Search Results (838)

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. Full article

In response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. The method is optimized according to the structural characteristics of cotton pickers, and a lightweight improved YOLOv11 algorithm is designed for cotton fire detection in cotton pickers. The backbone of the model is replaced with the MobileNetV2 network to achieve effective model lightweighting. In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. Furthermore, a visual attention mechanism named CBAM-ECA (Convolutional Block Attention Module-Efficient Channel Attention) is designed to suit the complex working conditions of cotton pickers. This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. To further improve localization performance and accelerate convergence, the loss function is also modified. These improvements enable the model to achieve higher precision in fire detection while ensuring fast and accurate localization. Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. The detection accuracy for flaming combustion, smoldering combustion, and overall detection is improved by 1.4%, 3%, and 1.9%, respectively, with an increase of 2.4% in mAP (mean average precision). Compared to other models—YOLOv3-tiny, YOLOv5, YOLOv8, and YOLOv10—the proposed method achieves higher detection accuracy by 5.9%, 7%, 5.9%, and 5.3%, respectively, and shows improvements in mAP by 5.4%, 5%, 4.8%, and 6.3%. The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. These improvements lay a solid foundation for fire prevention and suppression in cotton collection boxes on cotton pickers. Full article

Deep Reinforcement Learning (DRL) algorithms often exhibit significant performance variability across different training runs, even with identical settings. This paper investigates the hypothesis that a key contributor to this variability is the divergence in the observation spaces explored by individual learning agents. We conducted an empirical study using Proximal Policy Optimization (PPO) agents trained on eight Atari environments. We analyzed the collected agent trajectories by qualitatively visualizing and quantitatively measuring the divergence in their explored observation spaces. Furthermore, we cross-evaluated the learned actor and value networks, measuring the average absolute TD-error, the RMSE of value estimates, and the KL divergence between policies to assess their functional similarity. We also conducted experiments where agents were trained from identical network initializations to isolate the source of this divergence. Our findings reveal a strong correlation: environments with low-performance variance (e.g., Freeway) showed high similarity in explored observation spaces and learned networks across agents. Conversely, environments with high-performance variability (e.g., Boxing, Qbert) demonstrated significant divergence in both explored states and network functionalities. This pattern persisted even when agents started with identical network weights. These results suggest that differences in experiential trajectories, driven by the stochasticity of agent–environment interactions, lead to specialized agent policies and value functions, thereby contributing substantially to the observed inconsistencies in DRL performance. Full article

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article

As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications. Full article

Background/Objectives: The bidirectional selection of the Roman low- (RLA) and Roman high-avoidance (RHA) rat strains for extremely slow vs. very rapid acquisition of the two-way (shuttle-box) avoidance response has generated two divergent phenotypic profiles: RHA rats exhibit a behavioural pattern and gene expression profile in the frontal cortex and hippocampus (HPC) that are relevant to social and attentional/cognitive schizophrenia-linked symptoms; on the other hand, RLA rats display phenotypic traits linked to increased anxiety and sensitivity to stress-induced depression-like behaviours. The present studies aimed to evaluate the enduring and potentially positive effects of neonatal handling-stimulation (NH) on the traits differentiating these two strains of rats. Methods: We evaluated the effects of NH on anxious behaviour, prepulse inhibition of startle (PPI), spatial working memory, and hormone responses to stress in adult rats of both strains. Furthermore, given the proposed involvement of neuronal/synaptic plasticity and neurotrophic factors in the development of anxiety, stress, depression, and schizophrenia-related symptoms, using Western blot (WB) we assessed the effects of NH on the content of brain-derived neurotrophic factor (BDNF), its trkB receptor and Polysialilated-Neural Cell Adhesion Molecule (PSA-NCAM), in the prefrontal cortex (PFC), anterior cingulate cortex (ACg), ventral (vHPC), and dorsal (dHPC) hippocampus of adult rats from both strains. Results: NH increased novelty-induced exploration and reduced anxiety, particularly in RLA rats, attenuated the stress-induced increment in corticosterone and prolactin plasma levels, and improved PPI and spatial working memory in RHA rats. These effects correlated to long-lasting increases of BDNF and PSA-NCAM content in PFC, ACg, and vHPC. Conclusions: Collectively, these findings show enduring and distinct NH effects on neuroendocrine and behavioural and cognitive processes in both rat strains, which may be linked to neuroplastic and synaptic changes in the frontal cortex and/or hippocampus. Full article

