71 research outputs found
The Existence of Exponential Attractor for Discrete Ginzburg-Landau Equation
This paper studies the following discrete systems of the complex Ginzburg-Landau equation: iuËm-(α-iΔ)(2um-um+1-um-1)+iÎșum+ÎČum2Ïum=gm,ââmâZ. Under some conditions on the parameters α,âΔ,âÎș,âÎČ, and Ï, we prove the existence of exponential attractor for the semigroup associated with these discrete systems
Onset of nonlinear electroosmotic flow under AC electric field
Nonlinearity of electroosmotic flows (EOFs) is ubiquitous and plays a crucial
role in the mass and energy transfer in ion transport, specimen mixing,
electrochemistry reaction, and electric energy storage and utilizing. When and
how the transition from a linear regime to a nonlinear one is essential for
understanding, prohibiting or utilizing nonlinear EOF. However, suffers the
lacking of reliable experimental instruments with high spatial and temporal
resolutions, the investigation of the onset of nonlinear EOF still stays in
theory. Herein, we experimentally studied the velocity fluctuations of EOFs
driven by AC electric field via ultra-sensitive fluorescent blinking tricks.
The linear and nonlinear AC EOFs are successfully identified from both the time
trace and energy spectra of velocity fluctuations. The critical electric field
() separating the two statuses is determined and is discovered by
defining a generalized scaling law with respect to the convection velocity
() and AC frequency () as ~. The
universal control parameters are determined with surprising accuracy for
governing the status of AC EOFs. We hope the current investigation could be
essential in the development of both theory and applications of nonlinear EOF
Electrokinetic origin of swirling flow on nanoscale interface
The zeta () potential is a pivotal metric for characterizing the
electric field topology within an electric double layer - an important
phenomenon on phase interface. It underpins critical processes in diverse
realms such as chemistry, biomedical engineering, and micro/nanofluidics. Yet,
local measurement of potential at the interface has historically
presented challenges, leading researchers to simplify a chemically homogenized
surface with a uniform potential. In the current investigation, we
present evidence that, within a microchannel, the spatial distribution of
potential across a chemically homogeneous solid-liquid interface can
become two-dimensional (2D) under an imposed flow regime, as disclosed by a
state-of-art fluorescence photobleaching electrochemistry analyzer (FLEA)
technique. The potential' s propensity to become increasingly negative
downstream, presents an approximately symmetric, V-shaped pattern in the
spanwise orientation. Intriguingly, and of notable significance to chemistry
and engineering, this 2D potential framework was found to
electrokinetically induce swirling flows in tens of nanometers, aligning with
the streamwise axis, bearing a remarkable resemblance to the well-documented
hairpin vortices in turbulent boundary layers. Our findings gesture towards a
novel perspective on the genesis of vortex structures in nanoscale.
Additionally, the FLEA technique emerges as a potent tool for discerning
potential at a local scale with high resolution, potentially
accelerating the evolution and applications of novel surface material
Continuous micro-current stimulation to upgrade methanolic wastewater biodegradation and biomethane recovery in an upflow anaerobic sludge blanket (UASB) reactor
The dispersion of granules in upflow anaerobic sludge blanket (UASB) reactor represents a critical technical issue in methanolic wastewater treatment. In this study, the potentials of coupling a microbial electrolysis cell (MEC) into an UASB reactor for improving methanolic wastewater biodegradation, long-term process stability and biomethane recovery were evaluated. The results indicated that coupling a MEC system was capable of improving the overall performance of UASB reactor for methanolic wastewater treatment. The combined system maintained the comparatively higher methane yield and COD removal efficiency over the single UASB process through the entire process, with the methane production at the steady-state conditions approaching 1504.7 ± 92.2 mL-CH4 Lâ1-reactor dâ1, around 10.1% higher than the control UASB (i.e. 1366.4 ± 71.0 mL-CH4 Lâ1-reactor dâ1). The further characterizations verified that the input of external power source could stimulate the metabolic activity of microbes and reinforced the EPS secretion. The produced EPS interacted with Fe2+/3+ liberated during anodic corrosion of iron electrode to create a gel-like three-dimensional [-Fe-EPS-]n matrix, which promoted cell-cell cohesion and maintained the structural integrity of granules. Further observations via SEM and FISH analysis demonstrated that the use of bioelectrochemical stimulation promoted the growth and proliferation of microorganisms, which diversified the degradation routes of methanol, convert the wasted CO2 into methane and accordingly increased the process stability and methane productivity
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Multivariate Time Series (MTS) widely exists in real-word complex systems,
such as traffic and energy systems, making their forecasting crucial for
understanding and influencing these systems. Recently, deep learning-based
approaches have gained much popularity for effectively modeling temporal and
spatial dependencies in MTS, specifically in Long-term Time Series Forecasting
(LTSF) and Spatial-Temporal Forecasting (STF). However, the fair benchmarking
issue and the choice of technical approaches have been hotly debated in related
