Polish Consumers’ Understanding of Different Front-of-Package Food Labels: A Randomized Experiment

Dietary practices are a key behavioral factor in chronic disease prevention; one strategy for improving such practices population-wise involves front-of-package labels (FoPL). This online randomized study, conducted in a quota-based sample of 1159 Polish adults (mean age = 40.9 ± 15.4 years), assessed the objective understanding of five FoPL: Health Star Rating, Multiple Traffic Lights, NutriScore, Reference Intakes (RI) and Warning Label. Objective understanding was evaluated by comparing results of two nutritional quality ranking tasks (without/with FoPL) using three food categories (breakfast cereals, cakes, pizza). Associations between FoPL exposure and objective understanding were assessed via multivariable ordinal logistic regression. Compared to RI and across food categories, significant improvement in objective understanding was seen for NutriScore (OR = 2.02; 95% CI: 1.41–2.91) and Warning Label (OR = 1.61; 95% CI: 1.12–2.32). In age-stratified analyses, significant improvement in objective understanding compared to RI emerged mainly among adults aged 18–30 years randomized to NutriScore (all food categories: OR = 3.88; 95% CI: 2.04–7.36; cakes: OR = 6.88; 95% CI: 3.05–15.51). Relative to RI, NutriScore was associated with some improvement in objective understanding of FoPL across and within food categories, especially among young adults. These findings contribute to the ongoing debate about an EU-wide FoPL model.

A meritocratic network formation model for the rise of social media influencers

Many of today’s most used online social networks such as Instagram, YouTube, Twitter, or Twitch are based on User-Generated Content (UGC). Thanks to the integrated search engines, users of these platforms can discover and follow their peers based on the UGC and its quality. Here, we propose an untouched meritocratic approach for directed network formation, inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We theoretically and numerically analyze the network equilibria properties under different meeting probabilities: while featuring common real-world networks properties, e.g., scaling law or small-world effect, our model predicts that the expected in-degree follows a Zipf’s law with respect to the quality ranking. Notably, the results are robust against the effect of recommendation systems mimicked through preferential attachment based meeting approaches. Our theoretical results are empirically validated against large data sets collected from Twitch, a fast-growing platform for online gamers.

Constructing the Resistivity-to-Sediment-Type Transform for the Interpretation of Airborne EM Data

A novel Markov Chain Monte Carlo (MCMC) based methodology was developed for the transformation of resistivity, derived from airborne electromagnetic (AEM) data, into sediment type. This methodology was developed and tested using AEM data and well sediment type and resistivity logs from Butte and Glenn Counties in the Californian Central Valley. Our methodology accounts for the spatially varying sensitivity of the AEM method by constructing different transforms separated based on the sensitivity of the AEM method. The large spatial separation that typically exists between the AEM data and the wells with sediment type logs was avoided by planning the acquisition of AEM data so as to fly as close as possible to the well locations. We had 55 locations with sediment type logs and AEM data separated by 100 m, determined to be the maximum acceptable separation distance. Differences in vertical resolution between the AEM method and the sediment type logs were addressed by modeling the physics of the AEM measurement, allowing for a comparison of field and AEM data generated during the MCMC process. The influence of saturation state was captured by creating one set of transforms for the region above the top of the saturated zone and another for below. Using the set of transforms developed at the 55 locations, an inverse distance weighting scheme that included a well quality ranking was used to construct a set of 12 (six sensitivity bins, and two saturation states) resistivity-to-sediment-type transforms at every AEM data location. These represent a set of transforms that accommodate the variation in AEM sensitivity and are independent of the inversion used to retrieve the resistivity model. These transforms thus avoid two of the significant limitations common to resistivity-to-sediment-type transforms used to interpret AEM data.

Egyetemi tanári pályázatok minimális publikációs követelményeinek változása és várható hatása a társadalomtudományok területén

Összefoglaló. Az egyetemi tanári pályázat összeállításához és értékeléséhez összeállított, a Magyar Akkreditációs Bizottság által elfogadott módosított útmutató 2020. szeptember 1-jétől lépett hatályba. A módosított követelményrendszer vizionálja a jelenlegi értékmérők átalakulását. A tudományos teljesítmény súlypontjaivá a folyóirat-publikációk váltak, azonban a hazai közlemények önmagukban nem elegendőek, a nemzetközi láthatóság és minőségi rangsor feltétele a nemzetközi publikálás, amely egyúttal a tudományos fokozatok elérésének is feltételévé vált. Jelen tanulmány a gazdaságtudomány, valamint a társadalomtudományok területén működő magyarországi egyetemek oktatóinak publikációs tevékenységén keresztül vizsgálja a módosított követelményrendszer hatását és következményeit az egyetemi tanári pályázat, valamint tágabb értelemben a nemzetközi tudományos elismertség vonatkozásában. Summary. The modified Guidelines for the Compilation and Evaluation of the Applications for the Position of University Full Professors approved by the Hungarian Accreditation Committee was to enter into force on the 1st of September in 2020. The reshaped requirement system envisions the transformation of current assessment criteria. Journal publications have become the focus of scientific performance, however, publications in domestic journals alone are not enough; apart from being required for achieving international scientific visibility and quality ranking, international publications have also become a prerequisite for scientific degrees. The present study examines the impact and consequences of the modified requirement system on university professorships and, in a broader sense, on their international scientific recognition through the publication activities of lecturers from Hungarian universities in the fields of Economics and social sciences.

Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search

In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products.