EngineeringUK

Contactless manipulation of samples, particularly the ability to dynamically handle multiple fragile specimens while maintaining their integrity and viability, is crucial for various applications in biology, medicine, engineering, and physics. While hydrodynamic tweezers have emerged as a promising approach for gentle, label-free manipulation of a wide range of sample types and sizes, they typically have limited flexibility in terms of dynamic control, making it challenging to realize high-resolution and programmable manipulation of multiple samples. Here, we introduce the Streaming-based Tweezers for Routing, Engineering, And Manipulation of multiparticles (STREAM) with sub-wavelength resolution. The platform employs an array of piezoelectric plates arranged in a space-reciprocal pattern to generate acoustic streaming, creating localized trapping points. The mechanism of particle trapping and the improvement of routing resolution via multiunit activation were investigated. Subsequently, a convolutional neural network (CNN) with arbitrary voltage combination as the input and predicted trapping position as the output was integrated into the system. The CNN calibration is vital to the system as it enhances the platform’s performance, enabling precise control of the trapping positions beyond traditional physical unit size limitations. We demonstrated the STREAM platform’s capabilities through single particle routing with sub-wavelength precision, simultaneous manipulation of multiple particles, and on-demand assembly of samples. The STREAM platform opens new possibilities for applications requiring precise and dynamic control of particles and samples, with the potential to advance fields including biology, chemistry, and materials science.

Abstract The LOw Frequency ARray (LOFAR) radio telescope possesses the unique capability to measure ultra‐high energy cosmic rays as well as image lightning discharges. This study presents a comparison between the inferred thunderstorm charge structures derived from cosmic‐ray measurements and from lightning flashes. Our results show a basic triple‐layered distribution: a positive upper layer, a main negative layer, and a positive lower layer. However, our cosmic‐ray measurement shows a bottom‐heavy structure, where the charge in the upper positively charged layer is smaller than that in the lower one. This is consistent with practically all lightning observations with LOFAR, showing well‐developed negative leader structures at altitudes below those where positive leaders are seen. This is very different from the vast majority of thundercloud charge structures seen around the world.

The complex interaction of spin, valley and lattice degrees of freedom allows natural materials to create exotic topological phenomena. The interplay between topological wave materials and hydrodynamics could offer promising opportunities for visualizing topological physics and manipulating bioparticle unconventionally. Here we present topological acoustofluidic chips to illustrate the complex interaction between elastic valley spin and nonlinear fluid dynamics. We created valley streaming vortices and chiral swirling patterns for backward-immune particle transport. Using tracer particles, we observed arrays of clockwise and anticlockwise valley vortices due to an increase in elastic spin density. Moreover, we discovered exotic topological pressure wells in fluids, creating nanoscale trapping fields for manipulating DNA molecules. We also found a 93.2% modulation in the bandwidth of edge states, dependent on the orientation of the substrate’s crystallographic structure. Our study sets the stage for uncovering topological acoustofluidic phenomena and visualizing elastic valley spin, revealing the potential for topological-material applications in life sciences.

Abstract Undergraduate research is a high-impact learning experience, but undergraduate students interested in research often face high barriers to entry, such as limited guidance, confusing application processes, and feelings of not belonging. These barriers are especially prohibitive to first-year students, first-generation students, and members of underrepresented groups, and COVID-19 exacerbated the difficulty of undergraduates joining research by reducing opportunities to gain meaningful research experiences. To address these challenges, we designed a novel extracurricular education program named Starting an Undergraduate Research Experience (SURE) in the fall 2020 with the goal of lowering barriers to entry for engineering students to gain research experiences. Over 150 students enrolled in the workshop series throughout both semesters and were put into small groups led by research-experienced undergraduate peer mentors to cement skills from the workshop and to answer students’ questions on a one-on-one basis. A pre- and post-workshop survey was administered to measure students’ major interests, how they define research and engineering, sense of belonging, and perceived barriers to getting involved in research. After participating in SURE, students reported lower perceived barriers to entry in research and were more likely to obtain a research position.

