DSC – Digital Supply Chain
The "Digital Supply Chain" Research Group
Consistent and seamless data exchange between and within companies is critical to success. Nevertheless, many companies are still in the early stages of developing and implementing a digital supply chain (DSC).
The DSC Research Group supports companies in their search for application-oriented, technical, and organizational solutions and in the subsequent implementation of a digitized supply chain for today and tomorrow.
In its work, the DSC Research Group combines a scientifically sound approach with practice-oriented methods, current architectural concepts, and innovative technologies at the cutting edge of research and practice. These stem from the following areas:
Enterprise Application Integration and Service-Oriented Architectures for integrating existing IT systems and designing IT and application architectures
(Industrial) Internet of Things solutions for capturing near-real-time data from operational processes and integrating it into application systems
Artificial Intelligence for better utilization of data to support decision-making and process automation
Research
The DSC research group’s work focuses on the following questions:
How can real-time and near-real-time data be collected in production and logistics, integrated into higher-level application systems, and used to make better decisions?
What opportunities arise from the intelligent analysis of operational data across the supply chain and across companies?
How can this potential be strategically leveraged?
How can the end-to-end exchange of information in supply chains be structured from both an organizational and technical perspective?
How—and with which information technologies—can processes in production, logistics, and supply chain management be optimized and digitized?
What architectural approaches and technologies can be used to design and implement future-proof IT architectures and the corresponding information and application systems?
Do you have a project idea or research need? Contact us!
The DSC research group is currently involved in the following projects:
KINCHI
AI and Order Processing in the Cloud, with Smart Integration of Tradespeople Apps
Small businesses often use multiple specialized applications for digital order processing. The lack of integration between these tools means that data such as addresses or order details must be entered manually multiple times. This extra effort leads to errors, both internally and in communications with customers and partners. There is a lack of an integrated solution that efficiently embeds the various applications into a skilled trades business’s workflow to simplify order processing.
The KINCHI project aims to enable skilled trades businesses to fully digitize their order processing using a cloud platform. This platform integrates existing ERP systems and apps by providing a unified interface. Skilled trades businesses can continue using their familiar software solutions, while the platform handles data exchange and the optimization of, for example, quotes and cost estimates through AI services.
The KINCHI platform boosts productivity and efficiency in the skilled trades by simplifying administrative tasks and freeing up more time for core business activities. Software companies benefit from reduced interface complexity and new AI-powered business models. Compatibility with KINCHI is becoming a hallmark of efficient processes. End customers enjoy a more efficient, customer-friendly service.
Solution
Based on a process analysis of skilled trades businesses, a standardized order processing workflow is defined. This forms the basis for KINCHI’s data model and interface concept, which coordinates data exchange between various software solutions via a central platform. This platform utilizes existing technology standards and offers AI services to support trade-related processes.
The Mission of the DSC Research Group
The DSC research group is leading the KINCHI project. It is responsible for developing the reference process, requirements engineering, and the joint development of the platform concept. Based on the platform concept, the DSC research group is also leading the specification of the data model and interfaces, as well as the development of a business model. During the implementation phase, the group’s primary focus will be on developing AI services.
More Information
Funded by the
Federal Ministry of Education and Research
Project Management Agency
Karlsruhe Project Management Agency
Project Partner
Official project website
Contact
move.mORe
Multimodal Logistics in the Upper Rhine Region
The logistics industry faces challenges such as supply chain disruptions, sustainability, a shortage of skilled workers, technological disruption, rising customer expectations, and regulatory requirements. These challenges call for adaptability, investment in technology, sustainability initiatives, and strategic developments.
The goal of the project is to share the knowledge and experience of the Universities of Offenburg and Karlsruhe with regional companies and to promote exchange among them. Through close collaboration, the aim is to create an ecosystem in the Upper Rhine region that identifies and further develops common future topics in production and distribution logistics, including multimodal scenarios.
The “Multimodal Logistics” project is part of the move.mORe transfer project of the Hochschulen Offenburg and Karlsruhe.
By jointly addressing future-oriented topics, companies gain the advantage of being able to tackle these issues with greater impact and with additional input from research and practice. This also makes it possible to implement topics that are too extensive for individual companies to handle on their own. The topics are identified and prioritized through a regular roadmapping process. This helps companies tackle long-term issues in a structured manner.
