Search Results (1,029)

Efficient and safe path planning is the core prerequisite for realizing the autonomous crushing operation of the hydraulic crushing robotic arm in the mine chute. Foundational sampling-based algorithms, specifically standard RRT* and Informed RRT*, have problems such as unattainable targets, high computational redundancy, and hydraulic commutation shock in this highly constrained context. Therefore, this paper proposes a novel path planning method based on an improved Informed RRT* algorithm. Firstly, an axis-aligned bounding box (AABB) is constructed to approximately replace the obstacles, which not only facilitates collision detection but also enables the end of the robotic arm to accurately reach the target point. Secondly, an adaptive hierarchical strategy based on inverse kinematics perception and an artificial potential field guidance mechanism are used to construct the elevated obstacle-crossing corridor, achieving dimensionality-reduced path search and reducing ineffective collision detection. Finally, cubic non-uniform B-spline and seven-segment S-shaped velocity planning are combined to complete trajectory smoothing. Simulation results show that the success rate of the proposed planning algorithm is 100%, the number of generated nodes is reduced by 90.1%, and the trajectory achieves C² continuity, providing command-level smoothing to act as a feedforward mitigation against potential hydraulic oscillations. Path-planning experiments are carried out on the hydraulic crushing robotic arm, and the average positioning error of the end robotic arm reaching position is 30 mm, meeting the accuracy requirements and providing a reliable solution for the safe operation of heavy-duty robotic arms. Full article

Background: At present, there are very limited studies evaluating autonomous artificial intelligence (AI) agents in oral oncology or healthcare education, and no studies have directly compared traditional frontier large language models (LLMs) with autonomous AI agents in the field of pediatric oral oncologic pathology. This study evaluated the performance of AI LLMs and an autonomous AI scientific agent in generating engaging and accessible patient information sheets for common pediatric oral pathologic conditions and rare head and neck tumors. The development of high-quality patient education materials is particularly important in pediatric pathology because parents must often navigate complex and emotionally sensitive diagnoses, including rare tumors and developmental lesions, for which accessible, patient-friendly educational resources are frequently unavailable. AI-generated patient information sheets therefore represent a potential strategy to improve communication, understanding, and shared decision-making for families facing these uncommon conditions. Methods: AI-generated patient information sheets from popular frontier chatbots on various pediatric pathological conditions and rare tumors were evaluated by five platforms (Eli5a v2.0, ChatGPT-5.4, Claude Sonnet 4.6, Perplexity and Doximity), in a blinded fashion, by three expert evaluators using the Global Quality Score (GQS), DISCERN score, understandability score and actionability score using PEMAT, and the Flesch-Kincaid Grade Level. Because the same 20 conditions were assessed on every platform, observations were paired; platforms were compared using Friedman tests with paired Wilcoxon signed-rank post hoc tests and Holm correction, and interrater reliability was quantified using an absolute-agreement intraclass correlation coefficient (ICC). Results: Platform differences were significant for all five outcomes (all p < 0.001). Claude Sonnet 4.6 obtained the best overall information quality (GQS 4.25 ± 0.39), scoring significantly higher than all other platforms, while Perplexity, Eli5a v2.0 and ChatGPT-5.4 performed similarly to one another and better than Doximity. For information reliability, Doximity (DISCERN 70.50 ± 3.20) and Claude Sonnet 4.6 (70.25 ± 3.02) performed best, while Eli5a v2.0 recorded the lowest DISCERN score (54.50 ± 4.26). Eli5a v2.0 obtained the best results for patient-centered communication, with the highest understandability (PEMAT-U 95.05 ± 1.23) and readability (FKGL 5.45 ± 0.51) and an actionability score among the highest of the five platforms (PEMAT-A 89.00 ± 2.62, not significantly different from Perplexity or Doximity). Interrater absolute agreement for GQS was poor to moderate (ICC(2,1) = 0.236; ICC(2,3) = 0.481). Conclusion: No single platform was superior across all evaluated domains. Claude Sonnet 4.6 led in overall quality whereas Eli5a v2.0 achieved the highest understandability and the most appropriatereading grade level. Eli5a’s actionability was high but did not differ significantly from Perplexity or Doximity. These findings indicate a trade-off between information quality and reliability on one hand and lay accessibility on the other. Platform selection should therefore be matched to the communication task, and all AI-generated patient materials require clinician review before use. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most common chronic liver disease in children and adolescents, paralleling the global increase in pediatric obesity. Despite its growing clinical impact, the diagnosis and staging of pediatric MASLD/MASH remain challenging due to the limitations of current non-invasive tools and the complexity of histopathological evaluation. Liver biopsy remains the reference standard for assessing disease activity and fibrosis stage; however, its invasive nature and the substantial inter-observer variability among pathologists highlight the need for more objective and reproducible approaches. In this review, we summarize the current challenges in the diagnosis and management of pediatric MASLD/MASH and discuss the emerging role of artificial intelligence (AI)-driven models in this field. We explore the application of machine learning and deep learning approaches for non-invasive assessment of hepatic steatosis, as well as their potential to improve digital pathology-based evaluation of steatosis, hepatocellular ballooning, inflammation, and fibrosis. Furthermore, we discuss the opportunities and limitations associated with AI implementation in clinical practice, including algorithmic bias, interpretability, data quality, and the need for external validation. Full article

To address redundant and tortuous paths generated for tracked mowers in unstructured orchard environments and the limited adaptability of fixed-gain controllers to nonuniform-curvature paths, this study proposes a joint optimization method combining an improved rapidly exploring random tree (IRRT) planner with fuzzy PD (F-PD) tracking control. The planner uses an obstacle-density-based adaptive step size to balance search efficiency and obstacle-avoidance safety, an improved artificial potential field to bias random samples toward the goal, and cubic B-spline smoothing to generate continuous paths. Based on a differential-steering kinematic model, the F-PD controller uses heading error and its rate of change as inputs and adjusts proportional and derivative gains online through fuzzy inference. Across the three simulated environments, the average reductions in path length, node count, and computation time achieved by IRRT relative to conventional RRT were 10.6%, 11.7%, and 66.7%, respectively. The maximum lateral error of F-PD was 0.43 m, versus 1.42 m for PID. Field tests showed that the IRRT–F-PD combination reduced cumulative operation time and cumulative relative fuel consumption by 30.5% and 26.6%, respectively, compared with RRT–PID. The proposed method improves planning efficiency and curved-path tracking for autonomous orchard mowing. Full article

The use of artificial intelligence (AI) in endoscopic ultrasonography (EUS) is receiving increasing attention, particularly in the field of pancreatic diseases, where early and accurate diagnosis remains a major clinical challenge. This narrative review focuses on current and emerging applications of AI in pancreatic EUS, covering both image-based diagnostic support and the analysis of samples obtained through EUS-guided tissue acquisition. The first part of the review discusses how AI is being applied to improve EUS image interpretation, ranging from lesion detection to characterization and differentiation between benign and malignant pancreatic findings. The review also discusses early evidence and future perspectives for real-time procedural support, where diagnostic performance remains highly operator-dependent. The second part explores a less frequently discussed but equally relevant area: the use of AI in the analysis of cytological specimens obtained through EUS-guided fine-needle aspiration or fine-needle biopsy. Although cytopathology may appear to lie outside the traditional clinical scope of EUS, it represents an essential step in the diagnostic workflow of pancreatic diseases. Recent developments in AI-assisted digital cytology and pathology have shown promising potential to support and standardize cytological interpretation, with possible benefits in terms of diagnostic consistency, reproducibility, and turnaround time. By bridging imaging and pathology, AI may enhance the entire pancreatic EUS workflow, contributing to more efficient, accurate, and personalized diagnostic pathways in pancreatic disease management. Full article

Antimicrobial peptides (AMPs) are increasingly regarded as next-generation antimicrobial agents because of their broad-spectrum activity, rapid killing, antibiofilm potential, immunomodulatory properties, and mechanisms of action that differ from those of many conventional antibiotics. Despite these advantages, their clinical translation remains limited by proteolytic instability, hemolysis or cytotoxicity, poor pharmacokinetics, salt and serum sensitivity, production costs, and delivery challenges. The AMP field is therefore shifting from natural peptide discovery toward integrated engineering pipelines that combine rational peptide modification, biomaterial-based delivery, high-throughput screening, and artificial intelligence (AI), particularly machine learning (ML) and deep learning approaches. Chemical and structural modifications, including D-amino acid substitution, N-glycine substitution, cyclization, lipidation, PEGylation, terminal amidation, hydrocarbon stapling, hybridization, sequence truncation, metal coordination, and biomaterial immobilization, are being used to improve stability, potency, selectivity, antibiofilm activity, and tissue localization. In parallel, AI-guided approaches, including ML, deep learning, and generative modeling, enable large-scale exploration of diverse peptide sources, including microbiomes and extinct proteomes, to entirely new sequences, while supporting optimization of potency, selectivity, stability, toxicity, and synthesizability. This focused review summarizes recent advances in AMP engineering, biomaterial-assisted delivery, and AI-guided discovery for biofilm-associated infections in the context of antimicrobial resistance. Full article

In complex three-dimensional airspace, UAV trajectory planning is subject to stringent real-time constraints and must rapidly generate collision-free, near-optimal and curvature-continuous feasible flight paths within a limited computational window. Although mainstream bidirectional RRT-based algorithms improve the basic search speed through parallel dual-tree expansion, they still suffer from inherent limitations, including blind sampling, fixed expansion strategies and poor environmental adaptability. To address these limitations, this study proposes a Dynamic Hybrid Multi-strategy RRT (DHM-RRT*) algorithm. In the sampling stage, a hybrid strategy combining frontier-density adaptive sampling, Halton low-discrepancy sampling and uniform random sampling is adopted. In the expansion stage, a four-level progressive expansion mechanism is designed, comprising goal-directed expansion, dual-distance scoring tangent-cone obstacle avoidance, improved artificial-potential-field guidance and random fallback expansion. The failure rate of each strategy is estimated online using an exponential moving average, and the expansion probabilities are dynamically and adaptively assigned. After path generation, path quality is further improved through greedy direct connection near the stitching seam and B-spline smoothing. The algorithm was independently evaluated in three MATLAB three-dimensional obstacle environments and compared with the best-performing baseline algorithm in each environment. The proposed algorithm reduced the average path length by 1.78%, 0.42% and 5.49%, respectively, and reduced the planning time by 38.89%, 34.78% and 29.03%, respectively. The simulation results demonstrate that the proposed algorithm provides clear advantages in convergence speed, path length, smoothness and environmental robustness under complex obstacle constraints. Full article

As a common benign bone tumor, the pathological diagnosis of chondrogenic tumors needs to accurately analyze the calcification pattern of the cartilage matrix, key spatial topology information, and other important indicators. However, chondrogenic tumors are a heterogeneous group of tumors and a rare disease: doctors lack sufficient reference data and experience in diagnosis. At the same time, complex spatial structural information also increases the difficulty of diagnosis, which leads to inconsistencies in the diagnosis’s results of different doctors. In contrast, with the development of technology, artificial intelligence (AI), with its fast, accurate, and robust characteristics, can effectively improve the efficiency and accuracy of diagnosis. However, the application of AI in this field is still unrecognized, so it is urgent to develop a model that can help doctors in diagnosis to improve accuracy and efficiency. In this study, we propose XChondNet, an explainable spatial-context-aware synergistic deep feature fusion model for WSI-based chondrogenic tumor classification. The model proposes a fusion mechanism of pathological and positional features so that the model can effectively perceive spatial structural information and a parallel classifier mechanism based on potential coding, which can effectively solve the problem of class imbalance in chondrogenic tumor data. We evaluated the XChondNet model on our chondrogenic tumor dataset and the experimental results verified its effectiveness in the classification of the chondrogenic tumor subtype. In the test phase, XChondNet consistently achieved superior performance across different feature extractors. Compared with two strong MIL baselines, DTFD-MIL and RRT-MIL, XChondNet improved the average ACC from 85.16% to 86.58% (1.42 percentage points), with statistically significant differences confirmed by a paired t-test ($p=2.61\times {10}^{-5}$) and Wilcoxon signed-rank test ($p=0.0078$). Moreover, the attention maps of XChondNet explicitly reflect regions consistent with pathologists’ diagnostic concerns, enhancing the interpretability of AI-assisted diagnosis. Full article

