Unique Signal Research Articles

Temporal Resolution of Acoustic Process Emissions for Monitoring Joint Gap Formation in Laser Beam Butt Welding

With the increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Although its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high-power (3.5 kW) and high-speed (12 m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging the optimization of the signal duration. Our results also suggest that large gaps (>0.3mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2mm weld seam.

A multi-scale self-supervised hypergraph contrastive learning framework for video question answering

Video question answering (VideoQA) is a challenging video understanding task that requires a comprehensive understanding of multimodal information and accurate answers to related questions. Most existing VideoQA models use Graph Neural Networks (GNN) to capture temporal–spatial interactions between objects. Despite achieving certain success, we argue that current schemes have two limitations: (i) existing graph-based methods require stacking multi-layers of GNN to capture high-order relations between objects, which inevitably introduces irrelevant noise; (ii) neglecting the unique self-supervised signals in the high-order relational structures among multiple objects that can facilitate more accurate QA. To this end, we propose a novel Multi-scale Self-supervised Hypergraph Contrastive Learning (MSHCL) framework for VideoQA. Specifically, we first segment the video from multiple temporal dimensions to obtain multiple frame groups. For different frame groups, we design appearance and motion hyperedges based on node semantics to connect object nodes. In this way, we construct a multi-scale temporal–spatial hypergraph to directly capture high-order relations among multiple objects. Furthermore, the node features after hypergraph convolution are injected into a Transformer to capture the global information of the input sequence. Second, we design a self-supervised hypergraph contrastive learning task based on the node- and hyperedge-dropping data augmentation and an improved question-guided multimodal interaction module to enhance the accuracy and robustness of the VideoQA model. Finally, extensive experiments on three benchmark datasets demonstrate the superiority of our proposed MSHCL compared with stat-of-the-art methods.

Coupled harmonic oscillators model with two connected point masses for application in photo-induced force microscopy

Abstract To comprehensively describe the operation of photo-induced force microscopy (PiFM), we have developed a model based on coupled harmonic oscillators. This model features two point masses connected by massless elastic wires, offering greater intuitiveness compared to existing PiFM models. It simplifies these models into a unified theoretical framework. By solving the equations of motion using adjusted oscillator parameters, we have successfully replicated all dynamic features from previous theories. These features include resonance frequencies and shapes of eigenmodes, as well as the responses to various external forces in the two PiFM modes: direct coupling and sideband coupling. Furthermore, by integrating our model with a recently developed photo-induced thermal expansion force model, which covers both tip-enhanced and global expansions, we have managed to uncover the underlying physical mechanism responsible for the unique signal behaviors observed in sideband coupling mode, where the signal plot, as a function of sample thickness, unexpectedly exhibits a peak followed by a valley, rather than a proportionally increasing signal. Our study has the potential to enhance the comprehension of various other physical phenomena associated with PiFM in the future.

Thermal Runaway Early Warning and Risk Estimation Based on Gas Production Characteristics of Different Types of Lithium-Ion Batteries

Gas production analysis during the thermal runaway (TR) process plays a crucial role in early fire accident detection in electric vehicles. To assess the TR behavior of lithium-ion batteries and perform early warning and risk estimation, gas production and analysis were conducted on LiNixCoyMn1-x-yO2/graphite and LiFePO4/graphite cells under various trigger conditions. The findings indicate that the unique gas signals can provide TR warnings earlier than temperature, voltage, and pressure signals, with an advanced warning time ranging from 16 to 26 min. A new parameter called the thermal runaway degree (TRD) is introduced, which is the product of the molar quantity of gas production and the square root of the maximum temperature during the TR process. TRD is proposed to evaluate the severity of TR. The research reveals that TRD is influenced by the energy density of cells and the trigger conditions of TR. This parameter allows for a quantitative assessment of the safety risk associated with different battery types and the level of harm caused by various abuse conditions. Despite the uncertainties in the TR process, TRD demonstrates good repeatability (maximum relative deviation < 5%) and can be utilized as a characteristic parameter for risk estimation in lithium-ion batteries.

