Global Preference Research Articles

Study of the perception and the acceptability of mayonnaise ingredients among Mexican consumers and its global preference

Introduction: Due to the interest in healthcare through the diet, there is an increase in the reformulation of products to reduce their fat content. The mayonnaise is a dressing product recognized by its high content of fats (75 %); the reduction of fat content implies the use of additives that can be perceived as harmful and are poorly acceptable by the consumers. The objectives of this study were to study the perceptions and the acceptability of the ingredients used in mayonnaise, as well as the global preference of these products. Methodology: The preferences of traditional and low-fat mayonnaise were studied using a survey applied to 303 participants. The perception and acceptability of ingredients (on a scale of 1 – 5) was determined with focus groups of 12 participants. Results: The selection of mayonnaise was based on the nutritional content (in women) and on a specific commercial brand (in men) (p<0.05). The low-fat content and the ingredients from natural sources are perceived as healthy characteristics of the product (64 % and 27 % from the total of the participants, respectively). The 18 – 29 years old participants demonstrated more interest in products with probiotics, while the people from 51 – 60 age range preferred ingredients from natural sources (p<0.05). The ingredients with familiar names such as egg yolk, vinegar, lime juice, spices, and mustard showed high acceptability scores (>4 acceptability score). Conclusions: The low fat- mayonnaise is the most demanded in the market. However, there is interest in mayonnaise containing ingredients with natural sources or supplemented with probiotics. The perception and the acceptability of the ingredients are positively influenced by its nature (simple names) and origin (from natural sources).

The periodic table of fermented foods: limitations and opportunities.

Fermentation is one of the oldest methods of food processing and accounts for a substantial proportion of human foods, including not only staple foods such as bread, cereal porridges or fermented legumes but also fermented vegetables, meats, fish and dairy, alcoholic beverages as well as coffee, cocoa and condiments such as vinegar, soy sauce and fish sauces. Adding the regional varieties to these diverse product categories makes for an almost immeasurable diversity of fermented foods. The periodic table of fermented foods aims to map this diversity on the 118 entries of the periodic table of chemical elements. While the table fails to represent the diversity of fermented foods, it represents major fermentation substrates, product categories, fermentation processes and fermentation organisms. This communication not only addresses limitations of the graphical display on a "periodic table of fermented foods", but also identifies opportunities that relate to questions that are facilitated by this graphical presentation: on the origin and purpose of food fermentation, which fermented foods represent "indigenous" foods, differences and similarities in the assembly of microbial communities in different fermentations, differences in the global preferences for food fermentation, the link between microbial diversity, fermentation time and product properties, and opportunities of using traditional food fermentations as template for development of new products. KEY POINTS: • Fermented foods are produced in an almost immeasurable diversity. • Fermented foods were mapped on a periodic table of fermented foods. • This table facilitates identification of communalities and differences of products.

Improved recommender systems by denoising ratings in highly sparse datasets through individual rating confidence

Collaborative filtering (CF) is the most successful approach of Recommender Systems that has been applied in a wide range of applications. In this approach, historical rating data is exploited to calculate similarity between pairs of users and predict user preference over unseen items. The similarity is calculated using users' global preferences estimated from their previous interactions with items. To this end, commonly rated items by a given pair of users are utilized to calculate the similarity. In a sparse dataset, there are limited co-rated items. In addition, inconsistent behaviours of users in rating items may add some noise to data, which makes the ratings more untrustworthy leading to less accuraterecommendations. In this manuscript, we introduce an individual ratings confidence measure (IRC) to calculate the confidence of a given rating to each item by the target user. IRC consists of 5 factors that help to have more information about the interest and boredom of users. User ratings are denoised by prioritizing extreme and high-confidence ratings, and finally, the denoised ratings are used to obtain similarity values. This approach leads to a more accurate neighbourhood selection and rating prediction. We show the superiority of the proposed method when compared with state-of-the-art recommender algorithms over some well-known benchmark datasets.

