Using Real Options to Evaluate Investments in Ethanol Facilities

This paper uses real option analysis to evaluate investment decisions in ethanol facilities. First, we consider the option to expand the scale of a conventional ethanol plant. Second, we evaluate the option to choose a production technology given three drymilling choices – a conventional natural gas-fueled plant, a stover-fueled plant, and a stover-plus-syrup-fueled plant. We develop input-output coefficients and annual cash flow projections for a hypothetical small ethanol plant (50 million gallon capacity) using available industry and market price data. Scenario analysis is done to evaluate the effect of profitability and volatility on the option to expand. We find that the best decision during 2001-07 is often to expand, since the net present values of the investment project are positive. However, there are states in the binomial tree where it is best to wait. In relatively few such states the expansion project is simply rejected. During the early part of the period low profitability and high volatility more frequently favor strategies of waiting to invest until prices and profitability improve. During the latter part of the period (2005-07), profitability is sharply higher and most often the best strategy is to invest in the expansion. This result is consistent with the observed rapid increase in industry production capacity during 2005- 07. However, more recent market developments, sharply higher corn and natural gas prices and slightly higher ethanol prices during late 2007-early 2008, have combined to sharply reduce expected plant cash flow and profitability and cash flow volatility. The implication is that plant investment plans in 2008 would be increasingly placed on hold, which the real option model correctly predicts. The real option analysis of technology choice indicates that the stover-fueled technologies are most often chosen when compared to a natural gas-fueled conventional technology based on the prices that existed during 2001-2007.Financial Economics, Resource /Energy Economics and Policy,

A fully differentiable GNN-based PDE Solver: With Applications to Poisson and Navier-Stokes Equations

In this study, we present a novel computational framework that integrates the finite volume method with graph neural networks to address the challenges in Physics-Informed Neural Networks(PINNs). Our approach leverages the flexibility of graph neural networks to adapt to various types of two-dimensional unstructured grids, enhancing the model's applicability across different physical equations and boundary conditions. The core innovation lies in the development of an unsupervised training algorithm that utilizes GPU parallel computing to implement a fully differentiable finite volume method discretization process. This method includes differentiable integral and gradient reconstruction algorithms, enabling the model to directly solve partial-differential equations(PDEs) during training without the need for pre-computed data. Our results demonstrate the model's superior mesh generalization and its capability to handle multiple boundary conditions simultaneously, significantly boosting its generalization capabilities. The proposed method not only shows potential for extensive applications in CFD but also establishes a new paradigm for integrating traditional numerical methods with deep learning technologies, offering a robust platform for solving complex physical problems

Framework for quality assurance of ultrahigh dose rate clinical trials investigating FLASH effects and current technology gaps

FLASH radiation therapy (FLASH-RT), delivered with ultrahigh dose rate (UHDR), may allow patients to be treated with less normal tissue toxicity for a given tumor dose compared with currently used conventional dose rate. Clinical trials are being carried out and are needed to test whether this improved therapeutic ratio can be achieved clinically. During the clinical trials, quality assurance and credentialing of equipment and participating sites, particularly pertaining to UHDR-specific aspects, will be crucial for the validity of the outcomes of such trials. This report represents an initial framework proposed by the NRG Oncology Center for Innovation in Radiation Oncology FLASH working group on quality assurance of potential UHDR clinical trials and reviews current technology gaps to overcome. An important but separate consideration is the appropriate design of trials to most effectively answer clinical and scientific questions about FLASH. This paper begins with an overview of UHDR RT delivery methods. UHDR beam delivery parameters are then covered, with a focus on electron and proton modalities. The definition and control of safe UHDR beam delivery and current and needed dosimetry technologies are reviewed and discussed. System and site credentialing for large, multi-institution trials are reviewed. Quality assurance is then discussed, and new requirements are presented for treatment system standard analysis, patient positioning, and treatment planning. The tables and figures in this paper are meant to serve as reference points as we move toward FLASH-RT clinical trial performance. Some major questions regarding FLASH-RT are discussed, and next steps in this field are proposed. FLASH-RT has potential but is associated with significant risks and complexities. We need to redefine optimization to focus not only on the dose but also on the dose rate in a manner that is robust and understandable and that can be prescribed, validated, and confirmed in real time. Robust patient safety systems and access to treatment data will be critical as FLASH-RT moves into the clinical trials

Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network

In recent years, the development of deep learning is noticeably influencing the progress of computational fluid dynamics. Numerous researchers have undertaken flow field predictions on a variety of grids, such as MAC grids, structured grids, unstructured meshes, and pixel-based grids which have been many works focused on. However, predicting unsteady flow fields on unstructured meshes remains challenging. When employing graph neural networks (GNNs) for these predictions, the message-passing mechanism can become inefficient, especially with denser unstructured meshes. Furthermore, unsteady flow field predictions often rely on autoregressive neural networks, which are susceptible to error accumulation during extended predictions. In this study, we integrate the traditional finite volume method to devise a spatial integration strategy that enables the formulation of a physically constrained loss function. This aims to counter the error accumulation that emerged in autoregressive neural networks during long-term predictions. Concurrently, we merge vertex-center and cell-center grids from the finite volume method, introducing a dual message-passing mechanism within a single GNN layer to enhance the message-passing efficiency. We benchmark our approach against MeshGraphnets for unsteady flow field predictions on unstructured meshes. Our findings indicate that the methodologies combined in this study significantly enhance the precision of flow field predictions while substantially minimizing the training time cost. We offer a comparative analysis of flow field predictions, focusing on cylindrical, airfoil, and square column obstacles in two-dimensional incompressible fluid dynamics scenarios. This analysis encompasses lift coefficient, drag coefficient, and pressure coefficient distribution comparison on the boundary layers

