Growth and chemotaxis of nematodes reduced upon exposure to Third Fork Creek surface water

Background Third Fork Creek is a historically impaired urban stream that flows through the city of Durham, North Carolina. Caenorhabditis elegans (C. elegans) are non-parasitic, soil and aquatic dwelling nematodes that have been used frequently as a biological and ecotoxicity model. We hypothesize that exposure to Third Fork Creek surface water will inhibit the growth and chemotaxis of C. elegans. Using our ring assay model, nematodes were enticed to cross the water samples to reach a bacterial food source which allowed observation of chemotaxis. The total number of nematodes found in the bacterial food source and the middle of the plate with the water source was recorded for 3 days. Results Our findings suggest a reduction in chemotaxis and growth on day three in nematodes exposed to Third Fork Creek water samples when compared to the control (p value < 0.05). These exploratory data provide meaningful insight to the quality of Third Fork Creek located near a Historically Black University. Conclusions Further studies are necessary to elucidate the concentrations of the water contaminants and implications for human health. The relevance of this study lies within the model C. elegans that has been used in a plethora of human diseases and exposure research but can be utilized as an environmental indicator of water quality impairment.

Hybrid Prepreg Tapes for Composite Manufacturing: A Case Study

The aim of this research was the preparation and characterization of hybrid prepreg tapes from glass multifilament roving (circular cross-section). The fiber, roving, and tape strength distribution was characterized by exploratory data analysis tools (especially quantile-quantile plot) and modeled by the three parameters’ Weibull distribution. For estimation of Weibull model parameters, the noniterative technique based on the so-called Weibull moments was used. It was shown that the prepared hybrid prepreg tapes prepared by controlled mechanical spreading technology developed by the authors improved mechanical tensile properties and can be used for the preparation of composites of complicated forms by robotic winding.

Citizen Science for Traffic Monitoring: Investigating the Potentials for Complementing Traffic Counters with Crowdsourced Data

Traffic counts are among the most frequently employed data to assess the traffic patterns and key performance indicators of next generation sustainable cities. Automatised counting is often based on conventional traffic monitoring systems such as inductive loop counters (ILCs). These are costly to install, maintain, and support. In this paper, we investigate the possibilities to complement and potentially replace the existing traffic monitoring infrastructure with crowdsourcing solutions. More precisely, we investigate the capabilities to predict the ILC-obtained data using Telraam counters, low-cost camera counters voluntarily employed by citizens and freely accessible by the general public. In this context, we apply different exploratory data analysis approaches and demonstrate a regression procedure with a selected set of regression models. The presented analysis is demonstrated on different urban and highway road segments in Slovenia. Our results show that the data obtained from low-cost and easily accessible counters can be used to replace the existing traffic monitoring infrastructure in different scenarios. These results confirm the prospective to directly apply the citizen engagement in the process of planning and maintaining sustainable future cities.

E-Commerce Customers Behavior Research Using Cohort Analysis: A Case Study of COVID-19

Cohort analysis is a new practical method for e-commerce customers’ research, trends in their behavior, and experience during the COVID-19 crisis. The purpose of the research is to validate the efficiency of this method on the e-commerce records data set and find out the critical factors associated with customer awareness and loyalty levels. The cohort analysis features engineering, descriptive statistics, and exploratory data analysis are the main methods used to reach the study purpose. The research results showed that cohort analysis could answer various business questions and successfully solve real-world problems in e-commerce customer research. It could be extended to analyze user satisfaction with a platform’s technical performance and used for infrastructure monitoring. Obtained insights on e-commerce customers’ awareness and loyalty levels show the likeliness of a user to make a purchase or interact with the platform. Key e-business aspects from a customer point of view are analyzed and augment the user-experience understanding to strengthen customers’ relationships in e-commerce.

MIXED CLASS TEACHING AS AN EMERGING TREND ACCELERATED BY COVID-19

The COVID-19 pandemic has essentially accelerated the pace of the teaching transformation. Mixed (also hyflex) class teaching has become indispensable in medical, engineering, teacher and other fields of education when only online teaching is not enough to ensure the continuity of the instruction. The research aim is to identify scenarios of mixed class teaching underpinning the elaboration of implications for higher education. The present research used both - theoretical and empirical methods. The theoretical methods included the analysis of scientific literature, theoretical modelling, systematisation, synthesis, comparison, and generalisation. The empirical study carried out in June 2021 was exploratory. Data were collected through the analysis of published studies. The collected data were processed via content analysis. The present research allows concluding that teaching has undergone significant changes in different historical periods. The findings of the empirical study facilitate the conclusion on the existence of two scenarios of mixed class teaching, namely HOT (Here or There) and COIL (Collaborative Online International Learning). Both scenarios are oriented to students’ learning, teaching in these scenarios is neither segmented nor structured. The novel contribution of the research is revealed in the implications on mixed class teaching for higher education. Future research work was proposed.

BONNIE: Building Online Narratives from Noteworthy Interaction Events

Nowadays, we have access to data of unprecedented volume, high dimensionality, and complexity. To extract novel insights from such complex and dynamic data, we need effective and efficient strategies. One such strategy is to combine data analysis and visualization techniques, which are the essence of visual analytics applications. After the knowledge discovery process, a major challenge is to filter the essential information that has led to a discovery and to communicate the findings to other people, explaining the decisions they may have made based on the data. We propose to record and use the trace left by the exploratory data analysis, in the form of user interaction history, to aid this process. With the trace, users can choose the desired interaction steps and create a narrative, sharing the acquired knowledge with readers. To achieve our goal, we have developed the BONNIE ( Building Online Narratives from Noteworthy Interaction Events ) framework. BONNIE comprises a log model to register the interaction events, auxiliary code to help developers instrument their own code, and an environment to view users’ own interaction history and build narratives. This article presents our proposal for communicating discoveries in visual analytics applications, the BONNIE framework, and the studies we conducted to evaluate our solution. After two user studies (the first one focused on history visualization and the second one focused on narrative creation), our solution has showed to be promising, with mostly positive feedback and results from a Technology Acceptance Model ( TAM ) questionnaire.