Wolf is Coming—Dynamic Classification Prediction Model of Vespa Mandarinia

Given the threat of Vespa mandarinia invasion to ecological balance, according to the data and information provided, the dynamic reproduction model of Vespa mandarinia is established by using natural domain interpolation, and the variation law of total bumblebee with time, latitude, and longitude is obtained. At the same time, we established the classification prediction model by using a neural network and established the mapping relationship between time and space to evaluation grade.We meshed the area provided by the title, assigned values to the location of Vespa mandarinia (VM), and established a VM diffusion model with natural neighborhood interpolation. Its propagation process is simulated by cellular automata. It is determined that VM spreads in a circular shape centered at (122.93174°W, 48.93457°N) and (122.57376°W, 49.07848°N) in the Washington area, with the farthest distance being 1184.4 km and 985 km respectively.We set up a classification prediction model for better classification. According to the image upload time and location, SVM and neural network are used for classification prediction, and the classification accuracy is 74.26% and 97.60%, respectively, and the neural network has higher classification accuracy. So we choose the neural network

A Study on Economic Impact in the Context of American Election Based on AHP

To assess the economic impact of the different policies of the Trump and Biden candidates, we formulate metrics on five aspects: Covid-19 prevention and control measures, environmental protection policies, taxation, health care reform, foreign trade. Moreover, each metric is subdivided into several secondary metrics, making a three-tier hierarchical structure. Take environmental protection policy as an example: Without direct data under Biden’s policies, we collected data on U.S. CO2 emissions and U.S. oil consumption during Obama’s presidency as Biden’s legacy. First, use the analytic hierarchy process (AHP) to select indicators that can reflect the U.S. economy and determine the weight of each indicator. For the U.S. economy, Biden scored 2.6498, Trump 2.3502, suggesting that the election of Biden might make things better for the economy. For China’s economy, Biden scored 0.6810 and Trump 0.3245, meaning Biden could give the Chinese economy more room to grow. To reduce the influence of AHP subjectivity on the results, the Pearson correlation coefficient is introduced to establish the P-AHP model. Take the impact on China’s economy. Biden scored 0.5846 and Trump 0.4154

Policy Reflection and Demystify on Street Vendors in the Context of COVID-19—Based on the Empirical Investigation of Pingfang District in Harbin

At the beginning of 2020, the epidemic raged in China. Faced with the great pressure of economic downturn, the state has issued policies to relax the limitations on the street vending economy, making it promote employment and maintaining social stability in the stage of economic restart and recovery. This paper discussed policies related to street vendor economy under the background of epidemic in Pingfang District and gave reasonable suggestions for the orderly and stable development of the vendor economy in the post-epidemic era

A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks

The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in language comprehension and generation. However, the extremely high data and computational resource requirements for the performance of LLMs compel developers to resort to outsourcing training or utilizing third-party data and computing resources. These strategies may expose the model within the network to maliciously manipulated training data and processing, providing an opportunity for attackers to embed a hidden backdoor into the model, termed a backdoor attack. Backdoor attack in LLMs refers to embedding a hidden backdoor in LLMs that causes the model to perform normally on benign samples but exhibit degraded performance on poisoned ones. This issue is particularly concerning within communication networks where reliability and security are paramount. Despite the extensive research on backdoor attacks, there remains a lack of in-depth exploration specifically within the context of LLMs employed in communication networks, and a systematic review of such attacks is currently absent. In this survey, we systematically propose a taxonomy of backdoor attacks in LLMs as used in communication networks, dividing them into four major categories: input-triggered, prompt-triggered, instruction-triggered, and demonstration-triggered attacks. Furthermore, we conduct a comprehensive analysis of the benchmark datasets. Finally, we identify potential problems and open challenges, offering valuable insights into future research directions for enhancing the security and integrity of LLMs in communication networks

Collaborative multidisciplinary management and expertise of cT2-3 locally advanced operable esophageal squamous cell carcinoma:two case reports

Background: The accurate clinical staging of esophageal squamous cell carcinoma (ESCC) is pivotal for guiding treatment strategies. However, the current precision in staging for clinical T (cT)2 and cT3 stages remains unsatisfactory. This article discusses the role of multidisciplinary teams (MDTs) in the clinical staging and formulation of neoadjuvant treatment strategies for locally advanced operable ESCC. These challenges underscore the importance of precise staging in the decision-making process for appropriate therapeutic interventions.Case Description: Through the lens of two patient case studies with locally advanced resectable ESCC, the article showcases the intricate process of treatment planning undertaken by MDTs. It captures a range of expert perspectives from Japan, China, Hong Kong (China), Korea, the USA, and Europe, focusing on the challenges of differentiating between cT2 and cT3 stages of the disease, which is a critical determinant in the management and therapeutic approach for patients.Conclusions: The article concludes that the accurate staging of ESCC is a cornerstone in determining the most suitable treatment strategies. It underscores the vital role that MDTs play in both clinical staging and the decision-making process for treatment. Highlighting the limitations in current diagnostic methods, the article emphasizes the urgent need for advanced research and the refinement of diagnostic tools to improve the precision of staging, particularly between the cT2 and cT3 stages. It suggests that future research should consider whether a reclassification of these stages could be warranted to enhance treatment planning and outcomes for patients with ESCC.<br/

Enhancing Statistical Inference of Generalized Linear Regression Models Under Data Uncertainty

