Gear optimization

The use of formal numerical optimization methods for the design of gears is investigated. To achieve this, computer codes were developed for the analysis of spur gears and spiral bevel gears. These codes calculate the life, dynamic load, bending strength, surface durability, gear weight and size, and various geometric parameters. It is necessary to calculate all such important responses because they all represent competing requirements in the design process. The codes developed here were written in subroutine form and coupled to the COPES/ADS general purpose optimization program. This code allows the user to define the optimization problem at the time of program execution. Typical design variables include face width, number of teeth and diametral pitch. The user is free to choose any calculated response as the design objective to minimize or maximize and may impose lower and upper bounds on any calculated responses. Typical examples include life maximization with limits on dynamic load, stress, weight, etc. or minimization of weight subject to limits on life, dynamic load, etc. The research codes were written in modular form for easy expansion and so that they could be combined to create a multiple reduction optimization capability in future

Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond

Recent advancements in multimodal techniques open exciting possibilities for models excelling in diverse tasks involving text, audio, and image processing. Models like GPT-4V, blending computer vision and language modeling, excel in complex text and image tasks. Numerous prior research endeavors have diligently examined the performance of these Vision Large Language Models (VLLMs) across tasks like object detection, image captioning and others. However, these analyses often focus on evaluating the performance of each modality in isolation, lacking insights into their cross-modal interactions. Specifically, questions concerning whether these vision-language models execute vision and language tasks consistently or independently have remained unanswered. In this study, we draw inspiration from recent investigations into multilingualism and conduct a comprehensive analysis of model's cross-modal interactions. We introduce a systematic framework that quantifies the capability disparities between different modalities in the multi-modal setting and provide a set of datasets designed for these evaluations. Our findings reveal that models like GPT-4V tend to perform consistently modalities when the tasks are relatively simple. However, the trustworthiness of results derived from the vision modality diminishes as the tasks become more challenging. Expanding on our findings, we introduce "Vision Description Prompting," a method that effectively improves performance in challenging vision-related tasks

Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs

Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities and have excelled in a variety of natural language processing (NLP)tasks in recent years. Despite the fact that most LLMs are trained predominantly in English, multiple studies have demonstrated their comparative performance in many other languages. However, fundamental questions persist regarding how LLMs acquire their multi-lingual abilities and how performance varies across different languages. These inquiries are crucial for the study of LLMs since users and researchers often come from diverse language backgrounds, potentially influencing their utilization and interpretation of LLMs' results. In this work, we propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings. We investigate the phenomenon of across-language generalizations in LLMs, wherein insufficient multi-lingual training data leads to advanced multi-lingual capabilities. To accomplish this, we employ a novel back-translation-based prompting method. The results show that GPT exhibits highly translating-like behaviour in multilingual settings.Comment: Paper accepted to EMNLP 202

4,6-Bis[5-methyl-3-(trifluoro­meth­yl)pyrazol-1-yl]pyrimidine

The complete mol­ecule of the the title compound, C14H10F6N6, is generated by crystallographic twofold symmetry, with two C atoms lying on the roatation axis. The dihedral angle between the central and peripheral rings is 25.97 (8)°

Research on a Model of Extracting Persons\u27 Information Based on Statistic Method and Conceptual Knowledge

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

Fumigant Activity of Eight Plant Essential Oils Against Workers of Red Imported Fire Ant, Solenopsis invicta

Plant essential oils from eight plant species were tested for their insecticidal activities against the red imported fire ant, Solenopsis invcita, by using a fumigation bioassay. This study reveals that the mortalities after treatment of the workers of red imported fire ants varied according to the classification of workers, oil type, dosage, and exposure time. Among the essential oils tested, strong insecticidal activity was observed with the essential oils of camphor (Cinnamomum camphora), artemisia annua (Artemisia annua), eucalyptus (Eucalyptus globulus), mugwort (Artemisia argyi), and wintergreen (Ilex chinensis). Ant mortalities from chrysanthemum oil (Dendranthema indicum), turpentine oil (Pinus massoniana), and forsythia oil (Forsythia suspense) treatments were significantly lower than those from the previously mentioned five essential oil treatments. This study showed that camphor, artemisia annua, eucalyptus, mugwort, and wintergreen oils may have potential to be used as substitutes for chemical insecticides

6′,7′-Dimeth­oxy-1′,2′-dihydro­spiro­[cyclo­hexane-1,2′-quinazolin]-4′(3′H)-one

In the title compound, C15H20N2O3, prepared from the reaction of 2-amino-4,5-dimeth­oxy­benzonitrile and cyclo­hexa­none, the six-membered diaza ring assumes an envelope conformation. In the crystal, inversion dimers are formed by pairs of N—H⋯O hydrogen bonds. Futher N—H⋯O hydrogen bonds link the dimers into a two-dimensional structure parallel to (001)