Graph neural networks (GNNs) are becoming popular in various real-world applications. However, hardware-level security is a concern when GNN models are mapped to emerging neuromorphic computing architectures such as memristor-based crossbars. We identify a vulnerability of memristor-mapped GNNs and propose an attack mechanism based on the identified vulnerability. The proposed attack tampers memristor-mapped graph-structured data of a GNN by injecting adversarial edges to the graph and inducing slow-to-write errors in crossbars. We present a defense mechanism based on the write-verify (WV) scheme. We analyze the effectiveness of the WV-based defense and provide theoretical security guarantees. This analysis also provides guidance for selecting appropriate design parameters for the WV scheme to ensure its effectiveness in countering slow-to-write errors induced by attacks. Experimental results for the proposed attack show that there is a 5.72× increase in the success rate compared to a software-based baseline. We also demonstrate the efficacy of the WV-based defense in mitigating all slow-to-write errors induced by the proposed attack.

Magnetic recording devices are still competitive in the storage density race with solid-state devices thanks to new technologies such as two-dimensional magnetic recording (TDMR). TDMR offers remarkable storage density increase without the need for new magnetic materials; however, advanced data processing schemes are needed to guarantee reliability. Data patterns where a bit is surrounded by complementary bits at the four positions with Manhattan distance 1 on the TDMR grid are called plus isolation (PIS) patterns, and they are error-prone. Recently, we introduced lexicographically-ordered constrained (LOCO) codes, namely optimal plus LOCO (OP-LOCO) codes, with minimal redundancy that prevent these patterns from being written in a TDMR device. However, in the high-density regime or the low-energy regime (as the device ages), additional error-prone patterns emerge, specifically data patterns where a bit is surrounded by complementary bits at only three positions with Manhattan distance 1, and we call them incomplete plus isolation (IPIS) patterns. In this paper, we present capacity-achieving codes that forbid both PIS and IPIS patterns in TDMR systems with wide read heads. Because of their shape, we collectively call the PIS and IPIS patterns rotated T isolation (RTIS) patterns, and we call the new codes optimal T LOCO (OT-LOCO) codes. We analyze OT-LOCO codes and derive their simple encoding-decoding rule that allows reconfigurability. We also present a novel bridging idea for these codes to further increase the rate. Our simulation results demonstrate that OT-LOCO codes not only remarkably outperform OP-LOCO codes, but also entirely eliminate media noise effects at practical TD densities in the range [0.6, 0.8) with high rates in the range [0.81, 0.83]. To further preserve the storage capacity, we suggest using OP-LOCO codes, which have higher rates than OT-LOCO codes, early in the device lifetime, then employing the reconfiguration property to switch to OT-LOCO codes later in the device lifetime. While the point of reconfiguration on the density/energy axis is decided manually at the moment, the next step is to use machine learning to make that decision based on the TDMR device status. Moreover, we introduce another coding scheme to remove RTIS patterns in TDMR systems which offers lower complexity, lower error propagation, and track separation, at the expense of a limited rate loss.

In intensive care units (ICUs), critically ill patients are monitored with electroencephalography (EEG) to prevent serious brain injury. EEG monitoring is constrained by clinician availability, and EEG interpretation can be subjective and prone to interobserver variability. Automated deep-learning systems for EEG could reduce human bias and accelerate the diagnostic process. However, existing uninterpretable (black-box) deep-learning models are untrustworthy, difficult to troubleshoot, and lack accountability in real-world applications, leading to a lack of both trust and adoption by clinicians. We developed an interpretable deep-learning system that accurately classifies six patterns of potentially harmful EEG activity - seizure, lateralized periodic discharges (LPDs), generalized periodic discharges (GPDs), lateralized rhythmic delta activity (LRDA), generalized rhythmic delta activity (GRDA), and other patterns - while providing faithful case-based explanations of its predictions. The model was trained on 50,697 total 50-second continuous EEG samples collected from 2711 patients in the ICU between July 2006 and March 2020 at Massachusetts General Hospital. EEG samples were labeled as one of the six EEG patterns by 124 domain experts and trained annotators. To evaluate the model, we asked eight medical professionals with relevant backgrounds to classify 100 EEG samples into the six pattern categories - once with and once without artificial intelligence (AI) assistance - and we assessed the assistive power of this interpretable system by comparing the diagnostic accuracy of the two methods. The model's discriminatory performance was evaluated with area under the receiver-operating characteristic curve (AUROC) and area under the precision-recall curve. The model's interpretability was measured with task-specific neighborhood agreement statistics that interrogated the similarities of samples and features. In a separate analysis, the latent space of the neural network was visualized by using dimension reduction techniques to examine whether the ictal-interictal injury continuum hypothesis, which asserts that seizures and seizure-like patterns of brain activity lie along a spectrum, is supported by data. The performance of all users significantly improved when provided with AI assistance. Mean user diagnostic accuracy improved from 47 to 71% (P<0.04). The model achieved AUROCs of 0.87, 0.93, 0.96, 0.92, 0.93, and 0.80 for the classes seizure, LPD, GPD, LRDA, GRDA, and other patterns, respectively. This performance was significantly higher than that of a corresponding uninterpretable black-box model (with P<0.0001). Videos traversing the ictal-interictal injury manifold from dimension reduction (a two-dimensional representation of the original high-dimensional feature space) give insight into the layout of EEG patterns within the network's latent space and illuminate relationships between EEG patterns that were previously hypothesized but had not yet been shown explicitly. These results indicate that the ictal-interictal injury continuum hypothesis is supported by data. Users showed significant pattern classification accuracy improvement with the assistance of this interpretable deep-learning model. The interpretable design facilitates effective human-AI collaboration; this system may improve diagnosis and patient care in clinical settings. The model may also provide a better understanding of how EEG patterns relate to each other along the ictal-interictal injury continuum. (Funded by the National Science Foundation, National Institutes of Health, and others.).

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, to improve continuous AF detection in ambulatory settings towards a population-wide screening use case, we face several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5 M 30-second records from 24,100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open-source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency.

The urban heat island effect causes increased heat stress in urban areas. Cool roofs and urban greening have been promoted as mitigation strategies to reduce this effect. However, evaluating their efficacy remains a challenge, as potential temperature reductions depend on local characteristics. Existing methods to characterize their efficacy, such as computational fluid dynamics and urban canopy models, are computationally burdensome and require a high degree of expertise to employ. We propose a data-driven approach to overcome these hurdles, inspired by recent innovations in spatial causal inference. This approach allows for estimates of hypothetical interventions to reduce the urban heat island effect. We demonstrate this approach by modeling evening temperature in Durham, North Carolina, using readily retrieved air temperature, land cover, and satellite data. Hypothetical interventions such as lining streets with trees, cool roofs, and changing parking lots to green space are estimated to decrease evening temperatures by a maximum of 0.7–0.9 ^{circ } hbox {C}, with reduced effects on temperature as a function of distance from the intervention. Because of the ease of data access, this approach may be applied to other cities in the U.S. to help them come up with city-specific solutions for reducing urban heat stress.

The addition of surface acoustic wave (SAW) technologies to microfluidics has greatly advanced lab-on-a-chip applications due to their unique and powerful attributes, including high-precision manipulation, versatility, integrability, biocompatibility, contactless nature, and rapid actuation. However, the development of SAW microfluidic devices is limited by complex and time-consuming micro/nanofabrication techniques and access to cleanroom facilities for multistep photolithography and vacuum-based processing. To simplify the fabrication of SAW microfluidic devices with customizable dimensions and functions, we utilized the additive manufacturing technique of aerosol jet printing. We successfully fabricated customized SAW microfluidic devices of varying materials, including silver nanowires, graphene, and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). To characterize and compare the acoustic actuation performance of these aerosol jet printed SAW microfluidic devices with their cleanroom-fabricated counterparts, the wave displacements and resonant frequencies of the different fabricated devices were directly measured through scanning laser Doppler vibrometry. Finally, to exhibit the capability of the aerosol jet printed devices for lab-on-a-chip applications, we successfully conducted acoustic streaming and particle concentration experiments. Overall, we demonstrated a novel solution-based, direct-write, single-step, cleanroom-free additive manufacturing technique to rapidly develop SAW microfluidic devices that shows viability for applications in the fields of biology, chemistry, engineering, and medicine.