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We study the effect of railroads, the single largest public investment in colonial India, on human capital. Using district-level data on literacy and two different identification strategies, we find railroads had positive effects on literacy, in particular on male and English literacy. We show that railroads increased literacy by raising secondary and elite primary, rather than vernacular primary schooling. Our mediation analysis suggests that non-agricultural income, urbanisation, and opportunities for skilled employment are important mechanisms, while agricultural income is not.

Knowledge is key to competitive advantage, and organization leaders and managers are called to implement, maintain and enhance key knowledge management systems and processes. However, knowledge is inherently invisible, intangible, indefinite and resistant to quantification. Hence assessing investments in such systems and processes remains fundamentally challenging. The research described in this article builds upon a growing stream of knowledge visualization, measurement and dynamics work to develop a method for assessing knowledge flow efficiency, cost and performance in the organization. Results from application to archetypical organization systems and processes are highly promising, and they elucidate a novel decision support capability.

The Trident Room Podcast host Sydney Murkins sits down and has a conversation with Lt. Col. Chris Dellow, USMC. This episode was recorded on January 22, 2023.

In this episode, our guests discuss important themes regarding the future of automation and its implications for naval combat, hosted by Karl Flynn. This episode was recorded on November 30th, 2022.

Recent events, such as the death of George Floyd and the resulting Black Lives Matter (BLM) protests, the post elections attack on the U.S. Capitol, and the COVID-19 pandemic, have brought to light the challenges leaders in both private and public/governmental organizations face when communicating with employees about sensitive events. At both the national and global levels these issues are often tied to deep and long-held societal divisions. The U.S. Navy has frequently taken a progressive stance in supporting diversity and inclusion, and the Navy's recent steps to encourage inclusion and engage in necessary conversations are consistent with these actions. However, recent events and efforts to converse about these events have highlighted the challenges inherent in such communication. This study builds upon previous research focused on the tensions between participation and organizational control in online workplace communities (Aten & Salem, 2020). This research explored Sailors' experiences of sensitive conversations on various topics and of necessary conversations directed by Navy leadership following the attacks on the U.S. Capitol. This current research describes Sailors' perceptions of drivers and outcomes of sensitive conversations. It identifies four needed individual and organizational capacities to support successful conversations about sensitive subjects: metacognition, emotional regulation, cultural curiosity, and communication competence. Participants’ responses and related literature highlight two key conclusions. First, Sailors disagree on how the Navy should manage sensitive conversations: majority and minority groups’ perceptions differ on what topics require conversation, and fail to recognize that the inability to engage effectively in sensitive conversations is a military vulnerability. Second, Navy communication often demonstrates an inadequate capacity for managing these conversations: leaders lack self-awareness, emotional regulation is challe

Controversy and hype surround the divergent propositions that ChatGPT and other Generative Al innovations are dangerous and should be banned, or are benign and should be embraced. What should we make of the claim by Henry Kissinger, Eric Schmidt, and Dan Huttenlocher in their Wall Street Journal article that recent developments in Generative Al will bring challenges and change as significant as the printing press and Enlightenment? Will Generative Al technologies fundamentally change how we think, find work, organize society, and conduct international relations and war?

This report details a novel method which has the potential for improving the U.S. Navy’s ability to perform continuous assurance on autonomous and other cyberphysical systems. Specifically, this report presents a novel technique for simulation-driven data generation of explainable machine-learned correctness properties, called ML-assertions, for the purpose of subsequent runtime verification. The method brings the task of providing formal guarantees about the dependability of autonomous systems from the realm of doctoral-level experts into the domain of system developers and engineers. Preliminary experimentation demonstrates that ML-assertions can be utilized for behavior prediction in complex multi-agent systems, serving as a state-of-the-art method for conducting verification and validation on autonomous cyberphysical systems.

Emerging threats have focused U.S. Navy operating concepts on agile, distributed tactical forces in the littoral and maritime zones. Given the nature of a threat and its location, medical evacuation via air may be infeasible due to hostile conditions or distance, requiring a shi to a surface or subsurface strategy. Medical demand and adversary movements are unpredictable in warfare, therefore a routing decision framework that accounts for uncertainty is required. Using the principals of the U.S. Marine Corps Rapid Response Planning Process and online optimization, this article proposes a decision framework for surface medical evacuation routing against an adversary given uncertain demand that can be applied to manned and autonomous transport operations.

