An Automated Algorithm for Decline Analysis

Oil and gas wells are regularly monitored for their production rates. For a variety of reasons, typical production rate data is noisy and highly discontinuous, and we wish to use this data to extrapolate trends in the production rate to forecast future production and ultimate cumulative reserve recovery. The proposed solution consists of three main steps: (1) Segmentation of Data, (2) Curve fitting, and (3) a Decision Process. Segmentation of Data attempts to identify intervals in the data where a single trend is dominant. A curve from an appropriate family of functions is then fitted to this interval of data. The Decision Process gauges the quality of the trends identified and either formulates a final answer or, if the program cannot come to a reliable answer, ' flags' the well to be looked at by an operator

Analyzing Network Traffic for Malicious Hacker Activity

Since the Internet came into life in the 1970s, it has been growing more than 100% every year. On the other hand, the solutions to detecting network intrusion are far outpaced. The economic impact of malicious attacks in lost revenue to a single e-commerce company can vary from 66 thousand up to 53 million US dollars. At the same time, there is no effective mathematical model widely available to distinguish anomaly network behaviours such as port scanning, system exploring, virus and worm propagation from normal traffic. PDS proposed by Random Knowledge Inc., detects and localizes traffic patterns consistent with attacks hidden within large amounts of legitimate traffic. With the network’s packet traffic stream being its input, PDS relies on high fidelity models for normal traffic from which it can critically judge the legitimacy of any substream of packet traffic. Because of the reliability on an accurate baseline model for normal network traffic, in this workshop, we concentrate on modelling normal network traffic with a Poisson process

Studies on bacterial speck of tomatoes caused by Pseudomonas syringae pv tomato : a thesis presented in fulfilment of a Masterate of Science by thesis only at Massey University

Page 42 is missing from the original copy.The taxonomy of the causal agent of bacterial speck of tomatoes is discussed and the trinomial Pseudomonas syringae pathovar tomato (Okabe) Young, Dye and Wilkie is adopted. A vacuum infiltration method of artificially inoculating seed was used and P.syringae pv tomato was detected in both artificially and naturally infested seed using sensitive enrichment culture techniques. The pathogen can remain viable between seed harvest and sowing in association with seed but seed-plant transfer was only occasionally demonstrated. The acid seed extraction method and other germicidal seed treatments were evaluated for their effect on the seedborne pathogen. Streptomycin sulphate as a slurry treatment (2.5g a.i./Kg of seed) just prior to seed sowing was the only totally effective seed treatment tested. The potential for survival in infected crop debris, soil and on alternative hosts was shown. However, the pathogen was not isolated from weeds in infected tomato crops and no conclusive evidence of systemic infection was found

Managing direct energy use now and in the future

[Background and Introduction]: Agricultural producers are currently aware of increasing energy costs. This has occurred before the scientific and political debate on climate change has been resolved and a decision made on the best policy instruments to be used to respond. In parallel to this discussion, the on farm assessment of direct energy inputs (i.e. diesel and electricity) enables farmers to react positively to the potential of rising energy costs while contributing to a reduction in greenhouse gasses (GHGs) regardless of the scientific and policy debate surrounding climate change and emissions reduction. Previous work undertaken by the National Centre for Engineering in Agriculture (NCEA) has studied direct on farm energy use involving a number of case study cotton farms to understand the range, costs and contributions of energy use to cotton production and greenhouse gas emissions. The results from this work showed that energy use varies depending on the cropping enterprise and the farming system and that there are significant opportunities to reduce energy and costs. In comparison the greenhouse gas emissions (GHGs) from direct energy use can be similar and in fact greater than the GHGs generated by soil / fertiliser / water interactions. Improving on farm energy use would appear to be as important as improving nitrogen efficiency. A more detailed study undertaken by the NCEA on a large cotton farm in the Gwydir Valley (reference) identified significant reductions in energy resulting from the adoption of reduced tillage systems. The study showed that the adoption of a minimum tillage system had reduced energy costs (and greenhouse emissions) by 12% since 2000 and developing a 'near zero till' system had the potential to reduce this to 24% less than 2000 energy costs. It is evident from this work that there is substantial scope to improve energy use efficiency in cotton production systems, but to enable more growers to identify where they can improve, further development of tools, processes and human capacity is required. In the cropping sector a number of practice changes and technology developments have been, or are being, adopted which can be expected to reduce fuel / energy use or energy use intensity. Examples include minimum / zero tillage, controlled traffic, a range of precision ag technologies, planting of GM crops, some water use efficiency measures and use of legumes in crop rotations. Unfortunately, because the primary driver for the adoption of these practices and technologies has not been energy costs or efficiency, relatively few studies have considered the energy savings or efficiencies associated with them. Within highly mechanised agricultural productions systems such as the Australian Cotton Industry direct energy inputs (i.e. diesel and electricity) represent a major cost to the grower and potentially a significant proportion of the total green house gas (GHG) emissions. Previous studies by Baillie and Chen (2008) have reported significant savings in energy for both a refinement in current practices (i.e. up to 30 % for individual operations) and a change in practice (10 – 20% across the farming system) through energy assessment