Contexts and Context-awareness Revisited from an Intelligent Environments Perspective

ABSTRACT Context is a useful concept somehow unconsciously used by humans in daily life problem-solving. Recently, several subareas of computer science have been increasingly trying to rely on this concept to design systems with practical use in certain predefined circumstances. The perception is that imbuing in the system certain context-awareness qualities can support intelligent decision-making in specific practical situations. Despite a significant number of implemented systems that aim at providing context-awareness, there is a lack of commonly accepted and used methodologies and tools. At the root of this, is the lack of agreement on a set of good principles or standards, which can act as a guide to the scientific community and the developers interested in this class of systems. There have been some extensive surveys on the use of context, still there is no theoretical corpus emerging that we can use to discuss the essential concepts making up the fabric of contexts and its use by system developers. Here we attempted such enterprise at a level, which is more formal than popular surveys, in a way that is not implementation dependent and in a way that highlights key concepts of relevance to developers. We reassessed first the basic concepts identifying the need to more prominently consider system beneficiaries’ satisfaction. We then transfer explicitly these values to a more formal outline of the basic componentgs and the operations which emerge as relevant. We identify and highlight the tasks of context activation, comparison, influence, construction, and interaction. We hint at how these may work in practice and explained these through examples. We show how the theory is flexible enough by generalizing it to multiusers so that optimization of global preferences and expectations is used to drive system development and system behavior.

Economic preferences and state-level corruption: global evidence and mechanism

ABSTRACT Using newly published data from the Global Preference Survey which cover 76 countries (Falk et al., 2018), we document robust evidence that economic preferences are partly responsible for state-level corruption. Specifically, fixed effects estimation results show that patience, trust, and altruism discourage corruption, while negative reciprocity encourages corruption. We examine the role of economic freedom and government effectiveness as the mechanisms underlying the effect of negative reciprocity. We also find that the effects of patience and negative reciprocity are greater in a more stable political environment, and smaller in a society with faster economic growth.

Future ethical implications and potential health consequences of nanotechnology research in Jordan: A scientometric analysis


 
 
 
 Purpose: To evaluate the implanted legislation and regulations in Jordan, in order to minimize nanotechnology research-related health concerns.
 Methods: In the first part, all nanotechnology published research in Scopus database and the Jordanian National Database for Researchers were mined. The growth in nanotechnology research was analyzed by calculating the relative growth rate (RGR), doubling time (DT), and compound annual growth rate (CAGR). Thereafter, the results were compared to global preferences. Furthermore, this research explored all possible factors that may influence decision-makers and health policies in Jordan.
 Results: Data analysis showed that the quantity of nanotechnology research in Jordan is exponentially increasing with RGR (1.96), DT (0.37), and CAGR (33 %) very close to the figures obtained worldwide. The trend line analysis (R2 = 0.9638) expected that more than 65 % of nanotechnology research will be produced within the next 5 years. Moreover, no legislation has been adapted in Jordan to regulate nanotechnology research and industry.
 Conclusions: Adopting nanotechnology without having any corresponding legislation constitutes a violation of the ethical codes of research, and ignorance of public safety. Moreover, the research suggests a set of steps to organize the crafting of nanotechnology and research in Jordan, and other similar contexts in developing countries.
 
 
 

Does Trust Associate with Political Regime?

Since trust correlates with economic development and in turn economic development associates with political regime, we conjecture that there may be a relationship between trust and political regime. Without looking for any casual inference, we investigate if trust aggregated on the country level correlates with the country's political regime. Specifically, we are interested whether trust correlates positively with the level of democracy in cross-sectional observations. We analyse data on trust from 76 countries using the Global Preference Survey and investigate the correlations with five separate democracy indices (Polity2, Economist Intelligence Unit’s Index of Democracy, Freedom House, MaxRange and Unified Democracy Score). We do not find any significant association, with or without taking into account other factors (e.g., regional location, economic development, geographic conditions, culture) as well. Trust does not correlate with cornerstones of democracy either, measured by five components of the EIU index. A robustness check using an alternative measure of trust from the World Values Survey reaches the same results. The present study supersedes the working paper version (Khayouti et al., 2020).

