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Kant's moral education ideology is an important component of his entire ideological system and a brilliant gem in the history of human educational thought. Kant's moral education philosophy is mainly reflected in stimulating the intrinsic moral motivation of the educated and cultivating their kind will, advocating for the construction of moral freedom and spiritual meaning based on the principle of formality and universality as the basis for their own will. However, the current moral education in schools is still fundamentally based on material practical principles such as utilitarianism, emotion, self love, and reputation, which leads to low efficiency and formalization of moral education practice. In this sense, moral education cannot truly cultivate the sense of responsibility and obligation of the educated, let alone generate a Kantian sense of moral respect and sublimity from the depths of their hearts. The author believes that universities can change the way learners think and enhance people's moral motivation through the role of role models in moral education. By playing a role model, laying the foundation for the construction of ideas, and then making free choices, true freedom can be achieved. This article analyzes the connotation and characteristics of exemplary education in Kant's educational philosophy based on current research experience and relevant academic literature, and further explains the role of exemplary education in Kant's moral education philosophy. Finally, combining the role of role models, this paper focuses on exploring the contemporary value of the concept of role models in Kant's moral education ideas, aiming to draw on the essence of Kant's role model education and promote the reform and innovation of moral education in Chinese universities.

NPS NRP Executive Summary ; It is imperative to adopt more advanced business intelligence (BI) methodologies and tools to conduct comprehensive statistical and deep analyses to understand the entire spectrum of the Navy logistics enterprise including maintenance, supply, transportation, health services, general engineering, and finance. The ultimate goal is to enhance total force readiness and project combat power across the whole range of military operations and spectrum of conflict at any time. The objective of this proposal is to apply advanced business intelligence methodologies and tools to conduct comprehensive statistical and deep analyses of the readiness impacts resulting from not funding spares requirements. The research questions are listed as follows: Conduct a comparison of Fleet Demands against requirements in the FRWQ. When not funded, spares requirements accumulate in a Financially Restricted Work Que (FRWQ) awaiting resourcing. In the meantime, the systems these parts support are still fielded and the Fleet still generates requirements to replace these parts. Conduct an assessment of items in the FRWQ against high priority demands (CASREPs, NMCS, Crossdecks, etc.) Deliver the designs of the tool to score and prioritize the items in the FRWQ against maritime requirements data CASREP (impact to the weapon system, WSEC code, if critical) and aviation readiness data NMCS. The tool needs to take an input of FRWQ and match and score against CASREPs, NMCS, and CrossDecks, output a priority list. We propose to use business intelligence including tools such as Tableau or Microsoft power BI, and data mining tools such as Orange and lexical link analysis (LLA) to perform comprehensive statistics and deep analysis to address the research questions. If successful, the resulted research will help improve and determine the most efficient and effective method of stocking, forward staging, or contracting for the materials that have the highest likelihood of demand, balanced with the potential impact of failure, ...

NPS NRP Executive Summary ; Project Summary: The project concerns two concepts for naval warfighting: common tactical air picture (CTAP) and combat identification (CID). The CTAP process collects, processes, and analyzes data from a vast network of sensors, platforms, and decision makers and provides situational awareness to air warfare decision makers in a kill chain process. The CID process locates and labels critical airborne objects (as friendly, hostile, or neutral) with high precision and efficiency based on a CTAP as part of the core kill chain process. The existing methods of CTAP and CID involve wide ranges of participating platforms, such as destroyers, cruisers, carriers, fighter attack aircraft and tactical airborne early warning aircraft; participating sensors, such as Radar, Forward Looking Infrared (FLIR), Identification Friend or Foe (IFF), Precision Participation Location Identifier (PPLI), and National Technical Means (NTM); and Participating Networks and Systems, such as the Aegis combat system, Cooperative Engagement Capability (CEC), and Link-16. CID is notoriously a very difficult function, often more art than science process is still very manual, and decision makers can experience cognitive overload, so analytics is just one aspect of CID. Challenges in the CID process include (1) extremely short time for fusion, decision-making, and targeting; (2) uncertain and/or missing data outside sensor (e.g. radar, radio) ranges; (3) manual decision-making; (4) heterogeneous data sources for fusion and decision-making; and (5) multiple decision-makers in the loop. The CTAP and CID problems can be seen as both Big Data and no data. On one hand, the data used for CID comes from a combination of massively cooperative and non-cooperative sensors, organic sensors, and non-sensor information (where, typically, each sensor collects certain attributes). The big CID data then needs to be fused over time and space since they are collected in a distributed fashion. On the other hand, adversaries often conceal and change their true intentions, therefore rare or no data are observed for analyzing anomalous and adversarial behavior. Therefore, the CTAP, CID, and kill chain problems are challenging application areas for analytic, machine learning (ML), or artificial intelligence (AI) methods. n the past, we demonstrated that Big Data techniques and deep analytics can provide potentially useful information assisting the CTAP and CID. Big Data (BD) techniques allow the distributed acquisition and fusion of disparate and crucial combat data in real-time or near real-time. This year, we made advancements as follows: - We investigated specifically the Defense Information Systems Agency (DISA)'s Big Data Platform (BDP). BDP is designed for real time processing of Big Data beginning at ingestion and ultimately presenting useful data visualizations that may alert decision makers for anomalies. - We analyzed the Automatic Dependent Surveillance-Broadcast (ADS-B). ADS-B functions with Global Positioning System (GPS) satellite, rather than radar technology, more accurately observe and track air traffic. Aircraft equipped with an ADS-B Out transmitter sends position, altitude, heading, that relays the information to air traffic controllers and other aircraft. Our work in progress applies various Big Data, and deep learning including ML/AI tools to predict if a flight is commercial or military. ; OPNAV N2/N6 CID ; NPS Naval Research Program ; NPS-18-N193-A ; Approved for public release; distribution is unlimited.

NPS NRP Executive Summary ; Project Summary: The U.S. Marine Corps prides itself on the ability to successfully operate within a dynamic environment in an expedient and expeditious manner. To achieve power projection requirements, high states of aircraft, vessel, equipment, and personnel readiness are to be preserved. No small accomplishment, this burden lies heavily on supply professionals and the agility of the supply network in support of operating forces. Supply Chain Management (SCM) must cultivate dynamic supply chains, fluid as the maneuver units they support. Though military action is reactive at times, proactive preparations foster an ability to increase momentum and gain the initiative. SCM should also adopt a proactive mindset, forecasting readiness requirements through critical and balanced consideration. The purpose of this quantitative research is to assess the supply chain network of a deployed Multi- Mission Vertical and/or Short Takeoff and Landing (MV-22) Osprey squadron, discover inefficiencies that exist, and make recommendations to increase supply chain productivity. Specifically, this study takes advantage of Microsoft Excel and Big Data (BD) techniques to sort through structured and unstructured data. Collaboration with the Department of the Navy (DoN) for requirements and attaining data sets will permit the application of Excel and Lexical Link Analysis (LLArelationships between given data sets. Extraction of variables such as response times and aircraft part), a text-mining software, to deriveavailability will measure SCM strengths and draw attention to deficiencies. Interpreted relationships helps determine opportunities, shortfalls, and favorable and unfavorable conditions within the organizational and intermediate maintenance levels of the Osprey supply chain. The results observed will be the basis forsupply chain improvement recommendations and assist with enhancing DoN SCM productivity. ; Research POC Name: CAPT Eric ; NPS-18-N026-B ; Approved for public release; distribution is unlimited.