Multiplexed Gas Sensor: Fabrication Strategies, Recent Progress, and Challenges.

Due to miscellaneous toxic gases in the vicinity, there is a burgeoning need for advancement in the existing gas sensing technology not only for the survival of mankind but also for the industries based in various fields such as beverage, forestry, health care, environmental monitoring, agriculture, and military security. A gas sensor must be highly selective toward a specific gas in order to avoid incorrect signals while responding to nontarget gases. This may lead to complex scenarios depicting sensor defects, such as low selectivity and cross-sensitivity. Therefore, a multiplex gas sensor is required to address the problems of cross selectivity by combining different gas sensors, signal processing, and pattern recognition techniques along with the currently employed gas sensing technologies. The different sensing materials used in these sensor arrays will produce a unique response signal for developing a set of identifiers as the input that can be used to recognize a specific gas by its "fingerprint". This review provides a comprehensive review of chemiresistive-based multiplex gas sensors, including various fabrication strategies from expensive to low-cost techniques, advances in sensing materials, and a gist of various pattern recognition techniques used for both rigid and flexible gas sensor applications. Finally, the review assesses the current state-of-the-art in multiplex gas sensor technology and discusses various challenges for future research in this direction.

Cascade Reaction Regulated Electrochemiluminescence via Dual-Atomic-Site Catalysts.

Single-atom catalysts (SACs), a novel kind of electrocatalysts with full metal utilization, have been developed as unique signal amplifiers in several sensing platforms. Herein, based on theoretical prediction of the oxygen reduction reaction (ORR) mechanism on different atom sites, we constructed dual-atomic-site catalysts (DACs), Fe/Mn-N-C, to catalyze luminol-dissolved oxygen electrochemiluminescence (ECL). Computational simulation indicated that the weak adsorption of OH* on a single Fe site was overcome by introducing Mn as the secondary metallic active site, resulting in a synergic dual-site cascade mechanism. The superior catalytic activity of Fe/Mn-N-C DACs for the ORR was proven by the highly efficient cathodic luminol ECL, surpassing the performance of single-site catalysts (SACs), Fe-N-C and Mn-N-C. Furthermore, the ECL system, enhanced by a cascade reaction, exhibited remarkable sensitivity to ascorbic acid, with a detection limit of 0.02 nM. This research opens up opportunities for enhancing both the ECL efficiency and sensing performance by employing a rational atomic-scale design for DACs.

Phase Separation of DNA-Encoded Artificial Cells Boosts Signal Amplification for Biosensing.

Life-like hierarchical architecture shows great potential for advancing intelligent biosensing, but modular expansion of its sensitivity and functionality remains a challenge. Drawing inspiration from intracellular liquid-liquid phase separation, we discovered that a DNA-encoded artificial cell with a liquid core (LAC) can enhance peroxidase-like activity of Hemin and its DNA G-quadruplex aptamer complex (DGAH) without substrate-selectivity, unlike its gelled core (GAC) counterpart. The LAC is easily engineered as an ultrasensitive biosensing system, benefiting from DNA's high programmability and unique signal amplification capability mediated by liquid-liquid phase separation. As proof of concept, its versatility was successfully demonstrated by coupling with two molecular recognition elements to monitor tumor-related microRNA and profile cancer cell phenotypes. This scalable design philosophy offers new insights into the design of next generation of artificial cells-based biosensors.

Phase Separation of DNA‐Encoded Artificial Cells Boosts Signal Amplification for Biosensing

AbstractLife‐like hierarchical architecture shows great potential for advancing intelligent biosensing, but modular expansion of its sensitivity and functionality remains a challenge. Drawing inspiration from intracellular liquid‐liquid phase separation, we discovered that a DNA‐encoded artificial cell with a liquid core (LAC) can enhance peroxidase‐like activity of Hemin and its DNA G‐quadruplex aptamer complex (DGAH) without substrate‐selectivity, unlike its gelled core (GAC) counterpart. The LAC is easily engineered as an ultrasensitive biosensing system, benefiting from DNA′s high programmability and unique signal amplification capability mediated by liquid‐liquid phase separation. As proof of concept, its versatility was successfully demonstrated by coupling with two molecular recognition elements to monitor tumor‐related microRNA and profile cancer cell phenotypes. This scalable design philosophy offers new insights into the design of next generation of artificial cells‐based biosensors.