Partner choice does not predict prosociality across countries.

Why does human prosociality vary around the world? Evolutionary models and laboratory experiments suggest that possibilities for partner choice (i.e. the ability to leave unprofitable relationships and strike up new ones) should promote cooperation across human societies. Leveraging the Global Preferences Survey (n = 27,125; 27 countries) and the World Values Survey (n = 54,728; 32 countries), we test this theory by estimating the associations between relational mobility, a socioecological measure of partner choice, and a wide variety of prosocial attitudes and behaviours, including impersonal altruism, reciprocity, trust, collective action and moral judgements of antisocial behaviour. Contrary to our pre-registered predictions, we found little evidence that partner choice is related to prosociality across countries. After controlling for shared causes of relational mobility and prosociality - environmental harshness, subsistence style and geographic and linguistic proximity - we found that only altruism and trust in people from another religion are positively related to relational mobility. We did not find positive relationships between relational mobility and reciprocity, generalised trust, collective action or moral judgements. These findings challenge evolutionary theories of human cooperation which emphasise partner choice as a key explanatory mechanism, and highlight the need to generalise models and experiments to global samples.

Patience, Risk-Taking, and Human Capital Investment Across Countries

Abstract Patience and risk-taking—two preference components that steer intertemporal decision-making—are fundamental to human capital investment decisions. To understand how they contribute to international skill differences, we combine Programme for International Student Assessment tests with the Global Preference Survey. We find that opposing effects of patience (positive) and risk-taking (negative) together account for two-thirds of the cross-country variation in student skills. In an identification strategy addressing unobserved residence country features, we find similar results when assigning migrant students their country-of-origin preferences in models with residence country fixed effects. Associations of national preferences with family and school inputs suggest that both may act as channels.

Leadership First: Reimagining DEI Together

This article is a thoughtful integrative compilation of literature from scholars well known in their field of diversity, equity, and inclusion all of whom have brought welcome changes for the next generations. There is a new look to DEI and leadership matters. Even prior to the pandemic, the future of work was unfolding. Organizations continue to focus on digitalization, remote work, employee empowerment and workforce diversity. Similarly, leaders are reinventing themselves to transform through the ability to traverse a path of immense changes adapting to global trends and consumer preferences.

Graph neural networks with global noise filtering for session-based recommendation

Session-based recommendation leverages anonymous sessions to predict which item a user is most likely to click on next. While previous approaches capture items-transition patterns within current session and neighbor sessions, they do not accurately filter out noise within session or widen the range of feasible data in a more reasonable way. In a current session, the user may accidentally click on an unrelated item, resulting in the fact that, the users’ primary intents from neighbor sessions, may mismatch the current session. Thereby, we propose a new framework, dubbed Graph Neural Networks with Global Noise Filtering for Session-based Recommendation (GNN-GNF), aiming to filter noisy data and exploit items-transition patterns in a more comprehensive and reasonable manner. In simple terms, GNN-GNF contains two parts: data preprocessing and model learning. In data preprocesing, an item-level filter module is used to obtain the main intent of user and a session-level filter module is designed to filter the sessions unrelated to the target session intent by means of edge matching. In model learning, we consider both local-level interest obtained by an aggregation of the items representing the main intent of user within a session, and global-level interest deduced from a global graph. We take two kinds of neighbor aggregations, summation and interactive aggregation, respectively, to iteratively derive the representation of the central node in the global graph. Finally, GNN-GNF concatenates the local and global preference to characterize the current session, towards better recommendation prediction. Experiments on two datasets demonstrate that GNN-GNF can achieve competitive results. The source code is available at:https://github.com/Fenglixia/GNF.