During implementation, companies can also utilize infrastructure from the network, such as University labs or facilities belonging to the participating companies.
Solution
The multimodal logistics concept represents a forward-looking vision based on three key pillars. These pillars are not merely independent elements; rather, they form an inseparable unit whose strength stems from their interdependence, as can be seen in the figure above.
The first work package aims to establish the “Logistics on the Upper Rhine” innovation network. This network enables local companies to actively participate and jointly tackle logistical challenges. It also promotes knowledge exchange and provides a platform where companies can communicate with one another on an equal footing.
Initially, joint “roadmap workshops” will be held to identify future logistics issues facing the companies. These workshops will examine six different levels, taking into account both internal and external factors, in order to identify and prioritize short-, medium-, and long-term challenges.
The second work package involves the establishment of a decentralized logistics campus. The laboratory infrastructure at the universities of Hochschule Offenburg and Karlsruhe will be expanded to include IoT technologies, which will be used for tracking, tracing, and simulating transport logistics processes (HSO) as well as for analyzing and developing interface technologies between information technology and operational technology (HKA). A mobile lab, equipped with IoT sensors, storage media, and infrastructure for workshops, enables direct on-site research at companies and opens up new avenues for knowledge transfer.
The third work package focuses on the implementation projects resulting from the first two work packages. The identified topics will be implemented with the support of the research infrastructure and in collaboration with the participating companies in the innovation network.
The Mission of the DSC Research Group
The DSC research group leads the move.mORe subproject “Multimodal Logistics” and oversees the innovation network. Its responsibilities also include the development and construction of the decentralized campus, which features the mobile logistics laboratory. The third focus is on implementing the projects that arise from the research roadmap.
More Information
Supported by
a joint initiative of the Federal Ministry of Education and Research and the Joint Science Conference (GWK)
Project Sponsor
VDI Technology Center GmbH, Düsseldorf
Project partners
Research collaboration between Karlsruhe University of Applied Sciences and Hochschule Offenburg
Official project website:
HyConnect
Connecting Hydrogen Supply and Demand for Sustainable Transportation Logistics
Organizing hydrogen logistics for road freight transport currently poses major challenges for the stakeholders involved. A key problem is the lack of integration between planning and inventory data at refueling stations and among transportation companies. This lack of coordination hinders investments in vehicle fleets and infrastructure and exacerbates the well-known “chicken-and-egg problem” of hydrogen mobility.
The HyConnect project in North Rhine-Westphalia aims to overcome these hurdles through an innovative digital platform. The focus is on developing and piloting a solution that processes and optimizes real-time data using AI-based algorithms. The platform integrates capacity management, dynamic pricing, and reservation systems via a universal, GPT-based data interface. This enables existing resources to be networked across organizational structures and utilized in a dynamically optimized manner.
By precisely synchronizing hydrogen supply and demand, HyConnect enables significantly greater planning reliability for refueling stations and logistics companies. This increases the efficiency of hydrogen use, reduces costs, and enhances the economic appeal of hydrogen as an energy carrier in transportation. In this way, the project makes a significant contribution to reducing CO2 emissions, strengthens North Rhine-Westphalia’s position as a leading innovation hub for sustainable mobility, and could serve as a model for other regions.
Solution
HyConnect’s technological approach is based on three key pillars that establish a seamless connection between hydrogen infrastructure and logistics:
AI-based universal data interface: To simplify communication between fueling station operators and freight forwarders, we are developing a universal interface based on GPT technologies. This automates the integration of various software solutions and enables efficient, cross-platform data exchange in real time.
AI-driven pricing: Machine learning models are used to analyze the price elasticity of hydrogen in order to generate precise price forecasts. This dynamic pricing optimizes the utilization of refueling station infrastructure and increases the economic appeal for all stakeholders.
Central Reservation Platform: At the heart of the solution is a digital platform that enables binding reservations of hydrogen capacity. It serves as a tool for directly balancing supply and demand, thereby significantly increasing planning certainty for logistics companies.
By modeling these business processes and semantic data structures, HyConnect creates the necessary foundation to technologically bridge the “chicken-and-egg problem” of hydrogen mobility.
