Characteristics of a micro-fin evaporator: Theoretical analysis and experimental verification

A theoretical analysis and experimental verification on the characteristics of a micro-fin evaporator using R290 and R717 as refrigerants were carried out. The heat capacity and heat transfer coefficient of the micro-fin evaporator were investigated under different water mass flow rate, different refrigerant mass flow rate, and different inner tube diameter of micro-fin evaporator. The simulation results of the heat transfer coefficient are fairly in good agreement with the experimental data. The results show that heat capacity and the heat transfer coefficient of the micro-fin evaporator increase with increasing logarithmic mean temperature difference, the water mass flow rate and the refrigerant mass flow rate. Heat capacity of the micro-fin evaporator for diameter 9.52 mm is higher than that of diameter 7.00 mm with using R290 as refrigerant. Heat capacity of the micro-fin evaporator with using R717 as refrigerant is higher than that of R290 as refrigerant. The results of this study can provide useful guidelines for optimal design and operation of micro-fin evaporator in its present or future applications

Biosynthesis of Putrescine from L-arginine Using Engineered Escherichia coli Whole Cells

Putrescine, a biogenic amine, is a highly valued compound in medicine, industry, and agriculture. In this study, we report a whole-cell biocatalytic method in Escherichia coli for the production of putrescine, using L-arginine as the substrate. L-arginine decarboxylase and agmatine ureohydrolase were co-expressed to produce putrescine from L-arginine. Ten plasmids with different copy numbers and ordering of genes were constructed to balance the expression of the two enzymes, and the best strain was pACYCDuet-speB-speA. The optimal concentration of L-arginine was determined to be 20 mM for this strain. The optimum pH of the biotransformation was 9.5, and the optimum temperature was 45 °C; under these conditions, the yield of putrescine was 98%. This whole-cell biocatalytic method appeared to have great potential for the production of putrescine.</jats:p

DEWP: Deep Expansion Learning for Wind Power Forecasting

Wind is one kind of high-efficient, environmentally-friendly and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a more and more important role in supplying electricity. Though growing dramatically in recent years, the amount of generated wind power can be directly or latently affected by multiple uncertain factors, such as wind speed, wind direction, temperatures, etc. More importantly, there exist very complicated dependencies of the generated power on the latent composition of these multiple time-evolving variables, which are always ignored by existing works and thus largely hinder the prediction performances. To this end, we propose DEWP, a novel Deep Expansion learning for Wind Power forecasting framework to carefully model the complicated dependencies with adequate expressiveness. DEWP starts with a stack-by-stack architecture, where each stack is composed of (i) a variable expansion block that makes use of convolutional layers to capture dependencies among multiple variables; (ii) a time expansion block that applies Fourier series and backcast/forecast mechanism to learn temporal dependencies in sequential patterns. These two tailored blocks expand raw inputs into different latent feature spaces which can model different levels of dependencies of time-evolving sequential data. Moreover, we propose an inference block corresponding for each stack, which applies multi-head self-attentions to acquire attentive features and maps expanded latent representations into generated wind power. In addition, to make DEWP more expressive in handling deep neural architectures, we adapt doubly residue learning to process stack-by-stack outputs. Finally, we present extensive experiments in the real-world wind power forecasting application on two datasets from two different turbines to demonstrate the effectiveness of our approach.Comment: Accepted by TKD

ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation

We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are necessary to articulate objects. ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose. Our framework unifies grasping and articulation within a single policy guided by a single hand pose reference. Moreover, to facilitate the training of the precise finger control required for articulation, we present a learning curriculum with increasing difficulty. It starts with single-hand manipulation of stationary objects and continues with multi-agent training including both hands and non-stationary objects. To evaluate our method, we introduce Dynamic Object Grasping and Articulation, a task that involves bringing an object into a target articulated pose. This task requires grasping, relocation, and articulation. We show our method's efficacy towards this task. We further demonstrate that our method can generate motions with noisy hand-object pose estimates from an off-the-shelf image-based regressor.Comment: 3DV-2024 camera ready. Project page: https://eth-ait.github.io/artigrasp

Thermal Properties and Biodegradability Studies of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)

For investigating the relationship between thermal properties and biodegradability of poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), several films of PHBV containing different polyhydroxyvalerate (HV) fractions were subjected to degradation in different conditions for up to 49 days. Differential scanning calorimetry (DSC), thermogravimetry (TG), specimen weight loss and scanning electron microscopy (SEM) were performed to characterize the thermal properties and enzymatic biodegradability of PHBV. The experimental results suggest that the degradation rates of PHBV films increase with decreasing crystallinity; the degradability of PHBV occurring from the surface is very significant under enzymatic hydrolysis; the crystallinity of PHBV decreased with the increase of HV fraction in PHBV; and no decrease in molecular weight was observed in the partially-degraded polymer.Ningbo Natural Science Foundation (Grant 2006A610043)Science and Technology Department of Zhejiang Provincial Government (Grant 2007R10020

Investigating high energy proton proton collisions with a multi-phase transport model approach based on PYTHIA8 initial conditions

The striking resemblance of high multiplicity proton-proton (pp) collisions at the LHC to heavy ion collisions challenges our conventional wisdom on the formation of the Quark-Gluon Plasma (QGP). A consistent explanation of the collectivity phenomena in pp will help us to understand the mechanism that leads to the QGP-like signals in small systems. In this study, we introduce a transport model approach connecting the initial conditions provided by PYTHIA8 with subsequent AMPT rescatterings to study the collective behavior in high energy pp collisions. The multiplicity dependence of light hadron productions from this model is in reasonable agreement with the pp $\sqrt{s}=13$ TeV experimental data. It is found in the comparisons that both the partonic and hadronic final state interactions are important for the generation of the radial flow feature of the pp transverse momentum spectra. The study also shows that the long range two particle azimuthal correlation in high multiplicity pp events is sensitive to the proton sub-nucleon spatial fluctuations