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Objective: Charcot foot is a rare complication to neuropathy and can cause severe foot deformities and ulcerations, which often require prolonged antibiotical treatment. The objective of this retrospective study was to investigate whether this treatment is associated to impaired renal function. Results: In total, 163 patients were included, of whom 105 (64%) had received β-lactam antibiotics for a mean total duration of 13.0 months. There was a significant increase in the urine albumin/creatinine ratio in the group that received antibiotics (p = 0.017), and the use of antibiotics was associated to a subsequent diagnosis of nephropathy (p = 0.01). Patients treated with antibiotics had a 21.9% risk of developing subsequent nephropathy versus 5.2% for patients not treated with antibiotics. We suggest increased awareness on signs of nephropathy in patients with severe Charcot foot. [ABSTRACT FROM AUTHOR]

Limited natural daylight in Nordic Countries means artificial lighting is a critical factor in industrial plant production. The electricity cost of artificial lights accounts for a large percentage of the overall cost of plant production. The optimal use of artificial lighting in plant production can be formulated as a multi-objective problem (MOP) to achieve optimal plant growth while minimizing electricity cost. In previous work, for solving this MOP, a Genetic Algorithm (GA) was used to create a Pareto Frontier (PF), which contains solutions representing a trade-off for using artificial lighting against plant production objectives. The PF was updated immediately once a non-dominated child-solution was found by comparing the dominance with solutions in the PF. Besides, in addition to the PF, the initial random population is also reused as a parent source in the evolution process. When the genetic evolution process terminated, a priority-based selection mechanism was used to select a final solution from the PF. In this paper, an alternative evolution strategy is proposed and compared with the previous GA evolution strategy. By this alternative strategy, all child-solutions are only compared with their parents during the evolution process, and the non-dominated child-solutions are collected into a candidate list. The PF is then updated at the end of each generation by comparing solutions on the PF with the collected candidate solutions. In this alternative strategy, the PF is the only source of parent-solution during the evolution process. In addition, a posterior normalization is implemented in the dominance evaluation, and social welfare metrics (SWs) are applied as an alternative to the priority-based selection mechanism to avoid the explicit ranking of objectives. The experimental results show that the proposed alternative evolution strategy outperforms the previous strategy on dramatically avoiding local minima. [ABSTRACT FROM AUTHOR]

The project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility's performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of "system of systems" that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid. [ABSTRACT FROM AUTHOR]

Digitalisation potentials in the electricity sector are frequently discussed around the world, especially in Europe which has the largest interconnected continental electricity grid in the world. The analysis and comparison of electricity ecosystems between countries can help to enhance international understanding and cooperation. It can also enable businesses to expand. However, little literature has covered the cross-national comparisons of digitalisation potentials in the electricity sector. This paper uses the business ecosystem architecture development methodology to identify commonalities and differences between two electricity ecosystems: Germany and Denmark. The result shows that there are many similarities between the two countries, but the roles of market framework provider, market supervision, and metering point operator are performed by different actors. By comparing the value chain segments, the main differences between Denmark and Germany are the share of renewable energy generation, the organisation of the transmission system, smart meter installation & operations, and the national electricity data hub. Based on the comparisons, six recommendations for the digitalisation of the electricity ecosystem are proposed: digitalisation for enabling more renewable energy resources for electricity generation, digitalisation in the electricity grids, digitalisation ib. the electricity markets, digitalisation on the demand side, especially the transport sector, and regulation-driven digitalisation of the electricity ecosystem. [ABSTRACT FROM AUTHOR]

In a deregulated market context, industrial consumers often have multiple market participation options available to bid their flexible consumption in electricity markets and thereby reduce their electricity bill. Yet most participation strategies for demand response are developed in a fixed and predefined set of submarkets. Meanwhile, little literature has compared multiple market options for market participants. Therefore, this paper proposes a comparative approach between available market options to evaluate savings from different market participation options. More specifically, this study implements an optimisation program in Python to investigate the impacts of changes in an industrial process' flexibility on savings with different market participation options. The optimisation program is tested with a case study of an industrial cooling process in three Danish submarkets (day-ahead, intraday, and regulating power markets). The market participation options are formed by different combinations of these three submarkets, and the type and amount of process flexibility are varied by changing time and load constraints in the optimisation program. The results show that bidding in market options with multiple submarkets yields higher savings than single-market bidding, but that increases in available flexibility impact savings in each market option differently. Increased flexibility will only bring additional savings if it allows to take further advantage of price variations in a market option. Additionally, increases in savings with flexibility depend on the considered type of flexibility. These changes in relative savings between market options at each flexibility level imply that the optimal market option is not a static choice for a process with variable operating conditions. The optimal market option for an industrial consumer depends not only on market price signals, but also on the type and amount of available flexibility. [ABSTRACT FROM AUTHOR]

