Emerging Frontiers: Exploring the Impact of Generative AI Platforms on University Quantitative Finance Examinations

This study evaluated three Artificial Intelligence (AI) large language model (LLM) enabled platforms - ChatGPT, BARD, and Bing AI - to answer an undergraduate finance exam with 20 quantitative questions across various difficulty levels. ChatGPT scored 30 percent, outperforming Bing AI, which scored 20 percent, while Bard lagged behind with a score of 15 percent. These models faced common challenges, such as inaccurate computations and formula selection. While they are currently insufficient for helping students pass the finance exam, they serve as valuable tools for dedicated learners. Future advancements are expected to overcome these limitations, allowing for improved formula selection and accurate computations and potentially enabling students to score 90 percent or higher

A Methodology for Engineering Collaborative and ad-hoc Mobile Applications using SyD Middleware

Today’s web applications are more collaborative and utilize standard and ubiquitous Internet protocols. We have earlier developed System on Mobile Devices (SyD) middleware to rapidly develop and deploy collaborative applications over heterogeneous and possibly mobile devices hosting web objects. In this paper, we present the software engineering methodology for developing SyD-enabled web applications and illustrate it through a case study on two representative applications: (i) a calendar of meeting application, which is a collaborative application and (ii) a travel application which is an ad-hoc collaborative application. SyD-enabled web objects allow us to create a collaborative application rapidly with limited coding effort. In this case study, the modular software architecture allowed us to hide the inherent heterogeneity among devices, data stores, and networks by presenting a uniform and persistent object view of mobile objects interacting through XML/SOAP requests and responses. The performance results we obtained show that the application scales well as we increase the group size and adapts well within the constraints of mobile devices

Diethyl 2,6-dimethyl-4-(5-phenyl-1H-pyrazol-4-yl)-1,4-dihydro­pyridine-3,5-dicarboxyl­ate

In the title compound, C22H25N3O4, the dihydro­pyridine ring adopts a flattened boat conformation. The pyrazole ring makes a dihedral angle of 29.04 (5)° with the benzene ring. The mol­ecular structure is stabilized by an intra­molecular C—H⋯O hydrogen bond which generates an S(9) ring motif. In the crystal, mol­ecules are linked via N—H⋯O and C—H⋯N hydrogen bonds into a two-dimensional network parallel to the ab plane. The crystal structure is further consolidated by weak C—H⋯π inter­actions

Elastic shape matching of parameterized surfaces using square root normal fields.

In this paper we define a new methodology for shape analysis of parameterized surfaces, where the main issues are: (1) choice of metric for shape comparisons and (2) invariance to reparameterization. We begin by defining a general elastic metric on the space of parameterized surfaces. The main advantages of this metric are twofold. First, it provides a natural interpretation of elastic shape deformations that are being quantified. Second, this metric is invariant under the action of the reparameterization group. We also introduce a novel representation of surfaces termed square root normal fields or SRNFs. This representation is convenient for shape analysis because, under this representation, a reduced version of the general elastic metric becomes the simple \ensuremathL2\ensuremathL2 metric. Thus, this transformation greatly simplifies the implementation of our framework. We validate our approach using multiple shape analysis examples for quadrilateral and spherical surfaces. We also compare the current results with those of Kurtek et al. [1]. We show that the proposed method results in more natural shape matchings, and furthermore, has some theoretical advantages over previous methods

Correlation analysis of lidar derived optical parameters for investigations on thin cirrus features at a tropical station Gadanki(13.5ºN and 79.2ºE), India

The optical characterization of thin cirrus clouds is very important to understand its radiative effects. The optical parameters of cirrus clouds namely extinction

Allocation and Inventory Policies for Reels in Printed Circuit Board Assemblies

In Printed Circuit Board (PCB) assemblies, various types of reels loaded with different components are extensively utilized. We examine the inventory and allocation policies across assembly lines when the number and size of the reels substantially affect the assembly efficiency (e.g., when the number of available slots in a chip shooter is relatively limited). Critical features of the policies are illustrated via numerical examples

Macro-physical, optical and radiative properties of tropical cirrus clouds and its temperature dependence at Gadanki (13.5° N, 79.2° E) observed by ground based lidar

The macro-physical and optical properties of cirrus clouds and its temperature dependencies have been investigated at the National Atmospheric Research Laboratory (NARL; 13.5° N, 79.2° E), Gadanki, Andhra Pradesh, India; an inland tropical station during the period of observation January to December 2009 using a ground based pulsed monostatic lidar system data and radiosonde measurements. Based on the analysis of measurements the cirrus macrophysical properties such as occurrence height, mid cloud temperature, cloud geometrical thickness, and optical properties such as extinction coefficient, optical depth, depolarization ratio and lidar ratio have been determined. The variation of cirrus macrophysical and optical properties with mid cloud temperature have also been studied. The cirrus clouds mean height has been generally observed in the range of 9-17 km with a peak occurrence at 13-14 km. The cirrus mid cloud temperatures were in the range from -81 °C to -46 °C. The cirrus geometrical thickness ranges from 0.9-4.5 km and 56% of cirrus occurrences have thickness 1.0 -2.7 km. The monthly cirrus optical depth ranges from 0.01-0.47, but most (>80%) of the cirrus have values less than 0.1. The monthly mean cirrus extinction ranges from 2.8E-06 to 8E-05 and depolarization ratio and lidar ratio varies from 0.13 to 0.77 and 2 to 52 respectively. The temperature and thickness dependencies on cirrus optical properties have also been studied. A maximum cirrus geometrical thickness of 4.5 km is found at temperatures around – 46 °C with an indication that optical depth increases with increasing thickness and mid cloud temperature. The cloud radiative properties such as outgoing long-wave radiation (OLR) flux and cirrus IR forcing are studied. OLR flux during the cirrus occurrence days ranged from 348-456 W/m2 with a low value in the monsoon period. The cirrus IR forcing varied from 3.13 – 110.54 W/m2 and shows a peak at monsoon period

In-situ STEM imaging of growth and phase change of individual CuAlX precipitates in Al alloy

Age-hardening in Al alloys has been used for over a century to improve its mechanical properties. However, the lack of direct observation limits our understanding of the dynamic nature of the evolution of nanoprecipitates during age-hardening. Using in-situ (scanning) transmission electron microscopy (S/TEM) while heating an Al-Cu alloy, we were able to follow the growth of individual nanoprecipitates at atomic scale. The heat treatments carried out at 140, 160, 180 and 200 degrees C reveal a temperature dependence on the kinetics of precipitation and three kinds of interactions of nano-precipitates. These are precipitate-matrix, precipitate-dislocation, and precipitate-precipitate interactions. The diffusion of Cu and Al during these interactions, results in diffusion-controlled individual precipitate growth, an accelerated growth when interactions with dislocations occur and a size dependent precipitateprecipitate interaction: growth and shrinkage. Precipitates can grow and shrink at opposite ends at the same time resulting in an effective displacement. Furthermore, the evolution of the crystal structure within an individual nanoprecipiate, specifically the mechanism of formation of the strengthening phase,theta', during heat-treatment is elucidated by following the same precipitate through its intermediate stages for the first time using in-situ S/TEM studies