In the last decade, developments in tropical geometry have provided a number of uses directly applicable to problems in statistical learning. The TML package is the first R package which contains a comprehensive set of tools and methods used for basic computations related to tropical convexity, visualization of tropically convex sets, as well as supervised and unsupervised learning models using the tropical metric under the max-plus algebra over the tropical projective torus. Primarily, the TML package employs a Hit and Run Markov chain Monte Carlo sampler in conjunction with the tropical metric as its main tool for statistical inference. In addition to basic computation and various applications of the tropical HAR sampler, we also focus on several supervised and unsupervised methods incorporated in the TML package including tropical principal component analysis, tropical logistic regression and tropical kernel density estimation.

Deep neural networks show great success when input vectors are in an Euclidean space. However, those classical neural networks show a poor performance when inputs are phylogenetic trees, which can be written as vectors in the tropical projective torus. Here we propose tropical embedding to transform a vector in the tropical projective torus to a vector in the Euclidean space via the tropical metric. We introduce a tropical neural network where the first layer is a tropical embedding layer and the following layers are the same as the classical ones. We prove that this neural network with the tropical metric is a universal approximator and we derive a backpropagation rule for deep neural networks. Then we provide TensorFlow 2 codes for implementing a tropical neural network in the same fashion as the classical one, where the weights initialization problem is considered according to the extreme value statistics. We apply our method to empirical data including sequences of hemagglutinin for influenza virus from New York. Finally, we show that a tropical neural network can be interpreted as a generalization of a tropical logistic regression.

A previous analysis of pathways to reach net zero naval operations quantified Department of the Navy (DON) operational systems emissions and identified potential pathways to meet the 2050 reduction targets prioritized in executive orders and DOD climate strategies. The analysis generalized illustrative pathways allocating reduction potential for alternative fuels, hydrogen, unmanned systems, batteries, operational efficiencies, advanced nuclear energy, renewable energy, and carbon capture and sequestration. This follow-on, Phase II analysis considered the DON-unique challenges for integrating emissions reduction measures and cited reported climate science to assess the impact of four specific pathways: alternative force structure, advanced nuclear energy, platform carbon capture and storage, and sustainable fuels. The analysis assumed the Department will not accept any measure that could reduce operational capabilities on ships or aircraft and deduced that by 2050 conventional gas turbine and diesel engines will still demand up to 28 million barrels of F-76 and JP-5 fuels. The study concludes that alternative force structure concepts, like unmanned systems, could realize some emissions reductions by 2050; however, because future force design is responding to strategic competition against pacing adversaries, the future force will likely demand more operational energy offsetting those reductions. Advances in nuclear energy and non-drop-in alternative fuels like ammonia and methanol could enable some future capabilities, supporting the added energy requirement and reducing emissions, but will not likely proliferate to scale to support 2050 targets. Advances in platform carbon capture technologies are being tested in commercial maritime applications, but the power demand and CO2 storage requirements will likely preclude commissioned warships and naval aircraft. Sustainable fuels, and specifically drop-in fuels, offer the greatest potential for significant emissions redu

In a rapidly evolving maritime warfare landscape, the U.S. Navy and its allies require their crews to quickly identify optimal strategies for vessel engagements to ensure freedom of the seas. This necessity becomes more pronounced given the potential grave consequences of sub-optimal maneuvers, as illustrated by the cases of the USS John McCain and USS Fitzgerald. Recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) offer a promising solution. There have been significant strides in implementing AI to outperform human experts in complex games such as Chess, Poker, and StarCraft, which now have the potential to also benefit real-time decision-making and wargaming in the naval domain. This study explores the potential for Reinforcement Learning (RL) techniques to be applied to naval contexts, which could provide valuable decision support tools to ship captains and their staff by suggesting optimal movement strategies in complex maritime scenarios. In this study, exemplar naval scenarios were designed and modeled within a combat simulation environment, AI agents (consisting of a mix of rule-based, method-based, and value-based approaches) were designed, and the performances of these agents were evaluated and compared. The aim was to assess the agents’ ability to identify optimal movements against a rule-based adversary, while also comparing these performances against human-level play. The insights drawn from this study contribute to ongoing research aimed at developing effective decision aids for ship captains in real-world operations.

Complex sensor networks are an enormously large and expensive set of systems, software, sensors, and emitters that generate data contributing to battlespace awareness and combat identification. Multiple sensor network configurations can collect so much data that an ineffective “Data Rich, Information Poor” (DRIP) situation results. The desired solution for actionable information is coordinating high-value units (HVUs) to support actions such as identification and targeting. Timely detection of unknown signals is currently inadequate to maintain situational awareness at a tactical level. Few analysts have the experience and data access required to make effective use of the available data. Those who do are not close enough to the edge to support warfighting’s tactical and operational levels. Automating the workflow and processes of well-seasoned analysts combined with new AI and modeling technologies would enable improved and more timely extraction of essential information from the data and support better situational awareness and tactical decision-making. The motivation for analysis of combat and target identification stems from the significant pressure usually applied to make rapid, effective, and informed decisions. The goal of target ID is to analyze the threat of a potential target in order to make informed decisions about engaging it. The benefits of leveraging these graph-based methodologies include greater situational awareness with automation tools to distill contextualized information at the tactical and operational levels.