The local integration preference of the Tf1 retrotransposon in Schizosaccharomyces pombe

Transposons are mobile DNAs that can move to different locations in host genomes. The integration site selection of transposons is critical for both themselves and host cells. Studies on the integration of retrotransposons and retroviruses have focused more on the global preference than on the local preference. The local preferences of retrotransposons are usually weak and of large diversity. Here, we analyzed hundreds of thousands of independent integration events of the Tf1 retrotransposon in Schizosaccharomyces pombe. The consensus sequence at the Tf1 integration sites shows a palindromic pattern, which can be divided into four sections, each of them contains one or more CGnTA units with a period of 10 base pairs, indicating interaction with subunits of the integrase oligomer in the pre-integration complex. Moreover, the analysis on the nucleosome occupancy flanking Tf1 target sites shows that Tf1 integration favors regions with one entire nucleosome depletion.

Technological potential of the use of ultrasound and freeze concentration in Fuyu persimmon juice

This research aimed to propose a protocol for the processing of whole persimmon juice and to evaluate the effects of ultrasound treatment in relation to microbiological, sensory, and antioxidant aspects. The persimmon juice was submitted to freeze concentration, and the physical-chemical, total phenolic compounds (TPC), and antioxidant capacity were investigated. The sample treated with ultrasound (60% amplitude for 20 min) obtained a satisfactory reduction in total coliforms as well as the highest amount of TPC and total flavonoids compared with the control. This sample also showed a global preference in the sensory evaluation. Regarding the use of freeze concentration, two cycles of this process resulted in an increase in sugars, organic acids, and TPC. These results suggest that these nonthermal technologies can potentially be applied in relation to persimmon beverages, instead of the traditional pasteurization and concentration methods, to improve sensory quality and safety or to enhance functional properties. Novelty impact statement This study suggests that ultrasound treatment applied in persimmon juice processing can reduce microbiological load while preserving phenolic compounds and antioxidant capacity. Freeze concentration is also a technology that can be incorporated by the beverage industry in order to improve functional and sensory properties.

Comparative genomic analysis reveals metabolic flexibility of Woesearchaeota

The archaeal phylum Woesearchaeota, within the DPANN superphylum, includes phylogenetically diverse microorganisms that inhabit various environments. Their biology is poorly understood due to the lack of cultured isolates. Here, we analyze datasets of Woesearchaeota 16S rRNA gene sequences and metagenome-assembled genomes to infer global distribution patterns, ecological preferences and metabolic capabilities. Phylogenomic analyses indicate that the phylum can be classified into ten subgroups, termed A–J. While a symbiotic lifestyle is predicted for most, some members of subgroup J might be host-independent. The genomes of several Woesearchaeota, including subgroup J, encode putative [FeFe] hydrogenases (known to be important for fermentation in other organisms), suggesting that these archaea might be anaerobic fermentative heterotrophs.

A likelihood‐based ORESTE method for failure mode and effect analysis (FMEA) based risk analysis problem under interval type‐2 fuzzy environment

AbstractThe failure mode and effect analysis (FMEA) model is an effective safety system analysis method to identify and analyze potential risks. Nevertheless, the traditional FMEA possesses deficiencies associating with questionable multiplication procedure, overlooking uncertainty and interaction relationships among risk factors. Besides, determining the risk priority considering the detailed interrelation among the failure modes is beyond the capability of current FMEA models, especially in the case of complex preference information. Thus, this paper proposed an extended FMEA by integrating ORESTE (organization, rangement et Synthèse de données relarionnelles, in French) method, Heronian Mean (HM) operator, and interval type‐2 fuzzy sets (IT2FSs). First, the IT2FSs are utilized to depict the complex risk preference information. Second, the HM operator, as an aggregated tool, is utilized to obtain collective evaluation information, which can fully reflect the overall correlations among individual risk assessment information. Then, an extended ORESTE method based on the likelihood of IT2FSs is utilized to obtain the priority ranking orders of failure modes. In this extended method, a new IT2FSs global preference score is constructed to determine the weak ranking. And, three new preference intensities based on the likelihood of IT2FSs are constructed to determine PIR (preference, indifference, and incomparability) structure which can show the detailed relationships between failure modes. Finally, an illustrative example of a nuclear reheat valve system is selected to verify its application and efficiency.