The Mission of the DSC Research Group
Within the HyConnect project, the DSC research group is responsible for the requirements analysis and the specification of the AI-based universal data interface and AI-driven pricing. In doing so, the group is investigating and implementing innovative approaches such as large language models (LLMs) and GPT-based technologies to automate data exchange between gas stations and logistics companies across different organizational structures. In addition, the group is developing data-driven machine learning models to forecast the price elasticity of hydrogen. Alongside the software implementation of the interfaces and services, the DSC group is responsible for modeling the underlying business processes and semantic data structures.
More Information
Funded by the
Ministry of the Environment, Nature Conservation, and Transportation (https://www.umwelt.nrw.de/) in cooperation with the Ministry of Economic Affairs, Industry, Climate Protection, and Energy of the State of North Rhine-Westphalia (https://www.wirtschaft.nrw/ministerium)
Project Management Agency
Project Management Jülich (https://www.ptj.de/)
Project partners
PEM RWTH Aachen University (https://www.pem.rwth-aachen.de/go/id/fecr/)
H2 Mobility Deutschland GmbH & Co. KG (https://h2-mobility.de/)
MANSIO GmbH (https://www.mansio-logistics.com/)
H2 Green Power & Logistics AG (https://www.h2greenpowerlog.de/)
The DSC research group has already conducted research on the following projects:
KINLI
Artificial Intelligence for Sustainable Food Quality
More and more people in Germany are prioritizing high-quality meat. They are concerned about animal welfare standards and sustainable production. In practice, food safety is ensured by companies setting limits on cooking temperatures, for example, which allows them to respond proactively to noncompliance. However, this also leads to waste and higher costs. Companies are thus faced with the challenge of producing safe, high-quality, sustainable food that is consistent with animal welfare at a price the general public is willing to pay.
The KINLI project aims to develop an approach to proactively ensure quality and safety standards while minimizing waste. The project will use artificial intelligence (AI) to identify problems before they even occur. With the help of AI, KINLI aims to use data to predict when problems might arise in production so that appropriate measures can be taken to prevent them.
The project’s results will help companies meet consumer demands while remaining economically viable.
Solution
A key outcome of the project will be a central data platform that collects all necessary information along the supply chains from many different sources and makes it available for use by AI services. The AI services use the data to alert employees to potential problems and assist them in resolving these issues.
The project focuses on two areas as examples:
In cooked ham production, machine data and images are combined to meet consumer expectations regarding the ham’s appearance and texture while minimizing waste. The AI is designed to optimize machine parameters and provide guidance on meat processing.
In turkey farming, AI is used to analyze images and environmental data to ensure that the birds grow up healthy and safe. Farmers receive alerts about anomalies and can thus take early action to improve animal welfare and health.
Another key part of the project is ensuring that people can actually understand and use the AI’s recommendations, regardless of whether they’ve been working in the industry for a long time or are new to it.
The Mission of the DSC Research Group
The DSC research group uses data mining to gather the necessary data—which is generated at various points in the supply chain and, when combined, provides insights into the overall process—and analyzes it to identify overarching correlations. This includes, in particular, machine data from ham production and environmental data from turkey farming. The findings are implemented in AI services that generate results that are explainable and comprehensible to employees. In addition, DSC supports the development of the overall concept and the implementation of the data platform. This includes not only various interoperable interfaces but also a holistic data model spanning the entire food supply chain. Demonstrators are being developed for each of the various services to clearly illustrate how they work.
More Information
Funded by
the Federal Ministry of Food and Agriculture pursuant to a resolution of the German Bundestag
Project Sponsor
Federal Agency for Agriculture and Food
Project Partner
Kolsert KG
Official Project Website
Contact
LogIKTram
Logistics Concept and ICT Platform for Freight Transport in Trams and Light Rail Vehicles
Businesses and individuals expect flexible, fast, and secure delivery of their goods. However, conventional delivery truck traffic not only contributes to increased road congestion and, consequently, traffic jams, but also burdens local residents with noise and exhaust emissions. So far, however, environmentally friendly alternatives have struggled to gain traction. Consequently, logistics service providers’ systems are typically designed exclusively for conventional road transport, and alternatives such as schedule-based freight transport within the local public transit system are not taken into account.