Wireless mobile telephone service is now a dominant mode of communication throughout the world, with new technologies enabling ever expanding digital services that support commerce, government, and society. A lack of coverage in mobile services is inconvenient under normal conditions, but can exacerbate dangerous situations during emergencies. This report models and measures mobile telephone coverage in the U.S. Virgin Islands, a Caribbean territory that is still recovering from two devastating hurricanes in 2017. We present a physics‐based model designed to predict wireless coverage based on characteristics of the transmitting antennae and surrounding topography. We then present the results of ground measurements intended to validate our predictions. Overall, we confirm the anecdotal experience that there are significant “dead zones” in mobile coverage throughout the territory—both through our numerical modeling and primary data collection efforts—which can cause problems for public safety. However, our predicted coverage maps cannot be treated as authoritative at this time, due to incomplete data for transmitting towers. We recommend additional study and identify next steps required to generate authoritative coverage maps, along with the potential benefits of doing so.

The US Navy’s Culture of Excellence Perform to Plan effort seeks to empower warfighting capability by fostering psychological, physical, and emotional toughness. To meet this goal, the Navy needs to understand what encourages signature behaviors and reduces destructive behaviors and how these behaviors impact readiness. This study proposed a path to develop critical insight to encourage signature behaviors and to counter destructive behaviors. Researchers developed a design for a mixed-methods, explanatory sequential study to answer three research questions: (1) What are the rates of signature and destructive behaviors during phases of OFRP? (2) Do rates differ by command type? (3) How do signature and destructive behaviors impact readiness? This project resulted in the design of a study to investigate the surge in destructive behavior during the maintenance phase of the Optimized Fleet Response Plan (OFRP). The detailed proposal prepared by the research team was reviewed by the Naval Postgraduate School Institutional Review Board and later by the Office of Management and Budget. However, final approval was not received in time to conduct the research prior to the expiration of funds.

Using detailed data on evaluation and promotion outcomes of active-duty sailors and officers from 2014 to 2022, we find that earning a recommendation of Early Promote (EP) and receiving individual trait average scores above the reporting senior cumulative average (RSCA) are most predictive of promotion across ranks and communities. We find mixed results for socio-demographic and evaluation characteristics assumed to have a negative impact on promotion. Being non-white or a "1 of 1" evaluation (non-competitive summary group) is negatively associated with promotion in some rank and community combinations. Being female or married is positively associated with promotion in some rank and community combinations. However, these results are inconsistent across all ranks and communities, and we fail to find consistent patterns in the data that point to racial or gender bias substantively disadvantaging minorities or women.

This research has investigated some new and existing technologies that might be employed to improve the performance of high frequency Navy shore sites. The focus is primarily on the antenna. Conventional HF antennas like wires and loops have low gain and nearly isotropic radiation patterns. An array of these simple elements can overcome these limitations and provide increased gain, improved SNR on receive, low sidelobes, and scanned directive beams. Digital beamforming with electronically scanned phased arrays is of special interest. This analysis reveals that several shore site configurations are possible. For a particular case, the best selection will depend on the circumstances of the site geography, available land area, local electromagnetic issues (i.e., interference, radiation restrictions, etc.) and the desired coverage. A detailed tradeoff study is recommended for each site.

Periodically, environmental monitoring satellites are moved into on-orbit storage to await a call to duty. With a contract cost for four satellites of $1.4 billion, alternatives to storage are investigated and suggested in this study. Assuming a readership unfamiliar with the civilian satellite program, significant elaboration of program details is presented in a mild brief review of the literature, extracting the current state-of-the art novel suggestions. Next, novel utilizations are proposed. Eight novel applications are found in the literature: Oil palm mapping, cloud mapping, cloud property and atmospheric state estimation, hurricane prediction and analysis, surface urban heat island, Earth–distribution of solar radiation, fire location and burning area estimation, hail-producing storm detection. Five novel applications are proposed: Bio–radar, stereo winds, imager re-calibration using GOES-R, nuclear detonation detection, and communications relay.

