Joseph James Salvo

Structural Elucidation of a Putative Conidial Pigment Intermediate in Aspergillus parasiticus

Abstract A novel, hydroxylated naphtho[2,3-b]pyran has been isolated from a laccase-deficient strain of Aspergillus parasiticus and characterized through spectroscopic means.

A grid-based clustering method for mining frequent trips from large-scale, event-based telematics datasets

Telematics systems that integrate wireless communications with sensor-based monitoring and location-aware applications have been widely deployed for mobile asset tracking and condition monitoring. In asset tracking field, exploring the data that relate to asset behaviors is critical to understand asset utilization, efficiency, distribution, operation, and many other important aspects in the supply chain. Prior work on analyzing GPS-based patterns has mainly been performed on time-based datasets. In this paper, we describe a scalable clustering algorithm to discover frequently repeated trips from large-scale, event-based telematics datasets collected via a satellite-based tracking system. We first transform GPS traces into a list of trips. Then we present a grid-based hierarchical clustering algorithm to discover frequent spatial patterns among all trips. We evaluate the effectiveness of the proposed algorithm against a large-scale, real-world dataset collected from tracking over a hundred of thousand assets and prove its feasibility. Through these experimental results, we show that the proposed algorithm significantly reduces the computational time needed for clustering as opposed to the traditional hierarchical clustering based on pair-wise comparison.

Toward an Intelligent Brokerage Platform: Mining Backhaul Opportunities in Telematics Data

In commercial transportation operations, one of the largest wasteful expenditures is the movement of tractor trailers with little or no cargo. Analysis of interfleet data shows many lost opportunities for identifying backhauling loads—cargo that could have been moved by an otherwise empty trailer on its return from a delivery point to its home base. Brokerage systems that facilitate matching of load-sharing and backhaul opportunities currently do not incorporate monitoring of real-time, geo-based information, analysis of historical geo-based information, and user-calibrated preferences from all brokerage participants. Future intelligent brokerage systems will need to provide a full range of services including supply chain visibility and automated identification of potential collaborations based on historical trends. In this paper an algorithm is described for identifying load-sharing and backhaul opportunities based on the detection of patterns in large-scale, event-based telematics network data.

Analysis of State Transition Diagrams for RFID-Based Two-Way Access Control

The logic for determining the passing direction of a person through a two-way access control portal is rather complicated. This paper analyzes the state transition diagrams based on sensor fusion data to clarify the confusing decision making logic. An RFID-based access control system, coupled with infrared sensors, is implemented in a two-way portal to verify the proposed technique

Mining trips from telematics dataset for value-added logistics applications in asset tracking systems

With the recent improvements and cost reductions in GPS technology and wireless communication, telematics systems that integrate wireless communications with sensor-based monitoring and location-aware applications have been widely deployed for mobile asset tracking and condition monitoring. In such telematics systems, mining patterns from GPS datasets and exploring the useful data for asset behaviors is vital in order to understand asset utilization, efficiency, distribution, operation, and many other important aspects in the supply chain. In this paper we mine trips based on heuristic rules from a large-scale, real world, event-based telematics dataset that is noisy and sparse in nature. A series of logistics applications built on trip analysis are presented, including ETA prediction, Mileage estimation, Backhaul detection, and Supply chain network analysis.

Cloud Computing-Based Marketplace for Collaborative Design and Manufacturing

This paper introduces an open-source, interoperable platform for real-time collaboration in complex product lifecycle development across multiple companies. Each segment of the lifecycle, including product conception, design, analysis, prototyping, component sourcing, manufacturing and assembly, logistics and delivery, and services from installation to maintenance, repair and overhaul, can benefit from this collaboration through easy access, development, deployment, and integration of heterogeneous models and data. The platform is built on an elastic cloud-computing environment, which provides efficient scaling of computational performance needs to support the collaboration platform. We believe that this platform will enable organizations of all sizes to enter a new digital age of integrated product design, manufacturing and service systems.

A Hybrid Statistical Method for Accurate Prediction of Supplier Delivery Times of Aircraft Engine Parts

Aircraft engine assembly operations require thousands of parts provided by several geographically distributed suppliers. A majority of the operation steps are sequential, necessitating the availability of all the parts at appropriate times for these steps to be completed successfully. Thus, being able to accurately predict the availabilities of parts based on supplier deliveries is critical to minimizing the delays in meeting the customer demands. However, such accurate prediction is challenging due to the large lead times of these parts, limited knowledge of supplier capacities and capabilities, macroeconomic trends affecting material procurement and transportation times, and unreliable delivery date estimates provided by the suppliers themselves. We address these challenges by developing a statistical method that learns a hybrid stepwise regression — generalized multivariate gamma distribution model from historical transactional data on closed part purchase orders and is able to infer part delivery dates sufficiently before the supplier-promised delivery dates for open purchase orders. The hybrid form of the model makes it robust to data quality and short-term temporal effects as well as biased toward overestimating rather than underestimating the part delivery dates. Test results on real-world purchase orders demonstrate effective performance with low prediction errors and constantly high ratios of true positive to false positive predictions.Copyright