Integrated Platform for Whole Building HVAC System Automation and Simulation

Integrated optimal control strategies can reduce the overall building HVAC system energy consumption as well as improved air quality resulting in improved health and cognitive function for the occupants. However, it is time consuming to quantitatively evaluate the design-intended building HVAC automation system performance before on-site deployment, because: 1) the building and HVAC system design specs are in 2D or 3D drawings that require significant efforts to develop the system steady state or dynamic models based on them; 2) the building HVAC control strategies are designed and implemented in building automation (BA) system that could not smoothly connect with the building HVAC system steady state or dynamic models for performance evaluation through close-loop simulation. This paper presents the tool chain of an integrated simulation platform for building HVAC system automation and simulation as well as its implementation in a real case. First, building information from a Revit BIM model is automatically parsed to an EnergyPlus building energy model. Second, the HVAC system model is quickly populated with a scalable HVAC system library in Dymola. Third, the HVAC controls are developed in WebCTRL, a building HVAC automation system by Automated Logic Corporation (ALC). Finally, both the building energy model and HVAC system model are wrapped up as Functional Mock-up Units (FMU) and connected with embedded simulator in WebCTRL to perform close-loop building automation system performance simulation. A real case study, a chiller plant system in a hotel building, is conducted to verify the scalability and benefit of the developed tool chain. The case study demonstrates the values in identifying both HVAC automation system design-intended control issues and improvement areas for integrated optimal controls. This platform enables testing of building HVAC control strategies before on-site deployment, which reduces the labor and time required for building HVAC control development-to-market process and ensure the delivering quality. Furthermore, this platform can be calibrated with metered real-time data from the specific building HVAC system and serve as its “digital twin” that empowers the system fault detection, diagnostics and predictive maintenance

Creative Agents: Empowering Agents with Imagination for Creative Tasks

We study building embodied agents for open-ended creative tasks. While existing methods build instruction-following agents that can perform diverse open-ended tasks, none of them demonstrates creativity -- the ability to give novel and diverse task solutions implicit in the language instructions. This limitation comes from their inability to convert abstract language instructions into concrete task goals in the environment and perform long-horizon planning for such complicated goals. Given the observation that humans perform creative tasks with the help of imagination, we propose a class of solutions for creative agents, where the controller is enhanced with an imaginator that generates detailed imaginations of task outcomes conditioned on language instructions. We introduce several approaches to implementing the components of creative agents. We implement the imaginator with either a large language model for textual imagination or a diffusion model for visual imagination. The controller can either be a behavior-cloning policy learned from data or a pre-trained foundation model generating executable codes in the environment. We benchmark creative tasks with the challenging open-world game Minecraft, where the agents are asked to create diverse buildings given free-form language instructions. In addition, we propose novel evaluation metrics for open-ended creative tasks utilizing GPT-4V, which holds many advantages over existing metrics. We perform a detailed experimental analysis of creative agents, showing that creative agents are the first AI agents accomplishing diverse building creation in the survival mode of Minecraft. Our benchmark and models are open-source for future research on creative agents (https://github.com/PKU-RL/Creative-Agents).Comment: The first two authors contribute equall

MiRNA-let-7b smanjuje proliferacijsku aktivnost i razvoj folikularnih stanica putem ciljnog gena MAP3K1

To date, it has not yet been determined if the apoptosis of follicular granulosa cells (FGCs) is mediated by miR- let-7b via MAP3K1. In the present study, FGCs were transfected with a miR-let-7b mimic at different doses (0, 40, 60, 80, 100 and 120 μM), and were allocated into the control group (CG), and MIM-1, MIM-2, MIM-3, MIM-4 and MIM- 5, respectively. Expression levels of miR-let-7b and mitogen-activated protein kinase kinase kinase 1 (MAP3K1) mRNAs and proteins were determined using RT-PCR and Western blots. Luciferase report assay was applied to verify the targeting relationship between miR-let-7b and MAP3K1. The results revealed that the proliferation activity of FGCs in the MIM-4 group was significantly lower than that of the CG and MIM-1 groups (P<0.05). The MiR-let-7b mimic obviously reduced expression levels of miR-let-7b of the FGCs. The largest reduction was found in MIM-4. Levels of MAP3K1 mRNAs and proteins in the MIM-3 and MIM-4 groups were lower than that of the CG (P<0.05 or P<0.01). Co-transfection of let-7b mimic significantly inhibited luciferase activity (P<0.05) as compared with the CG. In conclusion, miR-let-7b may obviously depress the cell viability and accelerate apoptosis of ovine FGCs. Higher doses of miR-let-7b mimic (80 μM and 100 μM) could significantly depress expressions of miR-let-7b mRNAs, MAP3K1 mRNAs and protein in ovine FGCs. MiR-let-7b promoted FGCs apoptosis by inhibiting the MAP3K1 gene.Do danas nije definirano je li apoptoza folikularnih granuloza-stanica (FGCs) posredovana prekursorom miR-let- 7b putem gena MAP3K1. U ovom je istraživanju FGC transfeciran miR-let-7b imitatorom u različitim dozama (0, 40, 60, 80, 100 i 120 μM) i potom razvrstan u kontrolnu skupinu (CG), te u skupine označene kao MIM-1, MIM-2, MIM-3, MIM-4 i MIM-5. Razine ekspresije mRNA i proteina miR-let-7b i mitogenom-aktivirane proteinske kinase kinase kinase1 (MAP3K1) određene su uporabom RT-PCR-a i Western blot-a. Test luciferaze je primijenjen kako bi se potvrdio ciljni odnos između miR-let-7b i MAP3K1. Rezultati su pokazali da je proliferacijska aktivnost FGC-a u skupini MIM-4 bila znakovito manja nego u kontrolnoj skupini i MIM-1 skupini (P<0,05) što ide u prilog pretpostavci da miR-let-7b imitator može u FGCs smanjiti razinu ekpresije miR-let-7b. Najveće smanjenje utvrđeno je u skupini MIM-4. Razine MAP3K1 mRNAs i proteina u skupinama MIM-3 i MIM-4 bile su niže nego u kontrolnoj skupini (P<0,05; P<0,01). Transfekcija let-7b imitatorom znakovito je inhibirala aktivnost luciferaze (P<0,05) u usporedbi s kontrolnom skupinom. Zaključno, miR-let-7b može smanjiti opstojnost stanice i ubrzati apoptozu ovčjih FGC-a. Veće doze miR-let-7b mimic (80 μM i 100 μM) mogu znakovito smanjiti ekspresiju miR-let-7b mRNAs, MAP3K1 mRNAs i proteina u ovčjih FGC-a. MiR-let-7b putem inhibirajućeg gena MAP3K1 doprinosi apoptozi folikularnih granuloza-stanica (FGCs)

