Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</jats:p

Evaluating the Impact of an Integrated Urban Design of Transport Infrastructure and Public Space on Human Behavior and Environmental Quality: A Case Study in Beijing

Urban transport infrastructure can result in the physical, psychological and environmental separation of neighborhoods, public spaces and pedestrian networks, leading to negative impacts on citizens’ daily commutes, social activities and the quality of the ecosystem. An integrated design of transport infrastructure and public space is beneficial for mediating these negative impacts. In this paper, we propose an integrated methodology, which combines urban design, computational scenario evaluation and decision-making processes, based on a conceptual model of human and ecological needs-driven planning. To evaluate the impacts of the road network and public space design on individual outdoor activities, travel behavior and air pollution, an agent-based model is demonstrated. This model is then applied to a case study in Beijing, leading to hourly traffic volume maps and car-related air pollution heat maps of a baseline road network-public space design

Cross-cultural color-odor associations

Colors and odors are associated; for instance, people typically match the smell of strawberries to the color pink or red. These associations are forms of crossmodal correspondences. Recently, there has been discussion about the extent to which these correspondences arise for structural reasons (i.e., an inherent mapping between color and odor), statistical reasons (i.e., covariance in experience), and/or semantically-mediated reasons (i.e., stemming from language). The present study probed this question by testing color-odor correspondences in 6 different cultural groups (Dutch, Netherlands-residing-Chinese, German, Malay, Malaysian-Chinese, and US residents), using the same set of 14 odors and asking participants to make congruent and incongruent color choices for each odor. We found consistent patterns in color choices for each odor within each culture, showing that participants were making non-random color-odor matches. We used representational dissimilarity analysis to probe for variations in the patterns of color-odor associations across cultures; we found that US and German participants had the most similar patterns of associations, followed by German and Malay participants. The largest group differences were between Malay and Netherlands-resident Chinese participants and between Dutch and Malaysian-Chinese participants. We conclude that culture plays a role in color-odor crossmodal associations, which likely arise, at least in part, through experience

Odor-color associations differ with verbal descriptors for odors : A comparison of three linguistically diverse groups

People appear to have systematic associations between odors and colors. Previous research has emphasized the perceptual nature of these associations, but little attention has been paid to what role language might play. It is possible odor-color associations arise through a process of labeling; that is, participants select a descriptor for an odor and then choose a color accordingly (e.g., banana odor → "banana" label → yellow). If correct, this would predict odor-color associations would differ as odor descriptions differ. We compared speakers of Dutch (who overwhelmingly describe odors by referring to the source; e.g., smells like banana) with speakers of Maniq and Thai (who also describe odors with dedicated, abstract smell vocabulary; e.g., musty), and tested whether the type of descriptor mattered for odor-color associations. Participants were asked to select a color that they associated with an odor on two separate occasions (to test for consistency), and finally to label the odors. We found the hunter-gatherer Maniq showed few, if any, consistent or accurate odor-color associations. More importantly, we found the types of descriptors used to name the smells were related to the odor-color associations. When people used abstract smell terms to describe odors, they were less likely to choose a color match, but when they described an odor with a source-based term, their color choices more accurately reflected the odor source, particularly when the odor source was named correctly (e.g., banana odor → yellow). This suggests language is an important factor in odor-color cross-modal associations

Effects of Visual Priming on Taste-Odor Interaction

Little is known about the influence of visual characteristics other than colour on flavor perception, and the complex interactions between more than two sensory modalities. This study focused on the effects of recognizability of visual (texture) information on flavor perception of odorized sweet beverages

Lack of effect of high-protein vs. high-carbohydrate meal intake on stress-related mood and eating behavior

