The Existence of Exponential Attractor for Discrete Ginzburg-Landau Equation

This paper studies the following discrete systems of the complex Ginzburg-Landau equation: iu˙m-(α-iε)(2um-um+1-um-1)+iκum+βum2σum=gm,  m∈Z. Under some conditions on the parameters α, ε, κ, β, and σ, 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 ($E_{A,C}$) separating the two statuses is determined and is discovered by defining a generalized scaling law with respect to the convection velocity ($U$) and AC frequency ($f_f$) as $E_{A,C}$~${f_f}^{0.48-0.027U}$. 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 ($\zeta$) 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 $\zeta$ potential at the interface has historically presented challenges, leading researchers to simplify a chemically homogenized surface with a uniform $\zeta$ potential. In the current investigation, we present evidence that, within a microchannel, the spatial distribution of $\zeta$ 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 $\zeta$ 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 $\zeta$ 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 $\zeta$ 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 L−1-reactor d−1, around 10.1% higher than the control UASB (i.e. 1366.4 ± 71.0 mL-CH4 L−1-reactor d−1). 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 “source” with the outward flow and the “sink” 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 “sink,” 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