling (1.3 ± 2.2/10), and interference with daily activities (1.3 ± 2.2/10). Overall surgical experience was highly rated (9.7 ± 0.8/10). Final satisfaction scores were exceptionally high, with all patients indicating they would undergo robotic-assisted surgery again and recommend the procedure to others. Conclusions: Robotic-assisted full-arch implant rehabilitation demonstrated a high level of clinical accuracy, with submillimetric coronal and apical deviations and low angular discrepancies. In addition, treatment was associated with low postoperative morbidity, excellent patient acceptance, and very high satisfaction. These findings support the potential of autonomous robotic implant surgery as a predictable and patient-centered approach for full-arch rehabilitation, although larger controlled studies are required to confirm these results. Full article

Scoliosis is a complex three-dimensional spinal deformity that requires early detection, accurate radiological assessment, individualized treatment planning, and long-term monitoring. In recent years, artificial intelligence (AI) has emerged as a promising tool across multiple stages of scoliosis management, including screening, automated imaging analysis, deformity classification, prediction of progression, surgical planning, postoperative outcome assessment, and patient education. This narrative review summarizes current and emerging applications of AI in scoliosis, with particular emphasis on studies published after 2023. Deep learning algorithms, including convolutional neural networks, U-Net-based architectures, transformer models, and generative approaches, have demonstrated high accuracy in automated Cobb angle measurement, vertebral segmentation, coronal and sagittal parameter assessment, and radiation-free screening using surface topography or smartphone-based photographs. Machine learning models have also shown potential in predicting curve progression, treatment response, risk of postoperative complications, and patient-reported outcomes by integrating radiological, clinical, biomechanical, and, increasingly, multimodal data. In parallel, large language models and generative AI tools are being investigated for patient education, communication support, readability improvement, and research hypothesis generation. Despite these advances, important limitations remain, including limited external validation, dataset heterogeneity, potential algorithmic bias, insufficient interpretability, and incomplete integration into clinical workflows. Moreover, while AI systems show strong performance in automated measurement and screening tasks, their role in complex therapeutic decision-making, such as Lenke classification, fusion-level selection, and autonomous surgical planning, remains experimental. Overall, AI has the potential to improve the precision, efficiency, and personalization of scoliosis care; however, prospective multicentre studies, transparent reporting, explainable model design, and regulatory validation are essential before widespread clinical implementation. Full article

To address the scarcity of bridge defect data in the harsh environments of cold regions, as well as the parameter redundancy and edge platform deployment challenges of existing algorithms, this paper proposes an intelligent inspection system for bridge surface defects in cold regions based on parameter sharing and feature enhancement. The system first constructs a large-scale dataset called CRBD (Cold-Region Bridge Defect), which contains 10,129 high-resolution images and finely classifies defects into four standardized categories: Crack, Spalling, Patch, and Seepage. Subsequently, a lightweight detection network called BridgeNet is designed. Its core parameter sharing and feature enhancement detection head stabilizes training via group normalization, significantly reduces the parameter count through cross-scale global sharing and structural reparameterization, and improves bounding-box regression accuracy by incorporating a distribution focal loss mechanism. On this basis, an airborne real-time image processing and intelligent perception pipeline is constructed, which establishes the complete workflow for autonomous unmanned aerial vehicle inspections. The experimental results demonstrate that with a lightweight architecture of only 2.26 M parameters and a model size of 4.98 M, BridgeNet achieves a mean Average Precision of 61.4% and an F1 Score of 60.9%. Furthermore, it exhibits excellent real-time inference speed on heterogeneous edge mobile platforms and maintains robust overall perception stability under various extreme physical disturbances. Full article

Autonomous field robots are increasingly used in precision agriculture for monitoring, phenotyping, spraying, and site-specific intervention, yet reliable autonomy remains difficult under vegetation occlusion, uneven terrain, variable illumination, and intermittent communications. This review provides a deployment-oriented synthesis of navigation and sensor-fusion methods for agricultural robots, with emphasis on what is practical under field conditions rather than only in laboratory settings. The literature was examined through a structured narrative-review workflow using Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and related citation tracking, with primary emphasis on studies published between 2015 and 2025. We compare global, local, and hybrid planning methods; motion-control strategies such as PID, Pure Pursuit, and MPC; and localization pipelines that combine GNSS, IMU, LiDAR, cameras, odometry, and SLAM or Kalman-family fusion. Beyond algorithm summaries, the review links method selection to agricultural deployment constraints, including GNSS degradation, dynamic obstacles, compute limits, ROS 2 integration, time synchronization, and coordinate-frame management. The synthesis shows that no single stack is optimal across all crop systems: lightweight GNSS/IMU-based solutions remain attractive in structured open fields, whereas orchards, vineyards, and other occluded environments benefit more from tighter multi-sensor fusion and SLAM-supported localization. Finally, the review distills design guidance for sensing, planning, validation, and digital-twin-supported testing, and identifies research gaps related to robustness, benchmarking, safety, and scalable on-farm deployment. Full article