We use data from the Current Population Survey to identify factors associated with food insecurity in the civilian setting, then use a machine learning model to predict rates of food insecurity using the same characteristics based on administrative pay and personnel records from the Department of Defense. We next wargame potential policy solutions, including the new Basic Needs Allowance (BNA) to assess how various policies might change the risk of food insecurity. Policies in the prediction wargame change the threshold for eligibility for the BNA, add additional income based on number of dependents, increase spouse employment, and change SNAP eligibility. There are 6 major takeaways from this research: Takeaway 1: Having a larger family size and if the head of house is a woman, divorced, Black, or Hispanic are associated with a higher probability of being food insecure (risk factors). Takeaway 2: Being from a military family, being married, having more education, and more income are associated with a lower probability of being food insecure (protective factors). Takeaway 3: Few service members have income levels at or below BNA eligibility criteria. Takeaway 4: The FY2023 NDAA's method of eligibility for the BNA will not significantly reduce military food insecurity. Takeaway 5: We estimate that 6.9% of the military is likely to be food insecure. Takeaway 6: Moderate increases in benefits or income will not eliminate food insecurity. We take our results as an indication that food insecurity is a multi-faceted issue that will not be solved with money alone. Military members-and their families-should not face food insecurity. The current BNA will not change much about current rates of food insecurity. Indeed, moderate tweaks to pay are unlikely to have a meaningful effect on rates of food insecurity. Instead, a more comprehensive approach to food insecurity is needed.

This is the Phase II follow-up from risk-based decision analysis in capital budgeting and stochastic portfolio optimization. The research purpose is to propose a novel, reusable, extensible, adaptable, and comprehensive advanced analytical process employing strategic game theory with Integrated Risk Management to help the DOD with capital budgeting under uncertainty, with Monte Carlo risk-simulation, predictive analytics, and stochastic portfolio optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. Game theory applications will include areas of repeated finite and infinite games with complete and incomplete information in strategic and extended forms. The research approach will include, when appropriate, theoretical mathematical formulations, recommended data collection/integration, and optimization approaches, as well as practical implementation recommendations to test the benefits and viability of the proposed approach to generate optimal budget allocations and strategies informed by the reactions of other nations or internal players (U.S. Congress, organizations within DOD). The PI is an expert in modeling uncertainty and risk-based stochastic portfolio optimization decision analytics and strategic real options and game theoretical constructs. Whenever appropriate, strategic and extended forms will be applied. The assumption in game theory includes the rationality of the decision makers and updated information using Bayes’ law. The idea is to analyze Bertrand, Cournot, and Nash equilibria with pure and mixed strategies with dominant and dominated conditions in repeated games to determine the recommended method. We use military SMEs to determine probabilistic payoff structures engaged in sequential and simultaneous moves to identify equilibrium states in the main game tree to subgame perfect Nash equilibrium as worked-out examples and illustrations of the ap

A system and method for clock-skew-based covert communication in which a message formed of message bits is mapped to corresponding symbols having predetermined clock skew values. For each corresponding symbol, an offset value is calculated and added to each timestamp in a predetermined quantity of outgoing TCP segments to generated altered TCP segments, such that an artificial clock skew is induced as measured by a receiver. A clock skew value is determined from each predetermined quantity of TCP segments and mapped to corresponding symbol. The symbols are then mapped to corresponding message bits, and the message is determined from the bits. In this way a message can be sent from a transmitter to a receiver in a way that is covert during transmission and deciphered at the receiver.

The disclosure provides a method a method for generating a heterogeneous carbon-bonded material using an activated carbon support a solution comprising a material precursor and a chemical agent. The material precursor is typically a salt such as SnCI₂, and the chemical agent is a substance which thermally decomposes to generate reducing gases. The mixture is heated in an inert, nonreactive atmosphere to generate the reducing gases and remove surface groups from the carbon support, allowing material such as metal from the material precursor to nucleate and directly bond to the sites. The method typically utilizes high specific surface area carbon and may produce a p lurality of metal particles having an average diameter of less than about 20 nm dispersed on and strongly bonded to the underlying carbon support.

An apparatus for use in a friction stir operation, such as friction stir welding (FSW) or friction stir processing (FSP). The apparatus may have a rotating tool adapted to be plunged into a material, where the material is susceptible to being softened by heating. The rotating tool may further be adapted to be advanced along a surface of the material. An optical energy generating subsystem may be used to heat a portion of the material using optical energy as the tool is advanced along the material.

Disclosed herein is a design, simulation, and hardware build of a photonic compressed sensing Nyquist folding receiver that is able to directly undersample wideband RF signals and detect the original Nyquist zone and frequency information. According to an exemplary embodiment, this is achieved by generating a frequency modulated optical impulse train for directly undersampling the RF environment at the antenna. The impulse train undersamples the signals using an optical modulator configuration at 1550 nm and collects the detected samples in a low pass interpolation filter which is subsequently processed to extract the undersampled signals.