<p>Abstract</p> <p>Background</p> <p>Consumption of meals with different macronutrients, especially high in carbohydrates, may influence stress-related eating behavior. We aimed to investigate whether consumption of high-protein vs. high-carbohydrate meals influences stress-related mood, food reward, i.e. 'liking' and 'wanting', and post-meal energy intake.</p> <p>Methods</p> <p>Participants (n = 38, 19m/19f, age = 25 ± 9 y, BMI = 25.0 ± 3.3 kg/m²) came to the university four times, fasted, once for a stress session receiving a high-protein meal, once for a rest session receiving a high-protein meal, once for a stress session receiving a high-carbohydrate meal and once for a rest session receiving a high-carbohydrate meal (randomized cross-over design). The high-protein and high-carbohydrate test meals (energy percentage protein/carbohydrate/fat 65/5/30 vs. 6/64/30) matched for energy density (4 kJ/g) and daily energy requirements (30%). Stress was induced using an ego-threatening test. Pre- and post-meal 'liking' and 'wanting' (for bread, filling, drinks, dessert, snacks, stationery (non-food alternative as control)) was measured by means of a computer test. Following the post-meal 'wanting' measurement, participants received and consumed their wanted food items (post-meal energy intake). Appetite profile (visual analogue scales), mood state (Profile Of Mood State and State Trait Anxiety Inventory questionnaires), and post-meal energy intake were measured.</p> <p>Results</p> <p>Participants showed increased feelings of depression and anxiety during stress (P < 0.01). Consumption of the test meal decreased hunger, increased satiety, decreased 'liking' of bread and filling, and increased 'liking' of placebo and drinks (P < 0.0001). Food 'wanting' decreased pre- to post-meal (P < 0.0001). The high-protein vs. high-carbohydrate test meal induced lower subsequent 'wanting' and energy intake (1.7 ± 0.3 MJ vs. 2.5 ± 0.4 MJ) only in individuals characterized by disinhibited eating behavior (factor 2 Three Factor Eating Questionnaire, n = 16), during rest (P ≤ 0.01). This reduction in 'wanting' and energy intake following the high-protein meal disappeared during stress.</p> <p>Conclusions</p> <p>Consumption of a high-protein vs. high-carbohydrate meal appears to have limited impact on stress-related eating behavior. Only participants with high disinhibition showed decreased subsequent 'wanting' and energy intake during rest; this effect disappeared under stress. Acute stress overruled effects of consumption of high-protein foods.</p> <p>Trial registration</p> <p>The study was registered in the Dutch Trial Register (<a href="http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=2040">NTR1904</a>). The protocol described here in this study deviates from the trial protocol approved by the Medical Ethical Committee of the Maastricht University as it comprises only a part of the approved trial protocol.</p

Modulations of the Chicken Cecal Microbiome and Metagenome in Response to Anticoccidial and Growth Promoter Treatment

With increasing pressures to reduce or eliminate the use of antimicrobials for growth promotion purposes in production animals, there is a growing need to better understand the effects elicited by these agents in order to identify alternative approaches that might be used to maintain animal health. Antibiotic usage at subtherapeutic levels is postulated to confer a number of modulations in the microbes within the gut that ultimately result in growth promotion and reduced occurrence of disease. This study examined the effects of the coccidiostat monensin and the growth promoters virginiamycin and tylosin on the broiler chicken cecal microbiome and metagenome. Using a longitudinal design, cecal contents of commercial chickens were extracted and examined using 16S rRNA and total DNA shotgun metagenomic pyrosequencing. A number of genus-level enrichments and depletions were observed in response to monensin alone, or monensin in combination with virginiamycin or tylosin. Of note, monensin effects included depletions of Roseburia, Lactobacillus and Enterococcus, and enrichments in Coprococcus and Anaerofilum. The most notable effect observed in the monensin/virginiamycin and monensin/tylosin treatments, but not in the monensin-alone treatments, was enrichments in Escherichia coli. Analysis of the metagenomic dataset identified enrichments in transport system genes, type I fimbrial genes, and type IV conjugative secretion system genes. No significant differences were observed with regard to antimicrobial resistance gene counts. Overall, this study provides a more comprehensive glimpse of the chicken cecum microbial community, the modulations of this community in response to growth promoters, and targets for future efforts to mimic these effects using alternative approaches

