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.
This study affirms the long-term safety and efficacy of scleral contact lens use in patients with keratoconus.
This study aimed to evaluate the safety and efficacy of contemporary scleral contact ...lenses in the visual rehabilitation of the keratoconic population.
A retrospective study of keratoconic subjects examined between 2013 and 2018 was conducted. Subjects were included regardless of age, sex, pre-existing morbidity, or scleral lens design. Only eyes fit successfully with scleral contact lenses for ≥1 year were included. Exclusion criteria were prior corneal surgery, dystrophy, degeneration, and trauma.
A total of 157 eyes of 86 subjects met the study criteria. The mean Keratoconus Severity Score at initial fitting was 3.6 ± 1.0. Lenses were gas-permeable and nonfenestrated, with a mean overall diameter of 15.8 ± 0.6 mm and 70.1% toric scleral periphery. Physiological adverse events occurred in 9.6% of eyes, including microbial keratitis (0.6%), phlyctenulosis (0.6%), corneal abrasion (1.3%), contact lens-induced acute red eye (1.3%), corneal infiltrative events (1.3%), pingueculitis (1.3%), and hydrops (3.2%). Lens-related adverse events were documented in 55.4% of eyes. Adverse events related to surface issues included poor wetting in 1.9%, handling in 3.8%, reservoir fogging in 7.0%, lens intolerance in 7.6%, deposit in 8.9%, and broken lenses in 26.1% of eyes. The most common management strategies involved refits (54.0% of interventions), patient reeducation (29.5%), medical treatment (5.5%), surgical referral (6.8%), adjustment to wear time (2.5%), surface treatment (1.2%), and lens replacement (0.6%). Best-corrected distance logMAR visual acuity improved significantly from a mean of 0.50 in spectacles to a mean of 0.08 in scleral lenses (P < .0001). During the study period, 14.6% of eyes lost best-corrected scleral lens visual acuity, all from keratoconus progression.
Consistent with other groups, our study demonstrates excellent safety and efficacy of scleral contact lenses in subjects with keratoconus.
To identify texture features of multiparametric MRI (mp-MRI) for pre-treatment prediction of bone metastases (BM) in patients with prostate cancer (PCa).
One-hundred and seventy-six patients with ...clinicopathologically confirmed PCa were enrolled，and the data was gathered from January 2008 to January 2018. A total of 976 texture features were extracted from T2-weighted (T2-w) and dynamic contrast-enhanced T1-weighted (DCE T1-w) MRI. Step regression, ridge regression and LASSO regression method model was applied to select features and develop the predicting model for BM. The performance of the radiomics features, PSA level and Gleason Score were explored with the respect to the receiver operating characteristics (ROC) curve. Multivariable logistic regression analysis starting with the following clinical risk factors (PSA level, Gleason Score and age) and imaging biomarkers were applied to develop diagnostic model for BM in PCa.
The texture features, which consisted of 15 selected features, were significantly associated with BM (P < 0.01). The combined MRI features derived from T2-w and DCE T1-w showed better prognostic performance (AUC = 0.898) than features derived from single sequence (T2WI AUC = 0.875, DCE T1-w AUC = 0.870) and Gleason Score (AUC = 0.731) for pre-treatment prediction of BM in PCa. MRI -based imaging biomarker combined with clinical risk factors (free PSA, age and Gleason score) yielded the highest AUC(AUC = 0.916). Multivariate regression analysis showed that the imaging biomarker was an independent risk factor for the detection of bone metastases along with f-PSA level (free PSA) and Gleason score.
Multiparametric MRI-based texture feature was significant predictor for BM in PCa. Clinical risk factors combined with MRI-based texture feature could further improve the prediction performance, which provide an illustrative example of precision medicine and may affect treatment strategies.
Infrastructure is the lifeline for fulfilling most of the basic needs that support the well-being and prosperity of human society. To sustain improvements in quality of life, China, as a developing ...economy, needs more and better infrastructure, despite facing massive funding shortages. An enormous amount of its private capital is locked up because of the many obstacles to private investment. This study introduces a blockchain-based financing instrument for unlocking massive private capital to fund infrastructure projects in China. It draws on a literature review and expert survey to identify the major holdups preventing private capital from funding infrastructure projects and to compare the existing instruments used in infrastructure financing. A fuzzy AHP-SWOT analysis is conducted to reveal the main internal factors (strengths and weaknesses) and external factors (opportunities and threats) in using blockchain-based finance in infrastructure projects. Finally, a conceptual framework for a blockchain-based infrastructure financing system is formulated to take advantage of the strength and opportunities, meanwhile, counter the weakness and threats, and the framework is also validated through its deployment on Hyperledger Fabric.
Branching structure is often used as a supporting structure of the grid shell due to its geometrical and force-transferring features, and the rationality of its shape is very important. The ...“physical” and “numerical” hanging models can be used for the joint form-finding of the branching structure and free-form grid shell. However, slack elements may exist in the equilibrium model which corresponds to the inefficient members in the form-found branching structure. To solve this problem, a form-finding method of branching structure based on dynamic relaxation is proposed in this study. The proposed method clusters the elements of the branching model and equalizes the axial forces of the elements in the same cluster, in other words, there are no slack elements in the equilibrium branching model. This method overcomes the defect that the equilibrium branching model may have slack elements and needs many manual adjustments during the procedure of determining the rational shape of a branching structure, and effectively prevents the inefficient members existing in the form-found structure. Numerical examples are provided to demonstrate the characteristics of the proposed method and its effectiveness is verified as well.
