SlipChip for immunoassays in nanoliter volumes

This article describes a SlipChip-based approach to perform bead-based heterogeneous immunoassays with multiple nanoliter-volume samples. As a potential device to analyze the output of the chemistrode, the performance of this platform was tested using low concentrations of biomolecules. Two strategies to perform the immunoassay in the SlipChip were tested: (1) a unidirectional slipping method to combine the well containing a sample with a series of wells preloaded with reagents and (2) a back-and-forth slipping method to introduce a series of reagents to a well containing the sample by reloading and slipping the well containing the reagent. The SlipChips were fabricated with hydrophilic surfaces on the interior of the wells and with hydrophobic surfaces on the face of the SlipChip to enhance filling, transferring, and maintaining aqueous solutions in shallow wells. Nanopatterning was used to increase the hydrophobic nature of the SlipChip surface. Magnetic beads containing the capture antibody were efficiently transferred between wells and washed by serial dilution. An insulin immunoenzymatic assay showed a detection of limit of ∼13 pM. A total of 48 droplets of nanoliter volume were analyzed in parallel, including an on-chip calibration. The design of the SlipChip is flexible to accommodate other types of immunoassays, both heterogeneous and homogeneous. This work establishes the possibility of using SlipChip-based immunoassays in small volumes for a range of possible applications, including analysis of plugs from a chemistrode, detection of molecules from single cells, and diagnostic monitoring

Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection

Multivariate time-series data exhibit intricate correlations in both temporal and spatial dimensions. However, existing network architectures often overlook dependencies in the spatial dimension and struggle to strike a balance between long-term and short-term patterns when extracting features from the data. Furthermore, industries within the business community are hesitant to share their raw data, which hinders anomaly prediction accuracy and detection performance. To address these challenges, the authors propose a dynamic circular network-based federated dual-view learning approach. Experimental results from four open-source datasets demonstrate that the method outperforms existing methods in terms of accuracy, recall, and F1_score for anomaly detection

Driving mechanism of consumer migration behavior under the COVID-19 pandemic

IntroductionChina is now in the post-period of COVID-19 epidemic prevention and control. While facing normalized epidemic prevention and control, consumers behavioral intention and decision-making will still be influenced by the epidemic's development and the implementation of specific epidemic prevention measures in the medium to long term. With the impact of external epidemic prevention environment and measures, consumers' channel behavior has changed. How to better promote channel integration by adopting consumers' channel migration behavior is important for channel coordination strategies.MethodsThis paper takes fresh product retailing under normal epidemic prevention and control as an example and examines the change in channel migration behavior. Based on the value-based adoption model (VAM), this paper discusses the influence of channel characteristics and channel switching costs on channel migration intention, the mediating effect of perceived value between various influencing factors and channel migration intention, and the moderating effect of channel switching cost on perceived value and channel migration intention. Thus, an empirical study was carried out with 292 samples to verify the hypotheses.ResultsThe results show that under normal epidemic prevention and control, the influencing factors in the VAM model have a significant impact on channel migration intention; perceived value plays a mediating role between various influencing factors and channel migration intention.DiscussionsThe COVID-19 pandemic has had a significant effect on daily life and purchasing behavior. In the context of this pandemic, we have confirmed that consumers will probably change to other retailers when the usefulness, entertainment, and cost meet their expectation for purchasing fresh products. Channel characteristics have versatile features, such as channel structure and supply chain mode, which affect consumer behaviors in different ways. The perceived value comes from expectations and experience. Retailers should try to keep their products fresh and provide consumers with a high-level shopping experience during sale

A three-DOF ultrasonic motor using four piezoelectric ceramic plates in bonded-type structure

A three-DOF ultrasonic motor is presented in this paper. The proposed motor consists of four piezoelectric ceramic plates and a mental base with a flange that can fix the motor on a rack. The proposed motor takes advantage of a longitudinal mode and two bending modes, different hybrids of which can realize three-DOF actuation. Because of symmetric structure of the proposed motor, the resonance frequencies of the two bending modes are identical. And the resonance frequency of the longitudinal mode was tuned closed to the ones of the bending modes by adjusting the structural parameters in modal analysis. Then trajectories of nodes on the driving foot were obtained by the transient analysis to verify the feasibility of driving principle. Experiments including vibration shape test and output characteristic test were executed. The starting voltages of the rotation along horizontal axes are about 10 Vp-p. Under driving voltages of 200 Vp-p, the output velocities of three DOF can reach 280 rpm, 277 rpm and 327 rpm, respectively. The results of the experiments indicate that the proposed motor is characterized by low starting voltages and high output velocities

TSViT: A Time Series Vision Transformer for Fault Diagnosis

Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) face limitations in capturing temporal features (i.e., the variation of vibration signals over time). To address this issue, this paper introduces a novel model, the Time Series Vision Transformer (TSViT), specifically designed for fault diagnosis. On one hand, TSViT model integrates a convolutional layer to segment vibration signals and capture local features. On the other hand, it employs a transformer encoder to learn long-term temporal information. The experimental results with other methods on two distinct datasets validate the effectiveness and generalizability of TSViT with a comparative analysis of its hyperparameters' impact on model performance, computational complexity, and overall parameter quantity. TSViT reaches average accuracies of 100% and 99.99% on two test sets, correspondingly

The chemistrode: A droplet-based microfluidic device for stimulation and recording with high temporal, spatial, and chemical resolution

Microelectrodes enable localized electrical stimulation and recording, and they have revolutionized our understanding of the spatiotemporal dynamics of systems that generate or respond to electrical signals. However, such comprehensive understanding of systems that rely on molecular signals—e.g., chemical communication in multicellular neural, developmental, or immune systems—remains elusive because of the inability to deliver, capture, and interpret complex chemical information. To overcome this challenge, we developed the ‘‘chemistrode,’’ a plug-based microfluidic device that enables stimulation, recording, and analysis of molecular signals with high spatial and temporal resolution. Stimulation with and recording of pulses as short as 50 ms was demonstrated. A pair of chemistrodes fabricated by multilayer soft lithography recorded independent signals from 2 locations separated by 15 μm. Like an electrode, the chemistrode does not need to be built into an experimental system—it is simply brought into contact with a chemical or biological substrate, and, instead of electrical signals, molecular signals are exchanged. Recorded molecular signals can be injected with additional reagents and analyzed off-line by multiple, independent techniques in parallel (e.g., fluorescence correlation spectroscopy, MALDI-MS, and fluorescence microscopy). When recombined, these analyses provide a time-resolved chemical record of a system’s response to stimulation. Insulin secretion from a single murine islet of Langerhans was measured at a frequency of 0.67 Hz by using the chemistrode. This article characterizes and tests the physical principles that govern the operation of the chemistrode to enable its application to probing local dynamics of chemically responsive matter in chemistry and biology