De-icers: What is safe for the environment and is effective?

The purpose of this experiment was to determine the affect that various de-icers (both organic and inorganic based) had on the health of grass, as well as determine the efficiency of the de-icer in melting ice to then compare and contrast the data to find the best overall de-icer in terms of effectiveness and environmental impact

De-icers: What is safe for the environment and is effective?

De-icers are a common chemical product that cities and homeowners across the world use to melt ice on the roads and around their home. The purpose of this experiment was to determine the effect of various de-icers on grass health and compare it to the efficiency from which these de-icers actually melt ice. This was done through placing pots of soil into separate containers each filled with a little bit of a different de-icer solution to test its effect on grass by measuring the percentage of green grass is left in each pot. The efficiency was tested by filling different containers with ice and pouring each of the different de-icers on it and then after a set time, any water in the container was poured out and the container was massed to find out how much ice melted. This project and the results that come from it have very relevant implications as de-icers are a commonly used product throughout the world and with how often they’re used, the impact they can have on the environment can be substantially negative. Finding the least environmentally impactful de-icer as well as the one that is the most economically efficient one was the main focal point of this project

Fluctuations in Mass-Action Equilibrium of Protein Binding Networks

We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by relatively slow changes in total concentrations (copy numbers) of interacting proteins. The second type, to which we refer to as spontaneous, is caused by quickly decaying thermodynamic deviations away from the equilibrium of the system. As such they are amenable to methods of equilibrium statistical mechanics used in our study. We investigate the effects of network connectivity on these fluctuations and compare them to their upper and lower bounds. The collective effects are shown to sometimes lead to large power-law distributed amplification of spontaneous fluctuations as compared to the expectation for isolated dimers. As a consequence of this, the strength of both types of fluctuations is positively correlated with the overall network connectivity of proteins forming the complex. On the other hand, the relative amplitude of fluctuations is negatively correlated with the abundance of the complex. Our general findings are illustrated using a real network of protein-protein interactions in baker's yeast with experimentally determined protein concentrations.Comment: 4 pages, 3 figure

Identifying Social Influence in Networks Using Randomized Experiments

The recent availability of massive amounts of networked data generated by email, instant messaging, mobile phone communications, micro blogs, and online social networks is enabling studies of population-level human interaction on scales orders of magnitude greater than what was previously possible.1\u272 One important goal of applying statistical inference techniques to large networked datasets is to understand how behavioral contagions spread in human social networks. More precisely, understanding how people influence or are influenced by their peers can help us understand the ebb and flow of market trends, product adoption and diffusion, the spread of health behaviors such as smoking and exercise, the productivity of information workers, and whether particular individuals in a social network have a disproportion ate amount of influence on the system

Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks

We examine how firms can create word of mouth peer influence and social contagion by incorporating viral features into their products. Word of mouth is generally considered to more effectively promote peer influence and contagion when it is personalized and active. Unfortunately, econometric identification of peer influence is non-trivial. We therefore use a randomized field experiment to test the effectiveness of passive-broadcast and active-personalized viral messaging capabilities in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users. Surprisingly, we find that passive-broadcast viral messaging generates a 246% increase in local peer influence and social contagion, while adding active-personalized viral messaging only generates an additional 98% increase in contagion. Although active-personalized messaging is more effective per message and is correlated with more user engagement and product use, it is used less often and therefore generates less total peer adoption in the network than passive-broadcast messaging

Design of Randomized Experiments in Networks

Over the last decade, the emergence of pervasive online and digitally enabled environments has created a rich source of detailed data on human behavior. Yet, the promise of big data has recently come under fire for its inability to separate correlation from causation-to derive actionable insights and yield effective policies. Fortunately, the same online platforms on which we interact on a day-to-day basis permit experimentation at large scales, ushering in a new movement toward big experiments. Randomized controlled trials are the heart of the scientific method and when designed correctly provide clean causal inferences that are robust and reproducible. However, the realization that our world is highly connected and that behavioral and economic outcomes at the individual and population level depend upon this connectivity challenges the very principles of experimental design. The proper design and analysis of experiments in networks is, therefore, critically important. In this work, we categorize and review the emerging strategies to design and analyze experiments in networks and discuss their strengths and weaknesses

Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks

We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246% increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98% increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks

Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment

We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges make it difficult to econometrically identify peer influence in networks. Though some recent studies use experiments to identify influence, they have not investigated the social or structural conditions under which influence is strongest. By randomly manipulating messages sent by adopters of a Facebook application to their 1.3 million peers, we identify the moderating effect of tie strength and structural embeddedness on the strength of peer influence. We find that both embeddedness and tie strength increase influence. However, the amount of physical interaction between friends, measured by coappearance in photos, does not have an effect. This work presents some of the first large-scale in vivo experimental evidence investigating the social and structural moderators of peer influence in networks. The methods and results could enable more effective marketing strategies and social policy built around a new understanding of how social structure and peer influence spread behaviors in society

Leveraging Volunteer Fact Checking to Identify Misinformation about COVID-19 in Social Media

Identifying emerging health misinformation is a challenge because its manner and type are often unknown. However, many social media users correct misinformation when they encounter it. From this intuition, we implemented a strategy that detects emerging health misinformation by tracking replies that seem to provide accurate information. This strategy is more efficient than keyword-based search in identifying COVID-19 misinformation about antibiotics and a cure. It also reveals the extent to which misinformation has spread on social networks