Large ensembles of atmospheric dispersion simulations are necessary for uncertainty quantification, source attribution, and emergency response, however, physics-based models like HYSPLIT might be too computationally expensive for operational use. In addition, they have not fully benefited from recent advances in artificial-intelligence-based surrogate modelling, which offer the potential to reduce this computational burden. This study introduces the Dual-Head Mass-Consistent Plume Surrogate (DM-CPS), a machine-learning surrogate to promote the prediction of PM_2.5 transport at a much higher speed without compromising the geometry and integrated pollutant mass of the plume. The proposed framework utilizes two types of complementary histogram gradient-boosting models: one trained in the logarithmic concentration domain to reconstruct the plume morphology, and one trained in the original concentration domain to estimate the total pollutant burden. The outputs of the two branches are then combined through a field-level mass-consistency scaling process that preserves the spatial structure of the plume while enforcing a physically consistent integrated pollutant mass. The model is trained on a GDAS-driven ensemble of 96 HYSPLIT simulations over Almaty, Kazakhstan, which includes 1.88 million grid-cell records and physics-informed wind-aligned features. DM-CPS achieves the highest operational dispersion skill (FAC2 = 0.57) with a significant reduction in geometric bias (MG: 2.75 → 1.68) and an improvement in the median integrated-mass ratio (0.13 → 0.23) while maintaining plume structure (SSIM ≈ 0.83) compared to random forest, multilayer perceptron, and conventional gradient-boosted baselines. The surrogate predicts an entire concentration field in about 35 ms, a factor up to five times faster than running HYSPLIT. The cross-regime evaluation also shows a significant gap of generalization in the presence of unseen, out-of-training meteorological data, again emphasizing the need for increased diversity of meteorological training data for operational use. The results show that physically informed surrogate modeling can significantly speed up the prediction of atmospheric dispersion, which is fundamental to run large ensembles. Full article

The rapid acquisition of spatial information over affected areas is critical for emergency decision-making and disaster assessment in complex environments. Unmanned aerial vehicles (UAVs) have become an important tool for disaster information acquisition due to their rapid deployment and flexible observation capabilities. However, multi-UAV remote sensing information acquisition in complex obstacle-laden environments still faces challenges such as insufficient coverage efficiency, limited obstacle avoidance capability, and weak cooperation. This paper proposes a multi-UAV cooperative remote sensing coverage method based on an improved rotational potential field (IRPF). A disaster area model is first constructed. The artificial potential field is then enhanced by integrating separation forces and rotational guidance mechanisms to improve obstacle avoidance and information acquisition capability in complex environments, while coverage feedback is incorporated to enable dynamic observation region allocation. The experimental results show that the proposed method achieves a multi-run success rate of 100.0% over 20 independent experiments, with a final coverage rate of 93.3% in the representative scenario. The UAV system reaches the predefined 85% coverage threshold at step 106 and maintains zero collisions throughout the entire process. This method provides an effective approach for multi-UAV cooperative coverage planning in simulated complex environments, providing a potential approach for cooperative coverage planning in simulated disaster environments. Full article

Beaver dams can substantially modify flow conditions and increase local water retention, particularly in small urban and peri-urban streams exposed to hydrological alterations and increasing water deficits. This study evaluates the influence of beaver dams on hydraulic conditions, retention capacity, and water resources in the Junikowski Stream in Poznań, Poland. Field surveys, geodetic measurements, and spatial data were used to develop a one-dimensional hydraulic model in HEC-RAS. Three management scenarios were analysed: a channel without impoundment structures, the 2022 configuration including beaver dams and two artificial weirs, and the 2025 configuration representing a more developed beaver-dam cascade together with the functioning weirs. Simulations were conducted for a range of characteristic and probability flows to assess changes in water levels, inundation extent, and retained water volume. The results show that beaver dams exert the strongest effect under low-flow conditions, when they significantly increase water levels and improve local retention. Their hydraulic influence decreases with increasing discharge, although they continue to affect the spatial distribution of water in the valley. The proposed artificial structure may partly maintain retention benefits in the event of beaver dam degradation or removal. The findings demonstrate that beaver dams may function as effective nature-based solutions supporting water retention and potentially contributing to drought resilience and sustainable management of urban stream valleys. Full article

To address the challenge of rapid and precise obstacle avoidance for unmanned aerial vehicles (UAVs) in complex urban environments, rugged canyons, and other unstructured environments, this paper proposes a vision-based navigation algorithm. By combining the strengths of deep reinforcement learning (DRL) and convolutional neural networks (CNNs), this algorithm enables efficient navigation and obstacle avoidance in dynamic environments. First, to improve training efficiency, an autoencoder is used to extract latent spatial vectors from depth images, which are then used as input features for DRL. Second, an artificial potential field (APF) is introduced into the reward function to enhance obstacle avoidance performance in dynamic environments. Third, a CNN-based adaptive mode-switching mechanism is designed to meet navigation requirements under different environmental conditions. This mechanism can automatically identify environmental features based on real-time input data and dynamically adjust the UAV’s navigation strategy. To evaluate the proposed method, simulation experiments were conducted in static and dynamic scenarios, together with a preliminary indoor flight test. Under the evaluated conditions, the proposed method achieved favorable navigation success rates and path efficiency compared with the selected visual DRL baselines. The results also indicate cross-scenario transferability to the tested environments without environment-specific retraining. Full article

