Evaluating the effectiveness of two training formats for grain dust explosion prevention

Grain dust explosions result in fatalities, injuries, and downtimes in industry operations. Industry training has been implemented to educate workers on grain dust hazards and prevention tools and methods but no comprehensive evaluation has taken place. This research used the decision-making simulation to evaluate the effectiveness of two training formats for grain dust explosion programming using a four-level Kirkpatrick evaluation model. In addition, the association between the format of training and the decision choices made by workers and the information they used to make decision choices were examined. This research also examined the association between workers\u27 level of perceived training effectiveness and the decision choices made by workers and the information they used to make decision choices. A web-based survey was used as a platform for the decision-making simulation. The survey was sent to 260 individuals who had completed an online or face-to-face grain dust explosion prevention training. Results from this research suggest that both the online and face-to-face training were effective in terms of delivering knowledge and increasing the awareness of grain dust hazards. The format of training was not found to be significantly associated with workers\u27 decision choices and information used to make a decision choice. Similarly, workers\u27 level of perceived training effectiveness was not found to be significantly associated with workers decision choices and information used to make a decision choice. Implications and recommendations for the grain dust explosion prevention training offered in online and face-to-face formats are shared

Predicting Non-Fungible Token (NFT) Collections: A Contextual Generative Approach

Non-fungible tokens (NFTs) are digital assets stored on a blockchain representing real-world objects such as art or collectibles. It is a multibillion-dollar market, where the number of NFT collections increased over 100% in 2022; there are currently more than 80K collections on the Ethereum blockchain. Each collection, containing numerous tokens of a particular theme, has its unique characteristics. In this paper, we take a contextual generative approach that learns these diverse characteristics of NFT collections and generates the potential market value predictions of newly minted ones. We model NFTs as a series of transactions. First, meaningful contexts capturing the characteristics of various collections are derived using unsupervised learning. Next, our generative approach leverages these contexts to learn better characterizations of established NFT collections with differing market capitalization values. Finally, given a new collection in an early stage, the approach generates future transaction series for this emerging collection. Comprehensive experiments demonstrate that our approach closely predicts the potential value of NFT collections

Rado Numbers and SAT Computations

Given a linear equation $\mathcal{E}$, the $k$-color Rado number $R_k(\mathcal{E})$ is the smallest integer $n$ such that every $k$-coloring of $\{1,2,3,\dots,n\}$ contains a monochromatic solution to $\mathcal E$. The degree of regularity of $\mathcal E$, denoted $dor(\mathcal E)$, is the largest value $k$ such that $R_k(\mathcal E)$ is finite. In this article we present new theoretical and computational results about the Rado numbers $R_3(\mathcal{E})$ and the degree of regularity of three-variable equations $\mathcal{E}$. % We use SAT solvers to compute many new values of the three-color Rado numbers $R_3(ax+by+cz = 0)$ for fixed integers $a,b,$ and $c$. We also give a SAT-based method to compute infinite families of these numbers. In particular, we show that the value of $R_3(x-y = (m-2) z)$ is equal to $m^3-m^2-m-1$ for $m\ge 3$. This resolves a conjecture of Myers and implies the conjecture that the generalized Schur numbers $S(m,3) = R_3(x_1+x_2 + \dots x_{m-1} = x_m)$ equal $m^3-m^2-m-1$ for $m\ge 3$. Our SAT solver computations, combined with our new combinatorial results, give improved bounds on $dor(ax+by = cz)$ and exact values for $1\le a,b,c\le 5$. We also give counterexamples to a conjecture of Golowich

Chelating Compounds and Immobilized Tethered Chelators

Novel di- and tripodal compounds useful as chelators, intermediates for their production and a method for treating an aqueous solution to remove trivalent metal ions are presented

Removing Aluminum from Solution Using Chelating Compounds and Immobilized Tethered Chelators

Methods are described for removing aluminum from a solution using novel di- and tripodal compounds as chelators

Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions

The assessment of cybersecurity Capture-The-Flag (CTF) exercises involves participants finding text strings or ``flags'' by exploiting system vulnerabilities. Large Language Models (LLMs) are natural-language models trained on vast amounts of words to understand and generate text; they can perform well on many CTF challenges. Such LLMs are freely available to students. In the context of CTF exercises in the classroom, this raises concerns about academic integrity. Educators must understand LLMs' capabilities to modify their teaching to accommodate generative AI assistance. This research investigates the effectiveness of LLMs, particularly in the realm of CTF challenges and questions. Here we evaluate three popular LLMs, OpenAI ChatGPT, Google Bard, and Microsoft Bing. First, we assess the LLMs' question-answering performance on five Cisco certifications with varying difficulty levels. Next, we qualitatively study the LLMs' abilities in solving CTF challenges to understand their limitations. We report on the experience of using the LLMs for seven test cases in all five types of CTF challenges. In addition, we demonstrate how jailbreak prompts can bypass and break LLMs' ethical safeguards. The paper concludes by discussing LLM's impact on CTF exercises and its implications

A Titin mutation defines roles for circulation in endothelial morphogenesis

AbstractMorphogenesis of the developing vascular network requires coordinated regulation of an extensive array of endothelial cell behaviors. Precisely regulated signaling molecules such as vascular endothelial growth factor (VEGF) direct some of these endothelial behaviors. Newly forming blood vessels also become subjected to novel biomechanical forces upon initiation of cardiac contractions. We report here the identification of a recessive mouse mutation termed shrunken-head (shru) that disrupts function of the Titin gene. Titin was found to be required for the initiation of proper heart contractions as well as for maintaining the correct overall shape and orientation of individual cardiomyocytes. Cardiac dysfunction in shrunken-head mutant embryos provided an opportunity to study the effects of lack of blood circulation on the morphogenesis of endothelial cells. Without blood flow, differentiating endothelial cells display defects in their shapes and patterns of cell–cell contact. These endothelial cells, without exposure to blood circulation, have an abnormal distribution within vasculogenic vessels. Further effects of absent blood flow include abnormal spatial regulation of angiogenesis and elevated VEGF signaling. The shrunken-head mutation has provided an in vivo model to precisely define the roles of circulation on cellular and network aspects of vascular morphogenesis