A Road Maintenance Management Information System for Counties and Cities

County Highway Departments in Indiana follow guidelines approved by the State Board of Accounts, mainly for highway resource accounting and not for maintenance activity costing. The existing Daily Work Report Form was modified to help maintenance activity costing with more precise reporting of road location, equipment and material use and also for storage on a microcomputer database. The Highway Extension and Research Project for Indiana Counties and Cities (HERPICC) at Purdue University tested the recommended procedures in a pilot project with White County Highway Department in Indiana, U.S.A.. A user-friendly program was developed using the R-Base 5000 database software for maintenance activity accounting and has been recommended for use by county highway and city street departments in Indiana. In addition, organizational requirements such as maintenance staff training, requirements for road inventory and road section demarcation, and special considerations for unpaved road maintenance activities such as dragging, grading, spot regraveling and the reporting required are described

Development of analytic intermodal freight networks for use within a GIS

The paper discusses the practical issues involved in constructing intermodal freight networks that can be used within GIS platforms to support inter-regional freight routing and subsequent (for example, commodity flow) analysis. The procedures described can be used to create freight-routable and traffic flowable interstate and intermodal networks using some combination of highway, rail, water and air freight transportation. Keys to realistic freight routing are the identification of intermodal transfer locations and associated terminal functions, a proper handling of carrier-owned and operated sub-networks within each of the primary modes of transport, and the ability to model the types of carrier services being offered

A classification-based framework for predicting and analyzing gene regulatory response

BACKGROUND: We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences ("motifs") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment ("parents"). GeneClass formulates the learning task as a classification problem — predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of a decision tree. METHODS: In the current work, we introduce a new, robust version of the GeneClass algorithm that increases stability and computational efficiency, yielding a more scalable and reliable predictive model. The improved stability of the prediction tree enables us to introduce a detailed post-processing framework for biological interpretation, including individual and group target gene analysis to reveal condition-specific regulation programs and to suggest signaling pathways. Robust GeneClass uses a novel stabilized variant of boosting that allows a set of correlated features, rather than single features, to be included at nodes of the tree; in this way, biologically important features that are correlated with the single best feature are retained rather than decorrelated and lost in the next round of boosting. Other computational developments include fast matrix computation of the loss function for all features, allowing scalability to large datasets, and the use of abstaining weak rules, which results in a more shallow and interpretable tree. We also show how to incorporate genome-wide protein-DNA binding data from ChIP chip experiments into the GeneClass algorithm, and we use an improved noise model for gene expression data. RESULTS: Using the improved scalability of Robust GeneClass, we present larger scale experiments on a yeast environmental stress dataset, training and testing on all genes and using a comprehensive set of potential regulators. We demonstrate the improved stability of the features in the learned prediction tree, and we show the utility of the post-processing framework by analyzing two groups of genes in yeast — the protein chaperones and a set of putative targets of the Nrg1 and Nrg2 transcription factors — and suggesting novel hypotheses about their transcriptional and post-transcriptional regulation. Detailed results and Robust GeneClass source code is available for download from

How do we integrate skills and content in classics? An inquiry into students’ use of sources

Engagement with primary sources is a key feature of arts and humanities subjects, particularly classics and ancient history. Recent instructional trends emphasise integrating skills with content, particularly in the first year of higher education. We investigate how successfully first-year university students used a variety of sources in an integrated skills and content course, through analysis of 84 final essays. Most students used four to nine sources in a 1500 word essay, but only one type of ancient source. The findings express the need to move from debates about whether to integrate skills or not, to greater discuss how key discipline-specific skills are integrated into content-based courses. Cognitive apprenticeship theory, and a thematic approach used in museum education, are used to reflect on the findings and discuss how teachers might better support students in this key aspect of the discipline

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Integrating tropical research into biology education is urgently needed

Understanding tropical biology is important for solving complex problems such as climate change, biodiversity loss, and zoonotic pandemics, but biology curricula view research mostly via a temperatezone lens. Integrating tropical research into biology education is urgently needed to tackle these issues. The tropics are engines of Earth systems that regulate global cycles of carbon and water, and are thus critical for management of greenhouse gases. Compared with higher-latitude areas, tropical regions contain a greater diversity of biomes, organisms, and complexity of biological interactions. The tropics house the majority of the world’s human population and provide important global commodities from species that originated there: coffee, chocolate, palm oil, and species that yield the cancer drugs vincristine and vinblastine. Tropical regions, especially biodiversity hotspots, harbor zoonoses, thereby having an important role in emerging infectious diseases amidst the complex interactions of global environmental change and wildlife migration [1]. These well-known roles are oversimplified, but serve to highlight the global biological importance of tropical systems. Despite the importance of tropical regions, biology curricula worldwide generally lack coverage of tropical research. Given logistical, economic, or other barriers, it is difficult for undergraduate biology instructors to provide their students with field-based experience in tropical biology research in a diverse range of settings, an issue exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. Even in the tropics, field-based experience may be limited to home regions. When tropical biology is introduced in curricula, it is often through a temperate- zone lens that does not do justice to the distinct ecosystems, sociopolitical histories, and conservation issues that exist across tropical countries and regions [2]. The tropics are often caricatured as distant locations known for their remarkable biodiversity, complicated species interactions, and unchecked deforestation. This presentation, often originating from a colonial and culturally biased perspective, may fail to highlight the role of tropical ecosystems in global environmental and social challenges that accompany rising temperatures, worldwide biodiversity loss, zoonotic pandemics, and the environmental costs of ensuring food, water, and other ecosystem services for humans [3]