Energy is both a basic resource needed for economic growth and an essential tool for economic recovery. The topic of resilience is becoming increasingly prominent in the energy-economic domain and ...has also entered policy discourse. Yet the measuring method of resilience based on post-disruption events and the relationship between energy consumption and economic recovery are far from settled. This paper develops the idea of resilience and proposes a model to evaluate the economic recovery ability of an economy from the perspective of energy consumption. It also proposes a decoupling model to address the impact of energy-related elements on economic recovery. These ideas are then used for a preliminary empirical analysis of 14 countries against the context of the 2007–2008 financial crisis. The analysis showed that developing countries generally performed better than developed countries, that energy consumption is not a necessity for promoting economic recovery, and that energy-economic decoupling has a positive effect on economic recovery.
•An ensemble bagging tree model (EBT) was used to predict institutional building electricity demand.•Three prediction modules representing different semesters of the test building were developed.•The ...proposed ensemble bagging tree was proven to be effective for short-term building energy prediction.•The proposed variable selection method reduces the computation time of EBT without sacrificing its prediction accuracy.
Broadly speaking, building energy use prediction can be classified into two categories based on modeling approaches namely engineering and Artificial Intelligence (AI). While engineering approach requires solving physical equations representing the thermal performance of systems and components that constitute the buildings, the AI-based approach uses historical data to predict future performance. Although engineering approach estimates energy use with greater accuracy, it falls short in the overall complexity of model building and simulation in which detailed data that represent the building geometry, systems, configurations, and occupant schedule is needed. Whereas, the AI-based approach offers a rapid prediction of building energy use and, if appropriately trained and tested, may be used for quick and efficient decision-making of energy use reduction. Nevertheless, for robust integration with and to improve automated building systems management and intelligence, the need for consistent, stable, and higher prediction accuracy cannot be understated. To alleviate the instability issue, and to improve prediction accuracy, we have exploited and tested an ensemble learning technique, ‘Ensemble Bagging Trees’ (EBT), using data obtained from meteorological systems and building-level occupancy and meters.Results showed that the proposed EBT model predicted hourly electricity demand of the test building with improved accuracy of Mean Absolute Prediction Error that ranged from 2.97% to 4.63%. Additionally, results showed that proposed variable selection method could reduce the computation time of EBT by 38–41% without sacrificing the prediction accuracy. The proposed ensemble learning model that exemplifies improved prediction accuracy over other AI techniques can be used for real-time applications such as system fault detection and diagnosis.
The paper develops and compares a comprehensive range of configurations of empirical modeling techniques for solving the truck classification by weigh-in-motion problem. A review of existing ...artificial neural network approaches to the problem is followed by an in-depth comparison with support vector machines. Three main model formats are considered: (i) a monolithic structure with a one versus all strategy for selecting truck type; (ii) an array of sub-models each dedicated to one truck type with a one versus all truck type selection strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks. Overall, the SVM approach was found to outperform the ANN based models. The paper concludes with some suggestions for extending the work to a broader scope of problems.
The paper is concerned with the development and comparison of alternative machine learning methods of determining the type of truck crossing a bridge from the dynamic response it induces within the ...bridge structure, the so-called weigh-in-motion problem. Weigh-in-motion is a rich engineering problem presenting many challenges for current machine learning technologies, and for this reason is proposed as a benchmark for guiding and assessing advances in the application of this field of artificial intelligence. A review is first provided of existing methods of determining truck types and loading attributes using both machine learning and heuristic search techniques. The most promising approach to date, that of artificial neural networks, is then compared to support vector machines in a comprehensive study considering a range of configurations of both modeling techniques. A local scatter point smoothing schema is adopted as a means of selecting an optimal set of design parameters for each model type. Three main model formats are considered: (i) a monolithic model structure with a one-versus-all truck type classification strategy; (ii) an array of sub-models each dedicated to one truck type with a one-versus-all classification strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks in a one-versus-one classification strategy. Overall, the formats that used an array of sub-models performed best at truck classification, with the support vector machines having a slight edge over the artificial neural networks. The paper concludes with some suggestions for extending the work to a broader scope of problems.
The 21st century is witnessing a fast-paced digital revolution. A significant trend is that cyber and physical environments are being unprecedentedly entangled with the emergence of Internet of ...Things (IoT). IoT has been widely immersed into various domains in the industry. Among those areas where IoT would make significant impacts are building construction, operation, and management by facilitating high-class services, providing efficient functionalities, and moving towards sustainable development goals. So far, IoT itself has entered an ambiguous phase for industrial utilization, and there are limited number of studies focusing on the application of IoT in the building industry. Given the promising future impact of IoT technologies on buildings, and the increasing interests in interdisciplinary research among academics, this paper investigates the state-of-the-art projects and adoptions of IoT for the development of smart buildings within both academia and industry contexts. The wide-ranging IoT concepts are provided, covering the necessary breadth as well as relevant topic depth that directly relates to smart buildings. Current enabling technologies of IoT, especially those applied to buildings and related areas are summarized, which encompasses three different layers based on the conventional IoT architecture. Afterwards, several recent applications of IoT technologies on buildings towards the critical goals of smart buildings are selected and presented. Finally, the priorities and challenges of successful and seamless IoT integration for smart buildings are discussed. Besides, this paper discusses the future research questions to advance the implementation of IoT technologies in both building construction and operation phases. The paper argues that a mature adoption of IoT technologies in the building industry is not yet realized and, therefore, calls for more attention from researchers in the relevant fields from the application perspective.
•The common technologies of Internet of Things (IoT) used in the building industry on a layering basis are summarized.•The potentials of IoT technology application towards the development of smart buildings are recognized and highlighted.•An outline for developing IoT architecture to implement critical functionalities of smart buildings is provided.•Current trends and priorities, and future research areas of IoT application in the building industry are presented.
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