The LogIKTram project aims to combine efficient transshipment and transport procedures that enable the use of a light rail system for inner-city and regional freight transport. In addition, the necessary information and communication technology (ICT) is being developed. The methods and results are being developed and applied using the city of Karlsruhe as a case study. At the same time, the project ensures that the findings can be transferred to and utilized by other cities and regions.
The development of the logistics concept is intended to demonstrate whether light rail-based freight transport is feasible. At the same time, the creation of a planning model and the establishment of an ICT platform will lay the groundwork for an exemplary operational process. By synthesizing the expectations of a wide range of stakeholders, additional premises for the concept’s implementation are taken into account. The project results based on this—regarding conceivable processes, potential transport capacities, and economic feasibility calculations—serve as a basis for decision-making by the stakeholder groups regarding potential implementation.
Solution
Based on stakeholder interviews, an analysis of the transportation needs of potential goods tram customers, and the local conditions of the light rail network, a logistics concept for the Karlsruhe region will first be developed. This concept will form the basis for a model for strategic, tactical, and operational transportation planning. Requirements for the ICT platform will then be derived from the logistics concept and the planning model. In addition, requirements for transshipment and transport procedures will be developed based on the logistics concept. The ICT platform and the transshipment and transport procedures will be implemented and demonstrated as prototypes, and will be tested and further developed in simulation models to assess their feasibility and impact on traffic flows.
The Mission of the DSC Research Group
Within the project, Hochschule Offenburg is responsible for developing the logistics concept, the operator and planning model, as well as designing and implementing the necessary software functionalities for freight forwarders and shippers on the ICT platform.
For the requirements analysis, the DSC research group uses transport data from potential Gütertram customers, data from the light rail operator, publicly available data from OpenStreetMap (OSM), and information on local public transit schedules and stops via the General Transit Feed Specification (GTFS). Based on this data, feasibility analyses are also conducted for the implementation of the concepts.
To implement the functionalities on the ICT platform, the DSC research group adapts existing data formats for planning and booking transportation and supplements them with new, specific data formats. These are intended to ensure smooth communication between stakeholders and, consequently, the efficient handling of transportation via the ICT platform.
More Information
Funded by
the Federal Ministry for Economic Affairs and Climate Action pursuant to a resolution of the German Bundestag
Project Management Agency
DLR Project Management (German Aerospace Center)
Project partner
MARLO Consultants GmbH
Official project website
Contact
Laboratories
The DSC research group currently oversees two labs:
Digital Supply Chain
The Digital Supply Chain Lab maps the physical process and data exchange along a supply chain—from the supplier through the manufacturer to the customer. The Lab provides a learning and working environment for companies and students where current concepts and digital technologies can be tested and further developed in real-world processes. At the same time, work on demonstrators in research projects is intended to ensure the targeted development of practical project outcomes at an early stage.
Demonstrators
Supply Chain Demonstrator: Real-time transparency through RFID support for a closed-loop container system and the entire order fulfillment process from supplier to customer
Industrial IoT Demonstrator: Collection of data in a “batch size 1” manufacturing process using Industrial IoT technologies, integration of the data into higher-level systems, and linking with the Supply Chain Demonstrator
LogIKTram Project: ICT platform for regional freight transport via freight tram in the Karlsruhe region; demonstration of tactical and operational transport planning
EDI Demo: Demonstration of data exchange between ERP systems via UN/EDIFACT
Additional demonstrators are being planned for the KINCHI, KINLI, and move.mORe projects
Services for Businesses
Companies can use the lab, for example, for the following topics.
Exploring current technologies
Joint development of prototypes and demonstrators for implementing the technologies within their own companies
Integrating and demonstrating their own products and solutions
Joint research and development projects
Seminars and training sessions
Offer for Students
The lab offers the following opportunities for students
Learning about current technologies in courses
Student projects
Final theses
Technologies
The technological focus is on
tracking and tracing in supply chains
Real-time location systems (RTLS)
(Industrial) IoT solutions
Artificial intelligence and machine learning
Interfaces and data transmission
Future-Proof IT and Software Architectures
Mobile Logistics Lab
A mobile logistics lab in the form of a panel van expands the Digital Supply Chain Lab and the laboratory infrastructure at Hochschule Offenburg. The vehicle was acquired as part of the move.mORe project, specifically within the “multimodal logistics” subproject. The lab is based on a modified Renault Master e-Tech with an electric drive system, which also reflects the project’s commitment to sustainability. The vehicle is operational and ready to fulfill its mission.
