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Given the emergence of China as a political and economic power in the 21st century, there is increased interest in analyzing Chinese news articles to better understand developing trends in China. Because of the volume of the material, automating the categorization of Chinese-language news articles by headline text or titles can be an effective way to sort the articles into categories for efficient review. A 383,000-headline dataset labeled with 15 categories from the Toutiao website was evaluated via natural language processing to predict topic categories. The influence of six data preparation variations on the predictive accuracy of four algorithms was studied. The simplest model (Naïve Bayes) achieved 85.1% accuracy on a holdout dataset, while the most complex model (Neural Network using BERT) demonstrated 89.3% accuracy. The most useful data preparation steps were identified, and another goal examined the underlying complexity and computational costs of automating the categorization process. It was discovered the BERT model required 170x more time to train, was slower to predict by a factor of 18,600, and required 27x more disk space to save, indicating it may be the best choice for low-volume applications when the highest accuracy is needed. However, for larger-scale operations where a slight performance degradation is tolerated, the Naïve Bayes algorithm could be the best choice. Nearly one in four records in the Toutiao dataset are duplicates, and this is the first published analysis with duplicates removed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Infrastructure managers require timely and accurate state information to diagnose, prioritize, and repair the substantial infrastructure assets supporting modern society. Challenges in obtaining sufficient information can often be attributed to inadequate data collection procedures (phone calls, paper reports, etc.) or a general lack of knowledge or ability on the part of the reporting individual to accurately convey what is actually wrong with the facility. Fortunately, modern smart-phone technology offers the potential to improve maintenance work requests by providing better geolocation and problem description accuracy. An experiment simulating real-world maintenance requests was conducted comparing smart-phones with traditional verbal work order request systems. Usefulness and description accuracy ratios revealed smartphone systems generated more useful information regardless of submitter background or experience. However, interestingly the smart-phone applications did not improve asset geolocation and actually negatively impacted the ability of maintenance personnel to accurately relocate the asset needing service. Given the ubiquitous nature of smartphone technology, the potential exists to turn any citizen into an infrastructure sensor. This study takes a step toward understanding the benefits, as well as the limitations, of the smart-phone based work order submission systems. [ABSTRACT FROM AUTHOR]

A review of the article " Integrating Cost as a Decision Variable in Wargames" by Joshua N. Reese, Jonathan D. Ritschel, Brentt Langhals, which appeared in the periodical " Air & Space Power Journal on Winter 2021 is presented. Topics include on the US military critical cost, American taxpayers with various military engagements and cost of wargames decisions reflecting fiscal and operational realities.

Nearly one-half of all construction projects exceed planned costs and schedule, globally [1]. Owners and construction managers can analyze historical project performance data to inform cost and schedule overrun risk-reduction strategies. Though, the majority of open-source project datasets are limited by the number of projects, data dimensionality, and location. A significant global customer of the construction industry, the Department of Defense (DoD) maintains a vast database of historical project data that can be used to determine the sources and magnitude of construction schedule and cost overruns for many continental and international locations. The selection of data provided by the authors is a subset of the U.S. Federal Procurement Data System-Next Generation (FPDS-NG), which stores contractual obligations made by the U.S. Federal Government [2]. The data comprises more than ten fiscal years (1 Oct 2009 - 04 June 2020) of construction contract attributes that will enable researchers to investigate spatiotemporal schedule and cost performance by, but not limited to: contract type, construction type, delivery method, award date, and award value. To the knowledge of the authors, this is the most extensive open-source dataset of its kind, as it provides access to the contract data of 132,662 uniquely identified construction projects totaling $865 billion. Because the DoD's facilities and infrastructure construction requirements and use of private construction firms are congruent with the remainder of the public sector and the private sector, results obtained from analyses of this dataset may be appropriate for broader application., Competing Interests: The Air Force Civil Engineer Center (AFCEC) is the sponsor of this ongoing research effort. The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.