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The reference building models in the EnergyPlus tool do not consider the stack effect. This is not an issue for small and low-rise buildings. However, this is significant in evaluating the heating and cooling load of high-rise buildings. This study evaluates and quantifies this stack effect in building energy simulation, EnergyPlus. The high-rise commercial building model in the EnergyPlus reference building set was used as a simulation test bed. The vertical multi-stories shaft zones were added inside the core zone in the existing building model. Then, the airflow network model was set in EnergyPlus so the internal airflow including the vertical and horizontal directions through the shaft zone as well as the core and perimeter zones were configured. The simulation model was calibrated against the actual pressure distribution between the core and perimeter zones in all floors of high-rise buildings from the literature. This model calibration was conducted manually by changing the discharge coefficient of the openings between thermal zones. The heating and cooling load of the proposed model were compared to the existing model which does not consider the vertical airflow through the shaft zones. The quantification with relative load mismatch was analyzed by floors and environment including the temperature difference between indoor and outdoor, and wind speed. Moreover, the impact of the air mixing and infiltration on the heating and cooling load was quantified. The result of this study reveals the significance of the stack effect in calculating the heating and cooling load.

This study investigates the impact of an active workstation on heating and cooling energy consumption in commercial buildings. The improved performance and health of office workers are well-known and supported by several surveys and studies. However, the preferred and revised indoor air temperature for different activities of the employees, such as standing and walking, directly impacts the energy consumption of the building. Therefore, this study systematically quantifies the impact of indoor environments on building energy consumption using a simulation tool. Based on literature reviews with multidisciplinary fields, revised setpoint temperature, metabolic rate, and required outdoor air rate due to the level of active workstation (standing and walking) are applied to EnergyPlus reference building models. The impact on heating and cooling energy consumption is analyzed by considering several representative cities in the United States. Their average sensitivity was found to be 7.28% lower and 14.28% higher for heating and cooling energy, respectively. The total increment of $13,887 represents approximately 0.16% of the total profit of the corporation. This increment can be justified given the benefits to workers' health, which include lower insurance costs and improved performance. This suggests that it is economically desirable to deploy active workstations widely. [ABSTRACT FROM AUTHOR]

This study investigated the applicability of a tracking photovoltaic (PV) system installed in the roof area of a commercial building. Because PVWatts is the only PV module with a tracking feature in EnergyPlus, its electricity generation was validated through comparisons with detailed PV modules in EnergyPlus. The tracking PV system generated 26.8–35.5% more electricity annually than a fixed system in the climate of Incheon (S. Korea). The load coverage analysis of the tracking PV system was conducted with the reference commercial building model in EnergyPlus. Approximately 14% of the total building electric demand, including heating, ventilation, and air-conditioning; lighting; and equipment, was met by one PV array. Finally, the life cycle cost analysis of the tracking PV system was conducted by considering the net present value, which includes the initial installation and operation costs. The initial investment was returned after approximately 8 years, assuming between two and six tracking PV arrays were installed. Moreover, up to 26.8% cost savings were achieved in 15 years compared to the case without any PV arrays. [ABSTRACT FROM AUTHOR]

In this paper, a combined method is proposed to improve the accuracy of an electrical equivalent circuit model (EECM) and state‐of‐charge (SOC) estimation in situations where a lithium‐ion battery is frequently charged and discharged. To calculate the terminal voltage in the dynamic region, where charging and discharging are frequently repeated at 0.1 s intervals, a specific region is determined and historical weight is selected, based on the amount of charge in that region, to reflect the model parameters. The historical weights are applied to the charging and discharging parameters to improve the estimated performance of the EECM in estimating the terminal voltage of the battery. Also, the improved EECM is applied to the extended Kalman filter to improve the performance of SOC estimation. A 21,700 cylindrical lithium nickel manganese cobalt oxide‐type battery was used for verification, and the parameters required for modeling were extracted through experiments that were conducted to determine electrical characteristics. This paper compared two models not considering the history of charging or discharging characteristics with a model considering history to verify the accuracy of voltage and SOC estimation results. The result shows that the voltage and SOC estimation errors of the proposed combined method are the lowest in the three cases. [ABSTRACT FROM AUTHOR]

In multi-user Redirected Walking (RDW), the space subdivision method divides a shared physical space into sub-spaces and allocates a sub-space to each user. While this approach has the advantage of precluding any collisions between users, the conventional space subdivision method suffers from frequent boundary resets due to the reduction of available space per user. To address this challenge, in this study, we propose a space subdivision method called Optimal Space Partitioning (OSP) that dynamically divides the shared physical space in real-time. By exploiting spatial information of the physical and virtual environment, OSP predicts the movement of users and divides the shared physical space into optimal sub-spaces separated with shutters. Our OSP framework is trained using deep reinforcement learning to allocate optimal sub-space to each user and provide optimal steering. Our experiments demonstrate that OSP provides higher sense of immersion to users by minimizing the total number of reset counts, while preserving the advantage of the existing space subdivision strategy: ensuring better safety to users by completely eliminating the possibility of any collisions between users beforehand. Our project is available at https://github.com/AppleParfait/OSP-Archive. [ABSTRACT FROM AUTHOR]

Simple Summary: As of recently, cancer is considered a major cause of death in developed and developing countries. Therefore, there is an urgent need for improvements in existing diagnostic methods for effective early diagnosis. However, cross-contamination of cancer cell lines results in the development of inappropriate treatments that cannot be administered to patients. To address this issue, we propose an automatic cancer cell taxonomy with high accuracy using optical images of cells obtained through low-scale benchtop optical microscopy. Specifically, we built a deep-learning-based framework to classify cervical, hepatocellular, breast, and lung cancer cells. The experimental results demonstrated that the proposed deep-learning-based approach facilitates the automatic identification of cancer cells. Moreover, our findings provide important insights into the design of convolutional neural networks for various clinical tasks that utilize microscopic images. Microscopic image-based analysis has been intensively performed for pathological studies and diagnosis of diseases. However, mis-authentication of cell lines due to misjudgments by pathologists has been recognized as a serious problem. To address this problem, we propose a deep-learning-based approach for the automatic taxonomy of cancer cell types. A total of 889 bright-field microscopic images of four cancer cell lines were acquired using a benchtop microscope. Individual cells were further segmented and augmented to increase the image dataset. Afterward, deep transfer learning was adopted to accelerate the classification of cancer types. Experiments revealed that the deep-learning-based methods outperformed traditional machine-learning-based methods. Moreover, the Wilcoxon signed-rank test showed that deep ensemble approaches outperformed individual deep-learning-based models (p < 0.001) and were in effect to achieve the classification accuracy up to 97.735%. Additional investigation with the Wilcoxon signed-rank test was conducted to consider various network design choices, such as the type of optimizer, type of learning rate scheduler, degree of fine-tuning, and use of data augmentation. Finally, it was found that the using data augmentation and updating all the weights of a network during fine-tuning improve the overall performance of individual convolutional neural network models. [ABSTRACT FROM AUTHOR]

In this paper, we show that for a nonsingular projective curve and a positive integer k , the k -th secant bundle is the blowup of the k -th secant variety along the (k − 1) -th secant variety. This answers a question raised in the recent paper of the authors on secant varieties of curves. [ABSTRACT FROM AUTHOR]

Let X ⊆ ℙ N be a non-degenerate normal projective variety of codimension e and degree d with isolated ℚ -Gorenstein singularities. We prove that the Castelnuovo–Mumford regularity reg (𝒪 X) ≤ d − e , as predicted by the Eisenbud–Goto regularity conjecture. Such a bound fails for general projective varieties by a recent result of McCullough–Peeva. The main techniques are Noma's classification of non-degenerate projective varieties and Nadel vanishing for multiplier ideals. We also classify the extremal and the next to extremal cases. [ABSTRACT FROM AUTHOR]

The geometric and algebraic properties of smooth projective varieties with 1‐regular structure sheaf are well understood, and the complete classification of these varieties is a classical result. The aim of this paper is to study the next case: smooth projective varieties with 2‐regular structure sheaf. First, we give a classification of such varieties using adjunction mappings. Next, under suitable conditions, we study the syzygies of section rings of those varieties to understand the structure of the Betti tables, and show a sharp bound for Castelnuovo–Mumford regularity. [ABSTRACT FROM AUTHOR]

We generalize Kawamata's product formula for volumes of canonical divisors to arbitrary divisors using Okounkov bodies. [ABSTRACT FROM AUTHOR]