A Modulated Approach for Improving MFSK RADARS to Resolve Mutual Interference on Autonomous Vehicles (AVs).

This paper proposes a novel automotive radar waveform involving the theory behind M-ary frequency shift key (MFSK) radar systems. Along with the MFSK theory, coding schemes are studied to provide a solution to mutual interference. The proposed MFSK waveform consists of frequency increments throughout the range of 76 GHz to 81 GHz with a step value of 1 GHz. Instead of stepping with a fixed frequency, a triangular chirp sequence allows for static and moving objects to be detected. Therefore, automotive radars will improve Doppler estimation and simultaneous range of various targets. In this paper, a binary coding scheme and a combined transform coding scheme used for radar waveform correlation are evaluated in order to provide unique signals. AVs have to perform in an environment with a high number of signals being sent through the automotive radar frequency band. Efficient coding methods are required to increase the number of signals that are generated. An evaluation method and experimental data of modulated frequencies as well as a comparison with other frequency method systems are presented.

Targeted Workflow Investigating Variations in the Tear Proteome by Liquid Chromatography Tandem Mass Spectrometry.

Proteins in tears have an important role in eye health and have been shown as a promising source of disease biomarkers. The goal of this study was to develop a robust, sensitive, and targeted method for profiling tear proteins to examine the variability within a group of healthy volunteers over three days. Inter-individual and inter-day variabilities were examined to contribute to understanding the normal variations in the tear proteome, as well as to establish which proteins may be better candidates as eventual biomarkers of specific diseases. Tear samples collected on Schirmer strips were subjected to bottom-up proteomics, and resulting peptides were analyzed using an optimized targeted method measuring 226 proteins by liquid chromatography-scheduled multiple reaction monitoring. This method was developed using an in-house database of identified proteins from tears compiled from high-resolution data-dependent liquid chromatography tandem mass spectrometry data. The measurement of unique peptide signals can help better understand the dynamics of each of these proteins in tears. Some interesting trends were seen in specific pathways or protein classes, including higher variabilities for those involved in glycolysis, glutathione metabolism, and cytoskeleton proteins and lower variation for those involving the degradation of the extracellular matrix. The overall aim of this study was to contribute to the field of tear proteomics with the development of a novel and targeted method that is highly amenable to the clinical laboratory using high flow LC and commonly used triple quadrupole mass spectrometry while ensuring that protein quantitation was reported based on unique peptides for each protein and robust peak areas with data normalization. These results report on variabilities on over 200 proteins that are robustly detected in tear samples from healthy volunteers with a simple sample preparation procedure.

Geographic distribution of the Cross Seamount beaked whale based on acoustic detections

AbstractBeaked whales produce frequency‐modulated echolocation pulses that appear to be species‐specific, allowing passive acoustic monitoring to play a role in understanding spatio‐temporal patterns. The Cross Seamount beaked whale is known only from its unique echolocation signal (BWC) with no confirmed species identification. This beaked whale spans the Pacific Ocean from the Mariana Archipelago to Baja California, Mexico, south to the equator, but only as far north as latitude 29°N. Within these warm waters, 92% of BWC detections occurred at night, 6% during crepuscular periods, and only 2% during daylight hours. Detections of BWC signals on drifting recorders with a vertical hydrophone array at 150 m depth demonstrated that foraging often occurred shallow in the water column (<150 m). No other species of beaked whale to date has been documented foraging in waters this shallow. Given their nocturnal, shallow foraging dives, this species appears to prefer prey that may be available in the water column only during those hours. The foraging behavior of Cross Seamount beaked whales appears to be unique among all beaked whales, and these findings contribute additional ecological and acoustic information which can help guide future efforts to identify this cryptic whale.

We make a nice pair: Pairing the mID with a NeuroTechnology privacy enhancing technology improves mID download intentions

Mobile identification (mID) allows users to prove identity across many situations like when traveling through an airport; however, the personally identifiable information in the mID, if mishandled, poses a great threat to privacy. One solution is NeuroTechnology privacy enhancing technologies (PETs), which can be paired with the mID to authenticate users using one's unique neuro-proprioceptive signals that are safer than traditional security methods like facial recognition. Across two studies, we explore how TAM constructs impact mID download intentions (DI) and how pairing the PET with the mID impacts DI. In Study 1 (N = 465), mID-specific privacy concerns, anxiety (mID-ANX), general privacy concerns, and perceived privacy risk (mID-PPR) were strong negative predictors of DI. In Study 2 (N = 420), pairing the NeuroTechnology PET with the mID led to decreased mID-PPR and mID-ANX, and increased DI. An experimental mediation model demonstrated that pairing the NeuroTechnology PET with the mID was linked to higher DI because of decreased mID-PPR and mID-ANX leading to greater mID positive attitudes. Because privacy concerns and anxiety are barriers to technology acceptance in the TAM literature, NeuroTechnology PETs provide a solution to reduce privacy concerns and anxiety, and improve technology adoption.

Search and Study of Marked Code Structures for a Spatially Distributed System of Small-Sized Airborne Radars.

When forming the radar situation of a terrain, in order to increase its information content and to extract useful information, multi-position spatially distributed systems for integrating multi-angle radar data established by small-sized UAV-based airborne radars are used. In this case, each radar station belonging to a multi-position system as a probing signal must have its own unique marked signal. Such a setup will allow the signals reflected from ground objects and zones to be "attached" to specific receiving-transmitting positions of the multi-position system. This requirement results from the fact that each transceiver position emits one probing signal, and then receives all the echo signals reflected from the underlying surface and previously emitted by other radar devices of the multi-position system. Such a setup of multi-position systems requires the researcher to look for and investigate specialized systems of marked code structures used to modulate the probing signals in order to identify them in a joint radar channel. Thus, the problem at hand is how to look for and investigate novel marked code structures used to generate a system of probing signals, the use of which will allow it to be "attached" to a specific receiving-transmitting position of a multi-position onboard system and to identify them in a joint radar channel in the course of the remote sensing of the underlying surface. The purpose of this work is to conduct a study on the subject of the squeak and choice of a system of code structures that have a low level of side lobes of autocorrelation functions and uniformly distributed values of the levels of the cross-correlation function. To achieve this goal, the relevance of the study is substantiated in the introduction. The second section analyzes the level of side lobes for classical and modified Barker codes with an asymmetric alphabet. On the basis of this analysis, it was concluded that it is expedient to apply this approach for codes longer than Barker codes. Therefore, in the third section, the features of the generation of M-sequences are considered. The fourth section presents a technique for searching for new marked code structures, taking into account their mutual correlation properties for modifying M-sequences in order to implement multi-positional systems. The fifth section presents computer experiments on the search for marked code structures based on the modifications of M-sequences and presents the numerical characteristics of the correlation properties of the considered marked codes. And finally, in the sixth section, the final conclusions of the study are presented and recommendations are given for their further practical application. The practical significance of this study lies in the fact that the proposed new marked code structures are necessary for the synthesis of probing signals in the implementation of spatially distributed systems that function for the high-probability detection and high-precision determination of the coordinates of physical objects and are also necessary for the formation of radar images in a multi-position mode.

