A CASE STUDY OF THE NAVY EXCHANGE SERVICE COMMAND WEST COAST DISTRIBUTION CENTER’S PLANNING APPROACH TO AUTOMATION AND ROBOTICS

This study investigates the planning process for implementing automation and robotics in the warehouse operations of the Navy Exchange Service Command (NEXCOM), a military retailer, within the Department of Defense. The study explores the current situation of warehouse operations, distribution center, and distribution processes at NEXCOM and why the retailer is motivated to shift and improve processes with advanced technologies. The research examines specifics to the unique challenges and considerations when integrating automation and robotics faced by a government-owned entity, focusing on the decision-making process within the military retail context. A qualitative analysis and a case study of NEXCOM’s West Coast Distribution Center in Chino, CA, provides insights into these complexities. Additionally, the research explores other alternatives to improve processes without the use of automation and robotics to aid in the decision-making process and evaluation of integrating and implementing automation and robotics.Distribution Statement A. Approved for public release: Distribution is unlimited.Lieutenant, United States NavyLieutenant Commander, United States NavyLieutenant Commander, United States Nav

ARTist: The Android Runtime Instrumentation and Security Toolkit

We present ARTist, a compiler-based application instrumentation solution for Android. ARTist is based on the new ART runtime and the on-device dex2oat compiler of Android, which replaced the interpreter-based managed runtime (DVM) from Android version 5 onwards. Since dex2oat is yet uncharted, our approach required first and foremost a thorough study of the compiler suite's internals and in particular of the new default compiler backend Optimizing. We document the results of this study in this paper to facilitate independent research on this topic and exemplify the viability of ARTist by realizing two use cases. Moreover, given that seminal works like TaintDroid hitherto depend on the now abandoned DVM, we conduct a case study on whether taint tracking can be re-instantiated using a compiler-based instrumentation framework. Overall, our results provide compelling arguments for preferring compiler-based instrumentation over alternative bytecode or binary rewriting approaches.Comment: 13 page