work. Such controversies significantly hinder our understanding of progress in
this field. Thus, this paper aims to address these controversies to present
insights into advancements achieved. To resolve benchmarking issues, we
introduce BasicTS, a benchmark designed for fair comparisons in MTS
forecasting. BasicTS establishes a unified training pipeline and reasonable
evaluation settings, enabling an unbiased evaluation of over 30 popular MTS
forecasting models on more than 18 datasets. Furthermore, we highlight the
heterogeneity among MTS datasets and classify them based on temporal and
spatial characteristics. We further prove that neglecting heterogeneity is the
primary reason for generating controversies in technical approaches. Moreover,
based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct
an exhaustive and reproducible performance and efficiency comparison of popular
models, providing insights for researchers in selecting and designing MTS
forecasting models
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
Instant delivery services, such as food delivery and package delivery, have
achieved explosive growth in recent years by providing customers with
daily-life convenience. An emerging research area within these services is
service Route\&Time Prediction (RTP), which aims to estimate the future service
route as well as the arrival time of a given worker. As one of the most crucial
tasks in those service platforms, RTP stands central to enhancing user
satisfaction and trimming operational expenditures on these platforms. Despite
a plethora of algorithms developed to date, there is no systematic,
comprehensive survey to guide researchers in this domain. To fill this gap, our
work presents the first comprehensive survey that methodically categorizes
recent advances in service route and time prediction. We start by defining the
RTP challenge and then delve into the metrics that are often employed.
Following that, we scrutinize the existing RTP methodologies, presenting a
novel taxonomy of them. We categorize these methods based on three criteria:
(i) type of task, subdivided into only-route prediction, only-time prediction,
and joint route\&time prediction; (ii) model architecture, which encompasses
sequence-based and graph-based models; and (iii) learning paradigm, including
Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively,
we highlight the limitations of current research and suggest prospective
avenues. We believe that the taxonomy, progress, and prospects introduced in
this paper can significantly promote the development of this field
A fluidic platform for mobility evaluation of zebrafish with gene deficiency
IntroductionZebrafish is a suitable animal model for molecular genetic tests and drug discovery due to its characteristics including optical transparency, genetic manipulability, genetic similarity to humans, and cost-effectiveness. Mobility of the zebrafish reflects pathological conditions leading to brain disorders, disrupted motor functions, and sensitivity to environmental challenges. However, it remains technologically challenging to quantitively assess zebrafish's mobility in a flowing environment and simultaneously monitor cellular behavior in vivo.MethodsWe herein developed a facile fluidic device using mechanical vibration to controllably generate various flow patterns in a droplet housing single zebrafish, which mimics its dynamically flowing habitats.ResultsWe observe that in the four recirculating flow patterns, there are two equilibrium stagnation positions for zebrafish constrained in the droplet, i.e., the âsourceâ with the outward flow and the âsinkâ with the inward flow. Wild-type zebrafish, whose mobility remains intact, tend to swim against the flow and fight to stay at the source point. A slight deviation from streamline leads to an increased torque pushing the zebrafish further away, whereas zebrafish with motor neuron dysfunction caused by lipin-1 deficiency are forced to stay in the âsink,â where both their head and tail align with the flow direction. Deviation angle from the source point can, therefore, be used to quantify the mobility of zebrafish under flowing environmental conditions. Moreover, in a droplet of comparable size, single zebrafish can be effectively restrained for high-resolution imaging.ConclusionUsing the proposed methodology, zebrafish mobility reflecting pathological symptoms can be quantitively investigated and directly linked to cellular behavior in vivo
A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems
The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications
Sodium tanshinone IIA sulfonate mediates electron transfer reaction in rat heart mitochondria
In this paper, an electron transfer reaction mediated by sodium tanshinone IIA sulfonate (STS) was studied in rat heart mitochondria. It was found that STS could stimulate mitochondrial NADH oxidation dose-dependently and partly restore NADH oxidation in the presence of respiratory inhibitor (rotenone or antimycin A or KCN). It was likely that STS could accept electrons from complex I similar to ferricyanide and could be converted to its semiquinone form that could then reduce oxygen molecule. The data also showed that cytochrome c (Cyt c) could be reduced by STS in the presence of KCN, or STS could transfer the electron to oxygen directly. Free radicals were involved in the process. The results suggest that STS may protect ischemia-reperfusion injury through an electron transfer reaction in mitochondria against forming reactive oxygen radicals
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