Accurate information on atmospheric temperature profiles is crucial for improving numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which thereby fail to meet the high spatial resolution required for NWP. In this study, we present an integrated framework as an engineering refinement combining the variation method with an artificial neural network (Var-ANN) to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) aboard the polar-orbiting satellite FY-3C. The variation method is first applied to construct a spatially consistent reference field from available station observations, and this field is then used as the training target for a back-propagation neural network that learns the empirical relationship between satellite brightness temperatures and corrected atmospheric temperature. The calibrated temperature profiles were evaluated against independent radiosonde observations and further tested through assimilation into the Weather Research and Forecasting (WRF) model for precipitation simulation over the TP. Results indicate that the Var-ANN calibration reduces the root-mean-square error (RMSE) by approximately 60% and the mean bias from approximately −5 °C to −0.7 °C relative to radiosonde observations. In two WRF case studies, the calibrated profiles show potential for improving precipitation forecast skill, although the limited sample size precludes robust conclusions about operational forecast improvements. The Var-ANN framework provides a practical approach for enhancing the utility of FY-3C VASS temperature products for NWP applications over data-sparse complex terrain. Full article

Campus waste-sorting mobile manipulators operate in cluttered environments. This paper presents a task-oriented framework decoupling mobile-base navigation from manipulator motion. Its contribution is the safety-verified integration of goal-biased sampling, adaptive step-size adjustment, artificial potential field (APF)-guided directional correction, collision-recovery direction selection, and collision-checked bidirectional tree connection within Improved-BiRRT. After obtaining a feasible path, visibility-based pruning and piecewise cubic Hermite interpolating polynomial (PCHIP) smoothing are applied, followed by segment-wise collision verification with fallback to the verified pruned path. MATLAB R2024b simulations cover simple campus, complex campus, and narrow-passage environments. RRT, GoalBias-RRT, BiRRT, and Improved-BiRRT are evaluated under identical settings and post-processing. In the narrow-passage environment, Improved-BiRRT achieves a mean raw feasible-path length of $32.155\pm 1.935\mathrm{m}$, a mean final collision-verified path length of $27.041\pm 0.327\mathrm{m}$, and $39.25\pm 12.64$ iterations. Holm-adjusted Wilcoxon rank-sum tests show significantly lower final path lengths and iteration counts than baselines in the complex campus and narrow-passage environments ($p_{\mathrm{adj}}<0.05$). A five-target validation yields a cumulative collision-verified path length of $136.171\mathrm{m}$ and a cumulative MATLAB online planning time of $0.1305\mathrm{s}$. The results demonstrate feasibility for static two-dimensional campus waste-sorting tasks with predefined target ordering. Full article

Vertical farming (VF) offers a smart way to grow crops in stacked layers inside controlled indoor environments. By doing so, it uses far less land and water than traditional open-field agriculture, making it a promising solution for cities with limited space and resources. In recent years, artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies have begun to transform vertical farming. These tools are moving the industry away from rigid, rule-based systems toward more flexible, data-driven operations that can adapt in real time. This paper presents a systematic review of 208 peer-reviewed studies from 2015 to 2025. It explores how AI, ML, and IoT are applied across the VF ecosystem, focusing on key areas such as computer vision for disease detection, crop growth and yield prediction, smart climate control, and precision nutrient and irrigation management. This review examines the performance of different algorithms, including Convolutional Neural Networks (CNNs), Random Forest, XGBoost, and LSTMs across hydroponic, aeroponic, and aquaponic systems. The review also covers IoT setups with multi-sensor networks, edge-cloud computing, and automated control systems. Commercial farms have shown real gains in resource efficiency and shorter supply chains. However, challenges remain: high energy use (especially from LED lighting, which makes up 40–60% of costs), expensive setup, scattered datasets, and limited real-world testing. Many high-accuracy claims (>95%) come from lab conditions and need better validation in actual farms. Overall, AI-powered vertical farming has strong potential to support resilient urban food systems. Future work should focus on lightweight edge AI models, improved data standards, explainable AI, and robust life cycle assessments to ensure the benefits outweigh the environmental and economic costs. Full article