Konsortialabschlussbericht Agri-Gaia

1. Derzeitiger Stand von Wissenschaft und Technik: Die deutsche Agrarbranche weist bereits einen fortgeschrittenen Digitalisierungsgrad auf. Kommunikation zwischen Landmaschinen und Serverinfrastrukturen sowie Datenanalyse sind etablierte Praktiken. Das Projekt konnte auf Vorarbeiten aus den Bereichen dezentrale Plattformen und landwirtschaftliche Datenverarbeitung aufbauen. Trotz dieser Grundlagen fehlte eine branchenspezifische KI-Infrastruktur für die Agrarwirtschaft. Zu Projektbeginn existierte keine Interoperabilität zwischen Datensätzen und KI-Modellen, und B2B-Entwicklungen waren nur durch Kooperation mit großen Technologiekonzernen möglich. 2. Begründung/Zielsetzung der Untersuchung: Agri-Gaia zielte auf die Entwicklung eines KI-Ökosystems für mittelständische Agrar- und Ernährungsunternehmen in Anlehnung an die GAIA-X-Infrastruktur. Kernziele waren die Schaffung einer B2B-Plattform mit nutzerfreundlichen KI-Modulen und die Vernetzung von Anwendern mit Entwicklern, um insbesondere mittelständischen Betrieben ohne eigene KI-Expertise Zugang zu entsprechenden Technologien ermöglichen. Konkrete Aufgaben umfassten die Entwicklung einer Plattform mit Datei- und Algorithmenmarktplatz sowie die Demonstration praktischer Anwendungen wie gezielte Nährstoffausbringung oder selektives Hacken zur Pestizidreduktion. 3. Methoden: Aufgrund der Diversität der Arbeitspakete und Usecases wurde eine breite Vielfalt an Methoden verwendet. 4. Ergebnisse: Agri-Gaia hat erfolgreich die technischen Grundlagen für ein KI-Ökosystem für die Agrar- und Ernährungsindustrie entwickelt. Zentrale Ergebnisse umfassen eine Open-Source-Softwareplattform für KI-Anwendungen, Tools für synthetische Datengenerierung (die zur Gründung eines DFKI-Spin-Offs führten), ein semantisches Datenmanagement-System sowie erfolgreiche Use-Cases in den Bereichen Edge-Computing für Landmaschinen und Prozessoptimierung. Trotz anfänglicher Herausforderungen durch die verzögerte GAIA-X-Infrastruktur konnten leistungsfähige Lösungen für branchenspezifische KI-Anwendungen bereitgestellt werden. 5. Schlussfolgerung und Anwendungsmöglichkeiten: Das Projekt hat die Grundlage für eine nachhaltige KI-Nutzung in der deutschen Agrarwirtschaft geschaffen. Die Ergebnisse ermöglichen kürzere Realisierungszeiträume für KI-Produkte durch standardisierte Infrastrukturen und stärken die Wettbewerbsfähigkeit mittelständischer Unternehmen durch vereinfachten Zugang zu KI-Technologien. Die Entwicklungen werden in neuen Projekten fortgeführt und künftig durch die neu gegründete Agrotech Valley Technology GmbH kommerzialisiert. Die Integration in europäische Initiativen unterstreicht die internationale Relevanz des Projekts und rechtfertigt die öffentliche Förderung als wichtigen Beitrag zur technologischen Weiterentwicklung des Agrartechnik-Sektors.1. Current State of Science and Technology: The German agricultural sector already shows an advanced level of digitalization. Communication between agricultural machinery and server infrastructures as well as data analysis are established practices. The project was able to build on previous work in the areas of decentralized platforms and agricultural data processing. Despite these foundations, there was a lack of industry specific AI infrastructure for the agricultural sector. At the beginning of the project, there was no interoperability between datasets and AI models, and B2B developments were only possible through cooperation with large technology corporations. 2. Justification/Objective of the Investigation: Agri-Gaia aimed to develop an AI ecosystem for medium-sized agricultural and food companies based on the GAIA-X infrastructure. Core objectives were the creation of a B2B platform with user-friendly AI modules and the networking of users with developers, particularly to enable medium-sized businesses without their own AI expertise to access relevant technologies. Concrete tasks included the development of a platform with a file and algorithm marketplace as well as the demonstration of practical applications such as targeted nutrient application or selective hoeing for pesticide reduction. 3. Methods: Due to the diversity of work packages and use cases, a wide variety of methods was employed. 4. Results: Agri-Gaia has successfully developed the technical foundations for an AI ecosystem for the agricultural and food industry. Key results include an open-source software platform for AI applications, tools for synthetic data generation (which led to the establishment of a DFKI spin-off), a semantic data management system, and successful use cases in the areas of edge computing for agricultural machinery and process optimization. Despite initial challenges due to the delayed GAIA-X infrastructure, powerful solutions for industry-specific AI applications could be provided. 5. Conclusion and Application Possibilities: The project has created the foundation for sustainable AI use in German agriculture. The results enable shorter implementation periods for AI products through standardized infrastructures and strengthen the competitiveness of medium-sized companies through simplified access to AI technologies. The developments are being continued in new projects and will be commercialized in the future by the newly founded Agrotech Valley Technology GmbH. The integration into European initiatives underlines the international relevance of the project and justifies the public funding as an important contribution to the technological advancement of the agricultural technology sector

Meet the Team: A Quality Improvement Initiative to Improve Family Knowledge of Their Care Team

OBJECTIVES Hospitalized families often have poor knowledge of care team members, which can negatively impact communication. Local baseline data revealed that few families had knowledge of team members. Our primary aim was to increase the percentage of families able to identify a member of their team to 75% over 1 year and sustain use of our improvement tools over 6 months. METHODS We conducted a quality improvement initiative at a tertiary pediatric academic center. Plan-do-study-act cycles were used to implement and test 3 main interventions: (1) a “Meet the Team” form (MTTF), a visual handout outlining care team members; (2) verbal introductions at the start of patient- and family-centered rounds (PFCR); and (3) data sharing regarding family feedback about tool use. The outcome measure was the percentage of families successfully identifying team members. Process measures were the percentage of families who received the MTTF and the percentage of PFCR that included verbal introductions. Balancing measures included rounds length. RESULTS We conducted structured interviews of 141 families and observed 11 597 PFCR events. There was an increase in the percentage of families who could identify a team member from 10% to 84%. The percentage of PFCR events that included verbal introductions revealed special cause variation, increasing from 40% to 80%. Rounds length held steady at ∼11 minutes per patient. CONCLUSIONS Implementing paired interventions of MTTF distribution and verbal team introductions was associated with increased family knowledge of team members and no change in rounds length. </jats:sec