Building energy use prediction plays an important role in building energy management and conservation as it can help us to evaluate building energy efficiency, conduct building commissioning, and ...detect and diagnose building system faults. Building energy prediction can be broadly classified into engineering, Artificial Intelligence (AI) based, and hybrid approaches. While engineering and hybrid approaches use thermodynamic equations to estimate energy use, the AI-based approach uses historical data to predict future energy use under constraints. Owing to the ease of use and adaptability to seek optimal solutions in a rapid manner, the AI-based approach has gained popularity in recent years. For this reason and to discuss recent developments in the AI-based approaches for building energy use prediction, this paper conducts an in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple single AI-based prediction models improves the prediction accuracy manifold. This paper elaborates the principles, applications, advantages and limitations of these AI-based prediction methods and concludes with a discussion on the future directions of the research on AI-based methods for building energy use prediction.
Accurate building energy prediction plays an important role in improving the energy efficiency of buildings. This paper proposes a homogeneous ensemble approach, i.e., use of Random Forest (RF), for ...hourly building energy prediction. The approach was adopted to predict the hourly electricity usage of two educational buildings in North Central Florida. The RF models trained with different parameter settings were compared to investigate the impact of parameter setting on the prediction performance of the model. The results indicated that RF was not very sensitive to the number of variables (mtry) and using empirical mtry is preferable because it saves time and is more accurate. RF was compared with regression tree (RT) and Support Vector Regression (SVR) to validate the superiority of RF in building energy prediction. The prediction performances of RF measured by performance index (PI) were 14–25% and 5–5.5% better than RT and SVR, respectively, indicating that RF was the best prediction model in the comparison.
Moreover, an analysis based on the variable importance of RF was performed to identify the most influential features during different semesters. The results showed that the most influential features vary depending on the semester, indicating the existence of different operational conditions for the tested buildings. A further comparison between RF trained with yearly and monthly data indicated that the energy usage prediction for educational buildings could be improved by taking into consideration their energy behavior changes during different semesters.
In this work, novel AlSi10Mg composites modified by SiC nanoparticles have been successfully produced by a combined route including solvent-assisted dispersion, low-energy planetary ball milling and ...selective laser melting. The effect of laser-energy-input (EV) on the microstructure evolution and mechanical properties of the composites was systematically studied. The results showed that the hardly dissolved or reacted SiC nanoparticles tended to be distributed at the cell boundaries, significantly refining the matrix grains by the promotion of heterogeneous nucleation process and the Zener pinning effect. However, the weak metallurgical bonding and the formation of Al4C3 phase caused a detrimental impact to tensile properties. On the contrary, the consumption of SiC nanoparticles at higher EV significantly enhanced the strength and elongation to fracture of the composites through the homogenization of microstructure, better metallurgical bonding, and the joint action of SiC/Al4SiC4/Al interface. Specifically, the AlSi10Mg/SiC composite prepared at 210 W presented excellent mechanical performance (131.7 HV0.1 for hardness, 101 GPa for modulus, and 440 MPa for strength) with a good ductility (7.4%).
Time–frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide ...the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time–frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260× speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000–8300× for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations.
•A new highly efficient Bayesian updating method called BUAK is proposed.•Simple Rejection Sampling with Multiple Auxiliary Random Variables is introduced.•SRS-MARV features improved acceptance rates ...for decomposed limit state functions.•Adaptive Kriging-based reliability analysis is deeply integrated into BUAK.•BUAK reduces the computational demand by 1 to 3 orders of magnitude compared to BUS.
Bayesian updating offers a powerful tool for probabilistic calibration and uncertainty quantification of models as new observations become available. By reformulating Bayesian updating into a structural reliability problem via introducing an auxiliary random variable, the state-of-the-art Bayesian updating with structural reliability method (BUS) has showcased large potential to achieve higher accuracy and efficiency compared with conventional approaches based on Markov Chain Monte Carlo simulations. However, BUS faces a number of limitations. The transformed reliability problem often involves a very rare event especially when the number of observations increases. This along with the fact that conventional reliability analysis techniques are not efficient, and often not capable of accurately estimating the probability of rare events, unavoidably lead to a very large number of evaluations of the likelihood function and simultaneously insufficient accuracy of the derived posterior distributions. To overcome these limitations, we propose Simple Rejection Sampling with Multiple Auxiliary Random Variables (SRS-MARV), where the limit state function in BUS is decomposed into a system reliability problem with multiple limit state functions. The main advantage of this approach is that the acceptance rate of each decomposed limit state function is significantly improved, which facilitates effective integration of adaptive Kriging-based reliability analysis into SRS-MARV. Moreover, a new stopping criterion is proposed for efficient, adaptive training of the Kriging model. The proposed method called BUAK is shown to be highly computationally efficient and accurate based on results of comprehensive investigations for three diverse benchmark problems. Compared to the state-of-the-art methods, BUAK substantially reduces the computational demand by one to three orders of magnitude, therefore, facilitating the application of Bayesian updating to computationally very intensive models.
The cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4)/B7 and programmed death 1 (PD-1)/ programmed cell death-ligand 1 (PD-L1) are two most representative immune checkpoint pathways, which ...negatively regulate T cell immune function during different phases of T-cell activation. Inhibitors targeting CTLA-4/B7 and PD1/PD-L1 pathways have revolutionized immunotherapies for numerous cancer types. Although the combined anti-CTLA-4/B7 and anti-PD1/PD-L1 therapy has demonstrated promising clinical efficacy, only a small percentage of patients receiving anti-CTLA-4/B7 or anti-PD1/PD-L1 therapy experienced prolonged survival. Regulation of the expression of PD-L1 and CTLA-4 significantly impacts the treatment effect. Understanding the in-depth mechanisms and interplays of PD-L1 and CTLA-4 could help identify patients with better immunotherapy responses and promote their clinical care. In this review, regulation of PD-L1 and CTLA-4 is discussed at the levels of DNA, RNA, and proteins, as well as indirect regulation of biomarkers, localization within the cell, and drugs. Specifically, some potential drugs have been developed to regulate PD-L1 and CTLA-4 expressions with high efficiency. Keywords: PD-L1, CTLA-4, Cancer immunotherapy, Regulatory mechanism, Drug intervention
•AlSi10Mg alloy was successfully prepared by selective laser melting.•Two regimes of heat treatments were carried out for SLM AlSi10Mg alloy.•The residual stress of the A1 sample was reduced to be ...−13 MPa.•The A1 sample exhibited high strength of 273.2 MPa with plasticity of 15.3%.
In the present work, the model alloy of AlSi10Mg was prepared by selective laser melting, and further annealed by two kinds of heat treatment regimes, i.e. A1 (300 °C/2h+ water quench) and A2 (535 °C/1h+ water quench + 190 °C/10 h+ furnace quench). The samples were investigated by XRD, SEM, EBSD, room temperature tensile and nanoindentation tests, to understand the effect of heat treatments on the phase constituents, microstructure, residual stress and mechanical properties of the laser additive manufactured AlSi10Mg alloy. The experimental results showed that, the heat treatment method of A1 is an effective heat treatment regime for eliminating the residual stress and improving the comprehensive mechanical properties for structural applications.
Gliomas are the common type of brain tumors originating from glial cells. Epidemiologically, gliomas occur among all ages, more often seen in adults, which males are more susceptible than females. ...According to the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5), standard of care and prognosis of gliomas can be dramatically different. Generally, circumscribed gliomas are usually benign and recommended to early complete resection, with chemotherapy if necessary. Diffuse gliomas and other high-grade gliomas according to their molecule subtype are slightly intractable, with necessity of chemotherapy. However, for glioblastoma, feasible resection followed by radiotherapy plus temozolomide chemotherapy define the current standard of care. Here, we discuss novel feasible or potential targets for treatment of gliomas, especially IDH-wild type glioblastoma. Classic targets such as the p53 and retinoblastoma (RB) pathway and epidermal growth factor receptor (EGFR) gene alteration have met failure due to complex regulatory network. There is ever-increasing interest in immunotherapy (immune checkpoint molecule, tumor associated macrophage, dendritic cell vaccine, CAR-T), tumor microenvironment, and combination of several efficacious methods. With many targeted therapy options emerging, biomarkers guiding the prescription of a particular targeted therapy are also attractive. More pre-clinical and clinical trials are urgently needed to explore and evaluate the feasibility of targeted therapy with the corresponding biomarkers for effective personalized treatment options.
Stochastic resonance (SR) is a phenomenon wherein an information-carrying signal is enhanced via noise in a nonlinear system. This phenomenon enables living beings to adapt to noisy environments and ...use environmental noise to obtain useful information. A novel activation function of the echo state network (ESN) based on bistable SR is proposed in this study. Instead of using the tanh activation function—which is representative of the traditional threshold activation function—the bistable SR activation function is used to improve the noise adaptability of the ESN. Further, the proposed activation function provides a short-term memory (STM) ability that is not provided by the widely used threshold activation function, and thus, a physical reservoir can be designed using the proposed function. An STM task and a parity check task are used to verify the short-term memory and nonlinear ability of the bistable SR activation function. Further, two different prediction benchmarks prove that the proposed activation function can improve the noise adaptability of ESN. Finally, a visual recognition task is performed to demonstrate the potential of the SR activation function for physical reservoir computing.
Oxygen evolution reaction (OER) is of crucial importance to sustainable energy and environmental engineering, and layered double hydroxides (LDHs) are among the most active catalysts for OER in ...alkaline conditions, but the reaction mechanism for OER on LDHs remains controversial. Distinctive types of reaction mechanisms have been proposed for the O-O coupling in OER, yet they compose a coupled reaction network with competing kinetics dependent on applied potentials. Herein, we combine grand-canonical methods and micro-kinetic modeling to unravel that the nature of dominant mechanism for OER on LDHs transitions among distinctive types as a function of applied potential, and this arises from the interplay among applied potential and competing kinetics in the coupled reaction network. The theory-predicted overpotentials, Tafel slopes, and findings are in agreement with the observations of experiments including isotope labelling. Thus, we establish a computational methodology to identify and elucidate the potential-dependent mechanisms for electrochemical reactions.
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