How to Exploit the Digitalization Potential of Business Processes

Process improvement is the most value-adding activity in the business process management (BPM) lifecycle. Despite mature knowledge, many approaches have been criticized to lack guidance on how to put process improvement into practice. Given the variety of emerging digital technologies, organizations not only face a process improvement black box, but also high uncertainty regarding digital technologies. This paper thus proposes a method that supports organizations in exploiting the digitalization potential of their business processes. To achieve this, action design research and situational method engineering were adopted. Two design cycles involving practitioners (i.e., managers and BPM experts) and end-users (i.e., process owners and participants) were conducted. In the first cycle, the method’s alpha version was evaluated by interviewing practitioners from five organizations. In the second cycle, the beta version was evaluated via real-world case studies. In this paper, detailed results of one case study, which was conducted at a semiconductor manufacturer, are included

Applying Bayesian model averaging for uncertainty estimation of input data in energy modelling

Background Energy scenarios that are used for policy advice have ecological and social impact on society. Policy measures that are based on modelling exercises may lead to far reaching financial and ecological consequences. The purpose of this study is to raise awareness that energy modelling results are accompanied with uncertainties that should be addressed explicitly. Methods With view to existing approaches of uncertainty assessment in energy economics and climate science, relevant requirements for an uncertainty assessment are defined. An uncertainty assessment should be explicit, independent of the assessor&#8217;s expertise, applicable to different models, including subjective quantitative and statistical quantitative aspects, intuitively understandable and be reproducible. Bayesian model averaging for input variables of energy models is discussed as method that satisfies these requirements. A definition of uncertainty based on posterior model probabilities of input variables to energy models is presented. Results The main findings are that (1) expert elicitation as predominant assessment method does not satisfy all requirements, (2) Bayesian model averaging for input variable modelling meets the requirements and allows evaluating a vast amount of potentially relevant influences on input variables and (3) posterior model probabilities of input variable models can be translated in uncertainty associated with the input variable. Conclusions An uncertainty assessment of energy scenarios is relevant if policy measures are (partially) based on modelling exercises. Potential implications of these findings include that energy scenarios could be associated with uncertainty that is presently neither assessed explicitly nor communicated adequately

Genomic Characterization of Methanomicrobiales Reveals Three Classes of Methanogens

BACKGROUND:Methanomicrobiales is the least studied order of methanogens. While these organisms appear to be more closely related to the Methanosarcinales in ribosomal-based phylogenetic analyses, they are metabolically more similar to Class I methanogens. METHODOLOGY/PRINCIPAL FINDINGS:In order to improve our understanding of this lineage, we have completely sequenced the genomes of two members of this order, Methanocorpusculum labreanum Z and Methanoculleus marisnigri JR1, and compared them with the genome of a third, Methanospirillum hungatei JF-1. Similar to Class I methanogens, Methanomicrobiales use a partial reductive citric acid cycle for 2-oxoglutarate biosynthesis, and they have the Eha energy-converting hydrogenase. In common with Methanosarcinales, Methanomicrobiales possess the Ech hydrogenase and at least some of them may couple formylmethanofuran formation and heterodisulfide reduction to transmembrane ion gradients. Uniquely, M. labreanum and M. hungatei contain hydrogenases similar to the Pyrococcus furiosus Mbh hydrogenase, and all three Methanomicrobiales have anti-sigma factor and anti-anti-sigma factor regulatory proteins not found in other methanogens. Phylogenetic analysis based on seven core proteins of methanogenesis and cofactor biosynthesis places the Methanomicrobiales equidistant from Class I methanogens and Methanosarcinales. CONCLUSIONS/SIGNIFICANCE:Our results indicate that Methanomicrobiales, rather than being similar to Class I methanogens or Methanomicrobiales, share some features of both and have some unique properties. We find that there are three distinct classes of methanogens: the Class I methanogens, the Methanomicrobiales (Class II), and the Methanosarcinales (Class III)