Background/objectives: In 9/2020, the United States Food and Drug Administration )FDA( posted a black box warning for all benzodiazepines, addressing their association with serious risks of abuse, addiction, physical dependence, and withdrawal reactions. We evaluated changes in benzodiazepine dispensing rate trends after this warning. Methods: The dataset of Clalit Health Services (Israel’s largest insurer, with 5 million members) was used to identify and collect benzodiazepine dispensing data for all patients who were dispensed these drugs at least once during the study period (1/2017–12/2021). The dispensing rate (number of patients who were dispensed benzodiazepines per month divided by the number of patients alive during that month) was calculated for each month in the study period. Linear regression and change point regression were used to review the change in trend before and after the black box warning. New users of benzodiazepines after the black box warning were analyzed by age. Results: A total of 639,515 patients using benzodiazepines were reviewed. The mean benzodiazepine dispensing rate per month was 0.21 and ranged from 0.17 (in 2/2017) to 0.24 (in 3/2020). No significant change in trend was observed before vs. after the black box warning (slopes of 0.00675 percentage points per month and 0.00001 percentage points per month, respectively; p = 0.38). The change point regression analysis identified a change point in 4/2019, which is prior to the black box warning. New users were younger after the black box warning compared to before this warning. Conclusions: The FDA black box warning did not affect the dispensing rate of benzodiazepines. Full article

Polyvinyl alcohol (PVA) hydrogels have emerged as versatile materials due to their exceptional biocompatibility and tunable mechanical properties, showing great promise for flexible sensors, smart wound dressings, and tissue engineering applications. However, rational design remains challenging due to complex structure–property relationships involving multiple formulation parameters. This study presents an interpretable machine learning framework for predicting PVA hydrogel tensile strain properties with emphasis on mechanistic understanding, based on a comprehensive dataset of 350 data points collected from a systematic literature review. XGBoost demonstrated superior performance after Optuna-based optimization, achieving R² values of 0.964 for training and 0.801 for testing. SHAP analysis provided unprecedented mechanistic insights, revealing that PVA molecular weight dominates mechanical performance (SHAP importance: 84.94) through chain entanglement and crystallization mechanisms, followed by degree of hydrolysis (72.46) and cross-linking parameters. The interpretability analysis identified optimal parameter ranges and critical feature interactions, elucidating complex non-linear relationships and reinforcement mechanisms. By addressing the “black box” limitation of machine learning, this approach enables rational design strategies and mechanistic understanding for next-generation multifunctional hydrogels. Full article

Plastic pollution in marine environments is a globally recognized concern that poses ecological and economic threats. While 80% of plastic originates from land, 20% comes from sea-based sources like shipping and fishing. Comprehensive assessments of fishing-related plastics are limited but crucial for mitigation. This study analyzed the distribution and temporal evolution of three fishing-related items (EPS fish boxes, fragments, and buoys) along the Bocassette spit in the northern Adriatic Sea, a region with high fishing and aquaculture activity. UAV monitoring (November 2019, June/October 2020) and structured interviews with Po Delta fishermen were conducted. The collected debris was mainly EPS, with boxes (54.8%) and fragments (39.6%). Fishermen showed strong awareness of degradation, identifying plastic as the primary litter type and reporting gear loss. Litter concentrated in active dunes and the southern sector indicates human and riverine influence. Persistent items (61%) at higher elevations suggest longer residence times. Mapped EPS boxes could generate billions of micro-particles (e.g., ~10¹³). The results reveal a complex interaction between natural processes and human activities in litter distribution. This highlights the need for integrated management strategies, like improved waste management, targeted cleanup, and community involvement, to reduce long-term impacts on vulnerable coastal ecosystems. Full article

Existing frameworks for classifying the fidelity for object detection tasks do not consider false positive likelihood and object uniqueness. Inspired by the Detection, Recognition, Identification (DRI) framework proposed by Johnson 1958, we propose a new modified framework that defines three categories as visible anomaly, identifiable anomaly, and unique identifiable anomaly (AIU) as determined by human interpretation of imagery or geophysical data. These categories are designed to better capture false positive rates and emphasize the importance of identifying unique versus non-unique targets compared to the DRI Index. We then analyze visual, thermal, and multispectral UAV imagery collected over a seeded minefield and apply the AIU Index for the landmine detection use-case. We find that RGB imagery provided the most value per pixel, achieving a 100% identifiable anomaly rate at 125 pixels on target, and the highest unique target classification compared to thermal and multispectral imaging for the detection and identification of surface landmines and UXO. We also investigate how the AIU Index can be applied to machine learning for the selection of training data and informing the required action to take after object detection bounding boxes are predicted. Overall, the anomaly, identifiable anomaly, and unique identifiable anomaly index prescribes essential context for false-positive-sensitive or resolution-poor object detection tasks with applications in modality comparison, machine learning, and remote sensing data acquisition. Full article

