A double interaction-based financing group decisionmaking framework considering uncertain information and inconsistent assessment

Financing group decision-making (FGDM), which is an important stage of project financing, has unique characteristics: large investments and long payback horizons. Its evaluation results are likely to be distorted if we ignore the uncertain information and inconsistent assessment during the decision-making process. In this study, we propose a double interaction-based FGDM framework under uncertain information and inconsistent assessment. We modify the weight setting of evidence reasoning and aggregation method of probabilistic linguistic term sets to process the above two issues. The proposed framework is applied in a detailed case study analysis to display its effectiveness and stability. We expect the double interaction-based group decision-making framework under uncertain information and inconsistent assessment to be a useful tool to understand FGDM processes

Flexible Superwettable Tapes for On-Site Detection of Heavy Metals

Bioinspired superwettable micropatterns that combine superhydrophobicity and superhydrophilicity have been proved to exhibit outstanding capacity in controlling and patterning microdroplets and possessed new functionalities and possibilities in emerging sensing applications. Here, we introduce a flexible tape-based superhydrophilic–superhydrophobic tape toward on-site heavy metals monitoring. On such a superwettable tape, capillarity-assisted superhydrophilic microwells allow directly anchoring indicators in fixed locations and sampling into a test zone via simple dip-pull from an origin specimen solution. In contrast, the superhydrophobic substrate could confine the microdroplets in the superhydrophilic microwells for reducing the amount of analytical solution. The tape-based microchip also displays excellent flexibility against stretching, bending, and torquing for expanding wearable and portable sensing devices. Qualitative and quantitative colorimetric assessments of multiplex heavy metal analyses (chromium, copper, and nickel) by the naked eye are also achieved. The superwettable tape-based platforms with a facile operation mode and accessible signal read-out represent unrevealed potential for on-site environmental monitoring

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, and yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density

Stability and Mechanical Properties of w1-X Mox b4.2 (X=0.0-1.0) From First Principles

Heavy transition-metal tetraborides (e.g., tungsten tetraboride, molybdenum tetraboride, and molybdenum-doped tungsten tetraboride) exhibit superior mechanical properties, but solving their complex crystal structures has been a long-standing challenge. Recent experimental x-ray and neutron diffraction measurements combined with first-principles structural searches have identified a complex structure model for tungsten tetraboride that contains a boron trimer as an unusual structural unit with a stoichiometry of 1:4.2. In this paper, we expand the study to binary MoB4.2 and ternary W1-xMoxB4.2 (x=0.0-1.0) compounds to assess their thermodynamic stability and mechanical properties using a tailor-designed crystal structure search method in conjunction with first-principles energetic calculations. Our results reveal that an orthorhombic MoB4.2 structure in Cmcm symmetry matches well the experimental x-ray diffraction patterns. For the synthesized ternary Mo-doped tungsten tetraborides, a series of W1-xMoxB4.2 structures are theoretically designed using a random substitution approach by replacing the W to Mo atoms in the Cmcm binary crystal structure. This approach leads to the discovery of several W1-xMoxB4.2 structures that are energetically superior and stable against decomposition into binary WB4.2 and MoB4.2. The structural and mechanical properties of these low-energy W1-xMoxB4.2 structures largely follow the Vegard\u27s law. Under changing composition parameter x=0.0-1.0, the superior mechanical properties of W1-xMoxB4.2 stay in a narrow range. This unusual phenomenon stems from the strong covalent network with directional bonding configurations formed by boron atoms to resist elastic deformation. The findings offer insights into the fundamental structural and physical properties of ternary W1-xMoxB4.2 in relation to the binary WB4.2/MoB4.2 compounds, which open a promising avenue for further rational optimization of the functional performance of transition-metal borides that can be synthesized under favorable experimental conditions for wide applications

Learning Interpretable BEV Based VIO without Deep Neural Networks

Monocular visual-inertial odometry (VIO) is a critical problem in robotics and autonomous driving. Traditional methods solve this problem based on filtering or optimization. While being fully interpretable, they rely on manual interference and empirical parameter tuning. On the other hand, learning-based approaches allow for end-to-end training but require a large number of training data to learn millions of parameters. However, the non-interpretable and heavy models hinder the generalization ability. In this paper, we propose a fully differentiable, and interpretable, bird-eye-view (BEV) based VIO model for robots with local planar motion that can be trained without deep neural networks. Specifically, we first adopt Unscented Kalman Filter as a differentiable layer to predict the pitch and roll, where the covariance matrices of noise are learned to filter out the noise of the IMU raw data. Second, the refined pitch and roll are adopted to retrieve a gravity-aligned BEV image of each frame using differentiable camera projection. Finally, a differentiable pose estimator is utilized to estimate the remaining 3 DoF poses between the BEV frames: leading to a 5 DoF pose estimation. Our method allows for learning the covariance matrices end-to-end supervised by the pose estimation loss, demonstrating superior performance to empirical baselines. Experimental results on synthetic and real-world datasets demonstrate that our simple approach is competitive with state-of-the-art methods and generalizes well on unseen scenes

Leveraging BEV Representation for 360-degree Visual Place Recognition

This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial awareness. Our method extracts image features using standard convolutional networks and combines the features according to pre-defined 3D grid spatial points. To alleviate the mechanical and time misalignments between cameras, we further introduce deformable attention to learn the compensation. Upon the BEV feature representation, we then employ the polar transform and the Discrete Fourier transform for aggregation, which is shown to be rotation-invariant. In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets, including on-the-road and off-the-road scenarios. The experimental results verify the hypothesis that BEV can benefit VPR by its superior performance compared to baseline methods. To the best of our knowledge, this is the first trial of employing BEV representation in this task