Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R² value of 0.94. As a comparative measure, other machine learning algorithms resulted in R² values of 0.50–0.94. Additionally, the data from each location was modeled separately with R² values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data.

Numerous achievements have been made recently by researchers in the areas of fiber laser beam combining and power scaling. Industry has demonstrated multi-kW power from a single fiber amplifier, and a US national laboratory has coherently combined eight fiber amplifiers totaling 4 kW. This paper will survey the recent literature and then focus on fiber laser results from the Laser Division, Directed Energy Directorate of the Air Force Research Laboratory (AFRL). Progress has been made in the power scaling of narrow-linewidth fiber amplifiers, and we are transitioning lessons learned from PCF power scaling into monolithic architectures. SBS suppression has been achieved using a variety of techniques to lower the Brillioun gain, including acoustically tailored fiber, laser gain competition resulting from multitone seeding and inducing a longitudinal thermal gradient. We recently demonstrated a 32-channel coherent beam combination result using AFRL's phaselocking technique and are focused on exploring the limitations of this technique including linewidth broadening, kW-induced phase nonlinearities and auto-tuning methods for large channel counts. Additionally, we have recently refurbished our High Energy Laser Joint Technology Office-sponsored 16-amplifier fiber testbed to meet strict PER, spatial drift, power stability and beam quality requirements.

Marijuana is the most commonly abused drug for military personnel tested at the Air Force Drug Testing Laboratory. A publicly available dataset of drug use, personality trait scores and demographic data was modeled with logistic regression, decision tree and neural network models to determine the extent to which marijuana use can be predicted using personality traits. While the logistic regression model had lower performance than the neural network model, it matched the sensitivity of prior work (0.80), achieved a high level of significance (p < 0.05) and yielded valuable inferences. It implied that younger, less educated individuals who exhibit sensation-seeking behavior and are open to experience tend to be at higher risk for THC use. A method for performing an iterative multidimensional neural network hyperparameter search is presented, and two iterations of a 6-dimensional search were performed. Metrics were used to select a family of 8 promising models from a cohort of 4600 models, and the best NN model’s 0.87 sensitivity improved upon the literature. The model met an f1 overfitting threshold on the test and holdout datasets, and an accuracy sensitivity analysis on a holdout-equivalent dataset yielded a 95% CI of 0.86 ± 0.04. These results have the potential to increase the efficacy of drug prevention and intervention programs.

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.

Remote and contingency operations, including military and disaster-relief activities, often require the use of temporary facilities powered by inefficient diesel generators that are expensive to operate and maintain. Site planners can reduce operating costs by increasing shelter insulation and augmenting generators with photovoltaic-battery hybrid energy systems, but they must select the optimal design configuration based on the region’s climate to meet the power demand at the lowest cost. To assist planners, this paper proposes an innovative, climate-optimized, hybrid energy system selection model capable of selecting the facility insulation type, solar array size, and battery backup system to minimize the annual operating cost. To demonstrate the model’s capability in various climates, model performance was evaluated for applications in southwest Asia and the Caribbean. For a facility in Southwest Asia, the model reduced fuel consumption by 93% and saved $271 thousand compared to operating a diesel generator. The simulated facility in the Caribbean resulted in more significant savings, decreasing fuel consumption by 92% and saving $291 thousand. This capability is expected to support planners of remote sites in their ongoing effort to minimize fuel supply requirements and annual operating costs of temporary facilities.

Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition.