Search Results (11,207)

Traditional evaluations of artificial intelligence (AI) systems in the dynamic, operator-dependent, and time-sensitive field of interventional radiology (IR), focusing solely on algorithmic performance, often fail to capture their real-world clinical impact. This narrative review aims to provide an overview of the current state of the art of AI integration in IR through human–AI interaction (HAI), while offering a critical perspective on their clinical integration, limitations, and future directions. A comprehensive survey of recent literature was performed, focusing on AI applications across procedural phases. The review emphasizes systems providing decision support, real-time procedural verification, and immersive interfaces (augmented and virtual reality), while critically evaluating determinants of effective clinical adoption. AI has shown preliminary potential to support operator performance in selected interventional radiology tasks, although most applications remain experimental, retrospective, or evaluated in phantom or preclinical settings. Potential benefits include structuring uncertainty in patient selection and procedural planning, supporting assessment of device positioning and treatment outcomes, and integrating AI-derived outputs into the operator’s spatial field through immersive technologies. The clinical utility of these systems appears to be influenced by human–AI interaction, with interpretability, workflow integration, and trust calibration representing key determinants of effective use beyond algorithmic accuracy alone. The potential value of AI in interventional radiology appears to derive from its integration into human decision-making rather than from standalone predictive performance alone. A human-centered, interaction-based model supports understanding current applications, address challenges, and guide the development of adaptive, real-time systems for dynamic procedural environments. Full article

Building recognition and detection in remote sensing images are of great significance for urban planning, spatial database updating, and the construction of urban geographic information systems. For remote sensing images with complex background information, variations in the size of building objects make automatic building detection and recognition challenging, thereby affecting the recognition accuracy of deep learning models. At the same time, the lack of a standardized workflow for converting detection results into vector data formats makes it difficult to directly transform building detection results into usable GIS-compatible vector data. Based on the Cascade R-CNN model, an intelligent building recognition model for remote sensing images and a vectorization workflow for the recognition results are proposed. To address the issue of building recognition accuracy in remote sensing images, an intelligent building recognition model comprising ResNet101, a Feature Pyramid Network (FPN), a Region Proposal Network (RPN), and a cascade detector is proposed, which enhances the recognition precision and localization capability of building objects in multi-scale remote sensing images. To address the efficiency issue of vectorizing detection results, a procedural conversion method for building detection results in remote sensing images is proposed, which converts raster recognition results into GIS-compatible vector files through data verification, information extraction, boundary construction, polygon generation, and format conversion. Experiments show that the intelligent recognition model achieves a recall of 0.958, a miss rate of 0.042, a precision of 0.963, and an F1-score of 0.960. In addition, mAP@0.5, mAP@0.5:0.95, and mean IoU reach 0.954, 0.793, and 0.742, respectively, indicating good performance in building detection and localization. Compared with manual vectorization, the automated workflow reduces the processing time for 57 raster files from 25.4 min to 3.1 min, corresponding to an 87.8% reduction in processing time. These results indicate that the proposed method improves building recognition accuracy while enhancing the efficiency of converting recognition results into GIS vector data, showing application potential for urban spatial information extraction. Full article

Conventional signature-based defenses no longer protect the heterogeneous, large-scale infrastructures that the Internet of Things (IoT) now constitutes. Large language models (LLMs) and agentic artificial intelligence (AI)—systems that autonomously perceive, reason, plan, and act—open a path to self-defending IoT ecosystems, but the integrating literature remains fragmented. Within the IEEE Xplore, ACM Digital Library, and MDPI literature, this survey is, to the best of our knowledge, among the first systematic reviews of agentic AI and LLM-driven approaches for autonomous IoT cybersecurity. Following a PRISMA 2020 protocol, we analyze 153 peer-reviewed studies published between 2020 and 2026 in IEEE Xplore, the ACM Digital Library, and MDPI journals. We organize the corpus along a four-pillar taxonomy: agent architecture (single- vs. multi-agent), reasoning strategy (chain-of-thought, ReAct, plan-and-solve, tool use), action scope (detection, response, threat hunting, vulnerability discovery, deception), and deployment topology (edge, fog, cloud). We synthesize four flagship application domains, consolidate datasets and benchmarks, and analyze open challenges including hallucination, prompt-injection robustness, explainability, privacy, latency, and governance. A 2026 research roadmap identifies federated agentic learning, verifiable autonomous reasoning, trustworthy multi-agent collaboration, and resource-hardened edge agents as high-priority directions. A companion reproducibility kit—prompt templates, reference single- and multi-agent loops, and an Edge-IIoTset-style evaluation harness, released as illustrative scaffolding rather than a validated framework—is released publicly and archived on Zenodo (DOI 10.5281/zenodo.20726552). Full article