UniM2^2AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving

Masked Autoencoders (MAE) play a pivotal role in learning potent representations, delivering outstanding results across various 3D perception tasks essential for autonomous driving. In real-world driving scenarios, it's commonplace to deploy multiple sensors for comprehensive environment perception. While integrating multi-modal features from these sensors can produce rich and powerful features, there is a noticeable gap in MAE methods addressing this integration. This research delves into multi-modal Masked Autoencoders tailored for a unified representation space in autonomous driving, aiming to pioneer a more efficient fusion of two distinct modalities. To intricately marry the semantics inherent in images with the geometric intricacies of LiDAR point clouds, the UniM$^2$AE is proposed. This model stands as a potent yet straightforward, multi-modal self-supervised pre-training framework, mainly consisting of two designs. First, it projects the features from both modalities into a cohesive 3D volume space, ingeniously expanded from the bird's eye view (BEV) to include the height dimension. The extension makes it possible to back-project the informative features, obtained by fusing features from both modalities, into their native modalities to reconstruct the multiple masked inputs. Second, the Multi-modal 3D Interactive Module (MMIM) is invoked to facilitate the efficient inter-modal interaction during the interaction process. Extensive experiments conducted on the nuScenes Dataset attest to the efficacy of UniM$^2$AE, indicating enhancements in 3D object detection and BEV map segmentation by 1.2\%(NDS) and 6.5\% (mIoU), respectively. Code is available at https://github.com/hollow-503/UniM2AE.Comment: Code available at https://github.com/hollow-503/UniM2A

Multi-Agent Robust Control Synthesis from Global Temporal Logic Tasks

This paper focuses on the heterogeneous multi-agent control problem under global temporal logic tasks. We define a specification language, called extended capacity temporal logic (ECaTL), to describe the required global tasks, including the number of times that a local or coupled signal temporal logic (STL) task needs to be satisfied and the synchronous requirements on task satisfaction. The robustness measure for ECaTL is formally designed. In particular, the robustness for synchronous tasks is evaluated from both the temporal and spatial perspectives. Mixed-integer linear constraints are designed to encode ECaTL specifications, and a two-step optimization framework is further proposed to realize task-satisfied motion planning with high spatial robustness and synchronicity. Simulations are conducted to demonstrate the expressivity of ECaTL and the efficiency of the proposed control synthesis approach.Comment: 7 pages, 3 figure

EMID: An Emotional Aligned Dataset in Audio-Visual Modality

In this paper, we propose Emotionally paired Music and Image Dataset (EMID), a novel dataset designed for the emotional matching of music and images, to facilitate auditory-visual cross-modal tasks such as generation and retrieval. Unlike existing approaches that primarily focus on semantic correlations or roughly divided emotional relations, EMID emphasizes the significance of emotional consistency between music and images using an advanced 13-dimension emotional model. By incorporating emotional alignment into the dataset, it aims to establish pairs that closely align with human perceptual understanding, thereby raising the performance of auditory-visual cross-modal tasks. We also design a supplemental module named EMI-Adapter to optimize existing cross-modal alignment methods. To validate the effectiveness of the EMID, we conduct a psychological experiment, which has demonstrated that considering the emotional relationship between the two modalities effectively improves the accuracy of matching in abstract perspective. This research lays the foundation for future cross-modal research in domains such as psychotherapy and contributes to advancing the understanding and utilization of emotions in cross-modal alignment. The EMID dataset is available at https://github.com/ecnu-aigc/EMID

Focusing light with a metal film coated patchy particle

Microsphere-assisted super-resolution imaging is a promising technique that can significantly enhance the resolution of conventional optical microscopes. The focus of a classical microsphere is called photonic nanojet, which is a symmetric high-intensity electromagnetic field. Recently, patchy microspheres have been reported to have superior imaging performance than pristine microspheres, and coating microspheres with metal films leads to the formation of photonic hooks, which can enhance the imaging contrast of microspheres. Understanding the influence of metal patches on the near-field focusing of patchy particles is important for the rational design of a nanostructured microlens. In this work, we theoretically and experimentally showed that the light waves can be focused and engineered using patchy particles. When coating dielectric particles with Ag films, light beams with a hook-like structure or S-shaped structure can be generated. Simulation results show that the waveguide ability of metal films and the geometric asymmetry of patchy particles cause the formation of S-shaped light beams. Compared with classical photonic hooks, S-shaped photonic hooks have a longer effective length and a smaller beam waist at far-field region. Experiments were also carried out to demonstrate the generation of classical and S-shaped photonic hooks from patchy microspheres