Fast-food offerings in the United States in 1986, 1991, and 2016 show large increases in food variety, portion size, dietary energy, and selected micronutrients

BACKGROUND US national survey data shows fast food accounted for 11% of daily caloric intake in 2007-2010. OBJECTIVE To provide a detailed assessment of changes over time in fast-food menu offerings over 30 years, including food variety (number of items as a proxy), portion size, energy, energy density, and selected micronutrients (sodium, calcium, and iron as percent daily value [%DV]), and to compare changes over time across menu categories (entrées, sides, and desserts). DESIGN Fast-food entrées, sides, and dessert menu item data for 1986, 1991, and 2016 were compiled from primary and secondary sources for 10 popular fast-food restaurants. STATISTICAL ANALYSIS Descriptive statistics were calculated. Linear mixed-effects analysis of variance was performed to examine changes over time by menu category. RESULTS From 1986 to 2016, the number of entrées, sides, and desserts for all restaurants combined increased by 226%. Portion sizes of entrées (13 g/decade) and desserts (24 g/decade), but not sides, increased significantly, and the energy (kilocalories) and sodium of items in all three menu categories increased significantly. Desserts showed the largest increase in energy (62 kcal/decade), and entrées had the largest increase in sodium (4.6% DV/decade). Calcium increased significantly in entrées (1.2%DV/decade) and to a greater extent in desserts (3.9% DV/decade), but not sides, and iron increased significantly only in desserts (1.4% DV/decade). CONCLUSIONS These results demonstrate broadly detrimental changes in fast-food restaurant offerings over a 30-year span including increasing variety, portion size, energy, and sodium content. Research is needed to identify effective strategies that may help consumers reduce energy intake from fast-food restaurants as part of measures to improve dietary-related health issues in the United States.Accepted manuscrip

Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation

Advances in image processing and computer vision in the latest years have brought about the use of visual features in artwork recommendation. Recent works have shown that visual features obtained from pre-trained deep neural networks (DNNs) perform very well for recommending digital art. Other recent works have shown that explicit visual features (EVF) based on attractiveness can perform well in preference prediction tasks, but no previous work has compared DNN features versus specific attractiveness-based visual features (e.g. brightness, texture) in terms of recommendation performance. In this work, we study and compare the performance of DNN and EVF features for the purpose of physical artwork recommendation using transactional data from UGallery, an online store of physical paintings. In addition, we perform an exploratory analysis to understand if DNN embedded features have some relation with certain EVF. Our results show that DNN features outperform EVF, that certain EVF features are more suited for physical artwork recommendation and, finally, we show evidence that certain neurons in the DNN might be partially encoding visual features such as brightness, providing an opportunity for explaining recommendations based on visual neural models.Comment: DLRS 2017 workshop, co-located at RecSys 201

Optimise initial spare parts inventories: an analysis and improvement of an electronic decision tool.

Control of spare parts is very difficult as demands can be very low (once in a few years is no exception), while the consequences of a stockout can be severe. While in the past many companies choose to have very large spares inventories, one now observe trends in areas with good transportation connections to keep spare parts at the suppliers. Hence it is very important to make a good selection of which spare parts to stock at the start-up of new plants. To this end Shell Global Solutions has developed an electronic decision tool, called E-SPIR. In this report we analyse the decision rules used in it. We consider stockout penalties and advise to use criticality classifications instead. Furthermore, we investigate minimum stock levels, demand distributions and order quantities.

Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems

We study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact

Analyzing the impact of excess inventory of California Glam to control the inventories of distributors by integrating product and distributor segmentation concept in the supply chain

The main purpose of this work is to document the methodology, objectives, application process and results analysis of an organizational intervention for the supply chain of a new born beauty and cosmetics company California Glam which is emerging as a key player in beauty and cosmetic space in Mexico