The NBS Cord Time Sharing System

Computer with on-line remote device time-sharing, and command system

Smart Pricing: Linking Pricing Decisions with Operational Insights

The past decade has seen a virtual explosion of information about customers and their preferences. This information potentially allows companies to increase their revenues, in particular since modern technology enables price changes to be effected at minimal cost. At the same time, companies have taken major strides in understanding and managing the dynamics of the supply chain, both their internal operations and their relationships with supply chain partners. These two developments are narrowly intertwined. Pricing decisions have a direct effect on operations and visa versa. Yet, the systematic integration of operational and marketing insights is in an emerging stage, both in academia and in business practice. This article reviews a number of key linkages between pricing and operations. In particular, it highlights different drivers for dynamic pricing strategies. Through the discussion of key references and related software developments we aim to provide a snapshot into a rich and evolving field.supply chain management;inventory;capacity;dynamic pricing;operations-marketing interface

Crime and Social media

Purpose-The study complements the scant macroeconomic literature on the development outcomes of social media by examining the relationship between Facebook penetration and violent crime levels in a cross-section of 148 countries for the year 2012. Design/methodology/approach-The empirical evidence is based on Ordinary Least Squares (OLS), Tobit and Quantile regressions. In order to respond to policy concerns on the limited evidence on the consequences of social media in developing countries, the dataset is disaggregated into regions and income levels. The decomposition by income levels included: low income, lower middle income, upper middle income and high income. The corresponding regions include: Europe and Central Asia, East Asia and the Pacific, Middle East and North Africa, Sub-Saharan Africa and Latin America. Findings-From OLS and Tobit regressions, there is a negative relationship between Facebook penetration and crime. However, Quantile regressions reveal that the established negative relationship is noticeable exclusively in the 90th crime decile. Further, when the dataset is decomposed into regions and income levels, the negative relationship is evident in the Middle East and North Africa (MENA) while a positive relationship is confirmed for sub-Saharan Africa. Policy implications are discussed. Originality/value- Studies on the development outcomes of social media are sparse because of a lack of reliable macroeconomic data on social media. This study primarily complemented three existing studies that have leveraged on a newly available dataset on Facebook

Periodic Review, Push Inventory Policies for Remanufacturing

Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. This research is focused on product recovery, and in particular on production control and inventory management in the remanufacturing context. We study a remanufacturing facility that receives a stream of returned products according to a Poisson process. Demand is uncertain and also follows a Poisson process. The decision problems for the remanufacturing facility are when to release returned products to the remanufacturing line and how many new products to manufacture. We assume that remanufactured products are as good as new. In this paper, we employ a "push" policy that combines these two decisions. It is well known that the optimal policy parameters are difficult to find analytically; therefore, we develop several heuristics based on traditional inventory models. We also investigate the performance of the system as a function of return rates, backorder costs and manufacturing and remanufacturing lead times; and we develop approximate lower and upper bounds on the optimal solution. We illustrate and explain some counter-intuitive results and we test the performance of the heuristics on a set of sample problems. We find that the average error of the heuristics is quite low.inventory;reverse logistics;remanufacturing;environment;heuristics

Antigone

Thesis (M.A.)--Boston University Bibliography at end of Section II