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The introduction of “should cost” in 2011 required all Major Defense Acquisition Programs (MDAP) to create efficiencies and improvements to reduce a program’s “will-cost” estimate. Realistic “will-cost” estimates are a necessary condition for the “should cost” analysis to be effectively implemented. Owing to the inherent difficulties in establishing a program’s will-cost estimate, this paper aims to propose a new model to infuse realism into this estimate.

Using historical data from 73 Departments of Defense programs as recorded in the selected acquisition reports (SARs), the analysis uses mixed stepwise regression to predict a program’s cost from Milestone B (MS B) to initial operational capability (IOC).

The presented model explains 83 per cent of the variation in the program acquisition cost. Significant predictor variables include: projected duration (months from MS B to IOC); the amount of research development test and evaluation (RDT&E) funding spent at the start of MS B; whether the program is considered a fixed-wing aircraft; whether a program is considered an electronic system program; whether a program is considered ACAT I at MS B; and the program size relative to the total program’s projected acquisition costs at MS B.

The model supports the “will-cost and should-cost” requirement levied in 2011 by providing an objective and defensible cost for what a program should actually cost based on what has been achieved in the past. A quality will-cost estimate provides a starting point for program managers to examine processes and find efficiencies that lead to reduced program costs.

The purpose of the present study was the development of a methodology for translating predicted rates of decompression sickness (DCS), following tower escape from a sunken submarine, into predicted probability of survival, a more useful statistic for making operational decisions.

Predictions were made, using existing models, for the probabilities of a range of DCS symptoms following submarine tower escape. Subject matter expert estimates of the effect of these symptoms on a submariner’s ability to survive in benign weather conditions on the sea surface until rescued were combined with the likelihoods of the different symptoms occurring using standard probability theory. Plots were generated showing the dependence of predicted probability of survival following escape on the escape depth and the pressure within the stricken submarine.

Current advice on whether to attempt tower escape is based on avoiding rates of DCS above approximately 5%–10%. Consideration of predicted survival rates, based on subject matter expert opinion, suggests that the current advice might be considered as conservative in the distressed submarine scenario, as DCS rates of 10% are not anticipated to markedly affect survival rates.

According to the authors’ knowledge, this study represents the first attempt to quantify the effect of different DCS symptoms on the probability of survival in submarine tower escape.

Recent legislation resulted in an elevation of operating and support (O&S) costs’ relative importance for decision-making in Department of Defense programs. However, a lack of research in O&S hinders a cost analyst’s abilities to provide accurate sustainment estimates. Thus, the purpose of this paper is to investigate when Air Force aircraft O&S costs stabilize and to what degree. Next, a parametric O&S model is developed to predict median O&S costs for use as a new tool for cost analyst practitioners.

Utilizing the Air Force total ownership cost database, 44 programs consisting of 765 observations from 1996 to 2016 are analyzed. First, stability is examined in three areas: total O&S costs, the six O&S cost element structures and by aircraft type. Next, stepwise regression is used to predict median O&S costs per total active inventory (CPTAI) and identify influential variables.

Stability results vary by category but generally are found to occur approximately five years from initial operating capability. The regression model explains 89.01 per cent of the variance in the data set when predicting median O&S CPTAI. Aircraft type, location of lead logistics center and unit cost are the three largest contributing factors.

Results from this research provide insight to cost analysts on when to start using actual O&S costs as a baseline for estimates in lieu of analogous cost program data and also derives a new parametric O&S estimating tool designed as a cross-check to current estimating methodologies.

To achieve a high container handling efficiency at transshipment hub ports, there are a variety of scheduling problem as ship-to-berth assignment (BAP), container-to-yard arrangement (YAP), etc. As it is difficult to acquire the actual data of an existing terminal under various circumstances, this study aims to develop the time estimation model of container handling. Additionally, to achieve an efficient handling of containers at the yard, this study proposes the way to optimize the yard arrangement along with the berth allocation simultaneously by using estimated handling time.