Background: Stabilization of surgical guides in fully edentulous patients remains a clinical challenge due to mucosal resilience and potential micromovement, even when fixation pins are used. Guide instability may affect drilling accuracy and overall workflow predictability. This proof-of-concept case series describes a stabilization approach based on pre-placed tripod reference implants with multi-unit coupling, designed to create a mechanically defined prosthetic docking platform for fully guided implant surgery. Methods: Three fully edentulous patients requiring implant-supported rehabilitation were treated using a two-stage protocol. Three temporary reference implants were inserted in a tripod configuration 7–10 days prior to definitive surgery. Multi-unit abutments were mounted on the reference implants, and intraoral scanning was performed to design a surgical guide indexed to the prosthetic interfaces. During implant placement, the guide was screw-retained to the reference implants via the multi-unit connections. Postoperative implant positions were evaluated radiographically by superimposing postoperative datasets onto the preoperative planning model. Intraoperative guide stability, surgical events, and early postoperative outcomes were recorded. Results: Stable guide fixation was achieved in all three cases without detectable intraoperative displacement. Implant placement was completed as planned in each patient, and removal of the temporary reference implants was uneventful. No intraoperative or early postoperative complications were observed. Mean coronal, apical, and angular deviations between planned and achieved implant positions were 0.70 ± 0.16 mm, 0.39 ± 0.13 mm, and 3.30 ± 0.59°, respectively. These preliminary findings, derived from four treated arches, were comparable to ranges reported in selected studies on fully guided implant surgery; however, no direct statistical comparison with previously published datasets was performed. Conclusions: Within the limitations of this proof-of-concept case series, temporary reference implants arranged in a tripod configuration provided a stable and reproducible prosthetic indexing platform for guided implant surgery in fully edentulous patients. Further prospective studies involving larger patient cohorts and controlled comparative designs with conventional mucosa-supported or fixation-pin-supported surgical guides are required to evaluate the reproducibility, clinical performance, and long-term applicability of this stabilization concept. Full article

Sublobar pulmonary resection has become an increasingly adopted approach for early-stage non-small cell lung cancer, driven by evidence that anatomical segmentectomy can achieve oncological outcomes comparable to lobectomy in selected patients. Safe execution of sublobar resection depends on accurate preoperative identification of segmental bronchovascular anatomy, which demonstrates substantial variability. Conventional two-dimensional (2D) computed tomography (CT) imposes significant limitations on anatomical interpretation, particularly at the segmental and subsegmental level. Three-dimensional (3D) bronchovascular modelling provides patient-specific representations of segmental anatomy and relationships that address these limitations. This narrative review examines the current and emerging roles of 3D modelling in personalised thoracic surgery. It discusses the anatomical basis for its application, the limitations of conventional imaging, and the contribution of 3D modelling to preoperative planning and intraoperative decision making. It also considers broader applications, current limitations, and future directions, with emphasis on how patient-specific 3D modelling can support more tailored operative strategies and more individualised surgical care. Full article