Melatonin enhances the anti-tumor effect of fisetin by inhibiting COX-2/iNOS and NF-κB/p300 signaling pathways.

Melatonin is a hormone identified in plants and pineal glands of mammals and possesses diverse physiological functions. Fisetin is a bio-flavonoid widely found in plants and exerts antitumor activity in several types of human cancers. However, the combinational effect of melatonin and fisetin on antitumor activity, especially in melanoma treatment, remains unclear. Here, we tested the hypothesis that melatonin could enhance the antitumor activity of fisetin in melanoma cells and identified the underlying molecular mechanisms. The combinational treatment of melanoma cells with fisetin and melatonin significantly enhanced the inhibitions of cell viability, cell migration and clone formation, and the induction of apoptosis when compared with the treatment of fisetin alone. Moreover, such enhancement of antitumor effect by melatonin was found to be mediated through the modulation of the multiply signaling pathways in melanoma cells. The combinational treatment of fisetin with melatonin increased the cleavage of PARP proteins, triggered more release of cytochrome-c from the mitochondrial inter-membrane, enhanced the inhibition of COX-2 and iNOS expression, repressed the nuclear localization of p300 and NF-κB proteins, and abrogated the binding of NF-κB on COX-2 promoter. Thus, these results demonstrated that melatonin potentiated the anti-tumor effect of fisetin in melanoma cells by activating cytochrome-c-dependent apoptotic pathway and inhibiting COX-2/iNOS and NF-κB/p300 signaling pathways, and our study suggests the potential of such a combinational treatment of natural products in melanoma therapy

PF-DMD: Physics-fusion dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics

The DMD (Dynamic Mode Decomposition) method has attracted widespread attention as a representative modal-decomposition method and can build a predictive model. However, the DMD may give predicted results that deviate from physical reality in some scenarios, such as dealing with translation problems or noisy data. Therefore, this paper proposes a physics-fusion dynamic mode decomposition (PFDMD) method to address this issue. The proposed PFDMD method first obtains a data-driven model using DMD, then calculates the residual of the physical equations, and finally corrects the predicted results using Kalman filtering and gain coefficients. In this way, the PFDMD method can integrate the physics-informed equations with the data-driven model generated by DMD. Numerical experiments are conducted using the PFDMD, including the Allen-Cahn, advection-diffusion, and Burgers' equations. The results demonstrate that the proposed PFDMD method can significantly reduce the reconstruction and prediction errors by incorporating physics-informed equations, making it usable for translation and shock problems where the standard DMD method has failed

GlyphControl: Glyph Conditional Control for Visual Text Generation

Recently, there has been a growing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-aware text encoders like ByT5 and require retraining of text-to-image models, our approach leverages additional glyph conditional information to enhance the performance of the off-the-shelf Stable-Diffusion model in generating accurate visual text. By incorporating glyph instructions, users can customize the content, location, and size of the generated text according to their specific requirements. To facilitate further research in visual text generation, we construct a training benchmark dataset called LAION-Glyph. We evaluate the effectiveness of our approach by measuring OCR-based metrics and CLIP scores of the generated visual text. Our empirical evaluations demonstrate that GlyphControl outperforms the recent DeepFloyd IF approach in terms of OCR accuracy and CLIP scores, highlighting the efficacy of our method.Comment: Technical report. The codes will be released at https://github.com/AIGText/GlyphControl-releas