Machine-Vision-Based Roadway Health Monitoring and Assessment: Development of a Shape-Based Pavement-Crack-Detection Approach

State highway agencies (SHAs) routinely employ semi-automated and automated image-based methods for network-level pavement-cracking data collection, and there are different types of pavement-cracking data collected by SHAs for reporting and management purposes. The main objective of this proof-of-concept research was to develop a shape-based pavement-crack-detection approach for the reliable detection and classification of cracks from acquired two-dimensional (2D) concrete and asphalt pavement surface images. The developed pavement-crack-detection algorithm consists of four stages: local filtering, maximum component extraction, polynomial fitting of possible crack pixels, and shape metric computation and filtering. After completing the crack-detection process, the width of each crack segment is computed to classify the cracks. In order to verify the developed crack-detection approach, a series of experiments was conducted on real pavement images without and with cracks at different severities. The developed shape-based pavement crack detection algorithm was able to detect cracks at different severities from both asphalt and concrete pavement images. Further, the developed algorithm was able to compute crack widths from the images for crack classification and reporting purposes. Additional research is needed to improve the robustness and accuracy of the developed approach in the presence of anomalies and other surface irregularities

Consumer Dynamic Usage Allocation and Learning under Multi-Part Tariffs

Multipart tariffs are widely favored within service industries as an efficient means of mapping prices to differential levels of consumer demand. Whether they benefit consumers, however, is far less clear as they pose individuals with a potentially difficult task of dynamically allocating usage over the course of each billing cycle. In this paper we explore this welfare issue by examining the ability of individuals to optimally allocate consumption over time in a stylized cellular-phone usage task for which there exists a known optimal dynamic utilization policy. Actual call behavior over time is modeled using a dynamic choice model that allows decision makers to both discount the future (be myopic) and be subject to random errors when making call decisions. Our analysis provides a “half empty, half full” view of intuitive optimality. Participants rapidly learn to exhibit farsightedness, yet learning is incomplete with some level of allocation errors persisting even after repeated experience. We also find evidence for an asymmetric effect in which participants who are exogenously switched from a low (high) to high (low) allowance plan make more (fewer) errors in the new plan. The effect persists even when participants make their own plan choices. Finally, interventions that provide usage information to help participants eradicate errors have limited effectiveness

A Cross-Cohort Changepoint Model for Customer-Base Analysis

We introduce a new methodology that can capture and explain differences across a series of cohorts of new customers in a repeat-transaction setting. More specifically, this new framework, which we call a vector changepoint model, exploits the underlying regime structure in a sequence of acquired customer cohorts to make predictive statements about new cohorts for which the firm has little or no longitudinal transaction data. To accomplish this, we develop our model within a hierarchical Bayesian framework to uncover evidence of (latent) regime changes for each cohort-level parameter separately, while disentangling cross-cohort changes from calendar-time changes. Calibrating the model using multicohort donation data from a nonprofit organization, we find that holdout predictions for new cohorts using this model have greater accuracy—and greater diagnostic value—compared to a variety of strong benchmarks. Our modeling approach also highlights the perils of pooling data across cohorts without accounting for cross-cohort shifts, thus enabling managers to quantify their uncertainty about potential regime changes and avoid “old data” aggregation bias

Automated shape-based pavement crack detection approach

Pavements are critical man-made infrastructure systems that undergo repeated traffic and environmental loadings. Consequently, they deteriorate with time and manifest certain distresses. To ensure long-lasting performance and appropriate level of service, they need to be preserved and maintained. Highway agencies routinely employ semiautomated and automated image-based methods for network-level pavement-cracking data collection, and there are different types of pavement-cracking data collected by highway agencies for reporting and management purposes. We design a shape-based crack detection approach for pavement health monitoring, which takes advantage of spatial distribution of potential cracks. To achieve this, we first extract Potential Crack Components (PCrCs) from pavement images. Next, we employ polynomial curve to fit all pixels within these components. Finally, we define a Shape Metric (SM) to distinguish crack blocks from background. We experiment the shape-based crack detection approach on different datasets, and compare detection results with an alternate method that is based on Support Vector Machines (SVM) classifier. Experimental results prove that our approach has the capability to produce higher detections and fewer false alarms. Additional research is needed to improve the robustness and accuracy of the developed approach in the presence of anomalies and other surface irregularities

Unique properties of Plasmodium falciparum porphobilinogen deaminase

The hybrid pathway for heme biosynthesis in the malarial parasite proposes the involvement of parasite genome-coded enzymes of the pathway localized in different compartments such as apicoplast, mitochondria, and cytosol. However, knowledge on the functionality and localization of many of these enzymes is not available. In this study, we demonstrate that porphobilinogen deaminase encoded by the Plasmodium falciparum genome (PfPBGD) has several unique biochemical properties. Studies carried out with PfPBGD partially purified from parasite membrane fraction, as well as recombinant PfPBGD lacking N-terminal 64 amino acids expressed and purified from Escherichia coli cells (ΔPfPBGD), indicate that both the proteins are catalytically active. Surprisingly, PfPBGD catalyzes the conversion of porphobilinogen to uroporphyrinogen III (UROGEN III), indicating that it also possesses uroporphyrinogen III synthase (UROS) activity, catalyzing the next step. This obviates the necessity to have a separate gene for UROS that has not been so far annotated in the parasite genome. Interestingly, ΔPfP-BGD gives rise to UROGEN III even after heat treatment, although UROS from other sources is known to be heat-sensitive. Based on the analysis of active site residues, a ΔPfPBGDL116K mutant enzyme was created and the specific activity of this recombinant mutant enzyme is 5-fold higher than ΔPfPBGD. More interestingly, ΔPfPBGDL116K catalyzes the formation of uroporphyrinogen I (UROGEN I) in addition to UROGEN III, indicating that with increased PBGD activity the UROS activity of PBGD may perhaps become rate-limiting, thus leading to non-enzymatic cyclization of preuroporphyrinogen to UROGEN I. PfPBGD is localized to the apicoplast and is catalytically very inefficient compared with the host red cell enzyme