The Five Domains Model has become one of the most widely adopted frameworks in animal welfare science and practice. The Model is now applied in a range of ways; among the most prominent are (1) as a framework for systematic and structured welfare assessment and (2) as an organising structure for planning and communicating appropriate (i.e., welfare-focused) care provisions, education, and standards. This paper focuses on these two applications and proposes a corresponding update to the affiliated Five Provisions and Welfare Aims. Specifically, we revise: (1) Provision 4 from “Appropriate Behaviour” to “Appropriate Choices” to reflect the 2020 update of the Model incorporating human–animal interactions and the 2023 operationalisation of agency in Domain 4; (2) Provision 2 from “Good Environment” to “Good Living Space” to resolve ambiguity with Domain 4’s “Interactions with the Environment”; and (3) Provision 5 from “Positive Mental Experiences” to “Integrated Care,” which captures consistent delivery of the first four provisions over time and across all those who interact with the animal. This update also pairs Provision 5 with a welfare aim that specifies the integrated mental state the animal should experience as a result. This change makes the distinction between care (provisions) and welfare (aims) consistent throughout the framework. It also makes explicit the integrative role of Provision 5, which parallels Domain 5’s role in the Model. We then describe the reasoning process that distinguishes welfare assessment from welfare-focused care provision. Welfare assessment uses the domain structure as a reasoning pathway, with the assessor using indicators and their impacts in Domains 1 to 4 to infer named mental (affective) experiences in Domain 5. Planning and communicating appropriate (i.e., welfare-focused) care uses the same structure to organise information about what is provided to animals, without executing the inferential step to Domain 5. Drawing on examples from organisations that use the Model for different purposes, we show that both applications are legitimate but produce different outputs. The Five Provisions framework, with its dual structure of provisions paired with welfare aims, serves the care planning and communication function more effectively than does the Model’s domain structure alone. Recognising these different uses also helps to locate where recent critiques of the Model apply and where they do not. Finally, we propose that the provisions and welfare aims framework can supplement “needs” language in legislation and policy to better reflect the distinction between animal care and animal welfare. Full article

Background/Objectives: Periodontitis is a highly prevalent chronic disease requiring accurate diagnosis for effective treatment planning. Artificial intelligence (AI) has emerged as a potential tool to support clinical decision-making. This study aimed to compare the diagnostic performance and classification error patterns of a large language model (LLM) and a neural network (NN) in periodontitis classification according to the current staging and grading system. Methods: This retrospective study included 110 patients with periodontal disease. Clinical and demographic variables (age, sex, smoking status, number of teeth, API, BOP, PPD, and CAL) were analyzed. Reference diagnoses were established by two experts. Cases were evaluated using an LLM and a neural network. Model performance was assessed using accuracy, confusion matrices, and Cohen’s kappa coefficient, along with error analysis. Results: The LLM achieved 62% accuracy for stage and 63% for grade classification (κ = 0.48). The neural network showed higher performance, with 85% accuracy for stage and 79% for grade (κ = 0.79 and κ = 0.67, respectively). The LLM more often underestimated disease severity, whereas the neural network tended to overestimate progression. Differences between models were statistically significant (p < 0.0001). Conclusions: In this dataset and classification task, the task-specific neural network demonstrated higher diagnostic performance than the evaluated large language model. However, the findings should be interpreted in light of the fundamentally different training paradigms and intended applications of these AI systems. Further research is required to optimize and validate AI-based approaches for clinical use. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis, biological heterogeneity, and persistent challenges in staging and treatment selection. This narrative review summarizes current and emerging applications of AI across lung cancer screening and early detection, imaging-based staging and prognostication, tissue and liquid biopsy-based tumor characterization, treatment planning, surgical and intraoperative guidance, and drug discovery. In imaging, deep learning models have demonstrated high performance in pulmonary nodule detection, risk stratification, and prediction of molecular alterations, while also showing promise in improving screening efficiency and reducing interpretive variability. In pathology and liquid biopsy domains, AI enables prediction of driver mutations, immunotherapy response, and survival outcomes directly from histopathology slides, circulating tumor DNA, and other blood-based biomarkers, facilitating minimally invasive precision oncology approaches. In treatment planning and delivery, AI systems are being developed to support clinical decision-making, surgical planning (through advanced image segmentation and delineation of operative anatomy), and intraoperative navigation through robotic and computer vision-enabled platforms. Despite these advances, significant barriers remain, including limited real-world validation, algorithmic biases, workflow integration issues, and unresolved ethical and legal concerns. Future progress will depend on the development of transparent, clinically validated, and generalizable AI systems that augment rather than replace the expertise of clinical providers and healthcare teams. Active engagement from pulmonologists, oncologists, radiologists, and thoracic surgeons will be essential in guiding safe implementation and ensuring that AI-driven innovations translate into meaningful improvements in patient outcomes. Full article

Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

Objectives: We aimed to descriptively evaluate the feasibility and clinical utility of TowardPi BO (4K ultra-HD microscope integrated with a 400 kHz swept-source intraoperative optical coherence tomography (SS-iOCT) system) in managing various ophthalmic surgical conditions in a real-world setting. Methods: We analyzed surgical videos and data from 123 consecutive cases that underwent elective surgery with the assistance of this SS-iOCT system at Beijing Tongren Hospital between 2 September 2025 and 10 February 2026. Cases were included when the iOCT provided critical, real-time information that directly influenced surgical decision-making or technique modification. Cases were excluded if iOCT served only routine confirmatory or educational purposes without altering the surgical plan. Results: A total of 72 surgical cases were included, comprising 7 intraocular lens implantations with ciliary sulcus fixation, 19 macular holes, 3 cases of macular hole retinal detachment (MHRD), 4 cases of macular schisis with or without foveal detachment (MSRD), 12 cases of submacular hemorrhage, 20 cases of rhegmatogenous retinal detachment (RRD), and 7 intraocular mass lesions. The 400 kHz SS-iOCT significantly aided in surgical visualization, guided real-time decision-making, and prompted modifications in surgical techniques. Conclusions: To our knowledge, this is the first real-world study to evaluate the application of a 400 kHz SS-iOCT system across a wide spectrum of ophthalmic conditions, including its novel use in intraocular tumors. From routine to complex surgical cases, SS-iOCT enhances surgical precision and facilitates real-time decision-making, ultimately contributing to improved surgical outcomes. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

The past few years has witnessed the rapid development of China’s logistics industry. However, the industry still faces problems such as uneven regional development, low-cost efficiency, insufficient technology application, and pressure for green transformation. To support more effective policy and strategic planning, this study used composite location entropy, spatial autocorrelation analysis, and kernel density estimation to analyze the spatiotemporal evolution of logistics industry agglomeration based on China’s city-level panel data from 2010 to 2023. Geographic detectors and geographically weighted regression were used to explore its driving mechanisms from multiple perspectives. The results indicated that (1) China’s logistics industry agglomeration exhibited a decreasing gradient from east to west and the regional disparities gradually narrowed down over time. (2) China’s logistics industry showed significantly positive spatial autocorrelation, characterized mainly by high-high and low-low clusters. Northeastern China experienced the most active and tortuous local spatial evolution of logistics agglomeration, while Eastern China exhibited high tortuosity but stable spatial structure. Western China showed a smooth evolution, and Central China followed a relatively independent evolutionary path. Spatially, China’s logistics industry presented a pattern of high concentration in the southeast and sparse distribution in the northwest, with high-value zones expanding toward the central and western regions. (3) Transportation accessibility was the primary factor influencing logistics industry agglomeration, and the interaction among factors was stronger than the effect of individual factors. Specifically, the degree of openness exhibited a driving pattern centered on coastal areas and decreasing towards inland regions; the level of commercial development showed a positive correlation in the west and a negative correlation in the east; the spatial pattern of transportation capacity shifted from a pronounced east–west polarization to a more fragmented multi-cluster distribution; and transportation accessibility demonstrated spatial heterogeneity, with positive correlation in the southeast coastal areas and negative correlation in the west. Full article

In this paper, the maximum product spacing method of estimation has been investigated under progressive Type-I censoring scheme. This estimation method has not previously been considered in the life-testing literature, particularly under this censoring scheme. The optimality of the sampling plans under progressive Type-I censoring was studied using different criteria and proposed censoring plans. The applicability of the distribution was examined using the Chen distribution, which is capable of modeling various reliability behaviors. A Monte Carlo simulation was conducted to assess the efficiency of the maximum product spacing method and the optimality of the sampling plans. Finally, an engineering application was analyzed considering progressive Type-I censoring. Full article