Nowadays, society and business rely heavily on Information and Communication Technology (ICT) systems, which are progressing faster than ever. To stay on pace with them, focus is shifted towards integration of individual ICT systems into complex systems, which offers more functionality than simply the sum of individual systems. In this regard, Cyber-Physical Systems (CPSs) have gained significant importance and System-of-Systems (SoS) approach has been suggested for modeling complex CPSs to achieve a higher level goal, by dynamically building a large system with existing autonomous, and heterogeneous constituent systems (CSs). An important challenge in a system of Cyber-Physical Systems (SoCPSs) is to develop seamless collaboration between autonomous constituent-CPSs (CCPSs) to coordinate their operations. In this paper, we propose an agent based coordination mechanism to coordinate resource allocation and demand in SoCPSs. The approach models each CCPS as an agent and describes how multiple autonomous CCPSs, i.e., Virtual Power Plant (VPP), Commercial Greenhouse Growers (CGGs), communicate and collaborate with each other asynchronously through negotiation and how potential conflicts between CCPSs with conflicting goals are resolved. The efficacy of the proposed mechanism is validated through simulation of different real-world acyclic SoCPSs topologies. The results show that proposed approach is able to balance the individual requirements of multiple connected CPSs while achieving SoCPSs' mission. [ABSTRACT FROM AUTHOR]

Simulations, especially agent-based simulation, are able to facilitate the investigation of smart energy solutions and business models, and their impacts on the energy system and involved stakeholders. Technical details, alternatives, and multiple options for what-if scenarios influence simulation quality, but no methodology available to support the investigation. This paper proposes a method for identifying technical details of smart energy solutions in the energy system and identifying research gaps in the smart grid context with EV solutions as an example. The method includes the investigation of the state-of-the-art EV solutions by scoping review and the allocation of the scoping review results into the Smart Grid Architecture Model framework with three dimensions (Domains, Zones, and interoperability layers). The quantitative scoping review results in a total number of 240 references and 10 references match the criteria based on the qualitative scoping review. The results show that the most popular EV use case within the targeted scope is the V2G concept, and 6 out of the 10 references discuss the EVs' potentials to work as energy storage. Seventeen features are identified by mapping the EV use cases (solutions and business models) into the three dimensions (domain, zone, and interoperability layers) of the SGAM framework. The process at the Zone layer is the most popularly covered (mentioned 64 times), and enterprise at the Zone layer and communication in the interoperability layer are the least covered (mentioned 4 times each). [ABSTRACT FROM AUTHOR]

Modeling and simulation have been popularly used for system investigation and evaluation. With proper evaluation, distribution system operators can decide on a reasonable course of action for encouraging energy flexibility and make predictions on the recommended timing and magnitude of system updates under different scenarios. However, there is no efficient tool for system operators to quickly set up and perform simulations of alternative scenarios for system updates before planning their course of action, without much experience with programming or system modeling. This paper proposes an agent-based modeling framework for developing agent-based simulation models of business ecosystems that can be applied to multiple evaluation scenarios by simple configuration of agents and roles. There are two steps in this proposed framework: Step 1 – Interface and role interactions design and Step 2 – Agent architecture and connections design. In addition, the framework depends on a pre-step that covers mapping and architecture development of the business ecosystem to be modeled. The framework is demonstrated with a case study of an energy business ecosystem consisting of an electricity distribution grid with 137 connected domestic consumers. The case study shows that the proposed agent-based modeling framework supports the development of agent-based models for simulating energy business ecosystems. To verify the behavior of the developed agent-based simulation models, a verification procedure of the agent models is briefly discussed, which includes unit, integration, and system testing approaches similar to the ones used in software testing. [ABSTRACT FROM AUTHOR]

Digitalization is playing an important role in the emerging practice of Building-to-Grid (B2G). However, the majority of the literature only covers either the grid side, the demand side, or the technical aspect of B2G integration, and an overview of the digitalization in B2G and the involved stakeholders is missing. To fill these gaps, this paper proposes a definition of the B2G ecosystem and provides an overview of the digitalization of the B2G ecosystem with six aspects (B2G goals and themes, B2G technologies, B2G data sources and data management, and B2G related stakeholders). This paper also discusses five emerging technologies (5G, IoT, big data, artificial intelligence, and blockchain) and three main challenges of the applications of the emerging technologies in the B2G ecosystem (Security and privacy, interoperability and scalability, coordination, and dysfunctional emerging behaviors of collective intelligent controls). Lastly, this paper recommends future research directions in the Building-to-Grid ecosystem (especially ecosystem modeling and simulation), B2G's role in smart cities, sustainability, resilience, and harmonization of the B2G ecosystem, and other emerging technologies in B2G. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort. [ABSTRACT FROM AUTHOR]

Worldwide buildings are responsible for about 40% of the overall consumption and contribute to an average of 30% percent of the global carbon emissions. Nevertheless, most current buildings lack efficient energy management systems because such solutions are very expensive, especially when necessary instrumentation needs to be installed after the building's construction. As an alternative, we purpose the use of IoT sensor networks to retrofit existing medium and large-sized buildings to provide energy management capabilities in a cost-effective way. An IoT network auto-configuration platform for building energy management was developed. In order to efficiently manage metadata related to location and devices, a database using dynamic QR codes was created. Furthermore, we discuss the potential and shortcomings of different sensor-gateway pairing strategies that are applicable to an auto-configuring system. Lastly, we share our implementation of these concepts and demonstrate their use in a medium-sized building case study. The results show a trade-off between optimal configuration and total configuration time with a focus on the quality of the communication signal strength. The proposal provided the necessary automation for a cost-effective energy management system that can be deployed in both new constructions and existing buildings. [ABSTRACT FROM AUTHOR]