•Monte Carlo simulations give validation of experimental Curie temperature for Pr2FeCrO6.•Pr2FeCrO6 shows good magneto-dielectric response and magnetoresistance behavior.•These results are promising ...to use Pr2FeCrO6 for spintronics applications.
Evidence for Curie temperature (TC = 550 K) in the double perovskite Pr2FeCrO6 is resuscitated with Monte Carlo simulations, temperature dependence of resistivity and dielectric constant. It is concluded that Curie temperature of Pr2FeCrO6 is 550 K in R 3 symmetry. Magnetoresistance measurement in Pr2FeCrO6 at room temperature shows a value of 44%. All these results confirm the expediences of using Pr2FeCrO6 materials in multiferroic-based magnetic tunnel junctions for spintronics applications.
The dangers of mindless behaviors remain better defined than their remedies. Even as mindfulness becomes increasingly prevalent, we lack clarity on three key questions: What is mindfulness? How does ...mindfulness training operate? And why might mindfulness matter for organizations? In this article I introduce a new conceptualization of mindfulness, which I call metacognitive practice. Metacognitive practice is so named because it blends insights from metacognition and practice theory to answer these three key questions. First, when seen as metacognitive practice, mindfulness is not a single mode of information processing to be applied in all situations. Instead, it is a metacognitive process by which people adjust their mode of information processing to their current situation. Second, this metacognitive process is made possible by three specific beliefs that supersede lay beliefs about human information processing. A core function of mindfulness training, thus, is to provide a context that cultivates these beliefs. Third, when these beliefs are put into practice, people gain greater agency in how they respond to situations. This matters for organizations, because as people interrelate their individual actions into a collective response, metacognitive practice can get embedded in amplifying processes that transform the organization-or in fragmentation processes that threaten it.
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.
The use of Amazon’s Mechanical Turk (MTurk) in management research has increased over 2,117% in recent years, from 6 papers in 2012 to 133 in 2019. Among scholars, though, there is a mixture of ...excitement about the practical and logistical benefits of using MTurk and skepticism about the validity of the data. Given that the practice is rapidly increasing but scholarly opinions diverge, the Journal of Management commissioned this review and consideration of best practices. We hope the recommendations provided here will serve as a catalyst for more robust, reproducible, and trustworthy MTurk-based research in management and related fields.
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.
Uniform and porous graphene nanoflake films (GNFs) have been investigated as a support for catalytic Pt nanoclusters in direct methanol electro-oxidation. Pt nanoclusters of varying thickness are ...deposited on GNFs using magnetron sputtering, and their effects on the electrocatalytic activity for oxidizing methanol are systemically studied. GNF supported Pt nanoclusters with ultralow catalyst loading exhibit high performance for methanol electrocatalytic oxidation with a large mass-specific peak current density and a ratio of forward to backward peak currents up to 1.4. These characteristics compare favorably to the majority of Pt−C based electrodes, except for those of carbon nanotubes with Pt decoration on both the inner and the outer wall surfaces. The results obtained are ascribed to a highly coupled network made of high-density 2−4 nm Pt monolayer nanoclusters on both the basal and edge planes of each nanoflakes of graphene. GNFs are a promising support material for developing next-generation advanced Pt based fuel cells and their relevant electrodes in the field of energy.
Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical ...conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.
Carbon nanotubes (CNTs) have gained much interest from academia and industry due to their unique properties that include high electrical and thermal conductivity, high mechanical strength, high ...aspect ratio, high surface area and chemical resistance. Although composite structures containing CNTs are probably the most commercially advanced applications in the market, the area that holds most promise is in electronic applications. Low temperature CVD growth of high quality CNTs can be utilized in many applications particularly next generation IoTs, wearable electronic devices, TSVs, interconnects, and sensors. CNT growth temperature generally reported in literature ranges from 600 to 1000 °C, which is not suitable for temperature sensitive substrates. However, there is ongoing research to achieve CNT growth at low temperatures, with a number reporting the growth below 550 °C. In this review, we examine and discuss various techniques and approaches adopted to achieve growth of carbon nanotubes at low temperatures and its effect on various parameters of CNTs.
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Deep Neural Networks (DNN) have shown early promise for inverse design with their ability to arrive at working designs much faster than conventional optimization techniques. Current approaches, ...however, require complicated workflows involving training more than one DNN to address the problem of non-uniqueness in the inversion and the emphasis on speed has overshadowed the far more important consideration of solution optimality. We propose and demonstrate a simplified workflow that pairs forward-model DNN with evolutionary algorithms which are widely used for inverse gg design. Our evolutionary search in forward-model space is global and exploits the massive parallelism of modern GPUs for a speedy inversion. We propose a hybrid approach where the DNN is used only for preselection and initialization that is more effective at optimization than a standalone DNN and performs nearly as well as a vanilla evolutionary search with a significantly reduced function evaluation budget. We finally show the utility of an iterative procedure for building the training dataset which further boosts the effectiveness of this approach.
•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.
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