A qualitative approach to examining the rules of a community college and industry partnership.

The purpose of this study was to qualitatively examine the governance structure of a successful community college and industry advisory board that collaborate to improve regional workforce development initiatives. The institutional analysis and development (IAD) framework was used as a lens to describe the partnership. The results determined that there were many informal rules that governed the relationship between the community college and industry partners, which led to successful implementation of decisions. The community college leaders created the informal rules with the purpose of encouraging involvement among industry stakeholders, sharing power among all the participants, and facilitating communication. The findings are consistent with the literature in collaboration. Frequent and open communication, outcomes that benefit all stakeholders, and other positive institutional designs aid in the success of a community college and industry partnership. Keywords: community college, collaboration, industry, partnerships, industry advisory board, governance, institutional analysis and development framework, rules, cooperation, regional workforce development

Financial analysis of Company XYZ

Master of AgribusinessDepartment of Agricultural EconomicsMajor Professor Not ListedCompany XYZ is a small business, established in October 2021, that services a variety of vehicles for repairs and modifications. The current business model has reached its maximum workload output given shop space constraints. The objective of this study is to see how well Company XYZ is tracking or meeting company goals. Utilizing financial statements, invoices, and social media marketing, this study provides insight into the company’s future success and growth. An interview with the owner of Company XYZ provided information about future goals for the business, as well as relevant background information about their experiences in the automotive aftermarket industry. By completing a ratio analysis for Company XYZ from its start in October 2021 through December 2022, the study captured a broad view of the financial health and past success of Company XYZ. Completing another analysis of January and February 2023 numbers, provided information on the annual trend line used to inform future decisions. The comparison of Company XYZ’s numbers with industry numbers shows how well they perform against their competitors. The invoicing turnover rate (the number of days it takes an invoice to close) provided a clear picture of how the company is tracking towards their 2023 goal of 20 completed invoices each month with a minimum profit of $700 per invoice. Past profitability data showing where the company has been will help guide where the owners focus needs to fall for meeting future goals. Company XYZ uses social media marketing to reach its customer base and promote sales. Analyzing sales revenue resulting directly from one social media post. This provides a marketing model to facilitate the minimum number and type of social media marketing that will be needed to achieve company objectives. The two categories created for social media posts were family-related and job-related posts. Correlating the number of posts with total monthly sales provides insight into how significant social media marketing is in creating additional sales. Financial ratios indicated Company XYZ is financially healthy in comparison to the automotive repair industry. Invoices showed profitability for all but one out of 91 invoices. Social media posts overall drew in additional sales. When split into two categories, family-related posts had little impact on sales, while job related posts had a higher impact, although not statistically significant. Total posts overall compared to sales showed an even stronger impact on sales each month but was also not statistically significant. Additional factors play into what brings in monthly sales that were not discovered in this analysis

Auditory Cortical Detection and Discrimination Correlates with Communicative Significance

Plasticity studies suggest that behavioral relevance can change the cortical processing of trained or conditioned sensory stimuli. However, whether this occurs in the context of natural communication, where stimulus significance is acquired through social interaction, has not been well investigated, perhaps because neural responses to species-specific vocalizations can be difficult to interpret within a systematic framework. The ultrasonic communication system between isolated mouse pups and adult females that either do or do not recognize the calls' significance provides an opportunity to explore this issue. We applied an information-based analysis to multi- and single unit data collected from anesthetized mothers and pup-naïve females to quantify how the communicative significance of pup calls affects their encoding in the auditory cortex. The timing and magnitude of information that cortical responses convey (at a 2-ms resolution) for pup call detection and discrimination was significantly improved in mothers compared to naïve females, most likely because of changes in call frequency encoding. This was not the case for a non-natural sound ensemble outside the mouse vocalization repertoire. The results demonstrate that a sensory cortical change in the timing code for communication sounds is correlated with the vocalizations' behavioral relevance, potentially enhancing functional processing by improving its signal to noise ratio

Adaptive stimulus optimization for sensory systems neuroscience

In this paper, we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical optimal stimulus paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the iso-response paradigm which finds sets of stimuli giving rise to constant responses, and the system identification paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of non-linear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify non-linear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and toward a new paradigm of real-time model estimation and comparison

Modeling second-order boundary perception: A machine learning approach.

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing

Object Clusters or Spectral Energy? Assessing the Relative Contributions of Image Phase and Amplitude Spectra to Trypophobia

Trypophobia refers to the visual discomfort experienced by some people when viewing clustered patterns (e.g., clusters of holes). Trypophobic images deviate from the 1/f amplitude spectra typically characterizing natural images by containing excess energy at mid-range spatial frequencies. While recent work provides partial support for the idea of excess mid-range spatial frequency energy causing visual discomfort when viewing trypophobic images, a full factorial manipulation of image phase and amplitude spectra has yet to be conducted. Here, we independently manipulated the phase and amplitude spectra of 31 Trypophobic images using a standard Fast Fourier Transform (FFT). Participants rated the four different versions of each image for levels of visual comfort, and completed the Trypophobia Questionnaire (TQ). Images having the original phase spectra intact (with either original or 1/f amplitude) explained the most variance in comfort ratings and were rated lowest in comfort. However, images with the original amplitude spectra but scrambled phase spectra were rated higher in comfort, with a smaller amount of variance in comfort attributed to the amplitude spectrum. Participant TQ scores correlated with comfort ratings only for images having the original phase spectra intact. There was no correlation between TQ scores and comfort levels when participants viewed the original amplitude / phase-scrambled images. Taken together, the present findings show that the phase spectrum of trypophobic images, which determines the pattern of small clusters of objects, plays a much larger role than the amplitude spectrum in determining visual comfort