To obtain the handling time based on various situations of the terminal operated, the discrete simulation model of container handling is constructed. The model to estimate the handling time of a quay crane assigned to a relevant ship by multiple regression analysis is developed. To find a feasible solution to minimize the total service time which includes YAP and BAP simultaneously, a genetic algorithm based on heuristics is developed.

The proposed regression model has high performance to estimate the time spent of container handling. In the total service time, the proposed approach outperformed the existing 2-step process approach.

As it is difficult to acquire the actual information of an existing marine terminal under various circumstances, the paper contains a regression model to estimate the container handling time based on simulation data, and the regression model is used in an optimization model to minimize the ship turnaround time.

This research provides some evidence by the vine copula approach, suggesting the significant and symmetric causal relation between subsections of Baltic Exchange and hence concluding that investing in different indexes, which is currently a risk diversification system, is not a correct risk reduction strategy.

The daily observations of Baltic Capesize Index (BCI), Baltic Handysize Index (BHSI), Baltic Dirty Tanker Index (BDTI) and Baltic LNG Tanker Index (BLNG) over an eight-year period have been used. After collecting data, calculating the return and estimating the marginal distribution of return rates for each of the indexes applying asymmetric power generalized autoregressive conditional heteroskedasticity and autoregressive moving average (APGARCH-ARMA), and with the assumption of skew student's t-distribution, the dependence of Baltic indexes was modeled based on Vine-R structures.

A positive and symmetrical correlation was observed between the study groups. High and low tail dependence is observed between all four indexes. In other words, the sector business groups associated with each of these indexes react similarly to the extreme events of other groups. The BHSI has a pivotal role in examining the dependency structure of Baltic Exchange indexes. That is, in addition to the direct dependence of Baltic groups, the dependence of each group on the BHSI can transmit accidents and shocks to other groups.

Since the Baltic Exchange indexes are tradable, these findings have implications for portfolio design and hedging strategies for investors in shipping markets.

Vine copula structures proves the causal relationship between different Baltic Exchange indexes, which are derived from different types of markets.

Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.

The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.

These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.

Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.

Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.

The purpose of this paper is to empirically examine the relationship between intensity of competition and technical efficiency of large European container ports, accounting for regional diversities and spatial aspects of inter-port competition.

The analysis consists of applying a stochastic production frontier approach to a dataset of 77 large European container ports over the period 2002-2012, with inefficiency terms simultaneously modeled as a function of (among other factors) a constructed index of competitive intensity at different spatial levels.

The results indicate that there is no significant negative effect of competitive intensity on efficiency. In fact, for competing European ports within a proximity of 300 km, a higher level of competition is found to be associated with a higher level of technical efficiency.

The originality of the paper stems from its particular focus on European port regions and its novel findings in this context, which have implications for the discussions regarding pro-competitive port policy and regulation in the European Union.

Liner shipping plays a crucial role in facilitating the movement of manufactured goods around the world. While previous literature has shown that liner shipping is an important trade driver, potential differences across trade routes and world regions have not as yet been explored. This paper examines whether the impact of liner shipping on bilateral trade flows differs significantly across world regions, as well as exploring other geographical patterns.

Using state-of-the-art gravity modelling, this paper investigates the impact of the UNCTAD's Liner Shipping Bilateral Connectivity Index on bilateral trade in manufactured goods using a comprehensive database of disaggregated trade data for the period from 2006 to 2019.

The results show that the trade effect of liner shipping is greater in long-distance and interregional bilateral flows. For some regions, such as North America and Oceania, the effect is greater than the world average, while for others, such as Africa and South America, the effect is significantly smaller. The trade effects of liner shipping connectivity on the main east–west routes are average, but clear asymmetry emerges when analysing China's inward and outward trade flows separately.

The results of this paper show that the major east–west routes determine the baseline trade effects of liner shipping, demonstrate that some north–south trades such as those involving Oceania generate larger trade effects and confirm that the trade effects of liner shipping can be improved for some world regions such as South America and Africa.

