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Advances in building control have shown significant potential for improving building energy performance and decarbonization. One of the challenges to realizing those savings is the correct implementation of such advanced control strategies and regularly verifying their actual operational performance. Currently, the verification is often manually conducted, which is time-consuming, adhoc, and error-prone. To address this challenge, we introduced an automated control verification framework, aiming to create automatic verification of control performance in buildings. This framework integrates interpretations of the control requirements from recent energy codes and knowledge of conducting control verification into a knowledge base. The verification is based on time series data and thus is applicable for both trended data from a building automation system and simulation results of building energy simulations. This paper discusses the new version of the control performance verification framework, the expansion of the verification library, and the newly developed key performance indicators (KPIs) for control verification. It also provides an evaluation of the tool using both simulation and real building operation data and also an end-to-end demonstration case from importing a data set equipped with a semantic model, automatically instantiating control verification cases, and then conducting the verification and reporting the result.

Development of new building HVAC control algorithms has grown due to needs for energy efficiency and operational flexibility. However, case studies demonstrating new algorithms are largely individualized, making algorithm performance difficult to compare directly. In addition, the effort and expertise required to implement case studies in real or simulated buildings limits rapid prototyping potential. Therefore, this paper presents the Building Optimization Testing Framework (BOPTEST) and associated software for simulation-based benchmarking of building HVAC control algorithms. A containerized run-time environment (RTE) enables rapid, repeatable deployment of common building emulators representing different system types. Emulators use Modelica to represent realistic physical dynamics, embed baseline control, and enable overwriting supervisory and local-loop control signals. Finally, a common set of key performance indicators are calculated within the RTE and reported to the user. This paper details the design and implementation of software and demonstrates its usage to benchmark a Model Predictive Control strategy.

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

Hamstring strains are usually a result of a high mechanical stress produced by a quick extensive contraction or a violent stretch of the muscle group. This study aimed the effectiveness of INDIBA therapy (Group B) compared with the use of TECAR therapy (Group A). The first stage of rehabilitation represented first two weeks which were mainly based on the application of radiofrequency therapies to the posterior level of the thigh, progressive exercises and cryotherapy. Numeric Pain Rating Scale, manual muscle testing and range of motion had been used to evaluate the subjects, a significant advantage being shown for group B in the pain assessment. [ABSTRACT FROM AUTHOR]

COVID-19 significantly impacted the population by affecting physical health; social distancing and isolation influenced psychological health. This may have negative consequences, especially for older people. There is a lack of studies about the association between COVID-19 and exercise capacity among the elderly and improving quality of life after SARS-CoV-2 infection. This study aims to identify the potential sequelae of the COVID-19 disease regarding physical function and quality of life among people over 65 years old. This study recruited a total of 30 participants. A 6-minute walking test, somatic and functional measurements (including weight, height, HR, blood pressure and SpO 2 %) and the World Health Organisation Quality of Life-BREF Questionnaire were used to assess aerobic capacity and quality of life. Experiencing COVID-19 can negatively impact exercise capacity. The results suggest that men may have worse sequelae than women after experiencing COVID-19. The lower values of SpO 2 in the COVID-19 group during the 6-MWT indicate a reduction in the gas diffusion capacity, which can be attributed to potential lung damage following having contracted the disease. Lockdown periods seem to have had a significant impact on the physical health, relationships and environment of the elderly people included in this study. We can conclude that physical effort may potentially impact exercise capacity and quality of life among post-COVID-19 elderly in a positive way, but further studies are needed to confirm its benefits.

Networked dynamical systems are common throughout science in engineering; e.g., biological networks, reaction networks, power systems, and the like. For many such systems, nonlinearity drives populations of identical (or near-identical) units to exhibit a wide range of nontrivial behaviors, such as the emergence of coherent structures (e.g., waves and patterns) or otherwise notable dynamics (e.g., synchrony and chaos). In this work, we seek to infer (i) the intrinsic physics of a base unit of a population, (ii) the underlying graphical structure shared between units, and (iii) the coupling physics of a given networked dynamical system given observations of nodal states. These tasks are formulated around the notion of the Universal Differential Equation, whereby unknown dynamical systems can be approximated with neural networks, mathematical terms known a priori (albeit with unknown parameterizations), or combinations of the two. We demonstrate the value of these inference tasks by investigating not only future state predictions but also the inference of system behavior on varied network topologies. The effectiveness and utility of these methods are shown with their application to canonical networked nonlinear coupled oscillators.

In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided.