Investigating structural and functional aspects of the brain’s criticality in stroke

This paper addresses the question of the brain’s critical dynamics after an injury such as a stroke. It is hypothesized that the healthy brain operates near a phase transition (critical point), which provides optimal conditions for information transmission and responses to inputs. If structural damage could cause the critical point to disappear and thus make self-organized criticality unachievable, it would offer the theoretical explanation for the post-stroke impairment of brain function. In our contribution, however, we demonstrate using network models of the brain, that the dynamics remain critical even after a stroke. In cases where the average size of the second-largest cluster of active nodes, which is one of the commonly used indicators of criticality, shows an anomalous behavior, it results from the loss of integrity of the network, quantifiable within graph theory, and not from genuine non-critical dynamics. We propose a new simple model of an artificial stroke that explains this anomaly. The proposed interpretation of the results is confirmed by an analysis of real connectomes acquired from post-stroke patients and a control group. The results presented refer to neurobiological data; however, the conclusions reached apply to a broad class of complex systems that admit a critical state

Implementation of building a thermal model to improve energy efficiency of the central heating system - a case study

This paper presents the concept of an innovative control of a central heating system in a multifamily building based on the original thermodynamic model, the resulting architecture of the control system, and the originally designed and manufactured wireless temperature sensors for thermal zones. The novelty of this solution is the developed layers of the control system: distributed measurement and correction analysis, which is based on the existing infrastructure and the local HVAC controller. This approach allows for the effective use of the measured temperature data from thermal zones and finally sending the value of the calculated correction of settings to the controller. Moreover, in the analytical layer, a model was also implemented that calculates the necessary amount of energy based on data from the subsystem of temperature sensors located in the thermal zones of the building. The use of the algorithmic strategy presented in this paper extends the functionality and significantly improves the energy efficiency of the existing, classic, reference heating control algorithm by implementing additional control loops. Additionally, it enables integration with demand-side response systems. The presented concept was successfully tested, achieving real energy savings for heating by 12%. These results are described in a case-study format. The authors believe that this concept can be used in other buildings and thus will have a positive impact on the energy savings used to maintain thermal comfort in buildings and significantly reduce CO2 emissions

The architecture for testing central heating control algorithms with feedback from wireless temperature sensors

The energy consumption of buildings is a significant contributor to overall energy con- sumption in developed countries. Therefore, there is great demand for intelligent buildings in which energy consumption is optimized. Online control is a crucial aspect of such optimization. The imple- mentation of modern algorithms that take advantage of developments in information technology, artificial intelligence, machine learning, sensors, and the Internet of Things (IoT) is used in this context. In this paper, an architecture for testing central heating control algorithms as well as the control algorithms of the heating system of the building is presented. In particular, evaluation metrics, the method for seamless integration, and the mechanism for real-time performance monitoring and control are put forward. The proposed tools have been successfully tested in a residential building, and the conducted tests confirmed the efficiency of the proposed solution

Implementation of a demand elasticity model in the building energy management system

Nowadays, crucial part of modern Building Automation and Control Systems (BACS) is electric energy management. An active demand side management is very important feature of a Building Energy Management Systems (BEMS) integrated within the BACS. Since demand value changes in time and depends on various events, factors and parameters, a demand elasticity model has been proposed to provide reliable information about current and expected energy demand. In this paper we propose extension of this model with respect to parameters available in the BACS, determining energy demand level. Real data from the BACS had been imported into a calculation algorithm and proposed approach has been verified in simulation. For easy implementation of the demand elasticity model in the BACS, an extension for logical interface with a new functional profile has been proposed and described. It is ready for integration within the BACS with Internet of Things paradigm

Energy Flexometer: Transactive Energy-Based Internet of Things Technology

Effective Energy Management with an active Demand Response (DR) is crucial for future smart energy system. Increasing number of Distributed Energy Resources (DER), local microgrids and prosumers have an essential and real influence on present power distribution system and generate new challenges in power, energy and demand management. A relatively new paradigm in this field is transactive energy (TE), with its value and market-based economic and technical mechanisms to control energy flows. Due to a distributed structure of present and future power system, the Internet of Things (IoT) environment is needed to fully explore flexibility potential from the end-users and prosumers, to offer a bid to involved actors of the smart energy system. In this paper, new approach to connect the market-driven (bottom-up) DR program with current demand-driven (top-down) energy management system (EMS) is presented. Authors consider multi-agent system (MAS) to realize the approach and introduce a concept and standardize the design of new Energy Flexometer. It is proposed as a fundamental agent in the method. Three different functional blocks have been designed and presented as an IoT platform logical interface according to the LonWorks technology. An evaluation study has been performed as well. Results presented in the paper prove the proposed concept and design

Erythromycin Scavenging from Aqueous Solutions by Zeolitic Materials Derived from Fly Ash

Erythromycin (EA) is an antibiotic whose concentration in water and wastewater has been reported to be above the standard levels. Since the methods used so far to remove EA from aquatic environments have not been effective, the development of effective methods for EA removal is necessary. In the present study, fly ash (FA)-based zeolite materials, which have not been investigated as EA sorbents before, were used. The possibilities of managing waste FA and using its transformation products for EA sorption were presented. The efficiency of EA removal from experimental solutions and real wastewater was evaluated. In addition, the sorbents’ mineral composition, chemical composition, and physicochemical properties and the effects of adsorbent mass, contact time, initial EA concentration, and pH on EA removal were analyzed. The EA was removed within the first 2 min of the reaction with an efficiency of 99% from experimental solutions and 94% from real wastewater. The maximum adsorption capacities were 314.7 mg g−1 for the fly ash-based synthetic zeolite (NaP1_FA) and 363.0 mg g−1 for the carbon–zeolite composite (NaP1_C). A fivefold regeneration of the NaP1_FA and NaP1_C showed no significant loss of adsorption efficiency. These findings indicate that zeolitic materials effectively remove EA and can be further investigated for removing other pharmaceuticals from water and wastewater

Energy flexometer:transactive energy-based internet of things technology

\u3cp\u3eEffective Energy Management with an active Demand Response (DR) is crucial for future smart energy system. Increasing number of Distributed Energy Resources (DER), local microgrids and prosumers have an essential and real influence on present power distribution system and generate new challenges in power, energy and demand management. A relatively new paradigm in this field is transactive energy (TE), with its value and market-based economic and technical mechanisms to control energy flows. Due to a distributed structure of present and future power system, the Internet of Things (IoT) environment is needed to fully explore flexibility potential from the end-users and prosumers, to offer a bid to involved actors of the smart energy system. In this paper, new approach to connect the market-driven (bottom-up) DR program with current demand-driven (top-down) energy management system (EMS) is presented. Authors consider multi-agent system (MAS) to realize the approach and introduce a concept and standardize the design of new Energy Flexometer. It is proposed as a fundamental agent in the method. Three different functional blocks have been designed and presented as an IoT platform logical interface according to the LonWorks technology. An evaluation study has been performed as well. Results presented in the paper prove the proposed concept and design.\u3c/p\u3