The proposed Regional Comprehensive Economic Partnership (RCEP) will become a large trade agreement in Asia, which has brought together the ten members of Association of Southeast Asian Nations (ASEAN) and five of the neighbors’ countries. Under the trend of globalization, the progress of the transportation industry and regional integration will increase the volume of trade, therefore maritime performance is intrinsically linked to trade. In fact, few studies have examined regional integration in the context of seaborne. This paper aims to use the cluster analysis and Poisson quasi-maximum likelihood (PQML) gravity model to investigate the trading bloc phenomenon and relation between trade and marine transportation.

In this paper, hierarchical clustering analysis and tree diagrams are used to identify functional areas characterized by bilateral trade intensity and bilateral liner shipping connectivity indices. Regional reorganizations that have occurred within Asian countries were studied. This study illustrates that these trading blocs have a positive impact on trade when maritime transport, production and trading networks have developed between regions. A gravity model was constructed using worldwide trade data for 2007, 2010 and 2015. The study considered free trade agreement (FTA)/common market (CM) of EU, RCEP and North American Free Trade Agreement (NAFTA) as regional dummies and designed a real trade bloc induction variable. In addition, the study did not use the commonly adopted ordinary least squares (OLS) estimation but used the PQML method to estimate the gravity equation to overcome the problem of a large number of zero trade observations. Preliminary results show that regional integration cannot guarantee the establishment of intraregional trade but depends on the stage of economic development and regional industrial characteristics.

The major findings are summarized as follows. Both liner shipping connectivity and logistics performance have significant advantages with positive coefficients in each regression results. The creation of intraregional trade is not guaranteed, depending on the characteristics of the trade and the stage of economic development of the region. For RCEP, the effect created by intra-regional trade is better than the EU. Instead, the “nominal” intra-RCEP trade was significantly below the “real” trading blocs. For RCEP, the effect created by intra-regional trade is better than that of the EU. Instead, “nominal” intra-RCEP trade is much lower than “real” trading blocs. The real trading bloc between East Asia and Taiwan clearly exists, and the bloc phenomenon is becoming more and more significant. This result shows that Taiwan’s trade flow with East Asia is higher than the normal level relationship implied by its corresponding economic and geographical conditions.

This paper focuses on new empirical work done for this study is on the potential impact on trade. Earlier studies that have discussed and/or provided estimates of the benefits to the RCEP plan from improved transport and supply chain connectivity are cited. Marine transportation performance inherently links to economies of commerce. Few studies have examined regional integration in the context of maritime transportation, which reflects the lack of a mix of trade economists and maritime logistics research in the existing literature. This paper attempts to investigate the trading bloc phenomenon formed by regional integration (such as RCEP) and the relation between trade and marine transportation. With the official entry into force of the RCEP in 2020, it will promote increased trade and demand for logistics and maritime transport services in East Asia.

This paper aims to address the following questions: is good liner shipping connectivity a requisite for merchandise imports plus exports? What is the average of merchandise imports plus exports of the countries neighboring China? Do the merchandise imports plus exports of these countries correspond to each country’s own merchandise imports plus exports or liner shipping connectivity index (LSCI)?

The authors spatially analyze liner shipping connectivity and merchandise imports plus exports using 2016 data and a common framework for linear regression to establish the relationship amongst a country’s LSCI and its merchandise imports plus exports and between its merchandise imports plus exports and those of its neighbors. Merchandise imports plus exports of countries are not necessarily independent of each other, and countries that are contiguous may produce similar observations.

North America and Western Europe comprised clusters of countries that participated more actively in the international trading system, while Africa’s countries had less international trade than average. The study identifies and quantifies the geographical ripple of transport infrastructure on merchandise trade from a national perspective. Notably, a spatially lagged term improved the model’s ability to account for variations in merchandise imports plus exports across countries.

The spatial lag of merchandise imports plus exports can contribute to specifying the spread of merchandise imports plus exports beyond what the authors would anticipate from a country’s network of liner shipping.