Subject Assessment of Thermal Transition in a Museum: a Case Study

Thermal sensation and comfort evaluation schemes typically address thermally adapted people under static circumstances. A disregard of thermal evaluation processes pertaining to transitional states may result in inappropriate temperature settings, inefficient thermal control, and poor thermal comfort. Thus, recently studies have been carried out, which consider thermal perception under dynamic (transitional) conditions. This paper represents an example of such a study. It investigates people's subjective thermal sensation assessment immediately after a spatial transition, i.e., entering or exiting a building or moving between different spaces within a building. Field experiments were conducted in the Museum of Art History (Kunsthistorisches Museum) in Vienna, Austria. Multiple groups of participants moved through a predefined route throughout the building. This route involved five spatial transitions. Immediately after each transition, the participants expressed their thermal sensation vote (TSV) via a questionnaire. Participants' responses were analyzed in the context of monitored temperature differences between the spaces along the participants' route through the building

Exploring Indoor Thermal environment and cognitive performance in a short-term occupancy setting

It is general knowledge that the thermal comfort strongly influences people's wellbeing, health, and productivity. Many studies point to a significant relationship between working performance and indoor thermal conditions. This contribution presents the results of a related large scale experiment with a group of architectural students. Participants were separated in two groups, placed in two identical rooms (seated at tables), and shown a brief video lecture. One of the test rooms was heated, the other one was cool. After watching the video, participants were asked to work on a test involving a few multiple choice and open questions. The test cells were monitored with regard to temperature, relative humidity and CO2 concentration. We discuss the test performance of the two groups of participants in the context of the corresponding indoor climate conditions

An Efficient Distributed Nash Equilibrium Seeking with Compressed and Event-triggered Communication

Distributed Nash equilibrium (NE) seeking problems for networked games have been widely investigated in recent years. Despite the increasing attention, communication expenditure is becoming a major bottleneck for scaling up distributed approaches within limited communication bandwidth between agents. To reduce communication cost, an efficient distributed NE seeking (ETC-DNES) algorithm is proposed to obtain an NE for games over directed graphs, where the communication efficiency is improved by event-triggered exchanges of compressed information among neighbors. ETC-DNES saves communication costs in both transmitted bits and rounds of communication. Furthermore, our method only requires the row-stochastic property of the adjacency matrix, unlike previous approaches that hinged on doubly-stochastic communication matrices. We provide convergence guarantees for ETC-DNES on games with restricted strongly monotone mappings and testify its efficiency with no sacrifice on the accuracy. The algorithm and analysis are extended to a compressed algorithm with stochastic event-triggered mechanism (SETC-DNES). In SETC-DNES, we introduce a random variable in the triggering condition to further enhance algorithm efficiency. We demonstrate that SETC-DNES guarantees linear convergence to the NE while achieving even greater reductions in communication costs compared to ETC-DNES. Finally, numerical simulations illustrate the effectiveness of the proposed algorithms

Expression of mTOR conduction pathway in human osteosarcoma MG-63 cells and their stem cells, and the inhibitory effect of different doses of rapamycin

Purpose: To investigate the expressions of rapamycin target protein (mTOR) conduction pathway in human osteosarcoma MG-63 cells and their stem cells, and to examine the inhibitory effect of different doses of rapamycin.Methods: mTOR mRNA in osteosarcoma stem-like cells and human osteosarcoma MG-63 cells were determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The cells were treated with different doses of rapamycin and divided into low dose group (0.5 mg), medium dose group (1.0 mg), high dose group (2.0 mg) and blank (control) group. Apoptosis and cell cycle of MG-63 cells were determined by flow cytometry, while proliferation of MG-63 cells up was assessed by CCK-8 kit.Results: mTOR in human osteosarcoma MG-63 cells was significantly lower than that in osteosarcoma stem-like cells. Compared with the control group, mRNA expression levels of mTOR in MG-63 cells and osteosarcoma stem-like cells were significantly decreased after treatment with different concentrations of rapamycin (p < 0.05). MG-63 cells treated with various doses of rapamycin exhibited a significant decrease in their proliferation, compared with control group, while only the high rapamycin concentration group exhibited a significant decrease in osteosarcoma stem-like cell proliferation (p < 0.05). Treatment with rapamycin in MG-63 cells and osteosarcoma stem-like cells resulted in a significant increase in apoptosis, prolonged G0/G1 phase and shortened S phase (p < 0.05).Conclusion: Rapamycin inhibits the expression of mTOR mRNA in osteosarcoma stem-like and MG-63 cells. It also inhibits the proliferation and cell cycle formation of osteosarcoma stem-like cells and MG-63 cells via mTOR signal pathway. These findings may provide a new target for the treatment of osteosarcoma

Conglomerate Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions

In an era where scientific experimentation is often costly, multi-fidelity emulation provides a powerful tool for predictive scientific computing. While there has been notable work on multi-fidelity modeling, existing models do not incorporate an important ``conglomerate'' property of multi-fidelity simulators, where the accuracies of different simulator components (modeling separate physics) are controlled by different fidelity parameters. Such conglomerate simulators are widely encountered in complex nuclear physics and astrophysics applications. We thus propose a new CONglomerate multi-FIdelity Gaussian process (CONFIG) model, which embeds this conglomerate structure within a novel non-stationary covariance function. We show that the proposed CONFIG model can capture prior knowledge on the numerical convergence of conglomerate simulators, which allows for cost-efficient emulation of multi-fidelity systems. We demonstrate the improved predictive performance of CONFIG over state-of-the-art models in a suite of numerical experiments and two applications, the first for emulation of cantilever beam deflection and the second for emulating the evolution of the quark-gluon plasma, which was theorized to have filled the Universe shortly after the Big Bang