Long-time dynamics of a competition model with nonlocal diffusion and free boundaries: Vanishing and spreading of the invader

In this work, we investigate the long-time dynamics of a two species competition model of Lotka-Volterra type with nonlocal diffusions. One of the species, with density $v(t,x)$, is assumed to be a native in the environment (represented by the real line $\R$), while the other species, with density $u(t,x)$, is an invading species which invades the territory of $v$ with two fronts, $x=g(t)$ on the left and $x=h(t)$ on the right. So the population range of $u$ is the evolving interval $[g(t), h(t)]$ and the reaction-diffusion equation for $u$ has two free boundaries, with $g(t)$ decreasing in $t$ and $h(t)$ increasing in $t$, and the limits $h_\infty:=h(\infty)\leq \infty$ and $g_\infty:=g(\infty)\geq -\infty$ thus always exist. We obtain detailed descriptions of the long-time dynamics of the model according to whether $h_\infty-g_\infty$ is $\infty$ or finite. In the latter case, we reveal in what sense the invader $u$ vanishes in the long run and $v$ survives the invasion, while in the former case, we obtain a rather satisfactory description of the long-time asymptotic limit for both $u(t,x)$ and $v(t,x)$ when a certain parameter $k$ in the model is less than 1. This research is continued in a separate work, where sharp criteria are obtained to distinguish the case $h_\infty-g_\infty=\infty$ from the case $h_\infty-g_\infty$ is finite, and new phenomena are revealed for the case $k\geq 1$. The techniques developed in this paper should have applications to other models with nonlocal diffusion and free boundaries

Long-time dynamics of a competition model with nonlocal diffusion and free boundaries: Chances of successful invasion

This is a continuation of our work \cite{dns-part1} to investigate the long-time dynamics of a two species competition model of Lotka-Volterra type with nonlocal diffusions, where the territory (represented by the real line $\R$) of a native species with density $v(t,x)$, is invaded by a competitor with density $u(t,x)$, via two fronts, $x=g(t)$ on the left and $x=h(t)$ on the right. So the population range of $u$ is the evolving interval $[g(t), h(t)]$ and the reaction-diffusion equation for $u$ has two free boundaries, with $g(t)$ decreasing in $t$ and $h(t)$ increasing in $t$. Let $h_\infty:=h(\infty)\leq \infty$ and $g_\infty:=g(\infty)\geq -\infty$. In \cite{dns-part1}, we obtained detailed descriptions of the long-time dynamics of the model according to whether $h_\infty-g_\infty$ is $\infty$ or finite. In the latter case, we demonstrated in what sense the invader $u$ vanishes in the long run and $v$ survives the invasion, while in the former case, we obtained a rather satisfactory description of the long-time asymptotic limits of $u(t,x)$ and $v(t,x)$ when the parameter $k$ in the model is less than 1. In the current paper, we obtain sharp criteria to distinguish the case $h_\infty-g_\infty=\infty$ from the case $h_\infty-g_\infty$ is finite. Moreover, for the case $k\geq 1$ and $u$ is a weak competitor, we obtain biologically meaningful conditions that guarantee the vanishing of the invader $u$, and reveal chances for $u$ to invade successfully. In particular, we demonstrate that both $h_\infty=\infty=-g_\infty$ and $h_\infty=\infty$ but $g_\infty$ is finite are possible; the latter seems to be the first example for this kind of population models, with either local or nonlocal diffusion

Trusta: Reasoning about Assurance Cases with Formal Methods and Large Language Models

Assurance cases can be used to argue for the safety of products in safety engineering. In safety-critical areas, the construction of assurance cases is indispensable. Trustworthiness Derivation Trees (TDTs) enhance assurance cases by incorporating formal methods, rendering it possible for automatic reasoning about assurance cases. We present Trustworthiness Derivation Tree Analyzer (Trusta), a desktop application designed to automatically construct and verify TDTs. The tool has a built-in Prolog interpreter in its backend, and is supported by the constraint solvers Z3 and MONA. Therefore, it can solve constraints about logical formulas involving arithmetic, sets, Horn clauses etc. Trusta also utilizes large language models to make the creation and evaluation of assurance cases more convenient. It allows for interactive human examination and modification. We evaluated top language models like ChatGPT-3.5, ChatGPT-4, and PaLM 2 for generating assurance cases. Our tests showed a 50%-80% similarity between machine-generated and human-created cases. In addition, Trusta can extract formal constraints from text in natural languages, facilitating an easier interpretation and validation process. This extraction is subject to human review and correction, blending the best of automated efficiency with human insight. To our knowledge, this marks the first integration of large language models in automatic creating and reasoning about assurance cases, bringing a novel approach to a traditional challenge. Through several industrial case studies, Trusta has proven to quickly find some subtle issues that are typically missed in manual inspection, demonstrating its practical value in enhancing the assurance case development process.Comment: 38 page

An advanced YOLOv3 method for small object detection

Small object detection is a very challenging task in the field of object detection because it is easily affected by large object occlusion and small object itself has relatively little feature information. Aiming at the problem that the YOLOv3 network does not consider the context semantic relationship of small object detection, the detection accuracy of small objects is not high. In this paper, we propose a small object detection network combining multi-level fusion and feature augmentation. First, the feature enhancement module is introduced into the deep layer of the backbone extraction network to enhance the feature information of small objects in the feature map. Second, a multi-level feature fusion module is proposed to better capture the contextual semantic relationship of small objects. In addition, the strategy of combining Soft-NMS and CIOU is used to solve the problem of missed detection of occluded small objects. At last, The ablation experiment of the MS COCO2017 object detection task proves the effectiveness of several modules introduced in this paper for small object detection. The experimental results on the MS COCO2017, VOC2007, and VOC2012 datasets show that the AP of this method is 16.5%, 8.71%, and 9.68% higher than that of YOLOv3, respectively. All experiments show that the method proposed in this paper has better detection performance for small object detection

Slicing Recognition of Aircraft Integral Panel Generalized Pocket

AbstractTo automatically obtain a machining area in numerical control (NC) programming, a data model of generalized pocket is established by analyzing aircraft integral panel characteristics, and a feature recognition approach is proposed. First, by reference to the practical slice-machining process of an aircraft integral panel, both the part and the blank are sliced in the Z-axis direction; hence a feature profile is created according to the slicing planes and the contours are formed by the intersection of the slicing planes with the part and its blank. Second, the auxiliary features of the generalized pocket are also determined based on the face type and the position, to correct the profile of the pocket. Finally, the generalized pocket feature relationship tree is constructed by matching the vertical relationships among the features. Machining feature information produced by using this method can be directly used to calculate the cutter path. The validity and practicability of the method is verified by NC programming for aircraft panels