Deubiquitination plays a pivotal role in regulating plant responses to abiotic stress, growth, and development. Among the deubiquitinase (DUB) families, ubiquitin-specific proteases (UBPs) constitute the largest group. Despite this, limited research has been conducted on the functional characteristics of the UBP gene family in soybean (Glycine max). In this study, we identified 52 UBP gene family members in soybean, all of which harbored UCH (ubiquitin C-terminal hydrolase) domains with short yet evolutionarily conserved Cys-box and His-box. These genes were phylogenetically classified into 14 distinct groups; GmUBP genes within the same group shared analogous patterns of conserved domains and motifs. Moreover, a synteny analysis reveals that the GmUBP family has undergone extensive gene duplication events and shares a close evolutionary relationship with Arabidopsis thaliana. We conducted a focused analysis on GmUBP7, which is a gene exhibiting high expression levels in soybean seeds. Intriguingly, this gene exhibited several haplotypes in natural soybean varieties, with significant differences being observed in relation to seed traits, such as 100-seed weight, total fatty acid content, and protein content among different haplotypes. Collectively, the findings from this study provide a foundation for the functional characterization of GmUBP genes, offering new insights into the regulatory network underlying seed development in soybean. Full article

The integration of Artificial Intelligence (AI) into manufacturing is transforming the industry by advancing predictive maintenance, quality control, and supply chain optimisation, while also driving the shift from Industry 4.0 towards a more human-centric and sustainable vision. This emerging paradigm, known as Industry 5.0, emphasises resilience, ethical innovation, and the symbiosis between humans and intelligent systems, with AI playing a central enabling role. However, challenges such as the “black box” nature of AI models, data biases, ethical concerns, and the lack of robust frameworks for trustworthiness hinder its widespread adoption. This paper provides a comprehensive survey of AI trustworthiness in the manufacturing industry, examining the evolution of industrial paradigms, identifying key barriers to AI adoption, and examining principles such as transparency, fairness, robustness, and accountability. It offers a detailed summary of existing toolkits and methodologies for explainability, bias mitigation, and robustness, which are essential for fostering trust in AI systems. Additionally, this paper examines challenges throughout the AI pipeline, from data collection to model deployment, and concludes with recommendations and research questions aimed at addressing these issues. By offering actionable insights, this study aims to guide researchers, practitioners, and policymakers in developing ethical and reliable AI systems that align with the principles of Industry 5.0, ensuring both technological advancement and societal value. Full article

Bronchopulmonary dysplasia (BPD) remains a significant complication of premature birth and neonatal intensive care. While much is known about the drivers of lung injury, few studies have addressed the interrelationships between oxidative stress, inflammation, and downstream events, such as endoplasmic reticulum (ER) stress. In this review, we explore the concept of a “destructive cycle” in which these drivers self-amplify to push the lung into a state of maladaptive repair. Animal models, primarily the hyperoxic rat pup model, support a sequential progression from the generation of reactive oxygen species (ROS) and inflammation to endoplasmic reticulum (ER) stress and mitochondrial injury. We highlight how these intersecting pathways offer not just therapeutic targets but also opportunities for interventions that reprogram system-wide responses. Accordingly, we explore the potential of systems pharmacology therapeutics (SPTs) to address the multifactorial nature of BPD. As a prototype SPT, we describe the development of N-acetyl-L-lysyl-L-tyrosyl-L-cysteine amide (KYC), a systems chemico-pharmacology drug (SCPD), which is selectively activated in inflamed tissues and modulates key nodal targets such as high-mobility group box-1 (HMGB1) and Kelch-like ECH-associated protein-1 (Keap1). Collectively, the data suggest that future therapies may require a coordinated, network-level approach to break the destructive cycle and enable proper regeneration rather than partial repair. Full article

This paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. Two key process evaluation indicators were developed: specific electric energy consumption and an extended indicator that also includes gas usage. Using process data collected from the SCADA system and results from industrial factorial experiments, regression artificial neural network models were developed, with controllable process parameters used as inputs. In the next phase, black-box optimisation was performed using Bayesian and genetic algorithms to identify optimal mill operating settings. The results demonstrate significant improvements in energy efficiency, with energy savings reaching up to 48% in selected cases. The proposed methodology can be effectively applied to enhance energy performance in similar industrial grinding processes. Full article