This dissertation addresses the challenges of data uncertainty in statistical inference. It focuses on two primary research topics concerning the impact of uncertainty on covariates (influential factors) and the uncertainty on the response variable in generalized regression models. It brings opportunities as well as challenges in estimation and prediction when data uncertainty is involved.In the research, the issue of data uncertainty affecting the response variable has been studied, particularly when data heterogeneity is present. To address this challenge, a finite mixture Weibull regression modeling method is proposed. This approach explicitly considers the presence of potential data heterogeneity by introducing a latent variable to represent sub-populations and employing the Expectation-Maximization (EM) algorithm in the estimation of the latent variable. When addressing the impact of measurement error on covariates, existing research has extensively explored this area. However, two significant challenges remain to be addressed. The first is the presence of mixture error, where both classical and Berkson errors coexist, introducing bias to the model inference. The second challenge lies in the computational and mathematical burden associated with generalized linear regression models. Overcoming these difficulties is important for improving the accuracy, efficiency, and interpretability of the generalized linear regression model in the presence of measurement errors. This dissertation delves into the investigation of two specific types of generalized linear regression models, namely, the Poisson regression for count data modeling and the Weibull regression for time-to-event data analysis. Both models are analyzed in the context of covariates affected by mixture error. In the dissertation, two innovative approaches are proposed to address the impact of mixture error in two types of generalized linear regression models. For the Poisson regression model, an error-structure adapted quasi-likelihood estimation method is proposed, while for the Weibull regression model, an error-structure adapted Markov Chain Monte Carlo (MCMC) estimation method is proposed. These methods are designed to effectively address the challenges posed by mixture error in both models. To demonstrate the effectiveness of our proposed methods, numerical case studies using data from a Valley Fever investigation and the Fram- ingham Heart Study were conducted. The research work presented in this dissertation provides a portfolio of solutions to improve the estimation and prediction of generalized linear regression in the presence of data uncertainty. The effectiveness and efficiency of the proposed methodologies have been demonstrated and justified via numerical simulation case studies.Release after 08/18/202

Étude d'un renfort en lin unidirectionnel destiné aux matériaux composites biosourcés

In this Ph.D work, unidirectional flax fiber composite (UD biobased composite) has been designed and manufactured based on the hot platen press process. Plant fiber composites usually exhibit two regions under tensile load, but three regions have been identified in this work. A phenomenological model, previously developed to describe the tensile mechanical behavior of twisted plant yarn composites, has been tested with the UD biobased composite. We show that the addition of a strengthening phenomenon to the previous model is necessary to simulate correctly the third region. A second mechanical model has also been developed for experimental identification of the effective mechanical properties of flax reinforcement when embeded in matrix. A statistical distribution of local orientation of UD reinforcement was obtained allowing taking the fiber orientation into account. To that end, structure tensor method was applied to optical images of flax ply. Furthermore, this model allows the effect of porosity on mechanical properties to be studied. Both models provide effective forecast of the mechanical behavior of unidirectional flax fiber composite. Besides the mechanic models, sorption behavior of UD flax composite also has been analyzed. Langmuir's model and Fick's model were applied on our UD composite. The results show that the unidirectional configuration of the flax reinforcement promotes the water sorption from the associated composites.Dans cette thèse, un composite unidirectionnel à renfort lin (composite UD biosourcé) a été développé et élaboré par la technique de presse à chaud. Le comportement en traction des composites à renfort végétal montre en général deux domaines, mais un troisième domaine est identifié dans ce travail. Un modèle phénoménologique développé précédemment pour décrire le comportement en traction d'un composite à renfort en fils torsadés a été testé avec le composite UD biosourcé. Nous montrons que l'ajout d'un phénomène de consolidation au modèle précédent est nécessaire pour simuler correctement le troisième domaine. Un second modèle mécanique a été par ailleurs développé pour identifier expérimentalement les propriétés mécaniques effectives du renfort en lin lorsqu'il est piégé dans la matrice. La distribution statistique de l'orientation locale du renfort a été mesurée pour pouvoir prendre en compte l'orientation des fibres. Pour cela, la technique du tenseur de structure a été appliquée sur des images optiques du pli de lin. Par ailleurs, ce modèle permet d'étudier l'influence des porosités sur les propriétés mécaniques. Les deux modèles permettent d'effectuer des prévisions efficaces du comportement mécanique du composite de fibre de lin unidirectionnel. En complément des modèles de mécanique, le comportement en sorption du composite de lin UD a également été analysé. Le modèle de Langmuir et le modèle de Fick ont été appliqués sur nos composites UD. Les résultats montrent que la configuration unidirectionnelle du renfort de lin favorise la sorption d'eau des composites associés.Résumé en anglai

Location Privacy of ADS-B for General Aviation

utomatic Dependent Surveillance Broadcast (ADS-B) is a satellite-based system that makes an aircraft equipped with it periodically generates ADS-B broadcasts including the aircraft’s identifier, position, velocity, etc. Since the broadcasts can enhance navigation capability remarkably, it has replaced radar and becomes the backbone of the air traffic management (ATM) of next generation. However, the leak of identity and location privacy allows adversaries to track flights easily. Especially in general aviation, people’s travel destination may be related to personal privacy, commercial secrets and other important information that need to be protected. In this paper, the author studies a well-known location privacy protection scheme named random silent period which enhances the uncertainty by mix the target plane with other planes in the anonymity set and entropy is used to evaluate the uncertainty. Furthermore, we proposes a new correlation tracking method to improve the uncertainty. The evaluation of location privacy demonstrates that the method is more practical and accurate than the simple tracking method. Keywords— ADS-B; location privacy; random silent period; entropy; trackin