Image-based Flight Control of Unmanned Aerial Vehicles (UAVs) for Material Handling in Custom Manufacturing

This study introduces an approach for and the challenges in employing unmanned aerial vehicles (UAVs) for material handling in the emerging industrial custom manufacturing environments. Compared with conventional industrial robotic systems, UAVs offer enhanced flexibility for the design and on-the-fly variation of the pathways and workflow to optimally perform multiple tasks on demand, besides offering favorable cost and dimensional footprint factors. A fundamental challenge to the deployment of UAVs in manufacturing and other indoor industrial settings lies in ensuring the accuracy of a drone’s localization and flight path. Earlier approaches based on using multiple sensors (e.g., GPS, IMU) to improve the localization accuracy of UAVs are considered ineffective in indoor environments. In fact, few investigations have tackled the issues arising due to the limited space and complicated components and moving entities, human presence in shop-floor environments. Towards addressing this challenge, a pose estimation method that employs just a single camera onboard with a UAV, together with multiple ArUco markers positioned strategically over the shop-floor is implemented to track the real-time location of a UAV. A Kalman filter is applied to mitigate noise effects for pose estimation. To assess the performance of this method, several experiments were carried out in Texas A&M University’s manufacturing labs. The result suggests that Kalman filter can reduce the variance of pose estimation by 88.48% compared to a conventional camera and marker-based motion tracking method (~ 27 cm) and can localize (via averaging) the position to within 8 cm of the actual target location

BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation

In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying. Existing research methods show that it is possible to capture the heterogeneous interests of users through different types of behaviors. However, most multi-behavior approaches have limitations in learning the relationship between different behaviors. In this paper, we propose a novel multilayer perceptron (MLP)-based heterogeneous sequential recommendation method, namely behavior-aware multilayer perceptron (BMLP). Specifically, it has two main modules, including a heterogeneous interest perception (HIP) module, which models behaviors at multiple granularities through behavior types and transition relationships, and a purchase intent perception (PIP) module, which adaptively fuses subsequences of auxiliary behaviors to capture users' purchase intent. Compared with mainstream sequence models, MLP is competitive in terms of accuracy and has unique advantages in simplicity and efficiency. Extensive experiments show that BMLP achieves significant improvement over state-of-the-art algorithms on four public datasets. In addition, its pure MLP architecture leads to a linear time complexity

Learning graph-Fourier spectra of textured surface images for defect localization

In the realm of industrial manufacturing, product inspection remains a significant bottleneck, with only a small fraction of manufactured items undergoing inspection for surface defects. Advances in imaging systems and AI can allow automated full inspection of manufactured surfaces. However, even the most contemporary imaging and machine learning methods perform poorly for detecting defects in images with highly textured backgrounds, that stem from diverse manufacturing processes. This paper introduces an approach based on graph Fourier analysis to automatically identify defective images, as well as crucial graph Fourier coefficients that inform the defects in images amidst highly textured backgrounds. The approach capitalizes on the ability of graph representations to capture the complex dynamics inherent in high-dimensional data, preserving crucial locality properties in a lower dimensional space. A convolutional neural network model (1D-CNN) was trained with the coefficients of the graph Fourier transform of the images as the input to identify, with classification accuracy of 99.4%, if the image contains a defect. An explainable AI method using SHAP (SHapley Additive exPlanations) was used to further analyze the trained 1D-CNN model to discern important spectral coefficients for each image. This approach sheds light on the crucial contribution of low-frequency graph eigen waveforms to precisely localize surface defects in images, thereby advancing the realization of zero-defect manufacturing

Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models

Pattern recognition through the fusion of RGB frames and Event streams has emerged as a novel research area in recent years. Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition. However, we posit that these methods may suffer from key issues like sematic gaps and small-scale backbone networks. In this study, we introduce a novel pattern recognition framework that consolidates the semantic labels, RGB frames, and event streams, leveraging pre-trained large-scale vision-language models. Specifically, given the input RGB frames, event streams, and all the predefined semantic labels, we employ a pre-trained large-scale vision model (CLIP vision encoder) to extract the RGB and event features. To handle the semantic labels, we initially convert them into language descriptions through prompt engineering, and then obtain the semantic features using the pre-trained large-scale language model (CLIP text encoder). Subsequently, we integrate the RGB/Event features and semantic features using multimodal Transformer networks. The resulting frame and event tokens are further amplified using self-attention layers. Concurrently, we propose to enhance the interactions between text tokens and RGB/Event tokens via cross-attention. Finally, we consolidate all three modalities using self-attention and feed-forward layers for recognition. Comprehensive experiments on the HARDVS and PokerEvent datasets fully substantiate the efficacy of our proposed SAFE model. The source code will be made available at https://github.com/Event-AHU/SAFE_LargeVLM.Comment: In Peer Revie

Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer

Designing molecules with desirable properties, such as drug-likeliness and high binding affinities towards protein targets, is a challenging problem. In this paper, we propose the Dual-Space Optimization (DSO) method that integrates latent space sampling and data space selection to solve this problem. DSO iteratively updates a latent space generative model and a synthetic dataset in an optimization process that gradually shifts the generative model and the synthetic data towards regions of desired property values. Our generative model takes the form of a Latent Prompt Transformer (LPT) where the latent vector serves as the prompt of a causal transformer. Our extensive experiments demonstrate effectiveness of the proposed method, which sets new performance benchmarks across single-objective, multi-objective and constrained molecule design tasks

