Cooking and the Books: A Guide to Restaurant Accounting

The restaurant industry is known for particularly low profit margins; this project aims to understand where restaurants spend money and how expenses can be allocated for in this fast-paced environment. Through research of various cost accounting methods and the adaptability of those methods to restaurant culture, activity based costing (ABC) provided the most useful data for the restaurant. This project focuses on small business restaurants, specifically those that serve pizza. The backbone of experimentation for applying these accounting processes is a local pizza restaurant, in which the managers are unaware of how food cost and operating expenses could be combined to provide meaningful cost information. This study analyzes the process of implementing ABC in this restaurant to ultimately contribute to the formation of a deliverable. The product of this process is a guide which will walk restaurant owners through the application of ABC. This research and application process is intended to demonstrate to restaurant owners the purpose and ease of understanding the expenses of the business

Gender, Local Knowledge, and Lessons Learnt in Documenting and Conserving Agrobiodiversity

gender, biodiversity, agrobiodiversity, indigenous knowledge, conservation, sustainable management, genetic

Signal2Image Modules in Deep Neural Networks for EEG Classification

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as `base models'. We compare the accuracy and time performance of four S2Is (`signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201

Media stylistics

In this chapter we review the concept of ‘media stylistics’. In particular, we disentangle the polysemy of these two terms which, when combined, describe but can also obscure work in this area; and we discuss key themes and concerns which emerge. Through analysis of two short extracts of media discourse in English, we elaborate a distinction between two alternative emphases: study of media language as concerned with the capabilities associated with changing technologies for conveying linguistic messages (e.g. language use in telegraphy, radio, or instant messaging); and study of media language as commentary on modern society’s dominant communication forms, which tend to take an electronic ‘media’ form. In the first emphasis, media discourse is important in understanding the social functions of language and as regards social change. In the second emphasis, media language is more a matter of linguistic resources being used to communicate within an array of contemporary media choices whose availability is simply taken as a social fact. In later stages of the chapter we examine interaction between these different emphases at the level of media ‘genres'. In the formation of media genres, we argue, patterns of linguistic choice are superimposed on a given technical infrastructure and history of media capabilities. Distinctive media styles gradually evolve from each such combination to serve specific and changing expressive and communicative purposes. We conclude with discussion of the implications of this view of media technologies and forms as regards the development of new communicative styles on the Internet

Gender, local knowledge, and lessons learnt in documenting and conserving agrobiodiversity

This paper explores the linkages between gender, local knowledge systems and agrobiodiversity for food security by using the case study of LinKS, a regional FAO project in Mozambique, Swaziland, Zimbabwe and Tanzania over a period of eight years and now concluded. The project aimed to raise awareness on how rural men and women use and manage agrobiodiversity, and to promote the importance of local knowledge for food security and sustainable agrobiodiversity at local, institutional and policy levels by working with a diverse range of stakeholders to strengthen their ability to recognize and value farmers’ knowledge and to use gender-sensitive and participatory approaches in their work. This was done through three key activities: capacity building, research and communication. The results of the LinKS study show clearly that men and women farmers hold very specific local knowledge about the plants and animals they manage. Local knowledge, gender and agrobiodiversity are closely interrelated. If one of these elements is threatened, the risk of losing agrobiodiversity increases, having negative effects on food security. Increased productivity, economic growth and agricultural productivity are important elements in poverty reduction. The diverse and complex agroecological environment of Sub-Saharan Africa requires that future efforts be based on more localized solutions while maintaining a global outlook. Food security will have to build much more on local knowledge and agrobiodiversity with a clear understanding of gender implications while keeping in mind the continuously changing global socioeconomic and political conditions. – gender ; biodiversity ; agrobiodiversity ; indigenous knowledge ; conservation ; sustainable management ; genetic resources ; participation ; livelihood

Searching for a continuum 4D field theory arising from a 5D non-abelian gauge theory

The anisotropic 5D SU(2) Yang-Mills model has been widely investigated on the lattice during the last decade. In the case where all dimensions are large in size, it was previously claimed that there is a new phase in the phase diagram, called the Layer phase. In this phase, the gauge fields would be localized on 4D layers. Previous works claim that the phase transition to the Layer phase is of second order, which would allow a continuum limit to be taken. We present the extension of the previous work to large lattices, for which we found a first order phase transition. This leaves the scenario that this 5D theory can be dimensionally reduced to a continuum 4D field theory, doubtful.Comment: 6 pages, 2 figures - talk presented at the 31st International Symposium on Lattice Field Theory - Lattice 2013, Mainz, German

Learning from life-logging data by hybrid HMM: a case study on active states prediction

In this paper, we have proposed employing a hybrid classifier-hidden Markov model (HMM) as a supervised learning approach to recognize daily active states from sequential life-logging data collected from wearable sensors. We generate synthetic data from real dataset to cope with noise and incompleteness for training purpose and, in conjunction with HMM, propose using a multiobjective genetic programming (MOGP) classifier in comparison of the support vector machine (SVM) with variant kernels. We demonstrate that the system with either algorithm works effectively to recognize personal active states regarding medical reference. We also illustrate that MOGP yields generally better results than SVM without requiring an ad hoc kernel

Dysregulated placental microRNAs in Early and Late onset Preeclampsia

Copyright © 2017. Published by Elsevier Ltd.INTRODUCTION: To determine the miRNA expression profile in placentas complicated by Preeclampsia (PE) and compare it to uncomplicated pregnancies. METHODS: Sixteen placentas from women with PE, [11 with early onset PE (EOPE) and 5 with late onset PE (LOPE)], as well as 8 placentas from uncomplicated pregnancies were analyzed using miRNA microarrays. For statistical analyses the MATLAB® simulation environment was applied. The over-expression of miR-518a-5p was verified using Quantitative Real-Time Polymerase Chain Reaction. RESULTS: Forty four miRNAs were found dysregulated in PE complicated placentas. Statistical analysis revealed that miR-431, miR-518a-5p and miR-124* were over-expressed in EOPE complicated placentas as compared to controls, whereas miR-544 and miR-3942 were down-regulated in EOPE. When comparing the miRNA expression profile in cases with PE and PE-growth restricted fetuses (FGR), miR-431 and miR-518a-5p were found over-expressed in pregnancies complicated by FGR. DISCUSSION: Since specific miRNAs can differentiate EOPE and LOPE from uncomplicated placentas, they may be considered as putative PE-specific biomarkers. MiR-518a-5p emerged as a potential diagnostic indicator for EOPE cases as well as for PE-FGR complicated placentas, indicating a potential link to the severity of the disease.Peer reviewe