The preservation of submerged cultural heritage depends on the ability to locate, document, and monitor sites before they are degraded or lost. Although the North American Great Lakes contain thousands of exceptionally well-preserved shipwrecks, their large geographic extent and diverse operating environments present significant challenges for efficient archeological survey. This study presents a multi-platform autonomous survey framework developed and implemented during 2021–2022 field campaigns in Lake Michigan and Lake Ontario. The framework integrates autonomous underwater vehicles (AUVs), autonomous surface vehicles (ASVs), crewed vessels, side-scan sonar, multibeam bathymetry, magnetometry, optical imaging, and field-based data review within a hierarchical workflow comprising wide-area assessment (WAA) reconnaissance, high-resolution geophysical (HRG) mapping, adaptive mission refinement, and visual confirmation. The surveys produced 19.72 km² of geophysical coverage, including side-scan sonar mosaics, bathymetric surfaces, magnetic anomaly maps, and optical imagery that supported archeological interpretation. A case study from Lake Ontario demonstrates the framework’s effectiveness through the confirmation of a previously undocumented wooden shipwreck using complementary acoustic, magnetic, and visual datasets. Beyond the individual discoveries, the results demonstrate how integrated autonomous systems improve survey efficiency, support adaptive decision-making, and provide scalable methods for digital documentation, baseline site characterization, long-term monitoring, and preservation of submerged cultural heritage in freshwater and marine environments. Full article

The construction industry faces significant challenges in materials management, including inefficient supply chains and limited adoption of Circular Economy (CE) goals, which blockchain may address through automated tracking and verification systems. This systematic review examines blockchain technology applications in construction materials management to support CE strategies. Following PRISMA guidelines, 138 articles were selected from 1891 publications across four databases covering 2018 to 2025. The findings present a lifecycle-based framework across five building stages integrating smart contracts, IoT sensors, digital material passports, and tokenized waste exchange systems. Blockchain enables automated supply chain transparency, eliminates manual verification, and facilitates continuous material tracking. This research contributes by transforming conventional materials management into autonomous, data-driven workflows through a blockchain-enabled framework that systematically maps automated solutions for tracking, compliance, and circular resource flows across five building lifecycle stages, enabling practitioners to implement automated CE strategies. Full article

End-to-end autonomous driving is an emerging technology and a prominent research focus in both industry and academia. By integrating perception, localization, decision-making, and control into a single model, end-to-end systems aim to streamline the traditional modular pipeline while introducing new challenges in safety validation and interpretability. Unlike existing surveys that predominantly catalog algorithms, this review proposes a novel function-oriented taxonomy by categorizing architectures into perception-integrated and planning-integrated paradigms. Beyond the architectural dimension, the analysis delves into critical safety and interpretability, emphasizing the fundamental gap between theoretical design and the reliability required for real-world deployment. Industrial applicability is examined through real-world examples of data closed-loop workflows and simulation testing, addressing practical constraints in latency and computing resources. Finally, the review addresses critical challenges, particularly long-tail data scarcity and the deficiency in human-like decision-making and outlines future directions toward achieving robust autonomy. Full article

Objectives: To systematically evaluate the diagnostic accuracy, clinical applicability, and methodological maturity of artificial intelligence (AI)-based methods for temporomandibular disorders (TMD), temporomandibular joint (TMJ) abnormalities, and related cranio-cervico-mandibular (CCM) musculoskeletal conditions compared with conventional diagnostic methods and accepted reference standards. Materials and Methods: This systematic review and exploratory diagnostic test accuracy meta-analysis was conducted in accordance with PRISMA 2020 and PRISMA-DTA. The protocol was retrospectively registered in PROSPERO (CRD420261428138). PubMed/MEDLINE, Embase, and Scopus were searched from database inception through February 2026. Eligibility for the primary synthesis was restricted to published studies in English or Spanish involving adults aged 18 years or older. All extracted records were re-audited article by article to align the evidence with the diagnostic question. The domain-specific quantitative synthesis was restricted to TMJ osteoarthritis studies with explicit 2 × 2 diagnostic data or a unique, verifiable reconstruction from reported class totals and sensitivity/specificity. Risk of bias was assessed with QUADAS-2. Results: From 1471 records identified, 174 entered the master extraction dataset. After reclassification, 84 records were retained for primary TMD/TMJ qualitative synthesis, 8 as secondary CCM musculoskeletal evidence, 31 as conventional or reference standard supporting evidence, 33 as methodological or contextual evidence, 4 as differential orofacial pain evidence, and 14 as excluded or minimal-background records. Twenty-one studies were assessed as potential diagnostic accuracy candidates. Three TMJ osteoarthritis studies contributed to the domain-specific exploratory meta-analysis: two with explicit 2 × 2 data and one with a reproducible reconstruction. Pooled sensitivity was 0.791 (95% CI: 0.700–0.861) and pooled specificity was 0.869 (95% CI: 0.811–0.911). Heterogeneity was substantial for sensitivity (I² = 68.2%) and moderate for specificity (I² = 57.3%). Conclusions: AI demonstrates promising performance in selected image-based TMJ osteoarthritis tasks. Nevertheless, the evidence remains exploratory because only three studies were quantitatively comparable, one table was reconstructed, and modalities and validation designs differed. AI should be interpreted as an augmentative decision support tool rather than a replacement for MRI, CBCT, or validated clinical frameworks such as DC/TMD. Clinical Relevance: AI may support image-based TMD/TMJ workflows, but present evidence does not justify autonomous diagnosis or replacement of established clinical and imaging reference standards. Full article