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The United States Air Force often provides effective airlift for cargo distribution, but is at times inefficient. This paper aims to address the under-utilization of military airlift cargo compartments that plagues the airlift system.

The authors examine seven techniques designed to increase cargo compartment utilization and increase airlift utilization rates. The techniques were applied through load planning software to 30 real-world movements consisting of 159 sorties. They then ran each post-technique movement through a modeled flight environment to obtain cycle movement data. The metrics gained from both the load planning software and the modeled environment were regressed to provide statistical understanding regarding how well each technique influenced cost savings.

The results showed a 24 per cent elimination of aircraft required and a savings of $14.5m. Extrapolation of the authors’ findings to four years of airlift mission data revealed an estimated annual savings of $1.6bn.

This research effort provides multiple options to improve the efficiency and effectiveness of military airlift.

Among nascent entrepreneurial ventures, are some types of bootstrapping techniques more successful than others? We compare externally oriented and internally oriented techniques with respect to the likelihood of becoming an operational venture; and we compare cash-increasing and cost-decreasing techniques with respect to becoming operational. Using data from the first Panel Study of Entrepreneurial Dynamics, we find evidence suggesting that when bootstrapping a new venture, the percentage of cash-increasing and cost-decreasing externally oriented bootstrapping techniques that a ventureʼs owners use are positive predictors of subsequent positive cash flow (one and two years later). But, internally oriented techniques are not related to subsequent cash flow.

Evaluating warfighter lethality is a critical aspect of military performance. Raw metrics such as marksmanship speed and accuracy can provide some insight, yet interpreting subtle differences can be challenging. For example, is a speed difference of 300 milliseconds more important than a 10% accuracy difference on the same drill? Marksmanship evaluations must have objective methods to differentiate between critical factors while maintaining a holistic view of human performance.

Monte Carlo simulations are one method to circumvent speed/accuracy trade-offs within marksmanship evaluations. They can accommodate both speed and accuracy implications simultaneously without needing to hold one constant for the sake of the other. Moreover, Monte Carlo simulations can incorporate variability as a key element of performance. This approach thus allows analysts to determine consistency of performance expectations when projecting future outcomes.

The review divides outcomes into both theoretical overview and practical implication sections. Each aspect of the Monte Carlo simulation can be addressed separately, reviewed and then incorporated as a potential component of small arms combat modeling. This application allows for new human performance practitioners to more quickly adopt the method for different applications.

Performance implications are often presented as inferential statistics. By using the Monte Carlo simulations, practitioners can present outcomes in terms of lethality. This method should help convey the impact of any marksmanship evaluation to senior leadership better than current inferential statistics, such as effect size measures.

This paper aims to address the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015. During this time, higher rates of conflict transition occurred than normally observed in previous studies for certain Middle Eastern and North African countries.

Previous prediction models decrease in accuracy during times of volatile conflict transition. Also, proper strategies for handling the Arab Spring have been highly debated. This paper identifies which countries were affected by the Arab Spring and then applies data analysis techniques to predict a country’s tendency to suffer from high-intensity, violent conflict. A large number of open-source variables are incorporated by implementing an imputation methodology useful to conflict prediction studies in the future. The imputed variables are implemented in four model building techniques: purposeful selection of covariates, logical selection of covariates, principal component regression and representative principal component regression resulting in modeling accuracies exceeding 90 per cent.

Analysis of the models produced by the four techniques supports hypotheses which propose political opportunity and quality of life factors as causations for increased instability following the Arab Spring.

Of particular note is that the paper addresses the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015 through data analytics. This paper considers various open-source, readily available data for inclusion in multiple models of identified Arab Spring nations in addition to implementing a novel imputation methodology useful to conflict prediction studies in the future.

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.

Retaining employee loyalty after restructuring is a problem for all types of businesses. The major issue concerns how management and employees can establish a new, mutually acceptable "psychological contract" which ensures employee loyalty but not lifelong employment. Eighteen small businesses in Fairfield County, Connecticut were surveyed to investigate loyalty and motivation after downsizing. A significant correlation was found between loyalty and motivation. Furthermore, several motivational techniques employed were inconsistent with employee needs. Suggestions are offered on how to retain employee loyalty.

Increased economic activity in sub-Saharan Africa (SSA) has given rise to increased demand for port development. Given the often scarce availability of national public funding, port institutional reform programmes have been implemented to pave the way for the inclusion of external port investors. Notwithstanding this fact, some sub-Saharan African Governments remain institutionally locked into the notion that state-owned enterprises remain an appropriate vehicle for port terminal operations. This, despite the fact that terminal operational concessions globally and within the continent of Africa are increasingly being managed by global terminal operators. Given this context, this study aims to evaluate different port valuation and funding strategies. Two research questions form the core of this research: what is the financial value of a concession? What is the most cost advantageous funding strategy? The methodology is applied to the development of a two-berth container terminal in SSA.

After reviewing a range of financial valuation and funding techniques, the study presents valuation and funding model applicability-fit tests. Thereafter, a suitable valuation technique is selected and applied to the case study providing a concession valuation. Different funding strategies are applied to the valuation model to determine the cost implications of each funding instrument given the local context and institutional constraints applicable to SSA. Finally, the study discusses the significance of the results to potential SSA port investors by highlighting the impact of each funding approach on key financial metrics.

The study presents a range of financial investment appraisal results for the case study concession in consideration of four specific funding strategies. The highest concession valuation could be attributed to a higher debt ratio as a principal funding strategy. In addition, this funding approach (100% debt) realised the shortest payback period and the highest internal rate of return values. The authors, however, maintain that the optimal funding strategy for a concession depends ultimately on the financial goals of the investor.

This research makes a contribution to the existing literature on port finance and development by presenting a structured approach to the evaluation of the valuation and funding techniques, which can be used in terminal development subject to the specific local context and institutional constraints (in this case applicable to SSA). The study provides practical insight into the potential cost of the considered terminal concession for private or public sector participants and a view of the most cost advantageous funding strategy available for interested investors.

Marksmanship data is a staple of military and law enforcement evaluations. This ubiquitous nature creates a critical need to use all relevant information and to convey outcomes in a meaningful way for the end users. The purpose of this study is to demonstrate how simple simulation techniques can improve interpretations of marksmanship data.

This study uses three simulations to demonstrate the advantages of small arms combat modeling, including (1) the benefits of incorporating a Markov Chain into Monte Carlo shooting simulations; (2) how small arms combat modeling is superior to point-based evaluations; and (3) why continuous-time chains better capture performance than discrete-time chains.

The proposed method reduces ambiguity in low-accuracy scenarios while also incorporating a more holistic view of performance as outcomes simultaneously incorporate speed and accuracy rather than holding one constant.

This process determines the probability of winning an engagement against a given opponent while circumventing arbitrary discussions of speed and accuracy trade-offs. Someone wins 70% of combat engagements against a given opponent rather than scoring 15 more points. Moreover, risk exposure is quantified by determining the likely casualties suffered to achieve victory. This combination makes the practical consequences of human performance differences tangible to the end users. Taken together, this approach advances the operations research analyses of squad-level combat engagements.

For more than a century, marksmanship evaluations have used point-based systems to classify shooters. However, these scoring methods were developed for competitive integrity rather than lethality as points do not adequately capture combat capabilities. The proposed method thus represents a major shift in the marksmanship scoring paradigm.

This article examines the creation of an accurate market projection designed with easy-to-use, cost-effective data analytic techniques. Many of the techniques explored are derived from the subdisciplines of decision support and data warehousing found in the information technology arena. Two significant contributions are presented: a simple mathematical technique that eliminates the need for heuristics, and the simplification of the process to the point where no computer or sophisticated statistical analysis is needed.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.