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War of Control Hijacking
Published in Uzzal Sharma, Parmanand Astya, Anupam Baliyan, Salah-ddine Krit, Vishal Jain, Mohammad Zubair Khan, Advancing Computational Intelligence Techniques for Security Systems Design, 2023
Ragini Karwayun, Monika Sainger
Control flow defines the order in which each instruction or function call of a computer program is executed. Code written using an imperative programming language has a certain control flow; the statements are used to change a program's state. However, the declarative programming paradigm does not explicitly describe the control flow of a code; it rather defines the structure and elements of computer programs expressing the logic of computation.
Energy performance gap of a nearly Zero Energy Building (nZEB) in Denmark: the influence of occupancy modelling
Published in Building Research & Information, 2020
C. Carpino, E. Loukou, P. Heiselberg, N. Arcuri
Efforts in the latter direction have been made, for example, by Hong, Sun, Chen, Taylor-Lange, and Yan (2016a) implementing a new occupant behaviour modelling tool consisting of an occupant behaviour functional mock-up unit enabling co-simulation with building energy modelling programmes. Similarly, Pang et al. (2016) developed a real-time simulation framework to improve building operation that can interact with different software through an open standard interface. Stochastic methods are being used to develop predictive occupancy and activity models that can more accurately represent real scenarios. Furthermore, applications using equation-based object-oriented modelling languages are emerging in this context (Wang, Hong, & Jia, 2019; Wetter, Bonvini, & Nouidui, 2016). Such applications, by exploiting computer algebra, allow complex problems, which could not be adequately described by traditional imperative programming (Hong, Taylor-Lange, D’Oca, Yan, & Corgnati, 2016b), to be efficiently analysed. However, these technologies are not yet an integral part of the building energy simulation and are not fully supported by the software currently in use.
Real-time predictive control of HVAC systems for factory building using lightweight data-driven model
Published in Journal of Building Performance Simulation, 2023
Various simulation engines, including EnergyPlus, ESP-r, and TRNSYS, have been developed for building energy assessments to support design and operation (Crawley et al. (2008)). These tools are based on imperative programming languages that assign values to functions, declare the execution sequence of these functions, and then change the state using the predicted value. EnergyPlus (DOE 2022), for example, is an integrated, simultaneous solution that calculates the thermal loads of thermal zones using a heat balance engine at a time step, and the HVAC system simulation module responds to the former calculation (Crawley et al. 2001). As detailed calculation methods for fenestration surfaces (LBNL 2019) and airflow networks (Walton 1989) have been developed and augmented, the calculation sequence has become more complex. EnergyPlus offers various built-in control strategies for HVAC systems and runtime scripting systems for user-defined controls. However, because of the model structure that tightly connects submodules for numerical solutions, it is difficult to combine control models with building, HVAC, and electrical system models in real time, which are required for implementing optimal control of the corresponding systems (Wetter, Bonvini, and Nouidui 2016). Consequently, additional coupling software packages (e.g. building controls virtual test bed (BCVTB)) are developed to exchange calculation results between the simulation engines and the control scripts during simulations (Wetter and Haves 2008). Despite these additional works, coupling software packages have been widely selected for model predictive control and training data-driven control models. Corbin, Henze, and May-Ostendorp (2013) coupled EnergyPlus and MATLAB for real-time MPC with a particle swarm optimization algorithm. Coffey et al. (2010) coupled TRNSYS and a genetic algorithm to optimize demand response in office buildings. Ahn and Park (2020) coupled EnergyPlus and Python for training a Deep Q network, a reinforcement learning algorithm, to obtain an optimal control policy.
NewSQL Database Management System Compiler Errors: Effectiveness and Usefulness
Published in International Journal of Human–Computer Interaction, 2022
Possibly due to the increasingly ubiquitous nature of data, and the rising popularity of data analytics and data science, query languages, SQL in particular, have received increasing scholarly attention (Taipalus & Seppänen, 2020). Current educational research seems rather unanimous with the view that learning SQL is difficult (Miedema et al., 2021; Shin, 2020; Taipalus & Perälä, 2019). Usability concerns in query formulation have been explained by human factors, such as cognitive load (Shin, 2020; Smelcer, 1995), data model and real-world mismatch (Borthick et al., 2001; Sutcliffe et al., 2000), and different user characteristics (Ashkanasy et al., 2007; Bak & Meyer, 2011). Additionally, it has been shown that different environmental aspects, such as database complexity (Taipalus, 2020a) and database representation (Shin, 2020; Siau et al., 2004) have an effect on query writing. Finally, different measures for engaging and helping the end-user have been proposed in scientific literature, e.g., query visualization and previews (Taipalus, 2019; Tanin et al., 2000), cosmetic alterations (Dong & Khandwala, 2019), different natural language interfaces (Ribeiro & Moreira, 2003), and the facilitation of query reuse (Allen & Parsons, 2010; Toorn et al., 2022). However, error message research has not extended from programming languages to query languages, and the latest studies on the effects of SQL compiler error messages on query formulation seem to be published in the 1980s (Reisner, 1981; Welty & Stemple, 1981), until a recent comparison of SQL compilers of traditional RDBMS in 2021 (Taipalus et al., 2021). The differences in the SQL standard (ISO/IEC, 2016a, 2016b) between the 1980s and 2020s, as well as differences between SQL and imperative programming languages, and the potential threats to the generalizability of scientific results induced thereof have been highlighted in a previous study (Taipalus & Seppänen, 2020). Regarding usability, due to its declarative nature, SQL is arguably a “blacker box” to a software developer than an imperative programming language.