A Universal Feature Extractor Based on Self-Supervised Pre-Training for Fault Diagnosis of Rotating Machinery under Limited Data

To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully leverage the potential of easily obtainable unlabeled data from other devices. In light of this, this paper proposes a novel network architecture, named Signal Bootstrap Your Own Latent (SBYOL), which utilizes unlabeled vibration signals to address the challenging issues of variable working conditions, strong noise, and limited data in rotating machinery fault diagnosis. The architecture consists of a self-supervised pre-training-based fault feature recognition network and a diagnosis network based on knowledge transfer. The fault feature recognition network uses ResNet-18 as the backbone network for self-supervised pre-training and transfers the trained fault feature extractor to the target diagnostic object. Additionally, a unique vibration signal data augmentation technique, time–frequency signal transformation (TFST), is proposed specifically for rotating machinery fault diagnosis, which addresses the key task of contrastive learning and achieves high-precision fault diagnosis with very few labeled samples. Experimental results demonstrate that the proposed diagnostic model outperforms other methods in both extremely limited sample and strong noise scenarios and can transfer unlabeled data utilization between similar and even different device types.

Signaling pathways in pancreatic ductile adenocarcinoma and potential therapeutic targets

Pancreatic ductile adenocarcinoma (PDAC) has a dismal prognosis, with an overall 5-year survival of <10%. At present, PDAC is treated using systemic chemotherapeutic regimens, which have shown survival benefit in clinical trials. Unfortunately, the survival benefit offered by the current standards do not greatly impact the 5-year overall survival statistics with the disease and are associated with toxicity. The large majority of PDACs are associated with a mutation in Kirsten Ras (KRAS), which results in constative activation of downstream signaling resulting in oncogenesis, tumor progression, cellular survival, and metastasis. Due to the lack of druggable sites, designing direct KRAS inhibitors have proven difficult and extensive effort has been placed in finding upstream or downstream targets as potential therapeutic avenues. The epidermal growth factor receptor (EGFR), hedgehog (HH), and mTOR signaling pathways have all gained recent attention as potential candidates for targeted PDAC therapies. Erlotinib, an EGFR small-molecule inhibitor, has shown promise in preclinical studies against PDAC. It is currently the only Food and Drug Administration (FDA) approved targeted therapy for PDAC when used in conjunction with gemcitabine. However, clinical trials comparing erlotinib plus gemcitabine to gemcitabine alone have demonstrated only modest statistical significance in overall survival. Due to the unique hypovascular microenvironment in PDAC, designated by the term desmoplasia, the HH signaling pathway has also gained recent research interest. Recent studies have shown lithium, a divalent cation originally FDA approved for bipolar disorder, to inhibit PDAC progression through its mechanism of glycogen synthase 3 inhibition in the HH pathway. Metformin, a biguanide medication used in type II diabetes mellitus, has been shown to inhibit mammalian target of rapamycin complex 1 (mTORC1) signaling indirectly through its activation of AMPK. Preclinical studies have demonstrated tumor regression, induction of apoptosis, and effects on the microenvironment in PDAC through the inhibition of mTORC1 by metformin. We present compelling scientific rationale, based on unique signal transduction pathways, tumor pathophysiology, and therapeutics potential for the combination of erlotinib, lithium, and metformin for the treatment of PDAC.

Semi-Selective Array for the Classification of Purines with Surface Plasmon Resonance Imaging and Deep Learning Data Analysis.

In process analytics or environmental monitoring, the real-time recording of the composition of complex samples over a long period of time presents a great challenge. Promising solutions are label-free techniques such as surface plasmon resonance (SPR) spectroscopy. They are, however, often limited due to poor reversibility of analyte binding. In this work, we introduce how SPR imaging in combination with a semi-selective functional surface and smart data analysis can identify small and chemically similar molecules. Our sensor uses individual functional spots made from different ratios of graphene oxide and reduced graphene oxide, which generate a unique signal pattern depending on the analyte due to different binding affinities. These patterns allow four purine bases to be distinguished after classification using a convolutional neural network (CNN) at concentrations as low as 50 μM. The validation and test set classification accuracies were constant across multiple measurements on multiple sensors using a standard CNN, which promises to serve as a future method for developing online sensors in complex mixtures.

Doppler Radar-Based Human Speech Recognition Using Mobile Vision Transformer

As one of the important vital features of the human body, the acquisition of a speech signal plays an important role in human–computer interaction. In this study, voice sounds are gathered and identified using Doppler radar. The skin on the neck vibrates when a person speaks, which causes the vocal cords to vibrate as well. The vibration signal received by the radar will produce a unique micro-Doppler signal according to words with different pronunciations. Following the conversion of these signals into micro-Doppler feature maps, these speech signal maps are categorized and identified. The speech recognition method used in this paper is on neural networks. CNN convolutional neural networks have a lower generalization and accuracy when there are insufficient training samples and sample extraction bias, and the training model is not suitable for use on mobile terminals. MobileViT is a lightweight transformers-based model that can be used for image classification tasks. MobileViT uses a lightweight attention mechanism to extract features with a faster inference speed and smaller model size while ensuring a higher accuracy. Our proposed method does not require large-scale data collection, which is beneficial for different users. In addition, the learning speed is relatively fast, with an accuracy of 99.5%.

Improving Single-Molecule Antibody Detection Selectivity through Optimization of Peptide Epitope Presentation in OmpG Nanopore.

Outer membrane protein G (OmpG) is a monomeric porin found in Escherichia coli, which possesses seven flexible loops. OmpG has been engineered as a nanopore sensor, where its loops can host affinity epitopes for selective detection of biological molecules. In this study, we investigated various loop positions to incorporate a FLAG peptide antigen epitope in the most flexible loop 6 and tested the efficacy and sensitivity of these nanopore constructs in antibody detection. We observed an OmpG construct containing inserted FLAG sequence, which exhibited strong interaction with anti-FLAG antibodies in flow cytometry; however, it could not translate molecule interactions into a readable signal in current recordings. Further optimization of the peptide presentation strategy by replacing specific sections of loop 6 sequences with the FLAG tag created a construct capable of generating unique and distinct signals when interacting with various monoclonal or polyclonal anti-FLAG clones IgG antibodies in the mixture. The peptide display scheme demonstrated in this study can be generalized for the engineering of OmpG sensors, which can be used for screening and validating positive clones during antibody development, as well as for real-time quality control of cell cultures in monoclonal antibody production.

The Essence of Consciousness Revisiting the Neural Structural Features in Human Conscious Flow

No matter when or from which direction a neural signal is transmitted, the neural signal expression of a neuron within a neural region is unique. This unique neural signal expression is determined by the nature of the neuron, which is determined by the neurotransmitters it secretes. In the nervous system, the particlization process of neural signals can create structural relationships between neurons within a neural region. Within a neural region, the extreme combinations of the particlization process of neural signals from different neurons generate consciousness. The specific and non-specific neural tracts together form a reflex cross-section composed of the neural structures within the thalamic reflex pathway. The neural structures that make up the thalamic reflex cross-section are easily controlled by the thalamus, affecting the neural activity of various neural structures within the entire cerebral cortex, and forming neural reflex responses throughout the cerebral cortex

Self-checking dual-modal aptasensor based on hybrid Z-scheme heterostructure of Zn-defective CdS/ZnS for oxytetracycline detection

Electrochemical detection methods have been widely used for trace target detection with satisfactory results. However, most of the existing electrochemical sensors rely only on single signal output, which inevitably suffer from the interference of the complex matrix of real samples. Herein, we proposed a dual-modal aptasensor for oxytetracycline assay with self-checking function by integrating photoelectrochemical (PEC) and electrochemical (EC) signal outputs in one analysis system. Zn-defective CdS/ZnS heterostructure was synthesized and served as the photo-electroactive substrate for constructing the biorecognition process, while methylene blue (MB) was used as a dual-functional probe to enhance both PEC and EC signals. Due to the high activity of Zn-defective CdS/ZnS heterojunction and the unique dual-modal signal readout strategy, the biosensing platform exhibits superior analytical performance with the relatively wide linear range (0.01–50 ng mL−1), lower detection limits of 1.86 pg mL−1 (PEC mode) and 3.08 pg mL−1 (EC mode), as well as good selectivity, stability and reproducibility. The proposed dual-model analytical system with self-checking function is envisioned to provide a new approach for sensitive and reliable biosensing.