Positive solutions for nonlinear semipositone nth-order boundary value problems

In this paper, we investigate the existence of positive solutions for a class of nonlinear semipositone $n$th-order boundary value problems. Our approach relies on the Krasnosel'skii fixed point theorem. The result of this paper complement and extend previously known result

Existence of positive solutions for nth-order boundary value problem with sign changing nonlinearity

In this paper, we investigate the existence of positive solutions for singular $n$th-order boundary value problem $u^{(n)}(t)+a(t)f(t,u(t))=0,\quad 0\le t\le1,$ $u^{(i)}(0)=u^{(n-2)}(1)=0,\quad 0\le i\le n-2,$ where $n\ge2$, $a\in C((0,1),[0,+\infty))$ may be singular at $t=0$ and (or) $t=1$ and the nonlinear term $f$ is continuous and is allowed to change sign. Our proofs are based on the method of lower solution and topology degree theorem

STP-LWE: A Variant of Learning with Error for a Flexible Encryption

We construct a flexible lattice based scheme based on semitensor product learning with errors (STP-LWE), which is a variant of learning with errors problem. We have proved that STP-LWE is hard when LWE is hard. Our scheme is proved to be secure against indistinguishable chosen message attacks, and it can achieve a balance between the security and efficiency in the hierarchical encryption systems. In addition, our scheme is almost as efficient as the dual encryption in GPV08

A Construction of Multisender Authentication Codes with Sequential Model from Symplectic Geometry over Finite Fields

Multisender authentication codes allow a group of senders to construct an authenticated message for a receiver such that the receiver can verify authenticity of the received message. In this paper, we construct multisender authentication codes with sequential model from symplectic geometry over finite fields, and the parameters and the maximum probabilities of deceptions are also calculated

Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models

Emergency management urgently requires comprehensive knowledge while having a high possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence(AI) supported decision-making under that circumstance is of vital importance. Recent emerging large language models (LLM) provide a new direction for enhancing targeted machine intelligence. However, the utilization of LLM directly would inevitably introduce unreliable output for its inherent issue of hallucination and poor reasoning skills. In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages. The study constructs a structured emergency knowledge graph and guides LLMs to reason over it via a prompt chain. In real-world evaluations, E-KELL receives scores of 9.06, 9.09, 9.03, and 9.09 in comprehensibility, accuracy, conciseness, and instructiveness from a group of emergency commanders and firefighters, demonstrating a significant improvement across various situations compared to baseline models. This work introduces a novel approach to providing reliable emergency decision support.Comment: 26 pages, 6 figure