Use Cases
The mobile logistics lab opens up new possibilities for knowledge transfer, as it can operate independently of stationary laboratory infrastructure. The vehicle is flexibly configured to cover various use cases. Among other things, the vehicle can be used for the following use cases:
Collection and analysis of logistics flows in intralogistics and distribution logistics
Setting up demonstrators and conducting trials for tracking & tracing as well as real-time location systems (RTLS) both indoors and outdoors
Collection and gathering of logistics-related data from project partners or in trials and test setups
Conducting joint workshops directly on-site
Presentation and demonstration of project results to the interested public
Technologies
The vehicle is equipped with a wide range of technologies, including
RFID and UWB hardware with corresponding software, GPS and Galileo-based localization
Wi-Fi and IoT networks
Storage and servers
Workstations
Workshop materials
Interactive display
Industry
Do you know, at all times and in real time, where specific goods are located within your supply chains? Can you reliably estimate your customers’ future needs far enough in advance? Do you know how to meet these needs and how changes affect not only your own production and logistics processes but also the entire supply chain—for example, through the bullwhip effect?
These and many other questions must be addressed by companies in an interconnected and volatile world that is becoming increasingly fast-paced, unpredictable, uncertain, and complex. We work with companies in a practical, application-oriented manner to design and implement solutions for the digitalized supply chain.
We support you in particular with the following topics:
Strategic planning in the areas of IoT, AI, and the digital transformation of your supply chains, as well as the resulting impacts on corporate IT
Concept development and proof-of-concept (prototype, demonstrator) for AI services and IoT applications to capture, analyze, and utilize data throughout operational processes in production and logistics
Design of future-proof IT and application architectures, as well as support during their implementation
Support for software selection in the areas of ERP, MES, IoT platforms, and enterprise application integration
More Information
Team
Student Employees
Christian Friedrich
Elisa Busam
Haritha Thurpati
Miguel Karacaoglu
Michael Schweizer
Alumni
Robin Brischle
Jonas Ziegler
Tim Zeiser
Working Methods
The work within the DSC team is just as diverse as the research areas in which the DSC research group is active.
The DSC team offers:
Collaborative development and implementation of projects
Open and transparent communication
A culture of knowledge sharing and collaboration
The option to work remotely for greater flexibility
The DSC research group works with the following tools and technologies:
Location
Since December 1, 2023, the entire DSC research group has been housed at Schwedenstraße 7 in 77723 Gengenbach, which enables seamless collaboration among the individual DSC research divisions. The new location offers:
a modern work environment in bright rooms
ergonomic workstations with height-adjustable desks
extensive, innovative IT equipment
ample space for personal growth
Job Opportunities
The DSC Research Group is always looking for student assistants and research associates who are passionate about research and innovation. Doctoral studies are also available in various fields.
Job Openings for Students
Are you looking for a position as a student assistant?
If so, we cordially invite you to send a brief message to Prof. Dr.-Ing. Theo Lutz, indicating which projects or subject areas you are interested in. We will get back to you as soon as possible.
We are always looking for support in the following areas:
Project management
Development of browser-based front ends
Development and integration of backend solutions using Java and Python
Artificial Intelligence and Machine Learning
Job Openings for Graduates
To make the digital transformation of the supply chain a reality, we need people from a wide variety of disciplines, because the diversity of our projects not only offers an exciting range of challenges but also a unique opportunity to contribute your individual skills and expertise to our ongoing research projects. The DSC Research Group brings together the fields of Betriebswirtschaft, Wirtschaftsingenieurwesen, and Informatik, thereby ensuring an interdisciplinary perspective on our research projects.
As part of our team, you’ll have the opportunity to contribute to groundbreaking projects, bring innovative ideas to the table, and make a significant contribution to the further development of the digital supply chain.
We are constantly looking for new team members—both research assistants and student assistants—who are passionate about research and innovation and want to actively contribute to our projects. In addition, the DSC Research Group offers the opportunity to pursue a Ph.D. in various fields.
If you’d like to join our team and are interested in helping shape the future of the digital supply chain, don’t hesitate to submit an application.