Economic preferences and trade outcomes

Integrating the Global Preference Survey (GPS) and its data of unique scope on national preference structures in patience, risk attitude and reciprocity into a gravity framework, this paper is the first to explore a potential influence on international trade outcomes of economic and social preferences in a unified setting. Adding to the evidence on preferences’ importance for aggregate outcomes, the authors find marked differences in trade flows and relationships, both on the country-level and between bilateral partners. Their main results suggest that countries differing in their willingness to behave negatively reciprocal tend to trade significantly less amongst each other due to the destabilizing effect of unexpected punishments. On the other hand, countries that are patient or risk-averse tend to shift towards exporting more differentiated goods as opposed to homogeneous goods and vice versa. We propose term and risk transformation considerations as the driving mechanisms for this relationship.

Development & Evaluation of E-Learning Module Based on Visual and Global Preferences Using a User-Centered Design Approach

Students develop various learning styles based on their preferences and learning habits. To serve different learning styles in a class with a number of students using the conventional face-to-face teaching method is not practical; therefore, the idea of personalized e-Learning to accommodate differences in learning style has arisen. Building on this idea, this research intends to provide an alternative interaction design for e-Learning modules by developing content based on user needs using the User-Centered Design methodology. Due to a lack of e-Learning content for visual and global preferences in the Felder-Silverman learning styles, User-Centered Design is chosen as the basis to design the e-Learning module. The result consists of an alternative design and a proposed interface design. The alternative design describes learning objects and navigation of the e-Learning module. The proposed interface design is a prototype of an interactive e-Learning module. After being evaluated, the prototype satisfies the user's expectations in terms of content translation, content navigation, and interactivity throughout the module.

Sequential Learning-based IaaS Composition

We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.

Worldwide antipsychotic drug search intensities: pharmacoepidemological estimations based on Google Trends data

Prescription patterns of antipsychotic drugs (APDs) are typically sourced from country-specific data. In this study, a digital pharmacoepidemiological approach was used to investigate APD preferences globally. Publicly available data on worldwide web search intensities in Google for 19 typical and 22 atypical APDs were temporally and spatially normalized and correlated with reported prescription data. The results demonstrated an increasing global preference for atypical over typical APDs since 2007, with quetiapine, olanzapine, risperidone, and aripiprazole showing the largest search intensities in 2020. Cross-sectional analysis of 122 countries in 2020 showed pronounced differences in atypical/typical APD preferences that correlated with gross domestic product per capita. In conclusion, the investigation provides temporal and spatial assessments of global APD preferences and shows a trend towards atypical APDs, although with a relative preference for typical APDs in low-income countries. Similar data-sourcing methodologies allow for prospective studies of other prescription drugs.

Collective Reflective Equilibrium in Practice (CREP) and controversial novel technologies.

In this paper, we investigate how data about public preferences may be used to inform policy around the use of controversial novel technologies, using public preferences about autonomous vehicles (AVs) as a case study. We first summarize the recent ‘Moral Machine’ study, which generated preference data from millions of people regarding how they think AVs should respond to emergency situations. We argue that while such preferences cannot be used to directly inform policy, they should not be disregarded. We defend an approach that we call ‘Collective Reflective Equilibrium in Practice’ (CREP). In CREP, data on public attitudes function as an input into a deliberative process that looks for coherence between attitudes, behaviours and competing ethical principles. We argue that in cases of reasonable moral disagreement, data on public attitudes should play a much greater role in shaping policies than in areas of ethical consensus. We apply CREP to some of the global preferences about AVs uncovered by the Moral Machines study. We intend this discussion both as a substantive contribution to the debate about the programming of ethical AVs, and as an illustration of how CREP works. We argue that CREP provides a principled way of using some public preferences as an input for policy, while justifiably disregarding others.