Current understanding of CTLA-4: from mechanism to autoimmune diseases

Autoimmune diseases (ADs) are characterized by the production of autoreactive lymphocytes, immune responses to self-antigens, and inflammation in related tissues and organs. Cytotoxic T-lymphocyte antigen 4 (CTLA-4) is majorly expressed in activated T cells and works as a critical regulator in the inflammatory response. In this review, we first describe the structure, expression, and how the signaling pathways of CTLA-4 participate in reducing effector T-cell activity and enhancing the immunomodulatory ability of regulatory T (Treg) cells to reduce immune response, maintain immune homeostasis, and maintain autoimmune silence. We then focused on the correlation between CTLA-4 and different ADs and how this molecule regulates the immune activity of the diseases and inhibits the onset, progression, and pathology of various ADs. Finally, we summarized the current progress of CTLA-4 as a therapeutic target for various ADs

Causal relationship between depression and metabolic dysfunction-associated steatotic liver disease: a bidirectional Mendelian randomized study

BackgroundWith the global rise in obesity, metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as the most common chronic liver disease. Concurrently, depression is a highly prevalent mental disorder. As the incidence of MASLD and depression continues to increase, a growing body of research indicates a potential association between the two conditions. However, the direction of causality between depression and MASLD remains uncertain. To address this gap, our study utilizes a two-sample Mendelian randomization (MR) approach to explore the bidirectional causal relationship between depression and MASLD.MethodsWe extracted single nucleotide polymorphisms (SNPs) associated with depression and MASLD from pooled data of genome-wide association studies (GWAS). A comprehensive assessment of possible causality was also performed. Possible mediating effects of liver enzymes on MASLD were also assessed.ResultsA total of three GWAS pooled data on depression as well as GWAS data related to MASLD and GWAS data on four liver enzymes were used in this study. Our findings indicated a strong causal relationship between depression and MASLD (OR, 1.557; 95% CI, 1.097–2.211; P = 0.016). And we found a mediating effect of gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). ALT 10% (95% CI: 7% - 13%, P< 0.0002). AST, 4.14% (95% CI: 2.34% - 5.94%, P < 0.05). GGT 0.19% (95% CI: 0.15% - 0.22%, P< 0.000000002). However, we did not find a mediating effect of alkaline phosphatase (ALP). Our inverse MR analysis did not reveal any causal relationship between MASLD and depression.ConclusionsThe MR analysis revealed a positive causal relationship between depression and MASLD, while no reverse causal relationship was identified. Liver enzymes may mediate the role between depression and MASLD

Climate change : strategies for mitigation and adaptation

The sustainability of life on Earth is under increasing threat due to humaninduced climate change. This perilous change in the Earth's climate is caused by increases in carbon dioxide and other greenhouse gases in the atmosphere, primarily due to emissions associated with burning fossil fuels. Over the next two to three decades, the effects of climate change, such as heatwaves, wildfires, droughts, storms, and floods, are expected to worsen, posing greater risks to human health and global stability. These trends call for the implementation of mitigation and adaptation strategies. Pollution and environmental degradation exacerbate existing problems and make people and nature more susceptible to the effects of climate change. In this review, we examine the current state of global climate change from different perspectives. We summarize evidence of climate change in Earth’s spheres, discuss emission pathways and drivers of climate change, and analyze the impact of climate change on environmental and human health. We also explore strategies for climate change mitigation and adaptation and highlight key challenges for reversing and adapting to global climate change

Image-based Flight Control of Unmanned Aerial Vehicles (UAVs) for Material Handling in Custom Manufacturing

This study introduces an approach for and the challenges in employing unmanned aerial vehicles (UAVs) for material handling in the emerging industrial custom manufacturing environments. Compared with conventional industrial robotic systems, UAVs offer enhanced flexibility for the design and on-the-fly variation of the pathways and workflow to optimally perform multiple tasks on demand, besides offering favorable cost and dimensional footprint factors. A fundamental challenge to the deployment of UAVs in manufacturing and other indoor industrial settings lies in ensuring the accuracy of a drone’s localization and flight path. Earlier approaches based on using multiple sensors (e.g., GPS, IMU) to improve the localization accuracy of UAVs are considered ineffective in indoor environments. In fact, few investigations have tackled the issues arising due to the limited space and complicated components and moving entities, human presence in shop-floor environments. Towards addressing this challenge, a pose estimation method that employs just a single camera onboard with a UAV, together with multiple ArUco markers positioned strategically over the shop-floor is implemented to track the real-time location of a UAV. A Kalman filter is applied to mitigate noise effects for pose estimation. To assess the performance of this method, several experiments were carried out in Texas A&M University’s manufacturing labs. The result suggests that Kalman filter can reduce the variance of pose estimation by 88.48% compared to a conventional camera and marker-based motion tracking method (~ 27 cm) and can localize (via averaging) the position to within 8 cm of the actual target location