Publications
Reviewed Paperes
Pack, C.I.; Zeiser, T.; Beecks, C.; Lutz, T. KINLI: Time Series Forecasting for Monitoring Poultry Health in Complex Pen Environments. Animals 2025, 15, 3180. https://doi.org/10.3390/ani15213180
Tim Zeiser, Alexander Prange, Corinna Köters, Maik Schürmeyer, and Theo Lutz. Parameter Optimization for a Lake Injector. Industry 4.0 Science, 2024, Vol. 40, No. 6, pp. 40–46. https://doi.org/10.30844/I4SD.24.6.40
T. Zeiser, D. Ehret, T. Lutz, and J. Saar, “Explainable AI in Manufacturing,” 2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC), Funchal, Portugal, 2024, pp. 1–8, doi: 10.1109/ICE/ITMC61926.2024.10794363.
U. Ertem, T. Lutz, and T. Zeiser, “Bibliometric Analysis as a Means of Efficiently Assessing Trends in Artificial Intelligence,” 2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC), Funchal, Portugal, 2024, pp. 1–7, doi: 10.1109/ICE/ITMC61926.2024.10794216.
Ziegler, J.; Menzer, M.; Lutz, T.; Dittrich, I.: Data formats for communication between freight tram operator and forwarder. In: M. Shafik (ed.): Emerging Cutting-Edge Developments in Intelligent Traffic and Transportation Systems: Proceedings of the 7th International Conference (ICITT 2023), Incorporating the 7th International Conference on Communication and Network Technology (ICCNT) (2024) 50, pp. 295–306
Fäßler, Lisa; Dittrich, Ingo; Lutz, Theo; Ziegler, Jonas; Frindik, Roland; Koch, Günter: Logistics Concept for Freight Transport by Tram. Analysis of Logistical Requirements for a Freight Tram Concept, in: Internationales Verkehrswesen 74 (2022) 3, pp. 46–51.
Ziegler, Jonas; Dittrich, Ingo; Lutz, Theo; Fäßler, Lisa: Planning Challenges in Intermodal Transport. Data Models for the Exchange of Planning Data for Regional Freight Transport. In: Industry 4.0 Management 38 (2022) 6, pp. 59–62.
Seifert, Benjamin; Lutz, Theo: Machine Learning in Supply Chain Management. An Overview of Existing Approaches Based on the SCOR Model. In: Industry 4.0 Management 37 (2021) 2, pp. 49–51.
Bruder, Lukas; Neumayer, Dirk A.; Lutz, Theo: Selection Criteria for IoT Platforms. Informed Selection of a Suitable IoT Platform Based on Commonly Used Criteria. In: Industrie 4.0 Management 37 (2021) 4, pp. 55–58.
Knapp, Matthias; Lutz, Theo: Master Data Taxonomy—A systematic approach to assess and migrate master data, 2021 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC), 2021, pp. 1–8, doi:10.1109/ICE/ITMC52061.2021.9570250
Unreviewed Papers
Zeiser, T., Köters, C., Lutz, T., Schürmayer, M., Köppen, L., Prange, A.: KINLI—Optimizing Supply Chains with Artificial Intelligence to Predictively Promote Animal Welfare and Food Safety. In: Der Lebensmittelbrief (2024) May/June, Vol. 35, pp. 48–49
Lutz, T.; Shuvo, S.; Brischle, R.: KINCHI: Smart Digitalization of Order Processing in the Skilled Trades. In: Forschung im Fokus (2023) 26, pp. 43–45
Dittrich, I.; Ziegler, J.; Lutz, T.; Menzer, M.: LogIKTram: Concepts for Regional Freight Transport via Light Rail. In: Forschung im Fokus (2023) 26, pp. 46–49
Lutz, T.; Ziegler, J.; Zeiser, T.: KINLI: Artificial Intelligence for Sustainable Food Quality. In: Research in Focus (2023) 26, pp. 112–113
Sautter, S.; Haigis, N.; Baumert, M.; Lutz, T.: Machine Learning in Production Planning and Control. In: Research in Focus (2022) 25, pp. 78–81
Fäßler, L.; Dittrich, I.; Lutz, T.; & Ziegler, J.: LogIKTram: Sustainable tram-based freight transport. In: Research in Focus (2022) 25, pp. 66–68
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